Abstract
Background: Twelve separate streams of empirical data make a strong case for cannabis-induced accelerated aging including hormonal, mitochondriopathic, cardiovascular, hepatotoxic, immunological, genotoxic, epigenotoxic, disruption of chromosomal physiology, congenital anomalies, cancers including inheritable tumorigenesis, telomerase inhibition and elevated mortality. Methods: Results from a recently published longitudinal epigenomic screen were analyzed with regard to the results of recent large epidemiological studies of the causal impacts of cannabis. We also integrate theoretical syntheses with prior studies into these combined epigenomic and epidemiological results. Results: Cannabis dependence not only recapitulates many of the key features of aging, but is characterized by both age-defining and age-generating illnesses including immunomodulation, hepatic inflammation, many psychiatric syndromes with a neuroinflammatory basis, genotoxicity and epigenotoxicity. DNA breaks, chromosomal breakage-fusion-bridge morphologies and likely cycles, and altered intergenerational DNA methylation and disruption of both the histone and tubulin codes in the context of increased clinical congenital anomalies, cancers and heritable tumors imply widespread disruption of the genome and epigenome. Modern epigenomic clocks indicate that, in cannabis-dependent patients, cannabis advances cellular DNA methylation age by 25–30% at age 30 years. Data have implications not only for somatic but also stem cell and germ line tissues including post-fertilization zygotes. This effect is likely increases with the square of chronological age. Conclusion: Recent epigenomic studies of cannabis exposure provide many explanations for the broad spectrum of cannabis-related teratogenicity and carcinogenicity and appear to account for many epidemiologically observed findings. Further research is indicated on the role of cannabinoids in the aging process both developmentally and longitudinally, from stem cell to germ cell to blastocystoids to embryoid bodies and beyond.
Keywords: cannabis, genotoxicity, epigenotoxicity, aging, ageing, teratology, DNA methylation
1. Introduction
Aging is the ubiquitous fate of biota and involves progressive loss of function [1]. Whilst the unkempt appearance and often poor physical and/or mental health of the patient chronically dependent on drugs including cannabis is widely appreciated, formal studies of cellular aging following chronic drug exposure are curiously absent from the literature. Major recent advances in various fields including epigenomics, epidemiology, stem-cell physiology and the mechanics of mitotic and meiotic cell division provide a unique opportunity to conduct an investigative review of the interaction of cannabis exposure and aging with a view to stimulating formal investigation of the field with epigenomic and other aging biomarkers.
Whilst teratology and cancerogenesis are well recognized aspects of genotoxicity and are now well documented in relation to cannabis-related genotoxicity, accelerated aging is the third well recognized aspect of genotoxicity generally [2], which presently lacks a detailed, coordinated and comprehensive review of its phenomenology and underlying theoretical and mechanistic bases with regard to cannabis and cannabinoids. The present paper addresses this gap.
Major hallmarks of biological aging include genomic instability, epigenomic alterations, telomere attrition, cellular senescence, mitochondrial dysfunction, altered intercellular communication, stem-cell exhaustion, difficulty with nutrient utilization and loss of proteostasis [1,3,4,5,6,7]. It is important to note that most of these pathways are now known to interact with the epigenome. Another frequently cited theory of aging is the free oxygen radical theory. Oxyradicals have also been shown to interact with epigenomic pathways via P16INK4A [8].
In 1942, Conrad Waddington hypothesized that epigenomic states constrained cell lineage differentiation to certain “valleys” so that cell specification within the major types was energetically constrained [9]. This profound insight had several implications including that differentiated cells do not readily transdifferentiate into a different cell type. Moreover, cells usually differentiate from a less differentiated progenitor state into a more highly differentiated state so that the biological age of cells in terms of numbers of cell divisions is encoded and recorded epigenetically, together with many other immune, metabolic and in neurological tissues, electrical, memories [10]. This usual process of differentiation from multipotent progenitors into progeny with increasingly restricted fate is known as canalization [11].
In 2006, Takahashi and Yamanaka screened 20 putative stem-cell factors to define the minimal signaling core group required to induce and maintain pluripotent stem cells. The four factors they defined were called OSKM factors (Oct3/4, Sox2, Klf4 and cMyc) or simply Yamanaka factors [12]. These authors used these factors to induce mouse fibroblasts to dedifferentiate back into embryonic stem cells thereby showing that the biological clock could be reversed. Elegant studies by other groups with Yamanaka factors or similar have since replicated these findings in other systems including recovery of aged rodent pancreatic islets and skeletal muscle crush [13], recovery of cardiac function and reversal of heart failure after rodent myocardial infarction [14], and recovery of vision after traumatic optic nerve crush injury, glaucoma, cataract and age-related blindness in old rats [15]. They were even able to restore and rejuvenate the aged cells of a mouse model of progeria [13]. Not only does this collection of studies generalize the Yamanaka findings relating to tissue and organismal age reversal, but, as observed by leading aging researchers, they also provide powerful evidence for the primacy of epigenomic regulation of the aging process overall [16]. In this context, the various hallmarks of aging mentioned above are now probably best understood from their relationship to the complex and multi-layered epigenomic regulatory pathways.
Cannabis dependence is defined as the state which exists when individuals become physically or mentally unwell after ceasing exposure to cannabis [17]. Cannabis withdrawal is characterized by a spectrum of symptoms including anxiety, irritability, dysphoria, craving, sleeping difficulties, abdominal cramps, muscle aches and diarrhea [17]. Chronic exposure may be defined as exposure which occurs during a period exceeding six months [17]. Daily cannabis exposure is operationally defined as being cannabis exposure on all or most days each month or twenty or more days per month [18].
Chronic cannabis dependence is characterized by many of the age defining hallmarks mentioned above with DNA breaks, fusions and bridges well described [19,20,21,22,23,24] and potentially including the breakage-fusion-bridge cycle (where chromosomal breaks lead to aberrant interchromosomal joinings and which causes chromosomal bridges to form when the chromosomes separate in anaphase which then leads to further breaks when the chromosomes are pulled apart in telophase so that the cycle repeats) [25]; major changes in DNA methylation [26,27] which have been shown to be transmissible to sperm and to a subsequent generation of offspring [26,27,28,29,30,31,32]; telomere attrition [33,34]; immunomodulation including heritable immunomodulation [35,36,37,38,39,40]; inhibition of mitochondrial function including increased free radical generation [41,42,43,44]; impairment of DNA, RNA and protein synthesis and cell growth [45,46,47,48,49] and thus stem-cell impairment and widespread negative trophic and functional effects in many tissues [47,48,50,51]. Chronic exposure is also associated with increased rates of many cancers [52,53,54,55,56,57,58,59,60,61,62,63,64,65,66]; a suppressed endocrine state [67,68,69,70,71,72,73] and impaired male and female fertility [67,71,74]. Thus, significant long-term exposure to cannabinoids recapitulates and accelerates many of the significant features of physiological aging.
The most prominent of the various biophysiological clocks which have been described to measure biological as compared to chronological aging are epigenomic clocks based on DNA methylation [75,76,77,78]. Cardiovascular [79,80,81,82], immunological [83], transcriptomic, microRNA, proteomic and metabolomic clocks have also been released [84,85,86,87,88,89,90].
1.1. Key Definitions
Genomic instability is a major mechanism in cancer, congenital anomalies, neurodevelopmental defects and aging. Genomic instability refers to cellular mutations and includes changes to the nucleic acid sequence, chromosomal rearrangements aneuploidy, copy number variations, circular DNA and microchromsomes [91,92,93,94,95,96,97,98,99,100,101,102].
“Canalization” refers to the process described in Waddington’s famous theory of cellular differentiation like a marble rolling down a landscape of hills and valleys and finding its energetically most favorable point, progressively becoming more terminally differentiated [9].
“Yamanaka factors” are those four cellular transcription factors originally described by Yamanaka and colleagues for potentiate the de-differentiation of terminally differentiated fibroblasts into induced pluripotential stem cells. The four factors identified were Oct3/4 (POU5F1, POU Class 5 Homeobox 1), Sox2 (SRY-Box transcription factor 2), c-Myc (MYC protooncogene, BHLH Transcription Factor), and Klf4 (KLF Transcription factor 4).
“Epigenomic regulatory pathways” refer to the many mechanisms of gene regulation including: DNA methylation, post-translational histone modifications, micro-RNAs, long non-coding RNAs, involvement in topologically defined domains and adjacency to transcription factories, closeness to the nuclear envelope (which suppresses gene transcription), involvement tin euchromatin or heterochromatin structure (the former promoting and the latter suppressing transcription), various post-transcriptional modifications of RNA including C6-adenosyl methylation, circular DNA structure and microchromsomes, amongst others.
1.2. Outline
The plan of this review is as follows. Firstly, twelve independent streams of empirical data for accelerated aging will be presented to make a strong case for accelerated biological aging associated with chronic cannabis exposure to set the context for the following discussion. Secondly, evidence for perturbation of some fundamental cellular machinery by cannabis exposure and withdrawal will be presented including alteration of the epigenomic machinery itself, modulation of various stem-cell factors and epigenomic interference with the chromosomal machinery of cell division. Epigenetic changes in brain and cardiovascular function are briefly considered as changes in these organs not only reflect but drive systemic aging, i.e., they are not only age-defining illnesses but also age-generating disorders. Thirdly, since cancer and congenital anomalies (birth defects) are both age-related disorders and are clinical reflections of genotoxicity and/or epigenotoxicity and are heightened after cannabis exposure [25,66,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118], contemporary USA and European epidemiological findings are reviewed and form the backdrop for a contextual exploration of the recent powerful longitudinal epigenomic data published by Schrott and Murphy and colleagues on changes in the DNA methylome of human sperm after cannabis exposure and withdrawal annotated for many benign and malignant conditions [27]. These datasets are augmented by other recent organ specific studies highlighting genes of particular interest which are then interrogated in the Schrott data. Consideration is also given to genotoxic effects of cannabinoids more broadly including cannabidiol and Δ8-tetrahydrocannabinol (Δ8THC).
These matters are set out in tabular form in Table 1.
Table 1.
Outline of Paper.
| No. | Streams of Evidence | Focus of the Discussion |
|---|---|---|
| Section 3.1.1 | Clinical syndromes | Clinical phenomenology |
| Section 3.1.2 | Mitochondrial inhibition | Cellular systems and mechanisms |
| Section 3.1.3 | DNA Methylation | Cellular systems and mechanisms |
| Section 3.1.4 | Mental illnesses | Organ systems |
| Section 3.1.5 | Cardiovascular age | Organ systems |
| Section 3.1.6 | Endocrine suppression | Organ systems |
| Section 3.1.7 | Liver inflammation | Organ systems |
| Section 3.1.8 | Cancer | Heath disorders and Population impacts |
| Section 3.1.9 | Inheritable cancer | Heath disorders and Population impacts |
| Section 3.1.10 | Congenital Anomalies | Heath disorders and Population impacts |
| Section 3.1.11 | Telomerase inhibition | Cellular systems and mechanisms |
| Section 3.1.12 | Elevated Mortality rate | Epidemiological Studies |
| Pathogenetic Field of Interest | ||
| Section 3.2.1 | Epigenomic Overview | Cellular systems and mechanisms |
| Section 3.2.2 | Stem-Cell Factors | Cellular systems and mechanisms |
| Section 3.2.3 | Chromosomal Mechanics | Cellular systems and mechanisms |
| Section 3.2.4 | Centromeres and Kinetochores | Cellular systems and mechanisms |
| Section 3.2.5 | Prefrontal cortex and Brain | Organ systems |
| Section 3.2.6 | Cardiovascular System | Organ systems |
| Section 3.2.7 | Teratogenesis | Analysis DNA Methylation data and epidemiological impacts |
| Section 3.2.8 | Carcinogenesis | Analysis DNA Methylation data and epidemiological impacts |
It is concluded that these metrics collectively point towards cannabinoid-exposed tissues being of advanced biological age resulting in age related morbidity, and that this process is driven by cannabinoid-disruption of the human epigenome, with increasing global cannabis exposure to a much greater extent than is commonly realized [119], having far-reaching public health implications for the current and future generations
2. Methods
Literature Review. Evidence was overviewed from the authors prior knowledge of studies examining cannabis effects on mechanisms of ageing. A literature search was conducted of PubMed on 30 November 2022 using the two sets of search terms “cannabis AND aging” and “cannabinoids AND aging”. Identified articles were manually searched. In total, 48 and 108 articles were identified from the raw searches. However, these dealt generally with only specific organ systems of aging (such as Alzheimer’s disease or pancreatic aging) and not the whole field of the pathobiology of aging itself; or alternatively hypothesized about unproven aging preventative actions. Thus, it was not possible to identify any recent reviews of the impacts of cannabis or cannabinoids on the fundamental pathobiology of aging. This finding formally demonstrates the novelty of the present study.
The 12 streams of evidence referenced flow from cellular systems and mechanisms (Epigenomic Overview) through organ systems (Prefrontal Cortex and Brain), to health disorders including cancer (Carcinogenesis), to population impacts (on birth defects and cancer).
Data. Data on rates of congenital anomalies are taken from published reports in USA [103] and Europe [115,120]. Data on cancer rates are taken from published reports on USA [112,113,114,121] and Europe [121,122]. Epigenomic DNA methylation data were taken from the EWAS (Epigenome Wide Association Study) report of Schrott and colleagues relating to cannabis dependence and withdrawal in human sperm before and 11 weeks after a period of cannabis dependence [27]. Genes of interest were searched in the 359-page pdf document which comprises the supplementary Schrott database.
Analysis. Statistical processing of code to derive relevant descriptive statistics was performed in R Studio 1.4.1717 based on R version 4.1.1 and both data and code are available as supplementary files in the following Mendeley repository https://data.mendeley.com/datasets/sngdkpg8gy/1 (doi:10.17632/sngdkpg8gy.1) (accessed on 10 December 2022. Full address is: https://data.mendeley.com/datasets/sngdkpg8gy).
Ethics. Ethical approval for this study was provided from the Human Research Ethics Committee of the University of Western Australia number RA/4/20/4724 on 24 September 2021.
3. Results and Discussion
3.1. Streams of Evidence for Cannabinoid Acceleration of Aging
Twelve independent empirical data streams both independently and collectively indicate accelerated biological aging associated with chronic cannabis exposure.
3.1.1. Clinical Syndromes
Long-term cannabis dependence is characterized by a cluster of syndromes which are themselves age defining illnesses including: neuroinflammation from the many mental illnesses [123,124,125,126,127,128,129,130,131,132]; steatohepatitis and cirrhosis progression [133,134,135,136]; myocardial infarction, cerebrovascular disorders and cardiac arrythmia [17,137,138,139]; immunomodulation [35,36,37,38,39]; endocrine suppression [67,68,69,70,71,72,73]; impaired male and female fertility [67,71,74]; cancers [52,53,54,55,56,57,58,59,60,61,62,63,64,65,66]; congenital anomalies [66,103,108,109,110,111,115,116,118]; genotoxicity including DNA breaks, telomere loss and mitotic and meiotic errors [19,33,140]; epigenotoxicity including altered DNA methylation [26,27,28,29,30,31,32] and histone physiology [141,142].
3.1.2. Mitochondrial Inhibition
Mitochondrial inhibition is well described in lymphocytes, neurons, sperm, hepatocytes and oocytes following cannabis exposure [41,42,43,44,140,143,144]. Mitochondria carry all of the cannabinoid signal transduction machinery found in the plasmalemma [44,145,146,147]. Since mitochondria supply energy and epigenomic substrates to the nucleus and interact with it closely via mitohormetic and mitonuclear balance systems [148,149] metabolic inhibition implies epigenomic disruption. Mitochondrial inhibition is well established as one of the key hallmarks of aging [1,150,151,152,153,154,155,156,157,158] and implicated pathophysiological pathways include such novel mechanisms as the leakage of mitochondrial DNA into the cytosol and stimulation of innate γ-interferon-dependent immunity via the cGAS-STING pathway [156].
3.1.3. DNA Methylation
Many studies have documented extensive alteration of DNA methylation following cannabis administration in both rats and humans [26,27,28,29,30,31,32,159,160]. Moreover, an elegant study has proven not only that the epigenome controls the aging process but that reversion of epigenomic age can heal traumatic optic nerve injury, glaucoma and geriatric blindness as normally only seen in neonatal life [15]. Extensive reduction in histone synthesis has been demonstrated including reduced phosphorylated and acetylated isoforms [49,141].
3.1.4. Mental Illnesses
Cannabis is associated with many mental illnesses including depression, stress, anxiety, PTSD, other substance dependence, bipolar disorder, schizophrenia and suicide [123,124,125,126,127,128,129,130,131,132] all of which are characterized by neuroinflammation [161,162,163,164,165,166,167,168], which is one of the hallmarks of the aged and dementing brain [169,170,171,172]. Not only is neuroinflammation an age defining illness it is also an age causing illness as it induces systemic inflammation throughout the body (“inflamm-aging”) [4,173]. Cannabis exposure was recently shown to be causally related to all four indices of mental dysfunction (depressive symptoms, any mental illness, severe mental illness and suicidal thinking) tracked by the annual nationwide massive National Survey of Drug Use and Health in a space time and causal inferential analysis [106]. Cannabis exposure has also been linked with the development of autism-like and ADHD-like syndromes in children [117,174] in spacetime and causal inferential studies [175] and in epigenomic studies [26,159,176,177]. An extensive literature and many meta-analyses strongly connect cannabis use and the development of schizophrenia by many mechanisms [17,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196].
3.1.5. Cardiovascular Age
Biological age as cardiovascular physiological age has been measured directly biophysically in cannabis dependence and been found to be advanced above controls [81]. An effect size of 12% and a positive dose-response relationship (p < 0.002) were demonstrated.
3.1.6. Endocrine Suppression
Widespread suppression of many key endocrine systems including luteinizing hormone (in males and females), testosterone, prolactin (chronic effect), growth hormone, estradiol and progesterone, Graafian follicle maturation, vasopressin and pregnancy including reduced fertility have been demonstrated in association with chronic cannabis use [67,68,69,70,71,72,73]. It has also been demonstrated in combined opioid-cannabinoid-dependent patients that the reversal of the FSH/LH ratio, a key clinical biomarker of the perimenopause, happened 20 years earlier [197]. Ovarian failure has also been shown to invariably be due to DNA damage [198]. Hormonal signals are rapidly transduced by the epigenome [199]. Hormonal failure and reproductive senescence represent age-defining and age-generating illnesses [1,157,158,200].
3.1.7. Liver Inflammation
Liver inflammation, cirrhosis and cancer have also been linked with cannabinoid exposure [133,134,135,136]. In that hepatic inflammation causes systemic inflammation, insulin resistance and dysmetabolism [201], generally these are also age-defining and age-generating illnesses. Moreover, the complex multi-way interaction between dysmetabolic and immunopathic changes is increasingly being defined and emphasized [202].
3.1.8. Cancer
Clinical genotoxicity is expressed as heightened rates of many cancers including liver, breast, pancreas, diverse leukemias and lymphomas, oropharyngeal, thyroid, urinary, esophageal and testicular tumors [52,53,54,55,56,57,58,59,60,61,62,63,64,65,66]. Genotoxicity is also one of the well-established key hallmarks of cellular aging [1,157,158,203].
3.1.9. Inheritable Cancer
Several cannabis-related cancers occur in the pediatric age group and are therefore evidence of heritable carcinogenesis [204,205] and therefore combine both teratogenesis and malignancy in the one case. This has been found for acute myeloid and lymphoid leukemias and total pediatric cancer [65,66,104,105,206] and for rhabdomyosarcoma and neuroblastoma [207,208]. One recent survey of the cannabis-exposed DNA methylome showed 487 hits for various malignancies [27].
3.1.10. Congenital Anomalies
Clinical genotoxicity is also expressed as congenital anomalies. As a majority of congenital anomalies, particularly those affecting the heart and chromosomal systems, are known to be related to parental age [209,210] the congenital anomaly rate becomes a surrogate or biomarker for biological age. Dozens of congenital anomalies have been described following prenatal or community cannabis exposure in Hawaii, Colorado, Canada, Australia, USA and Europe affecting particularly limbs, central nervous, cardiovascular, gastrointestinal, uronephrological and chromosomal systems [66,103,108,109,110,111,115,116,118]. Hundreds of positive hits were recorded on a DNA methylome screen for all the organ systems involved including mitochondria, chromosomes, microtubules, body axis and embryonic growth [27].
3.1.11. Telomerase Inhibition
Cannabis inhibits the activity of telomerase one of the key enzymes controlling aging [27,211]. Telomerase reverse transcriptase (TERT) is the key enzyme tasked with maintenance of telomere length and thus chromosomal length maintenance during cell division.
3.1.12. Elevated Mortality Rate
Mortality has been shown to be very elevated in cannabis users in several studies [212,213,214,215,216,217,218,219,220,221,222,223] at 30% over 30 years [223] and in another had a standardized mortality index of 14.61 (C.I. 9.21–23.19) over 14 years [222]. Whilst drug overdose, suicide and AIDS were the leading causes of death, cannabis itself predisposes to other drug use and mental illness [106,224,225,226]. Mortality is of course a hard end point for aging albeit in this context the pathway is complex.
“The sections that follow integrate cannabis ageing theories from eight pathogenetic fields”.
3.2. Pathogenetic Field of Interest
3.2.1. Epigenomic Overview
Longitudinal epigenomic data published by Schrott and colleagues on changes in the DNA methylome of human sperm after cannabis dependence and withdrawal [27] provide an explanation for the broad spectrum of cannabis-related teratogenicity and cancerogenicity mentioned above.
Table 2 presents a re-formatted extract of the Schrott data looking at the epigenomic modulation of the key epigenomic machinery itself [27]. As shown in the Table, most of these perturbations of DNA methylation occur in introns within genes but some are in upstream presumably promoter regions and some are in downstream enhancer regions.
Table 2.
Overview of Cannabis Impacts on Epigenetic Machinery, Schrott EWAS Data.
| Nearest Gene Name | Chromosome Number | Nearest Gene Number | Dependency Status | Functional Annotation | Page | Distance from Nearest Gene | Relative Position | p-Value | Bonferroni Adjusted p-Value |
|---|---|---|---|---|---|---|---|---|---|
| DNA Methyltransferases | |||||||||
| DNMT1 | 19 | ENSG00000130816 | Withdrawal | Maintenance DNA methyltransferase | 156 | 0 | Intron | 1.89 × 10−6 | 0.010563 |
| DNMT1 | 19 | ENSG00000130816 | Withdrawal | Maintenance DNA methyltransferase | 179 | 0 | Intron | 4.81 × 10−6 | 0.016176 |
| DNMT3B | 20 | ENSG00000088305 | Dependence | de novo DNA methyltransferase | 109 | 0 | Intron | 1.22 × 10−5 | 0.023205 |
| DNMT3B | 20 | ENSG00000088305 | Withdrawal | de novo DNA methyltransferase | 125 | 1067 | Upstream | 2.08 × 10−8 | 0.001062 |
| DNMT3A | 2 | ENSG00000119772 | Withdrawal | de novo DNA methyltransferase | 194 | 0 | Intron | 7.57 × 10−6 | 0.020149 |
| DNA Demethylases | |||||||||
| TET1 | 10 | ENSG00000138336 | Dependence | Ten-Eleven translocase | 107 | 0 | Intron | 1.18 × 10−5 | 0.022782 |
| TET1P1 | 13 | ENSG00000232204 | Dependence | Pseudogene for TET | 63 | 36,150 | Downstream | 4.14 × 10−6 | 0.013905 |
| TET1P1 | 13 | ENSG00000232204 | Dependence | Pseudogene for TET | 85 | 47,940 | Upstream | 7.47 × 10−6 | 0.018443 |
| TET1P1 | 13 | ENSG00000232204 | Dependence | Pseudogene for TET | 98 | 9930 | Downstream | 9.97 × 10−6 | 0.021086 |
| TET1P1 | 13 | ENSG00000232204 | Dependence | Pseudogene for TET | 98 | 55,192 | Upstream | 6.32 × 10−6 | 0.018533 |
| Others | |||||||||
| UHRF1 | 19 | ENSG00000276043 | Withdrawal | Integrator of epigenetic information | 128 | 0 | Intron | 5.74 × 10−8 | 0.001782 |
| UHRF1BP1L | 12 | ENSG00000111647 | Withdrawal | Regulator of UHRF1 | 155 | 0 | Intron | 1.79 × 10−6 | 0.010239 |
| UHRF1BP1L | 12 | ENSG00000111647 | Withdrawal | Regulator of UHRF1 | 233 | 0 | Intron | 1.67 × 10−5 | 0.028881 |
| DPPA2 | 3 | ENSG00000163530 | Dependence | Developmental Pluripotency Associated 2 | 40 | 15,599 | Downstream | 1.66 × 10−6 | 0.009001 |
| DPPA2 | 3 | ENSG00000163530 | Dependence | Developmental Pluripotency Associated 2 | 133 | 6894 | Downstream | 1.90 × 10−7 | 0.003298 |
| DPPA2P1 | Y | ENSG00000223915 | Withdrawal | Pseudogene for DPPA2A | 135 | 26,055 | Upstream | 2.78 × 10−7 | 0.004034 |
| Telomerase | |||||||||
| TERT | 5 | ENSG00000223915 | Dependence | Telomerase | 44 | 4227 | Upstream | 2.82 × 10−6 | 0.012582 |
| Polycomb Repressors | |||||||||
| PCGF6 in PRC1 | 10 | ENSG00000156374 | Dependence | Polycomb Repressive Complex 1 | 65 | 0 | Intron | 4.37 × 10−6 | 0.014300 |
| PCGF6 in PRC1 | 10 | ENSG00000156374 | Withdrawal | Polycomb Repressive Complex 1 | 137 | 0 | Intron | 4.03 × 10−7 | 0.004978 |
| EZH2 in PRC2 | 7 | ENSG00000180628 | Dependence | Polycomb Repressive Complex 2 | 94 | 0 | Intron | 9.22 × 10−6 | 0.020342 |
| Chromatin Remodellers | |||||||||
| SMARCA2 | 9 | ENSG00000080503 | Dependence | SWI/SNF Matrix, Actin Chromatin Regulator 2 | 6 | 0 | Intron | 5.27 × 10−9 | 0.000438 |
| SMARCA2 | 9 | ENSG00000080503 | Dependence | SWI/SNF Matrix, Actin Chromatin Regulator 2 | 62 | 3071 | Downstream | 4.00 × 10−6 | 0.013641 |
| SMARCA2 | 9 | ENSG00000080503 | Dependence | SWI/SNF Matrix, Actin Chromatin Regulator 2 | 114 | 0 | Intron | 1.34 × 10−5 | 0.024371 |
| SMARCA4 | 19 | ENSG00000127616 | Withdrawal | SWI/SNF Matrix, Actin Chromatin Regulator 4 | 145 | 9567 | Upstream | 8.86 × 10−7 | 0.007300 |
| SMARCA4 | 19 | ENSG00000127616 | Withdrawal | SWI/SNF Matrix, Actin Chromatin Regulator 4 | 199 | 9258 | Upstream | 8.54 × 10−6 | 0.021311 |
DNA methyltransferases 1 (DNMT1) and 3A and 3B (DNMT3A, DNMT3B) are the main enzymes which are responsible for laying down the methylation signals on DNA both from conception and in response to many signals thereafter. TET1 (ten-eleven translocase) is the main enzyme responsible for removing the methylation signals. It oxidizes the methylcytosines of CpG dinucleotides and introduces a hydroxyl group which is then oxidized in subsequent steps with the effect of removing the methylation mark. Hence the first lines of this Table show that both writing and erasing the key DNA methylation marks are disturbed by cannabis dependence or withdrawal. Here it is important to note that most habitual cannabis users go through withdrawal daily which is one of the major motivations to repeat use and making withdrawal a major and defining feature of clinical cannabis dependence [227].
UHRF1 (ubiquitin-like containing PHD and RING finger domains 1) is a key enzyme which is involved with both DNA methylation and histone modifications [228]. It recruits both DNMT1 to write DNA methylation marks and histone deacetylases which control access by the transcription machinery [228]. Its tudor-like and PHD- domains recognize and bind histone 3 trimethylated at lysine 9 (H3K9me3) and unmethylated arginine-2 (H3R2me0) and recruits chromatin proteins. Hence this enzyme is regarded as a key epigenomic hub coordinating the activities of the DNA methylation and histone regulatory systems. It regulates both the retinoblastoma gene product and the P53 damage checkpoint. Its expression levels peak in late G1 and it controls the G1/S transition of the cell cycle. It plays a key role in the regulation of pericentric chromatin and thus kinetochore function and chromosomal segregation. It is also involved in DNA repair. It is a known oncogene and has been implicated in liver cancer amongst others [229]. Hence its perturbation can be predicted to have a major effect on epigenomic regulation.
DPPA3 (Developmental PluriPotency Associated protein 3) has been shown to protect the epigenome of the oocyte from methylation [230]. Whilst DPPA3 was not identified in the spermatocyte EWAS conducted by Schrott team DPPA2 was identified as indicated.
TERT (telomerase reverse transcriptase) is a key enzyme responsible for maintaining the length of telomeres and is key to maintaining pluripotency in stem cells and germ cells and is often highly induced in cancer cells. Telomeres are protective caps on the ends of chromosomes and because some length is lost with each cell replication event they usually shorten with age. Since telomere attrition is one of the key chromosomal hallmarks of aging the regulation of telomere length is a key metric for the cellular aging clock. This important finding of cannabinoid interference with this key cellular enzyme has also been reported by others [211].
The polycomb repressive complex (Table 2) is one of the main epigenomic complexes which silence heterochromatin long term. Therefore, interference with these activities can be expected to have long-term consequences for cellular health.
SMARCA2 and SMARCA4 are SWI/SNF (SWItch/Sucrose NonFermentable) ATP-dependent modifiers of chromatin which change nucleosome position in an energy-dependent manner and therefore rearrange the genome and make new sections available for transcription. Modulation of these epigenomic controllers was recently shown to have a very positive effect in advanced castrate resistant prostate cancer which was addicted to their activities [231]. Since the SWI/SNF system is a major rearranger of chromatin perturbation of this system carries major downstream implications for cellular health. SMARCA2 and SMARCA4 (also known as Brahma, BRM and Brahma-related Gene 1, BRG1) were also recently determined to be key determinants of differentiation and canalization of precursor mesodermal cells into a cardiac fate [11].
Not only is DNA methylated but so too are histone proteins. There were 161 hits in the Schrott database for histone methyltransferases which write this mark onto the histone code (some top hits shown in Supplementary Table S1) and 199 hits for the histone demethylases which remove this mark (of which an extract is shown in Supplementary Table S2).
Histone acetylation is a key mark on histone tails. By neutralizing the charge of histone tails histone acetylation opens up chromatin and makes it available for gene transcription. This key acetylation mark is written onto the histone code by histone acetyl transferases and removed by histone deacetylases. Eleven hits in the Schrott data for each of these which were noted in both cannabis dependence and withdrawal are detailed in Supplementary Tables S3 and S4 respectively.
Thus, this brief introductory overview provides good evidence of major changes not only of the DNA methylome but of the central machinery which writes and erases and coordinates the epigenetic code on both DNA and histones. Key chromosomal areas such as the telomeres and centromeres are also impacted which thereby directly impacts processes such as aging (via accelerated telomere loss) and cellular division (via disruptions of centromere/kinetochore function).
3.2.2. Stem-Cell Factors
Takahashi and Yamanaka published their seminal and ground-breaking paper on the use of four defined recombinant stem-cell factors to maintain and induce the pluripotential state of embryonic stem cells in 2006 [12]. Proof of the induced stem-cell concept was provided by their demonstration that they were able to revert mouse fibroblasts to embryonic stem cells by the use of their four defined factors OSKM. These induced embryonic stem (iPS) cells went on to contribute to viable mouse embryos after injection into blastocysts. Intermittent use of the OSKM factors, a technique known as partial reprogramming, was able both to rescue a mouse model of progeria and to dramatically accelerate injury recovery to skeletal muscle and pancreatic islets in aged mice [13] and was able to improve cardiac function after myocardial infarction in a mouse model [232]. Inducible expression of OSK in retinal ganglion cells was able to restore vision in a manner only seen in neonatal mouse pups after glaucoma, optic nerve crush and extreme age in old mice [15].
As shown in Table 3, there were 11 hits in the Schrott database for the Yamanaka stem-cell factors. The name of the Oct3/4 gene has since been changed to POU5F1. SOX2, KLF4 and MYC were positively identified but Nanog was not identified.
Table 3.
Cannabis Impacts on Yamanaka Stem-Cell Factors, Schrott EWAS Data.
| Nearest Gene Name | Chromosome Number | Nearest Gene Number | Dependency Status | Functional Annotation | Page | Distance from Nearest Gene | Relative Position | p-Value | Bonferroni Adjusted p-Value |
|---|---|---|---|---|---|---|---|---|---|
| POU5F1P2 | 8 | ENSG00000253382 | Dependence | Oct3/4 Pseudogene | 5 | 2871 | Downstream | 1.49 × 10−9 | 0.000216 |
| SOX2-OT | 3 | ENSG00000242808 | Dependence | Sox2 Overlapping Transcript | 6 | 0 | Intron | 5.25 × 10−9 | 0.000438 |
| SOX2-OT | 3 | ENSG00000242808 | Dependence | Sox2 Overlapping Transcript | 48 | 0 | Intron | 2.38 × 10−6 | 0.017245 |
| SOX2-OT | 3 | ENSG00000242808 | Dependence | Sox2 Overlapping Transcript | 88 | 0 | Intron | 8.12 × 10−6 | 0.019185 |
| SOX2-OT | 3 | ENSG00000242808 | Withdrawal | Sox2 Overlapping Transcript | 116 | 0 | Intron | 1.40 × 10−5 | 0.024849 |
| SOX2-OT | 3 | ENSG00000242808 | Withdrawal | Sox2 Overlapping Transcript | 146 | 0 | Intron | 9.74 × 10−7 | 0.007679 |
| SOX2-OT | 3 | ENSG00000242808 | Withdrawal | Sox2 Overlapping Transcript | 211 | 0 | Intron | 1.11 × 10−5 | 0.023974 |
| Klf4 | 9 | ENSG00000136826 | Dependence | Kruppel-like factor 4 | 117 | 12,186 | Upstream | 1.41 × 10−5 | 0.024968 |
| MycBP2 | 13 | ENSG00000005810 | Dependence | Myc Binding Protein 2 | 49 | 0 | Intron | 2.50 × 10−6 | 0.010960 |
| MycBP2 | 13 | ENSG00000005810 | Withdrawal | Myc Binding Protein 2 | 153 | 0 | Intron | 1.58 × 10−6 | 0.009647 |
| Myc | 8 | ENSG00000136826 | Withdrawal | Myc proto-oncogene | 227 | 23,489 | Downstream | 1.49 × 10−5 | 0.027466 |
A modification of the Yamanaka protocol using slightly different stem-cell factors where Klf4 was replaced by Lin28 was also shown to induce iPS induction [14]. Stem-cell factors used by these researchers and also by Yamanaka were further investigated in the Schrott data with results shown in Supplementary Table S5. As there were 146 hits for Ras, 230 hits for Catenin and 185 hits for Kit in this database only a leading selection is shown in the Table. Hits for PAX7, one of the skeletal muscle master transcription factors and Lin28 are also shown at the bottom of the Table. Supplementary Table S6 provides an expanded list of some of the hits for Kit.
There is also a powerful and well documented multi-way link between immune activation, dysmetabolic changes and the aging process. For example, a recent study showed that much of the effect of calorie restriction, which has been well demonstrated to induce life extension in flies, worms and mice, when applied in humans was mediated by PLA2G7 (platelet activating factor acetyl hydrolase/phospholipase A2 group VII) [202]. PLA2G7 is found in cholesterol-rich low density lipoprotein particles and PLA2G7 oxidizes saturated lipids and activates vessel wall macrophages, lymphocytes and endothelial cells. It thereby stands at the intersection of immunity and metabolic processes.
A research group from Stanford developed a biological clock based on immune biomarkers and found that CXCR9 was the key chemokine which accounted for most of the variance they identified [83]. A sizeable literature exists around NAD (nicotinamide adenine dinucleotide) metabolism and the links between its normal dramatic age-dependent decline and the ageing process itself [233,234,235,236,237,238,239,240,241]. The key rate limiting enzyme in the NAD biosynthetic pathway is nicotinamide phosphoribosyl transferase (NAMPT) which acts as the gateway to this pathway [148]. It was therefore of interest to learn if these key immune and metabolic mediators were identified in the Schrott EWAS. The results of this investigation are shown in Supplementary Table S7. Both PLA2G7 and NAMPT were positively identified. CXCR9 was not identified but CXCR13 was found.
3.2.3. Chromosomal Mechanics
During the process of cell division at the beginning of prometaphase, the nuclear membrane breaks down and what has very properly been called the “mammoth” supramolecular mitotic and meiotic machine involving the mitotic spindle begins to form [242]. The process takes place on the large scale of the whole cell cytoplasm and each of its innumerable steps are tightly regulated, carefully choreographed and finely coordinated by elegant and sophisticated mechanisms. The implication of this vastness, complexity and sophistication is that the delicate process of cell division is open to perturbation and disruption at numerous steps.
Whilst the process of cell division is well known to students of biology the world over from watching time lapsed video micrographs, it is less well known that in the human oocyte the process is highly error prone with error rates of 60–90% being reported even when young oocytes are used [243,244,245,246,247,248,249] and this error rate is known to rise sharply with age [243,244,245,246,248,249]. The bipolar alignment of the mitotic spindle with two spindle poles is critical to directing the cell to divide into two daughter cells during the subsequent anaphase separation. Whilst most species have a pair of centrioles and pericentriolar material (called centrosomes) which direct this process this is absent from higher (non-rodent) mammalian species including humans. Such species organize their spindle poles using acentriolar microtubule organizing centers (aMTOC) organized by NUMA (nuclear mitotic apparatus protein) and the kinesin motor protein KIFCI (kinesin family member C1) to draw the microtubules together [248]. Supplementation of human oocytes with KIFC1 largely rescued the high mitotic error rate [248] and in mice its knockdown via degron mediated destruction increased the error rate of bovine and modified aMTOC-free mouse oocytes to be highly similar to that of the human oocyte [248]. In actual fact, the number of poles in human oocytes mitotic spindles oscillates dynamically during oocyte maturation over several hours from several poles to just one pole and most frequently settles at just two spindle poles [245]. This implies that NUMA and KIFC1 are key to the integrity and reliability of the inherently error-prone oogenesis mitotic process in humans [248].
In worms, a kinesin-12 protein (KLP-18, kinesin-like protein), dynein (and its binding partner dynactin) and a kinesin-5 member (BMK-1, Big Mitogen Activated Protein 1) are required to prevent spindle splaying [247,250].
The anaphase-promoting complex/cyclosome (APC/C) is known to be a key organizer of the mitotic spindle and to determine when all the paired chromosomes are aligned satisfactorily on the metaphase plate and thus licences and controls the chromosomal separation of anaphase [249,251]. In human-derived HEK293 cell lines it was shown that APC/C also localizes to the centrosome where its activity is controlled by Cep152 (Centrosomal Protein 152) in complex with Cep 57 and Cep 63 [249].
Tubulin is also subject to numerous post-translational modifications particularly acetylation, polyglutaminylation and tyrosinylation [252]. Acetylation is key to the formation of the tubulin polymers of the mitotic spindle and this is controlled by lysine (K) acetyltransferase and histone deacetylases (HDAC) particularly HDAC3, HDAC6 and HDAC11 and the sirtuin (SIRT) HDAC’s SIRT2 in meiosis I and SIRT1 in meiosis II [245]. The process is also sensitive to oxidative stress and ROS (reactive oxygen species) are known to play important roles in both folliculogenesis and oocyte maturation but excessive ROS levels have been linked to shrinkage of the width and length of the mitotic spindle, disruption of the spindle asters, chromosomal misalignment in metaphase II, chromosomal disassembly in meiosis I and II and increased aneuploidy rates [245]. Adducts of ROS including 4-hydroxynonenal form and co-localize with α-, β- and γ-tubulins [245]. Ovarian ROS production also rises with age [245].
Importantly, acetylation of lysine-40 on polymerized α-tubulin by α-tubulin acetyl transferase 1 (ATAT1) occurs on the inner surface of the microtubule and allows for running repairs to be undertaken on the polymer when the microtubules is stressed or bent thereby adding greatly to the structural strength and flexibility of the structure [253]. Without K-40 alpha-tubulin acetylation, the microtubules remain brittle and bending leads to microtubule fracture and chromosomal derailment, isolation, aneuploidy and micronucleus development during the anaphase disjunction. Unlike female meiosis, cell division in the fertilized zygote is organized around centriole-containing centrosomes which are derived from the paternal gamete as those associated with the female pronucleus are rudimentary [243,244]. It is therefore clear that interference with any of these structural, binding, signaling or motor proteins will lead to an elevated error rate of human female gametogenesis [248].
Supplementary Table S8 therefore presents the hits identified in the Schrott database for NUMA, CEP and kinesin- and dynein-dynactin motor proteins. It is noted that KIF14 is an alternate nomenclature for KIFC3 which was noted to be critical [248]. Hits in intron, exon and enhancer regions are noted. There were 218 hits for kinesin motors and these hits were some of the strongest hits identified in cannabis dependency in both Schrott’s Tables S1 and S4 [27]. Some of the top-scoring kinesin motor protein hits are detailed in Supplementary Table S9. It is noted that these results for the DNA methylome come from sperm so it remains to be determined how the detailed results from oocytes might compare.
When one considers tubulins in the database of Schrott and colleagues, 106 hits are obtained. Some of those for tubulin (not including the pseudogenes) and ATAT1 are shown in Supplementary Table S10. This Table also shows epigenomic hits identified for some of the key enzymes which write and modify the tubulin code including acetylation, tyrosinylation/detyrosinylation and acetylation. In total, 86 of the hits observed for tubulin are for TUBB6 (β-tubulin 6 class V) and these appear as the most significant of all of the functional annotations in the Schrott Table S4 for cannabis dependence as partially extracted in Supplementary Table S11. TUBB6 epimutations are also linked with many cancers [27].
3.2.4. Centromeres and Kinetochores
In addition to the poles, organization and microtubular rays of the mitotic and meiotic spindles the points of attachment of the chromosomes to the microtubules also form a key locus of control for the whole mitotic process and a key point of vulnerability at which xenotoxins may impact. Somewhat confusingly the combination of the central repetitive non-coding DNA at the center of the chromosome (the centromere) together with its accompanying histones and proteins is (also) referred to as the centrosome. The key marker for the development of the centromere is the substitution of histone 3 (H3) for its derivative CENPA (Centrosomal protein A) and the formation of neocentromeres can be induced by the forced expression of CENPA along chromosomal arms [254]. A multiprotein complex of 16 other centrosomal proteins called the kinetochore is then assembled on the centrosome at CENPA to form a large multimolecular complex which binds to the growing plus ends of 25–30 microtubules for each chromosome.
Detailed descriptions of the protein composition of the kinetochore have appeared [254,255,256]. When these proteins are run through the Schrott database 109 hits are obtained for the 19 proteins listed in Table 4. Interestingly, 86 of these hits are for CENPN which is the equal second protein to assemble alongside CENPA at the very commencement of kinetochore assembly. Some of the most significant hits for CENPN are shown in Supplementary Table S12 and are extracted from the Table S4 in Schrott’s dataset for cannabis dependence. They are notable for their very high levels of statistical significance along with their association with uniformly malignant disorders. With the exception of SPC24, all the hits identified were in cannabis dependence rather than cannabis withdrawal.
Table 4.
Cannabis Impacts on Centrosomal Proteins, Schrott EWAS Data.
| Nearest Gene Name | Nearest Gene Number | Chromosome Number | Relative Location | Distance to Nearest Gene (Bases) | Number of Annotations | p-Value | Bonferroni-Adjusted p-Value |
|---|---|---|---|---|---|---|---|
| Centrosomal Proteins | |||||||
| CENPIP1 | ENSG00000224778 | 13 | Upstream | 1100 | 1 | 2.38 × 10−9 | 0.000279 |
| CENPF | ENSG00000117724 | 1 | Downstream | 72,569 | 3 | 2.98 × 10−8 | 0.001109 |
| CNEPVL3 | ENSG00000224109 | X | Downstream | 2146 | 1 | 2.80 × 10−6 | 0.001153 |
| CENPK | ENSG00000123219 | 5 | Intron | 0 | 1 | 8.01 × 10−6 | 0.019098 |
| CNEPP | ENSG00000188312 | 9 | Intron | 0 | 2 | 8.26 × 10−6 | 0.019330 |
| CNEPJ | ENSG00000151849 | 13 | Exon | 0 | 1 | 4.66 × 10−7 | 0.005279 |
| CNEPUP1 | ENSG00000255075 | 11 | Upstream | 8401 | 1 | 2.81 × 10−6 | 0.012567 |
| INCENP | ENSG00000149503 | 11 | Intron | 0 | 1 | 3.07 × 10−6 | 0.013077 |
| CNEPO | ENSG00000138092 | 2 | Exon | 0 | 1 | 6.25 × 10−6 | 0.018393 |
| CNEPI | ENSG00000102384 | X | Intron | 0 | 2 | 7.54 × 10−6 | 0.020123 |
| CNEPL | ENSG00000120334 | 1 | Intron | 0 | 1 | 8.22 × 10−6 | 0.020943 |
| CNEPX | ENSG00000169689 | 17 | Exon | 0 | 1 | 9.35 × 10−6 | 0.022176 |
| CNEPC | ENSG00000145241 | 4 | Intron | 0 | 1 | 9.60 × 10−6 | 0.002248 |
| CENPV | ENSG00000166582 | 17 | Upstream | 13,237 | 2 | 1.63 × 10−5 | 0.002861 |
| CENPN | ENSG00000166451 | 16 | 86 | 7.73 × 10−20 | |||
| Others | |||||||
| KNL1 | ENSG00000137812 | 15 | 3UTR | 0 | 1 | 7.71 × 10−7 | 0.006173 |
| ZWINT | ENSG00000122952 | 10 | Downstream | 58,081 | 1 | 6.00 × 10−6 | 0.016644 |
| NUF2 | ENSG00000143228 | 1 | Intron | 0 | 1 | 1.12 × 10−6 | 0.007421 |
| SPC24 | ENSG00000161888 | 19 | 3UTR | 0 | 1 | 1.61 × 10−6 | 0.009713 |
| Sumoylation | |||||||
| SUMO1 | ENSG00000112701 | 2 | Intron | 0 | 1 | 1.25 × 10−5 | 0.023445 |
| ZNF451 | ENSG00000226803 | 6 | Intron | 0 | 1 | 2.22 × 10−6 | 0.011398 |
| SENP6 | ENSG00000112701 | 6 | Intron | 0 | 1 | 3.12 × 10−6 | 0.013217 |
| SENP7 | ENSG00000138468 | 3 | Intron | 0 | 1 | 4.73 × 10−6 | 0.014903 |
| SENP7 | ENSG00000138468 | 3 | Intron | 0 | 1 | 1.16 × 10−5 | 0.024458 |
Table 4 also includes details on the addition of the Small Ubiquitin-like MOdifier (SUMO) protein to histones. Sumoylation is a key post-translational modification (PTM) of many proteins which has been shown to be critically involved in many key genomic functions such as DSB repair, DNA transcription and replication and chromosomal segregation and synapsis [257,258]. Sumoylation is a foundational post-translational modification on many proteins including RNA polymerase II which forms the basis for the addition of sometimes lengthy chains of PTM’s which control these key genomic activities [258]. Δ9THC acting via CB1Rs has been shown to directly modulate P53 (the “guardian of the genome”) and Mdm2 (murine double minute, one of its key controlling proteins) [259]. As documented in the lower segment of Table 4, it was demonstrated in the Schrott EWAS that SUMO1 itself, one of the key E3 SUMO ligases which attaches the PTM to proteins, ZNF451 (zinc finger 451) and two of the SUMO endopeptidase proteins (SENP6 and SENP7) which cleave the SUMO PTM’s are affected epigenomically by cannabis dependence and withdrawal.
Since centromeres form the site of attachment of the chromosomes to the mitotic spindle, it follows that centromeric stability is key to maintenance of genomic stability [2]. In fact, centromeres are intrinsically “stiffer” and more fragile than the rest of the chromosome and represent “hot spots” for double stranded break (DSB) occurrence and chromosomal rearrangements [2]. Accurate repair of these breaks by homologous recombination is therefore essential to genome stability. Homologous recombination is normally understood to be suppressed in the G1 phase of the cell cycle. However, it has recently been reported that CENPA together with its chaperone HJURP (Holliday Junction Recognition Protein) and dimethylation of H3 (H3K4me2) permit invasion of the double stranded DNA by the DNA-RNA hybrids (R-loops) and licences the assembly of the RAD51 (RAD51 Recombinase)—BRCA1 (BRCA1 DNA Repair Associated 1)—BRCA2 complex which is the core complex of the main high fidelity homologous recombination (HR) pathway. Inhibition of HR necessarily leads to activation of much lower fidelity pathways such as microhomology-mediated end joining mediated by RAD52 and compromises genomic stability [2]. These investigators were able to demonstrate that RAD51 inhibition greatly increased centromeric breaks and centromeric translocations in NIH3T3 cells (as immortalized embryonic fibroblast cell line). Inhibition of both RAD51 and RAD52 together, inhibited both major repair pathways and blocked the formation of chromosomal translocations [2].
These findings lend special significance then to the combined demonstration in Supplementary Table S13 of much greater epigenomic interference with RAD51 than RAD52 by cannabis dependence and withdrawal (9 hits vs. 1) in the Schrott data and the well documented increased rate of chromosomal translocations seen experimentally after cannabis exposure [19,20,21,22,23,24,260,261,262].
3.2.5. Prefrontal Cortex and Brain
It is of interest to consider the representation in the Schrott EWAS of some of the key genes and pathways which are believed to be central to brain development. DSCAM (Down syndrome cell adhesion molecule) is most highly expressed in the fetal brain and retina where it is involved in neuronal self-avoidance, axon growth cone guidance, amacrine and retinal ganglion cell dendrite arborisation, commissural midline crossing in the spinal cord, homophilic synapse development and congenital heart disease [263,264]. It is overexpressed in Down syndrome and this has been implicated in some of the development of intellectual impairment in that disorder [264]. Supplementary Table S14 sets out the 14 EWAS hits in the Schrott database for DSCAM.
DLGAP2 (DLG associated protein) is an autism associated candidate gene also implicated in schizophrenia which has previously been linked with paternal cannabis exposure in sperm EWAS Studies [27]. It was thus of interest to see if the present study confirmed these earlier results. Supplementary Table S15 shows that indeed these results were strongly confirmed by the present EWAS series.
It was shown in the last decade that one of the main reasons for the relatively very enlarged frontal lobes of the human brain is the increased activity of Robo (Roundabout) signaling in the frontal cortex which leads to a greatly expanded neurogenesis in the frontal lobes and hyperproliferation of dedicated neural progenitor cells which feed into the exuberant frontal lobar growth [265,266,267]. Slits 1–3 form the natural ligand for robo receptors. The system is involved in both nervous system development and patterning and axonal guidance and also in arterial pathfinding and steering [209]. It has also been shown that this activity is blocked by cannabinoids [268]. It was therefore fascinating to observe that SRGAP2C (SLIT-ROBO Rho GTPase Activating Protein 2C) was identified by genomic screens and comparative genetics across many species to be the gene responsible for the exuberant outgrowth of the human forebrain neocortex [269]. Indeed, inducible expression of the forebrain of mice increased the cortical neuronal density and the synaptic short and long range corticocortical and bidirectional thalamocortical connectivity of layer 2/3 pyramidal cortical cells, enhancing their computational power and the rodents’ ability to quickly learn complex sensory-discriminant tasks [269].
For these reasons, it was of interest to observe how this system performed in the Schrott EWAS. Supplementary Table S16 sets out five results for Slits, Supplementary Table S17 sets out 26 results for Robo and Supplementary Table S18 sets out the eight results for SRGAP2C and its natural antagonist and controller SRGAP2B.
Another system which has also been shown to induce the relative overgrowth of the enlarged human forebrain is retinoic acid (RA). It was recently shown that high concentrations of RA at the frontal pole decline to lower and more normal levels at the posterior of the prefrontal neocortex in the premotor cortex [270]. The enzyme at the anterior pole which is chiefly responsible for synthesizing the high levels of RA is ALDH1A1 (aldehyde dehydrogenase 1 family member 1), the RA signal is transduced by the retinoid receptors RXRG and RARB, and RA is catabolized near the premotor cortex by CYP26B1 which is part of the cytochrome P450 system [270].
It was thus of interest to examine how these systems were affected in the Schrott EWAS. Supplementary Table S19 lists 11 hits for ALDH1 including two hits for ALDH1A1 and cadherin and protocadherin (PCDH17) which also function in this pathway. Indeed there were 156 hits for protocadherin 17 from the very lowest p-vale of 7.73 × 10−20 [27]. The nine hits for retinoid receptors are disclosed in Supplementary Table S20. Although CYP26B was not identified in the Schrott screen there were twelve hits for CYP2 series cytochromes including CYP20A1, CYP27A1, CYP27C1 and CYP27C2; and CYP2B7P, CYP2C1, CYP2C18, CYP2C61P and CYP2W1.
3.2.6. Cardiovascular System
Aging of the cardiovascular system is known to be a critical determinant and driver of systemic aging [271,272,273,274,275,276]. Indeed, it is said that one is as “old as one’s arteries” [157,158,277,278,279]. This is true at both the macrovascular level, with myocardial infarction being a major cause of death in developed nations, and at the microvascular levels where capillaries and sinusoids often form critical elements of many stem-cell niches [157,278,279]. Moreover, a two-way crosstalk has recently been defined between major cardiovascular disorders (myocardial infarction, hypertension and atherosclerosis) and the bone marrow haemopoietic stem-cell niche where endothelial inflammation in one compartment directly signals to the stem-cell compartment of the other system [280,281]. For these reasons, consideration of the epigenomic findings in the Schrott cannabis exposure and withdrawal data of relevance to arterial health are central to any consideration of cannabinoid-related aging processes. A detailed consideration of the cardiovascular hits in the Schrott study is deferred until the later section on teratology (see Supplementary Table S25).
It is of interest to consider the genomic processes controlling arterial health. The key genes involved in generating arteries from embryonic angioblasts are listed as sonic hedgehog (shh), vascular endothelial growth factor (VEGF), notch and ephrin B2 [209]. These genes and pathways were therefore screened through the Schrott dataset and the hits identified in Table 5A,B were identified. PTCH1 is the main shh receptor. Gli3 (GLI family zinc finger 3) is one of the key transcription factors which mediates shh signaling in the nucleus [282]. Gli3 scored 185 hits in the Schrott EWAS data of which only a selection has been extracted for illustration. PSENEN (Presenilin enhancer, gamma secretase subunit) is a key plasmalemma bound enzyme which processes the shh ligand after receptor binding. SUFU (SUFU negative regulator of hedgehog signaling) inhibits shh [283].
Table 5.
Cannabis Impacts on Sonic Hedgehog Signaling, Schrott EWAS Data.
| (A) | |||||||||
| Nearest Gene Name | Nearest Gene Number | Page No. | Annotation | Chromosome Number | Dependency Status | Relative Position | Distance to Nearest Gene | p-Value | Bonferroni Adjusted p-Value |
| PTCH1 | ENSG00000185920 | 58 | Shh Receptor | 9 | Dependence | Intron | 0 | 3.46 × 10−6 | 0.012789 |
| PTCHD1-AS | ENSG00000233067 | 91 | lnc Promoter/enhancer | X | Dependence | Intron | 0 | 8.61 × 10−6 | 0.019678 |
| PTCHD1-AS | ENSG00000233067 | 129 | lnc Promoter/enhancer | X | Withdrawal | Intron | 0 | 8.21 × 10−8 | 0.002096 |
| PTCHD4 | ENSG00000244694 | 138 | Shh Receptor; Otopalatodigital syndrome | 6 | Withdrawal | Intron | 0 | 4.21 × 10−7 | 0.005104 |
| PTCH1 | ENSG00000185920 | 185 | Shh Receptor | 9 | Withdrawal | Intron | 0 | 5.80 × 10−6 | 0.017679 |
| SUFU | ENSG00000161996 | 207 | Hedgehog Inhibitor | 16 | Withdrawal | Exon | 0 | 1.01 × 10−5 | 0.022942 |
| Gli3 | ENSG00000106571 | 78 | Shh mediator | 7 | Dependence | Downstream | 81232 | 6.35 × 10−6 | 0.017090 |
| Gli3 | ENSG00000106571 | 99 | Shh mediator | 7 | Dependence | Intron | 0 | 1.00 × 10−5 | 0.021181 |
| Gli3 | ENSG00000106571 | 124 | Shh mediator | 7 | Withdrawal | Downstream | 20318 | 8.23 × 10−9 | 0.000646 |
| Gli3 | ENSG00000106571 | 182 | Shh mediator | 7 | Withdrawal | Intron | 0 | 5.28 × 10−6 | 0.001687 |
| Gli3 | ENSG00000106571 | 231 | Shh mediator | 7 | Withdrawal | Intron | 0 | 1.62 × 10−5 | 0.028539 |
| (B) | |||||||||
| Nearest Gene Name | Nearest Gene Number | Page No. | Annotation | Chromosome Number | Dependency Status | Number Genes Identified | Function | p-Value | |
| PTCH1 | ENSG00000185920 | 237 | Notch Processing | 9 | KEGG Pathway | 31 | Notch Processing | 0.044117 | |
| PTCH1 | ENSG00000185920 | 238 | Skin cancer | 9 | KEGG Pathway | 54 | Notch Processing | 0.067770 | |
| PSENEN | ENSG00000185920 | 326 | Cutaneous melanoma | 19 | Withdrawal | 110 | Notch Processing | 0.000008 | |
| Gli3 | ENSG00000106571 | 325 | Skin lesion | 7 | Withdrawal | 115 | Notch transcription factor | 1.65 × 10−6 | |
| Gli3 | ENSG00000106571 | 325 | Head and Neck SCC | 7 | Withdrawal | 53 | Notch transcription factor | 3.59 × 10−6 | |
| Gli3 | ENSG00000106571 | 325 | Skin cancer | 7 | Withdrawal | 113 | Notch transcription factor | 4.79 × 10−6 | |
| Gli3 | ENSG00000106571 | 325 | Lung adenocarcinoma | 7 | Withdrawal | 42 | Notch transcription factor | 5.84 × 10−6 | |
| Gli3 | ENSG00000106571 | 325 | Cancer | 7 | Withdrawal | 149 | Notch transcription factor | 7.17 × 10−6 | |
| Gli3 | ENSG00000106571 | 326 | Large bowel cancer | 7 | Withdrawal | 120 | Notch transcription factor | 7.45 × 10−6 | |
| Gli3 | ENSG00000106571 | 326 | Cutaneous melanoma | 7 | Withdrawal | 110 | Notch transcription factor | 7.71 × 10−6 | |
| Gli3 | ENSG00000106571 | 326 | High-grade astrocytoma | 7 | Withdrawal | 82 | Notch transcription factor | 8.42 × 10−6 | |
| Gli3 | ENSG00000106571 | 326 | Abdominal adenocarcinoma | 7 | Withdrawal | 135 | Notch transcription factor | 8.46 × 10−6 | |
| Gli3 | ENSG00000106571 | 327 | Solid cancer | 7 | Withdrawal | 150 | Notch transcription factor | 9.16 × 10−6 | |
| Gli3 | ENSG00000106571 | 327 | Head and Neck cancer | 7 | Withdrawal | 137 | Notch transcription factor | 9.54 × 10−6 | |
| Gli3 | ENSG00000106571 | 327 | Sensory development | 7 | Withdrawal | 18 | Notch transcription factor | 1.30 × 10−5 | |
| Gli3 | ENSG00000106571 | 327 | Carcinoma | 7 | Withdrawal | 148 | Notch transcription factor | 1.38 × 10−5 | |
Supplementary Table S21A,B list genes involved in the notch signaling pathway identified in the Schrott screen. JAG1 is a canonical notch ligand. Notch 1-3 are notch receptors. RBPJ (Recombination Signal Binding Protein for Immunoglobulin Kappa J Region) is an important transcriptional regulator of notch signaling. PSENEN also processes the notch ligand at the cell membrane [284].
Supplementary Table S22A,B list the six hits in the Schrott database relating to VEGF and EphrinB2 signaling. Both VEGF and EphrinB2 are key signaling and transduction factors involved in mediating numerous major morphogenic decisions and pathways [209,251].
In this regard, fascinating recent detailed studies have appeared on the profound impact of prenatal cannabinoid (as Δ9THC) exposure on cardiac development. Robinson and colleagues showed that prenatal exposure to Δ9THC led to cardiac wall thickening in three week old mice and thickening and hypertrophy of the semilunar valves and increased ventricular septal defects [285]. Myocardial cell proliferation was increased and cardiac function was reduced with lower ejection fraction, fractional shortening and cardiac output.
Lee and co-workers demonstrated rat fetal growth restriction following in utero exposure to Δ9THC, smaller hearts and reduced a heart to body weight ratio at birth [286]. By three weeks of post-natal life this has been reversed by post-natal catchup growth which resulted in larger but stiffer ventricular wall thickness and a corresponding reduction in cardiac output. This was linked with increased expression of collagens I and III, reduced matrix metalloproteinase 2 and increased glycogen synthase kinase 3β signaling all of which are linked with cardiac remodeling. This study is highly significant as it relates the smaller hearts at birth to subsequent cardiac stiffness and reduced cardiac output, all of which are age related changes [277]. These changes in early postnatal life are known to be causally related to increased incidence of adult heart disease in later life which is the leading cause of death globally [285,286,287].
Many congenital anomalies and cancers in USA and European epidemiological datasets have been shown to be heightened after cannabis exposure. The following sections on these cannabinoid-related teratogenic and carcinogenic findings are respectively reviewed using the epigenomic data on changes in the DNA methylome of human sperm after cannabis exposure and withdrawal with a focus on genotoxicity and/or epigenotoxicity.
3.2.7. Cannabinoid-Related Teratogenesis
The consistent association between congenital anomalies and cannabis exposure provides functional examples of how cannabis ageing mechanisms contribute to inter-generational disability. Table 6 directly compares the congenital anomalies which were found to be cannabis-associated in USA [103] with those identified in recent reports in the larger European dataset [115]. In total, 45/62 congenital anomalies were found to be cannabis-associated in the US dataset compared to 89/95 in the larger European dataset [103,115]. These concerning findings are noted to be highly concordant with those of other investigators in recent large population-based series [66,107,108,109,110,111,116,118,288,289]. These data are presented to introduce and contextualize the system-based narrative discussion undertaken in the following sections.
Table 6.
Comparative Lists of Significantly Cannabinoid-Associated Congenital Anomalies in Europe and USA.
| No. | Europe | USA | ||||||
|---|---|---|---|---|---|---|---|---|
| Congenital Anomaly | Term | Model | p-Value | Congenital Anomaly | Term | Model | p-Value | |
| 1 | Abdominal Wall Defects | pm.Resin.Daily | Categorical | 3.01 × 10−120 | ||||
| 2 | All Anomalies | Daily_Use | Categorical | <2.2 × 10−320 | ||||
| 3 | Amniotic band | pm.Resin.Daily | Categorical | 1.09 × 10−47 | ||||
| 4 | Anencephalus and similar | Resin_THC | Categorical | 1.53 × 10−212 | ||||
| 5 | Annular Pancreas | Daily_Use | Categorical | 1.52 × 10−13 | ||||
| 6 | Anophthalmos | Daily_Use | Categorical | 1.06 × 10−6 | ||||
| 7 | Ano-rectal atresia and stenosis | pm.Resin.Daily | Categorical | 4.03 × 10−39 | Large intestinal and Rectal atresia/stenosis | Cannabidiol_Estimates | Continuous | 0.0040 |
| 8 | Anotia | Herb_THC | Categorical | 4.63 × 10−13 | Anotia/microtia | LM_Cannabis | Continuous | 7.57 × 10−4 |
| 9 | Aortic atresia/interrupted aortic arch | LM.Cann_Resin_THC | Categorical | 5.71 × 10−25 | Interrupted aortic arch | LM_Cannabis | Continuous | 3.40 × 10−6 |
| 10 | Aortic Valve stenosis/atresia | Herb_THC | Categorical | 7.14 × 10−13 | Aortic valve stenosis | LM_Cannabis | Continuous | 0.0019 |
| 11 | Arhinencephaly/holoprosencephaly | LM_Herb.Daily | Continuous | 0.0052 | ||||
| 12 | Arterial Truncus | pm.Herb.Daily | Categorical | 9.92 × 10−7 | ||||
| 13 | Atrial septal defect (ASD) | Herb_THC | Categorical | <2.2 × 10−320 | Atrial septal defect (ASD) | LM_Cannabis | Continuous | 0.0378 |
| 14 | Atrioventricular septal defect (AVSD) | pm.Resin.Daily | Categorical | 1.65 × 10−101 | Atrioventricular septal defect (AVSD) | LM_Cannabis | Categorical | 0.0470 |
| 15 | Bilateral renal agenesis including Potter syndrome | Herb_THC | Categorical | 1.08 × 10−47 | Renal agenesis/hypoplasia | LM_Cannabis | Continuous | 7.34 × 10−4 |
| 16 | Bile duct atresia | Daily_Use | Categorical | 1.00 × 10−40 | Biliary atresia | Cannabidiol_Estimates | Continuous | 2.43 × 10−4 |
| 17 | Bladder Extrophy/Epispadias | pm.Resin.Daily | Categorical | 1.56 × 10−18 | Bladder extrophy | LM_Cannabis | Continuous | 0.0170 |
| 18 | Choanal Atresia | Herb_THC | Categorical | 7.34 × 10−94 | Choanal atresia | Δ9THC_Estimates | Continuous | 0.0033 |
| 19 | Chromosomal | Daily_Use | Categorical | <2.2 × 10−320 | Chromosomal | LM_Cannabis | Mixed Effects | 9.38 × 10−30 |
| 20 | Cleft lip with or without palate | Herb_THC | Categorical | 1.80 × 10−101 | Cleft lip with and without cleft palate | Cannabidiol_Estimates | Categorical | 0.0159 |
| 21 | Cleft palate | Herb_THC | Categorical | 1.79 × 10−34 | Cleft palate alone | LM_Cannabis | Continuous | 0.0014 |
| 22 | Cloacal exstrophy | LM_Cannabis | Categorical | 2.13 × 10−86 | ||||
| 23 | Club foot-talipes equinovarus | Daily_Use | Categorical | 4.23 × 10−292 | Clubfoot | LM_Cannabis | Continuous | 3.16 × 10−5 |
| 24 | Coarctation Aorta | Daily_Use | Categorical | 5.78 × 10−33 | Coarctation of the aorta | LM_Cannabis | Categorical | 9.74 × 10−45 |
| 25 | Congenital cataract | Daily_Use | Categorical | 4.88 × 10−66 | Congenital cataract | LM_Cannabis | Continuous | 0.0479 |
| 26 | Congenital glaucoma | Daily_Use | Categorical | 1.52 × 10−43 | ||||
| 27 | Congenital Heart | pm.Herb.Daily | Categorical | <2.2 × 10−320 | ||||
| 28 | Conjoined twins | Daily_Use | Categorical | 8.62 × 10−14 | ||||
| 29 | Craniosynostosis | Daily_Use | Categorical | 5.72 × 10−155 | ||||
| 30 | Cystic adenomatous malformation of lung | Daily_Use | Categorical | 4.05 × 10−80 | ||||
| 31 | Diaphragmatic Hernia | Daily_Use | Categorical | 8.77 × 10−57 | Diaphragmatic hernia | LM_Cannabis | Categorical | 2.11 × 10−8 |
| 32 | Digestive system | pm.Herb.Daily | Categorical | 1.61 × 10−264 | ||||
| 33 | Double outlet right ventricle | pm.Herb.Daily | Categorical | 1.28 × 10−46 | Double outlet right ventricle | LM_Cannabis | Categorical | 7.31 × 10−4 |
| 34 | Down Syndrome | Daily_Use | Categorical | <2.2 × 10−320 | Trisomy 21 (Down syndrome) | LM_Cannabis | Categorical | 4.02 × 10−26 |
| 35 | Duodenal stenosis/atresia | Herb_THC | Categorical | 1.50 × 10−10 | ||||
| 36 | Ear, face and neck | Daily_Use | Categorical | 3.38 × 10−44 | ||||
| 37 | Ebstein’s Anomaly | pm.Resin.Daily | Categorical | 3.23 × 10−17 | ||||
| 38 | Edward syndrome/Trisomy 18 | Daily_Use | Categorical | <2.2 × 10−320 | Edward syndrome/Trisomy 18 | LM_Cannabis | Categorical | 1.06 × 10−61 |
| 39 | Encephalocele | pm.Resin.Daily | Categorical | 4.76 × 10−21 | Encephalocele | LM_Cannabis | Continuous | 0.0013 |
| 40 | Epispadias | LM_Cannabis | Continuous | 0.0111 | ||||
| 41 | Eye | Daily_Use | Categorical | 2.27 × 10−175 | ||||
| 42 | Fetal alcohol syndrome | pm.Resin.Daily | Categorical | 5.88 × 10−57 | ||||
| 43 | Gastroschisis | Herb_THC | Categorical | 6.55 × 10−39 | ||||
| 44 | Genetic syndromes + microdeletions | pm.Herb.Daily | Categorical | 1.38 × 10−228 | Deletion 22q11.2 | LM_Cannabis | Continuous | 0.0024 |
| 45 | Genital | pm.Herb.Daily | Categorical | 2.55 × 10−243 | ||||
| 46 | Hip dislocation and/or dysplasia | Daily_Use | Categorical | <2.2 × 10−320 | Congenital hip dislocation | LM_Cannabis | Categorical | 7.27 × 10−70 |
| 47 | Hirschsprung’s disease | Daily_Use | Categorical | 2.54 × 10−88 | Hirschsprung disease (congenital megacolon) | LM_Cannabis | Categorical | 6.69 × 10−6 |
| 48 | Holoprosencephaly/Arhinencephaly | LM_Cannabis | Categorical | 1.22 × 10−72 | Holoprosencephaly | LM_Cannabis | Categorical | 2.90 × 10−12 |
| 49 | Hydrocephalus | pm.Herb.Daily | Categorical | 1.76 × 10−110 | ||||
| 50 | Hydronephrosis | Herb_THC | Categorical | <2.2 × 10−320 | ||||
| 51 | Hypoplastic Left Heart | Daily_Use | Categorical | 3.37 × 10−61 | Hypoplastic left heart syndrome | LM_Cannabis | Continuous | 0.0047 |
| 52 | Hypoplastic right heart | Resin_THC | Categorical | 2.85 × 10−59 | ||||
| 53 | Hypospadias | pm.Herb.Daily | Categorical | 2.92 × 10−177 | Hypospadias | LM_Cannabis | Continuous | 1.16 × 10−5 |
| 54 | Klinefelter syndrome | Daily_Use | Categorical | 1.75 × 10−41 | ||||
| 55 | Large intestinal and Rectal atresia/stenosis | Cannabidiol_Estimates | Continuous | 0.0040 | ||||
| 56 | Lateral anomalies | LM.Cann_Herb_THC | Categorical | 2.36 × 10−48 | ||||
| 57 | Limb anomalies | pm.Herb.Daily | Categorical | <2.2 × 10−320 | ||||
| 58 | Limb reductions | Daily_Use | Categorical | 8.20 × 10−65 | Limb deficiencies (reduction defects) | LM_Cannabis | Continuous | 0.0134 |
| 59 | Lower limb Reduction deformity | LM_Cannabis | Continuous | 0.0420 | ||||
| 60 | Maternal infections resulting in malformations | Daily_Use | Categorical | 4.15 × 10−87 | ||||
| 61 | Microphthalmos/Anophthalmos | Daily_Use | Categorical | 1.25 × 10−55 | Microphthalmos/Anophthalmos | Δ9THC_Estimates | Continuous | 0.0045 |
| 62 | Mitral valve anomalies | pm.Herb.Daily | Categorical | 8.99 × 10−58 | ||||
| 63 | Multicystic renal dysplasia | pm.Resin.Daily | Categorical | 6.70 × 10−251 | ||||
| 64 | Nervous system | pm.Herb.Daily | Categorical | <2.2 × 10−320 | ||||
| 65 | Neural Tube Defects | Resin_THC | Categorical | 9.97 × 10−269 | ||||
| 66 | Obstructive genitourinary defect | Cannabidiol_Estimates | Categorical | 2.22 × 10−15 | ||||
| 67 | Oesophageal stenosis/atresia | Daily_Use | Categorical | 3.49 × 10−44 | Oesophageal atresia/tracheoesophageal fistula | LM_Cannabis | Continuous | 4.83 × 10−6 |
| 68 | Omphalocele | pm.Resin.Daily | Categorical | 4.94 × 10−131 | Omphalocele | LM_Cannabis | Continuous | 0.0025 |
| 69 | Oro-facial clefts | Herb_THC | Categorical | 3.99 × 10−133 | ||||
| 70 | Patau syndrome/trisomy 13 | Daily_Use | Categorical | 1.08 × 10−144 | Patau syndrome/trisomy 13 | LM_Cannabis | Continuous | 2.08 × 10−7 |
| 71 | PDA as only CHD in term infants (>=37 weeks) | pm.Herb.Daily | Categorical | 2.14 × 10−20 | ||||
| 72 | Polydactyly | pm.Resin.Daily | Categorical | 1.46 × 10−292 | ||||
| 73 | Posterior urethral valve and/or prune belly | pm.Resin.Daily | Categorical | 1.28 × 10−42 | Congenital posterior urethral valves | LM_Cannabis | Continuous | 1.18 × 10−4 |
| 74 | Pulmonary valve atresia | Daily_Use | Categorical | 1.42 × 10−27 | Pulmonary valve atresia | Cannabidiol_Estimates | Categorical | 1.02 × 10−5 |
| 75 | Pulmonary valve stenosis | Daily_Use | Categorical | 2.09 × 10−95 | ||||
| 76 | Respiratory | pm.Herb.Daily | Categorical | 2.57 × 10−203 | ||||
| 77 | Severe CHD | Herb_THC | Categorical | 1.81 × 10-317 | ||||
| 78 | Severe microcephaly | pm.Herb.Daily | Categorical | 3.17 × 10−148 | ||||
| 79 | Single ventricle | Daily_Use | Categorical | 1.03 × 10−25 | Single ventricle | LM_Cannabis | Categorical | 0.0060 |
| 80 | Situs inversus | Daily_Use | Categorical | 1.42 × 10−44 | ||||
| 81 | Skeletal dysplasias | Daily_Use | Categorical | 5.12 × 10−74 | ||||
| 82 | Small Intestine stenosis/atresia | pm.Herb.Daily | Categorical | 8.23 × 10−31 | Small intestinal atresia/stenosis | Cannabidiol_Estimates | Continuous | 3.39 × 10−6 |
| 83 | Spina Bifida | Resin_THC | Categorical | 3.93 × 10−84 | Spina bifida without anencephalus | Δ9THC_Estimates | Continuous | 0.0008 |
| 84 | Syndactyly | pm.Resin.Daily | Categorical | 3.47 × 10−16 | ||||
| 85 | Teratogenic syndromes with malformations | Daily_Use | Categorical | 1.42 × 10−139 | ||||
| 86 | Tetralogy of Fallot | Daily_Use | Categorical | 3.12 × 10−47 | Tetralogy of Fallot | LM_Cannabis | Continuous | 0.0168 |
| 87 | Total Anomalous Pulmonary Venous Return | Herb_THC | Categorical | 4.07 × 10−09 | Total anomalous pulmonary venous connection | LM_Cannabis | Continuous | 0.0299 |
| 88 | Transposition of great vessels | Resin_THC | Categorical | 9.96 × 10−33 | Transposition of great arteries | Cannabidiol_Estimates | Continuous | 0.0479 |
| 89 | Turner syndrome | Daily_Use | Categorical | 1.10 × 10−146 | Turner syndrome | LM_Cannabis | Categorical | 7.69 × 10−49 |
| 90 | Tricuspid valve stenosis/atresia | Daily_Use | Categorical | 6.86 × 10−24 | ||||
| 91 | Urinary | pm.Resin.Daily | Categorical | <2.2 × 10−320 | ||||
| 92 | Valproate syndrome | Daily_Use | Categorical | 1.57 × 10−7 | ||||
| 93 | Vascular disruption anomalies | Herb_THC | Categorical | 3.46 × 10−101 | ||||
| 94 | VATER/VACTERL | pm.Herb.Daily | Categorical | 2.43 × 10−36 | ||||
| 95 | Ventricular septal defect (VSD) | pm.Resin.Daily | Categorical | <2.2 × 10−320 | Ventricular septal defect | LM_Cannabis | Continuous | 0.0021 |
Abbreviations: pm—Past month cannabis use. LM.Cann—Last Month Cannabis Use. Herb_THC—THC concentration of cannabis herb. Resin_THC—THC concentration of cannabis herb. DailyUse—Percent using daily or almost daily. LM_Herb.Daily = LM.Cann × DailyUse. LM.Cann_Herb_THC = LM.Cann × Herb_THC. LM.Cann_Resin_THC = LM.Cann × Resin_THC. pm.Herb.Daily = pm × Herb_THC × Daily_Use. pm.Resin.Daily = pm × Resin_THC × Daily_Use.
The p-values which relate to these various anomalies may be extracted from the Schrott EWAS database as indicated in Supplementary Table S23. This Table provides a list of 245 systems, targets and annotations ordered by their system for all of the above EWAS hits. The above table demonstrates cross-nationally consistent associations between cannabis exposure and varied congenital abnormalities. The sections that follow evaluates evidence associating cannabis exposure with epigenomic mechanisms for congenital abnormalities.
Supplementary Tables S24–S32 present the systems-based interrogation of the Schrott database for the cardiovascular, central nervous, face, general, limb, gastrointestinal, chromosomal, uronephrological and body wall systems respectively. Examination of these Supplementary Tables demonstrates that they offer profound insights into the possible pathogenesis of the congenital anomalies described in Table 6.
Supplementary Table S24 describes 73 central nervous system EWAS hits and lists features such as brain size, brain formation, forebrain patterning, development of many kinds of synapses, head development, head size, movement and viability of cerebral cortex cells, neurite growth, neuronal growth, neuronogenesis and neuronal outgrowth and proliferation, brain cell migration, axonogenesis and outgrowth which would be consistent not only with defects such as brain growth and size (microcephalus and anencephalus) but also defects of brain function such as epileptiform disorders, autism [117,174,288,290,291], intellectual disability (mental retardation) and many mental illnesses in childhood and later life [290,291,292,293,294,295,296,297,298,299,300]. Many disorders of eye development are also noted which is consistent with the finding of microphthalmia in both the USA and European series. Many disorders of inner ear development are noted consistent with the findings of microtia and anotia in the USA and European datasets. Associations are also reported with some malignant brain conditions which is consistent with earlier reports [59].
Supplementary Table S25 shows the 29 EWAS hits which are linked with the 23 cardiovascular anomalies in Europe and the eleven cardiovascular anomalies in USA. Hypoplasia of the cardiac chambers is mentioned both in Supplementary Table S25 and reported for both left and right ventricles in the congenital anomaly (CA) list of Table 6. Septal defects are reported in the EWAS list and in the CA list for both atria and ventricles. Anomalies of the atrioventricular valves/endocardial cushions are mentioned in the EWAS hit list and mitral and tricuspid valvular anomalies including Fallot’s teratology are mentioned in the CA teratological list. Many defects of vasculogenesis, angiogenesis, pulmonary venogenesis and vascular breakdown are mentioned on the EWAS list and the cardiovascular anomalies of transposition of the great arteries, total anomalous pulmonary venous return, vascular disruptions, VACTERL (vertebral, anal, cardiac, tracheoesophageal atresia, renal and limb) syndrome, aortic arch anomalies, coarctation of the aorta, severe cardiac congenital anomalies, double outlet right ventricle, tetralogy of Fallot and others were identified on the CA list.
Supplementary Table S26 lists 22 EWAS hits of interest for facial development. Development of the face has been shown to impact brain development embryologically as the organizers for both regions interact during gestation and both are controlled by strong anterior gradients of sonic hedgehog and retinoic acid [209]. Supplementary Table S26 lists anomalies of the head, palate, nose, lens, iris and ear which relate to listed CAs of microcephaly, cleft lip and palate (which may involve the nasolabial groove), congenital cataract (in both USA and Europe) and anotia/microtia (in both USA and Europe). Importantly the severe CA holoprosencephaly which is strongly associated with abnormal brain development was identified as a strong association of cannabis teratogenesis in Europe and a weak association in USA [103,115].
Supplementary Table S27 lists 60 hits from the Schrott EWAS dataset relating to “general” issues which do not readily classify under other systems. In total, 36 (60%) of these hits relate to cannabis dependence and 24 (40%) to cannabis withdrawal. The EWAS list provides fascinating and powerful insights to the observed teratological profile documented in Table 6. Defects of cell growth, embryonic growth, organismal growth and embryonic morphogenesis head up the Table. Defects of most major DNA activities are comprehended including synthesis, binding, recombination, transcription, translation, repair, recombination, replication, and synapsis (crossing over) are shown. Defective RNA translation is indicated. Defects of chromosomal synapsis, homologous pairing, assembly and synapsis are shown.
Mitochondrial defects are listed. This is important as mitochondria supply both the energy for genomic and epigenomic reactions and the underlying substrates for the epigenomic machinery. Two hits for microtubular impairment are shown, one each in cannabis dependence and withdrawal. This may relate to anomalous chromosomal mis-segregation disorders for chromosomal trisomies and monosomies affecting chromosomes 13, 18, 21 and X (Supplementary Table S27). Reproductive defects are indicated with diminished ovarian reserve—a hallmark of ovarian ageing—and three hits for breast cells which potentially relate to recently reported elevated rates of breast malignancy in USA in relation to cannabis consumption [66,112,113,114,121].
Anomalies of body trunk and body axis development are shown. In total, 22 anomalies of bone development are listed consistent with very elevated rates of VACTERL syndrome reported from Europe.
Supplementary Table S28 reports six hits for limb anomaly development consistent with major limb anomalies including limb reductions reported from both Europe and USA. These studies may be extended further as indicated in Table 7. It is known that morphogens such as retinoic acid, fibroblast growth factors (FGFs) and Wnts play pivotal roles in the three dimension temporally sequenced complex choreography of limb development [209]. Genes such as Meis1/2 (Meis homeobox), FGF4, RXRA (retinoid X receptor) and RARB (Retinoic Acid Receptor B), TBX4/5 (T-box transcription factor), Wnt’s, shh, GREM1/2 (Gremlin), CHD7 (Chromodomain Helicase DNA binding protein 7), TMEM107 (Transmembrane Protein 107), MEGF8 (Multiple EGF-like domains 8), BMP4, and GLI3 play key roles [27,209,301].
Table 7.
Epigenomic Hits for Limb Congenital Anomalies Extended Exploration, Schrott EWAS Database.
| Gene Acronym | Gene Name | Gene Number | Functional Annotation | Status | Page Number | Number of Genes Annotated | p-Value |
|---|---|---|---|---|---|---|---|
| Meis1 | Meis Homeobox 1 | ENSG00000143995 | Withdrawal | 194 | 37 | 7.55 × 10−6 | |
| Meis1 | Meis Homeobox 1 | ENSG00000143995 | Cancer growth | Withdrawal | 325 | 149 | 7.17 × 10−6 |
| Meis1 | Meis Homeobox 1 | ENSG00000143995 | Sensory organ development | Withdrawal | 327 | 18 | 1.30 × 10−5 |
| Meis1 | Meis Homeobox 1 | ENSG00000143995 | Eye formation | Withdrawal | 328 | 15 | 2.81 × 10−5 |
| Meis1 | Meis Homeobox 1 | ENSG00000143995 | Cancer | Withdrawal | 329 | 151 | 4.32 × 10−5 |
| Meis1 | Meis Homeobox 1 | ENSG00000143995 | Lens formation | Withdrawal | 333 | 4 | 9.17 × 10−5 |
| Meis1 | Meis Homeobox 1 | ENSG00000143995 | Cancer | Withdrawal | 334 | 88 | 1.22 × 10−4 |
| Meis1 | Meis Homeobox 1 | ENSG00000143995 | Eye formation | Withdrawal | 334 | 11 | 1.23 × 10−4 |
| Meis2 | Meis Homeobox 2 | ENSG00000134138 | Withdrawal | 134 | 97 | 2.36 × 10−7 | |
| Meis2 | Meis Homeobox 2 | ENSG00000134138 | Withdrawal | 181 | 1 | 0.016676 | |
| Meis2 | Meis Homeobox 2 | ENSG00000134138 | Withdrawal | 209 | 1 | 0.023289 | |
| Meis2 | Meis Homeobox 2 | ENSG00000134138 | Upper Aerodigestive SCC | Withdrawal | 325 | 40 | 1.28 × 10−6 |
| Meis2 | Meis Homeobox 2 | ENSG00000134138 | Upper Aerodigestive SCC | Withdrawal | 325 | 53 | 3.59 × 10−6 |
| Meis2 | Meis Homeobox 2 | ENSG00000134138 | Cranial nerve abnormality | Withdrawal | 325 | 7 | 6.34 × 10−6 |
| Meis2 | Meis Homeobox 2 | ENSG00000134138 | Cancer | Withdrawal | 325 | 149 | 7.17 × 10−6 |
| FGFs | Fibroblast Growth Factor | Withdrawal | 175 | ||||
| FGFR1OP | FGF Receptor 1 Oncogene Partner | ENSG00000213066 | Withdrawal | 13 | 1 | 0.002226 | |
| FGF5 | Fibroblast Growth Factor 5 | ENSG00000138675 | Withdrawal | 21 | 1 | 0.004362 | |
| FGF14 | Fibroblast Growth Factor 14 | ENSG00000102466 | Withdrawal | 25 | 1 | 0.005329 | |
| FGFR2 | Fibroblast Growth Factor Receptor 2 | ENSG00000066468 | Withdrawal | 28 | 1 | 0.005981 | |
| FGF14 | Fibroblast Growth Factor 14 | ENSG00000102466 | Dependence | 30 | 1 | 8.68 × 10−7 | |
| FGF12 | Fibroblast Growth Factor 12 | ENSG00000114279 | Dependence | 41 | 1 | 0.009199 | |
| FGF12 | Fibroblast Growth Factor 12 | ENSG00000114279 | Dependence | 54 | 1 | 0.001187 | |
| FGF3 | Fibroblast Growth Factor 3 | ENSG00000186895 | Dependence | 81 | 1 | 0.017663 | |
| FGFRL1 | FGF Receptor Like 3 | ENSG00000127418 | Dependence | 86 | 1 | 0.018855 | |
| FGF14 | Fibroblast Growth Factor 14 | ENSG00000102466 | Dependence | 106 | 1 | 0.002259 | |
| FGF4 | Fibroblast Growth Factor 4 | ENSG00000122642 | Dependence | 17 | 7 | 2.34 × 10−7 | |
| FGF4 | Fibroblast Growth Factor 4 | ENSG00000122642 | KEGG: Rap1 signaling | 236 | 41 | 0.000353 | |
| FGF4 | Fibroblast Growth Factor 4 | ENSG00000122642 | KEGG: actin cytoskeleton | 237 | 37 | 0.004586 | |
| FGF4 | Fibroblast Growth Factor 4 | ENSG00000122642 | KEGG: melanoma | 237 | 15 | 0.021590 | |
| FGF4 | Fibroblast Growth Factor 4 | ENSG00000122642 | KEGG: MAP kinase pathway | 237 | 39 | 0.029222 | |
| FGF4 | Fibroblast Growth Factor 4 | ENSG00000122642 | KEGG: Cancer pathways | 238 | 54 | 0.067770 | |
| FGF4 | Fibroblast Growth Factor 4 | ENSG00000122642 | KEGG: Ras signaling | 328 | 38 | 0.008745 | |
| RXRA | Retinoid X Receptor Alpha | ENSG00000186350 | Withdrawal | 125 | 1 | 1.48 × 10−8 | |
| RXRG | Retinoid X Receptor Gamma | ENSG00000143171 | Withdrawal | 136 | 1 | 3.40 × 10−7 | |
| RXRA | Retinoid X Receptor Alpha | ENSG00000186350 | Withdrawal | 144 | 1 | 8.40 × 10−7 | |
| RARA | Retinoic Acid Receptor Alpha | ENSG00000131759 | Dependence | 44 | 1 | 1.95 × 10−6 | |
| RARB | Retinoic Acid Receptor Beta | ENSG00000077092 | Dependence | 73 | 1 | 5.54 × 10−6 | |
| RARB | Retinoic Acid Receptor Beta | ENSG00000077092 | Withdrawal | 124 | 1 | 7.94 × 10−9 | |
| RARB | Retinoic Acid Receptor Beta | ENSG00000077092 | Withdrawal | 168 | 1 | 3.25 × 10−6 | |
| RARB | Retinoic Acid Receptor Beta | ENSG00000077092 | Withdrawal | 190 | 1 | 6.89 × 10−6 | |
| RARB | Retinoic Acid Receptor Beta | ENSG00000077092 | Withdrawal | 215 | 1 | 1.20 × 10−5 | |
| RARA | Retinoic Acid Receptor Alpha | ENSG00000131759 | KEGG: Cancer pathways | 238 | 54 | 0.067777 | |
| WNT’s | Wnt’s | Withdrawal | 203 | ||||
| WNT7B | Wnt family member 7B | ENSG00000188064 | Dependence | 74 | 1 | 5.78 × 10−6 | |
| WNT7A | Wnt family member 7A | ENSG00000154764 | Dependence | 119 | 1 | 1.47 × 10−0 | |
| WNT7A | Wnt family member 7A | ENSG00000154764 | Dependence | 123 | 1 | 4.13 × 10−9 | |
| WNT3A | Wnt family member 3A | ENSG00000154342 | Head and neck cancer | Withdrawal | 239 | 356 | 7.73 × 10−20 |
| WNT8B | Wnt family member 8B | ENSG00000075290 | Head and neck cancer | Withdrawal | 239 | 342 | 7.74 × 10−20 |
| TBX4 | T-Box transcription factor 4 | ENSG00000121075 | Dependence | 52 | 1 | 2.72 × 10−6 | |
| TBX4 | T-Box transcription factor 4 | ENSG00000121075 | Withdrawal | 235 | 1 | 1.71 × 10−5 | |
| TBX5-AS1 | T-Box transcription factor 5 Antisense 1 | ENSG00000255399 | Withdrawal | 202 | 1 | 9.18 × 10−6 | |
| CHD7 | Chromodomain Helicase DNA Binding Protein 7 | ENSG00000171316 | Dependence | 37 | 124 | 1.37 × 10−6 | |
| CHD7 | Chromodomain Helicase DNA Binding Protein 7 | ENSG00000171316 | Upper aerodigestive SCC | Withdrawal | 325 | 40 | 1.28 × 10−6 |
| CHD7 | Chromodomain Helicase DNA Binding Protein 7 | ENSG00000171316 | Upper aerodigestive SCC | Withdrawal | 325 | 115 | 1.65 × 10−6 |
| CHD7 | Chromodomain Helicase DNA Binding Protein 7 | ENSG00000171316 | Skin lesion | Withdrawal | 325 | 53 | 3.59 × 10−6 |
| CHD7 | Chromodomain Helicase DNA Binding Protein 7 | ENSG00000171316 | Skin cancer | Withdrawal | 325 | 113 | 4.79 × 10−6 |
| CHD7 | Chromodomain Helicase DNA Binding Protein 7 | ENSG00000171316 | Cancer | Withdrawal | 325 | 149 | 7.17 × 10−6 |
| CHD7 | Chromodomain Helicase DNA Binding Protein 7 | ENSG00000171316 | Large bowel adenocarcinoma | Withdrawal | 326 | 120 | 7.45 × 10−6 |
| CHD7 | Chromodomain Helicase DNA Binding Protein 7 | ENSG00000171316 | Cutaneous melanoma | Withdrawal | 326 | 110 | 7.71 × 10−6 |
| CHD7 | Chromodomain Helicase DNA Binding Protein 7 | ENSG00000171316 | High grade astocytoma | Withdrawal | 326 | 82 | 8.42 × 10−6 |
| CHD7 | Chromodomain Helicase DNA Binding Protein 7 | ENSG00000171316 | Abdominal adenocarcinoma | Withdrawal | 326 | 135 | 8.46 × 10−6 |
| CHD7 | Chromodomain Helicase DNA Binding Protein 7 | ENSG00000171316 | Solid organ cancer | Withdrawal | 327 | 150 | 9.16 × 10−6 |
| CHD7 | Chromodomain Helicase DNA Binding Protein 7 | ENSG00000171316 | Head and neck cancer | Withdrawal | 327 | 137 | 9.54 × 10−6 |
| CHD7 | Chromodomain Helicase DNA Binding Protein 7 | ENSG00000171316 | Sensory organ development | Withdrawal | 327 | 18 | 1.30 × 10−5 |
| CHD7 | Chromodomain Helicase DNA Binding Protein 7 | ENSG00000171316 | Carcinoma | Withdrawal | 327 | 148 | 1.38 × 10−5 |
| CHD7 | Chromodomain Helicase DNA Binding Protein 7 | ENSG00000171316 | Upper aerodigestive SCC | Withdrawal | 327 | 44 | 1.60 × 10−43 |
| MEGF8 | Multiple EGF-like domains 8 | ENSG00000105429 | Skin lesion | Withdrawal | 325 | 105 | 1.65 × 10−6 |
| MEGF8 | Multiple EGF-like domains 8 | ENSG00000105429 | Skin cancer | Withdrawal | 325 | 113 | 4.79 × 10−6 |
| MEGF8 | Multiple EGF-like domains 8 | ENSG00000105429 | Cranial nerve abnormality | Withdrawal | 325 | 7 | 6.34 × 10−6 |
| MEGF8 | Multiple EGF-like domains 8 | ENSG00000105429 | Cancer | Withdrawal | 325 | 149 | 7.17 × 10−6 |
| MEGF8 | Multiple EGF-like domains 8 | ENSG00000105429 | Large bowel adenocarcinoma | Withdrawal | 326 | 120 | 7.45 × 10−6 |
| MEGF8 | Multiple EGF-like domains 8 | ENSG00000105429 | Cutaneous melanoma | Withdrawal | 326 | 110 | 7.71 × 10−6 |
| MEGF8 | Multiple EGF-like domains 8 | ENSG00000105429 | High grade astocytoma | Withdrawal | 326 | 82 | 8.42 × 10−6 |
| MEGF8 | Multiple EGF-like domains 8 | ENSG00000105429 | Abdominal adenocarcinoma | Withdrawal | 326 | 135 | 8.46 × 10−6 |
| MEGF8 | Multiple EGF-like domains 8 | ENSG00000105429 | Solid organ cancer | Withdrawal | 327 | 150 | 9.16 × 10−6 |
| MEGF8 | Multiple EGF-like domains 8 | ENSG00000105429 | Head and neck cancer | Withdrawal | 327 | 137 | 9.54 × 10−6 |
| MEGF8 | Multiple EGF-like domains 8 | ENSG00000105429 | Carcinoma | Withdrawal | 327 | 148 | 1.38 × 10−5 |
| MEGF8 | Multiple EGF-like domains 8 | ENSG00000105429 | Carcinoma | Withdrawal | 329 | 151 | 4.32 × 10−5 |
| MEGF8 | Multiple EGF-like domains 8 | ENSG00000105429 | Squamous cell tumor | Withdrawal | 332 | 65 | 7.59 × 10−5 |
| MEGF8 | Multiple EGF-like domains 8 | ENSG00000105429 | Preaxial polydactyly | Withdrawal | 333 | 3 | 9.19 × 10−5 |
| TMEM107 | Transmembrane protein 107 | ENSG00000179029 | Upper aerodigestive SCC | Withdrawal | 325 | 22 | 1.28 × 10−6 |
| TMEM107 | Transmembrane protein 107 | ENSG00000179029 | Cancer | Withdrawal | 325 | 149 | 7.17 × 10−6 |
| TMEM107 | Transmembrane protein 107 | ENSG00000179029 | Solid organ cancer | Withdrawal | 327 | 150 | 9.16 × 10−6 |
| TMEM107 | Transmembrane protein 107 | ENSG00000179029 | Head and neck cancer | Withdrawal | 327 | 137 | 9.54 × 10−6 |
| TMEM107 | Transmembrane protein 107 | ENSG00000179029 | Carcinoma | Withdrawal | 327 | 148 | 1.38 × 10−5 |
| TMEM107 | Transmembrane protein 107 | ENSG00000179029 | Carcinoma | Withdrawal | 329 | 151 | 4.32 × 10−5 |
| TMEM107 | Transmembrane protein 107 | ENSG00000179029 | Squamous cell tumor | Withdrawal | 331 | 65 | 7.59 × 10−5 |
| TMEM107 | Transmembrane protein 107 | ENSG00000179029 | Preaxial polydactyly | Withdrawal | 333 | 3 | 9.19 × 10−5 |
| TMEM107 | Transmembrane protein 107 | ENSG00000179029 | Squamous cell tumor | Withdrawal | 334 | 64 | 1.45 × 10−4 |
| TMEM107 | Transmembrane protein 107 | ENSG00000179029 | Head and neck cancer | Withdrawal | 335 | 127 | 1.75 × 10−4 |
| TMEM107 | Transmembrane protein 107 | ENSG00000179029 | Cancer | Withdrawal | 337 | 79 | 2.83 × 10−4 |
| TMEM107 | Transmembrane protein 107 | ENSG00000179029 | Head abnormalities | Withdrawal | 338 | 21 | 3.27 × 10−4 |
| TMEM107 | Transmembrane protein 107 | ENSG00000179029 | Haemopoietic stimulation | Withdrawal | 338 | 23 | 3.51 × 10−4 |
| BMP4 | Bone morphogenetic protein 4 | ENSG00000125378 | Upper aerodigestive SCC | Withdrawal | 325 | 166 | 1.28 × 10−6 |
| BMP4 | Bone morphogenetic protein 4 | ENSG00000125378 | Upper aerodigestive SCC | Withdrawal | 325 | 115 | 1.65 × 10−6 |
| BMP4 | Bone morphogenetic protein 4 | ENSG00000125378 | Cranial nerve abnormality | Withdrawal | 325 | 7 | 6.34 × 10−6 |
| BMP4 | Bone morphogenetic protein 4 | ENSG00000125378 | Cancer | Withdrawal | 325 | 149 | 7.17 × 10−6 |
| BMP4 | Bone morphogenetic protein 4 | ENSG00000125378 | Large bowel adenocarcinoma | Withdrawal | 326 | 120 | 7.45 × 10−6 |
| BMP4 | Bone morphogenetic protein 4 | ENSG00000125378 | Abdominal adenocarcinoma | Withdrawal | 326 | 135 | 8.46 × 10−6 |
| BMP4 | Bone morphogenetic protein 4 | ENSG00000125378 | Solid organ cancer | Withdrawal | 327 | 150 | 9.16 × 10-=6 |
| BMP4 | Bone morphogenetic protein 4 | ENSG00000125378 | Head and neck cancer | Withdrawal | 327 | 137 | 9.54 × 10−6 |
| BMP4 | Bone morphogenetic protein 4 | ENSG00000125378 | Sensory organ development | Withdrawal | 327 | 18 | 1.30 × 10−5 |
| BMP4 | Bone morphogenetic protein 4 | ENSG00000125378 | Carcinoma | Withdrawal | 327 | 148 | 1.38 × 10−5 |
| BMP4 | Bone morphogenetic protein 4 | ENSG00000125378 | Upper aerodigestive SCC | Withdrawal | 327 | 44 | 1.60 × 10−5 |
| BMP4 | Bone morphogenetic protein 4 | ENSG00000125378 | Carcinoma | Withdrawal | 328 | 119 | 2.47 × 10−5 |
| BMP4 | Bone morphogenetic protein 4 | ENSG00000125378 | Eye formation | Withdrawal | 328 | 15 | 2.81 × 10−5 |
| BMP4 | Bone morphogenetic protein 4 | ENSG00000125378 | Upper GIT carcinoma | Withdrawal | 328 | 75 | 3.42 × 10−5 |
| BMP4 | Bone morphogenetic protein 4 | ENSG00000125378 | GIT adenocarcinoma | Withdrawal | 328 | 121 | 3.56 × 10−5 |
| GREM1 | GREM1, DAN family BMP antagonist | ENSG00000126873 | Withdrawal | 171 | 1 | 3.61 × 10−6 | |
| GREM2 | GREM2, DAN family BMP antagonist | ENSG00000180875 | Withdrawal | 85 | 1 | 9.90 × 10−6 | |
| GLI3 | GLI zinc finger family 3 | ENSG00000106571 | Skin lesion | Withdrawal | 325 | 183 | 1.28 × 10−6 |
| GLI3 | GLI zinc finger family 3 | ENSG00000106571 | Head and neck squamous carcinoma | Withdrawal | 325 | 53 | 1.65 × 10−6 |
| GLI3 | GLI zinc finger family 3 | ENSG00000106571 | Skin cancer | Withdrawal | 325 | 113 | 3.59 × 10−6 |
| GLI3 | GLI zinc finger family 3 | ENSG00000106571 | Lung adenocarcinoma | Withdrawal | 325 | 42 | 4.79 × 10−6 |
| GLI3 | GLI zinc finger family 3 | ENSG00000106571 | Cancer | Withdrawal | 325 | 149 | 7.17 × 10−6 |
| GLI3 | GLI zinc finger family 3 | ENSG00000106571 | Large bowel adenocarcinoma | Withdrawal | 326 | 120 | 7.45 × 10−6 |
| GLI3 | GLI zinc finger family 3 | ENSG00000106571 | Cutaneous melanoma | Withdrawal | 326 | 110 | 7.71 × 10−6 |
| GLI3 | GLI zinc finger family 3 | ENSG00000106571 | High grade astocytoma | Withdrawal | 326 | 82 | 8.42 × 10−6 |
| GLI3 | GLI zinc finger family 3 | ENSG00000106571 | Abdominal adenocarcinoma | Withdrawal | 326 | 135 | 8.46 × 10−6 |
| GLI3 | GLI zinc finger family 3 | ENSG00000106571 | Solid organ cancer | Withdrawal | 327 | 150 | 9.16 × 10−6 |
| GLI3 | GLI zinc finger family 3 | ENSG00000106571 | Head and neck cancer | Withdrawal | 327 | 137 | 9.54 × 10−6 |
| GLI3 | GLI zinc finger family 3 | ENSG00000106571 | Sensory organ development | Withdrawal | 327 | 18 | 1.30 × 10−5 |
| GLI3 | GLI zinc finger family 3 | ENSG00000106571 | Carcinoma | Withdrawal | 327 | 148 | 1.38 × 10−5 |
| GLI3 | GLI zinc finger family 3 | ENSG00000106571 | Upper aerodigestive SCC | Withdrawal | 327 | 44 | 1.60 × 10−43 |
Key: The first entry in each type of gene is in bold. This signifies the gene class. Its initial entry signifies the number of entries for that gene in the data set.
Some of the hits from the Schrott EWAS data are extracted and illustrated in Table 7. Numbers shown in bold on the right-hand side of the second column on the right are the total hits for that gene. The other numbers listed in the “Numbers of genes column” are the numbers of genes identified with the particular DNA methylation pattern identified and listed in the Schrott dataset. Hence Meis1 had 37 hits in the EWAS, Meis2 97 hits, FGFs 175 hits, FGF4 7 hits, RXR/RARs 10 hits, CHD7 124 hits, MEGF8 105 hits, TMEM107 232 hits, BMP4 166 hits and Gli3 183 hits. Together, this accounts for 1129 hits in these major morphogens and gene pathways which is a very substantial number of perturbations compromising limb morphogenesis.
In total, 37 gastrointestinal EWAS hits are listed in Supplementary Table S29 which relate to the many gastrointestinal congenital anomalies reported in Table 6 which affect most of the major gastrointestinal organs. 27/37 (73%) relate to cannabis dependence and 10 (27%) are in withdrawal. Supplementary Table S29 also lists most of the gastrointestinal organs. Cancer and carcinoma are prominently identified.
Supplementary Table S30 lists four Schrott EWAS hits for chromosomal disorders. Given that trisomies 13, 18 and 21, Turners, Klinefelters and genomic deletions along with all chromosomal disorders are all listed in Table 6 this is highly important. As discussed in earlier sections on the underlying subcellular pathoaetiology, it is not clear if these chromosomal disorders relate to epigenomic, microtubular, kinetochore, centrosome or related problems or possibly some combination of these aberrations.
The eight identified EWAS hits for renal disorders are shown in Supplementary Table S31. These clearly cover most aspects of uronephrological development. These relate to the many uronephrological CAs identified in Table 6 including overall urinary anomalies, multicystic renal disease, obstructive genitourinary disorder, congenital posterior urethral valve, renal agenesis, bladder extrophy and hydronephrosis. Importantly, renal agenesis was a strong association of cannabis teratogenesis in both USA and Europe. This fits with the above pathophysiological narrative as sonic hedgehog and retinoic acid are major morphogens in renal and urinary development [209].
Supplementary Table S32 lists 15 EWAS hits for body wall development. In total, 7/15 (46.7%) are in cannabis dependence and 8 (53.3%) are in cannabis withdrawal. Body trunk and body axis development are prominent as is development of the abdomen. Growth and differentiation of embryonic tissues is clearly predominant in the lower part of the Table.
These various Tables may be combined by body system as shown in Supplementary Table S33. This Table does not include the extended studies listed above for congenital limb anomalies. Supplementary Figure S1 presents the summary of the p-values as the negative log of the p-value as boxplots. Non-overlapping notches indicate statistically significant differences. Gastrointestinal, chromosomal and neurological defects appear towards the right end of the graph.
Table 8 provides the mean and median p-value for each system. A significantly rising trend by body system is noted (β-est. = 1.21, Student’s t = 7.65, p = 4.69 × 10−13; Adj R Squ. = 0.1908, F = 58.53, df = 1, 243, p = 4.69 × 10−13).
Table 8.
Summary Epigenomic Hits for All Congenital Anomalies.by Organ System, Schrott EWAS Database.
| System | Mean p-Value | Median p-Value |
|---|---|---|
| Gastrointestinal | 0.0011 | 7.45 × 10−6 |
| Chromosomes | 0.0018 | 1.31 × 10−4 |
| Neurological | 0.0035 | 6.15 × 10−4 |
| Cardiovascular | 0.0011 | 0.0011 |
| Face | 0.0021 | 0.0014 |
| Body Wall | 0.0018 | 0.0016 |
| General | 0.0026 | 0.0017 |
| Uronephrology | 0.0021 | 0.0022 |
| Limb | 0.0036 | 0.0037 |
If one considers 39 of the (arguably) most significant target organs of interest the results for mean and median p-value shown in Supplementary Table S34 are revealed which are plotted graphically in Supplementary Figure S2. Gastrointestinal, liver, brain, atrioventricular valves, head and chromosomes appear towards the right-hand side of this Figure as most severely affected. Again, the trend across this graph is highly statistically significant (β-est. = 0.31, Student’s t = 9.23, p = 6.82 × 10−18; Adj R Squ. = 0.2565, F = 85.16, df = 1, 243, p = 6.82 × 10−18).
Comparison of p-values between dependence and withdrawal shows that those in dependence are much lower than those in withdrawal (median (log P) ± IQR: cannabis dependence −7.66 (−10.56, −6.34); cannabis withdrawal −5.96 (−7.26, −5.17); t = 6.341, df = 187.12, p = 1.65 × 10−9). These findings are illustrated graphically in the boxplot of Supplementary Figure S3.
These data may be summarized by target organ as shown in Table 9. The number of annotations cited in the Schrott EWAS data by target for cannabis dependence and withdrawal is shown in Figure 1. The gene numbers identified in each condition by target are shown in Supplementary Figure S4. Figure 2 compares the relative p-values in each condition by target organ.
Table 9.
Contrast of Epigenomic Hits for All Congenital Anomalies.by Organ Target, Cannabis Dependence vs. Withdrawal, Schrott EWAS Database.
| Target | Cannabis Dependence | Cannabis Withdrawal | ||||||
|---|---|---|---|---|---|---|---|---|
| Number of Annotations | Cumulative Genes | Minimum p-Value | Median p-Value | Number of Annotations | Cumulative Genes | Minimum p-Value | Median p-Value | |
| Gastrointestinal | 8 | 2561 | 4.60 × 10−16 | 1.13 × 10−15 | - | - | - | - |
| Large Intestine | 5 | 1240 | 7.65 × 10−15 | 6.40 × 10−14 | 3 | 363 | 7.45 × 10−6 | 6.80 × 10−5 |
| Esophagus | 4 | 393 | 3.15 × 10−13 | 9.40 × 10−4 | 3 | 69 | 0.0020 | 0.0028 |
| Neurological | 8 | 710 | 5.33 × 10−8 | 4.45 × 10−4 | 1 | 2 | 7.20 × 10−4 | 7.20 × 10−4 |
| Heart | 5 | 53 | 8.83 × 10−8 | 1.57 × 10−4 | - | - | - | - |
| Liver | 2 | 404 | 1.28 × 10−7 | 1.79 × 10−7 | - | - | - | - |
| Brain | 6 | 750 | 1.39 × 10−7 | 1.86 × 10−5 | 1 | 3 | 1.16 × 10−4 | 1.16 × 10−4 |
| Pancreas | 8 | 769 | 9.10 × 10−7 | 1.25 × 10−5 | 4 | 112 | 0.0052 | 0.0061 |
| Embryo | 9 | 285 | 8.20 × 10−6 | 3.61 × 10−4 | - | - | - | - |
| Atrioventricular valves | 3 | 13 | 9.04 × 10−6 | 4.00 × 10−5 | - | - | - | - |
| Neurons | 14 | 336 | 9.27 × 10−6 | 1.88 × 10−4 | 3 | 11 | 0.0020 | 0.0031 |
| DNA | 12 | 373 | 1.50 × 10−5 | 0.0011 | 5 | 33 | 3.58 × 10−4 | 0.0070 |
| Chromosomes | 3 | 16 | 1.60 × 10−5 | 7.90 × 10−5 | 1 | 1 | 0.0070 | 0.0070 |
| Cardiovascular | 4 | 85 | 2.10 × 10−5 | 0.0019 | - | - | - | - |
| Synapse | 15 | 308 | 3.12 × 10−5 | 0.0018 | 7 | 36 | 1.43 × 10−4 | 0.0013 |
| Microtubules | 1 | 58 | 3.30 × 10−5 | 3.30 × 10−5 | 1 | 24 | 0.0045 | 0.0045 |
| Embryo | 6 | 93 | 3.60 × 10−5 | 0.0018 | 2 | 8 | 0.0023 | 0.0046 |
| Ventricle | 4 | 23 | 5.10 × 10−5 | 6.09 × 10−4 | - | - | - | - |
| Body | 5 | 132 | 7.80 × 10−5 | 0.0016 | 2 | 51 | 1.93 × 10−4 | 3.74 × 10−4 |
| Eye | 6 | 65 | 7.90 × 10−5 | 0.0010 | 13 | 73 | 2.80 × 10−5 | 6.89 × 10−4 |
| Cerebrum | 2 | 153 | 1.20 × 10−4 | 7.35 × 10−4 | 4 | 22 | 7.41 × 10−4 | 0.0020 |
| Head | 1 | 47 | 1.20 × 10−4 | 1.20 × 10−4 | - | - | - | - |
| Bone | 7 | 50 | 1.40 × 10−4 | 0.0018 | 14 | 48 | 1.93 × 10−4 | 0.0070 |
| Sensory | 1 | 29 | 1.64 × 10−4 | 1.64 × 10−4 | - | - | - | - |
| Body Axis | 1 | 1 | 1.93 × 10−4 | 1.93 × 10−4 | - | - | - | - |
| Urinary system | 1 | 17 | 2.20 × 10−4 | 2.20 × 10−4 | 1 | 8 | 0.0044 | 0.0044 |
| Kidney | 5 | 84 | 4.29 × 10−4 | 0.0022 | 1 | 4 | 0.0042 | 0.0042 |
| Breast | 1 | 3 | 5.73 × 10−4 | 5.73 × 10−4 | 2 | 9 | 0.0021 | 0.0023 |
| Granulocytes | 1 | 3 | 5.73 × 10−4 | 5.73 × 10−4 | - | - | - | - |
| Ear | 6 | 36 | 7.20 × 10−4 | 0.0021 | 5 | 21 | 1.65 × 10−4 | 8.04 × 10−4 |
| Atria | 4 | 15 | 8.55 × 10−4 | 0.0017 | - | - | - | - |
| Body trunk | 1 | 50 | 0.0015 | 0.0015 | - | - | - | - |
| Myogenesis | 2 | 4 | 0.0018 | 0.0018 | - | - | - | - |
| Vertebra | 1 | 3 | 0.0049 | 0.0049 | - | - | - | - |
| Limb | - | - | - | - | 6 | 18 | 9.20 × 10−5 | 0.0037 |
| Nose | - | - | - | - | 1 | 3 | 0.0011 | 0.0011 |
| Ovarian reserve | - | - | - | - | 1 | 2 | 0.0031 | 0.0031 |
| Mitochondria | - | - | - | - | 1 | 1 | 0.0070 | 0.0070 |
| Palate | - | - | - | - | 1 | 1 | 0.0070 | 0.0070 |
Figure 1.
Number of epigenomic annotations in the Schrott database for target organs by dependency status in (A) cannabis dependence and (B) withdrawal.
Figure 2.
Significance levels (as p-values) of target organs by dependency status in (A) cannabis dependence and (B) withdrawal in the Schrott database.
3.2.8. Cannabinoid-Related Carcinogenesis
The consistent association between varied cancers and cannabis exposure provides further examples of how cannabis ageing mechanisms contribute to disease. Table 10 sets out the most significant associations of various cancers with cannabis or cannabinoids in USA and Europe [112,113,114,121]. The Table lists the minimum p-value, the model type and the primary correlate of the various cancers listed. Two of the main features of this Table are the number of cancers listed and the commonality between the USA and European experience which are the two largest datasets on this issue available internationally.
Table 10.
Comparative Lists of Significantly Cannabinoid-Associated Cancers in Europe and USA.
| No. | Europe | USA | |||||
|---|---|---|---|---|---|---|---|
| Model | Cancer | Minimum p-Value | Model | Correlate | Cancer | Minimum p-Value | |
| 1 | Categorical | Acute Lymphoid Leukemia | 8.70 × 10−24 | Categorical | Δ9THC | Acute Lymphoid Leukemia | 7.65 × 10−25 |
| 2 | Continuous | Acute Myeloid Leukemia | 2.11 × 10−4 | Categorical | Δ9THC | Acute Myeloid Leukemia | 3.11 × 10−110 |
| 3 | Categorical | Cannabidiol | All_Cancer | <2.2 × 10−320 | |||
| 4 | Categorical | Anus | 6.71 × 10−35 | ||||
| 5 | Categorical | Bladder | <2.2 × 10−320 | Categorical | Cannabidiol | Bladder | <2.2 × 10−320 |
| 6 | Continuous | Brain.Medulloblastoma | 5.64 × 10−42 | Categorical | Cannabidiol | Brain | 5.67 × 10−33 |
| 7 | Categorical | Breast | 4.03 × 10−17 | Categorical | Δ9THC | Breast | 8.06 × 10−146 |
| 8 | Continuous | Chronic Lymphoid Leukemia | 1.20 × 10−34 | Categorical | Cannabidiol | Chronic Lymphoid Leukemia | 2.98 × 10−12 |
| 9 | Continuous | Chronic Myeloid Leukemia | 1.32 × 10−32 | Categorical | Δ9THC | Chronic Myeloid Leukemia | 1.52 × 10−12 |
| 10 | Categorical | Colorectum | 6.14 × 10−242 | Categorical | Cannabidiol | Colorectum | <2.2 × 10−320 |
| 11 | Categorical | Corpus uteri | 2.28 × 10−4 | ||||
| 12 | Categorical | Esophagus | 1.12 × 10−110 | Categorical | Cannabidiol | Esophagus | 2.31 × 10−43 |
| 13 | Categorical | Gallbladder | 2.24 × 10−4 | ||||
| 14 | Continuous | Hepatocellular Cancer | 2.29 × 10−42 | ||||
| 15 | Categorical | Hodgkin lymphoma | 1.80 × 10−8 | Categorical | Cannabidiol | Hodgkins | 1.22 × 10−30 |
| 16 | Categorical | Kaposi sarcoma | 1.16 × 10−7 | Categorical | Cannabidiol | Kaposi | 4.75 × 10−29 |
| 17 | Categorical | Kidney | 7.46 × 10−5 | Continuous | Cannabinol | Kidney | 0.0067 |
| 18 | Categorical | Larynx | <2.2 × 10−320 | ||||
| 19 | Categorical | Liver | <2.2 × 10−320 | Categorical | Δ9THC | Liver | <2.2 × 10−320 |
| 20 | Categorical | Lung | 1.45 × 10−8 | Categorical | Cannabidiol | Lung | 6.87 × 10−194 |
| 21 | Categorical | Melanoma of skin | <2.2 × 10−320 | Categorical | Cannabidiol | Melanoma | <2.2 × 10−320 |
| 22 | Categorical | Mesothelioma | 3.37 × 10−111 | ||||
| 23 | Categorical | Multiple myeloma | 6.92 × 10−8 | Categorical | Δ9THC | Multiple myeloma | 1.73 × 10−30 |
| 24 | Categorical | Non-Hodgkin lymphoma | 1.60 × 10−44 | Categorical | Cannabidiol | Non-Hodgkin lymphoma | 3.15 × 10−145 |
| 25 | Continuous | Oropharynx | 7.02 × 10−21 | Continuous | Δ9THC | Oropharynx | 3.21 × 10−6 |
| 26 | Categorical | Ovary.Germ Cell Tumor | 1.07 × 10−38 | Categorical | Cannabidiol | Ovary | 2.49 × 10−312 |
| 27 | Categorical | Pancreas | 4.09 × 10−9 | Categorical | Δ9THC | Pancreas | 4.57 × 10−166 |
| 28 | Categorical | Penis | 1.64 × 10−19 | ||||
| 29 | Categorical | Prostate | <2.2 × 10−320 | Categorical | Cannabidiol | Prostate | <2.2 × 10−320 |
| 30 | Categorical | Cannabidiol | Stomach | 2.30 × 10−192 | |||
| 31 | Categorical | Testis | 3.83 × 10−81 | Continuous | Cannabinol | Testis | 1.47 × 10−5 |
| 32 | Continuous | Testis.Non-Seminoma Germ | 1.25 × 10−75 | ||||
| 33 | Categorical | Testis.Seminoma | 5.14 × 10−58 | ||||
| 34 | Categorical | Thyroid | <2.2 × 10−320 | Categorical | Δ9THC | Thyroid | <2.2 × 10−320 |
| 35 | Continuous | Vulva | 8.88 × 10−44 | ||||
The above table demonstrates cross-nationally consistent associations between cannabis exposure and varied cancers. The sections that follow evaluate evidence associating cannabis exposure with cancer epigenomic mechanisms.
Supplementary Table S35 extracts all of the p-values applicable to 20 of these tumors comprehended by the Schrott EWAS dataset. Supplementary Table S36 summarizes the data of the preceding Table for minimum, mean and median significance levels by tumor type and is ordered by descending minimum p-value. The cumulative gene number includes duplicate mentions for some genes. Thyroid, melanoma and urinary cancers head this list. When the list is ordered by median p-value thyroid, testis, stomach, liver and oropharyngeal tumors head the list (Supplementary Table S37). Some of these key data are shown in Supplementary Figure S5 which lists the number of annotations, the cumulative gene number and the negative log of the p-value for each tumor type.
Because the Schrott dataset is elegantly organized into both cannabis dependence and withdrawal it may be categorized for 19 tumors in cannabis dependence as shown in Supplementary Table S38, which is listed in descending order of minimum p-value. The cumulative gene number again includes duplicate mentions for some genes. This list is headed by thyroid, melanoma and urinary cancers. When the same list is ordered by median p-value the order of significance is thyroid, melanoma, stomach, colorectal urinary and testis cancer as indicated in Supplementary Table S39. Some of these key data are illustrated graphically in Figure 3 which lists the number of cancers, the cumulative gene number from the Schrott EWAS dataset, and the negative log of the p-value for each tumor type.
Figure 3.
(A) Numbers of gene annotations, (B) numbers of genes affected and (C) negative logarithm of p-value by cancer type—cannabis dependence Schrott data.
Supplementary Table S40 lists the applicable p-values for cannabis withdrawal for 18 tumor types and is ordered by minimum p-value. The list is headed by melanoma, brain, oropharynx and esophageal cancers. These significance levels are noted to be lower than those in the preceding Tables. When the list is sorted by median p-value oropharynx, melanoma, brain, urinary, acute myeloid leukemia and testicular cancer head the list (Supplementary Table S41). These results are illustrated graphically in Figure 4 which shows, respectively, the number of gene annotations, the cumulative gene number and the negative log of the significance levels by tumor type.
Figure 4.
(A) Numbers of gene annotations, (B) numbers of genes affected and (C) negative logarithm of p-value by cancer type—cannabis withdrawal Schrott data.
Supplementary Figure S6 directly compares the significance levels of the tumors by cannabis dependency status. It is observed that the tumors are in a very different order and that the level of significance is generally much lower in cannabis withdrawal than in cannabis dependence.
Table 11 directly compares the significance levels and gene numbers for the various tumors types in dependence and withdrawal. Whilst the overall pattern is clearly that there are more genes implicated and at higher levels of statistical significance by cannabis dependence than cannabis withdrawal, there are a few notable exceptions to this pattern.
Table 11.
Contrast of Cannabis Dependence and Withdrawal Significance Levels and Gene Numbers, Schrott Data.
| Cancer | Minimum p-Value Dependence | Minimum p-Value Withdrawal | p-Value Ratio Dependence/Withdrawal | Total Gene Number Dependence | Total Gene Number Withdrawal | Gene Number Ratio Dependence/Withdrawal |
|---|---|---|---|---|---|---|
| Thyroid | 1.21 × 10−17 | 0.0014 | 1.17 × 1014 | 637 | 115 | 5.54 |
| Melanoma | 3.70 × 10−15 | 7.71 × 10−6 | 2.08 × 109 | 579 | 225 | 2.57 |
| Urinary | 2.54 × 10−14 | 2.16 × 10−4 | 8.50 × 109 | 1191 | 679 | 1.75 |
| Esophagus | 3.15 × 10−13 | 6.80 × 10−5 | 2.16 × 108 | 465 | 117 | 3.97 |
| Stomach | 3.15 × 10−13 | 6.80 × 10−5 | 2.16 × 108 | 443 | 102 | 4.34 |
| Colorectal | 7.27 × 10−13 | 6.17 × 10−4 | 8.49 × 108 | 1734 | 452 | 3.84 |
| Testis | 1.14 × 10−8 | 6.75 × 10−4 | 5.92 × 104 | 304 | 60 | 5.07 |
| Liver | 1.17 × 10−8 | NA | NA | 890 | NA | NA |
| Prostate | 2.88 × 10−8 | 5.33 × 10−4 | 1.85 × 104 | 399 | 158 | 2.53 |
| Breast | 3.25 × 10−8 | 0.0013 | 3.91 × 104 | 674 | 177 | 3.81 |
| Brain | 5.33 × 10−8 | 8.42 × 10−6 | 157.97 | 2779 | 947 | 2.93 |
| Oropharynx | 1.25 × 10−7 | 1.60 × 10−5 | 128.00 | 195 | 44 | 4.43 |
| Pancreas | 9.10 × 10−7 | 0.0052 | 5.73 × 103 | 769 | 112 | 6.87 |
| ALL | 4.08 × 10−5 | 6.01 × 10−4 | 14.73 | 23 | 118 | 0.19 |
| NHL | 4.08 × 10−5 | 6.11 × 10−4 | 14.98 | 322 | 43 | 7.49 |
| Ovary | 1.16 × 10−4 | 0.0070 | 60.43 | 529 | 1 | 529.00 |
| CML | 2.13 × 10−4 | 0.0021 | 9.95 | 11 | 11 | 1.00 |
| AML | 8.96 × 10−4 | 6.26 × 10−4 | 0.70 | 11 | 36 | 0.31 |
| Kidney | 0.00101 | NA | NA | 89 | NA | NA |
| Myeloma | NA | 0.0016 | NA | NA | 10 | NA |
Key: CML—Chronic Myeloid Leukemia; CLL—Chronic Lymphoid Leukemia; NHL—Non-Hodgkins Lymphoma.
Both acute myeloid leukemia (AML) and acute lymphoid leukemia (ALL) have a lower gene number dependence/withdrawal ratio than unity. AML also has a lower minimum (and median and mean) p-value dependence/withdrawal ratio. Data are listed by the gene number ratio in Supplementary Table S42 and ovarian, Non-Hodgkins, pancreas thyroid and testicular cancers are noted to head up the list.
Some of these data are shown graphically in Figure 5 which lists the log of the ratio of the minimum p-values, the log of the gene number for dependence/withdrawal and the log the gene number for the withdrawal/dependence ratio. In this way, the distinctly higher withdrawal/dependence ratios in the pediatric AML and ALL cancers are highlighted.
Figure 5.
Log plots of significance levels for (A) ratio of p-values between cannabis dependence and withdrawal, (B) log of the dependence/withdrawal ratio of total gene numbers affected between cannabis dependence and withdrawal and (C) log of the withdrawal/dependence ratio of total gene numbers affected between cannabis dependence and withdrawal; each by tumor type from the Schrott EWAS data.
3.3. Implications of Findings
From such a very broad array of objective reported results, basic cellular mechanisms and highly concordant epidemiological findings in both addiction medicine and aging science, it is necessary in discussing these results to highlight just a few key findings which are of particular importance to the overall flow of the main themes of this review and the major concepts presented. More detailed discussions have been presented in the references cited and other exhaustive and encyclopaedic sources [302,303,304,305,306,307]. The study is the first to combine and connect data from a broad range of genotoxic areas. Perhaps the most striking finding is the extraordinarily accurate predictive power of the epigenomic results to apparently explain the epidemiologically observed mutagenic and teratological phenomenology. This accuracy provides confirmation of the validity of the cannabis ageing mechanisms outlined in this paper. The epigenomic results of the Schrott group [27] not only predict with great accuracy such disparate findings as the high rates of atrial septal defect widely observed in Canada, Australia, Colorado, Hawaii, USA and Europe [103,107,108,111,114,115,116,118] and elevated rates of ventricular septal defect noted by the American Academy of Pediatrics and the American Heart Association and elsewhere [103,109,111,115,308], but also the mechanistically closely related pattern of congenital cardiac and renal anomalies which both share critical sensitivity to inhibition of the notch, sonic hedgehog and retinoic acid morphogenic pathways. Both atrial septal and ventricular septal defects feature prominently in the spectrum of cannabis teratological defects, and also in the multisyndromic VACTERL syndrome which formally relates renal, cardiac and limb anomalies (amongst others) and was the most strongly significantly cannabis-associated of all the European birth defects studied [114].
Findings also explain with extraordinary accuracy 20 cancers which are shared commonly between the epigenomic and epidemiological datasets featuring prominently liver, breast, pancreas, diverse leukemias and lymphomas, oropharyngeal, thyroid, urinary, esophageal and testicular tumors. These findings also accord closely with older published data which link cannabis to exposure of a range of tumors including lung, head and neck, larynx, brain, prostate, testis and urothelium [52,53,54,55,56,57,58,59,60,61,62].
The likely foundational importance of cannabis-induced epigenotoxicity implies that not only has the salience of epigenomic disturbances emerged as being pre-eminent from the mechanistic confusion surrounding the aging process itself [16] but in a similar way it appears that with time and further research the epigenomic perturbations induced by cannabis dependence and withdrawal are likely to be shown to be foundational in understanding the plethoric and protean manifestations of cannabinoid-induced mutagenesis, teratogenesis, carcinogenesis and indeed aging [2].
This foundational centrality of epigenotoxicity to the understanding of cannabinoid toxicity is highly reminiscent of the central understanding which the fundamentally epigenomic nature of fetal alcohol syndrome has been shown to display [309,310,311,312,313,314,315,316,317,318,319,320]. Indeed, fetal alcohol syndrome has been shown to be primarily mediated epigenomically via cannabinoid type 1 receptors (CB1Rs) [321,322,323,324,325,326,327,328,329,330,331]. It should come therefore as little surprise to note that cannabinoids can also act via CB1Rs with a unique spectrum of clinical manifestations.
One major corollary of the finding of the primacy of epigenomic toxicity is that at least some of these changes are likely to be epigenetically inheritable. Indeed, a heritable pediatric fetal cannabinoid syndrome, analogous to fetal alcohol syndrome has been previously proposed [321,322,323,327,328,329,330,332,333,334]. In the case of the pediatric cancers acute myeloid and lymphoid leukemia [65,66,206], this implies not only heritable teratogenicity but also heritable teratogenic carcinogenicity [204,205]. This finding likely also applies to other pediatric tumors previously linked with parental cannabis exposure such as rhabdomyosarcoma, neuroblastoma and astrocytoma [207,208,321,322,323,327,328,329,330,332,333,334].
It was noted that the ratios of the most significant p-values were inverted for the pediatric tumor ALL, and for AML of which some cases occur early in life. This suggests the intriguing possibility that it is the cannabis withdrawal state following birth which triggers and launches the leukemogenic gene cassettes of childhood.
Many other features of the above series of results stand out prominently. The high numbers and wide ranges of both congenital anomalies-45/62 in USA and 89/95 in Europe (Table 6)-and cancers-33/40 in Europe and 25/28 in USA (Table 10)-are striking both in their own right and by virtue of the range of tissues and organ systems affected. As these observations have been made previously [66,103,112,113,114,115,116,120,121,122], they do not form the primary focus of the present investigation. What is more important for the present discussion of cannabis-related aging and its mechanisms is the relationship of oncogenicity and teratogenesis to aging related processes.
The North Carolina group reported that the significance of the DMR’s in cannabis dependence was higher than cannabis withdrawal [27]. Hence most of the ratios for the gene numbers affected in Table 11 were expected. However, the higher gene numbers affected in ALL (primarily a pediatric cancer) and AML (occasionally a pediatric cancer) and the higher significance level in AML found in withdrawal were unexpected and raise the intriguing possibility that the cannabis withdrawal state following birth may trigger leukemogenic gene activation. Whether this holds true for the other pediatric cancers previously related to cannabis remains to be studied. Moreover, this topic was shown to be of much greater significance beyond the field of pediatric cancer by the recent finding that many adult haemopoietic tumors have been shown to commence in fetal life [335], a finding which these latter investigators note may also apply more widely to the field of solid organ tumorigenesis.
One of the prominent findings to emerge from the above epidemiological overview was the salience of chromosomal disorders in both the congenital anomaly and the cancer datasets. Trisomies or monosomies of chromosomes 13, 18, 21 and X (including syndromes described by Turner and Klinefelter) were observed directly [66,103,115,120]. Moreover, strong signals were detected for acute lymphoid leukemia (which has been shown to often involve translocations between chromosomes 4, 9, 10, 11 and 22) [105,336] and testicular cancer [105,112,113,114,121,122] (which has been shown to implicate chromosomes 1, 7, 8, 11, 12, 13, 18, 21, X and Y) [337]. The total length of these chromosomes together comprehends 1754 megabases of the 3000 megabases, or 59%, of the whole human genome directly impacted by cannabinoid-related genotoxicity/epigenotoxicity. Deletions of chromosome 22 in USA and microdeletions in Europe were also significantly cannabis-associated [66,103,115]. These data make the issue of chromosomal non-segregation, non-disjunction, aneuploidy, chromosomal breaks and translocations and subsequent teratogenic malignancy a very prominent feature of cannabis related genotoxicity. As described in considerable detail in the pathophysiological review, multiple direct and epigenomic pathways exist which comfortably explain and may account for these prominent and important clinical findings of hundred megabase scale epi/genotoxic activities.
Given so much powerful evidence for cannabinoid-related epigenotoxicity, the possibility that these epigenomic changes are potentially reflected as pro-ageing effects effectively accelerating natural aging warrants particularly careful consideration. On this issue, three tissues are of particular and pivotal importance namely: spermatocytes, oocytes and zygotes.
3.4. Spermatocytes
Classic photomicrographs of cannabis exposed sperm featuring multiple (up to four) heads, multiple tails, obviously deformed heads and tails on a background of proteinaceous and inflamed tissue [24] along with gross chromosomal translocations and ring and chain formation [23,260] give an obviously degenerate genotoxic appearance. Multiple cannabinoids are known to induce adverse mitochondrial effects, reduced energy charge and increased free radial flux [33,140] which are all changes that are well established as being age related. It has been shown that cannabinoid signaling via CB1R has a deleterious effect on sperm chromatin which increases along the epididymis, altered histone-protamine substitution via inhibition of transition protein 2 (TNP2) and leads to genome DNA fragmentation with compromise of male fertility [34]. Moreover, the above demonstration of cannabinoid-related gross changes to the tubulin code and meiotic apparatus (Supplementary Tables S9–S13) implies that not only are the microtubules of the sperm flagellum disrupted but so also are those comprising the sperm centrioles and first and second meiotic spindles. Since all of these various changes are age-defining and age-causing disorders, this implies that the age of cannabinoid-exposed sperm is advanced.
3.5. Oocytes
Diminished ovarian reserve was noted in the epigenomic dataset of Schrott (Supplementary Table S27; Schrott [27] Page 349) and is both a defining feature of female aging [1] and an important cause thereof [200]. Gross and severe morphological changes were noted in cannabis exposed oocytes induced to divide including chromosomal nucleoplasmic bridges, non-disjunctions, tripolar, quadripolar and pentapolar cell divisions along with an extremely high (20%) rate of oocyte death with just a single cell division. Moreover, oocyte depletion has been attributed primarily to failure of DNA damage repair [230]. As noted above, cannabis has been shown to suppress pituitary FSH secretion thereby interfering with the normal female hormonal cycle. All of these are clearly age-related and age-inducing changes.
3.6. Zygotes
Since both sperm and oocytes bear many chromosomal, genetic and epigenetic features of aging, it seems clear that these changes would persist in the pronuclei of the fertilized zygote and carry important influences into the first few rounds of zygotic cell division which are epigenetically controlled from the time of fertilization. These deleterious changes would be compounded by aberrant histone and protamine changes in sperm and by the disrupted tubulin code known to be borne by sperm. Together, these changes indicate that not only are the gametes themselves aged, but so too must the fertilized zygote be aged from—and actually even prior to—conception. It is noted again that the fragile process of human female meiosis is highly error prone ordinarily [248] which suggests that the tolerance for error under the influence of external xenobiotic genotoxic agents is very narrow indeed. These considerations raise the intriguing and very concerning possibility that the zygote itself may manifest advanced epigenomic age from even before fertilization and conception. It is noted that the newly described method of analysis of blastocystoid bodies derived from induced human pluripotential embryonic stem cells (iPS) might provide an ideal and ethical laboratory method to formally assess these issues [338].
It was recently shown that a key part in sperm maturation is played by the addition of mRNA exosomes (as epididymosomes) in the tail of the epididymis during sperm maturation. These extracellular packages of mRNA play a key part in early embryonic development during the initial divisions of the fertilized zygote and are under close control at several points by CB1R-mediated cannabinoid control [339]. Interference with this normal mechanism led to profound perturbation of sperm maturation, fertility and function. In this regard, the human system closely mirrors that seen in mice.
3.7. Cannabidiol and Δ8THC
At the time of writing, cannabidiol and Δ8THC have been allowed to freely penetrate culture without restriction in many places and have been made available in cookies, sauces, lollies, candies, crackers and in solid translucent blocks often being marketed as “legal weed”.
In such a context, it is important to note that it was found long ago that the genotoxic moiety of cannabinoids lies primarily in their central olevitol nucleus, an activity which is little modified by their various side chains [340,341]. This important finding implicates most cannabinoids in genotoxic effects.
Cannabidiol has an experimental [24,342,343,344,345] and an epidemiological literature describing its genotoxic effects in both cancer [112,113,114] and congenital anomalies [103]. Cannabidiol is also genotoxic by virtue of its involvement in signaling via the nuclear receptor—transcription factor PPARγ (Peroxisome proliferator receptor gamma) [346,347,348,349,350,351,352,353], by its inhibition of mitochondrial respiration which forms the energetic and co-factor substrate basis for the epigenomic machinery [41,42,354,355,356,357,358,359,360,361], and by its interaction at higher doses [362,363,364,365,366,367,368,369,370] with the cannabinoid type 1 receptors present on mitochondria themselves [44,145,146,147,371,372,373,374]. Importantly, the PPARγ nuclear signal is transduced by binding to retinoic acid receptors (RXR) which together then bind the genome [375]. Similarly, Δ8THC has been epidemiologically implicated in both cancer [376] and birth defects [377].
A recent very concerning paper demonstrated not only that many cannabinoids (including Δ9THC, Δ8THC and cannabidiol) could freely pass into the milk of dairy cattle fed legal hemp (with nominally less that 0.3% THC content) but that the cannabinoid concentration in milk could rise to a level where the total recommended daily dose of Δ9THC was exceeded [119]. Moreover, the cows themselves became obviously ataxic and “stoned” and stood motionless for extended period, not moving and not eating, apparently “stoned”. They were also ataxic and had difficulty walking. After cessation of the hemp/cannabinoid feed, these changes abruptly declined. Most concerningly, the levels of cannabinoid found in the feed when analyzed by state-of-the-art tandem liquid chromatography/gas chromatography—mass spectrometry (LCGC-MS) techniques were more than ten times those found with the standard legally prescribed tests for cannabinoid, a finding which necessarily impugns and indicts so called legally safe “low-THC” hemp products and imperils public heath and safety.
Moreover, such findings dramatically and eloquently illustrate the florid manner in which such grossly affected animals in the food chain might pass on the severe genotoxic cannabinoid-mediated damage (which includes limblessness) as has been chronicled in recent reports from France and Germany [378,379,380,381].
4. Conclusions
Many metrics, including hormonal, mitochondriopathic, cardiovascular, hepatotoxic, immunological, genotoxic, epigenotoxic, disruption of chromosomal physiology, congenital anomalies, cancers including inheritable tumorigenesis, telomerase inhibition and elevated mortality point towards cannabinoid-exposed tissues being of advanced biological age. Evidence from many studies indicates extensive perturbation of the human epigenome by exposure to many cannabinoids. Since the epigenome has emerged as the key and central mediator of the panorganismal aging process [13,14,15,16,202,382], it becomes of primary importance to investigate its likely implication in aging processes directly by the application of late-generation epigenomic clocks [383,384,385,386,387,388,389]. The likely involvement of spermatogonia, oocyte and fertilized zygote in this accelerated aging process increases the importance of this enquiry for the health of subsequent generations, an enquiry which is heightened and intensified by the transgenerational transmission of cannabinoid-related epigenotoxicity in human sperm [26,27], to subsequent rodent generations [28,29,30,31,32,390], for pediatric brain function and development including autistic-like disorders [117,174,288,290,292,295,296,297,298,299,391] and through the heritable passage of many birth defects [103,108,109,110,111,115,118,120] including several pediatric cancers [63,64,65,66,105,121]. Inversion of the ratio of the minimum p-values between dependence and withdrawal for ALL and AML may imply that it is the activation of leukemogenic gene cassettes by the withdrawal state occasioned by birth which gives rise to these pediatric cancers. The genotoxic, epigenotoxic, mutagenic and teratological issues raised are clearly very serious and have been shown several times to greatly outweigh those attributable to tobacco and alcohol [103,112,113,114,115]. These changes carry such far-reaching public health implications that they are worthy of investigation by the most advanced multiomics techniques including multichannel single cell epigenomic and 3D chromosomal topological techniques with appropriate resourcing to exhaustively perform these investigations in a translational multigenerational context.
Acknowledgments
All authors had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijerph192416721/s1. Table S1: Histone (Lysine) Methyltransferases; Table S2: Histone (L:ysine) Demethylases; Table S3: Histone Acetyltransferases; Table S4: Histone Deacetylases; Table S5: Other Stem Cell Factors; Table S6: Funtional Annotations of Kit; Table S7: Age-Related Immunometabolic changes; Table S8: Oocyte-Centrosome DNA Methylation Alterations; Table S9: Funtional Annotations of Kinesins; Table S10: Funtional Annotations of Tubulins; Table S11: Funtional Annotations of Tubulins-2; Table S12: Funtional Annotations of CENPN; Table S13: Funtional Annotations of RAD51/52; Table S14: Funtional Annotations of DSCAM; Table S15: Funtional Annotations of DGALP2; Table S16: Funtional Annotations of Slit; Table S17: Funtional Annotations of Robo; Table S18: Funtional Annotations of SRGAP2; Table S19: Funtional Annotations of Alcohol Dehydrogenase (ALDH); Table S20: Funtional Annotations of Retinoid Receptors; Table S21: Funtional Annotations of Notch Receptors; Table S22: Funtional Annotations of VEGF, EFNB2; Table S23: p-Values of Teratological Significance from Schrott; Table S24: Annotations from the Schrott Database for Central Nervous System Abnormalities; Table S25: Annotations from the Schrott Database for Cardiovascular System Abnormalities; Table S26: Annotations from the Schrott Database for Orofacial Abnormalities; Table S27: Annotations from the Schrott Database for General Abnormalities; Table S28: Annotations from the Schrott Database for Limb Abnormalities; Table S29: Annotations from the Schrott Database for Gastrointestinal System Abnormalities; Table S30: Annotations from the Schrott Database for Chromomsomal Abnormalities; Table S31: Annotations from the Schrott Database for Uronephrological System Abnormalities; Table S32: Annotations from the Schrott Database for Body Wall Abnormalities; Table S33: Summary of Teratological Findings of Schrott Database by Organ System & Target; Table S34: Summary of Significance of Schrott DNA Methylation hits by Target; Table S35: Significance Levels for Cancers from Schrott Dataset; Table S36: Summary of Significance Levels for Overall Cancers from Schrott Dataset—Ordered by Minimum p-Value; Table S37: Summary of Significance Levels for Overall Cancers from Schrott Dataset—Ordered by Median p-Value; Table S38: Summary of Significance Levels for Cancers in Cannabis Dependency from Schrott Dataset-Ordered by Minimum p-Value; Table S39: Summary of Significance Levels for Cancers in Cannabis Dependency from Schrott Dataset—Ordered by Median p-Value; Table S40: Summary of Significance Levels for Cancers in Cannabis Withdrawal from Schrott Dataset-Ordered by Minimum p-Value; Table S41: Summary of Significance Levels for Cancers in Cannabis Withdrawal from Schrott Dataset-Ordered by Median p-Value; Table S42:Contrast Between Gene Numebrs and Significance Levels in Withdrawal and Dependency Ordered by Gene Number Ratio. Figure S1: Overall results-negative logarithm of p-Values for congenital anomalies from Schrott Database by organ system. Figure S2: Overall results-negative logarithm of p-Values for congenital anomalies from Schrott Database by organ target. Figure S3: Overall results–Boxplot of negative logarithm of grouped p-Values for congenital anomalies from Schrott Database comparing cannabis dependency with cannabis withdrawal. Figure S4: Number of genes annotated in the Schrott database for target organs by dependency status in (A) cannabis dependence and (B) withdrawal. Figure S5: (A) Numbers of gene annotations, (B) numbers of genes affected and (C) negative logarithm of p-value by cancer type-overall Schrott data. Figure S6: Direct comparison between p-values for cannabis cancer relationships between (A) cannabis dependence and (B) cannabis withdrawal, Schrott data.
Author Contributions
A.S.R. assembled the data, designed and conducted the analyses, and wrote the first manuscript draft. G.K.H. provided technical and logistic support, co-wrote the paper, assisted with gaining ethical approval, provided advice on manuscript preparation and general guidance to study conduct. A.S.R. had the idea for the article, performed the literature search, wrote the first draft and is the guarantor for the article. All authors have read and agreed to the published version of the manuscript.
Institutional Review Board Statement
Ethics Approval and Consent to Participate. The Human Research Ethics Committee of the University of Western Australia provided ethical approval for the study to be undertaken 24 September 2021 (No. RA/4/20/4724).
Informed Consent Statement
Patient consent was not applicable.
Data Availability Statement
All data generated or analyzed during this study are included in this published article and its supplementary information files. Data along with the relevant R code have been made publicly available on the Mendeley Database Repository and can be accessed from this URL https://data.mendeley.com/datasets/sngdkpg8gy/1 (doi:10.17632/sngdkpg8gy.1) (accessed on 10 December 2022).
Conflicts of Interest
The authors declare that they have no competing interests.
Funding Statement
This research received no external funding. No funding organization played any role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Footnotes
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.Lopez-Otin C., Blasco M.A., Partridge L., Serrano M., Kroemer G. The hallmarks of aging. Cell. 2013;153:1194–1217. doi: 10.1016/j.cell.2013.05.039. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Yilmaz D., Furst A., Meaburn K., Lezaja A., Wen Y., Altmeyer M., Reina-San-Martin B., Soutoglou E. Activation of homologous recombination in G1 preserves centromeric integrity. Nature. 2021;600:748–753. doi: 10.1038/s41586-021-04200-z. [DOI] [PubMed] [Google Scholar]
- 3.Franceschi C., Bonafe M., Valensin S., Olivieri F., De Luca M., Ottaviani E., De Benedictis G. Inflamm-aging: An evolutionary perspective on immunosenescence. Ann. N. Y. Acad. Sci. 2000;908:244–254. doi: 10.1111/j.1749-6632.2000.tb06651.x. [DOI] [PubMed] [Google Scholar]
- 4.Salvioli S., Monti D., Lanzarini C., Conte M., Pirazzini C., Bacalini M.G., Garagnani P., Giuliani C., Fontanesi E., Ostan R., et al. Immune system, cell senescence, aging and longevity--inflamm-aging reappraised. Curr. Pharm. Des. 2013;19:1675–1679. [PubMed] [Google Scholar]
- 5.Beausejour C.M., Campisi J. Ageing: Balancing regeneration and cancer. Nature. 2006;443:404–405. doi: 10.1038/nature05221. [DOI] [PubMed] [Google Scholar]
- 6.Busuttil R.A., Dollé M., Campisi J., Vijga J. Genomic instability, aging, and cellular senescence. Ann. N. Y. Acad. Sci. 2004;1019:245–255. doi: 10.1196/annals.1297.041. [DOI] [PubMed] [Google Scholar]
- 7.Campisi J. The biology of replicative senescence. Eur. J. Cancer. 1997;33:703–709. doi: 10.1016/S0959-8049(96)00058-5. [DOI] [PubMed] [Google Scholar]
- 8.Rayess H., Wang M.B., Srivatsan E.S. Cellular senescence and tumor suppressor gene p16. Int. J. Cancer. 2012;130:1715–1725. doi: 10.1002/ijc.27316. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Waddington C.H. Organizers and Genes. Volume 1 Cambridge University Press; Cambridge, UK: 1940. [Google Scholar]
- 10.Gonzales K.A.U., Polak L., Matos I., Tierney M.T., Gola A., Wong E., Infarinato N.R., Nikolova M., Luo S., Liu S., et al. Stem cells expand potency and alter tissue fitness by accumulating diverse epigenetic memories. Science. 2021;374:eabh2444. doi: 10.1126/science.abh2444. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Hota S.K., Rao K.S., Blair A.P., Khalilimeybodi A., Hu K.M., Thomas R., So K., Kameswaran V., Xu J., Polacco B.J., et al. Brahma safeguards canalization of cardiac mesoderm differentiation. Nature. 2022;602:129–134. doi: 10.1038/s41586-021-04336-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Takahashi K., Yamanaka S. Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell. 2006;126:663–676. doi: 10.1016/j.cell.2006.07.024. [DOI] [PubMed] [Google Scholar]
- 13.Ocampo A., Reddy P., Martinez-Redondo P., Platero-Luengo A., Hatanaka F., Hishida T., Li M., Lam D., Kurita M., Beyret E., et al. In Vivo Amelioration of Age-Associated Hallmarks by Partial Reprogramming. Cell. 2016;167:1719–1733.e1712. doi: 10.1016/j.cell.2016.11.052. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Chen Y., Lüttmann F.F., Schoger E., Schöler H.R., Zelarayán L.C., Kim K.P., Haigh J.J., Kim J., Braun T. Reversible reprogramming of cardiomyocytes to a fetal state drives heart regeneration in mice. Science. 2021;373:1537–1540. doi: 10.1126/science.abg5159. [DOI] [PubMed] [Google Scholar]
- 15.Lu Y., Brommer B., Tian X., Krishnan A., Meer M., Wang C., Vera D.L., Zeng Q., Yu D., Bonkowski M.S., et al. Reprogramming to recover youthful epigenetic information and restore vision. Nature. 2020;588:124–129. doi: 10.1038/s41586-020-2975-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Schultz M.B., Sinclair D.A. When stem cells grow old: Phenotypes and mechanisms of stem cell aging. Development. 2016;143:3–14. doi: 10.1242/dev.130633. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Volkow N.D., Baler R.D., Compton W.M., Weiss S.R. Adverse Health Effects of Marijuana Use. N. Engl. J. Med. 2014;370:2219–2227. doi: 10.1056/NEJMra1402309. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Substance Abuse and Mental Health Services Administration Key Substance Use and Mental Health Indicators in the United States: Results from the 2020 National Survey on Drug Use and Health (NSDUH) [(accessed on 1 October 2021)]; Available online: https://www.samhsa.gov/data/sites/default/files/reports/rpt35325/NSDUHFFRPDFWHTMLFiles2020/2020NSDUHFFR1PDFW102121.pdf.
- 19.Morishima A. Effects of cannabis and natural cannabinoids on chromosomes and ova. NIDA Res. Monogr. 1984;44:25–45. [PubMed] [Google Scholar]
- 20.Morishima A., Henrich R.T., Jayaraman J., Nahas G.G. Hypoploid metaphases in cultured lymphocytes of marihuana smokers. Adv. Biosci. 1978;22–23:371–376. doi: 10.1016/b978-0-08-023759-6.50032-9. [DOI] [PubMed] [Google Scholar]
- 21.Leuchtenberger C., Leuchtenberger R. Morphological and cytochemical effects of marijuana cigarette smoke on epithelioid cells of lung explants from mice. Nature. 1971;234:227–229. doi: 10.1038/234227a0. [DOI] [PubMed] [Google Scholar]
- 22.Leuchtenberger C., Leuchtenberger R., Schneider A. Effects of marijuana and tobacco smoke on human lung physiology. Nature. 1973;241:137–139. doi: 10.1038/241137a0. [DOI] [PubMed] [Google Scholar]
- 23.Stenchever M.A., Kunysz T.J., Allen M.A. Chromosome breakage in users of marihuana. Am. J. Obstet. Gynecol. 1974;118:106–113. doi: 10.1016/S0002-9378(16)33653-5. [DOI] [PubMed] [Google Scholar]
- 24.Huang H.F.S., Nahas G.G., Hembree W.C. Effects of Marijuana Inhalation on Spermatogenesis of the Rat. In: Nahas G.G., Sutin K.M., Harvey D.J., Agurell S., editors. Marijuana in Medicine. Volume 1. Human Press; Totowa, NY, USA: 1999. pp. 359–366. [Google Scholar]
- 25.Reece A.S., Hulse G.K. Causal inference multiple imputation investigation of the impact of cannabinoids and other substances on ethnic differentials in US testicular cancer incidence. BMC Pharmacol. Toxicol. 2021;22:40–71. doi: 10.1186/s40360-021-00505-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Murphy S.K., Itchon-Ramos N., Visco Z., Huang Z., Grenier C., Schrott R., Acharya K., Boudreau M.H., Price T.M., Raburn D.J., et al. Cannabinoid exposure and altered DNA methylation in rat and human sperm. Epigenetics. 2018;13:1208–1221. doi: 10.1080/15592294.2018.1554521. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Schrott R., Murphy S.K., Modliszewski J.L., King D.E., Hill B., Itchon-Ramos N., Raburn D., Price T., Levin E.D., Vandrey R., et al. Refraining from use diminishes cannabis-associated epigenetic changes in human sperm. Environ. Epigenetics. 2021;7:dvab009. doi: 10.1093/eep/dvab009. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.DiNieri J.A., Wang X., Szutorisz H., Spano S.M., Kaur J., Casaccia P., Dow-Edwards D., Hurd Y.L. Maternal cannabis use alters ventral striatal dopamine D2 gene regulation in the offspring. Biol. Psychiatry. 2011;70:763–769. doi: 10.1016/j.biopsych.2011.06.027. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Szutorisz H., DiNieri J.A., Sweet E., Egervari G., Michaelides M., Carter J.M., Ren Y., Miller M.L., Blitzer R.D., Hurd Y.L. Parental THC exposure leads to compulsive heroin-seeking and altered striatal synaptic plasticity in the subsequent generation. Neuropsychopharmacology. 2014;39:1315–1323. doi: 10.1038/npp.2013.352. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Szutorisz H., Hurd Y.L. Epigenetic Effects of Cannabis Exposure. Biol. Psychiatry. 2016;79:586–594. doi: 10.1016/j.biopsych.2015.09.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Szutorisz H., Hurd Y.L. High times for cannabis: Epigenetic imprint and its legacy on brain and behavior. Neurosci. Biobehav. Rev. 2018;85:93–101. doi: 10.1016/j.neubiorev.2017.05.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Watson C.T., Szutorisz H., Garg P., Martin Q., Landry J.A., Sharp A.J., Hurd Y.L. Genome-Wide DNA Methylation Profiling Reveals Epigenetic Changes in the Rat Nucleus Accumbens Associated with Cross-Generational Effects of Adolescent THC Exposure. Neuropsychopharmacology. 2015;40:2993–3005. doi: 10.1038/npp.2015.155. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Rossato M., Pagano C., Vettor R. The cannabinoid system and male reproductive functions. J. Neuroendocrinol. 2008;20((Suppl. 1)):90–93. doi: 10.1111/j.1365-2826.2008.01680.x. [DOI] [PubMed] [Google Scholar]
- 34.Chioccarelli T., Cacciola G., Altucci L., Lewis S.E., Simon L., Ricci G., Ledent C., Meccariello R., Fasano S., Pierantoni R., et al. Cannabinoid receptor 1 influences chromatin remodeling in mouse spermatids by affecting content of transition protein 2 mRNA and histone displacement. Endocrinology. 2010;151:5017–5029. doi: 10.1210/en.2010-0133. [DOI] [PubMed] [Google Scholar]
- 35.Kaplan B.L., Springs A.E., Kaminski N.E. The profile of immune modulation by cannabidiol (CBD) involves deregulation of nuclear factor of activated T cells (NFAT) Biochem. Pharmacol. 2008;76:726–737. doi: 10.1016/j.bcp.2008.06.022. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Eisenstein T.K., Meissler J.J. Effects of Cannabinoids on T-cell Function and Resistance to Infection. J. Neuroimmune Pharmacol. 2015;10:204–216. doi: 10.1007/s11481-015-9603-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Chiurchiu V. Endocannabinoids and Immunity. Cannabis Cannabinoid Res. 2016;1:59–66. doi: 10.1089/can.2016.0002. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Kaplan B.L.F. Evaluation of Marijuana Compounds on Neuroimmune Endpoints in Experimental Autoimmune Encephalomyelitis. Curr. Protoc. Toxicol. 2018;75:11.25.1–11.25.22. doi: 10.1002/cptx.43. [DOI] [PubMed] [Google Scholar]
- 39.Bindukumar B., Mahajan S.D., Reynolds J.L., Hu Z., Sykes D.E., Aalinkeel R., Schwartz S.A. Genomic and proteomic analysis of the effects of cannabinoids on normal human astrocytes. Brain Res. 2008;1191:1–11. doi: 10.1016/j.brainres.2007.10.062. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Yang X., Hegde V.L., Rao R., Zhang J., Nagarkatti P.S., Nagarkatti M. Histone modifications are associated with Delta9-tetrahydrocannabinol-mediated alterations in antigen-specific T cell responses. J. Biol. Chem. 2014;289:18707–18718. doi: 10.1074/jbc.M113.545210. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Wu H.Y., Huang C.H., Lin Y.H., Wang C.C., Jan T.R. Cannabidiol induced apoptosis in human monocytes through mitochondrial permeability transition pore-mediated ROS production. Free Radic. Biol. Med. 2018;124:311–318. doi: 10.1016/j.freeradbiomed.2018.06.023. [DOI] [PubMed] [Google Scholar]
- 42.Chiu P., Karler R., Craven C., Olsen D.M., Turkanis S.A. The influence of delta9-tetrahydrocannabinol, cannabinol and cannabidiol on tissue oxygen consumption. Res. Commun. Chem. Pathol. Pharmacol. 1975;12:267–286. [PubMed] [Google Scholar]
- 43.Harkany T., Horvath T.L. (S)Pot on Mitochondria: Cannabinoids Disrupt Cellular Respiration to Limit Neuronal Activity. Cell Metab. 2017;25:8–10. doi: 10.1016/j.cmet.2016.12.020. [DOI] [PubMed] [Google Scholar]
- 44.Hebert-Chatelain E., Desprez T., Serrat R., Bellocchio L., Soria-Gomez E., Busquets-Garcia A., Pagano Zottola A.C., Delamarre A., Cannich A., Vincent P., et al. A cannabinoid link between mitochondria and memory. Nature. 2016;539:555–559. doi: 10.1038/nature20127. [DOI] [PubMed] [Google Scholar]
- 45.McClean D.K., Zimmerman A.M. Action of delta 9-tetrahydrocannabinol on cell division and macromolecular synthesis in division-synchronized protozoa. Pharmacology. 1976;14:307–321. doi: 10.1159/000136610. [DOI] [PubMed] [Google Scholar]
- 46.Thomas J., Tilak S., Zimmerman S., Zimmerman A.M. Action of delta 9-tetrahydrocannabinol on the pool of acid soluble nucleotides. Cytobios. 1984;40:71–85. [PubMed] [Google Scholar]
- 47.Tahir SK, Zimmerman AM: Influence of marihuana on cellular structures and biochemical activities. Pharmacol. Biochem. Behav. 1991;40:617–623. doi: 10.1016/0091-3057(91)90372-9. [DOI] [PubMed] [Google Scholar]
- 48.Parker S.J., Zuckerman B.S., Zimmermann A.M. The Effects of Maternal Marijuana Use During Pregnancy on Fetal Growth. In: Nahas G.G., Sutin K.M., Harvey D.J., Agurell S., editors. Marijuana in Medicine. Volume 1. Humana Press; Totowa, NY, USA: 1999. pp. 461–468. [Google Scholar]
- 49.Mon M.J., Haas A.E., Stein J.L., Stein G.S. Influence of psychoactive and nonpsychoactive cannabinoids on cell proliferation and macromolecular biosynthesis in human cells. Biochem. Pharmacol. 1981;30:31–43. doi: 10.1016/0006-2952(81)90282-3. [DOI] [PubMed] [Google Scholar]
- 50.Zimmerman A.M., Raj A.Y. Influence of cannabinoids on somatic cells in vivo. Pharmacology. 1980;21:277–287. doi: 10.1159/000137442. [DOI] [PubMed] [Google Scholar]
- 51.Tahir S.K., Trogadis J.E., Stevens J.K., Zimmerman A.M. Cytoskeletal organization following cannabinoid treatment in undifferentiated and differentiated PC12 cells. Biochem. Cell Biol. 1992;70:1159–1173. doi: 10.1139/o92-162. [DOI] [PubMed] [Google Scholar]
- 52.Aldington S., Harwood M., Cox B., Weatherall M., Beckert L., Hansell A., Pritchard A., Robinson G., Beasley R. Cannabis use and risk of lung cancer: A case-control study. Eur. Respir. J. 2008;31:280–286. doi: 10.1183/09031936.00065707. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 53.Voirin N., Berthiller J., Benhaïm-Luzon V., Boniol M., Straif K., Ayoub W.B., Ayed F.B., Sasco A.J. Risk of lung cancer and past use of cannabis in Tunisia. J. Thorac. Oncol. 2006;1:577–579. doi: 10.1097/01243894-200607000-00013. [DOI] [PubMed] [Google Scholar]
- 54.Berthiller J., Straif K., Boniol M., Voirin N., Benhaïm-Luzon V., Ayoub W.B., Dari I., Laouamri S., Hamdi-Cherif M., Bartal M., et al. Cannabis smoking and risk of lung cancer in men: A pooled analysis of three studies in Maghreb. J. Thorac. Oncol. 2008;3:1398–1403. doi: 10.1097/JTO.0b013e31818ddcde. [DOI] [PubMed] [Google Scholar]
- 55.Zhang Z.F., Morgenstern H., Spitz M.R., Tashkin D.P., Yu G.P., Marshall J.R., Hsu T.C., Schantz S.P. Marijuana use and increased risk of squamous cell carcinoma of the head and neck. Cancer Epidemiol. Biomark. Prev. 1999;8:1071–1078. [PubMed] [Google Scholar]
- 56.Hashibe M., Ford D.E., Zhang Z.F. Marijuana smoking and head and neck cancer. J. Clin. Pharmacol. 2002;42((Suppl. 11)):103S–107S. doi: 10.1002/j.1552-4604.2002.tb06010.x. [DOI] [PubMed] [Google Scholar]
- 57.Sidney S., Quesenberry C.P., Jr., Friedman G.D., Tekawa I.S. Marijuana use and cancer incidence (California, United States) Cancer Causes Control. 1997;8:722–728. doi: 10.1023/A:1018427320658. [DOI] [PubMed] [Google Scholar]
- 58.Daling J.R., Doody D.R., Sun X., Trabert B.L., Weiss N.S., Chen C., Biggs M.L., Starr J.R., Dey S.K., Schwartz S.M. Association of marijuana use and the incidence of testicular germ cell tumors. Cancer. 2009;115:1215–1223. doi: 10.1002/cncr.24159. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 59.Efird J.T., Friedman G.D., Sidney S., Klatsky A., Habel L.A., Udaltsova N.V., Van Den Eeden S., Nelson L.M. The risk for malignant primary adult-onset glioma in a large, multiethnic, managed-care cohort: Cigarette smoking and other lifestyle behaviors. J. Neurooncol. 2004;68:57–69. doi: 10.1023/B:NEON.0000024746.87666.ed. [DOI] [PubMed] [Google Scholar]
- 60.Moiche Bokobo P., de la Presa M.A., Cuesta Angulo J. Transitional cell carcinoma in a young heavy marihuana smoker. Arch. Esp. Urol. 2001;54:165–167. [PubMed] [Google Scholar]
- 61.Chacko J.A., Heiner J.G., Siu W., Macy M., Terris M.K. Association between marijuana use and transitional cell carcinoma. Urology. 2006;67:100–104. doi: 10.1016/j.urology.2005.07.005. [DOI] [PubMed] [Google Scholar]
- 62.Nieder A.M., Lipke M.C., Madjar S. Transitional cell carcinoma associated with marijuana: Case report and review of the literature. Urology. 2006;67:200. doi: 10.1016/j.urology.2005.08.006. [DOI] [PubMed] [Google Scholar]
- 63.Bluhm E.C., Daniels J., Pollock B.H., Olshan A.F. Maternal use of recreational drugs and neuroblastoma in offspring: A report from the Children’s Oncology Group (United States) Cancer Causes Control. 2006;17:663–669. doi: 10.1007/s10552-005-0580-3. [DOI] [PubMed] [Google Scholar]
- 64.Hashibe M., Straif K., Tashkin D.P., Morgenstern H., Greenlandm S., Zhang Z.F. Epidemiologic review of marijuana use and cancer risk. Alcohol. 2005;35:265–275. doi: 10.1016/j.alcohol.2005.04.008. [DOI] [PubMed] [Google Scholar]
- 65.Robison L.L., Buckley J.D., Daigle A.E., Wells R., Benjamin D., Arthur D.C., Hammond G.D. Maternal drug use and risk of childhood nonlymphoblastic leukemia among offspring. An epidemiologic investigation implicating marijuana (a report from the Childrens Cancer Study Group) Cancer. 1989;63:1904–1911. doi: 10.1002/1097-0142(19890515)63:10<1904::AID-CNCR2820631006>3.0.CO;2-W. [DOI] [PubMed] [Google Scholar]
- 66.Reece A.S., Hulse G.K. Epidemiological Overview of Multidimensional Chromosomal and Genome Toxicity of Cannabis Exposure in Congenital Anomalies and Cancer Development. Sci. Rep. 2021;11:13892–13912. doi: 10.1038/s41598-021-93411-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 67.Borowska M., Czarnywojtek A., Sawicka-Gutaj N., Woliński K., Płazińska M.T., Mikołajczak P., Ruchała M. The effects of cannabinoids on the endocrine system. Endokrynol. Pol. 2018;69:705–719. doi: 10.5603/EP.a2018.0072. [DOI] [PubMed] [Google Scholar]
- 68.Meah F., Lundholm M., Emanuele N., Amjed H., Poku C., Agrawal L., Emanuele M.A. The effects of cannabis and cannabinoids on the endocrine system. Rev. Endocr. Metab. Disord. 2021;23:401–420. doi: 10.1007/s11154-021-09682-w. [DOI] [PubMed] [Google Scholar]
- 69.Battista N., Pasquariello N., Di Tommaso M., Maccarrone M. Interplay between endocannabinoids, steroids and cytokines in the control of human reproduction. J. Neuroendocrinol. 2008;20((Suppl. 1)):82–89. doi: 10.1111/j.1365-2826.2008.01684.x. [DOI] [PubMed] [Google Scholar]
- 70.Battista N., Rapino C., Di Tommaso M., Bari M., Pasquariello N., Maccarrone M. Regulation of male fertility by the endocannabinoid system. Mol. Cell Endocrinol. 2008;286((Suppl. 1)):S17–S23. doi: 10.1016/j.mce.2008.01.010. [DOI] [PubMed] [Google Scholar]
- 71.Battista N., Bari M., Maccarrone M. Endocannabinoids and Reproductive Events in Health and Disease. Handb. Exp. Pharmacol. 2015;231:341–365. doi: 10.1007/978-3-319-20825-1_12. [DOI] [PubMed] [Google Scholar]
- 72.Smith C.G., Asch R.H. Acute, short-term, and chronic effects of marijuana on the female primate reproductive function. NIDA Res. Monogr. 1984;44:82–96. [PubMed] [Google Scholar]
- 73.Mendelson J.H., Mello N.K. Effects of marijuana on neuroendocrine hormones in human males and females. NIDA Res. Monogr. 1984;44:97–114. [PubMed] [Google Scholar]
- 74.Hillard C.J. Endocannabinoids and the Endocrine System in Health and Disease. In: Pertwee R.G., editor. Endocannabinoids. Springer International Publishing; Cham, Switzerland: 2015. pp. 317–339. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 75.Teschendorff A.E., Menon U., Gentry-Maharaj A., Ramus S.J., Weisenberger D.J., Shen H., Campan M., Noushmehr H., Bell C.G., Maxwell A.P., et al. Age-dependent DNA methylation of genes that are suppressed in stem cells is a hallmark of cancer. Genome Res. 2010;20:440–446. doi: 10.1101/gr.103606.109. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 76.Horvath S., Zhang Y., Langfelder P., Kahn R.S., Boks M.P., van Eijk K., van den Berg L.H., Ophoff R.A. Aging effects on DNA methylation modules in human brain and blood tissue. Genome Biol. 2012;13:R97. doi: 10.1186/gb-2012-13-10-r97. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 77.Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013;14:R115. doi: 10.1186/gb-2013-14-10-r115. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 78.Hannum G., Guinney J., Zhao L., Zhang L.I., Hughes G., Sadda S., Klotzle B., Bibikova M., Fan J.B., Gao Y., et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol. Cell. 2013;49:359–367. doi: 10.1016/j.molcel.2012.10.016. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 79.Reece A.S., Norman A., Hulse G.K. Acceleration of cardiovascular-biological age by amphetamine exposure is a power function of chronological age. Heart Asia. 2017;9:30–38. doi: 10.1136/heartasia-2016-010832. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 80.Reece A.S., Hulse G.K. Reduction in arterial stiffness and vascular age by naltrexone-induced interruption of opiate agonism: A cohort study. BMJ Open. 2013;3:e002610. doi: 10.1136/bmjopen-2013-002610. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 81.Reece A.S., Norman A., Hulse G.K. Cannabis Exposure as an Interactive Cardiovascular Risk Factor and Accelerant of Organismal Ageing—A Longitudinal Study. BMJ Open. 2016;6:e011891–e011900. doi: 10.1136/bmjopen-2016-011891. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 82.Reece A.S., Hulse G.K. Impact of Lifetime Opioid Exposure on Arterial Stiffness and Vascular Age: Cross-sectional and Longitudinal Studies in Men and Women. BMJ Open. 2014;4:e004521. doi: 10.1136/bmjopen-2013-004521. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 83.Sayed N., Huang Y., Nguyen K., Krejciova-Rajaniemi Z., Grawe A.P., Gao T., Tibshirani R., Hastie T., Alpert A., Cui L., et al. An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging. Nat. Aging. 2021;1:598–615. doi: 10.1038/s43587-021-00082-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 84.Yu Z., Zhai G., Singmann P., He Y., Xu T., Prehn C., Römisch-Margl W., Lattka E., Gieger C., Soranzo N., et al. Human serum metabolic profiles are age dependent. Aging Cell. 2012;11:960–967. doi: 10.1111/j.1474-9726.2012.00865.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 85.Menni C., Kastenmüller G., Petersen A.K., Bell J.T., Psatha M., Tsai P.C., Gieger C., Schulz H., Erte I., John S., et al. Metabolomic markers reveal novel pathways of ageing and early development in human populations. Int. J. Epidemiol. 2013;42:1111–1119. doi: 10.1093/ije/dyt094. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 86.Lawton K.A., Berger A., Mitchell M., Milgram K.E., Evans A.M., Guo L., Hanson R.W., Kalhan S.C., Ryals J.A., Milburn M.V. Analysis of the adult human plasma metabolome. Pharmacogenomics. 2008;9:383–397. doi: 10.2217/14622416.9.4.383. [DOI] [PubMed] [Google Scholar]
- 87.Ishikawa M., Maekawa K., Saito K., Senoo Y., Urata M., Murayama M., Tajima Y., Kumagai Y., Saito Y. Plasma and serum lipidomics of healthy white adults shows characteristic profiles by subjects’ gender and age. PLoS ONE. 2014;9:e91806. doi: 10.1371/journal.pone.0091806. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 88.Holly A.C., Melzer D., Pilling L.C., Henley W., Hernandez D.G., Singleton A.B., Bandinelli S., Guralnik J.M., Ferrucci L., Harries L.W. Towards a gene expression biomarker set for human biological age. Aging Cell. 2013;12:324–326. doi: 10.1111/acel.12044. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 89.Hertel J., Friedrich N., Wittfeld K., Pietzner M., Budde K., Van der Auwera S., Lohmann T., Teumer A., Völzke H., Nauck M., et al. Measuring Biological Age via Metabonomics: The Metabolic Age Score. J. Proteome Res. 2016;15:400–410. doi: 10.1021/acs.jproteome.5b00561. [DOI] [PubMed] [Google Scholar]
- 90.Collino S., Montoliu I., Martin F.P.J., Scherer M., Mari D., Salvioli S., Bucci L., Ostan R., Monti D., Biagi E., et al. Metabolic signatures of extreme longevity in northern Italian centenarians reveal a complex remodeling of lipids, amino acids, and gut microbiota metabolism. PLoS ONE. 2013;8:e56564. doi: 10.1371/annotation/5fb9fa6f-4889-4407-8430-6dfc7ecdfbdd. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 91.Tang M., Bolderson E., O’Byrne K.J., Richard D.J. Tumor Hypoxia Drives Genomic Instability. Front. Cell Dev. Biol. 2021;9:626229. doi: 10.3389/fcell.2021.626229. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 92.Salmaninejad A., Ilkhani K., Marzban H., Navashenaq J.G., Rahimirad S., Radnia F., Yousefi M., Bahmanpour Z., Azhdari S., Sahebkar A. Genomic Instability in Cancer: Molecular Mechanisms and Therapeutic Potentials. Curr. Pharm. Des. 2021;27:3161–3169. doi: 10.2174/1381612827666210426100206. [DOI] [PubMed] [Google Scholar]
- 93.Li H., Zimmerman S.E., Weyemi U. Genomic instability and metabolism in cancer. Int. Rev. Cell Mol. Biol. 2021;364:241–265. doi: 10.1016/bs.ircmb.2021.05.004. [DOI] [PubMed] [Google Scholar]
- 94.Cardoso A.P.F., Banerjee M., Nail A.N., Lykoudi A., States J.C. miRNA dysregulation is an emerging modulator of genomic instability. Semin. Cancer Biol. 2021;76:120–131. doi: 10.1016/j.semcancer.2021.05.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 95.De Majo F., Martens L., Hegenbarth J.C., Rühle F., Hamczyk M.R., Nevado R.M., Andrés V., Hilbold E., Bär C., Thum T., et al. Genomic instability in the naturally and prematurely aged myocardium. Proc. Natl. Acad. Sci. USA. 2021;118:e2022974118. doi: 10.1073/pnas.2022974118. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 96.Bonora M., Missiroli S., Perrone M., Fiorica F., Pinton P., Giorgi C. Mitochondrial Control of Genomic Instability in Cancer. Cancers. 2021;13:1914. doi: 10.3390/cancers13081914. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 97.Keefe D.L. Telomeres and genomic instability during early development. Eur. J. Med. Genet. 2020;63:103638. doi: 10.1016/j.ejmg.2019.03.002. [DOI] [PubMed] [Google Scholar]
- 98.Freitas M.O., Gartner J., Rangel-Pozzo A., Mai S. Genomic Instability in Circulating Tumor Cells. Cancers. 2020;12:3001. doi: 10.3390/cancers12103001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 99.Duijf P.H., Nanayakkara D., Nones K., Srihari S., Kalimutho M., Khanna K.K. Mechanisms of Genomic Instability in Breast Cancer. Trends Mol. Med. 2019;25:595–611. doi: 10.1016/j.molmed.2019.04.004. [DOI] [PubMed] [Google Scholar]
- 100.Tubbs A., Nussenzweig A. Endogenous DNA Damage as a Source of Genomic Instability in Cancer. Cell. 2017;168:644–656. doi: 10.1016/j.cell.2017.01.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 101.Andor N., Maley C.C., Ji H.P. Genomic Instability in Cancer: Teetering on the Limit of Tolerance. Cancer Res. 2017;77:2179–2185. doi: 10.1158/0008-5472.CAN-16-1553. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 102.Negrini S., Gorgoulis V.G., Halazonetis T.D. Genomic instability—An evolving hallmark of cancer. Nat. Rev. Mol. Cell Biol. 2010;11:220–228. doi: 10.1038/nrm2858. [DOI] [PubMed] [Google Scholar]
- 103.Reece A.S., Hulse G.K. Geotemporospatial and causal inference epidemiological analysis of US survey and overview of cannabis, cannabidiol and cannabinoid genotoxicity in relation to congenital anomalies 2001–2015. BMC Pediatr. 2022;22:47–124. doi: 10.1186/s12887-021-02996-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 104.Reece A.S., Hulse G.K. A geospatiotemporal and causal inference epidemiological exploration of substance and cannabinoid exposure as drivers of rising US pediatric cancer rates. BMC Cancer. 2021;21:197–230. doi: 10.1186/s12885-021-07924-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 105.Reece AS, Hulse GK: Cannabinoid exposure as a major driver of pediatric acute lymphoid Leukaemia rates across the USA: Combined geospatial, multiple imputation and causal inference study. BMC Cancer. 2021;21:984–1017. doi: 10.1186/s12885-021-08598-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 106.Reece A.S., Hulse G.K. Co-occurrence across time and space of drug- and cannabinoid- exposure and adverse mental health outcomes in the National Survey of Drug Use and Health: Combined geotemporospatial and causal inference analysis. BMC Public Health. 2020;20:1655–1669. doi: 10.1186/s12889-020-09748-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 107.Reece A.S., Hulse G.K. Contemporary epidemiology of rising atrial septal defect trends across USA 1991–2016: A combined ecological geospatiotemporal and causal inferential study. BMC Pediatr. 2020;20:539–550. doi: 10.1186/s12887-020-02431-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 108.Reece A.S., Hulse G.K. Canadian Cannabis Consumption and Patterns of Congenital Anomalies: An Ecological Geospatial Analysis. J. Addict. Med. 2020;14:e195–e210. doi: 10.1097/ADM.0000000000000638. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 109.Reece A.S., Hulse G.K. Broad Spectrum epidemiological contribution of cannabis and other substances to the teratological profile of northern New South Wales: Geospatial and causal inference analysis. BMC Pharmacol. Toxicol. 2020;21:75–103. doi: 10.1186/s40360-020-00450-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 110.Reece A.S., Hulse G.K. Cannabis Consumption Patterns Explain the East-West Gradient in Canadian Neural Tube Defect Incidence: An Ecological Study. Glob. Pediatr. Health. 2019;6:2333794X19894798. doi: 10.1177/2333794X19894798. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 111.Reece A.S., Hulse G.K. Cannabis Teratology Explains Current Patterns of Coloradan Congenital Defects: The Contribution of Increased Cannabinoid Exposure to Rising Teratological Trends. Clin. Pediatr. 2019;58:1085–1123. doi: 10.1177/0009922819861281. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 112.Reece A.S., Hulse G.K. Geotemporospatial and Causal Inferential Epidemiological Overview and Survey of USA Cannabis, Cannabidiol and Cannabinoid Genotoxicity Expressed in Cancer Incidence 2003–2017: Part 3—Spatiotemporal, Multivariable and Causal Inferential Pathfinding and Exploratory Analyses of Prostate and Ovarian Cancers. Arch. Public Health. 2022;80:100–136. doi: 10.1186/s13690-022-00813-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 113.Reece A.S., Hulse G.K. Geotemporospatial and Causal Inferential Epidemiological Overview and Survey of USA Cannabis, Cannabidiol and Cannabinoid Genotoxicity Expressed in Cancer Incidence 2003–2017: Part 2—Categorical Bivariate Analysis and Attributable Fractions. Arch. Public Health. 2022;80:100–135. doi: 10.1186/s13690-022-00812-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 114.Reece A.S., Hulse G.K. Geotemporospatial and Causal Inferential Epidemiological Overview and Survey of USA Cannabis, Cannabidiol and Cannabinoid Genotoxicity Expressed in Cancer Incidence 2003–2017: Part 1—Continuous Bivariate Analysis. Arch. Public Health. 2022;80:99–133. doi: 10.1186/s13690-022-00811-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 115.Reece A.S., Hulse G.K. Cannabinoid- and Substance- Relationships of European Congenital Anomaly Patterns: A Space-Time Panel Regression and Causal Inferential Study. Environ. Epigenetics. 2022;8:dvab015. doi: 10.1093/eep/dvab015. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 116.Reece A.S., Hulse G.K. Cannabis in Pregnancy—Rejoinder, Exposition and Cautionary Tales. Psychiatric Times, 9 October 2020. [(accessed on 1 November 2022)]. Available online: https://www.psychiatrictimes.com/view/cannabis-pregnancy-rejoinder-exposition-cautionary-tales.
- 117.Reece A.S., Hulse G.K. Epidemiological Associations of Various Substances and Multiple Cannabinoids with Autism in USA. Clin. Pediatr. Open Access. 2019;4:155. doi: 10.35248/2572-0775.19.4.155. [DOI] [Google Scholar]
- 118.Forrester M.B., Merz R.D. Risk of selected birth defects with prenatal illicit drug use, Hawaii, 1986–2002. J. Toxicol. Environ. Health A. 2007;70:7–18. doi: 10.1080/15287390600748799. [DOI] [PubMed] [Google Scholar]
- 119.Wagner B., Gerletti P., Fürst P., Keuth O., Bernsmann T., Martin A., Schäfer B., Numata J., Lorenzen M.C., Pieper R. Transfer of cannabinoids into the milk of dairy cows fed with industrial hemp could lead to Δ9-THC exposure that exceeds acute reference dose. Nature Food. 2022;3:921–932. doi: 10.1038/s43016-022-00623-7. [DOI] [PubMed] [Google Scholar]
- 120.Reece A.S., Hulse G.K. Cannabinoid Genotoxicity and Congenital Anomalies: A Convergent Synthesis of European and USA Datasets. In: Preedy V., Patel V., editors. Cannabis, Cannabinoids and Endocannabinoids. Volume 1. Elsevier; London, UK: 2022. in press . [Google Scholar]
- 121.Reece A.S., Hulse G.K. Cannabis Genotoxicity and Cancer Incidence: A Highly Concordant Synthesis of European and USA Datasets. In: Preedy V., Patel V., editors. Cannabis, Cannabinoids and Endocannabinoids. Volume 1. Elsevier; London, UK: 2022. in press . [Google Scholar]
- 122.Reece A.S., Hulse G.K. Epidemiological Overview of Cannabis- and Substance- Carcinogenesis in Europe: A Lagged Causal Inferential Panel Regression Modelling and Marginal Effects Study. 2022. Manuscript Submitted . [DOI] [PMC free article] [PubMed]
- 123.Fine J.D., Moreau A.L., Karcher N.R., Agrawal A., Rogers C.E., Barch D.M., Bogdan R. Association of Prenatal Cannabis Exposure with Psychosis Proneness Among Children in the Adolescent Brain Cognitive Development (ABCD) Study. JAMA Psychiatry. 2019;76:762–764. doi: 10.1001/jamapsychiatry.2019.0076. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 124.Grech A., Van Os J., Jones P.B., Lewis S.W., Murray R.M. Cannabis use and outcome of recent onset psychosis. Eur. Psychiatry. 2005;20:349–353. doi: 10.1016/j.eurpsy.2004.09.013. [DOI] [PubMed] [Google Scholar]
- 125.Henquet C., Murray R., Linszen D., van Os J. The environment and schizophrenia: The role of cannabis use. Schizophr. Bull. 2005;31:608–612. doi: 10.1093/schbul/sbi027. [DOI] [PubMed] [Google Scholar]
- 126.Bartoli F., Crocamo C., Carra G. Cannabis use disorder and suicide attempts in bipolar disorder: A meta-analysis. Neurosci. Biobehav. Rev. 2019;103:14–20. doi: 10.1016/j.neubiorev.2019.05.017. [DOI] [PubMed] [Google Scholar]
- 127.Hanna R.C., Perez J.M., Ghose S. Cannabis and development of dual diagnoses: A literature review. Am. J. Drug Alcohol Abus. 2017;43:442–455. doi: 10.1080/00952990.2016.1213273. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 128.Marwaha S., Winsper C., Bebbington P., Smith D. Cannabis Use and Hypomania in Young People: A Prospective Analysis. Schizophr. Bull. 2018;44:1267–1274. doi: 10.1093/schbul/sbx158. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 129.Tyler E., Jones S., Black N., Carter L.A., Barrowclough C. The relationship between bipolar disorder and cannabis use in daily life: An experience sampling study. PLoS ONE. 2015;10:e0118916. doi: 10.1371/journal.pone.0118916. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 130.Dierker L., Selya A., Lanza S., Li R., Rose J. Depression and marijuana use disorder symptoms among current marijuana users. Addict. Behav. 2018;76:161–168. doi: 10.1016/j.addbeh.2017.08.013. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 131.Filbey F.M., Aslan S., Lu H., Peng S.L. Residual Effects of THC via Novel Measures of Brain Perfusion and Metabolism in a Large Group of Chronic Cannabis Users. Neuropsychopharmacology. 2018;43:700–707. doi: 10.1038/npp.2017.44. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 132.Gobbi G., Atkin T., Zytynski T., Wang S., Askari S., Boruff J., Ware M., Marmorstein N., Cipriani A., Dendukuri N., et al. Association of Cannabis Use in Adolescence and Risk of Depression, Anxiety, and Suicidality in Young Adulthood: A Systematic Review and Meta-analysis Cannabis Use in Adolescence and Risk of Depression, Anxiety, and Suicidality in Young AdulthoodCannabis Use in Adolescence and Risk of Depression, Anxiety, and Suicidality in Young Adulthood. JAMA Psychiatry. 2019;76:426–434. doi: 10.1001/jamapsychiatry.2018.4500. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 133.Hézode C., Roudot-Thoraval F., Nguyen S., Grenard P., Julien B., Zafrani E.S., Pawlostky J.M., Dhumeaux D., Lotersztajn S., Mallat A. Daily cannabis smoking as a risk factor for progression of fibrosis in chronic hepatitis C. Hepatology. 2005;42:63–71. doi: 10.1002/hep.20733. [DOI] [PubMed] [Google Scholar]
- 134.Patsenker E., Stickel F. Cannabinoids in liver diseases. Clin. Liver Dis. 2016;7:21–25. doi: 10.1002/cld.527. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 135.Mai P., Yang L., Tian L., Wang L., Jia S., Zhang Y., Liu X., Yang L., Li L. Endocannabinoid System Contributes to Liver Injury and Inflammation by Activation of Bone Marrow-Derived Monocytes/Macrophages in a CB1-Dependent Manner. J. Immunol. 2015;195:3390–3401. doi: 10.4049/jimmunol.1403205. [DOI] [PubMed] [Google Scholar]
- 136.Patsenker E., Stoll M., Millonig G., Agaimy A., Wissniowski T., Schneider V., Mueller S., Brenneisen R., Seitz H.K., Ocker M., et al. Cannabinoid receptor type I modulates alcohol-induced liver fibrosis. Mol. Med. 2011;17:1285–1294. doi: 10.2119/molmed.2011.00149. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 137.Barber P.A. Cannabis and Stroke. In: Preedy V.R., editor. Handbook of Cannabis and Related Pathologies: Biology, Pharmacology and Treatment. Volume 1. Academic Press; New York, NY, USA: 2017. pp. 486–493. [Google Scholar]
- 138.Menahem S. Handbook of Cannabis and Related Pathologies: Biology, Pharmacology and Treatment. Volume 1. Preedy, V.R. Academic Press; New York, NY, USA: 2017. Cardiovascular Effects of Cannabis Usage; pp. 481–485. [Google Scholar]
- 139.Volkow N.D., Compton W.M., Weiss S.R. Adverse health effects of marijuana use. N. Engl. J. Med. 2014;371:878–879. doi: 10.1056/NEJMra1402309. [DOI] [PubMed] [Google Scholar]
- 140.Rossato M., Ion Popa F., Ferigo M., Clari G., Foresta C. Human sperm express cannabinoid receptor Cb1, the activation of which inhibits motility, acrosome reaction, and mitochondrial function. J. Clin. Endocrinol. Metab. 2005;90:984–991. doi: 10.1210/jc.2004-1287. [DOI] [PubMed] [Google Scholar]
- 141.Mon M.J., Haas A.E., Stein J.L., Stein G.S. Influence of psychoactive and nonpsychoactive cannabinoids on chromatin structure and function in human cells. Biochem. Pharmacol. 1981;30:45–58. doi: 10.1016/0006-2952(81)90282-3. [DOI] [PubMed] [Google Scholar]
- 142.Wang J., Yuan W., Li M.D. Genes and pathways co-associated with the exposure to multiple drugs of abuse, including alcohol, amphetamine/methamphetamine, cocaine, marijuana, morphine, and/or nicotine: A review of proteomics analyses. Mol. Neurobiol. 2011;44:269–286. doi: 10.1007/s12035-011-8202-4. [DOI] [PubMed] [Google Scholar]
- 143.Sarafian T.A., Habib N., Oldham M., Seeram N., Lee R.P., Lin L., Tashkin D.P., Roth M.D. Inhaled marijuana smoke disrupts mitochondrial energetics in pulmonary epithelial cells in vivo. Am. J. Physiol. Lung Cell Mol. Physiol. 2006;290:L1202–L1209. doi: 10.1152/ajplung.00371.2005. [DOI] [PubMed] [Google Scholar]
- 144.Sarafian T.A., Kouyoumjian S., Khoshaghideh F., Tashkin D.P., Roth M.D. Delta 9-tetrahydrocannabinol disrupts mitochondrial function and cell energetics. Am. J. Physiol. Lung Cell Mol. Physiol. 2003;284:L298–L306. doi: 10.1152/ajplung.00157.2002. [DOI] [PubMed] [Google Scholar]
- 145.Bénard G., Massa F., Puente N., Lourenço J., Bellocchio L., Soria-Gómez E., Matias I., Delamarre A., Metna-Laurent M., Cannich A., et al. Mitochondrial CB(1) receptors regulate neuronal energy metabolism. Nat. Neurosci. 2012;15:558–564. doi: 10.1038/nn.3053. [DOI] [PubMed] [Google Scholar]
- 146.Koch M., Varela L., Kim J.G., Kim J.D., Hernández-Nuño F., Simonds S.E., Castorena C.M., Vianna C.R., Elmquist J.K., Morozov Y.M., et al. Hypothalamic POMC neurons promote cannabinoid-induced feeding. Nature. 2015;519:45–50. doi: 10.1038/nature14260. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 147.Wolff V., Schlagowski A.I., Rouyer O., Charles A.L., Singh F., Auger C., Schini-Kerth V., Marescaux C., Raul J.S., Zoll J., et al. Tetrahydrocannabinol induces brain mitochondrial respiratory chain dysfunction and increases oxidative stress: A potential mechanism involved in cannabis-related stroke. Biomed. Res. Int. 2015;2015:323706. doi: 10.1155/2015/323706. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 148.Canto C., Menzies K.J., Auwerx J. NAD(+) Metabolism and the Control of Energy Homeostasis: A Balancing Act between Mitochondria and the Nucleus. Cell Metab. 2015;22:31–53. doi: 10.1016/j.cmet.2015.05.023. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 149.Bárcena C., Mayoral P., Quirós P.M. Mitohormesis, an Antiaging Paradigm. Int. Rev. Cell Mol. Biol. 2018;340:35–77. doi: 10.1016/bs.ircmb.2018.05.002. [DOI] [PubMed] [Google Scholar]
- 150.Balaban R.S., Nemoto S., Finkel T. Mitochondria, oxidants, and aging. Cell. 2005;120:483–495. doi: 10.1016/j.cell.2005.02.001. [DOI] [PubMed] [Google Scholar]
- 151.Gu L., Kwong J.M., Caprioli J., Piri N. DNA and RNA oxidative damage in the retina is associated with ganglion cell mitochondria. Sci. Rep. 2022;12:8705. doi: 10.1038/s41598-022-12770-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 152.Krammer U.D.B., Sommer A., Tschida S., Mayer A., Lilja S.V., Switzeny O.J., Hippe B., Rust P., Haslberger A.G. PGC-1α Methylation, miR-23a, and miR-30e Expression as Biomarkers for Exercise- and Diet-Induced Mitochondrial Biogenesis in Capillary Blood from Healthy Individuals: A Single-Arm Intervention. Sports. 2022;10:73. doi: 10.3390/sports10050073. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 153.Ng T.K.S., Wee H.N., Ching J., Kovalik J.P., Chan A.W., Matchar D.B. Plasma acylcarnitines as metabolic signatures of declining health-related quality of life measure in community-dwelling older adults: A combined cross-sectional and longitudinal pilot study. J. Gerontol. A Biol. Sci. Med. Sci. 2022;131:glac114. doi: 10.1093/gerona/glac114. [DOI] [PubMed] [Google Scholar]
- 154.Teng H., Hong Y., Cao J., Li H., Tian F., Sun J., Wen K., Han G., Whelchel A., Zhang X., et al. Senescence marker protein30 protects lens epithelial cells against oxidative damage by restoring mitochondrial function. Bioengineered. 2022;13:12955–12971. doi: 10.1080/21655979.2022.2079270. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 155.Wan W., Hua F., Fang P., Li C., Deng F., Chen S., Ying J., Wang X. Regulation of Mitophagy by Sirtuin Family Proteins: A Vital Role in Aging and Age-Related Diseases. Front. Aging Neurosci. 2022;14:845330. doi: 10.3389/fnagi.2022.845330. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 156.Zhong W., Rao Z., Xu J., Sun Y., Hu H., Wang P., Xia Y., Pan X., Tang W., Chen Z., et al. Defective mitophagy in aged macrophages promotes mitochondrial DNA cytosolic leakage to activate STING signaling during liver sterile inflammation. Aging Cell. 2022;21:e13622. doi: 10.1111/acel.13622. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 157.Hadley E.C., Lakatta E.G., Morrison-Bogorad M., Warner H.R., Hodes R.J. The future of aging therapies. Cell. 2005;120:557–567. doi: 10.1016/j.cell.2005.01.030. [DOI] [PubMed] [Google Scholar]
- 158.Kirkwood T.B. Understanding the odd science of aging. Cell. 2005;120:437–447. doi: 10.1016/j.cell.2005.01.027. [DOI] [PubMed] [Google Scholar]
- 159.Schrott R., Acharya K., Itchon-Ramos N., Hawkey A.B., Pippen E., Mitchell J.T., Kollins S.H., Levin E.D., Murphy S.K. Cannabis use is associated with potentially heritable widespread changes in autism candidate gene DLGAP2 DNA methylation in sperm. Epigenetics. 2019;15:161–173. doi: 10.1080/15592294.2019.1656158. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 160.Schrott R., Murphy S.K. Cannabis use and the sperm epigenome: A budding concern? Environ. Epigenetics. 2020;6:dvaa002. doi: 10.1093/eep/dvaa002. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 161.Troubat R., Barone P., Leman S., Desmidt T., Cressant A., Atanasova B., Brizard B., El Hage W., Surget A., Belzung C., et al. Neuroinflammation and depression: A review. Eur. J. Neurosci. 2021;53:151–171. doi: 10.1111/ejn.14720. [DOI] [PubMed] [Google Scholar]
- 162.Buckley P.F. Neuroinflammation and Schizophrenia. Curr. Psychiatry Rep. 2019;21:72. doi: 10.1007/s11920-019-1050-z. [DOI] [PubMed] [Google Scholar]
- 163.Benedetti F., Aggio V., Pratesi M.L., Greco G., Furlan R. Neuroinflammation in Bipolar Depression. Front. Psychiatry. 2020;11:71. doi: 10.3389/fpsyt.2020.00071. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 164.Najjar S., Pearlman D.M., Alper K., Najjar A., Devinsky O. Neuroinflammation and psychiatric illness. J. Neuroinflammation. 2013;10:43. doi: 10.1186/1742-2094-10-43. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 165.Imbeault S., Goiny M., Liu X., Erhardt S. Effects of IDO1 and TDO2 inhibition on cognitive deficits and anxiety following LPS-induced neuroinflammation. Acta Neuropsychiatr. 2020;32:43–53. doi: 10.1017/neu.2019.44. [DOI] [PubMed] [Google Scholar]
- 166.Paiva I.H.R., Duarte-Silva E., Peixoto C.A. The role of prebiotics in cognition, anxiety, and depression. Eur. Neuropsychopharmacol. 2020;34:1–18. doi: 10.1016/j.euroneuro.2020.03.006. [DOI] [PubMed] [Google Scholar]
- 167.Xu Y., Sheng H., Bao Q., Wang Y., Lu J., Ni X. NLRP3 inflammasome activation mediates estrogen deficiency-induced depression- and anxiety-like behavior and hippocampal inflammation in mice. Brain Behav. Immun. 2016;56:175–186. doi: 10.1016/j.bbi.2016.02.022. [DOI] [PubMed] [Google Scholar]
- 168.Zheng Z.H., Tu J.L., Li X.H., Hua Q., Liu W.Z., Liu Y., Pan B.X., Hu P., Zhang W.H. Neuroinflammation induces anxiety- and depressive-like behavior by modulating neuronal plasticity in the basolateral amygdala. Brain Behav. Immun. 2021;91:505–518. doi: 10.1016/j.bbi.2020.11.007. [DOI] [PubMed] [Google Scholar]
- 169.Cai Z., Hussain M.D., Yan L.J. Microglia, neuroinflammation, and beta-amyloid protein in Alzheimer’s disease. Int. J. Neurosci. 2014;124:307–321. doi: 10.3109/00207454.2013.833510. [DOI] [PubMed] [Google Scholar]
- 170.Calsolaro V., Edison P. Neuroinflammation in Alzheimer’s disease: Current evidence and future directions. Alzheimers Dement. 2016;12:719–732. doi: 10.1016/j.jalz.2016.02.010. [DOI] [PubMed] [Google Scholar]
- 171.Leng F., Edison P. Neuroinflammation and microglial activation in Alzheimer disease: Where do we go from here? Nat. Rev. Neurol. 2021;17:157–172. doi: 10.1038/s41582-020-00435-y. [DOI] [PubMed] [Google Scholar]
- 172.Lin L., Zheng L.J., Zhang L.J. Neuroinflammation, Gut Microbiome, and Alzheimer’s Disease. Mol. Neurobiol. 2018;55:8243–8250. doi: 10.1007/s12035-018-0983-2. [DOI] [PubMed] [Google Scholar]
- 173.Cannizzo E.S., Clement C.C., Sahu R., Follo C., Santambrogio L. Oxidative stress, inflamm-aging and immunosenescence. J. Proteomics. 2011;74:2313–2323. doi: 10.1016/j.jprot.2011.06.005. [DOI] [PubMed] [Google Scholar]
- 174.Reece A.S., Hulse G.K. Effect of Cannabis Legalization on US Autism Incidence and Medium Term Projections. Clin. Pediatr. Open Access. 2019;4:154. doi: 10.35248/2572-0775.19.4.154. [DOI] [Google Scholar]
- 175.Reece A.S., Hulse G.K. Impact of Converging Sociocultural and Substance-Related Trends on US Autism Rates: Combined Geospatiotemporal and Causal Inferential Analysis. Eur. Arch. Psychiatry Clinial Neurosci. 2022;19:7726–7752. doi: 10.1007/s00406-022-01446-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 176.Schrott R., Modliszewski J.L., Hawkey A.B., Grenier C., Holloway Z., Evans J., Pippen E., Corcoran D.L., Levin E.D., Murphy S.K. Sperm DNA methylation alterations from cannabis extract exposure are evident in offspring. Epigenetics Chromatin. 2022;15:33. doi: 10.1186/s13072-022-00466-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 177.Schrott R., Greeson K.W., King D., Symosko Crow K.M., Easley IV C.A., Murphy S.K. Cannabis alters DNA methylation at maternally imprinted and autism candidate genes in spermatogenic cells. Syst. Biol. Reprod. Med. 2022;68:357–369. doi: 10.1080/19396368.2022.2073292. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 178.Mona K., Ntlantsana V., Tomita A.M., Paruk S. Prevalence of cannabis use in people with psychosis in KwaZulu-Natal, South Africa. S. Afr. J. Psychiatry. 2022;28:1927. doi: 10.4102/sajpsychiatry.v28i0.1927. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 179.Mallet J., Godin O., Dansou Y., Mazer N., Scognamiglio C., Berna F., Boyer L., Capdevielle D., Chéreau I., D’Amato T., et al. Current (but not ex) cigarette smoking is associated with worse cognitive performances in schizophrenia: Results from the FACE-SZ cohort. Psychol. Med. 2022:1–12. doi: 10.1017/S0033291722002574. [DOI] [PubMed] [Google Scholar]
- 180.Little R., D’Mello D. A Cannabinoid Hypothesis of Schizophrenia: Pathways to Psychosis. Innov. Clin. Neurosci. 2022;19:38–43. [PMC free article] [PubMed] [Google Scholar]
- 181.Kayir H., Ruffolo J., McCunn P., Khokhar J.Y. Current Topics in Behavioral Neurosciences. Springer; Berlin/Heidelberg, Germany: 2022. The Relationship Between Cannabis, Cognition, and Schizophrenia: It’s Complicated. [DOI] [PubMed] [Google Scholar]
- 182.Ibarra-Lecue I., Unzueta-Larrinaga P., Barrena-Barbadillo R., Villate A., Horrillo I., Mendivil B., Landabaso M.A., Meana J.J., Etxebarria N., Callado L.F., et al. Cannabis use selectively modulates circulating biomarkers in the blood of schizophrenia patients. Addict. Biol. 2022;27:e13233. doi: 10.1111/adb.13233. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 183.Greco L.A., Reay W.R., Dayas C.V., Cairns M.J. Pairwise genetic meta-analyses between schizophrenia and substance dependence phenotypes reveals novel association signals with pharmacological significance. Transl. Psychiatry. 2022;12:403. doi: 10.1038/s41398-022-02186-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 184.Fusar-Poli L., Pries L.K., van Os J., Radhakrishnan R., Pençe A.Y., Erzin G., Delespaul P., Kenis G., Luykx J.J., Lin B.D., et al. The association between cannabis use and facial emotion recognition in schizophrenia, siblings, and healthy controls: Results from the EUGEI study. Eur. Neuropsychopharmacol. 2022;63:47–59. doi: 10.1016/j.euroneuro.2022.08.003. [DOI] [PubMed] [Google Scholar]
- 185.Dennen C.A., Blum K., Bowirrat A., Khalsa J., Thanos P.K., Baron D., Badgaiyan R.D., Gupta A., Braverman E.R., Gold M.S. Neurogenetic and Epigenetic Aspects of Cannabinoids. Epigenomes. 2022;6:27. doi: 10.3390/epigenomes6030027. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 186.Del Re E.C. Emerging themes in schizophrenia research at SIRS 2022: Inclusivity, equality and Cannabis impact on mental health. Schizophr. Res. 2022;250:39–40. doi: 10.1016/j.schres.2022.10.003. [DOI] [PubMed] [Google Scholar]
- 187.Crawford P., Go K.V. Schizophrenia. Am. Fam. Physician. 2022;106:388–396. [PubMed] [Google Scholar]
- 188.Argote M., Sescousse G., Brunelin J., Fakra E., Nourredine M., Rolland B. Association between formal thought disorder and cannabis use: A systematic review and meta-analysis. Schizophrenia. 2022;8:78. doi: 10.1038/s41537-022-00286-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 189.Pearson N.T., Berry J.H. Cannabis and Psychosis Through the Lens of DSM-5. Int. J. Environ. Res. Public Health. 2019;16:4149. doi: 10.3390/ijerph16214149. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 190.Vaucher J., Keating B.J., Lasserre A.M., Gan W., Lyall D.M., Ward J., Smith D.J., Pell J.P., Sattar N., Paré G., et al. Cannabis use and risk of schizophrenia: A Mendelian randomization study. Mol. Psychiatry. 2018;23:1287–1292. doi: 10.1038/mp.2016.252. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 191.Moustafa A.A., Salama M., Peak R., Tindle R., Salem A., Keri S., Misiak B., Frydecka D., Mohamed W. Interactions between cannabis and schizophrenia in humans and rodents. Rev. Neurosci. 2017;28:811–823. doi: 10.1515/revneuro-2016-0083. [DOI] [PubMed] [Google Scholar]
- 192.Marconi A., Di Forti M., Lewis C.M., Murray R.M., Vassos E. Meta-analysis of the Association Between the Level of Cannabis Use and Risk of Psychosis. Schizophr. Bull. 2016;42:1262–1269. doi: 10.1093/schbul/sbw003. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 193.Gage S.H., Hickman M., Zammit S. Association Between Cannabis and Psychosis: Epidemiologic Evidence. Biol. Psychiatry. 2016;79:549–556. doi: 10.1016/j.biopsych.2015.08.001. [DOI] [PubMed] [Google Scholar]
- 194.Pushpa-Rajah J.A., McLoughlin B.C., Gillies D., Rathbone J., Variend H., Kalakouti E., Kyprianou K. Cannabis and schizophrenia. Schizophr. Bull. 2015;41:336–337. doi: 10.1093/schbul/sbu168. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 195.D’Souza D.C., Sewell R.A., Ranganathan M. Cannabis and psychosis/schizophrenia: Human studies. Eur. Arch. Psychiatry Clin. Neurosci. 2009;259:413–431. doi: 10.1007/s00406-009-0024-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 196.Andréasson S., Engström A., Allebeck P., Rydberg U. Cannabis and schizophrenia. A longitudinal study of Swedish conscripts. Lancet. 1987;2:1483–1486. doi: 10.1016/S0140-6736(87)92620-1. [DOI] [PubMed] [Google Scholar]
- 197.Reece A.S., Thomas M.R., Norman A., Hulse G.K. Dramatic acceleration of reproductive aging, contraction of biochemical fecundity and healthspan-lifespan implications of opioid-induced endocrinopathy-FSH/LH ratio and other interrelationships. Reprod. Toxicol. 2016;66:20–30. doi: 10.1016/j.reprotox.2016.09.006. [DOI] [PubMed] [Google Scholar]
- 198.Ruth K.S., Day F.R., Hussain J., Martínez-Marchal A., Aiken C.E., Azad A., Thompson D.J., Knoblochova L., Abe H., Tarry-Adkins J.L., et al. Genetic insights into biological mechanisms governing human ovarian ageing. Nature. 2021;596:393–397. doi: 10.1038/s41586-021-03779-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 199.Berta D.G., Kuisma H., Välimäki N., Räisänen M., Jäntti M., Pasanen A., Karhu A., Kaukomaa J., Taira A., Cajuso T., et al. Deficient H2A.Z deposition is associated with genesis of uterine leiomyoma. Nature. 2021;596:398–403. doi: 10.1038/s41586-021-03747-1. [DOI] [PubMed] [Google Scholar]
- 200.Partridge L., Gems D., Withers D.J. Sex and death: What is the connection? Cell. 2005;120:461–472. doi: 10.1016/j.cell.2005.01.026. [DOI] [PubMed] [Google Scholar]
- 201.Kaymak I., Williams K.S., Cantor J.R., Jones R.G. Immunometabolic Interplay in the Tumor Microenvironment. Cancer Cell. 2021;39:28–37. doi: 10.1016/j.ccell.2020.09.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 202.Spadaro O., Youm Y., Shchukina I., Ryu S., Sidorov S., Ravussin A., Nguyen K., Aladyeva E., Predeus A.N., Smith S.R., et al. Caloric restriction in humans reveals immunometabolic regulators of health span. Science. 2022;375:671–677. doi: 10.1126/science.abg7292. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 203.Lombard D.B., Chua K.F., Mostoslavsky R., Franco S., Gostissa M., Alt F.W. DNA repair, genome stability, and aging. Cell. 2005;120:497–512. doi: 10.1016/j.cell.2005.01.028. [DOI] [PubMed] [Google Scholar]
- 204.Gröbner S.N., Worst B.C., Weischenfeldt J., Buchhalter I., Kleinheinz K., Rudneva V.A., Johann P.D., Balasubramanian G.P., Segura-Wang M., Brabetz S., et al. The landscape of genomic alterations across childhood cancers. Nature. 2018;555:321–327. doi: 10.1038/nature25480. [DOI] [PubMed] [Google Scholar]
- 205.Ma X., Liu Y.U., Liu Y., Alexandrov L.B., Edmonson M.N., Gawad C., Zhou X., Li Y., Rusch M.C., Easton J., et al. Pan-cancer genome and transcriptome analyses of 1,699 paediatric leukaemias and solid tumours. Nature. 2018;555:371–376. doi: 10.1038/nature25795. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 206.Trivers K.F., Mertens A.C., Ross J.A., Steinbuch M., Olshan A.F., Robison L.L. Children’s Cancer G: Parental marijuana use and risk of childhood acute myeloid leukaemia: A report from the Children’s Cancer Group (United States and Canada) Paediatr. Perinat. Epidemiol. 2006;20:110–118. doi: 10.1111/j.1365-3016.2006.00700.x. [DOI] [PubMed] [Google Scholar]
- 207.Grufferman S., Schwartz A.G., Ruymann F.B., Maurer H.M. Parents’ use of cocaine and marijuana and increased risk of rhabdomyosarcoma in their children. Cancer Causes Control. 1993;4:217–224. doi: 10.1007/BF00051316. [DOI] [PubMed] [Google Scholar]
- 208.Kuijten R.R., Bunin G.R., Nass C.C., Meadows A.T. Gestational and familial risk factors for childhood astrocytoma: Results of a case-control study. Cancer Res. 1990;50:2608–2612. [PubMed] [Google Scholar]
- 209.Carlson B.M. Human Embryology and Developmental Biology, 6 th ed. Volume 1 Elsevier; Pennsylvania, PA, USA: 2019. [Google Scholar]
- 210.Gill S.K., Broussard C., Devine O., Green R.F., Rasmussen S.A., Reefhuis J., National Birth Defects Prevention Study Association between maternal age and birth defects of unknown etiology: United States, 1997–2007. Birth Defects Res. A Clin. Mol. Teratol. 2012;94:1010–1018. doi: 10.1002/bdra.23049. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 211.Hussein N.A.E.M., El-Toukhy M.A.E.F., Kazem A.H., Ali M.E.S., Ahmad M.A.E.R., Ghazy H.M.R., El-Din A.M.G. Protective and therapeutic effects of cannabis plant extract on liver cancer induced by dimethylnitrosamine in mice. Alex. J. Med. 2014;50:241–251. doi: 10.1016/j.ajme.2014.02.003. [DOI] [Google Scholar]
- 212.Arendt M., Munk-Jørgensen P., Sher L., Jensen S.O. Mortality among individuals with cannabis, cocaine, amphetamine, MDMA, and opioid use disorders: A nationwide follow-up study of Danish substance users in treatment. Drug Alcohol Depend. 2011;114:134–139. doi: 10.1016/S0924-9338(11)71719-9. [DOI] [PubMed] [Google Scholar]
- 213.Calabria B., Degenhardt L., Hall W., Lynskey M. Does cannabis use increase the risk of death? Systematic review of epidemiological evidence on adverse effects of cannabis use. Drug Alcohol Rev. 2010;29:318–330. doi: 10.1111/j.1465-3362.2009.00149.x. [DOI] [PubMed] [Google Scholar]
- 214.Callaghan R.C., Cunningham J.K., Verdichevski M., Sykes J., Jaffer S.R., Kish S.J. All-cause mortality among individuals with disorders related to the use of methamphetamine: A comparative cohort study. Drug Alcohol Depend. 2012;125:290–294. doi: 10.1016/j.drugalcdep.2012.03.004. [DOI] [PubMed] [Google Scholar]
- 215.Davstad I., Allebeck P., Leifman A., Stenbacka M., Romelsjö A. Self-reported drug use and mortality among a nationwide sample of Swedish conscripts—A 35-year follow-up. Drug Alcohol Depend. 2011;118:383–390. doi: 10.1016/j.drugalcdep.2011.04.025. [DOI] [PubMed] [Google Scholar]
- 216.DeFilippis E.M., Singh A., Divakaran S., Gupta A., Collins B.L., Biery D., Qamar A., Fatima A., Ramsis M., Pipilas D., et al. Cocaine and Marijuana Use among Young Adults Presenting with Myocardial Infarction: The Partners YOUNG-MI Registry. J. Am. Coll. Cardiol. :2018. doi: 10.1016/j.jacc.2018.02.047. in press . [DOI] [PMC free article] [PubMed] [Google Scholar]
- 217.Desai R., Patel U., Sharma S., Amin P., Bhuva R., Patel M.S., Sharma N., Shah M., Patel S., Savani S., et al. Recreational Marijuana Use and Acute Myocardial Infarction: Insights from Nationwide Inpatient Sample in the United States. Cureus. 2017;9:e1816. doi: 10.7759/cureus.1816. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 218.Fridell M., Bäckström M., Hesse M., Krantz P., Perrin S., Nyhlén A. Prediction of psychiatric comorbidity on premature death in a cohort of patients with substance use disorders: A 42-year follow-up. BMC Psychiatry. 2019;19:150. doi: 10.1186/s12888-019-2098-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 219.Frost L., Mostofsky E., Rosenbloom J.I., Mukamal K.J., Mittleman M.A. Marijuana use and long-term mortality among survivors of acute myocardial infarction. Am. Heart J. 2013;165:170–175. doi: 10.1016/j.ahj.2012.11.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 220.Hser Y.I., Kagihara J., Huang D., Evans E., Messina N. Mortality among substance-using mothers in California: A 10-year prospective study. Addiction. 2012;107:215–222. doi: 10.1111/j.1360-0443.2011.03613.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 221.Muhuri P.K., Gfroerer J.C. Mortality associated with illegal drug use among adults in the United States. Am. J. Drug Alcohol Abus. 2011;37:155–164. doi: 10.3109/00952990.2011.553977. [DOI] [PubMed] [Google Scholar]
- 222.Pavarin R.M., Berardi D. Mortality risk in a cohort of subjects reported by authorities for cannabis possession for personal use. Results of a longitudinal study. Epidemiol. Prev. 2011;35:89–93. [PubMed] [Google Scholar]
- 223.Von Greiff N., Skogens L., Berlin M., Bergmark A. Mortality and Cause of Death-A 30-Year Follow-Up of Substance Misusers in Sweden. Subst. Use Misuse. 2018;53:2043–2051. doi: 10.1080/10826084.2018.1452261. [DOI] [PubMed] [Google Scholar]
- 224.Fergusson D.M., Boden J.M., Horwood L.J. Cannabis use and other illicit drug use: Testing the cannabis gateway hypothesis. Addiction. 2006;101:556–569. doi: 10.1111/j.1360-0443.2005.01322.x. [DOI] [PubMed] [Google Scholar]
- 225.Secades-Villa R., Garcia-Rodríguez O., Jin C.J., Wang S., Blanco C. Probability and predictors of the cannabis gateway effect: A national study. Int. J. Drug Policy. 2015;26:135–142. doi: 10.1016/j.drugpo.2014.07.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 226.Nkansah-Amankra S., Minelli M. Gateway hypothesis” and early drug use: Additional findings from tracking a population-based sample of adolescents to adulthood. Prev. Med. Rep. 2016;4:134–141. doi: 10.1016/j.pmedr.2016.05.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 227.Association A.P. Diagnostic and Statistical Manual of Mental Disorders (DSM-5) Volume 1 American Psychiatric Association; Washington, DC, USA: 2013. [Google Scholar]
- 228.GeneCards: The Human Gene Database: UHRF1. [(accessed on 1 June 2022)]. Available online: https://www.genecards.org/cgi-bin/carddisp.pl?gene=Uhrf1.
- 229.Mudbhary R., Hoshida Y., Chernyavskaya Y., Jacob V., Villanueva A., Fiel M.I., Chen X., Kojima K., Thung S., Bronson R.T., et al. UHRF1 overexpression drives DNA hypomethylation and hepatocellular carcinoma. Cancer Cell. 2014;25:196–209. doi: 10.1016/j.ccr.2014.01.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 230.Li Y., Zhang Z., Chen J., Liu W., Lai W., Liu B., Li X., Liu L., Xu S., Dong Q., et al. Stella safeguards the oocyte methylome by preventing de novo methylation mediated by DNMT1. Nature. 2018;564:136–140. doi: 10.1038/s41586-018-0751-5. [DOI] [PubMed] [Google Scholar]
- 231.Xiao L., Parolia A., Qiao Y., Bawa P., Eyunni S., Mannan R., Carson S.E., Chang Y., Wang X., Zhang Y., et al. Targeting SWI/SNF ATPases in enhancer-addicted prostate cancer. Nature. 2022;601:434–439. doi: 10.1038/s41586-021-04246-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 232.Yu J., Vodyanik M.A., Smuga-Otto K., Antosiewicz-Bourget J., Frane J.L., Tian S., Nie J., Jonsdottir G.A., Ruotti V., Stewart R., et al. Induced pluripotent stem cell lines derived from human somatic cells. Science. 2007;318:1917–1920. doi: 10.1126/science.1151526. [DOI] [PubMed] [Google Scholar]
- 233.Chi Y., Sauve A.A. Nicotinamide riboside, a trace nutrient in foods, is a vitamin B3 with effects on energy metabolism and neuroprotection. Curr. Opin. Clin. Nutr. Metab. Care. 2013;16:657–661. doi: 10.1097/MCO.0b013e32836510c0. [DOI] [PubMed] [Google Scholar]
- 234.Hollis F., van der Kooij M.A., Zanoletti O., Lozano L., Cantó C., Sandi C. Mitochondrial function in the brain links anxiety with social subordination. Proc. Natl. Acad. Sci. USA. 2015;112:15486–15491. doi: 10.1073/pnas.1512653112. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 235.Gómez C.A., Sutin J., Wu W., Fu B., Uhlirova H., Devor A., Boas D.A., Sakadžić S., Yaseen M.A. Phasor analysis of NADH FLIM identifies pharmacological disruptions to mitochondrial metabolic processes in the rodent cerebral cortex. PLoS ONE. 2018;13:e0194578. doi: 10.1371/journal.pone.0194578. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 236.Moon J., Kim H.R., Shin M.G. Rejuvenating Aged Hematopoietic Stem Cells Through Improvement of Mitochondrial Function. Ann. Lab. Med. 2018;38:395–401. doi: 10.3343/alm.2018.38.5.395. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 237.Kidd P.M. Neurodegeneration from mitochondrial insufficiency: Nutrients, stem cells, growth factors, and prospects for brain rebuilding using integrative management. Altern. Med. Rev. 2005;10:268–293. [PubMed] [Google Scholar]
- 238.Cimadamore F., Curchoe C.L., Alderson N., Scott F., Salvesen G., Terskikh A.V. Nicotinamide rescues human embryonic stem cell-derived neuroectoderm from parthanatic cell death. Stem Cells. 2009;27:1772–1781. doi: 10.1002/stem.107. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 239.Liu L.Y., Wang F., Zhang X.Y., Huang P., Lu Y.B., Wei E.Q., Zhang W.P. Nicotinamide phosphoribosyltransferase may be involved in age-related brain diseases. PLoS ONE. 2012;7:e44933. doi: 10.1371/journal.pone.0044933. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 240.Griffin S.M., Pickard M.R., Orme R.P., Hawkins C.P., Fricker R.A. Nicotinamide promotes neuronal differentiation of mouse embryonic stem cells in vitro. Neuroreport. 2013;24:1041–1046. doi: 10.1097/WNR.0000000000000071. [DOI] [PubMed] [Google Scholar]
- 241.Maruotti J., Sripathi S.R., Bharti K., Fuller J., Wahlin K.J., Ranganathan V., Sluch V.M., Berlinicke C.A., Davis J., Kim C., et al. Small-molecule-directed, efficient generation of retinal pigment epithelium from human pluripotent stem cells. Proc. Natl. Acad. Sci. USA. 2015;112:10950–10955. doi: 10.1073/pnas.1422818112. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 242.Black B.E. Preface to: Centromeres and Kinetochores. In: Black B.E., editor. Centromeres and Kinetochores. Volume 1. Springer; Cham, Switzerland: 2017. pp. v–viii. [Google Scholar]
- 243.Sathananthan A.H., Kola I., Osborne J., Trounson A., Ng S.C., Bongso A., Ratnam S. Centrioles in the beginning of human development. Proc. Natl. Acad. Sci. USA. 1991;88:4806–4810. doi: 10.1073/pnas.88.11.4806. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 244.Sathananthan A.H. Mitosis in the human embryo: The vital role of the sperm centrosome (centriole) Histol. Histopathol. 1997;12:827–856. [PubMed] [Google Scholar]
- 245.Blengini C.S., Schindler K. Acentriolar spindle assembly in mammalian female meiosis and the consequences of its perturbations on human reproduction. Biol. Reprod. 2021;106:253–263. doi: 10.1093/biolre/ioab210. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 246.Cavazza T., Takeda Y., Politi A.Z., Aushev M., Aldag P., Baker C., Choudhary M., Bucevičius J., Lukinavičius G., Elder K., et al. Parental genome unification is highly error-prone in mammalian embryos. Cell. 2021;184:2860–2877.e2822. doi: 10.1016/j.cell.2021.04.013. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 247.Cavin-Meza G., Kwan M.M., Wignall S.M. Multiple motors cooperate to establish and maintain acentrosomal spindle bipolarity in C. elegans oocyte meiosis. eLife. 2022;11:e72872. doi: 10.7554/eLife.72872. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 248.So C., Menelaou K., Uraji J., Harasimov K., Steyer A.M., Seres K.B., Bucevičius J., Lukinavičius G., Möbius W., Sibold C., et al. Mechanism of spindle pole organization and instability in human oocytes. Science. 2022;375:eabj3944. doi: 10.1126/science.abj3944. [DOI] [PubMed] [Google Scholar]
- 249.Tischer T., Yang J., Barford D. The APC/C targets the Cep152-Cep63 complex at the centrosome to regulate mitotic spindle assembly. J. Cell Sci. 2022;135:jcs259273. doi: 10.1242/jcs.259273. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 250.Segbert C., Barkus R., Powers J., Strome S., Saxton W.M., Bossinger O. KLP-18, a Klp2 kinesin, is required for assembly of acentrosomal meiotic spindles in Caenorhabditis elegans. Mol. Biol. Cell. 2003;14:4458–4469. doi: 10.1091/mbc.e03-05-0283. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 251.Alberts B., Johnson A., Lewis J., Morgan D., Raff M., Roberts K., Walter P., editors. Molecular Biology of the Cell. 6th ed. Garland Science; New York, NY, USA: 2014. [Google Scholar]
- 252.Moutin M.J., Bosc C., Peris L., Andrieux A. Tubulin post-translational modifications control neuronal development and functions. Dev. Neurobiol. 2021;81:253–272. doi: 10.1002/dneu.22774. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 253.Janke C., Montagnac G. Causes and Consequences of Microtubule Acetylation. Curr. Biol. 2017;27:R1287–R1292. doi: 10.1016/j.cub.2017.10.044. [DOI] [PubMed] [Google Scholar]
- 254.Hara M., Fukagawa T. Critical Foundation of the Kinetochore: The Constitutive Centromere—Associated Network (CCAN) In: Black B.E., editor. Centromeres and Kinetochores. Volume 1. Springer; Philadelphia, PA, USA: 2017. pp. 1–554. [DOI] [PubMed] [Google Scholar]
- 255.French B.T., Straight A.F. The Power of Xenopus Egg Extract for Reconstitution of Centromere and Kinetochore Function. In: Black B.E., editor. Centromeres and Kinetochores. Volume 1. Springer; Philadelphia, PA, USA: 2017. pp. 1–554. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 256.Grishchuk E.L. Biophysics of Microtubule End Coupling at the Kinetochore. In: Black B.E., editor. Centromeres and Kinetochores. Volume 1. Springer; Philadelphia, PA, USA: 2017. pp. 1–554. [DOI] [PubMed] [Google Scholar]
- 257.Hsu J.M., Huang J., Meluh P.B., Laurent B.C. The yeast RSC chromatin-remodeling complex is required for kinetochore function in chromosome segregation. Mol. Cell Biol. 2003;23:3202–3215. doi: 10.1128/MCB.23.9.3202-3215.2003. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 258.Ryu H.Y., Hochstrasser M. Histone sumoylation and chromatin dynamics. Nucleic Acids Res. 2021;49:6043–6052. doi: 10.1093/nar/gkab280. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 259.Gowran A., Murphy C.E., Campbell V.A. Delta(9)-tetrahydrocannabinol regulates the p53 post-translational modifiers Murine double minute 2 and the Small Ubiquitin MOdifier protein in the rat brain. FEBS Lett. 2009;583:3412–3418. doi: 10.1016/j.febslet.2009.09.056. [DOI] [PubMed] [Google Scholar]
- 260.Zimmerman A.M., Zimmerman S., Raj A.Y. Effects of Cannabinoids on Spermatogensis in Mice. In: Nahas G.G., Sutin K.M., Harvey D.J., Agurell S., editors. Marijuana and Medicine. 1st ed. Volume 1. Humana Press; Totowa, NY, USA: 1999. pp. 347–358. [Google Scholar]
- 261.Zimmerman S., Zimmerman A.M. Genetic effects of marijuana. Int. J. Addict. 1990;25:19–33. doi: 10.3109/10826089009067003. [DOI] [PubMed] [Google Scholar]
- 262.Henrich R.T., Nogawa T., Morishima A. In vitro induction of segregational errors of chromosomes by natural cannabinoids in normal human lymphocytes. Environ. Mutagen. 1980;2:139–147. doi: 10.1002/em.2860020206. [DOI] [PubMed] [Google Scholar]
- 263.GeneCards: Down Syndrome Cell Adhesion Molecule. [(accessed on 1 April 2022)]. Available online: https://www.genecards.org/cgi-bin/carddisp.pl?gene=DSCAM.
- 264.Grossman T.R., Gamliel A., Wessells R.J., Taghli-Lamallem O., Jepsen K., Ocorr K., Korenberg J.R., Peterson K.L., Rosenfeld M.G., Bodmer R., et al. Over-expression of DSCAM and COL6A2 cooperatively generates congenital heart defects. PLoS Genet. 2011;7:e1002344. doi: 10.1371/journal.pgen.1002344. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 265.Borrell V., Cárdenas A., Ciceri G., Galcerán J., Flames N., Pla R., Nóbrega-Pereira S., García-Frigola C., Peregrín S., Zhao Z., et al. Slit/Robo signaling modulates the proliferation of central nervous system progenitors. Neuron. 2012;76:338–352. doi: 10.1016/j.neuron.2012.08.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 266.Cárdenas A., Villalba A., de Juan Romero C., Picó E., Kyrousi C., Tzika A.C., Tessier-Lavigne M., Ma L., Drukker M., Cappello S., et al. Evolution of Cortical Neurogenesis in Amniotes Controlled by Robo Signaling Levels. Cell. 2018;174:590–606.e21. doi: 10.1016/j.cell.2018.06.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 267.Yeh M.L., Gonda Y., Mommersteeg M.T., Barber M., Ypsilanti A.R., Hanashima C., Parnavelas J.G., Andrews W.D. Robo1 modulates proliferation and neurogenesis in the developing neocortex. J. Neurosci. 2014;34:5717–5731. doi: 10.1523/JNEUROSCI.4256-13.2014. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 268.Alpár A., Tortoriello G., Calvigioni D., Niphakis M.J., Milenkovic I., Bakker J., Cameron G.A., Hanics J., Morris C.V., Fuzik J., et al. Endocannabinoids modulate cortical development by configuring Slit2/Robo1 signalling. Nat. Commun. 2014;5:4421. doi: 10.1038/ncomms5421. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 269.Schmidt E.R., Zhao H.T., Park J.M., Dipoppa M., Monsalve-Mercado M.M., Dahan J.B., Rodgers C.C., Lejeune A., Hillman E., Miller K.D., et al. A human-specific modifier of cortical connectivity and circuit function. Nature. 2021;599:640–644. doi: 10.1038/s41586-021-04039-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 270.Shibata M., Pattabiraman K., Lorente-Galdos B., Andrijevic D., Kim S.K., Kaur N., Muchnik S.K., Xing X., Santpere G., Sousa A.M., et al. Regulation of prefrontal patterning and connectivity by retinoic acid. Nature. 2021;598:483–488. doi: 10.1038/s41586-021-03953-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 271.Scuteri A., Orru M., Morrell C., Piras M.G., Taub D., Schlessinger D., Uda M., Lakatta E.G. Independent and additive effects of cytokine patterns and the metabolic syndrome on arterial aging in the SardiNIA Study. Atherosclerosis. 2011;215:459–464. doi: 10.1016/j.atherosclerosis.2010.12.023. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 272.Strait J.B., Lakatta E.G. Aging-associated cardiovascular changes and their relationship to heart failure. Heart Fail. Clin. 2012;8:143–164. doi: 10.1016/j.hfc.2011.08.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 273.Sutin A.R., Scuteri A., Lakatta E.G., Tarasov K.V., Ferrucci L., Costa P.T., Jr., Schlessinger D., Uda M., Terracciano A. Trait antagonism and the progression of arterial thickening: Women with antagonistic traits have similar carotid arterial thickness as men. Hypertension. 2010;56:617–622. doi: 10.1161/HYPERTENSIONAHA.110.155317. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 274.Wang M., Khazan B., Lakatta E. Central Arterial Aging and Angiotensin II Signaling. Curr. Hypertens. Rev. 2010;6:266–281. doi: 10.2174/157340210793611668. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 275.Wang M., Monticone R.E., Lakatta E.G. Arterial aging: A journey into subclinical arterial disease. Curr. Opin. Nephrol. Hypertens. 2010;19:201–207. doi: 10.1097/MNH.0b013e3283361c0b. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 276.Wang M., Zhang J., Spinetti G., Jiang L.Q., Monticone R., Zhao D., Cheng L., Krawczyk M., Talan M., Pintus G., et al. Angiotensin II activates matrix metalloproteinase type II and mimics age-associated carotid arterial remodeling in young rats. Am. J. Pathol. 2005;167:1429–1442. doi: 10.1016/S0002-9440(10)61229-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 277.Chien K.R., Karsenty G. Longevity and lineages: Toward the integrative biology of degenerative diseases in heart, muscle, and bone. Cell. 2005;120:533–544. doi: 10.1016/j.cell.2005.02.006. [DOI] [PubMed] [Google Scholar]
- 278.Lakatta E.G. Arterial aging is risky. J. Appl. Physiol. 2008;105:1321–1322. doi: 10.1152/japplphysiol.91145.2008. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 279.Le Couteur D.G., Lakatta E.G. A vascular theory of aging. J. Gerontol. A Biol. Sci. Med. Sci. 2010;65:1025–1027. doi: 10.1093/gerona/glq135. [DOI] [PubMed] [Google Scholar]
- 280.Itkin T., Rafii S. Cardiovascular diseases disrupt the bone-marrow niche. Nature. 2022;601:515–517. doi: 10.1038/d41586-021-03550-y. [DOI] [PubMed] [Google Scholar]
- 281.Rohde D., Vandoorne K., Lee I., Grune J., Zhang S., McAlpine C.S., Schloss M.J., Nayar R., Courties G., Frodermann V., et al. Bone marrow endothelial dysfunction promotes myeloid cell expansion in cardiovascular disease. Nat. Cardiovasc. Res. 2022;1:28–44. doi: 10.1038/s44161-021-00002-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 282.Matissek S.J., Elsawa S.F. GLI3: A mediator of genetic diseases, development and cancer. Cell Commun. Signal. 2020;18:54. doi: 10.1186/s12964-020-00540-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 283.Ungricht R., Guibbal L., Lasbennes M.C., Orsini V., Beibel M., Waldt A., Cuttat R., Carbone W., Basler A., Roma G., et al. Genome-wide screening in human kidney organoids identifies developmental and disease-related aspects of nephrogenesis. Cell Stem Cell. 2022;29:160–175.e167. doi: 10.1016/j.stem.2021.11.001. [DOI] [PubMed] [Google Scholar]
- 284.GeneCards: PSENEN. [(accessed on 1 April 2022)]. Available online: https://www.genecards.org/cgi-bin/carddisp.pl?gene=PSENEN&keywords=psenen.
- 285.Robinson G.I., Ye F., Lu X., Laviolette S.R., Feng Q. Maternal Delta-9-Tetrahydrocannabinol Exposure Induces Abnormalities of the Developing Heart in Mice. Cannabis Cannabinoid Res. 2022. ahead of print . [DOI] [PubMed]
- 286.Lee K., Laviolette S.R., Hardy D.B. Exposure to Δ9-tetrahydrocannabinol during rat pregnancy leads to impaired cardiac dysfunction in postnatal life. Pediatr. Res. 2021;90:532–539. doi: 10.1038/s41390-021-01511-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 287.Jameson J.L., Fauci A.S., Hauser S.L., Longo D.L., Jameson J.L., Loscalzo J., editors. Harrison’s Principles of Internal Medicine. 20th ed. McGraw Hill; New York, NY, USA: 2018. [Google Scholar]
- 288.Reece A.S., Hulse G.K. Gastroschisis and Autism-Dual Canaries in the Californian Coalmine. JAMA Surg. 2019;154:366–367. doi: 10.1001/jamasurg.2018.4694. [DOI] [PubMed] [Google Scholar]
- 289.Reece A.S., Hulse G.K. Cannabis and Pregnancy Don’t Mix. Mo. Med. 2020;117:530–531. [PMC free article] [PubMed] [Google Scholar]
- 290.Corsi D.J., Donelle J., Sucha E., Hawken S., Hsu H., El-Chaâr D., Bisnaire L., Fell D., Wen S.W., Walker M. Maternal cannabis use in pregnancy and child neurodevelopmental outcomes. Nat. Med. 2020;26:1536–1540. doi: 10.1038/s41591-020-1002-5. [DOI] [PubMed] [Google Scholar]
- 291.Corsi D.J., Walsh L., Weiss D., Hsu H., El-Chaar D., Hawken S., Fell D.B., Walker M. Association Between Self-reported Prenatal Cannabis Use and Maternal, Perinatal, and Neonatal Outcomes. JAMA. 2019;322:145–152. doi: 10.1001/jama.2019.8734. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 292.Brents L. Correlates and consequences of Prenatal Cannabis Exposure (PCE): Identifying and Characterizing Vulnerable Maternal Populations and Determining Outcomes in Exposed Offspring. In: Preedy V.R., editor. Handbook of Cannabis and Related Pathologies: Biology, Pharmacology, Diagnosis and Treatment. Volume 1. Academic Press; London, UK: 2017. pp. 160–170. [Google Scholar]
- 293.Fried P., Watkinson B., James D., Gray R. Current and former marijuana use: Preliminary findings of a longitudinal study of effects on IQ in young adults. CMAJ. 2002;166:887–891. [PMC free article] [PubMed] [Google Scholar]
- 294.Fried P.A., Smith A.M. A literature review of the consequences of prenatal marihuana exposure. An emerging theme of a deficiency in aspects of executive function. Neurotoxicol. Teratol. 2001;23:1–11. doi: 10.1016/S0892-0362(00)00119-7. [DOI] [PubMed] [Google Scholar]
- 295.Fried P.A., Watkinson B., Gray R. Neurocognitive consequences of marihuana—A comparison with pre-drug performance. Neurotoxicol. Teratol. 2005;27:231–239. doi: 10.1016/j.ntt.2004.11.003. [DOI] [PubMed] [Google Scholar]
- 296.Smith A.M., Mioduszewski O., Hatchard T., Byron-Alhassan A., Fall C., Fried P.A. Prenatal marijuana exposure impacts executive functioning into young adulthood: An fMRI study. Neurotoxicol. Teratol. 2016;58:53–59. doi: 10.1016/j.ntt.2016.05.010. [DOI] [PubMed] [Google Scholar]
- 297.Smith A.M., Longo C.A., Fried P.A., Hogan M.J., Cameron I. Effects of marijuana on visuospatial working memory: An fMRI study in young adults. Psychopharmacology. 2010;210:429–438. doi: 10.1007/s00213-010-1841-8. [DOI] [PubMed] [Google Scholar]
- 298.Smith A.M., Fried P.A., Hogan M.J., Cameron I. Effects of prenatal marijuana on visuospatial working memory: An fMRI study in young adults. Neurotoxicol. Teratol. 2006;28:286–295. doi: 10.1016/j.ntt.2005.12.008. [DOI] [PubMed] [Google Scholar]
- 299.Smith A.M., Fried P.A., Hogan M.J., Cameron I. Effects of prenatal marijuana on response inhibition: An fMRI study of young adults. Neurotoxicol. Teratol. 2004;26:533–542. doi: 10.1016/j.ntt.2004.04.004. [DOI] [PubMed] [Google Scholar]
- 300.Smith A., Fried P., Hogan M., Cameron I. The effects of prenatal and current marijuana exposure on response inhibition: A functional magnetic resonance imaging study. Brain Cogn. 2004;54:147–149. [PubMed] [Google Scholar]
- 301.Hockings N. Cuvier’s Objection, Morphogenesis and the Evolution Of Evolvability. 2020. [(accessed on 1 April 2022)]. Available online: https://www.researchgate.net/publication/342438770_Cuvier%27s_objection_morphogenesis_and_the_evolution_of_evolvability.
- 302.Reece A.S., Hulse G.K. Epidemiology of Cannabis: Genotoxicity and Neurotoxicity, Epigenomics and Aging. Volume 1. Elsevier; New York, NY, USA: 2023. Chapter 1: Close Parallels between Cannabis Use and Deteriorating US Mental Health at Four Levels Supports and Extends the Epidemiological Salience of Demonstrated Causal Mental Health Relationships: A Geospatiotemporal Study. in press . [Google Scholar]
- 303.Reece A.S., Hulse G.K. Epidemiology of Cannabis: Genotoxicity and Neurotoxicity, Epigenomics and Aging. Volume 1. Elsevier; New York, NY, USA: 2023. Chapter 2: Linked Rise of Cannabis Use and Autism Incidence Demonstrated by Close Three Level Geospatiotemporal Relationships, USA, 1990–2011. in press . [Google Scholar]
- 304.Reece A.S., Hulse G.K. Epidemiology of Cannabis: Genotoxicity and Neurotoxicity, Epigenomics and Aging. Volume 1. Elsevier; New York, NY, USA: 2023. Chapter 3: Geospatiotemporal and Causal Inferential Analysis of United States Congenital Anomalies as a Function of Multiple Cannabinoid- and Substance- Exposures: Phenocopying Thalidomide and Hundred Megabase-Scale Genotoxicity. in press . [Google Scholar]
- 305.Reece A.S., Hulse G.K. Epidemiology of Cannabis: Genotoxicity and Neurotoxicity, Epigenomics and Aging. Volume 1. Elsevier; New York, NY, USA: 2023. Chapter 4: Geospatiotemporal and Causal Inferential Epidemiological Survey and Exploration of Cannabinoid- and Substance- Related Carcinogenesis in USA 2003–2017. in press . [Google Scholar]
- 306.Reece A.S., Hulse G.K. Epidemiology of Cannabis: Genotoxicity and Neurotoxicity, Epigenomics and Aging. Volume 1. Elsevier; New York, NY, USA: 2023. Chapter 5: Multivalent Cannabinoid Epigenotoxicities and Multigenerational Aging. in press . [Google Scholar]
- 307.Reece A.S., Hulse G.K. Epidemiology of Cannabis: Genotoxicity and Neurotoxicity, Epigenomics and Aging. Elsevier; New York, NY, USA: 2023. [Google Scholar]
- 308.Jenkins K.J., Correa A., Feinstein J.A., Botto L., Britt A.E., Daniels S.R., Elixson M., Warnes C.A., Webb C.L. Noninherited risk factors and congenital cardiovascular defects: Current knowledge: A scientific statement from the American Heart Association Council on Cardiovascular Disease in the Young: Endorsed by the American Academy of Pediatrics. Circulation. 2007;115:2995–3014. doi: 10.1161/CIRCULATIONAHA.106.183216. [DOI] [PubMed] [Google Scholar]
- 309.Alberry B., Laufer B.I., Chater-Diehl E., Singh S.M. Epigenetic Impacts of Early Life Stress in Fetal Alcohol Spectrum Disorders Shape the Neurodevelopmental Continuum. Front. Mol. Neurosci. 2021;14:671891. doi: 10.3389/fnmol.2021.671891. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 310.Chang R.C., Thomas K.N., Mehta N.A., Veazey K.J., Parnell S.E., Golding M.C. Programmed suppression of oxidative phosphorylation and mitochondrial function by gestational alcohol exposure correlate with widespread increases in H3K9me2 that do not suppress transcription. Epigenetics Chromatin. 2021;14:27. doi: 10.1186/s13072-021-00403-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 311.Darbinian N., Selzer M.E. Oligodendrocyte pathology in fetal alcohol spectrum disorders. Neural Regen. Res. 2022;17:497–502. doi: 10.4103/1673-5374.314294. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 312.Gutherz O.R., Deyssenroth M., Li Q., Hao K., Jacobson J.L., Chen J., Jacobson S.W., Carter R.C. Potential roles of imprinted genes in the teratogenic effects of alcohol on the placenta, somatic growth, and the developing brain. Exp. Neurol. 2022;347:113919. doi: 10.1016/j.expneurol.2021.113919. [DOI] [PubMed] [Google Scholar]
- 313.Kruithof P., Ban S. A brief overview of fetal alcohol syndrome for health professionals. Br. J. Nurs. 2021;30:890–893. doi: 10.12968/bjon.2021.30.15.890. [DOI] [PubMed] [Google Scholar]
- 314.Legault L.M., Doiron K., Breton-Larrivée M., Langford-Avelar A., Lemieux A., Caron M., Jerome-Majewska L.A., Sinnett D., McGraw S. Pre-implantation alcohol exposure induces lasting sex-specific DNA methylation programming errors in the developing forebrain. Clin. Epigenetics. 2021;13:164. doi: 10.1186/s13148-021-01151-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 315.Lussier A.A., Bodnar T.S., Moksa M., Hirst M., Kobor M.S., Weinberg J. Prenatal Adversity Alters the Epigenetic Profile of the Prefrontal Cortex: Sexually Dimorphic Effects of Prenatal Alcohol Exposure and Food-Related Stress. Genes. 2021;12:1773. doi: 10.3390/genes12111773. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 316.Lussier A.A., Bodnar T.S., Weinberg J. Intersection of Epigenetic and Immune Alterations: Implications for Fetal Alcohol Spectrum Disorder and Mental Health. Front. Neurosci. 2021;15:788630. doi: 10.3389/fnins.2021.788630. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 317.Smith S.M., Virdee M.S., Eckerle J.K., Sandness K.E., Georgieff M.K., Boys C.J., Zeisel S.H., Wozniak J.R. Polymorphisms in SLC44A1 are associated with cognitive improvement in children diagnosed with fetal alcohol spectrum disorder: An exploratory study of oral choline supplementation. Am. J. Clin. Nutr. 2021;114:617–627. doi: 10.1093/ajcn/nqab081. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 318.Terracina S., Ferraguti G., Tarani L., Messina M.P., Lucarelli M., Vitali M., De Persis S., Greco A., Minni A., Polimeni A., et al. Transgenerational Abnormalities Induced by Paternal Preconceptual Alcohol Drinking. Findings from Humans and Animal Models. Curr. Neuropharmacol. 2021;20:1158–1173. doi: 10.2174/1570159X19666211101111430. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 319.Thomas K.N., Zimmel K.N., Roach A.N., Basel A., Mehta N.A., Bedi Y.S., Golding M.C. Maternal background alters the penetrance of growth phenotypes and sex-specific placental adaptation of offspring sired by alcohol-exposed males. FASEB J. 2021;35:e22035. doi: 10.1096/fj.202101131R. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 320.Wallén E., Auvinen P., Kaminen-Ahola N. The Effects of Early Prenatal Alcohol Exposure on Epigenome and Embryonic Development. Genes. 2021;12:1095. doi: 10.3390/genes12071095. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 321.Subbanna S., Shivakumar M., Psychoyos D., Xie S., Basavarajappa B.S. Anandamide-CB1 receptor signaling contributes to postnatal ethanol-induced neonatal neurodegeneration, adult synaptic, and memory deficits. J. Neurosci. 2013;33:6350–6366. doi: 10.1523/JNEUROSCI.3786-12.2013. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 322.Subbanna S., Nagre N.N., Umapathy N.S., Pace B.S., Basavarajappa B.S. Ethanol exposure induces neonatal neurodegeneration by enhancing CB1R Exon1 histone H4K8 acetylation and up-regulating CB1R function causing neurobehavioral abnormalities in adult mice. Int. J. Neuropsychopharmacol. 2014;18:pyu028. doi: 10.1093/ijnp/pyu028. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 323.Subbanna S., Nagre N.N., Shivakumar M., Joshi V., Psychoyos D., Kutlar A., Umapathy N.S., Basavarajappa B.S. CB1R-Mediated Activation of Caspase-3 Causes Epigenetic and Neurobehavioral Abnormalities in Postnatal Ethanol-Exposed Mice. Front. Mol. Neurosci. 2018;11:45. doi: 10.3389/fnmol.2018.00045. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 324.Joshi V., Subbanna S., Shivakumar M., Basavarajappa B.S. CB1R regulates CDK5 signaling and epigenetically controls Rac1 expression contributing to neurobehavioral abnormalities in mice postnatally exposed to ethanol. Neuropsychopharmacology. 2019;44:514–525. doi: 10.1038/s41386-018-0181-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 325.Shivakumar M., Subbanna S., Joshi V., Basavarajappa B.S. Postnatal Ethanol Exposure Activates HDAC-Mediated Histone Deacetylation, Impairs Synaptic Plasticity Gene Expression and Behavior in Mice. Int. J. Neuropsychopharmacol. 2020;23:324–338. doi: 10.1093/ijnp/pyaa017. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 326.Subbanna S., Basavarajappa B.S. Postnatal Ethanol-Induced Neurodegeneration Involves CB1R-Mediated β-Catenin Degradation in Neonatal Mice. Brain Sci. 2020;10:271. doi: 10.3390/brainsci10050271. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 327.Kulaga V., Shor S., Koren G. Correlation between drugs of abuse and alcohol by hair analysis: Parents at risk for having children with fetal alcohol spectrum disorder. Alcohol. 2010;44:615–621. doi: 10.1016/j.alcohol.2010.04.001. [DOI] [PubMed] [Google Scholar]
- 328.Shor S., Nulman I., Kulaga V., Koren G. Heavy in utero ethanol exposure is associated with the use of other drugs of abuse in a high-risk population. Alcohol. 2010;44:623–627. doi: 10.1016/j.alcohol.2009.08.008. [DOI] [PubMed] [Google Scholar]
- 329.Buchi K.F., Suarez C., Varner M.W. The prevalence of prenatal opioid and other drug use in Utah. Am. J. Perinatol. 2013;30:241–244. doi: 10.1055/s-0032-1323586. [DOI] [PubMed] [Google Scholar]
- 330.Subbanna S., Psychoyos D., Xie S., Basavarajappa B.S. Postnatal ethanol exposure alters levels of 2-arachidonylglycerol-metabolizing enzymes and pharmacological inhibition of monoacylglycerol lipase does not cause neurodegeneration in neonatal mice. J. Neurochem. 2015;134:276–287. doi: 10.1111/jnc.13120. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 331.Seleverstov O., Tobiasz A., Jackson J.S., Sullivan R., Ma D., Sullivan J.P., Davison S., Akkhawattanangkul Y., Tate D.L., Costello T., et al. Maternal alcohol exposure during mid-pregnancy dilates fetal cerebral arteries via endocannabinoid receptors. Alcohol. 2017;61:51–61. doi: 10.1016/j.alcohol.2017.01.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 332.Gal P., Sharpless M.K. Fetal drug exposure-behavioral teratogenesis. Drug Intell. Clin. Pharm. 1984;18:186–201. doi: 10.1177/106002808401800304. [DOI] [PubMed] [Google Scholar]
- 333.Faden V.B., Graubard B.I. Maternal substance use during pregnancy and developmental outcome at age three. J. Subst. Abuse. 2000;12:329–340. doi: 10.1016/S0899-3289(01)00052-9. [DOI] [PubMed] [Google Scholar]
- 334.Psychoyos D., Hungund B., Cooper T., Finnell R.H. A cannabinoid analogue of Delta9-tetrahydrocannabinol disrupts neural development in chick. Birth Defects Res. B Dev. Reprod. Toxicol. 2008;83:477–488. doi: 10.1002/bdrb.20166. [DOI] [PubMed] [Google Scholar]
- 335.Williams N., Lee J., Mitchell E., Moore L., Baxter E.J., Hewinson J., Dawson K.J., Menzies A., Godfrey A.L., Green A.R., et al. Life histories of myeloproliferative neoplasms inferred from phylogenies. Nature. 2022;602:162–168. doi: 10.1038/s41586-021-04312-6. [DOI] [PubMed] [Google Scholar]
- 336.Malouf C., Ottersbach K. Molecular processes involved in B cell acute lymphoblastic leukaemia. Cell Mol. Life Sci. 2018;75:417–446. doi: 10.1007/s00018-017-2620-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 337.Shen H., Shih J., Hollern D.P., Wang L., Bowlby R., Tickoo S.K., Thorsson V., Mungall A.J., Newton Y., Hegde A.M., et al. Integrated Molecular Characterization of Testicular Germ Cell Tumors. Cell Rep. 2018;23:3392–3406. doi: 10.1016/j.celrep.2018.05.039. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 338.Kagawa H., Javali A., Khoei H.H., Sommer T.M., Sestini G., Novatchkova M., Scholte op Reimer Y., Castel G., Bruneau A., Maenhoudt N., et al. Human blastoids model blastocyst development and implantation. Nature. 2022;601:600–605. doi: 10.1038/s41586-021-04267-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 339.Chioccarelli T., Falco G., Cappetta D., De Angelis A., Roberto L., Addeo M., Ragusa M., Barbagallo D., Berrino L., Purrello M., et al. FUS driven circCNOT6L biogenesis in mouse and human spermatozoa supports zygote development. Cell Mol. Life Sci. 2021;79:50. doi: 10.1007/s00018-021-04054-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 340.Nahas G.G. Keep Off the Grass. Volume 1 Elsevier; Middlebury, VT, USA: 1990. [Google Scholar]
- 341.Nahas G.G. Cannabis Physiopathology Epidemiology Detection. Volume 1 CRC Press Revivals; Boca Raton, FL, USA: 1990. [Google Scholar]
- 342.Russo C., Ferk F., Mišík M., Ropek N., Nersesyan A., Mejri D., Holzmann K., Lavorgna M., Isidori M., Knasmüller S. Low doses of widely consumed cannabinoids (cannabidiol and cannabidivarin) cause DNA damage and chromosomal aberrations in human-derived cells. Arch. Toxicol. 2019;93:179–188. doi: 10.1007/s00204-018-2322-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 343.Pucci M., Rapino C., Di Francesco A., Dainese E., D’Addario C., Maccarrone M. Epigenetic control of skin differentiation genes by phytocannabinoids. Br. J. Pharmacol. 2013;170:581–591. doi: 10.1111/bph.12309. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 344.Maor Y., Yu J., Kuzontkoski P.M., Dezube B.J., Zhang X., Groopman J.E. Cannabidiol inhibits growth and induces programmed cell death in kaposi sarcoma-associated herpesvirus-infected endothelium. Genes Cancer. 2012;3:512–520. doi: 10.1177/1947601912466556. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 345.Karmaus P.W., Wagner J.G., Harkema J.R., Kaminski N.E., Kaplan B.L. Cannabidiol (CBD) enhances lipopolysaccharide (LPS)-induced pulmonary inflammation in C57BL/6 mice. J. Immunotoxicol. 2013;10:321–328. doi: 10.3109/1547691X.2012.741628. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 346.Hind W.H., England T.J., O’Sullivan S.E. Cannabidiol protects an in vitro model of the blood-brain barrier from oxygen-glucose deprivation via PPARgamma and 5-HT1A receptors. Br. J. Pharmacol. 2016;173:815–825. doi: 10.1111/bph.13368. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 347.O’Sullivan S.E., Sun Y., Bennett A.J., Randall M.D., Kendall D.A. Time-dependent vascular actions of cannabidiol in the rat aorta. Eur. J. Pharmacol. 2009;612:61–68. doi: 10.1016/j.ejphar.2009.03.010. [DOI] [PubMed] [Google Scholar]
- 348.O’Sullivan S.E., Kendall D.A. Cannabinoid activation of peroxisome proliferator-activated receptors: Potential for modulation of inflammatory disease. Immunobiology. 2010;215:611–616. doi: 10.1016/j.imbio.2009.09.007. [DOI] [PubMed] [Google Scholar]
- 349.Hegde V.L., Singh U.P., Nagarkatti P.S., Nagarkatti M. Critical Role of Mast Cells and Peroxisome Proliferator-Activated Receptor gamma in the Induction of Myeloid-Derived Suppressor Cells by Marijuana Cannabidiol In Vivo. J. Immunol. 2015;194:5211–5222. doi: 10.4049/jimmunol.1401844. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 350.Ramer R., Heinemann K., Merkord J., Rohde H., Salamon A., Linnebacher M., Hinz B. COX-2 and PPAR-gamma confer cannabidiol-induced apoptosis of human lung cancer cells. Mol. Cancer Ther. 2013;12:69–82. doi: 10.1158/1535-7163.MCT-12-0335. [DOI] [PubMed] [Google Scholar]
- 351.Scuderi C., Steardo L., Esposito G. Cannabidiol promotes amyloid precursor protein ubiquitination and reduction of beta amyloid expression in SHSY5YAPP+ cells through PPARgamma involvement. Phytother. Res. 2014;28:1007–1013. doi: 10.1002/ptr.5095. [DOI] [PubMed] [Google Scholar]
- 352.De Filippis D., Esposito G., Cirillo C., Cipriano M., De Winter B.Y., Scuderi C., Sarnelli G., Cuomo R., Steardo L., De Man J.G., et al. Cannabidiol reduces intestinal inflammation through the control of neuroimmune axis. PLoS ONE. 2011;6:e28159. doi: 10.1371/journal.pone.0028159. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 353.Esposito G., Scuderi C., Valenza M., Togna G.I., Latina V., De Filippis D., Cipriano M., Carratù M.R., Iuvone T., Steardo L. Cannabidiol reduces Abeta-induced neuroinflammation and promotes hippocampal neurogenesis through PPARgamma involvement. PLoS ONE. 2011;6:e28668. doi: 10.1371/journal.pone.0028668. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 354.Chan J.Z., Duncan R.E. Regulatory Effects of Cannabidiol on Mitochondrial Functions: A Review. Cells. 2021;10:1251. doi: 10.3390/cells10051251. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 355.Olivas-Aguirre M., Torres-López L., Pottosin I., Dobrovinskaya O. Phenolic Compounds Cannabidiol, Curcumin and Quercetin Cause Mitochondrial Dysfunction and Suppress Acute Lymphoblastic Leukemia Cells. Int. J. Mol. Sci. 2020;22:204. doi: 10.3390/ijms22010204. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 356.Ryan D., Drysdale A.J., Lafourcade C., Pertwee R.G., Platt B. Cannabidiol targets mitochondria to regulate intracellular Ca2+ levels. J. Neurosci. 2009;29:2053–2063. doi: 10.1523/JNEUROSCI.4212-08.2009. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 357.Winklmayr M., Gaisberger M., Kittl M., Fuchs J., Ritter M., Jakab M. Dose-Dependent Cannabidiol-Induced Elevation of Intracellular Calcium and Apoptosis in Human Articular Chondrocytes. J. Orthop. Res. 2019;37:2540–2549. doi: 10.1002/jor.24430. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 358.Hayakawa K., Mishima K., Hazekawa M., Sano K., Irie K., Orito K., Egawa T., Kitamura Y., Uchida N., Nishimura R., et al. Cannabidiol potentiates pharmacological effects of Delta(9)-tetrahydrocannabinol via CB(1) receptor-dependent mechanism. Brain Res. 2008;1188:157–164. doi: 10.1016/j.brainres.2007.09.090. [DOI] [PubMed] [Google Scholar]
- 359.Silvestri C., Di Marzo V. The endocannabinoid system in energy homeostasis and the etiopathology of metabolic disorders. Cell Metab. 2013;17:475–490. doi: 10.1016/j.cmet.2013.03.001. [DOI] [PubMed] [Google Scholar]
- 360.Mato S., Victoria Sánchez-Gómez M., Matute C. Cannabidiol induces intracellular calcium elevation and cytotoxicity in oligodendrocytes. Glia. 2010;58:1739–1747. doi: 10.1002/glia.21044. [DOI] [PubMed] [Google Scholar]
- 361.Fišar Z., Singh N., Hroudová J. Cannabinoid-induced changes in respiration of brain mitochondria. Toxicol. Lett. 2014;231:62–71. doi: 10.1016/j.toxlet.2014.09.002. [DOI] [PubMed] [Google Scholar]
- 362.Alhamoruni A., Lee A.C., Wright K.L., Larvin M., O’Sullivan S.E. Pharmacological effects of cannabinoids on the Caco-2 cell culture model of intestinal permeability. J. Pharmacol. Exp. Ther. 2010;335:92–102. doi: 10.1124/jpet.110.168237. [DOI] [PubMed] [Google Scholar]
- 363.Da Silva J.A., Biagioni A.F., Almada R.C., de Souza Crippa J.A., Cecilio Hallak J.E., Zuardi A.W., Coimbra N.C. Dissociation between the panicolytic effect of cannabidiol microinjected into the substantia nigra, pars reticulata, and fear-induced antinociception elicited by bicuculline administration in deep layers of the superior colliculus: The role of CB1-cannabinoid receptor in the ventral mesencephalon. Eur. J. Pharmacol. 2015;758:153–163. doi: 10.1016/j.ejphar.2015.03.051. [DOI] [PubMed] [Google Scholar]
- 364.Laprairie R.B., Bagher A.M., Kelly M.E.M., Denovan-Wright E. Cannabidiol is a negative allosteric modulator of the cannabinoid CB1 receptor. Br. J. Pharmacol. 2015;172:4790–4805. doi: 10.1111/bph.13250. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 365.Stanley C.P., Hind W.H., Tufarelli C., O’Sullivan S.E. Cannabidiol causes endothelium-dependent vasorelaxation of human mesenteric arteries via CB1 activation. Cardiovasc. Res. 2015;107:568–578. doi: 10.1093/cvr/cvv179. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 366.Sartim A.G., Guimaraes F.S., Joca S.R. Antidepressant-like effect of cannabidiol injection into the ventral medial prefrontal cortex-Possible involvement of 5-HT1A and CB1 receptors. Behav. Brain Res. 2016;303:218–227. doi: 10.1016/j.bbr.2016.01.033. [DOI] [PubMed] [Google Scholar]
- 367.Hwang Y.S., Kim Y.J., Kim M.O., Kang M., Oh S.W., Nho Y.H., Park S.H., Lee J. Cannabidiol upregulates melanogenesis through CB1 dependent pathway by activating p38 MAPK and p42/44 MAPK. Chem. Biol. Interact. 2017;273:107–114. doi: 10.1016/j.cbi.2017.06.005. [DOI] [PubMed] [Google Scholar]
- 368.Silva N.R., Gomes F.V., Fonseca M.D., Mechoulam R., Breuer A., Cunha T.M., Guimaraes F.S. Antinociceptive effects of HUF-101, a fluorinated cannabidiol derivative. Prog. Neuropsychopharmacol. Biol. Psychiatry. 2017;79:369–377. doi: 10.1016/j.pnpbp.2017.07.012. [DOI] [PubMed] [Google Scholar]
- 369.Stern C.A., da Silva T.R., Raymundi A.M., de Souza C.P., Hiroaki-Sato V.A., Kato L., Guimarães F.S., Andreatini R., Takahashi R.N., Bertoglio L.J. Cannabidiol disrupts the consolidation of specific and generalized fear memories via dorsal hippocampus CB1 and CB2 receptors. Neuropharmacology. 2017;125:220–230. doi: 10.1016/j.neuropharm.2017.07.024. [DOI] [PubMed] [Google Scholar]
- 370.Fogaça M.V., Campos A.C., Coelho L.D., Duman R.S., Guimarães F.S. The anxiolytic effects of cannabidiol in chronically stressed mice are mediated by the endocannabinoid system: Role of neurogenesis and dendritic remodeling. Neuropharmacology. 2018;135:22–33. doi: 10.1016/j.neuropharm.2018.03.001. [DOI] [PubMed] [Google Scholar]
- 371.Mahoney J.M., Harris R.A. Effect of 9 -tetrahydrocannabinol on mitochondrial processes. Biochem. Pharmacol. 1972;21:1217–1226. doi: 10.1016/0006-2952(72)90283-3. [DOI] [PubMed] [Google Scholar]
- 372.Bartova A., Birmingham M.K. Effect of delta9-tetrahydrocannabinol on mitochondrial NADH-oxidase activity. J. Biol. Chem. 1976;251:5002–5006. doi: 10.1016/S0021-9258(17)33213-1. [DOI] [PubMed] [Google Scholar]
- 373.Hebert-Chatelain E., Reguero L., Puente N., Lutz B., Chaouloff F., Rossignol R., Piazza P.V., Benard G., Grandes P., Marsicano G. Cannabinoid control of brain bioenergetics: Exploring the subcellular localization of the CB1 receptor. Mol. Metab. 2014;3:495–504. doi: 10.1016/j.molmet.2014.03.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 374.Jimenez-Blasco D., Busquets-Garcia A., Hebert-Chatelain E., Serrat R., Vicente-Gutierrez C., Ioannidou C., Gómez-Sotres P., Lopez-Fabuel I., Resch-Beusher M., Resel E., et al. Glucose metabolism links astroglial mitochondria to cannabinoid effects. Nature. 2020;583:603–608. doi: 10.1038/s41586-020-2470-y. [DOI] [PubMed] [Google Scholar]
- 375.PPARG Peroxisome Proliferator Activated Receptor Gamma [Homo sapiens (Human)] [(accessed on 1 April 2022)]; Available online: https://www.ncbi.nlm.nih.gov/gene?Db=gene&Cmd=ShowDetailView&TermToSearch=5468.
- 376.Reece A.S., Hulse G.K. Epidemiology of Δ8THC–Related Carcinogenesis in USA: A Panel Regression and Causal Inferential Study. Int. J. Environ. Res. Public Health. 2022;19:7726–7752. doi: 10.3390/ijerph19137726. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 377.Reece A.S., Hulse G.K. Congenital Anomaly Epidemiological Correlates of Δ8THC Across USA 2003–2016: Panel Regression and Causal Inferential Study. Environ. Epigenetics. :2022. doi: 10.1093/eep/dvac012. in press . [DOI] [PMC free article] [PubMed] [Google Scholar]
- 378.Gant J. Scientists are baffled by spatter of babies born without hands or arms in France, as investigation fails to discover a cause. The Daily Mail. Jul 13, 2019.
- 379.Willsher K. Baby arm defects prompt nationwide investigation in France. The Guardian. Oct 31, 2018.
- 380.Agence France-Presse in Paris. France to investigate cause of upper limb defects in babies. The Guardian. Oct 21, 2018.
- 381.Babies Born with Deformed Hands Spark Investigation in Germany. [(accessed on 1 April 2022)]. Available online: https://edition.cnn.com/2019/09/16/health/hand-deformities-babies-gelsenkirchen-germany-intl-scli-grm/index.html.
- 382.Wang Y.X., Blau H.M. Reversing aging for heart repair. Science. 2021;373:1439–1440. doi: 10.1126/science.abl8679. [DOI] [PubMed] [Google Scholar]
- 383.Bejaoui Y., Razzaq A., Yousri N.A., Oshima J., Megarbane A., Qannan A., Potabattula R., Alam T., Martin G.M., Horn H.F., et al. DNA methylation signatures in Blood DNA of Hutchinson-Gilford Progeria syndrome. Aging Cell. 2022;21:e13555. doi: 10.1111/acel.13555. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 384.Do W.L., Nguyen S., Yao J., Guo X., Whitsel E.A., Demerath E., Rotter J.I., Rich S.S., Lange L., Ding J., et al. Associations between DNA methylation and BMI vary by metabolic health status: A potential link to disparate cardiovascular outcomes. Clin. Epigenetics. 2021;13:230. doi: 10.1186/s13148-021-01194-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 385.Fu K.A., Paul K.C., Lu A.T., Horvath S., Keener A.M., Bordelon Y., Bronstein J.M., Ritz B. DNA methylation-based surrogates of plasma proteins are associated with Parkinson’s disease risk. J. Neurol. Sci. 2021;431:120046. doi: 10.1016/j.jns.2021.120046. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 386.Gao T., Wilkins J.T., Zheng Y., Joyce B.T., Jacobs D.R., Jr., Schreiner P.J., Horvath S., Greenland P., Lloyd-Jones D., Hou L. Plasma lipid profiles in early adulthood are associated with epigenetic aging in the Coronary Artery Risk Development in Young Adults (CARDIA) Study. Clin. Epigenetics. 2022;14:16. doi: 10.1186/s13148-021-01222-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 387.Roberts J.D., Vittinghoff E., Lu A.T., Alonso A., Wang B., Sitlani C.M., Mohammadi-Shemirani P., Fornage M., Kornej J., Brody J.A., et al. Epigenetic Age and the Risk of Incident Atrial Fibrillation. Circulation. 2021;144:1899–1911. doi: 10.1161/CIRCULATIONAHA.121.056456. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 388.Schlosser P., Tin A., Matias-Garcia P.R., Thio C.H., Joehanes R., Liu H., Weihs A., Yu Z., Hoppmann A., Grundner-Culemann F., et al. Meta-analyses identify DNA methylation associated with kidney function and damage. Nat. Commun. 2021;12:7174. doi: 10.1038/s41467-021-27234-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 389.Tin A., Schlosser P., Matias-Garcia P.R., Thio C.H., Joehanes R., Liu H., Yu Z., Weihs A., Hoppmann A., Grundner-Culemann F., et al. Epigenome-wide association study of serum urate reveals insights into urate co-regulation and the SLC2A9 locus. Nat. Commun. 2021;12:7173. doi: 10.1038/s41467-021-27198-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 390.Ellis R.J., Bara A., Vargas C.A., Frick A.L., Loh E., Landry J., Uzamere T.O., Callens J.E., Martin Q., Rajarajan P., et al. Prenatal Δ(9)-Tetrahydrocannabinol Exposure in Males Leads to Motivational Disturbances Related to Striatal Epigenetic Dysregulation. Biol. Psychiatry. 2021;92:127–138. doi: 10.1016/j.biopsych.2021.09.017. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 391.Porath A.J., Fried P.A. Effects of prenatal cigarette and marijuana exposure on drug use among offspring. Neurotoxicol. Teratol. 2005;27:267–277. doi: 10.1016/j.ntt.2004.12.003. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
All data generated or analyzed during this study are included in this published article and its supplementary information files. Data along with the relevant R code have been made publicly available on the Mendeley Database Repository and can be accessed from this URL https://data.mendeley.com/datasets/sngdkpg8gy/1 (doi:10.17632/sngdkpg8gy.1) (accessed on 10 December 2022).