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. Author manuscript; available in PMC: 2024 Aug 20.
Published in final edited form as: J Neuroimmune Pharmacol. 2021 Nov 10;17(3-4):487–502. doi: 10.1007/s11481-021-10027-2

Meta-Analysis on Nicotine’s Modulation of HIV-Associated Dementia

Velu Krishnan 1,2, Michael Vigorito 1,3, Nikhil K Kota 1,2, Sulie L Chang 1,2
PMCID: PMC11334575  NIHMSID: NIHMS2000945  PMID: 34757527

Abstract

HIV-Associated Dementia (HAD) is a significant comorbidity that many HIV-patients face. Our study utilized QIAGEN Ingenuity Pathway Analysis (IPA) to identify and analyze molecular profiles and pathways underlying nicotine’s impact on HAD pathology. The Qiagen Knowledge Base (QKB) defines HAD as “Dementia associated with acquired immunodeficiency syndrome (disorder).” Although much remains unknown about HAD pathology, the curated research findings from the QKB shows 5 upregulated molecules that are associated with HAD + : CCL2 (Chemokine (C–C motif) ligand 2), L-glutamic acid, GLS (Glutaminase), POLG (DNA polymerase subunit gamma), and POLB (DNA polymerase subunit beta). The current study focused on these 5 HAD pathology molecules as the phenotype of interest. The Pathway Explorer tool of IPA was used to connect nicotine-associated molecules with the 5 HAD associated molecules (HAD pathology molecules) by connecting 29 overlapping molecules (including transcription regulators, cytokines, kinases, and other enzymes/proteins). The Molecule-Activity-Predictor (MAP) tool predicted nicotine-induced activation of the HAD pathology molecules indicating the exacerbation of HAD. However, alternative pathways with more holistic representations of molecular relationships revealed the potential of nicotine as a neuroprotective treatment. It was found that concurrent with nicotine treatment the individual inactivation of several of the intermediary molecules in the holistic pathways caused the downregulation of the HAD pathology molecules. These findings reveal that nicotine may have therapeutic properties for HAD when given alongside specific inhibitory drugs for one or more of the identified intermediary molecules.

Keywords: HIV-Associated Dementia (HAD), Nicotine, Ingenuity Pathway Analysis (IPA), Neuroinflammation, Neuronal Cell Death, Tumor Necrosis Factor (TNF)

Introduction

Human Immunodeficiency Virus (HIV)/Acquired Immunodeficiency Syndrome (AIDS) patients suffer from many complications associated with the pathologies of HIV-infection and AIDS. Although some therapies such as Highly Active Antiretroviral Therapy (HAART) can extend the lives of HIV-positive individuals and potentially decrease the incidence of HIV-associated dementia (HAD), the prevalence of dementia within the life span of people living with HIV/AIDS (PLWH/A) remains prevalent, between 10–20% (Pollicita et al. 2008; Fajardo-Ortiz et al. 2017; Cunningham et al. 2015; Ghafouri et al. 2006).

Dementia remains a prevalent symptom of many diseases of the central nervous system (CNS). HIV-infection is the leading cause of early-onset dementia in the younger U.S. population (less than 60 years of age) (Fajardo-Ortiz et al. 2017; Cunningham et al. 2015; Ghafouri et al. 2006). It is widely believed that neuronal damage/loss of neurons and neuroinflammation are the primary pathologies that cause the symptoms associated with dementia (Saha and Pahan 2003; Thompson et al. 2001). The pathology of HAD, also called the AIDS-dementia complex (ADC), remains largely unknown, but new findings and potential pathologies have been discovered about HAD which may lead to the discovery of possible therapies (Bouwman et al. 1998; Meucci et al. 1998).

The viral load of HIV in the cerebrospinal fluid is considered to be a major predictor of the development of HAD in HIV-infected patients (Meucci et al. 1998; Speechly et al. 2008; Davis et al. 2002). There are several neuropathologic findings that affect HAD patient’s brains and CNS. A common finding in all HIV Associated Neurological Disorders (HANDs) is the correlation between the severity of HAD and the atrophy of brain structures such as the gray matter of basal ganglia and deep white matter (Rocha et al. 2015). Additionally, white matter cortical atrophy is attributed to the accumulation of lipid macrophages (Crowe et al. 2010; Castellano et al. 2019).

Initial HIV infection is the root cause of the HAD pathology, where HIV-1 targets CD4 + helper T lymphocytes and CD8 + cytotoxic T lymphocytes (Al-Omoush et al. 2020). The so called “Trojan Horse Hypothesis” is the most compelling explanation, with support from studies on animal models and in vitro experimentation, of how HIV-1 infiltrates the CNS (Gooneratne et al. 2015; Kramer-Hammerle et al. 2005). HIV-1 enters the brain through the blood–brain barrier (BBB) as a passenger on infected cells that can cross the BBB such as CD4 + cells. The virus is then able to infect other cells of the CNS such as microglia and astrocytes, where they greatly express CD4/CCR5 receptors which are efficiently used by HIV-1 for infection/replication (Gooneratne et al. 2015; Kramer-Hammerle et al. 2005; Izquierdo-Useros et al. 2010; Clifford 2000).

Beyond the scope of HIV-1 infection, there are several proposed causes of the neuropathological damage that is observed in HAD and is widely believed to cause dementia. Some direct mechanisms of neuronal injury may be through viral proteins, such as Tat (transcriptional transactivator) (Mattson et al. 2005). Studies demonstrate that Tat is secreted at high-levels in vitro and that the neurotoxicity of Tat causes the accumulation of intracellular reactive oxygen species which damage neuronal cells (Saha and Pahan 2003; Thompson et al. 2001; Mattson et al. 2005). In addition, studies have shown that the excitation of capase-8 by Tat expression in HIV-1 CD4 + T-cell lines contributes to increased apoptosis of these neuronal cells (Ghafouri et al. 2006; Bartz and Emerman 1999). Another possible pathology of HAD, instead of cell damage and death, is neuroinflammation (Hong and Banks 2015). Viral proteins, such as Tat, gp120 and Vpr, are believed to participate in the HAD inflammatory cascade by promoting TNF-α (tumor necrosis factor) and IL-1 (interleukin) production in macrophages/microglia which promote the production of cytokines and chemokines including CCL2, thus leading to neurotoxicity (Kalliolias and Ivashkiv 2016; Banerjee et al. 2008; Anthony et al. 2005; Katuri et al. 2019; Bethel-Brown et al. 2012). Furthermore, in-vivo models indicate that proinflammatory cytokines, such as TNF-α and IL-1, can have neurotoxic effects damaging the BBB and exacerbating HAD by allowing for the increased entry of HIV-1 to cells of the CNS (Bethel-Brown et al. 2012; Kovalevich and Langford 2012; Cataldo 2010).

In looking towards possible therapies for HAD, one may consider nicotine and its possible neuroprotective effects. Some research has shown that nicotine provided neuroprotection against glutamate excitotoxicity and protected hippocampal neurons from cell death (Dong et al. 2020). Nicotine is a structural analog of endogenous ligands, acetylcholine (Ach). Ach acts on a distinct but heterogeneous class of cholinergic receptors defined as nicotinic receptors (nAChRs). In cholinergic neurons Ach is synthesized from Acetyl-S-CoA and choline by the enzyme Choline Acetyltransferase (ChAT) and released at the synapse as a neurotransmitter with a high affinity for nAChRs. Choline, a product of the rapid hydrolysis of Ach in the synapse by Acetylcholinesterase (AchE), also acts as a ligand of the nAChR although with a much lower affinity (Papke and Lindstrom 2020). At the neuromuscular junction and autonomic ganglia nAChRs are fast excitatory postsynaptic receptors, but in the central nervous system (CNS) nAChR are primarily on presynaptic structures and serve to modulate the release of other neurotransmitters such as glutamate. Also, nicotine has been found to affect intracellular signaling and activates signaling factors and kinase pathways to promote cell survival by inhibiting steps of the apoptosis signaling pathway (Chernyavsky 2015; Redza-Dutordoir and Averill-Bates 2016; Dineley et al. 2001).

Although not demonstrating unequivocal support, certain findings demonstrate that administering nicotine may have potential cognitive benefits. Some of the benefits found in Alzheimer’s/dementia patients included improved visual attention, information processing, and memory (Tian et al. 2017). Neural nAChR are widely distributed throughout the CNS and are involved in neural networks regulating arousal, motivation, and cognitive function and are involved in neurological pathologies (e.g., Alzheimer’s and Parkinson’s diseases), psychiatric disorders (e.g., depression, schizophrenia) and behavioral dysfunction including nicotine addiction. Nicotinic AChRs are pentamers forming a non-specific cation receptor channel. The hetero-pentamer subtype α4β2 receptors, for example, are abundant in the brain, exhibit a high affinity for nicotine, and may play a critical role in the rewarding effects driving nicotine addiction. The homo-pentamer α7nAChR is also widespread in the brain and plays a role in regulating cognition, although the affinity of α7nAChR for nicotine is low relative to the α4β2nAChR. Choline acts as a selective agonist for α7nAChR (Piovesana et al. 2021; Alkondon et al. 1997). Additionally, some research demonstrates that a combined therapy between galantamine and nicotine can inhibit the neuroinflammation caused by the viral protein gp120 (Bajrektarevic et al. 2019; Nicholatos et al. 2018). The sporadic findings on nicotine’s benefits as a neuroprotective agent conveys an important indication to the HAD pathology as a complex system that may require a combination of several therapeutic agents to treat.

Thus, we postulate that there are specific molecular pathways through which nicotine may act as a neuroprotective agent against HAD by leading to the inhibition of neuroinflammation and neuronal cell damage or apoptosis. If this hypothesis is correct, then there should be intermediary molecules that are both downstream of nicotine (i.e., that change when exposed to nicotine) and upstream of HAD-associated molecules (i.e., influence HAD). Furthermore, these intermediary molecules should be concordant with canonical pathways relating to neuroinflammation and neuronal cell death signaling.

This study conducted a network meta-analysis to investigate the impact that nicotine exposure has on HAD pathology through molecules common to biological pathways associated with HAD and with nicotine exposure. The bioinformatics tool by QIAGEN, Ingenuity Pathway Analysis (IPA), was used for data mining and generation of connectivity mapping that predict (simulate) the relationship between nicotine and HAD derived from the scientific literature in the QIAGEN Knowledge Base (QKB). The QKB is a massive database that is created from over seven million individually modeled relationships and connections between, diseases, drugs, chemicals, biological entities (i.e., genes, proteins, metabolites) and cellular processes (i.e., cell expression, etc.) along with the published results of omics experiments where the information is curated, by experts, from full-length scientific journal articles. Molecules associated with nicotine and HAD were obtained from the QKB and only those that were affected by nicotine and influenced HAD were initially analyzed as a unidirectional causal relationship of nicotine to intermediary molecules to HAD-associated molecules. To take a more holistic approach, this unidirectional relationship was then replaced with bidirectional relationships between the intermediary molecules and HAD-associated molecules. In addition, nicotine-associated molecules with no known relationship to HAD-associated molecules were included in bidirectional pathway simulations along with functional manipulations (i.e., activation/inactivation) applied to individual intermediary molecules. The network meta-analysis and pathway simulations suggest that nicotine can be a neuroprotective agent in terms of HAD pathology when combined with other inhibitory therapies for molecules involved in both or either the neuroinflammation and p38 MAPK (stress-induced apoptosis/autophagy) signaling pathways.

Methods and Materials

Ingenuity Pathway Analysis Software

The IPA Analysis Match CL license was purchased from QIAGEN for the use of all the features in the IPA software (QIAGEN Inc., https://www.qiagenbioinformatics.com/products). IPA was used as a bioinformatics tool to analyze data and interpret molecular relationships within the context of canonical pathways and biological systems identified in the scientific literature and stored in the QKB. In addition, several tools in IPA were used to test functional hypotheses by generating customized pathways (i.e., visual connectivity maps) to display the molecular networks using molecules associated with nicotine or HAD and their shared (intermediary) molecules (summarized in Fig. 1). The QIAGEN database is constantly reviewed and updated. The data used in the present analysis was retrieved from the QKB between January 8th, 2021 and March 21st, 2021.

Fig. 1.

Fig. 1

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The flow of steps and the bioinformatics tools available in IPA that were used to integrate and interpret the relationship between nicotine- and HAD-associated molecules. First, the “My Pathway” feature, along with the “Grow”, “Connect”, “Trim”, and “Pathway Explorer” tools, were used to identify molecules that are downstream of (affected by) nicotine and upstream of (that influence) the 5 HAD-associated molecules. Connectivity Maps were then generated to visualize a plausible unidirectional ( → ) relationship between nicotine- and HAD-associated molecules (hypothesis/simulation #1) as well as more holistic bidirectional ( ← → ) relationships (hypotheses/simulations #2–4). MAP was used to modulate molecules and establish predictions in silico. See Fig. 2 for the categorization of the intermediary molecules included in the Molecule Activity Analyses of the different hypotheses/simulations. Additionally, the ‘Downstream Effect Analysis’ tool was used for the quantification of the unidirectional relationship of hypothesis/simulation #1 and a “Canonical pathway analysis” (and a corresponding negative control) was also performed on the molecular dataset to establish functional relationships with classically characterized pathways

IPA “My Pathway”: Identifying Overlapping Molecules

Using the “My Pathway” feature, nicotine and HAD were added to a new network. Then the overlapping molecules between nicotine and HAD were added to the molecular network using the “Grow” and “Pathway Explorer” tools. The “Pathway Explorer” tool was utilized to initially specify that the molecules added between nicotine and HAD were downstream from nicotine but upstream from HAD. Furthermore, the “Connect” tool was utilized to connect all the molecules in the network together without discriminating between the directionality of the relationship in order to simulate how molecules and molecular networks in the human body are rarely isolated and affect each other. The “Trim” tool was also used to remove molecules that were not relevant in the analysis between nicotine exposure on HAD, such as those molecules that did not display known changes and are not naturally occurring in biological systems (i.e., chemical drugs, toxicants, chemicals, non-endogenous mammalian chemicals).

These molecular relationships added by the “Grow” and “Pathway Explorer” tools were analyzed by using the “Molecule Activity Predictor” (MAP) tool. This tool predicts the activity of molecules, diseases, or cellular functions when a molecule in the pathway has its activity either activated or inactivated to simulate a change in its expression. The MAP tool’s predicted activity, along with literature findings compiled in the QKB relating to HAD pathology, were used to interpret the relationship and effect of nicotine activation on HAD pathology. Furthermore, this interpretation was able to provide insight into nicotine’s potential neuroprotective properties.

Quantitative Analysis of the Effect that Nicotine Exposure has on HAD (Activation Z-Score)

The “Downstream Effect Analysis” algorithm was used to calculate the quantitative weightage given to the expression changes predicted by the MAP tool in simulating how nicotine activation would affect HAD (Kramer et al. 2014). The algorithm utilizes findings curated from the literature within the QKB as data points since the MAP tool within IPA also pulls its data from there to predict the activity changes. Krämer’s algorithm used 104 references as data points to calculate activation Z-scores for each intermediary molecule downstream of nicotine and upstream of HAD to infer the change in HAD expression due to nicotine activation as predicted by the MAP tool. The scale for the individual expression changes (z-score) associated with a molecule in the pathway falls between −2 and + 2, where −2 indicates a strong inhibitory relationship while + 2 indicates a strong excitation relationship. The analysis was used to calculate the magnitude change of the molecule’s activity and HAD pathology when exposed to nicotine. Additionally, the activation z-score provided the statistical significance of the pattern of activation relative to a random pattern of activation. Only the unidirectional dataset shown in Fig. 3 (Hypothesis/Simulation #1) was used for this analysis since the “Downstream Effect Analysis” only considers well-defined literature datapoints that are downstream from the initial directional edge (connection). Therefore, the formula was utilized to calculate a z-score.

Fig. 3.

Fig. 3

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Connectivity Map of the exclusively downstream relationships of the 14 molecules that are affected by nicotine exposure and influence HAD pathology. The molecules are listed in Table 1 with an (**) symbol

Core Analysis: Expression Analysis of Canonical Pathways (Overlapping p-values)

The “Core Expression Data Analysis” feature of IPA was used to analyze the molecular dataset for nicotine and HAD that were generated with the “My Pathway” build tools. The “Core Analysis” feature provides a “Canonical Pathway Analysis”, an “Upstream Analysis”, and an analysis of the diseases and cellular functions related to the utilized dataset. The “Canonical Pathway Analysis” determines the overlap of molecules between the inputted molecular dataset and the molecular dataset of the 705 canonical pathways within the QKB. The inputted dataset is rated with a right-tailed p-value from the Benjamini–Hochberg Corrected Fisher’s Exact Test to demonstrate the probability of finding a specific number of overlapping molecules from the inputted dataset in the canonical pathways stored within the QKB (overlap p-value). This analysis identifies the biological signaling and metabolic canonical pathways most likely associated with the dataset of molecules shared between nicotine-associated and HAD-associated molecules. In addition, the “Canonical Pathway Analysis” provided possible directional pathways that nicotine may be able to act as neuroprotective of HAD pathology. To prevent any biasing that may be present in the analysis from the QKB and ensure that the relationship between nicotine and HAD is specific to our dataset, a negative control “Canonical Pathway Analysis” was run between HAD and fertility due to there being no a-priori association between HAD and fertility.

Results

Molecules Associated with Nicotine and HAD

IPA’s “My Pathway” feature and build tools were used to identify the molecules associated with nicotine and molecules associated with HAD (Fig. 2). There were 357 molecules found to be associated with nicotine within the QKB. Meanwhile, there were only 8 molecules within the QKB directly related to HAD. Of these 8 molecules, 3 were drugs and therefore removed from further analyses due to them not naturally occurring in biological systems. The 5 molecules that are directly linked with HAD within the QKB (CCL2, L-glutamic acid, GLS, POLG and POLB) were chosen to be of focus in this study since a review of the relevant literature in the QKB revealed that the upregulation of these 5 molecules were directly associated with the pathology of HAD in humans (Huang et al. 2011; Scheu et al. 2017; Makinson et al. 2008). Thus, due to the small number of direct associations between molecules and HAD within the QKB, these 5 molecules became the target molecules representing HAD pathology. The current investigation examined how upstream molecular activity affects these 5 target molecules linked with HAD pathology (from here on these 5 molecules will be referred to as the HAD pathology molecules).

Fig. 2.

Fig. 2

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Categorization of 29 of the 32 overlapping molecules (3 + 12 + 14 + 3) identified by the My Pathway tools that are associated with nicotine and the 5 HAD pathology molecules (the 3 molecules downstream of nicotine and HAD pathology molecules are not shown). The relevant molecules were used to generate connectivity maps and to evaluate various hypotheses/simulations (see summary in Fig. 1) using the Molecule Activity Predictor (MAP) tool

Molecules Affected by Nicotine Exposure and Influencing HAD Pathology (Hypothesis/Simulation #1)

There were 2314 molecules associated with the HAD pathology molecules according to the QKB. Among the nicotine-associated molecules and the molecules associated with the HAD-pathology molecules, 32 were found to be overlapping. Of these 32 molecules, 3 were chemical drugs/toxicants that are not naturally present in biological systems and were thus not included in any analyses to maintain the accuracy of real biological systems. Furthermore, 12 overlapping molecules did not have a known unidirectional upstream relationship with the HAD pathology molecules and 3 overlapping molecules, while affected by nicotine, were downstream of the HAD pathology molecules. Although these two categories of molecules were excluded in the strictly unidirectional analysis of nicotine—> intermediary molecules—> 5 HAD pathology molecules (i.e., not included in that hypothesis/simulation #1), they were included in one or more of the subsequent simulations.

The remaining 14 molecules (noted with a “√” in column “14”, in Table 1) were downstream of (affected by) nicotine and were upstream of the HAD pathology molecules. Thus, they were further analyzed with the “Downstream Effect Analysis” tool to quantify their effect on the HAD pathology molecules under nicotine exposure. Of the 14 molecules, 11 molecules experienced excitation due to nicotine exposure: FN1, IL1, COL1A1, Akt, MAPK1, ERK1/2, STAT1, TP53, HIF1A, CREB1 and Ca2+. Meanwhile, 3 of these 14 molecules were inhibited due to nicotine exposure: IL1B, TNF and MYC. Yet, the expression of 4 of the 5 HAD pathology molecules were all increased regardless of the excitation or inhibition of the 14 molecules. These 14 molecules and the HAD pathology molecules were then organized into a connectivity map where the MAP tool was used to visually convey the influence that nicotine had on the HAD pathology molecules via the 14 intermediary molecules.

Table 1.

List of 34 Molecules Mediating the Nicotine to HAD Molecular Interaction

Symbol 12 14 3 Entrez Gene Name Molecular Family
Akt ✓ - Group
APP ✓ amyloid beta precursor protein Other
Ca2 + b ✓ - Chemical- endogenous mammalian
CCL2a C-C motif chemokine ligand 2 Cytokine
COL1A1 ✓ collagen type 1 alpha 1 chain Other
CREB1b ✓ cAMP responsive element binding protein 1 Transcription regulator
D-glucoseb ✓ - Chemical- endogenous mammalian
DLG4 ✓ discs large MAGUK scaffold protein 4 Kinase
ERK1/2ab ✓ - Group
FN1b ✓ fibronectin 1 Enzyme
GHRL ✓ ghrelin and obestatin prepropeptide Growth factor
GLSa glutaminase Enzyme
Gm21596/Hmgb1b ✓ high mobility group box 1 Transcription regulator
HIF1A ✓ hypoxia inducible factor 1 subunit alpha Transcription regulator
HMGB1b ✓ high mobility group box 1 Transcription regulator
IL1b ✓ - Group
IL1Bb ✓ interleukin 1 beta Cytokine
JUNB ✓ JunB proto-oncogene, AP-1 transcription factor subunit Transcription regulator
L-glutamic acida - Chemical- endogenous mammalian
MAP3K1b ✓ mitogen-activated protein kinase kinase kinase 1 Kinase
MAPK1b ✓ mitogen-activated protein kinase 1 Kinase
MAPTb ✓ microtubule associated protein tau Other
MYC ✓ MYC proto-oncogene, bHLH transcription factor Transcription regulator
NFKB1b ✓ nuclear factor kappa B subunit 1 Transcription regulator
PCNA ✓ proliferating cell nuclear antigen Enzyme
POLBa DNA polymerase beta Enzyme
POLGa DNA polymerase gamma, catalytic subunit Enzyme
POMC ✓ proopiomelanocortin Other
Reactive Oxygen Speciesbc ✓ - Chemical- endogenous mammalian
STAT1 ✓ signal transducer and activator of transcription 1 Transcription regulator
Testosteroneb ✓ - Chemical- endogenous mammalian
TNFb ✓ tumor necrosis factor Cytokine
TP53 ✓ tumor protein p53 Transcription regulator
XPC ✓ XPC complex subunit, DNA damage recognition and repair factor Transcription regulator
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Columns labeled 12, 14 and 3 identify the sets of overlapping molecules discovered with the My Pathway tools: 12 molecules with no known influence on HAD pathology molecules, 14 molecules affected by nicotine and influencing the HAD Pathology Molecules, and 3 molecules downstream of both nicotine and the HAD Pathology molecules

a

Indicates the 5 molecules directly associated with HAD (HAD Associated Pathology Molecules)

b

Indicates the molecules that can be inactivated when nicotine is activated in order to realize the neuroprotective effects of nicotine (ie. down- regulation of all 5 HAD associated Pathology Molecules)

c

Also a chemical toxicant but not removed from the network due to its relevancy to HAD pathology based on literature findings (Prochaska 2010)

Connectivity Pathway and Quantitative Analysis of molecules affected by Nicotine and Influencing HAD Pathology

The overall activity z-score for the change in the HAD pathology molecules was determined to be 5.23, which corresponds to a p – value = 1.67 ∗ 10−7 for a two-tailed distribution at a significance level 0.05 (Kramer et al. 2014). The magnitude of the change in expression of HAD is demonstrated in Fig. 4 where −2 represents a decrease in HAD expression and + 2 represents an increase in HAD expression.

Fig. 4.

Fig. 4

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Visual illustration of the quantitative analysis on the relationship between the 14 intermediary molecules that are affected by nicotine exposure and influence HAD pathology. The figure shows the individual contributions that these molecular changes have on the overall effect of nicotine exposure on HAD pathology

Expanded Connectivity Pathway Analysis of Downstream Relationships from Nicotine

However, it is important to note that the connectivity map generated in Fig. 3 based on the molecular dataset used to carry out the quantitative analysis displayed in Fig. 4 is unidirectional. Therefore, this molecular dataset used to generate Fig. 3 and Fig. 4 functions off the assumption that in the human body these pathways would be isolated, only present upstream from the 5 molecules associated with HAD. Therefore, to consider a more robust analysis a connectivity map was generated based on Fig. 3 but with the inclusion of the 12 molecules that were initially not included for there being no known relationship predicted to HAD. These molecules were connected to be upstream of the HAD pathway molecules using the “Connect” tool. This was done initially to examine how nicotine activation would affect HAD without the reciprocal effect of the 5 HAD pathology molecules may have on the intermediary molecules. Then the intermediary molecules were connected to each other in a bidirectional manner (either being upstream or downstream of each other) (Hypothesis/Simulation #2). This allowed for these molecules to be observed as they may be present in a human body where they would not be in an isolated system but instead actually affecting each other.

Utilizing the MAP tool, nicotine was once again activated and the predicted activity on these intermediary molecules and the HAD pathology molecules was examined. Due to the interconnectedness of the pathway with molecules acting in reciprocal manners upon each other, a quantitative analysis could not be carried out since the “Downstream Effect Analysis” relies on a molecular dataset where all the molecules are downstream from each other without interference (Kramer et al. 2014). However, the previous quantitative analysis demonstrated the general reliability of the MAP tool within the IPA software since Fig. 3 and the quantitative analysis used to generate Fig. 4 both conveyed that nicotine activation generally excited the 5 HAD associated pathology molecules as demonstrated by the positive z-scores of each intermediary molecule. The connectivity map displayed as Fig. 5 predicts that upon nicotine activation, there will be inhibition of the HAD pathology molecules.

Fig. 5.

Fig. 5

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Connectivity Map of the relationships of the 26 molecules (12 + 14) that are affected by nicotine exposure and influence HAD pathology. These intermediary molecules are interconnected between themselves to simulate reciprocal relationships (upstream and downstream) that molecules within the human body likely experience. This figure indicates that nicotine is possibly neuroprotective through certain pathways, especially if the reciprocal pathways of the 5 HAD associated pathology molecules are inactivated/blocked

Interconnected Connectivity Pathway Analysis of molecules mediating the relationship between Nicotine and HAD

This connectivity map generated in Fig. 5 still does not fully reflect how these pathways would function in the human body. Thus, the next step was to add the 3 molecules that were affected by nicotine but also downstream from the HAD pathology molecules and connect them all to see how reciprocal effects may influence the effects of nicotine exposure on HAD (Hypothesis/Simulation # 3). The intermediary molecules and HAD pathology molecules were connected to each other bidirectionally (Fig. 6). All of the molecules in the network were still ultimately downstream from nicotine as the focus of the study is to examine nicotine’s possible neuroprotective effects in terms of HAD. However, the interconnectedness of the rest of the molecules in the network is present in order simulate how these molecules would likely exist in the human body (free to interact with each other).

Fig. 6.

Fig. 6

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Connectivity Map of the relationships of the 29 intermediary molecules (12 + 14 + 3) that are affected by nicotine exposure and influence the 5 HAD associated pathology molecules (HAD pathology molecules). These intermediary molecules are interconnected with the HAD pathology molecules to best simulate how molecular pathways and systems likely affect each other within the human body since they are not isolated systems. This figure indicates that nicotine is likely neurotoxic through certain pathways that may need to first be inactivated in order to realize nicotine’s neuroprotective effects

Again, the MAP tool was utilized to activate nicotine and predict the effects on the intermediary molecules and ultimately on HAD. The MAP tool is able to visually quantify the predicted effect that nicotine activation would have on these molecules based on the saturation of the coloration: orange represents excitation (upregulation) and blue represents inhibition (downregulation) with the more saturated colors indicating a greater magnitude of change. Based on the connectivity map, when nicotine is activated, all 5 HAD pathology associated molecules are excited (upregulated).

Interconnected Connectivity Pathway Analysis of molecules mediating the relationship between Nicotine and HAD with Inhibition

We postulated that there may be some molecular pathways through which nicotine could act neuroprotectively in terms of HAD. The MAP tool within IPA allows for simultaneous activation and inactivation of molecules within a network. So, the MAP tool was utilized to generate connectivity maps where nicotine was activated but one of the intermediary molecules in the network was inactivated. For example, a connectivity map where nicotine is activated, and TNF is inactivated (Hypothesis/Simulation #4) is displayed below in Fig. 7 with interconnections between molecules to stimulate molecular interactions in the human body. From this connectivity map, it can be seen that when nicotine is activated and TNF is inactivated, the HAD pathology molecules are all inhibited. There are 16 other molecular pathways (17 in total) that when inhibited cause all 5 HAD associated pathology molecules to be inhibited. These molecules are listed in Table 1 with a “b” following their symbol name.

Fig. 7.

Fig. 7

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Connectivity Map of the relationships of the 29 molecules that are affected by nicotine exposure and influence the 5 HAD pathology molecules with TNF inactivated. All molecules are interconnected to best simulate how molecular pathways and systems likely affect each other within the human body since they are not isolated systems. This figure indicates that nicotine is likely neuroprotective when the TNF molecular pathway is inactivated concurrently. For a list of the 17 possible molecular pathways that are predicted to be neuroprotective when inactivated see T the molecules labeled with a (b) in Table 1

Canonical Pathway Analysis of the Interconnected Network of Molecules affected by Nicotine and Influencing HAD Pathology

The 34 molecules in the simulated pathways associated with nicotine and HAD were compared to the 705 well-defined canonical pathways within the QKB with the “Core Analysis, Expression Analysis” tool. The top 10 canonical pathways with the largest overlap p-values [-log(p-value)] are displayed in Fig. 8. The 1st canonical pathway found to have the most significant overlap with the 34 molecules related to the simulated nicotine and HAD pathways was the neuroinflammation signaling pathway with 3.9% overlap and a p – value = 1.21 ∗ 10−14 [−log(p-value) = 13.9]. The other pathway that was of interest was the p38 MAPK signaling canonical pathway. This pathway had an overlap of 5.9% and a p – value = 4.87 ∗ 10−9 [−log(p-value) = 8.31] and is of interest due to p38 MAPK signaling in apoptosis which is related to neuronal cell death and which is a pathology of HAD (Thompson et al. 2001; Mattson et al. 2005).

Fig. 8.

Fig. 8

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The top-ten canonical pathways of 339 canonical pathways with p < 0.05 ranked according to the overlap of the canonical pathway molecules and the 34 molecules associated with Nicotine and HAD

Due to the complexity and widespread functions of the immune system in addition to the commonality of signaling in apoptosis, it is possible that any canonical pathway analysis based on the findings within the QKB would result in overlapping molecules between the neuroinflammation and p38 MAPK signaling pathways. Thus, clear biasing favoring the inclusion of the p38 MAPK and neuroinflammation signaling pathways would weaken the support of the hypothesis that nicotine can act as a neuroprotective agent in terms of HAD by mediating decreases in neuroinflammation and neuronal cell death. To ensure that no biasing was present in the analysis, it was necessary to run a negative control analysis between HAD and something that had no a-priori associations to HAD. An analysis between HAD and fertility was finally run due to there being very limited evidence of a-priori associations between them. The 113 molecules associated with both HAD and fertility revealed the most significant canonical pathway being Glucocorticoid Receptor Signaling with an overlap of 4.45% and an overlap p – value = 2.01 ∗ 10−16. Both the p38 MAPK and neuroinflammation signaling pathways were not among the top 10 identified pathways in this negative control. Additionally, among those top 10 pathways only 3 pathways matched the top 10 identified in the nicotine and HAD analysis. Nevertheless, the result of this negative control increases the confidence that the p38 MAPK and neuroinflammation signaling are specifically related to the relationship between nicotine exposure and HAD, and not due to general and non-specific associations.

Discussion

The predicted pathways and simulations established in IPA indicate that nicotine is likely to be neurotoxic if it is free to target the interconnected molecular network of biological systems likely present in the human body (Fig. 3, 7 and 5) (Manoharan et al. 2016; Gianaros and Wager 2015; Medina-Cleghorn and Nomura 2014; Li and Agarwal 2009; Berger et al. 1998). The “Canonical Pathway Analysis”, our connectivity maps, and literature findings about HAD suggest that neuroinflammation and neuronal cell damage/death are the main pathological causes of HAD and that excitation of molecules involved in this pathway will lead to an exacerbation of HAD pathology. However, while our findings do indicate that nicotine can be neurotoxic on our simulated unidirectional and bidirectional pathways [Fig. 3 and 6], additional simulations provide plausible mechanisms for a neuroprotective effect of nicotine on HAD that can be exploited for treatment. For example, while a simulated bidirectional pathway including all 26 molecules (12 + 14) upstream of the HAD pathology molecules and the 3 downstream molecules predicts increased activation of these HAD-associated molecules (Fig. 6), the removal of the downstream molecules predicts nicotine-induced inhibition of the HAD-associated molecules (Fig. 5). Thus, nicotine may have a neuroprotective effect if administered simultaneously with inactivation of the specific molecular pathways downstream of HAD-associated molecules. Additional simulations also revealed that nicotine administration along with the inactivation of a selective subset of the upstream molecules (17 out of the 26) also results in the inhibition of the HAD pathology molecules (TNF example in Fig. 7; also see molecules denoted with b in Table 1).

Nicotine exposure has been shown to decrease systemic neuroinflammation by modulating dendritic cells (DCs) (Prochaska 2010). Nicotine exposure causes these cells to endocytose lower amounts of viral antigens upon infection by binding to the antigen receptors. Therefore, these DCs produce decreased amounts of pro-inflammatory cytokines such as IL-1B and TNF in response to infection (Prochaska 2010; Tao et al. 2019). This leads to a decrease in systemic inflammation and thus a decrease in the worsening of HAD. Additionally, exposure to nicotine decreases extracellular calcium ion concentration which prevents calcium-mediated cell damage from occurring (Kalra et al. 2004). The cholinergic system also plays an important role in the bi-directional communication between the peripheral immune system and the brain (Alkondon 1997). Through its afferent arm, the vagus nerve signals several brain nuclei after sensing activated immune cells in the periphery; the brain responds by suppressing peripheral proinflammatory cytokine release via its efferent arm by releasing Ach to activate α7nAChRs on immune cells such as macrophages, monocytes, dendritic cells, and T cells. The anti-inflammatory response is downstream of α7nAChRs activation, involving JAK2-STAT3 and P13K/Akt metabotropic signaling pathways (Pavlov and Tracey 2012; Piovesana et al. 2021). In the CNS astrocytes and microglia also express Ach receptors and the ChAT enzyme responsible for the synthesis of Ach, suggesting that glia cells regulate inflammatory responses by autocrine signaling (Han et al. 2017).

Furthermore, through α7nAChR-mediated pathways, nicotine is able to protect against HAD (Giunta et al. 2004; Dong et al. 2020). These studies demonstrated that nicotine inhibits the activations of MAPKs which reduces accumulation of Aβ in the cortex and hippocampus of in vitro models which leads to decreased neuronal cell damage/death. This can be seen within Fig. 7 from our study where nicotine activation with TNF inactivation causes molecules involved in both apoptosis signaling and neuroinflammation to be downregulated. In turn, this induces inhibition of the HAD pathology molecules which are normally upregulated in association with HAD (Huang et al. 2011; Scheu et al. 2017; Makinson et al. 2008). This downregulation (represented by the dark blue coloring) of these 5 molecules are interpreted as a prevention of the worsening of HAD based on the literature findings associated with these molecules within the QKB. Therefore, demonstrating that nicotine can act neuroprotectively by preventing damage/death to neuronal cells which is an associated pathology for the cause of HAD.

When the molecules associated with neuronal cell death (”Cell death of neuroglia” function from IPA) were added to the network shown in Fig. 9A and connected to the 29 intermediary molecules within the network, inhibition of MAPK1, MYC, HIF1A and ERK1/2 was predicted indicating nicotine’s neuroprotectivity since these molecules are involved in the p38 MAPK signaling pathway that functions in the apoptosis of neuronal cells (Fig. 9A) (Saha et al. 2020; Lu et al. 2019). The p38 MAPK signaling pathway has been shown to be involved in neuronal cell death in response to Aβ levels, which is a pathology of HAD (Cunningham et al. 2015; Thompson et al. 2001; Saha et al. 2020). Therefore, nicotine’s inhibitory effects on this apoptosis signaling cascade demonstrate nicotine’s possible neuroprotection in terms of HAD pathology.

Fig. 9.

Fig. 9

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Connectivity Map of the exclusively downstream relationships of the 14 molecules that are affected by nicotine exposure and influence HAD pathology with their connection to Cell Death of Neuroglia (pathology of HAD). These intermediary molecules are interconnected between themselves to simulate reciprocal relationships (upstream and downstream) that molecules within the human body likely experience. (a) This figure indicates that nicotine can be neuroprotective and prevent the worsening of HAD through the neural cell damage/death pathology pathway if the reciprocal pathways of the 5 HAD associated pathology molecules to the intermediary molecules are inhibited. (b) This figure indicates that nicotine can be neuroprotective and prevent the worsening of HAD through the neuroinflammation pathway if one of the 17 specific pathways are inactivated. For a list of the 17 possible molecular pathways to inactivate, see Table 1 where the molecules are labeled with a (b)

To further visualize how nicotine may affect neuroinflammation, the dominant pathology of HAD, the molecules associated with the inflammatory response (”Inflammatory response” function from IPA) was added to the network shown in Fig. 7 and connected to all 29 intermediary molecules associated with nicotine and the 5 HAD pathology molecules (Fig. 9B). When nicotine is activated and one of the 17 molecules of focus (denoted by a “b” in Table 1) are inactivated, the 5 molecules associated with HAD pathology are downregulated which indicates nicotine’s neuroprotective effects, since these 5 molecules are usually upregulated in their association to HAD (Huang et al. 2011; Scheu et al. 2017; Makinson et al. 2008). For example, when nicotine is activated and Tumor Necrosis Factor (TNF) is inactivated in the pathway, IL1 and IL-1B are downregulated. IL1 and IL-1B are important cytokines in the Interleukin-1 family and are associated with neuroinflammation (Shaftel et al. 2008). According to the connectivity map in Fig. 9B, this inhibition of the molecules involved in the neuroinflammation signaling pathway causes inhibition of the inflammatory response which leads to a downregulation of the HAD pathology molecules, which is interpreted as an inhibition of HAD pathology. This suggests that nicotine exposure with simultaneous inhibition of TNF (or one of the other 17 molecules to inactivate) will lead to a decrease in neuroinflammation and, thus an inhibition of HAD pathology.

As seen in Fig. 6, nicotine exposure without simultaneous inactivation of some intermediary molecule in this interconnected network increases both the molecular pathways of neuroinflammation and damage/death of neuronal cells. This would lead to an exacerbation of HAD pathology as seen by the upregulation of the HAD pathology molecules. Therefore, simultaneous inhibition of any of the 17 molecules listed in Table 1 is predicted to be necessary for nicotine’s neuroprotection effects against HAD. Two molecules of special interest to target concurrent with nicotine treatment are tumor necrosis factor (TNF) and interleukin 1 beta (IL-1B). Increased levels of TNF have been associated with increasing the permeability of the BBB and increasing the expression of adhesion molecules, such as intercellular adhesion molecule-1 (ICAM-1) and E-selectin, on astrocytes and endothelial cells which allow HIV-1 infected macrophages to pass through the BBB as explained by the “Trojan Horse” hypothesis (Gooneratne et al. 2015; Dobbie et al. 1999). Furthermore, TNF and IL-1B are associated with increasing the expression of monocyte chemoattractant protein-1 (MCP-1) which plays a role in the recruitment of monocytes and dendritic cells to areas of inflammation thereby increasing it (Gschwandtner et al. 2019; Perrin et al. 2005). Thus, these molecules significantly contribute to the neuroinflammation pathology of HAD. Additionally, TNF and IL-1B are associated with neuronal cell death because increased levels of TNF and IL-1B can lead to an excessive influx of Ca2 + into a neuron which causes the formation of nitric oxide, causing the neural cell to die by apoptosis/necrosis (Dobbie et al. 1999; Clarkson et al. 2017).

The ubiquity and diversity of AChRs, and the complex modulatory role of Ach in neuronal and immune regulation, has generated strong interest in the development of selective agonists of the cholinergic system as a promising target for the treatment of inflammation-associated neuro-psychiatric disorders (Piovesana et al. 2021; Alkondon et al. 1997). The potential therapeutic effects of selective agonists for nAChRs go beyond the traditional understanding of transient ion channel activation and will depend on a better understanding of the non-conducting (metabotopic) states of activated nAChRs, making this approach challenging (Papke and Lindstrom 2020). Although nicotine acts as a non-selective agonist of homo- and hetero- pentamer AChRs, our results suggest that another strategy to induce the neuroprotective properties of cholinergic system activation is to administer nicotine as an adjuvant to treatment of other pathways as revealed by our analysis.

There are certain limitations to this network meta-analysis that should be considered and could potentially affect the interpretation of the results of the study. The QKB is unable to make predictions in terms of activity about how nicotine activation would affect HAD directly (this is the reason that the ‘AIDS dementia complex’ node in the connectivity maps are uncolored). Therefore, the literature findings within the QKB associated with the 5 molecules directly associated with HAD (CCL2, GLS, L-glutamic acid, POLG, POLB) was identified as the main phenotype of interest (HAD-associated pathology molecules) to establish relevant causal networks as hypotheses to explain patterns relevant to HAD pathology. Additionally, due to the complexity and the connections that likely exist between these molecules and others within the human body, these analyses are still representative of isolated molecular pathways/networks despite the best efforts to simulate how these molecules would exist and affect each other within the human body. Therefore, in vivo and in vitro studies are needed in order to corroborate the prediction that nicotine can act as a neuroprotective agent in terms of HAD pathology when it is administered simultaneously with a substance that inhibits the neuroinflammation and/or p38 MAPK (apoptosis) signaling pathway.

The findings from the analyses in our in-silica study provide important information about developing nicotine as a possible therapeutic agent against HAD. The results from our study demonstrate how complicated and interconnected the pathology for HAD is within the human body. The study focused on the relationship that nicotine exposure had on HAD pathology. It was found that nicotine was seemingly neurotoxic in terms of HAD when it was exposed to an interconnected network of associated molecules. However, it was elucidated that nicotine could be made neuroprotective with simultaneous inhibition of specific pathways. This study found that nicotine is neuroprotective in terms of HAD pathology when it is activated along with molecular inactivation of molecules involved in either the neuroinflammation signaling pathway or p38 MAPK signaling pathway (apoptosis). Our study suggests that nicotine is a potential neuroprotective therapeutic agent for HAD pathology when it is administered along with inhibitory substances for neuroinflammation and/or neuronal cell apoptosis (cytokine or MAPK inhibitors) due to the complex nature of HAD pathology and the interconnected nature of biological pathways within the human body.

Acknowledgements

The authors thank Dr. Eric Seiser for initial use of QIAGEN Knowledge Base and QIAGEN Ingenuity Pathway Analysis tools.

Funding

This study was partially supported by National Institute of Health grants DA43448 and DA046258 to SLC.

Abbreviations

AIDS

Acquired Immunodeficiency Syndrome

ADC

AIDS-dementia complex

BBB

Blood brain barrier

CCL2

Chemokine (C-C motif) ligand 2

DCs

Dendritic cells

GLS

Glutaminase

HAART

Highly active antiretroviral therapy

HAD

HIV-Associated Dementia

HANDs

HIV-Associated neurological disorders

HIV

Human Immunodeficiency Virus

IPA

Ingenuity Pathway Analysis

IL-1β

Interleukin-1β

ICAM-1

Intercellular adhesion molecule-1

MAP

Molecule-Activity-Predictor

MCP-1

Monocyte chemoattractant protein-1

POLG

DNA polymerase subunit gamma

POLB

DNA polymerase subunit beta

QKB

Qiagen Knowledge Base

Tat

Transcriptional transactivator

TNF

Tumor Necrosis Factor

Footnotes

Conflicts of Interest/Competing Interests The authors declare that they have no conflict of interest/competing interests.

Availability of Data and Material

IPA Analysis Match CL license was purchased from QIAGEN LLC.

References

  1. Al-Omoush TK, Alzoubi KH, Khabour OF, Alsheyab FM, Abu-Siniyeh A, Al-Sawalha NA, Mayyas FA, Cobb CO, Eissenberg T (2020) The CHRNA5 Polymorphism (rs16969968) and its Association with Waterpipe Smoking Addiction among Jordanians. Arab J Basic Appl Sci 27(1):450–455. 10.1080/25765299.2020.1849491 [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Alkondon MPE, Cortes WS, Maelicke A, Albuquerque (1997. ) Choline is a selective agonist of alpha7 nicotinic acetylcholine receptors in the rat brain neurons. EX Eur J Neuroscience 9(12):2734–2742. 10.1111/j.1460-9568.1997.tb01702 [DOI] [PubMed] [Google Scholar]
  3. Anthony I, Ramage S, Carnie F, Simmonds P, Bell J (2005) Influence of HAART on HIV-related CNS disease and neuroinflammation. J Neuropathol Exp Neurol 64(6):529–536 [DOI] [PubMed] [Google Scholar]
  4. Bajrektarevic D, Corsini S, Nistri A, Tortora M (2019) Nicotine neuroprotection of brain neurons: the other side of nicotine addiction. In Neuroscience of Nicotine (pp 79–86). Elsevier [Google Scholar]
  5. Banerjee S, Walseth TF, Borgmann K, Wu L, Bidasee KR, Kannan MS, Ghorpade A (2008) CD38/cyclic ADP-ribose regulates astrocyte calcium signaling: implications for neuroinflammation and HIV-1-associated dementia. J Neuroimmune Pharmacol 3(3):154–164 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Bartz SR, Emerman M (1999) Human Immunodeficiency Virus Type 1 Tat Induces Apoptosis and Increases Sensitivity to Apoptotic Signals by Up-Regulating FLICE/Caspase-8. J Virol 73(3):1956. 10.1128/JVI.73.3.1956-1963.1999 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Berger F, Gage FH, Vijayaraghavan S (1998) Nicotinic receptor-induced apoptotic cell death of hippocampal progenitor cells. J Neurosci 18(17):6871–6881 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Bethel-Brown C, Yao H, Hu G, Buch S (2012) Platelet-derived growth factor (PDGF)-BB-mediated induction of monocyte chemoattractant protein 1 in human astrocytes: implications for HIV-associated neuroinflammation. J Neuroinflammation 9(1):1–14 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Bouwman F, Skolasky R, Hes D, Selnes O, Glass J, Nance-Sproson T, Royal W, Dal Pan G, McArthur JC (1998) Variable progression of HIV-associated dementia. Neurology 50(6):1814–1820 [DOI] [PubMed] [Google Scholar]
  10. Castellano P, Prevedel L, Valdebenito S, Eugenin EA (2019) HIV infection and latency induce a unique metabolic signature in human macrophages. Sci Rep 9(1):1–14 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Cataldo JK, Prochaska JJ, Glantz SA (2010) Cigarette smoking is a risk factor for Alzheimer’s Disease: an analysis controlling for tobacco industry affiliation. J Alzheimers Dis 19(2):465–480. 10.3233/JAD-2010-1240 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Chernyavsky AI, Shchepotin IB, Galitovkiy V, Grando SA (2015) “Mechanisms of tumor-promoting activities of nicotine in lung cancer: synergistic effects of cell membrane and mitochondrial nicotinic acetylcholine receptors”. BMC Cancer 15:152. 10.1186/s12885-015-1158-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Clarkson BD, Kahoud RJ, McCarthy CB, Howe CL (2017) Inflammatory cytokine-induced changes in neural network activity measured by waveform analysis of high-content calcium imaging in murine cortical neurons. Sci Rep 7(1):1–13 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Clifford DB (2000) Human immunodeficiency virus–associated dementia. Arch Neurol 57(3):321–324 [DOI] [PubMed] [Google Scholar]
  15. Crowe SM, Westhorpe CL, Mukhamedova N, Jaworowski A, Sviridov D, Bukrinsky M (2010) The macrophage: the intersection between HIV infection and atherosclerosis. J Leukoc Biol 87(4):589–598 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Cunningham EL, McGuinness B, Herron B, Passmore AP (2015) Dementia. Ulster Med J 84(2):79–87. https://www.ncbi.nlm.nih.gov/pubmed/26170481 [PMC free article] [PubMed] [Google Scholar]
  17. Davis HF, Skolasky RL Jr, Selnes OA, Burgess DM, McArthur JC (2002) Assessing HIV-associated dementia: modified HIV dementia scale versus the Grooved Pegboard. AIDS Read 12(1):29–31, 38 [PubMed] [Google Scholar]
  18. Dineley KT, Westerman M, Bui D, Bell K, Ashe KH, Sweatt JD (2001) β-Amyloid activates the mitogen-activated protein kinase cascade via hippocampal α7 nicotinic acetylcholine receptors: in vitro and in vivo mechanisms related to Alzheimer’s disease. J Neurosci 21(12):4125–4133 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Dobbie MS, Hurst RD, Klein NJ, Surtees RA (1999) Upregulation of intercellular adhesion molecule-1 expression on human endothelial cells by tumour necrosis factor-α in an in vitro model of the blood–brain barrier. Brain Res 830(2):330–336 [DOI] [PubMed] [Google Scholar]
  20. Dong Y, Bi W, Zheng K, Zhu E, Wang S, Xiong Y, Chang J, Jiang J, Liu B, Lu Z (2020) Nicotine prevents oxidative stress-induced hippocampal neuronal injury through α7-nAChR/Erk1/2 signaling pathway. Front Mol Neurosci 13:204. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Fajardo-Ortiz D, Lopez-Cervantes M, Duran L, Dumontier M, Lara M, Ochoa H, Castano VM (2017) The emergence and evolution of the research fronts in HIV/AIDS research. PLoS One 12(5):e0178293. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Ghafouri M, Amini S, Khalili K, Sawaya BE (2006) HIV-1 associated dementia: symptoms and causes. Retrovirology 3(1):1–11 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Gianaros PJ, Wager TD (2015) Brain-Body Pathways Linking Psychological Stress and Physical Health. Curr Dir Psychol Sci 24(4):313–321 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Giunta B, Ehrhart J, Townsend K, Sun N, Vendrame M, Shytle D, Tan J, Fernandez F (2004) Galantamine and nicotine have a synergistic effect on inhibition of microglial activation induced by HIV-1 gp120. Brain Res Bull 64(2):165–170 [DOI] [PubMed] [Google Scholar]
  25. Gooneratne SL, Richard J, Lee WS, Finzi A, Kent SJ, Parsons MS (2015) Slaying the Trojan Horse: Natural Killer Cells Exhibit Robust Anti-HIV-1 Antibody-Dependent Activation and Cytolysis against Allogeneic T Cells. J Virol 89(1):97. 10.1128/JVI.02461-14 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Gschwandtner M, Derler R, Midwood KS (2019) More than just attractive: how CCL2 influences myeloid cell behavior beyond chemotaxis. Front Immunol 10:2759. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Han B, Li X, Hao J (2017) The cholinergic anti-inflammatory pathway: An innovative treatment strategy for neurological diseases. Neurosci Biobehav Rev 77:358–368. 10.1016/j.neubiorev.2017.04.002 [DOI] [PubMed] [Google Scholar]
  28. Hong S, Banks WA (2015) Role of the immune system in HIV-associated neuroinflammation and neurocognitive implications. Brain Behav Immun 45:1–12 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Huang Y, Zhao L, Jia B, Wu L, Li Y, Curthoys N, Zheng JC (2011) Glutaminase dysregulation in HIV-1-infected human microglia mediates neurotoxicity: relevant to HIV-1-associated neurocognitive disorders. J Neurosci 31(42):15195–15204 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Izquierdo-Useros N, Naranjo-Gómez M, Erkizia I, Puertas MC, Borràs FE, Blanco J, Martinez-Picado J (2010) HIV and mature dendritic cells: Trojan exosomes riding the Trojan horse?. PLoS Pathog 6(3):e1000740. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Kalliolias GD, Ivashkiv LB (2016) TNF biology, pathogenic mechanisms and emerging therapeutic strategies. Nat Rev Rheumatol 12(1):49–62 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Kalra R, Singh SP, Pena-Philippides JC, Langley RJ, Razani-Boroujerdi S, Sopori ML (2004) Immunosuppressive and anti-inflammatory effects of nicotine administered by patch in an animal model. Clin Vaccine Immunol 11(3):563–568 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Katuri A, Bryant J, Heredia A, Makar TK (2019) Role of the inflammasomes in HIV-associated neuroinflammation and neurocognitive disorders. Exp Mol Pathol 108:64–72 [DOI] [PubMed] [Google Scholar]
  34. Kovalevich J, Langford D (2012) Neuronal toxicity in HIV CNS disease. Futur Virol 7(7):687–698 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Kramer A, Green J, Pollard J Jr, Tugendreich S (2014) Causal analysis approaches in Ingenuity Pathway Analysis. Bioinformatics 30(4):523–530. 10.1093/bioinformatics/btt703 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Kramer-Hammerle S, Rothenaigner I, Wolff H, Bell JE, Brack-Werner R (2005) Cells of the central nervous system as targets and reservoirs of the human immunodeficiency virus. Virus Res 111(2):194–213. 10.1016/j.virusres.2005.04.009 [DOI] [PubMed] [Google Scholar]
  37. Li Y, Agarwal P (2009) A pathway-based view of human diseases and disease relationships. PLoS One 4.2. https://dokumen.pub/in-silico-drug-design-repurposing-techniques-and-methodologies-1nbsped-0128161256-9780128161258.html [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Lu M, Wang Y, Zhan X (2019) The MAPK pathway-based drug therapeutic targets in pituitary adenomas. Front Endocrinol 10:330. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Makinson A, Moing VL, Kouanfack C, Laurent C, Delaporte E (2008) Safety of stavudine in the treatment of HIV infection with a special focus on resource-limited settings. Expert Opin Drug Saf 7(3):283–293 [DOI] [PubMed] [Google Scholar]
  40. Manoharan S, Guillemin GJ, Abiramasundari RS, Essa MM, Akbar M, Akbar MD (2016) The role of reactive oxygen species in the pathogenesis of Alzheimer’s disease, Parkinson’s disease, and Huntington’s disease: a mini review. Oxid Med Cell Longev 2016 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Mattson M, Haughey N, Nath A (2005) Cell death in HIV dementia. Cell Death Differ 12(1):893–904 [DOI] [PubMed] [Google Scholar]
  42. Medina-Cleghorn D, Nomura DK (2014) Exploring metabolic pathways and regulation through functional chemoproteomic and metabolomic platforms. Chem Biol 21(9):1171–1184 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Meucci O, Fatatis A, Simen AA, Bushell TJ, Gray PW, Miller RJ (1998) Chemokines regulate hippocampal neuronal signaling and gp120 neurotoxicity. Proc Natl Acad Sci 95(24):14500–14505. 10.1073/pnas.95.24.14500 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Nicholatos JW, Francisco AB, Bender CA, Yeh T, Lugay FJ, Salazar JE, Glorioso C, Libert S (2018) Nicotine promotes neuron survival and partially protects from Parkinson’s disease by suppressing SIRT6. Acta Neuropathol Commun 6(1):120. 10.1186/s40478-018-0625-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Papke RL, Lindstrom JM (2020) Nicotinic acetylcholine receptors: Conventional and unconventional ligands and signaling. Neuropharmacology, 168. 10.1016/j.neuropharm.2020.108021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Pavlov VA, Tracey KJ (2012) The vagus nerve and the inflammatory reflex–linking immunity and metabolism. Nat Rev Endocrinol 8(12):743–754. 10.1038/nrendo.2012.189 [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Perrin FE, Lacroix S, Avilés-Trigueros M, David S (2005) Involvement of monocyte chemoattractant protein-1, macrophage inflammatory protein-1α and interleukin-1β in Wallerian degeneration. Brain 128(4):854–866. 10.1093/brain/awh407 [DOI] [PubMed] [Google Scholar]
  48. Piovesana R, Salazar Intriago MS, Dini L, Tata AM (2021) Cholinergic Modulation of Neuroinflammation: Focus on α7 Nicotinic Receptor. Int J Mol Sci 22(9). 10.3390/ijms22094912 [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Pollicita MA, Svicher S, Ronga V, Perno L, Carlo F (2008) HIV-1-Associated Dementia During HAART Therapy. Recent Pat on CNS Drug Discov 3(23). 10.2174/157488908783421438 [DOI] [PubMed] [Google Scholar]
  50. Prochaska J (2010) Cigarette smoking is a risk factor for Alzheimer’s Disease: an analysis controlling for tobacco industry affilia. J Alzheimers Dis 19(2):465–480 [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Redza-Dutordoir M, Averill-Bates DA (2016) Activation of apoptosis signalling pathways by reactive oxygen species. Biochim Biophys Acta 1863(12):2977–2992. 10.1016/j.bbamcr.2016.09.012 [DOI] [PubMed] [Google Scholar]
  52. Rocha AJ, Nunes RH, Maia AC Jr (2015) Dementia in motor neuron disease: reviewing the role of MRI in diagnosis. Dementia & Neuropsychologia 9(4):369–379 [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Saha P, Guha S, Biswas SC (2020) P38K and JNK pathways are induced by amyloid-beta in astrocyte: Implication of MAPK pathways in astrogliosis in Alzheimer’s disease. Mol Cell Neurosci 108:103551. 10.1016/j.mcn.2020.103551 [DOI] [PubMed] [Google Scholar]
  54. Saha RN, Pahan K (2003) Tumor necrosis factor-α at the crossroads of neuronal life and death during HIV-associated dementia. J Neurochem 86(5):1057–1071. 10.1046/j.1471-4159.2003.01942.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Scheu S, Ali S, Ruland C, Arolt V, Alferink J (2017) The C-C Chemokines CCL17 and CCL22 and Their Receptor CCR4 in CNS Autoimmunity. Int J Mol Sci 18(11). 10.3390/ijms18112306 [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Shaftel SS, Griffin WST, O’Banion MK (2008) The role of interleukin-1 in neuroinflammation and Alzheimer disease: an evolving perspective. J Neuroinflammation 5(1):7. 10.1186/1742-2094-5-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Speechly CM, Bridges-Webb C, Passmore E (2008) The pathway to dementia diagnosis. Med J Aust 189(9):487–489. 10.5694/j.1326-5377.2008.tb02140.x [DOI] [PubMed] [Google Scholar]
  58. Tao X et al. (2019) Nicotine Protects Dendritic Cells from Apoptosis and Support DCs-dependent CD4+ T-cell Priming in vitro. Indian J Pharm Sci 81(6):1000–1010 [Google Scholar]
  59. Thompson KA, McArthur JC, Wesselingh SL (2001) Correlation between neurological progression and astrocyte apoptosis in HIV-associated dementia. Ann Neurol 49(6):745–752. 10.1002/ana.1011 [DOI] [PubMed] [Google Scholar]
  60. Tian J, Shi J, Li T, Li L, Wang Z, Li X, Lv Z, Zheng Q, Wei M, Wang Y (2017) Efficacy and Safety of an Herbal Therapy in Patients with Amnestic Mild Cognitive Impairment: A 24-Week Randomized Phase III Trial. Evid Based Complement Alternat Med 2017:4251747. 10.1155/2017/4251747 [DOI] [PMC free article] [PubMed] [Google Scholar]

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