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. Author manuscript; available in PMC: 2019 Jul 1.
Published in final edited form as: Brain Behav Immun. 2018 Apr 18;71:108–115. doi: 10.1016/j.bbi.2018.04.004

Recent Stimulant Use and Leukocyte Gene Expression In Methamphetamine Users with Treated HIV Infection

Adam W Carrico 1, Annesa Flentje 2, Kord Kober 2, Sulggi Lee 3, Peter Hunt 3, Elise D Riley 3, Steven Shoptaw 4, Elena Flowers 2,5, Samantha E Dilworth 3, Savita Pahwa 1, Bradley E Aouizerat 4
PMCID: PMC6003871  NIHMSID: NIHMS962687  PMID: 29679637

Abstract

Stimulant use may accelerate HIV disease progression through biological and behavioral pathways. However, scant research with treated HIV-positive persons has examined stimulant-associated alterations in pathophysiologic processes relevant to HIV pathogenesis. In a sample of 55 HIV-positive, methamphetamine-using sexual minority men with a viral load less than 200 copies/mL, we conducted RNA sequencing to examine patterns of leukocyte gene expression in participants who had a urine sample that was reactive for stimulants (n = 27) as compared to those who tested non-reactive (n = 28). Results indicated differential expression of 32 genes and perturbation of 168 pathways in recent stimulant users. We observed statistically significant differential expression of single genes previously associated with HIV latency, cell cycle regulation, and immune activation in recent stimulant users (false discovery rate p < 0.10). Pathway analyses indicated enrichment for genes associated with inflammation, innate immune activation, neuroendocrine hormone regulation, and neurotransmitter synthesis. Recent stimulant users displayed concurrent elevations in plasma levels of tumor necrosis factor – alpha (TNF-α) but not interleukin 6 (IL-6). Further research is needed to examine the bio-behavioral mechanisms whereby stimulant use may contribute to HIV persistence and disease progression.

Keywords: Cocaine, Gene Expression, HIV, Immune Activation, Inflammation, Methamphetamine, Reservoir

1. Introduction

Stimulant use may accelerate HIV disease progression via behavioral and biological mechanisms (Carrico, 2011; Salamanca et al., 2014). HIV-positive stimulant users are more likely to have difficulties with HIV disease management that contribute to elevated HIV viral load and hastened disease progression (Carrico, 2011; Carrico et al., 2011; Ellis et al., 2003). At the same time, recent findings from a cohort of HIV-positive sexual minority men receiving antiretroviral therapy (ART) indicate that those who engaged in more frequent stimulant use had 50% greater odds of clinical progression even after accounting for adherence and viral load (Carrico et al., 2014). Because most studies examining stimulant-associated immune dysregulation have relied on in vitro and animal models (Cabral, 2006), research with treated HIV-positive persons is needed to elucidate the biological pathways whereby stimulants may promote disease progression.

Sympathetic nervous system activation could partially mediate the immunomodulatory effects of stimulants in HIV, but findings from preclinical and human studies are mixed. Methamphetamine activates the sympathetic nervous system (Irwin et al., 2007; Makisumi et al., 1998) leading to elevated norepinephrine, which has been linked to greater HIV replication (Cole et al., 1998; Ironson et al., 2015). This is further supported by findings that sympathetic nervous activation and methamphetamine use may contribute to HIV persistence. For example, one prior study observed 3.9-fold greater rates of SIV replication at sites where lymph nodes juncture with catecholaminergic varicosities (Sloan et al., 2006). Among HIV-positive persons who were virally suppressed, greater sympathetic nervous system activation (measured by a shorter cardiac pre-ejection period) has also been associated with greater intracellular HIV RNA but not higher proviral HIV DNA (Hecht et al., 2017). Finally, recent findings with virally suppressed HIV-positive persons indicated that methamphetamine users had higher levels of proviral HIV DNA, but these results were not significant after adjusting for ART regimen (Massanella et al., 2015). Further research is needed to examine stimulant-associated alterations in biological processes relevant to HIV persistence.

Immune activation and inflammation have emerged as plausible, downstream mechanisms for the in vivo immunomodulatory effects of stimulants. In one laboratory-based study with HIV-negative men, cocaine infusion (versus placebo) was associated with decrements in monocyte expression of pro-inflammatory cytokine markers and decreased responsiveness of monocytes to lipopolysaccharide (Irwin et al., 2007). Cocaine-related reductions in monocyte expression of tumor necrosis factor - alpha (TNF-α) were partially attributable to greater increases in autonomic nervous system activation. Stimulant use could also contribute to immune dysregulation by upregulating indoleamine 2,3-dioxygenase (IDO). This is supported by a prior investigation that observed stimulant-associated elevations in neopterin and depleted tryptophan in HIV-positive persons after adjusting for ART adherence (Carrico et al., 2008). Studies with virally suppressed, HIV-positive methamphetamine users also indicate that recent stimulant use is associated greater monocyte activation (Carrico et al., In Press) as well as CD4+ and CD8+ T-cell proliferation, activation, and exhaustion at rest compared to those who did not use methamphetamine (Massanella et al., 2015). More research is clearly needed to elucidate the mechanisms of stimulant-associated immune dysregulation in treated HIV.

One important consideration is that stimulant use may influence multiple biological pathways and trigger opposing biological processes initiated to maintain homeostasis, obscuring the association of stimulant use with plasma immune markers. Gene expression is a transcriptomic approach that allows for exploration of multiple stimulant-associated alterations in leukocyte signaling, potentially providing greater clarity regarding the mechanisms of stimulant-associated biological alterations in treated HIV. For example, one in vitro study observed treatment of dendritic cells with methamphetamine led to altered gene expression for pathways governing chemokine regulation, cytokine production, signal transduction mechanisms, apoptosis, and cell cycle regulation (Mahajan et al., 2006).

The present study examined the association of recent stimulant use with leukocyte gene expression in a sample of HIV-positive, methamphetamine-using sexual minority men who were receiving effective ART. We hypothesized that participants who provided a urine sample that was reactive for stimulants (i.e., Stimulant Tox+) would display differential gene expression across multiple biological pathways relevant to HIV pathogenesis as compared to those who tested non-reactive for recent stimulant use (i.e., Stimulant Tox−). We also hypothesized that recent stimulant users would display elevated plasma levels of TNF-α and interleukin 6 (IL-6).

2. Methods

HIV-positive, methamphetamine-using sexual minority men were recruited for a randomized controlled trial from substance abuse treatment programs, HIV medical clinics, AIDS service organizations, the community, and referrals from active participants (Carrico et al., 2016). At an in-person screening visit, participants completed signed informed consent that included consent for specimen banking. All enrolled participants met the following inclusion criteria: 1) 18 years of age or older; 2) a sexual minority man; 3) documentation of HIV-positive serostatus (i.e., letter of diagnosis or ART medications other than Truvada matched to photo identification); and 4) provide a urine or hair sample at the screening visit that was reactive for methamphetamine. For the purposes of this study, participants were restricted to individuals who had an HIV viral load < 200 copies/mL.

Enrolled participants completed a separate baseline assessment approximately one week later that included a detailed battery of psychosocial measures, a urine sample for on-site toxicology testing, and peripheral venous blood sample to measure HIV disease markers. In addition, participants also provided two × 10 mL EDTA and two × 2.5 mL PAXgene® Blood RNA tubes (Qiagen, Inc.) for specimen banking. This study was approved by the Institutional Review Boards for the University of California, San Francisco as well as the University of Miami and Northwestern University. A certificate of confidentiality was obtained from the National Institute on Drug Abuse.

2.1. Measures

2.1.1. Demographics and health status

Participants completed a demographic questionnaire assessing age, race, ethnicity, and health-related factors such as current ART regimen.

2.1.2. ART adherence and persistence

Participants rated their adherence to each ART medication during the past 30 days using the visual analogue scale (Walsh et al., 2002), which was averaged to calculate the percentage of medications taken. Participants also indicated if they have experienced a treatment interruption, a period of two days or more in the past six months where all HIV medications were stopped without guidance from an HIV primary care provider. Participants without a treatment interruption (1) were compared to those with a treatment interruption (0) as a measure of ART persistence.

2.1.3. Psychiatric comorbidities

The Addiction Severity Index (ASI) was administered to assess the severity of alcohol and other substance use (McLellan et al., 1992). Depressive symptom severity was assessed using the 20-item Centers for the Epidemiologic Study - Depression (CES-D) scale (Radloff, 1977). Post-Traumatic Stress Disorder (PTSD) symptoms were measured with the PTSD Checklist – Civilian (PCL-C) version (Wilkins et al., 2011). Because depression and PTSD may have immunomodulatory effects, we examined whether they were potential confounders by comparing the Stimulant Tox+ and Stimulant Tox− groups.

2.1.4. HIV disease markers

HIV viral load testing was performed to detect plasma HIV RNA using the Abbott Real Time HIV-1 assay (Abbott Molecular, Inc.; Des Plaines, IL). This assay has a lower limit of detection of 40 copies/mL. CD4+ T-cell count was measured with whole blood using flow cytometry, and assays were performed by Quest Diagnostics.

2.1.5. On-site urine screening

Urine samples were collected for on-site toxicology screening using the iCup (Redwood Biotech, Inc.; Santa Rosa, CA), which is capable of detecting stimulant use within the past 72 hours. Results were used to identify participants who tested reactive for recent methamphetamine or cocaine use (i.e., Stimulant Tox+) versus those who were non-reactive for both (i.e., Stimulant Tox−). Among those who were Stimulant Tox+, all but one were reactive for methamphetamine. Given the relatively rapid temporal dynamics of leukocyte gene expression, we examined recent stimulant use as the primary predictor.

2.1.6. Plasma pro-inflammatory cytokines

Plasma levels of TNF-α and IL-6 were determined by the use of Human Quantikine Immunoassay using undiluted samples (R&D Systems, Minneapolis, MN) following the manufacturer’s instructions. Results were log10 transformed.

2.1.7. RNA sample preparation, sequencing, and data management

Total RNA was isolated from the PAXgene® tubes using the PAXgene® Blood miRNA Kit (Qiagen, Inc.). Total RNA for 62 unique samples was sent for library preparation and sequencing at the Vincent J. Coates Genomics Sequencing Laboratory at the University of California, Berkeley. Prior to library preparation, total RNA was treated with Ribo-Zero rRNA Removal Kit (Human, Mouse, Rat; Illumina Inc., San Diego, CA) to deplete cytoplasmic ribosomal RNA (O’Neil et al., 2013). RNA was then prepared for sequencing on an Apollo 324™ with PrepX™ RNAseq Library Prep reagents (WaferGen Biosystems, Fremont, CA) following the manufacturers protocol. Thirteen cycles of PCR amplification were used for single 6 base pair index addition and library fragment enrichment. Prepared libraries were then quantified on a Roche Light Cycler 480II (Roche Diagnostics Corp., Indianapolis, IN) using KAPA Illumina library quantitative PCR reagents (Roche Diagnostics Corp., Indianapolis, IN).

Sequencing was done on an Illumina HiSeq 4000 apparatus (Illumina Inc., San Diego, CA). Sixty-two samples were sequenced, with second samples from 10 library preparations included as technical replicates, for a total of 72 sequenced samples. Eight lanes were sequenced with 9 samples multiplexed per lane for 100 cycles of paired-end reads with a 1% PhiX v3 control library spike (Illumina Inc., San Diego, CA). Post-sequencing basecall files (bclfiles) were demultiplexed and converted into FASTQ file format using the bcl2fastq v2.17 software (Illumina Inc., San Diego, CA). Data was then posted and retrieved from a secured FTP site hosted by the core facility.

RNA sequencing (RNAseq) data processing was performed based on recently delineated best practices (Conesa et al., 2016; Kukurba and Montgomery, 2015). The fastq_illumina_filter (http://cancan.cshl.edu/labmembers/gordon/fastq_illumina_filter/) was used to retain only high quality reads. Illuimina adapters and leading or trailing low quality bases were removed and reads with an the average quality per base below 15 in a 4-base sliding window or below a minimum length of 36 bases were removed using Trimmomatic (Bolger et al., 2014). Reads were inspected with FASTQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). An unequal frequency of alleles was observed at the beginning of the reads across the majority of the libraries, so 12 bases were trimmed from all reads. Reads were again inspected with FASTQC with improved allele frequencies._The reference genome was prepared using the GRCh38 assembly (gencode.v24.GRCh38.p5.fa). Transcriptome annotations were obtained from the Gencode v24 primary assembly (gencode.v24.primary_assembly.annotation.gtf). Trimmed reads were aligned to the reference genome and annotation using the STAR aligner (Dobin et al., 2013).

Output SAM files were validated using ValidateSam. Read groups were added to the SAM file using the Picard tool AddOrReplaceReadGroups. Sorted files were inspected using RNA-SeQC (DeLuca et al., 2012). Abundance of RNA were estimated from aligned reads using featureCounts (Liao et al., 2014). Replicate count data were processed in edgeR (Robinson et al., 2010). Ensembl (http://www.ensembl.org) transcripts were annotated with Entrez gene ID and symbol (Maglott et al., 2011). Replication quality and repeatability was measured as Pearson correlations between the ten sample replicates pairs.

Lowly expressed tags were filtered out by retaining only those with 1 read per million in at least 27 samples. Count estimates were normalized with the trimmed means of M (TMM) values method (Robinson and Oshlack, 2010). TMM normalization was applied to the dataset in edgeR using calcNormFactors.

One sample from each replicate pair was excluded using a coin-flip, leaving a total of 62 samples for the analysis. Data exploration was performed with multi-dimensional scaling (MDS) plots for all samples to identify sample outliers and potential batch effects due to technical artifacts (e.g., RNA quality, processing laboratory, technician, sequencing lane). Four samples that were outliers in the MDS plots and three samples with an HIV viral load > 200 copies/mL were excluded, leaving 55 samples for further analysis. Surrogate Variable Analysis (SVA) was used to identify variation contributing to heterogeneity in the sample (e.g., batch effects) not due to the variable of interest, Stimulant Tox group membership (Leek and Storey, 2007). To identify which surrogate variables to include, all surrogate variables were tested for correlation with the phenotype, RNA integrity number (RIN), laboratory technician, sequencing lane, and library size. Significance was assessed at a nominal p-value of 0.05. Any surrogate variable which correlated with the phenotype (p < 0.10) was excluded.

2.2. Statistical Analyses

Independent samples t-tests and chi-square analyses were utilized to compare the Stimulant Tox+ and Stimulant Tox− groups on demographics, health status, psychiatric factors, and log10 plasma pro-inflammatory cytokine levels. Although there were no group differences in ART regimen, SVA gene expression analyses controlled for unmeasured sources of confounding.

Differential expression was determined under a variance modeling strategy that addresses the over-dispersion observed in gene expression count data in edgeR (Landau and Liu, 2013). A standard software package, edgeR, is widely used for differential gene expression analysis and performs well relative to other strategies (Rapaport et al., 2013; Soneson and Delorenzi, 2013). We followed best practices that have been previously articulated (Anders et al., 2013; Love et al., 2015). For this analysis, the overall dispersion as well as the gene-wise and tag-wise dispersion were estimated using general linear models (GLM) estimated using the Cox-Reid (CR)-adjusted likelihood method (Cox and Reid, 1987; McCarthy et al., 2012). Two of the twelve surrogate variables identified by SVA were correlated with Stimulant Tox group membership at p < 0.10 and were excluded. Gene-wise negative-binomial GLMs that adjusted for the 10 surrogate variables were fit. Differences between the Stimulant Tox+ and Stimulant Tox− groups were tested using likelihood ratio tests (p < 0.05). We adjusted for multiple hypothesis testing through estimation of the false discovery rate (FDR) under the Benjamini-Hochberg procedure with a significance cut-off of 10% with no minimal log-fold change (Benjamini and Hochberg, 1995; Hochberg and Benjamini, 1990). However, we report the unadjusted p-values for genes relevant to TNF-α as posthoc probing to characterize biological pathways underlying significantly elevated plasma levels in recent stimulant users.

Sequence loci data were annotated with Entrez gene IDs. The gene names were annotated using the HUGO Gene Nomenclature Committee resource database (Gray et al., 2013). In order to discover differences between Stimulant Tox+ and Stimulant Tox− groups in perturbations among genes that operate together in higher orders (i.e., pathways), we performed a competitive analysis of differentially perturbed pathways using the R package “GAGE” (Luo et al., 2009). A competitive approach tests if the gene set of a pathway is differentially expressed compared to the set of complementary genes (Emmert-Streib and Glazko, 2011; Goeman and Buhlmann, 2007). For GAGE pathway analyses, raw count data were preprocessed and normalized de novo following the author’s recommended instructions. Only loci annotated with ENTREZ IDs were included in the analysis.

Signaling pathway impact analysis (SPIA, Tarca et al., 2009), which integrates patterns of perturbation with the topology of the pathway, was used to identify pathway perturbations that differed between Stimulant Tox+ and Stimulant Tox− groups. Only loci annotated with ENTREZ IDs and unadjusted p values <0.001 in the differential gene expression analyses were retained for consideration in SPIA analyses.

3. Results

As shown in Table 1, the Stimulant Tox+ and Stimulant Tox− groups were generally comparable with regard to demographic and health-related factors, including no significant differences in ART regimen, ART adherence and persistence, prevailing CD4+ T-cell count, and the proportion with an HIV viral load below the limit of assay detection (less than 40 copies/mL). On average, Stimulant Tox+ participants reported 2.68 times more days using methamphetamine in the past 30 days (10.7 versus 4.0). Those who were Stimulant Tox+ displayed significantly higher log10 TNF-α levels (1.7 versus 1.6). Although log10 IL-6 were also higher in recent stimulant users (0.4 versus 0.3), this did not reach statistical significance (p = 0.10).

Table 1.

Demographic, health-related, and psychiatric factors as a function of recent stimulant use

Stimulant Tox+
(n = 27)		 Stimulant Tox−
(n = 28)
M (SD) M (SD)
Age 43.8 (7.2) 45.8 (8.2) t (53) = 0.93; p = 0.36
Time Since HIV Diagnosis (Years) 12.0 (8.3) 15.5 (7.5) t (53) = 1.66; p = 0.10
ART Adherence 77.8 (20.0) 87.8 (21.0) t (53) = 1.80; p = 0.08
CD4+ T-Cell Count (cells/mm3) 642.8 (298.5) 651.5 (286.5) t (53) = 0.11; p = 0.91
Methamphetamine Use Days (Past 30 Days) 10.7 (8.6) 4.0 (6.6) t (53) = −3.24; p < 0.01
ASI Drug Score 0.2 (0.1) 0.2 (0.1) t (53) = −0.61; p = 0.55
ASI Alcohol Score 0.2 (0.1) 0.1 (0.2) t (53) = −0.51; p = 0.62
Depressive Symptom Severity (CES-D) 27.6 (13.7) 21.3 (12.9) t (53) = −1.76; p = 0.08
PTSD Symptom Severity (PCL-C) 49.7 (11.9) 48.4 (15.1) t (53) = −0.36; p = 0.72
Log10 Interleukin-6 (IL-6) 0.4 (0.3) 0.3 (0.3) t (53) = −1.66; p = 0.10
Log10 Tumor Necrosis Factor – Alpha (TNF-α) 1.7 (0.20) 1.6 (0.20) t (53) = −2.34; p = 0.02
n (%) n (%)
Race/Ethnicity χ2 (3) = 3.07; p = 0.38
 Black/African American 1 (4%) 5 (17%)
 White 13 (48%) 13 (46%)
 Hispanic/Latino 10 (37%) 8 (29%)
 Other Ethnic Minority 3 (11%) 2 (7%)
ART Regimen χ2 (5) = 3.46; p = 0.63
 INSTI & NRTI 7 (26%) 6 (21%)
 Boosted PI & NRTI 9 (33%) 6 (21%)
 NNRTI & NRTI 6 (22%) 11 (39%)
 PI+NNRTI+NRTI 2 (8%) 1 (4%)
 Other HAART 3 (11%) 3 (11%)
 No HAART 0 (0%) 1 (4%)
ART persistence 11 (41%) 14 (50%) χ2 (1) = 0.48; p = 0.49
Undetectable HIV Viral Load (< 40 copies/mL) 22 (82%) 24 (86%) χ2 (1) = 0.18; p = 0.67
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Abbreviations: ART = Antiretroviral Therapy; ASI = Addiction Severity Index; CES-D = Center for the Epidemiologic Study - Depression Scale; HAART = Highly Active Anti-Retroviral Therapy; INSTI = Integrase Strand Transfer Inhibitor; M = mean; NNRTI = Non-Nucleoside Reverse Transcriptase Inhibitor; NRTI = Nucleoside Reverse Transcriptase Inhibitor; PCL-C = PTSD Checklist - Civilian Version Score; PI = Protease Inhibitor; PTSD = Post-Traumatic Stress Disorder; SD = Standard Deviation

Mean RIN was 7.2 (min. 6.3), while median library size was 14,060,000 reads; 20,426 transcripts were retained with at least 1 read per million in 10 samples from the total of 60,725 GENCODE transcript targets. We found high replicability for the count data with a mean within-replicate Pearson correlation coefficient of <0.9999. No batch effects were identified for sample preparation order, RIN value, RNA processing date or technician, or sequencing lane in either the first two dimensions of MDS plots. In line with values reported in other clinical datasets (McCarthy et al., 2012), the overall dispersion was observed to be 0.2381 (Biological Coefficient of Variation = 0.4879).

As shown in Table 2, 32 genes were differentially expressed in the Stimulant Tox+ group as compared to the Stimulant Tox− group. Results suggest that recent stimulant use was associated with upregulation of genes regulating HIV latency and survival of HIV-infected cells (i.e., PDL1, KAT2B, FCGR2A, LGALS3, HIST1H4A), cell cycle regulation (e.g., FOXO4, MED21), and immune activation (i.e., FCGR1CP, FCGR1B, RBM38, FCGR1A). We also observed downregulation of FAT1 and upregulation of FUT8, both of which are relevant to cancer. Results indicated downregulation of MAN2A2, GAA, and FAM210B, which are relevant to metabolic functioning (e.g., glycan synthesis and glycogen degradation). Finally, there was some evidence for differential expression of genes governing inflammation, neural and hormonal functioning, and cardiovascular risk.

Table 2.

Differentially expressed genes as a function of recent stimulant use.

Entrez Gene ID Name Fold Change Direction padjusted Primary Role(s)
HIV Latency
 29126 CD274 0.79 Up 0.0145 Decreased T-Cell Activation & PD-L1 Binding
 8850 KAT2B 0.27 Down 0.0691 Tat Transactivation & Chromatin Remodeling
 2212 FCGR2A 0.38 Up 0.0895 HIV Latency (CD32a)
 3958 LGALS3 0.52 Down 0.0990 Apoptosis & T-Cell Regulation
 8359 HIST1H4A 0.68 Up 0.0990 DNA Transcription
Cell Cycle
 6710 SPTB 0.65 Down 0.0497 Cell Membrane Stability
 9764 KIAA0513 0.24 Down 0.0672 Apoptosis & Cytoskeletal Regulation
 4303 FOXO4 0.55 Down 0.0681 Insulin Signaling
 23039 XPO7 0.36 Down 0.0895 Protein Export from Nucleus
 9412 MED21 0.27 Up 0.0990 RNA Transcription
 25893 TRIM58 0.59 Down 0.0990 Nuclear Polarization
Immune Activation
 100132417 FCGR1CP 1.17 Up 0.0145 Antibody-Dependent Cytotoxicity
 2210 FCGR1B 0.67 Up 0.0691 IFN-γ Signaling
 55544 RBM38 0.56 Down 0.0895 Cell Cycle Arrest
 2209 FCGR1A 0.62 Up 0.0990 IgG Binding
 5609 MAP2K7 0.37 Down 0.0990 Immune Activation & Apoptosis
Cancer
 2195 FAT1 1.31 Down 0.0637 Tumor Suppressor
 2530 FUT8 0.29 Up 0.0670 Malignancy & Metastasis
Metabolic
 4122 MAN2A2 0.35 Down 0.0141 Biosynthesis & Metabolism
 2548 GAA 0.33 Down 0.0990 Glycolysis
 116151 FAM210B 0.56 Down 0.0990 Mitochondrial Activity & Glycolysis
Inflammation
 3340 NDST1 0.30 Down 0.0895 Inflammation
Neural/Hormonal
 79098 C1orf116 1.18 Down 0.0145 Androgen-Specific Receptor
 11345 GABARAPL2 0.42 Down 0.0145 GABA Receptor Protein
 6535 SLC6A8 0.67 Down 0.0335 Creatinine Transport
 7504 XK 0.66 Down 0.0336 Amino Acid Transport & Calcium Ion Homeostasis
 79056 PRRG4 0.75 Up 0.0342 Calcium Ion Binding
 90134 KCNH7 0.55 Up 0.0342 Potassium Voltage-Gated Channel
 3759 KCNJ2 0.43 Up 0.0393 Potassium Voltage-Gated Channel
 23046 KIF21B 0.19 Down 0.0990 GABA
 133 ADM 0.62 Up 0.0990 Hormone Secretion & Angiogenesis
Cardiovascular
 2039 DMTN 0.50 Down 0.0393 Red Blood Cell Structure & Platelet Mobility
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In order to explore the plausible mechanisms underlying elevated TNF-α levels in plasma, we conducted posthoc assessment of genes relevant to the TNF superfamily. We observed upregulation of TNFSF13B (Entrez Gene ID = 10673; Fold Change = 0.35; punadjusted = 0.003), TNFSF10 (Entrez Gene ID = 8743; Fold Change = 0.24; punadjusted = 0.008), TNFAIP6 (Entrez Gene ID = 7130; Fold Change = 0.46; punadjusted = 0.033), and TNFRSF1A (Entrez Gene ID = 7132; Fold Change = 0.11; punadjusted = 0.036). These genes underlie B-cell stimulation, apoptosis, innate immune activation, and receptor expression relevant to TNF-α. There was no differential expression of TNF (Entrez Gene ID = 7124; Fold Change = 0.06; punadjusted = 0.79), which is linked to TNF-α production.

Evidence of two-directional perturbation of 168 KEGG pathways was observed (Supplemental Table 1). Table 3 summarizes those pathways with greatest relevance to HIV pathogenesis and neuropsychiatric disorders. Findings provided further support for two-directional perturbation of pathways relevant to HIV latency in recent stimulant users. Pathways relevant to HIV latency included: pathways in cancer, cell cycle, arachidonic acid metabolism, apoptosis, and the mTOR signaling pathway. Results also provided support for two-directional perturbation of pathways relevant to inflammation including: cytokine-cytokine receptor interaction, chemokine signaling pathway, MAPK signaling pathway, Jak-STAT signaling, and the Fc epsilon RI signaling pathway. We also observed two-directional perturbation of pathways relevant to the innate and acquired immune activation such as antigen processing and presentation, toll-like receptor signaling, natural killer cell cytotoxicity, and Fc gamma R-mediated phagocytosis, and T-cell activation. Two-directional perturbation of pathways linked to neuroendocrine hormone regulation (i.e., neuroactive ligand-receptor interaction and autoimmune thyroid disease) as well as synthesis of catecholamines (i.e., tyrosine metabolism) and serotonin (i.e., tryptophan metabolism) were also observed. These pathways are directly relevant the regulation of immunomodulatory neuroendocrine hormones as well as the etiology of neuropsychiatric disorders. Finally, we observed two-directional perturbation of pathways in cancer.

Table 3.

Perturbed pathways relevant to HIV and neuropsychiatric disorders as a function of recent stimulant use

KEGG ID Pathway Name Set Size Padjusted Primary Role
HIV Latency
 hsa04110 Cell Cycle 90 < 0.0001 Mitosis
 hsa04210 Apoptosis 64 < 0.0001 Programmed Cell Death
 hsa04150 mTOR Signaling Pathway 33 0.0969 Metabolism and Survival
 hsa00590 Arachidonic Acid Metabolism 32 < 0.0001 Lipid Metabolism
Inflammation
 hsa04060 Cytokine-Cytokine Receptor Interaction 175 < 0.0001 Regulation and Mobilization
 hsa04010 MAPK Signaling 163 < 0.0001 Proliferation, Differentiation, and Migration
 hsa04062 Chemokine Signaling 127 < 0.0001 Regulation and Mobilization
 hsa04630 Jak-STAT Signaling 99 < 0.0001 Antigen Presentation and Inflammation
 hsa04664 Fc Epsilon RI Signaling 45 0.0008 Cytokine Gene Transcription
Immune Activation
 hsa04510 Focal Adhesion 136 < 0.0001 Trafficking, Proliferation, and Differentiation of Leukocytes
 hsa04514 Cell Adhesion Molecules (CAMs) 84 < 0.0001 Leukocyte Trafficking
 hsa04650 Natural Killer Cell Cytotoxicity 81 < 0.0001 Natural Killer Cell Cytotoxicity
 hsa04670 Leukocyte Transendothelial Migration 79 0.0011 Leukocyte Trafficking
 hsa04660 T-Cell Receptor Signaling 74 < 0.0001 T-Cell Activation
 hsa04620 Toll-Like Receptor Signaling 70 < 0.0001 Innate Immune Activation
 hsa04666 Fc Gamma R-Mediated Phagocytosis 65 0.0002 Antibody-Dependent Cytotoxicity
 hsa04612 Antigen Processing and Presentation 58 < 0.0001 Immune Response
 hsa04662 B-Cell Receptor Signaling 49 0.0053 Immune Response
Neuropsychiatric
 hsa04080 Neuroactive Ligand-Receptor Interaction 196 < 0.0001 Neurotransmitters and Neuroendocrine Hormones
 hsa05320 Autoimmune Thyroid Disease 43 < 0.0001 Hypothyroidism
 hsa00380 Tryptophan Metabolism 28 < 0.0001 Serotonin Synthesis
 hsa00350 Tyrosine Metabolism 26 < 0.0001 Catecholamine and Thyroid Hormone Synthesis
Cancer
 hsa05200 Pathways in Cancer 234 < 0.0001 Apoptosis, Focal Adhesion, and Cellular Metabolic Processes
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4. Discussion

The results of the present study provide support for stimulant-associated alterations in patterns of gene expression relevant to HIV pathogenesis in a sample of methamphetamine users with treated HIV. Because this was a sample comprised entirely of methamphetamine users, this study examined dose-response associations of recent stimulant use. Below we briefly discuss these complex associations of recent stimulant use with alterations of gene expression patterns across multiple biological systems.

Recent stimulant use was associated with patterns of differential expression in genes previously associated with HIV persistence and residual immune dysregulation during ART suppression. Recent stimulant use was associated with upregulation of FCGR2A, which encodes for a marker (i.e., CD32a) that is expressed on CD4+ T-cells, potentially marking replication-competent cells (Descours et al., 2017). Recent stimulant users also demonstrated upregulation of PDL1, encoding the ligand for an immune exhaustion marker (i.e., programmed cell death-1 ligand) which is another putative cell surface marker identifying CD4+ T-cells with replication-competent HIV (Banga et al., 2016). Consistent with prior research (Massanella et al., 2015), our findings highlight that stimulant use may contribute to HIV persistence by facilitating HIV latency and persistent, low level HIV replication. Our results also suggest that stimulant use could lead to greater HIV persistence by inhibiting apoptosis of HIV-infected cells and metabolic dysregulation leading to expansion of the HIV reservoir in CD4+ T-cells (Chomont et al., 2009). Further research examining these biological pathways whereby stimulant use may contribute to HIV persistence is needed to inform the development of novel HIV cure interventions.

There was also some evidence that recent stimulant use is associated with dysregulation of gene expression patterns relevant to immunosurveillance. For example, recent stimulant users displayed downregulation of a gene relevant to tumor suppression (i.e., FAT1) and concurrent upregulation a gene that may promote metastasis (i.e., FUT8). These results are consistent with prior research where virally suppressed methamphetamine users displayed impaired proliferative responses after stimulation with various pathogens compared to those who did not use methamphetamine (Massanella et al., 2015). More research is warranted to examine the specific mechanisms that may underlie stimulant-associated impairments in immunosurveillance, including oncogenic viruses that are implicated in AIDS-related cancers.

Findings also provide some support for stimulant-associated alterations in gene expression relevant to immune activation and inflammation. We observed upregulation of multiple genes relevant to the Fc region of antibodies that are crucial for coordinating immune activation and supporting antibody-dependent cytotoxicity. As evidenced in the pathway analyses, this pattern of stimulant-associated increases in immune activation could contribute to greater inflammation. For example, alterations in Fc Epsilon RI signaling are linked to TNF transcription (Kraft and Bieber, 2001). We also observed upregulation of genes governing B-cell stimulation, apoptosis, innate immune activation, and receptor expression on immune cells relevant to TNF superfamily in recent stimulant users. This may partially explain why recent stimulant users in the present study displayed higher plasma levels of TNF-α. Findings are consistent with prior research in HIV-negative persons where reductions in monocyte expression of TNF-α following cocaine infusion were partially attributable to greater increases in autonomic nervous system activation (Irwin et al., 2007).

Consistent with prior in vitro research using dendritic cells (Mahajan et al., 2006), we observed stimulant-associated dysregulation of pathways governing chemokine regulation, cytokine production, apoptosis, and cell cycle regulation in leukocytes. Our findings also indicated that recent stimulant use was associated with two-directional, differential expression of pathways governing adhesion, trafficking, and cytotoxic activity of leukocytes. These results lend further support to the conclusion that stimulant use may disrupt immunosurveillance in treated HIV, which could have important implications for responding to novel pathogens, terminating inflammation, and eradicating neoplasias.

Finally, results of pathway analyses provide some evidence for stimulant-associated alterations in neuroendocrine hormone regulation and neurotransmitter synthesis. The acute effects of methamphetamine are attributable to sympathetic nervous system activation (Makisumi et al., 1998), resulting in the release of catecholamines such as norepinephrine at sympathetic nerve terminals and increased dopamine production in the central nervous system (Wilson et al., 1996). In addition, the hypothalamic-pituitary-adrenal (HPA) and hypothalamicpituitary-thyroidal (HPT) axes are thought to be dysregulated in chronic methamphetamine users (Carrico et al., 2018; Goeders, 2002; Li et al., 2013). This was observed in pathway analyses indicating possible dysregulation of the HPA and HPT axes. Recent stimulant use was also associated with two-directional, differential expression of pathways for metabolism of amino acid precursors for serotonin (i.e., tryptophan) as well as catecholamines such as norepinephrine and dopamine (i.e., tyrosine). These results are consistent with prior cross-sectional research where more frequent stimulant use was associated with depleted tryptophan in plasma among HIV-positive persons on ART (Carrico et al., 2008). Future research should determine whether stimulant-induced neuroendocrine dysregulation and perturbations in neurotransmitter synthesis mediate immunologic changes relevant to HIV pathogenesis.

A common aspect of studies of methamphetamine use and viral dynamics is the influence of ART adherence. In our study, we were only able to assess adherence using self-report measures and by restricting participants to those with an HIV RNA < 200 copies/mL. It is plausible that the gene expression changes observed here reflect transcriptional changes as a result of inadequate viral suppression rather than a direct effect of methamphetamine per se. Nonetheless, our analyses highlight several intriguing patterns suggestive of distinct inflammatory and latency-associated pathways that need to be further investigated in cohort studies using more rigorous measures of ART adherence in people who use methamphetamine.

This study has other limitations that deserve mention. First, recent stimulant use measured in urine reflects any use during the past 72 hours, and it is plausible that differential patterns of leukocyte gene expression reflect different phases of use like intoxication or withdrawal. Future research should assess methamphetamine and cocaine concentrations through quantitative toxicology measures. Second, all participants enrolled in this study were biologically confirmed methamphetamine users and further research is needed to examine differential patterns of leukocyte gene expression in comparison to non-users. Third, unhealthy alcohol use and other psychiatric comorbidities may independently influence gene expression. In fact, another recent study from our team led by Flentje and colleagues (2018) demonstrates that sexual minority stress processes are associated with differential leukocyte gene expression after controlling for recent stimulant use. Fourth, research is needed to examine differential gene expression as a function of chronic patterns of stimulant use, different route(s) of stimulant administration, and distinct typologies of polysubstance use. Future research should also measure other psychiatric and medical comorbidities in relation to gene expression in treated HIV. Finally, it is plausible that there are pharmacodynamic interactions between stimulant use and specific ART medications. Further research is needed to examine the influence of specific ART regimens as moderators.

Despite these limitations, results from this study provide some of the first evidence for potential stimulant-associated alterations of gene expression patterns relevant to HIV pathogenesis. These findings may help inform efforts to better understand potential biobehavioral mechanisms by which stimulant use may contribute to HIV persistence, dysregulate immunosurveillance, and disrupt neuroimmune signaling in treated HIV.

Supplementary Material

supplement

Highlights.

  • Documents stimulant-associated leukocyte gene expression in treated HIV infection

  • Elevated tumor necrosis factor – alpha (TNF-α) levels in recent stimulant users

  • Stimulant use may contribute to HIV persistence and residual immune dysregulation

Acknowledgments

This project was supported by the National Institute on Drug Abuse (R01-DA033854; Woods, Carrico, and Moskowitz, PIs). Additional funding was provided by pilot awards from the University of California, San Francisco Center for AIDS Research (P30-AI027763; Volberding, PI) and the National Institute on Drug Abuse (P50-009253; Guydish, PI; T32-DA007250; Sorensen, PI; K23DA039800; Flentje, PI). Additional support for assays of plasma cytokines was provided by the Miami Center for AIDS Research (P30-AI073961; Pahwa, PI). This work used the Vincent J. Coates Genomics Sequencing Laboratory at UC Berkeley, supported by NIH S10 OD018174 Instrumentation Grant. We would also like to thank Dr. Teri Leigler for her support of this project through the University of California, San Francisco Center for AIDS Research’s Virology Core (P30-AI027763). The team would like to express our gratitude to multiple staff who have contributed time and effort to the successful execution of this project including: Justin Lagana-Jackson, Paul Cotten, and Lara Coffin. Finally, we are grateful to the study participants who placed a great deal of trust in our team to collect and manage biological specimens for this project.

Footnotes

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