Detection of H3k4me3 Identifies Neurohiv Signatures, Genomic

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Title Detection of H3K4me3 Identifies NeuroHIV Signatures, Genomic Effects of Methamphetamine and Addiction Pathways in Postmortem HIV+ Brain Specimens that Are Not Amenable to Transcriptome Analysis.

Permalink https://escholarship.org/uc/item/4hq7z2rm

Journal Viruses, 13(4)

ISSN 1999-4915

Authors Basova, Liana Lindsey, Alexander McGovern, Anne Marie et al.

Publication Date 2021-03-24

DOI 10.3390/v13040544

Peer reviewed

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Article Detection of H3K4me3 Identiﬁes NeuroHIV Signatures, Genomic Effects of Methamphetamine and Addiction Pathways in Postmortem HIV+ Brain Specimens that Are Not Amenable to Transcriptome Analysis

Liana Basova 1, Alexander Lindsey 1, Anne Marie McGovern 1, Ronald J. Ellis 2 and Maria Cecilia Garibaldi Marcondes 1,*

1 San Diego Biomedical Research Institute, San Diego, CA 92121, USA; [email protected] (L.B.); [email protected] (A.L.); [email protected] (A.M.M.) 2 Departments of Neurosciences and Psychiatry, University of California San Diego, San Diego, CA 92103, USA; [email protected] * Correspondence: [email protected]

Abstract: Human postmortem specimens are extremely valuable resources for investigating translational hypotheses. Tissue repositories collect clinically assessed specimens from people with and without HIV, including age, viral load, treatments, substance use patterns and cognitive functions. One challenge is the limited number of specimens suitable for transcriptional studies, mainly due to poor RNA quality resulting from long postmortem intervals. We hypothesized that epigenomic Citation: Basova, L.; Lindsey, A.; signatures would be more stable than RNA for assessing global changes associated with outcomes McGovern, A.M.; Ellis, R.J.; of interest. We found that H3K27Ac or RNA Polymerase (Pol) were not consistently detected by Marcondes, M.C.G. Detection of H3K4me3 Identifies NeuroHIV Chromatin Immunoprecipitation (ChIP), while the enhancer H3K4me3 histone modification was Signatures, Genomic Effects of abundant and stable up to the 72 h postmortem. We tested our ability to use H3K4me3 in human Methamphetamine and Addiction prefrontal cortex from HIV+ individuals meeting criteria for methamphetamine use disorder or not Pathways in Postmortem HIV+ Brain (Meth +/−) which exhibited poor RNA quality and were not suitable for transcriptional profiling. Specimens that Are Not Amenable to Systems strategies that are typically used in transcriptional metadata were applied to H3K4me3 Transcriptome Analysis. Viruses 2021, peaks revealing consistent genomic activity differences in regions where addiction and neuronal 13, 544. https://doi.org/10.3390/ synapses pathway genes are represented, including genes of the dopaminergic system, as well as v13040544 inflammatory pathways. The resulting comparisons mirrored previously observed effects of Meth on suppressing gene expression and provided insights on neurological processes affected by Meth. The Academic Editor: Stefano Aquaro results suggested that H3K4me3 detection in chromatin may reflect transcriptional patterns, thus providing opportunities for analysis of larger numbers of specimens from cases with substance use Received: 17 February 2021 Accepted: 22 March 2021 and neurological deficits. In conclusion, the detection of H3K4me3 in isolated chromatin can be an Published: 24 March 2021 alternative to transcriptome strategies to increase the power of association using specimens with long postmortem intervals and low RNA quality.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in Keywords: postmortem interval; HIV; methamphetamine; brain; H3K4me3 published maps and institutional afﬁl- iations.

1. Introduction Postmortem human specimens represent an extremely valuable resource with direct Copyright: © 2021 by the authors. translational implications. Tissue repositories collect a great number of brain specimens Licensee MDPI, Basel, Switzerland. from HIV-positive (+) and negative individuals, with different viral loads, treatments and This article is an open access article ages. Most importantly, specimens may also be from subjects with different substance distributed under the terms and use disorder patterns and cognitive functions, generating opportunities for identifying conditions of the Creative Commons important molecular elements tied to recorded clinical and individual variables. Currently, Attribution (CC BY) license (https:// the analysis of the transcriptome can provide in-depth information about pathways, biolog- creativecommons.org/licenses/by/ ical processes and even cellular compartments disturbed by variables such as drug abuse. 4.0/).

Viruses 2021, 13, 544. https://doi.org/10.3390/v13040544 https://www.mdpi.com/journal/viruses Viruses 2021, 13, 544 2 of 24

Nevertheless, the use of a large fraction of the archived specimens may be limited for deter- mining global transcriptional changes due to problems with RNA quality. Postmortem time interval prior to tissue harvest is one of the main causes of poor RNA quality and presents a challenge to retrieve transcriptional information from a large number of human specimens, particularly the ones from drug users [1–3]. To address this challenge, we investigated whether changes in epigenetic marks such as enhancer histone modifications may be stable surrogates to indicate consequential information about gene transcription patterns, which would otherwise be lost due to poor RNA quality in postmortem specimens. This could enhance the investigation of global changes associated with HIV infection and neurological outcomes, as well as pathways involved in addiction, in a larger number of specimens. Epigenetic marks associated with transcription include enhancer histone modifications such as H3K4me3 and H3K27Ac in proximal promoters, as well as the presence of RNA Polymerase I/II at transcription starting sites (TSS) [4,5]. These and other epigenetic mechanisms, either enabling or silencing transcription, are highly conserved between eu- karyotic species [6–9]. We tested the effect of postmortem intervals on the stability of these epigenetic marks in mouse brain tissue and confirmed that the most stable modification over time was consistently found in human postmortem specimens with poor RNA quality. This information was used to assess signatures in prefrontal cortex specimens from HIV+ subjects that met the criteria of Methamphetamine (Meth) use disorder, or not. We found that both histone modifications were better preserved than RNA Polymerase and that H3K4me3 was more abundant than H3K27Ac. H3K4me3 was consistently found in postmortem human specimens and allowed the identification of promoter and gene activity in inflammatory and viral response genes, as well as in genes involved in addiction. We conclude that the characterization of the epigenetic landscape can enable the retrieval of useful information from precious specimens with otherwise poor RNA quality, offering an opportunity to expand the analysis of specimens in tissue repositories, such as the National NeuroAIDS Tissue Consortium (NNTC). Importantly, this may help increase the number of analyzed specimens, enhancing sample size and statistical power, while improving models that incorporate common confounders and heterogeneities of the human population to genetic and post-translational patterns.

2. Materials and Methods 2.1. Mouse Postmortem Specimens and ChIP-qPCR The studies were performed with the approval of the Institutional Animal Care and Use Committee (IACUC) of the San Diego Biomedical Research Institute (SDBRI). Eighteen 10 weeks old male C57Bl/6 mice were sacrificed using 5% isoflurane, and the bodies were maintained at room temperature for 0, 6, 24, 48, 72 and 96 h prior to dissection and brain harvesting. The brains were divided into two hemispheres, designated for RNA isolation and quality assessment performed as a service by the Microarray Core Facility at The Scripps Research Institute, La Jolla, as well as for chromatin isolation and cross linking. Mouse ChIP-qPCR was performed in isolated chromatin for the detection of H3K4me3, H3K27Ac and RNA Pol II with ChIP-grade antibodies. The reactions were performed in triplicate using 25 µg of mouse brain tissue chromatin and 3 µL of H3K4me3 antibody (Active Motif, Cat#39159, Carlsbad, CA, USA), 4 µg of H3K27Ac antibody (Active Motif, Cat#39133) or 4 µg of RNA Pol II antibody (Active Motif, Cat#91151). We performed qPCR using two positive control primers that worked well in similar assays (ACTB, GAPDH), as well as a negative control primer pair that amplifies a region in a gene desert on chromosome 6 (Untr6).

2.2. Human Postmortem Specimens and ChIP-qPCR Frozen human prefrontal cortex brain specimens were provided by the National NeuroAIDS Tissue Consortium (NNTC) upon request and kept at −80 ◦C until experi- mentation. All experiments were performed with Institutional IBC and IRB approval (IRB- 18-001-MCM). The prefrontal cortex specimens were selected among all male HIV+ cases Viruses 2021, 13, 544 3 of 24

(receiving ART and exhibiting criteria for global neuropsychological impairment [10–13], with available frozen and parafﬁn-embedded tissue. The specimens were divided in two groups based on meeting current stimulant abuse/dependence criteria at the last clinical evaluation and urine toxicology positive for methamphetamine (Meth− and Meth+, n = 3 case specimens/group). Three fragments of 1cm2 were separated from each frozen tissue specimen. Half of each fragment was used for RNA extraction and RNA quality assessment performed as a service by the Microarray Core Facility at The Scripps Research Institute, La Jolla. The remaining half was used for simultaneous chromatin isolation and cross- linking. ChIP reactions were performed using 30 µg of human HIV+ brain tissue chromatin and 4 µg of antibody against RNA Pol II, (Cat#39097, Active Motif, Carlsbad, CA, USA), H3K27Ac (Cat#39085, Active Motif) and H3K4me3 (Cat#39160, Active Motif). Following that, qPCR was performed using two positive control primers (ACTB and GAPDH), as well as a negative control primer pairs that amplify regions in a gene desert on chromosome 12 (Untr12) for human specimens.

2.3. ChIP-qPCR Statistical Analysis Statistical comparisons between postmortem intervals in ChiP-qPCR experiments in mouse and human preparations were performed using ANOVA, followed by Bonferroni’s posthoc test, in Prism 8.4.1 (Graphpad Software L.L.C., San Diego, CA, USA). Differences were considered statistically signiﬁcant at p < 0.05.

2.4. RNA Extraction and Quality Assessment Mouse and human tissue were homogenized with Kimble Kontes disposable pestles (Sigma-Aldrich, St. Louis, MO, USA), and RNA was extracted using RNAeasy Mini kit (Qiagen, Germantown, MD, USA). RNA quantiﬁcation was determined by spectropho- tometry using a NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies Inc., Wilmington, DE, USA). The OD260/280 ratio was used to evaluate the purity of the nucleic acid samples, and the quality of the extracted total RNA was determined with the Agilent 2100 Bioanalyzer (Santa Clara, CA, USA), at the La Jolla Scripps Research DNA Array Core as a service.

2.5. Chromatin Preparation Minced tissue was washed twice in ice cold PBS and treated with 1% formaldehyde (Sigma-Aldrich) for 12 min to crosslink the chromatin, as previously described [14,15]. The reaction was stopped with glycine added to a final concentration of 0.125 M. The pellets were lysed with lysis buffer containing molecular grade 85 mM KCl, 0.5% NP40 and 5 mM HEPES pH 8.0) supplemented with a protease inhibitor cocktail, incubated on ice for 15 min and centrifuged at 3500× g for 5 min to pellet the nuclei. The pellet was resuspended in nuclear lysis buffer (10 mM EDTA, 1% SDS, 50 mM Tris–HCl, pH 8.1) at a ratio 1:1 (v/w), incubated on ice for 10 min and stored at −80 ◦C until use. The pellets were sonicated, and DNA was sheared to an average length of 300–500 bp. The chromatin extraction was performed using Chromatin IP DNA Purification kit (Active Motif, Carlsbad, CA, USA). Genomic DNA (input) was prepared by treating aliquots of chromatin with RNase, proteinase K and heat (65 ◦C) to reverse cross-linking, followed by phenol and chloroform extractions and ethanol precipitation. Pellets were resuspended in 10 mM Tris, 1 mM EDTA, and the resulting DNA was quantified on a Nanodrop spectrophotometer (Thermo Fisher Scientific, Chicago, IL, USA). Results were used to calculate the total yield.

2.6. ChIP-Seq ChIP was performed using the ChIP-IT High Sensitivity Kit (Active Motif). ChIP-seq reactions were carried out with 30 ug of chromatin and anti-H3K4me3 antibody. The ChIP DNA was processed into an Illumina ChIP-Seq library and sequenced to generate reads, which were aligned to the H. sapiens genome annotation (Hg38 assembly) and unique aligns (removed duplicates) were obtained. A signal map showing fragment densities Viruses 2021, 13, 544 4 of 24

along the genome was visualized in the Integrated Genome Browser (IGB) and MACS peak finding was used to identify peaks. Control data was derived from positive and negative control alignments. The 75-nt sequence reads generated by Illumina sequencing (using NextSeq 500) were mapped to the genome using the BWA algorithm with default settings. Alignment information for each read was stored in the BAM format. Only reads that passed Illumina’s purity filter, aligned with no more than 2 mismatches and mapped uniquely to the genome were used in the subsequent analysis. In addition, duplicate reads were removed. Since the 50-ends of the aligned reads represent the end of ChIP/IP-fragments, the tags were extended in silico using Active Motif software at their 3’-ends to a length of 150–250 bp, depending on the average fragment length in the selected 200 bp library size. To identify the density of fragments along the genome, the genome was divided into 32-nt bins and the number of fragments in each bin was determined. This information in bigWig was visualized in UCSC genome browser. Intervals with tag enrichment were identified by chromosome number, start and end coordinates using MACS [16] and SICER [17], corresponding to significant enrichments in the ChIP/IP data compared to random Input. For normalization, the tag number of all samples was reduced by random sampling to the number of tags present in the smallest sample, using default settings. To compare peak metrics, overlapping Intervals were grouped into merged regions, defined by the start coordinate of the most upstream Interval and the end coordinate of the most downstream Interval. In locations where only one sample had an Interval, this defined the merged region. After defining the Intervals and Merged Regions, their genomic locations along with their proximities to gene annotations and other genomic features were determined and presented in .xls spreadsheets.

2.7. ChIP Analysis Genes with H3K4me3 peaks found within 10 kb of start/end were compared in the ChIP samples. Overlap between the samples was high (70–90% in pairwise comparisons). Active region identification settings were −7500 to 2500 from promoter start, with 10,000 upstream and downstream-margin. The comparison of all samples averaged by group and the peak metrics obtained in Bioconductor ChiPpeakAnno (www.bioconductor.org, 2020) [18] were used in combination with a present/absent peak call information to find genomic regions with H3K4me3 occupancy patterns. Sequencing statistics can be seen in Table1. The average fragment densities for 28,826 human genes (from TSS to termination site) and the corresponding promoters (from −1000 to +1000 nt relative to TSS) were determined. Peak values in gene bodies were corrected by length. For the analysis, the data files were normalized to the same number of unique alignments without duplicate reads. Intervals were determined using the SICER algorithm at a cutoff of FDR1E-10 and a Gap parameter of 600 bp (which merges peaks located within 600 bp of each other). Gene intervals (peaks) were determined using the SICER algorithm at a cutoff of FDR1E-10 and a Gap parameter of 600 bp (which merges peaks located within 600 bp of each other into a single “island”). The overlap between the samples was high (70–90% in pairwise comparisons). The tag densities/signal metrics relative to known gene annotations were examined and used in systems analysis.

Table 1. Sequencing statistics by group.

Average HIV+Meth- Average HIV+Meth+ Pooled Input H3K4me3 H3K4me3 hg38 Total number of reads 39,365,646 40,840,923 42,982,011 Total number of 35,790,215 38,438,156 39,813,903 alignments (hg38) Unique alignments (-q 25) 33,267,427 35,878,237 34,930,380 Unique alignments 23,200,850 26,581,519 32,071,577 (without duplicates) Viruses 2021, 13, 544 5 of 24

2.8. Systems Analysis Ratio and log 2-fold-change were calculated using tag densities/signal metrics relative to known gene annotations to group average signal values, with a cut off > and <1.5-fold, and p < 0.05 in pairwise comparisons performed in licensed JMP Pro 12 software (SAS Insti- tute Inc., Cary, NC, USA). Enrichment, processes and pathway annotations were performed using z scores by licensed Ingenuity Pathway Analysis software (IPA, Qiagen), conﬁrmed by DAVID Bioinformatics Resources 6.8 (https://david.ncifcrf.gov, 2020) and then visualized using local search features in GeneMANIA plugin [19–21] (www.genemania.org) in Cytoscape 3.7.0 platform [22](www.cytoscape.org, 2020) with Homo sapiens sources from Re- actome [23](www.reactome.org, 2020) and BioGRID_ORGANISM [24–26] (https://thebiogrid.org, 2020). Transcription factor-DNA interactions derived from merged peak regions were modeled using licensed TRANSFAC v2021.1 [27–29] (GeneXplain GmbH, Wolfenbuttel, Germany) with JASPAR 2020 position frequency matrix [30] and input provided in .BED format, restricted to H. sapiens nervous system.

2.9. In Situ Hybridization for HIV (vRNA) Detection Parafﬁn-embedded sections of prefrontal cortex (from the same subjects) were provided by the NNTC upon request. RNAScope 2.5 HD assay (Advanced Cell Diagnostics, ACD) was performed. Brieﬂy, pre-treatment was performed with RNAscope 2.5 HD Detection Kit (RED) (Cat# 322360), RNAscope 2.5 Pretreat Reagents-H202 and Protease Plus (Cat#322330), RNAscope Target Retrieval (Cat#322000) and RNAscope Wash Buffer (Cat#310091), following manual instructions. Probe set V-HIV1-clade B-C3 (Cat#425531- C3) targeting different segments within the gag-pol region, as described [31–33]. Im- ages were captured using the light feature of a Zeiss AXIO Observer.Z1 (Carl Zeiss AG, Oberkochen, Germany).

3. Results Peaks of enrichment in ChIP-seq data may suggest stable and predictive standards associated with transcription, especially the ones in promoter regions [5]. We selected 3 epigenetic marks associated with active transcriptional patterns: H3K27Ac, H3K4me3 and RNA Polymerase II (Pol). The stability of different epigenetic marks was tested in postmortem mouse specimens. Preservation of the most stable was conﬁrmed in human postmortem brain specimens with low RNA quality and used to compare prefrontal cortex signatures of Meth use disorder in the context of HIV infection.

3.1. Stability of Enhancer Epigenetic Marks in Mouse Postmortem Brains Overtime The stability of H3K27Ac, H3K4me3 and RNA Pol was tested in mouse brains, at different postmortem intervals, from 0 to 96 h time points. For that, C57Bl/6 mice were sacrificed and left at room temperature in groups of 3, for 6 h, 24, 48, 72 and 96 h before dissection and brain harvesting. Figure1 shows representative RNA electrophoresis indicating bands and increasing fragmentation over higher postmortem time intervals. The 2 histogram peaks in x-axis designate the 18 s and 28 s ribosomal RNA, respectively. The y-axis indicates fragment units (FU) derived from band density in relation to standards. Notice that fragment units decrease by about 50% at 24 h compared to 0 and 6 h, although 18 s and 28 s peaks in histograms may retain aspects of integrity. At 0–6 h, RIN was 8.8 ± 0.5. From 24 h on, RIN was consistently <7.2, reaching 2.5 ± 0.3 at 96 h. RIN < 7.5 is considered unfit for RNA studies, as it signifies a significant fragmentation, indicating poor quality that results in inaccurate transcriptional data. Viruses 2021, 13, 544 6 of 24

histogram peaks in x-axis designate the 18S and 28S ribosomal RNA, respectively. The y- axis indicates fragment units (FU) derived from band density in relation to standards. Notice that fragment units decrease by about 50% at 24 hrs compared to 0 and 6 h, although 18s and 28s peaks in histograms may retain aspects of integrity. At 0–6 h, RIN was

Viruses 2021, 13, 544 8.8 ± 0.5. From 24 hrs on, RIN was consistently <7.2, reaching 2.5 ± 0.3 at 96 h. RIN < 7.56 of is 24 considered unfit for RNA studies, as it signifies a significant fragmentation, indicating poor quality that results in inaccurate transcriptional data.

. FigureFigure 1. 1.RNARNA stability stability in inmouse mouse brains brains.. C57Bl/6 C57Bl/6 mice mice were were sacrificed sacrificed and and left left at atroom room temperature temperature forfor 0, 0,6, 6, 24, 24, 48, 48, 72 72 or or 96 96 h h before before dissection dissection (n = 3/interval). PCDH PCDH and A3C4 are AgilentAgilent RNARNA integrity integ- rityreference reference standards. standards. Their Their brains brains were were harvested, harvested, and and half half was was snap snap frozen frozen for for RNA RNA extraction. extraction. The TheRNA RNA quality quality was w testedas tested by ( Aby) gel(A) electrophoresis, gel electrophoresis, in an in Agilent an Agilent Bioanalyzer Bioanalyzer 2100. ( B2100.) RNA (B integrity) RNA integrity numbers (RIN) were derived from 18S and 28S fragment units (FU) in experimental spec- numbers (RIN) were derived from 18 s and 28 s fragment units (FU) in experimental specimens and imens and reference standards, as shown in 0, 24 and 96 h representative histograms. RIN < 7.5 is reference standards, as shown in 0, 24 and 96 h representative histograms. RIN < 7.5 is considered considered unfit for RNA studies, as it indicates a significant fragmentation. unfit for RNA studies, as it indicates a significant fragmentation. We used these mouse brain specimens to test the interaction between functional ge- We used these mouse brain specimens to test the interaction between functional nomicgenomic marks marks in mouse in mouse brains brains and andtime timein different in different postmortem postmortem intervals intervals.. For that, For we that, usedwe usedChIP- ChIP-qPCRqPCR to test to H3K4me3, test H3K4me3, H3K27Ac H3K27Ac and RNA and RNAPol II Polonto II promoters onto promoters of two of pri- two merprimer-amplified-amplified positive positive control control genomic genomic regions, regions, corresponding corresponding to to the the housekeeping housekeeping genesgenes beta beta actin (ACTB) (ACTB) and and glyceraldehyde-3-phosphate glyceraldehyde-3-phosphate dehydrogenase dehydrogenase (GAPDH), (GAPDH), which whichare permanent are permanent and constitutively and constitutively transcribed transcribed with a fixed with number a fixed of number occupancy of occupancy units across unitssamples, across as samples, well as aas negative well as a control negative primer control pair primer that amplifies pair that aamplifies region in a a region gene desert in a geneon chromosomedesert on chromosome 6 (Untr6), 6 as (Untr6), recommended as recommended by standard by standard quality control quality protocolscontrol pro- [34]. tocolsFigure [324].shows Figure the2 shows relative the increment relative increment of H3K4me3, of H3K4me3, H3K27Ac H3K27Ac and RNA and Pol RNA II binding Pol II bindingevents events in the amplifiedin the amplified ACTB ACTB and GAPDH and GAPDH genes comparedgenes compared to Untr6 to Untr6 negative negative control controlgenomic genomic region. region. RNA Pol RNA became Pol became undetectable undetectable faster than faster H3K27Ac than H3K27Ac and H3K4me3, and H3K4me3,indicating indicating less stability. less stability. Both histone Both modificationshistone modifications had a decrease had a decrease at 24 h, at were 24 h, similar were similarbetween between 24 and 24 72 and h and72 h suffered and suffered a drastic a drastic loss atloss 96 at h. 96 H3K4me3 h. H3K4me3 was was the modificationthe modifi- cationwith with stronger stronger signal signal over over input, input, as shown as shown in the in the increase increase of 200-foldof 200-fold above above input input at at the theGAPDH GAPDH gene. gene. This This indicates indicates that that H3K4me3 H3K4me3 may may have have a a better better detection detection stability stability up up to to72 72 h h of of postmortem postmortem interval. interval.

3.2. Stability of Functional Genomic Marks in Human Postmortem Prefrontal Cortex Specimens with RIN < 7.2 We hypothesized that H3K4me3 would be consistently detected in human specimens with poor RNA quality not considered adequate for transcriptional studies. We tested the same marks, H3K4me3, H3K27Ac and RNA Pol, in human postmortem prefrontal cortex specimens, which had a poor RNA quality (RIN < 6.2) and were not considered adequate for transcriptional studies. The specimens were obtained from the NNTC and were from ART-treated male subjects from the UCLA cohort between 2003 and 2011, following inclusion criteria described in methods. Table2 shows subject characteristics that were relevant for grouping.

Viruses 2021, 13, 544 7 of 24 Viruses 2021, 13, 544 7 of 24

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B RNA Pol H3K27Ac H3K4me3 RNA Pol H3K27Ac H3K4me3 RNA Pol H3K27Ac H3K4me3 Untr6 ACTB GAPDH 6 hr 24 hrs 48 hrs 72 hrs 96 hrs

FigureFigure 2. 2. PostmortemPostmortem stability stability of of genomic genomic epigenetic epigenetic marks marks positively positively associated associated with with transcription. transcription. Mice Mice were were sacrificed sacrificed andand maintained maintained at at room temperature forfor 6,6, 24,24, 48, 48, 72 72 and and 96 96 h h prior prior to to the the dissection dissection and and brain brain harvesting. harvesting. ChiP-qPCR ChiP-qPCR was wasperformed performed in isolated in isolated chromatin chromatin for thefor the detection detection of H3K4me3, of H3K4me3, H3K27Ac H3K27Ac and and RNA RNA Pol Pol II with II with ChIP-grade ChIP-grade antibodies. antibodies. The Thereactions reactions were were performed performed in triplicate in triplicate using using 25 µ g25 of μg mouse of mouse brain brain tissue tissue chromatin chromatin and 3 andµL of 3 H3K4me3μL of H3K4me3 antibody antibody (Active (ActiveMotif, Cat#39159),Motif, cat # 39159), 4 µg of 4 H3K27Ac μg of H3K27Ac antibody antibody (Active (Active Motif, Motif, Cat#39133) cat # 39133) or 4 µ gor of 4 RNAμg of PolRNA II Pol antibody II antibody (Active (Active Motif, Motif,Cat#91151). cat # 91151). We performed We performed qPCR usingqPCR two using positive two positive control primerscontrol primers that worked that wellworked in similar well in assays similar (ACTB, assays GAPDH), (ACTB, GAPDH), as well as a negative control primer pair that amplifies a region in a gene desert on chromosome 6 (Untr6). * p < as well as a negative control primer pair that amplifies a region in a gene desert on chromosome 6 (Untr6). * p < 0.05 in 0.05 in comparisons indicated by lines. comparisons indicated by lines. 3.2. Stability of Functional Genomic Marks in Human Postmortem Prefrontal Cortex Specimens Table 2. Specimen subject groups,with RIN viral < 7.2 load at CD4 nadir, ART prescribed at any given time and RNA integrity number (RIN) following extraction. We hypothesized that H3K4me3 would be consistently detected in human specimens with poor RNA quality not considered adequate for transcriptional studies. We tested the Group Case# Plasma VL at CD4 nadir ART History Frontal Cortex RNA RIN same marks, H3K4me3, H3K27Ac and RNA Pol, in human postmortem prefrontal cortex HIV+Meth- 4067specimens, 5.12 which had a poor ZDVRNA + quality 3TC, LPV (RIN + RTV, < 6.2) TFV and were not considered 6.6 adequate HIV+Meth- 4077 2.6 ATV, FTC, RTV, TFV, TFV + FTC 5.7 for transcriptional studies. The specimens were obtained from the NNTC and were from HIV+Meth- 4083 3.35 ZDV + 3TC, RFV, ATV, RTV, TFV/FTC 6.8 HIV+Meth+ 1163ART-treated 5.57 male subjects from DRV,the UCLA RTV, TFV cohort + FTC between 2003 and 2011, 4.6 following inclusion criteria described in methods.3TC, D4T, IDV,Table NVP, 2 shows RTV, ABC,subject characteristics that were rel- HIV+Meth+ 2074 4.57 5.2 evant for grouping. LPV + RTV, NFV, DDI, TFV HIV+Meth+ 4167 6.28 LPV + RTV, TFV + FTC 6.3 Legend: ART–Abbreviations:Table ZDV—zidovudine; 2. Specimen subject 3TC—lamivudine; groups, viral load LPV—lopinavir; at CD4 nadir, ATV—atazanavir; ART prescribed FTC—emtriditabine;at any given time and RTV—ritonavir; TFV—tenofovir;RNA DRV—darunavir; integrity number D4T—stavudine; (RIN) following IDV—indinavir; extraction. NVP—nevirapine; ABV—abacavir; NFV—nelfinavir; DDI—didanosine. Plasma VL at Frontal Cortex Group Case# ART History CD4 nadirDetection of viral RNA in matching paraffin embedded tissue sections byRNA RNAscope RIN HIV+Meth- 4067 5.12was detectable, althoughZDV not consistently + 3TC, LPV in+ RTV, all the TFV specimens (Figure3). Viral DNA6.6 was HIV+Meth- 4077 2.6not identified. ATV, FTC, RTV, TFV, TFV + FTC 5.7 Figure4 shows that a stronger increase over input was observed in both histone HIV+Meth- 4083 3.35 ZDV + 3TC, RFV, ATV, RTV, TFV/FTC 6.8 modifications compared to RNA Pol, with better results for H3K4me3. Like in mice, HIV+Meth+ 1163 5.57 DRV, RTV, TFV + FTC 4.6 H3K4me3 had a 200-fold increase in signal over input at the GAPDH gene, indicating HIV+Meth+ 2074 4.57cross-species 3TC, consistency.D4T, IDV, NVP, RTV, ABC, LPV + RTV, NFV, DDI, TFV 5.2 HIV+Meth+ 4167 6.28 LPV + RTV, TFV + FTC 6.3 Legend: ART–Abbreviations: ZDV—zidovudine; 3TC—lamivudine; LPV—lopinavir; ATV—atazanavir; FTC—emtriditabine; RTV—ritonavir; TFV—tenofovir; DRV—darunavir; D4T—stavudine; IDV—indinavir; NVP—nevirapine; ABV—abacavir; NFV—nelfinavir; DDI—didanosine.

Viruses 2021, 13, 544 8 of 24

Detection of viral RNA in matching paraffin embedded tissue sections by Viruses 2021, 13, 544 RNAscope was detectable, although not consistently in all the specimens8 of(Figure 24 3). Vi- ral DNA was not identified.

A) 4067 B) 4077 C) 4083

D) 2074 E) 4167 F) 1163

Figure 3. RNA scope in situ hybridization showing HIV-infected cells in prefrontal cortex. HIV Figure 3. RNA scope in situ hybridization showing HIV-infected cells in prefrontal cortex. HIV vRNA probes were used vRNA probes were used to detect active virus. Representative sections from the prefrontal cortex are to detect active virus. Representative sections from the prefrontal cortex are shown for specimens (A) 4067, (B) 4077, (C) 4083, (D) 2074,shown (E) 4167 for and specimens (F) 1163. (A Red) 4067, arrows (B) 4077, show ( Cexamples) 4083, (D of) 2074,positive (E) 4167staining and patterns (F) 1163. in Red pink, arrows which show correspond to expression ofexamples HIV vRNA of positive in the brain staining parenchyma. patterns in Magnification pink, which correspond 60X. to expression of HIV vRNA in the brain parenchyma. Magnification 60X. 3.3. Quality ControlsFigure in Genome-Wide 4 shows that Peaka stronger Data Confirms increase H3K4me3over input Stability was observed in Postmortem in both histone mod- Human Specimensifications compared to RNA Pol, with better results for H3K4me3. Like in mice, H3K4me3 had a 200-fold increase in signal over input at the GAPDH gene, indicating cross-species Given the relative stability of H3K4me3 in mouse and human specimens, this genomic consistency. unit was then selected for comparing two groups of HIV+ brain specimens represented by 6 cases with HIV-associated neurological diagnosis, divided in 2 subgroups, being 3 cases with no history of substance use and 3 cases that fit Meth-use criteria, based on self-reported recent and life-time use. Table1 shows the patient characteristics associated with the specimens used in signature comparisons. All unidentified subjects were 20–40-year-old males. Viral load at CD4 nadir, ART information and observations from available neuropsychological (NP) testing are shown in Table1. Prefrontal cortex specimens from these subjects had RIN values below 7.2. These were used to test the hypothesis that epigenetic marks are sufficiently stable to provide information from specimens that would otherwise have a limited value for the study of global changes. The results in Figure4A suggest that H3K4me3 signal is stronger than H3K27Ac, although both are stable in postmortem specimens up to 72 h postmortem. Figure4B shows H3K4me3 peaks in representative specimens, at the GAPDH gene. The quality of genome-wide H3K4me3 signal across samples was tested using different methods (Figure5). Active regions were merged within and between groups, revealing that specimens in both groups were stable regarding location of peaks relative to genomic coordinates and annotations mapped to the Human Hg38 assembly and visualized in pie charts (Figure5A). Tag distribution occurred predominantly in proximal promoters and introns, indicating that genomic activity was likely to be associated with transcriptional events, but also in regulatory regions, as opposed to Hg38 random controls in introns and non-coding regions (Figure5A). Differences in peak intensity have identified mutual as well as exclusive signatures, indicating that this strategy can distinguish between groups (Figure5B). For instance, 8525 active genomic regions were identified in HIV+Meth- specimens, which were not present in Meth users. Conversely, HIV+Meth+ specimens presented 7857 exclusive active regions. The majority of the active genomic regions were shared by specimens in both groups. The number of active regions per chromosome was also similar Viruses 2021, 13, 544 9 of 24

Viruses 2021, 13, 544 9 of 24 across cases and between groups (Figure5C), with large activity in chromosomes 1 and 19 and comparatively low activity in chromosomes 18, 21 and Y.

A) 250 *

c * 200

1 *

r 150

e 100

d 50 *

n * *

i *

B 0 HIV+Meth- HIV+Meth+ HIV+Meth- HIV+Meth+ HIV+Meth- HIV+Meth+ RNAPolII RNAPolII H3K27Ac H3K27Ac H3K4me3 H3K4me3 Untr12 ACTB GAPDH B) HIV+ Meth-

HIV+ Meth+

Pooled input

Figure 4. Comparison of detectable epigenetic marks in HIV+ prefrontal cortex specimens. Using ChIP-qPCR, we tested Figure 4. Comparison of detectable epigenetic marks in HIV+ prefrontal cortex specimens. Using our ability to detected genomic marks in postmortem human prefrontal cortex tissue with low RNA quality. (A) RNAPol, ChIP-qPCR, we tested our ability to detected genomic marks in postmortem human prefrontal cortex H3K27Ac and H3K4me3 were detected in 3 amplified genomic regions: A desert sequence in the human chromosome 12 was used as negative controltissue (white with bars), low ACTB RNA (gray quality. bars) (A )and RNAPol, GAPDH H3K27Ac (black bars) and H3K4me3promoters. were * p < detected0.05 compared in 3 ampliﬁed to Untr12 negative control. (B)genomic H3K4me3 regions: peaks in A the desert GAPDH sequence genomic in the sequence human in chromosome representative 12 waspostmortem used as negativespecimens control from both groups. * p < 0.05 in(white comparisons bars), ACTBindicated (gray by lines. bars) and GAPDH (black bars) promoters. * p < 0.05 compared to Untr12 negative control. (B) H3K4me3 peaks in the GAPDH genomic sequence in representative postmortem 3.3.specimens Quality from Controls both groups.in Genome * p <-W 0.05ide inPeak comparisons Data Confirms indicated H3K4me3 by lines. Stability in Postmortem Human Specimens Figure6 shows the H3K4me3 peak distribution patterns in both groups of specimens, bothGiven in average the relative plots and stability heatmaps of fromH3K4me3 tag distributions in mouse and across human target regionsspecimens, such this as gene ge- nomicbodies unit for allwas target then regionsselected (Figure for comparing6A,D), mapping two groups merged of HIV+ regions brain for specimens all peak regions represented(Figure by6B,E), 6 cases and transcriptionwith HIV-associated start site neurological (TSS, at 0 bp) diagnosis, proximal promotersdivided in (Figure2 subgroups,6C,F). beingThe heatmaps 3 cases with (values no history in z-axis/color, of substance regions use and in y -axis)3 cases are that used fit Meth for visualization-use criteria, of based read ondensities self-reported or H3K4me3 recent tagand distributions life-time use. (using Table bigWIG 1 shows metrics), the patient which characteristics are mapped associ- across atedtarget with regions. the specimens Peaks mapped used toin gene signature bodies comparisons. located between All −unidentified2 kb and +2 subjects kb, excluding were 20distal–40-year downstream-old males. and Viral upstream load at peak CD4 signals nadir, (FigureART information6D). Merged and Regions observations correspond from availableto all peak neuropsycho regions fromlogical−5 kb (NP) to +5 testing kb to includeare shown distal in promoterTable 1. Prefrontal and regulatory cortex regions specimens(Figure from6E). these Peaks subjects in proximal had RIN Promoters values below were located 7.2. These near were the TSS,used betweento test the− hypothesis1.5 kb and that+1.5 epigenetic kb (Figure6 marksF). These are heat sufficiently maps show stable H3K4me3 to provide distribution information patterns from that specimens characterize that woulddifferent other geneswise and have active a limited genomic value regions. for the Thestudy heatmaps of global were changes. clustered into 5 groups basedThe on results where in tags Figure were 4A located suggest within that the H3K4me3 gene sequence, signal is using stronger default than settings. H3K27Ac, The althoughclusters C1–C5 both are differed stable between in postmortem gene body, specimens merged upand to promoter 72 h postmortem. heatmaps, Figuredue to the 4B showscut-off H3K4me3 distance frompeaks the in representativ TSS used toe build specimens, each one at the of them.GAPDH These gene. patterns are not expectedThe quality to differ of atgenome least drastically-wide H3K4me3 between signal groups, across in samples spite of was the differencestested using in differ- Meth entuse, methods which might (Figure be 5). detected Active ratherregions by were a granular merged analysis within ofand peak between intensities. groups, Thus, reveal- the ingcomparison that specimens between in both HIV+Meth- groups were and HIV+Meth+ stable regarding in Figure location6 suggests of peaks that relative specimens to ge- nomicin both coordinates groups followed and annotations similar and mapped normal to pattern the Human distributions Hg38 assembly with parallel and visualized clusters, inindicating pie charts no (Figure aberrant 5A). antibody Tag distribution binding and occurred signal abovepredominantly pooled input in proximal controls, promoters adding up andto build introns, conﬁdence indicating on datathat quality.genomic The activity comparison was likely can be to also be associated established with and visualizedtranscrip- tional events, but also in regulatory regions, as opposed to Hg38 random controls in introns and non-coding regions (Figure 5A). Differences in peak intensity have identified

Viruses 2021, 13, 544 10 of 24

mutual as well as exclusive signatures, indicating that this strategy can distinguish between groups (Figure 5B). For instance, 8525 active genomic regions were identified in HIV+Methas average- plots specimens, (Figure 6whichA–C), whichwere not average present the in values Meth for users. all target Conversely, regions inHIV+Meth+ heatmaps specimensinto histograms presented and indicate 7857 exclusive tag distribution active regions in reference. The tomajority the TSS of in thex-axis. active Consistency genomic regionsacross specimens were shared was by further specimens conﬁrmed in both from groups. a pairwise The number comparison of active of regions the averaged per chro- tag mosomenumbers was for mergedalso similar regions across in the cases HIV+Meth- and between and groups HIV+Meth+ (Figure groups; 5C), with a scatter large plot activity was inproduced chromosomes with a 1 Pearson and 19 correlationand comparatively coefﬁcient low = activity 0.958 and in achromosomes slope = 0.977. 18, 21 and Y.

hg38_0_Random _Control 1_03XU_M ild_H3K4m e3_hg38 2_03XV_HAD_H3K4m e3_hg38 A) HIV+Meth- HIV+Meth+ Hg38 Random Controls DIST PROM (1−3 kb) PR5‘O−XUTR PROM (0−1 kb) DIST INTERGENIC DIST INTERGENIC EXON DIST PROM (1−3 kb) DIST PROM (1−3 kb) DIST DOWNSTR (1−3 kb) DIST DOWNSTR (1−3 kb)

PROX DOWNSTR (0−1 kb) PROX DOWNSTR (0−1 kb)

3‘−UTR 3‘−UTR DIST INTERGENIC PROX PROM (0−1 kb) PROX PROM (0−1 kb)

INTRON 5‘−UTR INTRON INTRON 5‘−UTR DIST DOWNSTR (1−3 kb) PROX DOWNSTR (0−1 kb) 3‘−UTR EXON EXON 4500 B) C) 4000

HIV+Meth- HIV+Meth+ 3500

3000

)

(

2500

o 2000 25561 C 8525 7857 1500 1000

500

0 1 2 3 4 5 6 7 8 9 X Y 10 11 12 13 14 15 16 17 18 19 20 21 22 Chromosome

1_HIV+Meth- H3K4me3 Intervals 2_HIV+Meth+ H3K4me3 Intervals # CpG Islands

Figure 5. Mapping of HeK4me3 active genomic regions in human postmortem brain specimens. ( A) Pie charts showing the distribution of H3K4me3 in gene sequences in both groups, with genome wide frequency of modiﬁcationsmodifications within introns, exons, exons, 5′ 5-0UTR,-UTR, proximal proximal (0(0–1–1 kb) kb) andand distal distal (1– (1–33 kb) kb) promoters, promoters, distal distal intergenic intergenic promoter promoter regions, regions, proximal proximal (0–1 (0–1kb) and kb) distaland distal (1–3 (1–3kb) kb)downstream downstream regulatory regulatory regions regions and and 3′- 3UTR.0-UTR. As As a control, a control, randomly randomly located located peaks peaks were were also also ran againagainstst the same genomic features features database database using using the the entire entire interval interval region region aligned aligned to to the the Human Human Hg38 Hg38 genome genome assembly. assembly. (B) Venn’sVenn’s diagram diagram showing showing genome genome wide wide exclusive exclusive and and mutual mutual merged merged active active regions regions in HIV+Meth- in HIV+Meth and- HIV+Meth+ and HIV+Meth+ cases cases (n = 3/group). (C) Number of HeK4me3 active peak intervals and CpG islands per chromosome in averaged (n = 3/group). (C) Number of HeK4me3 active peak intervals and CpG islands per chromosome in averaged HIV+Meth- HIV+Meth- and HIV+ Meth+ cases. and HIV+ Meth+ cases. Figure 6 shows the H3K4me3 peak distribution patterns in both groups of specimens, both in average plots and heatmaps from tag distributions across target regions such as gene bodies for all target regions (Figure 6A,D), mapping merged regions for all peak regions (Figure 6B,E), and transcription start site (TSS, at 0 bp) proximal promoters (Figure 6C,F). The heatmaps (values in z-axis/color, regions in y-axis) are used for visualization of read densities or H3K4me3 tag distributions (using bigWIG metrics), which are mapped across target regions. Peaks mapped to gene bodies located between −2 kb and +2 kb, excluding distal downstream and upstream peak signals (Figure 6D). Merged Re- gions correspond to all peak regions from −5 kb to +5 kb to include distal promoter and regulatory regions (Figure 6E). Peaks in proximal Promoters were located near the TSS, between −1.5 kb and +1.5 kb (Figure 6F). These heat maps show H3K4me3 distribution patterns that characterize different genes and active genomic regions. The heatmaps were

Viruses 2021, 13, 544 11 of 24

clustered into 5 groups based on where tags were located within the gene sequence, using default settings. The clusters C1–C5 differed between gene body, merged and promoter heatmaps, due to the cut-off distance from the TSS used to build each one of them. These patterns are not expected to differ at least drastically between groups, in spite of the differences in Meth use, which might be detected rather by a granular analysis of peak intensities. Thus, the comparison between HIV+Meth- and HIV+Meth+ in Figure 6 suggests that specimens in both groups followed similar and normal pattern distributions with parallel clusters, indicating no aberrant antibody binding and signal above pooled input controls, adding up to build confidence on data quality. The comparison can be also established and visualized as average plots (Figure 6A–C), which average the values for all target regions in heatmaps into histograms and indicate tag distribution in reference to the TSS in x-axis. Consistency across specimens was further confirmed from a pairwise comparison of the averaged tag numbers for merged regions in the HIV+Meth- and Viruses 2021, 13, 544 11 of 24 HIV+Meth+ groups; a scatter plot was produced with a Pearson correlation coefficient = 0.958 and a slope = 0.977.

Prom oters (TS S ) G enebodies M erg ed P eak Regions

0 Mild H3K4me3

4 H3K4me3 HIV+Meth- Mild H3K4me3 MildH H3K4me33K4me3 HIV+Meth- s

H3K4me3 HIV+Meth- t

0 A) B) 5 C) HAD H3K4me3

t n

t H3K4me3 HIV+Meth+

3 HADH 3H3K4me3K4me3 HIV+Meth+ 3 HADH H3K4me33K4me3 HIV+Meth+

n n Pooled Input

u o Pooled input u Pooled Input PooledPo Inputoled input

c Pooled input 0

I h

l 0

−2kb 0bp 0bp 2kb −5kb 0bp 5kb −5kb 0bp 5kb Interval Interval Interval D) E) F) H3K4me3 HIV+Meth- H3K4me3 HIV+Meth- H3K4me3 HIV+Meth+ Pooled input H3K4me3 HIV+Meth+ Pooled input H3K4me3 HIV+Meth- H3K4me3 HIV+Meth+ Pooled input

Interval Interval Interval ChIPseq counts

FigureFigure 6. 6.Average Average plots plots and and heatmaps. heatmaps Average. Average plots plots and and heatmaps heatmaps were were produced produced from from aggregated aggregated peak peak scores scores indicating indicat- taging distributions tag distributions (using (using bigWIG bigWIG metrics) metrics) across across target target regions regions identiﬁed identified as ( Aas,D (A) gene,D) gene bodies bodies (+/ (+/−2− kb),2 kb), (B (,BE,)E Merged) Merged RegionsRegions (+/ (+/−−5 kb) andand ((CC,F,F)) proximalproximal to to transcription transcription start start site site (TSS) (TSS) promoter promoter regions regions (+/ (+/−−1.51.5kb). kb). (A(A––CC)) Average Average plots plots correspondcorrespond to tohistograms histograms of the of average the average distribution distribution of all regions. of all regions. (D–F) Values (D–F) inValues heatmaps in heatmaps are represented are represented in z-axis/color in z- andaxis/color regions and in y regions-axis, using in y- aaxis, 5-cluster using default,a 5-cluster indicated default, by indicated C1–C5 inbyy -axisC1–C5 for in each y-axis region for each of interest. region of The interest. gradient The gradient blue-to-white color indicates high-to-low count in the corresponding interval region. blue-to-white color indicates high-to-low count in the corresponding interval region. 3.4. H3K4me3 Can Provide Insights into Biological Processes and Pathway Usage in Specimens 3.4. H3K4me3 Can Provide Insights into Biological Processes and Pathway Usage in Specimens withwith Limited Limited Value Value in in Global Global Transcriptome Transcriptome Studies Studies WeWe used used H3K4me3 H3K4me3 to to test tes ourt our ability ability to to use use epigenomics epigenomics as as an an alternative alternative to transcrip-to tran- tomescriptome strategies strategies and increase and increase power power in archived in archived human human tissues. tissues. Peak values Peak werevalues averaged, were andaveraged fold change, and fold between change HIV+Meth- between HIV+Meth and HIV+Meth+- and HIV+Meth+ groups was groups calculated. was Thecalculated. strong signalThe strong of H3K4me3 signal of and H3K4me3 differences and differences between groups between were groups detectable, were detectable, as exempliﬁed as ex- in Figureemplified7, showing in Figure a representative7, showing a representative randomly selected randomly genomic selected region genomic associated region with asso- theciated AATK with (Apoptosis-associated the AATK (Apoptosis tyrosine-associated kinase) tyrosine gene kinase) sequence gene in sequence chromosome in chromo- 17. A representativesome 17. A representative specimen from specimen the HIV+Meth+ from the HIV+Meth+ group showed group a higher showed H3K4me3 a higher peak in theH3K4me3 proximal peak promoter in the regionproximal compared promoter to aregion representative compared HIV+Meth- to a representative specimen, indicating that promoter activity is higher for this gene in the context of Meth, suggesting it is more likely to be transcribed in drug users, while activity within the intron did not differ between the specimens.

The application of a systems biology approach via Genemania and IPA has suggested that this strategy is able to provide important information on functional processes and pathway usage, while mapping effects of infection and co-morbidities on functional units of the genome. Overall, we found 6062 active intervals located upstream the gene sequence, of which 2210 were within proximal promoter regions (−1 kb–0) (Figure6F, C2). Of these, 1540 showed a high degree of connectivity based on pathway and physical interactions, a large fraction of them with detectable changes in peak intensity between specimens of the two groups (Figure8). An approach limited to genes containing H3Kme3 in proximal promoters produced signiﬁcant annotations in OMIM_DISEASE that include somatic cancer (p = 0.0003) and susceptibility to obesity (p = 0.006). Overrepresented functional annotations are within phosphoproteins (p = 5.3 × 10−103), alternative splicing (p = 5.8 × 10−31) and ubiquitin conjugation (p = 4.7 × 10−28). Biological processes (GOTERM) include positive regulation of GTPase activity (p = 6.8 × 10−10), Regulation of transcription Viruses 2021, 13, 544 12 of 24

(p = 3.2 × 10−9), Wnt signaling pathway (p = 1.8 × 10−8), Cell migration (p = 2.4 × 10−7), MAPK cascade (p = 1.7 × 10−7), neuron projection development (p = 3.0 × 10−6), positive regulation of apoptosis (p = 4.3 × 10−6), oxidative response (p = 6.8 × 10−5) and glial cell differentiation (p =6.6 × 10−4). Pathway annotations (KEGG) for genes with proximal promoter epigenetic activity are Pathways in cancer (p = 2.5 × 10−16), several pathways Viruses 2021, 13, 544 involved in neurotransmitter signaling and neurological pathogenesis, such as12 choliner- of 24 gic synapse (p = 1.5 × 10−7), oxytocin signaling (p = 6.8 × 10−7), glutamatergic synapse (p = 9.4 × 10−7), dopaminergic synapse (p = 3.9 × 10−6), cAMP signaling (p = 5.6 × 10−5), amphetamine addiction (p = 1.2 × 10−5), serotoninergic synapse (p = 9.4 × 10−4), morphine HIV+Meth- specimen, indicating that promoter activity is higher for this gene in the con- addiction (2.7 × 10−3) and endocannabinoid signaling (p = 2.5 × 10−3). Inﬂammatory and text of Meth, suggesting it is more likely to be transcribed in drug users, while activity chemokine pathways were also identiﬁed (p = 1.4 × 10−5). This indicates that epigenetic withinstrategies the intron like did this not can differ distinguish between disruptions the specimens. in neurotransmitter signaling.

Figure 7. Example of detectable H3K4me3 tag differences between HIV+Meth- and HIV+Meth+ Figurespecimens. 7. Example AATK of detectable Gene (apoptosis-associated H3K4me3 tag differences tyrosine between kinase), HIV+Meth with similar- and marks HIV+Meth+ in the gene specimens.regulatory AATK region, Gene but (apoptosis enhanced-associated at the promoter tyrosine in a kinase), HIV+Meth+ with representativesimilar marks specimen.in the gene Visualized regulatory region, but enhanced at the promoter in a HIV+Meth+ representative specimen. Visualized using Integrated Genome Browser. using Integrated Genome Browser. We have identified 2210 genes with active proximal promoter, with both increased (orangeThe application shades) andof a systems decreased biology (blue approach shades) promoter via Genemania activity and associated IPA has withsuggested Meth use that andthis whitestrategy nodes is able corresponding to provide to important no change information (Figure8). In on Figure functional8, it is important processes to and notice pathwaythe high usage, degree while of mapping connectivity effects between of infection the genes and thatco-morbidities have H3K4me3 on functional activity, indicating units of thethat genome. genomic Overall, activity we is anfound orchestrated 6062 active phenomenon. intervals located Nevertheless, upstream many the gene genes se- with quence,significant of which proximal 2210 were promoter within activityproximal did promoter not show regions functional (−1 kb or–0) physical (Figure interactions6F, C2). Of these,with each1540 othershowed and a remained high degree independent. of connectivity A complete based liston ofpathway these genes and andphysical fold changes interactions,between a large groups fraction is available of them in with Supplementary detectable changes Materials. in peak intensity between specimens of Thethe two genes groups in Figure (Figure8 were 8). furtherAn approach filtered limited based onto agenes significant containing +/ − H3Kme31.5-fold change in proximalcut off promoters in a comparison produced between significant HIV+Meth- annotations and HIV+Meth+.in OMIM_DISEASE Tight clustering that include criteria somaticproduced cancer two (p = high 0.0003) score and nodes, susceptibility calculated to in obesity Cytoscape (p = default0.006). Overrepresented network topology func- statistics tionalsettings, annotations based are on within the number phosphoproteins of neighboring (p = 5.3 nodes × 10 and−103), thealternative number splicing of connected (p = 5.8 pairs × 10−between31) and ubiquitin all neighbors. conjugation The first,(p = 4.7 a 5.2× 10 score−28). Biological node, indicates processes a cluster (GOTERM) where include each gene positiverepresented regulation by of node GTPase circles activity have ( anp = average 6.8 × 10− of10), 5.2 Regulation related neighboring of transcription genes (p and= 3.2 with × 10−interdependent9), Wnt signaling functional pathway domains(p = 1.8 × based10−8), Cell on genomic migration (pink (p = edge 2.4 × connectors), 10−7), MAPK physical cascade (red) (p = 1.7and × pathway10−7), neuron interactions projection (green development connectors) (p = (Figure 3.0 × 109A).−6), Thepositive genomic regulation regions of annotatedapop- tosisto (p genes = 4.3 × in 10 this−6), clusteroxidative exhibited response both (p increased= 6.8 × 10 (orange−5) and glial nodes) cell and differentiation decreased (blue (p =6.6 nodes) × 10−promoter4). Pathway activity, annotations or no (KEGG) change (whitefor genes nodes) with proximal in correlation promoter with epigenetic Meth use (Figureactivity9 A). are PathwaysIn order to in further cancer visualize (p = 2.5 the× 10 potential−16), several implications pathways ofinvolved those changes in neurotransmitter and relationships signalingbetween and theseneurological genes, we pathogenesis, performed asuch functional as cholinergic annotation synapse cluster (p = analysis 1.5 × 10− (Figure7), oxy-9B), tocinwhich signaling identified (p = 6.8 biological × 10−7), glutamatergic processes as synapse well as ( transcriptionp = 9.4 × 10−7), regulatorsdopaminergic (Figure synapse9B). For (p = instance,3.9 × 10−6), increased cAMP signaling promoter (p activity= 5.6 × 10 associated−5), amphetamine with Meth addiction included (p genes= 1.2 × annotated 10−5), serotoninergic synapse (p = 9.4 × 10−4), morphine addiction (2.7 × 10−3) and endocannabinoid signaling (p = 2.5 × 10−3). Inflammatory and chemokine pathways were also identified (p = 1.4 × 10−5). This indicates that epigenetic strategies like this can distinguish disruptions in neurotransmitter signaling.

Viruses 2021, 13, 544 13 of 24

to acute inflammation, macrophage infiltration, cell movement of antigen presenting cells as well as necrosis, apoptosis, and cell death of muscle cells. These changes were predicted as being orchestrated by IFNG, which showed a significant 2.3-fold increase in its promoter genomic activity (p < 0.0001). Other IFN pathway components such as IRF1 (1.8-fold, p < 0.0001) or transcriptional regulators JUN (1.8-fold, p < 0.001) and SOX2 (1.8-fold, p < 0.001) were also identified as key components that showed significant changes in the context of Meth (Figure9B). Overall, this suggests that the inflammatory process is enhanced in HIV+Meth users, in an IFN-dependent manner. Interestingly, genes with decreased promoter activity (shades of blue) shared connections with genes with increased promoter activity, based on physical protein–protein and genetic interactions, as well as shared domains or pathway, as shown in Figure9A, indicating potential regulators. Viruses 2021, 13, x FOR PEER REVIEW 13 of 25 Neurological pathways involved memory and cognition, as well as CREB signaling in neurons (Figure9B) were identified. In Figure9B, edges or connectors are directional.

NYNRIN

KIAA0141

ZNF341

FAM135B ZNF333

RNF38 ZDHHC17 DNAH11 ABCG4

DDI2 LMAN2L AK3

HS1BP3

METTL21A BAG3 SNN FUT9 CNTN4 PRSS35

FAM118A KAZN

MTFMT AGTPBP1

SLC7A1 CACNG8 PEAR1

SLC9A7 SEMA3C

RASGRF1 CACNA2D3

DDHD2 WDR45 MTMR6 BEAN1 IFI6 PLEKHO2 ZC3H7A LRP3 TANC2 CACNG4 FAM124A TOX2 CDKL2 ELMOD2

MCTP2 KIAA0930 PHF3 FBXL7 FAM214B MAP6 TSPAN7 SLC4A3 GIGYF1 PPFIA2 VSTM2A VSNL1 TCEAL8 EBF2 PTPN9 GYG2 GTDC1 CETN3 MUM1 UNC13A PSTPIP2 CEBPG ZNF681 FAM131C FAM19A4 POMGNT2 ALG10B ZXDA TANC1 SCAMP5 GPR12 RALGPS1 C11orf49 RNF187 KCNJ4 TMEM55A UNC5D WEE1 ARHGAP22 CAMKV EEPD1 POGK SYT17 ABO NCKAP5 RAB39A LINGO2 N4BP2L2 PPL GPRIN2 N4BP3 RAD54L SFRP5 KCTD6UNC5A ERC2 WDR44 SUMO3 ZNF800 MPP6

MEX3B ATP6V1G1 TEX2 ARMC9 METTL16 APP LDLRAP1 CARNMT1 ARID3B RASA3 FRZB EPC2 BTBD2 ZNF195 KCNJ12 ARHGAP12 NFE2L3 ADCYAP1R1 VPS8 CDC42SE2 TRIB1 WNT7B KLHL2 SFRP2 JMY VAPB OSBP2 ZBTB34 COG2 YWHAH EBF3 KCNK3 CHD6 ADGRL1 INA RASGRP1 WNT6 TOMM7 TMSB4X MPV17L TCP11L2 ZNF12 CAMK1D PXDC1 ZC3H6 SLC17A5 CNDP1 TNFAIP8L1 ZNF322 NOVA2 IFNGR2 COX5A RIMS4 ELMO1 JAG1 SNRK COG3 TBC1D1 CHGA PDLIM3 DTNB MARK1 POP7 HIKESHI OSBPL6 CEP97 FRYL ADI1 SETDB1 ZNF274IL1RAP KCNG2 PDSS2 PDZD4 GTF2E2 SYK ROR2 NSD1 SSH1 SV2B CACNA1D UCP2 RUFY3 CCZ1 ATCAY DNMT3A YTHDC2 KIAA1841 YTHDF1 PRSS12 MTMR2 NAPB MERTK RARRES3 NKD1 GPR37 HUNK SIRPA RIMS1 PTPRE GRIK3 FZD2 KLHL3 OSBPL3 CAMK2N1 CLEC2B LRRC8D IQGAP2 GRIN2B OSTM1 C6orf203 TSPAN13 LMO1 SLC16A7 PPFIA1 PTPN12 EPB41L4B ARC MSANTD3PDLIM1DEPDC1B ARAP3 ITPR3 CACNA1I ENPP1 SEPHS1 PID1 SRPK1 RCBTB2 BRSK2 PTPRG FZD7 ACSL3 CAMK2B AGPAT3 PRRC1 SOCS6 CACNA1A CENPC WBP2 IFNL2 C9orf72 RHPN2 WNK2 LIMK1 ARHGEF16 ABCC8 SDE2 KIAA1217 ZCCHC10 ARHGAP17HNRNPA1L2 GRB10 N4BP1 SFXN1 DAG1 KCTD12 RLIM LDLRAD3 BIK AGAP1 SNF8 CDH23 LAMC3 FEZ2 PHYHIP MTUS2 RANBP9 FZD5 KIF26B CDK5R2 CFL2 GGA2 PLXND1 MYCBP2 RHOB ADCY5 SCN5A HTR7 DLGAP3 SIK2 RRBP1 SOCS5 DCHS2 ARL4C DAPK2 RAB11FIP1 PPIF NKX3-1 CLPTM1L RPH3A FRA10AC1 AMER3 BMF DGKZ LUZP1 ANKRD6CACNA1HKLHL26 KCNG1 SNTB1 FBXO42 ANK1 NTRK2 COPS4 BCL7A KCNN2 TFDP1 PHF20 GPC3 IGFBP4 CHCHD6 AMOT SIK3 PARD3B ANOS1 KCNH2 LETMD1 GHR TAF1 FAM168B ZFP64 MTHFD1L WNT7A TTYH3 ACY3 INIP APBA2 PRICKLE1RARG DDX27 KLHDC2 TXNL1 TAF5 TRIM2 WHSC1 GK PAWRCOL12A1 AGTR1 STX1B CPLX1 FRMD5 KCNN3 WNT5B USP25 VDR PCDH9 CAPZB ALDH3A2 LLGL1 KCTD18 ARHGAP21 RGS10 ATF7IP2 ASB8 ABLIM2 TCEAL2 CDH22 PLEKHA1 KCNB1 CCKBR BACH2 HECW2 CXCL12 EGR1 SCD5 VPS37D PAG1 PNRC1 CDH8 PTGES3 NEDD4 RASGEF1A SRGAP1 LRP6 PRKCD ITPR2 RNF165 TMED3 CTGF RAP1B TIMP2 IGSF21 PIEZO1 ZIC3 RAB27B VPS18 JADE1 LIN9 RBX1 UCP1 NTSR1 NPAS2 CAB39 ADCY3SHANK2 AGO4 BATF3 WNT3 ABTB2SH3PXD2A SCYL1 LRP4 SNTA1 TBC1D14 MIA3KIDINS220RHOG SYT1 DOK4 RPH3AL LIMD1 CSPG4 SNRPC CTSZ LARP1B LSM14A ZNF423 BCL7B PDE3A SLC40A1NUDCD3 CCSER2 RRP15 TAF1B PTPN2HCRTR1MACF1 CNTNAP2 ADGRB1 UPF3B SLC6A3 PIP5K1B RFT1 IFNLR1 NR1D2 NKD2 EXPH5 PAFAH1B2 RIMBP2 CENPE GNG8 TERT AXIN2 PI4KB RTKN NTN1 UBE3A CELSR1 LRP12 ADCY1 REST SEH1L STK11 PIN4 RHOBTB1 MAPK8 CASK RGS17 SPRED1 ELF4 C14orf159 EPB41L1 AQP3 DENND1A KSR2 APC MSRB2 PRPF3 VAV2 SPSB1 PPARG SORCS2 GPT2 DUSP4 MINA MARK4NEDD4L PRR16 ALOX5 ZNF395 TNK2 F3 SMAD6 SH3PXD2B RAC1 GPC4 FOXO1 SMURF2PLCB1PDGFRB PLEKHA2APLN PPTC7 PRKAA1 DDX6 JAK3 DOT1L SOGA1 PDXK RAB27A SRCIN1 BDNF HP1BP3 ACAT1 CACNA2D1 GUCY1A2 STAC STK39 SIN3B KALRNOSBPL5 INSR LRRFIP1 CHMP6 FGF9 MYO18ANR3C2 CGNL1 COG1 ARHGEF28 CREB1 BARX2 FRAT1 PACSIN1 LRP1 IFNGR1 CNR1 PIK3C2A IMPA2 PALM EIF4A3 AK2 CREG1 CAND1 MLXIPL SOX13 UBE2G1 ZFYVE9 ARFGAP3UBE2K YPEL2 NGFR UBE4BCCDC88AKRAS IGF2BP2HABP4 EPHB6 FKBP4 MET KIFC1 DYRK2 DUSP10NDEL1 NTPCR CAMK2G RCC2 SCAF4 VIPR2 IGFBP2 PPP2R5E RPS6KA6 PCDH1 DCHS1 VPS37C NPC1 WWC1 TRIB2 GLRB NPM2 DNAJB6 DKK2 RFC5 PRKACB PLCH1 CDH4CCDC61 GJB2 LETM1 TTPA USP10 UBE2I MPP5 IRF8 ME2 PPP6R2 SGIP1 MAP4 WIPF1 GEMIN6 LMO4 GNA13LAMB2 ITPKB TSHZ2 GJB6 ATL2 GRIA2 REV1 DGKI RELN SSFA2 CTTN SIM2 C11orf84 FAT1 COBL DPYSL5 CTBP2 CFAP36 DBR1 LAMA3 GNAI1 FBXO17 ACTR5 CXCL1 IGF2R MCM9 MCC CCNA1 NCK2 LAMC1 RYR2 PLCB4 MTCL1 MX1 UHRF1BP1FCHSD2 HSPG2 RBM3 MTPAP CBFB RFC2 MYB UBASH3B EMSY SDC4 KHDRBS1RPS6KA5 PLRG1 LRRK2 ERBIN ID4 LAS1L PDE4A IQSEC2 HN1 PPP1R3C KRBOX4 EPPK1 HLA-A VANGL1 COPZ1 ARHGAP6 MARCKS SP1 KPNA2 ITSN2 TRPM7 PRMT2 SFRP1 YTHDF2 SMURF1 PDGFB GLDC NEDD1 HNF4G AMZ2 POGZ NELFCD RALYL TRIB3 WDR5 GNG7 MEIS2 RXRA TRIO SNX2 AIRE GRID2 KLHL5 CNNM4 PACSIN2 BAIAP2L1SSBP2 ATP6V1B2MYO5A GNB4 LGALS3 LIN54 NR2E1 GNA11 BRINP1 GALNT12CDK17 KIF1B RND3 ITGA6 PIK3CA PTPRJ GRIN1 DNMT3B UBB ACTN2 ATP6V1C1 KCNIP4 ANKS6 NUP58 STX2 FGFR3 ARRB1 MSI1 ARNT2WASF2 CELSR2 DERL1BDKRB2 SGCG NXN MICU1 ATP9A PDZD2 SF3B1 PHF20L1 ACTN4 CLSPN MEAF6 ABCC1 EPS15 NANS GORASP2 INPP4A GINS2 FECH EIF1AY RBM39 ZNF639 IL15 PIP4K2A GSTP1 ZMIZ2 JAK1 CLIP1 ADCY9 SYNJ2 SPG21 ERN1 DOCK11 CCDC9 ZFYVE27 FGFR2 MAP3K5DOCK6 RGS2 FLT3 MPDZ NF2 ELK3 CEP55 SSR3 MMGT1 TBC1D9B INTS4 XPOT EPHA8 BCL11B GOLGA3 FCHO2 SEL1L KITLG PCDH19 ARRDC4 DGCR6L TP53BP2 KLF16 EPB41L5 USP3SMARCC1 TCF7L1MYH9 GTF2B TSC1 BRD7 NCS1 C8orf33 MYBL2 BAMBI GADD45ASPRY2 PIAS4 MAP4K4PIK3CB CCNE1 RNF13 RASGEF1CPITRM1 FGF16 VCAN NCOA5 KIAA0355 RBM19 USP13 NAV2 NFASC CBX4 RASD2 CTCF MGMT KBTBD2 CUL3 CCDC39 BLMH EIF4B SMAD2 XPNPEP1 HNRNPF KSR1 IRF4 USP28 ABL2 YY1 TUBA1A PDGFC KIF20B KDELR2 CSRP1 RAP1GDS1 SNRPD1 ASCC2 C15orf59 PRKAR2B CARTPT CCNF RET KCTD7 ANKS1B KLF15 STAT5A VEGFA IL4R MCM3 GRIK4 RRM2 ACO1 RAI2 POU2F1 CHORDC1 FGF5 RDX ADRA1D NDUFA4 PARD6G FBXW8 PRDM1 SUZ12 PTPRM AHCY KIAA1211 KDELR3 KLF6 FBXL2 FERMT2LOXL2 ACVR1 UBAC1 RFC1 GAREM2 HLF DNAH5 TRIM24AFG3L2 CTSB FLNB RAP2A GRM7 SYTL2 MCU MTHFD1 BNIP3 SPIDR RAPGEF5 UST C20orf196 STAU1 STK11IP MAP3K8 RAB6B TRIM68 XPO4 DDX39A GATAD2APTK2 RBM5 PPP1CC MMP16 PRPF40B NOL4 ETV5 ASAP1 ZNF24 RABEP1 DDX60 NCOA1 TARDBPDYNC1H1WASF3 TCF24 DOK6 CPT1A MRPL19 TTK FKBP8 FOXK2 DRD1 TMEM248 PIK3R4 MTX2 PJA2 KNDC1 TXLNG RTN4R GAS7 APOOL NCOA6 PTPN14 KIFAP3 HDAC2 BCORL1 SLC1A3 ZBTB2 MKI67 AKT3 CNTN2 GATB DHRS7 PKNOX1MED9 SP4 OR3A1 DEK EFNB1 KMT2A STXBP1 PHC1 IRF5 PCMTD2 MTCH1 TFG PIM1 AP2S1 ACAP2 SLC1A1 KIF1A FBL FBXO25 ITGA9 MSL3 OTUD1 USP6NL ITGB4 TCF3 ATP2A3 ETS2 UGCG PCM1 FOPNL CRTC1 SCAF8 STRBP EPS8 CC2D2A BMP2K SEPP1 EGFR CMTM8 EZH2 RCAN1MECP2 PSAP DEPTOR RGPD8 YAF2 SMARCA4 BCR DUSP16 KPNB1 CDH2 TRAPPC8 PCGF3 PDS5A PUM3 GTF3A PRRG1 TRAPPC10 WWOX KAT2B CCDC53KCNJ3 UBE3C RNF19B CTNND2 ATP2B1 CSDE1 NCOR2 WIPI2 SHANK3 DACH2SYNCRIP PKP2 SMARCD3 DNAH8 GAL INTS10 VRK1 ZNF616APPL2MAPRE1 CADM1 ASB13 CUL5 NAMPT SYNE2 NECTIN2 DPYSL2 DSG2 TCF15 KIF11 FBXL4 RBMS2ANGPT1LRIG1 RABL2A ZNF592 DDIT4 PAFAH1B1 RBBP7GABARAPL1TTC1 ZNF131 SEMA3F INTS3 CDC42BPA SNX30 FAH ANXA6 FGF2 BRWD3 MEGF9 ADRB1 PSMC6 PDK3 SNAI1 PBX1 MYO10 ATR DPF1 PRKCB APLP2 SUN1 BRD4 NFIC PTPRZ1 SOS1 KCND2 ATP2B2 PDCL PXN ITGA11 ITGAV ULK1 SPR SSU72BRMS1L RAN TBL1XR1 ECE1 PTBP3 PDS5B SPIN1 SH3KBP1 CXCL2 CTNNB1 BCL9L REPS2 TUBB6 ITGB3 MLLT1 KPNA3 CTNNA2 PLEKHA5 DUSP14 DPY19L4 CYTH3 SVIL FBRSL1 PRDX1 NBEA RPF2 DIEXF ASS1 ARFGEF3 MED13L WDR43 MICALL1 HTR1F MPP3 RGS7 ROCK2 NUP210 CYFIP1GTF2IRD1 SMS PPARA PTPRT MAP3K10 ATIC SH3BP2 DHX30 SCML2 ANGPTL4 RUNX1T1 RNF126 WTAP RAD18 FANCD2 ATF7IP TSPAN14 SUN2 ARHGDIA BTRC SH3GL2 SIRT1 DPP6 RFX3 IQSEC1 CBX6 KLHL25 AJAP1 E2F1 SUSD1 SENP5 BCL11A LRRC47 TRAF3IP1 GRIK2 RPS6KA3 SPATS2L AKIRIN2 TIGAR CAMSAP2 USP9X ACVR1B YAP1 RHEB ZHX2 DZIP1 QSER1 GOLM1 ST8SIA2 XPO6 PPP2R2A ITGB1 CUX1 UBE2R2NGDN USP33KCTD15 TRPC4 KLF4 RNF169 WDR59 RHBDD1 MEF2A RXRG GPC1 DGKHGABRB2 L3MBTL1 DNAJC11 MIER3 SRRM1 KMT2C GALR1 USP22 RPA2 MAP3K1 SPAG9 ETV6 NFATC4 WIPF2 ZFHX3 SLC25A24IVNS1ABP NFIA EIF3E UPF1 ZNF605 SSX2IP KCNQ4 TNFRSF11A AP3D1 PPP1R1B RP9 MSH6 CDV3 BAZ1B PON2 ZBTB7A DNMBP TNRC6A FRMD4B ZFPM1 PMPCB GOLGA8A TUBB2B RGS12 HNRNPL STRN AP1G1 DDB2 STAT6 KIAA0922 GDF7 CRTC3 HMGCS1 SH3GL3 TIMM23 POLA1 PAM OBFC1 GRM8 VWA5B2 IFNAR2 CLTA COPS8 NT5DC3 KIAA1522 TCERG1 SNX6 MAPK13 TOP2A GTF2I ASXL2 PTPN5GOLGB1 SMO SLC32A1 ACVR1C SENP6 KLF2 ZNF596KIAA0368 RAPH1 ERI3 IL10RA BTAF1 ADAM19 TMEM177 STAT3 PTPN23 RAPGEF2 GUK1 EIF3B KIF18B DOK5 PARP1 RBFOX2 AKAP9 TFAP4 RASA2 SNAPC1 YEATS2 RERE TBC1D9 B3GNTL1 SARAF ZBTB16 MAPK4 SEC22B LAMP1 NFKBIA PHC2 FBLN2 FAM69B FOXO3 TUBB4A MAP2 RAB1A ZKSCAN7 PPP6R3 PLEKHB2 SERPINB6 MTHFD2 FDX1 RCL1 MSL1 CTTNBP2 DOCK7 FBN3 IMPDH1 PAIP1 KDM2A ECT2 ACVR2A BPTF GDF10 MMP24 BIRC3 MELTF VEGFC MAP3K3 TRIM33 SNX18 GATSL2 SNX8 PRKCA SNX7 AKAP1 FLT1 RPS6KA1 ZFR SEMA7A CBFA2T2 MAP3K14 ETV1 CSTF2 MOB1B CPNE8 EIF3D ITGB5 KIAA1549 TRIM26 PGF ZBTB32 CPSF6 XIAP ZNF746 ATG5 NLGN1 TNKS CUEDC1 NCAM2 GOLGA7 ADCK2 SMARCD2 GLIPR2 HMCN1 ZHX3 FST GCC2 TOM1L1 HIVEP2 ILKAP NOG AP1S2 PPP2R5C AGGF1 GHITM CAPN7 FAM208A RALGAPA1 GDF6 ABCE1 LZTS1 CDC42BPB HPF1 DHX40 FAM175A TTLL12 TFAP2C ROCK1 NEUROG1KCNE4 GRB14 CMIP ZNF787 ZNF318 CBX7 TJP1 DYNC1I1 TULP4 RYBP HELZ2 BICDL1 CALM2 TBC1D15 HBEGF NOS1AP MIER2 DNER NACC2 BICD2 KCTD17 PAQR3 KCNQ2 HDAC9 CLIP2 CLN6 CENPJ CEP170B RNF2 ZDHHC21 PIK3R1 MAL TBX3 CKM FAN1 RPS4Y1 RAB32 ATP2A2 ZNF827 CEP78 MTCH2 CAV2 ATXN2L PLXNC1 TERF2 LDOC1L CCND1 LSG1 TMX1 CAMSAP3 FXN ZZZ3 PIK3AP1 VGLL4 KCNA3 CSRP2 UCK2 MAN1A2 SLC9A2 SCARA3 STAG1 TRAK1 MYO16 CD99L2 PTPRN ETFA ORAI1 ZNF703 CHD1 SLC7A5 CDCA4 ATF7 TTBK1 KLF13 UBP1 BICD1FAM50B RANBP17 TAB3 KIF21A CCDC57 PSD4 MKNK2 PSPC1 PRR5 CYP1A1 GSPT1 POLQ RAB6A LRR1 OGDHL DLGAP4 UNC80 TBC1D2 HELB LRCH2 HAND1 GMFB MKL2 EFNA5 SLC6A9 PNMA3 LDLRAD4 OS9 FANK1 CARHSP1 WIPF3 ESYT2 NFIX TEAD4 RNF121 FAM57A CTNNBIP1 PMAIP1 GJA3 DOCK5 ITPK1 LINC00998 CRACR2A FBLN5 GLRA1 KIF13A CHST10 PTP4A2 ERRFI1 PDCD2 BBS9 NSMAF STIL LDOC1 RAB12 C2orf42 CLDN23 BMS1 SPTSSA

SYN2 CHM MFSD5

PDPN

METAP1D

EPHA6

CHST12 ENDOD1 STC2

CD276 VSTM2L

FAT4 RYK ADGRA3 BEND3

SOX3 ITM2C SENP7 SOBP

BNC2 SUMO2 SIMC1 HSDL2

KIF16B TRERF1

GTF2IRD2B

ZNF282

LAMP3 VPS54 TRIT1 SYT16 TMEM62 NKX6-1 NPTX1 AMPD3 JPH1 HHEX ELOVL6 SGPP2 ANKRD36B CCDC83 CAMTA1 STK17A SLC29A3 ALDH1A2 ALDH1L2 FAM20B CRISPLD2 TMEM184B SLC22A4 MGAT4A LRRC32 NPAS1 RABL2B PRDM7 ME3 ATP1B3 ZNF185 CMKLR1 ABHD17A C1orf21 JADE3 IFT27 GUCY2D PAPOLG C1orf159 POPDC3 FA2H RBM24 GALNT10 HEBP2 FAM89A ZNF699 PRRX2 SLIT3 ANKRD10 TSPAN3 EYA4 PANK3 SPOCK3 PLPP4 GMPR SLCO3A1 ANKH TSPAN9 FEM1C PERP RFX2 B3GALNT2 IGDCC4 FPGS MAN1C1 MEGF11 PREP KMO CWH43 SMIM21 DIRC3 FTCDNL1 LINC01314 SEMA4D KDSR FAM53B LYPLA1 ZFP37 FAM171A1 ACSF3 GMNC ZSCAN23 ENTPD4 NPTX2

NPTXR ABCA3 VPS53 HGH1 SYT14 SLC27A6 NKX2-2

TPBGL HNMT TVP23A DCAF12L2 GM2A RASL11A RNF144B ELAC1 KCNT2 ZSWIM4 CYS1 ZNF785 MRPS14 MCUR1 GOLGA8B UXS1 ANKRD60 MFSD1 SLC30A1 HCG27 TRABD2B HGSNAT SAMD5 AP3M2 SFMBT2 TMEM132C KIAA1671 TSPAN18 IGLON5 RBM20 LPIN2 C2CD4B ANKRD33B HSD17B11 NKX1-2 LYPD6B TMEM150C IFFO2 SLC45A4 CCDC66 LHFPL4 OTUD7A B4GALNT4 SPPL3 TENM2 FAM19A5 SLC35F1 FNIP2 TMEM114 RBP4 TMCO4 VSIG10 SRFBP1 ABHD17C FAM222A ACOT12 OTULIN KANSL1L TUNAR ANKRD36 PISD ASPHD2 TUBB8 ZBTB7C SEPN1 SLC4A9 TEKT1 ERICH3 SHROOM2 DESI2 TSNARE1 SLC10A7 FAM20A CCDC125 TTL CRTAC1 PWWP2B MFSD2A ZDHHC4 EVI5L GPR101 FAM78B TMEM266 ADAMTS20 TMEFF2

XKR6 FOXO6 ANKRD29 B4GALNT3 PTCHD1 TDRP CABP7 SLFN13 RBPMS2 RSPO2 ZSCAN10 ZNF99 TMEM178A PARS2 LANCL3 C14orf37 FBXO10 IRX3 MOGAT1 ZNF77 RIMKLA DMBX1 KIAA1644 SHROOM4 ZNF777 GPIHBP1 SHISA9 ZNF488 MAP3K15 ZNF852 FAM46B C16orf87 ZDHHC20 WBSCR17 ZNF286B SP8 C9orf129 ANKRD36C GPR26 CLEC2L GABRG1 NATD1 ARHGAP23 ISM1 GPR173 AMZ1 ELFN2 OAF ZNF804B NPFFR1 CELF5 PIANP C8orf46 NXPH1 ZNF541 TRIM16L MCOLN2 ULBP1 GGACT CDC42EP1 ANKRD31 TMEM219 RAD21L1 GSG1L ZNF589 AGPAT2 WDR63 JAKMIP3 RFPL2 VWA3B SP7 SLC38A9 FAM45A GADL1 ANKRD18B MIPOL1 FIGN PKDCC GXYLT1 TSPAN11 GRID1 NKAIN2 KCNH3 PRELID2 NMNAT3

SLC47A1 MDGA2 COL24A1 ETAA1 ADAMTS14 ZNF536 NUDT12 RASGEF1B OLFM2 TMEM2 LOXL4 MDGA1 SYNC RHBDD2 TMEM47 APCDD1 SCUBE1 LEPROTL1 NEURL1B EXT1 SGSM1 TMC7 METRNL CSMD1 ZNF44 FAM210B WDR60 RAMP1 PTPDC1 TMEM132B ADAMTS10 KCNH5 C5orf51 RBFOX3 TMEM98 TMEM185A GPR157 LRFN3 CELF6 ISL2 FMN2 PRSS27 COL23A1 ANO4 MMD PDCL3 WTIP CACHD1 ZUFSP FAXC ELOVL7 TSPAN33 ADAMTS7 LRRC63 ZXDB ARMC3 OSR1 ZNF618 SLC16A9 PXYLP1 C10orf35 ST6GAL2 CCDC117 C8orf48 CHRDL2 FBLN7 ZNF713 ZKSCAN2 RSPO4 BTBD3 PHACTR3 ZNF468 GPR137B MARVELD1 XKR4 CMC1 ZYG11B TMEM132D PAQR8 CNTNAP5 BMPER PPP1R14C DDX60L BRI3 PPP4R4

C15orf41 HS3ST4 HPS3 FAM102B SMOC2 TMEM192 C10orf107 CHST1 ZSWIM5 PIGZ SUSD6 WDFY2 IQCA1 FAM124B SLC19A3 CFAP43 PODXL ZNF223 PGBD5 FSTL5 ST6GALNAC3 SHISA6 CSRNP3 EEF2KMT DEF8 ASAP3 ABHD12B SLC7A10 ZSCAN30 PNPLA3 TTYH1 SLC16A10 B3GLCT ACSF2 ZNF362 TMEM164 CRYL1 ZNF608 STOX1 AAED1 RHBDF2 CRYGN GALNT13 CCDC170 STXBP6 TMEM218 CHIC1 ATG4C MVB12B PTTG1IP FAM104B OSTC SLC44A2 PPP4R2 SESTD1 NUDT7 ZMYND19 B3GALT1 SLC46A2 SFTPD KATNAL1 ZNF391 KIAA1211L CARD10 UBXN2B IGSF9B FAM69A CHST8 AHCYL2 FAM155A FAM220A RBFA B3GAT2 TRIM45 GLB1L2 SLC25A15 SMIM10L2B ABCC9 PHF24 GALNT18 WBSCR27 EDA XYLT2 MTMR7 ZNF169

TMEM38B TLE6 GRAMD1B CLSTN2 GPD1L BTN3A2 ELP6 GABRG3 FJX1 FAM150A CAMSAP1 GFRA2 B3GAT1 OSGIN2 ANKRD40 KIAA0232 RNF24 FAM189A1 CCZ1B THSD7B ECHDC3 CSGALNACT1 CTH ZNF254 INPP5A ZNF267 CDR2L SHROOM1 HRK HPSE2 ITIH5 YPEL1 TMEM241 ASIC1 PAIP2B PDE7A HCAR1 ZNF81 SULT4A1 HIVEP3 CMTM4 TMEM216 ASTN2 PUM2 FAM155B FAM102A TBC1D12 ACTR3B AP4E1 SLC9A6 LRRC20 PLEKHM1 HS6ST1 AK5 PRELID3A DKK3 LY6H APOL2 C1QL1 HEBP1 MICAL2 MFSD12 REEP3 MCTP1 VASH1 B4GALT5 UHRF1BP1L POU6F1 C2orf72 DIP2C CLCN4 PQLC2L GPBP1 C10orf11 SLC6A1 ZDHHC13 KIAA1549L BNIP2 SYNGR1 CERS4 NUDT14 GFOD1 WSCD2 IMMP2L PAPD4

NSUN7 NT5M DENND4C TET1 SORCS3 ZNF442 GPCPD1 SEC14L5 PLXDC2 CEP112 RTP4 LRRC9 NUDT4 GAREM1 KCNC1 SLC12A8 GPR148 C6orf62 DYM ADGRV1 DHRS3 ST6GALNAC2 SLC4A7 TMSB4Y ADAMTS3 FAM149A SERINC5 TMEM39B ZNRF1 AUH ADGRG6 ASXL3 PDLIM4 SERPINE2 NKAIN1 MT3 SLC22A3 KCNF1 DNAJC28 APOD AFAP1L1 AFF2 GABRA2 ACER3 FRMPD4 SAMD4A LHFPL2 NT5C2 HDDC2 FUT4 VSTM5 ZFP69B MLYCD KMT5B JPH3 TMEM120B FAM105A THEMIS2 SIL1 DOCK10 C4orf32 ZDHHC18 MFSD6 AIG1 ZNF697 PRODH ZFP41 RPS6KL1 ZCCHC2 PEX5L C11orf80 EPHX4 TMEM179 ZNF707 MMAA PITPNM3 LSAMP NDST3 ZDHHC15 CYB561 TMEM45A CACNB2 ZNF814 ADAM23 MFHAS1

PLEKHM2 ZSWIM2 AACS LBH BCAS4 AHSA2 UPK3A PCNX1 ATP8A2 HECA SMKR1 RELL1 FHOD3 CFAP221 RBMX2 BOLL LRP10 SERPINB1 CNTN5 USP31 C2CD2 ATP5G2 CNIH4 PDE8B PQLC1 SLC7A2 CTIF ADAMTSL3 GNPDA1 SLC39A3 GPX7 FAM174B ZNF432 DSCR3 FAM184B SLC25A33 ADAMTSL5 ZDHHC14 PLGRKT HLCS MTAP DCTD ATP8A1 WDR47 HS3ST2 MN1 C1orf106 ZNF316 NAP1L2 RALGPS2 PIEZO2 EN2 DPYD CHSY1 OPCML CMTM7 SNX10 RAB20 NMD3 CALCB FAM49A MAK16 ZNF354A FKBP9 MGLL EML5 C1GALT1 SERPING1 TENM1 DAGLA LHPP TSC22D2

Figure 8. Clustering of genes with active proximal promoter merged regions. A total of 6062 active intervals were located Figure 8. Clustering of genes with active proximal promoter merged regions. A total of 6062 active intervals were located − upstreamupstream the the gene gene sequence, sequence, of of which which 2210 2210 were were within within proximal proximal promoter promoter regions regions ( 1 kb–0)(−1 kb indicating–0) indicating pro-transcriptional pro-transcrip- activity.tional activity. Connectivity Connectivity based on based pathway on pathway (blue connectors) (blue connec andtors) physical and physical interactions interactions (red connectors) (red connectors) produced produced a cluster a ofcluster 1540 genes.of 1540Changes genes. Changes in peak in intensity peak intensity in HIV+Meth+ in HIV+Meth+ compared compared to HIV+Meth- to HIV+Meth are shown- are shown by color by (blue–decrease, color (blue–de- orange–increase,crease, orange–increase, white–no white change).–no change).

We have identified 2210 genes with active proximal promoter, with both increased (orange shades) and decreased (blue shades) promoter activity associated with Meth use and white nodes corresponding to no change (Figure 8). In Figure 8, it is important to notice the high degree of connectivity between the genes that have H3K4me3 activity, indicating that genomic activity is an orchestrated phenomenon. Nevertheless, many genes with significant proximal promoter activity did not show functional or physical interactions with each other and remained independent. A complete list of these genes and fold changes between groups is available in Supplementary Table S1. The genes in Figure 8 were further filtered based on a significant +/- 1.5-fold change cut off in a comparison between HIV+Meth- and HIV+Meth+. Tight clustering criteria produced two high score nodes, calculated in Cytoscape default network topology statistics settings, based on the number of neighboring nodes and the number of connected pairs between all neighbors. The first, a 5.2 score node, indicates a cluster where each gene represented by node circles have an average of 5.2 related neighboring genes and with interdependent functional domains based on genomic (pink edge connectors), physical (red) and pathway interactions (green connectors) (Figure 9A). The genomic regions annotated to genes in this cluster exhibited both increased (orange nodes) and decreased (blue nodes) promoter activity, or no change (white nodes) in correlation with Meth use (Figure 9A). In order to further visualize the potential implications of those changes and relationships between these genes, we performed a functional annotation cluster analysis (Figure 9B), which identified biological processes as well as transcription regulators (Figure 9B). For instance, increased promoter activity associated with Meth included genes annotated to acute inflammation, macrophage infiltration, cell movement of antigen presenting cells as well as necrosis, apoptosis, and cell death of muscle cells. These changes were predicted

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as being orchestrated by IFNG, which showed a significant 2.3-fold increase in its promoter genomic activity (p < 0.0001). Other IFN pathway components such as IRF1 (1.8- fold, p < 0.0001) or transcriptional regulators JUN (1.8-fold, p < 0.001) and SOX2 (1.8-fold, p < 0.001) were also identified as key components that showed significant changes in the context of Meth (Figure 9B). Overall, this suggests that the inflammatory process is enhanced in HIV+Meth users, in an IFN-dependent manner. Interestingly, genes with decreased promoter activity (shades of blue) shared connections with genes with increased promoter activity, based on physical protein–protein and genetic interactions, as well as shared domains or pathway, as shown in Figure 9A, indicating potential regulators. Neu- Viruses 2021, 13, 544 rological pathways involved memory and cognition, as well as CREB signaling in neurons14 of 24 (Figure 9B) were identified. In Figure 9B, edges or connectors are directional.

A) Proximal promoter +/-1.5-fold cutoff B) Functional Annotation C) Dopaminergic synapse signatures

C1orf210

TMEM125 TRIM59

MUM1L1 TIAM2 TAF7L IFI44L ISG15 SHROOM4 RSAD2 RGAG1

RHBDL2 MEPE IFI44 DMRT2 DIRAS3 TIAM2 IFI44L KLHL4 SPATA22 RGAG1 CLDN11 ADORA3 CWH43 KCNJ16 LAD1 HOXD3 HOXD8 BCAS1 ANO1 DMRT2 KLHL4 P4HA3 SCEL OPALIN BCAS1 ENPP6 KCNJ16 LAD1 ST18 HOXD8 GALNT15 GPM6B S100B ERMN C10orf90 RFX6 AHSP APOD SCEL OPALIN PRIMA1 CNDP1 USH1C HEYL ASPA FXYD1 TRIM54 HOXD4 CWH43 ST18 ENPP6 DMD ERMN MAG NKX2-1 S100B AHSP ANGPTL7 PLP1 ATP10B GSTM1 EDNRB CNDP1 USH1C FOLH1 TSPAN8MBPKLK6 IFNL2 APOD TRPM8 DRP2 COL4A5 MAL TIMP4 ABCA8 ART3 GRIP2 ASPA LPL EXOC3L2 MAG RNASE1 CDH10 FOLH1 ATP10B PROX1 KLK6 LAMA2 ITGA2 SPP1NGFR MYOD1 PLP1 EXOC3L2 ST14 MAL TIMP4 DRP2 GRIP2 CDH19 GSX1 PM20D1 TNFAIP6 NPY2R C2orf80 INS-IGF2 CDH10 SOX10 INS ERN2 NTSR2 KLK7 ITGA2 PROX1 LIPC SH3TC2 TH SPP1 MYOD1 PLA2G4A ST14 SH3D19 GJB1 PAX7 CDH19 GSX1 C2orf80 AZU1 INS C8orf4 PIWIL1 HAMP LIPC NPY2R NTSR2 WISP2 PAX8 BHLHE23 KLK7 EDA2R C1orf127 DMBT1 GPR26 CLPS DBH MTNR1B HTR4 SYT4 SLC6A18 TH GJB1 PAX7 AVPI1 AZU1 TIMP1 DIO3 PIWIL1 HAMP FAM19A4CACNA1B PAX8 BFSP2 WISP2 DNAH17 ATP12A HMCN2 AQP12B HTR4 GPR26 EDA2R CRB2 GPR139 DNAI2 DBH SYT4 KNCN MTNR1B UCN TERT PRSS50 TMPRSS11F CACNA1B DIO3 SCGB3A1 TNNT2 OPN4 DNAH17 BFSP2 ATP12A BTBD16 GPR55 LRRN4 GPR139 KNCN SYT3 TERT PRSS50 MYH6 FLT4 CCT8L2 TNNT2 KIAA1107 LRRN4 GPR55

CPA5

WBP2 FLT4 LDHAL6A KIAA1107 TUNAR CLPSL2 DTHD1 HIST1H3G HIST1H2BI DCDC2C TUNAR UMODL1

ZNF728 CLPSL1

Figure 9. Network of genes with significant changes in H3K4me3 dynamics within the proximal promoter in HIV+Meth+ Figure 9. Network of genes with significant changes in H3K4me3 dynamics within the proximal promoter in HIV+Meth+ postmortem prefrontal cortex specimens compared to HIV+Meth-. (A) A weighted gene network from in-promoter postmortem prefrontal cortex specimens compared to HIV+Meth-. (A) A weighted gene network from in-promoter en- − enrichmentrichment patterns patterns filtered filtered to +/ to-1.5 +/-fold1.5-fold differences, differences, where where circles circles in shades in shades of blue of indicate blue indicate decrease, decrease, and shades and shadesof orange of orangeindicate indicate enrichment, enrichment, with relationships with relationships based basedon genetic on genetic interactions interactions (pink (pinkedges), edges), physical physical interactions interactions (red (rededges) edges) and andpathway pathway (green (green edges). edges). (B) (FunctionalB) Functional Annotation Annotation derived derived from from IPA IPA related related to to genes genes with with in in-promoter-promoter modifications modifications andand predictionprediction ofof transcriptional transcriptional regulators. regulators. Edges Edges indicate indicate directional directional molecular molecular and and pathway pathway (orange) (orange) and biologicaland biological processes pro- (blue)cesses relationships.(blue) relationships. (C) Subnetwork (C) Subnetwork containing containing dopaminergic dopaminergic synapse synapse signatures signatures showing showing in-promoter in-promoter modifications, modifica- identifiedtions, identified by grouped by grouped by genetic by genetic interactions interactions (pink edges),(pink edges), physical physical interactions interactions (red edges) (red edges) and pathway and pathway (green edges)(green -6 (KEGG,edges) (KEGG,p = 3.9 × p 10= 3.9−6). x10 Node). Node sizes representsizes represent connection connection scores. scores. Blue colors Blue indicatecolors indicate decrease decrease and orange and indicatesorange indicates increase increase in H3K4me3 peak signals in averaged HIV+Meth+ compared to HIV+Meth- prefrontal cortex specimens. in H3K4me3 peak signals in averaged HIV+Meth+ compared to HIV+Meth- prefrontal cortex specimens.

A second significantsignificant high-scorehigh-score node node (4.8 (4.8 score) score) identified identified among among genes genes with with proximal proxi- promotermal promoter activity activity and significantand significant ><1.5-fold ><1.5-fold changes changes was was characterized characterized by dopaminergic by dopamin- ergicsynapses synapses signatures signatures (Figure (Figure9C), also 9C), including also including genes withgenes both with increased both increased and decreased and de- creasedactivity. activity. In this node,In this decreased node, decreased activity activity in the Tyrosinein the Tyrosine Hydroxylase Hydroxylase (TH) and (TH) PAX7 and promotersPAX7 promoters suggests suggest a losss ofa loss dopaminergic of dopaminergic function function and neural and neural progenitors progenitors in Meth in usersMeth usersin the in context the context of HIV of [HIV35]. In[35]. both In highboth high score score nodes nodes (Figure (Figure9A,C), 9A the,C), color-coded the color-coded line lineedges edges show show the the interaction interaction criteria criteria between between the the genes genes of of interest, interest, in in these these casescases basedbased predominantly in genetic and pathway interactions and indicating a coordinated process. The unbiasedunbiased analysisanalysis ofof all all genes genes with with HeK4me3 HeK4me3 active active regions regions (Figure (Figure6E), 6E), regardless regard- lessof position of position (in gene, (in gene, downstream, downstream, upstream upstream proximal proximal or upstream or upstream distal, distal, C1–C5), C1–C5), using us- a 1.5ing cuta 1.5 off cut and off 95% and confidence 95% confidence level in level detectable in detectable changes changes caused caused by Meth by in Meth the context in the contextof HIV, hasof HIV, led tohas the led identification to the identification of several of several gene clusters gene clusters and pathways and pathways annotated anno- to tatedaddiction to addiction (Figure (Figure10) and 10) neuronal and neuronal synapses synapses (Figure (Figure 11), expressing11), expressing a high a high degree degree of ofredundancy, redundancy, which which is evidencedis evidenced by by merging merging features. features. TheThe systemssystems analysisanalysis indicatesindicates differential activity in genes annotated to nicotine (Figure 1010A),A), morphine (Figure 10B),10B), amphetamine (Figure(Figure 10 10C)C) and and cocaine cocaine (Figure (Figure 10D) 10D) addiction. addiction. These These pathways pathways have have a high a degree of overlapping interactions that could be merged into a single network (Figure 10E).

Viruses 2021, 13, x FOR PEER REVIEW 15 of 25

Viruses 2021, 13, 544 15 of 24 high degree of overlapping interactions that could be merged into a single network (Fig- ure 10E). A complete list of these genes is in Supplementary Table S3.

A) Nicotine addiction B) Morphine addiction OPRM1 GRID2 GRK3 GRIN2D KCNJ6 GRIN1 GRIA4 GRIN3BGRIK4 CACNA1A GABRG1 ADCY8 ADCY7 GRIK5 ADCY4 GRIA2 GRIK1 CACNA1B GABRA5 GRIA3 GABBR2 DRD1 GRIK3 ADCY9 GRID1 GRK5 GRIA1 GRIN2A ADCY5 ADCY6 GABRG2 GABRE GRIN3A GRIN2B GRIK2 ADCY2 GRIN2C ADCY3 CHRNA4 GABRD GABRA4 CACNA1D ADCY1 GABRG2 CHRNB2 GABRB3 SSR4 GABRB3 CACNA1E GABRD PDE1B PDE7B CACNA1C PDE1A GABRB1 CACNA1A GABRG1 CACNA1F GABRG3 GLRA4 CACNA1B PDE4A PDE1C CACNA1S PDE7A GABRE C) Amphetamine addiction PDE4C D) Cocaine addiction

TH PTPN5 CREB3L1 SSR4 KARS SLC18A2 CREB3L4 HOXA4 CREB3L1 RACK1 GRASP CALML3 CALY MAOB KIF2C DRD1 CAMK2D LRP8 FOSB DLG4 MAOA SYNGAP1 GRIN2B DAB1 GRIN1 RELN CAMK2B GRIN2A CAMK2A GRIN2A GRIA2 MAOB GRIN1 GRIN2C DLG4 SYNGAP1 GRM2 GRIK4 PIK3R1 PAH GRIK2 GRIA1 CAMK2A HTR2A GRIN3B GRIK1 GRIN2B LRRC7 TH ADRB2 DRD1 GRIA4 CDK5R1 RACK1 SLC18A2 TPH2 CALY TPH1 PPP2R5D

COMT CANX CHMP5 Edges E) All addiction merged

CACNA1S CHMP5

PPP2R5D CREB3L1

CREB3L4 SLC18A2 CACNA1E MAOA MAOB CACNA1A GRIK2 FOSB CACNA1B GRIN2A SSR4 PTPN5 TH ADCY6 GRIK1 TPH2 GRIA1 CHRNB2 CACNA1CGRIN3B LRRC7 GRM2CALML3 CACNA1D CHRNA4 GRM5 CANX GRIA3 DRD1 CAMK2A HTR2A PIK3R1 ADCY10 GRIN1 CACNA1F HMP19 PDE4B GRIN2D HOXA4 KCNJ6 OPRM1 PDE1C GRK3 GABBR1 CAMK2D NPR1 GABRA1 LRP8 GLRA4 GRID2GUCY2C PDE7A GUCY1A3 ADCY1 PDE1B GRIK3 GRIK4 GRIK5 GRM1 CDK5R1 RELN GABRA2 GABRA3 GUCY2D PDE4A GRIA2 KIF2C SYNGAP1 GABRD GUCY1B3PDE9A GABBR2 Nodes PDE7B DLG4 GABRB3 NPR3GABRA4 ADCY4 TPH1 GRIA4 KARS CAMK2B ADRB2 GRIN2B GABRA5 CALY GABRB1 GUCY1A2 NSG1 GABRG3 ADCY7 -4 0 4 GRIN2C DAB1 GABRG1 ADCY9 ADCY2 GABRG2 ADCY8 GRK5 GRASP ADCY3 NPR2 PAH PDE1A GRID1 PDE4C GRIN3A GABRE ADCY5 COMT

Figure 10. Gene clusters annotated to drug addiction pathways in HIV+ prefrontal cortex and Figure 10. Genethe clusters effect annotated of Meth to use. drug The addiction analysis pathways of genes in withHIV+ H3K4me3prefrontal cortex active and regions, the effect including of Meth in-gene, use. The analysis of genes with H3K4me3 active regions, including in-gene, downstream distal and proximal, as well as upstream downstream distal and proximal, as well as upstream has resulted in gene clusters annotated to has resulted in gene clusters annotated to addiction. Genes overrepresented in pathways associated to (A) Nicotine addic- −5 tion (p = 2.2 x10-5addiction., Benjamini Genes 7.6 x10 overrepresented-4), (B) Morphine inaddiction pathways (p=1.9 associated x10-5, Benjamini to (A) Nicotine =1.3 x10- addiction3), (C) Amphetamine (p = 2.2 × 10addic-, −4 −5 −3 tion (p = 1.4 x10-4Benjamini, Benjamini=6.1 = 7.6 x10× -103) and), ((DB) Cocaine Morphine addiction addiction (p=1.1 (p x10= 1.9-3, Benjamini=3× 10 , Benjamini x10-2). (E =) 1.3These× 10 clusters), (C showed) Am- a 56% overlap andphetamine 100% connectivity addiction in (p merged= 1.4 features.× 10−4, Node Benjamini sizes represent = 6.1 × connection10−3) and scores. (D) CocaineBlue colors addiction in nodes (p = 1.1 × 10−3, Benjamini = 3 × 10−2). (E) These clusters showed a 56% overlap and 100% connectivity in merged features. Node sizes represent connection scores. Blue colors in nodes indicate decrease and orange indicates increase in H3K4me3 peak signals in averaged HIV+Meth+ compared

to HIV+Meth- prefrontal cortex specimens. Gray nodes indicate genes in the pathway for which we have not detected any H3K4me3 peak signal. Edge colors represent interaction criteria in connectors as deﬁned in the legend. Viruses 2021, 13, x FOR PEER REVIEW 16 of 25

indicate decrease and orange indicates increase in H3K4me3 peak signals in averaged HIV+Meth+ compared to HIV+Meth- prefrontal cortex specimens. Gray nodes indicate genes in the pathway for which we have not detected any H3K4me3 peak signal. Edge colors represent interaction criteria in connectors as defined in the legend.

Clusters annotated to synaptic pathways have also been able to distinguish HIV+Meth+ from HIV+Meth-, using the same approach including all merged HeK4me3 active regions (Figure 6E). For instance, high score gene clusters were annotated to glutamatergic (Figure 11A), dopaminergic (Figure 11B), cholinergic (Figure 11C), serotoninergic (Figure 11D), as well as GABAergic synapses (Figure 11E). Gene nodes were connected by protein–protein and genetic interactions, as well as pathway. Other high score pathways were annotated to neuronal functions, such as long-term potentiation (Figure 12A) and neuroactive ligand-receptor interactions (Figure 12B). The overlap between synaptic and neuroactive pathways was revealed by merging feature properties (Figure 12C). A complete list of these genes is in Supplementary Table S2. The large number of neural Viruses 2021, 13, 544 synapses genes with blue node color suggests that H3K4me3 is decreased in the context16 of 24 of Meth.

A) Glutamatergic synapse B) Dopaminergic synapse C) Cholinergic synapse

SSR4 MRFAP1 CHMP5 PLA2G4E NCS1 FLNA TPH2 CACNA1A DRD3 PAH PLA2G4A KCNQ2 DRD2 DRD5 CPT1C CACNA1A GRIA2 CHRNB2 CACNA1B DRD1 SLC18A3 CAMK2B CALY CHRNA2 GRIA2 GRIK2 CRAT SLC1A6 GRIA1 KCNQ3 PLCB2 TGM2 KCNJ6 PLCB1 CHAT CHRNA4 GRIK3 GRIN2B CACNA1A GRIA4 GRIN2A HMP19 CPT1B GRIK1 CAMK2D NSG1 CHRNA3 PLCB2 GRIN1 CALML3 CROT CREB3L1 CHRNA5 GRID2 SLC6A3 CAMK2B GPRC6A GRM2 KARS GRIN1 GRIN2B CPT2 GRIN2D GRM5 CAMK2A CHRNB4 GRIA1 GRIN3B SCN1A GRM8 CACNA1B GRIN2CGRIA3 LRRC7 CAMK2A GRIN2A KCNJ3 GRM6 CAMK2D TAS1R2 TH APBA1 KCNJ9 GRIK5 GRID1 SLC18A2 ADRB2 TAS1R1 ADCY8 MRFAP1 ADCY2 GRK3 PLCB1 KCNJ18 GRM1 GRM4 PLCB2 GRIN3A ADCY6 GRIK4 KCNJ6 PLCB1 ADCY5 HOXA4 ADCY1 ADCY8 ADCY2 CREB3L1 MAOB TAS1R3 CHMP5 KCNJ12 CHRM1 ADCY5 ADCY4 D) Serotoninergic synapse E) GABAergic synapse ADCY7

KCNJ6 ALOX12 ALOX12B GABBR2

ALOX15B GABRG1 HTR5A ALOX5 ALOX15 HTR6 GABRG2 GABRE IL4I1 ALOXE3 CACNA1E CACNA1A CHRFAM7A ADCY5 GABRB3 CACNA1B ADCY3 ADCY1 CACNA1C GABRD GLRA4 CACNA1FCACNA1S HTR4 ADCY7 GABRB3 ADCY4 GABRR3 ADCY2 MAOBPYROXD2 CACNA1D CACNA1B PLA2G4D ADCY10 KCNJ6 CACNA1E CACNA1C PLA2G4F ADCY9 ADCY6 CACNA1A PLCZ1 PLCB1 CACNA1D CACNA1S CYP4X1 PLA2G4B ADCY8 PLA2G4A PLCB4 CACNA1F SLC18A2 PLCB2 JMJD7-PLA2G4B SRC -4 0 4 HAP1 PLCB3 PLA2G4E PLA2G4C BICDL1

TRAK2 TRAK1

Figure 11. Gene clusters annotated to neurological synapses in HIV+ prefrontal corte cortexx and the effect of MethMeth use.use. The analysis of genes with H3K4me3 active regions, including in in-gene,-gene, downstream distal and proximal, proximal, as well well as as upstream upstream,, has resulted resulted in in gene gene clusters clusters annotated annotated to toneurological neurological synapses synapses and and function. function. Genes Genes overrepresented overrepresented in pathways in pathways asso- ciatedassociated to (A to) Glutamatergic (A) Glutamatergic synapse synapse (p =( p5.6= x10 5.6 ×-5, 10Benjamini−5, Benjamini = 1.3 x10 = 1.310-3), (B−)3 ),Dopaminergic (B) Dopaminergic synapse synapse (p = 2.2 (p = x10 2.2-5,× Benja-10−5, mini=7.6Benjamini x10 = 7.6-4), (×C)10 Cholinergic−4), (C) Cholinergic synapse (p synapse = 3.9 x10 (p-5),= Benjamini=1.2 3.9 × 10−5), Benjamini x10-3), (D) = Serotoninergic 1.2 × 10−3), ( Dsynapse) Serotoninergic (p = 1.8 x10 synapse-3, Ben- (jaminip = 1.8 =× 1.610 x10−3,-2 Benjamini) and E) GABAergic = 1.6 × 10 −synapse2) and ( E(p) = GABAergic 3.6 x10-4, Benjamini= synapse (p 3.2= 3.6 x10×-2).10 −4, Benjamini = 3.2 × 10−2).

Clusters annotated to synaptic pathways have also been able to distinguish HIV+Meth+ from HIV+Meth-, using the same approach including all merged HeK4me3 active regions (Figure6E). For instance, high score gene clusters were annotated to glutamatergic (Figure 11A), dopaminergic (Figure 11B), cholinergic (Figure 11C), serotoninergic (Figure 11D), as well as GABAergic synapses (Figure 11E). Gene nodes were connected by protein–protein and genetic interactions, as well as pathway. Other high score pathways were annotated to neuronal functions, such as long-term potentiation (Figure 12A) and neuroactive ligand-receptor interactions (Figure 12B). The overlap between synaptic and neuroactive pathways was revealed by merging feature properties (Figure 12C). The large number of neural synapses genes with blue node color suggests that H3K4me3 is decreased in the context of Meth. Viruses 2021, 13, x FOR PEER REVIEW 17 of 25

Viruses 2021, 13, 544 17 of 24

A) Long-term potentiation B) Neuroactive Ligand-Receptor Interactions

LRRC7 PRLR GABRE

SSR4 CAMK2D SYNGAP1 PTH2R CAMK2B CHRNB4 CHRNA3 PLCB2 P2RX2 ADCYAP1R1 CRHR2 GABRB3 GRM5 CAMK2A CHRNA4 GRIA4 PLCB1 GLP1R CHMP5 GLRA1 GABRD ADRB2 GRIA2GRIN2B HOMER1 GABRG2 ACTN4 PTGDR HTR5A ADCY8 ADRA1D GRIN2A GPR83 GRM1 GRIK2 GRIA1 LPAR3 SSTR4 GRM7 GRM2 GRM3 GRIK1 CALML3 OPRL1 GRIK1 GRIK3 OPRD1 DRD1 GRIK5 GRIN1 NTSR2 GRM5 GRIK4 MCHR1 GRIK3 GRIA3 GRIA1 GRID2 GRIN2D TGM2 HRH3 MTNR1A MTNR1B DRD5 GRIN2C GRIN2C PPP1R1C OPRM1 GRIN2A PPP1R1B HCRTR2 GRM4 GRIN2BGRIA4 TAS1R1 GHSR GRIA2 HTR4 UTS2R PPP1R1A S1PR3 OPRK1 GRIK2 GRIK4 HTR6 GRIN3A GRIK5 ADRB3 TAS1R3 OXTR CHRM1 GRIN2DGRIN1 C) All Synapse Merged DRD3 GRM6 NTSR1 GRM8 TAS1R2 ADRA1A PLA2G4E PLA2G4A PRLHR GRID1 GRIN3B ADCY8

ADCY2 SRC P2RX2 PLCB2 ADCYAP1R1 NSG1 SLC1A6 LPAR3 PRLH PTGDR TH CHRM1 PTH2R OXTR PLCB1 GRIK4 GRIK1 ADRB3 ADRA1A GHSR CALML3 HTR5A S1PR3 MTNR1A GRIN3A GRIK5 DRD5 SSTR4 NTSR2 HMP19 MTNR1B HTR4 GRIN1 KCNJ6 OPRL1 GRIN2C GRIA1 GPR83 PRLHR ALOX15 GRM8 GRIK3 DRD1 MCHR1 GRIN2A GRIN2D HAP1 OPRK1 GRIK2 NTSR1OPRD1 GRM6 GRM7 CAMK2A DRD3 UTS2R GRM4 HCRTR2 HTR1AADRA1D GRM5 GRIA3 HTR6 OPRM1 GRIA2 HRH3 CHAT GRIN2BCAMK2B GRIA4 GRK3 CALY GRM1 GRM2 CRHR2 GABBR2 GABRA5

CACNA1B CHRNB2 GABRB3 GLP1R GABRD GABRG2 PRLR SLC18A2 GLRA1 CACNA1A CHRNA4 KCNQ2 CHRNB4 CHRNA3 SCN1A BICDL1 SLC18A3 MAOB KCNJ12 -4 0 4

CYP4X1 CREB3L1 GABRE

Figure 12. Gene clusters annotated to neurological functions. (A) Long-term potentiation (p = 1.4 × 10−4, Figure 12. Gene clusters annotated to neurological functions. (A) Long-term potentiation (p = 1.4E-4, Benjamini = 6.2E-3). × −3 B p × −18 × −16 C Benjamini(B) Neuroactive = 6.2 ligand10 ).-(receptor) Neuroactive interacti ligand-receptorons (p = 6.6 x10 interactions-18, Benjamini ( == 6.63.3 x1010-16). (,C Benjamini) Merged =synaptic 3.3 10 clusters). ( )indicating Merged synapticredundancy clusters and indicating common redundancyinteractions. and Clusters common in Figures interactions. 11 and Clusters 12 showed in Figures a 52.7% 11 overlap and 12 showedand 100% a 52.7%connectivity overlap in andmerged 100% connectivityfeatures. Node in mergedsizes represent features. connection Node sizes scores. represent Blue connection colors indicate scores. decrease Blue colors and indicate orange decrease indicates and increase orange in indicatesH3K4me3 increase peak signals in H3K4me3 in averaged peak signals HIV+Meth+ in averaged compared HIV+Meth+ to HIV+Meth compared- prefrontal to HIV+Meth- cortex specimens. prefrontal Gray cortex nodes specimens. indicate Graygenes nodes in the indicate pathway genes for inwhich the pathwaywe have fornot whichdetected we any have H3K4me4 not detected peak any signal. H3K4me4 peak signal.

Interestingly,Interestingly, like like with with addiction addiction pathways, pathways, neurological neurological synapses synapses genes genes showed showed a a lowerlower number number of of H3K4me3 H3K4me3 active active peak peak regions regions in in the the prefrontal prefrontal cortex cortex from from HIV+Meth HIV+Meth usersusers compared compared to to non-Meth non-Meth users, users, suggesting suggesting differences differences in in enhancer enhancer chromatin chromatin remod- remod- elingeling events, events, while while epigenomic epigenomic alterations alterations such such as as the the one one we we are are measuring measuring have have the the capacitycapacity to to affectaffect thethe behavior of of the the identified identified gene gene networks. networks. However, However, epigenomic epigenomic fac- factorstors do do it it so so in in cooperation cooperation with with transcription transcription factorsfactors andand thethe respectiverespective binding binding motifs motifs mademade available available by by such such modifications. modifications. ChIP ChIP data data input input in in BED BED format format into into TRANSFAC TRANSFAC generatedgenerated a a functional functional analysis analysis report report where where prefrontal prefrontal cortex cortex was was the the most most significantly significantly −26 over-representedover-represented attribute attribute( p(p= = 2.292.29x10× 10-26). Cell). Cell type type attributes attributes characterized characterized by th bye over the - −12 over-representationrepresentation patterns patterns included included neurons neurons (p = 1.86 (p =x10 1.86-12), ×followed10 ), by followed T cells ( byp = T3.93 cellsx10- (p = 3.93 × 10−8), while innate immune cells (macrophages/microglia) were significantly under-represented (p = 3.77 × 10−6), suggesting potential dysfunction in that compartment regardless of drug use patterns.

Viruses 2021, 13, 544 18 of 24

To further help in the prediction of changes in the behavior of gene networks, and the effects of addiction, we modeled transcription factor usage to estimate the contribution of transcription factors affected and recruited by H3K4me3 in gene intervals, added 200bp ﬂanking regions. For that, we used TRANSFAC Match algorithm, which determined the frequency of transcription factor binding motifs in H3K4me3 intervals aligned to the GRCh38 human genome assembly from the Genome Reference Consortium (GCA_000001405.15 GCF_000001405.26). Table3 shows the 10 most frequent binding motifs, signiﬁcantly represented in active genomic regions in both HIV+Meth- and Meth+ group specimens.

Table 3. List of 10 most frequent transcription factor binding motifs identiﬁed in +/−200 bp from and within H3K4me3 intervals in HIV+ Meth- and Meth+ specimens. The most frequent consensus sequences are presented, along with the average number of binding sites per interval sequence throughout the genome.

Matrix Factor Consensus Sequence Classiﬁcation Average #Sites Interval Sequence V$SP1_13 Sp1 gcggctctgcggGGCGGggcgggg ZFC2H2 4.33 V$P53_03 P53 cgACATGGacacacatgggt P53 3.33 V$ETS2_06 c-Ets-2 ggCCGGAgaggctgcccctt ETS 2.85 V$JUNDFRA2_01 JUND:FRA2 aaTGACTcaa BZIP 2.5 V$ASCL1_03 MASH-1 cgCAGCTgcc BHLH 2.5 V$NR3C1_13 GR aacacaataTGTACa ZFC4-NR 2.33 V$CREBP1_01 ATF-2 tgaCGTCA BZIP 2.29 V$AP2ALPHA_03 AP2aA cgCGCCCccggctct BHSH 2.27 V$MYCMAX_03 cMyc:Max agttatgcACGTGtgtacca BHLH 2.15 V$HOXA5_03 HOXA5 AATTAgtg HOX 2

4. Discussion Postmortem specimens from humans are a valuable resource for understanding molecular dynamics taking place in a diversity of conditions. Even more critical is the fact that large association studies benefit from the integration with transcriptional profiles. However, RNA quality has been a limitation, encountered by our group and others [2,36,37]. It has been shown that small differences in RNA integrity affect gene expression quantification by introducing a moderate and pervasive bias in expression level estimates [38]. Postmortem interval is a major factor affecting RNA quality, along with years of storage [36,39]. This is an important issue particularly in specimens from substance user populations, with very diverse characteristics of interest [40]. Our results suggest that predictions on the behavior of gene networks can be derived from specimens that have a limited use in transcriptional profiling studies, by comparing epigenetic marker patterns that are preserved over longer postmortem intervals [41]. Epigenetic modifications, enhancers and regulators of transcriptional activity are likely protected by protein-based mechanisms that prevent physical damage to the genome and evolutionarily tailored to maintain the stability of gene expression patterns [42]. We tested the postmortem availability and preservation of two enhancer histone modifications, H3H4me3 and H3K27Ac, as well as RNAPol II at the TSS, all regarded as strong correlates of gene expression activity, using ChIP in mouse and human brain specimens [43–45]. In our studies in mice with controlled postmortem intervals, we found that H3K4me3 was more abundant than other active chromatin modifications, followed by H3K27Ac, and by RNAPol binding. Although this may reflect a difference of stability [41] developed for the preservation of genomic information, other factors might explain a significant relative difference in the abundance of these marks. For instance, the abundance of H3K4me3 could be associated to the number of histone modifications required to produce changes in the chromatin architecture and prime RNAPol recruitment as a single event. It may be also Viruses 2021, 13, 544 19 of 24

possible that not every histone modification will result in productive remodeling, which may depend on additional factors. Nevertheless, differences in stability are suggested by the negative slope. For instance, the slope of RNAPol compared to histone modifications indicates a faster decay or loss. Nevertheless, at 96 h postmortem, significant loss in all measures indicated that 72 h was the maximum postmortem interval for retrieving reliable data. H3K4me3 was more abundant than H3K27Ac and was detectable in postmortem tissues with larger intervals up to 72 h. The H3K4me3 epigenetic mark was also consistently found in human brain specimens with low RNA quality and used to examine frozen prefrontal cortex specimens preserved from HIV+ subjects. A higher activity based on the number of H3K4me3 active sites was found in chromosome 1, which is the largest human chromosome and reported involvement in heritable brain disorders [46]. Similarly, the highly active chromosome 19 contains genes such as APOE and Notch 3, whose polymorphisms have implications in, respectively, Alzheimer’s disease (AD) [47,48] and stroke and dementia associated with cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy [49,50]. HIV neurocognitive disorders share biomarkers and have similarities with AD [51]. Nevertheless, Meth had no effect on APOE gene activity. The similarity regarding mean gene activities within different chromosomes and across subjects indicates that potential differences between the groups are likely not due to disrupted enhancing epigenetic mechanisms, and they can be used for comparisons. This is also evidenced by similar H3K4me3, H3K27Ac and RNAPol tag ratios in housekeeping genes between groups. The analysis of H3K4me3 peak distribution was used predict signatures of Meth use disorder in the context of HIV. H3K4me3 promoter and gene activities are associated with transcriptional events in several systems [43,52,53]. Systems biology approaches were performed using activity that is exclusively in proximal promoters as well as including all gene regions. The majority of the in-promoter activity occurred in genes clustered in a large interactive network and annotated to inflammatory and viral responses, as well as to the dopaminergic system. Meth use was associated with enrichment of genomic activity in process-relevant genes such as IFNG and AATK, as well as loss of TH promoter activity [54], which is potentially associated with dopaminergic deficits. This finding suggests that Meth use increases genomic activities most likely to become transcriptional events that increase inflammation and apoptosis, with implications to neurodegeneration, dopaminergic deficits and decreased cognitive and executive functions, confirming previous observations from animal models of neuroHIV and from humans [55–61]. This indicates that epigenetic signatures are able to retrieve biologically meaningful data replicated by other systems and have the potential to provide incremental knowledge in association studies. In addition, this also suggests that biological processes associated with pathogenesis in the context of HIV and in drug users may have an epigenetic basis. The majority of the promoter and gene activity evidenced by H3K4me3 peaks, was suppressed in HIV+ Meth users, compared to HIV+ non-Meth users. These results also parallel previous transcriptional findings in a mouse model of HIV and Meth abuse, where Meth caused a significant suppression of gene transcription [57,62], particularly regarding the dopaminergic system [57,62,63]. This was also evident when the analysis included H3K4me3 peaks in all different gene regions, including downstream, upstream distal and upstream proximal (promoter region), as well as in exons, addiction and neuronal synapses gene networks were specifically identified, suggesting that genomic activity measures like the one we performed here can help predict changes associated with addiction and synaptic disorders. For instance, we distinguished pathways involved in drugs of abuse, in addition to amphetamines, with a granular precision based on various degrees of interaction. Among the pathways showing changes in gene activity in correlation with Meth use, we have identified nicotine, cocaine and opioids. We cautiously interpret this finding given the limited knowledge about the subjects’ history. However, polysubstance use is indeed prevalent among Meth abusers [64] and may be a factor. If this is the case, our strategy has the power to distinguish addictive disorders with a high degree of granularity. However, it Viruses 2021, 13, 544 20 of 24

is also important to acknowledge that molecular mechanisms involved in addiction have commonalities between different drugs [65–67]. This is evident in the overlap between addiction pathways, with common elements in reward circuitry and neurotransmitters. The gene networks associated with individual synaptic functions exhibited a common behavior, characterized by an overall decrease in genomic activity in Meth users in the context of HIV. This could result from mechanisms that favor and enhance the inflammatory process, which were identified here, leading to neuronal loss, including in the dopaminergic system, a hallmark of Meth and other substance use disorders [68,69]. Importantly, the patterns associated with Meth use strongly suggest a decrease in synaptic strength. This weakening was detectable in both inhibitory and excitatory γ-aminobutyric acid (GABA) and glutamatergic synapses projected from other brain regions to the prefrontal cortex, which was examined here. Along with dopaminergic projections into the prefrontal cortex, neuroactive ligand-receptor interactions play an important role in cognition and learning [70], temporal organization of behavior [71–74], as well as executive functioning that form the basis of planning, reasoning and thinking. In this area, a balance in neurotransmitters and neural networks is critical for the stability of the neuronal activation in that brain region [75–77]. Similar changes in circuitry have been previously described in correlation with Meth and other psychostimulants [78]. Moreover, the dopaminergic system is independently affected by HIV, with a described impact on addiction behaviors [79,80]. While our work suggests an epigenetic basis for neuronal circuitry disorders, it also shows such disorders can be distinguished in the brain of Meth users in the context of HIV based on epigenomic activity. In addition to the prediction of gene network behaviors, patterns of H3K4me4 peak activity can be used to predict transcription factor usage patterns, based on the occurrence of binding motif sequences within and near active intervals. We have performed this analysis and found that Sp1 was the most frequent binding motif. It has been shown that Sp1 makes a complex with NFkB to enhance HIV replication in vitro, in a process that is facilitated by Meth [81]. The second most abundant binding motif was p53, which has been described as a critical transcription factor in long term neurotoxic effects of Meth, particularly in dopaminergic neurons [82]. ETS2, which was another enriched binding motif, has been described as a transcriptional repressor of IL2 as well as of HIV, as it binds to the HIV-LTR-RATS element [83,84]. Thus, transcription factor binding predictions suggest potential important Meth effects on HIV replication and neurotoxicity. This study has limitations, including small sample size and the lack of HIV-negative specimens. Our epigenetic findings can inform processes occurring in the human brain but must be integrated with transcriptome data in specimens and models with higher RNA quality stability for validation. Nevertheless, since a large fraction of the H3K4me3 genomic activity occurs in proximal promoters followed by introns, it is likely to result into and correspond to transcriptional events. We demonstrate that important insights can be gained from epigenetic approaches and opens to the opportunity of investigations with larger sample sizes. We have shown the potential of using enhancer epigenetic marks for making predictions on the behavior of gene networks and for recovering important information from a significant number of specimens with limited use due to postmortem or storage intervals.

Supplementary Materials: The following are available online at https://www.mdpi.com/1999-491 5/13/4/544/s1, Table S1: Proximal Promoter node, Table S2: All synapses default node, Table S3: All addiction default node. Viruses 2021, 13, 544 21 of 24

Author Contributions: L.B. participated in the concept, prepared all brain cell pellets for ChIP, performed pathway analysis using IPA and other strategies, performed in situ hybridizations and participated in discussions; A.L. performed in situ hybridizations, examined sections and participated in discussions; A.M.M. performed in situ hybridizations and examination of sections; R.J.E. participated in the design of the study, in the selection of human specimens and in discussions; M.C.G.M. elaborated the concept and hypothesis, participated in the design and selection of human specimens, processed frozen human prefrontal cortex fragments till pellets, dissected mouse brains, performed all the systems analysis on Cytoscape and TRANSFAC, wrote the manuscript and obtained funding. All authors have read and agreed to the published version of the manuscript. Funding: R01DA036164 and R01DA047822 to M.C.G.M. Institutional Review Board Statement: This study was performed under approved institutional IRB 20-001 MCM. Informed Consent Statement: Not applicable. Data Availability Statement: All data relevant to this manuscript is available in Supplementary Materials. Acknowledgments: This study was supported by the R01DA036164 and R01DA047822 to M.C.G.M. The authors thank John Satterlee (National Institute on Drug Abuse, NIH) for discussions and suggestions, Madeleine Craske from Active Motif Inc. (Carlsbad, CA, USA) for technical advice and discussions, Fazila Kiyam, Krista Scrivner and Christine Auciello (San Diego Biomedical Research Institute, CA, USA) for administrative assistance. Conﬂicts of Interest: The authors declare no conﬂict of interest.

References 1. Fan, J.; Khanin, R.; Sakamoto, H.; Zhong, Y.; Michael, C.; Pena, D.; Javier, B.; Wood, L.D.; Iacobuzio-Donahue, C.A. Quantification of nucleic acid quality in postmortem tissues from a cancer research autopsy program. Oncotarget 2016, 7, 66906–66921. [CrossRef] 2. Zhu, Y.; Wang, L.; Yin, Y.; Yang, E. Systematic analysis of gene expression patterns associated with postmortem interval in human tissues. Sci. Rep. 2017, 7, 1–12. [CrossRef] 3. Tolliver, S.S.; Hearn, W.L.; Furton, K.G. Evaluating the Relationship Between Postmortem and Antemortem Morphine and Codeine Concentrations in Whole Blood. J. Anal. Toxicol. 2010, 34, 491–497. [CrossRef][PubMed] 4. Gates, L.A.; Foulds, C.E.; O’Malley, B.W. Histone Marks in the ‘Driver’s Seat’: Functional Roles in Steering the Transcription Cycle. Trends Biochem. Sci. 2017, 42, 977–989. [CrossRef][PubMed] 5. Karlić,R.; Chung, H.-R.; Lasserre, J.; Vlahoviˇcek,K.; Vingron, M. Histone modification levels are predictive for gene expression. Proc. Natl. Acad. Sci. USA 2010, 107, 2926–2931. [CrossRef][PubMed] 6. Huebert, D.J.; Kamal, M.; O’Donovan, A.; Bernstein, B.E. Genome-wide analysis of histone modifications by ChIP-on-chip. Methods 2006, 40, 365–369. [CrossRef][PubMed] 7. Bernstein, B.E.; Kamal, M.; Lindblad-Toh, K.; Bekiranov, S.; Bailey, D.K.; Huebert, D.J.; McMahon, S.; Karlsson, E.K.; Kulbokas, E.J.; Gingeras, T.R.; et al. Genomic Maps and Comparative Analysis of Histone Modifications in Human and Mouse. Cell 2005, 120, 169–181. [CrossRef] 8. Bernstein, B.E.; Humphrey, E.L.; Liu, C.L.; Schreiber, S.L. The use of Chromatin Immunoprecipitation Assays in Genome-Wide Analyses of Histone Modifications. Methods Enzym. 2003, 376, 349–360. [CrossRef] 9. Bernstein, B.E.; Humphrey, E.L.; Erlich, R.L.; Schneider, R.; Bouman, P.; Liu, J.S.; Kouzarides, T.; Schreiber, S.L. Methylation of histone H3 Lys 4 in coding regions of active genes. Proc. Natl. Acad. Sci. USA 2002, 99, 8695–8700. [CrossRef][PubMed] 10. Hightower, G.K.; Wong, J.K.; Letendre, S.L.; Umlauf, A.A.; Ellis, R.J.; Ignacio, C.C.; Heaton, R.K.; Collier, A.C.; Marra, C.M.; Clifford, D.B.; et al. Higher HIV-1 genetic diversity is associated with AIDS and neuropsychological impairment. Virology 2012, 433, 498–505. [CrossRef] 11. Everall, I.; Vaida, F.; Khanlou, N.; Lazzaretto, D.; Achim, C.; Letendre, S.; Moore, D.; Ellis, R.; Cherner, M.; Gelman, B.; et al. Cliniconeuropathologic correlates of human immunodeficiency virus in the era of antiretroviral therapy. J. Neurovirol. 2009, 15, 360–370. [CrossRef][PubMed] 12. Gelman, B.B.; Chen, T.; Lisinicchia, J.G.; Soukup, V.M.; Carmical, J.R.; Starkey, J.M.; Masliah, E.; Commins, D.L.; Brandt, D.; Grant, I.; et al. The National Neuro AIDS Tissue Consortium Brain Gene Array: Two Types of HIV-Associated Neurocognitive Impairment. PLoS ONE 2012, 7, e46178. [CrossRef][PubMed] 13. May, P.E.; Heithoff, A.J.; Wichman, C.S.; Phatak, V.S.; Moore, D.J.; Heaton, R.K.; Fox, H.S. Assessing Cognitive Functioning in People Living with HIV (PLWH): Factor Analytic Results From CHARTER and NNTC Cohorts. JAIDS J. Acquir. Immune Defic. Syndr. 2020, 83, 251–259. [CrossRef][PubMed] Viruses 2021, 13, 544 22 of 24

14. Basova, L.; Najera, J.A.; Bortell, N.; Wang, D.; Moya, R.; Lindsey, A.; Semenova, S.; Ellis, R.J.; Marcondes, M.C.G. Dopamine and its receptors play a role in the modulation of CCR5 expression in innate immune cells following exposure to Methamphetamine: Implications to HIV infection. PLoS ONE 2018, 13, e0199861. [CrossRef][PubMed] 15. Tjitro, R.; Campbell, L.A.; Basova, L.; Johnson, J.; Najera, J.A.; Lindsey, A.; Marcondes, M.C.G. Modeling the Function of TATA Box Binding Protein in Transcriptional Changes Induced by HIV-1 Tat in Innate Immune Cells and the Effect of Methamphetamine Exposure. Front. Immunol. 2019, 9, 3110. [CrossRef][PubMed] 16. Zhang, Y.; Liu, T.A.; Meyer, C.; Eeckhoute, J.; Johnson, D.S.; Bernstein, B.E.; Nussbaum, C.; Myers, R.M.; Brown, M.; Li, W.; et al. Model-based Analysis of ChIP-Seq (MACS). Genome Biol. 2008, 9, 1–9. [CrossRef][PubMed] 17. Zang, C.; Schones, D.E.; Zeng, C.; Cui, K.; Zhao, K.; Peng, W. A clustering approach for identification of enriched domains from histone modification ChIP-Seq data. Bioinformatics 2009, 25, 1952–1958. [CrossRef] 18. Sepulveda, J.L. Using R and Bioconductor in Clinical Genomics and Transcriptomics. J. Mol. Diagn. 2020, 22, 3–20. [CrossRef] 19. Montojo, J.; Zuberi, K.; Rodriguez, H.; Bader, G.D.; Morris, Q. GeneMANIA: Fast gene network construction and function prediction for Cytoscape. F1000Research 2014, 3, 153. [CrossRef] 20. Warde-Farley, D.; Donaldson, S.L.; Comes, O.; Zuberi, K.; Badrawi, R.; Chao, P.; Franz, M.; Grouios, C.; Kazi, F.; Lopes, C.T.; et al. The GeneMANIA prediction server: Biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res. 2010, 38, W214–W220. [CrossRef] 21. Franz, M.; Rodriguez, H.; Lopes, C.; Zuberi, K.; Montojo, J.; Bader, G.D.; Morris, Q. GeneMANIA update 2018. Nucleic Acids Res. 2018, 46, W60–W64. [CrossRef] 22. Shannon, P.; Markiel, A.; Ozier, O.; Baliga, N.S.; Wang, J.T.; Ramage, D.; Amin, N.; Schwikowski, B.; Ideker, T. Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks. Genome Res. 2013, 13, 2498–2504. [CrossRef] 23. Stein, L.D. Using the Reactome Database. Curr. Protoc. Bioinform. 2004, 7.[CrossRef] 24. Oughtred, R.; Rust, J.; Chang, C.; Breitkreutz, B.; Stark, C.; Willems, A.; Boucher, L.; Leung, G.; Kolas, N.; Zhang, F.; et al. The BioGRID database: A comprehensive biomedical resource of curated protein, genetic, and chemical interactions. Protein Sci. 2021, 30, 187–200. [CrossRef] 25. Oughtred, R.; Stark, C.; Breitkreutz, B.-J.; Rust, J.; Boucher, L.; Chang, C.; Kolas, N.; O’Donnell, L.; Leung, G.; McAdam, R.; et al. The BioGRID interaction database: 2019 update. Nucleic Acids Res. 2019, 47, D529–D541. [CrossRef] 26. Stark, C.; Breitkreutz, B.J.; Reguly, T.; Boucher, L.; Breitkreutz, A.; Tyers, M. BioGRID: A general repository for interaction datasets. Nucleic Acids Res. 2006, 34, D535–D539. [CrossRef] 27. Matys, V. Transfac (R) and its module TransCompel (R): Transcriptional gene regulation in eukaryotes. Nucleic Acids Res. 2006, 34, D108–D110. [CrossRef] 28. Wingender, E.; Chen, X.; Fricke, E.; Geffers, R.; Hehl, R.; Liebich, I.; Krull, M.; Matys, V.; Michael, H.; Ohnhäuser, R.; et al. The Transfac system on gene expression regulation. Nucleic Acids Res. 2001, 29, 281–283. [CrossRef][PubMed] 29. Wingender, E. Transfac: An integrated system for gene expression regulation. Nucleic Acids Res. 2000, 28, 316–319. [CrossRef] [PubMed] 30. Fornes, O.A.; Castro-Mondragon, J.; Khan, A.; Van Der Lee, R.; Zhang, X.A.; Richmond, P.; Modi, B.P.; Correard, S.; Gheorghe, M.; Baranašić,D.; et al. Jaspar 2020: Update of the open-access database of transcription factor binding profiles. Nucleic Acids Res. 2019, 48, D87–D92. [CrossRef][PubMed] 31. Wang, F.; Flanagan, J.; Su, N.; Wang, L.-C.; Bui, S.; Nielson, A.; Wu, X.; Vo, H.-T.; Ma, X.-J.; Luo, Y. RNAscope. J. Mol. Diagn. 2012, 14, 22–29. [CrossRef][PubMed] 32. Brands, C.; Morcock, D.; Estes, J.; Deleage, C. Next-generation Viral RNA/DNA in situ Hybridization Applications in Human Immunodeficiency Virus/Simian Immunodeficiency Virus Research. J. Vis. Exp. 2020, 160.[CrossRef][PubMed] 33. Deleage, C.; Chan, C.N.; Busman-Sahay, K.; Estes, J.D. Next-generation in situ hybridization approaches to define and quantify HIV and SIV reservoirs in tissue microenvironments. Retrovirology 2018, 15, 1–10. [CrossRef] 34. Mendoza-Parra, M.A.; Saleem, M.-A.M.; Blum, M.; Cholley, P.-E.; Gronemeyer, H. NGS-QC Generator: A Quality Control System for ChIP-Seq and Related Deep Sequencing-Generated Datasets. Adv. Struct. Saf. Stud. 2016, 1418, 243–265. [CrossRef] 35. Andersson, E.; Tryggvason, U.; Deng, Q.; Friling, S.; Alekseenko, Z.; Robert, B.; Perlmann, T.; Ericson, J. Identification of Intrinsic Determinants of Midbrain Dopamine Neurons. Cell 2006, 124, 393–405. [CrossRef] 36. Koppelkamm, A.; Vennemann, B.; Lutz-Bonengel, S.; Fracasso, T.; Vennemann, M. RNA integrity in post-mortem samples: Influencing parameters and implications on RT-qPCR assays. Int. J. Leg. Med. 2011, 125, 573–580. [CrossRef] 37. Ferrer, I.; Martinez, A.; Boluda, S.; Parchi, P.; Barrachina, M. Brain banks: Benefits, limitations and cautions concerning the use of post-mortem brain tissue for molecular studies. Cell Tissue Bank. 2008, 9, 181–194. [CrossRef] 38. Reiman, M.; Laan, M.; Rull, K.; Sõber, S. Effects of RNA integrity on transcript quantification by total RNA sequencing of clinically collected human placental samples. FASEB J. 2017, 31, 3298–3308. [CrossRef] 39. White, K.; Yang, P.; Li, L.; Farshori, A.; Medina, A.E.; Zielke, H.R. Effect of Postmortem Interval and Years in Storage on RNA Quality of Tissue at a Repository of the NIH NeuroBioBank. Biopreserv. Biobank. 2018, 16, 148–157. [CrossRef][PubMed] 40. Ahlner, J.; Holmgren, A.; Jones, A.W. Demographics and post-mortem toxicology findings in deaths among people arrested multiple times for use of illicit drugs and/or impaired driving. Forensic Sci. Int. 2016, 265, 138–143. [CrossRef] 41. Jarmasz, J.S.; Stirton, H.; Davie, J.R.; Del Bigio, M.R. DNA methylation and histone post-translational modification stability in post-mortem brain tissue. Clin. Epigenet. 2019, 11, 5. [CrossRef][PubMed] Viruses 2021, 13, 544 23 of 24

42. Almouzni, G.; Cedar, H. Maintenance of Epigenetic Information. Cold Spring Harb. Perspect. Biol. 2016, 8, a019372. [CrossRef] [PubMed] 43. Chen, K.; Chen, Z.; Wu, D.; Zhang, L.; Lin, X.; Su, J.; Rodriguez, B.; Xi, Y.; Xia, Z.; Chen, X.; et al. Broad H3K4me3 is associated with increased transcription elongation and enhancer activity at tumor-suppressor genes. Nat. Genet. 2015, 47, 1149–1157. [CrossRef] 44. Sato, Y.; Hilbert, L.; Oda, H.; Wan, Y.; Heddleston, J.M.; Chew, T.-L.; Zaburdaev, V.; Keller, P.; Lionnet, T.; Vastenhouw, N.; et al. Histone H3K27 acetylation precedes active transcription during zebrafish zygotic genome activation as revealed by live-cell analysis. Development 2019, 146, dev179127. [CrossRef] 45. Nikolov, D.B.; Burley, S.K. RNA polymerase II transcription initiation: A structural view. Proc. Natl. Acad. Sci. USA 1997, 94, 15–22. [CrossRef][PubMed] 46. Klar, A.J.S. The chromosome 1;11 translocation provides the best evidence supporting genetic etiology for schizophrenia and bipolar affective disorders. Genetics 2002, 160, 1745–1747. [PubMed] 47. Lacour, A.; Espinosa, A.; Louwersheimer, E.; Heilmann, S.; Hernández, I.; Wolfsgruber, S.; Fernández, V.; Wagner, H.; Rosende- Roca, M.; Mauleón, A.; et al. Genome-wide significant risk factors for Alzheimer’s disease: Role in progression to dementia due to Alzheimer’s disease among subjects with mild cognitive impairment. Mol. Psychiatry 2017, 22, 153–160. [CrossRef] 48. Moreno-Grau, S.M.-G.; Hernández, I.; Heilmann-Heimbach, S.; Ruiz, S.; Rosende-Roca, M.; Mauleón, A.; Vargas, L.; Gómez, O.R.; Alegret, M.; Espinosa, A.; et al. Genome-wide significant risk factors on chromosome 19 and the APOE locus. Oncotarget 2018, 9, 24590–24600. [CrossRef] 49. Joutel, A.; Corpechot, C.; Ducros, A.; Vahedi, K.; Chabriat, H.; Mouton, P.; Alamowitch, S.; Domenga, V.; Cécillion, M.; Maréchal, E.; et al. Notch3 Mutations in Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy (CADASIL), a Mendelian Condition Causing Stroke and Vascular Dementia. Ann. N. Y. Acad. Sci. 1997, 826, 213–217. [CrossRef] 50. Joutel, A.; Corpechot, C.; Ducros, A.; Vahedi, K.; Chabriat, H.; Mouton, P.; Alamowitch, S.; Domenga, V.; Cécillion, M.; Maréchal, E.; et al. Notch3 mutations in CADASIL, a hereditary adult-onset condition causing stroke and dementia. Nat. Cell Biol. 1996, 383, 707–710. [CrossRef] 51. Canet, G.; Dias, C.; Gabelle, A.; Simonin, Y.; Gosselet, F.; Marchi, N.; Makinson, A.; Tuaillon, E.; Van De Perre, P.; Givalois, L.; et al. HIV Neuroinfection and Alzheimer’s Disease: Similarities and Potential Links? Front. Cell. Neurosci. 2018, 12, 307. [CrossRef] [PubMed] 52. Okitsu, C.Y.; Hsieh, J.C.F.; Hsieh, C.-L. Transcriptional Activity Affects the H3K4me3 Level and Distribution in the Coding Region. Mol. Cell. Biol. 2010, 30, 2933–2946. [CrossRef][PubMed] 53. Akkers, R.C.; Van Heeringen, S.J.; Jacobi, U.G.; Janssen-Megens, E.M.; Françoijs, K.-J.; Stunnenberg, H.G.; Veenstra, G.J.C. A Hierarchy of H3K4me3 and H3K27me3 Acquisition in Spatial Gene Regulation in Xenopus Embryos. Dev. Cell 2009, 17, 425–434. [CrossRef][PubMed] 54. Daubner, S.C.; Le, T.; Wang, S. Tyrosine hydroxylase and regulation of dopamine synthesis. Arch. Biochem. Biophys. 2011, 508, 1–12. [CrossRef] 55. Najera, J.A.; Bustamante, E.A.; Bortell, N.; Morsey, B.; Fox, H.S.; Ravasi, T.; Marcondes, M.C.G. Methamphetamine abuse affects gene expression in brain-derived microglia of SIV-infected macaques to enhance inflammation and promote virus targets. BMC Immunol. 2016, 17, 1–19. [CrossRef] 56. Bortell, N.; Morsey, B.; Basova, L.; Fox, H.S.; Marcondes, M.C.G. Phenotypic changes in the brain of SIV-infected macaques exposed to methamphetamine parallel macrophage activation patterns induced by the common gamma-chain cytokine system. Front. Microbiol. 2015, 6, 900. [CrossRef] 57. Kesby, J.P.; Najera, J.A.; Romoli, B.; Fang, Y.; Basova, L.; Birmingham, A.; Marcondes, M.C.G.; Dulcis, D.; Semenova, S. HIV-1 TAT protein enhances sensitization to methamphetamine by affecting dopaminergic function. Brain Behav. Immun. 2017, 65, 210–221. [CrossRef] 58. Mediouni, S.; Marcondes, M.C.G.; Miller, C.A.; McLaughlin, J.P.; Valente, S.T. The cross-talk of HIV-1 Tat and methamphetamine in HIV-associated neurocognitive disorders. Front. Microbiol. 2015, 6, 1164. [CrossRef] 59. Ellis, C.; Hoffman, W.; Jaehnert, S.; Plagge, J.; Loftis, J.M.; Schwartz, D.; Huckans, M. Everyday Problems with Executive Dysfunction and Impulsivity in Adults Recovering from Methamphetamine Addiction. Addict. Disord. Their Treat. 2016, 15, 1–5. [CrossRef] 60. Kohno, M.; Loftis, J.M.; Huckans, M.; Dennis, L.E.; McCready, H.; Hoffman, W.F. The relationship between interleukin-6 and functional connectivity in methamphetamine users. Neurosci. Lett. 2018, 677, 49–54. [CrossRef][PubMed] 61. Loftis, J.M.; Janowsky, A. Neuroimmune Basis of Methamphetamine Toxicity. Int. Rev. Neurobiol. 2014, 118, 165–197. [CrossRef] [PubMed] 62. Basova, L.V.; Kesby, J.P.; Kaul, M.; Semenova, S.; Marcondes, M.C.G. Systems Biology Analysis of the Antagonizing Effects of HIV-1 Tat Expression in the Brain over Transcriptional Changes Caused by Methamphetamine Sensitization. Viruses 2020, 12, 426. [CrossRef] 63. Baek, E.J.; Kim, H.; Basova, L.A.; Rosander, A.; Kesby, J.P.; Semenova, S.; Marcondes, M.C.G. Sex differences and Tat expression affect dopaminergic receptor expression and response to antioxidant treatment in methamphetamine-sensitized HIV Tat transgenic mice. Neuropharmacology 2020, 178, 108245. [CrossRef][PubMed] Viruses 2021, 13, 544 24 of 24

64. Blackstone, K.; Iudicello, J.E.; Morgan, E.E.; Weber, E.; Moore, D.J.; Franklin, D.R.; Ellis, R.J.; Grant, I.; Woods, S.P. Human Immun- odeficiency Virus Infection Heightens Concurrent Risk of Functional Dependence in Persons with Long-Term Methamphetamine use. J. Addict. Med. 2013, 7, 255–263. [CrossRef][PubMed] 65. Koob, G.F.; Volkow, N.D. Neurocircuitry of Addiction. Neuropsychopharmacology 2009, 35, 217–238. [CrossRef] 66. Koob, G.F.; Volkow, N.D. Neurobiology of addiction: A neurocircuitry analysis. Lancet Psychiatry 2016, 3, 760–773. [CrossRef] 67. Volkow, N.D.; Koob, G.; Baler, R. Biomarkers in Substance Use Disorders. ACS Chem. Neurosci. 2015, 6, 522–525. [CrossRef] 68. Nickoloff-Bybel, E.A.; Calderon, T.M.; Gaskill, P.J.; Berman, J.W. HIV Neuropathogenesis in the Presence of a Disrupted Dopamine System. J. Neuroimmune Pharmacol. 2020, 15, 729–742. [CrossRef] 69. Gonçalves, J.; Baptista, S.; Martins, T.; Milhazes, N.; Borges, F.; Ribeiro, C.F.; Malva, J.O.; Silva, A.P. Methamphetamine-induced neuroinflammation and neuronal dysfunction in the mice hippocampus: Preventive effect of indomethacin. Eur. J. Neurosci. 2010, 31, 315–326. [CrossRef][PubMed] 70. Ott, T.; Nieder, A. Dopamine and Cognitive Control in Prefrontal Cortex. Trends Cogn. Sci. 2019, 23, 213–234. [CrossRef][PubMed] 71. Fuster, J.M. The Prefrontal Cortex—An Update. Neuron 2001, 30, 319–333. [CrossRef] 72. Fuster, J.M. Memory networks in the prefrontal cortex. Prog. Brain Res. 2000, 122, 309–316. [CrossRef] 73. Fuster, J.M. Prefrontal Cortex and the Bridging of Temporal Gaps in the Perception-Action Cycle. Ann. N. Y. Acad. Sci. 1990, 608, 318–336. [CrossRef] 74. Goldman-Rakic, P.S. Architecture of the Prefrontal Cortex and the Central Executive. Ann. N. Y. Acad. Sci. 1995, 769, 71–84. [CrossRef][PubMed] 75. Lisman, J.E.; Fellous, J.-M.; Wang, X.-J. A role for NMDA-receptor channels in working memory. Nat. Neurosci. 1998, 1, 273–275. [CrossRef] 76. Tegnér, J.; Compte, A.; Wang, X.-J. The dynamical stability of reverberatory neural circuits. Biol. Cybern. 2002, 87, 471–481. [CrossRef] 77. Rolls, E.T.; Loh, M.; Deco, G.; Winterer, G. Computational models of schizophrenia and dopamine modulation in the prefrontal cortex. Nat. Rev. Neurosci. 2008, 9, 696–709. [CrossRef] 78. Wearne, T.A.; Cornish, J.L. Inhibitory regulation of the prefrontal cortex following behavioral sensitization to amphetamine and/or methamphetamine psychostimulants: A review of GABAergic mechanisms. Prog. Neuro-Psychopharmacol. Biol. Psychiatry 2019, 95, 109681. [CrossRef][PubMed] 79. Gelman, B.B.; Lisinicchia, J.G.; Chen, T.; Johnson, K.M.; Jennings, K.; Freeman, D.H.; Soukup, V.M. Prefrontal Dopaminergic and Enkephalinergic Synaptic Accommodation in HIV-associated Neurocognitive Disorders and Encephalitis. J. Neuroimmune Pharmacol. 2012, 7, 686–700. [CrossRef][PubMed] 80. Gelman, B.B.; Spencer, J.A.; Holzer, C.E.; Soukup, V.M.; Iii, C.E.H. Abnormal Striatal Dopaminergic Synapses in National NeuroAIDS Tissue Consortium Subjects with HIV Encephalitis. J. Neuroimmune Pharmacol. 2006, 1, 410–420. [CrossRef][PubMed] 81. Skowronska, M.; McDonald, M.; Velichkovska, M.; Leda, A.R.; Park, M.; Toborek, M. Methamphetamine increases HIV infectivity in neural progenitor cells. J. Biol. Chem. 2018, 293, 296–311. [CrossRef][PubMed] 82. Hirata, H.; Cadet, J.L. p53-knockout mice are protected against the long-term effects of methamphetamine on dopaminergic terminals and cell bodies. J. Neurochem. 2002, 69, 780–790. [CrossRef][PubMed] 83. Panagoulias, I.; Karagiannis, F.; Aggeletopoulou, I.; Georgakopoulos, T.; Argyropoulos, C.P.; Akinosoglou, K.; Gogos, C.; Skoutelis, A.; Mouzaki, A. Ets-2 Acts as a Transcriptional Repressor of the Human Immunodeficiency Virus Type 1 through Binding to a Repressor-Activator Target Sequence of 50-LTR. Front. Immunol. 2018, 8, 1924. [CrossRef] 84. Panagoulias, I.; Georgakopoulos, T.; Aggeletopoulou, I.; Agelopoulos, M.; Thanos, D.; Mouzaki, A. Transcription Factor Ets-2 Acts as a Preinduction Repressor of Interleukin-2 (IL-2) Transcription in Naive T Helper Lymphocytes. J. Biol. Chem. 2016, 291, 26707–26721. [CrossRef][PubMed]