Journal of NeuroVirology

, Volume 24, Issue 3, pp 350–361 | Cite as

No reliable gene expression biomarkers of current or impending neurocognitive impairment in peripheral blood monocytes of persons living with HIV

  • Austin Quach
  • Steve Horvath
  • Natasha Nemanim
  • Dimitrios Vatakis
  • Mallory D. Witt
  • Eric N. Miller
  • Roger Detels
  • Peter Langfelder
  • Paul Shapshak
  • Elyse J. Singer
  • Andrew J. Levine


Events leading to and propagating neurocognitive impairment (NCI) in HIV-1-infected (HIV+) persons are largely mediated by peripheral blood monocytes. We previously identified expression levels of individual genes and gene networks in peripheral blood monocytes that correlated with neurocognitive functioning in HIV+ adults. Here, we expand upon those findings by examining if gene expression data at baseline is predictive of change in neurocognitive functioning 2 years later. We also attempt to validate the original findings in a new sample of HIV+ patients and determine if the findings are HIV specific by including HIV-uninfected (HIV−) participants as a comparison group. At two time points, messenger RNA (mRNA) was isolated from the monocytes of 123 HIV+ and 60 HIV− adults enrolled in the Multicenter AIDS Cohort Study and analyzed with the Illumina HT-12 v4 Expression BeadChip. All participants received baseline and follow-up neurocognitive testing 2 years after mRNA analysis. Data were analyzed using standard gene expression analysis and weighted gene co-expression network analysis with correction for multiple testing. Gene sets were analyzed for GO term enrichment. Only weak reproducibility of associations of single genes with neurocognitive functioning was observed, indicating that such measures are unreliable as biomarkers for HIV-related NCI; however, gene networks were generally preserved between time points and largely reproducible, suggesting that these may be more reliable. Several gene networks associated with variables related to HIV infection were found (e.g., MHC I antigen processing, TNF signaling, interferon gamma signaling, and antiviral defense); however, no significant associations were found for neurocognitive function. Furthermore, neither individual gene probes nor gene networks predicted later neurocognitive change. This study did not validate our previous findings and does not support the use of monocyte gene expression profiles as a biomarker for current or future HIV-associated neurocognitive impairment.


HIV-associated neurocognitive disorders neuroHIV Monocyte WGCNA Gene expression Biomarker 



We thank the participants and staff of the Multicenter AIDS Cohort Study in Los Angeles. This study was primarily funded by the National Institute for Drug Abuse [R01DA030913 (Levine and Horvath)]. The Los Angeles site of the Multicenter AIDS Cohort Study is funded by the National Institute of Allergy and Infectious Disease [U01-AI-35040 (Detels)], Los Angeles Biomedical Research Institute CTSI [UL1TR001881], and NIH/National Center for Advancing Translational Science (NCATS) UCLA CTSI [UL1TR000124].

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

13365_2018_625_MOESM1_ESM.docx (256 kb)
Supplemental Figure 1 Agreement of probe-GNF correlations from different sample sets. In order to assess reproducibility of the differential expression at the single probe level, correlations between the gene expression probe levels in peripheral monocytes and global neurocognitive function (GNF) from different sample sets were plotted against each other in order to assess inter-set agreement. Each point on the scatterplot represents a single gene expression probe with the correlation coefficient with GNF denoted on the x- and y-axes. Correlation coefficients are listed above each scatterplot. A) Correlations in the time point 1 HIV+ samples (n = 61) were plotted against correlations in time point 2 HIV+ samples (n = 62). B) Probe-GNF correlations in all HIV+ samples (n = 123) were plotted against correlations in all HIV− samples (n = 60). The HIV+ individuals with repeat measurements at both time points were excluded to avoid artificial inflation of agreement. (DOCX 256 kb)
13365_2018_625_MOESM2_ESM.docx (46 kb)
Supplemental Figure 2 Module preservation statistics between different HIV+ sample sets. The preservation of gene co-expression modules from time point 1 HIV+ samples were assessed by comparing in-group proportion of modules from time point 2 HIV+ samples versus permutated gene expression values to arrive at a permutation p-value. Points on the scatter plot represent modules as denoted by their color and label, where their vertical position denotes increasing negative log-scaled significance and their horizontal position indicates the number of genes in the module. The blue and red dotted lines represent nominal (p = 0.05) and Bonferroni significance (p = 0.00015) levels respectively. Ten thousand permutations were used in the computation, leading to achievable maximum significance of p = 0.0001, which was attained by a number of modules aligned horizontally at the top of the graph. (DOCX 46 kb)
13365_2018_625_MOESM3_ESM.docx (182 kb)
Supplemental Figure 3 Correlation to determine level of agreement of gene expression profiles within all samples. We found strong consistency between the gene expression profiles within and between individuals. Inter-individual variation was greater than the variation between repeat measurements on the same individual between time points, however even then the lowest inter-sample correlation was strong (r = 0.93). (DOCX 181 kb)
13365_2018_625_MOESM4_ESM.docx (96 kb)
Supplementary Table 1 (DOCX 96.4 kb)
13365_2018_625_MOESM5_ESM.xlsx (7.7 mb)
Supplementary Table 2 GO term enrichment of gene modules. The top 20 enriched GO terms for all module gene sets are presented. The horizontal portions of the table correspond to the modules as labeled on the left. Enrichment statistics are reported in the rightmost columns including Fisher’s exact test p-values. (XLSX 7866 kb)
13365_2018_625_MOESM6_ESM.xlsx (69 kb)
Supplementary Table 3 (XLSX 68 kb)


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Copyright information

© Journal of NeuroVirology, Inc. 2018

Authors and Affiliations

  • Austin Quach
    • 1
  • Steve Horvath
    • 1
    • 2
  • Natasha Nemanim
    • 3
  • Dimitrios Vatakis
    • 4
  • Mallory D. Witt
    • 5
    • 6
  • Eric N. Miller
    • 7
  • Roger Detels
    • 5
    • 8
  • Peter Langfelder
    • 1
  • Paul Shapshak
    • 9
  • Elyse J. Singer
    • 10
  • Andrew J. Levine
    • 10
  1. 1.Department of Human GeneticsDavid Geffen School of Medicine at the University of CaliforniaLos AngelesUSA
  2. 2.Department of BiostatisticsUniversity of CaliforniaLos AngelesUSA
  3. 3.Department of Neurology, National Neurological AIDS BankDavid Geffen School of Medicine at the University of CaliforniaLos AngelesUSA
  4. 4.Department of MedicineDavid Geffen School of Medicine at the University of CaliforniaLos AngelesUSA
  5. 5.David Geffen School of Medicine at the University of CaliforniaLos AngelesUSA
  6. 6.Los Angeles Biomedical Research Institute at Harbor-UCLA Medical CenterTorranceUSA
  7. 7.Department of Psychiatry and Biobehavioral ScienceDavid Geffen School of Medicine at the University of CaliforniaLos AngelesUSA
  8. 8.Department of Epidemiology, UCLA Fielding School of Public HealthLos AngelesUSA
  9. 9.Department of Medicine (Division of Infectious Disease and International Medicine), Morsani College of MedicineUniversity of South FloridaTampaUSA
  10. 10.Department of NeurologyDavid Geffen School of Medicine at the University of CaliforniaLos AngelesUSA

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