Characterization of host genetic expression patterns in HIV-infected individuals with divergent disease progression

Omic Technologies in HIV: Searching Transcriptional Signatures Involved in Long-Term Non-Progressor and HIV Controller Phenotypes

This article reviews the main discoveries achieved by transcriptomic approaches on HIV controller (HIC) and long-term non-progressor (LTNP) individuals, who are able to suppress HIV replication and maintain high CD4+ T cell levels, respectively, in the absence of antiretroviral therapy. Different studies using high throughput techniques have elucidated multifactorial causes implied in natural control of HIV infection. Genes related to IFN response, calcium metabolism, ribosome biogenesis, among others, are commonly differentially expressed in LTNP/HIC individuals. Additionally, pathways related with activation, survival, proliferation, apoptosis and inflammation, can be deregulated in these individuals. Likewise, recent transcriptomic studies include high-throughput sequencing in specific immune cell subpopulations, finding additional gene expression patterns associated to viral control and/or non-progression in immune cell subsets. Herein, we provide an overview of the main differentially expressed genes and biological routes commonly observed on immune cells involved in HIV infection from HIC and LTNP individuals, analyzing also different technical aspects that could affect the data analysis and the future perspectives and gaps to be addressed in this field.

DEAD-ly Affairs: The Roles of DEAD-Box Proteins on HIV-1 Viral RNA Metabolism

In order to ensure viral gene expression, Human Immunodeficiency virus type-1 (HIV-1) recruits numerous host proteins that promote optimal RNA metabolism of the HIV-1 viral RNAs (vRNAs), such as the proteins of the DEAD-box family. The DEAD-box family of RNA helicases regulates multiple steps of RNA metabolism and processing, including transcription, splicing, nucleocytoplasmic export, trafficking, translation and turnover, mediated by their ATP-dependent RNA unwinding ability. In this review, we provide an overview of the functions and role of all DEAD-box family protein members thus far described to influence various aspects of HIV-1 vRNA metabolism. We describe the molecular mechanisms by which HIV-1 hijacks these host proteins to promote its gene expression and we discuss the implications of these interactions during viral infection, their possible roles in the maintenance of viral latency and in inducing cell death. We also speculate on the emerging potential of pharmacological inhibitors of DEAD-box proteins as novel therapeutics to control the HIV-1 pandemic.

HIV-1 Infection Transcriptomics: Meta-Analysis of CD4+ T Cells Gene Expression Profiles

HIV-1 infection elicits a complex dynamic of the expression various host genes. High throughput sequencing added an expressive amount of information regarding HIV-1 infections and pathogenesis. RNA sequencing (RNA-Seq) is currently the tool of choice to investigate gene expression in a several range of experimental setting. This study aims at performing a meta-analysis of RNA-Seq expression profiles in samples of HIV-1 infected CD4+ T cells compared to uninfected cells to assess consistently differentially expressed genes in the context of HIV-1 infection. We selected two studies (22 samples: 15 experimentally infected and 7 mock-infected). We found 208 differentially expressed genes in infected cells when compared to uninfected/mock-infected cells. This result had moderate overlap when compared to previous studies of HIV-1 infection transcriptomics, but we identified 64 genes already known to interact with HIV-1 according to the HIV-1 Human Interaction Database. A gene ontology (GO) analysis revealed enrichment of several pathways involved in immune response, cell adhesion, cell migration, inflammation, apoptosis, Wnt, Notch and ERK/MAPK signaling.

Novel association of genetic variants in non-coding regulatory regions with HIV-1 infection

Host genetic variability interplays with the environment and variegating viral factors to determine the outcome in HIV-1/AIDS. Several GWAS studies have reported that genetic heterogeneity of individuals leads to differential HIV susceptibility. Proxy SNPs that are in Linkage Disequilibrium to the GWAS SNPs could be important targets in HIV pathogenesis and need to be analyzed further for their potential regulatory role. Current study thus aimed to identify novel proxy SNPs that may play a critical role in HIV susceptibility and disease progression. 372 SNPs, associated with HIV-1/AIDS pathogenesis, were retrieved via GWAS catalogue. 1854 proxy SNPs, in Linkage Disequilibrium (r² = 0.8) to the GWAS reported SNPs, were identified using the SNAP web tool. Regulatory functions of aforementioned 1854 polymorphic sites (GWAS SNPs and their proxy SNPs) were acquired from RegulomeDB. 178 of the proxy SNPs showed evidence of strong regulatory potential returning a score of ≤3. Among these regulatory SNPs, 22 had already been reported for their association with HIV/AIDS while 156 SNPs showed novel association. Three of these novel SNPs (g.rs6457282T>C, g.rs17064977C>T and g.rs3130350G>T) were validated using sequence specific PCR (SSP-PCR) on HIV-infected patients. For g.rs6457282T>C and rs17064977C>T, CT genotype was determined to be significantly associated with increased risk of HIV-1 infection (rs6457282T>C: OR = 9.5, 95% CI = 3.0792-29.3099, p = 0.0001; rs17064977C>T: OR = 8.1077, 95% CI = 3.1125-21.119, p = 0.0001). Moreover, the association of interacting protein partners of affected genes with HIV-1 elucidates the significance of corresponding SNPs in HIV disease outcome that further needs to be functionally deciphered.

Large-Scale "OMICS" Studies to Explore the Physiopatholgy of HIV-1 Infection

In this review, we present the main large-scale experimental studies that have been performed in the HIV/AIDS field. These “omics” studies are based on several technologies including genotyping, RNA interference, and transcriptome or epigenome analysis. Due to the direct connection with disease evolution, there has been a large focus on genotyping cohorts of well-characterized patients through genome-wide association studies (GWASs), but there have also been several invitro studies such as small interfering RNA (siRNA) interference or transcriptome analyses of HIV-1–infected cells. After describing the major results obtained with these omics technologies—including some with a high relevance for HIV-1 treatment—we discuss the next steps that the community needs to embrace in order to derive new actionable therapeutic or diagnostic targets. Only integrative approaches that combine all big data results and consider their complex interactions will allow us to capture the global picture of HIV molecular pathogenesis. This novel challenge will require large collaborative efforts and represents a huge open field for innovative bioinformatics approaches.

Meta-analysis of gene expression profiles in long-term non-progressors infected with HIV-1

Background

In the absence of antiretroviral treatments (ARTs), a small group of individuals infected with HIV, including long-term non-progressors (LTNPs) who maintain high levels of CD4+ T cells for more than 7–10 years in the absence of ART and in particular a subgroup of LTNPs, elite controllers (ECs), who have low levels of viremia, remain clinically and/or immunologically stable for years. However, the mechanism of stable disease progression in LTNPs and ECs needs to be elucidated to help those infected with HIV-1 remain healthy. In this study, to identify the characteristics of gene expression profiles and biomarkers in LTNPs, we performed a meta-analysis using multiple gene expression profiles among LTNPs, individuals infected with HIV-1 without ART, individuals infected with HIV-1 with ART, and healthy controls.

Methods

The gene expression profiles obtained from the Gene Expression Omnibus (GEO) microarray data repositories were classified into three groups: LTNPs versus healthy controls (first group, 3 studies), LTNPs versus patients infected with HIV-1 without ART (second group, 3 studies), and LTNPs versus patients infected with HIV-1 with ART (third group, 3 studies). In addition, we considered a fourth group, patients infected with HIV-1 without ART versus healthy controls (3 studies), to exclude genes associated with HIV-1 infection in the three groups. For each group, we performed a meta-analysis using the RankProd method to identify and compare the differentially expressed genes (DEGs) in the three groups.

Results

We identified the 14 common DEGs in the three groups when comparing them with each other. Most belonged to immune responses, antigen processing and presentation, the interferon-gamma-mediated signaling pathway, and T cell co-stimulation. Of these DEGs, PHLDA1 was up-regulated and ACTB and ACTG1 were down-regulated in all three groups. However, the rest of the up- or down-regulated genes were discordant in the three groups. Additionally, ACTB and ACTG1 are known to inhibit viral assembly and production, and THBS1 is known to inhibit HIV-1 infection.

Conclusions

These results suggest that significant genes identified in a meta-analysis provide clues to the cause of delayed disease progression and give a deeper understanding of HIV pathogenesis in LTNPs.

Electronic supplementary material

The online version of this article (10.1186/s12920-018-0443-x) contains supplementary material, which is available to authorized users.

Transcriptomic meta-analysis identifies gene expression characteristics in various samples of HIV-infected patients with nonprogressive disease

BACKGROUND

A small proportion of HIV-infected patients remain clinically and/or immunologically stable for years, including elite controllers (ECs) who have undetectable viremia (<50 copies/ml) and long-term nonprogressors (LTNPs) who maintain normal CD4⁺ T cell counts for prolonged periods (>10 years). However, the mechanism of nonprogression needs to be further resolved. In this study, a transcriptome meta-analysis was performed on nonprogressor and progressor microarray data to identify differential transcriptome pathways and potential biomarkers.

METHODS

Using the INMEX (integrative meta-analysis of expression data) program, we performed the meta-analysis to identify consistently differentially expressed genes (DEGs) in nonprogressors and further performed functional interpretation (gene ontology analysis and pathway analysis) of the DEGs identified in the meta-analysis. Five microarray datasets (81 cases and 98 controls in total), including whole blood, CD4⁺ and CD8⁺ T cells, were collected for meta-analysis.

RESULTS

We determined that nonprogressors have reduced expression of important interferon-stimulated genes (ISGs), CD38, lymphocyte activation gene 3 (LAG-3) in whole blood, CD4⁺ and CD8⁺ T cells. Gene ontology (GO) analysis showed a significant enrichment in DEGs that function in the type I interferon signaling pathway. Upregulated pathways, including the PI3K-Akt signaling pathway in whole blood, cytokine-cytokine receptor interaction in CD4⁺ T cells and the MAPK signaling pathway in CD8⁺ T cells, were identified in nonprogressors compared with progressors. In each metabolic functional category, the number of downregulated DEGs was more than the upregulated DEGs, and almost all genes were downregulated DEGs in the oxidative phosphorylation (OXPHOS) and tricarboxylic acid (TCA) cycle in the three types of samples.

CONCLUSIONS

Our transcriptomic meta-analysis provides a comprehensive evaluation of the gene expression profiles in major blood types of nonprogressors, providing new insights in the understanding of HIV pathogenesis and developing strategies to delay HIV disease progression.

Reconstructing the temporal progression of HIV-1 immune response pathways

Motivation: Most methods for reconstructing response networks from high throughput data generate static models which cannot distinguish between early and late response stages.

Results: We present TimePath, a new method that integrates time series and static datasets to reconstruct dynamic models of host response to stimulus. TimePath uses an Integer Programming formulation to select a subset of pathways that, together, explain the observed dynamic responses. Applying TimePath to study human response to HIV-1 led to accurate reconstruction of several known regulatory and signaling pathways and to novel mechanistic insights. We experimentally validated several of TimePaths’ predictions highlighting the usefulness of temporal models.

Availability and Implementation: Data, Supplementary text and the TimePath software are available from http://sb.cs.cmu.edu/timepath

Contact:zivbj@cs.cmu.edu

Supplementary information:Supplementary data are available at Bioinformatics online.

Analysis of Host Gene Expression Profile in HIV-1 and HIV-2 Infected T-Cells

HIV replication is closely regulated by a complex pathway of host factors, many of them being determinants of cell tropism and host susceptibility to HIV infection. These host factors are known to exert a positive or negative influence on the replication of the two major types of HIV, HIV-1 and HIV-2, thereby modulating virus infectivity, host response to infection and ultimately disease progression profiles characteristic of these two types. Understanding the differential regulation of host cellular factors in response to HIV-1 and HIV-2 infections will help us to understand the apparent differences in rates of disease progression and pathogenesis. This knowledge would aid in the discovery of new biomarkers that may serve as novel targets for therapy and diagnosis. The objective of this study was to determine the differential expression of host genes in response to HIV-1/HIV-2 infection. To achieve this, we analyzed the effects of HIV-1 (MN) and HIV-2 (ROD) infection on the expression of host factors in PBMC at the RNA level using the Agilent Whole Human Genome Oligo Microarray. Differentially expressed genes were identified and their biological functions determined. Host gene expression profiles were significantly changed. Gene expression profiling analysis identified a subset of differentially expressed genes in HIV-1 and HIV-2 infected cells. Genes involved in cellular metabolism, apoptosis, immune cell proliferation and activation, cytokines, chemokines, and transcription factors were differentially expressed in HIV-1 infected cells. Relatively few genes were differentially expressed in cells infected with HIV-2.

New Insights into the Disease Progression Control Mechanisms by Comparing Long-Term-Nonprogressors versus Normal-Progressors among HIV-1-Positive Patients Using an Ion Current-Based MS1 Proteomic Profiling

For decades, epidemiological studies have found significant differences in the susceptibility to disease progression among HIV-carrying patients. One unique group of HIV-1-positive patients, the long-term-nonprogressors (LTNP), exhibits far superior ability in virus control compared with normal-progressors (NP), which proceed to Acquired Immune Deficiency Syndrome (AIDS) much more rapidly. Nonetheless, elucidation of the underlying mechanisms of virus control in LTNP is highly valuable in disease management and treatment but remains poorly understood. Peripheral blood mononuclear cells (PBMC) have been known to play important roles in innate immune responses and thereby would be of great interest for the investigation of the mechanisms of virus defense in LTNP. Here, we described the first comparative proteome analysis of PBMC from LTNP (n = 10) and NP (n = 10) patients using a reproducible ion-current-based MS1 approach, which includes efficient and reproducible sample preparation and chromatographic separation followed by an optimized pipeline for protein identification and quantification. This strategy enables analysis of many biological samples in one set with high quantitative precision and extremely low missing data. In total, 925 unique proteins were quantified under stringent criteria without missing value in any of the 20 subjects, and 87 proteins showed altered expressions between the two patient groups. These proteins are implicated in key processes such as cytoskeleton organization, defense response, apoptosis regulation, intracellular transport, etc., which provided novel insights into the control of disease progressions in LTNP versus NP, and the expression and phosphorylation states of key regulators were further validated by immunoassay. For instance, (1) SAMH1, a potent and "hot" molecule facilitating HIV-1 defense, was for the first time found elevated in LTNP compared with NP or healthy controls; elevated proteins from IFN-α response pathway may also contribute to viral control in LTNP; (2) decreased proapoptotic protein ASC along with the elevation of antiapoptotic proteins may contribute to the less apoptotic profile in PBMC of LTNP; and (3) elevated actin polymerization and less microtubule assembly that impede viral protein transport were first observed in LTNP. These results not only enhanced the understanding of the mechanisms for nonprogression of LTNP, but also may afford highly valuable clues to direct therapeutic efforts. Moreover, this work also demonstrated the ion-current-based MS1 approach as a reliable tool for large-scale clinical research.

DC immunotherapy in HIV-1 infection induces a major blood transcriptome shift

OBJECTIVE

This study aimed to evaluate the effect of dendritic cell (DC) vaccination against HIV-1 on host gene expression profiles.

DESIGN

Longitudinal PBMC samples were collected from participants of the DC-TRN trial for immunotherapy against HIV. Microarray-assisted gene expression profiling was performed to evaluate the effects of vaccination and subsequent interruption of antiretroviral therapy on host genome expression. Data from the DC-TRN trial were compared with results from other vaccination trials.

METHODS

We used Affymetrix GeneChips for microarray gene expression analysis. Data were analyzed by principal component analysis and differential gene expression was assessed using linear modeling. Gene ontology enrichment and gene set analysis were used to characterize differentially expressed genes. Transcriptome analysis included comparison with PBMCs obtained from DC-vaccinated melanoma patients and of healthy individuals who received seasonal influenza vaccination.

RESULTS

DC-TRN immunotherapy in HIV-infected individuals resulted in a major shift in the transcriptome. Longitudinal analysis demonstrated that changes in the transcriptome sustained also during interruption of antiretroviral therapy. After DC-vaccination, the transcriptome was enriched for cellular immunity associated genes that were also induced in healthy adults who received live attenuated influenza virus vaccination. These beneficial responses were accompanied by detrimental signals of general immune activation.

CONCLUSIONS

The DC-TRN induced changes in the transcriptome were profound, lasting, and consisted of both protective signals and signatures of inflammation and immune exhaustion, with a net result of decreased viral load, without clinical benefit. Thus transcriptome analysis provides useful information, dissecting both positive and negative effects, for the evaluation of safety and efficacy of immunotherapeutic strategies.

MicroRNA regulation and its effects on cellular transcriptome in human immunodeficiency virus-1 (HIV-1) infected individuals with distinct viral load and CD4 cell counts

Background

Disease progression in the absence of therapy varies significantly in HIV-1 infected individuals. Both viral and host cellular molecules are implicated; however, the exact role of these factors and/or the mechanism involved remains elusive. To understand how microRNAs (miRNAs), which are regulators of transcription and translation, influence host cellular gene expression (mRNA) during HIV-1 infection, we performed a comparative miRNA and mRNA microarray analysis using PBMCs obtained from infected individuals with distinct viral load and CD4 counts.

Methods

RNA isolated from PBMCs obtained from HIV-1 seronegative and HIV-1 positive individuals with distinct viral load and CD4 counts were assessed for miRNA and mRNA profile. Selected miRNA and mRNA transcripts were validated using in vivo and in vitro infection model.

Results

Our results indicate that HIV-1 positive individuals with high viral load (HVL) showed a dysregulation of 191 miRNAs and 309 mRNA transcripts compared to the uninfected age and sex matched controls. The miRNAs miR-19b, 146a, 615-3p, 382, 34a, 144 and 155, that are known to target innate and inflammatory factors, were significantly upregulated in PBMCs with high viral load, as were the inflammatory molecules CXCL5, CCL2, IL6 and IL8, whereas defensin, CD4, ALDH1, and Neurogranin (NRGN) were significantly downregulated. Using the transcriptome profile and predicted target genes, we constructed the regulatory networks of miRNA-mRNA pairs that were differentially expressed between control, LVL and HVL subjects. The regulatory network revealed an inverse correlation of several miRNA-mRNA pair expression patterns, suggesting HIV-1 mediated transcriptional regulation is in part likely through miRNA regulation.

Conclusions

Results from our studies indicate that gene expression is significantly altered in PBMCs in response to virus replication. It is interesting to note that the infected individuals with low or undetectable viral load exhibit a gene expression profile very similar to control or uninfected subjects. Importantly, we identified several new mRNA targets (Defensin, Neurogranin, AIF) as well as the miRNAs that could be involved in regulating their expression through the miRNA-mRNA interaction.

Immunogenetics: Genome-Wide Association of Non-Progressive HIV and Viral Load Control: HLA Genes and Beyond

Very early after the identification of the human immunodeficiency virus (HIV), host genetics factors were anticipated to play a role in viral control and disease progression. As early as the mid-1990s, candidate gene studies demonstrated a central role for the chemokine co-receptor/ligand (e.g., CCR5) and human leukocyte antigen (HLA) systems. In the last decade, the advent of genome-wide arrays opened a new era for unbiased genetic exploration of the genome and brought big expectations for the identification of new unexpected genes and pathways involved in HIV/AIDS. More than 15 genome-wide association studies targeting various HIV-linked phenotypes have been published since 2007. Surprisingly, only the two HIV-chemokine co-receptors and HLA loci have exhibited consistent and reproducible statistically significant genetic associations. In this chapter, we will review the findings from the genome-wide studies focusing especially on non-progressive and HIV control phenotypes, and discuss the current perspectives.

Genome-wide search for the genes accountable for the induced resistance to HIV-1 infection in activated CD4+ T cells: apparent transcriptional signatures, co-expression networks and possible cellular processes

BACKGROUND

Upon co-stimulation with CD3/CD28 antibodies, activated CD4 + T cells were found to lose their susceptibility to HIV-1 infection, exhibiting an induced resistant phenotype. This rather unexpected phenomenon has been repeatedly confirmed but the underlying cell and molecular mechanisms are still unknown.

METHODS

We first replicated the reported system using the specified Dynal beads with PHA/IL-2-stimulated and un-stimulated cells as controls. Genome-wide expression and analysis were then performed by using Agilent whole genome microarrays and established bioinformatics tools.

RESULTS

We showed that following CD3/CD28 co-stimulation, a homogeneous population emerged with uniform expression of activation markers CD25 and CD69 as well as a memory marker CD45RO at high levels. These cells differentially expressed 7,824 genes when compared with the controls on microarrays. Series-Cluster analysis identified 6 distinct expression profiles containing 1,345 genes as the representative signatures in the permissive and resistant cells. Of them, 245 (101 potentially permissive and 144 potentially resistant) were significant in gene ontology categories related to immune response, cell adhesion and metabolism. Co-expression networks analysis identified 137 "key regulatory" genes (84 potentially permissive and 53 potentially resistant), holding hub positions in the gene interactions. By mapping these genes on KEGG pathways, the predominance of actin cytoskeleton functions, proteasomes, and cell cycle arrest in induced resistance emerged. We also revealed an entire set of previously unreported novel genes for further mining and functional validation.

CONCLUSIONS

This initial microarray study will stimulate renewed interest in exploring this system and open new avenues for research into HIV-1 susceptibility and its reversal in target cells, serving as a foundation for the development of novel therapeutic and clinical treatments.

Bridging HIV-1 cellular latency and clinical long-term non-progressor: an interactomic view

Development of an effective HIV management is enticed by the fact that long-term non-progressors (LTNP) restrict viral replication spontaneously, but is hindered by HIV-1 latency. Given that the most overlapping characteristics found between HIV-1 LTNP and latency, detailed analysis of the difference would disclose the essentials of latency. In this study, microarray data from our previous study was combined with HIV-1 latency and LTNP data obtained from NCBI GEO database. Principal variance component analysis and hierarchical clustering verified the removal of batch effect across platform. The analysis revealed a total of 456 differential expressed genes with >2-fold change and B-statistic >0. Bayesian inference was used to reconstitute the transcriptional network of HIV-1 latency or LTNP, respectively. Gene regulation was reprogrammed under different disease condition. By network interference, KPNA2 and ATP5G3 were identified as the hubs in latency network which mediate nuclear export and RNA processing. These data offer comparative insights into HIV-1 latency, which will facilitate the understanding of the genetic basis of HIV-1 latency in vivo and serve as a clue for future treatment dealing with key targets in HIV-1 latency.

Quantification of the host response proteome after mammalian reovirus T1L infection

All viruses are dependent upon host cells for replication. Infection can induce profound changes within cells, including apoptosis, morphological changes, and activation of signaling pathways. Many of these alterations have been analyzed by gene arrays to measure the cellular "transcriptome." We used SILAC (stable isotope labeling by amino acids in cell culture), combined with high-throughput 2-D HPLC/mass spectrometry, to determine relative quantitative differences in host proteins at 6 and 24 hours after infecting HEK293 cells with reovirus serotype 1 Lang (T1L). 3,076 host proteins were detected at 6 hpi, of which 132 and 68 proteins were significantly up or down regulated, respectively. 2,992 cellular proteins, of which 104 and 49 were up or down regulated, respectively, were identified at 24 hpi. IPA and DAVID analyses indicated proteins involved in cell death, cell growth factors, oxygen transport, cell structure organization and inflammatory defense response to virus were up-regulated, whereas proteins involved in apoptosis, isomerase activity, and metabolism were down-regulated. These proteins and pathways may be suitable targets for intervention to either attenuate virus infection or enhance oncolytic potential.

Gene array studies in HIV-1 infection

HIV-1-infected individuals exhibit remarkable variation in the onset of disease. Virus replication and disease progression depend on host cellular transcription and gene regulation in virus-specific target cells. Both viral and host factors are implicated in this differential regulation. Gene arrays and transcriptome analyses might shed light on why some infected individuals remain asymptomatic while others progress rapidly to AIDS. Here we review developments in HIV research using gene array technologies and the unifying concepts that have emerged from these studies. Gene set enrichment analysis has revealed gene signatures linked to disease progression involving pathways related to metabolism, apoptosis, cell-cycle dysregulation, and T-cell signaling. Macrophages contain anti-apoptotic signatures. Also, HIV-1 regulates previously under-emphasized cholesterol biosynthesis and energy production pathways. Notably, cellular pathways linked to a subset of HIV-infected individuals known as non-progressors contribute to survival and anti-viral responses.