Background

HIV-1 infection is characterized by the loss of number and dysfunction of CD4+ T cells and exhibits remarkable differences in clinical outcomes of treatment-naïve individuals [1]. As chronic progressors (CPs) or normal progressors (NPs), the majority of HIV-1-infected patients with progressive virus replication have chronic loss of CD4+ T cells and develop to AIDS in several years without any antiretroviral therapy (ART) [2, 3]. However, long-term nonprogressors (LTNPs) (≈5% of HIV-1-infected individuals), without progression of AIDS, maintain normal counts of CD4+ T cells (>500 cells/μl) and low viral load (LVL) without ART for many years [4, 5]. Moreover, several studies have found that LTNPs display a higher level of HIV-specific CD4+ and CD8+ T cell responses than that in chronic progressors [6, 7], which greatly slows disease progression to AIDS [5, 8, 9]. Although there are some known protective factors involved inHIV-1 disease progression or pathogenesis, such as specific protective HLA-B*57/B*27 alleles [10], the CCR5delta32 [11] and defective viruses [12] in LTNPs, the mechanisms of nonprogression in HIV-1 infection remains to be further explored.

MiRNAs are a class of small non-coding RNAs with the length of ≈22 nucleotides, which plays important roles in post-transcriptional regulation of genes. MiRNAs function to pair to 3′-untranslated regions (3′-UTR) of target mRNA, and almost all of miRNAs result in decreased target mRNA levels and/or protein translated [13]. MiRNAs have been demonstrated to suppress HIV-1 via decreasing HIV dependency factors (HDFs), miR-198 targets Cyclin T1 [14], miR-17/92 regulates P300/CBP-associated factor (PCAF) [15], and miR-15a/b, miR-16, miR-20a, miR-93, miR-106b bind to Pur-α and repress its expression [16]. It has also been proposed that miRNAs could either directly bind to HIV-1 RNA or affect cellular factors involved in HIV-1 replication [17]. MiRNAs can also modulate key regulatory molecules related to T cell exhaustion following HIV-1 infection [18]. MiR-9 regulates the expression level of Blimp-1 that considered as a T cell exhaustion marker [19], and let-7 miRNAs play a regulatory role in post-transcription of an immune inhibitory molecule, IL-10 [20]. MiR-125b, miR-150, miR-223, miR-28 and miR-382 [21], and miR-29a [22] have high abundance in resting CD4+ T cells, which contributes to inhibition of HIV-1. Furthermore, several miRNAs in peripheral blood mononuclear cells (PBMC) and plasma can predict the disease progression of HIV-1 infection, such as miR-31, miR-200c, miR-526a, miR-99a, miR-503 [23], and miR-150 [24]. Therefore, identification of deregulated miRNA expression profiles in different clinical outcomes of HIV-1 infection may be useful for further understanding the possible mechanisms associated with disease progression, pathogenesis and immunologic control.

However, there is no evidence that miRNA-mRNA co-expression profiles in different clinical outcomes of HIV-1 infection. Considering that CD4+ T cells are target cells of HIV-1 and the CD4+ T cell counts is employed to surveiller disease progression, we integrated miRNA and transcriptomic expression profiles data of CD4+ T cells in two series selected from GEO datasets in order to identify miRNA-mRNA crosstalk in HIV-1 infection. We have found numerous HIV-1 disease progression and pathogenesis-associated miRNAs and differentially regulated genes, then we constructed functional network of potential miRNA-mRNA pairs. Identification of genetic and/or epigenetic biomarkers may not only facilitate understanding of interaction between HIV-1 and host CD4+ T cells, but lead to develop novel markers for prediction of disease progression or therapeutic targets for HIV-1.

Methods

Dataset collection

The series GSE6740 was downloaded from the Gene Expression Omnibus (GEO) datasets (http://www.ncbi.nlm.nih.gov/geo/), contained 15 gene chips from 5 uninfected controls (UCs), 5 chronic progressors (CPs) and 5 long-term nonprogressors (LTNPs), which was analyzed using the platform, GPL96 (HG-U133A) Affymetrix Human Genome U133A Array. The series GSE24022 included miRNA microarray data of CD4+ T cells from 8 UCs, 7 LTNPs and 7 CPs, whose platform is Agilent-019118 Human miRNA Microarray 2.0 G4470B (miRNA ID version). These samples in the aforementioned series were divided into three comparison groups to perform subsequent analyses: the group of LTNPs versus UCs, CPs versus UCs, and LTNPs versus CPs, respectively.

Analyses of differentially expressed miRNAs (DEMs) and prediction of target genes

For the aberrantly miRNA expression profile analyses, the web analytical tool, GEO2R, was applied to identify DEMs with fold change (FC) > 2.0 and an adjusted p value <0.01. GEO2R (http://www.ncbi.nlm.nih.gov/geo/geo2r) is an R-based interactive web tool to identify differentially expressed genes via analyzing GEO data [25]. There are several softwares for prediction of miRNA targeting genes, but their algorithms are different and each of them has advantages and disadvantages. Therefore, it is necessary to combine with different software to reduce errors or biases. In this study, miRNA target genes were predicted using TargetScan v7.0 (http://www.targetscan.org/) [26] and miRDB v5.0 (http://www.mirdb.org/miRDB) [27]. Both of them utilize the latest miRNA data provided by miRBase v21. To reduce false-positive results, only common genes predicted by both softwares were chosen as target genes of deregulated miRNA for subsequent analysis.

Quality control, data preprocessing and analysis of differentially expressed genes (DEGs)

For the analyses of differentially expressed genes, the original data of the series GSE6740 were analyzed using the software Rv3.2.2 (https://www.r-project.org/). Initially, both index, including Relative Log Expression (RLE) and the Normalized Unscaled Standard Error (NUSE), were used to assess the quality of this microarray data [28]. Then, the method of Robust Multi-array Average (RMA) was applied to perform background adjustment, normalization and log transformation of the original microarray data [29]. Finally, the Linear Models for Microarray Data (LIMMA) package (http://bioconductor.org/biocLite.R) was used to identify differentially expressed genes (DEGs), which is a software package for constructing linear regression model [30]. The genes with FC > 1.5 and an adjusted p value <0.05 were regarded as DEGs.

Functional annotation and pathway enrichment analysis

The dysregulated genes in different disease stages were extracted as DEGs, which needed further functional annotation. Only genes that exhibited significant expression differences (p value <0.05 and FC > 1.5) were functionally annotated. These DEGs were analyzed using Database for Annotation, Visualization, and Integrated Discovery v6.7 (DAVID v6.7) that is a useful bioinformatics enrichment tool for GO terms, KEGG pathway, and gene-disease association (http://david.abcc.ncifcrf.gov/) [31]. To functionally annotate DEGs identified by the aforementioned three comparison groups, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) were analyzed with DAVID v6.7 [32]. Cytoscape (http://www.cytoscape.org/) was used in miRNA-mRNA network analysis [33].

Results

Identification of DEMs for prediction of disease progression during HIV-1 infection

Through a comprehensive analysis of miRNA expression profiling in different disease stages following HIV-1 infection, a list of aberrantly expressed miRNAs was included (Table 1). With at least twofold change and FDR-adjust p value of <0.01, we identified that 7 differentially expressed miRNAs (DEMs) in LTNPs, whose miR-342 was down-regulated and 6 miRNAs (miR-487b, miR-212, miR-494, miR-939, miR-1225 and miR-513a) were overexpressed in the LTNPs, compared with UCs, except of miR-768-5p because it overlaps an annotated snoRNA (HBII-239). Twenty DEMs were identified between CPs and UCs. Twelve miRNAs were higher and 7 DEMs were down-regulated in UCs, compared with CPs, whereas miR-923 that appeared to be a fragment of the 28S rRNA was removed, and miR-768-5p overlapped an annotated snoRNA (HBII-239) was not included. However, only miR-487b was overexpressed in LTNPs when 5 up-regulated miRNAs that also found in the group of CPs versus UCs were excluded. In addition, only miR-630 showed significantly differential expression among LTNPs, UCs and CPs, and the expression level of miR-630 was higher in CPs than that in LTNPs and UCs. It is well known that miR-630 relates to tumor cell growth, proliferation and metastasis [34, 35], involves in growth arrest of cancer cells [36], and can server as a prognostic marker for colorectal cancer [37] and gastric cancer [38], which implies that miR-630 may be a potential biomarker for prediction of disease progression during HIV-1 infection.

Table 1 Aberrantly expressed miRNAs and their predicted target gene numbers
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Analyses of the gene expression profiles of DEMs predicted target genes

Firstly, TargetScan v7.0 and miRDB v5.0 were used to predict deregulated miRNA target genes, and the common genes in both software were chosen. Totally, 1703 common genes were predicted as 7 DEMs target genes in the group of LTNPs versus UCs; 3006 common genes were predicted for 18 DEMs in the group of CPs versus UCs; and 175 target genes in the group of LTNPs versus CPs (Table 1).

After allowing for overlap between groups, 2629 target genes were predicted from differentially expressed miRNAs, however, the predicted target gene expression profiles still needed to be analyzed in order to elucidate the real miRNA-mRNA relationships in a pairwise manner. Next, we downloaded the series GSE6740 to perform identification of DEGs and functional annotation. To avoid the potential biases caused by inadequate quality of DNA array, both RLE and NUSE box plots were used to check the quality of these DNA arrays. Two DNA arrays GSM155202 (C102, Fig. 1b-1) and GSM155224 (L128, Fig. 1b-2) were excluded by the NUSE box plots analysis because of the arrays quality problems, which were not suitable for subsequent analysis. Finally, the gene expression profiles were divided into three different comparison groups, LTNPs versus UCs, CPs versus UCs, and LTNPs versus CPs, respectively. We identified that 478 genes were differentially expressed in LTNPs and 9 genes (RHOB, NCOA6, ATP8B1, CCL4, SEC31B, PTGER2, AVPR1B, MPI, and LOC285830) were up-regulated in LTNPs, compared with UCs. Besides, 799 differentially expressed genes (DEGs) were identified in the group of CPs versus UCs, and 424 DEGs were found in the comparison group of LTNPs versus CPs. It’s worth noting that 184 unique DEGs were only identified in the group of LTNPs versus CPs, including 38 up-regulated genes in LTNPs, such as CCL22, LILRB3, CCL7/MCP-3, TRAP1, TUBB1 and KLRG1; and 146 down-regulated genes, such as TMPO, BST2, RBX1, CCNA2, OAS2, FOXM1, EZH2, PAFF1, and so on, which may be involved in disease progression during HIV-1 infection (Additional file 2).

Fig. 1
figure 1

RLE and NUSE box plots of GSE6740. a RLE box plots. b NUSE box plots. NUSE is a very sensitive measure of noise or variation in the array data. C chronic progressors, L long-term nonprogressors, N uninfected controls

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Functional pathway analysis of DEGs in HIV-1 infection

GO and KEGG pathway analyses were performed with DAVID v6.7 to analyzed the differentially expressed genes (Additional file 1), which revealed that the DEGs between LTNPs and UCs were significantly enriched in plasma membrane, cytoplasm and nucleoplasm, including 9 up-regulated genes (RHOB, NCOA6, ATP8B1, CCL4, SEC31B, PTGER2, AVPR1B, MPI, and LOC285830), which involved in plasma membrane part (GO:0044459, p value = 0.016) and plasma membrane (GO:0005886, p value = 0.022). Further, gene ontology biological process (GO BP) analysis indicated that, compared to UCs, DEGs were significantly enriched in CPs’ immune system process (GO:0002376, p value = 1.6 × 10−8), defense response (GO:0006952, p value = 6.1 × 10−5), response to other organism (GO: 0051707, p value = 3.3 × 10−13), response to biotic stimulus (GO: 0009607, p value = 2.7 × 10−12), response to virus (GO: 0009615, p value = 9.2 × 10−9), response to external stimulus (GO:0006954, p value = 2.6 × 10−5), and inflammatory response (GO:0006954, p value = 6.6 × 10−6). Additionally, GO BP analysis showed that DEGs between CPs and LTNPs were related to immune system process (GO:0002376, p value = 8.5 × 10−5), response to other organism (GO: 0051707, p value = 2.5 × 10−6), response to biotic stimulus (GO: 0009607, p value = 9.9 × 10−6), response to virus (GO: 0009615, p value = 2.5 × 10−6), response to external stimulus (GO:0006954, p value = 4.1 × 10−4), and inflammatory response (GO:0006954, p value = 7.1 × 10−5), (Additional file 1). These results indicated that, in the CPs group, excessive immune activation may accelerate disease progression in chronic infection (genes: OAS1, ISG15, IFIT1, IFI27, IFI44L, and so on. Additional file 2). Furthermore, the DEGs between different groups were also subjected to KEGG pathway enrichment analysis. The KEGG pathway, RIG-I-like receptor signaling pathway was significantly enriched in CPs, compared to UCs (hsa04622, p value = 0.0038), and LTNPs (hsa04622, p value = 0.0039), revealing excessive innate immune response (genes: AZI2, DDX58, ISG15 and IRF7) in chronic infection compared to that in nonprogression or negative infection (Table 2).

Table 2 Enrichment of KEGG pathways with p < 0.05
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Screening of inversely correlated miRNA-mRNA pair candidates

Potential target genes identified based on microarray gene expression profiles were included in miRNA-mRNA crosstalk analysis if they met the two following criteria: (1) the expression level of miRNA and target genes are inversely correlated, because miRNAs function to degrade mRNA and/or inhibition of mRNA translation; (2) and the expression of target genes showed at least 1.5-fold change in different comparison groups, and an adjusted p value <0.05. Compared to UCs, we acquired 34 putative down-regulated target genes from up-regulated miRNAs that were identified in LTNPs, and 84 underexpressed genes in CPs (Additional file 2). The functional annotation of putative target genes showed differentially enriched GO terms between LTNPs and CPs. The highly enriched BP terms include regulation of cell communication (GO: 0010646), regulation of signal transduction (GO: 0009966), negative regulation of signal transduction (GO: 0009968), regulation of developmental process (GO: 0050793), and positive regulation of cell differentiation (GO: 0045579) in LTNPs but not UCs, while enzyme linked receptor protein signaling pathway (GO: 0007167), receptor quanylyl cyclase signaling pathway (GO: 0007168), regulation of body fluid level (GO: 0050878), and cellular amino acid derivative metabolic process (GO: 0006575) were enriched in CPs but not UCs. In addition, the most enriched MF terms were ion binding (GO: 0043167), quanylate cyclase activity (GO: 0004383), metal ion binding (GO: 0046872), and cation binding (GO: 0043169) were in CPs, and KEGG pathway analysis found two pathways endocytosis and purine metabolism, indicating miRNA-regulated genes may be involved in metabolism of chronic progressors (Table 3). After combining the gene expression profiles of the miRNA-mRNA pair candidates, the interactive networks of putative miRNA-mRNA pairs constructed with Cytoscape were shown in Fig. 2 and Additional file 3.

Table 3 Functional annotation of putative target genes with p < 0.05
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Fig. 2
figure 2

Genetic interactive networks for miRNA/mRNA pair candidates. a miRNA-mRNA interaction network from the group of LTNPs versus UCs; b miRNA-mRNA interaction network from the group of CPs versus UCs. CPs chronic progressors, LTNPs long-term nonprogressors, UCs uninfected controls

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Discussion

In our study, we firstly analyzed the differentially miRNAs profiles in LTNPs, CPs and UCs. Based on the cut-off value at >twofold change and the p value at <0.01, we investigated that 6 miRNAs were differentially expressed both in LTNPs and CPs, miR-342-5p (↓), miR-212-3p (↑), miR-494-3p (↑), miR-939-5p (↑), miR-1225-5p (↑), an miR-513a-5p (↑) in LTNPs and CPs, compared with UCs, indicating these deregulated miRNAs may be HIV-1-specific miRNAs of CD4+ T cells following HIV-1 infection. We also found that the expression levels of miR-575, miR-574-5p, miR-572, miR-513b-5p, miR-940 and miR-638 were higher in CPs than that in UCs, although they were not altered between LTNPs and CPs. Previous evidence indicated that suppressor of cytokine signaling 1 (SOCS1) protein is a target of miR-572 [39], and Miller et al. [40] have found that the expression level of suppressor of cytokine signaling 1 (SOCS1) protein in CD4+ T cells is lower in HIV-1 infected patients than that in healthy people, but SOCS1 mRNA level is higher in HIV-1 infection, indicating miR-572 may be related to sustained immune activation that promoted disease progression and pathogenesis following HIV-1 infection by directly targeting SOCS1. Besides, miR-940 can inhibit the growth of pancreatic ductal adenocarcinoma via targeting MyD88 [41] that involved in IL-33 mediated type 1 helper T cells (Th1) differentiation [42] (Th1 is pivotal in cellular immunity). We confirmed that let-7 family was down-regulated in CPs compared with UCs, which is consistent to findings of Swaminathan et al. [20].

Next, we applied TargetScan v7.0 and miRDB v5.0 to predict target genes of differentially expressed miRNAs and 2629 unique target genes predicted from three different comparison groups. Transcriptomic analysis of ex vivo CD4+ T cells from different clinical outcomes during HIV-1 infection, like LTNPs and CPs, we also found higher expression level of interferon-stimulated genes (ISGs), such as ISG-15 [43–45], IFI44, IFI44L, HERC6, IFI6, and so on, in CPs [46], indicating chronic immune activation, which is also differentially expressed between pathogenic (rhesus macaques [47–49]) and non-pathogenic (sooty mangabeys [50] or African green monkeys [51]) SIV infection, demonstrated by highly enriched GO terms and KEGG pathways, including response to virus (GO: 0009615), immune system process (0002376), and RIG-I-like receptor signaling pathway (hsa04622). Our findings confirm earlier studies that showed that a chronic interferon response or immune activation contributed to CD4+ T cells loss, pathogenesis and immune exhaustion in HIV-1 chronic infection [43, 44, 52, 53]. Moreover, it has been shown that immune inhibitory molecules, including LAG-3 [54] and CD160 [55], have higher levels in CPs than in LTNPs and UCs and are involved in immune exhaustion that accelerated HIV-1 disease progression. Additionally, we also identified 184 unique DEGs in LTNPs, which were involved in HIV/AIDS disease control or progression, including 38 up-regulated genes such as CCL22 (a soluble HIV-suppressive factor [56], LILRB3 (related to immune protection for HIV-1 infection) [57] and CCL7/MCP-3 (competed for HIV-1 gp120 binding) [58], and 146 down-regulated genes such as TMPO (involved in HIV-1 Tat-induced apoptosis of T cells) [59], BST2 (increased in SIV-infected rhesus monkeys) [60], RBX1 (involved in proteasomal degradation of APOBEC3G) [61], CCNA2 (contributed to loss of SAMHD1 ability to inhibit HIV-1) [62] and some unreported genes such as FOXM1, EZH2 and PAFF1 (Additional file 2).

Further, we analyzed negatively correlated miRNA-mRNA pair candidates, and the potential target genes were selected from the series GSE6740. We identified that thirty-four deregulated target genes with 5 up-regulated miRNAs were identified from the group of LTNPs versus UCs, and eighty-four repressed target genes from 10 up-regulated miRNAs in the group of LTNPs versus UCs, whose expression of miRNA and target genes showed negative correlation. The functional annotation revealed that miRNA-regulated genes may be involved in metabolic processes in chronic infection. There are several studies that have shown that down-regulation of CPPED1 expression improves glucose metabolism in adipocyte [63]; PCP4 plays an anti-apoptotic role in human breast cancer cells [64], and CBLL1 promotes cell proliferation in the early stages of tumor progression [65], whose genes were deregulated in CD4+ T cells of HIV-1-infected chronic progressors in our current study. We also demonstrate that the putative miRNA-mRNA pair candidates are involved in disease progression and pathogenesis. Inhibitory cytokine IL-10 contributes to dysregulated cytotoxic T cell function to HIV-1 infection, and IL-10 was verified to be the target gene of let-7 [20], which was down-regulated in CPs, compared with UCs. We have found that dysregulated CD100 in chronic HIV-1 infection, which is the putative target gene of miR-1225a-5p or miR-513a-5p. Loss of Sema4D/CD100 expression plays key roles in dysfunctional immunity during HIV-1 infection [66]. As the positive modulator of cellular apoptosis [67], MOAP1 was down-regulated in chronic infection, which implied that HIV-1 might employ cellular miRNAs to support persistent infection. The ubiquitin ligase Peli1 encoded by PELI1 inversely regulated T lymphocyte activation [68], whose expression level was decreased in our study, partly indicating hyperactivation of CD4+ T cells related to pathogenesis in HIV-1 infection [69].

However, we understood that there were limitations in our bioinformatics-based study. There were only 22 subjects (7 LTNPs, 7 CPs and 8 health controls) in the series of GSE24022 for miRNAs analysis and 13 subjects (4 LTNPs, 4CPs and 5 normal controls) in the series GSE6740 for DEGs. It is necessary to recruit more subjects in the future. We also recognized that there were a few differences between two series including the duration of infection, the definitions of disease stages of HIV-1 infection and chronic progression, viral load and CD4+ T cell counts. Therefore, it is necessary to be confirmed whether the level of deregulated miRNAs and putative target genes expression is actually altered in distinct disease progression of HIV-1 infection. The bioinformatics-based methods to obtain disease progression-related gene expression profiles and the interactive networks of miRNA-mRNA pair candidates via integrative analysis of miRNA-mRNA expression should be applied in integrative analyses of miRNA-mRNA expression profiles in different stages of HIV-1 infection, which will not only facilitate the understanding of the genetic basis of interaction between HIV-1 and host cells, but lead to the development of genetic markers for prediction of disease progression and therapy of HIV-1 in the future.

Conclusions

In summary, our integrative bioinformatics study showed that distinct transcriptional profiles in CD4+ T cells, including microRNAs and mRNAs, associated with different disease progression during HIV-1 infection, and identified a potential biomarker, miR-630, that may be employed to predict disease progression in HIV-1 infection.