Volume 5 Supplement 2
Differentially co-expressed interacting protein pairs discriminate samples under distinct stages of HIV type 1 infection
- Dukyong Yoon†1,
- Hyosil Kim†1,
- Haeyoung Suh-Kim2,
- Rae Woong Park1Email author and
- KiYoung Lee1, 3Email author
© Yoon et al; licensee BioMed Central Ltd. 2011
Published: 14 December 2011
Microarray analyses based on differentially expressed genes (DEGs) have been widely used to distinguish samples across different cellular conditions. However, studies based on DEGs have not been able to clearly determine significant differences between samples of pathophysiologically similar HIV-1 stages, e.g., between acute and chronic progressive (or AIDS) or between uninfected and clinically latent stages. We here suggest a novel approach to allow such discrimination based on stage-specific genetic features of HIV-1 infection. Our approach is based on co-expression changes of genes known to interact. The method can identify a genetic signature for a single sample as contrasted with existing protein-protein-based analyses with correlational designs.
Our approach distinguishes each sample using differentially co-expressed interacting protein pairs (DEPs) based on co-expression scores of individual interacting pairs within a sample. The co-expression score has positive value if two genes in a sample are simultaneously up-regulated or down-regulated. And the score has higher absolute value if expression-changing ratios are similar between the two genes. We compared characteristics of DEPs with that of DEGs by evaluating their usefulness in separation of HIV-1 stage. And we identified DEP-based network-modules and their gene-ontology enrichment to find out the HIV-1 stage-specific gene signature.
Based on the DEP approach, we observed clear separation among samples from distinct HIV-1 stages using clustering and principal component analyses. Moreover, the discrimination power of DEPs on the samples (70–100% accuracy) was much higher than that of DEGs (35–45%) using several well-known classifiers. DEP-based network analysis also revealed the HIV-1 stage-specific network modules; the main biological processes were related to “translation,” “RNA splicing,” “mRNA, RNA, and nucleic acid transport,” and “DNA metabolism.” Through the HIV-1 stage-related modules, changing stage-specific patterns of protein interactions could be observed.
DEP-based method discriminated the HIV-1 infection stages clearly, and revealed a HIV-1 stage-specific gene signature. The proposed DEP-based method might complement existing DEG-based approaches in various microarray expression analyses.
Human immunodeficiency virus type 1 (HIV-1) has been demonstrated to damage the human immune system, finally leading to acquired immunodeficiency syndrome (AIDS), which is characterized by vulnerability to life-threatening opportunistic infections. The natural progression of HIV-1 consists of the acute stage, the clinical latency stage, and AIDS . The acute stage (Acute), the first stage of HIV-1 infection, results from contamination with the HIV-1 virus through body fluids such as blood, semen, or vaginal fluid. In this stage, the copy number of HIV-1 virus rapidly increases, and the number of CD4+ T cells markedly decreases . However, most patients with HIV-1 infection recover from the acute stage without treatment within 3 to 6 weeks and have a period of clinical latency of 8 to 10 years . Although there are no clinical manifestations and the CD4+ T-cell count is almost recovered during the clinical latency stage, it has been reported that immune damage persistently occurs . Among the HIV-infected population, approximately 5 to 8% of patients remain clinically stable for decades. They have been referred to as long-term non-progressors (Non-progressive) . However, most patients undergo chronic progressive infection (Chronic) that finally leads to AIDS, at which point the CD4+ T-cell count drops below 200 cells/μL, and T cell-mediated immunity fails to protect the body from pathogens.
Several studies have attempted to reveal the mechanism of HIV-1 pathogenesis at the genomic level using microarray experiments. Using analysis of differentially expressed genes (DEGs) across HIV-1 infection stages, Hyrcza et al. found that expression of interferon-stimulated genes is increased in the early and chronic progressive stages . Li et al., by a similar DEG-based analysis using lymphatic tissue microarrays, showed that each stage has relatively different gene expression patterns . These studies have enhanced our knowledge about the pathogenic mechanism of HIV-1. One of the common limits of these studies, however, is that DEG-based expression analysis cannot identify an HIV-1 stage-specific gene signature that can clearly discriminate pathophysiologically similar stages, such as between Acute and Chronic stages or between Uninfected and Non-progressive stages [5, 6].
Recently, protein-interaction-based analyses with correlational designs have been successfully applied to discover a discriminant genetic signature for a specific condition, but not for an individual sample, using microarray analysis [7, 8]. These analyses usually have different assigned weights for an interacting protein pair based on degrees of correlation of expression levels under specific conditions. Genes or gene products do not work alone, but rather function in relationship with other genes or proteins in a real molecular setting . Moreover, the degree of correlation between members of an interacting protein pair under a specific condition might provide evidence for the degree of functional relationship under that condition. However, this approach requires multiple samples under a target condition to extract the genetic features for the condition; thus, it cannot be used for a genetic signature of a single sample, which is required to validate or test whether a new sample has a signature similar to those of other samples in a certain group.
Here, we suggest a novel protein-interaction-based method to capture a genetic signature for a single sample under a specific condition. To achieve this purpose, we assigned a co-expression (or co-changing) score to a protein–protein interaction by comparing the expression-change ratios of the two genes in a sample with representative values. After assigning co-expression scores for each sample, we found differentially co-expressed interacting protein pairs (DEPs) among conditions for a condition-specific signature. We applied the DEP-based method to samples representing the clinical stages of HIV-1 infection to discover an HIV-1 stage-specific signature.
Acquisition of HIV-1-infected gene expressions and human protein–protein interactions
Calculating a co-expression score between two interacting gene products
where Min (p, q) or Max (p, q) indicates the minimum or maximum value between p and q, respectively, and sign (x) indicates the sign of x. Note that the co-expression score ρ has a positive value if X and Y are simultaneously greater (or smaller) than R X and R Y ; otherwise, it has a negative value. Moreover, the co-expression score ρ has a higher absolute value if the absolute values of expression-changing ratios are similar between the two genes.
Identifying DEPs and DEGs
To identify DEPs for HIV-1 infection, analysis of variance (ANOVA) and geometric means of differences between median co-expression scores across individual stages were used (Figure1D). An interaction with a high value of -log10(p-value of an ANOVA test) × (geometric mean of differences of median co-expression scores) was considered significant, and interactions with higher degrees of significance than a specific cutoff value were selected as DEPs. A similar process was applied to select DEGs, except that expression levels, not co-expression scores, were used. To ensure a balanced comparison, the most highly significant DEGs were selected in a quantity equal to the number of genes. Here, several values from 0.5 to 1 were examined to identify the optimal cutoff value that provided the smallest number of DEPs and DEGs with the best accuracy.
PCA, clustering, and classification analyses of DEPs and DEGs
To estimate how DEPs reveal HIV-1 stage-specific characteristics, principal-component analysis (PCA) and several well-known classification and clustering methods were used (Figure1E). PCA transforms attribute values into new ones to create the linear projection of the data that accounts for the most variance in a low-dimensional subspace. Therefore, it provides snapshots of data that we can see at a glance. Global views of DEPs were compared with those of whole genes and DEGs using PCA. PCA was performed using the algorithm implemented in MATLAB, R2009b (MathWorks, Natick, MA, USA).
For clustering, we used hierarchical clustering (HCL) with the K-means methods implemented in MEV4.0 (Multiple Experiment Viewer, http://www.tm4.org) . The HCL method groups samples according to the degree of similarity between them based on feature information (here, DEPs or DEGs) without considering the class information (here, the HIV-1 stages). Therefore, it was possible to confirm whether the selected feature information of samples (i.e., DEPs or DEGs) is valuable for clustering samples according to stage. K-means clustering, like HCL, is an unsupervised learning method. However, K-means clustering was used to partition DEPs (or DEGs) into some number of clusters. Here, DEPs were clustered into six groups in each cell type (CD4+ and CD8+ cells). Stage-specific clusters in both CD4+ and CD8+ cells were then further characterized through GO term analysis.
For classification methods, we used the J48 decision tree, the SMO support-vector machine, and the multilayer perception artificial neural network, which were implemented in WEKA, version 3.6.3 . Leave-one-out cross-validation (LOOCV) using these methods was applied to estimate the classification performance of selected DEPs (or DEGs) for predicting the disease stages of HIV-1. LOOCV is one of the most popular validation methods; it trains prediction models using all samples except one and then tests the models with the remaining sample. This step then passes through as many iterations as there are samples . For performance measures, we used accuracy, sensitivity, and specificity from a confusion matrix.
DEP-based network-module identification
To discover HIV-related interaction sub-networks, the prepared PPIs and the list of genes in DEPs were imported into Cytoscape (http://www.Cytoscape.org)  with the median co-expression score for each stage (Figure1F). Next, we included the genes that directly interacted with the genes in DEPs. Using the extended network, MCODE was used to find sets of genes located at the area of dense DEPs (Figure1G). MCODE is a Cytoscape plug-in and is one of the most popular methods by which to find highly interconnected regions in a network . The score of a sub-network was calculated based on the complexity and density of the network. The top 10 modules with the highest network scores were considered significant since modules with higher network scores showed higher prediction accuracies in previous study . Among 10 significant modules, five modules containing DEPs were finally selected because none of the other five modules included any DEP. Note that a DEP in each sample has its own co-expression score. To find a representative HIV-related module under a specific stage, thus, we used a median value of multiple co-expression scores for an interaction and a median expression level for a protein, respectively.
Gene-ontology enrichment analysis
For the gene list in DEPs and DEGs, a functional annotation tool called the Database for Annotation, Visualization and Integrated Discovery (DAVID)  was applied to find functionally enriched terms. DAVID uses a Fisher’s exact test to determine whether the proportion of selected genes falling into each category differs from the baseline (here, all genes of Homo sapiens). For selected modules, BiNGO (the Biological Networks Gene-ontology tool)  was also used to conduct gene-ontology (GO) enrichment analysis. BiNGO, which is implemented as a plug-in for Cytoscape, maps the predominant functional themes of a given gene set on the GO hierarchy and outputs this mapping as a Cytoscape graph. Hypergeometric distribution was adopted to find a functional degree of overrepresentation of an HIV-related module using this method.
Identifying DEPs across HIV-1 stages
Protein pairs included in the top-30 DEPs
Name of Protein 1
Name of Protein 2
nuclear transport factor 2
cell division cycle 7 homolog (S. cerevisiae)
minichromosome maintenance complex component 3
vesicle-associated membrane protein 1 (synaptobrevin 1)
ADP-ribosylation factor GTPase activating protein 1
heat shock 70kDa protein 8
transcriptional adaptor 3
tenascin R (restrictin, janusin)
Rho/Rac guanine nucleotide exchange factor (GEF) 2
protein kinase C, iota
platelet-derived growth factor receptor, beta polypeptide
sorting nexin 2
replication factor C (activator 1) 5, 36.5kDa
polymerase (DNA directed), alpha 1, catalytic subunit
nuclear factor I/B
regulatory factor X, 1 (influences HLA class II expression)
eukaryotic translation initiation factor 3, subunit I
SMT3 suppressor of mif two 3 homolog 4 (S. cerevisiae)
collagen, type XVII, alpha 1
insulin receptor substrate 1
upstream binding transcription factor, RNA polymerase I
caveolin 1, caveolae protein, 22kDa
TNF receptor-associated factor 6
ribosomal protein S14
ribosomal protein S27a
heterogeneous nuclear ribonucleoprotein A2/B1
heterogeneous nuclear ribonucleoprotein H1 (H)
TATA box binding protein (TBP)-associated factor, 135kDa
chromobox homolog 3
vacuolar protein sorting 11 homolog (S. cerevisiae)
vacuolar protein sorting 45 homolog (S. cerevisiae)
ATP synthase, H+ transporting, mitochondrial Fo complex, subunit B1
ATP synthase, H+ transporting, mitochondrial Fo complex, subunit F2
polymerase (RNA) II (DNA directed) polypeptide G
splicing factor 3b, subunit 2, 145kDa
3-phosphoinositide dependent protein kinase-1
protein kinase C, theta
E1A binding protein p300
ribosomal protein S5
ribosomal protein L28
ELK1, member of ETS oncogene family
growth factor receptor-bound protein 10
ribosomal protein S13
ATPase family, AAA domain containing 3A
poly(A) binding protein, cytoplasmic 1
ribosomal protein S4, Y-linked 1
PRP4 pre-mRNA processing factor 4 homolog (yeast)
peptidylprolyl isomerase H (cyclophilin H)
zinc finger protein 36, C3H type, homolog (mouse)
eukaryotic translation initiation factor 2C, 4
ribosomal protein, large, P2
ribosomal protein L29
heat shock transcription factor 1
glycogen synthase kinase 3 beta
fused in sarcoma
The functional characteristics were also significantly different between DEPs and DEGs (Figure2C). The number of enriched GO terms using the 177 proteins of DEPs was 49, whereas it was 21 in the case of DEGs (the GO terms with >10 genes/proteins and p-value of <1.0×10–5 using DAVID tools). Among the enriched GO terms, 13 overlapped and were mainly associated with “translation” biological processes. Thirty-six GO terms included only in DEPs were related to responses against endogenous or exogenous stimuli (from transcription to mRNA processing). They were particularly associated with apoptosis (“positive regulation of apoptosis”, “positive regulation of programmed cell death”, and “positive regulation of cell death”), which is known to be an important factor in the progression of HIV by the resulting depletion of T helper cells . On the other hand, the GO terms only for DEGs included “response to virus” and “immune response”.
PCA results of DEPs and DEGs of HIV-1
Clustering results of DEPs in HIV-1
To discover stage-specific co-expressed pairs, we next clustered the 100 identified DEPs into six groups using a K-means clustering method. Different cell types might be associated with different DEP groups; therefore, we identified separate clusters using only CD4+ samples or only CD8+ samples. Of the six groups, four groups in each cell type showed stage-specific co-expression patterns. Surprisingly, the median co-expression scores of groups across samples were quite similar between CD4+ and CD8+ cells. Moreover, each pair of groups with a similar co-expression pattern shared many DEPs. Using the DEPs shared between CD4+ and CD8+ cells, we analyzed enriched GO terms (Figure4C). Common biological functions of all four clusters were “RNA splicing” and “mRNA processing”. The first cluster, which was composed of 14 DEPs, had lower co-expression scores only in the Uninfected stage. The genes of the 14 DEPs are known to play a major role in “transcription” in addition to the common functions. In contrast, the second cluster, which had 10 DEPs with lower co-expression scores only in the Acute stage, was involved in “translation” rather than “transcription”. The genes in the seven DEPs of the third cluster, which had higher co-expression scores only in the Non-progressive stages, are also known to play a role in “epigenetic processing”. Major functions of the last cluster (Chronic) were related to “repair processes” such as “cellular response to stress”, “response to radiation” and “DNA repair”.
Discriminant power of DEPs for HIV-1 stages
Next, we evaluated the influence of the number of DEPs (or DEGs) on the power for discriminating the HIV-1 stages. The performance was evaluated by a support-vector machine with various cutoff values for the degree of significance of DEPs from 0.5 to 1.0. Here, as many DEGs were selected as there were DEP genes. As shown in Figure5D, the highest accuracy was achieved at values of 0.6 and 0.7 (100% accuracy in classifying HIV-1 stages with DEPs and 82.5% accuracy with DEGs). Thus, in this study, we selected 0.7 as the cutoff value for DEP selection because it revealed the highest level of performance with a smaller number of DEPs and DEGs. As a result, 100 DEPs consisting of 177 genes and 177 DEGs were selected. Additionally, we investigated the impact of the number of selected features from PCA analysis with the selected DEPs and DEGs. In the PCA analysis using the 177 DEGs, about 20 principal components were required to achieve the best performance (Figure5E). For the selected DEP cases, however, only three principal components were required to obtain the best performance, which was the same accuracy as given by all components of PCA using DEPs. This also suggests that DEP-based features have meaningful discriminant information with respect to the HIV-1 stages.
Discovery of HIV-1 stage-specific network modules using DEPs
The purpose of this study was to develop a novel microarray data analysis method to discover the stage-specific protein pairs in HIV-1 infection. The developed novel method focuses on the expression co-changing patterns between interacting protein pairs rather than on expression levels of individual genes. Note that we here only considered known PPIs that contact physically or chemically in selecting DEPs rather than all possible pairs among all detected genes in microarray; both because expressions noisy and because physically or chemically contacting pairs can share biological function and thus their biological meaning can be easily interpreted. Strength of our method comparing existing correlation-based method is that it can capture a genetic signature for a single sample. Even though one or more samples are used for selecting representative expression level, our method can identify a genetic signature for a new single sample by comparing with known representative expression level if those levels were already known by previous study.
With this method, 100 DEPs were selected for the discriminant features of HIV-1 stages. A comparison between DEPs and DEGs revealed that DEPs more powerfully classified the ambiguous stages of HIV-1. This means that DEPs can provide additional information not included in DEGs. As shown in Figure2A, for example, the HIV-1-related proteins HNRNPM (600.8 under Acute and 595.0 under Chronic) and DHX9 (1477.7 under Acute and 1485.7 under Chronic) had similar expression levels between Acute and Chronic stages even though the variations within individual stages were relatively large (i.e., the expression level of DHX9 ranged from 420 to 1110 in the Chronic stage). Thus, the previous DEG-based approach missed both HNRNPM and DHX9 as the stage-specific genes for HIV-1. However, if we consider an expression co-changing pattern, the HNRNPM and DHX9 pair selected a significant feature of the HIV-1 stages because the co-changing scores were consistently positive in Acute samples but consistently negative in Chronic samples. In this respect, the DEP-based approach could well discriminate all four stages of HIV-1. Moreover, DEPs were enriched in more HIV-related GO terms, such as “apoptosis”, which is strongly associated with the spectrum of the progression of HIV infection. Additionally, there is distinct difference between the DEP-based approach and previous correlation-based network analyses [7, 8]. The biggest difference is that the DEP-based approach generates a distinct feature set with only one sample, whereas a correlation-based network approach finds a network feature with groups of samples under a specific condition. Thus, it is difficult to capture the characteristics of individual samples using the previous correlation-based network analyses. If there is a problem in predicting an unknown or new sample and if DEG-based analysis is unclear, then the DEP-based approach might be applicable.
The HIV-related network modules revealed by network analysis using DEPs correspond with the results of some earlier studies. Heterogeneous nuclear ribonucleoproteins (hnRNPs; complexes of RNA and protein) in modules 1 and 2 are known as HIV protein-synthesis modulators . In module 2, SF3B2 (splicing factor 3b, subunit 2, 145 kDa) modulates viral proliferation of HIV through interaction with Vpr (Viral Protein R) . SFRS2 (serine/arginine-rich splicing factor 2) influences the use of the HIV-1 splicing site . SNRPE (small nuclear ribonucleoprotein polypeptide E), one of the transcription elongation complexes, assembles with HIV Tat . DHX9 (DEAH (Asp-Glu-Ala-His) box polypeptide 9) affects the expression of HIV-1 . Furthermore, there is an association between PCF11 (PCF11, cleavage and polyadenylation factor subunit, homolog) and HIV-1 transcription. PCBP1 (poly(rC) binding protein 1) and YBX1 (Y-box binding protein 1) interact with Rev protein, a key regulator of HIV-1 gene expression . NUP62 (nucleoporin 62 kDa) is related to Rev-mediated viral RNA export by interacting with eIF-5A (eukaryotic translation-initiation factor 5A) . In contrast to NUP62, NUP155 (nucleoporin 155 kDa) is associated with the import of HIV DNA . All of these genes were included in DEPs but not in DEGs. From the module analysis with DEPs, it seems that changes in DNA and RNA metabolism are crucial in the clinical manifestations of HIV infection, and DEPs and HIV-related network modules might have the potential to assist in the elucidation of the pathogenesis of HIV-1 infection at the genomic and proteomic levels. However, further studies to seek biological confirmation are imperative to clarify the detailed roles of DEPs in specific HIV-1 stages.
We present a novel microarray data analysis method based on DEP by focusing on the expression co-changing patterns between interaction pairs. The DEP based algorithm was more powerful in classifying the ambiguous stages of HIV-1 and revealed the HIV-1 stage-specific network modules. The DEP-based method might contribute to complementation of existing DEG-based analyses.
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No.2010-0022887 and 2011-0018258). This research was also support by a grant (10182KFDA992-2301) from Korea Food & Drug Administration in 2011. H.S. was supported by the Priority Research Centers Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2010-0028294).
This article has been published as part of BMC Systems Biology Volume 5 Supplement 2, 2011: 22nd International Conference on Genome Informatics: Systems Biology. The full contents of the supplement are available online at http://www.biomedcentral.com/1752-0509/5?issue=S2.
- Pantaleo G, Graziosi C, Fauci AS: New concepts in the immunopathogenesis of human immunodeficiency virus infection. N Engl J Med. 1993, 328 (5): 327-335. 10.1056/NEJM199302043280508.View ArticlePubMedGoogle Scholar
- Chu C, Selwyn PA: Diagnosis and initial management of acute HIV infection. Am Fam Physician. 2010, 81 (10): 1239-1244.PubMedGoogle Scholar
- Grossman Z, Meier-Schellersheim M, Paul WE, Picker LJ: Pathogenesis of HIV infection: what the virus spares is as important as what it destroys. Nat Med. 2006, 12 (3): 289-295. 10.1038/nm1380.View ArticlePubMedGoogle Scholar
- Mothe B, Ibarrondo J, Llano A, Brander C: Virological, immune and host genetics markers in the control of HIV infection. Dis Markers. 2009, 27 (3): 105-120.PubMed CentralView ArticlePubMedGoogle Scholar
- Hyrcza MD, Kovacs C, Loutfy M, Halpenny R, Heisler L, Yang S, Wilkins O, Ostrowski M, Der SD: Distinct transcriptional profiles in ex vivo CD4+ and CD8+ T cells are established early in human immunodeficiency virus type 1 infection and are characterized by a chronic interferon response as well as extensive transcriptional changes in CD8+ T cells. J Virol. 2007, 81 (7): 3477-3486. 10.1128/JVI.01552-06.PubMed CentralView ArticlePubMedGoogle Scholar
- Li Q, Smith AJ, Schacker TW, Carlis JV, Duan L, Reilly CS, Haase AT: Microarray analysis of lymphatic tissue reveals stage-specific, gene expression signatures in HIV-1 infection. J Immunol. 2009, 183 (3): 1975-1982. 10.4049/jimmunol.0803222.PubMed CentralView ArticlePubMedGoogle Scholar
- Lin CC, Hsiang JT, Wu CY, Oyang YJ, Juan HF, Huang HC: Dynamic functional modules in co-expressed protein interaction networks of dilated cardiomyopathy. BMC Syst Biol. 2010, 4: 138-10.1186/1752-0509-4-138.PubMed CentralView ArticlePubMedGoogle Scholar
- Ma S, Shi M, Li Y, Yi D, Shia BC: Incorporating gene co-expression network in identification of cancer prognosis markers. BMC Bioinformatics. 2010, 11: 271-10.1186/1471-2105-11-271.PubMed CentralView ArticlePubMedGoogle Scholar
- Charbonnier S, Gallego O, Gavin AC: The social network of a cell: recent advances in interactome mapping. Biotechnol Annu Rev. 2008, 14: 1-28.View ArticlePubMedGoogle Scholar
- Lee K, Chuang HY, Beyer A, Sung MK, Huh WK, Lee B, Ideker T: Protein networks markedly improve prediction of subcellular localization in multiple eukaryotic species. Nucleic Acids Res. 2008, 36 (20): e136-10.1093/nar/gkn619.PubMed CentralView ArticlePubMedGoogle Scholar
- Salwinski L, Miller CS, Smith AJ, Pettit FK, Bowie JU, Eisenberg D: The Database of Interacting Proteins: 2004 update. Nucleic Acids Res. 2004, 32 (Database issue): D449-451.PubMed CentralView ArticlePubMedGoogle Scholar
- Bader GD, Donaldson I, Wolting C, Ouellette BF, Pawson T, Hogue CW: BIND--The Biomolecular Interaction Network Database. Nucleic Acids Res. 2001, 29 (1): 242-245. 10.1093/nar/29.1.242.PubMed CentralView ArticlePubMedGoogle Scholar
- Keshava Prasad TS, Goel R, Kandasamy K, Keerthikumar S, Kumar S, Mathivanan S, Telikicherla D, Raju R, Shafreen B, Venugopal A, et al: Human Protein Reference Database--2009 update. Nucleic Acids Res. 2009, 37 (Database issue): D767-772.PubMed CentralView ArticlePubMedGoogle Scholar
- Joshi-Tope G, Gillespie M, Vastrik I, D'Eustachio P, Schmidt E, de Bono B, Jassal B, Gopinath GR, Wu GR, Matthews L, et al: Reactome: a knowledgebase of biological pathways. Nucleic Acids Res. 2005, 33 (Database issue): D428-432.PubMed CentralView ArticlePubMedGoogle Scholar
- Rual JF, Venkatesan K, Hao T, Hirozane-Kishikawa T, Dricot A, Li N, Berriz GF, Gibbons FD, Dreze M, Ayivi-Guedehoussou N, et al: Towards a proteome-scale map of the human protein-protein interaction network. Nature. 2005, 437 (7062): 1173-1178. 10.1038/nature04209.View ArticlePubMedGoogle Scholar
- Stelzl U, Worm U, Lalowski M, Haenig C, Brembeck FH, Goehler H, Stroedicke M, Zenkner M, Schoenherr A, Koeppen S, et al: A human protein-protein interaction network: a resource for annotating the proteome. Cell. 2005, 122 (6): 957-968. 10.1016/j.cell.2005.08.029.View ArticlePubMedGoogle Scholar
- Ewing RM, Chu P, Elisma F, Li H, Taylor P, Climie S, McBroom-Cerajewski L, Robinson MD, O'Connor L, Li M, et al: Large-scale mapping of human protein-protein interactions by mass spectrometry. Mol Syst Biol. 2007, 3: 89-PubMed CentralView ArticlePubMedGoogle Scholar
- Ramani AK, Bunescu RC, Mooney RJ, Marcotte EM: Consolidating the set of known human protein-protein interactions in preparation for large-scale mapping of the human interactome. Genome Biol. 2005, 6 (5): R40-10.1186/gb-2005-6-5-r40.PubMed CentralView ArticlePubMedGoogle Scholar
- Saeed AI, Bhagabati NK, Braisted JC, Liang W, Sharov V, Howe EA, Li J, Thiagarajan M, White JA, Quackenbush J: TM4 microarray software suite. Methods Enzymol. 2006, 411: 134-193.View ArticlePubMedGoogle Scholar
- Hall Mark, F E, Holmes Geoffrey, Pfahringer Bernhard, Reutemann Peter, Witten Ian: The WEKA Data Mining Software: An Update. SIGKDD Explorations. 2009, 11 (1): 10-18. 10.1145/1656274.1656278.View ArticleGoogle Scholar
- Xu H, Lemischka IR, Ma'ayan A: SVM classifier to predict genes important for self-renewal and pluripotency of mouse embryonic stem cells. BMC Syst Biol. 2010, 4 (1): 173-10.1186/1752-0509-4-173.PubMed CentralView ArticlePubMedGoogle Scholar
- Cline MS, Smoot M, Cerami E, Kuchinsky A, Landys N, Workman C, Christmas R, Avila-Campilo I, Creech M, Gross B, et al: Integration of biological networks and gene expression data using Cytoscape. Nat Protoc. 2007, 2 (10): 2366-2382. 10.1038/nprot.2007.324.PubMed CentralView ArticlePubMedGoogle Scholar
- Bader GD, Hogue CW: An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics. 2003, 4: 2-10.1186/1471-2105-4-2.PubMed CentralView ArticlePubMedGoogle Scholar
- Huang da W, Sherman BT, Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009, 4 (1): 44-57.View ArticlePubMedGoogle Scholar
- Maere S, Heymans K, Kuiper M: BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics. 2005, 21 (16): 3448-3449. 10.1093/bioinformatics/bti551.View ArticlePubMedGoogle Scholar
- Alimonti JB, Ball TB, Fowke KR: Mechanisms of CD4+ T lymphocyte cell death in human immunodeficiency virus infection and AIDS. J Gen Virol. 2003, 84 (Pt 7): 1649-1661.View ArticlePubMedGoogle Scholar
- Bolinger C, Boris-Lawrie K: Mechanisms employed by retroviruses to exploit host factors for translational control of a complicated proteome. Retrovirology. 2009, 6: 8-10.1186/1742-4690-6-8.PubMed CentralView ArticlePubMedGoogle Scholar
- Hashizume C, Kuramitsu M, Zhang X, Kurosawa T, Kamata M, Aida Y: Human immunodeficiency virus type 1 Vpr interacts with spliceosomal protein SAP145 to mediate cellular pre-mRNA splicing inhibition. Microbes Infect. 2007, 9 (4): 490-497. 10.1016/j.micinf.2007.01.013.View ArticlePubMedGoogle Scholar
- Ropers D, Ayadi L, Gattoni R, Jacquenet S, Damier L, Branlant C, Stevenin J: Differential effects of the SR proteins 9G8, SC35, ASF/SF2, and SRp40 on the utilization of the A1 to A5 splicing sites of HIV-1 RNA. J Biol Chem. 2004, 279 (29): 29963-29973. 10.1074/jbc.M404452200.View ArticlePubMedGoogle Scholar
- Sobhian B, Laguette N, Yatim A, Nakamura M, Levy Y, Kiernan R, Benkirane M: HIV-1 Tat assembles a multifunctional transcription elongation complex and stably associates with the 7SK snRNP. Mol Cell. 2010, 38 (3): 439-451. 10.1016/j.molcel.2010.04.012.PubMed CentralView ArticlePubMedGoogle Scholar
- Bolinger C, Sharma A, Singh D, Yu L, Boris-Lawrie K: RNA helicase A modulates translation of HIV-1 and infectivity of progeny virions. Nucleic Acids Res. 2010, 38 (5): 1686-1696. 10.1093/nar/gkp1075.PubMed CentralView ArticlePubMedGoogle Scholar
- Klatt A, Zhang Z, Kalantari P, Hankey PA, Gilmour DS, Henderson AJ: The receptor tyrosine kinase RON represses HIV-1 transcription by targeting RNA polymerase II processivity. J Immunol. 2008, 180 (3): 1670-1677.View ArticlePubMedGoogle Scholar
- Hadian K, Vincendeau M, Mausbacher N, Nagel D, Hauck SM, Ueffing M, Loyter A, Werner T, Wolff H, Brack-Werner R: Identification of a heterogeneous nuclear ribonucleoprotein-recognition region in the HIV Rev protein. J Biol Chem. 2009, 284 (48): 33384-33391. 10.1074/jbc.M109.021659.PubMed CentralView ArticlePubMedGoogle Scholar
- Hofmann W, Reichart B, Ewald A, Muller E, Schmitt I, Stauber RH, Lottspeich F, Jockusch BM, Scheer U, Hauber J, et al: Cofactor requirements for nuclear export of Rev response element (RRE)- and constitutive transport element (CTE)-containing retroviral RNAs. An unexpected role for actin. J Cell Biol. 2001, 152 (5): 895-910. 10.1083/jcb.152.5.895.PubMed CentralView ArticlePubMedGoogle Scholar
- Lee K, Ambrose Z, Martin TD, Oztop I, Mulky A, Julias JG, Vandegraaff N, Baumann JG, Wang R, Yuen W, et al: Flexible use of nuclear import pathways by HIV-1. Cell Host Microbe. 2010, 7 (3): 221-233. 10.1016/j.chom.2010.02.007.PubMed CentralView ArticlePubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.