Associations between HIV and human pathways revealed by protein-protein interactions and correlated gene expression profiles

The Dynamic Linkage between Provirus Integration Sites and the Host Functional Genome Property Alongside HIV-1 Infections Associated with Antiretroviral Therapy

(1) Background: The HIV-1 latent reservoir harboring replication-competent proviruses is the major barrier in the quest for an HIV-1 infection cure. HIV-1 infection at all stages of disease progression is associated with immune activation and dysfunctional production of proinflammatory soluble factors (cytokines and chemokines), and it is expected that during HIV-1 infection, different immune components and immune cells, in turn, participate in immune responses, subsequently activating downstream biological pathways. However, the functional interaction between HIV-1 integration and the activation of host biological pathways is presently not fully understood. (2) Methods: In this work, I used genes targeted by proviruses from published datasets to seek enriched immunologic signatures and host biological pathways alongside HIV-1 infections based on MSigDb and KEGG over-representation analysis. (3) Results: I observed that different combinations of immunologic signatures of immune cell types and proinflammatory soluble factors appeared alongside HIV-1 infections associated with antiretroviral therapy. Moreover, enriched KEGG pathways were often related to "cancer specific types", "immune system", "infectious disease viral", and "signal transduction". (4) Conclusions: The observations in this work suggest that the gene sets harboring provirus integration sites may define specific immune cells and proinflammatory soluble factors during HIV-1 infections associated with antiretroviral therapy.

In silico prediction of HIV-1-host molecular interactions and their directionality

Human immunodeficiency virus type 1 (HIV-1) continues to be a major cause of disease and premature death. As with all viruses, HIV-1 exploits a host cell to replicate. Improving our understanding of the molecular interactions between virus and human host proteins is crucial for a mechanistic understanding of virus biology, infection and host antiviral activities. This knowledge will potentially permit the identification of host molecules for targeting by drugs with antiviral properties. Here, we propose a data-driven approach for the analysis and prediction of the HIV-1 interacting proteins (VIPs) with a focus on the directionality of the interaction: host-dependency versus antiviral factors. Using support vector machine learning models and features encompassing genetic, proteomic and network properties, our results reveal some significant differences between the VIPs and non-HIV-1 interacting human proteins (non-VIPs). As assessed by comparison with the HIV-1 infection pathway data in the Reactome database (sensitivity > 90%, threshold = 0.5), we demonstrate these models have good generalization properties. We find that the ‘direction’ of the HIV-1-host molecular interactions is also predictable due to different characteristics of ‘forward’/pro-viral versus ‘backward’/pro-host proteins. Additionally, we infer the previously unknown direction of the interactions between HIV-1 and 1351 human host proteins. A web server for performing predictions is available at http://hivpre.cvr.gla.ac.uk/.

Colored Network Motif Analysis by Dynamic Programming Approach: An Application in Host Pathogen Interaction Network

Network motifs are subgraphs of a network which are found with significantly higher frequency than that expected in similar random networks. Motifs are small building blocks of a network and they have emerged as a way to uncover topological properties of complex networks. A special yet not much explored type of motif is the 'colored motif' where color (type) of each node, and hence the edges, in the motif is distinguishable from each other. A traditional motif is defined as a recurring structure in a network, whereas colored motif introduces detailed information about the color of the nodes. G-trie is a data structure to efficiently store a given set of subgraphs by exploiting the topological overlaps within them. In this article we have implemented a modified g-trie to store colored subgraphs and developed a method to discover colored motifs. Our method uses an approximate enumeration for counting the subgraphs to reduce the runtime. We have applied our method to find colored motifs of size three in a host pathogen protein-protein interaction network having two types of proteins namely HIV-1 and human proteins, and four types of edges. Here, we have discovered eight motifs, six of which contain both HIV-1 and human proteins, while the remaining two contain only human proteins.

Data and Text Mining Help Identify Key Proteins Involved in the Molecular Mechanisms Shared by SARS-CoV-2 and HIV-1

Viruses can be spread from one person to another; therefore, they may cause disorders in many people, sometimes leading to epidemics and even pandemics. New, previously unstudied viruses and some specific mutant or recombinant variants of known viruses constantly appear. An example is a variant of coronaviruses (CoV) causing severe acute respiratory syndrome (SARS), named SARS-CoV-2. Some antiviral drugs, such as remdesivir as well as antiretroviral drugs including darunavir, lopinavir, and ritonavir are suggested to be effective in treating disorders caused by SARS-CoV-2. There are data on the utilization of antiretroviral drugs against SARS-CoV-2. Since there are many studies aimed at the identification of the molecular mechanisms of human immunodeficiency virus type 1 (HIV-1) infection and the development of novel therapeutic approaches against HIV-1, we used HIV-1 for our case study to identify possible molecular pathways shared by SARS-CoV-2 and HIV-1. We applied a text and data mining workflow and identified a list of 46 targets, which can be essential for the development of infections caused by SARS-CoV-2 and HIV-1. We show that SARS-CoV-2 and HIV-1 share some molecular pathways involved in inflammation, immune response, cell cycle regulation.

Reconnaissance of the candidate genes involved in the pathogenesis of human immunodeficiency virus and targeted by antiretroviral therapy

The expression levels of many genes change after treatment of human immunodeficiency virus (HIV)-infected subjects by antiretroviral drugs. High-throughput analysis of tremendous datasets led to the discovery of genes that are implicated in the treatment pathways. In this study, we performed a gene-enrichment analysis after determining the differentially expressed genes (DEGs) between untreated HIV-positive and HIV-negative subjects and also between treated HIV-positive subjects with antiretroviral therapy (ART; who receiving nucleoside reverse transcriptase inhibitor-based ART) and untreated HIV-positive cases in the peripheral blood mononuclear cells (PBMCs), adipose, and muscle tissues. In sum, the genes that activate inflammatory, immune response, proliferation, metabolism, and viral involvement pathways have different expression patterns in the untreated HIV-positive subjects and treated HIV-positive cases. Moreover, the expression levels of the genes including ACLY, ALDH18A1, HADHA, and YARS in the PBMCs tissue and HBEGF, PKN3, DEGS2, and EDN3 in the fat tissue were found to be different in the HIV-infected patients, which can be considered as new biomarkers for HIV infection.

Interaction of viral oncogenic proteins with the Wnt signaling pathway

It is estimated that up to 20% of all types of human cancers worldwide are attributed to viruses. The genome of oncogenic viruses carries genes that have protein products that act as oncoproteins in cell proliferation and transformation. The modulation of cell cycle control mechanisms, cellular regulatory and signaling pathways by oncogenic viruses, plays an important role in viral carcinogenesis. Different signaling pathways play a part in the carcinogenesis that occurs in a cell. Among these pathways, the Wnt signaling pathway plays a predominant role in carcinogenesis and is known as a central cellular pathway in the development of tumors. There are three Wnt signaling pathways that are well identified, including the canonical or Wnt/β-catenin dependent pathway, the noncanonical or β-catenin-independent planar cell polarity (PCP) pathway, and the noncanonical Wnt/Ca2+ pathway. Most of the oncogenic viruses modulate the canonical Wnt signaling pathway. This review discusses the interaction between proteins of several human oncogenic viruses with the Wnt signaling pathway.

Computational prediction of virus-human protein-protein interactions using embedding kernelized heterogeneous data

Pathogenic microorganisms exploit host cellular mechanisms and evade host defense mechanisms through molecular pathogen-host interactions (PHIs). Therefore, comprehensive analysis of these PHI networks should be an initial step for developing effective therapeutics against infectious diseases. Computational prediction of PHI data is gaining increasing demand because of scarcity of experimental data. Prediction of protein-protein interactions (PPIs) within PHI systems can be formulated as a classification problem, which requires the knowledge of non-interacting protein pairs. This is a restricting requirement since we lack datasets that report non-interacting protein pairs. In this study, we formulated the "computational prediction of PHI data" problem using kernel embedding of heterogeneous data. This eliminates the abovementioned requirement and enables us to predict new interactions without randomly labeling protein pairs as non-interacting. Domain-domain associations are used to filter the predicted results leading to 175 novel PHIs between 170 human proteins and 105 viral proteins. To compare our results with the state-of-the-art studies that use a binary classification formulation, we modified our settings to consider the same formulation. Detailed evaluations are conducted and our results provide more than 10 percent improvements for accuracy and AUC (area under the receiving operating curve) results in comparison with state-of-the-art methods.

Identifying differentially coexpressed module during HIV disease progression: A multiobjective approach

Microarray analysis based on gene coexpression is widely used to investigate the coregulation pattern of a group (or cluster) of genes in a specific phenotype condition. Recent approaches go one step beyond and look for differential coexpression pattern, wherein there exists a significant difference in coexpression pattern between two phenotype conditions. These changes of coexpression patterns generally arise due to significant change in regulatory mechanism across different conditions governed by natural progression of diseases. Here we develop a novel multiobjective framework DiffCoMO, to identify differentially coexpressed modules that capture altered coexpression in gene modules across different stages of HIV-1 progression. The objectives are built to emphasize the distance between coexpression pattern of two phenotype stages. The proposed method is assessed by comparing with some state-of-the-art techniques. We show that DiffCoMO outperforms the state-of-the-art for detecting differential coexpressed modules. Moreover, we have compared the performance of all the methods using simulated data. The biological significance of the discovered modules is also investigated using GO and pathway enrichment analysis. Additionally, miRNA enrichment analysis is carried out to identify TF to miRNA and miRNA to TF connections. The gene modules discovered by DiffCoMO manifest regulation by miRNA-28, miRNA-29 and miRNA-125 families.

Computational approaches for prediction of pathogen-host protein-protein interactions

Infectious diseases are still among the major and prevalent health problems, mostly because of the drug resistance of novel variants of pathogens. Molecular interactions between pathogens and their hosts are the key parts of the infection mechanisms. Novel antimicrobial therapeutics to fight drug resistance is only possible in case of a thorough understanding of pathogen-host interaction (PHI) systems. Existing databases, which contain experimentally verified PHI data, suffer from scarcity of reported interactions due to the technically challenging and time consuming process of experiments. These have motivated many researchers to address the problem by proposing computational approaches for analysis and prediction of PHIs. The computational methods primarily utilize sequence information, protein structure and known interactions. Classic machine learning techniques are used when there are sufficient known interactions to be used as training data. On the opposite case, transfer and multitask learning methods are preferred. Here, we present an overview of these computational approaches for predicting PHI systems, discussing their weakness and abilities, with future directions.

A review of in silico approaches for analysis and prediction of HIV-1-human protein-protein interactions

The computational or in silico approaches for analysing the HIV-1-human protein-protein interaction (PPI) network, predicting different host cellular factors and PPIs and discovering several pathways are gaining popularity in the field of HIV research. Although there exist quite a few studies in this regard, no previous effort has been made to review these works in a comprehensive manner. Here we review the computational approaches that are devoted to the analysis and prediction of HIV-1-human PPIs. We have broadly categorized these studies into two fields: computational analysis of HIV-1-human PPI network and prediction of novel PPIs. We have also presented a comparative assessment of these studies and proposed some methodologies for discussing the implication of their results. We have also reviewed different computational techniques for predicting HIV-1-human PPIs and provided a comparative study of their applicability. We believe that our effort will provide helpful insights to the HIV research community.

Alterations in the nuclear proteome of HIV-1 infected T-cells

Virus infection of a cell involves the appropriation of host factors and the innate defensive response of the cell. The identification of proteins critical for virus replication may lead to the development of novel, cell-based inhibitors. In this study we mapped the changes in T-cell nuclei during human immunodeficiency virus type 1 (HIV-1) at 20 hpi. Using a stringent data threshold, a total of 13 and 38 unique proteins were identified in infected and uninfected cells, respectively, across all biological replicates. An additional 15 proteins were found to be differentially regulated between infected and control nuclei. STRING analysis identified four clusters of protein-protein interactions in the data set related to nuclear architecture, RNA regulation, cell division, and cell homeostasis. Immunoblot analysis confirmed the differential expression of several proteins in both C8166-45 and Jurkat E6-1 T-cells. These data provide a map of the response in host cell nuclei upon HIV-1 infection.

Mycobacterial and HIV infections up-regulated human zinc finger protein 134, a novel positive regulator of HIV-1 LTR activity and viral propagation

BACKGROUND

Concurrent occurrence of HIV and Tuberculosis (TB) infections influence the cellular environment of the host for synergistic existence. An elementary approach to understand such coalition at the molecular level is to understand the interactions of the host and the viral factors that subsequently effect viral replication. Long terminal repeats (LTR) of HIV genome serve as a template for binding trans-acting viral and cellular factors that regulate its transcriptional activity, thereby, deciding the fate of HIV pathogenesis, making it an ideal system to explore the interplay between HIV and the host.

METHODOLOGY/PRINCIPAL FINDINGS

In this study, using biotinylated full length HIV-1 LTR sequence as bait followed by MALDI analyses, we identified and further characterized human-Zinc-finger-protein-134 (hZNF-134) as a novel positive regulator of HIV-1 that promoted LTR-driven transcription and viral production. Over-expression of hZNF-134 promoted LTR driven luciferase activity and viral transcripts, resulting in increased virus production while siRNA mediated knockdown reduced both the viral transcripts and the viral titers, establishing hZNF-134 as a positive effector of HIV-1. HIV, Mycobacteria and HIV-TB co-infections increased hZNF-134 expressions in PBMCs, the impact being highest by mycobacteria. Corroborating these observations, primary TB patients (n = 22) recorded extraordinarily high transcript levels of hZNF-134 as compared to healthy controls (n = 16).

CONCLUSIONS/SIGNIFICANCE

With these observations, it was concluded that hZNF-134, which promoted HIV-1 LTR activity acted as a positive regulator of HIV propagation in human host. High titers of hZNF-134 transcripts in TB patients suggest that up-regulation of such positive effectors of HIV-1 upon mycobacterial infection can be yet another mechanism by which mycobacteria assists HIV-1 propagation during HIV-TB co-infections. hZNF-134, an uncharacterized host protein, thus assumes a novel regulatory role during HIV-host interactions. Our study provides new insights into the emerging role of zinc finger proteins in HIV-1 pathogenesis.

Multitask learning for host-pathogen protein interactions

Motivation: An important aspect of infectious disease research involves understanding the differences and commonalities in the infection mechanisms underlying various diseases. Systems biology-based approaches study infectious diseases by analyzing the interactions between the host species and the pathogen organisms. This work aims to combine the knowledge from experimental studies of host–pathogen interactions in several diseases to build stronger predictive models. Our approach is based on a formalism from machine learning called ‘multitask learning’, which considers the problem of building models across tasks that are related to each other. A ‘task’ in our scenario is the set of host–pathogen protein interactions involved in one disease. To integrate interactions from several tasks (i.e. diseases), our method exploits the similarity in the infection process across the diseases. In particular, we use the biological hypothesis that similar pathogens target the same critical biological processes in the host, in defining a common structure across the tasks.

Results: Our current work on host–pathogen protein interaction prediction focuses on human as the host, and four bacterial species as pathogens. The multitask learning technique we develop uses a task-based regularization approach. We find that the resulting optimization problem is a difference of convex (DC) functions. To optimize, we implement a Convex–Concave procedure-based algorithm. We compare our integrative approach to baseline methods that build models on a single host–pathogen protein interaction dataset. Our results show that our approach outperforms the baselines on the training data. We further analyze the protein interaction predictions generated by the models, and find some interesting insights.

Availability: The predictions and code are available at: http://www.cs.cmu.edu/∼mkshirsa/ismb2013_paper320.html

Contact:j.klein-seetharaman@warwick.ac.uk

Supplementary information:Supplementary data are available at Bioinformatics online.

Detection of protein complexes using a protein ranking algorithm

Detecting protein complexes from protein-protein interaction (PPI) network is becoming a difficult challenge in computational biology. There is ample evidence that many disease mechanisms involve protein complexes, and being able to predict these complexes is important to the characterization of the relevant disease for diagnostic and treatment purposes. This article introduces a novel method for detecting protein complexes from PPI by using a protein ranking algorithm (ProRank). ProRank quantifies the importance of each protein based on the interaction structure and the evolutionarily relationships between proteins in the network. A novel way of identifying essential proteins which are known for their critical role in mediating cellular processes and constructing protein complexes is proposed and analyzed. We evaluate the performance of ProRank using two PPI networks on two reference sets of protein complexes created from Munich Information Center for Protein Sequence, containing 81 and 162 known complexes, respectively. We compare the performance of ProRank to some of the well known protein complex prediction methods (ClusterONE, CMC, CFinder, MCL, MCode and Core) in terms of precision and recall. We show that ProRank predicts more complexes correctly at a competitive level of precision and recall. The level of the accuracy achieved using ProRank in comparison to other recent methods for detecting protein complexes is a strong argument in favor of the proposed method.