Human immunodeficiency virus type 1, human protein interaction database at NCBI

Comparative analysis of retroviral Gag-host cell interactions: focus on the nuclear interactome

Retroviruses exploit a variety of host proteins to assemble and release virions from infected cells. To date, most studies that examined possible interacting partners of retroviral Gag proteins focused on host proteins that localize primarily to the cytoplasm or plasma membrane. Given the recent findings that several full-length Gag proteins localize to the nucleus, identifying the Gag-nuclear interactome has high potential for novel findings that reveal previously unknown host processes. In this study, we systematically compared nuclear factors identified in published HIV-1 proteomic studies which had used a variety of experimental approaches. In addition, to contribute to this body of knowledge, we report results from a mass spectrometry approach using affinity-tagged (His6) HIV-1 and RSV Gag proteins mixed with nuclear extracts. Taken together, the previous studies-as well as our own-identified potential binding partners of HIV-1 and RSV Gag involved in several nuclear processes, including transcription, splicing, RNA modification, and chromatin remodeling. Although a subset of host proteins interacted with both Gag proteins, there were also unique host proteins belonging to each interactome dataset. To validate one of the novel findings, we demonstrated the interaction of RSV Gag with a member of the Mediator complex, Med26, which is required for RNA polymerase II-mediated transcription. These results provide a strong premise for future functional studies to investigate roles for these nuclear host factors that may have shared functions in the biology of both retroviruses, as well as functions specific to RSV and HIV-1, given their distinctive hosts and molecular pathology.

Navigating the Landscape: A Comprehensive Review of Current Virus Databases

Viruses are abundant and diverse entities that have important roles in public health, ecology, and agriculture. The identification and surveillance of viruses rely on an understanding of their genome organization, sequences, and replication strategy. Despite technological advancements in sequencing methods, our current understanding of virus diversity remains incomplete, highlighting the need to explore undiscovered viruses. Virus databases play a crucial role in providing access to sequences, annotations and other metadata, and analysis tools for studying viruses. However, there has not been a comprehensive review of virus databases in the last five years. This study aimed to fill this gap by identifying 24 active virus databases and included an extensive evaluation of their content, functionality and compliance with the FAIR principles. In this study, we thoroughly assessed the search capabilities of five database catalogs, which serve as comprehensive repositories housing a diverse array of databases and offering essential metadata. Moreover, we conducted a comprehensive review of different types of errors, encompassing taxonomy, names, missing information, sequences, sequence orientation, and chimeric sequences, with the intention of empowering users to effectively tackle these challenges. We expect this review to aid users in selecting suitable virus databases and other resources, and to help databases in error management and improve their adherence to the FAIR principles. The databases listed here represent the current knowledge of viruses and will help aid users find databases of interest based on content, functionality, and scope. The use of virus databases is integral to gaining new insights into the biology, evolution, and transmission of viruses, and developing new strategies to manage virus outbreaks and preserve global health.

CRISPR library screening to develop HEK293-derived cell lines with improved lentiviral vector titers

Lentiviral (LV) vectors have emerged as powerful tools for treating genetic and acquired human diseases. As clinical studies and commercial demands have progressed, there has been a growing need for large amounts of purified LV vectors. To help meet this demand, we developed CRISPR library screening methods to identify genetic perturbations in human embryonic kidney 293 (HEK293) cells and their derivatives that may increase LV vector titers. Briefly, LV vector-based Human CRISPR Activation and Knockout libraries (Calabrese and Brunello) were used to modify HEK293 and HEK293T cells. These cell populations were then expanded, and integrated LV vector genomes were rescued by transfection. LV vectors were harvested, and the process of sequential transduction and rescue-transfection was iterated. Through this workflow, guide RNAs (gRNAs) that target genes that may suppress or enhance LV vector production were enriched and identified with Next-Generation Sequencing (NGS). Though more work is needed to test genes identified in this screen, we expect that perturbations of genes we identified here, such as TTLL12, which is an inhibitor of antiviral innate immunity may be introduced and multiplexed to yield cell lines with improved LV vector productivity.

Schlafen 12 restricts HIV-1 latency reversal by a codon-usage dependent post-transcriptional block in CD4+ T cells

Latency is a major barrier towards virus elimination in HIV-1-infected individuals. Yet, the mechanisms that contribute to the maintenance of HIV-1 latency are incompletely understood. Here we describe the Schlafen 12 protein (SLFN12) as an HIV-1 restriction factor that establishes a post-transcriptional block in HIV-1-infected cells and thereby inhibits HIV-1 replication and virus reactivation from latently infected cells. The inhibitory activity is dependent on the HIV-1 codon usage and on the SLFN12 RNase active sites. Within HIV-1-infected individuals, SLFN12 expression in PBMCs correlated with HIV-1 plasma viral loads and proviral loads suggesting a link with the general activation of the immune system. Using an RNA FISH-Flow HIV-1 reactivation assay, we demonstrate that SLFN12 expression is enriched in infected cells positive for HIV-1 transcripts but negative for HIV-1 proteins. Thus, codon-usage dependent translation inhibition of HIV-1 proteins participates in HIV-1 latency and can restrict the amount of virus release after latency reversal.

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.

Identification of Molecular Mechanisms Involved in Viral Infection Progression Based on Text Mining: Case Study for HIV Infection

Viruses cause various infections that may affect human lifestyle for durations ranging from several days to for many years. Although preventative and therapeutic remedies are available for many viruses, they may still have a profound impact on human life. The human immunodeficiency virus type 1 is the most common cause of HIV infection, which represents one of the most dangerous and complex diseases since it affects the immune system and causes its disruption, leading to secondary complications and negatively influencing health-related quality of life. While highly active antiretroviral therapy may decrease the viral load and the velocity of HIV infection progression, some individual peculiarities may affect viral load control or the progression of T-cell malfunction induced by HIV. Our study is aimed at the text-based identification of molecular mechanisms that may be involved in viral infection progression, using HIV as a case study. Specifically, we identified human proteins and genes which commonly occurred, overexpressed or underexpressed, in the collections of publications relevant to (i) HIV infection progression and (ii) acute and chronic stages of HIV infection. Then, we considered biological processes that are controlled by the identified protein and genes. We verified the impact of the identified molecules in the associated clinical study.

Exploration of Computational Aids for Effective Drug Designing and Management of Viral Diseases: A Comprehensive Review

BACKGROUND

Microbial diseases, specifically originating from viruses are the major cause of human mortality all over the world. The current COVID-19 pandemic is a case in point, where the dynamics of the viral-human interactions are still not completely understood, making its treatment a case of trial and error. Scientists are struggling to devise a strategy to contain the pandemic for over a year and this brings to light the lack of understanding of how the virus grows and multiplies in the human body.

METHODS

This paper presents the perspective of the authors on the applicability of computational tools for deep learning and understanding of host-microbe interaction, disease progression and management, drug resistance and immune modulation through in silico methodologies which can aid in effective and selective drug development. The paper has summarized advances in the last five years. The studies published and indexed in leading databases have been included in the review.

RESULTS

Computational systems biology works on an interface of biology and mathematics and intends to unravel the complex mechanisms between the biological systems and the inter and intra species dynamics using computational tools, and high-throughput technologies developed on algorithms, networks and complex connections to simulate cellular biological processes.

CONCLUSION

Computational strategies and modelling integrate and prioritize microbial-host interactions and may predict the conditions in which the fine-tuning attenuates. These microbial-host interactions and working mechanisms are important from the aspect of effective drug designing and fine- tuning the therapeutic interventions.

Circular RNA Profiles in Viremia and ART Suppression Predict Competing circRNA–miRNA–mRNA Networks Exclusive to HIV-1 Viremic Patients

Since the onset of the HIV-1/AIDS epidemic in 1981, 75 million people have been infected with the virus, and the disease remains a public health crisis worldwide. Circular RNAs (circRNAs) are derived from excised exons and introns during backsplicing, a form of alternative splicing. The relevance of unconventional, non-capped, and non-poly(A) transcripts to transcriptomics studies remains to be routinely investigated. Knowledge gaps to be filled are the interface between host-encoded circRNAs and viral replication in chronically progressed patients and upon treatment with antiviral drugs. We implemented a bioinformatic pipeline and repurpose publicly archived RNA sequence reads from the blood of 19 HIV-1-positive patients that previously compared transcriptomes during viremia and viremia suppression by antiretroviral therapy (ART). The in silico analysis identified viremic patients’ circRNA that became undetectable after ART. The circRNAs originated from a subset of host genes enriched in the HDAC biological pathway. These circRNAs and parental mRNAs held in common a small collection of miRNA response elements (MREs), some of which were present in HIV-1 mRNAs. The function of the MRE-containing target mRNA enriched the RNA polymerase II GO pathway. To visualize the interplay between individual circRNA–miRNA–target mRNA, important for HIV-1 and potentially other diseases, an Interactive Circos tool was developed to efficiently parse the intricately competing endogenous network of circRNA–miRNA–mRNA interactions originating from seven circRNA singled out in viremic versus non-viremic patients. The combined downregulation of the identified circRNAs warrants investigation as a novel antiviral targeting strategy.

A Novel Graph Topology-Based GO-Similarity Measure for Signature Detection From Multi-Omics Data and its Application to Other Problems

Large scale multi-omics data analysis and signature prediction have been a topic of interest in the last two decades. While various traditional clustering/correlation-based methods have been proposed, but the overall prediction is not always satisfactory. To solve these challenges, in this article, we propose a new approach by leveraging the Gene Ontology (GO)similarity combined with multiomics data. In this article, a new GO similarity measure, ModSchlicker, is proposed and the effectiveness of the proposed measure along with other standardized measures are reviewed while using various graph topology-based Information Content (IC)values of GO-term. The proposed measure is deployed to PPI prediction. Furthermore, by involving GO similarity, we propose a new framework for stronger disease-based gene signature detection from the multi-omics data. For the first objective, we predict interaction from various benchmark PPI datasets of Yeast and Human species. For the latter, the gene expression and methylation profiles are used to identify Differentially Expressed and Methylated (DEM)genes. Thereafter, the GO similarity score along with a statistical method are used to determine the potential gene signature. Interestingly, the proposed method produces a better performance ( 0.9 avg. accuracy and 0.95 AUC)as compared to the other existing related methods during the classification of the participating features (genes)of the signature. Moreover, the proposed method is highly useful in other prediction/classification problems for any kind of large scale omics data.

Comparative Analysis of T-Cell Spatial Proteomics and the Influence of HIV Expression

As systems biology approaches to virology have become more tractable, highly studied viruses such as HIV can now be analyzed in new unbiased ways, including spatial proteomics. We employed here a differential centrifugation protocol to fractionate Jurkat T cells for proteomic analysis by mass spectrometry; these cells contain inducible HIV-1 genomes, enabling us to look for changes in the spatial proteome induced by viral gene expression. Using these proteomics data, we evaluated the merits of several reported machine learning pipelines for classification of the spatial proteome and identification of protein translocations. From these analyses, we found that classifier performance in this system was organelle dependent, with Bayesian t-augmented Gaussian mixture modeling outperforming support vector machine learning for mitochondrial and endoplasmic reticulum proteins but underperforming on cytosolic, nuclear, and plasma membrane proteins by QSep analysis. We also observed a generally higher performance for protein translocation identification using a Bayesian model, Bayesian analysis of differential localization experiments, on row-normalized data. Comparative Bayesian analysis of differential localization experiment analysis of cells induced to express the WT viral genome versus cells induced to express a genome unable to express the accessory protein Nef identified known Nef-dependent interactors such as T-cell receptor signaling components and coatomer complex. Finally, we found that support vector machine classification showed higher consistency and was less sensitive to HIV-dependent noise. These findings illustrate important considerations for studies of the spatial proteome following viral infection or viral gene expression and provide a reference for future studies of HIV-gene-dropout viruses.

Collaborative Mining of Whole Genome Sequences for Intelligent HIV-1 Sub-Strain(s) Discovery

BACKGROUND

Effective global antiretroviral vaccines and therapeutic strategies depend on the diversity, evolution, and epidemiology of their various strains as well as their transmission and pathogenesis. Most viral disease-causing particles are clustered into a taxonomy of subtypes to suggest pointers toward nucleotide-specific vaccines or therapeutic applications of clinical significance sufficient for sequence-specific diagnosis and homologous viral studies. These are very useful to formulate predictors to induce cross-resistance to some retroviral control drugs being used across study areas.

OBJECTIVE

This research proposed a collaborative framework of hybridized (Machine Learning and Natural Language Processing) techniques to discover hidden genome patterns and feature predictors, for HIV-1 genome sequences mining.

METHOD

630 human HIV-1 genome sequences above 8500 bps were excavated from the National Center for Biotechnology Information (NCBI) database (https://www.ncbi.nlm.nih.gov) for 21 countries across different continents, Antarctica exempt. These sequences were transformed and learned using a self-organizing map (SOM). To discriminate emerging/new sub-strain(s), the HIV-1 reference genome was included as part of the input isolates/samples during the training. After training the SOM, component planes defining pattern clusters of the input datasets were generated, for cognitive knowledge mining and subsequent labelling of the datasets. Additional genome features including dinucleotide transmission recurrences, codon recurrences, and mutation recurrences, were finally extracted from the raw genomes to construct output classification targets for supervised learning.

RESULTS

SOM training explains the inherent pattern diversity of HIV-1 genomes as well as inter- and intra-country transmissions in which mobility might play an active role, as corroborated by literature. Nine sub-strains were discovered after disassembling the SOM correlation hunting matrix space attributed to disparate clusters. Cognitive knowledge mining separated similar pattern clusters bounded by a certain degree of correlation range, discovered by the SOM. A Kruskal-Wallis rank-sum test and Wilcoxon rank-sum test showed statistically significant variations in dinucleotide, codon, and mutation patterns.

CONCLUSION

Results of the discovered sub-strains and response clusters visualizations corroborate existing literature, with significant haplotype variations. The proposed framework would assist in the development of decision support systems for easy contact tracing, infectious disease surveillance, and studying the progressive evolution of the reference HIV-1 genome.

Identification of key immune regulatory genes in HIV-1 progression

Human immunodeficiency virus (HIV) infection causes acquired immunodeficiency syndrome (AIDS), one of the most devastating diseases affecting humankind. Here, we have proposed a framework to examine the differences among microarray gene expression data of uninfected and three different HIV-1 infection stages using module preservation statistics. We leverage the advantage of gene co-expression networks (GCN) constructed for each infection stages to detect the topological and structural changes of a group of differentially expressed genes. We examine the relationship among a set of co-expression modules by constructing a module eigengene network considering the overall similarity/dissimilarity among the genes within the modules. We have utilized different module preservation statistics with two composite statistics: "Z_summary" and "MedianRank" to examine the changes in co-expression patterns between modules. We have found several interesting results on the preservation characteristics of gene modules across different stages. Some genes are identified to be preserved in a pair of stages while altering their characteristics across other stages. We further validated the obtained results using permutation test and classification techniques. The biological significances of the obtained modules have also been examined using gene ontology and pathway-based analysis. Additionally, we have identified a set of key immune regulatory hub genes in the associated protein-protein interaction networks (PPINs) of the differentially expressed (DE) genes, which interacts with HIV-1 proteins and are likely to act as potential biomarkers in HIV-1 progression.

The large extracellular loop of CD63 interacts with gp41 of HIV-1 and is essential for establishing the virological synapse

Human immunodeficiency virus type 1 (HIV-1) persists lifelong in infected individuals and has evolved unique strategies in order to evade the immune system. One of these strategies is the direct cell-to-cell spread of HIV-1. The formation of a virological synapse (VS) between donor and target cell is important for this process. Tetraspanins are cellular proteins that are actively involved in the formation of a VS. However, the molecular mechanisms of recruiting host proteins for the cell–cell transfer of particles to the VS remains unclear. Our study has mapped the binding site for the transmembrane envelope protein gp41 of HIV-1 within the large extracellular loop (LEL) of CD63 and showed that this interaction occurs predominantly at the VS between T cells where viral particles are transferred. Mutations within the highly conserved CCG motif of the tetraspanin superfamily abrogated recruiting of expressed HIV-1 GFP fused Gag core protein and CD63 to the VS. This demonstrates the biological significance of CD63 for enhanced formation of a VS. Since cell–cell spread of HIV-1 is a major route of persistent infection, these results highlight the central role of CD63 as a member of the tetraspanin superfamily during HIV-1 infection and pathogenesis.

A systems-based method to repurpose marketed therapeutics for antiviral use: a SARS-CoV-2 case study

This study describes two complementary methods that use network-based and sequence similarity tools to identify drug repurposing opportunities predicted to modulate viral proteins. This approach could be rapidly adapted to new and emerging viruses. The first method built and studied a virus-host-physical interaction network; a three-layer multimodal network of drug target proteins, human protein-protein interactions, and viral-host protein-protein interactions. The second method evaluated sequence similarity between viral proteins and other proteins, visualized by constructing a virus-host-similarity interaction network. Methods were validated on the human immunodeficiency virus, hepatitis B, hepatitis C, and human papillomavirus, then deployed on SARS-CoV-2. Comparison of virus-host-physical interaction predictions to known antiviral drugs had AUCs of 0.69, 0.59, 0.78, and 0.67, respectively, reflecting that the scores are predictive of effective drugs. For SARS-CoV-2, 569 candidate drugs were predicted, of which 37 had been included in clinical trials for SARS-CoV-2 (AUC = 0.75, P-value 3.21 × 10-3). As further validation, top-ranked candidate antiviral drugs were analyzed for binding to protein targets in silico; binding scores generated by BindScope indicated a 70% success rate.

Proteomics approaches for the identification of protease substrates during virus infection

Proteases precisely and irreversibly catalyze the hydrolysis of peptide bonds, regulating the fate, localization, and activity of many proteins. Consequently, proteolytic activity plays an important role in fundamental cellular processes such as differentiation and migration, immunological and inflammatory reactions, apoptosis and survival. During virus infection, host proteases are involved in several processes, from cell entry to initiation, progression and resolution of inflammation. On the other hand, many viruses encode their own highly specific proteases, responsible for the proteolytic processing of viral proteins, but, at the same time, to cleave host proteins to corrupt antiviral host responses and adjust protein activity to favor viral replication. Traditionally, protease substrate identification has been addressed by means of hypothesis-driven approaches, but recent advances in proteomics have made a toolkit available to uncover the extensive repertoire of host proteins cleaved during infection, either by viral or host proteases. Here, we review the currently available proteomics-based methods that can and have contributed to the systematic and unbiased identification of new protease substrates in the context of virus-host interactions. The role of specific proteases during the course of virus infections will also be highlighted.

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.

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.

Reanalysis of Gene Expression Profiles of CD4+ T Cells Treated with HIV-1 Latency Reversal Agents

The human immunodeficiency virus (HIV-1) causes a progressive depletion of CD4+ T cells, hampering immune function. Current experimental strategies to fight the virus focus on the reactivation of latent HIV-1 in the viral reservoir to make the virus detectable by the immune system, by searching for latency reversal agents (LRAs). We hypothesize that if common molecular pathways elicited by the presence of LRAs are known, perhaps new, more efficient, “shock-and-kill” strategies can be found. Thus, the objective of the present study is to re-evaluate RNA-Seq assays to find differentially expressed genes (DEGs) during latency reversal via transcriptome analysis. We selected six studies (45 samples altogether: 16 negative controls and 29 LRA-treated CD4+ T cells) and 11 LRA strategies through a systematic search in Gene Expression Omnibus (GEO) and PubMed databases. The raw reads were trimmed, counted, and normalized. Next, we detected consistent DEGs in these independent experiments. AZD5582, romidepsin, and suberanilohydroxamic acid (SAHA) were the LRAs that modulated most genes. We detected 948 DEGs shared by those three LRAs. Gene ontology analysis and cross-referencing with other sources of the literature showed enrichment of cell activation, differentiation and signaling, especially mitogen-activated protein kinase (MAPK) and Rho-GTPases pathways.

The Impact of COVID-19 on the Insurance Industry

This study investigated the impact of COVID-19 on the insurance industry by studying the case of Ghana from March to June 2020. With a parallel comparison to previous pandemics such as SARS-CoV, H1N1 and MERS, we developed outlines for simulating the impact of the pandemic on the insurance industry. The study used qualitative and quantitative interviews to estimate the impact of the pandemic. Presently, the trend is an economic recession with decreasing profits but increasing claims. Due to the cancellation of travels, events and other economic losses, the Ghanaian insurance industry witnessed a loss currently estimated at GH Ȼ112 million. Our comparison and forecast predicts a normalization of economic indicators from January 2021. In the meantime, while the pandemic persists, insurers should adapt to working from remote locations, train and equip staff to work under social distancing regulations, enhance cybersecurity protocols and simplify claims/premium processing using e-payment channels. It will require the collaboration of the Ghana Ministry of Health, Banking Sector, Police Department, Customs Excise and Preventive Service, other relevant Ministries and the international community to bring the pandemic to a stop.

Insight in miRNome of Long-Term Non-Progressors and Elite Controllers Exposes Potential RNAi Role in Restraining HIV-1 Infection

Long-term non-progressors (LTNP) and elite controllers (EC) represent spontaneous natural models of efficient HIV-1 response in the absence of treatment. The main purposes of this work are to describe the miRNome of HIV-1 infected patients with different extreme phenotypes and identify potentially altered pathways regulated by differentially expressed (DE) miRNAs. The miRNomes from peripheral blood mononuclear cells (PBMCs) of dual phenotype EC-LTNP or LTNP with detectable viremia and HIV-infected patients with typical progression before and after treatment, were obtained through miRNA-Seq and compared among them. The administration of treatment produces 18 DE miRNAs in typical progressors. LTNP condition shows 14 DE miRNA when compared to typical progressors, allowing LTNP phenotype differentiation. A set of four miRNAs: miR-144-3p, miR-18a-5p, miR-451a, and miR-324 is strongly downregulated in LTNP and related to protein regulation as AKT, mTOR, ERK or IKK, involved in immune response pathways. Deregulation of 28 miRNA is observed between EC-LTNP and viremic-LTNP, including previously described anti-HIV miRNAs: miR-29a, associated with LTNP phenotype, and miR-155, targeting different pre-integration complexes such as ADAM10 and TNPO3. A holistic perspective of the changes observed in the miRNome of patients with different phenotypes of HIV-control and non-progression is provided.

The Role of Integrin α4β7 Signaling in Human Immunodeficiency Virus-1 Pathogenesis and Viral Entry in Primary CD4+ T Cells As Revealed by Comparative Phosphoproteomic Signatures

Integrin α₄β_7, a CD4 independent receptor of human immunodeficiency virus-1 (HIV-1) gp120, defines a subset of CD4⁺T cells preferentially targeted by HIV. It is also considered as a promising therapeutic target for HIV-1 infection. Despite its role in HIV acquisition and disease progression, HIV-1-mediated integrin α₄β₇ signaling has not been elucidated so far. In view of this, we determined phosphoproteomic signatures of HIV-1 gp120 signaling as well as signaling mediated by the integrin α₄β₇ ligand, mucosal vascular addressin cell adhesion molecule-1 (MAdCAM-1), in primary CD4⁺ T cells. This is the first comprehensive report on MAdCAM-1 signaling, which is believed to enhance HIV-1 replication. Importantly, we identified proteins associated with both classical and nonclassical integrin functions. We observed that HIV-1 gp120 signaling is associated with proteins that have previously not been associated with HIV-1 pathogenesis and thus, need to be explored further. There was a significant overlap in proteins identified by both MAdCAM-1 and HIV-1 gp120 signaling, which most likely represents cellular processes triggered upon interaction of HIV-1 gp120 with integrin α₄β_7. Pathway analysis revealed enrichment of processes that could facilitate viral replication as well as viral entry through endocytosis. Although these results warrant independent replication and further validation, they suggest the presence of additional potential therapeutic targets. These results also suggest that combinatorial approaches for targeting both HIV-1 gp120 and MAdCAM-1 signaling may be necessary for efficient control of HIV-1 infection as well as novel innovation strategies in HIV therapeutics.

A High-Throughput HIV-1 Drug Screening Platform, Based on Lentiviral Vectors and Compatible with Biosafety Level-1

HIV-1 infection is a complex, multi-step process involving not only viral, but also multiple cellular factors. To date, drug discovery methods have primarily focused on the inhibition of single viral proteins. We present an efficient and unbiased approach, compatible with biosafety level 1 (BSL-1) conditions, to identify inhibitors of HIV-1 reverse transcription, intracellular trafficking, nuclear entry and genome integration. Starting with a fluorescent assay setup, we systematically improved the screening methodology in terms of stability, efficiency and pharmacological relevance. Stability and throughput were optimized by switching to a luciferase-based readout. BSL-1 compliance was achieved without sacrificing pharmacological relevance by using lentiviral particles pseudo-typed with the mouse ecotropic envelope protein to transduce human PM1 T cells gene-modified to express the corresponding murine receptor. The cellular assay was used to screen 26,048 compounds selected for maximum diversity from a 200,640-compound in-house library. This yielded z' values greater than 0.8 with a hit rate of 3.3% and a confirmation rate of 50%. We selected 93 hits and enriched the collection with 279 similar compounds from the in-house library to identify promising structural features. The most active compounds were validated using orthogonal assay formats. The similarity of the compound profiles across the different platforms demonstrated that the reported lentiviral assay system is a robust and versatile tool for the identification of novel HIV-1 inhibitors.

Design, synthesis and biological evaluation of novel 2-(5-aryl-1H-imidazol-1-yl) derivatives as potential inhibitors of the HIV-1 Vpu and host BST-2 protein interaction

Novel ethyl 2-(5-aryl-1H-imidazol-1-yl)-acetates 17 and propionates 18, together with their acetic acid 19 and acetohydrazide 20 derivatives, were designed and synthesized using TosMIC chemistry. Biological evaluation of these newly synthesized scaffolds in the HIV-1 Vpu- Host BST-2 ELISA assay identified seven hits (17a, 17b, 17c, 17g, 18a, 20f and 20g) with greater than 50% inhibitory activity. These hits were validated in the HIV-1 Vpu- Host BST-2 AlphaScreen™ and six of the seven compounds were found to have comparable percentage inhibitory activities to those of the ELISA assay. Compounds 17b and 20g, with consistent percentage inhibitory activities across the two assays, had IC₅₀ values of 11.6 ± 1.1 μM and 17.6 ± 0.9 μM in a dose response AlphaScreen™ assay. In a cell-based HIV-1 antiviral assay, compound 17b exhibited an EC₅₀ = 6.3 ± 0.7 μM at non-toxic concentrations (CC₅₀ = 184.5 ± 0.8 μM), whereas compound 20g displayed antiviral activity roughly equivalent to its toxicity (CC₅₀ = 159.5 ± 0.9 μM). This data suggests that compound 17b, active in both cell-based and biochemical assays, provides a good starting point for the design of possible lead compounds for prevention of HIV-1 Vpu and host BST-2 protein binding in new anti-HIV therapeutics.

Evidence for Persistent Monocyte and Immune Dysregulation After Prolonged Viral Suppression Despite Normalization of Monocyte Subsets, sCD14 and sCD163 in HIV-Infected Individuals

BACKGROUND

People living with HIV on antiretroviral therapy (HIV/ART) experience excess non-AIDS comorbidities, and also remain at increased risk for certain infections and viral malignancies. Monocytes/macrophages are central to many of these comorbidities, and elevated plasma cytokines and immune activation during untreated infection are often incompletely reversed by ART and are also associated with comorbidities.

METHODS

We investigated monocyte surface markers, gene expression, and plasma cytokines in 11 HIV-infected older individuals (median 53 years) who started therapy with low CD4 counts (median 129 cells/µl), with elevated hsCRP (≥ 2mg/L) despite long-term ART (median 7.4 years), along with matched controls.

RESULTS

Frequency of monocyte subsets (based on CD14/CD16/CD163), were not different from controls, but surface expression of CD163 was increased (P = 0.021) while PD1 was decreased (P = 0.013) along with a trend for higher tissue factor (P = 0.096). As a group, HIV/ART participants had elevated plasma CCL2 (MCP-1; P = 0.0001), CXCL9 (MIG; P = 0.04), and sIL2R (P = 0.015), which were correlated, while sCD14 was not elevated. Principal component analysis of soluble markers revealed that 6/11 HIV/ART participants clustered with controls, while 5 formed a distinct group, driven by IL-10, CCL11, CXCL10, CCL2, CXCL9, and sIL2R. These individuals were significantly older than those clustering with controls. Transcriptomic analysis revealed multiple genes linked to immune functions including inflammation, immune cell development, and cell-cell signaling that were downregulated in HIV/ART monocytes and distinct from patterns in untreated subjects.

CONCLUSIONS

Long-term ART-treated individuals normalize monocyte subsets but exhibit immune dysregulation involving both aberrant inflammation and monocyte dysfunction, as well as inter-individual heterogeneity, suggesting complex mechanisms linking monocytes and HIV/ART comorbidities.

Targeting Virus-host Protein Interactions: Feature Extraction and Machine Learning Approaches

BACKGROUND

Targeting critical viral-host Protein-Protein Interactions (PPIs) has enormous application prospects for therapeutics. Using experimental methods to evaluate all possible virus-host PPIs is labor-intensive and time-consuming. Recent growth in computational identification of virus-host PPIs provides new opportunities for gaining biological insights, including applications in disease control. We provide an overview of recent computational approaches for studying virus-host PPI interactions.

METHODS

In this review, a variety of computational methods for virus-host PPIs prediction have been surveyed. These methods are categorized based on the features they utilize and different machine learning algorithms including classical and novel methods.

RESULTS

We describe the pivotal and representative features extracted from relevant sources of biological data, mainly include sequence signatures, known domain interactions, protein motifs and protein structure information. We focus on state-of-the-art machine learning algorithms that are used to build binary prediction models for the classification of virus-host protein pairs and discuss their abilities, weakness and future directions.

CONCLUSION

The findings of this review confirm the importance of computational methods for finding the potential protein-protein interactions between virus and host. Although there has been significant progress in the prediction of virus-host PPIs in recent years, there is a lot of room for improvement in virus-host PPI prediction.

Transcriptome Sequencing of Peripheral Blood Mononuclear Cells from Elite Controller-Long Term Non Progressors

The elite controller (EC)-long term non-progressor (LTNP) phenotype represent a spontaneous and advantageous model of HIV-1 control in the absence of therapy. The transcriptome of peripheral blood mononuclear cells (PBMCs) collected from EC-LTNPs was sequenced by RNA-Seq and compared with the transcriptomes from other phenotypes of disease progression. The transcript abundance estimation combined with the use of supervised classification algorithms allowed the selection of 20 genes and pseudogenes, mainly involved in interferon-regulated antiviral mechanisms and cell machineries of transcription and translation, as the best predictive genes of disease progression. Differential expression analyses between phenotypes showed an altered calcium homeostasis in EC-LTNPs evidenced by the upregulation of several membrane receptors implicated in calcium-signaling cascades and intracellular calcium-mobilization and by the overrepresentation of NFAT1/Elk-1-binding sites in the promoters of the genes differentially expressed in these individuals. A coordinated upregulation of host genes associated with HIV-1 reverse transcription and viral transcription was also observed in EC-LTNPs –i.e. p21/CDKN1A, TNF, IER3 and GADD45B. We also found an upregulation of ANKRD54 in EC-LTNPs and viremic LTNPs in comparison with typical progressors and a clear alteration of type-I interferon signaling as a consequence of viremia in typical progressors before and after receiving antiretroviral therapy.

Host-HIV-1 Interactome: A Quest for Novel Therapeutic Intervention

The complex nature and structure of the human immunodeficiency virus has rendered the cure for HIV infections elusive. The advances in antiretroviral treatment regimes and the development of highly advanced anti-retroviral therapy, which primarily targets the HIV enzymes, have dramatically changed the face of the HIV epidemic worldwide. Despite this remarkable progress, patients treated with these drugs often witness inadequate efficacy, compound toxicity and non-HIV complications. Considering the limited inventory of druggable HIV proteins and their susceptibility to develop drug resistance, recent attempts are focussed on targeting HIV-host interactomes that are essential for viral reproduction. Noticeably, unlike other viruses, HIV subverts the host nuclear pore complex to enter into and exit through the nucleus. Emerging evidence suggests a crucial role of interactions between HIV-1 proteins and host nucleoporins that underlie the import of the pre-integration complex into the nucleus and export of viral RNAs into the cytoplasm during viral replication. Nevertheless, the interaction of HIV-1 with nucleoporins has been poorly described and the role of nucleoporins during nucleocytoplasmic transport of HIV-1 still remains unclear. In this review, we highlight the advances and challenges in developing a more effective antiviral arsenal by exploring critical host-HIV interactions with a special focus on nuclear pore complex (NPC) and nucleoporins.

Anti-Tat Immunity in HIV-1 Infection: Effects of Naturally Occurring and Vaccine-Induced Antibodies Against Tat on the Course of the Disease

HIV-1 Tat is an essential protein in the virus life cycle, which is required for virus gene expression and replication. Most Tat that is produced during infection is released extracellularly and it plays a key role in HIV pathogenesis, including residual disease upon combination antiretroviral therapy (cART). Here, we review epidemiological and experimental evidence showing that antibodies against HIV-1 Tat, infrequently occurring in natural infection, play a protective role against disease progression, and that vaccine targeting Tat can intensify cART. In fact, Tat vaccination of subjects on suppressive cART in Italy and South Africa promoted immune restoration, including CD4+ T-cell increase in low immunological responders, and a reduction of proviral DNA even after six years of cART, when both CD4+ T-cell gain and DNA decay have reached a plateau. Of note, DNA decay was predicted by the neutralization of Tat-mediated entry of Env into dendritic cells by anti-Tat antibodies, which were cross-clade binding and neutralizing. Anti-Tat cellular immunity also contributed to the DNA decay. Based on these data, we propose the Tat therapeutic vaccine as a pathogenesis-driven intervention that effectively intensifies cART and it may lead to a functional cure, providing new perspectives and opportunities also for prevention and virus eradication strategies.

Analysis of networks of host proteins in the early time points following HIV transduction

Background

Utilization of quantitative proteomics data on the network level is still a challenge in proteomics data analysis. Currently existing models use sophisticated, sometimes hard to implement analysis techniques. Our aim was to generate a relatively simple strategy for quantitative proteomics data analysis in order to utilize as much of the data generated in a proteomics experiment as possible.

Results

In this study, we applied label-free proteomics, and generated a network model utilizing both qualitative, and quantitative data, in order to examine the early host response to Human Immunodeficiency Virus type 1 (HIV-1). A weighted network model was generated based on the amount of proteins measured by mass spectrometry, and analysis of weighted networks and functional sub-networks revealed upregulation of proteins involved in translation, transcription, and DNA condensation in the early phase of the viral life-cycle.

Conclusion

A relatively simple strategy for network analysis was created and applied to examine the effect of HIV-1 on host cellular proteome. We believe that our model may prove beneficial in creating algorithms, allowing for both quantitative and qualitative studies of proteome change in various biological and pathological processes by quantitative mass spectrometry.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2990-3) contains supplementary material, which is available to authorized users.

Augmenting subnetwork inference with information extracted from the scientific literature

Many biological studies involve either (i) manipulating some aspect of a cell or its environment and then simultaneously measuring the effect on thousands of genes, or (ii) systematically manipulating each gene and then measuring the effect on some response of interest. A common challenge that arises in these studies is to explain how genes identified as relevant in the given experiment are organized into a subnetwork that accounts for the response of interest. The task of inferring a subnetwork is typically dependent on the information available in publicly available, structured databases, which suffer from incompleteness. However, a wealth of potentially relevant information resides in the scientific literature, such as information about genes associated with certain concepts of interest, as well as interactions that occur among various biological entities. We contend that by exploiting this information, we can improve the explanatory power and accuracy of subnetwork inference in multiple applications. Here we propose and investigate several ways in which information extracted from the scientific literature can be used to augment subnetwork inference. We show that we can use literature-extracted information to (i) augment the set of entities identified as being relevant in a subnetwork inference task, (ii) augment the set of interactions used in the process, and (iii) support targeted browsing of a large inferred subnetwork by identifying entities and interactions that are closely related to concepts of interest. We use this approach to uncover the pathways involved in interactions between a virus and a host cell, and the pathways that are regulated by a transcription factor associated with breast cancer. Our experimental results demonstrate that these approaches can provide more accurate and more interpretable subnetworks. Integer program code, background network data, and pathfinding code are available at https://github.com/Craven-Biostat-Lab/subnetwork_inference

There is a multitude of publicly available databases that contain information about biological entities (i.e., genes, proteins, and other small molecules) as well as information about how these entities interact together. However, these databases are often incomplete. There is a wealth of information present in the text of the scientific literature that is not yet available in these databases. Using tools that mine the scientific literature we are able to extract some of this potentially relevant information. In this work we show how we can use publicly available databases in conjunction with the information extracted from the scientific literature to infer the networks that are involved in specific biological processes, such as viral replication and cancer tumor growth.

Investigating HIV-Human Interaction Networks to Unravel Pathogenic Mechanism for Drug Discovery: A Systems Biology Approach

BACKGROUND

Two big issues in the study of pathogens are determining how pathogens infect hosts and how the host defends itself against infection. Therefore, investigating host-pathogen interactions is important for understanding pathogenicity and host defensive mechanisms and treating infections.

METHODS

In this study, we used omics data, including time-course data from high-throughput sequencing, real-time polymerase chain reaction, and human microRNA (miRNA) and protein-protein interaction to construct an interspecies protein-protein and miRNA interaction (PPMI) network of human CD4+ T cells during HIV-1 infection through system modeling and identification.

RESULTS

By applying a functional annotation tool to the identified PPMI network at each stage of HIV infection, we found that repressions of three miRNAs, miR-140-5p, miR-320a, and miR-941, are involved in the development of autoimmune disorders, tumor proliferation, and the pathogenesis of T cells at the reverse transcription stage. Repressions of miR-331-3p and miR-320a are involved in HIV-1 replication, replicative spread, anti-apoptosis, cell proliferation, and dysregulation of cell cycle control at the integration/replication stage. Repression of miR-341-5p is involved in carcinogenesis at the late stage of HIV-1 infection.

CONCLUSION

By investigating the common core proteins and changes in specific proteins in the PPMI network between the stages of HIV-1 infection, we obtained pathogenic insights into the functional core modules and identified potential drug combinations for treating patients with HIV-1 infection, including thalidomide, oxaprozin, and metformin, at the reverse transcription stage; quercetin, nifedipine, and fenbendazole, at the integration/replication stage; and staurosporine, quercetin, prednisolone, and flufenamic acid, at the late stage.

Autism genes and the leukocyte transcriptome in autistic toddlers relate to pathogen interactomes, infection and the immune system. A role for excess neurotrophic sAPPα and reduced antimicrobial Aβ

Prenatal and early childhood infections have been implicated in autism. Many autism susceptibility genes (206 Autworks genes) are localised in the immune system and are related to immune/infection pathways. They are enriched in the host/pathogen interactomes of 18 separate microbes (bacteria/viruses and fungi) and to the genes regulated by bacterial toxins, mycotoxins and Toll-like receptor ligands. This enrichment was also observed for misregulated genes from a microarray study of leukocytes from autistic toddlers. The upregulated genes from this leukocyte study also matched the expression profiles in response to numerous infectious agents from the Broad Institute molecular signatures database. They also matched genes related to sudden infant death syndrome and autism comorbid conditions (autoimmune disease, systemic lupus erythematosus, diabetes, epilepsy and cardiomyopathy) as well as to estrogen and thyrotropin responses and to those upregulated by different types of stressors including oxidative stress, hypoxia, endoplasmic reticulum stress, ultraviolet radiation or 2,4-dinitrofluorobenzene, a hapten used to develop allergic skin reactions in animal models. The oxidative/integrated stress response is also upregulated in the autism brain and may contribute to myelination problems. There was also a marked similarity between the expression signatures of autism and Alzheimer's disease, and 44 shared autism/Alzheimer's disease genes are almost exclusively expressed in the blood-brain barrier. However, in contrast to Alzheimer's disease, levels of the antimicrobial peptide beta-amyloid are decreased and the levels of the neurotrophic/myelinotrophic soluble APP alpha are increased in autism, together with an increased activity of α-secretase. sAPPα induces an increase in glutamatergic and a decrease in GABA-ergic synapses creating and excitatory/inhibitory imbalance that has also been observed in autism. A literature survey showed that multiple autism genes converge on APP processing and that many are able to increase sAPPalpha at the expense of beta-amyloid production. A genetically programmed tilt of this axis towards an overproduction of neurotrophic/gliotrophic sAPPalpha and underproduction of antimicrobial beta-amyloid may explain the brain overgrowth and myelination dysfunction, as well as the involvement of pathogens in autism.

Convergent perturbation of the human domain-resolved interactome by viruses and mutations inducing similar disease phenotypes

An important goal of systems medicine is to study disease in the context of genetic and environmental perturbations to the human interactome network. For diseases with both genetic and infectious contributors, a key postulate is that similar perturbations of the human interactome by either disease mutations or pathogens can have similar disease consequences. This postulate has so far only been tested for a few viral species at the level of whole proteins. Here, we expand the scope of viral species examined, and test this postulate more rigorously at the higher resolution of protein domains. Focusing on diseases with both genetic and viral contributors, we found significant convergent perturbation of the human domain-resolved interactome by endogenous genetic mutations and exogenous viral proteins inducing similar disease phenotypes. Pan-cancer, pan-oncovirus analysis further revealed that domains of human oncoproteins either physically targeted or structurally mimicked by oncoviruses are enriched for cancer driver rather than passenger mutations, suggesting convergent targeting of cancer driver pathways by diverse oncoviruses. Our study provides a framework for high-resolution, network-based comparison of various disease factors, both genetic and environmental, in terms of their impacts on the human interactome.

Integrating Multifaceted Information to Predict Mycobacterium tuberculosis-Human Protein-Protein Interactions

Tuberculosis (TB) is one of the biggest infectious disease killers caused by Mycobacterium tuberculosis (MTB). Studying the protein-protein interactions (PPIs) between MTB and human can deepen our understanding of the pathogenesis of TB and offer new clues to the treatment against MTB infection, but the experimentally validated interactions are especially scarce in this regard. Herein we proposed an integrated framework that combined template-, domain-domain interaction-, and machine learning-based methods to predict MTB-human PPIs. As a result, we established a network composed of 13 758 PPIs including 451 MTB proteins and 3167 human proteins ( http://liulab.hzau.edu.cn/MTB/ ). Compared to known human targets of various pathogens, our predicted human targets show a similar tendency in terms of the network topological properties and enrichment in important functional genes. Additionally, these human targets largely have longer sequence lengths, more protein domains, more disordered residues, lower evolutionary rates, and older protein ages. Functional analysis demonstrates that these proteins show strong preferences toward the phosphorylation, kinase activity, and signaling transduction processes and the disease and immune related pathways. Dissecting the cross-talk among top-ranked pathways suggests that the cancer pathway may serve as a bridge in MTB infection. Triplet analysis illustrates that the paired targets interacting with the same partner are adjacent to each other in the intraspecies network and tend to share similar expression patterns. Finally, we identified 36 potential anti-MTB human targets by integrating known drug target information and molecular properties of proteins.

Evolution of In Silico Strategies for Protein-Protein Interaction Drug Discovery

The advent of advanced molecular modeling software, big data analytics, and high-speed processing units has led to the exponential evolution of modern drug discovery and better insights into complex biological processes and disease networks. This has progressively steered current research interests to understanding protein-protein interaction (PPI) systems that are related to a number of relevant diseases, such as cancer, neurological illnesses, metabolic disorders, etc. However, targeting PPIs are challenging due to their "undruggable" binding interfaces. In this review, we focus on the current obstacles that impede PPI drug discovery, and how recent discoveries and advances in in silico approaches can alleviate these barriers to expedite the search for potential leads, as shown in several exemplary studies. We will also discuss about currently available information on PPI compounds and systems, along with their usefulness in molecular modeling. Finally, we conclude by presenting the limits of in silico application in drug discovery and offer a perspective in the field of computer-aided PPI drug discovery.

Systematic detection of positive selection in the human-pathogen interactome and lasting effects on infectious disease susceptibility

Infectious disease has shaped the natural genetic diversity of humans throughout the world. A new approach to capture positive selection driven by pathogens would provide information regarding pathogen exposure in distinct human populations and the constantly evolving arms race between host and disease-causing agents. We created a human pathogen interaction database and used the integrated haplotype score (iHS) to detect recent positive selection in genes that interact with proteins from 26 different pathogens. We used the Human Genome Diversity Panel to identify specific populations harboring pathogen-interacting genes that have undergone positive selection. We found that human genes that interact with 9 pathogen species show evidence of recent positive selection. These pathogens are Yersenia pestis, human immunodeficiency virus (HIV) 1, Zaire ebolavirus, Francisella tularensis, dengue virus, human respiratory syncytial virus, measles virus, Rubella virus, and Bacillus anthracis. For HIV-1, GWAS demonstrate that some naturally selected variants in the host-pathogen protein interaction networks continue to have functional consequences for susceptibility to these pathogens. We show that selected human genes were enriched for HIV susceptibility variants (identified through GWAS), providing further support for the hypothesis that ancient humans were exposed to lentivirus pandemics. Human genes in the Italian, Miao, and Biaka Pygmy populations that interact with Y. pestis show significant signs of selection. These results reveal some of the genetic footprints created by pathogens in the human genome that may have left lasting marks on susceptibility to infectious disease.

Systems Biology Modeling to Study Pathogen-Host Interactions

Pathogen-host interactions (PHIs) underlie the process of infection. The systems biology view of the whole PHI system is superior to the investigation of the pathogen or host separately in understanding the infection mechanisms. Especially, the identification of host-oriented drug targets for the next-generation anti-infection therapeutics requires the properties of the host factors targeted by pathogens. Here, we provide an outline of computational analysis of PHI networks, focusing on the properties of the pathogen-targeted host proteins. We also provide information about the available PHI data and the related Web-based resources.

"cART intensification by the HIV-1 Tat B clade vaccine: progress to phase III efficacy studies"

INTRODUCTION

In spite of its success at suppressing HIV replication, combination antiretroviral therapy (cART) only partially reduces immune dysregulation and loss of immune functions. These cART-unmet needs appear to be due to persistent virus replication and cell-to-cell transmission in reservoirs, and are causes of increased patients' morbidity and mortality. Up to now, therapeutic interventions aimed at cART-intensification by attacking the virus reservoir have failed.

AREAS COVERED

We briefly review the rationale and clinical development of Tat therapeutic vaccine in cART-treated subjects in Italy and South Africa (SA). Vaccination with clade-B Tat induced cross-clade neutralizing antibodies, immune restoration, including CD4⁺ T cell increase particularly in low immunological responders, and reduction of proviral DNA. Phase III efficacy trials in SA are planned both in adult and pediatric populations.

EXPERT COMMENTARY

We propose the Tat therapeutic vaccine as a pathogenesis-driven intervention that effectively intensifies cART and may lead to a functional cure and provide new perspectives for prevention and virus eradication strategies.

Elucidating the in vivo interactome of HIV-1 RNA by hybridization capture and mass spectrometry

HIV-1 replication requires myriad interactions between cellular proteins and the viral unspliced RNA. These interactions are important in archetypal RNA processes such as transcription and translation as well as for more specialized functions including alternative splicing and packaging of unspliced genomic RNA into virions. We present here a hybridization capture strategy for purification of unspliced full-length HIV RNA-protein complexes preserved in vivo by formaldehyde crosslinking, and coupled with mass spectrometry to identify HIV RNA-protein interactors in HIV-1 infected cells. One hundred eighty-nine proteins were identified to interact with unspliced HIV RNA including Rev and Gag/Gag-Pol, 24 host proteins previously shown to bind segments of HIV RNA, and over 90 proteins previously shown to impact HIV replication. Further analysis using siRNA knockdown techniques against several of these proteins revealed significant changes to HIV expression. These results demonstrate the utility of the approach for the discovery of host proteins involved in HIV replication. Additionally, because this strategy only requires availability of 30 nucleotides of the HIV-RNA for hybridization with a capture oligonucleotide, it is readily applicable to any HIV system of interest regardless of cell type, HIV-1 virus strain, or experimental perturbation.

The Influence of HIV on the Evolution of Mycobacterium tuberculosis

HIV significantly affects the immunological environment during tuberculosis coinfection, and therefore may influence the selective landscape upon which M. tuberculosis evolves. To test this hypothesis whole genome sequences were determined for 169 South African M. tuberculosis strains from HIV-1 coinfected and uninfected individuals and analyzed using two Bayesian codon-model based selection analysis approaches: FUBAR which was used to detect persistent positive and negative selection (selection respectively favoring and disfavoring nonsynonymous substitutions); and MEDS which was used to detect episodic directional selection specifically favoring nonsynonymous substitutions within HIV-1 infected individuals. Among the 25,251 polymorphic codon sites analyzed, FUBAR revealed that 189-fold more were detectably evolving under persistent negative selection than were evolving under persistent positive selection. Three specific codon sites within the genes celA2b, katG, and cyp138 were identified by MEDS as displaying significant evidence of evolving under directional selection influenced by HIV-1 coinfection. All three genes encode proteins that may indirectly interact with human proteins that, in turn, interact functionally with HIV proteins. Unexpectedly, epitope encoding regions were enriched for sites displaying weak evidence of directional selection influenced by HIV-1. Although the low degree of genetic diversity observed in our M. tuberculosis data set means that these results should be interpreted carefully, the effects of HIV-1 on epitope evolution in M. tuberculosis may have implications for the design of M. tuberculosis vaccines that are intended for use in populations with high HIV-1 infection rates.

Iron overload down-regulates the expression of the HIV-1 Rev cofactor eIF5A in infected T lymphocytes

BACKGROUND

Changes in iron metabolism frequently accompany HIV-1 infection. However, while many clinical and in vitro studies report iron overload exacerbates the development of infection, many others have found no correlation. Therefore, the multi-faceted role of iron in HIV-1 infection remains enigmatic.

METHODS

RT-qPCR targeting the LTR region, gag, Tat and Rev were performed to measure the levels of viral RNAs in response to iron overload. Spike-in SILAC proteomics comparing i) iron-treated, ii) HIV-1-infected and iii) HIV-1-infected/iron treated T lymphocytes was performed to define modifications in the host cell proteome. Data from quantitative proteomics were integrated with the HIV-1 Human Interaction Database for assessing any viral cofactors modulated by iron overload in infected T lymphocytes.

RESULTS

Here, we demonstrate that the iron overload down-regulates HIV-1 gene expression by decreasing the levels of viral RNAs. In addition, we found that iron overload modulates the expression of many viral cofactors. Among them, the downregulation of the REV cofactor eIF5A may correlate with the iron-induced inhibition of HIV-1 gene expression. Therefore, we demonstrated that eiF5A downregulation by shRNA resulted in a significant decrease of Nef levels, thus hampering HIV-1 replication.

CONCLUSIONS

Our study indicates that HIV-1 cofactors influenced by iron metabolism represent potential targets for antiretroviral therapy and suggests eIF5A as a selective target for drug development.

Preservation affinity in consensus modules among stages of HIV-1 progression

Background

Analysis of gene expression data provides valuable insights into disease mechanism. Investigating relationship among co-expression modules of different stages is a meaningful tool to understand the way in which a disease progresses. Identifying topological preservation of modular structure also contributes to that understanding.

Methods

HIV-1 disease provides a well-documented progression pattern through three stages of infection: acute, chronic and non-progressor. In this article, we have developed a novel framework to describe the relationship among the consensus (or shared) co-expression modules for each pair of HIV-1 infection stages. The consensus modules are identified to assess the preservation of network properties. We have investigated the preservation patterns of co-expression networks during HIV-1 disease progression through an eigengene-based approach.

Results

We discovered that the expression patterns of consensus modules have a strong preservation during the transitions of three infection stages. In particular, it is noticed that between acute and non-progressor stages the preservation is slightly more than the other pair of stages. Moreover, we have constructed eigengene networks for the identified consensus modules and observed the preservation structure among them. Some consensus modules are marked as preserved in two pairs of stages and are analyzed further to form a higher order meta-network consisting of a group of preserved modules. Additionally, we observed that module membership (MM) values of genes within a module are consistent with the preservation characteristics. The MM values of genes within a pair of preserved modules show strong correlation patterns across two infection stages.

Conclusions

We have performed an extensive analysis to discover preservation pattern of co-expression network constructed from microarray gene expression data of three different HIV-1 progression stages. The preservation pattern is investigated through identification of consensus modules in each pair of infection stages. It is observed that the preservation of the expression pattern of consensus modules remains more prominent during the transition of infection from acute stage to non-progressor stage. Additionally, we observed that the module membership values of genes are coherent with preserved modules across the HIV-1 progression stages.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1590-3) contains supplementary material, which is available to authorized users.

Prediction of virus-host protein-protein interactions mediated by short linear motifs

BACKGROUND

Short linear motifs in host organisms proteins can be mimicked by viruses to create protein-protein interactions that disable or control metabolic pathways. Given that viral linear motif instances of host motif regular expressions can be found by chance, it is necessary to develop filtering methods of functional linear motifs. We conduct a systematic comparison of linear motifs filtering methods to develop a computational approach for predicting motif-mediated protein-protein interactions between human and the human immunodeficiency virus 1 (HIV-1).

RESULTS

We implemented three filtering methods to obtain linear motif sets: 1) conserved in viral proteins (C), 2) located in disordered regions (D) and 3) rare or scarce in a set of randomized viral sequences (R). The sets C,D,R are united and intersected. The resulting sets are compared by the number of protein-protein interactions correctly inferred with them - with experimental validation. The comparison is done with HIV-1 sequences and interactions from the National Institute of Allergy and Infectious Diseases (NIAID). The number of correctly inferred interactions allows to rank the interactions by the sets used to deduce them: D∪R and C. The ordering of the sets is descending on the probability of capturing functional interactions. With respect to HIV-1, the sets C∪R, D∪R, C∪D∪R infer all known interactions between HIV1 and human proteins mediated by linear motifs. We found that the majority of conserved linear motifs in the virus are located in disordered regions.

CONCLUSION

We have developed a method for predicting protein-protein interactions mediated by linear motifs between HIV-1 and human proteins. The method only use protein sequences as inputs. We can extend the software developed to any other eukaryotic virus and host in order to find and rank candidate interactions. In future works we will use it to explore possible viral attack mechanisms based on linear motif mimicry.

SILAC Based Proteomic Characterization of Exosomes from HIV-1 Infected Cells

Proteomics is the large-scale analysis of proteins. Proteomic techniques, such as liquid chromatography tandem mass spectroscopy (LC-MS/MS), can characterize thousands of proteins at a time. These powerful techniques allow us to have a systemic understanding of cellular changes, especially when cells are subjected to various stimuli, such as infections, stresses, and specific test conditions. Even with recent developments, analyzing the exosomal proteome is time-consuming and often involves complex methodologies. In addition, the resultant large dataset often needs robust and streamlined analysis in order for researchers to perform further downstream studies. Here, we describe a SILAC-based protocol for characterizing the exosomal proteome when cells are infected with HIV-1. The method is based on simple isotope labeling, isolation of exosomes from differentially labeled cells, and mass spectrometry analysis. This is followed by detailed data mining and bioinformatics analysis of the proteomic hits. The resultant datasets and candidates are easy to understand and often offer a wealth of information that is useful for downstream analysis. This protocol is applicable to other subcellular compartments and a wide range of test conditions.

The transcriptome of HIV-1 infected intestinal CD4+ T cells exposed to enteric bacteria

Global transcriptome studies can help pinpoint key cellular pathways exploited by viruses to replicate and cause pathogenesis. Previous data showed that laboratory-adapted HIV-1 triggers significant gene expression changes in CD4+ T cell lines and mitogen-activated CD4+ T cells from peripheral blood. However, HIV-1 primarily targets mucosal compartments during acute infection in vivo. Moreover, early HIV-1 infection causes extensive depletion of CD4+ T cells in the gastrointestinal tract that herald persistent inflammation due to the translocation of enteric microbes to the systemic circulation. Here, we profiled the transcriptome of primary intestinal CD4+ T cells infected ex vivo with transmitted/founder (TF) HIV-1. Infections were performed in the presence or absence of Prevotella stercorea, a gut microbe enriched in the mucosa of HIV-1-infected individuals that enhanced both TF HIV-1 replication and CD4+ T cell death ex vivo. In the absence of bacteria, HIV-1 triggered a cellular shutdown response involving the downregulation of HIV-1 reactome genes, while perturbing genes linked to OX40, PPAR and FOXO3 signaling. However, in the presence of bacteria, HIV-1 did not perturb these gene sets or pathways. Instead, HIV-1 enhanced granzyme expression and Th17 cell function, inhibited G1/S cell cycle checkpoint genes and triggered downstream cell death pathways in microbe-exposed gut CD4+ T cells. To gain insights on these differential effects, we profiled the gene expression landscape of HIV-1-uninfected gut CD4+ T cells exposed to bacteria. Microbial exposure upregulated genes involved in cellular proliferation, MAPK activation, Th17 cell differentiation and type I interferon signaling. Our findings reveal that microbial exposure influenced how HIV-1 altered the gut CD4+ T cell transcriptome, with potential consequences for HIV-1 susceptibility, cell survival and inflammation. The HIV-1- and microbe-altered pathways unraveled here may serve as a molecular blueprint to gain basic insights in mucosal HIV-1 pathogenesis.

Annexin A2 antibodies but not inhibitors of the annexin A2 heterotetramer impair productive HIV-1 infection of macrophages in vitro

During sexual transmission of human immunodeficiency virus (HIV), macrophages are initial targets for HIV infection. Secretory leukocyte protease inhibitor (SLPI) has been shown to protect against HIV infection of macrophages through interactions with annexin A2 (A2), which is found on the macrophage cell surface as a heterotetramer (A2t) consisting of A2 and S100A10. Therefore, we investigated potential protein-protein interactions between A2 and HIV-1 gp120 through a series of co-immunoprecipitation assays and a single molecule pulldown (SiMPull) technique. Additionally, inhibitors of A2t (A2ti) that target the interaction between A2 and S100A10 were tested for their ability to impair productive HIV-1 infection of macrophages. Our data suggest that interactions between HIV-1 gp120 and A2 exist, though this interaction may be indirect. Furthermore, an anti-A2 antibody impaired HIV-1 particle production in macrophages in vitro, whereas A2ti did not indicating that annexin A2 may promote HIV-1 infection of macrophages in its monomeric rather than tetrameric form.

A novel topological centrality measure capturing biologically important proteins

Topological centrality in protein interaction networks and its biological implications have widely been investigated in the past. In the present study, a novel metric of centrality-weighted sum of loads eigenvector centrality (WSL-EC)-based on graph spectra is defined and its performance in identifying topologically and biologically important nodes is comparatively investigated with common metrics of centrality in a human protein-protein interaction network. The metric can capture nodes from peripherals of the network differently from conventional eigenvector centrality. Different metrics were found to selectively identify hub sets that are significantly associated with different biological processes. The widely accepted metrics degree centrality, betweenness centrality, subgraph centrality and eigenvector centrality are subject to a bias towards super-hubs, whereas WSL-EC is not affected by the presence of super-hubs. WSL-EC outperforms other metrics of centrality in detecting biologically central nodes such as pathogen-interacting, cancer, ageing, HIV-1 or disease-related proteins and proteins involved in immune system processes and autoimmune diseases in the human interactome.

Stem-loop binding protein is a multifaceted cellular regulator of HIV-1 replication

A rare subset of HIV-1-infected individuals is able to maintain plasma viral load (VL) at low levels without antiretroviral treatment. Identifying the mechanisms underlying this atypical response to infection may lead to therapeutic advances for treating HIV-1. Here, we developed a proteomic analysis to compare peripheral blood cell proteomes in 20 HIV-1-infected individuals who maintained either high or low VL with the aim of identifying host factors that impact HIV-1 replication. We determined that the levels of multiple histone proteins were markedly decreased in cohorts of individuals with high VL. This reduction was correlated with lower levels of stem-loop binding protein (SLBP), which is known to control histone metabolism. Depletion of cellular SLBP increased promoter engagement with the chromatin structures of the host gene high mobility group protein A1 (HMGA1) and viral long terminal repeat (LTR), which led to higher levels of HIV-1 genomic integration and proviral transcription. Further, we determined that TNF-α regulates expression of SLBP and observed that plasma TNF-α levels in HIV-1-infected individuals correlated directly with VL levels and inversely with cellular SLBP levels. Our findings identify SLBP as a potentially important cellular regulator of HIV-1, thereby establishing a link between histone metabolism, inflammation, and HIV-1 infection.

A NMF based approach for integrating multiple data sources to predict HIV-1-human PPIs

BACKGROUND

Predicting novel interactions between HIV-1 and human proteins contributes most promising area in HIV research. Prediction is generally guided by some classification and inference based methods using single biological source of information.

RESULTS

In this article we have proposed a novel framework to predict protein-protein interactions (PPIs) between HIV-1 and human proteins by integrating multiple biological sources of information through non negative matrix factorization (NMF). For this purpose, the multiple data sets are converted to biological networks, which are then utilized to predict modules. These modules are subsequently combined into meta-modules by using NMF based clustering method. The integrated meta-modules are used to predict novel interactions between HIV-1 and human proteins. We have analyzed the significant GO terms and KEGG pathways in which the human proteins of the meta-modules participate. Moreover, the topological properties of human proteins involved in the meta modules are investigated. We have also performed statistical significance test to evaluate the predictions.

CONCLUSIONS

Here, we propose a novel approach based on integration of different biological data sources, for predicting PPIs between HIV-1 and human proteins. Here, the integration is achieved through non negative matrix factorization (NMF) technique. Most of the predicted interactions are found to be well supported by the existing literature in PUBMED. Moreover, human proteins in the predicted set emerge as 'hubs' and 'bottlenecks' in the analysis. Low p-value in the significance test also suggests that the predictions are statistically significant.

Uncovering New Pathogen-Host Protein-Protein Interactions by Pairwise Structure Similarity

Pathogens usually evade and manipulate host-immune pathways through pathogen-host protein-protein interactions (PPIs) to avoid being killed by the host immune system. Therefore, uncovering pathogen-host PPIs is critical for determining the mechanisms underlying pathogen infection and survival. In this study, we developed a computational method, which we named pairwise structure similarity (PSS)-PPI, to predict pathogen-host PPIs. First, a high-quality and non-redundant structure-structure interaction (SSI) template library was constructed by exhaustively exploring heteromeric protein complex structures in the PDB database. New interactions were then predicted by searching for PSS with complex structures in the SSI template library. A quantitative score named the PSS score, which integrated structure similarity and residue-residue contact-coverage information, was used to describe the overall similarity of each predicted interaction with the corresponding SSI template. Notably, PSS-PPI yielded experimentally confirmed pathogen-host PPIs of human immunodeficiency virus type 1 (HIV-1) with performance close to that of in vitro high-throughput screening approaches. Finally, a pathogen-host PPI network of human pathogen Mycobacterium tuberculosis, the causative agent of tuberculosis, was constructed using PSS-PPI and refined using filtration steps based on cellular localization information. Analysis of the resulting network indicated that secreted proteins of the STPK, ESX-1, and PE/PPE family in M. tuberculosis targeted human proteins involved in immune response and phagocytosis. M. tuberculosis also targeted host factors known to regulate HIV replication. Taken together, our findings provide insights into the survival mechanisms of M. tuberculosis in human hosts, as well as co-infection of tuberculosis and HIV. With the rapid pace of three-dimensional protein structure discovery, the SSI template library we constructed and the PSS-PPI method we devised can be used to uncover new pathogen-host PPIs in the future.