Host-pathogen Protein Interactions Research Articles

Quantification of Adaptive Immune Responses Against Protein-Binding Interfaces in the Streptococcal M1 Protein

Bacterial or viral antigens can contain subdominant protein regions that elicit weak antibody responses upon vaccination or infection although there is accumulating evidence that antibody responses against subdominant regions can enhance the protective immune response. One proposed mechanism for subdominant protein regions is the binding of host proteins that prevent antibody production against epitopes hidden within the protein binding interfaces. Here, we used affinity purification combined with quantitative mass spectrometry (AP-MS) to examine the level of competition between antigen-specific antibodies and host–pathogen protein interaction networks using the M1 protein from Streptococcus pyogenes as a model system. As most humans have circulating antibodies against the M1 protein, we first used AP-MS to show that the M1 protein interspecies protein network formed with human plasma proteins is largely conserved in naïve mice. Immunizing mice with the M1 protein generated a time-dependent increase of anti-M1 antibodies. AP-MS analysis comparing the composition of the M1-plasma protein network from naïve and immunized mice showed significant enrichment of 292 IgG peptides associated with 56 IgG chains in the immune mice. Despite the significant increase of bound IgGs, the levels of interacting plasma proteins were not significantly reduced in the immune mice. The results indicate that the antigen-specific polyclonal IgG against the M1 protein primarily targets epitopes outside the other plasma protein binding interfaces. In conclusion, this study demonstrates that AP-MS is a promising strategy to determine the relationship between antigen-specific antibodies and host-pathogen interaction networks that could be used to define subdominant protein regions of relevance for vaccine development.

Flagellin, a plant-defense-activating protein identified from Xanthomonas axonopodis pv. Dieffenbachiae invokes defense response in tobacco

BackgroundSecretome analysis is a valuable tool to study host-pathogen protein interactions and to identify new proteins that are important for plant health. Microbial signatures elicit defense responses in plants, and by that, the plant immune system gets triggered prior to pathogen infection. Functional properties of secretory proteins from Xanthomonas axonopodis pv. dieffenbachiae (Xad1) involved in priming plant immunity was evaluated.ResultsIn this study, the secretome of Xad1 was analyzed under host plant extract-induced conditions, and mass spectroscopic analysis of differentially expressed protein was identified as plant-defense-activating protein viz., flagellin C (FliC). The flagellin and Flg22 peptides both elicited hypersensitive reaction (HR) in non-host tobacco, activated reactive oxygen species (ROS) scavenging enzymes, and increased pathogenesis-related (PR) gene expression viz., NPR1, PR1, and down-regulation of PR2 (β-1,3-glucanase). Protein docking studies revealed the Flg22 epitope of Xad1, a 22 amino acid peptide region in FliC that recognizes plant receptor FLS2 to initiate downstream defense signaling.ConclusionThe flagellin or the Flg22 peptide from Xad1 was efficient in eliciting an HR in tobacco via salicylic acid (SA)-mediated defense signaling that subsequently triggers systemic immune response epigenetically. The insights from this study can be used for the development of bio-based products (small PAMPs) for plant immunity and health.

Understanding the mechanism of pathogenicity through interactome studies between Arachis hypogaea L. and Aspergillus flavus

Aspergillus flavus (A. flavus) infects the peanut seeds during pre-and post-harvest stages, causing seed quality destruction for humans and livestock consumption. Even though many resistant varieties were developed, the molecular mechanism of defense interactions of peanut against A. flavus still needs further investigation. Hence, an interologous host-pathogen protein interaction (HPPI) network was constructed to understand the subcellular level interaction mechanism between peanut and A. flavus. Out of the top 10 hub proteins of both organisms, protein phosphatase 2C and cyclic nucleotide-binding/kinase domain-containing protein and different ribosomal proteins were identified as candidate proteins involved in defense. Functional annotation and subcellular localization based characterization of HPPI identified protein SGT1 homolog, calmodulin and Rac-like GTP-binding proteins to be involved in defense response against fungus. The relevance of HPPI in infectious conditions was assessed using two transcriptome data which identified the interplay of host kinase class R proteins, bHLH TFs and cell wall related proteins to impart resistance against pathogen infection. Further, the pathogenicity analysis identified glycogen phosphorylase and molecular chaperone and allergen Mod-E/Hsp90/Hsp1 as potential pathogen targets to enhance the host defense mechanism. Hence, the computationally predicted host-pathogen PPI network could provide valuable support for molecular biology experiments to understand the host-pathogen interaction. SignificanceProtein-protein interactions execute significant cellular interactions in an organism and are influenced majorly by stress conditions. Here we reported the host-pathogen protein-protein interaction between peanut and A. flavus, and a detailed network analysis based on function, subcellular localization, gene co-expression, and pathogenicity was performed. The network analysis identified key proteins such as host kinase class R proteins, calmodulin, SGT1 homolog, Rac-like GTP-binding proteins bHLH TFs and cell wall related to impart resistance against pathogen infection. We observed the interplay of defense related proteins and cell wall related proteins predominantly, which could be subjected to further studies. The network analysis described in this study could be applied to understand other host-pathogen systems generally.

In-silico study of protein-protein interactions in wheat blast using docking and molecular dynamics simulation approach

Despite advancements in agricultural research and the introduction of modern biotechnological and farming techniques, food security remains a significant issue. Although the efforts of farmers to meet the demands of a growing population, many plant diseases caused by pathogens, through their effects on cell division and tissue growth, lead to the annual loss of countless food crops. The recently emerged wheat blast fungus Magnaporthe oryzae pathotype Triticum (MoT) poses a significant danger to worldwide wheat cultivation. The fungus is a highly varied lineage of the M. oryzae, responsible for causing rice blast disease. In spite of being a significant challenge to successful wheat production in South America since 1985, the underlying biology of the wheat blast pathogen is still not fully understood. The initial outbreak of the wheat blast in South Asia had a severe impact on wheat production, resulting in a complete loss of yield in affected fields. For the purpose of enhancing disease management, it’s vital to acquire a comprehensive comprehension of the infection biology of the fungus and its interaction with wheat plants on molecular levels. Host-pathogen protein interactions (HPIs) have the potential to reveal the pathogens’ mechanism for overcoming the host organism. The current study delves into the interactions between the host plant wheat and MoT through protein-protein interactions, molecular docking, and 100 ns molecular dynamic simulations. This research uncovers the structural and functional basis of these proteins, leading to improved plant health and production. Communicated by Ramaswamy H. Sarma

Assessment of GO-Based Protein Interaction Affinities in the Large-Scale Human-Coronavirus Family Interactome.

SARS-CoV-2 is a novel coronavirus that replicates itself via interacting with the host proteins. As a result, identifying virus and host protein-protein interactions could help researchers better understand the virus disease transmission behavior and identify possible COVID-19 drugs. The International Committee on Virus Taxonomy has determined that nCoV is genetically 89% compared to the SARS-CoV epidemic in 2003. This paper focuses on assessing the host-pathogen protein interaction affinity of the coronavirus family, having 44 different variants. In light of these considerations, a GO-semantic scoring function is provided based on Gene Ontology (GO) graphs for determining the binding affinity of any two proteins at the organism level. Based on the availability of the GO annotation of the proteins, 11 viral variants, viz., SARS-CoV-2, SARS, MERS, Bat coronavirus HKU3, Bat coronavirus Rp3/2004, Bat coronavirus HKU5, Murine coronavirus, Bovine coronavirus, Rat coronavirus, Bat coronavirus HKU4, Bat coronavirus 133/2005, are considered from 44 viral variants. The fuzzy scoring function of the entire host-pathogen network has been processed with ~180 million potential interactions generated from 19,281 host proteins and around 242 viral proteins. ~4.5 million potential level one host-pathogen interactions are computed based on the estimated interaction affinity threshold. The resulting host-pathogen interactome is also validated with state-of-the-art experimental networks. The study has also been extended further toward the drug-repurposing study by analyzing the FDA-listed COVID drugs.

On the choice of negative examples for prediction of host-pathogen protein interactions.

As practitioners of machine learning in the area of bioinformatics we know that the quality of the results crucially depends on the quality of our labeled data. While there is a tendency to focus on the quality of positive examples, the negative examples are equally as important. In this opinion paper we revisit the problem of choosing negative examples for the task of predicting protein-protein interactions, either among proteins of a given species or for host-pathogen interactions and describe important issues that are prevalent in the current literature. The challenge in creating datasets for this task is the noisy nature of the experimentally derived interactions and the lack of information on non-interacting proteins. A standard approach is to choose random pairs of non-interacting proteins as negative examples. Since the interactomes of all species are only partially known, this leads to a very small percentage of false negatives. This is especially true for host-pathogen interactions. To address this perceived issue, some researchers have chosen to select negative examples as pairs of proteins whose sequence similarity to the positive examples is sufficiently low. This clearly reduces the chance for false negatives, but also makes the problem much easier than it really is, leading to over-optimistic accuracy estimates. We demonstrate the effect of this form of bias using a selection of recent protein interaction prediction methods of varying complexity, and urge researchers to pay attention to the details of generating their datasets for potential biases like this.

Isobaric tags for relative and absolute quantification-based proteomic analysis of host-pathogen protein interactions in the midgut of Aedes albopictus during dengue virus infection.

Aedes albopictus (Ae. albopictus), an important vector of dengue virus (DENV), is distributed worldwide. Identifying host proteins involved in flavivirus replication in Ae. albopictus and determining their natural antiviral mechanisms are critical to control virus transmission. Revealing the key proteins related to virus replication and exploring the host-pathogen interaction are of great significance in finding new pathways of the natural immune response in Ae. albopictus. Isobaric tags for relative and absolute quantification (iTRAQ) was used to perform a comparative proteomic analysis between the midgut of Ae. albopictus infected with DENV and the control. 3,419 proteins were detected, of which 162 were ≥ 1.2-fold differentially upregulated or ≤ 0.8-fold differentially downregulated (p < 0.05) during DENV infections. Differentially expressed proteins (DEPs) were mainly enriched in ubiquitin ligase complex, structural constituent of cuticle, carbohydrate metabolism, and lipid metabolism pathways. We found that one of the DEPs, a putative pupal cuticle (PC) protein could inhibit the replication of DENV and interact with the DENV-E protein. In addition, the result of immunofluorescence (IF) test showed that there was co-localization between ubiquitin carboxyl-terminal hydrolase (UCH) protein and the DENV-E protein, and virus infection reduced the level of this protein. iTRAQ-based proteomic analysis of the Ae. albopictus midgut identified dengue infection-induced upregulated and downregulated proteins. The interaction between the PC and UCH proteins in the midgut of Ae. albopictus might exert a natural antiviral mechanism in mosquito.

Identification of key proteins in host–pathogen interactions between Mycobacterium tuberculosis and Homo sapiens: A systematic network theoretical approach

Tuberculosis (T.B.) is an infectious disease caused primarily by the bacterial pathogen Mycobacterium tuberculosis (Mtb) in humans. The emergence of various drug-resistant Mtb strains threatens to disrupt worldwide attempts to control the infection. The state of disease can be attributed to the host–pathogen protein interaction network. Hence in this study, we have analyzed the protein interaction network at the intra-species level, i.e., within Mtb using the Louvain community detection and at the inter-species level, i.e., between human and Mtb proteins. We observed a higher inter-connectedness in the intra-species protein network as compared to the inter-species network. This reflects a critical role of protein interaction network modulation during host–pathogenesis conditions. After analyzing 269 Mtb proteins and 2287 human proteins using the interlog approach, we observed a total of 640 host–pathogen protein interaction pairing involving 120 humans and 90 Mtb proteins. The H.P.I.s were filtered using functional protein annotations and publicly available experimental results. In the Mtb PPI network, Rv1286, Rv0462, Rv2215, Rv3559c, Rv3504, and Rv3523 are associated as a motif (a governing unit in a network) and identified as key proteins. This interaction network will help researchers better understand host–pathogen protein–protein interactions, as well as shed light on how Mtb interacts with its host.

Network analysis of host-pathogen protein interactions in microbe induced cardiovascular diseases

Large-scale visualization and analysis of HPIs involved in microbial CVDs can provide crucial insights into the mechanisms of pathogenicity. The comparison of CVD associated HPIs with the entire set of HPIs can identify the pathways specific to CVDs. Therefore, topological properties of HPI networks in CVDs and all pathogens was studied using Cytoscape3.5.1. Ontology and pathway analysis were done using KOBAS 3.0. HPIs of Papilloma, Herpes, Influenza A virus as well as Yersinia pestis and Bacillus anthracis among bacteria were predominant in the whole (wHPI) and the CVD specific (cHPI) network. The central viral and secretory bacterial proteins were predicted virulent. The central viral proteins had higher number of interactions with host proteins in comparison with bacteria. Major fraction of central and essential host proteins interacts with central viral proteins. Alpha-synuclein, Ubiquitin ribosomal proteins, TATA-box-binding protein, and Polyubiquitin-C &B proteins were the top interacting proteins specific to CVDs. Signaling by NGF, Fc epsilon receptor, EGFR and ubiquitin mediated proteolysis were among the top enriched CVD specific pathways. DEXDc and HELICc were enriched host mimicry domains that may help in hijacking of cellular machinery by pathogens. This study provides a system level understanding of cardiac damage in microbe induced CVDs.

In silico characterization, docking, and simulations to understand host-pathogen interactions in an effort to enhance crop production in date palms.

Food safety remains a significant challenge despite the growth and development in agricultural research and the advent of modern biotechnological and agricultural tools. Though the agriculturist struggles to aid the growing population's needs, many pathogen-based plant diseases by their direct impact on cell division and tissue development have led to the loss of tons of food crops every year. Though there are many conventional and traditional methods to overcome this issue, the amount and time spend are huge. Scientists have developed systems biology tools to study the root cause of the problem and rectify it. Host-pathogen protein interactions (HPIs) have a promising role in identifying the pathogens' strategy to conquer the host organism. In this paper, the interactions between the host Rhynchophorus ferrugineus (an invasive wood-boring pest that destroys palm) and the pathogens Proteus mirabilis, Serratia marcescens, and Klebsiella pneumoniae are comprehensively studied using protein-protein interactions, molecular docking, and followed by 200ns molecular dynamic simulations. This study elucidates the structural and functional basis of these proteins leading towards better plant health, production, and reliability.

Prediction of Prophylactic Peptide Vaccine Candidates for Human Papillomavirus (HPV): Immunoinformatics and Reverse Vaccinology Approaches

Background: Human Papillomavirus (HPV) is responsible for substantial morbidity and mortality worldwide. We predicted immunogenic promiscuous monovalent and polyvalent T-cell epitopes from the polyprotein of the Human Papillomavirus (HPV) using a range of bioinformatics tools and servers. Methods: We used immunoinformatics and reverse vaccinology-based approaches to design prophylactic peptides by antigenicity analysis, T-cell epitopes prediction, proteasomal and conservancy evaluation, host-pathogen protein interactions, and in silico binding affinity analysis. Results: We found two early proteins (E2 and E6) and two late proteins (L1 and L2) of HPV as potential vaccine candidates. Of these proteins (E2, E6, L1 &amp; L2), 2-epitopes of each candidate protein for multiple alleles of MHC class I and II bearing significant binding affinity (&gt;-6.0 kcal/mole). These potential epitopes for CD4+ and CD8+ T-cells were also linked to design polyvalent construct using GPGPG linkers. Cholera toxin B and mycobacterial heparin-binding hemagglutinin adjuvant with a molecular weight of 12.5 and 18.5 kDa were used for epitopes of CD4+ and CD8+ T-cells respectively. The molecular docking indicated the optimum binding affinity of HPV peptides with MHC molecules. This interaction showed that our predicted vaccine candidates are suitable to trigger the host immune system to prevent HPV infections. Conclusion: The predicted conserved T-cell epitopes would contribute to the imminent design of HPV vaccine candidates, which will be able to induce a broad range of immune-responses in a heterogeneous HLA population.

Prediction of host-pathogen protein interactions by extended network model.

Knowledge of the pathogen-host interactions between the species is essentialin order to develop a solution strategy against infectious diseases. In vitro methods take extended periods of time to detect interactions and provide very few of the possible interaction pairs. Hence, modelling interactions between proteins has necessitated the development of computational methods. The main scope of this paper is integrating the known protein interactions between thehost and pathogen organisms to improve the prediction success rate of unknown pathogen-host interactions. Thus, the truepositive rate of the predictions was expected to increase.In order to perform this study extensively, encoding methods and learning algorithms of several proteins were tested. Along with human as the host organism, two different pathogen organisms were used in the experiments. For each combination of protein-encoding and prediction method, both the original prediction algorithms were tested using only pathogen-host interactions and the same methodwas testedagain after integrating the known protein interactions within each organism. The effect of merging the networks of pathogen-host interactions of different species on the prediction performance of state-of-the-art methods was also observed. Successwas measured in terms of Matthews correlation coefficient, precision, recall, F1 score, and accuracy metrics. Empirical results showed that integrating the host and pathogen interactions yields better performance consistently in almost all experiments.

Uncovering physical interactions among human and Mycobacterium tuberculosis proteins

Background: Pathogens usually evade and manipulate host immune pathways through host-pathogen protein interactions. Uncovering these interactions is crucial for determining the mechanisms underlying pathogen infection and the defense system. The growing prevalence of tuberculosis (TB) infection in the world necessitated advances in TB research. With the rising information from several divisions of biosciences, computational approaches are promising to analyze and interpret the data at the system level. Methods: In the present study, in silico two-hybrid systems is employed on model organisms to predict physical interactions among proteins of Human and Mycobacterium tuberculosis (Mtb). Consistent protein interactions are identified by the Interlog method. Co-expression analysis and functional annotations are performed to infer significant Human and Mtb protein physical interactions (HMIs). Results: The interactions identified in this study support the current TB research through an improved understanding of the pathogen infection and survival mechanism. A network of HMIs highlighted dnaK as the most highly interacting protein. Further, dnaK, eno, tuf, and gap proteins are found to trigger toll-like receptor signaling pathways and initiate pathogenesis. Conclusion: The interactions proteins identified in this study may incline the researchers to explore for novel therapeutic intervention strategies.

In silico prediction of host-pathogen protein interactions in melioidosis pathogen Burkholderia pseudomallei and human reveals novel virulence factors and their targets.

The aerobic, Gram-negative motile bacillus, Burkholderia pseudomallei is a facultative intracellular bacterium causing melioidosis, a critical disease of public health importance, which is widely endemic in the tropics and subtropical regions of the world. Melioidosis is associated with high case fatality rates in animals and humans; even with treatment, its mortality is 20-50%. It also infects plants and is designated as a biothreat agent. B. pseudomallei is pathogenic due to its ability to invade, resist factors in serum and survive intracellularly. Despite its importance, to date only a few effector proteins have been functionally characterized, and there is not much information regarding the host-pathogen protein-protein interactions (PPI) of this system, which are important to studying infection mechanisms and thereby develop prevention measures. We explored two computational approaches, the homology-based interolog and the domain-based method, to predict genome-scale host-pathogen interactions (HPIs) between two different strains of B. pseudomallei (prototypical, and highly virulent) and human. In total, 76335 common HPIs (between the two strains) were predicted involving 8264 human and 1753 B. pseudomallei proteins. Among the unique PPIs, 14131 non-redundant HPIs were found to be unique between the prototypical strain and human, compared to 3043 non-redundant HPIs between the highly virulent strain and human. The protein hubs analysis showed that most B. pseudomallei proteins formed a hub with human dnaK complex proteins associated with tuberculosis, a disease similar in symptoms to melioidosis. In addition, drug-binding and carbohydrate-binding mechanisms were found overrepresented within the host-pathogen network, and metabolic pathways were frequently activated according to the pathway enrichment. Subcellular localization analysis showed that most of the pathogen proteins are targeting human proteins inside cytoplasm and nucleus. We also discovered the host targets of the drug-related pathogen proteins and proteins that form T3SS and T6SS in B. pseudomallei. Additionally, a comparison between the unique PPI patterns present in the prototypical and highly virulent strains was performed. The current study is the first report on developing a genome-scale host-pathogen protein interaction networks between the human and B. pseudomallei, a critical biothreat agent. We have identified novel virulence factors and their interacting partners in the human proteome. These PPIs can be further validated by high-throughput experiments and may give new insights on how B. pseudomallei interacts with its host, which will help medical researchers in developing better prevention measures.

A framework towards data analytics on host–pathogen protein–protein interactions

With the rapid development of high-throughput technologies, systems biology is now embracing a great opportunity made possible by the increased accumulation of data available online. Biological data analytics is considered as a critical means to contribute to a better understanding on such data through extraction of the latent features, relationships and the associated mechanisms. Therefore, it is important to evaluate how to involve data analytics from both computational and biological perspectives in practice. This paper has investigated interaction relationships in the proteomics area, which provide insights of the critical molecular processes within infection mechanisms. Specifically, we focused on host–pathogen protein–protein interactions, which represented the primary challenges associated with infectious diseases and drug design. Accordingly, a novel framework based on data analytics and machine learning techniques is detailed for analyzing these areas and we will describe the analytical results from host–pathogen protein–protein interactions (HP-PPI). Based on this framework, which serves as a pipeline solution for extracting and learning from the raw proteomics data, we have firstly evaluated several models from literature using different analytic technologies and performance measurements. An unsupervised deep learning model based on stacked denoising autoencoders, is subsequently proposed to capture higher level feature regarding the sequence information in the framework. The achieved performance indicates a superior capability of the unsupervised deep learning model in dealing with the host–pathogen protein interactions scenario among all of these models. The results will further help to enrich a theoretical and technical foundation for analyzing HP-PPI networks.

A modified transient gene expression protocol for subcellular protein localization analysis in rice

Transient protein expression is a useful technique for investigating the protein localization and functional analysis of stress responses in rice plants. Currently, available methods for in planta transient expression analysis include Agrobacterium-mediated transformation, protoplast transformation using polyethylene glycol or electroporation, and biolistic bombardment expression which have several disadvantages and are not well suited for the rice. Therefore, development of a method for rapid and efficient analysis of protein expression, subcellular localization, pathogen effector screening, and protein–protein interaction in rice is required. We developed a protocol for in planta gene expression analysis in sliced rice sheath cells by modifying and optimizing the biolistic particle bombardment technique. By obtaining thin sections (~400 μm) of rice sheath cells, auto-fluorescence from chlorophyll was eliminated. This system was validated through the localization of marker genes specifically expressed in nuclei, plasma membranes, and tonoplast. In addition, high transformation efficiency of 30% was achieved. Therefore, this protocol provides a new and rapid method for transient gene expression assay in rice. Protein secretion was examined in rice sheath cells using predicted secretory proteins from rice blast fungus, indicating that this method is applicable to plant–microbe interaction studies. The transient expression protocol established here is well optimized for protein localization, secretion, and host–pathogen protein interaction studies in rice. A typical experiment can be completed in three days.

Computational Health Engineering Applied to Model Infectious Diseases and Antimicrobial Resistance Spread

Infectious diseases are the primary cause of mortality worldwide. The dangers of infectious disease are compounded with antimicrobial resistance, which remains the greatest concern for human health. Although novel approaches are under investigation, the World Health Organization predicts that by 2050, septicaemia caused by antimicrobial resistant bacteria could result in 10 million deaths per year. One of the main challenges in medical microbiology is to develop novel experimental approaches, which enable a better understanding of bacterial infections and antimicrobial resistance. After the introduction of whole genome sequencing, there was a great improvement in bacterial detection and identification, which also enabled the characterization of virulence factors and antimicrobial resistance genes. Today, the use of in silico experiments jointly with computational and machine learning offer an in depth understanding of systems biology, allowing us to use this knowledge for the prevention, prediction, and control of infectious disease. Herein, the aim of this review is to discuss the latest advances in human health engineering and their applicability in the control of infectious diseases. An in-depth knowledge of host–pathogen–protein interactions, combined with a better understanding of a host’s immune response and bacterial fitness, are key determinants for halting infectious diseases and antimicrobial resistance dissemination.

A new sequence based encoding for prediction of host–pathogen protein interactions

Pathogen–host interactions are very important to figure out the infection process at the molecular level, where pathogen proteins physically bind to human proteins to manipulate critical biological processes in the host cell. Data scarcity and data unavailability are two major problems for computational approaches in the prediction of pathogen–host interactions. Developing a computational method to predict pathogen–host interactions with high accuracy, based on protein sequences alone, is of great importance because it can eliminate these problems. In this study, we propose a novel and robust sequence based feature extraction method, named Location Based Encoding, to predict pathogen–host interactions with machine learning based algorithms. In this context, we use Bacillus Anthracis and Yersinia Pestis data sets as the pathogen organisms and human proteins as the host model to compare our method with sequence based protein encoding methods, which are widely used in the literature, namely amino acid composition, amino acid pair, and conjoint triad. We use these encoding methods with decision trees (Random Forest, j48), statistical (Bayesian Networks, Naive Bayes), and instance based (kNN) classifiers to predict pathogen–host interactions. We conduct different experiments to evaluate the effectiveness of our method. We obtain the best results among all the experiments with RF classifier in terms of F1, accuracy, MCC, and AUC.

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.

Correction: Mechanisms of action of Coxiella burnetii effectors inferred from host-pathogen protein interactions.

[This corrects the article DOI: 10.1371/journal.pone.0188071.].