Insight into bacterial virulence mechanisms against host immune response via the Yersinia pestis-human protein-protein interaction network

Effect of anemoside B4 on milk whey in clinical mastitis-affected cows elucidated using tandem mass tag (TMT)-based quantitative proteomics

Intramuscular injection of anemoside B4 (AB4) has a superior therapeutic effect on clinical mastitis in lactating cows. Here, we explored AB4's effect on milk whey in clinical mastitis-affected cows using proteomics. Among fifty clinical mastitis cows received AB4 administration (0.05 ml/kg/day, for 7 days), twelve healed cows were selected and marked as group T. Twelve clinically heathy cows received the same dose of saline for 7 days, marked as group C. Collected milk whey of group T before and after AB4 administration marked as T1 and T2, respectively. The milk whey of group C after saline injection marked as C1. Milk whey protein changes were detected using tandem mass tag-based quantitative proteomic. We identified 872 quantifiable proteins in the samples. Among them, 511 proteins between T1 and C1, and 361 proteins between T2 and T1 were significantly altered. T1 than C1 had significantly more proteins associated with inflammatory damage and trans-endothelial migration of leukocytes, whereas these proteins were reduced in T2 treated with AB4. Compared with C, proteins associated with fibrin clot degradation and complement system activation were downregulated in T1 but upregulated in T2. In summary, AB4 can exert its therapeutic effect on clinical mastitis in cows mainly by reducing inflammatory damage, activating the complement system, inhibiting trans-endothelial migration of leukocytes, and promoting degradation of milk fibrin clots.

A pipeline of integrating transcriptome and interactome to elucidate central nodes in host-pathogens interactions

Investigating the complexity of host-pathogen interactions is challenging. Here, we outline a pipeline to identify important proteins and signaling molecules in human-viral interactomes. Firstly, we curate a comprehensive human interactome. Subsequently, we infer viral targets and transcriptome-specific human interactomes (VTTSHI) for papillomavirus and herpes viruses by integrating viral targets and transcriptome data. Finally, we reveal the common and shared nodes and pathways in viral pathogenesis following network topology and pathway enrichment analyses.

For complete details on the use and execution of this protocol, please refer to Kumar et al. (2020).

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Protocol for integrative analysis of transcriptome and proteome network data

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Network subgroup enrichment of two host-pathogen interaction networks

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Preprocessing of data and heterogeneous network integration

Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Host-microbiome protein-protein interactions capture disease-relevant pathways

Background

Host-microbe interactions are crucial for normal physiological and immune system development and are implicated in a variety of diseases, including inflammatory bowel disease (IBD), colorectal cancer (CRC), obesity, and type 2 diabetes (T2D). Despite large-scale case-control studies aimed at identifying microbial taxa or genes involved in pathogeneses, the mechanisms linking them to disease have thus far remained elusive.

Results

To identify potential pathways through which human-associated bacteria impact host health, we leverage publicly-available interspecies protein-protein interaction (PPI) data to find clusters of microbiome-derived proteins with high sequence identity to known human-protein interactors. We observe differential targeting of putative human-interacting bacterial genes in nine independent metagenomic studies, finding evidence that the microbiome broadly targets human proteins involved in immune, oncogenic, apoptotic, and endocrine signaling pathways in relation to IBD, CRC, obesity, and T2D diagnoses.

Conclusions

This host-centric analysis provides a mechanistic hypothesis-generating platform and extensively adds human functional annotation to commensal bacterial proteins.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-022-02643-9.

Annotation of Potential Vaccine Targets and Design of a Multi-Epitope Subunit Vaccine against Yersinia pestis through Reverse Vaccinology and Validation through an Agent-Based Modeling Approach

Yersinia pestis is responsible for plague and major pandemics in Asia and Europe. This bacterium has shown resistance to an array of drugs commonly used for the treatment of plague. Therefore, effective therapeutics measurements, such as designing a vaccine that can effectively and safely prevent Y. pestis infection, are of high interest. To fast-track vaccine development against Yersinia pestis, herein, proteome-wide vaccine target annotation was performed, and structural vaccinology-assisted epitopes were predicted. Among the total 3909 proteins, only 5 (rstB, YPO2385, hmuR, flaA1a, and psaB) were shortlisted as essential vaccine targets. These targets were then subjected to multi-epitope vaccine design using different linkers. EAAK, AAY, and GPGPG as linkers were used to link CTL, HTL, and B-cell epitopes, and an adjuvant (beta defensin) was also added at the N-terminal of the MEVC. Physiochemical characterization, such as determination of the instability index, theoretical pI, half-life, aliphatic index, stability profiling, antigenicity, allergenicity, and hydropathy of the ensemble, showed that the vaccine is highly stable, antigenic, and non-allergenic and produces multiple interactions with immune receptors upon docking. In addition, molecular dynamics simulation confirmed the stable binding and good dynamic properties of the vaccine-TLR complex. Furthermore, in silico and immune simulation of the developed MEVC for Y. pestis showed that the vaccine triggered strong immune response after several doses at different intervals. Neutralization of the antigen was observed at the third day of injection. Conclusively, the vaccine designed here for Y. pestis produces an immune response; however, further immunological testing is needed to unveil its real efficacy.

Artificial intelligence reveals roles of gut microbiota in driving human colorectal cancer evolution

With the rapid development of high-throughput sequencing and artificial intelligence (AI) techniques, gut mucosal microbiota begins to be recognized as critical drivers of human colorectal cancer (CRC). Various AI approaches have been designed to obtain effective information from enormous numbers of microbial cells residing in gut mucosal as well as cancer cells. These mainly include detection of microbial markers for early clinical diagnosis of stage-specific CRC, characterization of pathogenic bacterial activities via genomic and transcriptomic analyses, and prediction of interplay between bacterial drivers and host immune systems. Here I review the current progresses of AI applications in profiling gut microbiomes linked to CRC initiation and development. I further look forward to future AI research for improving our understanding of the roles of gut microbiota in CRC evolution.

An Ebola, Neisseria and Trypanosoma human protein interaction census reveals a conserved human protein cluster targeted by various human pathogens

Filovirus ebolavirus (ZE; Zaire ebolavirus, Bundibugyo ebolavirus), Neisseria meningitidis (NM), and Trypanosoma brucei (Tb) are serious infectious pathogens, spanning viruses, bacteria and protists and all may target the blood and central nervous system during their life cycle. NM and Tb are extracellular pathogens while ZE is obligatory intracellular, targetting immune privileged sites. By using interactomics and comparative evolutionary analysis we studied whether conserved human proteins are targeted by these pathogens. We examined 2797 unique pathogen-targeted human proteins. The information derived from orthology searches of experimentally validated protein-protein interactions (PPIs) resulted both in unique and shared PPIs for each pathogen. Comparing and analyzing conserved and pathogen-specific infection pathways for NM, TB and ZE, we identified human proteins predicted to be targeted in at least two of the compared host-pathogen networks. However, four proteins were common to all three host-pathogen interactomes: the elongation factor 1-alpha 1 (EEF1A1), the SWI/SNF complex subunit SMARCC2 (matrix-associated actin-dependent regulator of chromatin subfamily C), the dolichyl-diphosphooligosaccharide--protein glycosyltransferase subunit 1 (RPN1), and the tubulin beta-5 chain (TUBB). These four human proteins all are also involved in cytoskeleton and its regulation and are often addressed by various human pathogens. Specifically, we found (i) 56 human pathogenic bacteria and viruses that target these four proteins, (ii) the well researched new pandemic pathogen SARS-CoV-2 targets two of these four human proteins and (iii) nine human pathogenic fungi (yet another evolutionary distant organism group) target three of the conserved proteins by 130 high confidence interactions.

Prediction of human-Streptococcus pneumoniae protein-protein interactions using logistic regression

Streptococcus pneumoniae is a major cause of mortality in children under five years old. In recent years, the emergence of antibiotic-resistant strains of S. pneumoniae increases the threat level of this pathogen. For that reason, the exploration of S. pneumoniae protein virulence factors should be considered in developing new drugs or vaccines, for instance by the analysis of host-pathogen protein-protein interactions (HP-PPIs). In this research, prediction of protein-protein interactions was performed with a logistic regression model with the number of protein domain occurrences as features. By utilizing HP-PPIs of three different pathogens as training data, the model achieved 57-77 % precision, 64-75 % recall, and 96-98 % specificity. Prediction of human-S. pneumoniae protein-protein interactions using the model yielded 5823 interactions involving thirty S. pneumoniae proteins and 324 human proteins. Pathway enrichment analysis showed that most of the pathways involved in the predicted interactions are immune system pathways. Network topology analysis revealed β-galactosidase (BgaA) as the most central among the S. pneumoniae proteins in the predicted HP-PPI networks, with a degree centrality of 1.0 and a betweenness centrality of 0.451853. Further experimental studies are required to validate the predicted interactions and examine their roles in S. pneumoniae infection.

New Genotype of Yersinia pestis Found in Live Rodents in Yunnan Province, China

Yunnan Province, China is thought to be the original source of biovar Orientalis of Yersinia pestis, the causative agent of the third plague pandemic that has spread globally since the end of the 19th century. Although encompassing a large area of natural plague foci, Y. pestis strains have rarely been found in live rodents during surveillance in Yunnan, and most isolates are from rodent corpses and their fleas. In 2017, 10 Y. pestis strains were isolated from seven live rodents and three fleas in Heqing County of Yunnan. These strains were supposed to have low virulence to local rodents Eothenomys miletus and Apodemus chevrieri because the rodents were healthy and no dead animals were found in surrounding areas, as had occurred in previous epizootic disease. We performed microscopic and biochemical examinations of the isolates, and compared their whole-genome sequences and transcriptome with those of 10 high virulence Y. pestis strains that were isolated from nine rodents and one parasitic flea in adjacent city (Lijiang). We analyzed the phenotypic, genomic, and transcriptomic characteristics of live rodent isolates. The isolates formed a previously undefined monophyletic branch of Y. pestis that was named 1.IN5. Six SNPs, two indels, and one copy number variation were detected between live rodent isolates and the high virulence neighbors. No obvious functional consequence of these variations was found according to the known annotation information. Among genes which expression differential in the live rodent isolates compared to their high virulent neighbors, we found five iron transfer related ones that were significant up-regulated (| log₂ (FC) | > 1, p.adjust < 0.05), indicating these genes may be related to the low-virulence phenotype. The novel genotype of Y. pestis reported here provides further insights into the evolution and spread of plague as well as clues that may help to decipher the virulence mechanism of this notorious pathogen.

Elucidating the network features and evolutionary attributes of intra- and interspecific protein-protein interactions between human and pathogenic bacteria

Host–pathogen interaction is one of the most powerful determinants involved in coevolutionary processes covering a broad range of biological phenomena at molecular, cellular, organismal and/or population level. The present study explored host–pathogen interaction from the perspective of human–bacteria protein–protein interaction based on large-scale interspecific and intraspecific interactome data for human and three pathogenic bacterial species, Bacillus anthracis, Francisella tularensis and Yersinia pestis. The network features revealed a preferential enrichment of intraspecific hubs and bottlenecks for both human and bacterial pathogens in the interspecific human–bacteria interaction. Analyses unveiled that these bacterial pathogens interact mostly with human party-hubs that may enable them to affect desired functional modules, leading to pathogenesis. Structural features of pathogen-interacting human proteins indicated an abundance of protein domains, providing opportunities for interspecific domain-domain interactions. Moreover, these interactions do not always occur with high-affinity, as we observed that bacteria-interacting human proteins are rich in protein-disorder content, which correlates positively with the number of interacting pathogen proteins, facilitating low-affinity interspecific interactions. Furthermore, functional analyses of pathogen-interacting human proteins revealed an enrichment in regulation of processes like metabolism, immune system, cellular localization and transport apart from divulging functional competence to bind enzyme/protein, nucleic acids and cell adhesion molecules, necessary for host-microbial cross-talk.

Bacteria use structural imperfect mimicry to hijack the host interactome

Bacteria use protein-protein interactions to infect their hosts and hijack fundamental pathways, which ensures their survival and proliferation. Hence, the infectious capacity of the pathogen is closely related to its ability to interact with host proteins. Here, we show that hubs in the host-pathogen interactome are isolated in the pathogen network by adapting the geometry of the interacting interfaces. An imperfect mimicry of the eukaryotic interfaces allows pathogen proteins to actively bind to the host's target while preventing deleterious effects on the pathogen interactome. Understanding how bacteria recognize eukaryotic proteins may pave the way for the rational design of new antibiotic molecules.

A simple and rapid pipeline for identification of receptor-binding sites on the surface proteins of pathogens

Ligand-receptor interactions play a crucial role in the plethora of biological processes. Several methods have been established to reveal ligand-receptor interface, however, the majority of methods are time-consuming, laborious and expensive. Here we present a straightforward and simple pipeline to identify putative receptor-binding sites on the pathogen ligands. Two model ligands (bait proteins), domain III of protein E of West Nile virus and NadA of Neisseria meningitidis, were incubated with the proteins of human brain microvascular endothelial cells immobilized on nitrocellulose or PVDF membrane, the complex was trypsinized on-membrane, bound peptides of the bait proteins were recovered and detected on MALDI-TOF. Two peptides of DIII (~916 Da and ~2003 Da) and four peptides of NadA (~1453 Da, ~1810 Da, ~2051 Da and ~2433 Da) were identified as plausible receptor-binders. Further, binding of the identified peptides to the proteins of endothelial cells was corroborated using biotinylated synthetic analogues in ELISA and immunocytochemistry. Experimental pipeline presented here can be upscaled easily to map receptor-binding sites on several ligands simultaneously. The approach is rapid, cost-effective and less laborious. The proposed experimental pipeline could be a simpler alternative or complementary method to the existing techniques used to reveal amino-acids involved in the ligand-receptor interface.

Combating biothreat pathogens: ongoing efforts for countermeasure development and unique challenges

Research to discover and develop antibacterial and antiviral drugs with potent activity against pathogens of biothreat concern presents unique methodological and process-driven challenges. Herein, we review laboratory approaches for finding new antibodies, antibiotics, and antiviral molecules for pathogens of biothreat concern. Using high-throughput screening techniques, molecules that directly inhibit a pathogen’s entry, replication, or growth can be identified. Alternatively, molecules that target host proteins can be interesting targets for development when countering biothreat pathogens, due to the modulation of the host immune response or targeting proteins that interfere with the pathways required by the pathogen for replication. Monoclonal and cocktail antibody therapies approved by the Food and Drug Administration for countering anthrax and under development for treatment of Ebola virus infection are discussed. A comprehensive tabular review of current in vitro, in vivo, pharmacokinetic and efficacy datasets has been presented for biothreat pathogens of greatest concern. Finally, clinical trials and animal rule or traditional drug approval pathways are also reviewed.

Opinions; interpretations; conclusions; and recommendations are those of the authors and are not necessarily endorsed by the US Army.

Oncoviruses Can Drive Cancer by Rewiring Signaling Pathways Through Interface Mimicry

Oncoviruses rewire host pathways to subvert host immunity and promote their survival and proliferation. However, exactly how is challenging to understand. Here, by employing the first and to date only interface-based host-microbe interaction (HMI) prediction method, we explore a pivotal strategy oncoviruses use to drive cancer: mimicking binding surfaces-interfaces-of human proteins. We show that oncoviruses can target key human network proteins and transform cells by acquisition of cancer hallmarks. Experimental large-scale mapping of HMIs is difficult and individual HMIs do not permit in-depth grasp of tumorigenic virulence mechanisms. Our computational approach is tractable and 3D structural HMI models can help elucidate pathogenesis mechanisms and facilitate drug design. We observe that many host proteins are unique targets for certain oncoviruses, whereas others are common to several, suggesting similar infectious strategies. A rough estimation of our false discovery rate based on the tissue expression of oncovirus-targeted human proteins is 25%.

Host-directed therapies to combat tuberculosis and associated non-communicable diseases

Mycobacterium tuberculosis (Mtb) has coevolved with a human host to evade and exploit the immune system in multiple ways. Mtb is an enormously successful human pathogen that can remain undetected in hosts for decades without causing clinical disease. While tuberculosis (TB) represents a perfect prototype of host-pathogen interaction, it remains a major challenge to develop new therapies to combat mycobacterial infections. Additionally, recent studies emphasize on comorbidity of TB with different non-communicable diseases (NCDs), highlighting the impact of demographic and lifestyle changes on the global burden of TB. In the recent past, host-directed therapies have emerged as a novel and promising approach to treating TB. Drugs modulating host responses are likely to avoid the development of bacterial resistance which is a major public health concern for TB treatment. Interestingly, many of these drugs also form treatment strategies for non-communicable diseases. In general, technological advances along with novel host-directed therapies may open an exciting and promising research area, which can eventually deliver effective TB treatment as well as curtail the emergent synergy with NCDs.

Interface-Based Structural Prediction of Novel Host-Pathogen Interactions

About 20% of the cancer incidences worldwide have been estimated to be associated with infections. However, the molecular mechanisms of exactly how they contribute to host tumorigenesis are still unknown. To evade host defense, pathogens hijack host proteins at different levels: sequence, structure, motif, and binding surface, i.e., interface. Interface similarity allows pathogen proteins to compete with host counterparts to bind to a target protein, rewire physiological signaling, and result in persistent infections, as well as cancer. Identification of host-pathogen interactions (HPIs)-along with their structural details at atomic resolution-may provide mechanistic insight into pathogen-driven cancers and innovate therapeutic intervention. HPI data including structural details is scarce and large-scale experimental detection is challenging. Therefore, there is an urgent and mounting need for efficient and robust computational approaches to predict HPIs and their complex (bound) structures. In this chapter, we review the first and currently only interface-based computational approach to identify novel HPIs. The concept of interface mimicry promises to identify more HPIs than complete sequence or structural similarity. We illustrate this concept with a case study on Kaposi's sarcoma herpesvirus (KSHV) to elucidate how it subverts host immunity and helps contribute to malignant transformation of the host cells.

Identification of Antifungal Targets Based on Computer Modeling

Aspergillus fumigatus is a saprophytic, cosmopolitan fungus that attacks patients with a weak immune system. A rational solution against fungal infection aims to manipulate fungal metabolism or to block enzymes essential for Aspergillus survival. Here we discuss and compare different bioinformatics approaches to analyze possible targeting strategies on fungal-unique pathways. For instance, phylogenetic analysis reveals fungal targets, while domain analysis allows us to spot minor differences in protein composition between the host and fungi. Moreover, protein networks between host and fungi can be systematically compared by looking at orthologs and exploiting information from host⁻pathogen interaction databases. Further data&mdash;such as knowledge of a three-dimensional structure, gene expression data, or information from calculated metabolic fluxes&mdash;refine the search and rapidly put a focus on the best targets for antimycotics. We analyzed several of the best targets for application to structure-based drug design. Finally, we discuss general advantages and limitations in identification of unique fungal pathways and protein targets when applying bioinformatics tools.

Network biology discovers pathogen contact points in host protein-protein interactomes

In all organisms, major biological processes are controlled by complex protein-protein interactions networks (interactomes), yet their structural complexity presents major analytical challenges. Here, we integrate a compendium of over 4300 phenotypes with Arabidopsis interactome (AI-1MAIN). We show that nodes with high connectivity and betweenness are enriched and depleted in conditional and essential phenotypes, respectively. Such nodes are located in the innermost layers of AI-1MAIN and are preferential targets of pathogen effectors. We extend these network-centric analyses to Cell Surface Interactome (CSILRR) and predict its 35 most influential nodes. To determine their biological relevance, we show that these proteins physically interact with pathogen effectors and modulate plant immunity. Overall, our findings contrast with centrality-lethality rule, discover fast information spreading nodes, and highlight the structural properties of pathogen targets in two different interactomes. Finally, this theoretical framework could possibly be applicable to other inter-species interactomes to reveal pathogen contact points.

Mitigating the Impact of Antibacterial Drug Resistance through Host-Directed Therapies: Current Progress, Outlook, and Challenges

Increasing incidences of multidrug resistance in pathogenic bacteria threaten our ability to treat and manage bacterial infection. The development and FDA approval of novel antibiotics have slowed over the past decade; therefore, the adoption and improvement of alternative therapeutic strategies are critical for addressing the threat posed by multidrug-resistant bacteria. Host-directed therapies utilize small-molecule drugs and proteins to alter the host response to pathogen infection. Here, we highlight strategies for modulating the host inflammatory response to enhance bacterial clearance, small-molecule potentiation of innate immunity, and targeting of host factors that are exploited by pathogen virulence factors. Application of state-of-the-art "omic" technologies, including proteomics, transcriptomics, and image-omics (image-based high-throughput phenotypic screening), combined with powerful bioinformatics tools will enable the modeling of key signaling pathways in the host-pathogen interplay and aid in the identification of host proteins for therapeutic targeting and the discovery of host-directed small molecules that will regulate bacterial infection. We conclude with an outlook on research needed to overcome the challenges associated with transitioning host-directed therapies into a clinical setting.

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.

Prediction of Host-Pathogen Interactions for Helicobacter pylori by Interface Mimicry and Implications to Gastric Cancer

There is a strong correlation between some pathogens and certain cancer types. One example is Helicobacter pylori and gastric cancer. Exactly how they contribute to host tumorigenesis is, however, a mystery. Pathogens often interact with the host through proteins. To subvert defense, they may mimic host proteins at the sequence, structure, motif, or interface levels. Interface similarity permits pathogen proteins to compete with those of the host for a target protein and thereby alter the host signaling. Detection of host-pathogen interactions (HPIs) and mapping the re-wired superorganism HPI network-with structural details-can provide unprecedented clues to the underlying mechanisms and help therapeutics. Here, we describe the first computational approach exploiting solely interface mimicry to model potential HPIs. Interface mimicry can identify more HPIs than sequence or complete structural similarity since it appears more common than the other mimicry types. We illustrate the usefulness of this concept by modeling HPIs of H. pylori to understand how they modulate host immunity, persist lifelong, and contribute to tumorigenesis. H. pylori proteins interfere with multiple host pathways as they target several host hub proteins. Our results help illuminate the structural basis of resistance to apoptosis, immune evasion, and loss of cell junctions seen in H. pylori-infected host cells.

Structural host-microbiota interaction networks

Hundreds of different species colonize multicellular organisms making them "metaorganisms". A growing body of data supports the role of microbiota in health and in disease. Grasping the principles of host-microbiota interactions (HMIs) at the molecular level is important since it may provide insights into the mechanisms of infections. The crosstalk between the host and the microbiota may help resolve puzzling questions such as how a microorganism can contribute to both health and disease. Integrated superorganism networks that consider host and microbiota as a whole-may uncover their code, clarifying perhaps the most fundamental question: how they modulate immune surveillance. Within this framework, structural HMI networks can uniquely identify potential microbial effectors that target distinct host nodes or interfere with endogenous host interactions, as well as how mutations on either host or microbial proteins affect the interaction. Furthermore, structural HMIs can help identify master host cell regulator nodes and modules whose tweaking by the microbes promote aberrant activity. Collectively, these data can delineate pathogenic mechanisms and thereby help maximize beneficial therapeutics. To date, challenges in experimental techniques limit large-scale characterization of HMIs. Here we highlight an area in its infancy which we believe will increasingly engage the computational community: predicting interactions across kingdoms, and mapping these on the host cellular networks to figure out how commensal and pathogenic microbiota modulate the host signaling and broadly cross-species consequences.

Elucidation of host-pathogen protein-protein interactions to uncover mechanisms of host cell rewiring

Infectious diseases are the result of molecular cross-talks between hosts and their pathogens. These cross-talks are in part mediated by host-pathogen protein-protein interactions (HP-PPI). HP-PPI play crucial roles in infections, as they may tilt the balance either in favor of the pathogens' spread or their clearance. The identification of host proteins targeted by viral or bacterial pathogenic proteins necessary for the infection can provide insights into their underlying molecular mechanisms of pathogenicity, and potentially even single out pharmacological intervention targets. Here, we review the available methods to study HP-PPI, with a focus on recent mass spectrometry based methods to decipher bacterial-human infectious diseases and examine their relevance in uncovering host cell rewiring by pathogens.

Yersinia pestis YopK Inhibits Bacterial Adhesion to Host Cells by Binding to the Extracellular Matrix Adaptor Protein Matrilin-2

Pathogenic yersiniae harbor a type III secretion system (T3SS) that injects Yersinia outer protein (Yop) into host cells. YopK has been shown to control Yop translocation and prevent inflammasome recognition of the T3SS by the innate immune system. Here, we demonstrate that YopK inhibits bacterial adherence to host cells by binding to the extracellular matrix adaptor protein matrilin-2 (MATN2). YopK binds to MATN2, and deleting amino acids 91 to 124 disrupts binding of YopK to MATN2. A yopK null mutant exhibits a hyperadhesive phenotype, which could be responsible for the established Yop hypertranslocation phenotype of yopK mutants. Expression of YopK, but not YopK_Δ91-124, in a yopK mutant restored the wild-type phenotypes of adhesion and Yop translocation, suggesting that binding to MATN2 might be essential for YopK to inhibit bacterial adhesion and negatively regulate Yop translocation. A green fluorescent protein (GFP)-YopK fusion specifically binds to the endogenous MATN2 on the surface of HeLa cells, whereas GFP-YopK_Δ91-124 cannot. Addition of purified YopK protein during infection decreased adhesion of Y. pestis to HeLa cells, while YopK_Δ91-124 protein showed no effect. Taking these results together, we propose a model that the T3SS-secreted YopK hinders bacterial adhesion to HeLa cells by binding to MATN2, which is ubiquitously exposed on eukaryotic cells.

Immunomodulatory Yersinia outer proteins (Yops)-useful tools for bacteria and humans alike

Human-pathogenic Yersinia produce plasmid-encoded Yersinia outer proteins (Yops), which are necessary to down-regulate anti-bacterial responses that constrict bacterial survival in the host. These Yops are effectively translocated directly from the bacterial into the target cell cytosol by the type III secretion system (T3SS). Cell-penetrating peptides (CPPs) in contrast are characterized by their ability to autonomously cross cell membranes and to transport cargo - independent of additional translocation systems. The recent discovery of bacterial cell-penetrating effector proteins (CPEs) - with the prototype being the T3SS effector protein YopM - established a new class of autonomously translocating immunomodulatory proteins. CPEs represent a vast source of potential self-delivering, anti-inflammatory therapeutics. In this review, we give an update on the characteristic features of the plasmid-encoded Yops and, based on recent findings, propose the further development of these proteins for potential therapeutic applications as natural or artificial cell-penetrating forms of Yops might be of value as bacteria-derived biologics.

Centrality in the host-pathogen interactome is associated with pathogen fitness during infection

To perform their functions proteins must interact with each other, but how these interactions influence bacterial infection remains elusive. Here we demonstrate that connectivity in the host-pathogen interactome is directly related to pathogen fitness during infection. Using Y. pestis as a model organism, we show that the centrality-lethality rule holds for pathogen fitness during infection but only when the host-pathogen interactome is considered. Our results suggest that the importance of pathogen proteins during infection is directly related to their number of interactions with the host. We also show that pathogen proteins causing an extensive rewiring of the host interactome have a higher impact in pathogen fitness during infection. Hence, we conclude that hubs in the host-pathogen interactome should be explored as promising targets for antimicrobial drug design.

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.

Yersinia pestis in the Age of Big Data

As omics-driven technologies developed rapidly, genomics, transcriptomics, proteomics, metabolomics and other omics-based data have been accumulated in unprecedented speed. Omics-driven big data in biology have changed our way of research. "Big science" has promoted our understanding of biology in a holistic overview that is impossibly achieved by traditional hypothesis-driven research. In this chapter, we gave an overview of omics-driven research on Y. pestis, provided a way of thinking on Yersinia pestis research in the age of big data, and made some suggestions to integrate omics-based data for systems understanding of Y. pestis.

Using host-pathogen protein interactions to identify and characterize Francisella tularensis virulence factors

Background

Francisella tularensis is a select bio-threat agent and one of the most virulent intracellular pathogens known, requiring just a few organisms to establish an infection. Although several virulence factors are known, we lack an understanding of virulence factors that act through host-pathogen protein interactions to promote infection. To address these issues in the highly infectious F. tularensis subsp. tularensis Schu S4 strain, we deployed a combined in silico, in vitro, and in vivo analysis to identify virulence factors and their interactions with host proteins to characterize bacterial infection mechanisms.

Results

We initially used comparative genomics and literature to identify and select a set of 49 putative and known virulence factors for analysis. Each protein was then subjected to proteome-scale yeast two-hybrid (Y2H) screens with human and murine cDNA libraries to identify potential host-pathogen protein-protein interactions. Based on the bacterial protein interaction profile with both hosts, we selected seven novel putative virulence factors for mutant construction and animal validation experiments. We were able to create five transposon insertion mutants and used them in an intranasal BALB/c mouse challenge model to establish 50 % lethal dose estimates. Three of these, ΔFTT0482c, ΔFTT1538c, and ΔFTT1597, showed attenuation in lethality and can thus be considered novel F. tularensis virulence factors. The analysis of the accompanying Y2H data identified intracellular protein trafficking between the early endosome to the late endosome as an important component in virulence attenuation for these virulence factors. Furthermore, we also used the Y2H data to investigate host protein binding of two known virulence factors, showing that direct protein binding was a component in the modulation of the inflammatory response via activation of mitogen-activated protein kinases and in the oxidative stress response.

Conclusions

Direct interactions with specific host proteins and the ability to influence interactions among host proteins are important components for F. tularensis to avoid host-cell defense mechanisms and successfully establish an infection. Although direct host-pathogen protein-protein binding is only one aspect of Francisella virulence, it is a critical component in directly manipulating and interfering with cellular processes in the host cell.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-2351-1) contains supplementary material, which is available to authorized users.

Computational analysis of protein interaction networks for infectious diseases

Infectious diseases caused by pathogens, including viruses, bacteria and parasites, pose a serious threat to human health worldwide. Frequent changes in the pattern of infection mechanisms and the emergence of multidrug-resistant strains among pathogens have weakened the current treatment regimen. This necessitates the development of new therapeutic interventions to prevent and control such diseases. To cater to the need, analysis of protein interaction networks (PINs) has gained importance as one of the promising strategies. The present review aims to discuss various computational approaches to analyse the PINs in context to infectious diseases. Topology and modularity analysis of the network with their biological relevance, and the scenario till date about host–pathogen and intra-pathogenic protein interaction studies were delineated. This would provide useful insights to the research community, thereby enabling them to design novel biomedicine against such infectious diseases.

Improving the Understanding of Pathogenesis of Human Papillomavirus 16 via Mapping Protein-Protein Interaction Network

The human papillomavirus 16 (HPV16) has high risk to lead various cancers and afflictions, especially, the cervical cancer. Therefore, investigating the pathogenesis of HPV16 is very important for public health. Protein-protein interaction (PPI) network between HPV16 and human was used as a measure to improve our understanding of its pathogenesis. By adopting sequence and topological features, a support vector machine (SVM) model was built to predict new interactions between HPV16 and human proteins. All interactions were comprehensively investigated and analyzed. The analysis indicated that HPV16 enlarged its scope of influence by interacting with human proteins as much as possible. These interactions alter a broad array of cell cycle progression. Furthermore, not only was HPV16 highly prone to interact with hub proteins and bottleneck proteins, but also it could effectively affect a breadth of signaling pathways. In addition, we found that the HPV16 evolved into high carcinogenicity on the condition that its own reproduction had been ensured. Meanwhile, this work will contribute to providing potential new targets for antiviral therapeutics and help experimental research in the future.

A review on computational systems biology of pathogen-host interactions

Pathogens manipulate the cellular mechanisms of host organisms via pathogen-host interactions (PHIs) in order to take advantage of the capabilities of host cells, leading to infections. The crucial role of these interspecies molecular interactions in initiating and sustaining infections necessitates a thorough understanding of the corresponding mechanisms. Unlike the traditional approach of considering the host or pathogen separately, a systems-level approach, considering the PHI system as a whole is indispensable to elucidate the mechanisms of infection. Following the technological advances in the post-genomic era, PHI data have been produced in large-scale within the last decade. Systems biology-based methods for the inference and analysis of PHI regulatory, metabolic, and protein-protein networks to shed light on infection mechanisms are gaining increasing demand thanks to the availability of omics data. The knowledge derived from the PHIs may largely contribute to the identification of new and more efficient therapeutics to prevent or cure infections. There are recent efforts for the detailed documentation of these experimentally verified PHI data through Web-based databases. Despite these advances in data archiving, there are still large amounts of PHI data in the biomedical literature yet to be discovered, and novel text mining methods are in development to unearth such hidden data. Here, we review a collection of recent studies on computational systems biology of PHIs with a special focus on the methods for the inference and analysis of PHI networks, covering also the Web-based databases and text-mining efforts to unravel the data hidden in the literature.

Mining host-pathogen protein interactions to characterize Burkholderia mallei infectivity mechanisms

Burkholderia pathogenicity relies on protein virulence factors to control and promote bacterial internalization, survival, and replication within eukaryotic host cells. We recently used yeast two-hybrid (Y2H) screening to identify a small set of novel Burkholderia proteins that were shown to attenuate disease progression in an aerosol infection animal model using the virulent Burkholderia mallei ATCC 23344 strain. Here, we performed an extended analysis of primarily nine B. mallei virulence factors and their interactions with human proteins to map out how the bacteria can influence and alter host processes and pathways. Specifically, we employed topological analyses to assess the connectivity patterns of targeted host proteins, identify modules of pathogen-interacting host proteins linked to processes promoting infectivity, and evaluate the effect of crosstalk among the identified host protein modules. Overall, our analysis showed that the targeted host proteins generally had a large number of interacting partners and interacted with other host proteins that were also targeted by B. mallei proteins. We also introduced a novel Host-Pathogen Interaction Alignment (HPIA) algorithm and used it to explore similarities between host-pathogen interactions of B. mallei, Yersinia pestis, and Salmonella enterica. We inferred putative roles of B. mallei proteins based on the roles of their aligned Y. pestis and S. enterica partners and showed that up to 73% of the predicted roles matched existing annotations. A key insight into Burkholderia pathogenicity derived from these analyses of Y2H host-pathogen interactions is the identification of eukaryotic-specific targeted cellular mechanisms, including the ubiquitination degradation system and the use of the focal adhesion pathway as a fulcrum for transmitting mechanical forces and regulatory signals. This provides the mechanisms to modulate and adapt the host-cell environment for the successful establishment of host infections and intracellular spread.

Burkholderia species need to manipulate many host processes and pathways in order to establish a successful intracellular infection in eukaryotic host organisms. Burkholderia mallei uses secreted virulence factor proteins as a means to execute host-pathogen interactions and promote pathogenesis. While validated virulence factor proteins have been shown to attenuate infection in animal models, their actual roles in modifying and influencing host processes are not well understood. Here, we used host-pathogen protein-protein interactions derived from yeast two-hybrid screens to study nine known B. mallei virulence factors and map out potential virulence mechanisms. From the data, we derived both general and specific insights into Burkholderia host-pathogen infectivity pathways. We showed that B. mallei virulence factors tended to target multifunctional host proteins, proteins that interacted with each other, and host proteins with a large number of interacting partners. We also identified similarities between host-pathogen interactions of B. mallei, Yersinia pestis, and Salmonella enterica using a novel host-pathogen interactions alignment algorithm. Importantly, our data are compatible with a framework in which multiple B. mallei virulence factors broadly influence key host processes related to ubiquitin-mediated proteolysis and focal adhesion. This provides B. mallei the means to modulate and adapt the host-cell environment to advance infection.

Yersinia protein kinase A phosphorylates vasodilator-stimulated phosphoprotein to modify the host cytoskeleton

Pathogenic Yersinia species evolved a type III secretion system that injects a set of effectors into the host cell cytosol to promote infection. One of these effectors, Yersinia protein kinase A (YpkA), is a multidomain effector that harbours a Ser/Thr kinase domain and a guanine dissociation inhibitor (GDI) domain. The intercellular targets of the kinase and GDI domains of YpkA were identified to be Gαq and the small GTPases RhoA and Rac1, respectively, which synergistically induce cytotoxic effects on infected cells. In this study, we demonstrate that vasodilator-stimulated phosphoprotein (VASP), which is critical for regulation of actin assembly, cell adhesion and motility, is a direct substrate of YpkA kinase activity. Ectopic co-expression of YpkA and VASP in HEK293T cells leads to the phosphorylation of VASP at S157, and YpkA kinase activity is essential for VASP phosphorylation at this site. Moreover, YpkA directly phosphorylates VASP in in vitro kinase assay. YpkA-mediated VASP phosphorylation significantly inhibits actin polymerization and promotes the disruption of actin cytoskeleton, which inhibits the phagocytosis. Taken together, our study found a novel molecular mechanism used by YpkA to disrupt cytoskeleton dynamics, thereby promoting the anti-phagocytosis ability of pathogenic Yersiniae.

Novel Burkholderia mallei virulence factors linked to specific host-pathogen protein interactions

Burkholderia mallei is an infectious intracellular pathogen whose virulence and resistance to antibiotics makes it a potential bioterrorism agent. Given its genetic origin as a commensal soil organism, it is equipped with an extensive and varied set of adapted mechanisms to cope with and modulate host-cell environments. One essential virulence mechanism constitutes the specialized secretion systems that are designed to penetrate host-cell membranes and insert pathogen proteins directly into the host cell's cytosol. However, the secretion systems' proteins and, in particular, their host targets are largely uncharacterized. Here, we used a combined in silico, in vitro, and in vivo approach to identify B. mallei proteins required for pathogenicity. We used bioinformatics tools, including orthology detection and ab initio predictions of secretion system proteins, as well as published experimental Burkholderia data to initially select a small number of proteins as putative virulence factors. We then used yeast two-hybrid assays against normalized whole human and whole murine proteome libraries to detect and identify interactions among each of these bacterial proteins and host proteins. Analysis of such interactions provided both verification of known virulence factors and identification of three new putative virulence proteins. We successfully created insertion mutants for each of these three proteins using the virulent B. mallei ATCC 23344 strain. We exposed BALB/c mice to mutant strains and the wild-type strain in an aerosol challenge model using lethal B. mallei doses. In each set of experiments, mice exposed to mutant strains survived for the 21-day duration of the experiment, whereas mice exposed to the wild-type strain rapidly died. Given their in vivo role in pathogenicity, and based on the yeast two-hybrid interaction data, these results point to the importance of these pathogen proteins in modulating host ubiquitination pathways, phagosomal escape, and actin-cytoskeleton rearrangement processes.

Francisella tularensis subsp. tularensis induces a unique pulmonary inflammatory response: role of bacterial gene expression in temporal regulation of host defense responses

Pulmonary exposure to Francisella tularensis is associated with severe lung pathology and a high mortality rate. The lack of induction of classical inflammatory mediators, including IL1-β and TNF-α, during early infection has led to the suggestion that F. tularensis evades detection by host innate immune surveillance and/or actively suppresses inflammation. To gain more insight into the host response to Francisella infection during the acute stage, transcriptomic analysis was performed on lung tissue from mice exposed to virulent (Francisella tularensis ssp tularensis SchuS4). Despite an extensive transcriptional response in the lungs of animals as early as 4 hrs post-exposure, Francisella tularensis was associated with an almost complete lack of induction of immune-related genes during the initial 24 hrs post-exposure. This broad subversion of innate immune responses was particularly evident when compared to the pulmonary inflammatory response induced by other lethal (Yersinia pestis) and non-lethal (Legionella pneumophila, Pseudomonas aeruginosa) pulmonary infections. However, the unique induction of a subset of inflammation-related genes suggests a role for dysregulation of lymphocyte function and anti-inflammatory pathways in the extreme virulence of Francisella. Subsequent activation of a classical inflammatory response 48 hrs post-exposure was associated with altered abundance of Francisella-specific transcripts, including those associated with bacterial surface components. In summary, virulent Francisella induces a unique pulmonary inflammatory response characterized by temporal regulation of innate immune pathways correlating with altered bacterial gene expression patterns. This study represents the first simultaneous measurement of both host and Francisella transcriptome changes that occur during in vivo infection and identifies potential bacterial virulence factors responsible for regulation of host inflammatory pathways.

Do pathogens reduce genetic diversity of their hosts? Variable effects of sylvatic plague in black-tailed prairie dogs

Introduced diseases can cause dramatic declines in-and even the loss of-natural populations. Extirpations may be followed by low recolonization rates, leading to inbreeding and a loss of genetic variation, with consequences on population viability. Conversely, extirpations may create vacant habitat patches that individuals from multiple source populations can colonize, potentially leading to an influx of variation. We tested these alternative hypotheses by sampling 15 colonies in a prairie dog metapopulation during 7 years that encompassed an outbreak of sylvatic plague, providing the opportunity to monitor genetic diversity before, during and after the outbreak. Analysis of nine microsatellite loci revealed that within the metapopulation, there was no change in diversity. However, within extirpated colonies, patterns varied: In half of the colonies, allelic richness after recovery was less than the preplague conditions, and in the other half, richness was greater than the preplague conditions. Finally, analysis of variation within individuals revealed that prairie dogs present in recolonized colonies had higher heterozygosity than those present before plague. We confirmed plague survivorship in six founders; these individuals had significantly higher heterozygosity than expected by chance. Collectively, our results suggest that high immigration rates can maintain genetic variation at a regional scale despite simultaneous extirpations in spatially proximate populations. Thus, virulent diseases may increase genetic diversity of host populations by creating vacant habitats that allow an influx of genetic diversity. Furthermore, even highly virulent diseases may not eliminate individuals randomly; rather, they may selectively remove the most inbred individuals.

Systems biology of pathogen-host interaction: networks of protein-protein interaction within pathogens and pathogen-human interactions in the post-genomic era

Infectious diseases comprise some of the leading causes of death and disability worldwide. Interactions between pathogen and host proteins underlie the process of infection. Improved understanding of pathogen-host molecular interactions will increase our knowledge of the mechanisms involved in infection, and allow novel therapeutic solutions to be devised. Complete genome sequences for a number of pathogenic microorganisms, as well as the human host, has led to the revelation of their protein-protein interaction (PPI) networks. In this post-genomic era, pathogen-host interactions (PHIs) operating during infection can also be mapped. Detailed systematic analyses of PPI and PHI data together are required for a complete understanding of pathogenesis of infections. Here we review the striking results recently obtained during the construction and investigation of these networks. Emphasis is placed on studies producing large-scale interaction data by high-throughput experimental techniques.

Omics strategies for revealing Yersinia pestis virulence

Omics has remarkably changed the way we investigate and understand life. Omics differs from traditional hypothesis-driven research because it is a discovery-driven approach. Mass datasets produced from omics-based studies require experts from different fields to reveal the salient features behind these data. In this review, we summarize omics-driven studies to reveal the virulence features of Yersinia pestis through genomics, trascriptomics, proteomics, interactomics, etc. These studies serve as foundations for further hypothesis-driven research and help us gain insight into Y. pestis pathogenesis.

Interactome mapping for analysis of complex phenotypes: insights from benchmarking binary interaction assays

Protein interactions mediate essentially all biological processes and analysis of protein-protein interactions using both large-scale and small-scale approaches has contributed fundamental insights to the understanding of biological systems. In recent years, interactome network maps have emerged as an important tool for analyzing and interpreting genetic data of complex phenotypes. Complementary experimental approaches to test for binary, direct interactions, and for membership in protein complexes are used to explore the interactome. The two approaches are not redundant but yield orthogonal perspectives onto the complex network of physical interactions by which proteins mediate biological processes. In recent years, several publications have demonstrated that interactions from high-throughput experiments can be equally reliable as the high quality subset of interactions identified in small-scale studies. Critical for this insight was the introduction of standardized experimental benchmarking of interaction and validation assays using reference sets. The data obtained in these benchmarking experiments have resulted in greater appreciation of the limitations and the complementary strengths of different assays. Moreover, benchmarking is a central element of a conceptual framework to estimate interactome sizes and thereby measure progress toward near complete network maps. These estimates have revealed that current large-scale data sets, although often of high quality, cover only a small fraction of a given interactome. Here, I review the findings of assay benchmarking and discuss implications for quality control, and for strategies toward obtaining a near-complete map of the interactome of an organism.

Diversity in genetic in vivo methods for protein-protein interaction studies: from the yeast two-hybrid system to the mammalian split-luciferase system

The yeast two-hybrid system pioneered the field of in vivo protein-protein interaction methods and undisputedly gave rise to a palette of ingenious techniques that are constantly pushing further the limits of the original method. Sensitivity and selectivity have improved because of various technical tricks and experimental designs. Here we present an exhaustive overview of the genetic approaches available to study in vivo binary protein interactions, based on two-hybrid and protein fragment complementation assays. These methods have been engineered and employed successfully in microorganisms such as Saccharomyces cerevisiae and Escherichia coli, but also in higher eukaryotes. From single binary pairwise interactions to whole-genome interactome mapping, the self-reassembly concept has been employed widely. Innovative studies report the use of proteins such as ubiquitin, dihydrofolate reductase, and adenylate cyclase as reconstituted reporters. Protein fragment complementation assays have extended the possibilities in protein-protein interaction studies, with technologies that enable spatial and temporal analyses of protein complexes. In addition, one-hybrid and three-hybrid systems have broadened the types of interactions that can be studied and the findings that can be obtained. Applications of these technologies are discussed, together with the advantages and limitations of the available assays.

Host and virus protein interaction studies in understanding shrimp virus gene function

Protein-protein interaction studies have been widely used in several fields to characterize an unknown protein. This in turn helps to find out several pathways to understand a complex mechanism or discover a drug for treatment. Among the methods, yeast two-hybrid has widely been used in human, animal and plant research studies. This aspect of research has also been found useful in understanding the shrimp virus gene function. With respect to White spot syndrome virus, interaction studies have been applied to elucidate virus structure, understand the mode of entry of the virus, mechanism of virus replication and also to discover some of the host anti-viral proteins. Interaction studies on other shrimp viruses are scanty and only few reports available on Yellow head virus and Taura syndrome virus. All these findings are still in preliminary stage and lot more studies are necessary to have the clear picture. Protein interaction research on other shrimp viruses are still lacking. Considering all these, it appears that this field of research has a wide scope to understand the virulence mechanism of shrimp viruses where very little information is available till date.