The human-bacterial pathogen protein interaction networks of Bacillus anthracis, Francisella tularensis, and Yersinia pestis

Deep-HPI-pred: An R-Shiny applet for network-based classification and prediction of Host-Pathogen protein-protein interactions

Host-pathogen interactions (HPIs) are vital in numerous biological activities and are intrinsically linked to the onset and progression of infectious diseases. HPIs are pivotal in the entire lifecycle of diseases: from the onset of pathogen introduction, navigating through the mechanisms that bypass host cellular defenses, to its subsequent proliferation inside the host. At the heart of these stages lies the synergy of proteins from both the host and the pathogen. By understanding these interlinking protein dynamics, we can gain crucial insights into how diseases progress and pave the way for stronger plant defenses and the swift formulation of countermeasures. In the framework of current study, we developed a web-based R/Shiny app, Deep-HPI-pred, that uses network-driven feature learning method to predict the yet unmapped interactions between pathogen and host proteins. Leveraging citrus and CLas bacteria training datasets as case study, we spotlight the effectiveness of Deep-HPI-pred in discerning Protein-protein interaction (PPIs) between them. Deep-HPI-pred use Multilayer Perceptron (MLP) models for HPI prediction, which is based on a comprehensive evaluation of topological features and neural network architectures. When subjected to independent validation datasets, the predicted models consistently surpassed a Matthews correlation coefficient (MCC) of 0.80 in host-pathogen interactions. Remarkably, the use of Eigenvector Centrality as the leading topological feature further enhanced this performance. Further, Deep-HPI-pred also offers relevant gene ontology (GO) term information for each pathogen and host protein within the system. This protein annotation data contributes an additional layer to our understanding of the intricate dynamics within host-pathogen interactions. In the additional benchmarking studies, the Deep-HPI-pred model has proven its robustness by consistently delivering reliable results across different host-pathogen systems, including plant-pathogens (accuracy of 98.4% and 97.9%), human-virus (accuracy of 94.3%), and animal-bacteria (accuracy of 96.6%) interactomes. These results not only demonstrate the model's versatility but also pave the way for gaining comprehensive insights into the molecular underpinnings of complex host-pathogen interactions. Taken together, the Deep-HPI-pred applet offers a unified web service for both identifying and illustrating interaction networks. Deep-HPI-pred applet is freely accessible at its homepage: https://cbi.gxu.edu.cn/shiny-apps/Deep-HPI-pred/ and at github: https://github.com/tahirulqamar/Deep-HPI-pred.

High acidity organic waste degradation and the potential to bioremediation of heavy metals in soil by an acid-tolerant Serratia sp

Highly acidic citrus pomace (CP) is a byproduct of Pericarpium Citri Reticulatae production and causes significant environmental damage. In this study, a newly isolated acid-tolerant strain of Serratia sp. JS-043 was used to treat CP and evaluate the effect of reduced acid citrus pomace (RACP) in passivating heavy metals. The results showed that biological treatment could remove 97.56% of citric acid in CP, the organic matter in the soil increased by 202.60% and the catalase activity in the soil increased from 0 to 0.117 U g-1. Adding RACP into soil can increase the stabilization of Cu, Zn, As, Co, and Pb. Specifically, through the metabolism of strain JS-043, RACP was also involved in the stabilization of Zn and Pb, and Residual Fraction in the total pool of these metals increased by 10.73% and 10.54%, respectively. Finally, the genome sequence of Serratia sp. JS-043 was completed, and the genetic basis of its acid-resistant and acid-reducing characteristics was preliminarily revealed. JS-043 also contains many genes encoding proteins associated with heavy metal ion tolerance and transport. These findings suggest that JS-043 may be a high-potential strain to improve the quality of acidic organic wastes that can then be useful for soil bioremediation.

Identification of virulence-associated factors in Vibrio parahaemolyticus with special reference to moonlighting protein: a secretomics study

Vibrio parahaemolyticus causes seafood-borne gastroenteritis infection in human which can even lead to death. The pathogenic strain of V. parahaemolyticus secretes different types of virulence factors that are directly injected into the host cell by a different type of secretion system which helps bacteria to establish its own ecological niche within the organism. Therefore, the aim of this study was to isolate the extracellular secreted proteins from the trh positive strain of V. parahaemolyticus and identify them using two-dimensional gel electrophoresis and MALDI-TOFMS/MS. Seventeen different cellular proteins viz, Carbamoyl-phosphate synthase, 5-methyltetrahydropteroyltriglutamate, tRNA-dihydrouridine synthase, Glycerol-3-phosphate dehydrogenase, Orotidine 5'-phosphate decarboxylase, Molybdenum import ATP-binding protein, DnaJ, DNA polymerase IV, Ribosomal RNA small subunit methyltransferase G, ATP synthase subunit delta and gamma, Ribosome-recycling factor, 4-hydroxy-3-methylbut-2-en-1-yl diphosphate synthase, tRNA pseudouridine synthase B, Ditrans, polycis-undecaprenyl-diphosphate synthase, Oxygen-dependent coproporphyrinogen-III oxidase, and Peptide deformylase 2 were identified which are mainly involved in different metabolic and biosynthetic pathways. Furthermore, the molecular function of the identified proteins were associated with catalytic activity, ligase activity, transporter, metal binding, and ATP synthase when they are intercellular. However, to understand the importance of these secreted proteins in the infection and survival of bacteria inside the host cell, pathogen-host protein-protein interactions (PPIs) were carried out which identified the association of eight secreted proteins with 41 human proteins involved in different cellular pathways, including ubiquitination degradation, adhesion, inflammation, immunity, and programmed cell death. The present study provides unreported strategies on host-cell environment's survival and adaptation mechanisms for the successful establishment of infections and intracellular propagation.

Human-gut bacterial protein-protein interactions: understudied but impactful to human health

The human gut microbiome is associated with a wide range of diseases; yet, the mechanisms these microbes use to influence human health are not fully understood. Protein-protein interactions (PPIs) are increasingly identified as a potential mechanism by which gut microbiota influence their human hosts. Similar to some PPIs observed in pathogens, many disease-relevant human-gut bacterial PPIs function by interacting with components of the immune system or the gut barrier. Here, we highlight recent advances in these two areas. It is our opinion that there is a vastly unexplored network of human-gut bacterial PPIs that contribute to the prevention or pathogenesis of various diseases and that future research is warranted to expand PPI discovery.

Yersiniomics, a Multi-Omics Interactive Database for Yersinia Species

The genus Yersinia includes a large variety of nonpathogenic and life-threatening pathogenic bacteria, which cause a broad spectrum of diseases in humans and animals, such as plague, enteritis, Far East scarlet-like fever (FESLF), and enteric redmouth disease. Like most clinically relevant microorganisms, Yersinia spp. are currently subjected to intense multi-omics investigations whose numbers have increased extensively in recent years, generating massive amounts of data useful for diagnostic and therapeutic developments. The lack of a simple and centralized way to exploit these data led us to design Yersiniomics, a web-based platform allowing straightforward analysis of Yersinia omics data. Yersiniomics contains a curated multi-omics database at its core, gathering 200 genomic, 317 transcriptomic, and 62 proteomic data sets for Yersinia species. It integrates genomic, transcriptomic, and proteomic browsers, a genome viewer, and a heatmap viewer to navigate within genomes and experimental conditions. For streamlined access to structural and functional properties, it directly links each gene to GenBank, the Kyoto Encyclopedia of Genes and Genomes (KEGG), UniProt, InterPro, IntAct, and the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and each experiment to Gene Expression Omnibus (GEO), the European Nucleotide Archive (ENA), or the Proteomics Identifications Database (PRIDE). Yersiniomics provides a powerful tool for microbiologists to assist with investigations ranging from specific gene studies to systems biology studies. IMPORTANCE The expanding genus Yersinia is composed of multiple nonpathogenic species and a few pathogenic species, including the deadly etiologic agent of plague, Yersinia pestis. In 2 decades, the number of genomic, transcriptomic, and proteomic studies on Yersinia grew massively, delivering a wealth of data. We developed Yersiniomics, an interactive web-based platform, to centralize and analyze omics data sets on Yersinia species. The platform allows user-friendly navigation between genomic data, expression data, and experimental conditions. Yersiniomics will be a valuable tool to microbiologists.

Pathogen-driven cancers from a structural perspective: Targeting host-pathogen protein-protein interactions

Host-pathogen interactions (HPIs) affect and involve multiple mechanisms in both the pathogen and the host. Pathogen interactions disrupt homeostasis in host cells, with their toxins interfering with host mechanisms, resulting in infections, diseases, and disorders, extending from AIDS and COVID-19, to cancer. Studies of the three-dimensional (3D) structures of host-pathogen complexes aim to understand how pathogens interact with their hosts. They also aim to contribute to the development of rational therapeutics, as well as preventive measures. However, structural studies are fraught with challenges toward these aims. This review describes the state-of-the-art in protein-protein interactions (PPIs) between the host and pathogens from the structural standpoint. It discusses computational aspects of predicting these PPIs, including machine learning (ML) and artificial intelligence (AI)-driven, and overviews available computational methods and their challenges. It concludes with examples of how theoretical computational approaches can result in a therapeutic agent with a potential of being used in the clinics, as well as future directions.

PHILM2Web: A high-throughput database of macromolecular host–pathogen interactions on the Web

During infection, the pathogen’s entry into the host organism, breaching the host immune defense, spread and multiplication are frequently mediated by multiple interactions between the host and pathogen proteins. Systematic studying of host–pathogen interactions (HPIs) is a challenging task for both experimental and computational approaches and is critically dependent on the previously obtained knowledge about these interactions found in the biomedical literature. While several HPI databases exist that manually filter HPI protein–protein interactions from the generic databases and curated experimental interactomic studies, no comprehensive database on HPIs obtained from the biomedical literature is currently available. Here, we introduce a high-throughput literature-mining platform for extracting HPI data that includes the most comprehensive to date collection of HPIs obtained from the PubMed abstracts. Our HPI data portal, PHILM2Web (Pathogen–Host Interactions by Literature Mining on the Web), integrates an automatically generated database of interactions extracted by PHILM, our high-precision HPI literature-mining algorithm. Currently, the database contains 23 581 generic HPIs between 157 host and 403 pathogen organisms from 11 609 abstracts. The interactions were obtained from processing 608 972 PubMed abstracts, each containing mentions of at least one host and one pathogen organisms. In response to the coronavirus disease 2019 (COVID-19) pandemic, we also utilized PHILM to process 25 796 PubMed abstracts obtained by the same query as the COVID-19 Open Research Dataset. This COVID-19 processing batch resulted in 257 HPIs between 19 host and 31 pathogen organisms from 167 abstracts. The access to the entire HPI dataset is available via a searchable PHILM2Web interface; scientists can also download the entire database in bulk for offline processing.

Database URL: http://philm2web.live

Analysis of host-pathogen gene association networks reveals patient-specific response to streptococcal and polymicrobial necrotising soft tissue infections

BACKGROUND

Necrotising soft tissue infections (NSTIs) are rapidly progressing bacterial infections usually caused by either several pathogens in unison (polymicrobial infections) or Streptococcus pyogenes (mono-microbial infection). These infections are rare and are associated with high mortality rates. However, the underlying pathogenic mechanisms in this heterogeneous group remain elusive.

METHODS

In this study, we built interactomes at both the population and individual levels consisting of host-pathogen interactions inferred from dual RNA-Seq gene transcriptomic profiles of the biopsies from NSTI patients.

RESULTS

NSTI type-specific responses in the host were uncovered. The S. pyogenes mono-microbial subnetwork was enriched with host genes annotated with involved in cytokine production and regulation of response to stress. The polymicrobial network consisted of several significant associations between different species (S. pyogenes, Porphyromonas asaccharolytica and Escherichia coli) and host genes. The host genes associated with S. pyogenes in this subnetwork were characterised by cellular response to cytokines. We further found several virulence factors including hyaluronan synthase, Sic1, Isp, SagF, SagG, ScfAB-operon, Fba and genes upstream and downstream of EndoS along with bacterial housekeeping genes interacting with the human stress and immune response in various subnetworks between host and pathogen.

CONCLUSIONS

At the population level, we found aetiology-dependent responses showing the potential modes of entry and immune evasion strategies employed by S. pyogenes, congruent with general cellular processes such as differentiation and proliferation. After stratifying the patients based on the subject-specific networks to study the patient-specific response, we observed different patient groups with different collagens, cytoskeleton and actin monomers in association with virulence factors, immunogenic proteins and housekeeping genes which we utilised to postulate differing modes of entry and immune evasion for different bacteria in relationship to the patients' phenotype.

Structural insight into protein-protein interactions between intestinal microbiome and host

Protein-protein interactions between the microbiome and host organism play an important role in shaping host health. These host-modulating proteins have therapeutic potential in treating microbiome-linked disorders such as inflammatory bowel disease and obesity. Structural analysis of interacting proteins provides highly mechanistic insight into the domains driving these interactions and the resulting influence on host cell processes. Here, we briefly review recent publication of microbiome protein structures involved in host binding interactions, the effects of these interactions on host physiology, and the need for further study to increase the ability to detect proteins with therapeutic potential.

Computational Network Inference for Bacterial Interactomics

Since the large-scale experimental characterization of protein–protein interactions (PPIs) is not possible for all species, several computational PPI prediction methods have been developed that harness existing data from other species. While PPI network prediction has been extensively used in eukaryotes, microbial network inference has lagged behind. However, bacterial interactomes can be built using the same principles and techniques; in fact, several methods are better suited to bacterial genomes. These predicted networks allow systems-level analyses in species that lack experimental interaction data. This review describes the current network inference and analysis techniques and summarizes the use of computationally-predicted microbial interactomes to date.

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.

A core and pan gene map of Leptospira genus and its interactions with human host

Leptospira species are the etiological agent of an emerging zoonotic disease known as "Leptospirosis" that substantially affects both human health and economy across the globe. Despite the global importance of the disease, pathogenetic features, host-adaptation and proper diagnosis of this bacteria remains lacking. To accomplish these gaps, pan-genome of Leptospira genus was explored in the present study. The pan-genome of Leptospira genus was comprised of core (692) and accessory parts (softcore:1804, shell:6432, cloud:16,600). The functional analysis revealed the abundancy of "Translation, ribosomal structure and biogenesis" COG class in core-genes; whereas in accessory parts, genes involved in signal transduction was the most abundant. Furthermore, pathogen-host interaction (PHI) analysis of core and accessory proteins with human proteins showed the presence of a total of 599 and 510 interactions, respectively. There were eight hubs in core PHI network and five hubs in PHI network of accessory proteins. The human's proteins involved in these interactions were found functionally enriched in metabolic processes, responses to stimulus and immune system processes. Further, pan-genome based phylogeny separated the Leptospira genus in three major clades (belonging to P1, P2 and S) which relates with their pathogenicity level. Additionally, pathogenic and saprophytic clade specific genes of Leptospira have also been identified and functionally annotated for COG, KEGG and virulence factors. The results revealed the presence of 102 pathogenic and 215 saprophytic group specific gene clusters. The COG functional annotation of pathogen specific genes showed that defence mechanism followed by signal transduction mechanisms category were most significantly enriched COG categories; whereas in saprophytic group, signal transduction mechanisms was the most abundant COG, suggesting their role in adaptation and hence important for microbe's evolution and survival. In conclusion, this study provides a new insight of genomic features of Leptospira genus which may further be implemented for development of better control actions of the disease.

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 200 ns molecular dynamic simulations. This study elucidates the structural and functional basis of these proteins leading towards better plant health, production, and reliability.

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.

DeepViral: prediction of novel virus-host interactions from protein sequences and infectious disease phenotypes

MOTIVATION

Infectious diseases caused by novel viruses have become a major public health concern. Rapid identification of virus-host interactions can reveal mechanistic insights into infectious diseases and shed light on potential treatments. Current computational prediction methods for novel viruses are based mainly on protein sequences. However, it is not clear to what extent other important features, such as the symptoms caused by the viruses, could contribute to a predictor. Disease phenotypes (i.e., signs and symptoms) are readily accessible from clinical diagnosis and we hypothesize that they may act as a potential proxy and an additional source of information for the underlying molecular interactions between the pathogens and hosts.

RESULTS

We developed DeepViral, a deep learning based method that predicts protein-protein interactions (PPI) between humans and viruses. Motivated by the potential utility of infectious disease phenotypes, we first embedded human proteins and viruses in a shared space using their associated phenotypes and functions, supported by formalized background knowledge from biomedical ontologies. By jointly learning from protein sequences and phenotype features, DeepViral significantly improves over existing sequence-based methods for intra- and inter-species PPI prediction.

AVAILABILITY

Code and datasets for reproduction and customization are available at https://github.com/bio-ontology-research-group/DeepViral. Prediction results for 14 virus families are available at https://doi.org/10.5281/zenodo.4429824.

A Molecular Interaction Map of Klebsiella pneumoniae and Its Human Host Reveals Potential Mechanisms of Host Cell Subversion

Klebsiella pneumoniae is a leading cause of pneumonia and septicemia across the world. The rapid emergence of multidrug-resistant K. pneumoniae strains necessitates the discovery of effective drugs against this notorious pathogen. However, there is a dearth of knowledge on the mechanisms by which this deadly pathogen subverts host cellular machinery. To fill this knowledge gap, our study attempts to identify the potential mechanisms of host cell subversion by building a K. pneumoniae-human interactome based on rigorous computational methodology. The putative host targets inferred from the predicted interactome were found to be functionally enriched in the host's immune surveillance system and allied functions like apoptosis, hypoxia, etc. A multifunctionality-based scoring system revealed P53 as the most multifunctional protein among host targets accompanied by HIF1A and STAT1. Moreover, mining of host protein-protein interaction (PPI) network revealed that host targets interact among themselves to form a network (TTPPI), where P53 and CDC5L occupy a central position. The TTPPI is composed of several inter complex interactions which indicate that K. pneumoniae might disrupt functional coordination between these protein complexes through targeting of P53 and CDC5L. Furthermore, we identified four pivotal K. pneumoniae-targeted transcription factors (TTFs) that are part of TTPPI and are involved in generating host's transcriptional response to K. pneumoniae-mediated sepsis. In a nutshell, our study identifies some of the pivotal molecular targets of K. pneumoniae which primarily correlate to the physiological response of host during K. pneumoniae-mediated sepsis.

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.

Plant Phenolics as Pathogen-Carrier Immunogenicity Modulator Haptens

Background

Pathogens use multiple mechanisms to disrupt cell functioning in their host and allow pathogenesis. These mechanisms involve communication between the pathogen and the host cell through protein-protein interactions.

Methods

Protein-protein interactions chains referred to as signal transduction pathways are the processes by which a chemical or physical signal transmits through a cell as series of molecular events so the pathogen needs to intercept these molecular pathways at few positions to induce pathogenesis such as pathogen viability, infection or hypersensitivity.

Results

The pathogen nodes of interception are not necessarily the most immunogenic; so that novel immunogenicity-improvement strategies need to be developed thought a chemical conjugation of the pathogen-carrier nodes to develop an efficient immune response in order to block pathogenesis. On the other hand, if pathogen-carriers are immunogens; toleration ought to be induced by this conjugation avoiding hypersensitivity. Thus, this paper addresses the biological plausibility of plant-phenolics as pathogen-carrier immunogenicity modulator haptens.

Conclusion

The plant-phenolic compounds have in their structure functional groups such as hydroxyl, carbonyl, carboxyl, ester, or ether, capable of reacting with the amino or carbonyl groups of the amino acids of a pathogen-carrier to form conjugates. Besides, the varied carbon structures these phenolic compounds have; it is possible to alter the pathogen-carrier related factors that determine the immunogenicity: 1) Structural complexity, 2) Molecular size, 3) Structural heterogeneity, 4) Accessibility to antigenic determinants or epitopes, 5) Optical configuration, 6) Physical state, or 7) Molecular rigidity.

Pediatric Tuberculosis: The Impact of "Omics" on Diagnostics Development

Tuberculosis (TB) is a major public health concern for all ages. However, the disease presents a larger challenge in pediatric populations, partially owing to the lack of reliable diagnostic standards for the early identification of infection. Currently, there are no biomarkers that have been clinically validated for use in pediatric TB diagnosis. Identification and validation of biomarkers could provide critical information on prognosis of disease, and response to treatment. In this review, we discuss how the “omics” approach has influenced biomarker discovery and the advancement of a next generation rapid point-of-care diagnostic for TB, with special emphasis on pediatric disease. Limitations of current published studies and the barriers to their implementation into the field will be thoroughly reviewed within this article in hopes of highlighting future avenues and needs for combating the problem of pediatric tuberculosis.

Exploring the Diversity Within the Genus Francisella - An Integrated Pan-Genome and Genome-Mining Approach

Pan-genome analysis is a powerful method to explore genomic heterogeneity and diversity of bacterial species. Here we present a pan-genome analysis of the genus Francisella, comprising a dataset of 63 genomes and encompassing clinical as well as environmental isolates from distinct geographic locations. To determine the evolutionary relationship within the genus, we performed phylogenetic whole-genome studies utilizing the average nucleotide identity, average amino acid identity, core genes and non-recombinant loci markers. Based on the analyses, the phylogenetic trees obtained identified two distinct clades, A and B and a diverse cluster designated C. The sizes of the pan-, core-, cloud-, and shell-genomes of Francisella were estimated and compared to those of two other facultative intracellular pathogens, Legionella and Piscirickettsia. Francisella had the smallest core-genome, 692 genes, compared to 886 and 1,732 genes for Legionella and Piscirickettsia respectively, while the pan-genome of Legionella was more than twice the size of that of the other two genera. Also, the composition of the Francisella Type VI secretion system (T6SS) was analyzed. Distinct differences in the gene content of the T6SS were identified. In silico approaches performed to identify putative substrates of these systems revealed potential effectors targeting the cell wall, inner membrane, cellular nucleic acids as well as proteins, thus constituting attractive targets for site-directed mutagenesis. The comparative analysis performed here provides a comprehensive basis for the assessment of the phylogenomic relationship of members of the genus Francisella and for the identification of putative T6SS virulence traits.

SON DNA-binding protein mediates macrophage autophagy and responses to intracellular infection

Intracellular pathogens affect diverse host cellular defence and metabolic pathways. Here, we used infection with Francisella tularensis to identify SON DNA-binding protein as a central determinant of macrophage activities. RNAi knockdown of SON increases survival of human macrophages following F. tularensis infection or inflammasome stimulation. SON is required for macrophage autophagy, interferon response factor 3 expression, type I interferon response and inflammasome-associated readouts. SON knockdown has gene- and stimulus-specific effects on inflammatory gene expression. SON is required for accurate splicing and expression of GBF1, a key mediator of cis-Golgi structure and function. Chemical GBF1 inhibition has similar effects to SON knockdown, suggesting that SON controls macrophage functions at least in part by controlling Golgi-associated processes.

Systematic evaluation of machine learning methods for identifying human-pathogen protein-protein interactions

In recent years, high-throughput experimental techniques have significantly enhanced the accuracy and coverage of protein-protein interaction identification, including human-pathogen protein-protein interactions (HP-PPIs). Despite this progress, experimental methods are, in general, expensive in terms of both time and labour costs, especially considering that there are enormous amounts of potential protein-interacting partners. Developing computational methods to predict interactions between human and bacteria pathogen has thus become critical and meaningful, in both facilitating the detection of interactions and mining incomplete interaction maps. In this paper, we present a systematic evaluation of machine learning-based computational methods for human-bacterium protein-protein interactions (HB-PPIs). We first reviewed a vast number of publicly available databases of HP-PPIs and then critically evaluate the availability of these databases. Benefitting from its well-structured nature, we subsequently preprocess the data and identified six bacterium pathogens that could be used to study bacterium subjects in which a human was the host. Additionally, we thoroughly reviewed the literature on 'host-pathogen interactions' whereby existing models were summarized that we used to jointly study the impact of different feature representation algorithms and evaluate the performance of existing machine learning computational models. Owing to the abundance of sequence information and the limited scale of other protein-related information, we adopted the primary protocol from the literature and dedicated our analysis to a comprehensive assessment of sequence information and machine learning models. A systematic evaluation of machine learning models and a wide range of feature representation algorithms based on sequence information are presented as a comparison survey towards the prediction performance evaluation of HB-PPIs.

MicrobioLink: An Integrated Computational Pipeline to Infer Functional Effects of Microbiome-Host Interactions

Microbiome–host interactions play significant roles in health and in various diseases including autoimmune disorders. Uncovering these inter-kingdom cross-talks propels our understanding of disease pathogenesis and provides useful leads on potential therapeutic targets. Despite the biological significance of microbe–host interactions, there is a big gap in understanding the downstream effects of these interactions on host processes. Computational methods are expected to fill this gap by generating, integrating, and prioritizing predictions—as experimental detection remains challenging due to feasibility issues. Here, we present MicrobioLink, a computational pipeline to integrate predicted interactions between microbial and host proteins together with host molecular networks. Using the concept of network diffusion, MicrobioLink can analyse how microbial proteins in a certain context are influencing cellular processes by modulating gene or protein expression. We demonstrated the applicability of the pipeline using a case study. We used gut metaproteomic data from Crohn’s disease patients and healthy controls to uncover the mechanisms by which the microbial proteins can modulate host genes which belong to biological processes implicated in disease pathogenesis. MicrobioLink, which is agnostic of the microbial protein sources (bacterial, viral, etc.), is freely available on GitHub.

The Genome of the Great Gerbil Reveals Species-Specific Duplication of an MHCII Gene

The great gerbil (Rhombomys opimus) is a social rodent living in permanent, complex burrow systems distributed throughout Central Asia, where it serves as the main host of several important vector-borne infectious pathogens including the well-known plague bacterium (Yersinia pestis). Here, we present a continuous annotated genome assembly of the great gerbil, covering over 96% of the estimated 2.47-Gb genome. Taking advantage of the recent genome assemblies of the sand rat (Psammomys obesus) and the Mongolian gerbil (Meriones unguiculatus), comparative immunogenomic analyses reveal shared gene losses within TLR gene families (i.e., TLR8, TLR10, and the entire TLR11-subfamily) for Gerbillinae, accompanied with signs of diversifying selection of TLR7 and TLR9. Most notably, we find a great gerbil-specific duplication of the MHCII DRB locus. In silico analyses suggest that the duplicated gene provides high peptide binding affinity for Yersiniae epitopes as well as Leishmania and Leptospira epitopes, putatively leading to increased capability to withstand infections by these pathogens. Our study demonstrates the power of whole-genome sequencing combined with comparative genomic analyses to gain deeper insight into the immunogenomic landscape of the great gerbil and its close relatives.

In silico unravelling pathogen-host signaling cross-talks via pathogen mimicry and human protein-protein interaction networks

Pathogen-host protein interactions are fundamental for pathogens to manipulate host signaling pathways and subvert host immune defense. For most pathogens, very few or no experimental studies have been conducted to investigate their signaling cross-talks with host. In this study, we propose a computational framework to validate the biological assumption that human protein-protein interaction (PPI) networks alone are sufficient to infer pathogen-host PPIs via pathogen functional mimicry. Pathogen functional mimicry assumes that a pathogen functionally mimics and substitutes host counterpart proteins in order for the pathogen to get involved in or hijack the host cellular processes. Through pathogen functional mimicry defined via gene ontology (GO) semantic similarity, we first use the known human PPIs as templates to infer pathogen-host PPIs, and the PPIs are further used as training data to build an l₂-regularized logistic regression model for novel pathogen-host PPI prediction. Independent tests on the experimental data from human immunodeficiency virus and Francisella tularensis validate the effectiveness of the proposed pathogen functional mimicry technique. Performance comparisons also show that the proposed technique y excels the existing pathogen sequence mimicry approaches and transfer learning methods. The proposed framework provides a new avenue to study the experimentally less-studied pathogens in the worst scenarios that very few or no experimental pathogen-host PPIs are available. As two case studies, we apply the proposed framework to Salmonella typhimurium and Human respiratory syncytial virus to reconstruct the pathogen-host PPI networks and further investigate the interference of these two pathogens with human immune signaling and transcription regulatory system.

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%.

Prediction of human-Bacillus anthracis protein-protein interactions using multi-layer neural network

Motivation

Triplet amino acids have successfully been included in feature selection to predict human-HPV protein-protein interactions (PPI). The utility of supervised learning methods is curtailed due to experimental data not being available in sufficient quantities. Improvements in machine learning techniques and features selection will enhance the study of PPI between host and pathogen.

Results

We present a comparison of a neural network model versus SVM for prediction of host-pathogen PPI based on a combination of features including: amino acid quadruplets, pairwise sequence similarity, and human interactome properties. The neural network and SVM were implemented using Python Sklearn library. The neural network model using quadruplet features and other network features outperformance the SVM model. The models are tested against published predictors and then applied to the human-B.anthracis case. Gene ontology term enrichment analysis identifies immunology response and regulation as functions of interacting proteins. For prediction of Human-viral PPI, our model (neural network) is a significant improvement in overall performance compared to a predictor using the triplets feature and achieves a good accuracy in predicting human-B.anthracis PPI.

Availability and implementation

All code can be downloaded from ftp://ftp.sanbi.ac.za/machine_learning/.

Supplementary information

Supplementary data are available at Bioinformatics online.

A Two-Way Proteome Microarray Strategy to Identify Novel Mycobacterium tuberculosis-Human Interactors

Tuberculosis (TB) is still a serious threat to human health which is caused by mycobacterium tuberculosis (Mtb). The main reason for failure to eliminate TB is lack of clearly understanding the molecular mechanism of Mtb pathogenesis. Determining human Mtb-interacting proteins enables us to characterize the mechanism and identify potential molecular targets for TB diagnosis and treatment. However, experimentally systematic Mtb interactors are not readily available. In this study, we performed an unbiased, comprehensive two-way proteome microarray based approach to systematically screen global human Mtb interactors and determine the binding partners of Mtb effectors. Our results, for the first time, screened 84 potential human Mtb interactors. Bioinformatic analysis further highlighted these protein candidates might engage in a wide range of cellular functions such as activation of DNA endogenous promoters, transcription of DNA/RNA and necrosis, as well as immune-related signaling pathways. Then, using Mtb proteome microarray followed His tagged pull-down assay and Co-IP, we identified one interacting partner (Rv0577) for the protein candidate NRF1 and three binding partners (Rv0577, Rv2117, Rv2423) for SMAD2, respectively. This study gives new insights into the profile of global Mtb interactors potentially involved in Mtb pathogenesis and demonstrates a powerful strategy in the discovery of Mtb effectors.

Inferring pathogen-host interactions between Leptospira interrogans and Homo sapiens using network theory

Leptospirosis is the most emerging zoonotic disease of epidemic potential caused by pathogenic species of Leptospira. The bacterium invades the host system and causes the disease by interacting with the host proteins. Analyzing these pathogen-host protein interactions (PHPIs) may provide deeper insight into the disease pathogenesis. For this analysis, inter-species as well as intra-species protein interactions networks of Leptospira interrogans and human were constructed and investigated. The topological analyses of these networks showed lesser connectivity in inter-species network than intra-species, indicating the perturbed nature of the inter-species network. Hence, it can be one of the reasons behind the disease development. A total of 35 out of 586 PHPIs were identified as key interactions based on their sub-cellular localization. Two outer membrane proteins (GpsA and MetXA) and two periplasmic proteins (Flab and GlyA) participating in PHPIs were found conserved in all pathogenic, intermediate and saprophytic spp. of Leptospira. Furthermore, the bacterial membrane proteins involved in PHPIs were found playing major roles in disruption of the immune systems and metabolic processes within host and thereby causing infectious disease. Thus, the present results signify that the membrane proteins participating in such interactions hold potential to serve as effective immunotherapeutic candidates for vaccine development.

Victors: a web-based knowledge base of virulence factors in human and animal pathogens

Virulence factors (VFs) are molecules that allow microbial pathogens to overcome host defense mechanisms and cause disease in a host. It is critical to study VFs for better understanding microbial pathogenesis and host defense mechanisms. Victors (http://www.phidias.us/victors) is a novel, manually curated, web-based integrative knowledge base and analysis resource for VFs of pathogens that cause infectious diseases in human and animals. Currently, Victors contains 5296 VFs obtained via manual annotation from peer-reviewed publications, with 4648, 179, 105 and 364 VFs originating from 51 bacterial, 54 viral, 13 parasitic and 8 fungal species, respectively. Our data analysis identified many VF-specific patterns. Within the global VF pool, cytoplasmic proteins were more common, while adhesins were less common compared to findings on protective vaccine antigens. Many VFs showed homology with host proteins and the human proteins interacting with VFs represented the hubs of human-pathogen interactions. All Victors data are queriable with a user-friendly web interface. The VFs can also be searched by a customized BLAST sequence similarity searching program. These VFs and their interactions with the host are represented in a machine-readable Ontology of Host-Pathogen Interactions. Victors supports the 'One Health' research as a vital source of VFs in human and animal pathogens.

Review of computational methods for virus-host protein interaction prediction: a case study on novel Ebola-human interactions

Identification of potential virus-host interactions is useful and vital to control the highly infectious virus-caused diseases. This may contribute toward development of new drugs to treat the viral infections. Recently, database records of clinically and experimentally validated interactions between a small set of human proteins and Ebola virus (EBOV) have been published. Using the information of the known human interaction partners of EBOV, our main objective is to identify a set of proteins that may interact with EBOV proteins. Here, we first review the state-of-the-art, computational methods used for prediction of novel virus-host interactions for infectious diseases followed by a case study on EBOV-human interactions. The assessment result shows that the predicted human host proteins are highly similar with known human interaction partners of EBOV in the context of structure and semantics and are responsible for similar biochemical activities, pathways and host-pathogen relationships.

Bradyrhizobium diazoefficiens USDA 110- Glycine max Interactome Provides Candidate Proteins Associated with Symbiosis

Although the legume-rhizobium symbiosis is a most-important biological process, there is a limited knowledge about the protein interaction network between host and symbiont. Using interolog- and domain-based approaches, we constructed an interspecies protein interactome containing 5115 protein-protein interactions between 2291 Glycine max and 290 Bradyrhizobium diazoefficiens USDA 110 proteins. The interactome was further validated by the expression pattern analysis in nodules, gene ontology term semantic similarity, co-expression analysis, and luciferase complementation image assay. In the G. max-B. diazoefficiens interactome, bacterial proteins are mainly ion channel and transporters of carbohydrates and cations, while G. max proteins are mainly involved in the processes of metabolism, signal transduction, and transport. We also identified the top 10 highly interacting proteins (hubs) for each species. Kyoto Encyclopedia of Genes and Genomes pathway analysis for each hub showed that a pair of 14-3-3 proteins (SGF14g and SGF14k) and 5 heat shock proteins in G. max are possibly involved in symbiosis, and 10 hubs in B. diazoefficiens may be important symbiotic effectors. Subnetwork analysis showed that 18 symbiosis-related soluble N-ethylmaleimide sensitive factor attachment protein receptor proteins may play roles in regulating bacterial ion channels, and SGF14g and SGF14k possibly regulate the rhizobium dicarboxylate transport protein DctA. The predicted interactome provide a valuable basis for understanding the molecular mechanism of nodulation in soybean.

Transferring knowledge of bacterial protein interaction networks to predict pathogen targeted human genes and immune signaling pathways: a case study on M. tuberculosis

Background

Bacterial invasive infection and host immune response is fundamental to the understanding of pathogen pathogenesis and the discovery of effective therapeutic drugs. However, there are very few experimental studies on the signaling cross-talks between bacteria and human host to date.

Methods

In this work, taking M. tuberculosis H37Rv (MTB) that is co-evolving with its human host as an example, we propose a general computational framework that exploits the known bacterial pathogen protein interaction networks in STRING database to predict pathogen-host protein interactions and their signaling cross-talks. In this framework, significant interlogs are derived from the known pathogen protein interaction networks to train a predictive l₂-regularized logistic regression model.

Results

The computational results show that the proposed method achieves excellent performance of cross validation as well as low predicted positive rates on the less significant interlogs and non-interlogs, indicating a low risk of false discovery. We further conduct gene ontology (GO) and pathway enrichment analyses of the predicted pathogen-host protein interaction networks, which potentially provides insights into the machinery that M. tuberculosis H37Rv targets human genes and signaling pathways. In addition, we analyse the pathogen-host protein interactions related to drug resistance, inhibition of which potentially provides an alternative solution to M. tuberculosis H37Rv drug resistance.

Conclusions

The proposed machine learning framework has been verified effective for predicting bacteria-host protein interactions via known bacterial protein interaction networks. For a vast majority of bacterial pathogens that lacks experimental studies of bacteria-host protein interactions, this framework is supposed to achieve a general-purpose applicability. The predicted protein interaction networks between M. tuberculosis H37Rv and Homo sapiens, provided in the Additional files, promise to gain applications in the two fields: (1) providing an alternative solution to drug resistance; (2) revealing the patterns that M. tuberculosis H37Rv genes target human immune signaling pathways.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4873-9) contains supplementary material, which is available to authorized users.

Beyond degree and betweenness centrality: Alternative topological measures to predict viral targets

The availability of large-scale screens of host-virus interaction interfaces enabled the topological analysis of viral protein targets of the host. In particular, host proteins that bind viral proteins are generally hubs and proteins with high betweenness centrality. Recently, other topological measures were introduced that a virus may tap to infect a host cell. Utilizing experimentally determined sets of human protein targets from Herpes, Hepatitis, HIV and Influenza, we pooled molecular interactions between proteins from different pathway databases. Apart from a protein's degree and betweenness centrality, we considered a protein's pathway participation, ability to topologically control a network and protein PageRank index. In particular, we found that proteins with increasing values of such measures tend to accumulate viral targets and distinguish viral targets from non-targets. Furthermore, all such topological measures strongly correlate with the occurrence of a given protein in different pathways. Building a random forest classifier that is based on such topological measures, we found that protein PageRank index had the highest impact on the classification of viral (non-)targets while proteins' ability to topologically control an interaction network played the least important role.

A Perspective on Proteomics of Infectious Diseases

Pneumonia, HIV/AIDS, tuberculosis, malaria and several other diseases caused by pathogens largely contribute to the enormous burden of infectious diseases. Over the last few decades, the impact of infectious diseases on a population has been drastic and remains a major health concern even today. Despite advances in science and technology in this era of health and development, there is a substantial knowledge gap in our understanding of the molecular basis of these infectious diseases. The availability of valuable genomic information for a number of pathogens and their hosts has improved our understanding of disease pathogenesis but has not always been useful in addressing important biological questions. The primary reason lies in the fact that genes do not best reflect the status of a cell. Proteins represent the functional molecules of a cell and are ultimately responsible for controlling most aspects of cellular function. Their existence as different isoforms owing to posttranslational modifications suggests that many proteins can be produced by the same gene. Furthermore, not all mRNAs are translated at all times justifying the need to develop additional tools to study proteins as separate molecular entities. Their presence or absence under disease conditions, varying levels, different forms, and functions need to be carefully studied to understand molecular alterations in response to a disease. Here, we describe the applications of proteomics-based approaches to study infectious diseases with a note on the objectives of the Human Proteome Project (HPP)-Human Infectious Diseases (HID) project under the HUPO's flagship program.

Meloidogyne incognita PASSE-MURAILLE (MiPM) Gene Encodes a Cell-Penetrating Protein That Interacts With the CSN5 Subunit of the COP9 Signalosome

The pathogenicity of phytonematodes relies on secreted virulence factors to rewire host cellular pathways for the benefits of the nematode. In the root-knot nematode (RKN) Meloidogyne incognita, thousands of predicted secreted proteins have been identified and are expected to interact with host proteins at different developmental stages of the parasite. Identifying the host targets will provide compelling evidence about the biological significance and molecular function of the predicted proteins. Here, we have focused on the hub protein CSN5, the fifth subunit of the pleiotropic and eukaryotic conserved COP9 signalosome (CSN), which is a regulatory component of the ubiquitin/proteasome system. We used affinity purification-mass spectrometry (AP-MS) to generate the interaction network of CSN5 in M. incognita-infected roots. We identified the complete CSN complex and other known CSN5 interaction partners in addition to unknown plant and M. incognita proteins. Among these, we described M. incognita PASSE-MURAILLE (MiPM), a small pioneer protein predicted to contain a secretory peptide that is up-regulated mostly in the J2 parasitic stage. We confirmed the CSN5-MiPM interaction, which occurs in the nucleus, by bimolecular fluorescence complementation (BiFC). Using MiPM as bait, a GST pull-down assay coupled with MS revealed some common protein partners between CSN5 and MiPM. We further showed by in silico and microscopic analyses that the recombinant purified MiPM protein enters the cells of Arabidopsis root tips in a non-infectious context. In further detail, the supercharged N-terminal tail of MiPM (NTT-MiPM) triggers an unknown host endocytosis pathway to penetrate the cell. The functional meaning of the CSN5-MiPM interaction in the M. incognita parasitism is discussed. Moreover, we propose that the cell-penetrating properties of some M. incognita secreted proteins might be a non-negligible mechanism for cell uptake, especially during the steps preceding the sedentary parasitic phase.

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.

Neoteric advancement in TB drugs and an overview on the anti-tubercular role of peptides through computational approaches

Tuberculosis (TB) is a devastating threat to human health whose treatment without the emergence of drug resistant Mycobacterium tuberculosis (M. tuberculosis) is the million-dollar question at present. The pathogenesis of M. tuberculosis has been extensively studied which represents unique defence strategies by infecting macrophages. Several anti-tubercular drugs with varied mode of action and administration from diversified sources have been used for the treatment of TB that later contributed to the emergence of multidrug-resistant tuberculosis (MDR-TB) and extensively drug-resistant tuberculosis (XDR-TB). However, few of potent anti-tubercular drugs are scheduled for clinical trials status in 2017-2018. Peptides of varied origins such as human immune cells and non-immune cells, bacteria, fungi, and venoms have been widely investigated as anti-tubercular agents for the replacement of existing anti-tubercular drugs in future. In the present review, we spotlighted not only on the mechanisms of action and mode of administration of currently available anti-tubercular drugs but also the recent comprehensive report of World Health Organization (WHO) on TB epidemic, diagnosis, prevention, and treatment. The major excerpt of the study also inspects the direct contribution of different computational tools during drug designing strategies against M. tuberculosis in order to grasp the interplay between anti-tubercular peptides and targeted bacterial protein. The potentiality of some of these anti-tubercular peptides as therapeutic agents unlocks a new portal for achieving the goal of end TB strategy.

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.

Perturbed human sub-networks by Fusobacterium nucleatum candidate virulence proteins

BACKGROUND

Fusobacterium nucleatum is a gram-negative anaerobic species residing in the oral cavity and implicated in several inflammatory processes in the human body. Although F. nucleatum abundance is increased in inflammatory bowel disease subjects and is prevalent in colorectal cancer patients, the causal role of the bacterium in gastrointestinal disorders and the mechanistic details of host cell functions subversion are not fully understood.

RESULTS

We devised a computational strategy to identify putative secreted F. nucleatum proteins (FusoSecretome) and to infer their interactions with human proteins based on the presence of host molecular mimicry elements. FusoSecretome proteins share similar features with known bacterial virulence factors thereby highlighting their pathogenic potential. We show that they interact with human proteins that participate in infection-related cellular processes and localize in established cellular districts of the host-pathogen interface. Our network-based analysis identified 31 functional modules in the human interactome preferentially targeted by 138 FusoSecretome proteins, among which we selected 26 as main candidate virulence proteins, representing both putative and known virulence proteins. Finally, six of the preferentially targeted functional modules are implicated in the onset and progression of inflammatory bowel diseases and colorectal cancer.

CONCLUSIONS

Overall, our computational analysis identified candidate virulence proteins potentially involved in the F. nucleatum-human cross-talk in the context of gastrointestinal diseases.

ModuleAlign: module-based global alignment of protein-protein interaction networks

MOTIVATION

As an increasing amount of protein-protein interaction (PPI) data becomes available, their computational interpretation has become an important problem in bioinformatics. The alignment of PPI networks from different species provides valuable information about conserved subnetworks, evolutionary pathways and functional orthologs. Although several methods have been proposed for global network alignment, there is a pressing need for methods that produce more accurate alignments in terms of both topological and functional consistency.

RESULTS

In this work, we present a novel global network alignment algorithm, named ModuleAlign, which makes use of local topology information to define a module-based homology score. Based on a hierarchical clustering of functionally coherent proteins involved in the same module, ModuleAlign employs a novel iterative scheme to find the alignment between two networks. Evaluated on a diverse set of benchmarks, ModuleAlign outperforms state-of-the-art methods in producing functionally consistent alignments. By aligning Pathogen-Human PPI networks, ModuleAlign also detects a novel set of conserved human genes that pathogens preferentially target to cause pathogenesis.

AVAILABILITY

http://ttic.uchicago.edu/∼hashemifar/ModuleAlign.html

CONTACT

canzar@ttic.edu or j3xu.ttic.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

In silico search of inhibitors of Streptococcus mutans for the control of dental plaque

Biofilm is an extremely complex microbial community arranged in a matrix of polysaccharides and attached to a substrate. Its development is crucial in the pathophysiology of oral infections like dental caries, as well as in periodontal, pulp, and periapical diseases. Streptococcus mutans is one of the most effective microorganisms in lactic acid production of the dental biofilm. Identifying essential Streptococcus mutans proteins using bioinformatics methods helps to search for alternative therapies. To this end, the bacterial genomes of several Streptococcus mutans strains and representative strains of other cariogenic and non-cariogenic bacteria were analysed by identifying pathogenicity islands and alignments with other bacteria, and by detecting the exclusive genes of cariogenic species in comparison to the non-pathogenic ones. This study used tools for orthology prediction such as BLAST and OrthoMCL, as well as the server IslandViewer for the detection of pathogenicity islands. In addition, the potential interactome of Streptococcus mutans was rebuilt by comparing it to interologues of other species phylogenetically close to or associated with cariogenicity. This protocol yielded a final list of 20 proteins related to potentially virulent factors that can be used as therapeutic targets in future analyses. The EIIA and EIIC enzymatic subunits of the phosphotransferase system (PTS) were prioritized, as well as the pyruvate kinase enzyme, which are directly involved in the metabolism of carbohydrates and in obtaining the necessary energy for the microorganism's survival. These results will guide a subsequent experimental trial to develop new, safe, and effective molecules in the treatment of dental caries.

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.