Computational analysis of protein interaction networks for infectious diseases

Comprehensive analysis of intervention and control studies for the computational identification of dengue biomarker genes

Dengue fever, caused by the dengue virus (DENV), presents a significant global health concern, with millions of cases reported annually. Despite significant progress in understanding Dengue fever, effective prognosis and treatment remain elusive due to the complex clinical presentations and limitations in current diagnostic methods. The virus, transmitted primarily by the Aedes aegypti mosquito, exists in four closely related forms, each capable of causing flu-like symptoms ranging from mild febrile illness to severe manifestations such as plasma leakage and hemorrhagic fever. Although advancements in diagnostic techniques have been made, early detection of severe dengue remains difficult due to the complexity of its clinical presentations. This study conducted a comprehensive analysis of differential gene expression in dengue fever patients using multiple microarray datasets from the NCBI GEO database. Through bioinformatics approaches, 163 potential biomarker genes were identified, with some overlapping previously reported biomarkers and others representing novel candidates. Notably, AURKA, BUB1, BUB1B, BUB3, CCNA2, CCNB2, CDC6, CDK1, CENPE, EXO1, NEK2, ZWINT, and STAT1 were among the most significant biomarkers. These genes are involved in critical cellular processes, such as cell cycle regulation and mitotic checkpoint control, which are essential for immune cell function and response. Functional enrichment analysis revealed that the dysregulated genes were predominantly associated with immune response to the virus, cell division, and RNA processing. Key regulatory genes such as AURKA, BUB1, BUB3, and CDK1 are found to be involved in cell cycle regulation and have roles in immune-related pathways, underscoring their importance in the host immune response to Dengue virus infection. This study provides novel insights into the molecular mechanisms underlying Dengue fever pathogenesis, highlighting key regulatory genes such as AURKA and CDK1 that could serve as potential biomarkers for early diagnosis and targets for therapeutic intervention, paving the way for improved management of the disease.

Understanding gut microbiome-based machine learning platforms: A review on therapeutic approaches using deep learning

Human beings possess trillions of microbial cells in a symbiotic relationship. This relationship benefits both partners for a long time. The gut microbiota helps in many bodily functions from harvesting energy from digested food to strengthening biochemical barriers of the gut and intestine. But the changes in microbiota composition and bacteria that can enter the gastrointestinal tract can cause infection. Several approaches like culture-independent techniques such as high-throughput and meta-omics projects targeting 16S ribosomal RNA (rRNA) sequencing are popular methods to investigate the composition of the human gastrointestinal tract microbiota and taxonomically characterizing microbial communities. The microbiota conformation and diversity should be provided by whole-genome shotgun metagenomic sequencing of site-specific community DNA associating genome mapping, gene inventory, and metabolic remodelling and reformation, to ease the functional study of human microbiota. Preliminary examination of the therapeutic potency for dysbiosis-associated diseases permits investigation of pharmacokinetic-pharmacodynamic changes in microbial communities for escalation of treatment and dosage plan. Gut microbiome study is an integration of metagenomics which has influenced the field in the last two decades. And the incorporation of artificial intelligence and deep learning through "omics-based" methods and microfluidic evaluation enhanced the capability of identification of thousands of microbes.

Precision drug design against Acidovorax oryzae: leveraging bioinformatics to combat rice brown stripe disease

Bacterial brown stripe disease caused by Acidovorax oryzae is a major threat to crop yields, and the current reliance on pesticides for control is unsustainable due to environmental pollution and resistance. To address this, bacterial-based ligands have been explored as a potential treatment solution. In this study, we developed a protein-protein interaction (PPI) network for A. oryzae by utilizing shared differentially expressed genes (DEGs) and the STRING database. Using a maximal clique centrality (MCC) approach through CytoHubba and Network Analyzer, we identified hub genes within the PPI network. We then analyzed the genomic data of the top 10 proteins, and further narrowed them down to 2 proteins by utilizing betweenness, closeness, degree, and eigenvector studies. Finally, we used molecular docking to screen 100 compounds against the final two proteins (guaA and metG), and Enfumafungin was selected as a potential treatment for bacterial resistance caused by A. oryzae based on their binding affinity and interaction energy. Our approach demonstrates the potential of utilizing bioinformatics and molecular docking to identify novel drug candidates for precision treatment of bacterial brown stripe disease caused by A. oryzae, paving the way for more targeted and sustainable control strategies. The efficacy of Enfumafungin in inhibiting the growth of A. oryzae strain RS-1 was investigated through both computational and wet lab methods. The models of the protein were built using the Swiss model, and their accuracy was confirmed via a Ramachandran plot. Additionally, Enfumafungin demonstrated potent inhibitory action against the bacterial strain, with an MIC of 100 µg/mL, reducing OD600 values by up to 91%. The effectiveness of Enfumafungin was further evidenced through agar well diffusion assays, which exhibited the highest zone of inhibition at 1.42 cm when the concentration of Enfumafungin was at 100 µg/mL. Moreover, Enfumafungin was also able to effectively reduce the biofilm of A. oryzae RS-1 in a concentration-dependent manner. The swarming motility of A. oryzae RS-1 was also found to be significantly inhibited by Enfumafungin. Further validation through TEM observation revealed that bacterial cells exposed to Enfumafungin displayed mostly red fluorescence, indicating destruction of the bacterial cell membrane.

Protein Therapeutic Target Candidates Against Acinetobacter baumannii, a Pathogen of Concern to Planetary Health: A Network-Based Integrative Omics Drug Discovery Approach

Acinetobacter baumannii, an opportunistic gram-negative pathogen responsible for several nosocomial infections, has developed resistance to various antibiotics. Proteins involved in the two-component system (TCS), virulence, and antibiotic resistance (AR), help this pathogen in regulating antibiotic susceptibility and virulence mechanisms. The present study reports a network-based integrative omics approach to drug discovery to identify key regulatory proteins as therapeutic candidates against A. baumannii. We collected data on the TCS, virulence, and AR proteins from various databases (P2CS, VFDB, ARDB, and PAIDB), which were subjected to network, host-pathogen, and gene expression data analysis. Network analysis identified 43 hubs, and 10 proteins were found to be interacting with human proteins associated with vital pathways. Of the 53 (43 + 10) pathogen proteins, 46 had no orthologs in the human host. Twelve proteins, namely, RpfC, Wzc, OmpR, EnvZ, BfmS, PilG, histidine kinase, ABC 3 transport family protein, outer membrane porin OprD family, CsuD, Pgm, and LpxA, were differentially expressed in the resistant strain. We propose these proteins as key regulators that warrant evaluation as therapeutic target candidates in the future. Furthermore, structure prediction of ABC 3 transport family protein was performed as a case study. The findings from this study are poised to facilitate and inform drug discovery and development against A. baumannii.

Pharmacological Mechanism of NRICM101 for COVID-19 Treatments by Combined Network Pharmacology and Pharmacodynamics

Symptom treatments for Coronavirus disease 2019 (COVID-19) infection and Long COVID are one of the most critical issues of the pandemic era. In light of the lack of standardized medications for treating COVID-19 symptoms, traditional Chinese medicine (TCM) has emerged as a potentially viable strategy based on numerous studies and clinical manifestations. Taiwan Chingguan Yihau (NRICM101), a TCM designed based on a medicinal formula with a long history of almost 500 years, has demonstrated its antiviral properties through clinical studies, yet the pharmacogenomic knowledge for this formula remains unclear. The molecular mechanism of NRICM101 was systematically analyzed by using exploratory bioinformatics and pharmacodynamics (PD) approaches. Results showed that there were 434 common interactions found between NRICM101 and COVID-19 related genes/proteins. For the network pharmacology of the NRICM101, the 434 common interacting genes/proteins had the highest associations with the interleukin (IL)-17 signaling pathway in the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Moreover, the tumor necrosis factor (TNF) was found to have the highest association with the 30 most frequently curated NRICM101 chemicals. Disease analyses also revealed that the most relevant diseases with COVID-19 infections were pathology, followed by cancer, digestive system disease, and cardiovascular disease. The 30 most frequently curated human genes and 2 microRNAs identified in this study could also be used as molecular biomarkers or therapeutic options for COVID-19 treatments. In addition, dose-response profiles of NRICM101 doses and IL-6 or TNF-α expressions in cell cultures of murine alveolar macrophages were constructed to provide pharmacodynamic (PD) information of NRICM101. The prevalent use of NRICM101 for standardized treatments to attenuate common residual syndromes or chronic sequelae of COVID-19 were also revealed for post-pandemic future.

APPINetwork: an R package for building and computational analysis of protein-protein interaction networks

Background

Protein-protein interactions (PPIs) are essential to almost every process in a cell. Analysis of PPI networks gives insights into the functional relationships among proteins and may reveal important hub proteins and sub-networks corresponding to functional modules. Several good tools have been developed for PPI network analysis but they have certain limitations. Most tools are suited for studying PPI in only a small number of model species, and do not allow second-order networks to be built, or offer relevant functions for their analysis. To overcome these limitations, we have developed APPINetwork (Analysis of Protein-protein Interaction Networks). The aim was to produce a generic and user-friendly package for building and analyzing a PPI network involving proteins of interest from any species as long they are stored in a database.

Methods

APPINetwork is an open-source R package. It can be downloaded and installed on the collaborative development platform GitLab (https://forgemia.inra.fr/GNet/appinetwork). A graphical user interface facilitates its use. Graphical windows, buttons, and scroll bars allow the user to select or enter an organism name, choose data files and network parameters or methods dedicated to network analysis. All functions are implemented in R, except for the script identifying all proteins involved in the same biological process (developed in C) and the scripts formatting the BioGRID data file and generating the IDs correspondence file (implemented in Python 3). PPI information comes from private resources or different public databases (such as IntAct, BioGRID, and iRefIndex). The package can be deployed on Linux and macOS operating systems (OS). Deployment on Windows is possible but it requires the prior installation of Rtools and Python 3.

Results

APPINetwork allows the user to build a PPI network from selected public databases and add their own PPI data. In this network, the proteins have unique identifiers resulting from the standardization of the different identifiers specific to each database. In addition to the construction of the first-order network, APPINetwork offers the possibility of building a second-order network centered on the proteins of interest (proteins known for their role in the biological process studied or subunits of a complex protein) and provides the number and type of experiments that have highlighted each PPI, as well as references to articles containing experimental evidence.

Conclusion

More than a tool for PPI network building, APPINetwork enables the analysis of the resultant network, by searching either for the community of proteins involved in the same biological process or for the assembly intermediates of a protein complex. Results of these analyses are provided in easily exportable files. Examples files and a user manual describing each step of the process come with the package.

Immunomodulatory effects of microbiota-derived metabolites at the crossroad of neurodegenerative diseases and viral infection: network-based bioinformatics insights

Bidirectional cross-talk between commensal microbiota and the immune system is essential for the regulation of immune responses and the formation of immunological memory. Perturbations of microbiome-immune system interactions can lead to dysregulated immune responses against invading pathogens and/or to the loss of self-tolerance, leading to systemic inflammation and genesis of several immune-mediated pathologies, including neurodegeneration. In this paper, we first investigated the contribution of the immunomodulatory effects of microbiota (bacteria and fungi) in shaping immune responses and influencing the formation of immunological memory cells using a network-based bioinformatics approach. In addition, we investigated the possible role of microbiota-host-immune system interactions and of microbiota-virus interactions in a group of neurodegenerative diseases (NDs): Amyotrophic Lateral Sclerosis (ALS), Multiple Sclerosis (MS), Parkinson’s disease (PD) and Alzheimer’s disease (AD). Our analysis highlighted various aspects of the innate and adaptive immune response systems that can be modulated by microbiota, including the activation and maturation of microglia which are implicated in the development of NDs. It also led to the identification of specific microbiota components which might be able to influence immune system processes (ISPs) involved in the pathogenesis of NDs. In addition, it indicated that the impact of microbiota-derived metabolites in influencing disease-associated ISPs, is higher in MS disease, than in AD, PD and ALS suggesting a more important role of microbiota mediated-immune effects in MS.

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

In silico analysis of Leishmania proteomes and protein-protein interaction network: Prioritizing therapeutic targets and drugs for repurposing to treat leishmaniasis

Leishmaniasis is a serious world health problem and its current therapies have several limitations demanding to develop novel therapeutics for this disease. The present study aims to prioritize novel broad-spectrum targets using proteomics and protein-protein interaction network (PPIN) data for 11 Leishmania species. Proteome comparison and host non-homology analysis resulted in 3605 pathogen-specific conserved core proteins. Gene ontology analysis indicated their involvement in major molecular functions like DNA binding, transportation, dioxygenase, and catalytic activity. PPIN analysis of these core proteins identified eight hub proteins (viz., vesicle-trafficking protein (LBRM2903_190011800), ribosomal proteins S17 (LBRM2903_34004790) and L2 (LBRM2903_080008100), eukaryotic translation initiation factor 3 (LBRM2903_350086700), replication factor A (LBRM2903_150008000), U3 small nucleolar RNA-associated protein (LBRM2903_340025600), exonuclease (LBRM2903_200021800), and mitochondrial RNA ligase (LBRM2903_200074100)). Among the hub proteins, six were classified as drug targets and two as vaccine candidates. Further, druggability analysis indicated three hub proteins, namely eukaryotic translation initiation factor 3, ribosomal proteins S17 and L2 as druggable. Their three-dimensional structures were modelled and docked with the identified ligands (2-methylthio-N6-isopentenyl-adenosine-5'-monophosphate, artenimol and omacetaxine mepesuccinate). These ligands could be experimentally validated (in vitro and in vivo) and repurposed for the development of novel antileishmanial agents.

A Rice Protein Interaction Network Reveals High Centrality Nodes and Candidate Pathogen Effector Targets

Network science identifies key players in diverse biological systems including host-pathogen interactions. We demonstrated a scale-free network property for a comprehensive rice protein–protein interactome (RicePPInets) that exhibits nodes with increased centrality indices. While weighted k-shell decomposition was shown efficacious to predict pathogen effector targets in Arabidopsis, we improved its computational code for a broader implementation on large-scale networks including RicePPInets. We determined that nodes residing within the internal layers of RicePPInets are poised to be the most influential, central, and effective information spreaders. To identify central players and modules through network topology analyses, we integrated RicePPInets and co-expression networks representing susceptible and resistant responses to strains of the bacterial pathogens Xanthomonas oryzae pv. oryzae and X. oryzae pv. oryzicola (Xoc) and generated a RIce-Xanthomonas INteractome (RIXIN). This revealed that previously identified candidate targets of pathogen transcription activator-like (TAL) effectors are enriched in nodes with enhanced connectivity, bottlenecks, and information spreaders that are located in the inner layers of the network, and these nodes are involved in several important biological processes. Overall, our integrative multi-omics network-based platform provides a potentially useful approach to prioritizing candidate pathogen effector targets for functional validation, suggesting that this computational framework can be broadly translatable to other complex pathosystems.

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.

HyMM: hybrid method for disease-gene prediction by integrating multiscale module structure

MOTIVATION

Identifying disease-related genes is an important issue in computational biology. Module structure widely exists in biomolecule networks, and complex diseases are usually thought to be caused by perturbations of local neighborhoods in the networks, which can provide useful insights for the study of disease-related genes. However, the mining and effective utilization of the module structure is still challenging in such issues as a disease gene prediction.

RESULTS

We propose a hybrid disease-gene prediction method integrating multiscale module structure (HyMM), which can utilize multiscale information from local to global structure to more effectively predict disease-related genes. HyMM extracts module partitions from local to global scales by multiscale modularity optimization with exponential sampling, and estimates the disease relatedness of genes in partitions by the abundance of disease-related genes within modules. Then, a probabilistic model for integration of gene rankings is designed in order to integrate multiple predictions derived from multiscale module partitions and network propagation, and a parameter estimation strategy based on functional information is proposed to further enhance HyMM's predictive power. By a series of experiments, we reveal the importance of module partitions at different scales, and verify the stable and good performance of HyMM compared with eight other state-of-the-arts and its further performance improvement derived from the parameter estimation.

CONCLUSIONS

The results confirm that HyMM is an effective framework for integrating multiscale module structure to enhance the ability to predict disease-related genes, which may provide useful insights for the study of the multiscale module structure and its application in such issues as a disease-gene prediction.

Biomedical data, computational methods and tools for evaluating disease-disease associations

In recent decades, exploring potential relationships between diseases has been an active research field. With the rapid accumulation of disease-related biomedical data, a lot of computational methods and tools/platforms have been developed to reveal intrinsic relationship between diseases, which can provide useful insights to the study of complex diseases, e.g. understanding molecular mechanisms of diseases and discovering new treatment of diseases. Human complex diseases involve both external phenotypic abnormalities and complex internal molecular mechanisms in organisms. Computational methods with different types of biomedical data from phenotype to genotype can evaluate disease-disease associations at different levels, providing a comprehensive perspective for understanding diseases. In this review, available biomedical data and databases for evaluating disease-disease associations are first summarized. Then, existing computational methods for disease-disease associations are reviewed and classified into five groups in terms of the usages of biomedical data, including disease semantic-based, phenotype-based, function-based, representation learning-based and text mining-based methods. Further, we summarize software tools/platforms for computation and analysis of disease-disease associations. Finally, we give a discussion and summary on the research of disease-disease associations. This review provides a systematic overview for current disease association research, which could promote the development and applications of computational methods and tools/platforms for disease-disease associations.

Computational Identification of the Plausible Molecular Vaccine Candidates of Multidrug-Resistant Salmonella enterica

Salmonella enterica serovars are responsible for the life-threatening, fatal, invasive diseases that are common in children and young adults. According to the most recent estimates, globally, there are approximately 11–20 million cases of morbidity and between 128,000 and 161,000 mortality per year. The high incidence rates of diseases like typhoid, caused by the serovars Typhi and Paratyphi, and gastroenteritis, caused by the non-typhoidal Salmonellae, have become worse, with the ever-increasing pathogenic strains being resistant to fluoroquinolones or almost even the third generation cephalosporins, such as ciprofloxacin and ceftriaxone. With vaccination still being one of the chosen methods of eradicating this disease, identification of candidate proteins, to be utilized for effective molecular vaccines, has probably remained a challenging issue. In our study here, we portray the usage of computational tools to analyze and predict potential vaccine candidate(s) for the multi-drug resistant serovars of S. enterica.

Identification of viral-mediated pathogenic mechanisms in neurodegenerative diseases using network-based approaches

During the course of a viral infection, virus-host protein-protein interactions (PPIs) play a critical role in allowing viruses to replicate and survive within the host. These interspecies molecular interactions can lead to viral-mediated perturbations of the human interactome causing the generation of various complex diseases. Evidences suggest that viral-mediated perturbations are a possible pathogenic etiology in several neurodegenerative diseases (NDs). These diseases are characterized by chronic progressive degeneration of neurons, and current therapeutic approaches provide only mild symptomatic relief; therefore, there is unmet need for the discovery of novel therapeutic interventions. In this paper, we initially review databases and tools that can be utilized to investigate viral-mediated perturbations in complex NDs using network-based analysis by examining the interaction between the ND-related PPI disease networks and the virus-host PPI network. Afterwards, we present our theoretical-driven integrative network-based bioinformatics approach that accounts for pathogen-genes-disease-related PPIs with the aim to identify viral-mediated pathogenic mechanisms focusing in multiple sclerosis (MS) disease. We identified seven high centrality nodes that can act as disease communicator nodes and exert systemic effects in the MS-enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways network. In addition, we identified 12 KEGG pathways, 5 Reactome pathways and 52 Gene Ontology Immune System Processes by which 80 viral proteins from eight viral species might exert viral-mediated pathogenic mechanisms in MS. Finally, our analysis highlighted the Th17 differentiation pathway, a disease communicator node and part of the 12 underlined KEGG pathways, as a key viral-mediated pathogenic mechanism and a possible therapeutic target for MS disease.

In silico Analyses of Immune System Protein Interactome Network, Single-Cell RNA Sequencing of Human Tissues, and Artificial Neural Networks Reveal Potential Therapeutic Targets for Drug Repurposing Against COVID-19

Background: There is pressing urgency to identify therapeutic targets and drugs that allow treating COVID-19 patients effectively. Methods: We performed in silico analyses of immune system protein interactome network, single-cell RNA sequencing of human tissues, and artificial neural networks to reveal potential therapeutic targets for drug repurposing against COVID-19. Results: We screened 1,584 high-confidence immune system proteins in ACE2 and TMPRSS2 co-expressing cells, finding 25 potential therapeutic targets significantly overexpressed in nasal goblet secretory cells, lung type II pneumocytes, and ileal absorptive enterocytes of patients with several immunopathologies. Then, we performed fully connected deep neural networks to find the best multitask classification model to predict the activity of 10,672 drugs, obtaining several approved drugs, compounds under investigation, and experimental compounds with the highest area under the receiver operating characteristics. Conclusion: After being effectively analyzed in clinical trials, these drugs can be considered for treatment of severe COVID-19 patients. Scripts can be downloaded at https://github.com/muntisa/immuno-drug-repurposing-COVID-19.

Alignment of virus-host protein-protein interaction networks by integer linear programming: SARS-CoV-2

Motivation

Beside socio-economic issues, coronavirus pandemic COVID-19, the infectious disease caused by the newly discovered coronavirus SARS-CoV-2, has caused a deep impact in the scientific community, that has considerably increased its effort to discover the infection strategies of the new virus. Among the extensive and crucial research that has been carried out in the last months, the analysis of the virus-host relationship plays an important role in drug discovery. Virus-host protein-protein interactions are the active agents in virus replication, and the analysis of virus-host protein-protein interaction networks is fundamental to the study of the virus-host relationship.

Results

We have adapted and implemented a recent integer linear programming model for protein-protein interaction network alignment to virus-host networks, and obtained a consensus alignment of the SARS-CoV-1 and SARS-CoV-2 virus-host protein-protein interaction networks. Despite the lack of shared human proteins in these virus-host networks, and the low number of preserved virus-host interactions, the consensus alignment revealed aligned human proteins that share a function related to viral infection, as well as human proteins of high functional similarity that interact with SARS-CoV-1 and SARS-CoV-2 proteins, whose alignment would preserve these virus-host interactions.

A Comparative Genomics Approach for Shortlisting Broad-Spectrum Drug Targets in Nontuberculous Mycobacteria

Many members of nontuberculous mycobacteria (NTM) are opportunistic pathogens causing several infections in animals. The incidence of NTM infections and emergence of drug-resistant NTM strains are rising worldwide, emphasizing the need to develop novel anti-NTM drugs. The present study is aimed to identify broad-spectrum drug targets in NTM using a comparative genomics approach. The study identified 537 core proteins in NTM of which 45 were pathogen specific and essential for the survival of pathogens. Furthermore, druggability analysis indicated that 15 were druggable among those 45 proteins. These 15 proteins, which were core proteins, pathogen-specific, essential, and druggable, were considered as potential broad-spectrum candidates. Based on their locations in cytoplasm and membrane, targets were classified as drug and vaccine targets. The identified 15 targets were different enzymes, carrier proteins, transcriptional regulator, two-component system protein, ribosomal, and binding proteins. The identified targets could further be utilized by researchers to design inhibitors for the discovery of antimicrobial agents.

Integrative Network Biology Framework Elucidates Molecular Mechanisms of SARS-CoV-2 Pathogenesis

COVID-19 (coronavirus disease 2019) is a respiratory illness caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although the pathophysiology of this virus is complex and largely unknown, we employed a network-biology-fueled approach and integrated transcriptome data pertaining to lung epithelial cells with human interactome to generate Calu-3-specific human-SARS-CoV-2 interactome (CSI). Topological clustering and pathway enrichment analysis show that SARS-CoV-2 targets central nodes of the host-viral network, which participate in core functional pathways. Network centrality analyses discover 33 high-value SARS-CoV-2 targets, which are possibly involved in viral entry, proliferation, and survival to establish infection and facilitate disease progression. Our probabilistic modeling framework elucidates critical regulatory circuitry and molecular events pertinent to COVID-19, particularly the host-modifying responses and cytokine storm. Overall, our network-centric analyses reveal novel molecular components, uncover structural and functional modules, and provide molecular insights into the pathogenicity of SARS-CoV-2 that may help foster effective therapeutic design.

BacFITBase: a database to assess the relevance of bacterial genes during host infection

Bacterial infections have been on the rise world-wide in recent years and have a considerable impact on human well-being in terms of attributable deaths and disability-adjusted life years. Yet many mechanisms underlying bacterial pathogenesis are still poorly understood. Here, we introduce the BacFITBase database for the systematic characterization of bacterial proteins relevant for host infection aimed to enable the identification of new antibiotic targets. BacFITBase is manually curated and contains more than 90 000 entries with information on the contribution of individual genes to bacterial fitness under in vivo infection conditions in a range of host species. The data were collected from 15 different studies in which transposon mutagenesis was performed, including top-priority pathogens such as Acinetobacter baumannii and Campylobacter jejuni, for both of which increasing antibiotic resistance has been reported. Overall, BacFITBase includes information on 15 pathogenic bacteria and 5 host vertebrates across 10 different tissues. It is freely available at www.tartaglialab.com/bacfitbase.

Integrative Network Biology Framework Elucidates Molecular Mechanisms of SARS-CoV-2 Pathogenesis

COVID-19 (Coronavirus disease 2019) is a respiratory illness caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). While the pathophysiology of this deadly virus is complex and largely unknown, we employ a network biology-fueled approach and integrate multiomics data pertaining to lung epithelial cells-specific co-expression network and human interactome to generate Calu-3-specific human-SARS-CoV-2 Interactome (CSI). Topological clustering and pathway enrichment analysis show that SARS-CoV-2 target central nodes of host-viral network that participate in core functional pathways. Network centrality analyses discover 28 high-value SARS-CoV-2 targets, which are possibly involved in viral entry, proliferation and survival to establish infection and facilitate disease progression. Our probabilistic modeling framework elucidates critical regulatory circuitry and molecular events pertinent to COVID-19, particularly the host modifying responses and cytokine storm. Overall, our network centric analyses reveal novel molecular components, uncover structural and functional modules, and provide molecular insights into SARS-CoV-2 pathogenicity that may foster effective therapeutic design. Funding: This work was supported by the National Science Foundation (IOS-1557796) to M.S.M., and U54 ES 030246 from NIH/NIEHS to M. A. Conflict of Interest: The authors declare no competing interests. The authors also declare no financial interests.

A Parametric Targetability Evaluation Approach for Vitiligo Proteome Extracted through Integration of Gene Ontologies and Protein Interaction Topologies

Vitiligo is a well-known skin disorder with complex etiology. Vitiligo pathogenesis is multifaceted with many ramifications. A computational systemic path was designed to first propose candidate disease proteins by merging properties from protein interaction networks and gene ontology terms. All in all, 109 proteins were identified and suggested to be involved in the onset of disease or its progression. Later, a composite approach was employed to prioritize vitiligo disease proteins by comparing and benchmarking the properties against standard target identification criteria. This includes sequence-based, structural, functional, essentiality, protein-protein interaction, vulnerability, secretability, assayability, and druggability information. The existing information was seamlessly integrated into efficient pipelines to propose a novel protocol for assessment of targetability of disease proteins. Using the online data resources and the scripting, an illustrative list of 68 potential drug targets was generated for vitiligo. While this list is broadly consistent with the research community's current interest in certain specific proteins, and suggests novel target candidates that may merit further study, it can still be modified to correspond to a user-specific environment, either by adjusting the weights for chosen criteria (i.e., a quantitative approach) or by changing the considered criteria (i.e., a qualitative approach).

Delineating the Plausible Molecular Vaccine Candidates and Drug Targets of Multidrug-Resistant Acinetobacter baumannii

Nosocomial infections have become alarming with the increase of multidrug-resistant bacterial strains of Acinetobacter baumannii. Being the causative agent in ~80% of the cases, these pathogenic gram-negative species could be deadly for hospitalized patients, especially in intensive care units utilizing ventilators, urinary catheters, and nasogastric tubes. Primarily infecting an immuno-compromised system, they are resistant to most antibiotics and are the root cause of various types of opportunistic infections including but not limited to septicemia, endocarditis, meningitis, pneumonia, skin, and wound sepsis and even urinary tract infections. Conventional experimental methods including typing, computational methods encompassing comparative genomics, and combined methods of reverse vaccinology and proteomics had been proposed to differentiate and develop vaccines and/or drugs for several outbreak strains. However, identifying proteins suitable enough to be posed as drug targets and/or molecular vaccines against the multidrug-resistant pathogenic bacterial strains has probably remained an open issue to address. In these cases of novel protein identification, the targets either are uncharacterized or have been unable to confer the most coveted protection either in the form of molecular vaccine candidates or as drug targets. Here, we report a strategic approach with the 3,766 proteins from the whole genome of A. baumannii ATCC19606 (AB) to rationally identify plausible candidates and propose them as future molecular vaccine candidates and/or drug targets. Essentially, we started with mapping the vaccine candidates (VaC) and virulence factors (ViF) of A. baumannii strain AYE onto strain ATCC19606 to identify them in the latter. We move on to build small networks of VaC and ViF to conceptualize their position in the network space of the whole genomic protein interactome (GPIN) and rationalize their candidature for drugs and/or molecular vaccines. To this end, we propose new sets of known proteins unearthed from interactome built using key factors, KeF, potent enough to compete with VaC and ViF. Our method is the first of its kind to propose, albeit theoretically, a rational approach to identify crucial proteins and pose them for candidates of vaccines and/or drugs effective enough to combat the deadly pathogenic threats of A. baumannii.

Analysis of protein targets in pathogen-host interaction in infectious diseases: a case study on Plasmodium falciparum and Homo sapiens interaction network

Infection and disease progression is the outcome of protein interactions between pathogen and host. Pathogen, the role player of Infection, is becoming a severe threat to life as because of its adaptability toward drugs and evolutionary dynamism in nature. Identifying protein targets by analyzing protein interactions between host and pathogen is the key point. Proteins with higher degree and possessing some topologically significant graph theoretical measures are found to be drug targets. On the other hand, exceptional nodes may be involved in infection mechanism because of some pathway process and biologically unknown factors. In this article, we attempt to investigate characteristics of host-pathogen protein interactions by presenting a comprehensive review of computational approaches applied on different infectious diseases. As an illustration, we have analyzed a case study on infectious disease malaria, with its causative agent Plasmodium falciparum acting as 'Bait' and host, Homo sapiens/human acting as 'Prey'. In this pathogen-host interaction network based on some interconnectivity and centrality properties, proteins are viewed as central, peripheral, hub and non-hub nodes and their significance on infection process. Besides, it is observed that because of sparseness of the pathogen and host interaction network, there may be some topologically unimportant but biologically significant proteins, which can also act as Bait/Prey. So, functional similarity or gene ontology mapping can help us in this case to identify these proteins.

Systems Biology and Machine Learning in Plant-Pathogen Interactions

Systems biology is an inclusive approach to study the static and dynamic emergent properties on a global scale by integrating multiomics datasets to establish qualitative and quantitative associations among multiple biological components. With an abundance of improved high throughput -omics datasets, network-based analyses and machine learning technologies are playing a pivotal role in comprehensive understanding of biological systems. Network topological features reveal most important nodes within a network as well as prioritize significant molecular components for diverse biological networks, including coexpression, protein-protein interaction, and gene regulatory networks. Machine learning techniques provide enormous predictive power through specific feature extraction from biological data. Deep learning, a subtype of machine learning, has plausible future applications because a domain expert for feature extraction is not needed in this algorithm. Inspired by diverse domains of biology, we here review classic systems biology techniques applied in plant immunity thus far. We also discuss additional advanced approaches in both graph theory and machine learning, which may provide new insights for understanding plant-microbe interactions. Finally, we propose a hybrid approach in plant immune systems that harnesses the power of both network biology and machine learning, with a potential to be applicable to both model systems and agronomically important crop plants.

Paradigm Shift in Drug Re-purposing From Phenalenone to Phenaleno-Furanone to Combat Multi-Drug Resistant Salmonella enterica Serovar Typhi

Over recent years, typhoid fever has gained increasing attention with several cases reporting treatment failure due to multidrug resistant (MDR) strains of Salmonella enterica serovar Typhi. While new drug development strategies are being devised to combat the threat posed by these MDR pathogens, drug repurposing or repositioning has become a good alternative. The latter is considered mainly due to its capacity for saving sufficient time and effort for pre-clinical and optimization studies. Owing to the possibility of an unsuccessful repositioning, due to the mismatch in the optimization of the drug ligand for the changed biochemical properties of “old” and “new” targets, we have chosen a “targeted” approach of adopting a combined chemical moiety-based drug repurposing. Using small molecules selected from a combination of earlier approved drugs having phenalenone and furanone moieties, we have computationally delineated a step-wise approach to drug design against MDR Salmonella. We utilized our network analysis-based pre-identified, essential chaperone protein, SicA, which regulates the folding and quality of several secretory proteins including the Hsp70 chaperone, SigE. To this end, another crucial chaperone protein, Hsp70 DnaK, was also considered due to its importance for pathogen survival under the stress conditions typically encountered during antibiotic therapies. These were docked with the 19 marketed anti-typhoid drugs along with two phenalenone-furanone derivatives, 15 non-related drugs which showed 70% similarity to phenalenone and furanone derivatives and other analogous small molecules. Furthermore, molecular dynamics simulation studies were performed to check the stability of the protein-drug complexes. Our results showed the best binding interaction and stability, under the parameters of a virtual human body environment, with XR770, a phenaleno-furanone moiety based derivative. We therefore propose XR770, for repurposing for therapeutic intervention against emerging and significant drug resistance conferred by pathogenic Salmonella strains.

In silico Identification of the Indispensable Quorum Sensing Proteins of Multidrug Resistant Proteus mirabilis

Catheter-associated urinary tract infections (CAUTI) is an alarming hospital based disease with the increase of multidrug resistance (MDR) strains of Proteus mirabilis. Cases of long term hospitalized patients with multiple episodes of antibiotic treatments along with urinary tract obstruction and/or undergoing catheterization have been reported to be associated with CAUTI. The cases are complicated due to the opportunist approach of the pathogen having robust swimming and swarming capability. The latter giving rise to biofilms and probably inducible through autoinducers make the scenario quite complex. High prevalence of long-term hospital based CAUTI for patients along with moderate percentage of morbidity, cropping from ignorance about drug usage and failure to cure due to MDR, necessitates an immediate intervention strategy effective enough to combat the deadly disease. Several reports and reviews focus on revealing the important genes and proteins, essential to tackle CAUTI caused by P. mirabilis. Despite longitudinal countrywide studies and methodical strategies to circumvent the issues, effective means of unearthing the most indispensable proteins to target for therapeutic uses have been meager. Here, we report a strategic approach for identifying the most indispensable proteins from the genome of P. mirabilis strain HI4320, besides comparing the interactomes comprising the autoinducer-2 (AI-2) biosynthetic pathway along with other proteins involved in biofilm formation and responsible for virulence. Essentially, we have adopted a theoretical network model based approach to construct a set of small protein interaction networks (SPINs) along with the whole genome (GPIN) to computationally identify the crucial proteins involved in the phenomenon of quorum sensing (QS) and biofilm formation and thus, could be therapeutically targeted to fight out the MDR threats to antibiotics of P. mirabilis. Our approach utilizes the functional modularity coupled with k-core analysis and centrality scores of eigenvector as a measure to address the pressing issues.

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.

Incorporating interaction networks into the determination of functionally related hit genes in genomic experiments with Markov random fields

Motivation

Incorporating gene interaction data into the identification of ‘hit’ genes in genomic experiments is a well-established approach leveraging the ‘guilt by association’ assumption to obtain a network based hit list of functionally related genes. We aim to develop a method to allow for multivariate gene scores and multiple hit labels in order to extend the analysis of genomic screening data within such an approach.

Results

We propose a Markov random field-based method to achieve our aim and show that the particular advantages of our method compared with those currently used lead to new insights in previously analysed data as well as for our own motivating data. Our method additionally achieves the best performance in an independent simulation experiment. The real data applications we consider comprise of a survival analysis and differential expression experiment and a cell-based RNA interference functional screen.

Availability and implementation

We provide all of the data and code related to the results in the paper.

Supplementary information

Supplementary data are available at Bioinformatics online.

Dynamic and modular gene regulatory networks drive the development of gametogenesis

Gametogenesis is a complex process, which includes mitosis and meiosis and results in the production of ovum and sperm. The development of gametogenesis is dynamic and needs many different genes to work synergistically, but it is lack of global perspective research about this process. In this study, we detected the dynamic process of gametogenesis from the perspective of systems biology based on protein-protein interaction networks (PPINs) and functional analysis. Results showed that gametogenesis genes have strong synergistic effects in PPINs within and between different phases during the development. Addition to the synergistic effects on molecular networks, gametogenesis genes showed functional consistency within and between different phases, which provides the further evidence about the dynamic process during the development of gametogenesis. At last, we detected and provided the core molecular modules of different phases about gametogenesis. The gametogenesis genes and related modules can be obtained from our Web site Gametogenesis Molecule Online (GMO, http://gametsonline.nwsuaflmz.com/index.php), which is freely accessible. GMO may be helpful for the reference and application of these genes and modules in the future identification of key genes about gametogenesis. Summary, this work provided a computational perspective and frame to the analysis of the gametogenesis dynamics and modularity in both human and mouse.

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.

Network Tools for the Analysis of Proteomic Data

Recent advancements in high-throughput technologies such as mass spectrometry have led to an increase in the rate at which data is generated and accumulated. As a result, standard statistical methods no longer suffice as a way of analyzing such gigantic amounts of data. Network analysis, the evaluation of how nodes relate to one another, has over the years become an integral tool for analyzing high throughput proteomic data as they provide a structure that helps reduce the complexity of the underlying data.Computational tools, including pathway databases and network building tools, have therefore been developed to store, analyze, interpret, and learn from proteomics data. These tools enable the visualization of proteins as networks of signaling, regulatory, and biochemical interactions. In this chapter, we provide an overview of networks and network theory fundamentals for the analysis of proteomics data. We further provide an overview of interaction databases and network tools which are frequently used for analyzing proteomics data.

Comparative interactomics for virus-human protein-protein interactions: DNA viruses versus RNA viruses

Viruses are obligatory intracellular pathogens and completely depend on their hosts for survival and reproduction. The strategies adopted by viruses to exploit host cell processes and to evade host immune systems during infections may differ largely with the type of the viral genetic material. An improved understanding of these viral infection mechanisms is only possible through a better understanding of the pathogen-host interactions (PHIs) that enable viruses to enter into the host cells and manipulate the cellular mechanisms to their own advantage. Experimentally-verified protein-protein interaction (PPI) data of pathogen-host systems only became available at large scale within the last decade. In this study, we comparatively analyzed the current PHI networks belonging to DNA and RNA viruses and their human host, to get insights into the infection strategies used by these viral groups. We investigated the functional properties of human proteins in the PHI networks, to observe and compare the attack strategies of DNA and RNA viruses. We observed that DNA viruses are able to attack both human cellular and metabolic processes simultaneously during infections. On the other hand, RNA viruses preferentially interact with human proteins functioning in specific cellular processes as well as in intracellular transport and localization within the cell. Observing virus-targeted human proteins, we propose heterogeneous nuclear ribonucleoproteins and transporter proteins as potential antiviral therapeutic targets. The observed common and specific infection mechanisms in terms of viral strategies to attack human proteins may provide crucial information for further design of broad and specific next-generation antiviral therapeutics.