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Wolbachia are gram-negative endosymbiotic bacteria residing in the nematode host Brugia malayi that causes lymphatic filariasis in humans. Wolbachia settle in the host environment for their survival, growth and reproduction by maintaining an obligate mutualism relationship. However, the mechanism used by Wolbachia to maintain its mutualistic relationship with the nematode is poorly understood. Therefore, to elucidate the host-pathogen interaction (HPI) mechanism of Wolbachia and Brugia malayi, we used interolog-based approach for identification of host-pathogen protein-protein interactions (HP-PPIs) network and domain-based approach for the validation. The inter-species HP-PPIs network contained 392 proteins (258 Brugia malayi and 134 Wolbachia proteins) connected by 829 edges. Further, based on interolog approach, we identified 24 pathogen (Wolbachia) and 33 host (Brugia malayi) proteins involved in HPI. This study also reported 8 hub genes namely bma-atp-1, Bm1_25145, A0A0K0JKQ9, fusA, bma-eef-2, rplB, Bma-rpl-2, and bma-eftu-2 involved in maintaining an endosymbiotic relationship between Wolbachia and Brugia malayi. Furthermore, we also reported six-pair of host-pathogen interactions containing numerous pairs of shared domains that might be crucial for the establishment of mutualistic relationship between Wolbachia and Brugia malayi. The functional analysis revealed most of the Brugia malayi proteins are involved in the generation of precursor metabolites showing catalytic activity in the intracellular anatomical structures and organelles. The subcellular localization reported host and pathogen proteins were located in mitochondria and cytoplasm respectively. The Brugia malayi pathways involved in HPI were metabolic pathway, oxidative phosphorylation pathway, and spliceosome pathways, indicates that for maintaining mutualism energy-yielding pathways are targeted. Overall, this work provides new insights into the mechanism of HPIs between Wolbachia and Brugia malayi, and will help researchers a deeper understanding of the intracellular pathogenic activity and endosymbiotic relationship of Wolbachia with its host Brugia malayi.

Although a large number of databases are available for regulatory elements, a bottleneck has been created by the lack of bioinformatics tools to predict the interaction modes of regulatory elements. To reduce this gap, we developed the Arabidopsis Transcription Regulatory Factor Domain/Domain Interaction Analysis Tool–liquid/liquid phase separation (LLPS), oligomerization, GO analysis (ART FOUNDATION-LOG), a useful toolkit for protein–nucleic acid interaction (PNI) and protein–protein interaction (PPI) analysis based on domain–domain interactions (DDIs). LLPS, protein oligomerization, the structural properties of protein domains, and protein modifications are major components in the orchestration of the spatiotemporal dynamics of PPIs and PNIs. Our goal is to integrate PPI/PNI information into the development of a prediction model for identifying important genetic variants in peaches. Our program unified interdatabase relational keys based on protein domains to facilitate inference from the model species. A key advantage of this program lies in the integrated information of related features, such as protein oligomerization, LOG analysis, structural characterizations of domains (e.g., domain linkers, intrinsically disordered regions, DDIs, domain–motif (peptide) interactions, beta sheets, and transmembrane helices), and post-translational modification. We provided simple tests to demonstrate how to use this program, which can be applied to other eukaryotic organisms. [ABSTRACT FROM AUTHOR]

Protein tyrosine kinases (PTKs) are a large enzyme family that regulates many cellular processes. The key to their broad role in signaling is their tunable substrate specificity and regulatory mechanisms that allow each to respond to appropriate regulatory signals and phosphorylate the correct physiological protein substrates. Thus, in addition to the general PTK catalytic platform, each PTK acquires unique structural motifs that confer a unique combination of catalytic and regulatory properties. Understanding the structural basis for these properties is essential for understanding and manipulating the PTK-based signaling networks in normal and cancer cells. C-terminal Src kinase (Csk) and its homolog, Csk-homologous kinase (Chk), phosphorylate Src family kinases on a C-terminal Tyr residue and negatively regulate their kinase activity. While this regulatory function is biologically essential, Csk and Chk have also been excellent model PTKs for dissecting the structural basis of PTK catalysis and regulation. In this article, we review the structure-function studies of Csk and Chk that shed light on the regulatory and catalytic mechanisms of protein tyrosine kinases in general.

There exist several databases that provide virus-host protein interactions. While most provide curated records of interacting virus-host protein pairs, information on the strain-specific virulence factors or protein domains involved, is lacking. Some databases offer incomplete coverage of influenza strains because of the need to sift through vast amounts of literature (including those of major viruses including HIV and Dengue, besides others). None have offered complete, strain specific protein-protein interaction records for the influenza A group of viruses. In this paper, we present a comprehensive network of predicted domain-domain interaction(s) (DDI) between influenza A virus (IAV) and mouse host proteins, that will allow the systematic study of disease factors by taking the virulence information (lethal dose) into account. From a previously published dataset of lethal dose studies of IAV infection in mice, we constructed an interacting domain network of mouse and viral protein domains as nodes with weighted edges. The edges were scored with the Domain Interaction Statistical Potential (DISPOT) to indicate putative DDI. The virulence network can be easily navigated via a web browser, with the associated virulence information (LD50 values) prominently displayed. The network will aid influenza A disease modeling by providing strain-specific virulence levels with interacting protein domains. It can possibly contribute to computational methods for uncovering influenza infection mechanisms mediated through protein domain interactions between viral and host proteins. It is available at https://iav-ppi.onrender.com/home.

Introduction. Infection caused by Mycobacterium tuberculosis (M. tb ) is still a leading cause of mortality worldwide with estimated 1.4 million deaths annually. Hypothesis/Gap statement. Despite macrophages' ability to kill bacterium, M. tb can grow inside these innate immune cells and the exploration of the infection has traditionally been characterized by a one-sided relationship, concentrating solely on the host or examining the pathogen in isolation. Aim. Because of only a handful of M. tb -host interactions have been experimentally characterized, our main goal is to predict protein-protein interactions during the early phases of the infection. Methodology. In this work, we performed an integrative computational approach that exploits differentially expressed genes obtained from Dual RNA-seq analysis combined with known domain-domain interactions. Results. A total of 2381 and 7214 genes were identified as differentially expressed in M. tb and in THP-1-like macrophages, respectively, revealing different transcriptional profiles in response to infection. Over 48 h of infection, the host-pathogen network revealed 25 016 PPIs. Analysis of the resulting predicted network based on cellular localization information of M. tb proteins, indicated the implication of interacting nodes including the bacterial PE/PPE/PE_PGRS family. In addition, M. tb proteins interacted with host proteins involved in NF-kB signalling pathway as well as interfering with the host apoptosis ability via the potential interaction of M. tb TB16.3 with human TAB1 and M. tb GroEL2 with host protein kinase C delta, respectively. Conclusion. The prediction of the full range of interactions between M. tb and host will contribute to better understanding of the pathogenesis of this bacterium and may provide advanced approaches to explore new therapeutic targets against tuberculosis.

As one of the most studied Apicomplexan parasite Cryptosporidium, Cryptosporidium parvum ( C. parvum ) causes worldwide serious diarrhea disease cryptosporidiosis, which can be deadly to immunodeficiency individuals, newly born children, and animals. Proteome-wide identification of protein-protein interactions (PPIs) has proven valuable in the systematic understanding of the genome-phenome relationship. However, the PPIs of C. parvum are largely unknown because of the limited experimental studies carried out. Therefore, we took full advantage of three bioinformatics methods, i.e., interolog mapping (IM), domain-domain interaction (DDI)-based inference, and machine learning (ML) method, to jointly predict PPIs of C. parvum . Due to the lack of experimental PPIs of C. parvum , we used the PPI data of Plasmodium falciparum ( P. falciparum ), which owned the largest number of PPIs in Apicomplexa, to train an ML model to infer C. parvum PPIs. We utilized consistent results of these three methods as the predicted high-confidence PPI network, which contains 4,578 PPIs covering 554 proteins. To further explore the biological significance of the constructed PPI network, we also conducted essential network and protein functional analysis, mainly focusing on hub proteins and functional modules. We anticipate the constructed PPI network can become an important data resource to accelerate the functional genomics studies of C. parvum as well as offer new hints to the target discovery in developing drugs/vaccines., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Author(s).)