Pathway based therapeutic targets identification and development of an interactive database CampyNIBase of Campylobacter jejuni RM1221 through non-redundant protein dataset

Two-component regulatory systems in Helicobacter pylori and Campylobacter jejuni: Attractive targets for novel antibacterial drugs

Two-component regulatory systems (TCRS) are ubiquitous signal transduction mechanisms evolved by bacteria for sensing and adapting to the constant changes that occur in their environment. Typically consisting of two types of proteins, a membrane sensor kinase and an effector cytosolic response regulator, the TCRS modulate via transcriptional regulation a plethora of key physiological processes, thereby becoming essential for bacterial viability and/or pathogenicity and making them attractive targets for novel antibacterial drugs. Some members of the phylum Campylobacterota (formerly Epsilonproteobacteria), including Helicobacter pylori and Campylobacter jejuni, have been classified by WHO as “high priority pathogens” for research and development of new antimicrobials due to the rapid emergence and dissemination of resistance mechanisms against first-line antibiotics and the alarming increase of multidrug-resistant strains worldwide. Notably, these clinically relevant pathogens express a variety of TCRS and orphan response regulators, sometimes unique among its phylum, that control transcription, translation, energy metabolism and redox homeostasis, as well as the expression of relevant enzymes and virulence factors. In the present mini-review, we describe the signalling mechanisms and functional diversity of TCRS in H. pylori and C. jejuni, and provide an overview of the most recent findings in the use of these microbial molecules as potential novel therapeutic targets for the development of new antibiotics.

Comprehensive Analysis of Chemical Structures That Have Been Tested as CFTR Activating Substances in a Publicly Available Database CandActCFTR

Background: Cystic fibrosis (CF) is a genetic disease caused by mutations in CFTR, which encodes a chloride and bicarbonate transporter expressed in exocrine epithelia throughout the body. Recently, some therapeutics became available that directly target dysfunctional CFTR, yet research for more effective substances is ongoing. The database CandActCFTR aims to provide detailed and comprehensive information on candidate therapeutics for the activation of CFTR-mediated ion conductance aiding systems-biology approaches to identify substances that will synergistically activate CFTR-mediated ion conductance based on published data. Results: Until 10/2020, we derived data from 108 publications on 3,109 CFTR-relevant substances via the literature database PubMed and further 666 substances via ChEMBL; only 19 substances were shared between these sources. One hundred and forty-five molecules do not have a corresponding entry in PubChem or ChemSpider, which indicates that there currently is no single comprehensive database on chemical substances in the public domain. Apart from basic data on all compounds, we have visualized the chemical space derived from their chemical descriptors via a principal component analysis annotated for CFTR-relevant biological categories. Our online query tools enable the search for most similar compounds and provide the relevant annotations in a structured way. The integration of the KNIME software environment in the back-end facilitates a fast and user-friendly maintenance of the provided data sets and a quick extension with new functionalities, e.g., new analysis routines. CandActBase automatically integrates information from other online sources, such as synonyms from PubChem and provides links to other resources like ChEMBL or the source publications. Conclusion: CandActCFTR aims to establish a database model of candidate cystic fibrosis therapeutics for the activation of CFTR-mediated ion conductance to merge data from publicly available sources. Using CandActBase, our strategy to represent data from several internet resources in a merged and organized form can also be applied to other use cases. For substances tested as CFTR activating compounds, the search function allows users to check if a specific compound or a closely related substance was already tested in the CF field. The acquired information on tested substances will assist in the identification of the most promising candidates for future therapeutics.

Efficacy of Plant Growth-Promoting Bacteria Bacillus cereus YN917 for Biocontrol of Rice Blast

Bacillus cereus YN917, obtained from a rice leaf with remarkable antifungal activity against Magnaporthe oryzae, was reported in our previous study. The present study deciphered the possible biocontrol properties. YN917 strain exhibits multiple plant growth-promoting and disease prevention traits, including production of indole-3-acetic acid (IAA), ACC deaminase, siderophores, protease, amylase, cellulase, and β-1,3-glucanase, and harboring mineral phosphate decomposition activity. The effects of the strain YN917 on growth promotion and disease prevention were further evaluated under detached leaf and greenhouse conditions. The results revealed that B. cereus YN917 can promote seed germination and seedling plant growth. The growth status of rice plants was measured from the aspects of rice plumule, radicle lengths, plant height, stem width, root lengths, fresh weights, dry weights, and root activity when YN917 was used as inoculants. YN917 significantly reduced rice blast severity under detached leaf and greenhouse conditions. Genome analysis revealed the presence of gene clusters for biosynthesis of plant promotion and antifungal compounds, such as IAA, tryptophan, siderophores, and phenazine. In summary, YN917 can not only be used as biocontrol agents to minimize the use of chemical substances in rice blast control, but also can be developed as bio-fertilizers to promote the rice plant growth.

Recognition of plausible therapeutic agents to combat COVID-19: An omics data based combined approach

Coronavirus disease-2019 (COVID-19), caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), has become an immense threat to global public health. In this study, we performed complete genome sequencing of a SARS-CoV-2 isolate. More than 67,000 genome sequences were further inspected from Global Initiative on Sharing All Influenza Data (GISAID). Using several in silico techniques, we proposed prospective therapeutics against this virus. Through meticulous analysis, several conserved and therapeutically suitable regions of SARS-CoV-2 such as RNA-dependent RNA polymerase (RdRp), Spike (S) and Membrane glycoprotein (M) coding genes were selected. Both S and M were chosen for the development of a chimeric vaccine that can generate memory B and T cells. siRNAs were also designed for S and M gene silencing. Moreover, six new drug candidates were suggested that might inhibit the activity of RdRp. Since SARS-CoV-2 and SARS-CoV-1 have 82.30% sequence identity, a Gene Expression Omnibus (GEO) dataset of Severe Acute Respiratory Syndrome (SARS) patients were analyzed. In this analysis, 13 immunoregulatory genes were found that can be used to develop type 1 interferon (IFN) based therapy. The proposed vaccine, siRNAs, drugs and IFN based analysis of this study will accelerate the development of new treatments.

Novel Approaches for Systems Biology of Metabolism-Oriented Pathogen-Human Interactions: A Mini-Review

Pathogenic microorganisms exploit host metabolism for sustained survival by rewiring its metabolic interactions. Therefore, several metabolic changes are induced in both pathogen and host cells in the course of infection. A systems-based approach to elucidate those changes includes the integrative use of genome-scale metabolic networks and molecular omics data, with the overall goal of better characterizing infection mechanisms for novel treatment strategies. This review focuses on novel aspects of metabolism-oriented systems-based investigation of pathogen-human interactions. The reviewed approaches are the generation of dual-omics data for the characterization of metabolic signatures of pathogen-host interactions, the reconstruction of pathogen-host integrated genome-scale metabolic networks, which has a high potential to be applied to pathogen-gut microbiota interactions, and the structure-based analysis of enzymes playing role in those interactions. The integrative use of those approaches will pave the way for the identification of novel biomarkers and drug targets for the prediction and prevention of infectious diseases.