Identifying co-targets to fight drug resistance based on a random walk model

Abstract- Drug resistance has now posed more severe and emergent threats to human health and infectious disease treatment. However, the wet-lab approaches alone to counter drug resistance have so far achieved limited success in understanding the underlying mechanisms and pathways of drug resistance. Our approach applied A * heuristic search algorithm in order to extract drug response pathways from protein-protein interaction networks and to identify the co-target for effective antibacterial drugs. In this paper, we chose one of the killer infectious diseases, Mycobacterium Tuberculosis as our test bed. The results showed that the acetyl-CoA carboxylase is believed to be involved in fatty acid and mycolic acid biosynthesis and is strongly associated with the drug resistance mechanisms. Our analysis are consistent with the recent experimental results and also found alanine and glycine rich membrane and cell wall-associated lipoproteins to be potential co-targets for countering drug resistance. keywords: Drug resistance, Co-target, Random walk, Mycobacterium Tuberculosi

targetTB: A target identification pipeline for Mycobacterium tuberculosis through an interactome, reactome and genome-scale structural analysis

<p>Abstract</p> <p>Background</p> <p>Tuberculosis still remains one of the largest killer infectious diseases, warranting the identification of newer targets and drugs. Identification and validation of appropriate targets for designing drugs are critical steps in drug discovery, which are at present major bottle-necks. A majority of drugs in current clinical use for many diseases have been designed without the knowledge of the targets, perhaps because standard methodologies to identify such targets in a high-throughput fashion do not really exist. With different kinds of 'omics' data that are now available, computational approaches can be powerful means of obtaining short-lists of possible targets for further experimental validation.</p> <p>Results</p> <p>We report a comprehensive <it>in silico </it>target identification pipeline, targetTB, for <it>Mycobacterium tuberculosis</it>. The pipeline incorporates a network analysis of the protein-protein interactome, a flux balance analysis of the reactome, experimentally derived phenotype essentiality data, sequence analyses and a structural assessment of targetability, using novel algorithms recently developed by us. Using flux balance analysis and network analysis, proteins critical for survival of <it>M. tuberculosis </it>are first identified, followed by comparative genomics with the host, finally incorporating a novel structural analysis of the binding sites to assess the feasibility of a protein as a target. Further analyses include correlation with expression data and non-similarity to gut flora proteins as well as 'anti-targets' in the host, leading to the identification of 451 high-confidence targets. Through phylogenetic profiling against 228 pathogen genomes, shortlisted targets have been further explored to identify broad-spectrum antibiotic targets, while also identifying those specific to tuberculosis. Targets that address mycobacterial persistence and drug resistance mechanisms are also analysed.</p> <p>Conclusion</p> <p>The pipeline developed provides rational schema for drug target identification that are likely to have high rates of success, which is expected to save enormous amounts of money, resources and time in the drug discovery process. A thorough comparison with previously suggested targets in the literature demonstrates the usefulness of the integrated approach used in our study, highlighting the importance of systems-level analyses in particular. The method has the potential to be used as a general strategy for target identification and validation and hence significantly impact most drug discovery programmes.</p

Genetic signatures of Mycobacterium tuberculosis Nonthaburi genotype revealed by whole genome analysis of isolates from tuberculous meningitis patients in Thailand.

Genome sequencing plays a key role in understanding the genetic diversity of Mycobacterium tuberculosis (M.tb). The genotype-specific character of M. tb contributes to tuberculosis severity and emergence of drug resistance. Strains of M. tb complex can be classified into seven lineages. The Nonthaburi (NB) genotype, belonging to the Indo-Oceanic lineage (lineage 1), has a unique spoligotype and IS6110-RFLP pattern but has not previously undergone a detailed whole genome analysis. In addition, there is not much information available on the whole genome analysis of M. tb isolates from tuberculous meningitis (TBM) patients in public databases. Isolates CSF3053, 46-5069 and 43-13838 of NB genotype were obtained from the cerebrospinal fluids of TBM Thai patients in Siriraj Hospital, Bangkok. The whole genomes were subjected to high throughput sequencing. The sequence data of each isolate were assembled into draft genome. The sequences were also aligned to reference genome, to determine genomic variations. Single nucleotide polymorphisms (SNPs) were obtained and grouped according to the functions of the genes containing them. They were compared with SNPs from 1,601 genomes, representing the seven lineages of M. tb complex, to determine the uniqueness of NB genotype. Susceptibility to first-line, second-line and other antituberculosis drugs were determined and related to the SNPs previously reported in drug-resistant related genes. The assembled genomes have an average size of 4,364,461 bp, 4,154 genes, 48 RNAs and 64 pseudogenes. A 500 base pairs deletion, which includes ppe50, was found in all isolates. RD239, specific for members of Indo Oceanic lineage, and RD147c were identified. A total of 2,202 SNPs were common to the isolates and used to classify the NB strains as members of sublineage 1.2.1. Compared with 1,601 genomes from the seven lineages of M. tb complex, mutation G2342203C was found novel to the isolates in this study. Three mutations (T28910C, C1180580T and C152178T) were found only in Thai NB isolates, including isolates from previous study. Although drug susceptibility tests indicated pan-susceptibility, non-synonymous SNPs previously reported to be associated with resistance to anti-tuberculous drugs; isoniazid, ethambutol, and ethionamide were identified in all the isolates. Non-synonymous SNPs were found in virulence genes such as the genes playing roles in apoptosis inhibition and phagosome arrest. We also report polymorphisms in essential genes, efflux pumps associated genes and genes with known epitopes. The analysis of the TBM isolates and the availability of the variations obtained will provide additional resources for global comparison of isolates from pulmonary tuberculosis and TBM. It will also contribute to the richness of genomic databases towards the prediction of antibiotic resistance, level of virulence and of origin of infection

Association Between Circulating CD4+ T Cell Methylation Signatures of Network-Oriented SOCS3 Gene and Hemodynamics in Patients Suffering Pulmonary Arterial Hypertension

Pathogenic DNA methylation changes may be involved in pulmonary arterial hypertension (PAH) onset and its progression, but there is no data on potential associations with patient-derived hemodynamic parameters. The reduced representation bisulfite sequencing (RRBS) platform identified N= 631 differentially methylated CpG sites which annotated to N= 408 genes (DMGs) in circulating CD4(+) T cells isolated from PAH patients vs. healthy controls (CTRLs). A promoter-restricted network analysis established the PAH subnetwork that included 5 hub DMGs (SOCS3, GNAS, ITGAL, NCOR2, NFIC) and 5 non-hub DMGs (NR4A2, GRM2, PGK1, STMN1, LIMS2). The functional analysis revealed that the SOCS3 gene was the most recurrent among the top ten significant pathways enriching the PAH subnetwork, including the growth hormone receptor and the interleukin-6 signaling. Correlation analysis showed that the promoter methylation levels of each network-oriented DMG were associated individually with hemodynamic parameters. In particular, SOCS3 hypomethylation was negatively associated with right atrial pressure (RAP) and positively associated with cardiac index (CI) (vertical bar r vertical bar &gt;= 0.6). A significant upregulation of the SOCS3, ITGAL, NFIC, NCOR2, and PGK1 mRNA levels (qRT-PCR) in peripheral blood mononuclear cells from PAH patients vs. CTRLs was found (P &lt;= 0.05). By immunoblotting, a significant upregulation of the SOCS3 protein was confirmed in PAH patients vs. CTRLs (P &lt; 0.01). This is the first network-oriented study which integrates circulating CD4(+) T cell DNA methylation signatures, hemodynamic parameters, and validation experiments in PAH patients at first diagnosis or early follow-up. Our data suggests that SOCS3 gene might be involved in PAH pathogenesis and serve as potential prognostic biomarker

Integrated Bio-Entity Network: A System for Biological Knowledge Discovery

A significant part of our biological knowledge is centered on relationships between biological entities (bio-entities) such as proteins, genes, small molecules, pathways, gene ontology (GO) terms and diseases. Accumulated at an increasing speed, the information on bio-entity relationships is archived in different forms at scattered places. Most of such information is buried in scientific literature as unstructured text. Organizing heterogeneous information in a structured form not only facilitates study of biological systems using integrative approaches, but also allows discovery of new knowledge in an automatic and systematic way. In this study, we performed a large scale integration of bio-entity relationship information from both databases containing manually annotated, structured information and automatic information extraction of unstructured text in scientific literature. The relationship information we integrated in this study includes protein–protein interactions, protein/gene regulations, protein–small molecule interactions, protein–GO relationships, protein–pathway relationships, and pathway–disease relationships. The relationship information is organized in a graph data structure, named integrated bio-entity network (IBN), where the vertices are the bio-entities and edges represent their relationships. Under this framework, graph theoretic algorithms can be designed to perform various knowledge discovery tasks. We designed breadth-first search with pruning (BFSP) and most probable path (MPP) algorithms to automatically generate hypotheses—the indirect relationships with high probabilities in the network. We show that IBN can be used to generate plausible hypotheses, which not only help to better understand the complex interactions in biological systems, but also provide guidance for experimental designs

Nutrients, herbal bioactive derivatives and commensal microbiota as tools to lower the risk of SARS-CoV-2 infection

The SARS-CoV-2 outbreak has infected a vast population across the world, causing more than 664 million cases and 6.7 million deaths by January 2023. Vaccination has been effective in reducing the most critical aftermath of this infection, but some issues are still present regarding re-infection prevention, effectiveness against variants, vaccine hesitancy and worldwide accessibility. Moreover, although several old and new antiviral drugs have been tested, we still lack robust and specific treatment modalities. It appears of utmost importance, facing this continuously growing pandemic, to focus on alternative practices grounded on firm scientific bases. In this article, we aim to outline a rigorous scientific background and propose complementary nutritional tools useful toward containment, and ultimately control, of SARS-CoV-2 infection. In particular, we review the mechanisms of viral entry and discuss the role of polyunsaturated fatty acids derived from α-linolenic acid and other nutrients in preventing the interaction of SARS-CoV-2 with its entry gateways. In a similar way, we analyze in detail the role of herbal-derived pharmacological compounds and specific microbial strains or microbial-derived polypeptides in the prevention of SARS-CoV-2 entry. In addition, we highlight the role of probiotics, nutrients and herbal-derived compounds in stimulating the immunity response

Host-directed therapy for bacterial infections -Modulation of the phagolysosome pathway-

Bacterial infections still impose a significant burden on humanity, even though antimicrobial agents have long since been developed. In addition to individual severe infections, the f fatality rate of sepsis remains high, and the threat of antimicrobial-resistant bacteria grows with time, putting us at inferiority. Although tremendous resources have been devoted to the development of antimicrobial agents, we have yet to recover from the lost ground we have been driven into. Looking back at the evolution of treatment for cancer, which, like infectious diseases, has the similarity that host immunity eliminates the lesion, the development of drugs to eliminate the tumor itself has shifted from a single-minded focus on drug development to the establishment of a treatment strategy in which the de-suppression of host immunity is another pillar of treatment. In infectious diseases, on the other hand, the development of therapies that strengthen and support the immune system has only just begun. Among innate immunity, the first line of defense that bacteria encounter after invading the host, the molecular mechanisms of the phagolysosome pathway, which begins with phagocytosis to fusion with lysosome, have been elucidated in detail. Bacteria have a large number of strategies to escape and survive the pathway. Although the full picture is still unfathomable, the molecular mechanisms have been elucidated for some of them, providing sufficient clues for intervention. In this article, we review the host defense mechanisms and bacterial evasion mechanisms and discuss the possibility of host-directed therapy for bacterial infection by intervening in the phagolysosome pathway

Going viral : an integrated view on virological data analysis from basic research to clinical applications

Viruses are of considerable interest for several fields of life science research. The genomic richness of these entities, their environmen- tal abundance, as well as their high adaptability and, potentially, pathogenicity make treatment of viral diseases challenging. This thesis proposes three novel contributions to antiviral research that each concern analysis procedures of high-throughput experimen- tal genomics data. First, a sensitive approach for detecting viral genomes and transcripts in sequencing data of human cancers is presented that improves upon prior approaches by allowing de- tection of viral nucleotide sequences that consist of human-viral homologs or are diverged from known reference sequences. Sec- ond, a computational method for inferring physical protein contacts from experimental protein complex purification assays is put for- ward that allows statistically meaningful integration of multiple data sets and is able to infer protein contacts of transiently binding protein classes such as kinases and molecular chaperones. Third, an investigation of minute changes in viral genomic populations upon treatment of patients with the mutagen ribavirin is presented that first characterizes the mutagenic effect of this drug on the hepatitis C virus based on deep sequencing data.Viren sind von beträchtlichem Interesse für die biowissenschaftliche Forschung. Der genetische Reichtum, die hohe Vielfalt, wie auch die Anpassungsfähigkeit und mögliche Pathogenität dieser Organismen erschwert die Behandlung von viralen Erkrankungen. Diese Promotionsschrift enthält drei neuartige Beiträge zur antiviralen Forschung welche die Analyse von experimentellen Hochdurchsatzdaten der Genomik betreffen: erstens, ein sensitiver Ansatz zur Entdeckung viraler Genome und Transkripte in Sequenzdaten humaner Karzinome, der die Identifikation von viralen Nukleotidsequenzen ermöglicht, die von Referenzgenomen ab- weichen oder homolog zu humanen Faktoren sind. Zweitens, eine computergestützte Methode um physische Proteinkontakte von experimentellen Proteinkomplex-Purifikationsdaten abzuleiten welche die statistische Integration von mehreren Datensätzen erlaubt um insbesondere Proteinkontakte von flüchtig interagierenden Proteinklassen wie etwa Kinasen und Chaperonen aus den Daten ableiten zu können. Drittens, eine Untersuchung von kleinsten Änderungen viraler Genompopulationen während der Behandlung von Patienten mit dem Mutagen ribavirin die zum ersten Mal die mutagene Wirkung dieses Medikaments auf das Hepatitis C Virus mittels Tiefensequenzdaten nachweist