DisintegrinDB: The first integrated database resource of disintegrins from snake venoms.

Nowadays, a large number of databases have been developed gathering different types of therapeutic peptides including antimicrobial, antiviral and scorpion toxins peptides facilitating the searching for these molecules and their structural characteristics and pharmacology. Disintegrins, a family of small non-enzymatic and cysteine-rich proteins found in the snake venom may have a potential role in terms of novel therapeutic leads for cancer treatment. Despite their therapeutic effect, no database dedicated to disintegrins is available yet. Indeed, accessible information related to disintegrins are either scattered or fragmented in different databases from which it becomes extremely difficult to collect all the properties related to a particular disintegrin without exploring numerous databases available through distinct websites. Here, we propose DisintegrinDB as a first unique resource centralizing data related to disintegrins from snake venom. DisintegrinDB aims to facilitate the search on a given disintegrin and centralizes all the information on these peptides, helping researchers to retrieve all relevant related information.

The First Snake Venom KTS/Disintegrins-Integrin Interactions Using Bioinformatics Approaches.

Snake venom contains a number of active molecules that have been shown to possess high anti-tumor activities; disintegrins are an excellent example among these. Their ability to interact and bind with integrins suggests that they could be very valuable molecules for the development of new cancer therapeutic approaches. However, in the absence of a clear Lysine-Threonine-Serine (KTS) Disintegrins Integrin interaction model, the exact compound features behind it are still unknown. In this study, we investigated the structural characteristics of three KTS-disintegrins and the interaction mechanisms with the α1ß1 integrin receptor using in silico bioinformatics approaches. Normal mode analysis showed that the flexibility of the KTSR motif and the C-terminal region play a key role and influence the KTS-Disintegrin-integrin interaction. Protein-protein docking also suggested that the interaction involving the KTSR motif is highly dependent on the residue following K21, S23 and R24. These findings contribute to a better understanding of the KTS-Disintegrin-Integrin structural differences and their interactions with α1ß1 receptors, which could improve the selection process of the best active molecules for antitumor therapies.

Mycobacterium tuberculosis-THP-1 like macrophages protein-protein interaction map revealed through dual RNA-seq analysis and a computational approach.

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.

PHINDaccess Hackathons for COVID-19 and Host-Pathogen Interaction: Lessons Learned and Recommendations for Low- and Middle-Income Countries.

Hackathons are collaborative events that bring together diverse groups to solve predefined challenges. The COVID-19 pandemic caused by SARS-CoV-2 has emphasized the need for portable and reproducible genomics analysis pipelines to study the genetic susceptibility of the human host and investigate human-SARS-CoV-2 protein interactions. To build and strengthen institutional capacities in OMICS data analysis applied to host-pathogen interaction (HPI), the PHINDaccess project organized two hackathons in 2020 and 2021. These hackathons are aimed at developing bioinformatics pipelines related to the SARS-CoV-2 viral genome, its phylodynamic transmission, and the identification of human genome host variants, with a focus on addressing global health challenges, particularly in low- and middle-income countries (LMIC). This paper outlines the preparation, proceedings, and lessons learned from these hackathons, including the challenges faced by participants and our recommendations based on our experience for organizing hackathons in LMIC and beyond.

Relationships between Virulence Genes and Antibiotic Resistance Phenotypes/Genotypes in Campylobacter spp. Isolated from Layer Hens and Eggs in the North of Tunisia: Statistical and Computational Insights.

Globally, Campylobacter is a significant contributor to gastroenteritis. Efficient pathogens are qualified by their virulence power, resistance to antibiotics and epidemic spread. However, the correlation between antimicrobial resistance (AR) and the pathogenicity power of pathogens is complex and poorly understood. In this study, we aimed to investigate genes encoding virulence and AR mechanisms in 177 Campylobacter isolates collected from layer hens and eggs in Tunisia and to assess associations between AR and virulence characteristics. Virulotyping was determined by searching 13 virulence genes and AR-encoding genes were investigated by PCR and MAMA-PCR. The following genes were detected in C. jejuni and C. coli isolates: tet(O) (100%/100%), blaOXA-61 (18.82%/6.25%), and cmeB (100%/100%). All quinolone-resistant isolates harbored the Thr-86-Ile substitution in GyrA. Both the A2074C and A2075G mutations in 23S rRNA were found in all erythromycin-resistant isolates; however, the erm(B) gene was detected in 48.38% and 64.15% of the C. jejuni and C. coli isolates, respectively. The machine learning algorithm Random Forest was used to determine the association of virulence genes with AR phenotypes. This analysis showed that C. jejuni virulotypes with gene clusters encompassing the racR, ceuE, virB11, and pldA genes were strongly associated with the majority of phenotypic resistance. Our findings showed high rates of AR and virulence genes among poultry Campylobacter, which is a cause of concern to human health. In addition, the correlations of specific virulence genes with AR phenotypes were established by statistical analysis.