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%), bla_OXA-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.

Leishmaniasis is a protozoal vector-borne disease that affects both humans and animals. In the Mediterranean Basin, the primary reservoir hosts of Leishmania spp. are mainly rodents and canids. Lipidomic approaches have allowed scientists to establish Leishmania spp. lipid profiles for the identification of cell stage specific biomarkers, drug mechanisms of action, and host immune response. Using an in silico approach of global network interaction between genes involved in fatty acid (FA) synthesis followed by the GC-MS approach, we were able to characterize the fatty acid profiles of L. major derived from human and rodent hosts. Our results revealed that the lipid profile of L. major showed similarities and differences with those already reported for other Leishmania species. Phospholipids are the predominant lipid class. FA composition of rodent parasites was characterized by a lower abundance of the precursor C18:2(n-6). One of the rodent clones, which also expressed the lowest lipid abundance in PL and TAG, was the least sensitive clone to the miltefosine drug and has the lowest infection efficiency. Our findings suggest that the lipid composition variation may explain the response of the parasite toward treatment and their ability to infect their host.

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 α1β1 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 α1β1 receptors, which could improve the selection process of the best active molecules for antitumor therapies.

Background: The advent of high-throughput technologies, including cutting-edge sequencing devices, has revolutionized biomedical data generation and processing. Nevertheless, big data applications require novel hardware and software for parallel computing and management to handle the ever-growing data size and analysis complexity. On-premise, high-performance computing (HPC) is increasingly used in biomedical research for big data stewardship. Findings: In this work, we present Tunisia’s first high-performance computational infrastructure for omics research. Method: We highlight measurements and recommendations that may help institutions in other low- and middle-income countries that are eager to implement local HPC in facilities for bioinformatics research and omics data analyses.

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.