Search

The transition from MIRU-VNTR-based epidemiology studies in tuberculosis (TB) to genomic epidemiology has transformed how we track transmission. However, short-read sequencing is poor at analyzing repetitive regions such as the MIRU-VNTR loci. This causes a gap between the new genomic data and the large amount of information stored in historical databases. Long-read sequencing could bridge this knowledge gap by allowing analysis of repetitive regions. However, the feasibility of extracting MIRU-VNTRs from long reads and linking them to historical data has not been evaluated. In our study, an in silico arm, consisting of inference of MIRU patterns from long-read sequences (using MIRUReader program), was compared with an experimental arm, involving standard amplification and fragment sizing. We analyzed overall performance on 39 isolates from South Africa and confirmed reproducibility in a sample enriched with 62 clustered cases from Spain. Finally, we ran 25 consecutive incident cases, demonstrating the feasibility of correctly assigning new clustered/orphan cases by linking data inferred from genomic analysis to MIRU-VNTR databases. Of the 3,024 loci analyzed, only 11 discrepancies (0.36%) were found between the two arms: three attributed to experimental error and eight to misassigned alleles from long-read sequencing. A second round of analysis of these discrepancies resulted in agreement between the experimental and in silico arms in all but one locus. Adjusting the MIRUReader program code allowed us to flag potential in silico misassignments due to suboptimal coverage or unfixed double alleles. Our study indicates that long-read sequencing could help address potential chronological and geographical gaps arising from the transition from molecular to genomic epidemiology of tuberculosis., Importance: The transition from molecular epidemiology in tuberculosis (TB), based on the analysis of repetitive regions (VNTR-based genotyping), to genomic epidemiology transforms in the precision with which we track transmission. However, short-read sequencing, the most common method for performing genomic analysis, is poor at analyzing repetitive regions. This means that we face a gap between the new genomic data and the large amount of information stored in historical databases, which is also an obstacle to cross-national surveillance involving settings where only molecular data are available. Long-read sequencing could help bridge this knowledge gap by allowing analysis of repetitive regions. Our study demonstrates that MIRU-VNTR patterns can be successfully inferred from long-read sequences, allowing the correct assignment of new cases as clustered/orphan by linking new data extracted from genomic analysis to historical MIRU-VNTR databases. Our data may provide a starting point for bridging the knowledge gap between the molecular and genomic eras in tuberculosis epidemiology., Competing Interests: The authors declare no conflict of interest.

Drug resistance is a major cause of the inefficacy of conventional cancer therapies, and often accompanied by severe side effects. Thus, there is an urgent need to develop novel drugs with low cytotoxicity, high selectivity and minimal acquired chemical resistance. Peptide-based drugs (less than 0.5 kDa) have emerged as a potential approach to address these issues due to their high specificity and potent anticancer activity. In this study, we developed a support vector machine model (SVM) to detect the potential anticancer properties of novel peptides by scanning the American University in Cairo (AUC) Red Sea metagenomics library. We identified a novel 37-mer antimicrobial peptide through SVM pipeline analysis and characterized its anticancer potential through in silico cross-examination. The peptide sequence was further modified to enhance its anticancer activity, analyzed for gene ontology, and subsequently synthesized. To evaluate the anticancer properties of the modified 37-mer peptide, we assessed its effect on the viability and morphology of SNU449, HepG2, SKOV3, and HeLa cells, using an MTT assay. Additionally, we evaluated the migration capabilities of SNU449 and SKOV3 cells using a scratch-wound healing assay. The targeted selectivity of the modified peptide was examined by evaluating its hemolytic activity on human erythrocytes. Treatment with the peptide significantly reduced cell viability and had a critical impact on the morphology of hepatocellular carcinoma (SNU449 and HepG2), and ovarian cancer (SKOV3) cells, with a marginal effect on cervical cancer cell lines (HeLa). The viability of a human fibroblast cell line (1Br-hTERT) was also significantly reduced by peptide treatment, as were the proliferation and migration abilities of SNU449 and SKOV3 cells. The annexin V assay revealed programmed cell death (apoptosis) as one of the potential cellular death pathways in SNU449 cells upon peptide treatment. Finally, the peptide exhibited antimicrobial effects on both gram-positive and gram-negative bacterial strains. The findings presented here suggest the potential of our novel peptide as a potent anticancer and antimicrobial agent., (© 2023. The Author(s).)