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Abstract Background The COVID-19 pandemic remains a global public health concern. Advances in sequencing technologies has allowed for high numbers of SARS-CoV-2 whole genome sequence (WGS) data and rapid sharing of sequences through global repositories to enable almost real-time genomic analysis of the pathogen. WGS data has been used previously to group genetically similar viral pathogens to reveal evidence of transmission, including methods that identify distinct clusters on a phylogenetic tree. Identifying clusters of linked cases can aid in the regional surveillance and management of the disease. In this study, we present a novel method for producing stable genomic clusters of SARS-CoV-2 cases, cov2clusters, and compare the accuracy and stability of our approach to previous methods used for phylogenetic clustering using real-world SARS-CoV-2 sequence data obtained from British Columbia, Canada. Results We found that cov2clusters produced more stable clusters than previously used phylogenetic clustering methods when adding sequence data through time, mimicking an increase in sequence data through the pandemic. Our method also showed high accuracy when predicting epidemiologically informed clusters from sequence data. Conclusions Our new approach allows for the identification of stable clusters of SARS-CoV-2 from WGS data. Producing high-resolution SARS-CoV-2 clusters from sequence data alone can a challenge and, where possible, both genomic and epidemiological data should be used in combination.

Abstract Background Antibiotic resistance is a growing global health concern prompting researchers to seek alternatives to conventional antibiotics. Antimicrobial peptides (AMPs) are attracting attention again as therapeutic agents with promising utility in this domain, and using in silico methods to discover novel AMPs is a strategy that is gaining interest. Such methods can sift through large volumes of candidate sequences and reduce lab screening costs. Results Here we introduce AMPlify, an attentive deep learning model for AMP prediction, and demonstrate its utility in prioritizing peptide sequences derived from the Rana [Lithobates] catesbeiana (bullfrog) genome. We tested the bioactivity of our predicted peptides against a panel of bacterial species, including representatives from the World Health Organization’s priority pathogens list. Four of our novel AMPs were active against multiple species of bacteria, including a multi-drug resistant isolate of carbapenemase-producing Escherichia coli. Conclusions We demonstrate the utility of deep learning based tools like AMPlify in our fight against antibiotic resistance. We expect such tools to play a significant role in discovering novel candidates of peptide-based alternatives to classical antibiotics.

Antimicrobial peptides (AMPs) are a diverse class of short, often cationic biological molecules that present promising opportunities in the development of new therapeutics to combat antimicrobial resistance. Newly developed in silico methods offer the ability to rapidly discover numerous novel AMPs with a variety of physiochemical properties. Herein, using the rAMPage AMP discovery pipeline, we bioinformatically identified 51 AMP candidates from amphibia and insect RNA-seq data and present their in-depth characterization. The studied AMPs demonstrate activity against a panel of bacterial pathogens and have undetected or low toxicity to red blood cells and human cultured cells. Amino acid sequence analysis revealed that 30 of these bioactive peptides belong to either the Brevinin-1, Brevinin-2, Nigrocin-2, or Apidaecin AMP families. Prediction of three-dimensional structures using ColabFold indicated an association between peptides predicted to adopt a helical structure and broad-spectrum antibacterial activity against the Gram-negative and Gram-positive species tested in our panel. These findings highlight the utility of associating the diverse sequences of novel AMPs with their estimated peptide structures in categorizing AMPs and predicting their antimicrobial activity.

Antibiotic resistance is a global health crisis increasing in prevalence every day. To combat this crisis, alternative antimicrobial therapeutics are urgently needed. Antimicrobial peptides (AMPs), a family of short defense proteins, are produced naturally by all organisms and hold great potential as effective alternatives to small molecule antibiotics. Here, we present rAMPage, a scalable bioinformatics discovery platform for identifying AMP sequences from RNA sequencing (RNA-seq) datasets. In our study, we demonstrate the utility and scalability of rAMPage, running it on 84 publicly available RNA-seq datasets from 75 amphibian and insect species—species known to have rich AMP repertoires. Across these datasets, we identified 1137 putative AMPs, 1024 of which were deemed novel by a homology search in cataloged AMPs in public databases. We selected 21 peptide sequences from this set for antimicrobial susceptibility testing against Escherichia coli and Staphylococcus aureus and observed that seven of them have high antimicrobial activity. Our study illustrates how in silico methods such as rAMPage can enable the fast and efficient discovery of novel antimicrobial peptides as an effective first step in the strenuous process of antimicrobial drug development.

We evaluated the performance of 16S and internal transcribed spacer (ITS) region amplification and sequencing of rDNA from clinical specimens, for the respective detection and identification of bacterial and fungal pathogens. Direct rDNA amplification of 16S and ITS targets from clinical samples was performed over a 4-year period and reviewed. All specimens were from sterile sites and submitted to a reference laboratory for evaluation. Results of 16S and ITS were compared to histopathology, Gram and/or calcofluor stain microscopy results. A total of 277 16S tests were performed, with 64 (23%) positive for the presence of bacterial DNA. Identification of an organism was more likely in microscopy positive 16S samples 14/21 (67%), compared to 35/175 (20%) of microscopy negative samples. A total of 110 ITS tests were performed, with 14 (13%) positive. The yield of microscopy positive ITS samples, 9/44 (21%), was higher than microscopy negative samples 3/50 (6%). Given these findings, 16S and ITS are valuable options for culture negative specimens from sterile sites, particularly in the setting of positive microscopy findings. Where microscopy results are negative, the limited sensitivity of 16S and ITS in detecting and identifying an infectious agent needs to be considered.

Background. Clostridium difficile is a major cause of gastrointestinal illness. Epidemic NAP1 strains contain toxins A and B, a deletion in repressor tcdC, and a binary toxin. Objectives. To determine the molecular epidemiology of C. difficile in British Columbia and compare between two time points in one region. Methods. C. difficile isolates from hospital and community laboratories (2008) and one Island Health hospital laboratory (2013) were characterized by pulsed-field gel electrophoresis, PCR-ribotyping, toxin possession, tcdC genotype, and antimicrobial susceptibility. Results. In 2008, 42.7% of isolates had NAP1 designation. Hospital-collected isolates were associated with older patients and more NAP1 types. Unlike other isolates, most NAP1 isolates possessed binary toxin and a 19 bp loss in tcdC. All isolates were susceptible to metronidazole and vancomycin. A 2013 follow-up revealed a 28.9% decrease in NAP1 isolates and 20.0% increase in isolates without NAP designation in one region. Then, community-associated cases were seen in younger patients, while NAP types were evenly distributed. Isolates without NAP designation did not cluster with a PFGE pattern or ribotype. Conclusions. Evaluation of C. difficile infections within British Columbia revealed demographic associations, epidemiological shifts, and characteristics of strain types. Continuous surveillance of C. difficile will enable detection of emerging strains.