Emerging pre-clinical safety assessments for potential probiotic strains: a review

The field of antibiotic drug discovery and the monitoring of new antibiotic resistance elements have yet to fully exploit the power of the genome revolution. Despite the fact that the first genomes sequenced of free living organisms were those of bacteria, there have been few specialized bioinformatic tools developed to mine the growing amount of genomic data associated with pathogens. In particular, there are few tools to study the genetics and genomics of antibiotic resistance and how it impacts bacterial populations, ecology, and the clinic. We have initiated development of such tools in the form of the Comprehensive Antibiotic Research Database (CARD; http://arpcard.mcmaster.ca). The CARD integrates disparate molecular and sequence data, provides a unique organizing principle in the form of the Antibiotic Resistance Ontology (ARO), and can quickly identify putative antibiotic resistance genes in new unannotated genome sequences. This unique platform provides an informatic tool that bridges antibiotic resistance concerns in health care, agriculture, and the environment.

The gastrointestinal tract is often considered as a key organ involved in the digestion of food and providing nutrients to the body for proper maintenance. However, this system is composed of organs that are extremely complex. Among the different parts, the intestine is viewed as an incredible surface of contact with the environment and is colonised by hundreds of trillions of gut microbes. The role of the gut barrier has been studied for decades, but the exact mechanisms involved in the protection of the gut barrier are various and complementary. Among them, the integrity of the mucus barrier is one of the first lines of protection of the gastrointestinal tract. In the past, this ‘slimy’ partner was mostly considered a simple lubricant for facilitating the progression of the food bolus and the stools in the gut. Since then, different researchers have made important progress, and currently, the regulation of this mucus barrier is gaining increasing attention from the scientific community. Among the factors influencing the mucus barrier, the microbiome plays a major role in driving mucus changes. Additionally, our dietary habits (ie, high-fat diet, low-fibre/high-fibre diet, food additives, pre- probiotics) influence the mucus at different levels. Given that the mucus layer has been linked with the appearance of diseases, proper knowledge is highly warranted. Here, we debate different aspects of the mucus layer by focusing on its chemical composition, regulation of synthesis and degradation by the microbiota as well as some characteristics of the mucus layer in both physiological and pathological situations.

ARG-ANNOT (Antibiotic Resistance Gene-ANNOTation) is a new bioinformatic tool that was created to detect existing and putative new antibiotic resistance (AR) genes in bacterial genomes. ARG-ANNOT uses a local BLAST program in Bio-Edit software that allows the user to analyze sequences without a Web interface. All AR genetic determinants were collected from published works and online resources; nucleotide and protein sequences were retrieved from the NCBI GenBank database. After building a database that includes 1,689 antibiotic resistance genes, the software was tested in a blind manner using 100 random sequences selected from the database to verify that the sensitivity and specificity were at 100% even when partial sequences were queried. Notably, BLAST analysis results obtained using the rmtF gene sequence (a new aminoglycoside-modifying enzyme gene sequence that is not included in the database) as a query revealed that the tool was able to link this sequence to short sequences (17 to 40 bp) found in other genes of the rmt family with significant E values. Finally, the analysis of 178 Acinetobacter baumannii and 20 Staphylococcus aureus genomes allowed the detection of a significantly higher number of AR genes than the Resfinder gene analyzer and 11 point mutations in target genes known to be associated with AR. The average time for the analysis of a genome was 3.35 ± 0.13 min. We have created a concise database for BLAST using a Bio-Edit interface that can detect AR genetic determinants in bacterial genomes and can rapidly and easily discover putative new AR genetic determinants.