Bioinformatic detection of horizontally transferred DNA in bacterial genomes

Unlocking the genetic potential of Lacticaseibacillus rhamnosus strains: Medical applications of a promising probiotic for human and animal health

Lacticaseibacillus rhamnosus is a group of probiotic strains that have gained popularity for their potential health benefits such as promoting digestive health, boosting the immune system, improving lactose digestion, preventing and treating antibiotic-associated diarrhea, reducing the severity and duration of certain infections, and preventing the formation of dental plaque. In particular, L. rhamnosus strains SD4 and SD11 have promising human and animal health applications due to their ability to inhibit the growth of harmful pathogens. This study presents an in silico genomic analysis of L. rhamnosus strains SD4 and SD11. We analyzed draft genomes and conducted comparative genome analyses against several other probiotic strains, aiming to gain insights into the genomes of the two strains and to compare them to related strains isolated from other sources. We also aimed to clarify the functional mechanisms and adaptation of these strains to specific environments. Comprehensive insights into the genomes of L. rhamnosus SD4 and SD11 could enhance our understanding of their capacity to colonize, adapt, and exhibit probiotic properties after administration. This study holds significance in advancing our understanding of the potential health benefits associated with these strains and in elucidating the underlying mechanisms responsible for their effectiveness in humans and animals.

DUGMO: tool for the detection of unknown genetically modified organisms with high-throughput sequencing data for pure bacterial samples

Background

The European Community has adopted very restrictive policies regarding the dissemination and use of genetically modified organisms (GMOs). In fact, a maximum threshold of 0.9% of contaminating GMOs is tolerated for a “GMO-free” label. In recent years, imports of undescribed GMOs have been detected. Their sequences are not described and therefore not detectable by conventional approaches, such as PCR.

Results

We developed DUGMO, a bioinformatics pipeline for the detection of genetically modified (GM) bacteria, including unknown GM bacteria, based on Illumina paired-end sequencing data. The method is currently focused on the detection of GM bacteria with – possibly partial – transgenes in pure bacterial samples. In the preliminary steps, coding sequences (CDSs) are aligned through two successive BLASTN against the host pangenome with relevant tuned parameters to discriminate CDSs belonging to the wild type genome (wgCDS) from potential GM coding sequences (pgmCDSs). Then, Bray-Curtis distances are calculated between the wgCDS and each pgmCDS, based on the difference of genomic vocabulary. Finally, two machine learning methods, namely the Random Forest and Generalized Linear Model, are carried out to target true GM CDS(s), based on six variables including Bray-Curtis distances and GC content. Tests carried out on a GM Bacillus subtilis showed 25 positive CDSs corresponding to the chloramphenicol resistance gene and CDSs of the inserted plasmids. On a wild type B. subtilis, no false positive sequences were detected.

Conclusion

DUGMO detects exogenous CDS, truncated, fused or highly mutated wild CDSs in high-throughput sequencing data, and was shown to be efficient at detecting GM sequences, but it might also be employed for the identification of recent horizontal gene transfers.

MetaCHIP: community-level horizontal gene transfer identification through the combination of best-match and phylogenetic approaches

BACKGROUND

Metagenomic datasets provide an opportunity to study horizontal gene transfer (HGT) on the level of a microbial community. However, current HGT detection methods cannot be applied to community-level datasets or require reference genomes. Here, we present MetaCHIP, a pipeline for reference-independent HGT identification at the community level.

RESULTS

Assessment of MetaCHIP's performance on simulated datasets revealed that it can predict HGTs with various degrees of genetic divergence from metagenomic datasets. The results also indicated that the detection of very recent gene transfers (i.e. those with low levels of genetic divergence) from metagenomics datasets is largely affected by the read assembly step. Comparison of MetaCHIP with a previous analysis on soil bacteria showed a high level of consistency for the prediction of recent HGTs and revealed a large number of additional non-recent gene transfers, which can provide new biological and ecological insight. Assessment of MetaCHIP's performance on real metagenomic datasets confirmed the role of HGT in the spread of genes related to antibiotic resistance in the human gut microbiome. Further testing also showed that functions related to energy production and conversion as well as carbohydrate transport and metabolism are frequently transferred among free-living microorganisms.

CONCLUSION

MetaCHIP provides an opportunity to study HGTs among members of a microbial community and therefore has several applications in the field of microbial ecology and evolution. MetaCHIP is implemented in Python and freely available at https://github.com/songweizhi/MetaCHIP .

Monitoring plasmid-mediated horizontal gene transfer in microbiomes: recent advances and future perspectives

The emergence of antimicrobial resistant bacteria constitutes an increasing global health concern. Although it is well recognized that the cornerstone underlying this phenomenon is the dissemination of antimicrobial resistance via plasmids and other mobile genetic elements, the antimicrobial resistance transfer routes remain largely uncharted. In this review, we describe different methods for assessing the transfer frequency and host ranges of plasmids within complex microbiomes. The discussion is centered around the critical evaluation of recent advances for monitoring the fate of fluorescently tagged plasmids in bacterial communities through the coupling of fluorescence activated cell sorting and next generation sequencing techniques. We argue that this approach constitutes an exceptional tool for obtaining quantitative data regarding the extent of plasmid transfer, key disseminating taxa, and possible propagation routes. The integration of this information will provide valuable insights on how to develop alternative avenues for fighting the rise of antimicrobial resistant pathogens, as well as the means for constructing more comprehensive risk assessment models.

Kullback Leibler divergence in complete bacterial and phage genomes

The amino acid content of the proteins encoded by a genome may predict the coding potential of that genome and may reflect lifestyle restrictions of the organism. Here, we calculated the Kullback–Leibler divergence from the mean amino acid content as a metric to compare the amino acid composition for a large set of bacterial and phage genome sequences. Using these data, we demonstrate that (i) there is a significant difference between amino acid utilization in different phylogenetic groups of bacteria and phages; (ii) many of the bacteria with the most skewed amino acid utilization profiles, or the bacteria that host phages with the most skewed profiles, are endosymbionts or parasites; (iii) the skews in the distribution are not restricted to certain metabolic processes but are common across all bacterial genomic subsystems; (iv) amino acid utilization profiles strongly correlate with GC content in bacterial genomes but very weakly correlate with the G+C percent in phage genomes. These findings might be exploited to distinguish coding from non-coding sequences in large data sets, such as metagenomic sequence libraries, to help in prioritizing subsequent analyses.

Identifying pathogenicity islands in bacterial pathogenomics using computational approaches

High-throughput sequencing technologies have made it possible to study bacteria through analyzing their genome sequences. For instance, comparative genome sequence analyses can reveal the phenomenon such as gene loss, gene gain, or gene exchange in a genome. By analyzing pathogenic bacterial genomes, we can discover that pathogenic genomic regions in many pathogenic bacteria are horizontally transferred from other bacteria, and these regions are also known as pathogenicity islands (PAIs). PAIs have some detectable properties, such as having different genomic signatures than the rest of the host genomes, and containing mobility genes so that they can be integrated into the host genome. In this review, we will discuss various pathogenicity island-associated features and current computational approaches for the identification of PAIs. Existing pathogenicity island databases and related computational resources will also be discussed, so that researchers may find it to be useful for the studies of bacterial evolution and pathogenicity mechanisms.