Metagenomics is the study of genetic materials directly sampled from natural habitats. It has the potential to reveal previously hidden diversity of microscopic life largely due to the existence of highly parallel and low-cost next-generation sequencing technology. Conventional approaches align metagenomic reads onto known reference genomes to identify microbes in the sample. Since such a collection of reference genomes is very large, the approach often needs high-end computing machines with large memory which is not often available to researchers. Alternative approaches follow an alignment-free methodology where the presence of a microbe is predicted using the information about the unique k-mers present in the microbial genomes. However, such approaches suffer from high false positives due to trading off the value of k with the computational resources. In this article, we propose a highly efficient metagenomic sequence classification (MSC) algorithm that is a hybrid of both approaches. Instead of aligning reads to the full genomes, MSC aligns reads onto a set of carefully chosen, shorter and highly discriminating model sequences built from the unique k-mers of each of the reference sequences.Microbiome researchers are generally interested in two objectives of a taxonomic classifier: (i) to detect prevalence, i.e. the taxa present in a sample, and (ii) to estimate their relative abundances. MSC is primarily designed to detect prevalence and experimental results show that MSC is indeed a more effective and efficient algorithm compared to the other state-of-the-art algorithms in terms of accuracy, memory and runtime. Moreover, MSC outputs an approximate estimate of the abundances.The implementations are freely available for non-commercial purposes. They can be downloaded from https://drive.google.com/open?id=1XirkAamkQ3ltWvI1W1igYQFusp9DHtVl. © The Author(s) 2019. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: email@example.com.
Comparative genomic analysis of Lactobacillus mucosae LM1 identifies potential niche-specific genes and pathways for gastrointestinal adaptation.
Lactobacillus mucosae is currently of interest as putative probiotics due to their metabolic capabilities and ability to colonize host mucosal niches. L. mucosae LM1 has been studied in its functions in cell adhesion and pathogen inhibition, etc. It demonstrated unique abilities to use energy from carbohydrate and non-carbohydrate sources. Due to these functions, we report the first complete genome sequence of an L. mucosae strain, L. mucosae LM1. Analysis of the pan-genome in comparison with closely-related Lactobacillus species identified a complete glycogen metabolism pathway, as well as folate biosynthesis, complementing previous proteomic data on the LM1 strain. It also revealed common and unique niche-adaptation genes among the various L. mucosae strains. The aim of this study was to derive genomic information that would reveal the probable mechanisms underlying the probiotic effect of L. mucosae LM1, and provide a better understanding of the nature of L. mucosae sp. Copyright © 2017 Elsevier Inc. All rights reserved.
Mobilome of Brevibacterium aurantiacum Sheds Light on Its Genetic Diversity and Its Adaptation to Smear-Ripened Cheeses.
Brevibacterium aurantiacum is an actinobacterium that confers key organoleptic properties to washed-rind cheeses during the ripening process. Although this industrially relevant species has been gaining an increasing attention in the past years, its genome plasticity is still understudied due to the unavailability of complete genomic sequences. To add insights on the mobilome of this group, we sequenced the complete genomes of five dairy Brevibacterium strains and one non-dairy strain using PacBio RSII. We performed phylogenetic and pan-genome analyses, including comparisons with other publicly available Brevibacterium genomic sequences. Our phylogenetic analysis revealed that these five dairy strains, previously identified as Brevibacterium linens, belong instead to the B. aurantiacum species. A high number of transposases and integrases were observed in the Brevibacterium spp. strains. In addition, we identified 14 and 12 new insertion sequences (IS) in B. aurantiacum and B. linens genomes, respectively. Several stretches of homologous DNA sequences were also found between B. aurantiacum and other cheese rind actinobacteria, suggesting horizontal gene transfer (HGT). A HGT region from an iRon Uptake/Siderophore Transport Island (RUSTI) and an iron uptake composite transposon were found in five B. aurantiacum genomes. These findings suggest that low iron availability in milk is a driving force in the adaptation of this bacterial species to this niche. Moreover, the exchange of iron uptake systems suggests cooperative evolution between cheese rind actinobacteria. We also demonstrated that the integrative and conjugative element BreLI (Brevibacterium Lanthipeptide Island) can excise from B. aurantiacum SMQ-1417 chromosome. Our comparative genomic analysis suggests that mobile genetic elements played an important role into the adaptation of B. aurantiacum to cheese ecosystems.
The human microbiome includes trillions of bacteria, many of which play a vital role in host physiology. Numerous studies have now detected bacterial DNA in first-pass meconium and amniotic fluid samples, suggesting that the human microbiome may commence in utero. However, these data have remained contentious due to underlying contamination issues. Here, we have used a previously described method for reducing contamination in microbiome workflows to determine if there is a fetal bacterial microbiome beyond the level of background contamination. We recruited 50 women undergoing non-emergency cesarean section deliveries with no evidence of intra-uterine infection and collected first-pass meconium and amniotic fluid samples. Full-length 16S rRNA gene sequencing was performed using PacBio SMRT cell technology, to allow high resolution profiling of the fetal gut and amniotic fluid bacterial microbiomes. Levels of inflammatory cytokines were measured in amniotic fluid, and levels of immunomodulatory short chain fatty acids (SCFAs) were quantified in meconium. All meconium samples and most amniotic fluid samples (36/43) contained bacterial DNA. The meconium microbiome was dominated by reads that mapped to Pelomonas puraquae. Aside from this species, the meconium microbiome was remarkably heterogeneous between patients. The amniotic fluid microbiome was more diverse and contained mainly reads that mapped to typical skin commensals, including Propionibacterium acnes and Staphylococcus spp. All meconium samples contained acetate and propionate, at ratios similar to those previously reported in infants. P. puraquae reads were inversely correlated with meconium propionate levels. Amniotic fluid cytokine levels were associated with the amniotic fluid microbiome. Our results demonstrate that bacterial DNA and SCFAs are present in utero, and have the potential to influence the developing fetal immune system.