RfaH is a virulence factor from Escherichia coli whose C-terminal domain (CTD) undergoes a dramatic α-to-β conformational transformation. The CTD in its α-helical fold is stabilized by interactions with the N-terminal domain (NTD), masking an RNA polymerase binding site until a specific recruitment site is encountered. Domain dissociation is triggered upon binding to DNA, allowing the NTD to interact with RNA polymerase to facilitate transcription while the CTD refolds into the β-barrel conformation that interacts with the ribosome to activate translation. However, structural details of this transformation process in the context of the full protein remain to be elucidated. Here, we explore the mechanism of the α-to-β conformational transition of RfaH in the full-length protein using a dual-basin structure-based model. Our simulations capture several features described experimentally, such as the requirement of disruption of interdomain contacts to trigger the α-to-β transformation, confirms the roles of previously indicated residues E48 and R138, and suggests a new important role for F130, in the stability of the interdomain interaction. These native basins are connected through an intermediate state that builds up upon binding to the NTD and shares features from both folds, in agreement with previous in silico studies of the isolated CTD. We also examine the effect of RNA polymerase binding on the stabilization of the β fold. Our study shows that native-biased models are appropriate for interrogating the detailed mechanisms of structural rearrangements during the dramatic transformation process of RfaH.

The Atacama Desert hosts diverse ecosystems including salt flats and shallow Andean lakes. Several heavy metals are found in the Atacama Desert, and microorganisms growing in this environment show varying levels of resistance/tolerance to copper, tellurium, and arsenic, among others. Herein, we report the genome sequence and comparative genomic analysis of a new Exiguobacterium strain, sp. SH31, isolated from an altiplanic shallow athalassohaline lake. Exiguobacterium sp. SH31 belongs to the phylogenetic Group II and its closest relative is Exiguobacterium sp. S17, isolated from the Argentinian Altiplano (95% average nucleotide identity). Strain SH31 encodes a wide repertoire of proteins required for cadmium, copper, mercury, tellurium, chromium, and arsenic resistance. Of the 34 Exiguobacterium genomes that were inspected, only isolates SH31 and S17 encode the arsenic efflux pump Acr3. Strain SH31 was able to grow in up to 10 mM arsenite and 100 mM arsenate, indicating that it is arsenic resistant. Further, expression of the ars operon and acr3 was strongly induced in response to both toxics, suggesting that the arsenic efflux pump Acr3 mediates arsenic resistance in Exiguobacterium sp. SH31.

Background Our understanding of the composition, function, and health implications of human microbiota has been advanced by high-throughput sequencing and the development of new genomic analyses. However, trade-offs among alternative strategies for the acquisition and analysis of sequence data remain understudied. Methods We assessed eight popular taxonomic profiling pipelines; MetaPhlAn2, metaMix, PathoScope 2.0, Sigma, Kraken, ConStrains, Centrifuge and Taxator-tk, against a battery of metagenomic datasets simulated from real data. The metagenomic datasets were modeled on 426 complete or permanent draft genomes stored in the Human Oral Microbiome Database and were designed to simulate various experimental conditions, both in the design of a putative experiment; read length (75–1,000 bp reads), sequence depth (100K–10M), and in metagenomic composition; number of species present (10, 100, 426), species distribution. The sensitivity and specificity of each of the pipelines under various scenarios were measured. We also estimated the relative root mean square error and average relative error to assess the abundance estimates produced by different methods. Additional datasets were generated for five of the pipelines to simulate the presence within a metagenome of an unreferenced species, closely related to other referenced species. Additional datasets were also generated in order to measure computational time on datasets of ever-increasing sequencing depth (up to 6 × 107). Results Testing of eight pipelines against 144 simulated metagenomic datasets initially produced 1,104 discrete results. Pipelines using a marker gene strategy; MetaPhlAn2 and ConStrains, were overall less sensitive, than other pipelines; with the notable exception of Taxator-tk. This difference in sensitivity was largely made up in terms of runtime, significantly lower than more sensitive pipelines that rely on whole-genome alignments such as PathoScope2.0. However, pipelines that used strategies to speed-up alignment between genomic references and metagenomic reads, such as kmerization, were able to combine both high sensitivity and low run time, as is the case with Kraken and Centrifuge. Absent species genomes in the database mostly led to assignment of reads to the most closely related species available in all pipelines. Our results therefore suggest that taxonomic profilers that use kmerization have largely superseded those that use gene markers, coupling low run times with high sensitivity and specificity. Taxonomic profilers using more time-consuming read reassignment, such as PathoScope 2.0, provided the most sensitive profiles under common metagenomic sequencing scenarios. All the results described and discussed in this paper can be visualized using the dedicated R Shiny application (https://github.com/microgenomics/HumanMicrobiomeAnalysis). All of our datasets, pipelines and results are made available through the GitHub repository for future benchmarking.

Piscine orthoreovirus (PRV) belongs to the family Reoviridae and has been described mainly in association with salmonid infections. The genome of PRV consists of about 23,600 bp, with 10 segments of double-stranded RNA, classified as small (S1 to S4), medium (M1, M2 and M3) and large (L1, L2 and L3); these range approximately from 1000 bp (segment S4) to 4000 bp (segment L1). How the genetic variation among PRV strains affects the virulence for salmonids is still poorly understood. The aim of this study was to describe the molecular phylogeny of PRV based on an extensive sequence analysis of the S1 and M2 segments of PRV available in the GenBank database to date (May 2020). The analysis was extended to include new PRV sequences for S1 and M2 segments. In addition, subgenotype classifications were assigned to previously published unclassified sequences. It was concluded that the phylogenetic trees are consistent with the original classification using the PRV genomic segment S1, which differentiates PRV into two major genotypes, I and II, and each of these into two subgenotypes, designated as Ia and Ib, and IIa and IIb, respectively. Moreover, some clusters of country- and host-specific PRV subgenotypes were observed in the subset of sequences used. This work strengthens the subgenotype classification of PRV based on the S1 segment and can be used to enhance research on the virulence of PRV.

As the field of microbiomics advances, the burden of computational work that scientists need to perform in order to extract biological insight has grown accordingly. Likewise, while human microbiome analyses are increasingly shifting toward a greater integration of various high-throughput data types, a core number of methods form the basis of nearly every study. In this unit, we present step-by-step protocols for five core stages of human microbiome research. The protocols presented in this unit provide a base case for human microbiome analysis, complete with sufficient detail for researchers to tailor certain aspects of the protocols to the specificities of their data. © 2017 by John Wiley & Sons, Inc.