Shine: To explore specific, sensitive and conserved biomarkers from massive microbial genomic data within intrapopulations.

To improve the quality of nucleic acid detection reagents, we provided a new strategy, Shine, to explore specific, sensitive and conserved biomarkers from massive microbial genomic data within intrapopulations in order to improve detection sensitivity and accuracy. It is obvious that the more comprehensive genomic data are, the more effective the detection biomarkers. Here, we demonstrated that our method could detect undiscovered multicopy conserved species-specific or even subspecies-specific target fragments, according to several clinical projects. In particular, this approach was effective for any pathogenic microorganism even in incompletely assembled motifs. Based on our strategy, the detection device designed with quantitative PCR primers and probes for systematic and automated detection of pathogenic microorganisms in biological samples may cover all pathogenic microorganisms without limits based on genome annotation. On the website https://bioinfo.liferiver.com.cn, users may select different configuration parameters depending on the purpose of the project to realize routine clinical detection practices. Therefore, it is recommended that our strategy is suitable to identify shared universal phylogenetic markers with few false positive or false negative errors and to automate the design of minimal primers and probes to detect pathogenic communities with cost-effective predictive power.

Metagenome Assembly and Metagenome-Assembled Genome of “ Candidatus Aramenus sulfurataquae” from Thermal Sediments from the Los Azufres Volcanic Complex

A plethora of hot springs are found at the Los Azufres volcanic complex in Mexico, and studies are needed to determine their microbial genomic diversity. Here, we report a metagenome of hot spring sediments and a metagenome-assembled genome of “ Candidatus Aramenus sulfurataquae.” This study reveals novel genomic sequences of Sulfolobales archaea.

Microbial trait evolution is dominated by frequent and rare pulsed evolution

On the macroevolutionary timescale, does trait evolution proceed gradually or by rapid bursts (pulses) separated by long periods of stasis? Although studies have shown pulsed evolution is prevalent in animals, our knowledge about the tempo and mode of evolution across the tree of life is very limited. This long-standing debate calls for a test in bacteria and archaea, the most ancient and diverse forms of life with unique population genetic properties. Using a likelihood-based framework, we analyzed patterns of microbial genomic trait evolution on a broad macroevolutionary timescale. Here we show that pulsed evolution is both prevalent and predominant in microbes. For the first time, we detected two distinct types of pulsed evolution that are predicted by the punctuated equilibrium and quantum evolution theories. Our findings suggest that major bacterial lineages originated in quick bursts and pulsed evolution is common across the tree of life despite drastically different population genetic properties of animals, plants and microbes.

Gene-level metagenomics identifies genome islands associated with immunotherapy response

Researchers must be able to generate experimentally testable hypotheses from sequencing-based observational microbiome experiments to discover the mechanisms underlying the influence of gut microbes on human health. We describe a novel bioinformatics tool for identifying testable hypotheses based on gene-level metagenomic analysis of WGS microbiome data (geneshot). By applying geneshot to two independent previously published cohorts, we identified microbial genomic islands consistently associated with response to immune checkpoint inhibitor (ICI)-based cancer treatment in culturable type strains. The identified genomic islands are within operons involved in type II secretion, TonB-dependent transport, and bacteriophage growth. These results, as well as the underlying methodology, inform further mechanistic studies and facilitate the development of microbiome-enhanced therapeutics.

Microbial Diversity of Fermented Greek Table Olives of Halkidiki and Konservolia Varieties from Different Regions as Revealed by Metagenomic Analysis

Current information from conventional microbiological methods on the microbial diversity of table olives is insufficient. Next-generation sequencing (NGS) technologies allow comprehensive analysis of their microbial community, providing microbial identity of table olive varieties and their designation of origin. The purpose of this study was to evaluate the bacterial and yeast diversity of fermented olives of two main Greek varieties collected from different regions—green olives, cv. Halkidiki, from Kavala and Halkidiki and black olives, cv. Konservolia, from Magnesia and Fthiotida—via conventional microbiological methods and NGS. Total viable counts (TVC), lactic acid bacteria (LAB), yeast and molds, and Enterobacteriaceae were enumerated. Microbial genomic DNA was directly extracted from the olives’ surface and subjected to NGS for the identification of bacteria and yeast communities. Lactobacillaceae was the most abundant family in all samples. In relation to yeast diversity, Phaffomycetaceae was the most abundant yeast family in Konservolia olives from the Magnesia region, while Pichiaceae dominated the yeast microbiota in Konservolia olives from Fthiotida and in Halkidiki olives from both regions. Further analysis of the data employing multivariate analysis allowed for the first time the discrimination of cv. Konservolia and cv. Halkidiki table olives according to their geographical origin.

Machine learning models to identify patient and microbial genetic factors associated with carbapenem-resistant Klebsiella pneumoniae infection

BackgroundAmong patients colonized with carbapenem-resistant Klebsiella pneumoniae (CRKP), only a subset develop clinical infection. While patient characteristics may influence risk for infection, it remains unclear if the genetic background of CRKP strains contributes to this risk. We applied machine learning to quantify the capacity of patient characteristics and microbial genotypes to discriminate infection and colonization, and identified patient and microbial features associated with infection across multiple healthcare facilities.MethodsMachine learning models were built using whole-genome sequences and clinical metadata from 331 patients colonized or infected with CRKP across 21 long-term acute care hospitals. To quantify variation in performance, we built models using 100 different train/test splits of the entire dataset, and urinary and respiratory site-specific subsets, and evaluated predictive performance on each test split using the area under the receiver operating characteristics curve (AUROC). Patient and microbial features predictive of infection were identified as those consistently important for predicting infection based on average change in AUROC when included in the model.FindingsWe found that patient and genomic features were only weakly predictive of clinical CRKP infection vs. colonization (AUROC IQRs: patient=0·59-0·68, genomic=0·55-0·61, combined=0·62-0·68), and that one feature set did not consistently outperform the other (genomic vs. patient p=0·4). Comparable model performances were observed for anatomic site-specific models (combined AUROC IQRs: respiratory=0·61-0·71, urinary=0·54-0·64). Strong genomic predictors of infection included the presence of the ICEKp10 mobile genetic element carrying an iron acquisition system (yersiniabactin) and a toxin (colibactin), along with disruption of an O-antigen biosynthetic gene in a sub-lineage of the epidemic ST258 clone. Teasing apart sequential evolutionary steps in the context of clinical metadata indicated that altered O-antigen biosynthesis increased association with the respiratory tract, and subsequent acquisition of ICEKp10 was associated with increased virulence.InterpretationOur results support the need for rigorous machine learning frameworks to gain realistic estimates of the performance of clinical models of infection. Moreover, integrating microbial genomic and clinical data using such a framework can help tease apart the contribution of microbial genetic variation to clinical outcomes.FundingCenters for Disease Control and Prevention, National Institutes of Health, National Science FoundationResearch in contextEvidence before this studyWe searched PubMed for “crkp” OR “carbapenem resistant klebsiella pneumoniae” AND “infection” AND “machine learning” for papers published up to April 14, 2020 and found no results. Substituting “machine learning” with “bacterial genome-wide association studies” produced one relevant paper investigating pathogenicity-associated loci in K. pneumoniae clinical isolates. When we searched for “infection” AND “machine learning” AND “genom*” AND “clinical”, there was one relevant result - a study that used clinical and bacterial genomic features in a machine learning model to identify clonal differences related to Staphylococcus aureus infection outcome.Added value of this studyTo our knowledge, this is the first study to integrate clinical and genomic data to study anatomic site-specific colonization and infection across multiple healthcare facilities. Using this method, we identified clinical features associated with CRKP infection, as well as a sub-lineage of CRKP with potentially altered niche-specific adaptation and virulence. This method could be used for other organisms and other clinical outcomes to evaluate performance of predictive models and identify features that are consistently associated with clinical outcomes of interest across facilities or geographic regions.Implications of all the available evidenceFew studies have combined patient and microbial genomic data to study important clinical outcomes. However, those that have done this, including ours, have identified clinical and/or genomic features associated with the outcome of interest that provide a foundation for future epidemiological, clinical, and biological studies to better understand bacterial infections and clinical outcomes.

Optimization of subsampling, decontamination, and DNA extraction of difficult peat and silt permafrost samples

ABSTRACTThis study aims to act as a methodological guide for contamination monitoring, decontamination, and DNA extraction for peaty and silty permafrost samples with low biomass or difficult to extract DNA. We applied a biological tracer, either only in the field or both in the field and in the lab, via either spraying or painting. Spraying in the field followed by painting in the lab resulted in a uniform layer of the tracer on the core sections. A combination of bleaching, washing, and scraping resulted in complete removal of the tracer leaving sufficient material for DNA extraction, while other widely used decontamination methods did not remove all detectable tracer. In addition, of four widely used commercially available DNA extraction kits, only a modified ZymoBIOMICS™ DNA Microprep kit was able to acquire PCR amplifiable DNA. Permafrost chemical parameters, age, and soil texture did not have an effect on decontamination efficacy; however, the permafrost type did influence DNA extraction. Based on these findings, we developed recommendations for permafrost microbiologists to acquire contaminant-free DNA from permafrost with low biomass.IMPORTANCEPermafrost has the capacity to preserve microbial and non-microbial genomic material for millennia; however, major challenges are associated with permafrost samples, including decontamination of samples and acquiring pure DNA. Contamination of samples during coring and post coring handling and processing could affect downstream analyses and interpretations. Despite the use of multiple different decontamination and DNA extraction methods in studies of permafrost, the efficacy of these methods is not well known. We used a biological tracer to test the efficacy of previously published decontamination methods, as well as a bleach-based method we devised, on two chemically and structurally different permafrost core sections. Our method was the only one that removed all detectable tracer. In addition, we tested multiple DNA extraction kits and modified one that is able to acquire pure, PCR amplifiable DNA from silty, and to some extent from peaty, permafrost samples.

MiST 3.0: an updated microbial signal transduction database with an emphasis on chemosensory systems

Bacteria and archaea employ dedicated signal transduction systems that modulate gene expression, second-messenger turnover, quorum sensing, biofilm formation, motility, host-pathogen and beneficial interactions. The updated MiST database provides a comprehensive classification of microbial signal transduction systems. This update is a result of a substantial scaling to accommodate constantly growing microbial genomic data. More than 125 000 genomes, 516 million genes and almost 100 million unique protein sequences are currently stored in the database. For each bacterial and archaeal genome, MiST 3.0 provides a complete signal transduction profile, thus facilitating theoretical and experimental studies on signal transduction and gene regulation. New software infrastructure and distributed pipeline implemented in MiST 3.0 enable regular genome updates based on the NCBI RefSeq database. A novel MiST feature is the integration of unique profile HMMs to link complex chemosensory systems with corresponding chemoreceptors in bacterial and archaeal genomes. The data can be explored online or via RESTful API (freely available at https://mistdb.com).