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Despite increasing efforts across various disciplines, the fate, transport, and impact of synthetic plastics on the environment and public health remain poorly understood. To better elucidate the microbial ecology of plastic waste and its potential for biotransformation, we conducted a large-scale analysis of all publicly available meta-omic studies investigating plastics (n = 27) in the environment. Notably, we observed low prevalence of known plastic degraders throughout most environments, except for substantial enrichment in riverine systems. This indicates rivers may be a highly promising environment for discovery of novel plastic bioremediation products. Ocean samples associated with degrading plastics showed clear differentiation from non-degrading polymers, showing enrichment of novel putative biodegrading taxa in the degraded samples. Regarding plastisphere pathogenicity, we observed significant enrichment of antimicrobial resistance genes on plastics but not of virulence factors. Additionally, we report a co-occurrence network analysis of 10 + million proteins associated with the plastisphere. This analysis revealed a localized sub-region enriched with known and putative plastizymes—these may be useful for deeper investigation of nature’s ability to biodegrade man-made plastics. Finally, the combined data from our meta-analysis was used to construct a publicly available database, the Plastics Meta-omic Database (PMDB)—accessible at plasticmdb.org. These data should aid in the integrated exploration of the microbial plastisphere and facilitate research efforts investigating the fate and bioremediation potential of environmental plastic waste.

 

What a strain is and how many strains make up a natural bacterial population remain elusive concepts despite their apparent importance for assessing the role of intra-population diversity in disease emergence or response to environmental perturbations. To advance these concepts, we sequenced 138 randomly selectedSalinibacter ruberisolates from two solar salterns and assessed these genomes against companion short-read metagenomes from the same samples. The distribution of genome-aggregate average nucleotide identity (ANI) values among these isolates revealed a bimodal distribution, with four-fold lower occurrence of values between 99.2% and 99.8% relative to ANI >99.8% or <99.2%, revealing a natural “gap” in the sequence space within species. Accordingly, we used this ANI gap to define genomovars and a higher ANI value of >99.99% and shared gene-content >99.0% to define strains. Using these thresholds and extrapolating from how many metagenomic reads each genomovar uniquely recruited, we estimated that –although our 138 isolates represented about 80% of theSal. ruberpopulation– the total population in one saltern pond is composed of 5,500 to 11,000 genomovars, the great majority of which appear to be rare in-situ. These data also revealed that the most frequently recovered isolate in lab media was often not the most abundant genomovar in-situ, suggesting that cultivation biases are significant, even in cases that cultivation procedures are thought to be robust. The methodology and ANI thresholds outlined here should represent a useful guide for future microdiversity surveys of additional microbial species.

 

Tau-typing is an integrated analysis pipeline for identifying genes or genomic segments whose phylogenetic resolving power most closely resembles the genome-wide resolving power of an input collection of genomes using the Kendall Tau rank correlation statistic. The pipeline is implemented in Nextflow and uses Docker and Singularity containers to ensure reliable scalability and reproducibility of results. This pipeline is particularly suitable for organisms for which whole-genome sequencing remains unaffordable or unscalable for routine applications, such as protozoan parasites which are not amenable to laboratory culture-based methods.

Tau-typing is freely available at https://github.com/hseabolt/tautyping. The pipeline is implemented in Nextflow with Singularity support.

 

Abstract Background Microbes and their viruses are hidden engines driving Earth’s ecosystems from the oceans and soils to humans and bioreactors. Though gene marker approaches can now be complemented by genome-resolved studies of inter-(macrodiversity) and intra-(microdiversity) population variation, analytical tools to do so remain scattered or under-developed. Results Here, we introduce MetaPop, an open-source bioinformatic pipeline that provides a single interface to analyze and visualize microbial and viral community metagenomes at both the macro - and microdiversity levels. Macrodiversity estimates include population abundances and α- and β-diversity. Microdiversity calculations include identification of single nucleotide polymorphisms, novel codon-constrained linkage of SNPs, nucleotide diversity ( π and θ ), and selective pressures (pN/pS and Tajima’s D ) within and fixation indices ( F ST ) between populations. MetaPop will also identify genes with distinct codon usage. Following rigorous validation, we applied MetaPop to the gut viromes of autistic children that underwent fecal microbiota transfers and their neurotypical peers. The macrodiversity results confirmed our prior findings for viral populations (microbial shotgun metagenomes were not available) that diversity did not significantly differ between autistic and neurotypical children. However, by also quantifying microdiversity, MetaPop revealed lower average viral nucleotide diversity ( π ) in autistic children. Analysis of the percentage of genomes detected under positive selection was also lower among autistic children, suggesting that higher viral π in neurotypical children may be beneficial because it allows populations to better “bet hedge” in changing environments. Further, comparisons of microdiversity pre- and post-FMT in autistic children revealed that the delivery FMT method (oral versus rectal) may influence viral activity and engraftment of microdiverse viral populations, with children who received their FMT rectally having higher microdiversity post-FMT. Overall, these results show that analyses at the macro level alone can miss important biological differences. Conclusions These findings suggest that standardized population and genetic variation analyses will be invaluable for maximizing biological inference, and MetaPop provides a convenient tool package to explore the dual impact of macro - and microdiversity across microbial communities. 

Abstract Background High-throughput sequencing has increased the number of available microbial genomes recovered from isolates, single cells, and metagenomes. Accordingly, fast and comprehensive functional gene annotation pipelines are needed to analyze and compare these genomes. Although several approaches exist for genome annotation, these are typically not designed for easy incorporation into analysis pipelines, do not combine results from different annotation databases or offer easy-to-use summaries of metabolic reconstructions, and typically require large amounts of computing power for high-throughput analysis not available to the average user. Results Here, we introduce MicrobeAnnotator, a fully automated, easy-to-use pipeline for the comprehensive functional annotation of microbial genomes that combines results from several reference protein databases and returns the matching annotations together with key metadata such as the interlinked identifiers of matching reference proteins from multiple databases [KEGG Orthology (KO), Enzyme Commission (E.C.), Gene Ontology (GO), Pfam, and InterPro]. Further, the functional annotations are summarized into Kyoto Encyclopedia of Genes and Genomes (KEGG) modules as part of a graphical output (heatmap) that allows the user to quickly detect differences among (multiple) query genomes and cluster the genomes based on their metabolic similarity. MicrobeAnnotator is implemented in Python 3 and is freely available under an open-source Artistic License 2.0 from https://github.com/cruizperez/MicrobeAnnotator . Conclusions We demonstrated the capabilities of MicrobeAnnotator by annotating 100 Escherichia coli and 78 environmental Candidate Phyla Radiation (CPR) bacterial genomes and comparing the results to those of other popular tools. We showed that the use of multiple annotation databases allows MicrobeAnnotator to recover more annotations per genome compared to faster tools that use reduced databases and is computationally efficient for use in personal computers. The output of MicrobeAnnotator can be easily incorporated into other analysis pipelines while the results of other annotation tools can be seemingly incorporated into MicrobeAnnotator to generate summary plots. 

Mapping of short metagenomic (or metatranscriptomic) read data to reference isolate or single-cell genomes or metagenome-assembled genomes (MAGs) to assess microbial population relative abundance and/or structure represents an essential task of many studies across environmental and clinical settings. The filtering for the quality of the read match and assessment of read mapping results are frequently performed without visual aids or with the assistance of visualizations produced through ad-hoc, in-house approaches. Here, we introduce RecruitPlotEasy, a fully automated, user-friendly pipeline for these purposes that integrates statistical approaches to quantify intra-population sequence and gene-content diversity and identify co-occurring relative populations in the sample. Hence, RecruitPlotEasy should also greatly facilitate population genetics studies. RecruitPlotEasy is implemented in Python and R languages and is freely available open source software under the Artistic License 2.0 from https://github.com/KGerhardt/RecruitPlotEasy .