Benchmarking software to predict antibiotic resistance phenotypes in shotgun metagenomes using simulated data

The use of shotgun metagenomics for AMR detection is appealing because data can be generated from clinical samples with minimal processing. Detecting antimicrobial resistance (AMR) in clinical genomic data is an important epidemiological task, yet a complex bioinformatic process. Many software tools exist to detect AMR genes, but they have mostly been tested in their detection of genotypic resistance in individual bacterial strains. It is important to understand how well these bioinformatic tools detect AMR genes in shotgun metagenomic data. We developed a software pipeline, hAMRoaster (https://github.com/ewissel/hAMRoaster), for assessing accuracy of prediction of antibiotic resistance phenotypes. For evaluation purposes, we simulated a short read (Illumina) shotgun metagenomics community of eight bacterial pathogens with extensive antibiotic susceptibility testing profiles. We benchmarked nine open source bioinformatics tools for detecting AMR genes that 1) were conda or Docker installable, 2) had been actively maintained, 3) had an open source license, and 4) took FASTA or FASTQ files as input. Several metrics were calculated for each tool including sensitivity, specificity, and F1 at three coverage levels. This study revealed that tools were highly variable in sensitivity (0.25 - 0.99) and specificity (0.2 - 1) in detection of resistance in our synthetic FASTQ files despite similar databases and methods implemented. Tools performed similarly at all coverage levels (5x, 50x, 100x). Cohen’s kappa revealed low agreement across tools.

Taxonomic and Functional Diversity of Rhizosphere Microbiome Recruited From Compost Synergistically Determined by Plant Species and Compost

Compost is frequently served as the first reservoir for plants to recruit rhizosphere microbiome when used as growing substrate in the seedling nursery. In the present study, recruitment of rhizosphere microbiome from two composts by tomato, pepper, or maize was addressed by shotgun metagenomics and 16S rRNA amplicon sequencing. The 16S rRNA amplicon sequencing analysis showed that 41% of variation in the rhizosphere bacterial community was explained by compost, in contrast to 23% by plant species. Proteobacterial genera were commonly recruited by all three plant species with specific selections for Ralstonia by tomato and Enterobacteria by maize. These findings were confirmed by analysis of 16S rRNA retrieved from the shotgun metagenomics library. Approximately 70% of functional gene clusters differed more than sevenfold in abundance between rhizosphere and compost. Functional groups associated with the sensing and up-taking of C3 and C4 carboxylic acids, amino acids, monosaccharide, production of antimicrobial substances, and antibiotic resistance were over-represented in the rhizosphere. In summary, compost and plant species synergistically shaped the composition of the rhizosphere microbiome and selected for functional traits associated with the competition on root exudates.

Metagenomic and Metatranscriptomic Responses of Chemical Dispersant Application during a Marine Dilbit Spill

The global increase in marine transportation of dilbit (diluted bitumen) can increase the risk of spills, and the application of chemical dispersants remains a common response practice in spill events. To reliably evaluate dispersant effects on dilbit biodegradation over time, we set large-scale (1500 mL) microcosms without nutrients addition using low dilbit concentration (30 ppm). Shotgun metagenomics and metatranscriptomics were deployed to investigate microbial community responses to naturally and chemically dispersed dilbit. We found that the large-scale microcosms could produce more reproducible community trajectories than small-scale (250 mL) ones based on the 16S rRNA gene amplicon sequencing. In the early-stage large-scale microcosms, multiple genera were involved into the biodegradation of dilbit, while dispersant addition enriched primarily Alteromonas and competed for the utilization of dilbit, causing depressed degradation of aromatics. The metatranscriptomic based Metagenome Assembled Genomes (MAG) further elucidated early-stage microbial antioxidation mechanism, which showed dispersant addition triggered the increased expression of the antioxidation process genes of Alteromonas species. Differently, in the late stage, the microbial communities showed high diversity and richness and similar compositions and metabolic functions regardless of dispersant addition, indicating the biotransformation of remaining compounds can occur within the post-oil communities. These findings can guide future microcosm studies and the application of chemical dispersants for responding to a marine dilbit spill. Importance In this study, we employed microcosms to study the effects of marine dilbit spill and dispersant application on microbial community dynamics over time. We evaluated the impacts of microcosm scale and found that increasing the scale is beneficial for reducing community stochasticity, especially in the late stage of biodegradation. We observed that dispersant application suppressed aromatics biodegradation in the early stage (6 days) whereas exerting insignificant effects in the late stage (50 days), from both substances removal and metagenomic/metatranscriptomic perspectives. We further found that Alteromonas species are vital for the early-stage chemically dispersed oil biodegradation, and clarified their degradation and antioxidation mechanisms. The findings would help to better understand microcosm studies and microbial roles for biodegrading dilbit and chemically dispersed dilbit, and suggest that dispersant evaluation in large-scale systems and even through field trails would be more realistic after marine oil spill response.

The role of the microbiota in the management of intensive care patients

The composition of the gut microbiota is highly dynamic and changes according to various conditions. The gut microbiota mainly includes difficult-to-cultivate anaerobic bacteria, hence knowledge about its composition has significantly arisen from culture-independent methods based on next-generation sequencing (NGS) such as 16S profiling and shotgun metagenomics. The gut microbiota of patients hospitalized in intensive care units (ICU) undergoes many alterations because of critical illness, antibiotics, and other ICU-specific medications. It is then characterized by lower richness and diversity, and dominated by opportunistic pathogens such as Clostridioides difficile and multidrug-resistant bacteria. These alterations are associated with an increased risk of infectious complications or death. Specifically, at the time of writing, it appears possible to identify distinct microbiota patterns associated with severity or infectivity in COVID-19 patients, paving the way for the potential use of dysbiosis markers to predict patient outcomes. Correcting the microbiota disturbances to avoid their consequences is now possible. Fecal microbiota transplantation is recommended in recurrent C. difficile infections and microbiota-protecting treatments such as antibiotic inactivators are currently being developed. The growing interest in the microbiota and microbiota-associated therapies suggests that the control of the dysbiosis could be a key factor in the management of critically ill patients. The present narrative review aims to provide a synthetic overview of microbiota, from healthy individuals to critically ill patients. After an introduction to the different techniques used for studying the microbiota, we review the determinants involved in the alteration of the microbiota in ICU patients and the latter’s consequences. Last, we assess the means to prevent or correct microbiota alteration.

Dissecting the dominant hot spring microbial populations based on community-wide sampling at single-cell genomic resolution

With advances in DNA sequencing and miniaturized molecular biology workflows, rapid and affordable sequencing of single-cell genomes has become a reality. Compared to 16S rRNA gene surveys and shotgun metagenomics, large-scale application of single-cell genomics to whole microbial communities provides an integrated snapshot of community composition and function, directly links mobile elements to their hosts, and enables analysis of population heterogeneity of the dominant community members. To that end, we sequenced nearly 500 single-cell genomes from a low diversity hot spring sediment sample from Dewar Creek, British Columbia, and compared this approach to 16S rRNA gene amplicon and shotgun metagenomics applied to the same sample. We found that the broad taxonomic profiles were similar across the three sequencing approaches, though several lineages were missing from the 16S rRNA gene amplicon dataset, likely the result of primer mismatches. At the functional level, we detected a large array of mobile genetic elements present in the single-cell genomes but absent from the corresponding same species metagenome-assembled genomes. Moreover, we performed a single-cell population genomic analysis of the three most abundant community members, revealing differences in population structure based on mutation and recombination profiles. While the average pairwise nucleotide identities were similar across the dominant species-level lineages, we observed differences in the extent of recombination between these dominant populations. Most intriguingly, the creek’s Hydrogenobacter sp. population appeared to be so recombinogenic that it more closely resembled a sexual species than a clonally evolving microbe. Together, this work demonstrates that a randomized single-cell approach can be useful for the exploration of previously uncultivated microbes from community composition to population structure.

An Interplay between Viruses and Bacteria Associated with the White Sea Sponges Revealed by Metagenomics

Sponges are remarkable holobionts harboring extremely diverse microbial and viral communities. However, the interactions between the components within holobionts and between a holobiont and environment are largely unknown, especially for polar organisms. To investigate possible interactions within and between sponge-associated communities, we probed the microbiomes and viromes of cold-water sympatric sponges Isodictya palmata (n = 2), Halichondria panicea (n = 3), and Halichondria sitiens (n = 3) by 16S and shotgun metagenomics. We showed that the bacterial and viral communities associated with these White Sea sponges are species-specific and different from the surrounding water. Extensive mining of bacterial antiphage defense systems in the metagenomes revealed a variety of defense mechanisms. The abundance of defense systems was comparable in the metagenomes of the sponges and the surrounding water, thus distinguishing the White Sea sponges from those inhabiting the tropical seas. We developed a network-based approach for the combined analysis of CRISPR-spacers and protospacers. Using this approach, we showed that the virus–host interactions within the sponge-associated community are typically more abundant (three out of four interactions studied) than the inter-community interactions. Additionally, we detected the occurrence of viral exchanges between the communities. Our work provides the first insight into the metagenomics of the three cold-water sponge species from the White Sea and paves the way for a comprehensive analysis of the interactions between microbial communities and associated viruses.

Microbial Communities Associated With Stunted Growth Syndrome In Penaeus Vannamei Farming

Purpose: Stunted/slow growth syndrome is one of the yield-limiting diseases in Penaeus vannamei farming. Limited information is available on the aetiology of this condition, which needs to be studied to devise prophylactic measures to minimise the production losses. Amongst the factors that influence this condition, microbial communities in the growing environment play an important role. This study aimed at understanding major microbial associations of affected and healthy pond waters through shotgun metagenomics.Method: The water samples were filtered through vacuum filtration to extract suspended microbes. Subsequently, DNA was isolated from the filtrate using PowerSoil® DNA Isolation Kit. Libraries prepared from isolated DNA were sequenced using the shotgun metagenomic method on the Illumina HiSeq platform. The microbial profiling and their functional prediction of the shotgun metagenome sequences were carried out using stand-alone versions of Kaiju, OmicsBox respectively. Results: The taxonomic classification results revealed that species of Oceanospirillum, and vibrio were high in the disease sample, while Rhodobacteraceae bacterium and Neptunomonas were high in the healthy sample. The alpha diversity analysis showed slightly higher diversity in the healthy sample compared to the disease infected. The taxonomic biomarkers for healthy and infected states reported in previous studies were also observed in this study. The major functional associations of both the healthy and infected groups include amino acid transport and metabolism, cell wall/membrane/envelope biogenesis, and energy production and conversion. Conclusion: The study identified major taxonomical and functional associations of ponds affected and unaffected with stunted growth syndrome. These associations significantly varied between the samples, indicating dysbiosis of the microbial profiles in the pond waters. This dysbiosis could be a potential cause for the manifestation of stunted growth syndrome. Microbial associations along with other pond environmental factors need to be further explored for an in-depth understanding of stunted growth syndrome.

Diversity and Function of Wolf Spider Gut Microbiota Revealed by Shotgun Metagenomics

Wolf spiders (Lycosidae) are crucial component of integrated pest management programs and the characteristics of their gut microbiota are known to play important roles in improving fitness and survival of the host. However, there are only few studies of the gut microbiota among closely related species of wolf spider. Whether wolf spiders gut microbiota vary with habitats remains unknown. Here, we used shotgun metagenomic sequencing to compare the gut microbiota of two wolf spider species, Pardosa agraria and P. laura from farmland and woodland ecosystems, respectively. The results show that the gut microbiota of Pardosa spiders is similar in richness and abundance. Approximately 27.3% of the gut microbiota of P. agraria comprises Proteobacteria, and approximately 34.5% of the gut microbiota of P. laura comprises Firmicutes. We assembled microbial genomes and found that the gut microbiota of P. laura are enriched in genes for carbohydrate metabolism. In contrast, those of P. agraria showed a higher proportion of genes encoding acetyltransferase, an enzyme involved in resistance to antibiotics. We reconstructed three high-quality and species-level microbial genomes: Vulcaniibacterium thermophilum, Anoxybacillus flavithermus and an unknown bacterium belonging to the family Simkaniaceae. Our results contribute to an understanding of the diversity and function of gut microbiota in closely related spiders.

Salivary Microbiome in Adenoid Cystic Carcinoma Detected by 16S rRNA Sequencing and Shotgun Metagenomics

Microorganisms are confirmed to be closely related to the occurrence and development of cancers in human beings. However, there has been no published report detailing relationships between the oral microbiota and salivary adenoid cystic carcinoma (SACC). In this study, unstimulated saliva was collected from 13 SACC patients and 10 healthy controls. The microbial diversities, compositions and functions were comprehensively analyzed after 16S rRNA sequencing and whole-genome shotgun metagenomic sequencing. The alpha diversity showed no significant difference between SACC patients and healthy controls, while beta diversity showed a separation trend. The SACC patients showed higher abundances of Streptococcus and Rothia, while Prevotella and Alloprevotella were more abundant in healthy controls. The prevalent KEGG pathways, carbohydrate-active enzymes, antibiotic resistances and virulence factors as well as the biomarkers in SACC were determined by functional gene analysis. Our study preliminarily investigated the salivary microbiome of SACC patients compared with healthy controls and might be the basis for further studies on novel diagnostic and treatment strategies.

Comparative meta-omics for identifying pathogens associated with prosthetic joint infection

Prosthetic joint infections (PJI) are economically and personally costly, and their incidence has been increasing in the United States. Herein, we compared 16S rRNA amplicon sequencing (16S), shotgun metagenomics (MG) and metatranscriptomics (MT) in identifying pathogens causing PJI. Samples were collected from 30 patients, including 10 patients undergoing revision arthroplasty for infection, 10 patients receiving revision for aseptic failure, and 10 patients undergoing primary total joint arthroplasty. Synovial fluid and peripheral blood samples from the patients were obtained at time of surgery. Analysis revealed distinct microbial communities between primary, aseptic, and infected samples using MG, MT, (PERMANOVA p = 0.001), and 16S sequencing (PERMANOVA p < 0.01). MG and MT had higher concordance with culture (83%) compared to 0% concordance of 16S results. Supervised learning methods revealed MT datasets most clearly differentiated infected, primary, and aseptic sample groups. MT data also revealed more antibiotic resistance genes, with improved concordance results compared to MG. These data suggest that a differential and underlying microbial ecology exists within uninfected and infected joints. This study represents the first application of RNA-based sequencing (MT). Further work on larger cohorts will provide opportunities to employ deep learning approaches to improve accuracy, predictive power, and clinical utility.