Lightweight compositional analysis of metagenomes with FracMinHash and minimum metagenome covers

The identification of reference genomes and taxonomic labels from metagenome data underlies many microbiome studies. Here we describe two algorithms for compositional analysis of metagenome sequencing data. We first investigate the FracMinHash sketching technique, a derivative of modulo hash that supports Jaccard containment estimation between sets of different sizes. We implement FracMinHash in the sourmash software, evaluate its accuracy, and demonstrate large-scale containment searches of metagenomes using 700,000 microbial reference genomes. We next frame shotgun metagenome compositional analysis as the problem of finding a minimum collection of reference genomes that "cover" the known k-mers in a metagenome, a minimum set cover problem. We implement a greedy approximate solution using FracMinHash sketches, and evaluate its accuracy for taxonomic assignment using a CAMI community benchmark. Finally, we show that the minimum metagenome cover can be used to guide the selection of reference genomes for read mapping. sourmash is available as open source software under the BSD 3-Clause license at github.com/dib-lab/sourmash/.

MT-MAG: Accurate and interpretable machine learning based taxonomic assignment of metagenome-assembled genomes, with a partial classification option

We propose MT-MAG, a novel machine learning-based taxonomic assignment tool for hierarchically-structured local classification of metagenome-assembled genomes (MAGs). MT-MAG is capable of classifying large and diverse real metagenomic datasets, having analyzed for this study a total of 240 Gbp of data in the training set, and 7 Gbp of data in the test set. MT-MAG is, to the best of our knowledge, the first machine learning method for taxonomic assignment of metagenomic data that offers a "partial classification" option. MT-MAG outputs complete or a partial classification paths, and interpretable numerical classification confidences of its classifications, at all taxonomic ranks. MT-MAG is able to completely classify 48% more sequences than DeepMicrobes to the Species level (the only comparable taxonomic rank for DeepMicrobes), and it outperforms DeepMicrobes by an average of 33% in weighted accuracy, and by 89% in constrained accuracy.

The Periodontopathic Pathogen, Porphyromonas gingivalis, Involves a Gut Inflammatory Response and Exacerbates Inflammatory Bowel Disease

Periodontal disease (PD) is one of the most prevalent disorders globally and is strongly associated with many other diseases. Inflammatory bowel disease (IBD), an inflammatory condition of the colon and the small intestine, is reported to be associated with PD through undetermined mechanisms. We analyzed taxonomic assignment files from the Crohn’s Disease Viral and Microbial Metagenome Project (PRJEB3206). The abundance of Porphyromonadaceae in fecal samples was significantly different between patients with Crohn’s disease and control volunteers. Dextran sulfate sodium was used to induce colitis in mice to reveal the effect of this periodontopathic pathogen in vivo. After intrarectal implantation of Porphyromonas gingivalis (Pg)—the primary pathogen causing PD—the disease activity index score, colonic epithelial loss, and inflammatory cell infiltration were intensified. In addition, tumor necrosis factor-α and interleukin-6 showed the highest levels in Pg-infected colons. This revealed the importance of Pg in the exacerbation of IBD. Thus, simultaneous treatment of PD should be considered for people with IBD. Moreover, implantation of Pg in the rectum worsened the clinical symptoms of colitis in mice. Because Pg participates in the pathogenesis of IBD, reducing the chances of it entering the intestine might prevent the worsening of this disorder.

Phytool, a ShinyApp to homogenise taxonomy of freshwater microalgae from DNA barcodes and microscopic observations

Methods for biomonitoring of freshwater phytoplankton are evolving rapidly with eDNA-based methods, offering great complementarity with microscopy. Metabarcoding approaches have been more commonly used over the last years, with a continuous increase in the amount of data generated. Depending on the researchers and the way they assigned barcodes to species (bioinformatic pipelines and molecular reference databases), the taxonomic assignment obtained for HTS DNA reads might vary. This is also true for traditional taxonomic studies by microscopy with regular adjustments of the classification and taxonomy. For those reasons (leading to non-homogeneous taxonomies), gap-analyses and comparisons between studies become even more challenging and the curation processes to find potential consensus names are time-consuming. Here, we present a web-based application (Phytool), developed with ShinyApp (Rstudio), that aims to make the harmonisation of taxonomy easier and in a more efficient way, using a complete and up-to-date taxonomy reference database for freshwater microalgae. Phytool allows users to homogenise and update freshwater phytoplankton taxonomical names from sequence files and data tables directly uploaded in the application. It also gathers barcodes from curated references in a user-friendly way in which it is possible to search for specific organisms. All the data provided are downloadable with the possibility to apply filters in order to select only the required taxa and fields (e.g. specific taxonomic ranks). The main goal is to make accessible to a broad range of users the connection between microscopy and molecular biology and taxonomy through different ready-to-use functions. This study estimates that only 25% of species of freshwater phytoplankton in Phytobs are associated with a barcode. We plead for an increased effort to enrich reference databases by coupling taxonomy and molecular methods. Phytool should make this crucial work more efficient. The application is available at https://caninuzzo.shinyapps.io/phytool_v1/

Identification of individual root-knot nematodes using low coverage long-read sequencing

Root-knot nematodes (RKN; genus Meloidogyne) are polyphagous plant pathogens of great economic importance to agriculturalists globally. These species are small, diverse, and can be challenging for accurate taxonomic identification. Many of the most important crop pests confound analysis with simple genetic marker loci as they are polyploids of likely hybrid origin. Here we take a low-coverage, long-read genome sequencing approach to characterisation of individual root-knot nematodes. We demonstrate library preparation for Oxford Nanopore Technologies Flongle sequencing of low input DNA from individual juveniles and immature females, multiplexing up to twelve samples per flow cell. Taxonomic identification with Kraken 2 (a k-mer-based taxonomic assignment tool) is shown to reliably identify individual nematodes to species level, even within the very closely related Meloidogyne incognita group. Our approach forms a robust, low-cost, and scalable method for accurate RKN species diagnostics.

Identification and pathogenicity of Alternaria species associated with leaf blotch disease and premature defoliation in French apple orchards

Leaf blotch caused by Alternaria spp. is a common disease in apple-producing regions. The disease is usually associated with one phylogenetic species and one species complex, Alternaria alternata and the Alternaria arborescens species complex (A. arborescens SC), respectively. Both taxa may include the Alternaria apple pathotype, a quarantine or regulated pathogen in several countries. The apple pathotype is characterized by the production of a host-selective toxin (HST) which is involved in pathogenicity towards the apple. A cluster of genes located on conditionally dispensable chromosomes (CDCs) is involved in the production of this HST (namely AMT in the case of the apple pathotype). Since 2016, leaf blotch and premature tree defoliation attributed to Alternaria spp. have been observed in apple-producing regions of central and south-eastern France. Our study aimed to identify the Alternaria species involved in apple tree defoliation and assess the presence of the apple pathotype in French orchards. From 2016 to 2018, 166 isolates were collected and identified by multi-locus sequence typing (MLST). This analysis revealed that all these French isolates belonged to either the A. arborescens SC or A. alternata. Specific PCR detection targeting three genes located on the CDC did not indicate the presence of the apple pathotype in France. Pathogenicity was assessed under laboratory conditions on detached leaves of Golden Delicious and Gala apple cultivars for a representative subset of 28 Alternaria isolates. All the tested isolates were pathogenic on detached leaves of cultivars Golden Delicious and Gala, but no differences were observed between the pathogenicity levels of A. arborescens SC and A. alternata. However, the results of our pathogenicity test suggest that cultivar Golden Delicious is more susceptible than Gala to Alternaria leaf blotch. Implications in the detection of the Alternaria apple pathotype and the taxonomic assignment of Alternaria isolates involved in Alternaria leaf blotch are discussed.

Population study of the gut microbiome: associations with diet, lifestyle, and cardiometabolic disease

Background The human gut harbors trillions of microbes that play dynamic roles in health. While the microbiome contributes to many cardiometabolic traits by modulating host inflammation and metabolism, there is an incomplete understanding regarding the extent that and mechanisms by which individual microbes impact risk and development of cardiovascular disease (CVD). The Framingham Heart Study (FHS) is a multi-generational observational study following participants over decades to identify risk factors for CVD by correlating genetic and phenotypic factors with clinical outcomes. As a large-scale population-based cohort with extensive clinical phenotyping, FHS provides a rich landscape to explore the relationships between the gut microbiome and cardiometabolic traits. Methods We performed 16S rRNA gene sequencing on stool from 1423 participants of the FHS Generation 3, OMNI2, and New Offspring Spouse cohorts. Data processing and taxonomic assignment were performed with the 16S bioBakery workflow using the UPARSE pipeline. We conducted statistical analyses to investigate trends in overall microbiome composition and diversity in relation to disease states and systematically examined taxonomic associations with a variety of clinical traits, disease phenotypes, clinical blood markers, and medications. Results We demonstrate that overall microbial diversity decreases with increasing 10-year CVD risk and body mass index measures. We link lifestyle factors, especially diet and exercise, to microbial diversity. Our association analyses reveal both known and unreported microbial associations with CVD and diabetes, related prescription medications, as well as many anthropometric and blood test measurements. In particular, we observe a set of microbial species that demonstrate significant associations with CVD risk, metabolic syndrome, and type 2 diabetes as well as a number of shared associations between microbial species and cardiometabolic subphenotypes. Conclusions The identification of significant microbial taxa associated with prevalent CVD and diabetes, as well as risk for developing CVD, adds to increasing evidence that the microbiome may contribute to CVD pathogenesis. Our findings support new hypothesis generation around shared microbe-mediated mechanisms that influence metabolic syndrome, diabetes, and CVD risk. Further investigation of the gut microbiomes of CVD patients in a targeted manner may elucidate microbial mechanisms with diagnostic and therapeutic implications.

Human and Animal RNA Virus Diversity Detected by Metagenomics in Cameroonian Clams

Many recent pandemics have been recognized as zoonotic viral diseases. While their origins remain frequently unknown, environmental contamination may play an important role in emergence. Thus, being able to describe the viral diversity in environmental samples contributes to understand the key issues in zoonotic transmission. This work describes the use of a metagenomic approach to assess the diversity of eukaryotic RNA viruses in river clams and identify sequences from human or potentially zoonotic viruses. Clam samples collected over 2years were first screened for the presence of norovirus to verify human contamination. Selected samples were analyzed using metagenomics, including a capture of sequences from viral families infecting vertebrates (VirCapSeq-VERT) before Illumina NovaSeq sequencing. The bioinformatics analysis included pooling of data from triplicates, quality filtering, elimination of bacterial and host sequences, and a deduplication step before de novo assembly. After taxonomic assignment, the viral fraction represented 0.8–15% of reads with most sequences (68–87%) remaining un-assigned. Yet, several mammalian RNA viruses were identified. Contigs identified as belonging to the Astroviridae were the most abundant, with some nearly complete genomes of bastrovirus identified. Picobirnaviridae sequences were related to strains infecting bats, and few others to strains infecting humans or other hosts. Hepeviridae sequences were mostly related to strains detected in sponge samples but also strains from swine samples. For Caliciviridae and Picornaviridae, most of identified sequences were related to strains infecting bats, with few sequences close to human norovirus, picornavirus, and genogroup V hepatitis A virus. Despite a need to improve the sensitivity of our method, this study describes a large diversity of RNA virus sequences from clam samples. To describe all viral contaminants in this type of food, and being able to identify the host infected by viral sequences detected, may help to understand some zoonotic transmission events and alert health authorities of possible emergence.

Microbial Community Dynamics during Biodegradation of Crude Oil and Its Response to Biostimulation in Svalbard Seawater at Low Temperature

The development of oil exploration activities and an increase in shipping in Arctic areas have increased the risk of oil spills in this cold marine environment. The objective of this experimental study was to assess the effect of biostimulation on microbial community abundance, structure, dynamics, and metabolic potential for oil hydrocarbon degradation in oil-contaminated Arctic seawater. The combination of amplicon-based and shotgun sequencing, together with the integration of genome-resolved metagenomics and omics data, was applied to assess microbial community structure and metabolic properties in naphthenic crude oil-amended microcosms. The comparison of estimates for oil-degrading microbial taxa obtained with different sequencing and taxonomic assignment methods showed substantial discrepancies between applied methods. Consequently, the data acquired with different methods was integrated for the analysis of microbial community structure, and amended with quantitative PCR, producing a more objective description of microbial community dynamics and evaluation of the effect of biostimulation on particular microbial taxa. Implementing biostimulation of the seawater microbial community with the addition of nutrients resulted in substantially elevated prokaryotic community abundance (103-fold), a distinctly different bacterial community structure from that in the initial seawater, 1.3-fold elevation in the normalized abundance of hydrocarbon degradation genes, and 12% enhancement of crude oil biodegradation. The bacterial communities in biostimulated microcosms after four months of incubation were dominated by Gammaproteobacterial genera Pseudomonas, Marinomonas, and Oleispira, which were succeeded by Cycloclasticus and Paraperlucidibaca after eight months of incubation. The majority of 195 compiled good-quality metagenome-assembled genomes (MAGs) exhibited diverse hydrocarbon degradation gene profiles. The results reveal that biostimulation with nutrients promotes naphthenic oil degradation in Arctic seawater, but this strategy alone might not be sufficient to effectively achieve bioremediation goals within a reasonable timeframe.

Comprehensive Compositional Analysis of the Slit Lamp Bacteriota

Slit lamps are routinely used to examine large numbers of patients every day due to high throughput. Previous, cultivation-based results suggested slit lamps to be contaminated with bacteria, mostly coagulase-negative staphylococci, followed by micrococci, bacilli, but also Staphylococcus aureus. Our study aimed at obtaining a much more comprehensive, cultivation-independent view of the slit lamp bacteriota and its hygienic relevance, as regularly touched surfaces usually represent fomites, particularly if used by different persons. We performed extensive 16S rRNA gene sequencing to analyse the bacteriota, of 46 slit lamps from two tertiary care centers at two sampling sites, respectively. 82 samples yielded enough sequences for downstream analyses and revealed contamination with bacteria of mostly human skin, mucosa and probably eye origin, predominantly cutibacteria, staphylococci and corynebacteria. The taxonomic assignment of 3369 ASVs (amplicon sequence variants) revealed 19 bacterial phyla and 468 genera across all samples. As antibiotic resistances are of major concern, we screened all samples for methicillin-resistant Staphylococcus aureus (MRSA) using qPCR, however, no signals above the detection limit were detected. Our study provides first comprehensive insight into the slit lamp microbiota. It underlines that slit lamps carry a highly diverse, skin-like bacterial microbiota and that thorough cleaning and disinfection after use is highly recommendable to prevent eye and skin infections.