Rapid on-site detection of harmful algal blooms: real-time cyanobacteria identification using Oxford Nanopore sequencing.

With the increasing occurrence and severity of cyanobacterial harmful algal blooms (cHAB) at the global scale, there is an urgent need for rapid, accurate, accessible, and cost-effective detection tools. Here, we detail the RosHAB workflow, an innovative, in-the-field applicable genomics approach for real-time, early detection of cHAB outbreaks. We present how the proposed workflow offers consistent taxonomic identification of water samples in comparison to traditional microscopic analyses in a few hours and discuss how the generated data can be used to deepen our understanding on cyanobacteria ecology and forecast HABs events. In parallel, processed water samples will be used to iteratively build the International cyanobacterial toxin database (ICYATOX; http://icyatox.ibis.ulaval.ca) containing the analysis of novel cyanobacterial genomes, including phenomics and genomics metadata. Ultimately, RosHAB will (1) improve the accuracy of on-site rapid diagnostics, (2) standardize genomic procedures in the field, (3) facilitate these genomics procedures for non-scientific personnel, and (4) identify prognostic markers for evidence-based decisions in HABs surveillance.

Gene-based microbiome representation enhances host phenotype classification.

With the concomitant advances in both the microbiome and machine learning fields, the gut microbiome has become of great interest for the potential discovery of biomarkers to be used in the classification of the host health status. Shotgun metagenomics data derived from the human microbiome is composed of a high-dimensional set of microbial features. The use of such complex data for the modeling of host-microbiome interactions remains a challenge as retaining de novo content yields a highly granular set of microbial features. In this study, we compared the prediction performances of machine learning approaches according to different types of data representations derived from shotgun metagenomics. These representations include commonly used taxonomic and functional profiles and the more granular gene cluster approach. For the five case-control datasets used in this study (Type 2 diabetes, obesity, liver cirrhosis, colorectal cancer, and inflammatory bowel disease), gene-based approaches, whether used alone or in combination with reference-based data types, allowed improved or similar classification performances as the taxonomic and functional profiles. In addition, we show that using subsets of gene families from specific functional categories of genes highlight the importance of these functions on the host phenotype. This study demonstrates that both reference-free microbiome representations and curated metagenomic annotations can provide relevant representations for machine learning based on metagenomic data. IMPORTANCE Data representation is an essential part of machine learning performance when using metagenomic data. In this work, we show that different microbiome representations provide varied host phenotype classification performance depending on the dataset. In classification tasks, untargeted microbiome gene content can provide similar or improved classification compared to taxonomical profiling. Feature selection based on biological function also improves classification performance for some pathologies. Function-based feature selection combined with interpretable machine learning algorithms can generate new hypotheses that can potentially be assayed mechanistically. This work thus proposes new approaches to represent microbiome data for machine learning that can potentiate the findings associated with metagenomic data.

Mgll Knockout Mouse Resistance to Diet-Induced Dysmetabolism Is Associated with Altered Gut Microbiota.

Monoglyceride lipase (MGLL) regulates metabolism by catabolizing monoacylglycerols (MAGs), including the endocannabinoid 2-arachidonoyl glycerol (2-AG) and some of its bioactive congeners, to the corresponding free fatty acids. Mgll knockout mice (Mgll-/-) exhibit elevated tissue levels of MAGs in association with resistance to the metabolic and cardiovascular perturbations induced by a high fat diet (HFD). The gut microbiome and its metabolic function are disrupted in obesity in a manner modulated by 2-arachidonoyl glycerol (2-AG's) main receptors, the cannabinoid CB1 receptors. We therefore hypothesized that Mgll-/- mice have an altered microbiome, that responds differently to diet-induced obesity from that of wild-type (WT) mice. We subjected mice to HFD and assessed changes in the microbiomes after 8 and 22 weeks. As expected, Mgll-/- mice showed decreased adiposity, improved insulin sensitivity, and altered circulating incretin/adipokine levels in response to HFD. Mgll-/- mice on a chow diet exhibited significantly higher levels of Hydrogenoanaerobacterium, Roseburia, and Ruminococcus than WT mice. The relative abundance of the Lactobacillaceae and Coriobacteriaceae and of the Lactobacillus, Enterorhabdus, Clostridium_XlVa, and Falsiporphyromonas genera was significantly altered by HFD in WT but not Mgll-/- mice. Differently abundant families were also associated with changes in circulating adipokine and incretin levels in HFD-fed mice. Some gut microbiota family alterations could be reproduced by supplementing 2-AG or MAGs in culturomics experiments carried out with WT mouse fecal samples. We suggest that the altered microbiome of Mgll-/- mice contributes to their obesity resistant phenotype, and results in part from increased levels of 2-AG and MAGs.

Germ-free mice exhibit profound gut microbiota-dependent alterations of intestinal endocannabinoidome signaling.

The gut microbiota is a unique ecosystem of microorganisms interacting with the host through several biochemical mechanisms. The endocannabinoidome (eCBome), a complex signaling system including the endocannabinoid system, approximately 50 receptors and metabolic enzymes, and more than 20 lipid mediators with important physiopathologic functions, modulates gastrointestinal tract function and may mediate host cell-microbe communications there. Germ-free (GF) mice, which lack an intestinal microbiome and so differ drastically from conventionally raised (CR) mice, offer a unique opportunity to explore the eCBome in a microbe-free model and in the presence of a reintroduced functional gut microbiome through fecal microbiota transplant (FMT). We aimed to gain direct evidence for a link between the microbiome and eCBome systems by investigating eCBome alterations in the gut in GF mice before and after FMT. Basal eCBome gene expression and lipid profiles were measured in various segments of the intestine of GF and CR mice at juvenile and adult ages using targeted quantitative PCR transcriptomics and LC-MS/MS lipidomics. GF mice exhibited age-dependent modifications in intestinal eCBome gene expression and lipid mediator levels. FMT from CR donor mice to age-matched GF male mice reversed several of these alterations, particularly in the ileum and jejunum, after only 1 week, demonstrating that the gut microbiome directly impacts the host eCBome and providing a cause-effect relationship between the presence or absence of intestinal microbes and eCBome signaling. These results open the way to new studies investigating the mechanisms through which intestinal microorganisms exploit eCBome signaling to exert some of their physiopathologic functions.