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The heightened cardiovascular disease (CVD) risk observed among omnivores is thought to be linked, in part, to gut microbiota-dependent generation of trimethylamine-N-oxide (TMAO) from L-carnitine, a nutrient abundant in red meat. Gut microbial transformation of L-carnitine into trimethylamine (TMA), the precursor of TMAO, occurs via the intermediate γ-butyrobetaine (γBB). However, the interrelationship of γBB, red meat ingestion and CVD risks, as well as the gut microbial genes responsible for the transformation of γBB to TMA, are unclear. In the present study, we show that plasma γBB levels in individuals from a clinical cohort (n = 2,918) are strongly associated with incident CVD event risks. Culture of human faecal samples and microbial transplantation studies in gnotobiotic mice with defined synthetic communities showed that the introduction of Emergencia timonensis, a human gut microbe that can metabolize γBB into TMA, is sufficient to complete the carnitine → γBB → TMA transformation, elevate TMAO levels and enhance thrombosis potential in recipients after arterial injury. RNA-sequencing analyses of E. timonensis identified a six-gene cluster, herein named the γBB utilization (gbu) gene cluster, which is upregulated in response to γBB. Combinatorial cloning and functional studies identified four genes (gbuA, gbuB, gbuC and gbuE) that are necessary and sufficient to recapitulate the conversion of γBB to TMA when coexpressed in Escherichia coli. Finally, reanalysis of samples (n = 113) from a clinical, randomized diet, intervention study showed that the abundance of faecal gbuA correlates with plasma TMAO and a red meat-rich diet. Our findings reveal a microbial gene cluster that is critical to dietary carnitine → γBB → TMA → TMAO transformation in hosts and contributes to CVD risk.

18 Pages, 5 Figures. Sequencing data from this study have been submitted to the NCBI Sequence Read Archive (SRA; https://www.ncbi.nlm.nih.gov/sra) under study accession number SRP101446., High-throughput sequencing of full-length transcripts using long reads has paved the way for the discovery of thousands of novel transcripts, even in well-annotated mammalian species. The advances in sequencing technology have created a need for studies and tools that can characterize these novel variants. Here, we present SQANTI, an automated pipeline for the classification of long-read transcripts that can assess the quality of data and the preprocessing pipeline using 47 unique descriptors. We apply SQANTI to a neuronal mouse transcriptome using Pacific Biosciences (PacBio) long reads and illustrate how the tool is effective in characterizing and describing the composition of the full-length transcriptome. We perform extensive evaluation of ToFU PacBio transcripts by PCR to reveal that an important number of the novel transcripts are technical artifacts of the sequencing approach and that SQANTI quality descriptors can be used to engineer a filtering strategy to remove them. Most novel transcripts in this curated transcriptome are novel combinations of existing splice sites, resulting more frequently in novel ORFs than novel UTRs, and are enriched in both general metabolic and neural-specific functions. We show that these new transcripts have a major impact in the correct quantification of transcript levels by state-of-the-art short-read-based quantification algorithms. By comparing our iso-transcriptome with public proteomics databases, we find that alternative isoforms are elusive to proteogenomics detection. SQANTI allows the user to maximize the analytical outcome of long-read technologies by providing the tools to deliver quality-evaluated and curated full-length transcriptomes., This work has been partially funded by the University of Florida Preeminence hires program, the Spanish Ministry of Economy and Competitiveness grants BIO2015- 71658-R, BFU2014-57636-P, Spanish Ministry of Education grant FPU2013/02348, and GENCODE NIH grant 2U41 HG007234. This project has received funding from the Marie Curie International Research Staff Exchange Scheme within the 7th European Community Framework Program under grant agreement No 612583-DEANN.

High-throughput sequencing of full-length transcripts using long reads has paved the way for the discovery of thousands of novel transcripts, even in very well annotated organisms as mice and humans. Nonetheless, there is a need for studies and tools that characterize these novel isoforms. Here we present SQANTI, an automated pipeline for the classification of long-read transcripts that computes 47 descriptors that can be used to assess the quality of the data and of the preprocessing pipelines. We applied SQANTI to a neuronal mouse transcriptome using PacBio long reads and illustrate how the tool is effective in readily describing the composition of and characterizing the full-length transcriptome. We perform extensive evaluation of ToFU PacBio transcripts by PCR to reveal that an important number of the novel transcripts are technical artifacts of the sequencing approach, and that SQANTI quality descriptors can be used to engineer a filtering strategy to remove them. Most novel transcripts in this curated transcriptome are novel combinations of existing splice sites, result more frequently in novel ORFs than novel UTRs and are enriched in both general metabolic and neural specific functions. We show that these new transcripts have a major impact in the correct quantification of transcript levels by state-of-the-art short-read based quantification algorithms. By comparing our iso-transcriptome with public proteomics databases we find that alternative isoforms are elusive to proteogenomics detection and are variable in protein changes with respect to the principal isoform of their genes. SQANTI allows the user to maximize the analytical outcome of long read technologies by providing the tools to deliver quality-evaluated and curated full-length transcriptomes. SQANTI is available at https://bitbucket.org/ConesaLab/sqanti.