Metabolic syndrome (MetS) is a complex condition encompassing a constellation of cardiometabolic abnormalities. Oxylipins are a superfamily of lipid mediators regulating many cardiometabolic functions. Plasma oxylipin signature could provide a new clinical tool to enhance the phenotyping of MetS pathophysiology. A high-throughput validated mass spectrometry method, allowing for the quantitative profiling of over 130 oxylipins, was applied to identify and validate the oxylipin signature of MetS in two independent nested case/control studies involving 476 participants. We identified an oxylipin signature of MetS (coined OxyScore), including 23 oxylipins and having high performances in classification and replicability (cross-validated AUC_ROC of 89%, 95% CI: 85–93% and 78%, 95% CI: 72–85% in the Discovery and Replication studies, respectively). Correlation analysis and comparison with a classification model incorporating the MetS criteria showed that the oxylipin signature brings consistent and complementary information to the clinical criteria. Being linked with the regulation of various biological processes, the candidate oxylipins provide an integrative phenotyping of MetS regarding the activation and/or negative feedback regulation of crucial molecular pathways. This may help identify patients at higher risk of cardiometabolic diseases. The oxylipin signature of patients with metabolic syndrome enhances MetS phenotyping and may ultimately help to better stratify the risk of cardiometabolic diseases.

Given the role of the rumen microbiome in providing nutrients to the host ruminant, it is expected that rumen microbes contribute to inter-animal variations in feed efficiency. However, the link between microbial structure and an “efficient” host phenotype is unclear. We hypothesized that extreme residual feed intake (RFI) phenotypes would display distinctive microbiome features regardless of the diet. In this study, we selected the 32 most extreme RFI Charolais bulls from a cohort of 100 animals fed corn-silage (CS; n = 50) or grass-silage (GS; n = 50) based diets. Rumen samples were obtained 3 h after feeding, at slaughter, for fermentation and metataxonomic and metatranscriptomic microbial analysis. Volatile fatty acid profiles showed no differences between diets and between extreme RFI phenotypes (P > 0.05). Total bacteria and methanogen populations did not differ between extreme RFI phenotypes (P > 0.05), although methanogens expressed per liquid rumen digesta weight tended to decrease in the most efficient bulls compared to the least efficient ones (P = 0.10). The rumen microbial community structure differed between diets (P < 0.001), and between extreme RFI phenotypes in the GS diet. In the whole dataset, we identified Succiniclasticum, Saccharofermentans, Clostridia_258483 and CAG-238 as bacteria discriminant between extreme RFI phenotypes (q < 0.10). Within diets, these four genera were also discriminant in the GS diet and were all associated with the least efficient bulls. In contrast, in bulls fed the CS diet, only Saccharofermentans and Succiniclasticum were discriminant but they were positively associated with the most efficient bulls. Rumen microbial functional features did not differ between extreme RFI phenotypes but did differ between diets. In conclusion, the rumen microbiome was mainly influenced by diet, with the RFI phenotype being a marginal effector. Succiniclasticum, Saccharofermentans, Clostridia_258483, and CAG-238 were discriminant between extreme RFI phenotypes regardless of diet. However, the direction of the association with RFI was diet dependent, indicating a diet-RFI interaction and suggesting that these discriminating microbes may be suitable microbial indicator species for RFI only when considered in conjunction with the diet information.

With nearly 1700 species, Microsporidia represent a group of obligate intracellular eukaryotes with veterinary, economic and medical impacts. To help understand the biological functions of these microorganisms, complete genome sequencing is routinely used. Nevertheless, the proper prediction of their gene catalogue is challenging due to their taxon-specific evolutionary features. As innovative genome annotation strategies are needed to obtain a representative snapshot of the overall lifestyle of these parasites, the MicroAnnot tool, a dedicated workflow for microsporidian sequence annotation using data from curated databases of accurately annotated microsporidian genes, has been developed. Furthermore, specific modules have been implemented to perform small gene (<300 bp) and transposable element identification. Finally, functional annotation was performed using the signature-based InterProScan software. MicroAnnot’s accuracy has been verified by the re-annotation of four microsporidian genomes for which structural annotation had previously been validated. With its comparative approach and transcriptional signal identification method, MicroAnnot provides an accurate prediction of translation initiation sites, an efficient identification of transposable elements, as well as high specificity and sensitivity for microsporidian genes, including those under 300 bp.

Several microsporidia, obligate intracellular parasites, have evolved through genome reduction and compaction. Other microsporidia contrast with this trend exhibiting larger genomes, in part through the expansion of transposable elements (TEs). Anncaliia algerae belongs to the latter, and boasts an especially diverse host range. Exploring whether A. algerae’s wide host range is a consequence of TE expansion requires high-quality genomic and transcriptomic data. However, due to an AT-rich genome and abundant TE content, previous sequencing efforts yielded fragmented and/or incomplete genome assemblies. Here, using PacBio Hifi sequencing, we produced a near chromosome-level assembly and extensively annotated the genome through: a microsporidia specific annotation tool (MicroAnnot), de novo TE prediction, and a dual RNA-seq dataset. This approach allowed an in depth look into the genome architecture and ploidy of A. algerae, along with fine characterisation of the TEs. We also highlight three proteins likely involved in TE regulatory machinery in A. algerae.

With nearly 1,700 species, Microsporidia represent a group of obligate intracellular eukaryotes with veterinary, economic and medical impacts. To help in understanding the biological functions of these microorganisms, complete genome sequencing is used routinely. Nevertheless, the proper prediction of their gene catalogue is challenging due to taxon-specific evolutionary features. As innovative genome annotation strategies are needed to obtain a representative snapshot of the overall lifestyle of these parasites, the MicroAnnot tool, a dedicated workflow for microsporidian sequence annotation using data from curated databases of accurately annotated microsporidian genes, has been developed. Furthermore, specific modules have been implemented to perform small genes (< 300bp) and transposable element identification. Finally, functional annotation was performed using the signature-based InterProScan software. MicroAnnot’s accuracy has been verified by the re-annotation of four microsporidian genomes for which structural annotation had previously been validated. With its comparative approach and transcriptional signal identification method, MicroAnnot provides accurate prediction of translation initiation sites, efficient identification of transposable elements, as well as high specificity and sensitivity for microsporidian genes including those under 300 bp. Thanks to its web interface (https://microannot.org), MicroAnnot is available to the community to ensure high quality annotation of microsporidian genomes.