Mediterranean Sea heatwaves jeopardize greater amberjack's (Seriola dumerili) aquaculture productivity through impacts on the fish microbiota.

Climate change is dramatically increasing the frequency and severity of marine heatwaves (MHWs) in the Mediterranean basin, strongly affecting marine food production systems. However, how it will shape the ecology of aquaculture systems, and the cascading effects on productivity, is still a major knowledge gap. The present work aims to increase our understanding of future impacts, caused by raising water temperatures, on the interaction between water and fish microbiotas, and consequential effects upon fish growth. Thus, the bacterial communities present in the water tanks, and mucosal tissues (skin, gills and gut), of greater amberjack farmed in recirculatory aquaculture systems (RAS), at three different temperatures (24, 29 and 33 °C), were characterized in a longitudinal study. The greater amberjack (Seriola dumerili) is a teleost species with high potential for EU aquaculture diversification due to its fast growth, excellent flesh quality and global market. We show that higher water temperatures disrupt the greater amberjack's microbiota. Our results demonstrate the causal mediation exerted by this bacterial community shifts on the reduction of fish growth. The abundance of members of the Pseudoalteromonas is positively correlated with fish performance, whereas members of the Psychrobacter, Chryseomicrobium, Paracoccus and Enterovibrio are suggested as biomarkers for dysbiosis, at higher water temperatures. Hence, opening new evidence-based avenues for the development of targeted microbiota-based biotechnological tools, designed to increase the resilience and adaptation to climate change of the Mediterranean aquaculture industry.

The outcome of boosting mitochondrial activity in alcohol-associated liver disease is organ-dependent.

BACKGROUND AND AIMS: Alcohol-associated liver disease (ALD) accounts for 70% of liver-related deaths in Europe, with no effective approved therapies. Although mitochondrial dysfunction is one of the earliest manifestations of alcohol-induced injury, restoring mitochondrial activity remains a problematic strategy due to oxidative stress. Here, we identify methylation-controlled J protein (MCJ) as a mediator for ALD progression and hypothesize that targeting MCJ may help in recovering mitochondrial fitness without collateral oxidative damage. APPROACH AND RESULTS: C57BL/6 mice [wild-type (Wt)] Mcj knockout and Mcj liver-specific silencing (MCJ-LSS) underwent the NIAAA dietary protocol (Lieber-DeCarli diet containing 5% (vol/vol) ethanol for 10 days, plus a single binge ethanol feeding at day 11). To evaluate the impact of a restored mitochondrial activity in ALD, the liver, gut, and pancreas were characterized, focusing on lipid metabolism, glucose homeostasis, intestinal permeability, and microbiota composition. MCJ, a protein acting as an endogenous negative regulator of mitochondrial respiration, is downregulated in the early stages of ALD and increases with the severity of the disease. Whole-body deficiency of MCJ is detrimental during ALD because it exacerbates the systemic effects of alcohol abuse through altered intestinal permeability, increased endotoxemia, and dysregulation of pancreatic function, which overall worsens liver injury. On the other hand, liver-specific Mcj silencing prevents main ALD hallmarks, that is, mitochondrial dysfunction, steatosis, inflammation, and oxidative stress, as it restores the NAD + /NADH ratio and SIRT1 function, hence preventing de novo lipogenesis and improving lipid oxidation. CONCLUSIONS: Improving mitochondrial respiration by liver-specific Mcj silencing might become a novel therapeutic approach for treating ALD.

Multiomics signatures of type 1 diabetes with and without albuminuria.

Aims/hypothesis: To identify novel pathophysiological signatures of longstanding type 1 diabetes (T1D) with and without albuminuria we investigated the gut microbiome and blood metabolome in individuals with T1D and healthy controls (HC). We also mapped the functional underpinnings of the microbiome in relation to its metabolic role. Methods: One hundred and sixty-one individuals with T1D and 50 HC were recruited at the Steno Diabetes Center Copenhagen, Denmark. T1D cases were stratified based on levels of albuminuria into normoalbuminuria, moderate and severely increased albuminuria. Shotgun sequencing of bacterial and viral microbiome in stool samples and circulating metabolites and lipids profiling using mass spectroscopy in plasma of all participants were performed. Functional mapping of microbiome into Gut Metabolic Modules (GMMs) was done using EggNog and KEGG databases. Multiomics integration was performed using MOFA tool. Results: Measures of the gut bacterial beta diversity differed significantly between T1D and HC, either with moderately or severely increased albuminuria. Taxonomic analyses of the bacterial microbiota identified 51 species that differed in absolute abundance between T1D and HC (17 higher, 34 lower). Stratified on levels of albuminuria, 10 species were differentially abundant for the moderately increased albuminuria group, 63 for the severely increased albuminuria group while 25 were common and differentially abundant both for moderately and severely increased albuminuria groups, when compared to HC. Functional characterization of the bacteriome identified 23 differentially enriched GMMs between T1D and HC, mostly involved in sugar and amino acid metabolism. No differences in relation to albuminuria stratification was observed. Twenty-five phages were differentially abundant between T1D and HC groups. Six of these varied with albuminuria status. Plasma metabolomics indicated differences in the steroidogenesis and sugar metabolism and circulating sphingolipids in T1D individuals. We identified association between sphingolipid levels and Bacteroides sp. abundances. MOFA revealed reduced interactions between gut microbiome and plasma metabolome profiles albeit polar metabolite, lipids and bacteriome compositions contributed to the variance in albuminuria levels among T1D individuals. Conclusions: Individuals with T1D and progressive kidney disease stratified on levels of albuminuria show distinct signatures in their gut microbiome and blood metabolome.

microRNA-based signatures obtained from endometrial fluid identify implantative endometrium.

STUDY QUESTION: Is it possible to use free and extracellular vesicle-associated microRNAs (miRNAs) from human endometrial fluid (EF) samples as non-invasive biomarkers for implantative endometrium? SUMMARY ANSWER: The free and extracellular vesicle-associated miRNAs can be used to detect implantative endometrium in a non-invasive manner. WHAT IS KNOWN ALREADY: miRNAs and extracellular vesicles (EVs) from EF have been described as mediators of the embryo-endometrium crosstalk. Therefore, the analysis of miRNA from this fluid could become a non-invasive technique for recognizing implantative endometrium. This analysis could potentially help improve the implantation rates in ART. STUDY DESIGN, SIZE, DURATION: In this prospective study, we first optimized different protocols for EVs and miRNA analyses using the EF of a setup cohort (n = 72). Then, we examined differentially expressed miRNAs in the EF of women with successful embryo implantation (discovery cohort n = 15/validation cohort n = 30) in comparison with those for whom the implantation had failed (discovery cohort n = 15/validation cohort n = 30). Successful embryo implantation was considered when pregnancy was confirmed by vaginal ultrasound showing a gestational sac 4 weeks after embryo transfer (ET). PARTICIPANTS/MATERIALS, SETTING, METHODS: The EF of the setup cohort was obtained before starting fertility treatment during the natural cycle, 16-21 days after the beginning of menstruation. For the discovery and validation cohorts, the EF was collected from women undergoing frozen ET on Day 5, and the samples were collected immediately before ET. In this study, we compared five different methods; two of them based on direct extraction of RNA and the other three with an EV enrichment step before the RNA extraction. Small RNA sequencing was performed to determine the most efficient method and find a predictive model differentiating between implantative and non-implantative endometrium. The models were confirmed using quantitative PCR in two sets of samples (discovery and validation cohorts) with different implantation outcomes. MAIN RESULTS AND THE ROLE OF CHANCE: The protocols using EV enrichment detected more miRNAs than the methods based on direct RNA extraction. The two most efficient protocols (using polymer-based precipitation (PBP): PBP-M and PBP-N) were used to obtain two predictive models (based on three miRNAs) allowing us to distinguish between an implantative and non-implantative endometrium. The first Model 1 (PBP-M) (discovery: AUC = 0.93; P-value = 0.003; validation: AUC = 0.69; P-value = 0.019) used hsa-miR-200b-3p, hsa-miR-24-3p and hsa-miR-148b-3p. Model 2 (PBP-N) (discovery: AUC = 0.92; P-value = 0.0002; validation: AUC = 0.78; P-value = 0.0002) used hsa-miR-200b-3p, hsa-miR-24-3p and hsa-miR-99b-5p. Functional analysis of these miRNAs showed strong association with key implantation processes such as in utero embryonic development or transforming growth factor-beta signaling. LARGE SCALE DATA: The FASTQ data are available in the GEO database (access number GSE178917). LIMITATIONS, REASONS FOR CAUTION: One important factor to consider is the inherent variability among the women involved in the trial and among the transferred embryos. The embryos were pre-selected based on morphology, but neither genetic nor molecular studies were conducted, which would have improved the accuracy of our tests. In addition, a limitation in miRNA library construction is the low amount of input RNA. WIDER IMPLICATIONS OF THE FINDINGS: We describe new non-invasive protocols to analyze miRNAs from small volumes of EF. These protocols could be implemented in clinical practice to assess the status of the endometrium before attempting ET. Such evaluation could help to avoid the loss of embryos transferred to a non-implantative endometrium. STUDY FUNDING/COMPETING INTEREST(S): J.I.-P. was supported by a predoctoral grant from the Basque Government (PRE_2017_0204). This study was partially funded by the Grant for Fertility Innovation (GFI, 2011) from Merck (Darmstadt, Germany). It was also supported by the Spanish Ministry of Economy and Competitiveness MINECO within the National Plan RTI2018-094969-B-I00, the European Union's Horizon 2020 research and innovation program (860303), the Severo Ochoa Centre of Excellence Innovative Research Grant (SEV-2016-0644) and the Instituto de Salud Carlos III (PI20/01131). The funding entities did not play any role in the study design, collection, analysis and interpretation of data, writing of the report or the decision to submit the article for publication. The authors declare no competing interests.

A Comprehensive Metabolomics Analysis of Fecal Samples from Advanced Adenoma and Colorectal Cancer Patients.

Accurate diagnosis of colorectal cancer (CRC) still relies on invasive colonoscopy. Noninvasive methods are less sensitive in detecting the disease, particularly in the early stage. In the current work, a metabolomics analysis of fecal samples was carried out by ultra-high-performance liquid chromatography-tandem mass spectroscopy (UPLC-MS/MS). A total of 1380 metabolites were analyzed in a cohort of 120 fecal samples from patients with normal colonoscopy, advanced adenoma (AA) and CRC. Multivariate analysis revealed that metabolic profiles of CRC and AA patients were similar and could be clearly separated from control individuals. Among the 25 significant metabolites, sphingomyelins (SM), lactosylceramides (LacCer), secondary bile acids, polypeptides, formiminoglutamate, heme and cytidine-containing pyrimidines were found to be dysregulated in CRC patients. Supervised random forest (RF) and logistic regression algorithms were employed to build a CRC accurate predicted model consisting of the combination of hemoglobin (Hgb) and bilirubin E,E, lactosyl-N-palmitoyl-sphingosine, glycocholenate sulfate and STLVT with an accuracy, sensitivity and specificity of 91.67% (95% Confidence Interval (CI) 0.7753-0.9825), 0.7 and 1, respectively.

Interplay between Genome, Metabolome and Microbiome in Colorectal Cancer.

BACKGROUND: Colorectal cancer (CRC), a major health concern, is developed depending on environmental, genetic and microbial factors. The microbiome and metabolome have been analyzed to study their role in CRC. However, the interplay of host genetics with those layers in CRC remains unclear. METHODS: 120 individuals were sequenced and association analyses were carried out for adenoma and CRC risk, and for selected components of the microbiome and metabolome. The epistasis between genes located in cholesterol pathways was analyzed; modifiable risk factors were studied using Mendelian randomization; and the three omic layers were used to integrate their data and to build risk prediction models. RESULTS: We detected genetic variants that were associated to components of metabolome or microbiome and adenoma or CRC risk (e.g., in LINC01605, PROKR2 and CCSER1 genes). In addition, we found interactions between genes of cholesterol metabolism, and HDL cholesterol levels affected adenoma (p = 0.0448) and CRC (p = 0.0148) risk. The combination of the three omic layers to build risk prediction models reached high AUC values (>0.91). CONCLUSIONS: The use of the three omic layers allowed for the finding of biological mechanisms related to the development of adenoma and CRC, and each layer provided complementary information to build risk prediction models.

Integrative Analysis of Fecal Metagenomics and Metabolomics in Colorectal Cancer.

Although colorectal cancer (CRC) is the second leading cause of death in developed countries, current diagnostic tests for early disease stages are suboptimal. We have performed a combination of UHPLC-MS metabolomics and 16S microbiome analyses on 224 feces samples in order to identify early biomarkers for both advanced adenomas (AD) and CRC. We report differences in fecal levels of cholesteryl esters and sphingolipids in CRC. We identified Fusobacterium, Parvimonas and Staphylococcus to be increased in CRC patients and Lachnospiraceae family to be reduced. We finally described Adlercreutzia to be more abundant in AD patients' feces. Integration of metabolomics and microbiome data revealed tight interactions between bacteria and host and performed better than FOB test for CRC diagnosis. This study identifies potential early biomarkers that outperform current diagnostic tools and frame them into the stablished gut microbiota role in CRC pathogenesis.

Extracellular Vesicles From Liver Progenitor Cells Downregulates Fibroblast Metabolic Activity and Increase the Expression of Immune-Response Related Molecules.

Extracellular vesicles (EVs) mediate cell-to-cell crosstalk whose content can induce changes in acceptor cells and their microenvironment. MLP29 cells are mouse liver progenitor cells that release EVs loaded with signaling cues that could affect cell fate. In the current work, we incubated 3T3-L1 mouse fibroblasts with MLP29-derived EVs, and then analyzed changes by proteomics and transcriptomics. Results showed a general downregulation of protein and transcript expression related to proliferative and metabolic routes dependent on TGF-beta. We also observed an increase in the ERBB2 interacting protein (ERBIN) and Cxcl2, together with an induction of ribosome biogenesis and interferon-related response molecules, suggesting the activation of immune system signaling.

Gut microbiome and serum metabolome analyses identify molecular biomarkers and altered glutamate metabolism in fibromyalgia.

BACKGROUND: Fibromyalgia is a complex, relatively unknown disease characterised by chronic, widespread musculoskeletal pain. The gut-brain axis connects the gut microbiome with the brain through the enteric nervous system (ENS); its disruption has been associated with psychiatric and gastrointestinal disorders. To gain an insight into the pathogenesis of fibromyalgia and identify diagnostic biomarkers, we combined different omics techniques to analyse microbiome and serum composition. METHODS: We collected faeces and blood samples to study the microbiome, the serum metabolome and circulating cytokines and miRNAs from a cohort of 105 fibromyalgia patients and 54 age- and environment-matched healthy individuals. We sequenced the V3 and V4 regions of the 16S rDNA gene from faeces samples. UPLC-MS metabolomics and custom multiplex cytokine and miRNA analysis (FirePlex™ technology) were used to examine sera samples. Finally, we combined the different data types to search for potential biomarkers. RESULTS: We found that the diversity of bacteria is reduced in fibromyalgia patients. The abundance of the Bifidobacterium and Eubacterium genera (bacteria participating in the metabolism of neurotransmitters in the host) in these patients was significantly reduced. The serum metabolome analysis revealed altered levels of glutamate and serine, suggesting changes in neurotransmitter metabolism. The combined serum metabolomics and gut microbiome datasets showed a certain degree of correlation, reflecting the effect of the microbiome on metabolic activity. We also examined the microbiome and serum metabolites, cytokines and miRNAs as potential sources of molecular biomarkers of fibromyalgia. CONCLUSIONS: Our results show that the microbiome analysis provides more significant biomarkers than the other techniques employed in the work. Gut microbiome analysis combined with serum metabolomics can shed new light onto the pathogenesis of fibromyalgia. We provide a list of bacteria whose abundance changes in this disease and propose several molecules as potential biomarkers that can be used to evaluate the current diagnostic criteria.

Targeted UPLC-MS Metabolic Analysis of Human Faeces Reveals Novel Low-Invasive Candidate Markers for Colorectal Cancer.

Low invasive tests with high sensitivity for colorectal cancer and advanced precancerous lesions will increase adherence rates, and improve clinical outcomes. We have performed an ultra-performance liquid chromatography/time-of-flight mass spectrometry (UPLC-(TOF) MS)-based metabolomics study to identify faecal biomarkers for the detection of patients with advanced neoplasia. A cohort of 80 patients with advanced neoplasia (40 advanced adenomas and 40 colorectal cancers) and 49 healthy subjects were analysed in the study. We evaluated the faecal levels of 105 metabolites including glycerolipids, glycerophospholipids, sterol lipids and sphingolipids. We found 18 metabolites that were significantly altered in patients with advanced neoplasia compared to controls. The combinations of seven metabolites including ChoE(18:1), ChoE(18:2), ChoE(20:4), PE(16:0/18:1), SM(d18:1/23:0), SM(42:3) and TG(54:1), discriminated advanced neoplasia patients from healthy controls. These seven metabolites were employed to construct a predictive model that provides an area under the curve (AUC) median value of 0.821. The inclusion of faecal haemoglobin concentration in the metabolomics signature improved the predictive model to an AUC of 0.885. In silico gene expression analysis of tumour tissue supports our results and puts the differentially expressed metabolites into biological context, showing that glycerolipids and sphingolipids metabolism and GPI-anchor biosynthesis pathways may play a role in tumour progression.

Metabolic alterations in urine extracellular vesicles are associated to prostate cancer pathogenesis and progression.

Urine contains extracellular vesicles (EVs) that concentrate molecules and protect them from degradation. Thus, isolation and characterisation of urinary EVs could increase the efficiency of biomarker discovery. We have previously identified proteins and RNAs with differential abundance in urinary EVs from prostate cancer (PCa) patients compared to benign prostate hyperplasia (BPH). Here, we focused on the analysis of the metabolites contained in urinary EVs collected from patients with PCa and BPH. Targeted metabolomics analysis of EVs was performed by ultra-high-performance liquid chromatography-mass spectrometry. The correlation between metabolites and clinical parameters was studied, and metabolites with differential abundance in PCa urinary EVs were detected and mapped into cellular pathways. We detected 248 metabolites belonging to different chemical families including amino acids and various lipid species. Among these metabolites, 76 exhibited significant differential abundance between PCa and BPH. Interestingly, urine EVs recapitulated many of the metabolic alterations reported in PCa, including phosphathidylcholines, acyl carnitines, citrate and kynurenine. Importantly, we found elevated levels of the steroid hormone, 3beta-hydroxyandros-5-en-17-one-3-sulphate (dehydroepiandrosterone sulphate) in PCa urinary EVs, in line with the potential elevation of androgen synthesis in this type of cancer. This work supports urinary EVs as a non-invasive source to infer metabolic changes in PCa.