An important role for triglyceride in regulating spermatogenesis.

Drosophila is a powerful model to study how lipids affect spermatogenesis. Yet, the contribution of neutral lipids, a major lipid group which resides in organelles called lipid droplets (LD), to sperm development is largely unknown. Emerging evidence suggests LD are present in the testis and that loss of neutral lipid- and LD-associated genes causes subfertility; however, key regulators of testis neutral lipids and LD remain unclear. Here, we show LD are present in early-stage somatic and germline cells within the Drosophila testis. We identified a role for triglyceride lipase brummer (bmm) in regulating testis LD, and found that whole-body loss of bmm leads to defects in sperm development. Importantly, these represent cell-autonomous roles for bmm in regulating testis LD and spermatogenesis. Because lipidomic analysis of bmm mutants revealed excess triglyceride accumulation, and spermatogenic defects in bmm mutants were rescued by genetically blocking triglyceride synthesis, our data suggest that bmm-mediated regulation of triglyceride influences sperm development. This identifies triglyceride as an important neutral lipid that contributes to Drosophila sperm development, and reveals a key role for bmm in regulating testis triglyceride levels during spermatogenesis.

Gut metagenome-derived signature predicts hepatic decompensation and mortality in NAFLD-related cirrhosis.

BACKGROUND: There are limited data on the diagnostic accuracy of gut microbial signatures for predicting hepatic decompensation in patients with cirrhosis. AIMS: To determine whether a stool metagenome-derived signature accurately detects hepatic decompensation and mortality risk in cirrhosis secondary to non-alcoholic fatty liver disease (NAFLD) METHODS: Shotgun metagenomic sequencing was performed on faecal samples collected at study entry from a prospective cohort of adults with NAFLD-related cirrhosis. A Random Forest machine learning algorithm was utilised to identify a metagenomic signature of decompensated cirrhosis (defined by ascites, hepatic encephalopathy or variceal haemorrhage) and subsequently validated in an external cohort. A Cox proportional hazards regression model was used to examine predictors of all-cause mortality. RESULTS: In all, 25 adults with NAFLD-related cirrhosis (training cohort) were included. Among the 16 participants with decompensated cirrhosis, 33% had ascites, 56% had hepatic encephalopathy and 22% had experienced a variceal haemorrhage (not mutually exclusive). We identified a stool metagenomic signature comprising 13 discriminatory species that reliably distinguished decompensated NAFLD-related cirrhosis (diagnostic accuracy, 0.97, 95% confidence interval [CI] 0.96-0.99). Diagnostic accuracy of the 13-species signature remained high after adjustment for lactulose (area under the curve [AUC] 0.99) and rifaximin use (AUC 0.93). The discriminative ability of 13-species metagenomic signature was robust in an independent test cohort (AUC 0.95, 95% CI 0.81-1.00). The 13-species metagenomic signature (hazard ratio [HR] 1.54, 95% CI 1.10-2.15, p = 0.01) was a stronger predictor of mortality than the Model for End-Stage Liver Disease score (HR 1.25, 95% CI 1.03-1.53, p = 0.03). CONCLUSIONS: This study provides evidence for a gut metagenome-derived signature with high diagnostic accuracy for hepatic decompensation that predicts risk of mortality in NAFLD-related cirrhosis.

JPA: Joint Metabolic Feature Extraction Increases the Depth of Chemical Coverage for LC-MS-Based Metabolomics and Exposomics.

Extracting metabolic features from liquid chromatography-mass spectrometry (LC-MS) data has been a long-standing bioinformatic challenge in untargeted metabolomics. Conventional feature extraction algorithms fail to recognize features with low signal intensities, poor chromatographic peak shapes, or those that do not fit the parameter settings. This problem also poses a challenge for MS-based exposome studies, as low-abundant metabolic or exposomic features cannot be automatically recognized from raw data. To address this data processing challenge, we developed an R package, JPA (short for Joint Metabolomic Data Processing and Annotation), to comprehensively extract metabolic features from raw LC-MS data. JPA performs feature extraction by combining a conventional peak picking algorithm and strategies for (1) recognizing features with bad peak shapes but that have tandem mass spectra (MS2) and (2) picking up features from a user-defined targeted list. The performance of JPA in global metabolomics was demonstrated using serial diluted urine samples, in which JPA was able to rescue an average of 25% of metabolic features that were missed by the conventional peak picking algorithm due to dilution. More importantly, the chromatographic peak shapes, analytical accuracy, and precision of the rescued metabolic features were all evaluated. Furthermore, owing to its sensitive feature extraction, JPA was able to achieve a limit of detection (LOD) that was up to thousands of folds lower when automatically processing metabolomics data of a serial diluted metabolite standard mixture analyzed in HILIC(-) and RP(+) modes. Finally, the performance of JPA in exposome research was validated using a mixture of 250 drugs and 255 pesticides at environmentally relevant levels. JPA detected an average of 2.3-fold more exposure compounds than conventional peak picking only.