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BackgroundGestational diabetes mellitus (GDM) is a common metabolic disorder with important health implications for both mother and offspring. This study aims to assess the relationship between the Triglyceride Glucose (TyG) index and GDM and explore its clinical significance.MethodsA retrospective cohort study included 631 singleton pregnant women. The study collected data on the TyG index, glucose levels, and clinical outcomes from all participants. Research objectives were validated using logistic regression analysis and Receiver Operating Characteristic curves.ResultsWe found an independent correlation between TyG index and increased risk of GDM, with an odds ratio (OR) of 3.11 (95% confidence interval (CI): 2.09-4.63, P

Delays in sowing have significant effects on the grain yield, yield components, and grain protein concentrations of winter wheat. However, little is known about how delayed sowing affects these characteristics at different positions in the wheat spikes. In this study, the effects of sowing date were investigated in a winter wheat cultivar, Shannong 30, which was sown in 2019 and 2020 on October 8 (normal sowing) and October 22 (late sowing) under field conditions. Delayed sowing increased the partitioning of 13C-assimilates to spikes, particularly to florets at the apical section of a spike and those occupying distal positions on the same spikelet. Consequently, the increase in grain number was the greatest for the apical sections, followed by the basal and central sections. No significant differences were observed between sowing dates in the superior grain number in the basal and central sections, while the number in apical sections was significantly different. The number of inferior grains in each section also increased substantially in response to delayed sowing. The average grain weights in all sections remained unchanged under delayed sowing because there were parallel increases in grain number and 13C-assimilate partitioning to grains at specific positions in the spikes. Increases in grain number m–2 resulted in reduced grain protein concentrations as the limited nitrogen supply was diluted into more grains. Delayed sowing caused the greatest reduction in grain protein concentration in the basal sections, followed by the central and apical sections. No significant differences in the reduction of the grain protein concentration were observed between the inferior and superior grains under delayed sowing. In conclusion, a 2-week delay in sowing improved grain yield through increased grain number per spike, which originated principally from an increased grain number in the apical sections of spikes and in distal positions on the same spikelet. However, grain protein concentrations declined in each section because of the increased grain number and reduced N uptake.

Background: Gestational diabetes mellitus (GDM) is a common metabolic disorder with important health implications for both mother and offspring. This study aims to assess the relationship between the Triglyceride Glucose (TyG) index and GDM and explore its clinical significance. Methods: A retrospective cohort study included 631 singleton pregnant women. The study collected data on the TyG index, glucose levels, and clinical outcomes from all participants. Research objectives were validated using logistic regression analysis and Receiver Operating Characteristic curves. Results: We found an independent correlation between TyG index and increased risk of GDM, with an odds ratio (OR) of 3.11 (95% confidence interval (CI): 2.09-4.63, P<0.001). The spline model revealed a linear association between TyG index and GDM in early pregnancy (non-linear P=0.748), and the risk of GDM increased with the increase of TyG index. In addition, we found that TyG has high diagnostic performance in predicting GDM, with an AUC value of 0.668. Combined with Age, it can improve predictive performance, with an AUC value of 0.684. Compared to the lower quartile of the TyG index, women in the higher quartile have a higher incidence of premature birth, hypertensive disorders of pregnancy, and intrahepatic cholestasis of pregnancy (ICP). In the GDM group, the TyG index was negatively correlated with 25 (OH) D and TBIL, and positively correlated with FBG, TG, and TC. Conclusions: The TyG index is strongly linked to the development of GDM and is an independent risk factor for predicting it. Monitoring TyG levels in early pregnancy is valuable for identifying women at high risk for GDM. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau, known as the “Roof of the World” and “The Third Pole”, harbors numerous saline lakes primarily distributed in the Northern Tibetan Plateau. However, the challenging conditions of high altitude, low oxygen level, and harsh climate have limited investigations into the actinobacteria from these saline lakes. This study focuses on investigating the biodiversity and bioactive secondary metabolites of cultivable actinobacteria isolated from the sediments of four saline lakes on the Northern Tibetan Plateau. A total of 255 actinobacterial strains affiliated with 21 genera in 12 families of 7 orders were recovered by using the pure culture technique and 16S rRNA gene phylogenetic analysis. To facilitate a high-throughput bioactivity evaluation, 192 isolates underwent OSMAC cultivation in a miniaturized 24-well microbioreactor system (MATRIX cultivation). The antibacterial activity of crude extracts was then evaluated in a 96-well plate antibacterial assay. Forty-six strains demonstrated antagonistic effects against at least one tested pathogen, and their underlying antibacterial mechanisms were further investigated through a dual-fluorescent reporter assay (pDualrep2). Two Streptomyces strains (378 and 549) that produce compounds triggering DNA damage were prioritized for subsequent chemical investigations. Metabolomics profiling involving HPLC-UV/vis, UPLC-QTOF-MS/MS, and molecular networking identified three types of bioactive metabolites belonging to the aromatic polyketide family, i.e., cosmomycin, kidamycin, and hedamycin. In-depth analysis of the metabolomic data unveiled some potentially novel anthracycline compounds. A genome mining study based on the whole-genome sequences of strains 378 and 549 identified gene clusters potentially responsible for cosmomycin and kidamycin biosynthesis. This work highlights the effectiveness of combining metabolomic and genomic approaches to rapidly identify bioactive chemicals within microbial extracts. The saline lakes on the Northern Tibetan Plateau present prospective sources for discovering novel actinobacteria and biologically active compounds.

The reproducibility in microbiome studies is limited due to the lack of one gold-standard operating procedure. The aim of this study was to examine the impact of protocol variations on microbiome composition using metagenomic data sets from a single center. We assessed the variation in a data set consisted of 2,722 subjects, including 9 subcohorts harboring healthy subjects and patients with various disorders, such as inflammatory bowel disease, colorectal cancer, and type 2 diabetes. Two different DNA extraction kits, with or without lyticase, and two sample storage methods were compared. Our results indicated that DNA extraction had the largest impact on gut microbiota diversity among all host factors and sample operating procedures. Healthy subjects matched by age, body mass index, and sample operating methods exhibited reduced, yet significant differences (PERMANOVA, P < 0.05) in gut microbiota composition across studies. The variations contributed by DNA extraction were primarily driven by different recovery efficiency of gram-positive bacteria, e.g., phyla Firmicutes and Actinobacteria. This was further confirmed by a parallel comparison of fecal samples from five healthy subjects and a standard mock community. In addition, the DNA extraction method influenced DNA biomass, quality, and the detection of specific lineage-associated diseases. Sample operating approach and batch effects should be considered for cohorts with large sample size or longitudinal cohorts to ensure that source data were appropriately generated and analyzed. Comparison between samples processed with inconsistent methods should be dealt with caution. This study will promote the establishment of a sample operating standard to enhance our understanding of microbiome and translating in clinical practice.IMPORTANCEThe reproducibility of human gut microbiome studies has been suboptimal across cohorts and study design choices. One possible reason for the disagreement is the introduction of systemic biases due to differences in methodologies. In our study, we utilized microbial metagenomic data sets from 2,722 fecal samples generated from a single research center to examine the extent to which sample storage and DNA extraction influence the quantification of microbial composition and compared this variable with other sources of technical and biological variation. Our research highlights the impact of DNA extraction methods when analyzing microbiome data and suggests that the microbiome profile may be influenced by differences in the extraction efficiency of bacterial species. With metagenomics sequencing being increasingly used in clinical biology, our findings provide insight into the challenges using metagenomics sequencing in clinical diagnostics, where the detection of certain species and its abundance relative to a "healthy reference" is key.

In complex mammals, the importance and host-specificity of microbial communities have been demonstrated through their positive effects on host immune fitness or performance. However, whether host metabolic physiology homeostasis depends on a specific bacterial community exclusive to the host remains unclear. Here, we show that the coevolved host-specific microbiota is required to maintain diet-specific flexible and sufficient metabolic homeostasis through a high colonization rate, modulating gut metabolites, and related targets. Using germ-free (GF) mice, we tested whether the fitness benefiting the host metabolic phenotype of microbiota was host-specific. We demonstrated that GF mice associated with exogenous microbiota (human microbiota (HM)), which exhibited different and reduced gut microbial species diversity, significantly elevated metabolic rate, and exhibited metabolic insufficiency, all characteristics of GF mice. Strikingly, the absence of the host-specific microbiome attenuated high-fat diet-specific metabolism features. Different diets caused different metabolic changes in only host-specific microbiota-associated mice, not the host-microbiota mismatched mice. While RNA sequencing revealed subtle changes in the expression of genes in the liver, GF mice and HM mice showed considerably altered expression of genes associated with metabolic physiology compared to GF mice associated with host-specific microbiota. The effect of diet outweighed microbiota in the liver transcriptome. These changes occurred in the setting of decreased luminal short-chain fatty acids (SCFAs) and the secondary bile acid (BAs) pool and downstream gut signaling targets in HM and GF mice, which affects whole-body metabolism. These data indicate that a foreign microbial community provides little metabolic benefit to the host when compared to a host-specific microbiome, due to the colonization selection pressure and microbiota-derived metabolites dysfunction. Overall, microbiome fitness effects on the host metabolic phenotype were host-specific. Understanding the impact of the host-specificity of the microbiome on metabolic homeostasis may provide important insights for building a better probiotic., Highlights: Microbiome fitness effects on the host metabolic phenotype were host-specific in mammals.Human microbiota-associated mice exhibited lower host metabolic fitness or performance, and similar functional costs in GF mice.Different diets cause different metabolic changes only in host-specific microbiota-associated mice, not the host-microbiota mismatched mice.The defective gut microbiota in host-specific microbiota, microbial metabolites and related targets likely drive the metabolic homeostasis.

Nonalcoholic fatty liver disease (NAFLD) is multifactorial in nature, affecting over a billion people worldwide. The gut microbiome has emerged as an associative factor in NAFLD, yet mechanistic contributions are unclear. Here, we show fast food (FF) diets containing high fat, added cholesterol, and fructose/glucose drinking water differentially impact short- vs. long-term NAFLD severity and progression in conventionally-raised, but not germ-free mice. Correlation and machine learning analyses independently demonstrate FF diets induce early and specific gut microbiota changes that are predictive of NAFLD indicators, with corresponding microbial community instability relative to control-fed mice. Shotgun metagenomics showed FF diets containing high cholesterol elevate fecal pro-inflammatory effectors over time, relating to a reshaping of host hepatic metabolic and inflammatory transcriptomes. FF diet-induced gut dysbiosis precedes onset and is highly predictive of NAFLD outcomes, providing potential insights into microbially-based pathogenesis and therapeutics., Competing Interests: DECLARATION OF INTERESTS The authors declare no competing financial interests.