Obesity induced disruption on diurnal rhythm of insulin sensitivity via gut microbiome-bile acid metabolism.

The disruption of the diurnal rhythm has been recognized as a significant contributing factor to metabolic dysregulation. The important role of gut microbiota and bile acid metabolism has attracted extensive attention. However, the function of the gut microbiota-bile acid axis in regulating the diurnal rhythms of metabolic homeostasis remains largely unknown. Herein, we aimed to investigate the interplay between rhythmicity of host metabolism and gut microbiota-bile acid axis, as well as to assess the impact of obesity on them. We found that high fat diet feeding and Leptin gene deficiency (ob/ob) significantly disturbed the rhythmic patterns of insulin sensitivity and serum total cholesterol levels. The bile acid profiling unveiled a conspicuous diurnal rhythm oscillation of ursodeoxycholic acid (UDCA) in lean mice, concomitant with fluctuations in insulin sensitivity, whereas it was absent in obese mice. The aforementioned diurnal rhythm oscillations were largely desynchronized by gut microbiota depletion, suggesting the indispensable role of gut microbiota in diurnal regulation of insulin sensitivity and bile acid metabolism. Consistently, 16S rRNA sequencing revealed that UDCA-associated bacteria exhibited diurnal rhythm oscillations that paralleled the fluctuation in insulin sensitivity. Collectively, the current study provides compelling evidence regarding the association between diurnal rhythm of insulin sensitivity and gut microbiota-bile acid axis. Moreover, we have elucidated the deleterious effects of obesity on gut microbiome-bile acid metabolism in both the genetic obesity model and the diet-induced obesity model.

TasselLFANet: a novel lightweight multi-branch feature aggregation neural network for high-throughput image-based maize tassels detection and counting.

Accurately and rapidly counting the number of maize tassels is critical for maize breeding, management, and monitoring the growth stage of maize plants. With the advent of high-throughput phenotyping platforms and the availability of large-scale datasets, there is a pressing need to automate this task for genotype and phenotype analysis. Computer vision technology has been increasingly applied in plant science, offering a promising solution for automated monitoring of a large number of plants. However, the current state-of-the-art image algorithms are hindered by hardware limitations, which compromise the balance between algorithmic capacity, running speed, and overall performance, making it difficult to apply them in real-time sensing field environments. Thus, we propose a novel lightweight neural network, named TasselLFANet, with an efficient and powerful structure for accurately and efficiently detecting and counting maize tassels in high spatiotemporal image sequences. Our proposed approach improves the feature-learning ability of TasselLFANet by adopting a cross-stage fusion strategy that balances the variability of different layers. Additionally, TasselLFANet utilizes multiple receptive fields to capture diverse feature representations, and incorporates an innovative visual channel attention module to detect and capture features more flexibly and precisely. We conducted a series of comparative experiments on a new, highly informative dataset called MrMT, which demonstrate that TasselLFANet outperforms the latest batch of lightweight networks in terms of performance, flexibility, and adaptability, achieving an F1 measure value of 94.4%, a mAP.@5 value of 96.8%, and having only 6.0M parameters. Moreover, compared with the regression-based TasselNetV3-Seg† model, our proposed model achieves superior counting performance, with a mean absolute error (MAE) of 1.80, a root mean square error (RMSE) of 2.68, and a R2 of 0.99. The proposed model meets the accuracy and speed requirements of the vision system in maize tassel detection. Furthermore, our proposed method is reliable and unaffected by geographical changes, providing essential technical support for computerized counting in the field.

Development of a simultaneous quantification method for the gut microbiota-derived core nutrient metabolome in mice and its application in studying host-microbiota interaction.

The gut microbiota interacts with the host via production of various metabolites of dietary nutrients. Herein, we proposed the concept of the gut microbiota-derived core nutrient metabolome, which covers 43 metabolites in carbohydrate metabolism, glycolysis, tricarboxylic acid cycle and amino acid metabolism, and established a quantitative UPLC-Q/TOF-MS method through 3-nitrophenylhydrazine derivatization to investigate the influence of obesity on the gut microbiota in mice. All metabolites could be simultaneously analyzed via separation on a BEH C18 column within 18 min. The lower limits of quantification of most analytes were less than 1 µM. Validation results demonstrated suitability for the analysis of mouse fecal samples. The method was then applied to detect the gut microbiota-derived nutrient metabolome in the feces of high-fat diet induced obese (DIO) and ob/ob (leptin-deficient) mice, as well as obesity-prone (OP) and obesity-resistant (OR) mice. Compared to the control groups, there were 13, 23 and 10 differentially abundant metabolites detected in ob/ob, DIO and OP groups, respectively. Among them, amino acids including leucine, isoleucine, glycine, methionine, tyrosine and glutamine were co-downregulated in the obese or OP mice and exhibited inverse association with body weight. 16S rDNA analysis revealed that the genera Lactobacillus and Dubosiella were also inversely associated with body weight and positively correlated with fecal amino acids. Collectively, our work provides an effective and simplified method for simultaneous quantifying the gut microbiota-derived core nutrient metabolome in mouse feces, which could assist various future studies on host-microbiota metabolic interaction.

Limosilactobacillus reuteri and caffeoylquinic acid synergistically promote adipose browning and ameliorate obesity-associated disorders.

OBJECTIVE: High intake of caffeoylquinic acid (CQA)-rich dietary supplements, such as green coffee bean extracts, offers health-promoting effects on maintaining metabolic homeostasis. Similar to many active herbal ingredients with high pharmacological activities but low bioavailability, CQA has been reported as a promising thermogenic agent with anti-obesity properties, which contrasts with its poor oral absorption. Intestinal tract is the first site of CQA exposure and gut microbes might react quickly to CQA. Thus, it is of interest to explore the role of gut microbiome and microbial metabolites in the beneficial effects of CQA on obesity-related disorders. RESULTS: Oral CQA supplementation effectively enhanced energy expenditure by activating browning of adipose and thus ameliorated obesity-related metabolic dysfunctions in high fat diet-induced obese (DIO) mice. Here, 16S rRNA gene amplicon sequencing revealed that CQA treatment remodeled the gut microbiota to promote its anti-obesity actions, as confirmed by antibiotic treatment and fecal microbiota transplantation. CQA enriched the gut commensal species Limosilactobacillus reuteri (L. reuteri) and stimulated the production of short-chain fatty acids, especially propionate. Mono-colonization of L. reuteri or low-dose CQA treatment did not reduce adiposity in DIO mice, while their combination elicited an enhanced thermogenic response, indicating the synergistic effects of CQA and L. reuteri on obesity. Exogenous propionate supplementation mimicked the anti-obesity effects of CQA alone or when combined with L. reuteri, which was ablated by the monocarboxylate transporter (MCT) inhibitor 7ACC1 or MCT1 disruption in inguinal white adipose tissues to block propionate transport. CONCLUSIONS: Our data demonstrate a functional axis among L. reuteri, propionate, and beige fat tissue in the anti-obesity action of CQA through the regulation of thermogenesis. These findings provide mechanistic insights into the therapeutic use of herbal ingredients with poor bioavailability via their interaction with the gut microbiota. Video Abstract.

Discovery of Betulinic Acid Derivatives as Potent Intestinal Farnesoid X Receptor Antagonists to Ameliorate Nonalcoholic Steatohepatitis.

Farnesoid X receptor (FXR) has emerged as a promising therapeutic target for nonalcoholic steatohepatitis (NASH) because of its tightly interwoven relationship with bile acid homeostasis, inflammation, fibrosis, and glucose and lipid metabolism. Evidence showed that intestinal FXR antagonism exhibited remarkable metabolic improvements in mice. Herein, we developed a series of betulinic acid derivatives as potent intestinal FXR antagonists, and F6 was identified as the most potent one with an IC50 at 2.1 µM. F6 selectively inhibited intestinal FXR signaling and ameliorated the hepatic steatosis, inflammation, and fibrosis in Gubra-amylin NASH (GAN) and high-fat with methionine and choline deficiency (HFMCD) diet-induced NASH models. The beneficial effects were achieved by direct antagonism of intestinal FXR and feedback activation of hepatic FXR, thereby decreasing ceramides and repressing inflammasome activation in the liver. Collectively, our work substantially supports F6 as a promising drug candidate against NASH and demonstrates that antagonism of intestinal FXR signaling is a practical strategy for treating metabolic diseases.

Discovery of 9,11-Seco-Cholesterol Derivatives as Novel FXR Antagonists.

The farnesoid X receptor (FXR) plays an important role in the regulation of bile acid, lipid, and glucose homeostasis. Recent findings have shown that the inhibition of FXR is beneficial to improvement of related metabolic diseases and cholestasis. In the present work, 9,11-seco-cholesterol derivatives were designed and synthesized by cleaving the C ring of cholesterol and were identified as novel structures of FXR antagonists. Compound 9a displayed the best FXR antagonistic activity at the cellular level (IC50 = 4.6 µM) and decreased the expression of the target genes of FXR in vivo.

AtCaM4 interacts with a Sec14-like protein, PATL1, to regulate freezing tolerance in Arabidopsis in a CBF-independent manner.

Calmodulin (CaM), a multifunctional Ca2+ sensor, mediates multiple reactions involved in regulation of plant growth and responses to environmental stress. In this study, we found that AtCaM4 plays a negative role in freezing tolerance in Arabidopsis. The deletion of AtCaM4 resulted in enhanced freezing tolerance in cam4 mutant plants. Although AtCaM4 and AtCaM1 were cold-induced isoforms, cam4/cam1Ri double-mutant and cam4 single-mutant plants exhibited similar improvements in freezing tolerance, indicating that AtCaM4 plays major role. Furthermore, we found that AtCaM4 may influence freezing tolerance in a C-repeat binding factor (CBF)-independent manner as cold-induced expression patterns of CBFs did not change in the cam4/cam1Ri mutant. In addition, among the cold-responsive (COR) genes detected, KIN1, COR15b, and COR8.6 exhibited clearly enhanced expression over the long term in cam4/cam1Ri mutant plants exposed to cold stress. Using immunoprecipitation and mass spectrometry, we identified multiple candidate AtCaM4-interacting proteins. Co-immunoprecipitation assays confirmed the interaction of AtCaM4 with PATL1 in vivo and a phenotype analysis showed that patl1 mutant plants exhibited enhanced freezing tolerance. Thus, we conclude that AtCaM4 negatively regulates freezing tolerance in Arabidopsis by interacting with the novel CaM-binding protein PATL1.

Numerical simulation of failure behavior of granular debris flows based on flume model tests.

In this study, the failure behaviors of debris flows were studied by flume model tests with artificial rainfall and numerical simulations (PFC(3D)). Model tests revealed that grain sizes distribution had profound effects on failure mode, and the failure in slope of medium sand started with cracks at crest and took the form of retrogressive toe sliding failure. With the increase of fine particles in soil, the failure mode of the slopes changed to fluidized flow. The discrete element method PFC(3D) can overcome the hypothesis of the traditional continuous medium mechanic and consider the simple characteristics of particle. Thus, a numerical simulations model considering liquid-solid coupled method has been developed to simulate the debris flow. Comparing the experimental results, the numerical simulation result indicated that the failure mode of the failure of medium sand slope was retrogressive toe sliding, and the failure of fine sand slope was fluidized sliding. The simulation result is consistent with the model test and theoretical analysis, and grain sizes distribution caused different failure behavior of granular debris flows. This research should be a guide to explore the theory of debris flow and to improve the prevention and reduction of debris flow.