The gut microbiota (GM) is related to obesity and other metabolic diseases. To detect GM markers for obesity in patients with different metabolic abnormalities and investigate their relationships with clinical indicators, 1,914 Chinese adults were enrolled for 16S rRNA gene sequencing in this retrospective study. Based on GM composition, Random forest classifiers were constructed to screen the obesity patients with (Group OA) or without metabolic diseases (Group O) from healthy individuals (Group H), and high accuracies were observed for the discrimination of Group O and Group OA (areas under the receiver operating curve (AUC) equal to 0.68 and 0.76, respectively). Furthermore, six GM markers were shared by obesity patients with various metabolic disorders (Bacteroides, Parabacteroides, Blautia, Alistipes, Romboutsia and Roseburia). As for the discrimination with Group O, Group OA exhibited low accuracy (AUC = 0.57). Nonetheless, GM classifications to distinguish between Group O and the obese patients with specific metabolic abnormalities were not accurate (AUC values from 0.59 to 0.66). Common biomarkers were identified for the obesity patients with high uric acid, high serum lipids and high blood pressure, such as Clostridium XIVa, Bacteroides and Roseburia. A total of 20 genera were associated with multiple significant clinical indicators. For example, Blautia, Romboutsia, Ruminococcus2, Clostridium sensu stricto and Dorea were positively correlated with indicators of bodyweight (including waistline and body mass index) and serum lipids (including low density lipoprotein, triglyceride and total cholesterol). In contrast, the aforementioned clinical indicators were negatively associated with Bacteroides, Roseburia, Butyricicoccus, Alistipes, Parasutterella, Parabacteroides and Clostridium IV. Generally, these biomarkers hold the potential to predict obesity-related metabolic abnormalities, and interventions based on these biomarkers might be beneficial to weight loss and metabolic risk improvement.

Natural and artificial directional selections have resulted in significantly genetic and phenotypic differences across breeds in domestic animals. However, the molecular regulation of skeletal muscle diversity remains largely unknown. Here, we conducted transcriptome profiling of skeletal muscle across 27 time points, and performed whole-genome re-sequencing in Landrace (lean-type) and Tongcheng (obese-type) pigs. The transcription activity decreased with development, and the high-resolution transcriptome precisely captured the characterizations of skeletal muscle with distinct biological events in four developmental phases: Embryonic, Fetal, Neonatal, and Adult. A divergence in the developmental timing and asynchronous development between the two breeds was observed; Landrace showed a developmental lag and stronger abilities of myoblast proliferation and cell migration, whereas Tongcheng had higher ATP synthase activity in postnatal periods. The miR-24-3p driven network targeting insulin signaling pathway regulated glucose metabolism. Notably, integrated analysis suggestedSATB2andXLOC_036765contributed to skeletal muscle diversity via regulating the myoblast migration and proliferation, respectively. Overall, our results provide insights into the molecular regulation of skeletal muscle development and diversity in mammals.

Background: Horizontal Gene Transfer (HGT) is the process of transferring genetic materials between species. Through sharing genetic materials, microorganisms in the human microbiota form a network. The network can provide insights into understanding the microbiota. Here, we constructed the HGT networks from the gut microbiota sequencing data and performed network analysis to characterize the HGT networks of gut microbiota. Results: We constructed the HGT network and perform the network analysis to two typical gut microbiota datasets, a 283-sample dataset of Mother-to-Child and a 148-sample dataset of longitudinal inflammatory bowel disease (IBD) metagenome. The results indicated that (1) the HGT networks are scale-free. (2) The networks expand their complexities, sizes, and edge numbers, accompanying the early stage of lives; and microbiota established in children shared high similarity as their mother (p-value = 0.0138), supporting the transmission of microbiota from mother to child. (3) Groups harbor group-specific network edges, and network communities, which can potentially serve as biomarkers. For instances, IBD patient group harbors highly abundant communities of Proteobacteria (p-value = 0.0194) and Actinobacteria (p-value = 0.0316); children host highly abundant communities of Proteobacteria (p-value = 2.8785 $$e^{-5}$$e - 5 ) and Actinobacteria (p-value = 0.0015), and the mothers host highly abundant communities of Firmicutes (p-value = 8.0091 $$e^{-7}$$e - 7 ). IBD patient networks contain more HGT edges in pathogenic genus, including Mycobacterium, Sutterella, and Pseudomonas. Children’s networks contain more edges from Bifidobacterium and Escherichia. Conclusion: Hence, we proposed the HGT network constructions from the gut microbiota sequencing data. The HGT networks capture the host state and the response of microbiota to the environmental and host changes, and they are essential to understand the human microbiota.

Protein alignment is a basic step for many molecular biology researches. The BLOSUM matrices, especially BLOSUM62, are the de facto standard matrices for protein alignments. However, after widely utilization of the matrices for 15 years, programming errors were surprisingly found in the initial version of source codes for their generation. And amazingly, after bug correction, the “intended” BLOSUM62 matrix performs consistently worse than the “miscalculated” one. In this paper, we find linear relationships among the eigenvalues of the matrices and propose an algorithm to find optimal unified eigenvectors. With them, we can parameterize matrix BLOSUMx for any given variable x that could change continuously. We compare the effectiveness of our parameterized isentropic matrix with BLOSUM62. Furthermore, an iterative alignment and matrix selection process is proposed to adaptively find the best parameter and globally align two sequences. Experiments are conducted on aligning 13,667 families of Pfam database and on clustering MHC II protein sequences, whose improved accuracy demonstrates the effectiveness of our proposed method.

Despite that obesity is associated with many metabolic diseases, a significant proportion (10–30 %) of obese individuals is recognized as ‘metabolically healthy obeses’ (MHOs). The aim of the current study is to characterize the gut microbiome for MHOs as compared to ‘metabolically unhealthy obeses’ (MUOs). We compared the gut microbiome of 172 MHO and 138 MUO individuals from Chongqing (China) (inclined to eat red meat and food with a spicy taste), and performed validation with selected biomarkers in 40 MHOs and 33 MUOs from Quanzhou (China) (inclined to eat seafood and food with a light/bland taste). The genera Alistipes , Faecalibacterium and Odoribacter had increased abundance in both Chongqing and Quanzhou MHOs. We also observed different microbial functions in MUOs compared to MHOs, including an increased abundance of genes associated with glycan biosynthesis and metabolism. In addition, the microbial gene markers identified from the Chongqing cohort bear a moderate accuracy [AUC (area under the operating characteristic curve)=0.69] for classifying MHOs distinct from MUOs in the Quanzhou cohort. These findings indicate that gut microbiome is significantly distinct between MHOs and MUOs, implicating the potential of the gut microbiome in stratification and refined management of obesity.