No abstract

Introduction Bacterial dysbiosis has been described in patients with current depressive episode (CDE); however, the fungal composition in the gut has not been investigated in these patients. Methods Here, we characterized the fungal gut mycobiota in patients with CDE. We systematically characterized the microbiota and mycobiota in fecal samples obtained from 24 patients with CDE and 16 healthy controls (HC) using 16S rRNA gene‐ and ITS1‐based DNA sequencing, respectively. Results In patients with CDE, bacterial dysbiosis was characterized by an altered composition and reduced correlation network density, and the gut mycobiota was characterized by a relative reduction in alpha diversity and altered composition. Most notably, the CDE group had higher levels of Candida and lower level of Penicillium than the HC group. Compared with the HC group, the gut microbiota in patients with CDE displayed a significant disruption in the bacteria–fungi correlation network suggestive of altered interkingdom interactions. Furthermore, a gut microbial index based on the combination of eight genera (four bacterial and four fungal CDE‐associated genera) distinguished CDE patients from controls with an area under the curve of approximately 0.84, suggesting that the gut microbiome signature is a promising tool for disease classification. Conclusions Our findings suggest that both bacteria and fungi contribute to gut dysbiosis in patients with CDE. Future studies involving larger cohorts and metagenomic or metabolomic analyses may clarify the structure and potential roles and functions of the gut mycobiome and its impact on the development of CDE.

Among Chinese type 2 diabetic patients, increasing DR severity is significantly associated reduced kidney function and increased risk of CKD. These associations are independent of risk factors for CKD, suggesting that assessment of DR may provide useful information on the renal function and risk of kidney disease.

Genetic characterization of Chinese indigenous pig breeds is essential to promote scientific conservation and sustainable development of pigs. Here, we systematically surveyed the genomes of 75 unrelated Diannan small-ear (DSE) pigs from three diverse regions (Yingjiang County, Jinping County, and Sipsongpanna in Yunnan Province) to describe their population structures, genetic diversity, inbreeding coefficients, and selection signatures. First, these individuals were sequenced and genotyped using the genome reducing and sequencing (GGRS) protocol. A total of 438,038 autosomal single-nucleotide polymorphisms (SNPs) were obtained and used for subsequent statistical analysis. The results showed that these DSE pigs were clearly differentiated into three separate clades revealed by the population structure and principal component analysis, which is consistent with their geographical origins. Diannan small-ear pigs owned lower genetic diversity when compared with some other pig breeds, which demonstrated the need to strengthen the conservation strategies for DSE pigs. In addition, the inbreeding coefficients based on runs of homozygosity (ROH) length (F ROH) were calculated in each ROH length categories, respectively. And the results indicated that the ancient (up to 50 generations ago) inbreeding had greater impacts than recent (within the last five generations) inbreeding within DSE pigs. Some candidate selection signatures within the DSE pig population were detected through the ROH islands and integrated haplotype homozygosity score (iHS) methods. And genes associated with meat quality (COL15A1, RPL3L, and SLC9A3R2), body size (PALM2-AKAP2, NANS, TRAF7, and PACSIN1), adaptability (CLDN9 and E4F1), and appetite (GRM4) were identified. These findings can help to understand the genetic characteristics and provide insights into the molecular background of special phenotypes of DSE pigs to promote conservation and sustainability of the breed.

Genomic prediction (GP) has revolutionized animal and plant breeding. However, better statistical models that can improve the accuracy of GP are required. For this reason, in this study, we explored the genomic-based prediction performance of a popular machine learning method, the Support Vector Machine (SVM) model. We selected the most suitable kernel function and hyperparameters for the SVM model in eight published genomic data sets on pigs and maize. Next, we compared the SVM model with RBF and the linear kernel functions to the two most commonly used genome-enabled prediction models (GBLUP and BayesR) in terms of prediction accuracy, time, and the memory used. The results showed that the SVM model had the best prediction performance in two of the eight data sets, but in general, the predictions of both models were similar. In terms of time, the SVM model was better than BayesR but worse than GBLUP. In terms of memory, the SVM model was better than GBLUP and worse than BayesR in pig data but the same with BayesR in maize data. According to the results, SVM is a competitive method in animal and plant breeding, and there is no universal prediction model.