With expanding applications of next-generation sequencing in medical genetics, increasing computational methods are being developed to predict the pathogenicity of missense variants. Selecting optimal methods can accelerate the identification of candidate genes. However, the performances of different computational methods under various conditions have not been completely evaluated. Here, we compared 12 performance measures of 23 methods based on three independent benchmark datasets: (i) clinical variants from the ClinVar database related to genetic diseases, (ii) somatic variants from the IARC TP53 and ICGC databases related to human cancers and (iii) experimentally evaluated PPARG variants. Some methods showed different performances under different conditions, suggesting that they were not always applicable for different conditions. Furthermore, the specificities were lower than the sensitivities for most methods (especially, for the experimentally evaluated benchmark datasets), suggesting that more rigorous cutoff values are necessary to distinguish pathogenic variants. Furthermore, REVEL, VEST3 and the combination of both methods (i.e. ReVe) showed the best overall performances with all the benchmark data. Finally, we evaluated the performances of these methods with de novo mutations, finding that ReVe consistently showed the best performance. We have summarized the performances of different methods under various conditions, providing tentative guidance for optimal tool selection.

A growing number of genomic tools and databases were developed to facilitate the interpretation of genomic variants, particularly in coding regions. However, these tools are separately available in different online websites or databases, making it challenging for general clinicians, geneticists and biologists to obtain the first-hand information regarding some particular variants and genes of interest. Starting with coding regions and splice sties, we artificially generated all possible single nucleotide variants (n = 110 154 363) and cataloged all reported insertion and deletions (n = 1 223 370). We then annotated these variants with respect to functional consequences from more than 60 genomic data sources to develop a database, named VarCards (http://varcards.biols.ac.cn/), by which users can conveniently search, browse and annotate the variant- and gene-level implications of given variants, including the following information: (i) functional effects; (ii) functional consequences through different in silico algorithms; (iii) allele frequencies in different populations; (iv) disease- and phenotype-related knowledge; (v) general meaningful gene-level information; and (vi) drug–gene interactions. As a case study, we successfully employed VarCards in interpretation of de novo mutations in autism spectrum disorders. In conclusion, VarCards provides an intuitive interface of necessary information for researchers to prioritize candidate variations and genes.

Objectives. We aimed to explore the impact of gut microbiota in coronary heart disease (CHD) patients through high-throughput sequencing. Methods. A total of 29 CHD in-hospital patients and 35 healthy volunteers as controls were included. Nucleic acids were extracted from fecal samples, followed by α diversity and principal coordinate analysis (PCoA). Based on unweighted UniFrac distance matrices, unweighted-pair group method with arithmetic mean (UPGMA) trees were created. Results. After data optimization, an average of 121312 ± 19293 reads in CHD patients and 234372 ± 108725 reads in controls was obtained. Reads corresponding to 38 phyla, 90 classes, and 584 genera were detected in CHD patients, whereas 40 phyla, 99 classes, and 775 genera were detected in controls. The proportion of phylum Bacteroidetes (56.12%) was lower and that of phylum Firmicutes was higher (37.06%) in CHD patients than those in the controls (60.92% and 32.06%, P < 0.05). PCoA and UPGMA tree analysis showed that there were significant differences of gut microbial compositions between the two groups. Conclusion. The diversity and compositions of gut flora were different between CHD patients and healthy controls. The incidence of CHD might be associated with the alteration of gut microbiota.

Liver plays a central role in modulating blood glucose level. Our most recent findings suggested that supplementation with microbiota metabolite sodium butyrate (NaB) could ameliorate progression of type 2 diabetes mellitus (T2DM) and decrease blood HbA1c in db/db mice. To further investigate the role of butyrate in homeostasis of blood glucose and glycogen metabolism, we carried out the present study. In db/db mice, we found significant hypertrophy and steatosis in hepatic lobules accompanied by reduced glycogen storage, and expression of GPR43 was significantly decreased by 59.38 ± 3.33%; NaB administration significantly increased NaB receptor G-protein coupled receptor 43 (GPR43) level and increased glycogen storage in both mice and HepG2 cells. Glucose transporter 2 (GLUT2) and sodium-glucose cotransporter 1 (SGLT1) on cell membrane were upregulated by NaB. The activation of intracellular signaling Protein kinase B (PKB), also known as AKT, was inhibited while glycogen synthase kinase 3 (GSK3) was activated by NaB in both in vivo and in vitro studies. The present study demonstrated that microbiota metabolite NaB possessed beneficial effects on preserving blood glucose homeostasis by promoting glycogen metabolism in liver cells, and the GPR43-AKT-GSK3 signaling pathway should contribute to this effect.

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Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with a male-to-female prevalence of 4:1. However, the genetic mechanisms underlying this gender difference remain unclear. Mutation burden analysis, a TADA model, and co-expression and functional network analyses were performed on de novo mutations (DNMs) and corresponding candidate genes. We found that the prevalence of putative functional DNMs (loss-of-function and predicted deleterious missense mutations) in females was significantly higher than that in males, suggesting that a higher genetic load was required in females to reach the threshold for a diagnosis. We then prioritized 174 candidate genes, including 60 shared genes, 91 male-specific genes, and 23 female-specific genes. All of the three subclasses of candidate genes were significantly more frequently co-expressed in female brains than male brains, suggesting that compensation effects of the deficiency of ASD candidate genes may be more likely in females. Nevertheless, the three subclasses of candidate genes were co-expressed with each other, suggesting a convergent functional network of male and female-specific genes. Our analysis of different aspects of genetic components provides suggestive evidence supporting the female-protective effect in ASD. Moreover, further study is needed to integrate neuronal and hormonal data to elucidate the underlying gender difference in ASD.

The crosstalk between the gut microbiota and immune state of the host is an essential focus in academia and clinics. To explore the dynamic role of the microbiota in response to immune deficiency, we comprehensively assessed the microbiome of 90 mouse fecal samples, across three time points including two immunodeficiency models, namely severe combined immunodeficient (SCID) mice and non-obese diabetic SCID (NOD/SCID) mice, with BALB/cA as a control strain. Metagenomic analysis revealed a decrease in alpha diversity and the existence of a clear structural separation in the microbiota of immunodeficient mice. Although nuances exist between SCID and NOD/SCID mice, an increase in the protective microbiota, in particular Lactobacillus, contributed the most to the discrimination of immunodeficient and control mice. Further data regarding the red blood cell (RBC) concentration and serum IgA level during different stages of development support the concept of the microbiota alleviating the advancement of immune deficiency, which is called microbial compensation. Taken together, these results demonstrate the dynamic impact of immunodeficiency on the gut microbiota and the adaptive alteration of the microbiota that may influence the host state.

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