Xuejiao Wang

Polycystic ovary syndrome (PCOS) is a common endocrine and metabolic disorder in women. Gut microbiota has been implicated to play a critical role in metabolic diseases and may modulate the secretion of mediators of the brain-gut axis. Interaction between gut microbiota and the symptoms in PCOS still remains elusive. Here, we showed an altered gut microbiota significantly correlated with PCOS phenotype. There were 33 patients with PCOS (non-obese PCOS individuals, PN, n=12; obese PCOS individuals, PO, n=21) as well as 15 control subjects (non-obese control individuals, CN, n=9; obese control individuals, CO, n=6) enrolled in our study. The plasma levels of serotonin, ghrelin and peptide YY (PYY) were significantly decreased in patients with PCOS compared with controls, and have a significantly negative correlation with waist circumference and testosterone. Sequencing of the V3-V4 region of the 16S rRNA gene in fecal samples revealed the substantial differences of gut microbial species between the PCOS and non-obese controls. Bacterial species were clustered into 23 co-abundance groups (CAGs) based on the SparCC correlation coefficients of their relative abundance. The CAGs increased in PCOS, including the bacteria belonging to Bacteroides, Escherichia/Shigella and Streptococcus, were negatively correlated with ghrelin, and positively correlated with testosterone and BMI. Furthermore, the CAGs that were decreased in PCOS, including the bacteria from Akkermansia and Ruminococcaceae, showed opposite relationship with body-weight, sex-hormone and brain-gut peptides. In conclusion, gut microbial dysbiosis in women with PCOS is associated with the disease phenotypes.

The gut microbiota is crucial for human health, functioning as a complex adaptive system akin to a vital organ. To identify core health-relevant gut microbes, we followed the systems biology tenet that stable relationships signify core components. By analyzing metagenomic datasets from a high-fiber dietary intervention in type 2 diabetes and 26 case-control studies across 15 diseases, we identified a set of stably correlated genome pairs within co-abundance networks perturbed by dietary interventions and diseases. These genomes formed a "two competing guilds" (TCGs) model, with one guild specialized in fiber fermentation and butyrate production and the other characterized by virulence and antibiotic resistance. Our random forest models successfully distinguished cases from controls across multiple diseases and predicted immunotherapy outcomes through the use of these genomes. Our guild-based approach, which is genome specific, database independent, and interaction focused, identifies a core microbiome signature that serves as a holistic health indicator and a potential common target for health enhancement.

Crx (cone-rod homeobox) is a homeodomain transcription factor implicated in regulating the expression of photoreceptor and pineal genes. To identify proteins that interact with Crx in the retina, we carried out a yeast two-hybrid screen of a retinal cDNA library. One of the identified clones encodes Baf (barrier to autointegration factor), which was previously shown to have a role in mitosis and retroviral integration. Additional biochemical assays provided supporting evidence for a Baf-Crx interaction. The Baf protein is detectable in all nuclear layers of the mouse retina, including the photoreceptors and the bipolar cells where Crx is expressed. Transient transfection assays with a rhodopsin-luciferase reporter in HEK293 cells demonstrate that overexpression of Baf represses Crx-mediated transactivation, suggesting that Baf acts as a negative regulator of Crx. Consistent with this role for Baf, an E80A mutation of CRX associated with cone-rod dystrophy has a higher than normal transactivation potency but a reduced interaction with Baf. Although our studies did not identify a causative Baf mutation in retinopathies, we suggest that Baf may contribute to the phenotype of a photoreceptor degenerative disease by modifying the activity of Crx. In view of the ubiquitous expression of Baf, we hypothesize that it may play a role in regulating tissue- or cell type-specific gene expression by interacting with homeodomain transcription factors.

MOTIVATION: Accurate and rapid prediction of protein-ligand binding affinity is a great challenge currently encountered in drug discovery. Recent advances have manifested a promising alternative in applying deep learning-based computational approaches for accurately quantifying binding affinity. The structure complementarity between protein-binding pocket and ligand has a great effect on the binding strength between a protein and a ligand, but most of existing deep learning approaches usually extracted the features of pocket and ligand by these two detached modules. RESULTS: In this work, a new deep learning approach based on the cross-attention mechanism named CAPLA was developed for improved prediction of protein-ligand binding affinity by learning features from sequence-level information of both protein and ligand. Specifically, CAPLA employs the cross-attention mechanism to capture the mutual effect of protein-binding pocket and ligand. We evaluated the performance of our proposed CAPLA on comprehensive benchmarking experiments on binding affinity prediction, demonstrating the superior performance of CAPLA over state-of-the-art baseline approaches. Moreover, we provided the interpretability for CAPLA to uncover critical functional residues that contribute most to the binding affinity through the analysis of the attention scores generated by the cross-attention mechanism. Consequently, these results indicate that CAPLA is an effective approach for binding affinity prediction and may contribute to useful help for further consequent applications. AVAILABILITY AND IMPLEMENTATION: The source code of the method along with trained models is freely available at https://github.com/lennylv/CAPLA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.