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Xu, z.

Publications and source records attributed to Xu, z..

3 recordsLinked to original sources

Positive Selection on Rare Variants Underlying the Cold Adaptation of Wild Boar

The wide geographical distribution of Eurasian wild boar (Sus scrofa) offers a natural experiment to study the thermoregulation. Here, we conducted whole-genome resequencing and chromatin profiling experiments on the local populations from cold regions (northern and northeastern Asia) and warm regions (southeastern Asia and southern China). Using genome-wide scans of four methods, we detected candidate genes underlying cold-adaptation with significant enrichment of pathways related to thermogenesis, fat cell development, and adipose tissue regulation. We also found two enhancer variants under positive selection, an intronic variant of IGF1R (rs341219502) and an exonic variant of BRD4 (rs327139795), which showed the highest differentiation between cold and warm region populations of wild boar and domestic pigs. Moreover, these rare variants were absent in outgroup species and warm-region wild boar but nearly fixed in cold-region populations, suggesting their de novo origins in cold-region populations. The experiments of CUT&Tag chromatin profiling showed that rs341219502 of IGF1R is associated with the gain of three novel transcription factors involving regulatory changes in enhancer function, while rs327139795 of BRD4 could result in the loss of a phosphorylation site due to amino acid alteration. We also found three genes (SLCO1C1, PDE3A, and TTC28) with selection signals in both wild boar and native human populations from Siberia, which suggests convergent molecular adaptation in mammals. Our study shows the adaptive evolution of genomic molecules underlying the remarkable environmental flexibility of wild boar.

evolutionary biology↗

Multi_CycGT: a DL-based multimodal model for membrane permeability prediction of cyclic peptides

As a highly versatile therapeutic modality, cyclic peptides have gained significant attention due to their exceptional binding affinity, minimal toxicity and capacity to target the surface of conventionally "undruggable" proteins. However, the development of cyclic peptides with therapeutic effects by targeting intracellular biological targets has been hindered by the issue of limited membrane permeability. In this paper, we have conducted an extensive benchmarking analysis of a proprietary dataset consisting of 6941 cyclic peptides, employing machine learning and deep learning models. In addition, we propose an innovative multimodal model called Multi_CycGT which combines a Graph Convolutional Network (GCN) and a Transformer to extract 1D and 2D features. These encoded features are then fused for the prediction of cyclic peptide permeability. The cross-validation experiments demonstrate that the proposed Multi_CycGT model achieved the highest level of accuracy on the test set, with an accuracy value of 0.8206 and an AUC value of 0.8650. This paper introduces a pioneering deep learning-based approach that demonstrates enhanced effectiveness in predicting the membrane permeability of cyclic peptides. It also represents the first attempt in this field. We hope that this work will help to accelerate the design of cyclic peptide active drugs in medicinal chemistry and chemical biology applications.

bioinformatics↗

A multidrug resistant clinical P. aeruginosa isolate in the MLST550 clonal complex: uncoupled quorum sensing modulates the interplay of virulence and resistance

Pseudomonas aeruginosa is a prevalent and pernicious pathogen equipped with both extraordinary capabilities to infect the host and to develop antimicrobials resistance (AMR). Monitoring the emergence of AMR high risk clones and understanding the interplay of their pathogenicity and antibiotic resistance is of paramount importance to avoid resistance dissemination and to control P. aeruginosa infections. In this study, we report the identification of a multidrug resistant (MDR) P. aeruginosa strain PA154197 isolated from a blood stream infection in Hong Kong. PA154197 belongs to a distinctive MLST550 clonal complex shared by two international P. aeruginosa isolates VW0289 and AUS544. Comparative genome and transcriptome analysis with the reference strain PAO1 led to the identification of a variety of genetic variations in antibiotic resistance genes and the hyper-expression of three multidrug efflux pumps MexAB-OprM, MexEF-OprN, and MexGHI-OpmD in PA154197. Unlike many resistant isolates displaying an attenuated virulence, PA154197 produces a significantly high level of the P. aeruginosa major virulence factor pyocyanin (PYO) and displays an uncompromised virulence compared to PAO1. Further analysis revealed that the secondary quorum sensing system Pqs which primarily controls the PYO production is hyper-active in PA154197 independent of the master QS systems Las and Rhl. Together, these investigations disclose a unique, uncoupled QS mediated pathoadaptation mechanism in clinical P. aeruginosa which may account for the high pathogenic potentials and antibiotics resistance in the MDR isolate PA154197.

microbiology↗