Biology subjects

Pang, F.

Publications and source records attributed to Pang, F..

3 recordsLinked to original sources

bioRxiv2023

Antibiotic skeletal diversification via differential enoylreductase recruitment and module iteration in trans-acyltransferase polyketide synthases

Microorganisms are remarkable chemists capable of assembling complex molecular architectures that penetrate cells and bind biomolecular targets with exquisite selectivity. Consequently, microbial natural products have wide-ranging applications in medicine and agriculture. How the "blind watchmaker" of evolution creates skeletal diversity is a key question in contemporary natural products research. Comparative analysis of biosynthetic pathways to structurally related metabolites is an insightful approach to addressing this. Here we report comparative biosynthetic investigations of gladiolin, a polyketide antibiotic from Burkholderia gladioli with promising activity against multidrug resistant Mycobacterium tuberculosis, and entangien, a structurally related antibiotic produced by Sorangium cellulosum. Although these metabolites have very similar macrolide cores, their C21 side chains differ significantly in both length and degree of saturation. Surprisingly, the trans-acyltransferase polyketide synthases (PKSs) that assemble these antibiotics are almost identical, raising intriguing questions about mechanisms underlying structural diversification in this important class of biosynthetic assembly line. In vitro reconstitution of key biosynthetic transformations using simplified substrate analogues, combined with gene deletion and complementation experiments, enabled us to elucidate the origin of all structural differences in the C21 side chains of gladiolin and etnangien. The more saturated gladiolin side chain arises from a cis-acting enoylreductase (ER) domain in module 1 and in trans recruitment of a standalone ER to module 5 of the PKS. Remarkably, module 5 of the gladiolin PKS is intrinsically iterative in the absence of the standalone ER, accounting for the longer side chain in etnangien. These findings have important implications for biosynthetic engineering approaches to the creation of novel polyketide skeletons.

biochemistry↗

bioRxiv2022

DeepCCI: a deep learning framework for identifying cell-cell interactions from single-cell RNA sequencing data

With the rapid development of high throughput single-cell RNA sequencing (scRNA-seq) technologies, it is of high importance to identify Cell-cell interactions (CCIs) from the ever-increasing scRNA-seq data. However, limited by the algorithmic constraints, current computational methods based on statistical strategies ignore some key latent information contained in scRNA-seq data with high sparsity and heterogeneity. To address the issue, here, we developed a deep learning framework named DeepCCI to identify meaningful CCIs from scRNA-seq data. Applications of DeepCCI to a wide range of publicly available datasets from diverse technologies and platforms demonstrate its ability to predict significant CCIs accurately and effectively.

bioinformatics↗

bioRxiv2022

Influence of residual microbial nucleic acid in instruments for hip joint arthroplasty on false-positive results of metagenomic sequencing

The purpose of this study was to analyze the effect of residual microbial nucleic acid in instruments for hip joint arthroplasty on false-positive sequencing results. Samples were taken from 3 different acetabular reamer for hip arthroplasty in 7 different hospitals. The whole process was strictly aseptic, metagenomic next-generation sequencing (mNGS) was performed according to standard operating procedures. The sterility of instruments was confirmed by culture method. The sequencing results of specimens from different hospitals were compared to analyze the difference of background bacteria. Bioinformatics analysis and visualization were presented through R language. A total of 26 samples were processed by mNGS, including 24 instrument swab samples, 1 blank swab control, and 1 blank water control. 254,314,707 reads were sequenced in all samples. The results showed that 1.13% of Clean Reads can be matched to pathogenic microorganism genomes, of which bacterial sequences account for 87.48%, fungal sequences account for 11.18%, parasite sequences account for 1.26%, and virus sequences account for 0.06%. The results of PCA (Principal Component Analysis) demonstrated that the distribution of bacteria on the surface of instruments was significantly different between medical institutions. Through the Venn diagram, it was found that 465 species of bacteria in all region hospitals, Liaocheng Peoples Hospital had a maximum of 340 species of bacteria, followed by Guanxian County Peoples Hospital with 169 species. The clustering heat map illustrated that the distribution of bacterial groups in three different instrument samples in the same hospital was basically the same, and the bacterial genera varied significantly among hospitals. The residual microbial nucleic acid fragments are mainly bacterial DNA and represent differences in different medical institutions, The establishment of independent background bacterial libraries in different medical institutions can effectively improve the accuracy of mNGS diagnosis and help to exclude background microorganisms interference.

genomics↗