Biomedical subjects

Peng Zhou

Publications and source records attributed to Peng Zhou.

3 recordsLinked to original sources

PubMed2025

Metagenome-Based Characterization of the Gut Virome Signatures in Patients With Gout.

The gut microbiome has been implicated in the development of autoimmune diseases, including gout. However, the role of the gut virome in gout pathogenesis remains underexplored. We employed a reference-dependent virome approach to analyze fecal metagenomic data from 102 gout patients (77 in the discovery cohort and 25 in the validation cohort) and 86 healthy controls (HCs) (63 and 23 in each cohort). A subset of gout patients in the discovery cohort provided longitudinal samples at Weeks 2, 4, and 24. Our analysis revealed significant alterations in the gut virome of gout patients, including reduced viral richness and shifts in viral family composition. Notably, Siphoviridae, Myoviridae, and Podoviridae were depleted, while Quimbyviridae, Retroviridae, and Schitoviridae were enriched in gout patients. We identified 359 viral operational taxonomic units (vOTUs) associated with gout. Enriched vOTUs in gout patients predominantly consisted of Fusobacteriaceae, Bacteroidaceae, and Selenomonadaceae phages, while control-enriched vOTUs included Ruminococcaceae, Oscillospiraceae, and Enterobacteriaceae phages. Longitudinal analysis revealed that a substantial proportion of these virome signatures remained stable over 6 months. Functional profiling highlighted the enrichment of viral auxiliary metabolic genes, suggesting potential metabolic interactions between viruses and host bacteria. Notably, gut virome signatures effectively discriminated gout patients from HCs, with high classification performance in the validation cohort. This study provides the first comprehensive characterization of the gut virome in gout, revealing its potential role in disease pathogenesis and highlighting virome-based signatures as promising biomarkers for gout diagnosis and future therapeutic strategies.

Humans↗

PubMed2024

Development of SacB-based counterselection for efficient allelic exchange in Fusobacterium nucleatum.

Fusobacterium nucleatum, prevalent in the oral cavity, is significantly linked to overall human health. Our molecular comprehension of its role in oral biofilm formation and its interactions with the host under various pathological circumstances has seen considerable advancements in recent years, primarily due to the development of various genetic tools for DNA manipulation in this bacterium. Of these, counterselection-based unmarked in-frame mutation methods have proved notably effective. Under suitable growth conditions, cells carrying a counterselectable gene die, enabling efficient selection of rare, defined allelic exchange mutants. The sacB gene from Bacillus subtilis, encoding levansucrase, is a widely used counterselective marker partly due to the easy availability of sucrose. Yet, its potential application in F. nucleatum genetic study remains untested. We demonstrated that F. nucleatum cells expressing sacB in either a shuttle or suicide plasmid exhibit a lethal sensitivity to supplemental sucrose. Utilizing sucrose counterselection, we created an in-frame deletion of the F. nucleatum tonB gene, a critical gene for energy-dependent transport processes in Gram-negative bacteria, and a precise knock-in of the luciferase gene immediately following the stop codon of the hslO gene, the last gene of a five-gene operon possibly related to the natural competence of F. nucleatum. Post-counterselection with 5% sucrose, chromosomal plasmid loss occurred in all colonies, leading to gene alternations in half of the screened isolates. This sacB-based counterselection technique provides a reliable method for isolating unmarked gene mutations in wild-type F. nucleatum, enriching the toolkit for fusobacterial research.IMPORTANCEInvestigations into Fusobacterium nucleatum's role in related diseases significantly benefit from the strategies of creating unmarked gene mutations, which hinge on using a counterselective marker. Previously, the galk-based allelic exchange method, although effective, faced an inherent limitation-the need for a modified host. This study aims to surmount this limitation by substituting galK with sacB for gene modification in F. nucleatum. Our application of the sacB-based methodology successfully yielded a tonB in-frame deletion mutant and a luciferase gene knock-in at the precise chromosomal location in the wild-type background. The new method augments the existing toolkit for F. nucleatum research and has far-reaching implications due to the easy accessibility to the counterselection compound sucrose. We anticipate its broader adoption in further exploration, thereby reinforcing its critical role in propelling our understanding of F. nucleatum.

Fusobacterium nucleatum↗

PubMed2022

Flnc: Machine Learning Improves the Identification of Novel Long Noncoding RNAs from Stand-Alone RNA-Seq Data.

Long noncoding RNAs (lncRNAs) play critical regulatory roles in human development and disease. Although there are over 100,000 samples with available RNA sequencing (RNA-seq) data, many lncRNAs have yet to be annotated. The conventional approach to identifying novel lncRNAs from RNA-seq data is to find transcripts without coding potential but this approach has a false discovery rate of 30-75%. Other existing methods either identify only multi-exon lncRNAs, missing single-exon lncRNAs, or require transcriptional initiation profiling data (such as H3K4me3 ChIP-seq data), which is unavailable for many samples with RNA-seq data. Because of these limitations, current methods cannot accurately identify novel lncRNAs from existing RNA-seq data. To address this problem, we have developed software, Flnc, to accurately identify both novel and annotated full-length lncRNAs, including single-exon lncRNAs, directly from RNA-seq data without requiring transcriptional initiation profiles. Flnc integrates machine learning models built by incorporating four types of features: transcript length, promoter signature, multiple exons, and genomic location. Flnc achieves state-of-the-art prediction power with an AUROC score over 0.92. Flnc significantly improves the prediction accuracy from less than 50% using the conventional approach to over 85%. Flnc is available via GitHub platform.

RNA-seq↗