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Flore, E.

Publications and source records attributed to Flore, E..

2 recordsLinked to original sources

Unveiling the small protein landscape of uropathogenic E. coli

Uropathogenic Escherichia coli (UPEC) is the predominant causative agent of urinary tract infections. The UPEC strain, CFT073, has a mosaic genome containing 60 genomic islands enriched in virulence genes. In this study, we explore the small proteome of CFT073 using ribosome profiling (Ribo-seq) coupled with RNA sequencing (RNA-seq), and reveal 338 translated, previously unannotated, putative small open reading frames (sORFs) with less than 100 codons. Among them, 78 sORFs are located in intergenic regions. Fifteen intergenic sORFs encode a predicted transmembrane helix (smemORFs), of which seven are hypothetical or have not been characterized in other E. coli strains. The majority of the remaining putative sORFs encodes uncharacterized or novel soluble proteins (ssolORFs). Considering the evolution of many sORFs is likely to be recent, functional sORFs are more likely to have conserved gene neighborhood in other genomes. Searching into the genomes of 10 other E. coli strains and four enterobacteria species, synteny analysis reveals that 10 of 15 putative smemORFs, and 27 of 63 putative ssolORFs, have a homologous sequence in a similar genomic context in at least one other genome, supporting their likelihood of encoding functional proteins. Further validation of putative sORFs using proteomics, cell-free protein synthesis, and mNeonGreen tagging provide strong evidence for the translation of nine predicted intergenic sORFs. Notably, four previously uncharacterized small membrane proteins have increased abundance under oxidative stress, suggesting a potential role in the stress response. Together, our findings reveal the small proteome of an extraintestinal pathogenic E. coli and provide a catalog of putative sORFs for further investigation.

microbiology↗

EnrichMET: R Package for Integrated Pathway and Network Analysis for Metabolomics

Advances in metabolomics have significantly improved our understanding of cellular processes by enabling the identification of hundreds of metabolites in a single experiment. These developments provide valuable insights into complex metabolic networks. While efforts have been made to develop pathway enrichment analysis (PEA), existing implementation often require multiple steps, rely on web-based interfaces, or depend on R packages configuration that may affect reproducibility and ease of use. To overcome these limitations, we introduce EnrichMet, an R package for fast, flexible, and reproducible pathway enrichment analysis. EnrichMet modules support over-representation analysis of pathways, metabolite set enrichment analysis (MetSEA), and network-based pathway analysis. The package streamlines the workflow by combining curated pathway information from the Kyoto Encyclopedia of Genes and Genomics (KEGG) and employs Fishers Exact Test to identify significantly enriched pathways. Benchmark analyses show that enrichment on sample data completes in approximately 3 seconds. EnrichMet offers both a command-line and a user-friendly Shiny interface, enabling accessibility for users with or without programming experience. Through case studies on experimental metabolomics datasets, we demonstrated that EnrichMet delivers accurate and comprehensive pathway enrichment results while minimizing computational time and simplifying user interaction. Furthermore, its flexible framework supports extensions to other data types and knowledge bases beyond KEGG, as illustrated through a lipidomics case study. By unifying performance, reproducibility, usability, and visualization within a single package, EnrichMet facilitates deeper insights and promotes efficient, transparent, and reproducible research practices. Availability and implementation(https://github.com/biodatalab/enrichmet.git)

bioinformatics↗