Back

Improved in silico and in vitro methods for E. coli LPS outer core typing

Trofimova, E.; Westerman, R. P.; Jaschke, P. R.

2025-04-13 microbiology
10.1101/2025.04.13.648558 bioRxiv
Show abstract

The Gram-negative bacterial envelope comprises the outer membrane, periplasmic space with the peptidoglycan layer, and inner (cytoplasmic) membrane. A lipopolysaccharide (LPS) layer linked to the outer membrane is essential for survival in most species of bacteria, primarily by providing structural stability and regulating selective chemical permeability. These functions make the LPS layer a key pathogenicity determinant, protecting bacteria from host defences. At the same time, it serves as a common receptor for multiple bacteriophage orders, making it a crucial point of bacterial vulnerability. LPS outer core typing is traditionally performed using immunoblotting and PCR. With the increasing availability of sequenced genomes, PCR has emerged as the primary method for typing. This study presents a set of nine oligonucleotides designed for typing the five LPS outer core structures of Escherichia coli: R1, R2, R3, R4, and K-12, both in vitro and in silico. Our method was able to successfully type 99% of strains within a comprehensive dataset of 4549 complete genomes. Using these new methods, we identify previously unreported hybrid LPS structures involving R2 and K-12 outer core types and establish associations between LPS outer core types and E. coli phylogenetic groups, pathogenicity, O-antigen polysaccharides, and capsule types. We also introduce LPSTyper, a Python-based command-line tool that enables rapid and precise LPS outer core typing in E. coli genome sequences. Together, the expanded dataset of LPS outer core types and enhanced laboratory and computational tools for their detection provide new insights into LPS structural evolution and its role in E. coli ecology and pathogenicity. We anticipate these tools will improve the efficiency of future research and diagnostics of this important bacterial envelope structure. Impact StatementBacterial lipopolysaccharide (LPS) layer types are important for determining both pathogenicity potential and phage susceptibility. This study presents a robust and scalable framework for precise typing of Escherichia coli lipopolysaccharide (LPS) outer core types, enabled by the development of nine novel oligos targeting the waa locus. Using the LPSTyper tool for in silico analysis and a simple PCR-based method for in vitro validation, this approach allows rapid and accurate typing across thousands of genomes, supporting large-scale monitoring of LPS diversity in both commensal and pathogenic E. coli populations. Our findings reveal strong associations between LPS outer core types, O-antigens, capsules, phylogenetic groups, and pathotypes, highlighting the critical role of LPS structure in virulence, immune evasion, and environmental adaptation. The discovery of hybrid and novel waa locus arrangements further underscores the evolutionary plasticity of LPS biosynthesis. Overall, this work provides essential tools and insights for genomic surveillance and deepens our understanding of E. coli surface structure variability central to host-pathogen interactions and antimicrobial resistance. Data SummaryThe LPSTyper source code is freely available on GitHub under the Apache 2.0 licence: https://github.com/ellinium/LPSTyper. Sequencing data for all genomes used in this study are accessible via the NCBI database under BioProject accession number PRJNA1243169. Individual genome accession numbers are listed in Table 2. All supporting data are provided in Supplementary Data Files 1 and 2. O_TBL View this table: org.highwire.dtl.DTLVardef@951af3org.highwire.dtl.DTLVardef@11c790eorg.highwire.dtl.DTLVardef@4fb5e3org.highwire.dtl.DTLVardef@12e87caorg.highwire.dtl.DTLVardef@1d4fc1a_HPS_FORMAT_FIGEXP M_TBL O_FLOATNOTable 2.C_FLOATNO O_TABLECAPTIONReference E. coli strains used in in vitro PCR. C_TABLECAPTION C_TBL

Published in Microbial Genomics (predicted rank #1) · training set

Matching journals

The top 1 journal accounts for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.