Metagenomic Signatures of Gut Infections Caused by Different Escherichia coli Pathotypes

Abstract

Escherichia coli is a leading contributor to infectious diarrhea and child mortality worldwide, but it remains unknown how alterations in the gut microbiome vary for distinct E. coli pathotype infections and whether these signatures can be used for diagnostic purposes. Further, the majority of enteric diarrheal infections are not diagnosed with respect to their etiological agent(s) due to technical challenges. To address these issues, we devised a novel approach that combined traditional, isolate-based and molecular-biology techniques with metagenomics analysis of stool samples and epidemiological data. Application of this pipeline to children enrolled in a case-control study of diarrhea in Ecuador showed that, in about half of the cases where an E. coli pathotype was detected by culture and PCR, E. coli was likely not the causative agent based on the metagenome-derived low relative abundance, the level of clonality, and/or the virulence gene content. Our results also showed that diffuse adherent E. coli (DAEC), a pathotype that is generally underrepresented in previous studies of diarrhea and thus, thought not to be highly virulent, caused several small-scale diarrheal outbreaks across a rural to urban gradient in Ecuador. DAEC infections were uniquely accompanied by coelution of large amounts of human DNA and conferred significant shifts in the gut microbiome composition relative to controls or infections caused by other E. coli pathotypes. Our study shows that diarrheal infections can be efficiently diagnosed for their etiological agent and categorized based on their effects on the gut microbiome using metagenomic tools, which opens new possibilities for diagnostics and treatment. IMPORTANCE E. coli infectious diarrhea is an important contributor to child mortality worldwide. However, diagnosing and thus treating E. coli infections remain challenging due to technical and other reasons associated with the limitations of the traditional culture-based techniques and the requirement to apply Koch’s postulates. In this study, we integrated traditional microbiology techniques with metagenomics and epidemiological data in order to identify cases of diarrhea where E. coli was most likely the causative disease agent and evaluate specific signatures in the disease-state gut microbiome that distinguish between diffuse adherent, enterotoxigenic, and enteropathogenic E. coli pathotypes. Therefore, our methodology and results should be highly relevant for diagnosing and treating diarrheal infections and have important applications in public health.

Measuring Environmental Exposure to Enteric Pathogens in Low-Income Settings: Review and Recommendations of an Interdisciplinary Working Group

“… 46 , 162 Shotgun metagenomic approaches have also been employed, for example to distinguish between foodborne disease outbreak strains of Salmonella , 163 and to identify the likely causes of diarrheagenic E. coli in Ecuador. 164 The limitation of these techniques is not only the intensive resources they require, but it can also be difficult to attribute a specific pathogen to a disease outcome when multiple enteric pathogens are detected in stool simultaneously or when asymptomatic infections are common. Additionally, the sensitivity of these methods can vary.…”

Strain-level epidemiology of microbial communities and the human microbiome

“…This functional perspective on strains has captured a wide range of operational architectures, since some processes are wellconserved across entire clades (e.g., butyrate production in Faecalibacterium prausnitzii [20,21]). Others, conversely, are highly variable even within specific benign or pathogenic species-Escherichia coli in the gut being the most prominent example [22].…”

Advantages outweigh concerns about using genome sequence as type material for prokaryotic taxonomy

“…However, this is unlikely to be true for most-if not all-taxa because such new versions will mostly affect only a small number of genes or nucleotide substitution positions in the genome as analysis of mock datasets of known composition has revealed (Sczyrba et al, 2017) or the sequencing of the isolated 'Candidatus Macondimonas diazotrophica' that was almost identical to its corresponding MAG (e.g., ANI >99.9%) (Karthikeyan et al, 2019). It is even less likely that the affected genes by new genome versions would represent the species-diagnostic traits because these genes are often the hypothetical, mobile or prophage-associated genes found in multiple copies (and short contigs) in the genome (Pena-Gonzalez et al, 2019). It is also important to realize that for two genomes to accumulate~1% difference in their ANI value, more than 20 000 years of evolution would be required (Lawrence and Ochman, 1998), which represents a too long time to affect current taxonomy practice.…”