| Summary: | <i>Escherichia coli</i> (<i>E. coli</i>) F17 is one of the most common pathogens causing diarrhea in farm livestock. In the previous study, we accessed the transcriptomic and microbiomic profile of <i>E. coli</i> F17-antagonism (AN) and -sensitive (SE) lambs; however, the biological mechanism underlying <i>E. coli</i> F17 infection has not been fully elucidated. Therefore, the present study first analyzed the metabolite data obtained with UHPLC-MS/MS. A total of 1957 metabolites were profiled in the present study, and 11 differential metabolites were identified between <i>E. coli</i> F17 AN and SE lambs (i.e., FAHFAs and propionylcarnitine). Functional enrichment analyses showed that most of the identified metabolites were related to the lipid metabolism. Then, we presented a machine-learning approach (Random Forest) to integrate the microbiome, metabolome and transcriptome data, which identified subsets of potential biomarkers for <i>E. coli</i> F17 infection (i.e., GlcADG 18:0-18:2, ethylmalonic acid and <i>FBLIM1</i>); furthermore, the PCCs were calculated and the interaction network was constructed to gain insight into the crosstalk between the genes, metabolites and bacteria in <i>E. coli</i> F17 AN/SE lambs. By combing classic statistical approaches and a machine-learning approach, our results revealed subsets of metabolites, genes and bacteria that could be potentially developed as candidate biomarkers for <i>E. coli</i> F17 infection in lambs.
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