Machine learning models reveal microbial signatures in healthy human tissues, challenging the sterility of human organs.

ABSTRACT:

Background

The presence of microbes within healthy human internal organs still remains under question. Our study endeavors to discern microbial signatures within normal human internal tissues using data from the Genotype-Tissue Expression (GTEx) consortium. Machine learning (ML) models were developed to classify each tissue type based solely on microbial profiles, with the identification of tissue-specific microbial signatures suggesting the presence of distinct microbial communities inside tissues.

Methods

We analyzed 13,871 normal RNA-seq samples from 28 tissues obtained from the GTEx consortium. Unaligned sequencing reads with the human genome were processed using AGAMEMNON, an algorithm for metagenomic microbial quantification, with a reference database comprising bacter

SUBMITTER: Skoulakis A 

PROVIDER: S-EPMC11808598 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature