{"database":"GEO","file_versions":[{"headers":{"Content-Type":["application/json"]},"body":{"files":{"Other":["ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE343nnn/GSE343117/"]},"type":"primary"},"statusCode":"OK","statusCodeValue":200}],"scores":null,"additional":{"omics_type":["Other"],"species":["mouse gut metagenome"],"gds_type":["Other"],"full_dataset_link":["https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE343117"],"repository":["GEO"],"entry_type":["GSE"],"additional_accession":[]},"is_claimable":false,"name":"Mining Microbial Transcriptomes to Engineer Cell-Based Bacterial Biosensors in Gut-Resident Bacteroidaceae [BCSeq]","description":"The gastrointestinal tract is rich in metabolic, immune, and microbiome-derived signals that can inform the design of live biotherapeutics and diagnosis of intestinal disorders. Engineered cell-based biosensors can tap into this molecular information and report on their environment, yet their development in gut-resident symbionts has been limited by a lack of validated sensor systems. Here, we present a generalizable pipeline that leverages bacterial transcriptional profiling to identify environment-responsive systems for biosensor engineering. Candidate Sensors Systems (CSSs) mined from healthy, disease, and in vitro transcriptomes were assembled into a barcoded library in Bacteroidaceae chassis and screened in high-throughput in vivo to identify responsive promoters. A unique Bacteroidales ECF-type sigma factor operon with ties to sphingolipid metabolism and flux was highly responsive in chemically-induced colitis models. The biosensor responded robustly to disease and returned to baseline upon recovery, establishing an in vivo-driven strategy for discovering functional biosensors in non-model gut-resident bacteria.","dates":{"publication":"2026/08/24"},"accession":"GSE343117","cross_references":{"GSM":["GSM9946320","GSM9946364","GSM9946365","GSM9946321","GSM9946362","GSM9946363","GSM9946360","GSM9946361","GSM9946328","GSM9946329","GSM9946326","GSM9946327","GSM9946368","GSM9946324","GSM9946325","GSM9946369","GSM9946322","GSM9946366","GSM9946323","GSM9946367","GSM9946331","GSM9946375","GSM9946332","GSM9946376","GSM9946373","GSM9946374","GSM9946330","GSM9946371","GSM9946372","GSM9946370","GSM9946339","GSM9946337","GSM9946338","GSM9946335","GSM9946379","GSM9946336","GSM9946377","GSM9946333","GSM9946334","GSM9946378","GSM9946342","GSM9946343","GSM9946340","GSM9946341","GSM9946380","GSM9946381","GSM9946348","GSM9946349","GSM9946346","GSM9946347","GSM9946344","GSM9946345","GSM9946353","GSM9946354","GSM9946351","GSM9946352","GSM9946350","GSM9946359","GSM9946357","GSM9946358","GSM9946355","GSM9946356"],"GPL":["21051"],"GSE":["343117"],"taxon":["mouse gut metagenome"]}}