<HashMap><database>GEO</database><file_versions><headers><Content-Type>application/xml</Content-Type></headers><body><files><Other>ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE343nnn/GSE343117/</Other></files><type>primary</type></body><statusCode>OK</statusCode><statusCodeValue>200</statusCodeValue></file_versions><scores/><additional><omics_type>Other</omics_type><species>mouse gut metagenome</species><gds_type>Other</gds_type><full_dataset_link>https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE343117</full_dataset_link><repository>GEO</repository><entry_type>GSE</entry_type></additional><is_claimable>false</is_claimable><name>Mining Microbial Transcriptomes to Engineer Cell-Based Bacterial Biosensors in Gut-Resident Bacteroidaceae [BCSeq]</name><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.</description><dates><publication>2026/08/24</publication></dates><accession>GSE343117</accession><cross_references><GSM>GSM9946320</GSM><GSM>GSM9946364</GSM><GSM>GSM9946365</GSM><GSM>GSM9946321</GSM><GSM>GSM9946362</GSM><GSM>GSM9946363</GSM><GSM>GSM9946360</GSM><GSM>GSM9946361</GSM><GSM>GSM9946328</GSM><GSM>GSM9946329</GSM><GSM>GSM9946326</GSM><GSM>GSM9946327</GSM><GSM>GSM9946368</GSM><GSM>GSM9946324</GSM><GSM>GSM9946325</GSM><GSM>GSM9946369</GSM><GSM>GSM9946322</GSM><GSM>GSM9946366</GSM><GSM>GSM9946323</GSM><GSM>GSM9946367</GSM><GSM>GSM9946331</GSM><GSM>GSM9946375</GSM><GSM>GSM9946332</GSM><GSM>GSM9946376</GSM><GSM>GSM9946373</GSM><GSM>GSM9946374</GSM><GSM>GSM9946330</GSM><GSM>GSM9946371</GSM><GSM>GSM9946372</GSM><GSM>GSM9946370</GSM><GSM>GSM9946339</GSM><GSM>GSM9946337</GSM><GSM>GSM9946338</GSM><GSM>GSM9946335</GSM><GSM>GSM9946379</GSM><GSM>GSM9946336</GSM><GSM>GSM9946377</GSM><GSM>GSM9946333</GSM><GSM>GSM9946334</GSM><GSM>GSM9946378</GSM><GSM>GSM9946342</GSM><GSM>GSM9946343</GSM><GSM>GSM9946340</GSM><GSM>GSM9946341</GSM><GSM>GSM9946380</GSM><GSM>GSM9946381</GSM><GSM>GSM9946348</GSM><GSM>GSM9946349</GSM><GSM>GSM9946346</GSM><GSM>GSM9946347</GSM><GSM>GSM9946344</GSM><GSM>GSM9946345</GSM><GSM>GSM9946353</GSM><GSM>GSM9946354</GSM><GSM>GSM9946351</GSM><GSM>GSM9946352</GSM><GSM>GSM9946350</GSM><GSM>GSM9946359</GSM><GSM>GSM9946357</GSM><GSM>GSM9946358</GSM><GSM>GSM9946355</GSM><GSM>GSM9946356</GSM><GPL>21051</GPL><GSE>343117</GSE><taxon>mouse gut metagenome</taxon></cross_references></HashMap>