{"database":"GEO","file_versions":[{"headers":{"Content-Type":["application/json"]},"body":{"files":{"Other":["ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE325nnn/GSE325636/"]},"type":"primary"},"statusCode":"OK","statusCodeValue":200}],"scores":null,"additional":{"omics_type":["Transcriptomics"],"species":["Salmonella enterica"],"gds_type":["Expression profiling by high throughput sequencing"],"full_dataset_link":["https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE325636"],"repository":["GEO"],"entry_type":["GSE"],"additional_accession":[]},"is_claimable":false,"name":"Framework for analyzing bacterial responses to environmental signals: Salmonella’s response to nineteen host associated molecules","description":"Most RNAseq studies characterize gene expression in complex environments and long exposure times confounding analysis. Here Salmonella enterica serovar Typhimurium (Salmonella) was exposed in a minimal medium to 19 different metabolites associated with the host for just 10 minutes. This method successfully reduced background processes enabling the identification of compound specific gene changes. Exposure to ten of the compounds resulted in only 1-4 differentially expressed genes (DEGs), while exposure to cysteine and 5-oxoproline resulted in 20 and 34 DEGs respectively. This included transcriptional changes of up to 150-fold. Modulated genes encode import systems or enzymes for the integration of compounds into metabolism. Of note, while some responses were not previously known, many of these DEGs were expected from Escherichia coli data, implying many specific responses are transcription factor mediated. Exposure to seven compounds resulted in no specific responses, implying Salmonella does not sense these compounds, at least in tested conditions. Interestingly, Salmonella specifically down regulated SPI-2 in response to 5-oxoproline exposure, which was the only compound to significantly affect Salmonella growth, implying SPI-2 inhibition might be part of a stress response. Furthermore, the results imply the existence in Salmonella of an unknown tyramine dopamine sensor. Finally, a pipeline was developed which identifies differentially expressed metabolic pathways even when specific genes are not statistically modulated. This pipeline identified a number of expected and unexpected modified pathways. Importantly, in response to propionate exposure Salmonella activated enterobactin biosynthesis implying propionate might inhibit iron bioavailability. To conclude, this work lays the framework for analyzing specific bacterial responses to environmental signals. Such analyses allow resolving bacterial evolutionary programing which can be used to model bacterial transcriptional behavior in complex environments, to develop biosensors, and to engineer ligand specific transcriptional switches for bioengineering.","dates":{"publication":"2026/03/27"},"accession":"GSE325636","cross_references":{"GSM":["GSM9609953","GSM9609954","GSM9609955","GSM9609956","GSM9609957","GSM9609913","GSM9609914","GSM9609958","GSM9609915","GSM9609959","GSM9609916","GSM9609917","GSM9609918","GSM9609919","GSM9609950","GSM9609951","GSM9609952","GSM9609942","GSM9609943","GSM9609944","GSM9609945","GSM9609946","GSM9609947","GSM9609948","GSM9609949","GSM9609940","GSM9609941","GSM9609931","GSM9609932","GSM9609933","GSM9609934","GSM9609935","GSM9609936","GSM9609937","GSM9609938","GSM9609939","GSM9609970","GSM9609971","GSM9609972","GSM9609930","GSM9609920","GSM9609964","GSM9609921","GSM9609965","GSM9609966","GSM9609922","GSM9609923","GSM9609967","GSM9609924","GSM9609968","GSM9609969","GSM9609925","GSM9609926","GSM9609927","GSM9609928","GSM9609929","GSM9609960","GSM9609961","GSM9609962","GSM9609963"],"GPL":["36727","36728"],"GSE":["325636"],"taxon":["Salmonella enterica"]}}