Expression of virulence factors in is regulated by a wide range of transcriptional regulators, including proteins and small RNAs (sRNAs), at the level of transcription and/or translation. The locus consists of three overlapping transcripts generated from three distinct promoters, all containing the open reading frame (ORF). The 5' untranslated regions (UTRs) of these transcripts contain three separate regions ∼711, 409, and 146 nucleotides (nt) upstream of the translation start, the functions of which remain unknown. Recent transcriptome-sequencing (RNA-Seq) analysis and subsequent characterization indicated that two sRNAs, teg49 and teg48, are processed and likely produced from the P3 and P1 transcripts of the locus, respectively. In this report, we utilized a variety of promoter mutants and and mutants to ascertain the contributions of these factors to the generation of teg49. We also defined the transcriptional regulon of teg49, including virulence genes not regulated by SarA. Phenotypically, teg49 did not impact biofilm formation or affect overall SarA expression significantly. Comparative analyses of RNA-Seq data between the wild-type, teg49 mutant, and mutant strains indicated that ∼133 genes are significantly upregulated while 97 are downregulated in a teg49 deletion mutant in a-independent manner. An abscess model of skin infection indicated that the teg49 mutant exhibited a reduced bacterial load compared to the wild-type Overall, these results suggest that teg49 sRNA has a regulatory role in target gene regulation independent of SarA. The exact mechanism of this regulation is yet to be dissected.

The NCBI Gene Expression Omnibus (GEO) provides tools to query and download transcriptomic data. However, less than 4% of microbial experiments include the sample group annotations required to assess differential gene expression for high-throughput reanalysis, and data deposited after 2014 universally lack these annotations. Our algorithm GAUGE (general annotation using text/data group ensembles) automatically annotates GEO microbial data sets, including microarray and RNA sequencing studies, increasing the percentage of data sets amenable to analysis from 4% to 33%. Eighty-nine percent of GAUGE-annotated studies matched group assignments generated by human curators. To demonstrate how GAUGE annotation can lead to scientific insight, we created GAPE (GAUGE-annotated Pseudomonas aeruginosa and Escherichia coli transcriptomic compendia for reanalysis), a Shiny Web interface to analyze 73 GAUGE-annotated P. aeruginosa studies, three times more than previously available. GAPE analysis revealed that PA3923, a gene of unknown function, was frequently differentially expressed in more than 50% of studies and significantly coregulated with genes involved in biofilm formation. Follow-up wet-bench experiments demonstrate that PA3923 mutants are indeed defective in biofilm formation, consistent with predictions facilitated by GAUGE and GAPE. We anticipate that GAUGE and GAPE, which we have made freely available, will make publicly available microbial transcriptomic data easier to reuse and lead to new data-driven hypotheses. IMPORTANCE GEO archives transcriptomic data from over 5,800 microbial experiments and allows researchers to answer questions not directly addressed in published papers. However, less than 4% of the microbial data sets include the sample group annotations required for high-throughput reanalysis. This limitation blocks a considerable amount of microbial transcriptomic data from being reused easily. Here, we demonstrate that the GAUGE algorithm could make 33% of microbial data accessible to parallel mining and reanalysis. GAUGE annotations increase statistical power and, thereby, make consistent patterns of differential gene expression easier to identify. In addition, we developed GAPE (GAUGE-annotated Pseudomonas aeruginosa and Escherichia coli transcriptomic compendia for reanalysis), a Shiny Web interface that performs parallel analyses on P. aeruginosa and E. coli compendia. Source code for GAUGE and GAPE is freely available and can be repurposed to create compendia for other bacterial species.

Staphylococcus aureus , a commensal and opportunist pathogen, is responsible for a large number of human and animal infections, from benign to severe. Gene expression adaptation during infection requires a complex network of regulators, including transcriptional factors (TF) and sRNAs.

Researchers studying cystic fibrosis (CF) pathogens have produced numerous RNA-seq datasets which are available in the gene expression omnibus (GEO). Although these studies are publicly available, substantial computational expertise and manual effort are required to compare similar studies, visualize gene expression patterns within studies, and use published data to generate new experimental hypotheses. Furthermore, it is difficult to filter available studies by domain-relevant attributes such as strain, treatment, or media, or for a researcher to assess how a specific gene responds to various experimental conditions across studies. To reduce these barriers to data re-analysis, we have developed an R Shiny application called CF-Seq, which works with a compendium of 128 studies and 1,322 individual samples from 13 clinically relevant CF pathogens. The application allows users to filter studies by experimental factors and to view complex differential gene expression analyses at the click of a button. Here we present a series of use cases that demonstrate the application is a useful and efficient tool for new hypothesis generation. (CF-Seq: http://scangeo.dartmouth.edu/CFSeq/)

Researchers studying cystic fibrosis (CF) pathogens have produced numerous RNA-seq datasets which are available in the gene expression omnibus (GEO). Although these studies are publicly available, substantial computational expertise and manual effort are required to compare similar studies, visualize gene expression patterns within studies, and use published data to generate new experimental hypotheses. Furthermore, it is difficult to filter available studies by domain-relevant attributes such as strain, treatment, or media, or for a researcher to assess how a specific gene responds to various experimental conditions across studies. To reduce these barriers to data re-analysis, we have developed an R Shiny application called CF-Seq, which works with a compendium of 147 studies and 1,446 individual samples from 13 clinically relevant CF pathogens. The application allows users to filter studies by experimental factors and to view complex differential gene expression analyses at the click of a button. Here we present a series of use cases that demonstrate the application is a useful and efficient tool for new hypothesis generation. (CFSeq: http://scangeo.dartmouth.edu/CFSeq/)