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Histocompatibility is the ability to discriminate between self and non-self tissues, and has been described in species throughout the metazoa. Despite its universal presence, histocompatibility genes utilized by different phyla are unique-those found in sponges, cnidarians, ascidians, and vertebrates are not orthologous. Thus, the origins of these sophisticated recognition systems, and any potential functional commonalities between them, are not understood. We are studying histocompatibility in the botryllid ascidians, members of the chordate subphylum, Tunicata, which provide a powerful model to understand both the origins and functional aspects of this process. Histocompatibility in the botryllids occurs at the tips of an extracorporeal vasculature that come into contact when two individuals grow into proximity. If compatible, the vessels will fuse, forming a parabiosis between the two individuals. If incompatible, the two vessels will reject-an inflammatory reaction that results in melanin scar formation at the point of contact, blocking anastomosis. Compatibility is determined by a single, highly polymorphic locus called the fuhc with the following rules: individuals that share one or both fuhc alleles will fuse, while those who share neither will reject. The fuhc locus encodes at least six proteins with known roles in allorecognition. One of these genes, called uncle fester, is necessary and sufficient to initiate the rejection response. Here, we report the existence of genotype-specific expression levels of uncle fester, differing by up to eight-fold at the mRNA-level, and that these expression levels are constant and maintained for the lifetime of an individual. We also found that these differences had functional consequences: the expression level of uncle fester correlated with the speed and severity of the rejection response. These findings support previous conclusions that uncle fester levels modulate the rejection response, and may be responsible for controlling the variation observed in the timing and intensity of the reaction. The maintenance of genotype specific expression of uncle fester is also evidence of an education process reminiscent of that which occurs in mammalian Natural Killer cells. In turn, this suggests that while histocompatibility receptors and ligands evolve via convergent evolution, they may utilize conserved intracellular machinery to interpret binding events at the cell surface., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology.)

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/ )., (© 2022. The Author(s).)

Teg49 is a Staphylococcus aureus trans-acting regulatory sRNA derived from cleavage of the sarA P3 transcript. We showed by RNA-Seq here that the 5' trident-like structure in Teg49 regulates transcriptionally (direct and indirect) 22 genes distinct from sarA. Among these, Teg49 was noted to repress spn, encoding a 102 residue preprotein which yields the mature 73 residue peptide which inhibits the catalytic activity of myeloperoxidase in human neutrophils. Teg49 was found to regulate spn mRNA post-transcriptionally in strain SH1000 through 9-nt base-pairing between hairpin loop 2 of Teg49 and an exposed bulge of the spn mRNA. Mutations of the Teg49 binding site disrupted the repression of spn, leading to reduced degradation, and increased half-life of spn mRNA in the Teg49 mutant. The spn-Teg49 interaction was also confirmed with a synonymous spn mutation to yield enhanced spn expression in the mutant vs. the parent. The Teg49 mutant with increased spn expression exhibited enhanced resistance to MPO activity in vitro. Killing assays with human neutrophils showed that the Teg49 mutant was more resistant to killing after phagocytosis. Altogether, this study shows that Teg49 in S. aureus has a distinct and important regulatory profile whereby this sRNA modulates resistance to myeloperoxidase-mediated killing by human neutrophils., (© 2022 John Wiley & Sons Ltd.)

SarA, a transcriptional regulator of Staphylococcus aureus, is a major global regulatory system that coordinates the expression of target genes involved in its pathogenicity. Various studies have identified a large number of SarA target genes, but an in-depth characterization of the sarA regulon, including small regulatory RNAs (sRNAs), has not yet been done. In this study, we utilized transcriptome sequencing (RNA-Seq) and chromatin immunoprecipitation sequencing (ChIP-Seq) to determine a comprehensive list of SarA-regulated targets, including both mRNAs and sRNAs. RNA-Seq analysis indicated 390 mRNAs and 51 sRNAs differentially expressed in a Δ sarA mutant, while ChIP-Seq revealed 354 mRNAs and 55 sRNA targets in the S. aureus genome. We confirmed the authenticity of several novel SarA targets by Northern blotting and electrophoretic mobility shift assays. Among them, we characterized repression of sprG2 , a gene that encodes the toxin of a type I toxin-antitoxin system, indicating a multilayer lockdown of toxin expression by both SarA and its cognate antitoxin, SprF2. Finally, a novel SarA consensus DNA binding sequence was generated using the upstream promoter sequences of 15 novel SarA-regulated sRNA targets. A genome-wide scan with a deduced SarA motif enabled the discovery of new potential SarA target genes which were not identified in our RNA-Seq and ChIP-Seq analyses. The strength of this new consensus was confirmed with one predicted sRNA target. The RNA-Seq and ChIP-Seq combinatory analysis gives a snapshot of the regulation, whereas bioinformatic analysis reveals a permanent view of targets based on sequence. Altogether these experimental and in silico methodologies are effective to characterize transcriptional factor (TF) regulons and functions. IMPORTANCE 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. TF SarA influences virulence, metabolism, biofilm formation, and resistance to some antibiotics. SarA directly regulates expression of around 20 mRNAs and a few sRNAs. Here, we combined high-throughput expression screening methods combined with binding assays and bioinformatics for an in-depth investigation of the SarA regulon. This combinatory approach allowed the identification of 85 unprecedented mRNAs and sRNAs targets, with at least 14 being primary. Among novel SarA direct targets, we characterized repression of sprG2 , a gene that encodes the toxin of a toxin-antitoxin system, indicating a multilayer lockdown of toxin expression by both SarA and its cognate antitoxin, SprF2.

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., (Copyright © 2021 Li et al.)

Expression of virulence factors in Staphylococcus aureus is regulated by a wide range of transcriptional regulators, including proteins and small RNAs (sRNAs), at the level of transcription and/or translation. The sarA locus consists of three overlapping transcripts generated from three distinct promoters, all containing the sarA 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 sarA 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 sarA P3 and sarA P1 transcripts of the sarA locus, respectively. In this report, we utilized a variety of sarA promoter mutants and cshA and rnc 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 sarA mutant strains indicated that ∼133 genes are significantly upregulated while 97 are downregulated in a teg49 deletion mutant in a sarA -independent manner. An abscess model of skin infection indicated that the teg49 mutant exhibited a reduced bacterial load compared to the wild-type S. aureus 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., (Copyright © 2018 American Society for Microbiology.)