Bacterial RNA Biology on a Genome Scale

Abstract

Bacteria are an exceedingly diverse group of organisms whose molecular exploration is experiencing a renaissance. While the classical view of bacterial gene expression was relatively simple, the emerging view is more complex, encompassing extensive post-transcriptional control involving riboswitches, RNA thermometers, and regulatory small RNAs (sRNAs) associated with the RNA-binding proteins CsrA, Hfq, and ProQ, as well as CRISPR/Cas systems that are programmed by RNAs. Moreover, increasing interest in members of the human microbiota and environmental microbial communities has highlighted the importance of understudied bacterial species with largely unknown transcriptome structures and RNA-based control mechanisms. Collectively, this creates a need for global RNA biology approaches that can rapidly and comprehensively analyze the RNA composition of a bacterium of interest. We review such approaches with a focus on RNA-seq as a versatile tool to investigate the different layers of gene expression in which RNA is made, processed, regulated, modified, translated, and turned over.

Internal RNAs overlapping coding sequences can drive the production of alternative proteins in archaea

“…However, this simplicity has been challenged by the widespread use of high-resolution transcriptome mapping technologies [1,2]. Identification of overlapping genomic elements revealed modular operon organization that allows for conditional co-modulation of genes in bacteria and archaea [3,4].…”

The signal and the noise - characteristics of antisense RNA in complex microbial communities

“…We now show this to be a consistent phenomenon across different environments, suggesting biological relevance. The presence of asRNA might have regulatory properties, as high levels of asRNA could act to block gene expression (Bao et al 2015; Hör, Gorski, and Vogel 2018). Such information could for example partition genes into suppressed, intermediate, and unsuppressed based on certain cutoff values of percent asRNA (Bao et al 2015).…”

“…Of course, we believe the optimum would be to include asRNA as a separate entity into the analysis whenever possible, as important Information may be retrieved. For example, high levels of asRNA could have inhibitive effects on downstream protein production (Gorski, Vogel, and Doudna 2017; Hör, Gorski, and Vogel 2018). This could be investigated by including asRNA in standard differential expression analysis and screening for significant interactions between asRNA/mRNA pairs and their association with experimental treatment.…”

“…Transcriptomics is an established tool to quantify bacterial messenger RNA (mRNA) expression and regulation on a whole-organism level (Sorek and Cossart 2010), which increasingly also includes a wide repertoire of small regulatory non-coding RNA (sRNA) (Gorski, Vogel, and Doudna 2017; Hör, Gorski, and Vogel 2018). This is largely due to the introduction of next-generation RNA-sequencing (RNAseq), a technique initially applied mostly on human samples but which has also allowed unprecedented insight into the overall characteristics of complete microbial transcriptomes (Sorek and Cossart 2010).…”

No wisdom in the crowd: genome annotation in the era of big data – current status and future prospects

“…Furthermore, genome sequences are implicitly associated with available knowledge about biology, based on the notion that they must code for genes with their regulatory regions and especially protein coding sequences (CDSs). More recently, a large family of RNAs [regulatory 'noncoding' RNAs (Hor et al, 2018), riboswitches (Serganov and Nudler, 2013), ribozymes (Wilson and Lilley, 2015) and other miscellaneous RNAs (Nelson and Breaker, 2017)] completed the picture. As relevant metadata, this involved the tagging of gene sequences for control regions (Nikolaichik and Damienikan, 2016) as well as other elements of genome organization, and of course the source of the sequence, often a scientific article.…”