The Clostridium small RNome that responds to stress: the paradigm and importance of toxic metabolite stress in C. acetobutylicum.

BACKGROUND: Small non-coding RNAs (sRNA) are emerging as major components of the cell's regulatory network, several possessing their own regulons. A few sRNAs have been reported as being involved in general or toxic-metabolite stress, mostly in Gram- prokaryotes, but hardly any in Gram+ prokaryotes. Significantly, the role of sRNAs in the stress response remains poorly understood at the genome-scale level. It was previously shown that toxic-metabolite stress is one of the most comprehensive and encompassing stress responses in the cell, engaging both the general stress (or heat-shock protein, HSP) response as well as specialized metabolic programs. RESULTS: Using RNA deep sequencing (RNA-seq) we examined the sRNome of C. acetobutylicum in response to the native but toxic metabolites, butanol and butyrate. 7.5% of the RNA-seq reads mapped to genome outside annotated ORFs, thus demonstrating the richness and importance of the small RNome. We used comparative expression analysis of 113 sRNAs we had previously computationally predicted, and of annotated mRNAs to set metrics for reliably identifying sRNAs from RNA-seq data, thus discovering 46 additional sRNAs. Under metabolite stress, these 159 sRNAs displayed distinct expression patterns, a select number of which was verified by Northern analysis. We identified stress-related expression of sRNAs affecting transcriptional (6S, S-box &solB) and translational (tmRNA & SRP-RNA) processes, and 65 likely targets of the RNA chaperone Hfq. CONCLUSIONS: Our results support an important role for sRNAs for understanding the complexity of the regulatory network that underlies the stress response in Clostridium organisms, whether related to normophysiology, pathogenesis or biotechnological applications.

Experimental design, preprocessing, normalization and differential expression analysis of small RNA sequencing experiments.

Prior to the advent of new, deep sequencing methods, small RNA (sRNA) discovery was dependent on Sanger sequencing, which was time-consuming and limited knowledge to only the most abundant sRNA. The innovation of large-scale, next-generation sequencing has exponentially increased knowledge of the biology, diversity and abundance of sRNA populations. In this review, we discuss issues involved in the design of sRNA sequencing experiments, including choosing a sequencing platform, inherent biases that affect sRNA measurements and replication. We outline the steps involved in preprocessing sRNA sequencing data and review both the principles behind and the current options for normalization. Finally, we discuss differential expression analysis in the absence and presence of biological replicates. While our focus is on sRNA sequencing experiments, many of the principles discussed are applicable to the sequencing of other RNA populations.

Short-read sequencing technologies for transcriptional analyses.

The technological advances in DNA sequencing over the past five years have changed our approaches to gene expression analysis, fundamentally altering the basic methods used and in most cases driving a shift from hybridization-based approaches to sequencing-based approaches. Quantitative, tag-based studies of gene expression were one of the earliest applications of these next-generation technologies, but the tremendous depth of sequencing facilitates de novo transcript discovery, which replaces traditional expressed sequence tag (EST) sequencing. In addition, these technologies have created new opportunities for understanding the generation, stability, and decay of RNA and the impacts of chromatin differences on gene expression. As we review the impact of these methods on plant biology, we also mention published studies from animal systems when the methods are broadly applicable. We can anticipate that the published work over the past few years is a harbinger of much broader studies that are yet to be published and are sure to further advance our understanding of plant genomes in a field changing at a dizzying pace.