A framework for the computational prediction and analysis of non-coding RNAs in microbial environmental populations and their experimental validation

Abstract

Small regulatory RNAs and antisense RNAs play important roles in the regulation of gene expression in bacteria but are underexplored, especially in natural populations. While environmentally relevant microbes often are not amenable to genetic manipulation or cannot be cultivated in the laboratory, extensive metagenomic and metatranscriptomic datasets for these organisms might be available. Hence, dedicated workflows for specific analyses are needed to fully benefit from this information. Here, we identified abundant sRNAs from oceanic environmental populations of the ecologically important primary producer Prochlorococcus starting from a metatranscriptomic differential RNA-Seq (mdRNA-Seq) dataset. We tracked their homologs in laboratory isolates, and we provide a framework for their further detailed characterization. Several of the experimentally validated sRNAs responded to ecologically relevant changes in cultivation conditions. The expression of the here newly discovered sRNA Yfr28 was highly stimulated in low-nitrogen conditions. Its predicted top targets include mRNAs encoding cell division proteins, a sigma factor, and several enzymes and transporters, suggesting a pivotal role of Yfr28 in the coordination of primary metabolism and cell division. A cis-encoded antisense RNA was identified as a possible positive regulator of atpF encoding subunit b' of the ATP synthase complex. The presented workflow will also be useful for other environmentally relevant microorganisms for which experimental validation abilities are frequently limiting although there is wealth of sequence information available.

Riboregulation in bacteria: From general principles to novel mechanisms of the trp attenuator and its sRNA and peptide products

“…Metatranscriptomic approaches enable sRNA identification and differential analysis of the abundance of sRNAs and possibly of their predicted targets in microbiome communities. For functional validation and mechanistic analysis, related laboratory strains are used (Lott et al, 2020).…”

The Fate and Functionality of Alien tRNA Fragments in Culturing Medium and Cells of Escherichia coli

“…In bacteria, several types of regulatory RNAs are quite extensively studied as well, including small regulatory RNAs (sRNAs) encoded by their own genes [ 16 , 17 , 18 ] and acting in cis antisense RNAs (aRNAs), transcribed from their own promoters located within coding sequences of target genes [ 19 , 20 , 21 , 22 ]. Based on the ability of sRNAs to form stable secondary structures, tools have been created for their search on a genome scale [ 23 , 24 , 25 , 26 ], while aRNAs can be predicted using promoter search algorithms [ 27 ]. Another type of regulatory RNA that has attracted attention in the last decades is the processed fragments of transcripts that have independent functions in cells.…”

Weighted Gene Coexpression Network Analysis Identifies Crucial Genes Involved in Coronary Atherosclerotic Heart Disease

“…Several algorithms based study approaches have been explored to assess the potential underlying mechanisms for the gene networks that can provide overall comprehensions into various disorders [ 9 , 10 ]. Weighted gene coexpression network analysis (WGCNA) is a system biology method that could effectively differentiate gene coexpression networks of a multifaceted bioprocess to some specific modules [ 11 ].…”