Discovery of numerous novel small genes in the intergenic regions of the Escherichia coli O157:H7 Sakai genome

Abstract

In the past, short protein-coding genes were often disregarded by genome annotation pipelines. Transcriptome sequencing (RNAseq) signals outside of annotated genes have usually been interpreted to indicate either ncRNA or pervasive transcription. Therefore, in addition to the transcriptome, the translatome (RIBOseq) of the enteric pathogen Escherichia coli O157:H7 strain Sakai was determined at two optimal growth conditions and a severe stress condition combining low temperature and high osmotic pressure. All intergenic open reading frames potentially encoding a protein of ≥ 30 amino acids were investigated with regard to coverage by transcription and translation signals and their translatability expressed by the ribosomal coverage value. This led to discovery of 465 unique, putative novel genes not yet annotated in this E. coli strain, which are evenly distributed over both DNA strands of the genome. For 255 of the novel genes, annotated homologs in other bacteria were found, and a machine-learning algorithm, trained on small protein-coding E. coli genes, predicted that 89% of these translated open reading frames represent bona fide genes. The remaining 210 putative novel genes without annotated homologs were compared to the 255 novel genes with homologs and to 250 short annotated genes of this E. coli strain. All three groups turned out to be similar with respect to their translatability distribution, fractions of differentially regulated genes, secondary structure composition, and the distribution of evolutionary constraint, suggesting that both novel groups represent legitimate genes. However, the machine-learning algorithm only recognized a small fraction of the 210 genes without annotated homologs. It is possible that these genes represent a novel group of genes, which have unusual features dissimilar to the genes of the machine-learning algorithm training set.

Identifying small proteins by ribosome profiling with stalled initiation complexes

“…As shown here, translated smORFs are more prevalent than previously believed and are found in contexts that would be difficult to distinguish by other methods, including bioinformatics approaches that have been successfully employed previously (16,17). While traditional ribosome profiling can guide the prediction (53,54) or, in conjunction with experiments to verify protein synthesis, even support the annotation of intergenic smORFs in bacteria (27), ribosome profiling with stalled initiation complexes allows for new ORFs to be located in contexts that are generally-ignored, including within or overlapping other genes as shown here and by the group of Vázquez-Laslop and Mankin (35). These new, internal altORFs may represent a new class of functional and regulatory proteins that comprise an ever-expanding proteome.…”

An unexpected vestigial protein complex reveals the evolutionary origins of an s-triazine catabolic enzyme

“…There is an increasing body of evidence that small, overlooked open-reading frames often encode functional low-molecular weight proteins (32,33). It may be that a subset of these have structural roles, like that of AtzG, and are required to promote or enhance soluble protein expression or stability of their partner proteins.…”

Improving Bacterial Ribosome Profiling Data Quality

“…Thus, a single-codon resolution is achievable [2,6]. In contrast for prokaryotes, where reading frames can be detected in a sum signal, the resolution of standard ribosome footprinting methods lack precision for reading frame determination in individual genes [30]. An approach for increased precision uses RelE in addition to MNase.…”

“…Samples are loaded onto, e.g. a TBE-urea gel and certain fragment sizes are excised after comparison to a corresponding DNA or RNA ladder [5,30]. Another possibility for fragment size selection is to use a size exclusion spin-column, but this has rarely been used [39].…”

“…However, the necessary amount of reads is highly dependent on the characteristics of the experiment. If the particular interest lies in using ribosome profiling experiments to detect weakly expressed genes [28,30,56,57], a certain read number matching a gene is needed to confidently confirm expression (e.g. RPKM above a certain threshold).…”