bims-micpro 2020-11-08 papers

Cancer Lett. 2020 Nov 03. pii: S0304-3835(20)30572-3. [Epub ahead of print]

sORF-Encoded MicroPeptides: New players in inflammation, metabolism, and precision medicine.

Significant technological advances have enabled the discovery and identification of a new class of molecules, micropeptides or small ORF encoded peptides (SEPs) within non-coding RNAs (ncRNAs). As ncRNAs are well known to be transcriptionally silent, the discovery of SEPs implies that many ncRNAs are misannotated or play both coding and non-coding functions. SEPs have reportedly diverse regulatory roles in embryogenesis, myogenesis, inflammation, diseases, and cancer. SEPs appearing in different subcellular compartments show distinct functions. In this review, we summarized the functions of SEPs that have been characterized thus far. As SEPs are amenable to therapeutic development as biologics, understanding their underlying functions will provide novel targets for the treatment of inflammatory or metabolic disorders.

Keywords: Inflammation; SEPs; Small peptides; ncRNA

DOI: https://doi.org/10.1016/j.canlet.2020.10.038

Curr Genet. 2020 Nov 01.

Rules are made to be broken: a "simple" model organism reveals the complexity of gene regulation.

Andrea L Higdon, Gloria A Brar.

Global methods for assaying translation have greatly improved our understanding of the protein-coding capacity of the genome. In particular, it is now possible to perform genome-wide and condition-specific identification of translation initiation sites through modified ribosome profiling methods that selectively capture initiating ribosomes. Here we discuss our recent study applying such an approach to meiotic and mitotic timepoints in the simple eukaryote, budding yeast, as an example of the surprising diversity of protein products-many of which are non-canonical-that can be revealed by such methods. We also highlight several key challenges in studying non-canonical protein isoforms that have precluded their prior systematic discovery. A growing body of work supports expanded use of empirical protein-coding region identification, which can help relieve some of the limitations and biases inherent to traditional genome annotation approaches. Our study also argues for the adoption of less static views of gene identity and a broader framework for considering the translational capacity of the genome.

Keywords: Genome annotation; Meiosis; Near-cognate codons; Protein-coding regions; Ribosome profiling; Translation initiation

DOI: https://doi.org/10.1007/s00294-020-01121-8

Comput Struct Biotechnol J. 2020 ;18 2836-2850

Modelling of pathogen-host systems using deeper ORF annotations and transcriptomics to inform proteomics analyses.

Sebastien Leblanc, Marie A Brunet.

The Zika virus is a flavivirus that can cause fulminant outbreaks and lead to Guillain-Barré syndrome, microcephaly and fetal demise. Like other flaviviruses, the Zika virus is transmitted by mosquitoes and provokes neurological disorders. Despite its risk to public health, no antiviral nor vaccine are currently available. In the recent years, several studies have set to identify human host proteins interacting with Zika viral proteins to better understand its pathogenicity. Yet these studies used standard human protein sequence databases. Such databases rely on genome annotations, which enforce a minimal open reading frame (ORF) length criterion. An ever-increasing number of studies have demonstrated the shortcomings of such annotation, which overlooks thousands of functional ORFs. Here we show that the use of a customized database including currently non-annotated proteins led to the identification of 4 alternative proteins as interactors of the viral capsid and NS4A proteins. Furthermore, 12 alternative proteins were identified in the proteome profiling of Zika infected monocytes, one of which was significantly up-regulated. This study presents a computational framework for the re-analysis of proteomics datasets to better investigate the viral-host protein interplays upon infection with the Zika virus.

Keywords: AP-MS, affinity-purification mass spectrometry; Alternative ORFs; DEP, differentially expressed proteins; FDR, false discovery rate; FPKM, fragments per kilobase of exon model per million reads mapped; Flavivirus; HCIP, highly confident interacting proteins; HCMV, human cytomegalovirus; LFQ, label free quantification; MS, mass spectrometry; ORF, open reading frame; PSM, peptide spectrum match; Protein network; Proteogenomics; Proteome profiling; ZIKV, Zika virus; Zika; altProt, alternative protein; ncRNA, non-coding RNA; sORF, small open reading frame

DOI: https://doi.org/10.1016/j.csbj.2020.10.010