Dataset Information

Detection of SEPs by MS - Unraveling the hidden universe of small proteins in bacterial genomes

ABSTRACT: Identifying smORFs and SEPs is technically and computationally challenging. Experimentally, techniques as ribosome profiling (Ribo-Seq and mass spectroscopy (MS) are used. Ribo-Seq sequences the mRNA and does not provide the translated frame, thus identifying proteins encoded by overlapping ORFs is not feasible. Herein we have used MS to characterize smORFomes of different Mycoplasma species. This data is used to corroborate the predictions of a random forest classifier that in silico predicts all the putative SEPs encoded by different bacterial genomes.

INSTRUMENT(S): LTQ Orbitrap Velos

ORGANISM(S): Mycoplasma Hyopneumoniae Mycoplasma Mycoides Mycoplasma Pneumoniae Mycoplasma Capricolum Mycoplasma Genitalium Mycoplasma Gallisepticum Str. R

TISSUE(S): Cell Culture

SUBMITTER: Samuel Miravet Verde

LAB HEAD: Luis Serrano

PROVIDER: PXD008243 | Pride | 2019-03-01

REPOSITORIES: Pride

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Unraveling the hidden universe of small proteins in bacterial genomes.

Miravet-Verde Samuel S Ferrar Tony T Espadas-García Guadalupe G Mazzolini Rocco R Gharrab Anas A Sabido Eduard E Serrano Luis L Lluch-Senar Maria M

Molecular systems biology 20190222 2

Identification of small open reading frames (smORFs) encoding small proteins (≤ 100 amino acids; SEPs) is a challenge in the fields of genome annotation and protein discovery. Here, by combining a novel bioinformatics tool (RanSEPs) with "-omics" approaches, we were able to describe 109 bacterial small ORFomes. Predictions were first validated by performing an exhaustive search of SEPs present in <i>Mycoplasma pneumoniae</i> proteome via mass spectrometry, which illustrated the limitations of sh ...[more]

PMID: 30796087

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Project description:Long non-coding RNAs (lncRNAs) are generally defined as RNA transcripts longer than 200 nucleotides that are not translated into proteins. Recently, many small open reading frames (smORFs) embedded in lncRNA scripts have been verified to be able to encode functional polypeptides (namely lncRNA-SEPs here). Although collaborative analysis by advanced genomics, bioinformatics and proteomics largely drives SEPs discovery, the poor predictability, diminutive size and low abundance still challenge systematic identification of SEPs from different biological samples. Here, we took advantage of the NONCODE database that deposited with the most complete collection and annotation of lncRNA transcripts from different species to build a database that to maximally collect all putative small ORFs from human and mouse lncRNA transcripts. Two effective and complementary polypeptides enrichment strategies (30 kDa MWCO filter and C8 SPE column) were also integrated to further improve the discovery of novel lncRNA-SEPs. These efforts led to the discovery of 362 lncRNA-SEPs from 8 human cell lines and 238 lncRNA-SEPs from 3 mouse cell lines and 8 mouse tissues. 18 out of these lncRNA-SEPs were verified experimentally by multiple technologies including in vitro expression, immunoblotting and parallel reaction monitoring-based mass spectrometry (PRM-MS) in 293T cells. Further bioinformatic analysis reveals that the physical and chemical properties of these novel lncRNA-SEPs, such as amino acid composition and codon usage, are varied from canonical proteins. Intriguingly, nearly 70% of the identified lncRNA-SEPs were found to be initiated with non-AUG start codons. Collectively, the efficient workflows presented in this study enables us identify 600 novel lncRNA-SPEs from multiple cell lines and tissues, which should represent the largest number of MS-detected lncRNA-encoding SEPs ever reported to date. These novel lncRNA-SEPs not only could provide new clues for the annotation of the noncoding elements in the genome, but also could serve as a valuable resource for the functional characterization of individual lncRNA-SEPs.

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