Dataset Information

Deeply mining a universe of peptides Encoded by Long Noncoding RNAs

ABSTRACT: 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.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Homo Sapiens (human) Mus Musculus (mouse)

TISSUE(S): Spleen, Heart, Testis, Brain, Liver, Lung, Cell Culture, Kidney

SUBMITTER: Qing Zhang

LAB HEAD: Fuquan Yang

PROVIDER: PXD016981 | Pride | 2023-05-06

REPOSITORIES: Pride

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Dataset's files

Source:

			Action	DRS
	20161202_Hela_01_LncPEP_full.msf	Msf
	20161202_Hela_01_LncPEP_semi.msf	Msf
	20161202_Hela_01_unDigestion_lncPEP.msf	Msf
	20161202_Hela_02_LncPEP_full.msf	Msf
	20161202_Hela_02_LncPEP_semi.msf	Msf

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Publications

LncRNA-encoded microproteins: A new form of cargo in cell culture-derived and circulating extracellular vesicles.

Cai Tanxi T Zhang Qing Q Wu Bowen B Wang Jifeng J Li Na N Zhang Tingting T Wang Zhipeng Z Luo Jianjun J Guo Xiaojing X Ding Xiang X Xie Zhensheng Z Niu Lili L Ning Weihai W Fan Zhen Z Chen Xiaowei X Guo Xiangqian X Chen Runsheng R Zhang Hongwei H Yang Fuquan F

Journal of extracellular vesicles 20210712 9

Advancements in omics-based technologies over the past few years have led to the discovery of numerous biologically relevant peptides encoded by small open reading frames (smORFs) embedded in long noncoding RNA (lncRNA) transcripts (referred to as microproteins here) in a variety of species. However, the mechanisms and modes of action that underlie the roles of microproteins have yet to be fully characterized. Herein, we provide the first experimental evidence of abundant microproteins in extrac ...[more]

PMID: 34276900

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Project description:Many small open reading frames (smORFs) embedded in lncRNA transcripts have been shown to encode biologically functional polypeptides (smORFs-encoded polypeptides, SEPs) in different organisms. Despite significant advances in genomics, bioinformatics and proteomics that largely enabled the discovery of novel SEPs, their identification across different biological samples is still hampered by their poor predictability, diminutive size and low relative abundance. Here, we take advantage of NONCODE, a repository containing the most complete collection and annotation of lncRNA transcripts from different species, to build a novel database that attempts to maximize a collection of SEPs from human and mouse lncRNA transcripts. In order to further improve SEP discovery, we implemented two effective and complementary polypeptide enrichment strategies, 30 kDa MWCO filter and C8 SPE column. These combined strategies enabled us to discover 357 and 409 SEPs from, respectively, 8 human cell lines, and 3 mouse cell lines and 8 mouse tissues. Importantly, nineteen of the identified SEPs were then verified through in-vitro expression, immunoblotting, parallel reaction monitoring (PRM) and synthetic peptides. Subsequent bioinformatic analysis revealed that some of the physical and chemical properties of these novel SEPs, including amino acid composition and codon usage, are different from those commonly found in canonical proteins. Intriguingly, nearly 65% of the identified SEPs were found to be initiated with non-AUG start codons. Overall, the strategy presented in this study encompasses an efficient workflow that enabled us to identify 766 novel SEPs across multiple cell lines and tissues, which probably represents the largest number of SEPs detected by mass spectrometry reported to date. These novel SEPs might not only provide new clues for the annotation of noncoding elements in the genome but can also serve as a valuable resource for the functional characterization of individual SEPs.

Project description:LncRNA expression profiling for liver tissues of mice fed for a normal diet (NFD, 3mice) and a high-fat diet (HFD, 3mice) Summary: An abstract of the experiment and the data analysis. Experiment Workflow: A workflow of the experiment and the data analysis. Project Description: Sample and experiment information. Array Information: Mouse 8 x 60K LncRNA expression array information. Summary Table of Files for Data Delivery: Contains summary table of files for data delivery and the recommended software programs for viewing the data. Data Analysis for LncRNAs 1. Raw LncRNA data normalization and low intensity filtering: Raw signal intensities were normalized in quantile method by GeneSpring GX v11.5.1, and low intensity LncRNAs were filtered (LncRNAs that at least 6 out of 9 samples have flags in Present or Marginal were chosen for further analysis, these LncRNAs can be found from the LncRNA Expression Profiling Data.xls file). 2. Quality assessment of LncRNA data after filtering: Contains Box Plot and Scatter Plot for LncRNAs after filtering (This data can be found from the LncRNA Expression Profiling Data.xls file). 3. Differentially expressed LncRNAs screening: Contains differentially expressed genes with statistical significance that passed Volcano Plot filtering (Fold Change >= 2.0, P-value <= 0.05) (This data can be found from the Differentially Expressed LncRNAs.xls file). 4. Heat Map and Hierarchical Clustering: Hierarchical Clustering of Differentially Expressed LncRNAs (The heat map can be found from the LncRNA Expression Profiling Data.xls file). Data Analysis for mRNAs 1. Raw mRNA data normalization and low intensity filtering: Raw signal intensities were normalized in quantile method by GeneSpring GX v11.5.1, and low intensity mRNAs were filtered (mRNAs that at least 6 out of 9 samples have flags in Present or Marginal were chosen for further analysis, these mRNAs can be found from the mRNA Expression Profiling Data.xls file). 2. Quality assessment of mRNA data after filtering: Contains Box Plot and Scatter Plot for mRNAs after filtering (This data can be found from the mRNA Expression Profiling Data.xls file). 3. Differentially expressed mRNAs screening: Contains differentially expressed genes with statistical significance that passed Volcano Plot filtering (Fold Change >= 2.0, P-value <= 0.05) (This data can be found from the Differentially Expressed mRNAs.xls file). 4. Heat Map and Hierarchical Clustering: Hierarchical Clustering of Differentially Expressed mRNAs (The heat map can be found from the mRNA Expression Profiling Data.xls file). 5. Pathway analysis: Pathway analysis of the differentially expressed mRNAs. 6. GO analysis: GO term analysis of the differentially expressed mRNAs. LncRNA Classification and Subgroup Analysis 1. Rinn lincRNAs profiling: Contains profiling data of all lincRNAs based on John Rinn's papers (This data can be found from the Rinn lincRNAs profiling.xls file). 2. LincRNAs nearby coding gene data table: Contains the differentially expressed lincRNAs and nearby coding gene pairs (distance < 300 kb) (This data can be found from the LincRNAs nearby coding gene data table.xls file). Sample RNA Quality Control: Sample quality control data file from NanoDrop ND-1000 spectrophotometer and standard denaturing agarose gel electrophoresis. Methods: A brief introduction of methods for sample preparation, microarray design, experiment, and data analysis.