Dataset Information

A Hidden Human Proteome Encoded by the “Non-Coding” Genes

ABSTRACT: It has been a long debate whether the 98% “non-coding” fraction of human genome can encode functional proteins besides a “random noise” of translation. We used our established translatome sequencing (RNC-seq) to analyze human cells and found that up to 3330 long non-coding RNAs (lncRNAs) were bound to ribosomes and thus might be translated into proteins (with more than 50 amino acids). These new protein-coding genes distributed universally in all human chromosomes. We then used various experimental methods including mass spectrometry, immunoblotting, subcellular localization and phenotype assessments to verify the existence of such a hidden human proteome encoded by purported lncRNAs that can express functional proteins. These new proteins deviate from the canonical proteins in various physical and chemical properties, and emerged mostly in primates during evolution. In sum, we experimentally evidenced a hidden human functional proteome encoded by purported lncRNAs, suggesting that the human genome has to be systematically re-annotated.

INSTRUMENT(S): TripleTOF 5600

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Cell Culture

SUBMITTER: Yang Wang

LAB HEAD: Qing-Yu He

PROVIDER: PXD005291 | Pride | 2022-02-25

REPOSITORIES: Pride

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Publications

A hidden human proteome encoded by 'non-coding' genes.

Lu Shaohua S Zhang Jing J Lian Xinlei X Sun Li L Meng Kun K Chen Yang Y Sun Zhenghua Z Yin Xingfeng X Li Yaxing Y Zhao Jing J Wang Tong T Zhang Gong G He Qing-Yu QY

Nucleic acids research 20190901 15

It has been a long debate whether the 98% 'non-coding' fraction of human genome can encode functional proteins besides short peptides. With full-length translating mRNA sequencing and ribosome profiling, we found that up to 3330 long non-coding RNAs (lncRNAs) were bound to ribosomes with active translation elongation. With shotgun proteomics, 308 lncRNA-encoded new proteins were detected. A total of 207 unique peptides of these new proteins were verified by multiple reaction monitoring (MRM) and ...[more]

PMID: 31340039

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Project description:Motivation: Identification of eQTL, the genetic loci that contribute to heritable variation in gene expression, can be obstructed by factors that produce variation in expression profiles if these factors are unmeasured or hidden from direct analysis. Methods: We have developed a method for Hidden Expression Factor analysis (HEFT) that identifies individual and pleiotropic effects of eQTL in the presence of hidden factors. The HEFT model simultaneously accounts for the effects of genotypes while learning hidden factors, where we make use of the complete likelihood of a unified multivariate regression and factor analysis model to derive a ridge estimator for combined factor learning and detection of eQTL. HEFT requires no pre-estimation of hidden factor effects, no iterative model selection, it provides p-values, and is extremely fast, requiring just a few hours to complete an eQTL analysis of thousands of expression variables when analyzing hundreds of thousands of SNPs on a standard 8 core 2.6G desktop. Results: By analyzing simulated data, we demonstrate that HEFT can correct for an unknown number of hidden factors and outperforms related hidden factor methods for eQTL analysis, where the improved performance is particularly evident in the detection of eQTL with multivariate effects. To demonstrate a real-world application, we applied HEFT to identify eQTL affecting gene expression in human lung tissue for a study that included presumptive hidden factors. The analysis identified a number of eQTL with direct relevance to lung disease that could not be found without a hidden factor analysis, including cis-eQTL for GTF2H1 and MTRR, genes that have been independently associated with lung cancer. We have developed HEFT, a fast multivariate method that detect eQTLs by analyzing thousands of traits simultaneously in the presence of hidden factors. HEFT employs a combined regression factor analysis approach to analyze the gene expression and genotype data sets, looking for univariate or multivariate eQTLs that regulate gene expression, while simultaneously controlling for both orthogonal or non-orthogonal hidden factors. We show by extensive simulation that HEFT outperforms competing methods, and by applying HEFT to a study that included presumptive hidden factors, we identified a number of eQTL with direct relevance to lung disease that could not be found without a hidden factor analysis. HEFT analysis results file (includes all results, not just top hits) linked below as supplementary file.

Dataset Information

A Hidden Human Proteome Encoded by the “Non-Coding” Genes

Publications

A hidden human proteome encoded by 'non-coding' genes.

Similar Datasets

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