Co-regulation map of the human proteome enables identification of protein functions

Kustatscher, Georg; Grabowski, Piotr; Schrader, Tina A.; Passmore, Josiah B.; Schrader, Michael; Rappsilber, Juri

doi:10.1038/s41587-019-0298-5

Cited by 125 publications

(141 citation statements)

References 83 publications

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“…Higher gene expression of the 13 cell cycle pathway genes were associated with poorer overall survival in LUADs, p-value < 0.01. To verify the relationships at the protein level, we drew the PPI network ( Figure 8n) and co-regulation map (Figure 8o) for the human proteome, for a deeper identification of protein functions by ProteomeHD (URL: www.proteomeHD.net) [30].…”

Section: Validation Of Key Genesmentioning

confidence: 99%

mRNAsi Index: Machine Learning in Mining Lung Adenocarcinoma Stem Cell Biomarkers

Zhang

Tseng

Lien

et al. 2020

Genes

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Cancer stem cells (CSCs), characterized by self-renewal and unlimited proliferation, lead to therapeutic resistance in lung cancer. In this study, we aimed to investigate the expressions of stem cell-related genes in lung adenocarcinoma (LUAD). The stemness index based on mRNA expression (mRNAsi) was utilized to analyze LUAD cases in the Cancer Genome Atlas (TCGA). First, mRNAsi was analyzed with differential expressions, survival analysis, clinical stages, and gender in LUADs. Then, the weighted gene co-expression network analysis was performed to discover modules of stemness and key genes. The interplay among the key genes was explored at the transcription and protein levels. The enrichment analysis was performed to annotate the function and pathways of the key genes. The expression levels of key genes were validated in a pan-cancer scale. The pathological stage associated gene expression level and survival probability were also validated. The Gene Expression Omnibus (GEO) database was additionally used for validation. The mRNAsi was significantly upregulated in cancer cases. In general, the mRNAsi score increases according to clinical stages and differs in gender significantly. Lower mRNAsi groups had a better overall survival in major LUADs, within five years. The distinguished modules and key genes were selected according to the correlations to the mRNAsi. Thirteen key genes (CCNB1, BUB1, BUB1B, CDC20, PLK1, TTK, CDC45, ESPL1, CCNA2, MCM6, ORC1, MCM2, and CHEK1) were enriched from the cell cycle Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, relating to cell proliferation Gene Ontology (GO) terms, as well. Eight of the thirteen genes have been reported to be associated with the CSC characteristics. However, all of them have been previously ignored in LUADs. Their expression increased according to the pathological stages of LUAD, and these genes were clearly upregulated in pan-cancers. In the GEO database, only the tumor necrosis factor receptor associated factor-interacting protein (TRAIP) from the blue module was matched with the stemness microarray data. These key genes were found to have strong correlations as a whole, and could be used as therapeutic targets in the treatment of LUAD, by inhibiting the stemness features.

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Section: Validation Of Key Genesmentioning

confidence: 99%

mRNAsi Index: Machine Learning in Mining Lung Adenocarcinoma Stem Cell Biomarkers

Zhang

Tseng

Lien

et al. 2020

Genes

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“…Significant progress in describing the overall mitochondrial proteome has been made in recent years [ 20 , 21 ] and this has identified not only proteins that physically interact but also groups of proteins that are co-regulated [ 22 ] paving the way to link mitochondrial proteomics with normal and pathophysiological regulation. Two key observations are that a large fraction of the mitochondrial proteome is expressed in a tissue specific manner in mouse [ 23 ] and that global mitochondrial proteomic changes are observed during ageing [ 24 , 25 ].…”

Section: Regulation Of Mitochondrial Gene Expression and Age-relatmentioning

confidence: 99%

Intimate Relations—Mitochondria and Ageing

Webb¹,

Sideris²

2020

IJMS

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Mitochondrial dysfunction is associated with ageing, but the detailed causal relationship between the two is still unclear. We review the major phenomenological manifestations of mitochondrial age-related dysfunction including biochemical, regulatory and energetic features. We conclude that the complexity of these processes and their inter-relationships are still not fully understood and at this point it seems unlikely that a single linear cause and effect relationship between any specific aspect of mitochondrial biology and ageing can be established in either direction.

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“…Importantly we placed an emphasis on remodeling of proteome architecture between these diverse biological scenarios to extrapolate genome wide protein co-regulation and function. This approach has just recently been documented in humans (Kustatscher et al, 2019) and draws its power from deep coverage of the proteome as we have done here, quantifying close to 16,000 Arabidopsis proteins. Indepth sampling allowed quantitative measurement of all of the components of entire biochemical pathways such as JA synthesis in PTI, protein complex components such as RBF complexes and protein co-expression of genes in local neighborhoods such as cysteine rich kinases (CRKs), facilitating confident inference of protein abundance coregulation and functional and temporal connections for instance in seed development and dormancy, the establishment, maintenance and deconstruction of the photosynthetic machinery and in senescence and abscission.…”

Section: Chronology and Model Of Phytohormones In Ptimentioning

confidence: 93%

Reshaping of theArabidopsis thalianaproteome landscape and co-regulation of proteins in development and immunity

Bassal

Majovsky

Thieme

et al. 2020

Preprint

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24Proteome remodeling is a fundamental adaptive response and proteins in complex and 25 functionally related proteins are often co-expressed. Using a deep sampling strategy we 26 define Arabidopsis thaliana tissue core proteomes at around 10,000 proteins per tissue 27 and absolutely quantify (copy numbers per cell) nearly 16,000 proteins throughout the 28 plant lifecycle. A proteome wide survey of global post translational modification revealed 29 amino acid exchanges pointing to potential conservation of translational infidelity in 30 eukaryotes. Correlation analysis of protein abundance uncovered potentially new tissue 31 and age specific roles of entire signaling modules regulating transcription in 32 photosynthesis, seed development and senescence and abscission. Among others, the 33 data suggest a potential function of RD26 and other NAC transcription factors in seed 34 development related to desiccation tolerance as well as a possible function of Cysteine-35 rich Receptor-like Kinases (CRKs) as ROS sensors in senescence. All of the 36 components of ribosome biogenesis factor (RBF) complexes were co-expressed tissue 37 and age specifically indicating functional promiscuity in the assembly of these little 38 described protein complexes in Arabidopsis. Treatment of seedlings with flg22 for 16 39 hours allowed us to characterize proteome architecture in basal immunity in detail. The 40 results were complemented with parallel reaction monitoring (PRM) targeted 41 proteomics, phytohormone, amino acid and transcript measurements. We obtained 42 strong evidence of suppression of jasmonate (JA) and JA-Ile levels by deconjugation 43 and hydroxylation via IAA-ALA RESISTANT3 (IAR3) and JASMONATE-INDUCED 44 OXYGENASE 2 (JOX2) under the control of JASMONATE INSENSITIVE 1 (MYC2). 45 This previously unknown regulatory switch is another part of the puzzle of the as yet 46 understudied role of JA in pattern triggered immunity. The extensive coverage of the 47 Arabidopsis proteome in various biological scenarios presents a rich resource to plant 48 biologists that we make available to the community.49 50 51 52 53 Proteome biology is receiving increasing interest in the last years but still proves difficult 54 on a genome wide scale in plants. The proteome is the most fundamental active 55 determinant of an organism's phenotype and its landscape is large, complex and 56 dynamic, entailing changes in protein abundance, interaction, post translational 57 modification (PTM) and sub-cellular localization. 58 Steady state protein abundance at a certain time point is to a considerable part 59 determined by the abundance of its transcript and the latter's translation rate. Synthesis 60 is however only half of the equation governing protein abundance, indeed Arabidopsis 61 has more than 600 F-box proteins as components of diverse E3 ubiquitin ligase 62 complexes that direct protein degradation. Newer evidence has shown that post 63 transcriptional and translational mechanisms (Ponnala et al., 2014; Merchante et al., 64 2017) and...

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Co-regulation map of the human proteome enables identification of protein functions

Cited by 125 publications

References 83 publications

mRNAsi Index: Machine Learning in Mining Lung Adenocarcinoma Stem Cell Biomarkers

mRNAsi Index: Machine Learning in Mining Lung Adenocarcinoma Stem Cell Biomarkers

Intimate Relations—Mitochondria and Ageing

Reshaping of theArabidopsis thalianaproteome landscape and co-regulation of proteins in development and immunity

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