Wilhelm et al. reply

Wilhelm, Mathias; Hahne, Hannes; Savitski, Mikhail M.; Marx, Harald; Lemeer, Simone; Bantscheff, Marcus; Küster, Bernhard

doi:10.1038/nature22294

Cited by 10 publications

(9 citation statements)

References 4 publications

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“…In contrast, RNA and protein expression of EIF4A3 (a DEAD‐box RNA helicase involved in translation initiation) only spanned sixfold and 11‐fold between tissues and showed no correlation. We note that cases such as the latter are merely the result of technical variation in the measurement or genuinely similar expression levels in most tissues reflecting the roles of these proteins in central biological processes in all tissues (Appendix Fig S16; Wilhelm et al , ).…”

Section: Resultsmentioning

confidence: 96%

“…The relationship between mRNA and protein expression has been studied extensively over the past years and there continues to be debate in terms of how the various correlations that can be computed may be interpreted in terms of technical artefacts or biological meaning (Liu et al , ; Fortelny et al , ; Franks et al , ; Wilhelm et al , ). While it is beyond the scope of the current study to attempt to reconcile the different views, the extensive data on both mRNA and protein expression provided in this resource should help to eventually bring clarity.…”

Section: Resultsmentioning

confidence: 99%

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A deep proteome and transcriptome abundance atlas of 29 healthy human tissues

Wang

Eraslan

Wieland³

et al. 2019

Molecular Systems Biology

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599

558

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Genome‐, transcriptome‐ and proteome‐wide measurements provide insights into how biological systems are regulated. However, fundamental aspects relating to which human proteins exist, where they are expressed and in which quantities are not fully understood. Therefore, we generated a quantitative proteome and transcriptome abundance atlas of 29 paired healthy human tissues from the Human Protein Atlas project representing human genes by 18,072 transcripts and 13,640 proteins including 37 without prior protein‐level evidence. The analysis revealed that hundreds of proteins, particularly in testis, could not be detected even for highly expressed mRNA s, that few proteins show tissue‐specific expression, that strong differences between mRNA and protein quantities within and across tissues exist and that protein expression is often more stable across tissues than that of transcripts. Only 238 of 9,848 amino acid variants found by exome sequencing could be confidently detected at the protein level showing that proteogenomics remains challenging, needs better computational methods and requires rigorous validation. Many uses of this resource can be envisaged including the study of gene/protein expression regulation and biomarker specificity evaluation.

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Section: Resultsmentioning

confidence: 96%

Section: Resultsmentioning

confidence: 99%

A deep proteome and transcriptome abundance atlas of 29 healthy human tissues

Wang

Eraslan

Wieland³

et al. 2019

Molecular Systems Biology

Self Cite

599

558

View full text Add to dashboard Cite

show abstract

“…It is believed that protein amounts depend mainly on mRNA amounts under steady-state conditions [ 4 , 8 ], although the correlation value is still a matter of discussion [ 6 , 60 , 61 ]. Thus, a positive correlation between RA and PA datasets is expected.…”

Section: Resultsmentioning

confidence: 99%

A Trans-Omics Comparison Reveals Common Gene Expression Strategies in Four Model Organisms and Exposes Similarities and Differences between Them

Forés-Martos

Forte

Garcı́a-Martı́nez

et al. 2021

Cells

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The ultimate goal of gene expression regulation is on the protein level. However, because the amounts of mRNAs and proteins are controlled by their synthesis and degradation rates, the cellular amount of a given protein can be attained by following different strategies. By studying omics data for six expression variables (mRNA and protein amounts, plus their synthesis and decay rates), we previously demonstrated the existence of common expression strategies (CESs) for functionally related genes in the yeast Saccharomyces cerevisiae. Here we extend that study to two other eukaryotes: the yeast Schizosaccharomyces pombe and cultured human HeLa cells. We also use genomic data from the model prokaryote Escherichia coli as an external reference. We show that six-variable profiles (6VPs) can be constructed for every gene and that these 6VPs are similar for genes with similar functions in all the studied organisms. The differences in 6VPs between organisms can be used to establish their phylogenetic relationships. The analysis of the correlations among the six variables supports the hypothesis that most gene expression control occurs in actively growing organisms at the transcription rate level, and that translation plays a minor role. We propose that living organisms use CESs for the genes acting on the same physiological pathways, especially for those belonging to stable macromolecular complexes, but CESs have been modeled by evolution to adapt to the specific life circumstances of each organism.

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“…It is believed that protein amounts depend mainly on mRNA amounts under steady-state conditions [4,8], although the correlation value is still a matter of discussion [6,58,59].…”

Section: Comparisons and Correlations Between Variablesmentioning

confidence: 99%

A trans-omics comparison reveals common gene expression strategies in four model organisms and exposes similarities and differences between them

Forés-Martos

Forte

Garcı́a-Martı́nez

et al. 2020

Preprint

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The ultimate goal of gene regulation should focus on the protein level. However, as mRNA is an obligate intermediary, and because the amounts of mRNAs and proteins are controlled by their synthesis and degradation rates, the cellular amount of a given protein can be attained following different strategies. By studying omics datasets for six expression variables (mRNA and protein amounts, plus their synthesis and decay rates), we previously demonstrated the existence of common expression strategies (CES) for functionally-related genes in the yeast Saccharomyces cerevisiae. Here we extend that study to two other eukaryotes: the distantly related yeast Schizosaccharomyces pombe and cultured human HeLa cells. We also use genomic datasets from the model prokaryote Escherichia coli as an external reference. We show that CES are also present in all the studied organisms and the differences in them between organisms can be used to establish their phylogenetic relationships. The phenogram based on 6VP has the expected topology for the phylogeny of these four organisms, but shows interesting branch length differences to DNA sequence-based trees. The analysis of the correlations among the six variables supports that most gene expression control occurs in actively growing organisms at the transcription rate level, and that translation plays a minor role in it. We propose that all living cells use CES for the genes acting on the same physiological pathways, especially for those belonging to stable macromolecular complexes, but CES have been modeled by evolution to adapt to the specific life circumstances of each organism. The obtained phenograms may reflect both evolutionary constraints in expression strategies, and lifestyle convergences.

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Wilhelm et al. reply

Cited by 10 publications

References 4 publications

A deep proteome and transcriptome abundance atlas of 29 healthy human tissues

A deep proteome and transcriptome abundance atlas of 29 healthy human tissues

A Trans-Omics Comparison Reveals Common Gene Expression Strategies in Four Model Organisms and Exposes Similarities and Differences between Them

A trans-omics comparison reveals common gene expression strategies in four model organisms and exposes similarities and differences between them

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