Quantifying absolute gene expression profiles reveals distinct regulation of central carbon metabolism genes in yeast
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Abstract
In addition to controlled expression of genes by specific regulatory circuits, the abundance of proteins and transcripts can also be influenced by physiological states of the cell such as growth rate and metabolism. Here we examine the control of gene expression by growth rate and metabolism, by analyzing a multi-omics dataset consisting of absolute-quantitative abundances of the transcriptome, proteome, and amino acids in 22 steady-state yeast cultures. We find that transcription and translation are coordinately controlled by the cell growth rate via RNA polymerase II and ribosome abundance, but they are independently controlled by nitrogen metabolism via amino acid and nucleotide availabilities. Genes in central carbon metabolism, however, are distinctly regulated and do not respond to the cell growth rate or nitrogen metabolism as all other genes. Understanding these effects allows the confounding factors of growth rate and metabolism to be accounted for in gene expression profiling studies.
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Response to Reviewer #3 (Public Review):
[...] In general, while the findings are interesting and seemed to be mainly supported by the evidence, the manuscript is complicated to read. Evidence is scattered throughout the manuscript and needs to be gathered and compiled by the reader to check the results.
Response: we have re-structured the manuscript, re-written many sections of the text, and moved figures so that the flow and logic is improved. We have also added a section of text which describes the goals and strategy of the study (lines 118-130). Some of the writing is remiss: Figures 6A and 6C have the same caption and different graphs. Response: we apologize for this error, this has now been fixed (now Fig 7A and Fig 7-figure supplement 1).
It is also not clear how the differential expression calculations in Figure 1C were done: …
Response to Reviewer #3 (Public Review):
[...] In general, while the findings are interesting and seemed to be mainly supported by the evidence, the manuscript is complicated to read. Evidence is scattered throughout the manuscript and needs to be gathered and compiled by the reader to check the results.
Response: we have re-structured the manuscript, re-written many sections of the text, and moved figures so that the flow and logic is improved. We have also added a section of text which describes the goals and strategy of the study (lines 118-130). Some of the writing is remiss: Figures 6A and 6C have the same caption and different graphs. Response: we apologize for this error, this has now been fixed (now Fig 7A and Fig 7-figure supplement 1).
It is also not clear how the differential expression calculations in Figure 1C were done: what are the two conditions being compared?
Response: 1-way ANOVA comparing all conditions, we have added this on line 83 and in the figure legend (now Fig 1B).
Figure 7 encapsulates what is learnt from this paper but needs a more informative caption describing the full metabolic lesson learnt.
Response: we have given a more detailed description of the model (now Figure 8) on lines 361-375 and in the figure legend.
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Response to Reviewer #2 (Public Review):
[...] The main message of this paper was not sufficiently clear because at different places of the manuscript the authors highlight different aspects:
Response: we have re-structured our manuscript to improve the flow and logic of the paper. We have also added section of text which describes the goals and strategy of the study (lines 118-130).
Based on the title it seems that the "distinct regulation" is the key aspect. Notably, however, this point has only a minor role in the manuscript itself.
Response: Indeed the “distinct regulation” of CCM genes is the key aspect, we have re-structured the manuscript so that this point is clearer.
In the abstract, it seems that the key aspect is a "framework", although after having read the paper it was not clear what the authors mean with the term.
Res…
Response to Reviewer #2 (Public Review):
[...] The main message of this paper was not sufficiently clear because at different places of the manuscript the authors highlight different aspects:
Response: we have re-structured our manuscript to improve the flow and logic of the paper. We have also added section of text which describes the goals and strategy of the study (lines 118-130).
Based on the title it seems that the "distinct regulation" is the key aspect. Notably, however, this point has only a minor role in the manuscript itself.
Response: Indeed the “distinct regulation” of CCM genes is the key aspect, we have re-structured the manuscript so that this point is clearer.
In the abstract, it seems that the key aspect is a "framework", although after having read the paper it was not clear what the authors mean with the term.
Response: We have re-written the text throughout the manuscript so that the unclear terminology “framework” is no longer used.
Later in the manuscript the authors also use the term "coarse-graining approach", but it was not clear whether this is the same as the "framework".
Response: they are not the same, and we have addressed this clarity issue by re-writing the text throughout the manuscript so that the unclear terminology “framework” is no longer used. The term “coarse-graining approach” has also been removed and instead a detailed description of the method used in this analysis is given (lines 402-415, 465-469).
Beyond, throughout the manuscript, the authors make the point that global physiological parameters (such as growth rate) determine gene and protein expression level. Even though this point is important and often overlooked, it has been made before in several papers, which the authors also cite. Thus, this aspect mostly provides confirmation of previous work. Finally, at the end of the introduction, where the authors refer to "our findings... ", it is unclear to which findings they particularly refer to.
Response: We have re-written the abstract to improve clarity.
The manuscript could be clearer in certain specific aspects:
- The paper uses lots of terms that are not well defined: For instance, it is not explained well what the authors mean by "metabolic parameters". I know metabolite concentrations, and metabolic fluxes, but I don't know what metabolic parameters are. It is also not explained well what is meant with "global control mechanisms" and what is meant by "augment".
Response: We have re-written the text so that the unclear terminologies “metabolic parameters., “global control mechanisms”, and “augment” have either been removed or defined clearly throughout the text.
- Similarly, this lack of clarity also exists when the authors step from a particular analysis (i.e. a correlation) to a conclusion statement. The hard evidence supporting particular statements is not sufficiently explained.
Response: We have re-structured the manuscript and re-written many sections in the text to better explain our data and analysis strategy, and the conclusions drawn. We have also added section of text which describes the goals and strategy of the study and also highlights that correlations do not imply direct causation (lines 118-130).
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Reviewer #3 (Public Review):
The authors present here a very interesting and thorough systems biology study of S. cerevisiae involving 22 steady state conditions with different growth rates and nitrogen sources. Proteomics and transcriptomics data, as well as intracellular amino acid concentrations, are gathered in a study that, if only for the sheer amount of data, is quite unique.
The authors use differential expression analysis, clustering algorithms and correlations to divide the genes and proteins studied into a small number of groups whose behaviour can be generally categorized. For a starter there is a small group (~10%) that map to central carbon metabolism and seems to be regulated by cues not covered in this study (growth rate and metabolic parameters involving amino acid and nucleotide availability). The rest of genes (90%) …
Reviewer #3 (Public Review):
The authors present here a very interesting and thorough systems biology study of S. cerevisiae involving 22 steady state conditions with different growth rates and nitrogen sources. Proteomics and transcriptomics data, as well as intracellular amino acid concentrations, are gathered in a study that, if only for the sheer amount of data, is quite unique.
The authors use differential expression analysis, clustering algorithms and correlations to divide the genes and proteins studied into a small number of groups whose behaviour can be generally categorized. For a starter there is a small group (~10%) that map to central carbon metabolism and seems to be regulated by cues not covered in this study (growth rate and metabolic parameters involving amino acid and nucleotide availability). The rest of genes (90%) seem to have their transcript and protein levels heavily determined by growth rate and/or amino acid metabolism. For different growth rates, the expression of these genes and corresponding proteins seemed to be very correlated, and dependent on the availability of translation and transcription machinery (RNA polymerase and ribosomes). For different nitrogen sources, gene expression seemed dependent on amino acid and nucleotide availability.
These general rules are insightful and can provide a much more informative way to analyze multiomics data sets, by e.g. accounting for expected over/under expressions due to growth rate changes. Indeed, the authors attempt this for two cases: a distantly related yeast (S. pombe) and a human cancer cell model. While they are able to show that most transcript variation for S. pombe seems to be due to growth rate changes, the rest of the inferences do not seem very informative.
In general, while the findings are interesting and seemed to be mainly supported by the evidence, the manuscript is complicated to read. Evidence is scattered throughout the manuscript and needs to be gathered and compiled by the reader to check the results. Some of the writing is remiss: Figures 6A and 6C have the same caption and different graphs. It is also not clear how the differential expression calculations in Figure 1C were done: what are the two conditions being compared? Figure 7 encapsulates what is learnt from this paper but needs a more informative caption describing the full metabolic lesson learnt.
In summary, the data presented here is a golden data set that will make a great contribution to science, the general rules are interesting and seemed to be supported by the data, but to be more useful to readers the writing of the paper could be made clearer.
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Reviewer #2 (Public Review):
Using budding yeast, the authors have generated transcriptome and proteome data for a series of experimental conditions, augmented with measurement of some amino acid abundances. These data are subjected to a number of correlation and enrichment analyses. Based on those, the authors put forward a verbal "model of information flow, material flow and global control of material abundance".
The main message of this paper was not sufficiently clear because at different places of the manuscript the authors highlight different aspects: Based on the title it seems that the "distinct regulation" is the key aspect. Notably, however, this point has only a minor role in the manuscript itself. In the abstract, it seems that the key aspect is a "framework", although after having read the paper it was not clear what the …
Reviewer #2 (Public Review):
Using budding yeast, the authors have generated transcriptome and proteome data for a series of experimental conditions, augmented with measurement of some amino acid abundances. These data are subjected to a number of correlation and enrichment analyses. Based on those, the authors put forward a verbal "model of information flow, material flow and global control of material abundance".
The main message of this paper was not sufficiently clear because at different places of the manuscript the authors highlight different aspects: Based on the title it seems that the "distinct regulation" is the key aspect. Notably, however, this point has only a minor role in the manuscript itself. In the abstract, it seems that the key aspect is a "framework", although after having read the paper it was not clear what the authors mean with the term. Later in the manuscript the authors also use the term "coarse-graining approach", but it was not clear whether this is the same as the "framework". Beyond, throughout the manuscript, the authors make the point that global physiological parameters (such as growth rate) determine gene and protein expression level. Even though this point is important and often overlooked, it has been made before in several papers, which the authors also cite. Thus, this aspect mostly provides confirmation of previous work. Finally, at the end of the introduction, where the authors refer to "our findings... ", it is unclear to which findings they particularly refer to.
The manuscript could be clearer in certain specific aspects:
The paper uses lots of terms that are not well defined: For instance, it is not explained well what the authors mean by "metabolic parameters". I know metabolite concentrations, and metabolic fluxes, but I don't know what metabolic parameters are. It is also not explained well what is meant with "global control mechanisms" and what is meant by "augment".
Similarly, this lack of clarity also exists when the authors step from a particular analysis (i.e. a correlation) to a conclusion statement. The hard evidence supporting particular statements is not sufficiently explained.
-
Reviewer #1 (Public Review):
Nielsen and colleagues describe a large new multi-ome database containing combinations of absolute mRNA quantities, proteome and amino acid concentrations in a set of 14 yeast populations grown in various conditions in chemostats. Apart from being a valuable resource for colleagues, analysis of the data confirms the results of several previous seminal studies.
For example, the authors confirm the relatively high correlation between transcript and corresponding protein abundance. Moreover, it is shown that for most genes, changes in transcript abundance related to manipulated changes in growth rate largely reflected the availability of RNA polymerase II. Interestingly, this was not the case for genes involved in central carbon metabolism, suggesting that these are regulated separately, likely to maintain the …
Reviewer #1 (Public Review):
Nielsen and colleagues describe a large new multi-ome database containing combinations of absolute mRNA quantities, proteome and amino acid concentrations in a set of 14 yeast populations grown in various conditions in chemostats. Apart from being a valuable resource for colleagues, analysis of the data confirms the results of several previous seminal studies.
For example, the authors confirm the relatively high correlation between transcript and corresponding protein abundance. Moreover, it is shown that for most genes, changes in transcript abundance related to manipulated changes in growth rate largely reflected the availability of RNA polymerase II. Interestingly, this was not the case for genes involved in central carbon metabolism, suggesting that these are regulated separately, likely to maintain the cells' ATP levels. Similarly, manipulation of growth through the use of different nitrogen sources led to changes in transcription that correlated with certain amino-acid-derived metabolites (including nucleotides), but not with RNAPolII levels. Genes involved in central carbon metabolism are again an exception to this rule.
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Evaluation Summary:
This study has generated a large amount of solid data in the form of a new multi-ome database containing combinations of absolute mRNA quantities, proteome and amino acid concentrations in a set of 14 yeast populations grown in various conditions in chemostats. Apart from being a valuable resource for colleagues, analysis of the data confirms the results of several previous seminal studies.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 agreed to share their name with the authors.)
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