Dynamic multi-omics and mechanistic modeling approach uncovers novel mechanisms of kidney fibrosis progression
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Abstract
Kidney fibrosis, characterized by excessive extracellular matrix deposition, is a progressive disease that, despite affecting 10% of the population, lacks specific treatments and suitable biomarkers. This study presents a comprehensive, time-resolved multi-omics analysis of kidney fibrosis using an in vitro model system based on human kidney PDGFRβ+ mesenchymal cells aimed at unraveling disease mechanisms. Using transcriptomics, proteomics, phosphoproteomics, and secretomics we quantified over 14,000 biomolecules across seven time points following TGF-β stimulation. This revealed distinct temporal patterns in the expression and activity of known and potential kidney fibrosis markers and modulators. Data integration resulted in time-resolved multi-omic network models which allowed us to propose mechanisms related to fibrosis progression through early transcriptional reprogramming. Using siRNA knockdowns and phenotypic assays, we validated predictions and regulatory mechanisms underlying kidney fibrosis. In particular, we show that several early-activated transcription factors, including FLI1 and E2F1, act as negative regulators of collagen deposition and propose underlying molecular mechanisms. This work advances our understanding of the pathogenesis of kidney fibrosis and provides a resource to be further leveraged by the community.
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Referee #3
Evidence, reproducibility and clarity
In this study the authors sought to identify novel mechanisms underlying the progression of kidney fibrosis, by activating myofibroblast formation of a human kidney fibroblast cell line with TGF-beta, and collecting a time-series data set of transcriptome, proteome, phosphoproteome and secretome. They then performed a number of computational analyses to identify the key pathways and regulators that were driving the TGF-beta mediated responses in the early and late time points. They further validated several candidates experimentally with siRNA knockdowns, confirming FLI1 and E2F1 as two primary suppressors for myofibroblast …
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Referee #3
Evidence, reproducibility and clarity
In this study the authors sought to identify novel mechanisms underlying the progression of kidney fibrosis, by activating myofibroblast formation of a human kidney fibroblast cell line with TGF-beta, and collecting a time-series data set of transcriptome, proteome, phosphoproteome and secretome. They then performed a number of computational analyses to identify the key pathways and regulators that were driving the TGF-beta mediated responses in the early and late time points. They further validated several candidates experimentally with siRNA knockdowns, confirming FLI1 and E2F1 as two primary suppressors for myofibroblast activation.
Major comments: while all the experiments and data collections appeared to be carried out carefully, all data essentially came from one human PDGFRβ+ cell line derived from a previous study. Can this cell line fully represent the fibroblast populations in human kidneys? I could not find much information such as donor age, sex, or clinical conditions of the donor. It is unclear how much the cell line has been passaged, what is the level of clonality or the level of replication-induced senescence. How can we ensure that the mechanisms identified from one single cell line are robust and generalizable, truly representative of common kidney fibroblast cells or fibroblasts in general? The amount of multi-omics data collection was quite impressive, and I don't think it is realistic to repeat all those data generation experiments across multiple cell lines. Nonetheless, I feel that it is important to selectively validate some of the key findings on additional cell lines. On a related note, myofibroblast activation can be different between male and female in vivo and in vitro (https://www.biorxiv.org/content/10.1101/2024.10.02.615251v1.abstract). Is any of the findings in this study sex specific?
Minor comments:
Results section 2.1. Authors state "Specifically, we observed the activation of myofibroblast-specific gene expression as the fibrotic process progresses linking long-term patient data with in vitro data obtained over the course of hours". However, the transcriptomic data (Figure 1F) shows very low # of hits for these myofibroblast specific genes. Does this indicate that these cells are already in the myofibroblast state and that this is a model for TGFB stimulation of myofibroblasts? More clarification on this and what is being modeled (including starting and ending state of these cells) is needed. The authors tend to overstate how this in vitro model reflects complex disease phenotypes. The main issue is what is being modeled, which appears to be mostly TGF-B induced ECM production and possibly enhanced myofibroblast state signatures? On page 23: "To summarize, the integration of multi-omic data into time-resolved network models of early and late fibrotic responses revealed dynamic shifts in signaling pathways, transcription factor activities, and protein interactions, highlighting the temporal complexity of kidney fibrosis progression and identifying both well-known and novel regulatory factors for further investigation." Here it is not clear that the timeline used in this paper is recapitulating "late fibrotic processes" seen in vivo nor how it truly relates to kidney fibrosis progression. Also section 2.4: "To further validate the role of these transcription factors in the development of fibrotic diseases...". This is not something that this in vitro model can achieve. In section 2.4, the paragraph discussing E2F1 is poorly written, over uses the word "activity", and is not clear. Figure 3E: it is a bit of surprise to see HDAC1 being a node there connecting RELA to KLF4/FLI1. HDAC1 deacetylates histones and many transcription factors, hence the effects are likely to be very broad. Can the authors explain why it has such a high specificity in this context?
Significance
Overall, this is a nice study with several strengths. The time-series multi-omics data along the course of myofibroblast activation generated in this study is very impressive. While transcriptomic data collection is quite routine, the proteomics, phosphoproteomics, and secretomics data really lifted the significance of this study to another level. As demonstrated in their study, these data allowed the authors to carry out much more sophisticated computational analyses (which is another major strengths of this study), examining the responses in terms of gene regulation, protein production, modification, secretion at the early and late stages of fibrotic activation, formulating a mechanistic model. This study managed to get much closer to determining causal and direct regulation, compared with many other previous studies staying at the level of correlation and enrichments. Finally, some of the key regulators identified in their analyses were validated experimentally by siRNA knockdowns.
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Referee #2
Evidence, reproducibility and clarity
Summary:
The authors presented a comprehensive, time-resolved multi-omics analysis of kidney fibrosis using an in vitro model system based on human kidney PDGFRβ+ mesenchymal cells aimed at unraveling disease mechanisms. This research advanced our understanding of the pathogenesis of kidney fibrosis. However, this reviewer has several concerns.
Major comments:
1.Why does the 0.08h group not exist in Fig S1? What's more, the detection of ECM appears to be insufficient as it only reveals COL1 expression. 2.Fig S2A shows that p-smad2 has 11 bands, whereas Smad2 has 12 bands. Moreover, the repeatability of the two repeated trials is not …
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Referee #2
Evidence, reproducibility and clarity
Summary:
The authors presented a comprehensive, time-resolved multi-omics analysis of kidney fibrosis using an in vitro model system based on human kidney PDGFRβ+ mesenchymal cells aimed at unraveling disease mechanisms. This research advanced our understanding of the pathogenesis of kidney fibrosis. However, this reviewer has several concerns.
Major comments:
1.Why does the 0.08h group not exist in Fig S1? What's more, the detection of ECM appears to be insufficient as it only reveals COL1 expression. 2.Fig S2A shows that p-smad2 has 11 bands, whereas Smad2 has 12 bands. Moreover, the repeatability of the two repeated trials is not very excellent. Additionally, why not look at the phosphoproteomics data to see how p-smad2 changes? 3.The early-activated transcription factors screened by the author, including FLI1 and E2F1, act as negative regulators of collagen deposition, needs further verification.
Minor comments:
1.The graphical abstract and the abstract don't agree on how many time points there are-is it seven or eight? 2.For every group in the multi-omics, what is the n value?
Significance
The insights gained from this study not only advance our understanding of kidney fibrosis but also pave the way for the development of novel therapeutic strategies targeting this challenging condition. There is still much to be done, though. For instance, the author's screening of early-activated transcription factors, such as FLI1 and E2F1, which function as negative regulators of collagen deposition, requires additional confirmation.
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Referee #1
Evidence, reproducibility and clarity
Summary
This study showed measurements and integration of time-series multiple omics data of the human kidney PDGFR beta+ cells responding to TGF-beta stimuli. The authors also presented key pathways that were inferred based on estimating activities of TFs and kinases, and confirmed by knockdown experiments whose phenotypes can be observed by means of imaging.
Major concerns
- The content of Discussion is too thin. Particularly, it is uncommon to see a discussion section with no citations like this manuscript. Cite related studies and compare with the own results so that the authors can argue originality and novelty of this work. I …
Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.
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Referee #1
Evidence, reproducibility and clarity
Summary
This study showed measurements and integration of time-series multiple omics data of the human kidney PDGFR beta+ cells responding to TGF-beta stimuli. The authors also presented key pathways that were inferred based on estimating activities of TFs and kinases, and confirmed by knockdown experiments whose phenotypes can be observed by means of imaging.
Major concerns
- The content of Discussion is too thin. Particularly, it is uncommon to see a discussion section with no citations like this manuscript. Cite related studies and compare with the own results so that the authors can argue originality and novelty of this work. I also see some citations in Results. Usually it is opposite: little citations in Results section and many citations in Discussions.
- Put more emphasis on presenting biological relevances in order for readers to easily recognize them. I guess that Figs. 4C and 4F are examples of such biological findings.
- Draw the whole picture(s) of the integrated networks, not only subnetworks. If too much complicated, the complexity itself will be important information for readers.
- On SMAD2:
4a) The responses of p-SMAD2 in Fig. S2 are remarkably different in the two batches. The authors should discuss the reason of these outcomes. Which of the two batches exhibited similar responses to the phosphoproteome data?
4b) What possible reasons do authors think about that SMAD2/3 are not included in the transcriptional regulatory networks presented in Figs. 3 and 4 in spite of their importance in the TGFbeta signaling? Should be argued.
4c) What molecular mechanism can cause the increase in SERPINE1 expression dependent on TGFbeta? The mechanism may involve SMAD2/3 but neither presented nor argued. Should be clarified.
4d) It seems inconsistent that knockdown of the early-activated TFs cause extensive ECM accumulation in the knockdown experiment presented in Fig. 4B. Did the authors see suppression of ECM accumulation by knockdown of SMAD2/3? Should be presented.
Minor concerns
- Fig. 1D: Numbers in the Venn diagram of 'proteomics technologies' do not match with the numbers in another Venn diagram on the right hand side. Should be corrected or explained.
- Fig. 2B: 'INFalpha' should be IFNalpha, so is 'INFgamma'.
- Fig. 2B, Fig. S4C: What does the sign of 'Pathway enrichment score' mean? How is it calculated? Should be explained.
- Do not fit curves to data that should be drawn in line graphs (e.g. Figs. 3F, 4E, 4G etc.).
- How did the authors plot the regression curves presented in Fig. 4D? Should be clarified.
- What is 'PKN'? Maybe 'Prior Knowledge Network', but clearly spelled out when it first appears.
- Did the PNK-nodes in the networks exhibit quantitative changes in any of the omics data?
- What do the axes of the heatmaps mean in Fig. S3A? Why are there more categories than total sample numbers? Should be clarified.
Significance
The omics data were well measured under appropriate quality controls. Hence, this study will attract interests from specialists of kidney fibrosis and systems biologists. But there still remains concerns regarding arguments and data presentation of the manuscript.
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