Peer review of: Core functional nodes and sex-specific pathways in human ischaemic and dilated (2020)

Reviewers' comments: 

 

Reviewer #2 (Remarks to the Author): 

 

Summary 

The manuscript describes proteomic and metabolomic datasets from patients with ischemic and 
dilated cardiomyopathy relative to controls. There is a paucity of high-quality human heart failure 
proteomic data in the literature, and existing datasets have often been used in furtherance of 
specific hypotheses, largely relegated to supplements. Human HF proteomes, therefore, remain 
undercharacterized, particularly as it pertains to etiological and sex differences. Any study that 
addresses these lucidly would be a welcome advance. 

 

Evaluation Overview 

I have little doubt that the authors are sitting on a treasure trove of data. My primary 
disappointment is that the data is poorly showcased by the manuscript, which appears to be written 
as a short communique. Notwithstanding the potential impact of the human datasets, the 
manuscript, as currently laid out and written, would encounter headwinds at a middling proteomics 
journal, as the data presentation is quite awkward and opaque. This not simply an issue of 
“cosmetics”, as I understand that researchers can reasonably differ on how to present complex data 
sets. There are also quality control novelty issues. Therefore, I cannot recommend the manuscript 
for publication in its present form. 

 

Strength: The authors have provided a link that allows the reader to interact with the dataset and 
observe pathways and networks. The reviewer appreciates the care taken with the layout of the 
website at https://mengboli.shinyapps.io/heartomics/. 

 

Major Issues. 

1. Notwithstanding that the journal is called Nature Communications, the choice of reporting the 
study as a short communique, rather than a regular research article, is problematic. The lay of the 
research landscape is not adequately framed, the data itself is given short shrift in the figures and 
the text his highly descriptive rather than quantitative. Essentially the figures themselves are more 
metadata than actual data (particularly fig 2). The data is confined largely to the supplements. 
Finally, posited new findings are not adequately contextualized, given that there are likely hundreds 
of cardiac profiling studies that have preceded this one. 

 

2. The schematic in figure 1, does not do either the data workflow or the patient cohort justice. 
Firstly, the nature of the patient cohort should be addressed separately in a more detailed table. 
More on that later. The proteomic and metabolomic workflow is highly generic. The pictogram 
essentially says “we did proteomics and metabolomics”. It does not say what kind or proteomics 
(label-free? TMT?) or metabolomics (LC? GC?, a combination?). The green balloon says “Data 
processing & data analysis”. It does not say what kind of analysis (PCA? Statistical tests? Pathways? 
Networks?). If the point is to encapsulate the meat of the study in one glance, it could be greatly 
improved. 

 

3. On to patients. Figure 1b and 1c are insufficient documentation of the patient cohort. Ideally, this 
would be done in two ways. The manuscript would benefit from a table summarizing the patient 
vital statistics (height, weight, sex, race) but most importantly, documenting the cardiac functional 
parameters that led to their inclusion. What was the ejection fraction? A supplement containing 
detailed table documenting each patient, with vital statistics, height, age, weight, sex, ejection 
fraction, fractional shortening, left ventricle internal diameter, drug regimen, smoking history, etc. 
would also be useful. 

 

4. These major characteristics of the cohort should be incorporated into the text. 

 

5. I took a look at the dataset submission. The authors have submitted the raw data files. They have 
also submitted maxquant archive with all the pertinent peptide and protein particulars. What is 
lacking (this is common to many submissions) is a simple table of protein quantitation (e.g. sample # 
vs protein accession), essentially equivalent to a GEO matrix file that any user can open in excel or 
any other data analysis program. This is key if the dataset is to be mined as a resource to the broader 
community and not gather dust. 

 

 

6. The proteomic and metabolomic methods could use some fleshing out. As written, it is not clear 
how the metabolites were identified. MRM yes, but was this against a previously compiled database 
of standards? a search? What were the instrument settings, tolerances, etc.? 

For the proteomics section what were the MS1 and MS2 mass tolerances. It’s not clear what is being 
quantified. If the authors are using Maxquant, they using iBAQ or LFQ intensities? 

 

7. Particularly in the proteome, is there any missing data? What was its distribution? How much 
missing data would prompt exclusion of protein from the analysis? Was missing data imputed? If so 
how? 

 

8. There is no 10,000 ft view or data QC in the form of a principal component analysis (PCA). How 
noisy are the data sets? How different are the normal, ICM and DCM proteomes and metabolomes. 
Does diagnosis track with the first principal component? 

 

9. The authors are to be commended for going with a statistical framework like Limma which 
increases power. But they have chosen to go with binary comparisons (Limma modified t-test) and 
volcano plots, when the experimental design screams for a Limma/ANOVA multigroup comparison. 
This would maximize power by providing more data points for modeling. More importantly, though, 
it would allow the authors use an intuitive p-value-filtered, hierarchically-clustered heatmap that 
would showcase clusters defining the similarities and differences between normal, ICM and DCM 
groups. This would be a substantial improvement over the Venn diagrams in figure 2a, which are 
more metadata than data. Volcano plots are actual data but don’t show the relationships between 
ICM and DCM. For instance, in the current analysis there are 135 differentially expressed proteins 
shared between ICM vs. Donor and DCM vs Donor. Are they regulated in the same direction for each 
etiology? Presumably a mix of up and down. In a Multigroup LIMMA/ANOVA-heatmap layout that 
would jump out. The same goes for the metabolites 

 

10. Figure 2b is a highly uninformative display of metadata, based on the supplements. It shows the 
overlap between protein and metabolite pathways. Indeed integration of metabolite and protein 
data is difficult, but there are more sophisticated publically available tools that would likely help the 
authors substantially. Such tools allow you to load protein and/or gene, and metabolite levels and 
perform combined pathway enrichment and topological analysis. An example of this would be 
MetaboAnalyst. Where metabolite and protein data corroborate each other inference is 
strengthened. If performed downstream of cluster analysis as described above, the insights could be 
great. 

 

11. Or at the very minimum, the authors could find a way to present the tabular data in their 
supplements graphically in the manuscript, even if it’s as simple as a bar graph ranked by –log(p-
value) 

 

12. Because the authors have eschewed the traditional article format they do not adequately 
contextualize the significant body of established work on the link between both circulating and local 
cardiac levels of thyroid hormone and heart failure. This would include (but not be limited to) work 
suggesting a local cardiac T3 deficiency. E.g. the PNAS paper from several years ago showing that 
cardiac overexpression of the deiodinase rescues cardiac function while not affecting hypertrophy in 
a mouse TAC model. 

 

Trivieri, M.G., Oudit, G.Y., Sah, R., Kerfant, B.G., Sun, H., Gramolini, A.O., Pan, Y., Wickenden, A.D., 
Croteau, W., Morreale de Escobar, G., et al. (2006). Cardiac-specific elevations in thyroid hormone 
enhance contractility and prevent pressure overload-induced cardiac dysfunction. Proceedings of 
the National Academy of Sciences of the United States of America 103, 6043-6048. 

https://www.pnas.org/content/103/15/6043 

 

This reviewer is not affiliated in any way. So high cardiac T4 in the authors’ study might be consistent 
with a bottleneck caused by low cardiac de-iodinase activity. It is up to the authors to make the case 
for the novelty of their finding. 

 

13. In figure 3. The authors have performed network analysis on correlated proteins and metabolites 
to find co-regulated nodes. The networks, as presented, are OK, but again there is perhaps a missed 
opportunity really chunk down the data and present it in a graphically intuitive manner. First, there 
is nothing wrong with a correlation network, but we now all have access the latest StringdB 
databases https://string-db.org/ where the authors could build functional association networks 
based on gene correlation, protein-protein interaction, lit mining, and curated pathway databases. 
The resulting STRING score is far more robust than correlation alone. Secondly, the network of 135 
proteins surely has a few major functional submodules that can be extracted with tools like by 
network Markov Clustering (quite easily done in Cytoscape). Finally, would it not be more intuitive to 
create a single network, layout the major submodules that comprise the 135 DE proteins and color 
code the node body as the expression level in DCM the node periphery coded by expression in ICM? 
I guess I find the layout of 3a and 3b to be less optimally informative. I do understand that the 
networks include metabolites, but nevertheless. 

 

14. Enrichment of programs in females relative to males is addressed. But again this reviewer craves 
deeper understanding. The authors do not present either a PCA or a factor-ANOVA that might help 
describe the relative contributions of diagnosis, sex, or race to the overall variance in the data. Sum 
of squares analysis? F-ratios?. Again, my point with respect to figure 2 apply to 3cd and e. I 
suspected a multigroup comparison (2- or 3-way ANOVA) organized as a heatmap would be more 
intuitive. 

 

In closing, though I realize the critique might come across as “you should analyze the data my way”, 
that’s not really my intent. Rather, it is meant as a roadmap to address one of the major limitations 
of the present manuscript – that the bulk of the data is confined to the supplements and not 
adequately summarized in the manuscript. I do not feel like I have a grip on the extent of the 
similarities and differences between Donors, ICM, and DCM patients let alone how each is impacted 
by sex or race or any other aspect of patient history. This is partly due to figure composition and 
layout and partly due to the abbreviated form of the manuscript that offers little context as to how 
this dataset distinguishes itself from prior heart failure profiling studies. 

 

Minor Points 

This reviewer is confused. I thought iRT peptides were used in the context of MRM or DIA. How are 
they used in a a DDA workflow? 

 

 

 

Reviewer #3 (Remarks to the Author): 

 

Specific Comments to Authors 

 

1. Lines 32-34. Actually, there are now a fair number of papers using endomyocardial biopsy to 
assess functional genomics in the nonfailing and failing human heart, and even more extensive 
studies in explanted human hearts from prospectively maintained biobanks, with tissue taken at the 
time of transplantation or organ donation and immediately immersed in liquid N2 as in this study. 
Most of these studies dealt with various types of RNA expression rather than proteomics, however. 
There have been proteomic analyses published from explanted nonfailing and DCM hearts (e.g. 
Colak et al, PLoS One 2016; not referenced in current manuscript), but this paper represents the 
largest series and also looks at ischemic cardiomyopathy. 

 

2. Lines 39-41. Perturbed thyroid hormone signaling has been reported before in IDC human LVs. 

 

3. Methods, line 4 "These samples are not post-mortem." These are heart tissue samples removed 
immediately from hearts explanted at the time of cardiac transplantation or organ donation and 
placed immediately in liquid N2. This approach to obtaining and investigating human cardiac tissue 
samples has been ongoing and published extensively for nearly 40 years, since the early years of 
cardiac transplantation. "Not post mortem" is a bit misleading since the organ donors are brain 
dead, and the transplant recipient hearts are headed for the research lab and pathology 
department. It's true that the cardiac tissue is quite viable in a biologic sense b/c of the preservation 
methods uses in situ and on explantation, but there is no reason to declare that the methods here 
are unique or that "Our understanding of heart failure (HF) has been limited by lack of access to pre-
mortem human cardiac tissue." 

 

4. I could not find any information on baseline characteristics of the hearts used. At a minimum this 
would include by-group mean or median age, sex, race, LVEF, NYHA Class, medication classes and 
storage time. This information could be supplied in a Supplementary table.