Design and Development of a qPCR-Based Mitochondrial Analysis Workflow for Medical Laboratories

Round 1

Reviewer 1 Report

The authors provide a manuscript on a new software-based analysis workflow designed for the calculation/analysis of mtDNA in a clinical setting. The workflow is designed to assist in clinical practice and reference data demonstrates effective linear regression plots between mtDNAcn/mtDNAdr and age. Having such a technique available in the clinical setting is of great value and would provide relevant information in the diagnosis/treatment of mitochondrial-linked pathologies.

I have several questions/comments below:

1.)  The author’s state that their workflow helps with the calculation of “mitochondrial parameters”, yet only 1 parameter of mitochondria is used (mtDNA). This wording is confusing and should be changed throughout the manuscript to state that it is measuring mtDNA, unless other parameters will be added in the future (see comment 2).

2.)  To follow up on the 1^st comment, the manuscript highlights the ability of PlateFlow to quantify mtDNA (cn & dr) values, but is it possible for other measures of mitochondrial parameters (i.e. membrane polarization (JC-1), mitochondrial function (ATP production, proton leak), mitochondrial mass) to be adapted in the software? If so, this should be added to the text of the manuscript.

3.)  Have the authors checked for other confounders besides age in their regression model? Smokers and heavy alcohol drinkers would also have potentially dysregulated mtDNA levels. Is there an ability to add additional confounders to their software analysis? If so, this should be added to the text of the manuscript.

Minor comments:

1.)  The figure legends for Figures 4, 5, & 6 require more information for the reader to clearly understand what is being presented. Please state that 19427, 19428, and 19429 are 3 individual patients (correct?) and what the STDMix contained.

2.)  Why did STDMix have such low mtDNAcn? I cannot find in the text of the submitted manuscript what these samples contained.

3.)  Why was the mtDNAdr not 100% for H20? Was non-specific primer binding not excluded from the analysis?

Author Response

Thank you very much for your review. Here are my responses:

1.) The two parameters we are refering to are copy number and deletion ratio. You are correct that both parameters are determined from mtDNA. Therefore the wording was changed from "mitochondrial parameters" to "mtDNA parameters" to clarify.

2.) As the software currently focusses on qPCR, there are immediate plans to integrate the mentioned parameters in the software. They might be measured however in the laboratory as separate tests and combined in the laboratory information system. Out of curiosity, I have forwarded this questions to the laboratory, but have not received an answer yet.

3.) It is currently not possible to add additional confounders as the patient information available to the laboratory is too limited. Also for the specific variables you mentioned, I'm wondering if these should be considered confounders or risk factors?

Minor comments:

1.) I have extended the figure legends to make more clear that this is example data only. In addition I explained the data more in the text itself.

2.) The STDMix samples are calibrator candidates which are adjusted to have a copy number of exactly 1. This is therefore expected. I extended the text description to make this more clear.

3.) This is likely a combination of a suboptimal calibrator used and bad Ct cut-off values. The figure was inititially only meant as a screenshot to show the user interface and not to discuss experiment result. However as this was not made clear, I added a disclaimer in the text and also discuss the H2O value.

Reviewer 2 Report

Paper deals with important topics in Medics. The authors have developed a prototype of the qPCR-based Mitochondrial Analysis Workflow for Medical Laboratories.   

It has a logical structure. The paper is well-written and scientifically sound. The experimental section is good.

However, I have a number of suggestions:   

Suggestions:

1. Abstract should be extended by the obtained results in step by step model. 

2. I would suggest optimizing images in section #3, as they are too big for now.

3. The paper hasn’t had any Discussions.

4. The conclusion section should be extended with limitations and clearer explained advantages of the proposed method.

5. A lot of references are outdated and unlinked. Please fix it by using 3-5 years old papers in high-impact journals

Author Response

Dear Reviewer,

thank you for your time and your kind review.

Answers:

1. I'm not completely sure what you mean by "step by step model". As I'm already at the word count limit for the abstract, I'm hesitant to extend the abstract much further as this would mean removing other parts, which seem equally relevant to me.
2. Image sizes have been reduced as suggested
3. There is some Discussion as part of the Evaluation Section. I changed the heading to reflect this better
4. I extended the section as suggested
5. I replaced several references with newer ones as suggested. For other references this seemed not adequate as they were referencing primary sources or there was no recent good quality article.