Review Reports

Round 1

Reviewer 1 Report

The manuscript in presenting interesting data on the downregulation of Wnt signaling pathway in human dysmature cumulus cells revealed by NGS and used as a hallmark for evaluating oocyte quality.

There are some changes to be done to the paper.

Abstract: I would add the importance of this study as a background before the purpose.

Introduction:

• line 68-72 seems to duplicate the explanation of DCC
• at the end of the Introduction, there should be the aim of the study presented

References - editing - numbers are doubled for each reference 

The use of the English language is correct and there is no need for corrections.

The conclusions are supported by the data presented and the authors also present the possible use of this method as a tool for oocyte quality assessment.

 

 

Author Response

Point1: Abstract: I would add the importance of this study as a background before the purpose.

Response1: Thank you for providing these insights. In the Abstract, we have added the text as Background (line 27-29).

Point2: line 68-72 seems to duplicate the explanation of DCC

Response2: Thank you for pointing out. The following texts from line68-70 have been removed because the DCC explanatory document was duplicative.

Point3: at the end of the Introduction, there should be the aim of the study presented
Response3: Thank you for providing these insights. The purpose of this study was described in line85-88, but it may have been unclear, so we have revised the text (line 93-95).

Point4: References - editing - numbers are doubled for each reference 
Response4: Thank you for pointing out. All of the reference numbers were duplicated and have been corrected.

 

 

Reviewer 2 Report

To identify hallmarks for evaluating oocyte quality by investigating gene expression patterns in human cumulus cells surrounding oocytes.  The authors obtained cumulus cells from the cumulus-oocyte complex of infertile women treated with assisted reproductive technology.  Based on maturity level, the cumulus cells were classified into two categories, i.e., dysmature cumulus cell (DCC) and maturation cumulus cell.  DCCs were subjected to gene expression analysis using next-generation sequencing and compared with COCs that are in the process of maturation as controls.  The expression levels of genes involved in the Wnt signal/β-catenin pathway were significantly reduced in DCCs compared with those in control cells.  Moreover, the expression levels of genes involved in multiple pathways associated with apoptosis were also significantly reduced compared with those in control cells.  The authors concluded that DCCs showed significant decreases in apoptosis- and Wnt/β-catenin signaling-associated gene expression.  DCCs could be classified by morphological evaluation, and the method described in their study may be useful as an oocyte quality estimation tool.

I agree with the authors this is a unique study on human oocytes.  The manuscript should be circulated after revisions.

Major points:

1) what is the major difference between granulosa cells and cumulus cells?  The former contain only supported cells while the latter contain both oocytes and granulosa cells?

2) the authors need to explain why 2^nd-generation sequencing, not the first-generation sequencing, was necessary to draw any conclusions.

3) the authors need to explain why RPKM, ABNOVA, and GO were necessary and fit the study.

4) there should be TCF, APC, CCD2 or MYC analyzed in Fig 2.

Author Response

Point1: What is the major difference between granulosa cells and cumulus cells?  The former contain only supported cells while the latter contain both oocytes and granulosa cells?

Response1: You have asked an interesting question. Granulosa cells refer to mural granulosa cells and are the cells that exist around the follicle. On the other hand, cumulus cells refers to cumulus granulosa cells and are the cells that form the oocyte. Cumulus granulosa cells are heavily influenced by the oocyte, while mural granulosa cells are farther away from the oocyte and are less influenced by it.

 

Point2: The authors need to explain why 2nd-generation sequencing, not the first-generation sequencing, was necessary to draw any conclusions.

Response2: In this study, we comprehensively investigated gene expression patterns in human cumulus cells surrounding oocytes to identify features for assessing oocyte quality. Second-generation sequencing (NGS) can determine millions of sequences at once, which is suitable for the purpose of this study. On the other hand, first-generation sequencing (Sanger sequence) can only determine one sequence at a time and is not suitable for such analysis.

We explained this in the revised manuscript.

 

Line83-84

Using NGS, it is possible to comprehensively investigate gene expression in samples because it can determine millions of sequences at once.

 

Point3: The authors need to explain why RPKM, ABNOVA, and GO were necessary and fit the study.

Response3: The RPKM (Reads Per Kilobase per Million reads) values represent counts of reads mapping a feature (gene or exon). This value is commonly used as normalized expression value for gene expression analysis to allow comparison across samples for a particular gene, exon, or transcript, and also fit gene expression profiling analysis conducted in this study.

 

ANOVA ("Analysis of variance") compares the means of two or more independent groups in order to determine whether there is statistical evidence that the associated population means have significant differences. It is implemented in Strand NGS software as statistical tools to identify differentially expressed genes. ANOVA followed by post hoc analysis using Student-Newman-Keuls were necessary for statistical analysis of differentially expressed genes and fit our purpose of the analysis.

We revised manuscript to address this issue as follows:

 

Line 144-146

Differentially expressed genes in DCCs were identified based on a 2-fold change compared to the mean value of the control, and statistical significance based on p-value < 0.05, using one-way analysis of variance (ANOVA), followed by post hoc analysis using Student-Newman-Keuls.

 

GO analysis is a functional enrichment analysis of the significant genes based on biological process, molecular functions, and cellular components. It is helpful tool for identifying GO terms enriched for a given differentially expressed gene list and necessary to examine those biological processes, molecular functions, and cellular components that are impacted in the condition studied. The results of GO analysis suggested that genes whose function is related to Wnt signaling are significantly enriched, and it has been supported by additional pathway enrichment analysis. Based on these results, we presumed that these genes impair the transduction of the Wnt signaling pathway in the DCCs. Such analysis is necessary to discuss biological/functional meanings of the differentially expressed genes. We addressed the need for the GO analysis in the revised manuscript as follows:

 

Line 181-183

We performed GO analysis to examine the enrichment of biological processes, molecular function, and cellular components and identify biological characteristic for differentially expressed gene list.

 

Point4: There should be TCF, APC, CCD2 or MYC analyzed in Fig 2.

Response4: In gene expression profiling analysis, fold change of the expression value was examined in every gene annotated in UCSC annotation database. Based on the analysis results, box plots of differentially expressed genes shows the difference in gene expression in Figure 2a. On the other hand, box plots of TCF7, TCF7L2, TCFL1, APC, CCND2, and MYC are not shown, as their expression were not significantly different. We found no significant differences in gene expression for TCF, APC, CCD2, and MYC. We have added text for the explanation of Figure2b (line 207-208).