Fungal-bacteria interactions provide shelter for bacteria in Caesarean section scar diverticulum

Reviewer #1 (Public Review):

Summary:
Chen et al. describe the bacterial and fungal composition of cervical samples from women with/without Cesarean-section scar diverticulum (CSD) using whole metagenomic sequencing. Also, they report the metabolomic profile associated with CSD and built correlation networks at the taxonomical and taxonomic-metabolic levels to establish potential bacteria-fungi interactions. These interactions could be used, long-term, as therapeutic options to treat or prevent CSD.

Strengths:
- The authors have used advanced techniques in shotgun sequencing which is a powerful tool able to characterize the microbiome at the species (or lower) level and metabolomics.
- These are novel results showing the interaction of bacteria and fungi and present a wider view of the role of the microbiome in female infertility.

Weaknesses:
- This is a pilot study with only 24 cases and 24 controls. Because the human microbiota entails individual variability, this work should be confirmed with a higher sample size to achieve enough statistical power.
- The authors do not report here the use of blank controls. The use of this type of control is important to "subtract" the potential background from plasticware, buffer or reagents from the real signal. Lack of controls may lead to microbiome artefacts in the results. This can be seen in the results presented where the authors report some bacterial contaminants (Agrobacterium tumefaciensis, Aequorivita lutea, Chitinophagaceae, Marinobacter vinifirmus, etc) as part of the most common bacteria found in cervical samples.
- Samples used for this study were collected from the cervix. Why not collect samples from the uterine cavity and isthmocele fluid (for cases)? In their previous paper using samples from the same research protocol ((IRB no. 2019ZSLYEC-005S) they used endometrial tissue from the patients, so access to the uterine cavity was guaranteed.
- Through the use of shotgun genomics, results from all the genomes of the organisms present in the sample are obtained. However, the authors have only used the metagenomic data to infer the taxonomical annotation of fungi and bacteria.

Reviewer #2 (Public Review):

Summary: Shotgun data have been analysed to obtain fungal and bacterial organisms' abundance. Through their metabolic functions and through co-occurrence networks, a functional relationship between the two types of organisms can be inferred. By means of metabolomics, function-related metabolites are studied in order to deepen the fungus-bacteria synergy.

Strengths:
Data obtained from bacteria correlate with data from other authors.
The study of metabolic "interactions" between fungi and bacteria is quite new.
The inclusion of metabolomics data to support the results is a great contribution.

Weaknesses: Methodological descriptions are minimal.

Some example:
*The CON group (line 147) has not been defined. I supposed it is the control group.
* There are no statistics related to shotgun sequencing. How many reads have been sequenced? How many have been removed from the host? How many are left to study bacteria and fungi? Are these reads proportional among the 48 samples? If not, what method has been used to normalise the data?
* ggClusterNet has numerous algorithms to better display the modules of the microbiome network. Which one has been used?

Reviewer #3 (Public Review):

In the present study, Chen et al. revealed the fungal composition and explored its interaction with bacteria in Caesarean section scar diverticulum (CSD) patients. Performing metagenomic and mass spectrometry analysis, they found specific fungi could alter bacterial abundance through regulating the production of several metabolites such as Goyaglycoside A and Janthitrem E, which results in disruption of bacterial composition stability. Their study drew a conclusion that abnormal fungal composition and activity are essential drivers for bacterial dysbiosis in CSD patients. However, the results are not substantial enough and there are many format errors throughout the manuscript. In addition, I have some concerns or suggestions that may help to improve this work.

Major
1. Smoke or drink conditions, as well as diseases like hypertension and diabetes are important factors that could influence the metabolism of the host, thus the authors should add them in the exclusion criteria in the Methods.
2. The sample size of this study is not large enough to draw a convincing conclusion.