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  • Răzvan Lucian Jurca1,†,
  • Maria-Andreea Soporan2,3,† and
  • Ioana-Ecaterina Pralea2,*
  • et al.

Reviewer 1: Hossam El-Sherbiny Reviewer 2: Anonymous

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

 This study performed an exploratory proteomic analysis of maternal urine from pregnancies with normal karyotype and those affected by fetal aneuploidy. The article is interesting and provides useful information.

  • Please provide sentences related to the hypothesis.
  • Please discuss on what basis you selected the sample size

 

Author Response

Reviewer 1

Comments and Suggestions for Authors

This study performed an exploratory proteomic analysis of maternal urine from pregnancies with normal karyotype and those affected by fetal aneuploidy. The article is interesting and provides useful information.

Response: We thank the reviewer for the attention accorded to our paper and for the positive feedback.

 

  1. Please provide sentences related to the hypothesis.

Response: A clear presentation of our research hypothesis was formulated and included in the last part of the introduction. [Manuscript with track changes, lines 73-79]

 

  1. Please discuss on what basis you selected the sample size

Response: The sample size was determined primarily based on feasibility and the availability of clinically confirmed cases of fetal aneuploidy within the defined 11-month recruitment period, acknowledging the low population prevalence of these conditions and the ethical constraints associated with maternal sampling during pregnancy.

Given these factors, the study was designed as an exploratory, hypothesis-generating analysis aimed at detecting preliminary proteomic trends rather than achieving statistical power for biomarker validation.

Comparable exploratory proteomic investigations in pregnancies affected by fetal aneuploidy have included similar or smaller sample sizes (typically 6–15 cases per group), supporting the feasibility of our design.

Examples include:

 

  1. Tsangaris GTh et al. Proteomic analysis of amniotic fluid in pregnancies with Down syndrome. Proteomics. 2006;6(15):4410–4419. doi:10.1002/pmic.200600085
  2. Kolla V et al. Quantitative proteomics analysis of maternal plasma in Down syndrome pregnancies using iTRAQ. J Biomed Biotechnol. 2010;2010:952047. doi:10.1155/2010/952047
  3. Yao Y et al. Screening and identification of potential predictive biomarkers for Down’s syndrome from second trimester maternal serum. Expert Rev Proteomics. 2015;12(1):97–107. doi:10.1586/14789450.2015.979796
  4. Uriarte GAL et al. Proteomic profile of serum of pregnant women carrying a fetus with Down syndrome using nano-UPLC-Q-TOF-MS/MS. J Matern Fetal Neonatal Med. 2017;31(11):1483–1491. doi:10.1080/14767058.2017.1319923
  5. Gao L et al. Urinary proteomics for noninvasive prenatal screening of trisomy 21: New biomarker candidates. OMICS. 2021;25(11):738–744. doi:10.1089/omi.2021.0154

 

The following text was added to the Discussion section [Manuscript with track changes, lines 313-317]

“Sample size rationale. The cohort size was primarily determined by feasibility and the availability of clinically confirmed aneuploid pregnancies within the 11-month recruitment period, reflecting the naturally low prevalence of these conditions. The study was therefore designed as an exploratory, hypothesis-generating analysis aimed at capturing preliminary proteomic trends in maternal urine“.

Author Response File: Author Response.docx

Reviewer 2 Report

Comments and Suggestions for Authors

This preliminary study explores differences in the maternal urinary proteome between pregnancies with normal fetal karyotype and those affected by fetal aneuploidy using label-free quantitative proteomics. A total of 861 proteins were identified, among which 42 showed significant differential expression. The identified proteins and their associated biological pathways may provide insight into maternal–fetal metabolic communication in aneuploid pregnancies.

However, several minor issues should be addressed before the manuscript can be accepted:

  1. MS/MS acquisition parameters are not specified in the manuscript and should be clearly described.
  2. The authors allowed only one missed cleavage during database searching, whereas allowing up to two missed cleavages is standard for tryptic digestion. Please clarify the rationale for this setting.
  3. Multiple-testing correction is essential in quantitative proteomics to control the false discovery rate. The authors should explain why such correction was not applied.
  4. The authors should describe the variability within each sample group and clarify whether samples cluster consistently within each group in Figure 1B.

Author Response

Reviewer 2

 

Comments and Suggestions for Authors

This preliminary study explores differences in the maternal urinary proteome between pregnancies with normal fetal karyotype and those affected by fetal aneuploidy using label-free quantitative proteomics. A total of 861 proteins were identified, among which 42 showed significant differential expression. The identified proteins and their associated biological pathways may provide insight into maternal–fetal metabolic communication in aneuploid pregnancies.

 

Response: We thank the reviewer for taking the time to evaluate our manuscript and for the valuable comments regarding the aspects that need clarification and improvement.

 

However, several minor issues should be addressed before the manuscript can be accepted:

 

  1. MS/MS acquisition parameters are not specified in the manuscript and should be clearly described.

Response: We thank the reviewer for this comment. We have now added a detailed description of the MS/MS acquisition parameters for the SYNAPT G2-Si HDMS instrument, including source settings, collision energy ramps, and ion mobility parameters. The revised text is included in the Materials and Methods section. [Manuscript with track changes, lines 166-176]

 

(…)“The following source and analyzer settings were applied: capillary voltage was 2.5 kV, source temperature 80 °C, sampling cone 40 V, and cone gas flow 30 L/h. LC–MS data were acquired in data-independent acquisition (DIA) mode using the ion mobility enabled HDMSE mode. For IMS separation, a traveling-wave height of 40 V was applied, and the wave velocity was linearly ramped from 800 to 500 m/s across the full IMS cycle. Wave velocities in the trap and transfer cells were 311 m/s and 190 m/s, respectively, with wave heights of 4 V in both regions. Spectra were collected in resolution mode over the m/z range 50–2000, with a scan time of 0.5 s. For low-energy scans, the collision energy was fixed at 4 eV (trap) and 2 eV (transfer), while in high-energy scans, the transfer collision energy was ramped from 19 to 45 eV. Post-acquisition, lock mass correction was applied using the doubly charged monoisotopic ion of [Glu1]-Fibrinopeptide B (m/z 785.8426).”

 

  1. The authors allowed only one missed cleavage during database searching, whereas allowing up to two missed cleavages is standard for tryptic digestion. Please clarify the rationale for this setting.

Response: We thank the reviewer for this insightful comment. The decision to allow a maximum of one missed cleavage was based on published evidence showing that this parameter provides an optimal balance between proteome coverage and identification specificity when digestion efficiency is high supported by batch-based QC metrics report provided by the Progenesis QI for proteomics data processing software.  

Allowing a maximum of one missed cleavage is appropriate when digestion efficiency is high and peptide identification employs ion-mobility–enhanced LC–HDMS workflows as this setting reduces the theoretical search space, improves specificity, and helps maintain a lower false discovery rate (FDR) during peptide identification (10.1021/acs.analchem.2c03820). Also, data show that correctly identified peptides in high-quality datasets arise from peptides with zero or one missed cleavage (10.1021/acs.analchem.2c03820).

This one-missed-cleavage approach is also consistent with previously published Progenesis QI for Proteomics workflows using ion-mobility–based LC–MS data under similar conditions as those used in this study (10.1016/j.jprot.2021.104117).

Indeed, when two missed cleavages are allowed, there is a modest increase in peptide identifications, but this may come at the cost of increased computational complexity and a higher risk of false positives. Additionally, missed cleavage peptides are less likely to be optimal surrogates for quantitation (10.1021/pr301139y, 10.1021/pr500294d)”, particularly in label-free workflows and their inclusion does not significantly improve the identification of proteins.

The Progenesis QI for Proteomics QC report on missed cleavages proportions on this dataset (below) further confirms the choice of more stringent approach showing approximately 60% of peptides being fully cleaved, 36% with aa single missed cleavage, and <2% more than one.

 

This chart shows the distribution of missed cleavages per identified peptide ion across the whole experiment, assuming the proteins were treated with trypsin and using each peptide ion's highest scoring identification

 

The following text was added to the manuscript. [Manuscript with track changes, lines 183-187]

“Database searching was performed using trypsin as the digestion enzyme, allowing a maximum of one missed cleavage. This threshold was selected based on digestion efficiency metrics from Progenesis QI for Proteomics, which showed >95% of peptides were fully cleaved or contained a single missed cleavage.”  

 

  1. Multiple-testing correction is essential in quantitative proteomics to control the false discovery rate. The authors should explain why such correction was not applied.

Response: We thank the reviewer for this important observation. We fully acknowledge that multiple-testing correction is a critical step in large-scale quantitative proteomics to control the false discovery rate (FDR). However, in small exploratory cohorts such as the present study, stringent FDR correction (e.g., Benjamini–Hochberg at 0.05) can be overly conservative, eliminating all potentially meaningful features and thereby masking biologically relevant signals.

This limitation is well recognized in the proteomics literature, where unadjusted p-values combined with fold-change thresholds are commonly used for hypothesis generation in low-powered datasets (doi:10.1002/pmic.201600044). Accordingly, we have reported unadjusted p-values (p ≤ 0.05, |fold change| ≥ 1.2) and clearly stated that these findings are exploratory and require validation in larger, independent cohorts.

 

The following text was added to the Study Limitations and Future Directions section of the manuscript. [Manuscript with track changes, lines 486-491]

 

(…) “Considering the small exploratory cohorts such as the present study, stringent FDR correction (e.g., Benjamini–Hochberg at 0.05) can be overly conservative, eliminating all potentially meaningful features and thereby masking biologically relevant signals.  Consequently, unadjusted p-values (p ≤ 0.05) and fold-change thresholds (|fold change| ≥ 1.2) were applied to identify exploratory trends rather than definitive biomarkers."

Future studies should include larger, prospective cohorts to address inter-individual variability and enhance robustness. While this exploratory analysis identified 42 differentially abundant proteins distinguishing aneuploid from euploid pregnancies, these findings should be interpreted as preliminary.

 

  1. The authors should describe the variability within each sample group and clarify whether samples cluster consistently within each group in Figure 1B.

Response: We thank the reviewer for this suggestion. To better evaluate within-group variability and overall clustering behavior, we regenerated the heatmap using hierarchical clustering of all samples based on the normalized abundances of the 42 differentially abundant proteins. The updated version (now presented as Figure 1B) shows clearer grouping trends consistent with biological classification: most aneuploid pregnancy samples cluster together, forming a distinct branch, while control samples primarily group within a separate subcluster. Importantly, this variability did not obscure overall group-level separation, as confirmed by principal component analysis (PCA, Supplementary Figure S1), which showed partial segregation between aneuploid and euploid pregnancies along the first two principal components.

 

To clarify this in the manuscript, we have added the following sentence to the Figure 1 legend and Results section [Manuscript with track changes, lines 262-264].

 

“Hierarchical clustering of the 42 differentially abundant proteins revealed partial separation between aneuploid and control samples, with moderate within-group variability reflecting individual metabolic and physiological differences.”

 

Updated figure 1:

Figure 1. Proteome profile differentiation between study groups: (A) Volcano plot showing dif-ferential protein expression between fetal aneuploidy and control groups. Proteins with |fold change| ≥ 1.2 and p ≤ 0.05 were considered significantly differentially expressed. Red and blue dots indicate proteins with higher and lower abundance, respectively, while gray dots represent non-significant different proteins; (B) Heatmap illustrating the expression profiles of the 42 significantly differentially abundant proteins across all urine samples. Expression values were normalized and clustered using hierarchical clustering (distance: Euclidean; linkage: average).

Author Response File: Author Response.docx