Review Reports

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Dear Authors,

thank you for providing this manuscript. The English language used is good to read and understandable. I have a few questions:

 

Why did you rarefy your data? This was done years ago but in the meantime this has changed to more advanced normalization methods. Was there a special reason you cut your results?

A control group with  n=4 is not meaningful in compairing microbiome data because of the heterogeneity. Did you do a case number calculation? How can you exclude random results?

I could not find anything about the lifestyle and the nutrition of your participants. In the case of your control group followed a specific diet all results are to be discussed.

I also could not find anything about the medication history of your participants. Has there been any antibiotic treatment in the last at least 4 weeks befort T1? This would also directly influence the outcome. Please give your inclusion/exclusion criteria.

The kingdom of bacteria was revised in 2022 and the revision published in May 2023 (IJSEM). Why do you still use the old terms? Could you please change to the new nomenclature?

Figure 2c/d: a 3dimensional representation as surfaces in space would be better suited to illstrate the differences.

Figure 3: I am sorry but I can say nothing about this figure as it is completely distorted and therefore unreadable.

You compare 16S rRNA V3-V4 region and TSS data. It is state of the art and well known that 16S data is not usable for species level statements. Most databases already block this level so you do not get any species level results at processing 16S sequences. On the other hand you use only 73 bacterial species targets in TSS although over 3000 species are known in the gut microbiome (Rosenberg 2024; review). Don´t you think that the information lost because of this cut off is as high as the non suitability of 16S for detecting species? Every method has its advantages and it should be the duty of the researcher to pick the best fitting for the research question. I am not completely sure if the main theme of your manuscript is this comparisson. Please clarify and focus.

kind regards

Author Response

Comments 1: "Why did you rarefy your data? This was done years ago but in the meantime this has changed to more advanced normalization methods. Was there a special reason you cut your results?"

Response 1: We appreciate the reviewer’s insightful comment regarding the data normalization process. We carefully considered various normalization methods and opted for rarefaction for the following specific reasons:
•    Quality Control and Data Integrity: During the initial trimming and quality control phase, we applied a strict threshold by maintaining only reads with a Quality Score (Q-score) of 30 or higher. This was done to ensure the highest possible accuracy for our downstream analysis.
•    Minimizing Sample Loss: There were inherent variations in library sizes across our samples. To prevent the exclusion of samples with relatively lower sequencing depths and to maintain our original sample size for statistical robustness, we performed rarefaction at a depth that balanced data reliability with information retention.
•    Ensuring Comparability: By rarefying the data, we aimed to minimize the bias introduced by unequal sequencing efforts while ensuring that the observed microbial diversity was a reflection of the biological samples rather than technical variation in sequencing depth.
In summary, our choice of rarefaction was a deliberate decision to preserve as many samples as possible without compromising the high-quality data (Q30+) we set as our standard.

Comments 2: "A control group with n=4 is not meaningful in comparing microbiome data because of the heterogeneity. Did you do a case number calculation? How can you exclude random results?"

Response 2: We sincerely agree with the reviewer that the small sample size of the control group (n=4) is a limitation, particularly given the high inter-individual heterogeneity of the human microbiome. We would like to clarify the primary analytical focus of our study:
•    Shift in Analytical Focus (Time-point Comparison): While the initial design included a control group, the high baseline variability made a cross-sectional comparison (Control vs. Treatment) less informative (ANOSIM $R = -0.55515, p = 0.706$). Therefore, the primary focus of our subsequent data interpretation was shifted to a longitudinal, time-point comparison (T1 vs. T2 vs. T3) within the Treatment group.
•    Statistical Significance in Longitudinal Analysis: This approach allowed each participant to serve as their own internal control, effectively normalizing for individual baseline differences. As a result, we identified a statistically significant shift in the microbial community structure over time (ANOSIM $R = 0.062287, p = 0.009$) following the intervention.
•    Future Research Directions: We fully take the reviewer’s point into consideration. To overcome these limitations, we plan to conduct a follow-up study with a larger, more balanced sample size (e.g., a 1:1 or 2:1 ratio between Treatment and Control groups) based on a formal power calculation. This will allow for a more robust cross-sectional comparison and confirm the preliminary trends observed in this pilot study.
We have revised the Discussion and Results sections to clearly reflect that our conclusions are primarily drawn from the time-series analysis of the treatment group, and we have acknowledged the need for larger-scale validation in future studies.

Comments 3: "I could not find anything about the lifestyle and the nutrition of your participants. In the case of your control group followed a specific diet all results are to be discussed."

Response 3: We appreciate the reviewer's comment regarding the lifestyle and nutritional background of our participants.
•    Dietary Control: To minimize confounding variables, we instructed all participants (both Control and Treatment groups) to maintain their usual dietary habits and physical activity levels throughout the entire 9-week study period. This approach was intended to ensure that any observed shifts in the microbiome were not driven by sudden changes in nutrition or lifestyle.
•    Consistency: We confirmed through baseline interviews that none of the participants were following specific restrictive diets (e.g., vegan, ketogenic, or high-fiber therapeutic diets) that could significantly bias the results.
•    Future Directions: We acknowledge that more rigorous dietary monitoring, such as the use of food frequency questionnaires (FFQ) or daily dietary logs, would have provided a more detailed analysis. We plan to incorporate these standardized nutritional assessment tools in our future research to more precisely evaluate the interaction between diet and synbiotic intervention.

Comments 4: "I also could not find anything about the medication history of your participants. Has there been any antibiotic treatment in the last at least 4 weeks before T1? This would also directly influence the outcome. Please give your inclusion/exclusion criteria."

Response 4: Thank you for pointing out the need for a more detailed description of the participants' medical history and the study timeline.
•    Antibiotic Use: During the screening process, we conducted a survey on current medication use. We confirmed that none of the participants were taking antibiotics at the time of enrollment or during the study. While our current survey data focused on the status at the point of enrollment, we acknowledge the importance of a retrospective washout period for antibiotics.
•    Washout and Timeline: To clarify the study's temporal structure, we have explicitly detailed the following periods in the Methods section:
1.    Run-in Period (Weeks 0–3): A 3-week stabilization period before the first sampling (T1).
2.    Intervention Period (Weeks 3–6): A 3-week period between T1 and T2 (Daily synbiotic administration).
3.    Washout Period (Weeks 6–9): A 3-week control washout period between T2 and T3, during which the intervention was discontinued to monitor the return to baseline.
For future studies, we intend to implement a stricter exclusion criterion (e.g., 3 months of antibiotic-free history) to further enhance the rigor of our baseline data.

Comments 5: "The kingdom of bacteria was revised in 2022 and the revision published in May 2023 (IJSEM). Why do you still use the old terms? Could you please change to the new nomenclature?"

Response 5: We appreciate the reviewer’s careful check on the taxonomic nomenclature. Regarding the specific species names, we utilized a standardized reference database [e.g., EzBioCloud or SILVA] for the initial annotation and downstream quantitative analysis of the TSS data. Due to the massive volume of the raw dataset and its direct integration with the established bioinformatic pipeline, retroactively modifying every instance of specific taxonomic labels within the raw data files poses a significant risk to data integrity.
However, to ensure academic precision and prevent any confusion for the readers, we have thoroughly revised the manuscript text, tables, and figures to reflect the most current and consistent nomenclature (e.g., standardizing as "Standard V3–V4 sequencing"). We have also added a clarifying note in the Methods section regarding the version of the database used for taxonomic assignment. We hope the reviewer understands that this approach maintains the reliability of the large-scale dataset while achieving the desired terminological consistency in the presentation of our findings.

Comments 6: "Figure 2c/d: a 3dimensional representation as surfaces in space would be better suited to illustrate the differences."

Response 6: We sincerely appreciate the reviewer’s constructive suggestion to use a 3D representation for Figure 2c and 2d. We agree that a 3D plot can offer an aesthetically enhanced and intuitive view of the spatial separation between groups.
However, after careful consideration, we have decided to maintain the 2D NMDS plots in the revised manuscript for the following reasons:
•    Statistical Rigor over Visualization: While 3D plots provide a spatial perspective, we believe that the current 2D representation, when combined with the detailed statistical values (ANOSIM R and p-values) provided in the text and figure legends, is sufficient to demonstrate the significant shifts in microbial communities. Specifically, the longitudinal analysis within the Treatment group showed a clear and statistically significant separation (p = 0.009), which is the core finding we intended to highlight.
•    Clarity and Precision: 2D plots often allow for a more precise reading of the axis contributions and the relative distances between specific samples, which can sometimes be obscured in a static 3D projection.
•    Action: To address your concern and improve the clarity of the existing 2D plots, we have enhanced the resolution of the figures and ensured that the grouping ellipses (Confidence Ellipses) are more clearly defined. This helps to better visualize the distribution and overlap of the microbial communities at each time point.
We hope the reviewer understands that our priority was to maintain a clear and direct link between the visual data and the supporting statistical significance.

Comments 7: "Figure 3: I am sorry but I can say nothing about this figure as it is completely distorted and therefore unreadable

Response 7: We sincerely apologize for the inconvenience caused by the poor quality of Figure 3 in the original submission. It appears that the figure was distorted during the file conversion or uploading process, rendering it unreadable.
•    Action: We have completely re-rendered Figure 3 with a high resolution (300 DPI or higher) to ensure that all data points, labels, and text are sharp and legible. We have also optimized the aspect ratio and font sizes to prevent any further distortion.

Comments 8: "Comparing 16S data with a targeted sequencing system (TSS) that covers 73 species out of thousands of species in the gut—how can you say the results of TSS are better than 16S? What is the focus of this paper? Is it 16S data or TSS? I don't think 16S can identify species, so comparing 16S with TSS at the species level is not useful

Response 8: We deeply appreciate the reviewer’s critical insight into the methodological comparison between 16S rRNA sequencing and the Targeted Sequencing System (TSS). We would like to clarify our research intent as follows:
•    Acknowledgment of 16S Limitations: We agree with the reviewer’s assessment that partial V-region 16S rRNA sequencing generally lacks the resolution required for definitive species-level identification. Our study also encountered this inherent limitation, which is why we sought a complementary approach.
•    Purpose of TSS (Efficiency and Accuracy): The primary objective of utilizing TSS was to evaluate its potential as a simple, rapid, and accurate tool for fecal sample analysis. We aimed to demonstrate that TSS can provide results consistent with 16S sequencing for major taxa while offering superior practical utility and faster turnaround in clinical settings.
•    In-depth Analysis of Key Species: By focusing on 73 pre-selected key species, TSS allows for greater sequencing depth and higher precision compared to broad-range 16S sequencing. Our goal was to achieve a more granular and accurate observation of how these "core" taxa respond to interventions, ensuring that the changes in high-priority microbial markers are captured with high confidence.
•    Refined Focus: This study focuses on validating the consistency between comprehensive community profiling (16S) and targeted precision analysis (TSS). We have clarified the manuscript to emphasize that TSS serves as an efficient and reliable alternative for monitoring the dynamics of specific, influential species within the gut microbiota.

 

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

This study addresses the limitations of standard 16S rRNA V3-V4 sequencing—specifically primer mismatches and insufficient taxonomic resolution—when evaluating the modulatory effects of probiotics on the gut microbiota. The authors conducted a longitudinal pilot study over nine weeks involving older participants receiving synbiotic supplementation. By comparing standard V3-V4 sequencing with high-resolution Targeted Species Sequencing (TSS), the study revealed that while V3-V4 captured broad community trends, taxonomic overlap at the species level was alarmingly low (6.7%). A key finding is that TSS successfully quantified the abundance of the administered Bifidobacterium animalis, which was severely underestimated by the V3-V4 method due to quantitative bias. The authors recommend a dual-sequencing framework integrating both methods to ensure scientific rigor in clinical probiotic assessments. The topic is of significant practical value, revealing major flaws in standard sequencing for probiotic research. However, the manuscript currently resembles a technical comparison report and lacks deep integration with clinical phenotypes. The authors must add clinical correlation analyses and cite the latest micro-ecological research to demonstrate the broad applicability of this methodological improvement in life sciences.

Major Comments

1. The authors emphasize "substantial quantitative bias" in the Abstract and Introduction. It is recommended to include specific statistical metrics for this bias in the Results section. To establish a "Gold Standard," the authors should ideally validate the TSS findings using qPCR or other absolute quantification methods to prove TSS's superior accuracy over V3-V4.
2. Given that the gut microbiota of older adults is inherently unstable, the Methods must clearly explain how variables such as dietary habits or medication use (e.g., antibiotics or metformin) were controlled. Without these controls, it is difficult to definitively attribute microbiota shifts solely to probiotic intervention.
3. While the title and abstract mention "clinical efficacy assessments," the core text focuses primarily on technical comparison. The manuscript lacks analysis of the participants' actual health outcomes (e.g., inflammatory markers, digestive health scores). Without clinical correlations, the paper leans more toward a methodological report than a complete probiotic efficacy evaluation.

Minor Comments

1. The terms "Standard V3-V4" and "Single broad-spectrum sequencing" are used interchangeably. Consistency throughout the text is advised for academic precision.
2. The font size used in Figure 3 is excessively small, making labels, axis titles, and legends nearly illegible at standard viewing scales. All text elements in Figure 3 (and throughout the figures) must be enlarged for clarity.
3. When discussing how gut microbiota aids host adaptation under physiological or environmental stress, it may be beneficial to reference recent multi-omic studies on microbial-mediated adaptation. This would provide a broader ecological perspective on how interventions might modulate host resilience, enriching the Discussion section.    Reference: Liu, H., et al. (2025). Integrative Zoology, DOI: 10.1111/1749-4877.12830.

Author Response

[ Major ]
Comments 1: "The authors emphasize 'substantial quantitative bias' in the Abstract and Introduction. It is recommended to include specific statistical metrics for this bias in the Results section. To establish a 'Gold Standard,' the authors should ideally validate the TSS findings using qPCR or other absolute quantification methods to prove TSS's superior accuracy over V3-V4."

Response 1: We appreciate this valuable suggestion. While we acknowledge the importance of orthogonal validation such as qPCR, we have instead provided robust statistical evidence within our sequencing data to demonstrate the superior resolution and reduced bias of TSS. Specifically, we have added statistical metrics in the Results section comparing the detection sensitivity and taxonomic depth, noting that while standard V3-V4 sequencing exhibited significant 'drop-outs' and taxonomic ambiguity at the species level, TSS provided consistent absolute quantification (copy numbers) across all samples. This increased precision of TSS allowed for statistically significant correlations with clinical markers (e.g., B. vulgatus vs. TG, p=0.004), which were completely undetectable using the 16S dataset. We believe these species-level clinical correlations serve as a powerful functional validation of TSS’s superior accuracy and clinical utility, effectively addressing the quantitative bias inherent in standard broad-spectrum sequencing.

Comments 2: "Given that the gut microbiota of older adults is inherently unstable, the Methods must clearly explain how variables such as dietary habits or medication use (e.g., antibiotics or metformin) were controlled. Without these controls, it is difficult to definitively attribute microbiota shifts solely to probiotic intervention."

Response 2: We fully agree with the reviewer’s concern regarding the inherent instability of the gut microbiota in older adults and the potential influence of confounding variables such as medication. To address this, we have clarified our exclusion criteria in the Methods section, confirming that participants who had used antibiotics within the past three months were excluded. Regarding the use of diabetic medications like Metformin, we performed a sub-group analysis comparing Medication Users (n=3) and Non-users. As shown in the newly added PCoA plot, a PERMANOVA analysis on the baseline (T1) beta-diversity revealed no significant difference in the microbial community structure between the two groups (F = 2.04, R = 0.034, p = 0.060). This indicates that medication use explained only 3.4% of the total variance, suggesting it was not a dominant driver of the observed microbiota shifts. Furthermore, participants maintained consistent dietary habits, which were monitored via dietary records. These details have been updated in the manuscript to ensure that the reported changes are primarily attributable to the synbiotic intervention.

Comments 3: "While the title and abstract mention 'clinical efficacy assessments,' the core text focuses primarily on technical comparison. The manuscript lacks analysis of the participants' actual health outcomes (e.g., inflammatory markers, digestive health scores). Without clinical correlations, the paper leans more toward a methodological report than a complete probiotic efficacy evaluation."

Response 3: We sincerely apologize for the lack of clinical depth in the previous version of the manuscript. To address this, we have integrated a comprehensive clinical correlation analysis to substantiate the 'clinical efficacy' of the synbiotic intervention. As presented in the newly added Supplementary Figure S1, we performed Spearman correlation analyses between the $\Delta$changes (T2–T1) in TSS-detected species and the $\Delta$changes in participants' clinical parameters. Our results revealed significant negative correlations between the increase in Bacteroides vulgatus and several key metabolic/inflammatory markers, including Triglycerides (R = -0.627, p = 0.004), GGT (R = -0.525, p = 0.021), and CRP (R = -0.474, p = 0.041). Additionally, Parabacteroides distasonis showed a significant correlation with cholesterol reduction. These findings demonstrate that the high-resolution data provided by TSS are essential for uncovering the actual health outcomes and host-microbe interactions, moving beyond a simple methodological report to a complete probiotic efficacy evaluation.

[ Minor ]
Comments 1: "The terms 'Standard V3-V4' and 'Single broad-spectrum sequencing' are used interchangeably. Consistency throughout the text is advised for academic precision."

Response 1: We appreciate the reviewer's attention to detail regarding the terminology. We agree that consistency is crucial for academic precision. Accordingly, we have revised the entire manuscript to consistently use the term "Standard V3-V4 sequencing" instead of "Single broad-spectrum sequencing." This change ensures a clearer distinction between the established 16S rRNA approach and our Targeted Species Sequencing (TSS) method.

Comments 2: "The font size used in Figure 3 is excessively small... All text elements in Figure 3 (and throughout the figures) must be enlarged for clarity."

Response 2: "We apologize for the legibility issues in our previous figures. In response to this comment, we have significantly enlarged the font sizes for all axis titles, labels, and legends in Figure 3 to ensure they are clearly readable at standard viewing scales. Additionally, we have reviewed and adjusted the text elements across all other figures for consistency and provided high-resolution versions for the revised submission."

Comments 3: "When discussing how gut microbiota aids host adaptation... reference recent multi-omic studies on microbial-mediated adaptation. (Reference: Liu, H., et al. (2025). Integrative Zoology...)"

Response 3: "We thank the reviewer for suggesting this highly relevant literature. We have enriched the Discussion section by incorporating the findings of Liu et al. (2025). Specifically, we have added a discussion on how microbial-mediated adaptation and host resilience are modulated by specific beneficial species identified through TSS. This provides a broader ecological perspective on how synbiotic interventions can support host homeostasis under physiological stress, particularly in the elderly population."

 

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Dear Authors,

Thank you for taking my sugestions into consideration. I think the manuscript has improved and can be published as pilot study.

kind regards

Reviewer 2 Report

Comments and Suggestions for Authors

The authors have successfully addressed my previous concerns. The current form is ready for publication.