Identification of Dominant Microbes and Their Successions During Solid-State Fermentation of Luzhou-Flavour Liquor Based on High-Throughput Sequencing Following Culture

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The submitted manuscript provides valuable and original insight into microbial community dynamics during the solid-state fermentation of Luzhou-flavour liquor. The combination of high-throughput sequencing (HTS) and culture-dependent methods is well chosen and offers a comprehensive view of fungal and bacterial successions. The study is of high relevance to the field of traditional fermentation microbiology.

However, several points require improvement before publication:

1. Clarity of the Luzhou liquor production scheme
   The description of the fermentation and sampling process is informative, yet critical details are missing for full reproducibility and clarity:

   • Please provide a clear schematic diagram of the production and sampling process, including the transformation of layers across fermentation cycles.

   • Specify the exact composition and proportions of raw materials used (sorghum, rice, Daqu, water, etc.).

   • Clarify the volume/dimensions of the fermentation pits — e.g., is “8 × 10 m³” meant to indicate eight pits, each 10 m³, or a single pit measuring 8 m × 10 m?

2. Figures and tables

   • Several figures lack clear legends and layer identifiers. For example, in Figure 6 (correlation analysis), it's unclear which graphs correspond to which fermentation layer. Use consistent and labelled subplots (e.g., A = upper, B = middle, C = lower).

   • Ensure that what is written below the figures is properly formatted as a figure legend, not continuation of the main text.

3. Lack of a Conclusion section
   The manuscript omits a final conclusion or summary section. A concise paragraph that clearly restates the key findings and practical implications (e.g., which microbes are functionally most important for flavour development and quality control) would enhance readability and impact.

4. Enzyme activity interpretation
   In the section discussing enzyme activity (glucoamylase, liquefying enzymes), please clarify:

   • To which specific enzymes the described trends refer.

   • Revise statements that do not reflect actual data trends. For example, the sentence stating that enzyme activity "declined during days 12–44 in the upper layer" is not entirely accurate — the figure indicates fluctuations with a rebound at day 44.

5. Objective of the study

   • The objective of the study is not clearly stated at the end of the Introduction, as is customary in scientific writing. Please provide a clearly defined aim, differentiating it from methodology or background.

6. Highlight novel contributions

   • Emphasise more strongly what distinguishes your work from prior studies — particularly your simultaneous comparison of all three fermentation layers using both HTS and culturing approaches.

Author Response

Reviewer 1 Comments: The submitted manuscript provides valuable and original insight into microbial community dynamics during the solid-state fermentation of Luzhou-flavour liquor. The combination of high-throughput sequencing (HTS) and culture-dependent methods is well chosen and offers a comprehensive view of fungal and bacterial successions. The study is of high relevance to the field of traditional fermentation microbiology.

Response to Reviewer 1

Thank you very much for taking the time to review this manuscript. Please find our detailed responses below. The corresponding revisions are indicated in red font in the re-submitted files.

 

Reviewer 1 Comments

However, several points require improvement before publication:

Comment 1：Clarity of the Luzhou liquor production scheme
The description of the fermentation and sampling process is informative, yet critical details are missing for full reproducibility and clarity: Please provide a clear schematic diagram of the production and sampling process, including the transformation of layers across fermentation cycles. Specify the exact composition and proportions of raw materials used (sorghum, rice, Daqu, water, etc.). Clarify the volume/dimensions of the fermentation pits — e.g., is “8 × 10 m³” meant to indicate eight pits, each 10 m³, or a single pit measuring 8 m × 10 m?

Response：The production process of Luzhou-flavor liquor has been reported by us previously【Yang，et al.，2018】. owing to a complicated process and the restrictions of the length of this paper，the process flow diagram is not listed here.

The volume of the Luzhou-flavor liquor fermentation pool was modified (8–10 m³).

Comment 2：Figures and tables： Several figures lack clear legends and layer identifiers. For example, in Figure 6 (correlation analysis), it's unclear which graphs correspond to which fermentation layer. Use consistent and labelled subplots (e.g., A = upper, B = middle, C = lower). Ensure that what is written below the figures is properly formatted as a figure legend, not continuation of the main text.2.

Response：Figure legends and layer identifiers are perfected as follows：

Figure 1. Rarefaction curve analysis based on the Shannon indices for ITS1 (A-C) and 16S V3/V4 (a-c) from fermented grain samples taken separately from the upper (A), middle (B), and lower (C) layers.

Figure 2. Dynamic changes (A-C) and composition (a-c) of dominant fungi in fermented grains from the upper (A), middle (B), and lower (C) layers during Luzhou-flavor liquor production (THS data; A, upper; B, middle; C, lower layers; the numbers on the horizontal axis indicate days of fermentation).

Figure 3. Dynamic changes (A-C) and composition (a-c) of dominant bacteria in fermented grains from the upper (A), middle (B), and lower (C) layers during Luzhou-flavor liquor production (HTS data; A, upper; B, middle; C, lower layers; the numbers on the horizontal axis indicate days of fermentation).

Figure 4. Dominant microbial population succession in fermenting grains during brewing (A, upper; B, middle; C, lower layers) during Luzhou-flavor liquor production (HTS; the number means days).

Figure 5．Dynamics of glucoamylase (left) and liquefying enzyme (right) activities in fermented grains in the upper (A), middle (B), and lower (C) layers during Luzhou-flavor liquor production. 0A-44A, 0B-44B, and 0C-44C are the sample numbers of days 0–44 from the upper (A), middle (B), and lower (C) layers, respectively (the numbers indicate days; error bars indicate standard deviations).

Figure 6. Correlation between the dominant strains and enzymes (left) (saccharifying (S) and liquefying (L) enzymes) and volatile material (right) (Z, esters; S, acids; C, alcohols) in the upper (A), middle (B), and lower (C) fermenting grain layers during Luzhou-flavor liquor production. The dominant microorganisms in the mash are also shown, determined using the vegan statistical analysis package in R. The arrows represent the factors, while the numbers represent different microbial distributions. The length of the arrows represents the size of the correlation between the factors and the community distribution and species distribution, and the angle indicates the direction of the correlation.

 

Supplementary Data

Supplementary Figure S1. Shannon-Wiener analysis based on the Shannon indices for ITS1 (A-C) and 16S V3/V4 (a-c) from fermented grain samples taken separately from the upper (A), middle (B), and lower (C) layers (HTS data; A, upper; B, middle; C, lower layers; the numbers indicate days).

Supplementary Figure S2. Clustering analysis of ITS1 (A-C) and 16S V3/V4 (a-c) from the upper, middle, and lower fermented grain layers (HTS data). Clustering analysis was performed using UPGMA, a type of hierarchical clustering method based on unweighted UniFrac distance metrics (A, upper; B, middle; C, lower layers; the numbers indicate days).

Supplementary Figure S3. PCA analysis of fungal (upper) and bacterial (lower) community in fermenting grains (fungus: A, upper; B, middle; C, lower layers; bacteria: a, upper; b, middle; c, lower layers) during fermentation.

Table S1: Peak area percentage of volatile compounds in upper layer (A) fermented grains during Luzhou-flavour liquor production.

Table S2: Peak area percentage of volatile compounds in fermented grain middle layer (B) during Luzhou-flavour liquor production.

Table S3: Peak area percentage of volatile compounds in the fermented grain lower layer (C) during Luzhou-flavour liquor production.

 

Comment 3：Lack of a Conclusion section
The manuscript omits a final conclusion or summary section. A concise paragraph that clearly restates the key findings and practical implications (e.g., which microbes are functionally most important for flavour development and quality control) would enhance readability and impact.

Response：A conclusion section has been added, as follows.

Conclusion: To explore the mechanism underlying the microbial interactions involved in Luzhou wine brewing process, the changes in the microbial community of fermented grains were tracked using HTS and traditional culture separation technology. The dominant microorganisms in the early and late stages of fermentation were yeast, lactic acid bacteria, and spore-forming bacteria and yeast and lactic acid bacteria, respectively. In the three levels of fermented grains, the content of alcohol substances was the highest, with the middle and lower levels being higher than the upper level and with the lower-level acid substances being higher than those in the middle level. Finally, regarding the correlation of the phase of bacteria and volatile substances in the fermentation process, the microorganisms related to the volatile substances in the upper, middle, and lower levels were consistent.

 

Comment 4: Enzyme activity interpretation
In the section discussing enzyme activity (glucoamylase, liquefying enzymes), please clarify: To which specific enzymes the described trends refer. Revise statements that do not reflect actual data trends. For example, the sentence stating that enzyme activity "declined during days 12–44 in the upper layer" is not entirely accurate — the figure indicates fluctuations with a rebound at day 44.

Response: The change of glucoamylase activity (Left) and liquefying enzyme activity (Right) during the fermentation of fermented grains is shown in Figure 5, where A, B, and C, represent the upper, middle, and lower layers of the fermented grains, respectively. The liquefaction power of the upper layer of fermented grains increased rapidly from days 0 to 4, then gradually decreased. A trend of first rising and then decreasing appeared after the 12th day. The liquefaction power of the middle layer was generally low and only reached a higher level at approximately day 30. The liquefaction power of the lower layer of wine lees fluctuated greatly, with significant increases in liquefaction power on the 8th and 30th days and significant downwards trends on the 20th and 44th days. The difference in liquefaction enzyme activity among the three levels of the wine lees was also relatively large. Many factors affect the change of liquefaction enzyme activity, such as the community structure of microorganisms, water content, reducing sugar content, and acidity. Furthermore, the difference in factors among the levels is very large, which also causes the inconsistency in the enzyme activity changes among the layers.

 

Comment：Objective of the study

The objective of the study is not clearly stated at the end of the Introduction, as is customary in scientific writing. Please provide a clearly defined aim, differentiating it from methodology or background.

Response: In this study, we aimed to combine traditional culture (plate separation method) and HTS technology to analyse the microbial diversity and population succession during the solid-state brewing of Luzhou-flavour liquor to establish the dominant microorganisms.

Comment：Highlight novel contributions

Emphasise more strongly what distinguishes your work from prior studies — particularly your simultaneous comparison of all three fermentation layers using both HTS and culturing approaches.

Response: The purpose of this study was to combine traditional pure culture with modern molecular biology and use the combined method of traditional plate separation and high-throughput second generation sequencing technology so that both could support each other and avoid their own shortcomings, to obtain reliable experimental data.

 

 

 

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The comments  are stored  in the attachment.

Comments for author File: Comments.pdf

Author Response

Reviewer 2 Comments:

 This manuscript employs integrated high-throughput sequencing (HTS) and culture dependent methods to investigate spatiotemporal microbial succession (bacteria/fungi) across three layers (upper/middle/lower) of fermented grains during 44-day solid-state fermentation of Luzhou-flavor liquor. The work present very interesting research and key innovations include: Identification of layer-specific functional microbes (e.g., Lactobacillus parabuchneri exclusively in lower layer; Candida ethanolica abundance linked to liquor quality); Correlative analysis connecting dominant taxa (Pichia fermentans, Lactobacillus suebicus) with enzyme activities (glucoamylase/liquefying enzymes) and volatile flavor compounds;Multi-method validation (HTS + culturing) revealing 92.5% bacterial and 92.7% fungal coverage of dominant taxa.

 

Response: Thank you very much for taking the time to review this manuscript. Please find our detailed responses below. The corresponding revisions are are indicated in red font in the re-submitted files.

 

 

Comment 1: L24-L26 (Abstract): The fermentation duration (44 days) should be explicitly stated in the abstract to contextualize temporal microbial succession.

 

Response:  The brewing process of traditional Chinese Luzhou-flavour liquor involves solid-state fermentation for 2 months in an open environment with grains (fermentation substrates) and Daqu (saccharifying fermenter).

 

Comment 2: L142-L145 (Section 2.4.1): The "standard method" for DNA extraction (Narayan et al. 2016) lacks critical modifications (e.g., sample homogenization protocol, lysis buffer composition).

 

Response: We have added the following at the end of section 2.2: “Samples were obtained from different layers; three samples were collected at each point and mixed for further analysis by modification.”

 

Comment 3: L164-L168 (Section 2.4.2): Bioinformatics parameters for chimera removal (UCHIME algorithm settings) and OTU clustering identity threshold (e.g., 97%) require clarification.

Response:  Bioinformatics parameters for chimera removal use UCHIME algorithm settings, and OTU clustering identity used OTUs with 97% similarity.

 

Comment 4: L220-L223 (Section 3.1): The statement "OTU number was b > c > a on the 0th day" should be revised to "Lower-layer OTUs > middle-layer > upper-layer on day 0" for clarity.

Response: Yes, "OTU number was b > c > a on the 0th day" is revised to "Lower-layer OTUs > middle-layer > upper-layer on day 0" .

Comment 5: L344-L346 (Section 3.7): Correlations between microbes/enzymes/volatiles (Fig. 6) lack statistical validation (e.g., *p*-values or confidence intervals).

Response: In Fig. 6, A, B, and C represent the upper, middle, and lower layers of the mash, respectively. The canonical correlation analysis of the enzyme activity (left) and volatile substances (right) and dominant microorganisms in the mash are shown using the Vegan statistical analysis package in R language. In the left panel, the arrows represent the enzyme activity factors; S and L represent the saccharification and liquefaction activity, respectively. In the right panel, Z, S, and C represent esters, acids, and alcohols, respectively; the numbers represent different microbial distributions; the length of the arrows represent the degree correlation between the enzyme activity factors and community and species distribution—the longer the line, the greater the correlation, and vice versa.

 

Comment 6: Figure 6: Volatile compound quantification reports "peak area percentage" (Supplementary Tables S1-S3) instead of absolute concentrations (e.g., μg/g), limiting cross-study comparisons.

Response: The left and right of Fig 6 are the canonical correlation analysis of the enzyme activity (left, u/g) and the volatile substances (right, %) and dominant microorganisms in the mash by using the Vegan statistical analysis package in R language.

 

Comment 7: Supplementary Figures S1-S3: Axes in Shannon-Wiener/PCA plots (S1-S3) are unlabeled, and statistical groupings in cluster analyses (S2) lack explanatory legends.

Response: We apologize for this misunderstanding because the axes in the Shannon-Wiener/PCA plots (S1-S3) were already labelled.

Supplementary Figure S1. Shannon-Wiener analysis based on the Shannon indices for ITS1 (A-C) and 16S V3/V4 (a-c) from fermented grain samples taken separately from the upper (A), middle (B), and lower (C) layers (HTS data; A, upper; B, middle; C, lower layers; the numbers indicate days).

Supplementary Figure S2. Clustering analysis of ITS1 (A-C) and 16S V3/V4 (a-c) from the upper, middle, and lower fermented grain layers (HTS data). Clustering analysis was performed using UPGMA, a type of hierarchical clustering method based on unweighted UniFrac distance metrics (A, upper; B, middle; C, lower layers; the numbers indicate days).

Supplementary Figure S3. PCA analysis of fungal (upper) and bacterial (lower) community in fermenting grains (fungus: A, upper; B, middle; C, lower layers; bacteria: a, upper; b, middle; c, lower layers) during fermentation.

 

Comment 8: Discussion: The role of lower-layer-specific microbes (e.g., L. parabuchneri in lactic acid reduction) in enhancing caproic acid production ([28]) is underdeveloped.

Response: We have added ‘For example, the microbes in the lower layer may enhance caproic acid production [31]’.

 

Comment 9: Report GC-MS parameters (column, temperature program) for volatile compound analysis (Section 2.3).

Response: MS conditions: ion source temperature 200℃; interface temperature 250℃; ionization mode: electron ionization positive ion mode; electron energy: 70 eV; scanning mass range: m/z 33–450; solvent delay time: 2.3 min. Qualitative and semi-quantitative analysis: compounds with matching degree > 800 in the NIST11 database were retrieved, and the internal standard was used for quantitative analysis. The content of volatile flavour substances was calculated according to the ratio of the peak area of 4-octanol to flavour substances, and then multiplied by the dilution multiple to obtain the content of volatile flavour substances, and each sample was determined three times, and the average value was determined.

 

Comment 10: Add error bars and statistical significance markers (*p*-values) to Fig. 6. Correct microbial nomenclature in Figures 4 and text (e.g., Bacillus).

Response: In Fig. 6, A, B, and C represent the upper, middle, and lower layers of the mash, respectively. The canonical correlation analysis of the enzyme activity (left) and volatile substances (right) and dominant microorganisms in the mash are shown using the Vegan statistical analysis package in R language. In the left panel, the arrows represent the enzyme activity factors; S and L represent the saccharification and liquefaction activity, respectively. In the right panel, Z, S, and C represent esters, acids, and alcohols, respectively; the numbers represent different microbial distributions; the length of the arrows represent the degree correlation between the enzyme activity factors and community and species distribution—the longer the line, the greater the correlation, and vice versa. We confirm that there are no mistakes in microbial nomenclature in Figures 4 and the revised text.

 

Comment 11: Contrast succession patterns with Xiao et al. (2019) to emphasize novelty.

Response:

The succession of bacterial community in fermented grains was analysed using HTS technology, and the correlation between the alternation of communities at different fermentation stages and variation of environmental factors was analysed using the Mantel test [36]. The results showed that there were 397 genera of microorganisms in the fermentation process of fermented grains, and Lactobacillus, Bacillus, Weissella, Dysgonomonas, Comamonas, and Ruminococcaceae were the dominant genera (relative abundance > 1.0%). However, in the present study, the change rule of microorganisms in the upper, middle, and lower three levels of the mash was very similar. The number of bacteria was relatively high on days 0–12, comprising mainly Lactobacillus, Acetobacter, Bacillus, while the fungi were at a slow growth stage except for the rapid growth of P. fermentans. From 12 to 44 days, most of the bacteria disappeared, leaving only Lactobacillus, and L. acetotolerans increased significantly, while the fungi were mainly large numbers of some yeasts. The abundance of P. fermentans was still high, but it was unclear whether the numbers included dead strains from the previous period; P. exigua and P. kudriavzevii also showed a significant increase. Alternately, samples from each period of the fermentation cycle of the fermented grains were isolated and identified, with 258 strains of bacteria belonging to 30 species and 130 strains of fungi belonging to 15 species. By comparing these with the results of HTS analysis, the dominant microorganisms were determined to be B. subtilis subsp. inaquosorum, B. methylotrophicus, A. oxydans, A. cerevisiae, A. malorum, L. paracasei subsp. tolerans, B. amyloliquefaciens subsp. plantarum, A. aceti, L. brevis, A. pasteurianus subsp. pasteurianus, B. aryabhattai, Pseudomonas veronii, A. punensis, B. vanillea, L. pentosus, A. pomorum, A. sicerae, B. atrophaeus, L. buchneri, B. flexneri, L. hilgardii, B. sonorensis, L. parafarraginis, Staphylococcus epidermidis, L. xiangfangensis, L. plantarum subsp. plantarum, and L. acetotolerans, accounting for 92.5% of the total bacteria in the high-throughput group. The dominant fungi were C. rugopelliculosa, P. fermentans, S. cerevisiae, P. membranifaciens, C. humilis, S. fibuligera, C. cabralensis, P. kudriavzevii, C. ethanolica, and P. occidentalis, accounting for 92.7% of the high-throughput ITS data, indicating that the isolation and identification results were ideal and the experimental purpose was achieved.

 

 

Author Response File: Author Response.pdf