Impacts of biostimulation and bioaugmentation on woodchip bioreactor microbiomes

ABSTRACT Woodchip bioreactors (WBRs) are used to remove nutrients, especially nitrate, from subsurface drainage. The nitrogen removal efficiency of WBRs, however, is limited by low temperatures and the availability of labile carbon. Bioaugmentation and biostimulation are potential approaches to enhance nitrate removal of WBRs under cold conditions, but their effectiveness is still unclear. Here, we clarified the effects of bioaugmentation and biostimulation on the microbiomes and nitrate removal rates of WBRs. As a bioaugmentation treatment, we inoculated WBR-borne cold-adapted denitrifying bacteria Cellulomonas cellasea strain WB94 and Microvirgula aerodenitrificans strain BE2.4 into the WBRs located at Willmar, MN, USA. As a biostimulation treatment, acetate was added to the WBRs to promote denitrification. Woodchip samples were collected from multiple locations in each WBR before and after the treatments and used for the microbiome analysis. The 16S rRNA gene amplicon sequencing showed that the microbiomes changed by the treatments and season. The high-throughput quantitative PCR for nitrogen cycle genes revealed a higher abundance of denitrification genes at locations closer to the WBR inlet, suggesting that denitrifiers are unevenly present in WBRs. In addition, a positive relationship was identified between the abundance of M. aerodenitrificans strain BE2.4 and those of norB and nosZ in the WBRs. Based on generalized linear modeling, the abundance of norB and nosZ was shown to be useful in predicting the nitrate removal rate of WBRs. Taken together, these results suggest that the bioaugmentation and biostimulation treatments can influence denitrifier populations, thereby influencing the nitrate removal of WBRs. IMPORTANCE Nitrate pollution is a serious problem in agricultural areas in the U.S. Midwest and other parts of the world. Woodchip bioreactor is a promising technology that uses microbial denitrification to remove nitrate from agricultural subsurface drainage, although the reactor’s nitrate removal performance is limited under cold conditions. This study showed that the inoculation of cold-adapted denitrifiers (i.e., bioaugmentation) and the addition of labile carbon (i.e., biostimulation) can influence the microbial populations and enhance the reactor’s performance under cold conditions. This finding will help establish a strategy to mitigate nitrate pollution.

Major comments: 1.The authors repeatedly discuss sampling ports and how nitrogen cycling varies based on port.However, the figure demonstrating where ports are located on the bioreactors are not a part of the main figure.I suggest making Figure S4 your first figure to help guide readers the significance or sampling ports.In a similar vein, why did the authors only consider some ports with statistical analysis and not others (For example, Line 409-410)?This requires additional explanation.
2. The microbiome analysis should be expanded.The authors provide results on alpha and beta-diversity (ordinations), but that's it.I'd like to see a more thorough assessment of the microbiomes, atleast for significant time points that vary, as stated in the text and shown in Figure 3 PCoA.Specifically, what are the dominant taxa present in these (phyla, order, genera) when undergoing biostimulation and bioaugmentation.I also would recommend performing an indicator species analysis, or some other similar analysis like ANCOM, to see if there are specific OTUs which are greater or lower in one treatment versus another.This will provide a more broad snapshot.
Minor comments: L64: are, not is L89: remove "an" L102 and other areas: what do the values refer to?Please state this explicitly.L113-120: This is describing data and analysis so should go in methods L122: Figure 1, not S1.L126: Can authors please add statistical output for the paired t-tests here and throughout when its mentioned?L141: Not sure how a negative correlation with port number indicates this.I think adding the supplemental figure as stated above will help with this interpretation.L148 and in figures: Is it necessary to indicate or denote genes by "_[primer name]"?It may be more organized to represent a different way, if primer name is needed at all.L152-157: Should go in methods.L158: The alpha-diversity metrics analyzed are never discussed in the methods.This information should be included there.Also, do the authors only evaluate Shannon Diversity?What is the reason for only evaluating that alpha-diversity metric?L166: The sampling period/time is not easily distinguishable based on the PCoA.I suggest using maybe averages with centroids for each treatment per axis.This will reduce all the noisy data given and then provide the Figure 3 in supplements.L169: Can a table of the perMANOVA results and post-hoc pairwise comparisons be included in a table?L289: Is the inflow rate actually calculated?If so, please include.L293: More information on the woodchip composition would be nice.Perhaps this is common knowledge in the woodchip microbiome area, but it is not clear to me what types of wood make up woodchips for these experiments.L298: Include concentration of labile C addition.L302: How much flow rates was lowered?L307 and elsewhere: It's a bit unclear what the time of sampling was over the study period.I'm assuming its twice in May/June 2017, 4x in Oct/Nov 2017, and twice in spring 2018?Please explain.L310: This is where the port number comes into the picture of the sampling and is never acknowledged until this point.At first, it seemed random but throughout the paper it became clear the reasoning behind port sampling.Can the authors set up the explanation of their experimental design in regards to the ports and sampling regime at the beginning of the methods section?L390: I've never heard of this pipeline so excuse if I'm missing a detail here.Did the pipeline account for determining contaminant sequences, which are always present in 16S sequence datasets?Also, did it account for chimeric sequences?If not, this needs to be addressed.L399: Include rarefaction sequence level here (I think 5000 was stated elsewhere?)L410: Why port2 and 5? Again, the reasoning for port sampling needs to be clarified.

Reviewer #2 (Comments for the Author):
General comment Woodchip bioreactors (WBRs) are used to remove nutrients, especially nitrate, from subsurface drainage, but the nitrogen removal efficiency of WBRs is limited by low temperature and availability of labile carbon.Bioaugmentation and biostimulation are potential approaches to enhance nitrate removal of WBRs under cold conditions, and microbiome may work during the procedure.This study is aimed to study the impacts of Biostimulation (labile carbon: sodium acetate trihydrate) and Bioaugmentation (WBR-borne cold-adapted denitrifying bacteria) on Woodchip Bioreactor Microbiome.The top of this study is intriguing, and it is also a time-consuming work with many factors out of control, but it's better to analyze further.

Major comment
1.In the study, you used two kinds of technology to describe the impacts of Biostimulation and Bioaugmentation on Woodchip Bioreactor Microbiomes: Nitrogen cycle evaluation (NiCE) chip and 16S gene amplification sequencing, but there are 2 serious issues: 1.1 There are few contents about the microbiome, it should not only include the α-diversity and β-diversity, but also species of microorganisms and their changes with time and port.1.2 How to compromise the result of the 2 technologies.Are the genes generated from NiCE the same with the species or genus generated from 16S gene amplification sequencing?My suggestion is that use one as the main technology and make another complementary to support what you have found with the main technology.
2. Feyereisen et al have published the data of Nitrate-N load removal for biostimulation, bioaugmentation and control treatments (Feyereisen et al., 2022)., it's better to analyze the correlation of Nitrate-N removal to abundance of nitrogen cycle genes and/or microbiome changes.The study will be more amazing and significant after you correlate the Nitrate-N removal to gene changes.Without phenomenon or function of Woodchip bioreactor, only gene or microbiome changes sound not convictive.
Minority comment 1. Line 153-154 (Figure S2), it's more important to describe how deep the sequencing is, and is it deep enough to analyze further (| eg.Rarefaction and Shannon diversity of DNA sequences from samples) rather than describe how many reads you sequenced.
2. Figure S3.Shannon index of samples collected from different treatment groups.It's better to put P value on it or "NS (No significance)" on it.
3. line 193-194," Our NiCE chip data suggests that there are denitrification hotspots in WBRs.The distribution of nitrogen cycle genes inside WBRs is rather uneven" this is an intriguing point but few convictive contents described in the result.
4. The study used V4 primer to identify the microbiome with 16S amplification sequencing, which only provide information of genus.While primer 27f/1492r (5'-AGAGTTTGATCCTGGCTCAG-30/5'-CTACGGCTACCTTGTTACGA-30) is the most widely used primer for species-level identification (Frank et al., 2008) and the cost is not that much, so use bacterial universal primer 27f/1492r maybe better in this study.And you can get more useful information to support the practical application.

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General comment
Woodchip bioreactors (WBRs) are used to remove nutrients, especially nitrate, from subsurface drainage, but the nitrogen removal efficiency of WBRs is limited by low temperature and availability of labile carbon.Bioaugmentation and biostimulation are potential approaches to enhance nitrate removal of WBRs under cold conditions, and microbiome may work during the procedure.This study is aimed to study the impacts of Biostimulation (labile carbon: sodium acetate trihydrate) and Bioaugmentation (WBR-borne cold-adapted denitrifying bacteria) on Woodchip Bioreactor Microbiome.The top of this study is intriguing, and it is also a time-consuming work with many factors out of control, but it's better to analyze further.

Major comment
1.In the study, you used two kinds of technology to describe the impacts of Biostimulation and Bioaugmentation on Woodchip Bioreactor Microbiomes: Nitrogen cycle evaluation (NiCE) chip and 16S gene amplification sequencing, but there are 2 serious issues: 1.1 There are few contents about the microbiome, it should not only include the αdiversity and β-diversity, but also species of microorganisms and their changes with time and port.1.2 How to compromise the result of the 2 technologies.Are the genes generated from NiCE the same with the species or genus generated from 16S gene amplification sequencing?My suggestion is that use one as the main technology and make another complementary to support what you have found with the main technology.
2. Feyereisen et al have published the data of Nitrate-N load removal for biostimulation, bioaugmentation and control treatments (Feyereisen et al., 2022)., it's better to analyze the correlation of Nitrate-N removal to abundance of nitrogen cycle genes and/or microbiome changes.The study will be more amazing and significant after you correlate the Nitrate-N removal to gene changes.Without phenomenon or function of Woodchip bioreactor, only gene or microbiome changes sound not convictive.

Minority comment
1. Line 153-154 (Figure S2), it's more important to describe how deep the sequencing is, and is it deep enough to analyze further (| eg.Rarefaction and Shannon diversity of DNA sequences from samples) rather than describe how many reads you sequenced.
2. Figure S3.Shannon index of samples collected from different treatment groups.It's better to put P value on it or "NS (No significance)" on it.
3. line 193-194," Our NiCE chip data suggests that there are denitrification hotspots in WBRs.The distribution of nitrogen cycle genes inside WBRs is rather uneven" this is an intriguing point but few convictive contents described in the result.
4. The study used V4 primer to identify the microbiome with 16S amplification sequencing, which only provide information of genus.While primer 27f/1492r (5'-AGAGTTTGATCCTGGCTCAG-30/5'-CTACGGCTACCTTGTTACGA-30) is the most widely used primer for species-level identification (Frank et al., 2008) and the cost is not that much, so use bacterial universal primer 27f/1492r maybe better in this study.And you can get more useful information to support the practical application.

Response to Reviewers Reviewer #1:
This paper examines nitrate removal in woodchip bioreactors and which nitrogen cycling genes, and microbial consortia, may drive removal.The paper is written well and significant for the bioremediation field.The major considerations I have for the authors are regarding manuscript organization and explanation of the experimental design therefore more about presentation than about content.I believe the work presented is of high quality but could be improved as outlined below.Please see the comments below.
We appreciate that the reviewer recognized the significance of this study and provided valuable suggestions on improving the overall organization and the explanation of experimental design in this manuscript.
Major comments: 1.The authors repeatedly discuss sampling ports and how nitrogen cycling varies based on port.However, the figure demonstrating where ports are located on the bioreactors are not a part of the main figure.I suggest making Figure S4 your first figure to help guide readers the significance or sampling ports.In a similar vein, why did the authors only consider some ports with statistical analysis and not others (For example, Line 409-410)?This requires additional explanation.
We agree with the reviewer and have moved Figure S4 to the introduction (L82-87, now called Figure 1) and method sections (L292-296).
2. The microbiome analysis should be expanded.The authors provide results on alpha and beta-diversity (ordinations), but that's it.I'd like to see a more thorough assessment of the microbiomes, at least for significant time points that vary, as stated in the text and shown in Figure 3 PCoA.Specifically, what are the dominant taxa present in these (phyla, order, genera) when undergoing biostimulation and bioaugmentation.I also would recommend performing an indicator species analysis, or some other similar analysis like ANCOM, to see if there are specific OTUs which are greater or lower in one treatment versus another.This will provide a more broad snapshot.
We agree with the reviewer and have conducted additional analyses.We now show the relative abundance of major phyla in our samples (Fig. 4) and analyzed the changes in their relative abundance between different time points (season) and treatments.We have used both Kruskal-Wallis test and DESeq2 program to identify the differentially abundant taxa.
"Major phyla identified in this study include Acidobacteria, Actinobacteria, Bacteroidetes, Chloroflexi, Crenarchaeota, Euryachaeota, Firmicutes, Planctomycetes, Proteobacteria, and Verucomicrobia (Fig. 4).Proteobacteria was the most dominant phylum in WBRs with mean relative abundance of 54%.The relative abundances of these phyla were similar among the WBRs with different treatments, except for Chloroflexi and Planctomycetes (p < 0.05 by Kruskal-Wallis test).Nonetheless, the relative abundance of Acidobacteria, Actinobacteria, Bacteroidetes, Chloroflexi, Crenarchaeota, Euryarchaeota, Firmicutes, and Proteobacteria were significantly different between spring (May-June) and fall (Oct.-Nov.)(Fig. S3; p < 0.01 by Kruskal-Wallis test).The same taxa also exhibited differential abundance across different seasons by the DESeq2 analysis."(L144-154) "Proteobacteria occupied the largest proportion of the WBR microbiomes.Most of the bacterial genera that significantly increased their relative abundance in spring (p < 0.01 by DESeq2) belonged to the Proteobacteria phylum.Proteobacteria include many denitrifiers but also exhibit a vast range of morphological, physiological, and metabolic diversities (Kersters et al., 2006;Spain et al., 2009).Even though it plays a crucial role in the cycling of carbon, nitrogen, and sulfur, the highly diverse nature of Proteobacteria makes it difficult to assess its potential functions in WBRs.Additional analysis such as metagenomics may be necessary to assess the potential function of these microbes."(L212-219) We agree with the reviewer have added a description of NRR in the revised text."Nitrate removal rates (NRR in the units of g N m −3 d −1 ), calculated as the difference between inflow and outflow nitrate load, divided by time and the wetted volume of the WBR bed, were significantly higher for the biostimulation treatment than the bioaugmentation and control treatments for Spring 2017 (15.0, 5.8, and 4.4 mg N m -3 d -1 , respectively; p = 0.029) and Fall 2017 campaigns (5.6, 3.9, and 4.1 mg N m -3 d -1 , respectively; p = 0.095) (20)."(L99-104) L113-120: This is describing data and analysis so should go in methods We agree with the reviewer and have moved these sentences to the method section (L383-389).
Figure S1 is correct here.
L126: Can authors please add statistical output for the paired t-tests here and throughout when its mentioned?
We agree with the reviewer and have added a table to summarize the t-test statistics (Table S1).
L141: Not sure how a negative correlation with port number indicates this.I think adding the supplemental figure as stated above will help with this interpretation.
We thank the reviewer's comment.We have revised the text to clarify this sentence.Also, showing the WBR diagram earlier in the manuscript (Fig. 1) could also help readers understand the location of the ports."In addition, the port number was negatively related to almost all of the nitrogen cyclerelated genes, especially the genes involved in the denitrification process.The smaller port number means that the ports are located closer to the bioreactor inlet where nitrate and DO were the highest (Feyereisen et al., 2022).Taken together, these results suggest the uneven distribution of denitrification genes within WBR." (L131-135) L148 and in figures: Is it necessary to indicate or denote genes by "[primer name]"?It may be more organized to represent a different way, if primer name is needed at all.Thank you for the comment.Since microbial nitrogen cycling genes are very diverse, we used multiple assays to target some of the nitrogen cycling genes (e.g., nosZ gene was measured by four different assays).We wanted to show which specific assay could be used to explain the NRR here.L152-157: Should go in methods.
We agree with the reviewer and have moved them to the method section.
"Samples with less than 5,000 reads were removed.As a result, 158 of the 169 samples were used for the downstream analyses.For the 158 samples, the mean and median sequencing depths were 13,153 and 13,275 reads, respectively (Fig. S2)." (L398-401) L158: The alpha-diversity metrics analyzed are never discussed in the methods.This information should be included there.Also, do the authors only evaluate Shannon Diversity?What is the reason for only evaluating that alpha-diversity metric?
We agree with the reviewer and have described how we obtained the alpha diversity metrics in the method section (L474-479).We measured not only the Shannon index but also other alphadiversity metrics, including Simpson, Chao1, and ACE; however, we only presented the Shannon index results as the representative diversity index because results were similar among the metrics and the Shannon index considers both evenness and richness of diversity.We now clearly state this in the revised text.
"Alpha-diversity metrics (e.g., Shannon diversity index shown in Fig. S4) were similar among treatments (p = 0.47 by ANOVA)."(L155-156) "Alpha-diversity metrics including Shannon, Simpson, Chao1, and ACE indices were calculated using phyloseq."(L410-411) L166: The sampling period/time is not easily distinguishable based on the PCoA.I suggest using maybe averages with centroids for each treatment per axis.This will reduce all the noisy data given and then provide the Figure 3 in supplements.
Thank you for your suggestion.We created violin/barplots to show the centroids for each treatment per axis as the reviewer suggested; however, these figures do not show clear difference between treatments or seasons (please see below).To show the difference more clearly between sampling period/time, we now put ellipses on the PCoA plot (see Figure 5 below).L169: Can a table of the perMANOVA results and post-hoc pairwise comparisons be included in a table?
We thank the reviewer for this comment.We however do not think post-hoc pairwise comparison is needed in this case.We detected statistical significance only between seasons by PERMANOVA.Because there are only two treatments (spring vs. fall), post-hot pairwise comparison is not necessary.Also, no significance was detected between bioaugmentation, PCoA biostimulation, and control treatments by PERMANOVA, and therefore, post-hoc pairwise comparison will not provide meaningful results.
L289: Is the inflow rate actually calculated?If so, please include.
We agree with the reviewer and have included the approximate flow rate in the revised manuscript.
"The water flow rate to each bioreactor was controlled by manual valves and was relatively constant at 9 to 10 L/min."(L290-291) L293: More information on the woodchip composition would be nice.Perhaps this is common knowledge in the woodchip microbiome area, but it is not clear to me what types of wood make up woodchips for these experiments.
We agree with the reviewer and have added information about woodchip composition in the revised manuscript.
We agree with the reviewer and noted the range of labile C concentration in the revised manuscript.
"Beginning in Fall 2017, bed flow rate in the bioaugmentation treatment WBRs was stopped or reduced by two-third for 1 to 7 days after inoculation to improve effectiveness of introducing the microbes to the bed." (L303-305) L307 and elsewhere: It's a bit unclear what the time of sampling was over the study period.I'm assuming its twice in May/June 2017, 4x in Oct/Nov 2017, and twice in spring 2018?Please explain.
We collected samples twice in May/June 2017, three times in Oct/Nov 2017, and another three times in spring (May/June) 2018.We have revised the text to clarify this.
"Woodchip samples were collected twice in Spring (May-June) 2017, three times in Fall 2017 (October-November), and three times in Spring 2018 (a total of eight sampling dates)."(L307-309) L310: This is where the port number comes into the picture of the sampling and is never acknowledged until this point.At first, it seemed random but throughout the paper it became clear the reasoning behind port sampling.Can the authors set up the explanation of their experimental design in regards to the ports and sampling regime at the beginning of the methods section?
We agree with the reviewer.We now explain the ports early in the manuscript (Introduction section) as well as in the methods section.
"Each WBR had five vertical pipes filled with woodchip bags for sample collection and analysis (Fig. 1b)."(L86-87) "Five 15-cm diameter PVC pipes ("ports") were installed vertically along the length of each bed that were used for water quality monitoring and woodchip sampling (Fig. 1a).
Into each port was inserted a woodchip basket, which was filled with about 30 woodchip bags, each approximately 8-cm-diameter mesh bag containing about 100 g of woodchip (Fig. 1b)."(L292-296) L390: I've never heard of this pipeline so excuse if I'm missing a detail here.Did the pipeline account for determining contaminant sequences, which are always present in 16S sequence datasets?Also, did it account for chimeric sequences?If not, this needs to be addressed.
Yes, the raw sequences reads were quality-filtered, trimmed, and assembled by NINJA-SHI7.NINJA-SHI7 is a self learning pipeline that could reduce errors and remove technical contaminants from sequencing reads.More information could be found here: "Al-Ghalith GA, Hillmann B, Ang K, Shields-Cutler R, Knights D. SHI7 Is a Self-Learning Pipeline for Multipurpose Short-Read DNA Quality Control.mSystems.2018 Apr 24;3(3):e00202-17.doi: 10.1128/mSystems.00202-17.PMID: 29719872; PMCID: PMC5915699."L399: Include rarefaction sequence level here (I think 5000 was stated elsewhere?) We agree with the reviewer and have included the smallest number of sequences used for analysis.
"The numbers of sequences were normalized by rarefaction at the smallest number of sequences (6,873 reads)."(L407-409) L410: Why port2 and 5? Again, the reasoning for port sampling needs to be clarified.
We agree with the reviewer and have explained the reasoning for the port sampling in the text.
"Paired t-tests were conducted for the average of the twelve denitrification genes in samples collected from Port #2 (locatednear to the WBR inlet) and Port #5 (located near the WBR outlet) using Excel.We appreciate that the reviewer recognized the significance and importance of this study and provided valuable suggestions on improving the overall structure of this manuscript.

Major comment
1.In the study, you used two kinds of technology to describe the impacts of Biostimulation and Bioaugmentation on Woodchip Bioreactor Microbiomes: Nitrogen cycle evaluation (NiCE) chip and 16S gene amplification sequencing, but there are 2 serious issues: 1.1 There are few contents about the microbiome, it should not only include the α-diversity and β-diversity, but also species of microorganisms and their changes with time and port.
We agree with the reviewer and have conducted additional analyses.We now show the relative abundance of major phyla in our samples (Fig. 4) and analyzed the changes in their relative abundance between different time points (season) and treatments.We have used both Kruskal-Wallis test and DESeq2 program to identify the differentially abundant taxa.
"Major phyla identified in this study include Acidobacteria, Actinobacteria, Bacteroidetes, Chloroflexi, Crenarchaeota, Euryachaeota, Firmicutes, Planctomycetes, Proteobacteria, and Verucomicrobia (Fig. 4).Proteobacteria was the most dominant phylum in WBRs with mean relative abundance of 54%.The relative abundances of these phyla were similar among the WBRs with different treatments, except for Chloroflexi and Planctomycetes (p < 0.05 by Kruskal-Wallis test).Nonetheless, the relative abundance of Acidobacteria, Actinobacteria, Bacteroidetes, Chloroflexi, Crenarchaeota, Euryarchaeota, Firmicutes, and Proteobacteria were significantly different between spring (May-June) and fall (Oct.-Nov.)(Fig. S3; p < 0.01 by Kruskal-Wallis test).The same taxa also exhibited differential abundance across different seasons by the DESeq2 analysis."(L144-154) "Proteobacteria occupied the largest proportion of the WBR microbiomes.Most of the bacterial genera that significantly increased their relative abundance in spring (p < 0.01 by DESeq2) belonged to the Proteobacteria phylum.Proteobacteria include many denitrifiers but also exhibit a vast range of morphological, physiological, and metabolic diversities (Kersters et al., 2006;Spain et al., 2009).Even though it plays a crucial role in the cycling of carbon, nitrogen, and sulfur, the highly diverse nature of Proteobacteria makes it difficult to assess its potential functions in WBRs.Additional analysis such as metagenomics may be necessary to assess the potential function of these microbes."(L212-219) As the reviewer mentioned, the two technologies used (16S gene amplicon sequencing and the NiCE chip) are complementary to each other.While 16S analysis can show the structure of microbial communities, the NiCE chip analysis can provide quantitative information on the N cycle-related genes.We consider the 16S analysis as the main technology in this study.To explain the microbial community difference detected by the PCoA analysis (Fig. 5), we used the NiCE chip results in the CAP analysis (Fig. 6).Therefore, the results obtained from the two technologies are combined in this study.We have revised the manuscript to clarify this.
"Constrained analysis of principal coordinates (CAP) was used to explain the variations in the WBR microbiomes by environmental variables and the abundance of N cyclerelated genes (Fig. 6)."(L169-171) 2. Feyereisen et al have published the data of Nitrate-N load removal for biostimulation, bioaugmentation and control treatments (Feyereisen et al., 2022)., it's better to analyze the correlation of Nitrate-N removal to abundance of nitrogen cycle genes and/or microbiome changes.The study will be more amazing and significant after you correlate the Nitrate-N removal to gene changes.Without phenomenon or function of Woodchip bioreactor, only gene or microbiome changes sound not convictive.
Thank you for your valuable comment.We actually analyzed the correlation of Nitrate-N removal to abundance of nitrogen cycle genes.We used the generalized linear model (GLM) to explain the nitrate removal rate (NRR) by the abundance of different N-cycling function genes.
By using the GLM model, we found that the abundance of norB and nosZ had significantly and positively associated with NRR.
Minority comment 1. Line 153-154 (Figure S2), it's more important to describe how deep the sequencing is, and is it deep enough to analyze further (| eg.Rarefaction and Shannon diversity of DNA sequences from samples) rather than describe how many reads you sequenced.
We agree with the reviewer and have calculated the Good's coverage.The mean ± SD coverage are 0.761 ± 0.036.We now describe this in the revised manuscript.
"The numbers of sequences were normalized by rarefaction at the smallest number of sequences (6,873 reads).The mean ± standard deviation of the Good's coverage after rarefaction was 0.761 ± 0.036."(L407-410) 2. Figure S3.Shannon index of samples collected from different treatment groups.It's better to put P value on it or "NS (No significance)" on it.
We agree with the reviewer and have added the p-value (p = 0.47) to Figure S4 (previously Fig. S3). 3. line 193-194," Our NiCE chip data suggests that there are denitrification hotspots in WBRs.The distribution of nitrogen cycle genes inside WBRs is rather uneven" this is an intriguing point but few convictive contents described in the result.
We agree with the reviewer and have added more explanation for this statement.We also analyze the difference in the N-cycling genes abundance between port 2 and port 5 by t-test (Table S1) to support the uneven distribution of N-cycling functional genes.
"The heatmap of denitrification genes showed higher relative abundances in samples collected from port #2, which is near the bed inlet, compared to samples collected from ports #4 and #5, which are closer to the bed outlet (Fig. 2).This was also supported by paired t-test (Table S1)." (L113-116) "In addition, the port number was negatively related to almost all of the nitrogen cyclerelated genes, especially the genes involved in the denitrification process.The smaller port number means that the ports are located closer to the bioreactor inlet where nitrate and DO were the highest (Feyereisen et al., 2022).Taken together, these results suggest the uneven distribution of denitrification genes within WBR." (L131-135) 4. The study used V4 primer to identify the microbiome with 16S amplification sequencing, which only provide information of genus.While primer 27f/1492r (5'-AGAGTTTGATCCTGGCTCAG-30/5'-CTACGGCTACCTTGTTACGA-30) is the most widely used primer for species-level identification (Frank et al., 2008) and the cost is not that much, so use bacterial universal primer 27f/1492r maybe better in this study.And you can get more useful information to support the practical application.
We agree with the reviewer that long-read sequencing (e.g., with 27f/1492r) can provide species-level identification.While the 27f/1492r primer pair is frequently used to identify species with Sangar sequencing technology (e.g., for pure cultures), this primer pair is not commonly used to analyze complex microbial communities with short-read sequencers such as MiSeq.
Primers used in this study (515F/806R) is more commonly used to analyze complex microbial communities, including those for the Earth Microbiome Project.The use of long-read sequencers such as PacBio can allow us to use 27f/1492r primers to identify species in complex microbial communities, but these sequencers also have some drawbacks such as smaller number of sequence reads than MiSeq.Given the number of samples in this study is relatively large (n=169), we believe the use of 515F/806R is appropriate to meet our objectives.
• Manuscript: A .DOC version of the revised manuscript • Figures: Editable, high-resolution, individual figure files are required at revision, TIFF or EPS files are preferred

Figure 4 .
Figure 4. Relative abundance of archaeal/bacterial phyla in the woodchip samples as assessed by the 16S rRNA gene amplicon sequencing analysis.

Figure 4 .
Figure 4. Relative abundance of archaeal/bacterial phyla in the woodchip samples as assessed by the 16S rRNA gene amplicon sequencing analysis.

Figure S4 .
Figure S4.Shannon index of samples collected from different treatment groups.
WBRs) are used to remove nutrients, especially nitrate, from subsurface drainage, but the nitrogen removal efficiency of WBRs is limited by low temperature and availability of labile carbon.Bioaugmentation and biostimulation are potential approaches to enhance nitrate removal of WBRs under cold conditions, and microbiome may work during the procedure.This study is aimed to study the impacts of Biostimulation (labile carbon: sodium acetate trihydrate) and Bioaugmentation (WBR-borne cold-adapted denitrifying bacteria) on Woodchip Bioreactor Microbiome.The top of this study is intriguing, and it is also a timeconsuming work with many factors out of control, but it's better to analyze further.