Microbial assemblages and methanogenesis pathways impact methane production and foaming in manure deep-pit storages.

Foam accumulation in swine manure deep-pits has been linked to explosions and flash fires that pose devastating threats to humans and livestock. It is clear that methane accumulation within these pits is the fuel for the fire; it is not understood what microbial drivers cause the accumulation and stabilization of methane. Here, we conducted a 13-month field study to survey the physical, chemical, and biological changes of pit-manure across 46 farms in Iowa. Our results showed that an increased methane production rate was associated with less digestible feed ingredients, suggesting that diet influences the storage pit's microbiome. Targeted sequencing of the bacterial 16S rRNA and archaeal mcrA genes was used to identify microbial communities' role and influence. We found that microbial communities in foaming and non-foaming manure were significantly different, and that the bacterial communities of foaming manure were more stable than those of non-foaming manure. Foaming manure methanogen communities were enriched with uncharacterized methanogens whose presence strongly correlated with high methane production rates. We also observed strong correlations between feed ration, manure characteristics, and the relative abundance of specific taxa, suggesting that manure foaming is linked to microbial community assemblage driven by efficient free long-chain fatty acid degradation by hydrogenotrophic methanogenesis.

Introduction

Animal production has shifted from pasture systems to confinement facilities as larger, more specialized operations replace smaller less efficient farms to meet inexpensive protein demands [1]. These shifts have resulted in the separation of farrowing and finishing operations, implementation of liquid manure storage systems, and greater use of concentrates in animal rations for swine production. In the Midwestern United States, manure is predominately stored underground in 2.4–3 m deep pits for extended periods (6–12 months). Deep-pits are built within the swine production facility below a slatted floor housing animals to reduce nutrients loss and dilution [2].

Swine growers using deep-pit manure management systems have observed sporadic foam formation. Farms experiencing manure foaming face significant management issues as manure foam limits facility storage space and requires more frequent removal. More importantly, the foam traps methane and other gases resulting in potentially life-threatening fires and explosions [2]. In the Midwestern US, there has been an increase in the frequency of foaming events [3, 4]. The inability to replicate this phenomenon in the laboratory makes ascertaining the cause of foaming a challenge [5, 6]. Consequently, the only knowledge of its causes is mainly limited to its correlation with high (≥ 0.1 L methane / L manure • day) methane production rates (MPR), with little knowledge of its abiotic or biotic drivers. In this study, we performed a large-scale characterization of over 500 manure samples collected monthly for 13 months across 46 swine farms in Iowa to understand the dietary and microbial associations that drive manure physical-chemical change.

Materials and methods

Experimental design

Over 500 manure samples were collected from 46 farms deep pits (8ft) within Iowa over thirteen months (S1 Fig). The farms worked with two integrators and received feed from four feed mills (S1 Table). Data were collected to describe each farm’s feed ingredients. Manure sampling procedures and characterization (e.g., total solids, methane production rate) have previously been described [7]. Additional manure characterization (i.e., pH, moisture, organic N) was performed by Midwest Laboratories (Omaha, NE). At collection, the manure storage surface was characterized as non-foaming (no-foam, with direct access to liquids), crust-forming (crust, with a hard and dry layer on top), or foaming (foam, with visible bubbles) (S2 Fig).

DNA extraction and sequencing

DNA was extracted from samples obtained from the top layer of non-foaming pits or the transition layer below the foam or crust in other pits (S3 Fig, layer B). Additionally, a subset of manure slurry samples was used to characterize methanogen populations (S3 Fig, layer C). The genomic DNA was extracted from 200 mg manure samples using the FastDNA SPIN Kit for Soil (MP Biomedical).

Extracted DNA was sequenced to characterize bacterial and methanogen communities. To identify bacteria in manure samples, the V4-V5 region of the 16S rRNA gene was amplified with primers 515F 5’-GTGCCAGCMGCCGCGGTAA-3’ and 924R 5’-CCGTCAATTCMTTTRAGT-3’ with barcodes and Illumina adaptors added as previously described [8]. The methane-production gene, methyl-coenzyme A reductase (mcrA), was used to identify methanogens in manure samples. The mcrA gene was amplified using barcode and Illumina adaptor added primers mlas 5’-GGTGGTGTMGGDTTCACMCARTA-3’ and mcrA-rev 5’-CGTTCATBGCGTAGTTVGGRTAGT-3’ [9]. Every 50 ul PCR reaction contained 25uL 2X KAPA HiFi HotStart ReadyMix (KAPA Biosystems, Woburn, MA, USA), 500 uM each primer, 50 ng template DNA, and 21uL DNA-free water. Thermal cycling conditions for this reaction included an initial denaturation at 98°C for 45 sec., 30 cycles of 98°C for 10 sec., 55°C for 30 sec., 72°C for 30 sec., followed by a final extension at 72°C for 2 min. The first 15 cycles had a temperature ramp rate at 0.6°C/s, and the next 15 cycles had a temperature ramp rate at 3°C/s. The primer-dimers were removed using 0.8 X volume of AMPure® XP beads (Agencourt Bioscience, Beverly, MA, USA).

For sequencing library preparation, an equal amount of amplicons from each sample were pooled together. The pooled samples were sent to Roy J. Carver Biotechnology Center (Urbana, IL, USA) for sequencing on an Illumina MiSeq instrument with a 2 x 250 bp reads configuration using Nano Kit v2 (Illumina, San Diego, CA, USA). All sequences are deposited in National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) with accession numbers SRR5564278—SRR5564520 and SRR5566243—SRR5566590.

DNA sequence processing

Pair-ended bacterial 16S rRNA gene sequences were assembled using the Ribosomal Database Project (RDP) Paired-end Reads Assembler7 with minimal overlap of 80 bases (-o 80) and minimal assembled length 350 bases (–l 350). Assembled sequences with an expected maximum error-adjusted Q score less than 25 over the entire sequence were eliminated. Usearch (8.1, 64bit) [10] was used to remove chimeras de novo, followed by removing chimeras of known reference genes using the RDP 16S rRNA gene training set sequences (No. 15). High quality and chimera-filtered sequences were clustered at 97% sequence similarity by CD-HIT (4.6.1) [11], resulting in identifying unique operational taxonomic units (OTUs) and their abundance in each sample. CD-HIT was used because of its speed and previously demonstrated production of clusters highly similar to the actual number of OTUs from simulated complex data [12, 13]. Each representative OTU sequence’s taxonomy was identified based on RDP 16S rRNA database using RDP Classifier [14] with a confidence cutoff at 50% (-c 0.5). At least 98.26% of the OTUs could be identified at the bacterial phylum level. To preserve the microbial community composition and avoid additional biases, we opted to remove questionable OTUs and inadequately sequenced samples and use raw counts and relative abundance for downstream analyses [15, 16]. Specifically, OTUs that were observed fewer than five times across all samples were removed, and samples with less than 10,000 sequence reads (< 0.97 Good’s coverage index) were also excluded from the analysis, resulting in a total of 488 samples used for analyses (S2 Table).

The methanogen-associated mcrA gene sequences were processed similarly to bacterial 16S rRNA gene sequences with the following modifications. The assembled mcrA gene sequence length was restricted between 400 and 460 bases. After de novo chimera removal, a non-redundant version of the previously published mcrA database was used as the reference dataset to remove any chimeras that were missed by de novo methods [17]. The dataset was then used to construct a Basic Local Alignment Search Tool (BLAST) database to identify the methanogen OTU taxonomy using BLAST+ (2.2.30) [18]. The sequences matched were all significant (with a maximum expected error of 1E-78, and a minimum percent identity of 79%). Similar to the bacterial sequences, methanogen OTUs observed fewer than five times across all samples were removed, and samples with less than 4,000 sequences (< 0.99 Good’s coverage index) were excluded from the analysis (S2 Table).

Statistical analyses

Diet information and manure characteristics were analyzed for significant correlations with foam, crust, and no-foam manure using Bayes factor analysis [19]. This analysis was performed in R (3.2.4) using the package BayesFactor (0.9.12–2) [20]. Correlations were estimated between dietary inputs and manure characteristics. Monotonic relationships were evaluated using Spearman’s correlation analysis, and non-monotonic relationships were evaluated using the Hoeffding dependence test [21]. The significance of each relationship (p-value) was adjusted for false discovery rate using the method developed by Benjamini & Hochberg [22]. If the adjusted p-value of Spearman’s correlation was less than 0.05, the relationship was considered monotonic, and Spearman’s correlation coefficient (ρ) was used to determine how strong the correlation was. If the adjusted p-value of the Hoeffding dependence test was less than 0.05, but the adjusted p-value of Spearman’s correlation analysis was greater or equal to 0.05, the relationship was considered non-monotonic. The Hoeffiding dependence coefficient (D) was used to determine the strength of the correlation. The correlation analysis was performed in R (3.2.4) using packages Hmisc (3.17–3).

Similarities between bacterial communities identified in foam, no-foam, and crust samples were evaluated. To distinguish the most critical factors contributing to the bacterial community variations, we standardized the OTU abundance across all samples by the total number of sequences per sample. The Bray-Curtis distance was calculated to evaluate the community dissimilarities among samples. Permutational multivariate analysis (PERMANOVA) was used to independently test if diet ingredients and manure physical and chemical characteristics impacted microbial community variance between foaming and non-foaming storages. The community ordinations were performed in R (3.2.4) using packages Vegan (2.3–5). Additional descriptions of the statistical analyses are in the S1 File.

Results and discussion

Manure surface textures

Manure samples were visually categorized based on surface texture into three types: 1) foam, 2) no-foam, and 3) crust. Manure surface textures varied greatly from farm-to-farm and within a farm throughout the 13 months (S1 Fig). No distinct patterns of manure surface texture over time were identified within individual pits. Overall, we observed that 40–60% of pit samples collected each month were foam samples. Additionally, the proportion of no-foam samples collected decreased from over 50% in 2012 to less than 30% in 2013. The proportion of samples with crusts increased from less than 8% to above 32% from 2012 to 2013 (Fig 1). Together, these trends suggest that manure foaming was persistent, and long-term stored manures were likely to form crusts or foam during the time of this study.

Fig 1

The proportions of manure samples with different surface textures collected over 13 months, where “no-foam” represents non-foaming manure, “crust” represents crust-forming manure, and “foam” represents foaming manure.

The lines are the fitted trend lines showing the changes in percent of different type of samples collected over the study.

Specific feed ingredients were observed to influence manure foaming

The composition of swine feed was observed to influence characteristics of the manure surface texture in pits. No-foam manures were associated with more digestible feed, and foam and crust manure associated with the less digestible feed. In particular, soybean meal (SBM) levels were significantly higher in diets associated with no-foam manure, and the proportion of distiller’s dried grains with soluble (DDGS) was significantly higher in feed given to swine from foam and crust manures (Table 1). SBM is more digestible than DDGS due primarily to DDGS formulated diets having higher neutral detergent fiber (NDF) contents than SBM-based diets [23-25]. Lower digestibility of DDGS diets increases the amount of fecal output and significantly increases manure foaming potential (Table 1) [4, 24, 26, 27]. Increasing poorly digestible diet components, including NDF, can induce an anti-nutritive effect in swine [28]. While neutral detergent fiber has lower digestibility by pigs, the excreted partially degraded NDF particles are rich in plant polysaccharides. These polysaccharides are highly digestible by anaerobic microbial communities, which support manure fermentation in the storage pit. Manures from swine fed high fiber diets generally have increased methane production rates and organic N content [29, 30], both of which were significantly associated with manure foaming (Table 1). These results suggest the feed ingredients’ digestibility impacts the formation of different manure surface textures. Foaming was associated with less digestible diets, and crust-forming manure was associated with high crude protein, crude fiber, and acid detergent fiber (ADF) (Table 1).

Table 1

Diet and manure characteristics in manures with different surface textures.

	Number of Samples
Significant Contributors	No-foam	Crust	Foam	Observed Trends	BF10	P-value
Soybean Meala	157	95	228	No-foam > Crust > Foam	24.25	0.0396
Crude Proteinb	157	95	228	Crust > Foam > No-foam	23.94	0.0401
ADFb	157	95	228	Crust > Foam > No-foam	656.57	0.0015
Crude Fiberb	157	95	228	Crust > Foam > No-foam	2358.38	0.0004
Manure Temperature	149	96	223	Crust > Foam > No-foam	35922.64	< 0.0001
Manure Depth	162	98	228	Crust > Foam > No-foam	5.42E+08	< 0.0001
DDGSa	157	95	228	Foam > Crust > No-foam	40.23	0.0243
NDFb	157	95	228	Foam > Crust > No-foam	1920.51	0.0005
CH4 Production Rate (slurry)	143	77	176	Foam > Crust > No-foam	1.32E+09	< 0.0001
Organic N	141	77	172	Foam > No-foam > Crust	23.27	0.0412

a. Major diet ingedients and supplements.

b. Diet components as formulated.

c. P value calculated as p(H0|D).

Bayes factors are in column BF10. Column P-value shows the posterior likelihood of observed trends not occurring.

Significant correlations between different carbon compounds observed in deep pit manure

The formation of no-foam, crust, or foam manure-surfaces is likely due to various physical-chemical interactions. Thus, we identified the most significant correlations among manure physical-chemical properties (S4 Fig). We found that manure physical properties, especially a higher surface tension and lower foaming capacity, had the strongest correlations with non-foaming manures. In contrast, strong correlations among chemical properties were observed in foaming manures. Specifically the correlations among free long chain fatty acids (LCFA), short-chain fatty acids (SCFA), carbon content, and pH (Fig 2). Crust manures were correlated with physical and chemical measurements, such as surface tension and high manganese and potassium content. These observations suggest that the manure surface texture change from no-foam to foam is associated with a shift from the dominance of correlations among physical properties to correlations among chemical properties, with crusts as an intermediate.

Fig 2

The five strongest manure characteristic associations in foam (purple), crust (orange), and no-foam (green) manure samples. Bars extending towards left represent negative correlations and bars extending towards right represent positive correlations.

Chemical interactions have previously been associated with foaming. Yan et al (2015) found that the concentration of LCFA were the greatest in the manure’s foam layer, with lower LCFA concentration observed in the liquid portion of the foaming manure compared to non-foaming manure [31]. In this study, we only evaluated the concentration of LCFA in the liquid portion of the manure. We found that the LCFA from the swine manure were predominately C16 and C18 compounds. Consistent with the previous findings, the average concentration of LCFA in the foaming manure liquid was lower than the non-foam manure liquid, although the difference was not significant. However, the concentration of LCFA was significantly enriched in crust-forming manure, with 15 and 22 times more observed than no-foam and foaming manure, respectively. This is surprising as animals associated with the foaming manure were fed significantly more DDGS, which contains more lipids than other diet ingredients, such as SBM (Table 1) [6]. Dietary lipids are main sources of LCFA and it is natural to expect the amount of LCFA measured corresponds to the amount of lipids received. However, our observation that the concentration of LCFA in manure did not correlate with the amount of DDGS in diet suggests that the accumulation of LCFA in manure alone is insufficient to explain the manure foaming.

Acetic acid comprised more than 50% of the total SCFA measured from the pit manure samples, with propionic and butyric acid being the second and third in abundance. The proportion of each SCFA out of the total amount of SCFA detected did not differ significantly among manure types. However, in foaming manure, a significant strong positive correlation between LCFA and SCFA was observed, which was absent in no-foam and crust manure (Fig 2). This observation highlighted the potential for the chemical conversion of LCFA to SCFA as an essential step in forming foaming manure. Further, it would be consistent with the enrichment of LCFA only in crust manure, as it may be converted efficiently to SCFA in foaming manure. Generally, SCFA, including acetic acid, and LCFA are microbial metabolites produced by gastrointestinal microorganisms and are important nutrients consumed by host animals and gastrointestinal microorganisms [32-34]. As fecal matter is excreted into manure pits, these fatty acids likely continue to support microbial metabolism in manure pits. While various microorganisms directly assimilate SCFA, LCFA generally cannot be readily utilized by most microbes until it is degraded to SCFA [35-37]. The significant correlation between LCFA and SCFA in foaming manure suggests that the foaming manure-associated microorganisms are efficient at converting LCFA to SCFA and supporting the growth of methanogens. The study limitation is that only the endpoint accumulations of LCFA and SCFA were measured, which do not reflect the rate of depletion and production. However, our results indicate a clear hypothesis that increased conversion of LCFA to SCFA would result in foaming in manure pits. This observation is also consistent with previous results indicating that the degradation of LCFA to SCFA is an important fermentation step in methane production from lipids [38, 39].

There were significantly more SCFA (p = 0.0046) and acetic acid (p = 0.0014) in the no-foam manure than foaming manure. On average, 12.4 mg/g and 7.3 mg/g of SCFA was detected in the no-foam and foaming manure, respectively, while 7.5 mg/g and 4 mg/g of acetic acid was detected in the no-foam and foaming manure, respectively. SCFA is not the thermodynamically preferred microbial fermentation end-products and SCFA accumulation is known to inhibit methanogenesis in anaerobic fermentation [40]. Consequently, the MPR in non-foaming manure was significantly lower than those of foaming manure (Table 1). Although a large quantity of SCFA could reduce manure pH, which may impact methanogenesis as well [41], the pit manure pH averaged at 8.2 and did not differ significantly between manure types. Thus, SCFA likely had little influence on the manure pH and the manure pH did not contribute to the observed MPR differences among manure types.

Under anaerobic conditions, the breakdown of LCFA is carried out via acetogenesis [35, 42]. This process is endogenic and does not occur spontaneously under standard conditions. However, by coupling methanogenesis, an excess amount of SCFA can be removed and result in an overall exogenic reaction [42]. Therefore, methanogenesis is an important step in efficient anaerobic LCFA degradation. Together with our observations, manure foaming is associated with efficient anaerobic LCFA degradation with two interlinked components: acetogens that break down LCFA to SCFA and methanogens that remove SCFA and turn the overall reaction spontaneous. In addition, recent studies found that anaerobic fatty acid chain elongation could effectively conserve energy in the absence of methanogens [43, 44]. Thus, this could be an alternative SCFA processing pathway in no-foam manure if methanogenesis was indeed inhibited and should be evaluated further in future studies.

Foam, no-foam, and crust samples contain distinct bacterial and methanogenic communities

Consistent with the observation of varying metabolites in the varying manure textures, the microbial community composition in foaming and non-foaming manures significantly differed from each other (Fig 3). Variations among microbial communities associated with the different manure types were identified by sequencing the 16S rRNA gene and methyl coenzyme M reductase (mcrA) gene, conserved phylogenetic markers in bacteria and methanogens, respectively. Bray-Curtis dissimilarity revealed that the farm where the manure sample originated contributed to the largest variation in microbial community structures (R2 = 0.48 for bacteria, R2 = 0.83 for methanogens) (S3 Table). This is consistent with the findings of previous studies where individual storage tank explained the manure microbiome the best, better than diet, suggesting that the unique microbiome of each manure storage tank is contributing to the manure condition [5, 6]. Given the high variability among farms, we treated individual farms as experimental blocks and found that bacterial communities differed significantly, with the greatest variation (P = 0.001, R2 = 0.12) among no-foam and foaming manures (Fig 3A). Similar significance patterns were also observed in methanogenic communities (P = 0.001, R2 = 0.30) (Fig 3B).

Fig 3

Non-metric multidimensional scaling (NMDS) analysis of bacterial communities (panel A) and methanogen communities (panel B) by the Bray-Curtis distances calculated using the relative abundance of microbial operational taxonomic units (OTU). The ellipses represent 95% confidence level around the centroids of manure samples with different surface textures. The microbial community variations among manure samples with different surface textures were assessed using Permutational Multivariate Analysis of Variance Using Distance Matrices (PERMANOVA). The methane production rates (MPR) were modeled to overlay the observed community differences (Contour fitting). The grey background shows the fitted MPR based on the measured MPR, with darker grey represents higher MPR.

Next, we examined whether the microbial communities of a specific manure type had strong correlations with MPR. We compared MPR measurements to the distribution of observed taxa containing the mcrA gene in manure samples. The correlation between MPR and microbial community distribution revealed a strong association between increased MPR and manure pit methanogenic communities (R2 = 0.54, p < 0.001, Fig 3B). The methanogen communities of foaming manure differed from no-foam and crust-forming manure significantly (R2 = 0.3, p = 0.001) and was associated with high MPR, suggesting foaming manure may contain unique methanogens. Notably, although bacterial communities of non-foaming, crust-forming, and foaming manure also differed significantly (R2 = 0.12, p = 0.001), the bacterial community differences did not correlate well with MPR (R2 = 0.04) (Fig 3A). Overall, these results suggest that MPR in swine manure storage pits is significantly related to methanogen community compositions. The bacterial communities are not directly related to methane production but may indirectly contribute to the stability or persistence of the foam.

Overall, the bacterial communities in foaming manure and crust-forming manure were significantly more stable than those in non-foaming manure (Fig 4). This result is consistent with our observation that pits, once foaming, persist with foam continuously (S1 Fig). Andersen et al. (2018) previously found that the bacterial communities of no-foam manure were more prone to changes upon an antibiotic’s addition (i.e., ionophore). In contrast, the foaming manure communities were much more resistant to this perturbation [45]. This study further corroborates their findings and suggests foaming-manure microbial communities are less likely to shift with disturbances.

Fig 4

The manure-associated community dissimilarity as a function of time (y = a•exp(b•x)), where a smaller slope (b) suggests a smaller dissimilarity over time. Distribution of slopes was estimated by bootstrapping each group of samples 999 times. The slope estimated for non-foaming samples was significantly greater than those estimated for crust-forming and foaming samples (overlap coefficient = 0.0467).

To minimize the community variations unique to individual farms, microbial OTUs present in all samples associated with the same surface texture were selected to represent core no-foam, crust, and foam bacteria communities (e.g., “core” no-foam, crust, and foam communities). The number of shared and unique bacterial and archaeal taxa among the three manure textures that were significantly different in observed abundances were identified (Fig 5A, 5B). Shared between foaming and non-foaming manures were taxa associated with the phyla Bacteroidetes, Firmicutes, Proteobacteria, and Spirochaetes. These phyla were broadly present in manures, but their proportional abundances at the genus level differed significantly (BF10 > = 100) among no-foam, crust, and foam manures (Fig 5C), suggesting that different bacterial assemblages are associated with the varying manures. For example, broadly distributed in all manure samples were genes related to the archaeal phylum Euryarchaeota and specific unclassified archaea, but their specific abundances varied in different manure types. At the species level, the Euryacheaota Methanosphaera stadtmanae was significantly more abundant in no-foam manure (BF10 > = 20), and the unclassified archaea were the most abundant in foam manure (BF10 > = 20) (Fig 5D).

Fig 5

The distribution of shared and unique A) bacterial and B) methanogenic core of no-foam, crust, and foam manure samples. The numbers represent the operational taxonomic unit (OTU) counts. Panel C shows the ten most relatively abundant bacterial groups at the genus level where relative abundances were significantly different among no-foam, crust, and foam manures. Methanogen groups that differed significantly in relative abundance among different types of manure are shown in panel D. Individual bar represents the average of a microorganism relative abundance in the specified manure type, while individual error bar represents the 95% confidence interval calculated using bootstrapping method. Within panel C and D, the labels on the right side indicate the manure type in which the bacterial or methanogen groups were the most relatively abundant in.

Generally, we observed that deep-pit manures contained high acetic acid (average 4.4 mg/g). However, no sequences related to acetic acid-degrading methanogens, specifically Methanosarcinaceae- or Methanosaetaceae-affiliated mcrA genes, were detected. Members of these two families are the only known methanogens that can directly produce methane by cleaving acetic acid (acetoclastic methanogens) and are usually enriched in high acetate environments [46, 47]. Instead, hydrogenotrophic methanogens, such as Methanobacteria and Methanomicrobia, were abundant in the manure storage pits. Studies have found that ammonium can inhibit the growth of acetoclastic methanogens, and in an environment with a large amount of acetate but a lack of acetoclastic methanogens, acetate oxidation in conjunction with hydrogenotrophic methanogens may play an important role in anaerobic methane production [47, 48]. Therefore, methane production in swine manure store pit is likely solely carried out by hydrogenotrophic methanogens.

To better evaluate these taxa interactions and the metabolites associated with manures, relationships between relative taxa abundances and results from manure chemical analysis were explored. In no-foam manure samples, the SCFA concentration was positively correlated with a Bacilli (Lactobacillus) OTU and negatively correlated with Clostridia members (Fig 6). This result is in line with previous observations that lactic acid-producing bacteria may produce SCFA and subsequently inhibit the growth of Clostridia members [49, 50]. The manure SCFA content was also negatively correlated with a Turicibacter sp. (Erysipelotrichia member) in no-foam manure. Little is known about Turicibacter, except that they have been previously identified as important animal microbiota members[51]. In contrast to no-foam manures, no significant correlations between individual bacterial taxa and SCFA content were identified in foam manure (Fig 6).

Fig 6

The core significant and strong correlations between bacteria and methanogens, bacteria/methanogens and dietary inputs, and bacteria/methanogens and manure characteristics.

Individual rectangle labels represent dietary input, manure measurements, or bacterial and methanogenic groups at the class level. The solid lines represent positive correlations and the dashed lines represent negative correlations. A thicker line indicates a stronger correlation. Correlations observed in no-foam, crust, and foam manure were shown in green, orange, and purple, respectively. Looped correlations indicate that relationships were observed among members of the same microbial groups.

Different Clostridia members were positively correlated with the concentration of LCFA in both non-foaming and foaming manure, Sporobacter sp. in foaming manure, and Sporobacterium sp. in non-foaming manure (Fig 6). Unique to foaming manure, a single unclassified Firmicutes OTU was also positively correlated with the concentration of LCFA. Although Sporobacter and Sporobacterium members were previously reported in lipid abundant methane-producing anaerobic systems, they are not known to be LCFA degraders [52-54]. Therefore, we suspect that the unclassified Firmicutes played a role in converting LCFA to SCFA in the foaming manure. The lack of correlation between LCFA and bacteria in the crust-forming manure is consistent with the observation of LCFA accumulation, suggesting little degradation of LCFA occurred in the crust-forming manure.

Intriguingly, Clostridia were positively correlated with Methanomicrobia and Methanobacteria in both no-foam and crust-forming manure, respectively, while Bacteroidia were positively correlated with unclassified Archaea in foaming manure. While members of both Clostridia and Bacteroidia are known hydrogen producers that support methanogenesis, a high abundance of Bacteroidia has previously been found in high methane-producing anaerobic bioreactors [55-57]. The synergistic interactions between bacteria and methanogens could explain the uniquely high MPR observed in the foaming manure, despite hydrogenotrophic methanogenesis being the main methane-producing pathway in all manure storage pits in the study.

Conclusions

In summary, our key findings based on characterizing the foaming conditions of manure pits in 46 Iowa farms are: (1) foaming is associated with increased levels of indigestible fiber; (2) manure LCFA concentration is strongly correlated with the manure SCFA concentration in foaming manure only, suggesting direct conversion from LCFA to SCFA was an essential step in manure foaming; (3) different synergistic interactions between bacteria and methanogens correlated with different methane production rates; (4) specific taxa are associated with foaming, non-foaming, and crust manures; (5) a lower MPR in non-foaming pits is likely due to both the accumulation of SCFA and a less efficient combination of bacteria/methanogen group; (6) foaming microbial communities are relatively more stable compared to non-foaming microbial communities, suggesting the development of a mutualistic microbial relationship in the foaming manure.

Based on the observation of specific taxa in different manure textures, we hypothesize that there are differences in the efficiency of manure organic matter anaerobic fermentation involving the degradation of LCFA (Fig 7). Specifically, we observed that the microbial community of foaming manure can completely degrade manure organic matter to methane via LCFA and SCFA conversion, as evidenced by no significant accumulations of intermediate metabolites. In contrast, in crust-forming manure, manure organic matter fermentation is stalled during LCFA degradation, and we observed a significant LCFA accumulation. No significant correlation was observed between the concentrations of LCFA and SCFA in no-foam manure; however, the significant accumulation of SCFA may be due to the production of an excess amount of SCFA from manure organic matter or the inefficient utilization of SCFA in no-foam manure. The limitation of this study is the lack of fatty acid rate of production and consumption, which should be investigated in future studies. Despite the limitation, our hypothesis expands upon our key findings that foaming is most likely directly related to specific unclassified methanogens and their relationship with specific hydrogen producing bacteria, which are supported by metabolites in manures and their interactions with feed fiber. For management of foaming manures, future research would benefit from understanding the stability of the foam. We suggest that there are key taxa that are involved in the conversion (Firmicutes, foaming) or lack of conversion (Lactobacillus, non-foaming) of LCFA to SCFA. We highlight these taxa as potential membership that can be used as resources for foaming manure management. For example, the impacts of the addition of Lactobacillus for mitigating the transition of non-foaming to foaming pits would be a key area for future research. The observed stability of the foaming manure and its microbial communities present risks to managing foam and its methanogen production. For long term manure storage, these risks are further heightened as trends indicate that DDGS will have increased use in feed [58]. In this regard, the identification of alternative resources, such as microbial additions or treatments, would be of value for providing safe and sustainable manure management.

Fig 7

The predicted manure fermentation processes in crust-forming, non-foaming, and foaming manure.

The solid and dashed lines represent efficient and inefficient processes, respectively. The circles represent the fermentation by-product and the large circle indicates the accumulation of the by-product. The microorganisms that strongly and positively correlated with the by-product are listed above the circles.

The manure samples and their surface textures collected from 46 farms from October 2012 to October 2013.

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Manure samples with different surface textures, from left to right: Non-foaming, non-foaming, foaming, foaming, crust-forming, and foaming manure.

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The vertical profile of manure in the storage facilities.

Layer A describes manure surface texture. Manure characterizations and bacterial community analyses were performed on samples from layer B. Samples from layer C were used to measure methane production rates (MPR) and for methanogen community analyses.

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The significant correlations among dietary information and manure characteristics.

The major dietary inputs were separated into ingredients and supplements (red bars) and nutrients (blue bars). Green bars represent manure characteristics. Within each grid, the larger the square box, the stronger the correlation. The square box color in each grid shows the type of manure the correlation was observed in. A grid with multiple boxes suggests it is a common correlation found in manure with different surface textures. A grid with a single box suggests it is a unique correlation found in that particular type of manure as the color indicated.

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General information of farms where manure samples were collected from.

(PDF)

Click here for additional data file.

Final number of samples used in the study after removing samples with missing manure characteristics and dietary information and samples with low sequencing coverage.

The final average bacterial sequencing depth and coverage were reported in columns 3 and 4. The final average methanogen sequencing depth and coverage were reported in columns 6 and 7.

(PDF)

Click here for additional data file.

Permutational multivariate analysis of variance of microbial communities with different experimental factors.

Bray-Curtis dissimilarities were calculated using operational taxonomic unit (OTU) relative abundance.

(PDF)

Click here for additional data file.

Additional supporting analysis methods.

(DOCX)

Click here for additional data file.

9 Apr 2021

PONE-D-21-06556

Microbial assemblages and methanogenesis pathways impact on methane production and foaming in manure deep-pit storages

PLOS ONE

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Reviewer #1: Partly

Reviewer #2: Yes

Reviewer #3: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors compared physical and chemical properties of different types of swine manure pit samples (no-foam, foam, and crust), and made associations of these with the bacterial and methanogen community structure, and methane production rate. The work is interesting and generally well-performed, and it does shed light on some of the differences in these sample types. Despite these advances, the reviewer feels that the authors could have extended their observations by delving a bit more into the characteristics of the samples, particularly with regard to the SCFA and LCFA (see Specific comments below), as both types of compounds are major substrates or intermediates and their metabolic fates have a strong influence on methane production and, presumably, microbial community structure.

A second issue regards the pH of the samples, and how it might relate to SCFA concentration and to methane production. It appears from L66 and Supplemental Figure S3 that pH was measured, and there was a slight correlation of pH with some of the other measured characteristics (particularly nutrients), especially in the no-foam manures, but little correlation of pH with SCFA. This should perhaps be pointed out somewhere in the text. It would also be useful to indicate the range of pH values encountered, and whether they differed significantly among the manure types. As the aceticlastic methanogens tend to be more sensitive to acidic conditions than are the hydrogenotrophic methanogens, could pH explain the general absence of the former group in these samples?

Specific comments:

L50: The authors bring up the tendency of foaming to cause a potential for explosion, but in the Results we learn that the foaming samples showed lower rates of methane production than did the other samples. Presumably the foaming causes methane accumulation even if it is produced in lesser amounts. This should be mentioned in the Discussion.

L64-67: The methods description is inadequate. Reference 4 does not provide analytical methods, and no methods are described for LCFA and SCFA analysis.

L115, L126: How were these sequence numbers (10k for bacteria, 4k for methanogens) selected, as opposed to normalizing the number of reads selected to number of reads in the sample with the fewest reads?

L158-161: It would be very useful to be more specific regarding these sample types, perhaps by including photographs (even if included in Supplementary data).

L181-183: Another important difference between SBM and DDGS is the amount and type of LCFAs, which should have some bearing in discussion of the importance of LCFA and its metabolism in the three sample types.

L209-211: This seems like a superficial distinction, as the “physical interactions” must have some sort of underlying chemical basis (e.g., surface tension is probably governed by the relative contributions and hydrophobic/hydrophilic behavior of proteins, lipids and polysaccharides).

L228-254: In this section the authors discuss SCFA and acetic acid in the samples without providing any data on the amounts and distributions (i.e., different chain lengths) of these acids. Such information would be very useful in interpreting potential bottlenecks in LCFA degradation to SCFA, and SCFA catabolism to methane.

L256: This is an oversimplification. Perhaps the authors should add “under standard conditions”. The process of LCFA degradation to SCFA typically operates near equilibrium, so that it is quite sensitive to the relative concentrations of substrate and product. But it does not typically require input of energy, and the authors seem to suggest this in the following sentences, in which they state that methanogenesis provides thermodynamic displacement via H2 consumption, allowing SCFA catabolism to occur.

L320-321: It is not clear to the reviewer how the individual slope values for the three types of manures can be compared to yield statistically significant differences, as there is only a single slope value for each sample type..

L349-360: These are interesting observations, but they raise the question of where the acetic acid goes. Working back to comments to L228-254 above, is it possible that some members of the bacterial community might be engaged in chain elongation reactions, in which acetate is converted to longer SCFA such as butyrate and caproate? Chain elongation has been widely observed in waste pits and anaerobic reactors in which aceticlastic methanogenesis is limited (for example, see Zhu et al., DOI: 10.1038/srep14360, or Angenent et al., DOI: 10.1021/acs.est.5b04847).

L398-401: What is meant here by the vague statement regarding “the different potential involvement of bacteria or methanogens”?

L421-424: The reviewer is not convinced by this statement. Why can’t the accumulation of SCFA be due to impaired catabolism of SCFA, rather than invoking a greater production of SCFA from other feed components?

Table S2: Please define (in a footnote) the term “integrator”.

Minor edits:

L45-46: Delete “However,” and use rest of sentence as the first sentence of the next paragraph.

L50: Delete “conditions of”.

L185: Change “low” to “poorly”.

L229: Here and elsewhere in the manuscript, change “SCFA/acetic acid” to just “SCFA”, because (as pointed out in L233-234) acetic acid is one of the SCFA.

L277: Change “between” to “among”.

L329: Change “genera” to “genus”.

L347: Change “that the bacterial or methanogen groups were the most abundant in” to “in which the bacterial or methanogen groups were the most abundant”.

Reviewer #2: Manuscript entitled "Microbial assemblages and methanogenesis pathways impact on methane production and foaming in manure deep-pit storages" can be considered as high importance. This manuscript provides relatively new and unique dataset to tackle the human, animal and environmental risks associated with swine industry. The study provided a relatively large dataset to support the finding. Manuscript is well written and deserve publications.

Few things, however, authors are suggested to consider: 1) provide the details of farm (herd size; pit size; and existing manure management in farms where samples were collected); 2) Figure 1 requires further descriptions in Figure captions (currently not clear); 3) method section requires further clarifications improvement in terms of sampling protocol, and the strategies implemented in sample collection; 4) before ending results and discussion section, authors are suggested to provide major findings quantitatively, and limitations of the study; and 5) discussion on the composition of gas formation, ammonia, and microbial community identified in Figure 5,6.7. Any pathogens, in addition to methanogens were identified?

Further, in statistics table, authors are suggested to provide the sample numbers used for the analysis. In terms of feed characterization, not sure if the feed of individual farms has been characterized or the feed from mills which supplies the feed has been characterized. Each pit will have certain holding capacity, and the retention time will be having impacts of microbial community. Therefore, the conditions of pits (mixing, retention time, temperature, flushing frequency, and the frequencies of disloading of those pits are important consideration while describing the shift in microbial community under anaerobic conditions.

Reviewer #3: This manuscripts describes the bacterial and methanogenic communities of manure collected from 46 swine farms at several different time points. The authors visually classified the manure surface as either non-foaming, crust-forming or foaming at each sampling time.

Certain chemical properties of the manure were also assessed. The authors report that the swine diet influences the manure surface texture and that certain chemical and physical properties were correlated with the manure surface texture as well. The non-foaming and foaming manure bacterial and methanogenic microbial communities were most dissimilar from each other.

Major comments

The materials and methods for the manure sampling and chemical analysis appear to be missing. The superscript “4” is included at the end of the sentence on ln 65 but it is unclear if this is referring to REF 4 or something else.

Minor comments

In Table S2 should the P-values for Farm and Feed Mill for bacteria also be 0.001?

Can the x-axis in Fig. 5 C and D be converted to percent relative abundance rather than the current scale which is not very intuitive.

Ln 24: “an increased”

Ln 26-27: “…rRNA and archaeal mcrA genes…”

Ln 29: “and that the bacterial…”

Ln 31: “previously” should be removed since they are still uncharacterized.

Ln 33, 362: “relative abundance”

Ln 50: I would remove “conditions of”

Ln 81: “mcrA gene was”

Ln 111-113: What database was used for 16S rRNA gene classification?

Ln 121: “chimeras”

Ln 180: What is meant by “more significant” here? A greater concentration of DDGS? Also, what does the superscript “19” refer to here?

Ln 182: “NDF” should be first defined here rather than on ln 185.

Ln 205, 407: “were correlated”

Ln 228: The use of “SCFA/acetic acid” here and elsewhere is unclear to the review. Was acetic acid the only SCFA measured?

Ln 275: Remove “occurrence of”

Ln 338: “of shared and unique A) bacterial and B) methanogenic OTUs of no-foam…”

Ln 340: “most relatively abundant”

Ln 342: “that differed”; “are shown”

Ln 346: “relatively abundant”

Ln 361: “taxa”

Ln 364: “was positively correlated”; “Bacilli (Lactobacillus) OTU and…”

Ln 366: I would remove “members of”

Ln 367-368: “…content was also negatively”

Ln 371: “between individual bacterial taxa and”

Ln 377: “…or bacterial and methanogenic groups at the class level”

Ln 383, 393, 395: “were positively correlated”

Ln 386: “unclassified Firmicutes OTU was also positively…”

Ln 389: “suspect that the”

Ln 390: Use “absence of” or “lack of” rather than “missing” here.

Ln 397: “has previously been…”

Ln 404: “is strongly correlated”

Ln 407: Remove “observed”

Ln 416: “observed that the”

Ln 418: “as evidenced”

Ln 424: “matter”

Ln 429: “that there are”

**********

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Reviewer #1: No

Reviewer #2: Yes: Pramod Pandey

Reviewer #3: No

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24 May 2021

Response to Reviewers

We thank all reviewers for their time and thoughtful suggestions. We addressed each comment below and all line numbers in the responses are referring to the line numbers in the revised manuscript with tracked changes. These suggestions have helped to clarify and strengthen the manuscript, and we are grateful for the time and consideration of these reviews.

Reviewer #1: The authors compared physical and chemical properties of different types of swine manure pit samples (no-foam, foam, and crust), and made associations of these with the bacterial and methanogen community structure, and methane production rate. The work is interesting and generally well-performed, and it does shed light on some of the differences in these sample types. Despite these advances, the reviewer feels that the authors could have extended their observations by delving a bit more into the characteristics of the samples, particularly with regard to the SCFA and LCFA (see Specific comments below), as both types of compounds are major substrates or intermediates and their metabolic fates have a strong influence on methane production and, presumably, microbial community structure.

A second issue regards the pH of the samples, and how it might relate to SCFA concentration and to methane production. It appears from L66 and Supplemental Figure S3 (S3 Fig) that pH was measured, and there was a slight correlation of pH with some of the other measured characteristics (particularly nutrients), especially in the no-foam manures, but little correlation of pH with SCFA. This should perhaps be pointed out somewhere in the text. It would also be useful to indicate the range of pH values encountered, and whether they differed significantly among the manure types. As the aceticlastic methanogens tend to be more sensitive to acidic conditions than are the hydrogenotrophic methanogens, could pH explain the general absence of the former group in these samples?

Response: The reviewer made many helpful suggestions, and they are specifically addressed below. To address the reviewer’s comment on manure pH, the average pH was 8.17, 8.24, and 8.21 in no-foam, crust forming, and foaming manure, respectively. This slightly alkaline pH could be contributing to the absent of acetoclastic methanogens. This point has been added to the revised manuscript (L325-346).

Specific comments:

L50: The authors bring up the tendency of foaming to cause a potential for explosion, but in the Results we learn that the foaming samples showed lower rates of methane production than did the other samples. Presumably the foaming causes methane accumulation even if it is produced in lesser amounts. This should be mentioned in the Discussion.

Response: Our results are consistent with the tendency of foaming to cause a potential for explosion. In Table 1, foaming manure had significantly higher rates of methane production. We double checked the manuscript to ensure that results stated that foaming samples was associated with higher methane production consistently.

L64-67: The methods description is inadequate. Reference 4 does not provide analytical methods, and no methods are described for LCFA and SCFA analysis.

Response: We thank the reviewer for pointing it out. The correct reference with manure sampling and analyses has been added (L80).

L115, L126: How were these sequence numbers (10k for bacteria, 4k for methanogens) selected, as opposed to normalizing the number of reads selected to number of reads in the sample with the fewest reads?

Response: The sequence number cut offs were evaluated based on Good’s coverage and total sequencing depth. Rarefaction has been argued as not suitable for microbial compositional analysis because it can introduce more biases (McMurdie and Holmes, 2014; Willis, 2019). In general, it is much more important to remove samples that were inadequately sequenced and thus may bias representation due to low sequencing coverage. Because the swine manure bacterial community is more diverse than the methanogen community, we set the Good’s coverage at 97% for bacterial samples and 99% for methanogen samples, which reflects 10k and 4k sequences, respectively. We have also updated this in the manuscript for clarification (L139-144, L155-157).

McMurdie PJ, Holmes S: Waste not, want not: why rarefying microbiome data is inadmissible . PLoS Comput Biol. 2014, 10: 1003531-10.1371/journal.pcbi.1003531.

Willis, A. D. (2019). Rarefaction, alpha diversity, and statistics. Front. Microbiol. 10:2407. doi: 10.3389/fmicb.2019.02407

L158-161: It would be very useful to be more specific regarding these sample types, perhaps by including photographs (even if included in Supplementary data).

Response: Manure pictures with specific surface texture are shown below. They have been added to the Supplementary document as S2 Fig. Additional description to the sample types have also been added on L82-84.

The picture (S2 Fig) illustrates the different surface textures after short term lab incubation. From left to right they are: No foam, No foam, Foam, Foam, Crust, Foam.

L181-183: Another important difference between SBM and DDGS is the amount and type of LCFAs, which should have some bearing in discussion of the importance of LCFA and its metabolism in the three sample types.

Response: We thank the reviewer for the suggestion. We have added this to the discussion (L281-286).

L209-211: This seems like a superficial distinction, as the “physical interactions” must have some sort of underlying chemical basis (e.g., surface tension is probably governed by the relative contributions and hydrophobic/hydrophilic behavior of proteins, lipids and polysaccharides).

Response: We agree that the term “physical interactions” is not descriptive. The sentence has been updated to “These observations suggest that the manure surface texture change from no-foam to foam is associated with a shift from the dominance of correlations among physical properties to correlations among chemical properties, with crusts as an intermediate.” (L254-257).

L228-254: In this section the authors discuss SCFA and acetic acid in the samples without providing any data on the amounts and distributions (i.e., different chain lengths) of these acids. Such information would be very useful in interpreting potential bottlenecks in LCFA degradation to SCFA, and SCFA catabolism to methane.

Response: We thank the reviewer for the good suggestion. The information about SCFA distribution has been added to the revised manuscript (L289-L292, L318-L321).

L256: This is an oversimplification. Perhaps the authors should add “under standard conditions”. The process of LCFA degradation to SCFA typically operates near equilibrium, so that it is quite sensitive to the relative concentrations of substrate and product. But it does not typically require input of energy, and the authors seem to suggest this in the following sentences, in which they state that methanogenesis provides thermodynamic displacement via H2 consumption, allowing SCFA catabolism to occur.

Response: This has been updated in the revised manuscript as suggested by the reviewer (L347).

L320-321: It is not clear to the reviewer how the individual slope values for the three types of manures can be compared to yield statistically significant differences, as there is only a single slope value for each sample type.

Response: The statistical significance of the community stability slopes was calculated using bootstraping, which repeatedly sample slopes based on different points within a group at random.

L349-360: These are interesting observations, but they raise the question of where the acetic acid goes. Working back to comments to L228-254 above, is it possible that some members of the bacterial community might be engaged in chain elongation reactions, in which acetate is converted to longer SCFA such as butyrate and caproate? Chain elongation has been widely observed in waste pits and anaerobic reactors in which aceticlastic methanogenesis is limited (for example, see Zhu et al., DOI: 10.1038/srep14360, or Angenent et al., DOI: 10.1021/acs.est.5b04847).

Response: These are very good questions. Unfortunately, we did not measure medium chain fatty acids for this study. However, they are worth investigating for future studies. We have added the reviewer’s suggestions to the paper on L354-357.

L398-401: What is meant here by the vague statement regarding “the different potential involvement of bacteria or methanogens”?

Response: This sentence has been updated to clarify “the different potential involvement of bacteria or methanogens” as “The synergistic interactions between bacteria and methanogens could explain...” (L515-516).

L421-424: The reviewer is not convinced by this statement. Why can’t the accumulation of SCFA be due to impaired catabolism of SCFA, rather than invoking a greater production of SCFA from other feed components?

Response: We agree with the reviewer. This has been updated in the text on L545-546.

S2 Table: Please define (in a footnote) the term “integrator”.

Response: This has been added in the revised Supplementary Information L83.

Minor edits:

L45-46: Delete “However,” and use rest of sentence as the first sentence of the next paragraph.

Response: Updated on L48.

L50: Delete “conditions of”.

Response: Updated on L52.

L185: Change “low” to “poorly”.

Response: Updated on L217.

L229: Here and elsewhere in the manuscript, change “SCFA/acetic acid” to just “SCFA”, because (as pointed out in L233-234) acetic acid is one of the SCFA.

Response: This has been updated throughout the manuscript.

L277: Change “between” to “among”.

Response: Updated on L377.

L329: Change “genera” to “genus”.

Response: Updated on L432.

L347: Change “that the bacterial or methanogen groups were the most abundant in” to “in which the bacterial or methanogen groups were the most abundant”.

Response: Updated on L455.

Reviewer #2: Manuscript entitled "Microbial assemblages and methanogenesis pathways impact on methane production and foaming in manure deep-pit storages" can be considered as high importance. This manuscript provides relatively new and unique dataset to tackle the human, animal and environmental risks associated with swine industry. The study provided a relatively large dataset to support the finding. Manuscript is well written and deserve publications.

Few things, however, authors are suggested to consider:

Response: We thank the reviewer for the comments and suggestions. The specific updates and responses are below.

1) provide the details of farm (herd size; pit size; and existing manure management in farms where samples were collected);

Response: We added S1 Table in the supplementary material to provide the additional farm information.

2) Figure 1 requires further descriptions in Figure captions (currently not clear);

Response: We have updated the caption of Figure 1 (L203-205).

3) method section requires further clarifications improvement in terms of sampling protocol, and the strategies implemented in sample collection;

Response: We thank the reviewer for pointing this out. We updated the citation and detailed sampling protocols and strategies are described in the cited article (L80).

4) before ending results and discussion section, authors are suggested to provide major findings quantitatively, and limitations of the study;

Response: We thank the reviewer for the suggestion. We listed out our major findings on L519-535. Due to the descriptive nature of our study, many of the key findings cannot be expressed quantitatively, and after deliberation, felt that adding numbers the findings would interfere with the coherence of the summary. We added the limitations of the study on L546-548.

and 5) discussion on the composition of gas formation, ammonia, and microbial community identified in Figure 5,6.7.

Response: We thank the reviewer for the suggestions. Unfortunately, we do not have the information on gas compositions. We did not specifically discuss the negative correlation observed between ammonia and Gamma-Proteobacteria in the crust-forming manure because it deviates from the main findings of the study. Similarly, ammonia strongly correlated with potassium and TKN in all 3 manure types (S3 Fig), which were not unique to a specific foam type, and we do not know how they contributed to our overall findings.

Any pathogens, in addition to methanogens were identified?

Response: The reviewer raised a very good question. We used microbial 16S rRNA gene based sequencing method to identify bacteria in this study. While this method can identify a wide range of bacterial genera and sometimes species, it does not detect the specific virulence or pathogenicity. One may be able to infer the pathogenicity of a species based on its literature presence, however, it is not reliable. We considered this discussion outside the scope of the current study.

Further, in statistics table, authors are suggested to provide the sample numbers used for the analysis. In terms of feed characterization, not sure if the feed of individual farms has been characterized or the feed from mills which supplies the feed has been characterized. Each pit will have certain holding capacity, and the retention time will be having impacts of microbial community. Therefore, the conditions of pits (mixing, retention time, temperature, flushing frequency, and the frequencies of disloading of those pits are important consideration while describing the shift in microbial community under anaerobic conditions.

Response: The reviewer made many good suggestions. We added sample sizes to Table 1 (L241-242). Feed from individual farms were characterized and we also added S1 Table to address the reviewer’s question regarding the farms. We do not have all information the reviewer asked, but we did recognize the significant impact of pit conditions and hence used the core microbial communities of each manure type to address the major differences. We clarified this on L425-L427.

Reviewer #3: This manuscripts describes the bacterial and methanogenic communities of manure collected from 46 swine farms at several different time points. The authors visually classified the manure surface as either non-foaming, crust-forming or foaming at each sampling time.

Certain chemical properties of the manure were also assessed. The authors report that the swine diet influences the manure surface texture and that certain chemical and physical properties were correlated with the manure surface texture as well. The non-foaming and foaming manure bacterial and methanogenic microbial communities were most dissimilar from each other.

Response: We appreciate reviewer’s time and suggestions. The specific comments are addressed below.

Major comments

The materials and methods for the manure sampling and chemical analysis appear to be missing. The superscript “4” is included at the end of the sentence on ln 65 but it is unclear if this is referring to REF 4 or something else.

Response: We thank the reviewer for pointing it out. The correct reference on the manure sampling and chemical analysis has been added (L80).

Minor comments

In S2 Table should the P-values for Farm and Feed Mill for bacteria also be 0.001?

Response: S2 Table has been updated as S3 Table. The P-values for Farm and Feed Mill is 1.0 because Farm is used as blocking effect as stated in the footnote. We noticed the discrepancies in the methanogen PERMANOVA values and have updated them.

Can the x-axis in Fig. 5 C and D be converted to percent relative abundance rather than the current scale which is not very intuitive.

Response: We thank the reviewer for the suggestion. We have updated Fig. 5 to change the x-axis to percent scale.

Ln 24: “an increased”

Response: updated on L24.

Ln 26-27: “…rRNA and archaeal mcrA genes…”

Response: updated on L26.

Ln 29: “and that the bacterial…”

Response: updated on L28.

Ln 31: “previously” should be removed since they are still uncharacterized.

Response: updated on L30.

Ln 33, 362: “relative abundance”

Response: updated on L33 and L443.

Ln 50: I would remove “conditions of”

Response: updated on L52.

Ln 81: “mcrA gene was”

Response: updated on L96.

Ln 111-113: What database was used for 16S rRNA gene classification?

Response: We used RDP 16S rRNA database. This information has been added on L137.

Ln 121: “chimeras”

Response: updated on L150.

Ln 180: What is meant by “more significant” here? A greater concentration of DDGS? Also, what does the superscript “19” refer to here?

Response: The sentence and the reference have been updated to “... the proportion of distiller’s dried grains with soluble (DDGS) was significantly higher in feed given to swine from foam and crust manure...” (L212-213 and L215).

Ln 182: “NDF” should be first defined here rather than on ln 185.

Response: updated on L215.

Ln 205, 407: “were correlated”

Response: This sentence has been updated to “... strong correlations among chemical properties were observed in foaming manures. Specifically the correlations among...” (L250-L253).

Ln 228: The use of “SCFA/acetic acid” here and elsewhere is unclear to the review. Was acetic acid the only SCFA measured?

Response: Acetic acid was the largest component of SCFA measured. We updated the information on L289-L292. The use of “SCFA/acetic acid” has also be changed to “SCFA” throughout the paper.

Ln 275: Remove “occurrence of”

Response: updated on L374.

Ln 338: “of shared and unique A) bacterial and B) methanogenic OTUs of no-foam…”

Response: updated on L441.

Ln 340: “most relatively abundant”

Response: updated on L443.

Ln 342: “that differed”; “are shown”

Response: updated on L450-451.

Ln 346: “relatively abundant”

Response: updated on L455.

Ln 361: “taxa”

Response: updated on L469.

Ln 364: “was positively correlated”; “Bacilli (Lactobacillus) OTU and…”

Response: updated on L471 and L472.

Ln 366: I would remove “members of”

Response: updated on L478.

Ln 367-368: “…content was also negatively”

Response: updated on L480.

Ln 371: “between individual bacterial taxa and”

Response: updated on L484.

Ln 377: “…or bacterial and methanogenic groups at the class level”

Response: updated on L490.

Ln 383, 393, 395: “were positively correlated”

Response: updated on L496, 511, and 513.

Ln 386: “unclassified Firmicutes OTU was also positively…”

Response: updated on L499.

Ln 389: “suspect that the”

Response: updated on L507.

Ln 390: Use “absence of” or “lack of” rather than “missing” here.

Response: updated on L508.

Ln 397: “has previously been…”

Response: updated on L515.

Ln 404: “is strongly correlated”

Response: updated on L522.

Ln 407: Remove “observed”

Response: updated on L525.

Ln 416: “observed that the”

Response: updated on L539.

Ln 418: “as evidenced”

Response: updated on L541.

Ln 424: “matter”

Response: updated on L547.

Ln 429: “that there are”

Response: updated on L554.

Submitted filename: plosone_response_to_reviewers.docx

Click here for additional data file.

16 Jun 2021

PONE-D-21-06556R1

Microbial assemblages and methanogenesis pathways impact on methane production and foaming in manure deep-pit storages

PLOS ONE

Dear Dr. Yang,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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PLOS ONE

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Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

Reviewer #3: All comments have been addressed

**********

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

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Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #1: The authors have adequately addressed the reviewers comments. One additional question arises:

L386: This does not make sense to the reviewer. Hydrogenotrophic methanogens oxidize H2, and use the reducing equivalents to reduce CO2. How do they oxidize acetate?

Minor edits:

Title: Suggest deleting “on”.

L43 and L62: Please use SI units (meters, rather than ft).

L119, L131: Change “less” to “fewer”.

L256: Change “microorganism metabolism” to “microbial metabolism”.

L257: Change “many” to “most”.

L279-281: This is a little confusing as written. Suggest changing “Under standard anaerobic conditions, the breakdown of LCFA is carried out via acetogenesis [35,42]. This process is energy-consuming and non-spontaneous.”, to “Under anaerobic conditions, the breakdown of LCFA is carried out via acetogenesis [35, 42]. This process is endergonic and does not occur spontaneously under standard conditions.”

L298: Insert “, respectively” after “methanogens”.

L458: Change “fibers” to “fiber”.

Reviewer #2: Authors revised manuscript, and revised version of manuscripts responded answers to comments line by line, and issues raised have been resolved.

Reviewer #3: (No Response)

**********

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Reviewer #1: No

Reviewer #2: Yes: Pramod Pandey

Reviewer #3: No

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1 Jul 2021

Reviewer #1: The authors have adequately addressed the reviewers comments. One additional question arises:

L386: This does not make sense to the reviewer. Hydrogenotrophic methanogens oxidize H2, and use the reducing equivalents to reduce CO2. How do they oxidize acetate?

Response: We thank the reviewer for the question. Acetate oxidation in the absence of acetolastic methanogens is part of the overall anaerobic methane production pathway. However, acetate oxidation is carried out by microorganisms other than hydrogenotrophic methanogens. We rephrased the sentence to clear the confusion.

Minor edits:

Response: We thank the reviewer for the suggestions. The edits below have been incorporated.

Title: Suggest deleting “on”.

L43 and L62: Please use SI units (meters, rather than ft).

L119, L131: Change “less” to “fewer”.

L256: Change “microorganism metabolism” to “microbial metabolism”.

L257: Change “many” to “most”.

L279-281: This is a little confusing as written. Suggest changing “Under standard anaerobic conditions, the breakdown of LCFA is carried out via acetogenesis [35,42]. This process is energy-consuming and non-spontaneous.”, to “Under anaerobic conditions, the breakdown of LCFA is carried out via acetogenesis [35, 42]. This process is endergonic and does not occur spontaneously under standard conditions.”

L298: Insert “, respectively” after “methanogens”.

L458: Change “fibers” to “fiber”.

Reviewer #2: Authors revised manuscript, and revised version of manuscripts responded answers to comments line by line, and issues raised have been resolved.

Reviewer #3: (No Response)

Submitted filename: plosone_response_to_reviewers_2.docx

Click here for additional data file.

2 Jul 2021

Microbial assemblages and methanogenesis pathways impact methane production and foaming in manure deep-pit storages

PONE-D-21-06556R2

Dear Dr. Yang,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Alex V Chaves, PhD

Academic Editor

PLOS ONE

22 Jul 2021

PONE-D-21-06556R2

Microbial assemblages and methanogenesis pathways impact methane production and foaming in manure deep-pit storages

Dear Dr. Yang:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Prof Alex V Chaves

Academic Editor

PLOS ONE