Impact of Nutrient Composition on Rumen Microbiome Dynamics and Roughage Degradation

doi:10.21203/rs.3.rs-4700524/v1

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Impact of Nutrient Composition on Rumen Microbiome Dynamics and Roughage Degradation

https://doi.org/10.21203/rs.3.rs-4700524/v1

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Background: Ruminant animals such as goats rely on rumen microbial communities to degrade fibrous nutrients from roughages, facilitating their growth and development. This study investigates dynamic shifts in surface-attached rumen microbes in representative roughages: rice straw (RS), bamboo shoot sheet (BSS), and alfalfa (ALF). Four 14-month-old Min Dong goats with rumen fistulas were used, and the roughages were assessed at 4 h, 12 h, 24 h, 36 h, 48 h, and 72 h intervals. Microbiome composition and function were revealed through 16S rRNA and metagenomics sequencing.

Results: Prevotella and Treponema were the predominant genera in roughage degradation. Nutritional composition and tissue structure of roughages affected microbial attachment, causing variations in nutrient degradation rates. Microbials related to dry matter (DM) and crude protein (CP) degradation were abundant in early fermentation stages (4-12h) but decreased over time, while fiber-degrading microbials increased after 24 hours. Surface-attached microbials produced enzymes such as β-Glucosidase (BG), Endo-β-1,4-glucanase (C1), Exo-β-1,4-glucanase (Cx), and Neutral xylanase (NEX), with enzymatic activity correlating with the fiber content of the roughages.

Conclusions: These findings advance our understanding of microbial roles in ruminant nutrition and digestion. The interaction between microbial communities and rumen fermentation is pivotal for understanding collaborative gene encoding by goat rumen microbiota, which is critical for fiber degradation.

Rumen Microbiome Dynamics

Roughage Degradation

Nutrient Composition

Fiber Degradation

Enzymatic Activity

Bioinformatics.

The livestock industry confronts pronounced challenges related to the availability and quality of roughages, fundamental components of ruminant diets [1, 2]. Factors such as a lack of superior roughages, underdeveloped infrastructure, and restricted access to high-quality feed resources have impeded sector advancement, particularly in comparison to well-established livestock industries globally [3, 4]. Roughages, predominantly fibrous, are essential for ruminant digestion and nutrition due to their reliance on rumen microorganisms for fiber degradation and assimilation[5, 6].

Understanding microbial community attachment to feed particles is integral to rumen fermentation and digestion [7]. Different roughages exhibit varied dry matter (DM) degradation rates upon rumen entry, with the microbial composition attached to particles differing across feeds [8]. Comprehensive knowledge about rumen bacterial diversity, especially regarding feeds rich in cellulose and hemicellulose, remains limited [9–11]. The genus Prevotella, notably dominant, plays a significant role in enzyme families associated with fiber degradation [12–14]. While fungi and archaea, including Chytridiomycota and Euryarchaeota, are recognized as primary groups in the rumen, research on their dynamics in relation to diverse roughages is scant [15].

Traditional studies on rumen microbial communities were culture-dependent, thus limiting insights. However, advancements in next-generation sequencing (NGS) have facilitated culture-independent methodologies, offering a granular perspective on rumen bacterial community structures [16]. Recent 16S rRNA gene sequencing studies indicate that predominant bacterial communities in most ruminants encompass phyla like Bacteroidetes and Firmicutes among others [17, 18]. These communities, influenced by factors such as host species and diet, exhibit dynamism [19, 20]. Metagenomics research has further illuminated microbial functions and enzymes in livestock [21–24].

This study aims to determine whether rumen microorganisms have a preference for roughages with higher cellulose and hemicellulose content and to track changes in the microbial population associated with roughages as they degrade over a long period in the rumen of Mindong goats [25–27]. By investigating microbial reactions to various roughages in goats, this study, underpinned by NGS, aims to enhance roughage utilization efficiency and deepen understanding of microbial community dynamics. Through 16S rRNA gene and metagenomics-based analysis, we aim to gain a deeper understanding of the microbial communities linked to various roughages, offering insights for optimizing low-quality roughages in ruminants. Simultaneously, metagenomics sequencing will clarify the functional dynamics of these communities, uncovering the interaction between microbial attachment, fodder breakdown, fiber structure, and pH fluctuations.

Cellulase activity for rumen degradation of different feeds

The dry matter (DM) content of various roughage feeds was evaluated and presented in Table 1. Notably, RS and ALF exhibited DM contents exceeding 90%, significantly greater than that of BSS (P < 0.05). ALF had the highest crude protein (CP) content at 14.39%. RS contained the highest NDF and ADF contents at 77.52% and 49.73%, respectively, both markedly surpassing those of BSS and ALF (P < 0.05). Hemicellulose content (HC) was highest in RS and BSS compared to ALF (P < 0.05). Among the feeds, ALF had the highest Acid detergent lignin (ADL) content at 10.13%, followed by BSS and RS (P < 0.05).

Table 1

Nutrient content of different roughage raw materials (%)
Items	Groups
Items	RS	BSS	ALF
DM	93.03 ± 0.21^a	88.94 ± 0.48^b	92.04 ± 0.18^a
CP	5.41 ± 0.06^c	10.19 ± 0.17^b	14.39 ± 0.22^a
NDF	77.52 ± 0.70^a	72.77 ± 1.08^b	47.05 ± 0.27^c
ADF	49.73 ± 0.33^a	45.90 ± 0.45^b	34.95 ± 0.37^c
HC	27.79 ± 0.74^a	26.87 ± 0.64^a	12.09 ± 0.64^b
ADL	4.16 ± 0.53^c	7.77 ± 0.21^b	10.13 ± 0.43^a
Note: Different lower-case letters after the peer data indicate significant differences (P < 0.05) in each nutrient component of different roughages.

Meanwhile, β-glucosidase activity was measured as shown in Table 2. The β-glucosidase activity of all roughage types was lowest at 4 hours. The RS group exhibited a significant activity drop at 4 h compared to 12 h (P < 0.05). The BSS and ALF groups displayed a marked activity surge from 4 to 12 h (P < 0.05). The BSS group's activity at 4 and 72 h significantly exceeded that of the RS and ALF groups during the same intervals (P < 0.05).

Table 2

Changes of β-glucosidase activity at different time points (n mol /min/g)
Rumen retention time	Groups
Rumen retention time	RS	BSS	ALF
4 h	68.79 ± 4.14^Cc	130.91 ± 8.06^Da	91.90 ± 8.44^Bb
12 h	142.84 ± 4.32^Bc	164.73 ± 14.28^BCb	201.28 ± 24.66^Aa
24 h	151.66 ± 7.64^Bb	146.70 ± 6.27^Cb	172.84 ± 12.24^Aa
72 h	187.65 ± 5.46^Ab	221.93 ± 9.98^Aa	154.92 ± 10.93^Cc
Note: Different lower-case letters after the data in the same row indicate significant (P < 0.05) differences in enzyme activities among different groups at the same time point; different upper-case letters after the data in the same column indicate significant (P < 0.05) differences in degradation rates among different time points for the same group. Where RS is used to denote rice straw; BSS denotes bamboo shoot shells; and ALF denotes alfalfa.

The RS group's endoglucanase activity was at its lowest at 12 h, significantly lower than at other times (P < 0.05) shown in Table 3. The ALF group showed a significant rise in activity from 12 to 24 h (P < 0.05). In contrast, the BSS group's activity consistently increased from 4 to 72 h (P < 0.05).

Table 3

Changes of Endo-β-1,4-glucosidase activity at different time points (µg /h/g)
Rumen retention time	Groups
Rumen retention time	RS	BSS	ALF
4 h	2637.50 ± 99.28^Ba	1818.84 ± 120.62^Db	2581.33 ± 82.55^BCa
12 h	2048.08 ± 64.09^Db	2162.73 ± 41.22^Cb	2767.43 ± 197.84^Ba
24 h	2982.74 ± 86.77^Ab	2892.71 ± 100.94^Bb	3915.80 ± 140.09^Aa
72 h	2322.05 ± 98.77^Cb	3546.83 ± 205.62^Aa	2440.77 ± 51.82^Cb
Note: Different regular letters after the data in the same row indicate significant (P < 0.05) differences in enzyme activities among different groups at the same time point; different capital letters after the data in the same column indicate significant (P < 0.05) differences in degradation rates among different time points for the same group. Where RS is used to denote rice straw; BSS denotes bamboo shoot shells; and ALF denotes alfalfa.

Table 4 shows that exoglycanase activity across all roughage groups initially increased before declining. The RS group's activity peaked at 36 h, reaching 3855 nmol/min/g, significantly higher than at other time points (P < 0.05). The BSS group's activity consistently increased from 4 to 24 h (P < 0.05) and then significantly decreased from 24 to 72 h (P < 0.05).

Table 4

Changes of Exo-β-1,4-glucosidase activity at different time points (n mol /min/g)
Rumen retention time	Groups
Rumen retention time	RS	BSS	ALF
4 h	2176.75 ± 136.35^Ca	2163.86 ± 37.07^Da	1787.36 ± 9.07^Bb
12 h	2657.36 ± 37.46^Ba	2991.45 ± 201.60^BCa	1848.84 ± 43.52^Bb
24 h	2561.57 ± 253.38^Bb	3953.72 ± 332.54^Aa	2780.69 ± 195.49^Ab
72 h	3069.82 ± 148.35^Aa	2793.60 ± 93.33^Cab	2579.06 ± 59.86^Ab
Note: Different lower-case letters after the data in the same row indicate significant (P < 0.05) differences in enzyme activities among different groups at the same time point; different upper-case letters after the data in the same column indicate significant (P < 0.05) differences in degradation rates among different time points for the same group. RS is rice straw; BSS is bamboo shoot shells; and ALF is alfalfa.

Table 5 shows that neutral xylanase activity in each roughage group peaked at distinct intervals before subsequently diminishing. The RS group's activity significantly increased from 12 to 24h (P < 0.05). The BSS group's activity consistently increased from 4 to 24 h (P < 0.05).

Table 5

Changes of Neutral xylanase activity at different time points (n mol /min/g)
Rumen retention time	Groups
Rumen retention time	RS	BSS	ALF
4 h	110.92 ± 13.27^Bc	195.53 ± 28.47^Cb	257.71 ± 14.76^Ca
12 h	138.94 ± 24.33^Bc	256.01 ± 29.17^Bb	405.15 ± 29.60^Aa
24 h	316.07 ± 34.69^Ab	324.29 ± 12.51^Aab	364.73 ± 22.61^ABa
72 h	354.53 ± 16.75^Aa	269.40 ± 49.10^ABb	321.50 ± 11.09A^Bab
Note: Different lowercase letters after the peer data indicate significant (P < 0.05) differences in enzyme activity between groups at the same time point. RS is rice straw; BSS is bamboo shoot shells; and ALF is alfalfa.

Roughage-attached microbial community

The paired-end sequencing of the V3-V4 region of the 16S rRNA gene yielded 9,803,516 raw sequence pairs (averaging 75,996 sequences per sample) across 48 samples. After denoising and chimera removal, 6,484,419 clean sequences remained, averaging 50,266 sequences per sample Valid metagenomic data per sample ranged from 8.82 Gb to 13.57 Gb, with contig lengths of N50 between 399 and 726 bp. After redundancy removal, a non-redundant gene catalog was established, comprising 7,319,835 open reading frames (ORFs).

Microbial community composition was identified with 27 phyla and 362 genera via 16S rRNA gene sequencing. Predominant phyla across all treatment groups included Bacteroidota, Firmicutes, and Spirochaetota, with dominant genera such as Prevotella, Treponema, Ruminococcus, and Fibrobacter. The relative abundance of major bacterial phyla and genera attached to the roughage is illustrated in Supplementary Fig. 1A and 1B. Metagenomic sequencing also identified Archaea and Fungi at both phylum and genus levels (Supplementary Fig. 1C, 1D).

Additionally, the different species were calculated between RS, BSS, and ALF at log10 to investigate the biomarkers. RS, BSS, and ALF sample groups exhibited distinct bacterial populations across various sampling times. Notably, Firmicutes was the pattern biomarker in all roughage groups and at all sampling times at the phylum level. However, at the genus level, a more differential trend was observed. Specifically, at 4h, BSS and RS groups had Bacteroidota and Firmicutes as biomarkers at the phylum level (Fig. 1A). At the genus level, BSS had Prevotella, while RS had Pseudobutyrivibrio and Butyrivibrio as biomarkers (Fig. 1A). At 12h, RS, BSS, and ALF groups displayed Bacteroidota, Spirochaetota, and Firmicutes as phylum-level biomarkers (Fig. 1B). Genus-level biomarkers included Prevotella and Rikenellaceae_RC9_gut_group for BSS, Treponema for ALF, and Ruminococcus, Butyrivibrio, and Pseudobutyrivibrio for RS (Fig. 1B). At 24h, RS had Firmicutes as a phylum-level biomarker, while ALF had Butyrivibrio at the genus level (Fig. 1C). At 36h, RS and ALF had Saccharofermentans and Butyrivibrio as genus-level biomarkers, respectively (Fig. 1D). At 72h, RS and ALF groups' phylum-level biomarkers were Firmicutes and Spirochaetota, respectively (Fig. 1E). At the genus level, ALF had Butyrivibrio, while RS had Rikenellaceae_RC9_gut_group, Saccharofermentans, and Christensenellaceae_R7_group (Fig. 1E).

Functional insights into rumen degradation of roughages

Analysis of OG quantities in samples across different time points revealed that 22 OGs exhibited significant variations in quantity according to COG annotation results (Fig. 2A). Over half of the OGs in eggNOG were unidentified. The identified OGs with a relative abundance exceeding 5% included categories such as Energy production and conversion, Amino acid transport and metabolism, and Carbohydrate transport and metabolism. At 72h, the transport and metabolism of amino acids, lipids, nucleotides, carbohydrates, inorganic ions, and coenzymes, as well as translation, ribosomal structure, and biogenesis, were significantly elevated (P < 0.05) compared to other time points. Conversely, the relative abundance of energy production and conversion was notably higher at 4h than at other time points (P < 0.05). The relative abundances of intracellular transport, secretion, vesicular transport, and signaling mechanisms did not show significant differences (P > 0.05) across 4h, 12h, and 24h, but were significantly higher than at 72h (P < 0.05) (Fig. 2A).

Meanwhile, L3-level KEGG pathway analysis revealed time-dependent enrichment variations in several pathways. Enzymes crucial for carbohydrate degradation displayed varying relative abundances throughout the degradation process (Fig. 2B). Prevotella and Fibrobacter predominantly facilitated carbohydrate degradation in the rumen, with their roles changing over time. The abundance of ABC transporters, Aminoacyl-tRNA biosynthesis, Histidine metabolism, and Porphyrin and chlorophyll metabolism was significantly reduced at 24h compared to 72h (P < 0.05) (Fig. 2B). Similarly, the abundance of the Cell cycle - Caulobacter and Homologous recombination pathways was lower at 24h than at 72h (P < 0.05). At the 4-24h interval, Porphyrin and chlorophyll metabolism, Cell cycle - Caulobacter, Homologous recombination, and Purine metabolism exhibited reduced abundance (P < 0.05). he endocytosis pathway showed a significant decrease in abundance at 72h compared to 4h, 24h (P < 0.05), and 12h (P < 0.01). Both Starch and sucrose metabolism and terpene skeleton biosynthesis were less abundant at 72h than at 4h, 24h, and 12h (P < 0.01). However, Starch and sucrose metabolism and terpenoid backbone biosynthesis were more abundant at 72h than at 24h (P < 0.05), while the two-component system was more prevalent at 72h than at other time points (P < 0.01). In essence, the rumen degradation of various roughages induced shifts in microbial enzymes associated with carbohydrate metabolism, with Prevotella being the primary contributor, followed by other microbes.

Microbial correlation with roughage degradation parameters

During rumen digestion, the degradation rates of various components in the RS, BSS, and ALF groups, real-time pH, and cellulase activities were analyzed in relation to rumen microbes. CCA1 and CCA2 accounted for 22.7% and 14.6% of sample differences, respectively (Fig. 3). Eleven environmental factors significantly explained the microbial distribution across the three feed groups (P < 0.05) (Fig. 3). The microbial community most influenced the ADL degradation rate, which negatively correlated with the HC degradation rate and pH.

The RS group's Spearman rank correlation test highlighted significant relationships between degradation rates and specific bacterial taxa (Fig. 4). Positive correlations (P < 0.05) were noted between degradation rates of DM, CP, and HC with Lachnospiraceae bacterium and Treponema sp. The pH also positively correlated (P < 0.05) with Treponema sp., while a negative correlation (P < 0.05) was observed with the C1 enzyme (Fig. 4). Both β-glucosidase (BG) enzyme and C1 enzyme activity showed significant positive correlations (P < 0.05) with Lachnospiraceae bacterium (Fig. 4). Furthermore, DM and CP degradation rates positively correlated (P < 0.05) with Spirochaetia bacterium, whereas C1 enzyme negatively correlated (P < 0.05) with the same (Fig. 4).

Cellulase enzymes and glycoside hydrolase (GHs) correlation analysis

The results, annotated using the CAZys database, indicate that enzymes involved in carbohydrate degradation metabolism are categorized into five major classes: Glycoside Hydrolases (GHs), GlycosylTransferases (GTs), Carbohydrate Esterases (CEs), Polysaccharide Lyases (PLs), and Auxiliary Activities (AAs), with an additional category for Carbohydrate-Binding Modules (CBMs). The classification results are summarized in Supplementary Fig. 2A. The GHs family, with the highest gene count, includes GH2, GH3, GH5, and GH30, which show varying relative abundances at different time points (Supplementary Fig. 2B). Notably, GH9, GH10, and GH51 are associated with endo-β-1,4-glucanase activity, displaying distinct abundance patterns over time (Supplementary Fig. 2B). The GTs family, the second most numerous, includes GT2, which shows significant changes in abundance from 4 to 72 hours (P < 0.05) (Supplementary Fig. 2C). In the CEs family, CE1 and CE10 dominate, involved in xylan and polysaccharide degradation, with their abundance varying significantly over time (P < 0.05) (Supplementary Fig. 2D). The PLs family includes significant families like PL1, PL4, PL10, and PL11 (P < 0.05) (Supplementary Fig. 2E). The AAs family, particularly AA6, shows higher relative abundance across different roughage samples (Supplementary Fig. 2F). The CBMs family components play significant roles in the degradation of various carbohydrates, with specific CBMs like CBM13, CBM22, and CBM62 involved in bacterial connection and xylan binding (Supplementary Fig. 2G).

Furthermore, we analyzed the detailed correlation between four types of cellulase enzymes and their associated glycoside hydrolase (GH) families across different roughage samples and time points, as shown in Fig. 5. In the RS group, the Cx enzyme showed a highly significant positive correlation with GH 144 (P < 0.01) and a significant positive correlation with GH 2 (P < 0.05). The C1 enzyme was significantly positively correlated with GH 3 (P < 0.05). The BG enzyme exhibited significant positive correlations with GH 30, GH 116, and GH 44 (P < 0.05) in BSS group. The C1 enzyme was significantly positively correlated with GH 1 and GH 39 (P < 0.05). The Cx enzyme showed a highly significant negative correlation with GH 98 and GH 2 (P < 0.01) and a significant negative correlation with GH 51 (P < 0.05). The NEX enzyme was significantly negatively correlated with GH 98 (P < 0.05). Only the NEX enzyme in the ALF group had a significant positive correlation with GH 9 and GH 74 (P < 0.05).

This study delved into the interplay between degradation and microbial attachment across three roughages, examining this impact over varied sampling intervals. The research underscores the pivotal role of Ruminococcus in metabolizing complex carbohydrates into nutrients beneficial for the host [28]. Genera such as Ruminococcus, Treponema, Butyrivibrio, and Lanchnospiraceae exhibit significant correlations with the degradation of vital nutritional components, such as CP, NDF, and ADF, across different roughage types. Moreover, specific bacteria such as Rikenellaceae and Porphyromonadaceae are linked to particular roughage types, influencing the degradation rates of nutritional components [29]. These insights emphasize the integral role of rumen microbes in processing roughage to extract essential nutrients for the host animal [30, 31].

To discern the rumen microbial composition throughout the digestion process, we employed two sequencing techniques: 16S rRNA gene sequencing and metagenomic sequencing. The predominant phyla identified in this study, namely Bacteroidota, Fimicutes, Spirochaetota, Proteobacteria, and Fibrobacterota, have been reported for sheep and goats [32–34]. The shifts in microbial community structures attached to different roughage types varied during the rumen digestion process. Additionally, the proportion of identical microbial communities in different roughages differed, potentially influenced by the roughage type and rumen digestion duration [35]. At the genus level, Prevotella and Treponema emerged as dominant. The presence of pyruvate oxidation genes in Prevotella enables these species to flourish in nutrient-scarce environments, leading to their abundance during the early feed fermentation stages [30, 31, 36]. Concurrently, Chytridiomycota, Ascomycota, and Mucoromycota were identified as the primary anaerobic fungi phyla in the rumen for different roughages, consistent with findings in cows and goats [37]. Anaerobic fungi are instrumental in rumen fiber degradation, primarily utilizing rhizomorph systems and a spectrum of cellulolytic enzymes [38]. Roughage type and retention time seemingly did not influence the abundance of anaerobic fungi significantly. Ruminococcus plays a pivotal role in degrading complex carbohydrates, yielding essential nutrients [28], whereas Treponema and Butyrivibrio participate in volatile fatty acid synthesis through metabolic pathways [39]. The degradation rates of nutritional components such as CP, NDF, and ADF are significantly associated with Ruminococcus, Treponema, Butyrivibrio, and Lanchnospiraceae. Additionally, Rikenellaceae and Porphyromonadaceae contribute to short-chain fatty acid production through carbohydrate fermentation [40]. Consequently, different roughage types attract distinct carbohydrate-metabolizing bacterial groups. The degradation rates of the RS group's nutritional components were positively correlated with Rikenellaceae and Porphyromonadaceae, whereas in the ALF group, these rates exhibited negative correlations with the same bacteria. A study on Mongolian cattle indicated that as they mature, their rumen adapts to high-fiber forage, leading to an increased abundance of Rikenellaceae [41]. This suggests that the RS group's strong association with this bacterium might be attributed to its higher fiber content compared to BSS and ALF.

In our study, pathways related to amino acid, lipid, nucleotide, carbohydrate, inorganic ion, and coenzyme transport and metabolism, as well as translation and ribosomal structure biogenesis, exhibited an initial decline followed by an increase between 4 and 72 h. This trend suggests microbial competition for nutrients during the early fermentation stage [42], followed by enhanced growth due to nutrient release from cellulose degradation in later fermentation phases [43]. KEGG pathways analysis revealed that carbohydrate metabolism [44], amino acid metabolism [45], and energy metabolism pathways [45] had elevated relative abundances, underscoring their significance in the degradation of various roughage types [46]. Notably, ABC transporters were the most abundant functional pathway at level-3, indicating the diverse substrate-binding capabilities of the rumen microbiota [47, 48]. The enrichment of ABC transporters at 72 h might reflect the necessity to transport external nutrients into cells to support nitrogen metabolism as feed nutrients deplete [49]. Carbohydrate-active enzymes (CAZys) are crucial for breaking down complex carbohydrates [50, 51]. In this study, the GH43, GH13, GH3, GH2, and GH5 families were the most abundant. However, these enzymes exhibited a declining trend from 4 to 72 h, suggesting their role in the initial breakdown of feed due to their involvement in plant cell wall degradation [52, 53]. Other families, such as GH48, were significantly correlated with enzyme activities, and their abundance was associated with Firmicutes, Actinomycetes, Ascomycota, and Euryarchaeota [54, 55]. The rumen microbiota likely collaborates through different glycoside hydrolase families to optimize fiber degradation [56]. The correlation analysis unveiled positive associations between BG, C1, and Cx enzymes and several glycoside hydrolase genes, especially within the Firmicutes, Actinomycetes, and Ascomycota groups. This emphasizes their indispensable role in fiber degradation. NEX enzymes displayed significant correlations with glycoside hydrolase genes at various time points, particularly within the Firmicutes, Actinomycetes, and some Bacteroidetes groups. Their heightened activity after the 24-hour mark underscores the significance of NEX enzymes in hemicellulose degradation [57].

The degradation rates of RS, BSS, and ALF vary due to differences in nutritional composition, tissue structure, and rumen microbial communities. Higher fiber content in roughages correlates with increased GHs functional genes. Specific dominant microbial communities on each roughage surface led to distinct rumen degradation characteristics. Prevotella significantly contributes to enzyme family genes, particularly GH3, GH5, and GH31. A decrease in fiber content reduces the correlation between GHs and (hemi)cellulase activity. Goat rumen microbiota collaboratively encode different GH genes, playing a crucial role in (hemi)cellulase production and fiber degradation. These findings highlight the complex interactions between microbial communities and rumen fermentation, enhancing our understanding of microbiota's role in ruminant nutrition and digestive efficiency.

Animals and samples

The experimental protocol was approved by the Animal Care Committee of Fujian Agricultural and Forestry University (Fuzhou, China). A total of 4 Eastern Minnesota male goats weighing (26.6±2.35kg), 14 months old, healthy, dewormed and with a permanent rumen fistulation [58] were selected for this study. All goats were fed with a uniform diet (Supplementary Table 1). Roughage feeds, including rice straw (RS), bamboo shoot shells (BSS), and alfalfa (ALF), were collected and cut into 1-2cm fragments to dry in a drying oven at 65 °C for 48h, moistened for 2 hours, pulverized, and sieved through a 40-mesh standard sieve to determine the degradation rate. Roughage feeds were put into nylon bags with a 300-mesh aperture, dimensions 8cm x 13cm, sourced from the Beef Cattle Centre of China Agricultural University. All nylon bags were marked, placed in the rumen, removed after 4h, 8h, 12h, 24h, 36h, 48h, 72h, rinsed repeatedly with tap water, and then placed in an oven set at 65 °C and dried for 24 h, respectively. All bags were kept in constant weight.

Rumen degradation rate of roughage feeds nutritional components and pH

Feed nutritional components were determined using established methods [59]. D) and CP contents were ascertained using standard procedures [59]. NDF and ADF were analyzed as per Soest et al. [60], with HC derived from the difference between NDF and ADF. ADL was quantified via the ANKOM filter bag technique [61].

Degradation rate calculations utilized specific formulas, incorporating degradation parameters and effective degradation rates across various time intervals. In the effective degradation rate formula, the rumen outflow rate "k" was set at 0.0235 h-1, reflecting the crude feed and animal species metrics. pH measurements involved collecting 14 nylon bags (two for each time point) at intervals ranging from 4 to 72 hours. Following the 'synchronous insertion, batch extraction' method, we sequentially removed two nylon bags at each interval, extracting the rumen fluid to measure feed pH using an electronic pH meter.

Statistical degradation rates were processed using Excel 2020, with ANOVA conducted via SPSS 25.0. Degradation kinetics were further evaluated using SAS's nonlinear regression method, with Duncan's multiple comparison test for post-hoc analysis.

Next generation sequencing (NGS) and data processing

The NGS process and analysis were conducted by OE biotech Co., Ltd. (Shanghai, China). MagPure Soil Using the DNA LQ Kit (Magan), we extracted the genome DNA from nylon bags containing roughage feeds. To analyze the microbial diversity, we amplified the V3-V4 hyper-variable regions of the 16S rRNA gene using the universal primers 343F (5’-TACGGRAGGCAGCAG-3’) and 798R(5’-AGGGTATCTAATCCT-3’). Amplification products underwent purification and sequencing on the NovaSeq 6000 platform (Illumina, USA). PE reads were processed using Trimmomatic (v0.36) [62]， merged by FLASH [63], and removed chimera using UCHIME [64]. Tags with over 97% sequence identity were clustered into operational taxonomic units (OTUs) using VSEARCH (v2.22.1) [65]. Taxonomic annotations of OTUs were performed against the SILVA(v.138) database using the QIIME RDP classifier [66].

Metagenomics sequencing followed the manufacturer’s guidelines (Illumina, USA), with each sample prepared as a pair-end (PE) DNA library. Sequencing was conducted on the Novaseq 600 platform (Illumina, USA). PE reads were processed using Trimmomatic (v0.36) [62], and aligned against the host genome using bowtie2（v2.2.9） [67], Aligned reads were discarded, and metagenome assembly was executed using MEGAHIT (v1.1.2) [68]. Binning was conducted after assembly. Bowtie2 [67] and Samtools [69] were performed respectively to comprise and format converse for the contigs with more than 1500 bp. Metawrap [70], built with MetaBAT2 [71], Maxbin2 [72], and Concoct [73], used for generating the non-redundant Bins. ORFs from contigs over 200 bp were predicted using prodigal (v2.6.3) [74]. CD-HIT (v4.6.7) [75] was used to establish the non-redundant (NR) gene catalog from the ORFs. Gene abundance was determined by mapping high-quality reads against the NR gene catalog using bowtie2 (v2.2.9) [67]. Taxonomic assignments of genes were annotated against the NR database using DIAMOND (v.0.9.7) [76], and further compared against databases like KEGG [77], COG [78], and GO [79]. The gene sets were compared with the CAZy database[80] using the corresponding tool hmmscan (http://hmmer.org/) (v3.1b2) to obtain the information of the carbohydrate active enzyme corresponding to the gene and then calculated the carbohydrate activity using the sum of the gene abundances corresponding to the carbohydrate active enzyme abundance.

Cellulose and hemicellulose degrading enzyme activity

We conducted four enzymatic activity tests, β-Glucosidase (BG enzyme), Endo-β-1,4-glucanase (C1 enzyme), Exo-β-1,4-glucanase (Cx enzyme), and Neutral xylanase (NEX enzyme), respectively. The BG enzyme activity followed the kit instruction from Quanzhou Ruixin Biotechnology Co., Ltd. (20220801-RXWB0063-48) using approximately 0.1g contents from the nylon bag. The neutral xylanase (NEX enzyme) activity assay was conducted in a similar method to the BG enzyme assay, utilizing a kit from Quanzhou Ruixin Biotechnology Co., Ltd. (20220801-RXSH0794). Both C1 enzyme and Cx enzyme activity assays involved similar procedures of tissue weighing, homogenization in precooled ethanol, centrifugation, and sediment processing determined using the appropriate kits from Quanzhou Ruixin Biotechnology Co., Ltd. (20220801-RXWB0474-96 and 20220801-RXWB0232-96).

Bioinformatics and statistical analysis

Microbial community alpha and beta diversity were analyzed using QIIME[66]. The unweighted unifrac distance matrix and the weighted unifrac non-metric multidimensional scaling method (NMDS) were employed to evaluate sample beta diversity. LEfSe was used for species abundance variance analysis, and functional abundance prediction was done using PICRUSt2 [81]. Various R packages (v 3.2.0) facilitated PCA analysis, mapping, PCoA, NMDS equidistance matrix results, and graphical analysis for species or functional abundance profiles. Variance analysis was conducted using ANOVA and Kruskal Wallis calculations in R.

RS Rice straw

BSS Bamboo shoot sheath

ALF Alfalfa hay

DM Dry matter

CP Crude protein

CF Crude fiber

ORFs open reading frames

NDF Neutral detergent fiber

ADF Acid detergent fiber

HC Hemicellulose

ADL Acid detergent lignin

pH Pondus hydrogenii

BG B-glucosidase

GHs Glycoside Hydrolases

GTs Glycosyl Transferases

CE Carbohydrate Esterase

PLs Polysaccharide Lyases

AAs Auxiliary Activities

CBMs Carbohydrate-Binding Modules

C1 Endogram-β-1,4-glucanase

Cx Exocution-β-1,4-glucanase

NEX Neutral xylanase

CAZys Carbohydrate-active enzymes

Ethics approval and consent to participate

The experimental protocol was approved by the Animal Care Committee of Fujian Agricultural and Forestry University (Fuzhou, China).

Consent for publication

Not applicable in this section.

Availability of data and materials

The 16S rRNA gene amplicon and metagenome sequencing raw data were deposited in the NCBI BioProject database under the accession numbers PRJNA1091916.

Competing interests

The authors declare that they have no competing interests in this section.

Funding

This study was conducted under the Fujian Provincial Science and Technology Plan project (University Industry University Cooperation Project, 2023N5004; Spark Project, 2023S0007, 2023S0015, and 2023S0054; Foreign Cooperation Project, 2023I1009); Fujian Agriculture and Forestry University Rural Revitalization Service Team - Herbivorous Animal Industry Service Team (11899170139); Supported by the Science and Technology Innovation Special Fund of Fujian Agriculture and Forestry University (CXZX2020057A) all provided funding in support of this work. The authors declare that these funding sources had no influence on the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Authors' contributions

XY, KL and YL: Conceptualization, Statistical analysis, Data visualization, and Writing-original draft. MG and I. OMOOR: Sampling, Statistical analysis, Data visualization.

HL and SQ: Materials and Methodology. XJ, JL and ZM: prepared all figures and tables; JW or QG: Funding Acquisition and Project administration. All authors commented on and approved the final manuscript.

Acknowledgements

We thank the Mindong Goat Provincial Conservation Farm in Shouning County, Fujian Province for the goats.

Hernández-Castellano LE, Nally JE, Lindahl J, Wanapat M, Alhidary IA, Fangueiro D, et al. Dairy science and health in the tropics: challenges and opportunities for the next decades. Tropical Animal Health and Production. 2019;51:1009-17.
Devendra C, Leng R. Feed resources for animals in Asia: issues, strategies for use, intensification and integration for increased productivity. Asian-Australasian Journal of Animal Sciences. 2011;24(3):303-21.
Beigh YA, Ganai AM, Ahmad HA. Prospects of complete feed system in ruminant feeding: A review. Veterinary world. 2017;10(4):424.
Lardy G, Anderson VL. Alternative feeds for ruminants. 2009.
Nunes HP, Teixeira S, Maduro Dias CS, Borba AE. Alternative Forages as Roughage for Ruminant: Nutritional Characteristics and Digestibility of Six Exotic Plants in Azores Archipelago. Animals. 2022;12(24):3587.
Hassen A, Gemeda B, Selzer K, Nel T, Salem AZM, Elghandour MMMY, et al. Role of Exogenous Enzymes in Feed Digestibility and Reducing the Emission Intensity of Enteric Methane Production in Ruminants. In: Exogenous Enzymes as Feed Additives in Ruminants. Springer; 2023. p. 77-102.
Fernando SC, Purvis HT, Najar FZ, Sukharnikov LO, Krehbiel CR, Nagaraja TG, et al. Rumen Microbial Population Dynamics during Adaptation to a High-Grain Diet. Applied and Environmental Microbiology. 2010;76(22):7482-90; doi: doi:10.1128/AEM.00388-10.
Gharechahi J, Vahidi MF, Ding X-Z, Han J-L, Salekdeh GH. Temporal changes in microbial communities attached to forages with different lignocellulosic compositions in cattle rumen. FEMS Microbiology Ecology. 2020;96(6):fiaa069; doi: 10.1093/femsec/fiaa069.
Akin DE. Microscopic Evaluation of Forage Digestion by Rumen Microorganisms-a Review. Journal of Animal Science. 1979;48(3):701-10; doi: 10.2527/jas1979.483701x.
Li J, Lian H, Zheng A, Zhang J, Dai P, Niu Y, et al. Effects of different roughages on growth performance, nutrient digestibility, ruminal fermentation, and microbial community in weaned Holstein calves. Frontiers in Veterinary Science. 2022;9:864320.
Pu XX, Zhang XM, Li QS, Wang R, Zhang M, Zhang SZ, et al. Comparison of in situ ruminal straw fiber degradation and bacterial community between buffalo and Holstein fed with high-roughage diet. Frontiers in Microbiology. 2023;13:1079056.
Betancur-Murillo CL, Aguilar-Marín SB, Jovel J. Prevotella: A Key Player in Ruminal Metabolism. Microorganisms. 2023;11(1):1.
Dao T-K, Do T-H, Le N-G, Nguyen H-D, Nguyen T-Q, Le T-T-H, et al. Understanding the Role of Prevotella Genus in the Digestion of Lignocellulose and Other Substrates in Vietnamese Native Goats’ Rumen by Metagenomic Deep Sequencing. Animals. 2021;11(11):3257.
Kibegwa FM, Bett RC, Gachuiri CK, Machuka E, Stomeo F, Mujibi FD. Diversity and functional analysis of rumen and fecal microbial communities associated with dietary changes in crossbreed dairy cattle. PLOS ONE. 2023;18(1):e0274371; doi: 10.1371/journal.pone.0274371.
Boots B, Lillis L, Clipson N, Petrie K, Kenny DA, Boland TM, et al. Responses of anaerobic rumen fungal diversity (phylum Neocallimastigomycota) to changes in bovine diet. Journal of Applied Microbiology. 2013;114(3):626-35; doi: 10.1111/jam.12067.
Godoy-Vitorino F, Goldfarb KC, Karaoz U, Leal S, Garcia-Amado MA, Hugenholtz P, et al. Comparative analyses of foregut and hindgut bacterial communities in hoatzins and cows. The ISME journal. 2012;6(3):531-41.
Jami E, Mizrahi I. Composition and Similarity of Bovine Rumen Microbiota across Individual Animals. PLOS ONE. 2012;7(3):e33306; doi: 10.1371/journal.pone.0033306.
Zhong Y, Xue M, Liu J. Composition of Rumen Bacterial Community in Dairy Cows With Different Levels of Somatic Cell Counts. Frontiers in Microbiology. 2018;9; doi: 10.3389/fmicb.2018.03217.
Kocherginskaya SA, Aminov RI, White BA. Analysis of the rumen bacterial diversity under two different diet conditions using denaturing gradient gel electrophoresis, random sequencing, and statistical ecology approaches. Anaerobe. 2001;7(3):119-34.
Guo YX, Yang RC, Duan CH, Wang Y, Hao QH, Ji SK, et al. Effect of dioscorea opposite waste on growth performance, blood parameters, rumen fermentation and rumen bacterial community in weaned lambs. Journal of Integrative Agriculture. 2023;22(6):1833-46; doi: 10.1016/j.jia.2022.10.002.
Zhang XX, Lv QB, Yan QL, Zhang Y, Guo RC, Meng JX, et al. A Catalog of over 5,000 Metagenome-Assembled Microbial Genomes from the Caprinae Gut Microbiota. Microbiology Spectrum. 2022;10(6); doi: 10.1128/spectrum.02211-22.
Lam KN, Cheng JJ, Engel K, Neufeld JD, Charles TC. Current and future resources for functional metagenomics. Frontiers in Microbiology. 2015;6; doi: 10.3389/fmicb.2015.01196.
Cantarel BL, Coutinho PM, Rancurel C, Bernard T, Lombard V, Henrissat B. The Carbohydrate-Active EnZymes database (CAZy): an expert resource for Glycogenomics. Nucleic Acids Research. 2009;37:D233-D8; doi: 10.1093/nar/gkn663.
Hagen LH, Brooke CG, Shaw CA, Norbeck AD, Piao HL, Arntzen MO, et al. Proteome specialization of anaerobic fungi during ruminal degradation of recalcitrant plant fiber. Isme Journal. 2021;15(2):421-34; doi: 10.1038/s41396-020-00769-x.
Xue B, Wu M, Yue S, Hu A, Li X, Hong Q, et al. Changes in Rumen Bacterial Community Induced by the Dietary Physically Effective Neutral Detergent Fiber Levels in Goat Diets. Frontiers in Microbiology. 2022;13; doi: 10.3389/fmicb.2022.820509.
Wu Z-l, Yang X, Zhang J, Wang W, Liu D, Hou B, et al. Effects of forage type on the rumen microbiota, growth performance, carcass traits, and meat quality in fattening goats. Frontiers in Veterinary Science. 2023;10; doi: 10.3389/fvets.2023.1147685.
Utaaker KS, Chaudhary S, Kifleyohannes T, Robertson LJ. Global Goat! Is the Expanding Goat Population an Important Reservoir of Cryptosporidium? Frontiers in Veterinary Science. 2021;8; doi: 10.3389/fvets.2021.648500.
La Reau AJ, Suen G. The Ruminococci: key symbionts of the gut ecosystem. Journal of Microbiology. 2018;56(3):199-208; doi: 10.1007/s12275-018-8024-4.
Wang WW, Wang Y, Cui ZW, Yang Y, An XP, Qi JW. Fermented Wheat Bran Polysaccharides Intervention Alters Rumen Bacterial Community and Promotes Rumen Development and Growth Performance in Lambs. Frontiers in Veterinary Science. 2022;9; doi: 10.3389/fvets.2022.841406.
Nathani NM, Patel AK, Mootapally CS, Reddy B, Shah SV, Lunagaria PM, et al. Effect of roughage on rumen microbiota composition in the efficient feed converter and sturdy Indian Jaffrabadi buffalo (Bubalus bubalis). BMC Genomics. 2015;16(1):1116; doi: 10.1186/s12864-015-2340-4.
Zhang X, Han LJ, Gui LS, Raza SHA, Hou SZ, Yang BC, et al. Metabolome and microbiome analysis revealed the effect mechanism of different feeding modes on the meat quality of Black Tibetan sheep. Frontiers in Microbiology. 2023;13; doi: 10.3389/fmicb.2022.1076675.
Skarlupka JH, Kamenetsky ME, Jewell KA, Suen G. The ruminal bacterial community in lactating dairy cows has limited variation on a day-to-day basis. Journal of Animal Science and Biotechnology. 2019;10(1):66; doi: 10.1186/s40104-019-0375-0.
Wang Y, Cao P, Wang L, Zhao Z, Chen Y, Yang Y. Bacterial community diversity associated with different levels of dietary nutrition in the rumen of sheep. Appl Microbial Biotechnol. 2017;101(9)(1432-0614 (Electronic)):3717-28; doi: 10.1007/s00253-017-8144-5.
Malik PK, Trivedi S, Kolte AP, Sejian V, Bhatta R, Rahman H. Diversity of rumen microbiota using metagenome sequencing and methane yield in Indian sheep fed on straw and concentrate diet. Saudi Journal of Biological Sciences. 2022;29(8):103345; doi: https://doi.org/10.1016/j.sjbs.2022.103345.
Gharechahi J, Vahidi MF, Bahram M, Han J-L, Ding X-Z, Salekdeh GH. Metagenomic analysis reveals a dynamic microbiome with diversified adaptive functions to utilize high lignocellulosic forages in the cattle rumen. The ISME Journal. 2021;15(4):1108-20; doi: 10.1038/s41396-020-00837-2.
Rubino F, Carberry C, M Waters S, Kenny D, McCabe MS, Creevey CJ. Divergent functional isoforms drive niche specialisation for nutrient acquisition and use in rumen microbiome. The ISME Journal. 2017;11(4):932-44; doi: 10.1038/ismej.2016.172.
Barraza A, Montes-Sánchez JJ, Caamal-Chan MG, Loera-Muro A. Characterization of microbial communities from rumen and large intestine of lactating creole goats grazing in arid plant communities. Microbiology. 2021;167(10); doi: https://doi.org/10.1099/mic.0.001092.
Gresser U, Empl H Fau - Kamilli I, Kamilli I Fau - Zöllner N, Zöllner N. Pharmacokinetics of benzbromarone: evidence for a biliary-intestinal-biliary recirculation. Adv Exp Med Biol. 1991;309A(0065-2598 (Print)):147-50.
Wang W, Wang Y, Cui Z, Yang Y, An X, Qi J. Fermented Wheat Bran Polysaccharides Intervention Alters Rumen Bacterial Community and Promotes Rumen Development and Growth Performance in Lambs. Frontiers in Veterinary Science. 2022;9; doi: 10.3389/fvets.2022.841406.
Ozbayram EG, Akyol C, Ince B, Karakoc C, Ince O. Rumen bacteria at work: bioaugmentation strategies to enhance biogas production from cow manure. JOURNAL OF APPLIED MICROBIOLOGY. 2018;124(2):491-502; doi: 10.1111/jam.13668.
Ahmad AA, Zhang JB, Liang ZY, Du M, Yang YY, Zheng JS, et al. Age-dependent variations in rumen bacterial community of Mongolian cattle from weaning to adulthood. Bmc Microbiology. 2022;22(1); doi: 10.1186/s12866-022-02627-6.
Weimer PJ. Degradation of Cellulose and Hemicellulose by Ruminal Microorganisms. Microorganisms. 2022. doi: 10.3390/microorganisms10122345.
Zhao C, Wang L, Ma G, Jiang X, Yang J, Lv J, et al. Cellulase Interacts with Lactic Acid Bacteria to Affect Fermentation Quality, Microbial Community, and Ruminal Degradability in Mixed Silage of Soybean Residue and Corn Stover. Animals. 2021. doi: 10.3390/ani11020334.
Li Y, Gao J, Xue Y, Sun R, Sun X, Sun Z, et al. Nutrient availability of roughages in isocaloric and isonitrogenous diets alters the bacterial networks in the whole gastrointestinal tract of Hu sheep. BMC Microbiology. 2023;23(1):70; doi: 10.1186/s12866-023-02814-z.
Zhong H, Zhou J, Abdelrahman M, Xu H, Wu Z, Cui L, et al. The Effect of Lignin Composition on Ruminal Fiber Fractions Degradation from Different Roughage Sources in Water Buffalo (Bubalus bubalis). Agriculture. 2021. doi: 10.3390/agriculture11101015.
Pang K, Chai S, Yang Y, Wang X, Liu S, Wang S. Dietary forage to concentrate ratios impact on yak ruminal microbiota and metabolites. Frontiers in Microbiology. 2022;13; doi: 10.3389/fmicb.2022.964564.
Zhang YK, Zhang XX, Li FD, Li C, Li GZ, Zhang DY, et al. Characterization of the rumen microbiota and its relationship with residual feed intake in sheep. Animal. 2021;15(3):100161; doi: https://doi.org/10.1016/j.animal.2020.100161.
Zeng HB, Yin YY, Chen LX, Xu ZX, Luo Y, Wang Q, et al. Alterations in nutrient digestion and utilization associated with different residual feed intake in Hu sheep. ANIMAL NUTRITION. 2023;13:334-41; doi: 10.1016/j.aninu.2023.02.009.
Jiang H, Cao H-W, Chai Z-X, Chen X-Y, Zhang C-F, Zhu Y, et al. Dynamic alterations in yak (Bos grunniens) rumen microbiome in response to seasonal variations in diet. Physiological Genomics. 2022;54(12):514-25; doi: 10.1152/physiolgenomics.00112.2022.
Flint HJ, Scott KP, Duncan SH, Louis P, Forano E. Microbial degradation of complex carbohydrates in the gut. Gut microbes. 2012;3(4):289-306.
Lombard V, Golaconda Ramulu H, Drula E, Coutinho PM, Henrissat B. The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic acids research. 2014;42(D1):D490-D5.
Bohra V, Dafale NA, Purohit HJ. Understanding the alteration in rumen microbiome and CAZymes profile with diet and host through comparative metagenomic approach. Archives of microbiology. 2019;201:1385-97.
Souto BdM, de Araujo ACB, Hamann PRV, Bastos AdR, Cunha IdS, Peixoto J, et al. Functional screening of a Caatinga goat (Capra hircus) rumen metagenomic library reveals a novel GH3 β-xylosidase. Plos one. 2021;16(1):e0245118.
Yang S, Xie X, Ma J, He X, Li Y, Du M, et al. Selective isolation of Bifidobacterium from human faeces using pangenomics, metagenomics, and enzymology. Frontiers in Microbiology. 2021;12:649698.
Lynd LR, Weimer PJ, Van Zyl WH, Pretorius IS. Microbial cellulose utilization: fundamentals and biotechnology. Microbiology and molecular biology reviews. 2002;66(3):506-77.
Jose VL, Appoothy T, More RP, Arun AS. Metagenomic insights into the rumen microbial fibrolytic enzymes in Indian crossbred cattle fed finger millet straw. AMB Express. 2017;7:1-11.
Khatoon M, Jakhesara SJ, Rank D, Joshi CG, Kunjadiya AP. Exploration of rumen microbial and carbohydrate-active enzyme profiles in cattle fed coir a lignin-rich diet using a metagenomic approach. Gene. 2022;846:146868.
Castillo C, Hernández J. Ruminal Fistulation and Cannulation: A Necessary Procedure for the Advancement of Biotechnological Research in Ruminants. Animals. 2021;11(7):1870.
Cozzolino D, Labandera M. Determination of dry matter and crude protein contents of undried forages by near-infrared reflectance spectroscopy. Journal of the Science of Food and Agriculture. 2002;82(4):380-4; doi: https://doi.org/10.1002/jsfa.1050.
Sauveur B, Ferré R, Lacassagne L. CONSERVATION D'ŒUFS DE POULE SOUS ATMOSPHÈRE ENRICHIE EN GAZ CARBONIQUE. ACTION SUR LES RÉSULTATS D'ÉCLOSION. Annales De Zootechnie. 1967;16:351-6.
Komárek A, Manson H, Thiex NJ: Crude fiber deter-minations using the ANKOM system. In.; 1996.
Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30(15):2114-20; doi: 10.1093/bioinformatics/btu170.
Magoč T, Salzberg SL. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics. 2011;27(21):2957-63; doi: 10.1093/bioinformatics/btr507.
Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics. 2011;27(16):2194-200; doi: 10.1093/bioinformatics/btr381.
Rognes T, Flouri T, Nichols B, Quince C, Mahé F. VSEARCH: a versatile open source tool for metagenomics. PeerJ. 2016;4:e2584; doi: 10.7717/peerj.2584.
Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nature Methods. 2010;7(5):335-6; doi: 10.1038/nmeth.f.303.
Langmead B SS. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012 Mar 4.;9(4):357-9.; doi: doi:10.1038/nmeth.1923.
Li D, Liu C-M, Luo R, Sadakane K, Lam T-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics. 2015;31(10):1674-6; doi: 10.1093/bioinformatics/btv033.
Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009;25(16):2078-9; doi: 10.1093/bioinformatics/btp352.
Uritskiy GV, DiRuggiero J, Taylor J. MetaWRAP—a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome. 2018;6(1):158; doi: 10.1186/s40168-018-0541-1.
Kang DD, Li F, Kirton EA-O, Thomas AA-O, Egan RA-O, An H, et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. (2167-8359 (Print)).
Wu Y-W, Simmons BA, Singer SW. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics. 2016;32(4):605-7; doi: 10.1093/bioinformatics/btv638.
Alneberg J, Bjarnason BS, de Bruijn I, Schirmer M, Quick J, Ijaz UZ, et al. Binning metagenomic contigs by coverage and composition. Nature Methods. 2014;11(11):1144-6; doi: 10.1038/nmeth.3103.
Hyatt D, Chen G-L, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC bioinformatics. 2010;11(1):1-11.
Li W, Jaroszewski L, Godzik A. Clustering of highly homologous sequences to reduce the size of large protein databases. Bioinformatics. 2001;17(3):282-3.
Buchfink B, Reuter K, Drost H-G. Sensitive protein alignments at tree-of-life scale using DIAMOND. Nature methods. 2021;18(4):366-8.
Kanehisa M, Goto S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Research. 2000;28(1):27-30; doi: 10.1093/nar/28.1.27.
Galperin MY, Makarova KS, Wolf YI, Koonin EV. Expanded microbial genome coverage and improved protein family annotation in the COG database. Nucleic Acids Research. 2014;43(D1):D261-D9; doi: 10.1093/nar/gku1223.
Cole JR, Wang Q, Cardenas E, Fish J, Chai B, Farris RJ, et al. The Ribosomal Database Project: improved alignments and new tools for rRNA analysis. Nucleic Acids Res. 2009;37(Database issue):D141-5; doi: 10.1093/nar/gkn879.
Al-Jourani O, Benedict S, Ross J, Layton A, Peet Pvd, Marando VM, et al. Mining the human gut microbiome identifies mycobacterial <span class="sc">d</span>-arabinan degrading enzymes. bioRxiv. 2022:2022.07.22.500997; doi: 10.1101/2022.07.22.500997.
Douglas GM, Maffei VJ, Zaneveld JR, Yurgel SN, Brown JR, Taylor CM, et al. PICRUSt2 for prediction of metagenome functions. Nature Biotechnology. 2020;38(6):685-8; doi: 10.1038/s41587-020-0548-6.

No competing interests reported.

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Impact of Nutrient Composition on Rumen Microbiome Dynamics and Roughage Degradation

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Rumen degradation rate of roughage feeds nutritional components and pH

Next generation sequencing (NGS) and data processing

Cellulose and hemicellulose degrading enzyme activity

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