[1]

Johansen KS. 2016. Lytic polysaccharide monooxygenases: the microbial power tool for lignocellulose degradation. Trends in Plant Science 21:926−36

doi: 10.1016/j.tplants.2016.07.012
[2]

Shahab RL, Brethauer S, Davey MP, Smith AG, Vignolini S, et al. 2020. A heterogeneous microbial consortium producing short-chain fatty acids from lignocellulose. Science 369:eabb1214

doi: 10.1126/science.abb1214
[3]

King AJ, Cragg SM, Li Y, Dymond J, Guille MJ, et al. 2010. Molecular insight into lignocellulose digestion by a marine isopod in the absence of gut microbes. Proceedings of the National Academy of Sciences of the United States of America 107:5345−50

doi: 10.1073/pnas.0914228107
[4]

Lin L, Lai Z, Zhang J, Zhu W, Mao S. 2023. The gastrointestinal microbiome in dairy cattle is constrained by the deterministic driver of the region and the modified effect of diet. Microbiome 11:10

doi: 10.1186/s40168-022-01453-2
[5]

Hess M, Sczyrba A, Egan R, Kim TW, Chokhawala H, et al. 2011. Metagenomic discovery of biomass-degrading genes and genomes from cow rumen. Science 331:463−67

doi: 10.1126/science.1200387
[6]

Xie F, Jin W, Si H, Yuan Y, Tao Y, et al. 2021. An integrated gene catalog and over 10, 000 metagenome-assembled genomes from the gastrointestinal microbiome of ruminants. Microbiome 9:137

doi: 10.1186/s40168-021-01078-x
[7]

Artzi L, Bayer EA, Moraïs S. 2017. Cellulosomes: bacterial nanomachines for dismantling plant polysaccharides. Nature Reviews Microbiology 15:83−95

doi: 10.1038/nrmicro.2016.164
[8]

Gharechahi J, Vahidi MF, Sharifi G, Ariaeenejad S, Ding XZ, et al. 2023. Lignocellulose degradation by rumen bacterial communities: New insights from metagenome analyses. Environmental Research 229:115925

doi: 10.1016/j.envres.2023.115925
[9]

Seshadri R, Leahy SC, Attwood GT, Teh KH, Lambie SC, et al. 2018. Cultivation and sequencing of rumen microbiome members from the Hungate1000 Collection. Nature Biotechnology 36:359−67

doi: 10.1038/nbt.4110
[10]

Michalak L, Gaby JC, Lagos L, La Rosa SL, Hvidsten TR, et al. 2020. Microbiota-directed fibre activates both targeted and secondary metabolic shifts in the distal gut. Nature Communications 11:5773

doi: 10.1038/s41467-020-19585-0
[11]

Gálvez EJC, Iljazovic A, Amend L, Lesker TR, Renault T, et al. 2020. Distinct polysaccharide utilization determines interspecies competition between intestinal Prevotella spp. Cell Host & Microbe 28:838−852.E6

doi: 10.1016/j.chom.2020.09.012
[12]

Novy V, Aïssa K, Nielsen F, Straus SK, Ciesielski P, et al. 2019. Quantifying cellulose accessibility during enzyme-mediated deconstruction using 2 fluorescence-tagged carbohydrate-binding modules. Proceedings of the National Academy of Sciences of the United States of America 116:22545−51

doi: 10.1073/pnas.1912354116
[13]

Shi Q, Abdel-Hamid AM, Sun Z, Cheng Y, Tu T, et al. 2023. Carbohydrate-binding modules facilitate the enzymatic hydrolysis of lignocellulosic biomass: Releasing reducing sugars and dissociative lignin available for producing biofuels and chemicals. Biotechnology Advances 65:108126

doi: 10.1016/j.biotechadv.2023.108126
[14]

Moraïs S, Mizrahi I. 2019. Islands in the stream: from individual to communal fiber degradation in the rumen ecosystem. FEMS microbiology reviews 43:362−79

doi: 10.1093/femsre/fuz007
[15]

Gharechahi J, Vahidi MF, Ding XZ, Han JL, Salekdeh GH. 2020. Temporal changes in microbial communities attached to forages with different lignocellulosic compositions in cattle rumen. FEMS Microbiology Ecology 96:fiaa069

doi: 10.1093/femsec/fiaa069
[16]

Gharechahi J, Vahidi MF, Bahram M, Han JL, Ding XZ, et al. 2021. Metagenomic analysis reveals a dynamic microbiome with diversified adaptive functions to utilize high lignocellulosic forages in the cattle rumen. The ISME Journal 15:1108−20

doi: 10.1038/s41396-020-00837-2
[17]

Van Soest PJ, Robertson JB, Lewis BA. 1991. Methods for dietary fiber, neutral detergent fiber, and nonstarch polysaccharides in relation to animal nutrition. Journal of Dairy Science 74:3583−97

doi: 10.3168/jds.S0022-0302(91)78551-2
[18]

Van Keulen J, Young BA. 1977. Evaluation of acid-insoluble ash as a natural marker in ruminant digestibility studies. Journal of Animal Science 44:282−87

doi: 10.2527/jas1977.442282x
[19]

Yu Z, Morrison M. 2004. Improved extraction of PCR-quality community DNA from digesta and fecal samples. Biotechniques 36:808−12

doi: 10.2144/04365ST04
[20]

Magoč T, Salzberg SL. 2011. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27:2957−63

doi: 10.1093/bioinformatics/btr507
[21]

Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, et al. 2016. DADA2: High-resolution sample inference from Illumina amplicon data. Nature Methods 13:581−83

doi: 10.1038/nmeth.3869
[22]

Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, et al. 2019. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nature Biotechnology 37:852−57

doi: 10.1038/s41587-019-0209-9
[23]

Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, et al. 2013. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Research 41:D590−D596

doi: 10.1093/nar/gks1219
[24]

Bokulich NA, Kaehler BD, Rideout JR, Dillon M, Bolyen E, et al. 2018. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2's q2-feature-classifier plugin. Microbiome 6:90

doi: 10.1186/s40168-018-0470-z
[25]

Li J, Zhong H, Ramayo-Caldas Y, Terrapon N, Lombard V, et al. 2020. A catalog of microbial genes from the bovine rumen unveils a specialized and diverse biomass-degrading environment. Gigascience 9:giaa057

doi: 10.1093/gigascience/giaa057
[26]

Glendinning L, Genç B, Wallace RJ, Watson M. 2021. Metagenomic analysis of the cow, sheep, reindeer and red deer rumen. Scientific reports 11:1990

doi: 10.1038/s41598-021-81668-9
[27]

Wolff SM, Ellison MJ, Hao Y, Cockrum RR, Austin KJ, et al. 2017. Diet shifts provoke complex and variable changes in the metabolic networks of the ruminal microbiome. Microbiome 5:60

doi: 10.1186/s40168-017-0274-6
[28]

Li W, Han Y, Yuan X, Wang G, Wang Z, et al. 2017. Metagenomic analysis reveals the influences of milk containing antibiotics on the rumen microbes of calves. Archives of Microbiology 199:433−43

doi: 10.1007/s00203-016-1311-8
[29]

Wang L, Zhang G, Xu H, Xin H, Zhang Y. 2019. Metagenomic analyses of microbial and carbohydrate-active enzymes in the rumen of holstein cows fed different forage-to-concentrate ratios. Frontiers in Microbiology 10:649

doi: 10.3389/fmicb.2019.00649
[30]

Xue MY, Sun HZ, Wu XH, Liu JX, Guan LL. 2020. Multi-omics reveals that the rumen microbiome and its metabolome together with the host metabolome contribute to individualized dairy cow performance. Microbiome 8:64

doi: 10.1186/s40168-020-00819-8
[31]

Xue MY, Xie YY, Zhong Y, Ma XJ, Sun HZ, et al. 2022. Integrated meta-omics reveals new ruminal microbial features associated with feed efficiency in dairy cattle. Microbiome 10:32

doi: 10.1186/s40168-022-01228-9
[32]

Mu YY, Qi WP, Zhang T, Zhang JY, Mao SY. 2021. Gene function adjustment for carbohydrate metabolism and enrichment of rumen microbiota with antibiotic resistance genes during subacute rumen acidosis induced by a high-grain diet in lactating dairy cows. Journal of Dairy Science 104:2087−105

doi: 10.3168/jds.2020-19118
[33]

Wu X, Huang S, Huang J, Peng P, Liu Y, et al. 2021. Identification of the potential role of the rumen microbiome in milk protein and fat synthesis in dairy cows using metagenomic sequencing. Animals 11:1247

doi: 10.3390/ani11051247
[34]

Chen S, Zhou Y, Chen Y, Gu J. 2018. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34:i884−i890

doi: 10.1093/bioinformatics/bty560
[35]

Kang DD, Froula J, Egan R, Wang Z. 2015. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ 3:e1165

doi: 10.7717/peerj.1165
[36]

Li D, Liu CM, Luo R, Sadakane K, Lam TW. 2015. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31:1674−76

doi: 10.1093/bioinformatics/btv033
[37]

Wu YW, Simmons BA, Singer SW. 2016. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics 32:605−7

doi: 10.1093/bioinformatics/btv638
[38]

Alneberg J, Bjarnason BS, De Bruijn I, Schirmer M, Quick J, et al. 2014. Binning metagenomic contigs by coverage and composition. Nature Methods 11:1144−46

doi: 10.1038/nmeth.3103
[39]

Uritskiy GV, DiRuggiero J, Taylor J. 2018. MetaWRAP − a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome 6:158

doi: 10.1186/s40168-018-0541-1
[40]

Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. 2015. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Research 25:1043−55

doi: 10.1101/gr.186072.114
[41]

Olm MR, Brown CT, Brooks B, Banfield JF. 2017. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. The ISME Journal 11:2864−68

doi: 10.1038/ismej.2017.126
[42]

Seemann T. 2014. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30:2068−69

doi: 10.1093/bioinformatics/btu153
[43]

Nayfach S, Shi ZJ, Seshadri R, Pollard KS, Kyrpides NC. 2019. New insights from uncultivated genomes of the global human gut microbiome. Nature 568:505−10

doi: 10.1038/s41586-019-1058-x
[44]

Parks DH, Chuvochina M, Waite DW, Rinke C, Skarshewski A, et al. 2018. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nature Biotechnology 36:996

doi: 10.1038/nbt.4229
[45]

Segata N, Börnigen D, Morgan XC, Huttenhower C. 2013. PhyloPhlAn is a new method for improved phylogenetic and taxonomic placement of microbes. Nature Communications 4:2304

doi: 10.1038/ncomms3304
[46]

Letunic I, Bork P. 2016. Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees. Nucleic Acids Research 44:W242−W245

doi: 10.1093/nar/gkw290
[47]

Zhang H, Yohe T, Huang L, Entwistle S, Wu P, et al. 2018. dbCAN2: a meta server for automated carbohydrate-active enzyme annotation. Nucleic Acids Research 46:W95−W101

doi: 10.1093/nar/gky418
[48]

Potter SC, Luciani A, Eddy SR, Park Y, Lopez R, et al. 2018. HMMER web server: 2018 update. Nucleic Acids Research 46:W200−W204

doi: 10.1093/nar/gky448
[49]

Lombard V, Golaconda Ramulu H, Drula E, Coutinho PM, Henrissat B. 2014. The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Research 42:D490−D495

doi: 10.1093/nar/gkt1178
[50]

Stewart RD, Auffret MD, Roehe R, Watson M. 2018. Open prediction of polysaccharide utilisation loci (PUL) in 5414 public Bacteroidetes genomes using PULpy. bioRxiv Preprint:421024

doi: 10.1101/421024
[51]

Yeoman CJ, Fields CJ, Lepercq P, Ruiz P, Forano E, et al. 2021. In Vivo competitions between Fibrobacter succinogenes, Ruminococcus flavefaciens, and Ruminoccus albus in a gnotobiotic sheep model revealed by multi-omic analyses. mBio 12:e03533-20

doi: 10.1128/mBio.03533-20
[52]

Wylensek D, Hitch TCA, Riedel T, Afrizal A, Kumar N, et al. 2020. A collection of bacterial isolates from the pig intestine reveals functional and taxonomic diversity. Nature Communications 11:6389

doi: 10.1038/s41467-020-19929-w
[53]

Terry SA, Badhan A, Wang Y, Chaves AV, McAllister TA. 2019. Fibre digestion by rumen microbiota—a review of recent metagenomic and metatranscriptomic studies. Canadian Journal of Animal Science 99:678−92

doi: 10.1139/cjas-2019-0024
[54]

Wang K, Gao P, Geng L, Liu C, Zhang J, et al. 2022. Lignocellulose degradation in Protaetia brevitarsis larvae digestive tract: refining on a tightly designed microbial fermentation production line. Microbiome 10:90

doi: 10.1186/s40168-022-01291-2
[55]

Hagen LH, Brooke CG, Shaw CA, Norbeck AD, Piao H, et al. 2021. Proteome specialization of anaerobic fungi during ruminal degradation of recalcitrant plant fiber. The ISME Journal 15:421−34

doi: 10.1038/s41396-020-00769-x
[56]

Cabral L, Persinoti GF, Paixão DAA, Martins MP, Morais MAB, et al. 2022. Gut microbiome of the largest living rodent harbors unprecedented enzymatic systems to degrade plant polysaccharides. Nature Communications 13:629

doi: 10.1038/s41467-022-28310-y
[57]

Froidurot A, Julliand V. 2022. Cellulolytic bacteria in the large intestine of mammals. Gut Microbes 14:2031694

doi: 10.1080/19490976.2022.2031694
[58]

Beckham GT, Matthews JF, Peters B, Bomble YJ, Himmel ME, et al. 2011. Molecular-level origins of biomass recalcitrance: decrystallization free energies for four common cellulose polymorphs. The Journal of Physical Chemistry B 115:4118−27

doi: 10.1021/jp1106394
[59]

Zhang KD, Li W, Wang YF, Zheng YL, Tan FC, et al. 2018. Processive degradation of crystalline cellulose by a multimodular endoglucanase via a wirewalking mode. Biomacromolecules 19:1686−96

doi: 10.1021/acs.biomac.8b00340
[60]

Ding S, Cao J, Zhou R, Zheng F. 2007. Molecular cloning, and characterization of a modular acetyl xylan esterase from the edible straw mushroom Volvariella volvacea. FEMS Microbiology Letters 274:304−10

doi: 10.1111/j.1574-6968.2007.00844.x
[61]

Rivera-Chacon R, Pacífico C, Ricci S, Petri RM, Reisinger N, et al. 2024. Prolonged feeding of high-concentrate diet remodels the hindgut microbiome and modulates nutrient degradation in the rumen and the total gastrointestinal tract of cows. Journal of Dairy Science In Press

doi: 10.3168/jds.2024-24919
[62]

Xie F, Xu L, Wang Y, Mao S. 2021. Metagenomic sequencing reveals that high-grain feeding alters the composition and metabolism of cecal microbiota and induces cecal mucosal injury in sheep. mSystems 6:e00915-21

doi: 10.1128/mSystems.00915-21
[63]

Li Z, Wang X, Zhang Y, Yu Z, Zhang T, et al. 2022. Genomic insights into the phylogeny and biomass-degrading enzymes of rumen ciliates. The ISME Journal 16:2775−87

doi: 10.1038/s41396-022-01306-8
[64]

Peng X, Wilken SE, Lankiewicz TS, Gilmore SP, Brown JL, et al. 2021. Genomic and functional analyses of fungal and bacterial consortia that enable lignocellulose breakdown in goat gut microbiomes. Nature Microbiology 6:499−511

doi: 10.1038/s41564-020-00861-0