Biotechnology Bulletin ›› 2023, Vol. 39 ›› Issue (1): 295-304.doi: 10.13560/j.cnki.biotech.bull.1985.2022-0752
Previous Articles Next Articles
ZHANG Yan-feng(), DING Yan-ling, MA Ying, ZHOU Xiao-nan, YANG Chao-yun, SHI Yuan-gang, KANG Xiao-long()
Received:
2022-06-21
Online:
2023-01-26
Published:
2023-02-02
Contact:
KANG Xiao-long
E-mail:zhangyf6667@163.com;kangxl9527@126.com
ZHANG Yan-feng, DING Yan-ling, MA Ying, ZHOU Xiao-nan, YANG Chao-yun, SHI Yuan-gang, KANG Xiao-long. Comparative Analysis of Rumen and Fecal Microbial Characteristics Associated with Residual Feed Intake in Beef Cattle[J]. Biotechnology Bulletin, 2023, 39(1): 295-304.
样本名称Sample ID | 有效数据Valid data/Mb | Q20/% | Scaftigs总长Scaftigs total/bp | 开放阅读框ORFs | 完整基因Integrity:All |
---|---|---|---|---|---|
FB.H1 | 12 509.32 | 98.06 | 188 879 887 | 297 809 | 84 910(28.51%) |
FB.H2 | 13 567.54 | 97.81 | 276 330 484 | 405 700 | 139 819(34.46%) |
FB.H3 | 12 159.56 | 97.22 | 189 874 035 | 293 866 | 80 445(27.37%) |
FB.H4 | 12 053.01 | 97.03 | 135 845 081 | 208 630 | 59 756(28.64%) |
FB.H5 | 13 225.16 | 97.81 | 171 198 747 | 264 485 | 73 853(27.92%) |
FB.L1 | 13 375.02 | 97.98 | 290 986 051 | 436 592 | 131 731(30.17%) |
FB.L2 | 12 739.79 | 98.10 | 265 213 212 | 412 889 | 128 852(31.21%) |
FB.L3 | 12 652.81 | 98.06 | 228 899 989 | 359 316 | 103 408(28.78%) |
FB.L4 | 12 870.73 | 97.34 | 262 012 715 | 390 833 | 119 371(30.54%) |
FB.L5 | 12 315.37 | 97.47 | 234 495 499 | 351 948 | 109 337(31.07%) |
LWY.H1 | 13 319.20 | 97.90 | 148 596 432 | 243 354 | 60 663(24.93%) |
LWY.H2 | 13 041.11 | 97.72 | 292 975 636 | 396 572 | 107 715(27.16%) |
LWY.H3 | 13 532.42 | 97.85 | 319 093 131 | 480 760 | 147 723(30.73%) |
LWY.H4 | 13 226.23 | 98.02 | 272 607 966 | 428 244 | 131 496(30.71%) |
LWY.H5 | 12 721.76 | 97.44 | 173 570 140 | 274 033 | 70 778(25.83%) |
LWY.L1 | 12 771.32 | 97.99 | 282 351 096 | 394 302 | 154 391(39.16%) |
LWY.L2 | 12 488.25 | 98.00 | 210 931 091 | 321 579 | 87 924(27.34%) |
LWY.L3 | 13 391.11 | 97.64 | 245 419 502 | 300 276 | 66 510(22.15%) |
LWY.L4 | 13 351.27 | 97.03 | 296 978 984 | 387 232 | 119 832(30.95%) |
LWY.L5 | 13 734.49 | 97.00 | 275 938 988 | 359 633 | 95 278(26.49%) |
Table 1 Basic information statistics of each sample
样本名称Sample ID | 有效数据Valid data/Mb | Q20/% | Scaftigs总长Scaftigs total/bp | 开放阅读框ORFs | 完整基因Integrity:All |
---|---|---|---|---|---|
FB.H1 | 12 509.32 | 98.06 | 188 879 887 | 297 809 | 84 910(28.51%) |
FB.H2 | 13 567.54 | 97.81 | 276 330 484 | 405 700 | 139 819(34.46%) |
FB.H3 | 12 159.56 | 97.22 | 189 874 035 | 293 866 | 80 445(27.37%) |
FB.H4 | 12 053.01 | 97.03 | 135 845 081 | 208 630 | 59 756(28.64%) |
FB.H5 | 13 225.16 | 97.81 | 171 198 747 | 264 485 | 73 853(27.92%) |
FB.L1 | 13 375.02 | 97.98 | 290 986 051 | 436 592 | 131 731(30.17%) |
FB.L2 | 12 739.79 | 98.10 | 265 213 212 | 412 889 | 128 852(31.21%) |
FB.L3 | 12 652.81 | 98.06 | 228 899 989 | 359 316 | 103 408(28.78%) |
FB.L4 | 12 870.73 | 97.34 | 262 012 715 | 390 833 | 119 371(30.54%) |
FB.L5 | 12 315.37 | 97.47 | 234 495 499 | 351 948 | 109 337(31.07%) |
LWY.H1 | 13 319.20 | 97.90 | 148 596 432 | 243 354 | 60 663(24.93%) |
LWY.H2 | 13 041.11 | 97.72 | 292 975 636 | 396 572 | 107 715(27.16%) |
LWY.H3 | 13 532.42 | 97.85 | 319 093 131 | 480 760 | 147 723(30.73%) |
LWY.H4 | 13 226.23 | 98.02 | 272 607 966 | 428 244 | 131 496(30.71%) |
LWY.H5 | 12 721.76 | 97.44 | 173 570 140 | 274 033 | 70 778(25.83%) |
LWY.L1 | 12 771.32 | 97.99 | 282 351 096 | 394 302 | 154 391(39.16%) |
LWY.L2 | 12 488.25 | 98.00 | 210 931 091 | 321 579 | 87 924(27.34%) |
LWY.L3 | 13 391.11 | 97.64 | 245 419 502 | 300 276 | 66 510(22.15%) |
LWY.L4 | 13 351.27 | 97.03 | 296 978 984 | 387 232 | 119 832(30.95%) |
LWY.L5 | 13 734.49 | 97.00 | 275 938 988 | 359 633 | 95 278(26.49%) |
[1] |
Ley RE, Peterson DA, Gordon JI. Ecological and evolutionary forces shaping microbial diversity in the human intestine[J]. Cell, 2006, 124(4): 837-848.
doi: 10.1016/j.cell.2006.02.017 pmid: 16497592 |
[2] |
Ross EM, Moate PJ, Marett LC, et al. Metagenomic predictions: from microbiome to complex health and environmental phenotypes in humans and cattle[J]. PLoS One, 2013, 8(9): e73056.
doi: 10.1371/journal.pone.0073056 URL |
[3] |
Barlow GM, Yu A, Mathur R. Role of the gut microbiome in obesity and diabetes mellitus[J]. Nutr Clin Pract, 2015, 30(6): 787-797.
doi: 10.1177/0884533615609896 pmid: 26452391 |
[4] |
Wang J, Jia HJ. Metagenome-wide association studies: fine-mining the microbiome[J]. Nat Rev Microbiol, 2016, 14(8): 508-522.
doi: 10.1038/nrmicro.2016.83 pmid: 27396567 |
[5] | Roehe R, Dewhurst RJ, Duthie CA, et al. Bovine host genetic variation influences rumen microbial methane production with best selection criterion for low methane emitting and efficiently feed converting hosts based on metagenomic gene abundance[J]. PLoS Genet, 2016, 12(2): e1005846. |
[6] |
Camarinha-Silva A, Maushammer M, Wellmann R, et al. Host genome influence on gut microbial composition and microbial prediction of complex traits in pigs[J]. Genetics, 2017, 206(3): 1637-1644.
doi: 10.1534/genetics.117.200782 pmid: 28468904 |
[7] | Jami E, White BA, Mizrahi I. Potential role of the bovine rumen microbiome in modulating milk composition and feed efficiency[J]. PLoS One, 2014, 9(1): e85423. |
[8] |
Jewell KA, McCormick CA, Odt CL, et al. Ruminal bacterial community composition in dairy cows is dynamic over the course of two lactations and correlates with feed efficiency[J]. Appl Environ Microbiol, 2015, 81(14): 4697-4710.
doi: 10.1128/AEM.00720-15 URL |
[9] |
Nielsen MK, MacNeil MD, Dekkers JCM, et al. Review: Life-cycle, total-industry genetic improvement of feed efficiency in beef cattle: blueprint for the Beef Improvement Federation[J]. Prof Animal Sci, 2013, 29(6): 559-565.
doi: 10.15232/S1080-7446(15)30285-0 URL |
[10] |
VandeHaar MJ, Armentano LE, Weigel K, et al. Harnessing the genetics of the modern dairy cow to continue improvements in feed efficiency[J]. J Dairy Sci, 2016, 99(6): 4941-4954.
doi: S0022-0302(16)30165-5 pmid: 27085407 |
[11] |
Koch RM, Swiger LA, Chambers D, et al. Efficiency of feed use in beef cattle[J]. J Anim Sci, 1963, 22(2): 486-494.
doi: 10.2527/jas1963.222486x URL |
[12] |
Herd RM, Arthur PF. Physiological basis for residual feed intake[J]. J Anim Sci, 2009, 87(S14): E64-E71.
doi: 10.2527/jas.2008-1345 URL |
[13] |
Crews DHD Jr. Genetics of efficient feed utilization and national cattle evaluation: a review[J]. Genet Mol Res, 2005, 4(2): 152-165.
pmid: 16110437 |
[14] |
Trevizan N, Canesin RC, Branco RH, et al. Growth, ruminal and metabolic parameters and feeding behavior of Nellore cattle with different residual feed intake phenotypes[J]. Livest Sci, 2021, 244: 104393.
doi: 10.1016/j.livsci.2021.104393 URL |
[15] | Jubb KVF, Kennedy PC, Palmer N. Pathology of domestic animals[M]. Elsevier, 2015. |
[16] |
Berry DP, Crowley JJ. Cell Biology Symposium: genetics of feed efficiency in dairy and beef cattle[J]. J Anim Sci, 2013, 91(4): 1594-1613.
doi: 10.2527/jas.2012-5862 pmid: 23345557 |
[17] | Richardson E, Herd R. Biological basis for variation in residual feed intake in beef cattle. 2. Synthesis of results following divergent selection[J]. Animal Prod Sci, 2004, 44: 431-440. |
[18] |
Kayser W, Hill RA. Relationship between feed intake, feeding behaviors, performance, and ultrasound carcass measurements in growing purebred Angus and Hereford bulls[J]. J Anim Sci, 2013, 91(11): 5492-5499.
doi: 10.2527/jas.2013-6611 pmid: 23989865 |
[19] | Richardson EC. Possible physiological indicators for net feed conversion efficiency in beef cattle, 1996[C]. Sydney: Pergamon Press, 1996. |
[20] |
Guan LL, Nkrumah JD, et al. Linkage of microbial ecology to phenotype: correlation of rumen microbial ecology to cattle's feed efficiency[J]. FEMS Microbiol Lett, 2008, 288(1): 85-91.
doi: 10.1111/j.1574-6968.2008.01343.x pmid: 18785930 |
[21] | 杨朝云, 康晓龙, 淡新刚, 等. 不同剩余采食量水平的安格斯牛生长性状差异性分析[J]. 西南大学学报: 自然科学版, 2020, 42(2): 8-14. |
Yang CY, Kang XL, Dan XG, et al. Differential analysis of growth traits in Angus cattle on different levels of residual feed intake[J]. J Southwest Univ Nat Sci Ed, 2020, 42(2): 8-14. | |
[22] |
Sanford RA, Lloyd KG, et al. Microbial taxonomy Run amok[J]. Trends Microbiol, 2021, 29(5): 394-404.
doi: 10.1016/j.tim.2020.12.010 pmid: 33546975 |
[23] |
Terry SA, Badhan A, Wang Y, et al. Fibre digestion by rumen microbiota—a review of recent metagenomic and metatranscriptomic studies[J]. Canadian Journal of Animal Science, 2019, 99(4): 678-692.
doi: 10.1139/cjas-2019-0024 URL |
[24] |
Kenny DA, Fitzsimons C, Waters SM, et al. Invited review: improving feed efficiency of beef cattle - the current state of the art and future challenges[J]. Animal, 2018, 12(9): 1815-1826.
doi: 10.1017/S1751731118000976 pmid: 29779496 |
[25] |
Frey JC, Pell AN, et al. Comparative studies of microbial populations in the rumen, duodenum, ileum and faeces of lactating dairy cows[J]. J Appl Microbiol, 2010, 108(6): 1982-1993.
doi: 10.1111/j.1365-2672.2009.04602.x pmid: 19863686 |
[26] | Noel SJ, Olijhoek DW, McLean F, et al. Rumen and fecal microbial community structure of Holstein and jersey dairy cows as affected by breed, diet, and residual feed intake[J]. Animals(Basel), 2019, 9(8): 498. |
[27] |
Shabat SKB, Sasson G, et al. Specific microbiome-dependent mechanisms underlie the energy harvest efficiency of ruminants[J]. ISME J, 2016, 10(12): 2958-2972.
doi: 10.1038/ismej.2016.62 pmid: 27152936 |
[28] |
Mu YY, Lin XY, Wang ZH, et al. High-production dairy cattle exhibit different rumen and fecal bacterial community and rumen metabolite profile than low-production cattle[J]. MicrobiologyOpen, 2019, 8(4): e00673.
doi: 10.1002/mbo3.673 URL |
[29] |
Duncan SH, Louis P, Thomson JM, et al. The role of pH in determining the species composition of the human colonic microbiota[J]. Environ Microbiol, 2009, 11(8): 2112-2122.
doi: 10.1111/j.1462-2920.2009.01931.x pmid: 19397676 |
[30] |
Thomas F, Hehemann JH, et al. Environmental and gut bacteroidetes: the food connection[J]. Front Microbiol, 2011, 2: 93.
doi: 10.3389/fmicb.2011.00093 pmid: 21747801 |
[31] |
Jami E, Mizrahi I. Composition and similarity of bovine rumen microbiota across individual animals[J]. PLoS One, 2012, 7(3): e33306.
doi: 10.1371/journal.pone.0033306 URL |
[32] |
Bekele AZ, Koike S, Kobayashi Y. Genetic diversity and diet specificity of ruminal Prevotella revealed by 16S rRNA gene-based analysis[J]. FEMS Microbiol Lett, 2010, 305(1): 49-57.
doi: 10.1111/j.1574-6968.2010.01911.x URL |
[33] |
McGovern E, McGee M, Byrne CJ, et al. Investigation into the effect of divergent feed efficiency phenotype on the bovine rumen microbiota across diet and breed[J]. Sci Rep, 2020, 10(1): 15317.
doi: 10.1038/s41598-020-71458-0 pmid: 32948787 |
[34] | 吴宇佳, 迟晓培, 等. 肥胖者唾液微生物宏基因组学特点[J]. 北京大学学报: 医学版, 2018, 50(1): 5-12. |
Wu YJ, Chi XP, et al. Sal Ivary microbiome in people with obesity: a pilot study[J]. J Peking Univ Heal Sci, 2018, 50(1): 5-12. | |
[35] |
Lam S, Munro JC, Zhou M, et al. Associations of rumen parameters with feed efficiency and sampling routine in beef cattle[J]. Animal, 2018, 12(7): 1442-1450.
doi: 10.1017/S1751731117002750 pmid: 29122053 |
[36] |
Henderson G, Cox F, et al. Rumen microbial community composition varies with diet and host, but a core microbiome is found across a wide geographical range[J]. Sci Rep, 2015, 5: 14567.
doi: 10.1038/srep14567 pmid: 26449758 |
[37] |
Hegarty RS, Goopy JP, Herd RM, et al. Cattle selected for lower residual feed intake have reduced daily methane production[J]. J Anim Sci, 2007, 85(6): 1479-1486.
pmid: 17296777 |
[38] |
Nkrumah JD, Okine EK, Mathison GW, et al. Relationships of feedlot feed efficiency, performance, and feeding behavior with metabolic rate, methane production, and energy partitioning in beef cattle[J]. J Anim Sci, 2006, 84(1): 145-153.
doi: 10.2527/2006.841145x pmid: 16361501 |
[39] |
Johnson KA, Johnson DE. Methane emissions from cattle[J]. J Anim Sci, 1995, 73(8): 2483-2492.
doi: 10.2527/1995.7382483x pmid: 8567486 |
[40] |
Reinhold P, Sachse K, Kaltenboeck B. Chlamydiaceae in cattle: commensals, trigger organisms, or pathogens?[J]. Vet J, 2011, 189(3): 257-267.
doi: 10.1016/j.tvjl.2010.09.003 pmid: 20980178 |
[41] |
Luton PE, Wayne JM, Sharp RJ, et al. The mcrA gene as an alternative to 16S rRNA in the phylogenetic analysis of methanogen populations in landfillbbThe GenBank accession numbers for the mcrA sequences reported in this paper are AF414034-AF414051 and AF414007-AF414033.[J]. Microbiology, 2002, 148(11): 3521-3530.
doi: 10.1099/00221287-148-11-3521 URL |
[42] |
Chouchane L, Danguir J, Beji C, et al. Genetic variation in the stress protein hsp70-2 gene is highly associated with obesity[J]. Int J Obes, 2001, 25(4): 462-466.
doi: 10.1038/sj.ijo.0801545 URL |
[43] | Hardie DG, Ross FA, Hawley SA. AMPK: a nutrient and energy sensor that maintains energy homeostasis[J]. Nat Rev Mol Cell Biol, 2012, 13(4): 251-262. |
[44] |
Marshall MS. Ras target proteins in eukaryotic cells[J]. FASEB J, 1995, 9(13): 1311-1318.
doi: 10.1096/fasebj.9.13.7557021 pmid: 7557021 |
[45] |
Gadsden MH, McIntosh EM, Game JC, et al. dUTP pyrophosphatase is an essential enzyme in Saccharomyces cerevisiae[J]. EMBO J, 1993, 12(11): 4425-4431.
doi: 10.1002/j.1460-2075.1993.tb06127.x pmid: 8223452 |
[46] |
Wang W, Zheng SS, Li LX, et al. Comparative metagenomics of the gut microbiota in wild greylag geese(Anser anser)and ruddy shelducks(Tadorna ferruginea)[J]. MicrobiologyOpen, 2019, 8(5): e00725.
doi: 10.1002/mbo3.725 URL |
[47] | 张慧敏, 夏海磊, 黄强, 等. 海子水牛瘤胃微生物的宏基因组学分析[J]. 动物营养学报, 2017, 29(11): 4151-4161. |
Zhang HM, Xia HL, Huang Q, et al. Metagenomic analysis of microorganisms in rumen of haizi buffalo[J]. Chin J Animal Nutr, 2017, 29(11): 4151-4161. | |
[48] |
Jose VL, Appoothy T, More RP, et al. Metagenomic insights into the rumen microbial fibrolytic enzymes in Indian crossbred cattle fed finger millet straw[J]. AMB Express, 2017, 7(1): 13.
doi: 10.1186/s13568-016-0310-0 pmid: 28050853 |
[49] |
Stewart RD, Auffret MD, Warr A, et al. Assembly of 913 microbial genomes from metagenomic sequencing of the cow rumen[J]. Nat Commun, 2018, 9(1): 870.
doi: 10.1038/s41467-018-03317-6 pmid: 29491419 |
[1] | ZHANG Kun, YAN Chang, TIAN Xin-peng. Research Progress in Microbial Single Cell Separation Methods [J]. Biotechnology Bulletin, 2023, 39(9): 1-11. |
[2] | LI Huan-min, GAO Feng-tao, LI Wei-zhong, WANG Jin-qing, FENG Jia-li. Progress in Research and Application of Natural Bio-materials as Immobilized Carriers [J]. Biotechnology Bulletin, 2023, 39(7): 105-112. |
[3] | ZHANG Yu-han, FAN Yi, LI Ting-ting, PANG Shuang, LIU Wei, BAI Ke-yu, ZHANG Xi-mei. Microbial Enrichment on Leaf Surface and DNA Extraction Method Based on the Metagenomics Sequencing [J]. Biotechnology Bulletin, 2022, 38(3): 256-263. |
[4] | GAO Hui-hui, JIA Chen-bo, HAN Qin, SU Jian-yu, XU Chun-yan. Microbiological Mechanism of Root Rot of Lycium barbarum Ningqi-7 [J]. Biotechnology Bulletin, 2022, 38(12): 244-251. |
[5] | LI Jun-ling, MA Xiao-han, ZHANG Yu-dan, JIA Wei, XU Zi-cheng. Research Progress on the Relationship Between Soil Microorganism and Tobacco Bacterial Wilt [J]. Biotechnology Bulletin, 2020, 36(9): 88-99. |
[6] | GUO Wei, XUE Shuai, ZHANG Zhe-chao, DIAO Feng-wei, HU Jie, ZHANG Min, LIU Mei-chun, DING Sheng-li, JIA Bing-bing, SHI Zhong-qi. Research Progress on Bioremediation of Saline-alkali Grassland:A Review [J]. Biotechnology Bulletin, 2020, 36(7): 200-208. |
[7] | LIN Miao, WANG Kuo-peng, CHEN Ying-liang, SUN Wen-jing, FENG Li-mei, HU Zi-xuan. Effects of Ethanol on Metabolites and Bacterial Community of Rice Straw Cocultured with Rumen Fluid in vitro [J]. Biotechnology Bulletin, 2020, 36(2): 91-99. |
[8] | LIU Shu-jun, CHEN Miao, WANG Feng-zhong, BAO Yu-ming, XIN Feng-jiao, WEN Bo-ting. In Vitro Fermentation of Monosodium Glutamate with Human Gut Microbes [J]. Biotechnology Bulletin, 2020, 36(12): 104-112. |
[9] | GUO Jing, XIE Zhan-ling, LUO Tao, XUE Zhi-feng, GUO Jian-juan, LI Fa-xiong, ZHANG Xiu-juan. Comparative Study on Endophytic Fungi Diversity of Kobresia humilis in Floccularia luteovirens [J]. Biotechnology Bulletin, 2019, 35(11): 109-117. |
[10] | WANG Duan, YAO Xiang-mei, YE Jian. Research Progress on Multipartite Interactions Among Rhizosphere Microbe-Plants-Virus-Vector Insect [J]. Biotechnology Bulletin, 2018, 34(2): 54-65. |
[11] | WANG Zhu-jun, WANG Shang, LIU Yang-ying, FENG Kai, DENG Ye. The Applications of Metagenomics in the Detection of Environmental Microbes Involving in Nitrogen Cycle [J]. Biotechnology Bulletin, 2018, 34(1): 1-14. |
[12] | WANG Meng-jiao, FANG Hai-tian, LIU Hui-yan, HE Xiao-guang, WU Qing, ZHAO Bei-bei. Research Progresses on the Role of Transport Proteins NupC and NupG During the Nucleoside Secretion Processing in Microbial Cells [J]. Biotechnology Bulletin, 2017, 33(2): 24-29. |
[13] | SONG Wei-feng, LI Ming-cong, GAO Zheng. Research Progress on in situ Detection Methods of Microorganisms [J]. Biotechnology Bulletin, 2017, 33(10): 26-32. |
[14] | WANG Yu-xuan, WEI Wei, LI Ping-ping, ZHAO Yun, FU Wei-guo. Study Progress on Microorganism in Constructed Wetlands [J]. Biotechnology Bulletin, 2017, 33(10): 74-79. |
[15] | JIA Yan-ping, YIN Ai-ming, SUN Yan-mei, CHENG Shou-tao, WANG Xu-ming. Screen of Poly-γ-gamma Glutamic Acid Producing Bacteria and Their Glutamic Acid Fermentation Broth’s Function on the Drought Resistance of Maize [J]. Biotechnology Bulletin, 2017, 33(10): 135-142. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||