整合肠道菌群与小分子代谢物分析调控肉鸡饲料转化率的影响因素

doi:10.3864/j.issn.0578-1752.2025.06.013

Abstract

Abstract:

【Objective】 Feed Conversion Rate (FCR) is an important economic trait index in livestock and poultry production. Through the analysis of intestinal flora and small molecular metabolites, the factors affecting the regulation of FCR in broilers were explored. 【Method】 Good health condition, the same batch of 0-day-old Jatropha curcas chicks were selected, free to feed and drink. At 57 days of age, 500 males with normal development and similar body weight ((2 374.96 ± 214.39) g) were selected and transferred to the assay house, with an acclimatization period of 3 days. The official assay was performed at 60 days of age, with an experimental period of 18 days, and 20 birds with high feed conversion rate (HF) and low feed conversion rate (LF) were selected for slaughtering at the marketable age (78 days of age), respectively, based on the results of the automated feeding system. Twenty birds with HF and LF were selected for slaughter at the market age (78 days old) according to the results of the automatic feeding system. Chicken intestines were isolated for determination of intestinal morphology, while cecal chow was harvested for joint analysis of microbial and small molecule metabolite differences between the HF and LF cecums of broiler chickens and correlation analyses using 16S rRNA sequencing and globally precise non-targeted metabolomics. 【Result】 (1) Compared with the LF group, the daily weight gain in HF group was significantly decreased (P<0.05), while the FCR was significantly increased (P<0.05), the jejunal length was significantly decreased (P<0.05), rectal length was significantly increased(P<0.05), and jejunal villus height/crypt depth ratio was significantly decreased(P<0.05). (2) Compared with the LF group, Chao1, Shannon, Observed_species, and Faith_pd indices were significantly decreased in the HF group (P<0.05). The difference in compositional structure between HF and LF was large (P<0.05). Genera with LDA values greater than 3 in the LF group were Blautia_A, Barnesiella, Massilistercora, Papillibacter, Mitsuokella, Paramuribaculum, and UBA738. The genera with LDA values greater than 3 in the HF group were Desulfovibrio_R, Alloprevotella, and Intestinimonas. (3) Thirty-two differential metabolites were screened, among which Docosahexaenoic acid, beta-carotene, and D-Mannose 6-phosphate were more multiplicative. Among these differential metabolites fatty acyl, benzene and substituted derivatives were more frequent. (4) The microorganisms that showed significant negative correlation with FCR values were Papillibacter, Blautia_A, and Paramuribaculum. The metabolites that showed a significant negative correlation with FCR values were Phenylethylamine, beta-Carotene, Antibiotic JI-20A, trans-Cinnamate, Xanthosine, 3-Methoxyanthranilate, Phthalic acid, Niacinamide, gamma-L-Glutamyl-D-alanine, 5-Nitro-2-(3-phenylpropylamino) benzoic acid, Desaminotyrosine, and 1-palmitoylglycerophosphocholine. The metabolites that showed significant positive correlation with FCR values were Phosphorylcholine. Gamma-L-Glutamyl-D-alanine was significantly positively correlated with Blautia_A, while Phosphorylcholine was significantly positively correlated with Intestinimonas. 【Conclusion】 The broiler feed conversion was associated with intestinal flora and metabolites, while Papillibacter, Blautia_A, Barnesella, and Mitsuokella might be marker microorganisms affecting feed efficiency. Desmethyltyrosine, β-carotene, Xanthosine, gamma-L-Glutamyl-D-alanine, and Phosphorylcholine might be marker metabolites affecting feed efficiency. Intestinal flora could influence FCR directly or through microbial metabolites, and microbe-metabolite associations suggested specific pathways of action that may influence feed conversion.

Key words: broiler, feed conversion, 16S rRNA, gut microbes, metabolites, correlation analysis

JU XiaoJun, ZHANG Ming, LIU YiFan, JI GaiGe, SHAN YanJu, TU YunJie, ZOU JianMin, ZHANG HaiTao, BIAN LiangYong, SHU JingTing. Integration of Intestinal Flora and Small Molecule Metabolite to Analyze the Role of Factors Regulating Feed Conversion in Broiler Chickens[J].Scientia Agricultura Sinica, 2025, 58(6): 1223-1238.

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Figures/Tables 14

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Table 4

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Table 5

Differential metabolites"

名称Name	LF	HF	FC	P	FDR	VIP	分类Classifications
去氨酪氨酸Docosahexaenoic acid	109859745.60	9684848.25	11.34	0.00	0.74	2.46	脂肪酰基 Fatty acyl group
β-胡萝卜素beta-Carotene	1727034.87	234115.24	7.38	0.00	0.14	2.47
D-甘露糖-6-磷酸D-Mannose 6-phosphate	59557243.36	8247867.07	7.22	0.04	0.88	1.96	有机氧化合物 Organic oxygen compound
胆红素Bilirubin	3960199.86	603791.98	6.56	0.04	0.88	1.88	氮杂环化合物 Azacyclic compound
花生四烯酸Arachidonic acid	1108087983	170716250.30	6.49	0.02	0.83	2.07	脂肪酰基 Fatty acyl group
烟酰胺Niacinamide	24731690.15	3861812.29	6.4	0.02	0.65	2.46	吡啶及其衍生物 Pyridine and its derivatives
抗生素JI-20A Antibiotic JI-20A	34903423.53	7504761.19	4.65	0.02	0.63	2.05
苯乙酸酯Phenyl acetate	79999522.65	17505838.39	4.57	0.02	0.81	2.05
溶血磷脂酸LysoPA(16_0_0_0)	33600980.67	7397164.48	4.54	0.01	0.74	2.38
苯乙胺 Phenylethylamine	3453499.23	901232.84	3.83	0.01	0.55	2.30	苯及取代衍生物 Benzene and its substituted derivatives
D-苯丙氨酸D-Phenylalanine	92760286.87	27299352.28	3.4	0.04	0.88	1.90
2-氨基苯甲酸 2-Aminobenzoic acid	15065050.37	4551010.21	3.31	0.04	0.79	1.96	苯及取代衍生物 Benzene and its substituted derivatives
1-棕榈酰甘油磷酸胆碱1-palmitoylglycerophosphocholine	220264082.20	76449187.98	2.88	0.03	0.70	2.12	甘油磷脂Glycerophospholipid
酪醇Tyrosol	31538152.22	11038793.72	2.86	0.04	0.75	1.52	酚 Phenol
3-甲氧基邻氨基苯甲酸酯3-Methoxyanthranilate	8295554.24	3013310.76	2.75	0.03	0.72	2.12	苯及取代衍生物 Benzene and its substituted derivatives
5-硝基-2-(3-苯丙氨基)苯甲酸5-Nitro-2-(3-phenylpropylamino)benzoic acid	18535652.32	6781265.83	2.73	0.01	0.56	1.92
植物鞘氨醇Phytosphingosine	154682127.40	57961693.16	2.67	0.04	0.74	2.01	有机氮化合物 Organic nitrogen compound
二甲基甘氨酸 Dimethylglycine	9649870.37	3739267.29	2.58	0.04	0.88	2.01	羧酸及其衍生物 Carboxylic acid and its derivatives
反式肉桂酸盐trans-Cinnamate	30377892.69	11999622.56	2.53	0.03	0.70	2.00
邻苯二甲酸 Phthalic acid	121830710.50	54830998.99	2.22	0.02	0.81	2.08	苯及取代衍生物 Benzene and its substituted derivatives
4-羟基肉桂酸 4-Hydroxycinnamic acid	42766949.70	19655191.89	2.18	0.02	0.68	2.12	肉桂酸及其衍生物 Cinnamic acid and its derivatives
2-酮己酸 2-Ketohexanoic acid	11226883.12	5283975.25	2.12	0.04	0.75	1.66	酮酸及其衍生物 Ketoacid and its derivatives
γ-L-谷氨酰-D-丙氨酸gamma-L-Glutamyl-D-alanine	18952298.78	9587893.46	1.98	0.02	0.64	1.87	甘油磷脂 Glycerophospholipid
非那西丁Phenacetin	62700130.90	35189233.08	1.78	0.03	0.71	1.81
脱氨基酪氨酸Desaminotyrosine	64704533.84	36995641.20	1.75	0.01	0.78	2.13	苯丙酸 Benzoic acid
黄嘌呤核苷Xanthosine	31147891.16	19885108.84	1.57	0.02	0.84	2.09	嘌呤核苷 Purine nucleoside
山嵛酸Behenic acid	8073074.79	18131360.05	0.45	0.04	0.88	1.91	脂肪酰基 Fatty acyl group
1-羟基-1,2-二氢番茄红素1-Hydroxy-1,2-dihydrolycopene	2977717.56	8415772.56	0.35	0.05	0.83	2.05	有机氧化合物 Organic oxygen compound
邻甲酚o-Cresol	321228.22	939046.97	0.34	0.03	0.88	2.09	酚 Phenol
二十四烷酸Tetracosanoic acid	7493320.23	23641959.60	0.32	0.03	0.85	2.01	脂肪酰基 Fatty acyl group
磷酸胆碱Phosphorylcholine	23734633.96	81867790.22	0.29	0.01	0.47	1.85	有机氮化合物 Organic nitrogen compound
α-胡萝卜素Alpha-Carotene	1140587.13	4556420.00	0.25	0.02	0.67	1.85	戊二烯醇脂质 Pentadienyl lipid

Table 5

Fig. 6

Fig. 7

Fig. 8

Fig. 9

References 43

[1]	BLANDER J M, LONGMAN R S, ILIEV I D, SONNENBERG G F, ARTIS D. Regulation of inflammation by microbiota interactions with the host. Nature Immunology, 2017, 18: 851-860. doi: 10.1038/ni.3780 pmid: 28722709
[2]	DITTOE D K, OLSON E G, RICKE S C. Impact of the gastrointestinal microbiome and fermentation metabolites on broiler performance. Poultry Science, 2022, 101(5): 101786.
[3]	GRICE E A, SEGRE J A. The human microbiome: Our second genome. Annual Review of Genomics and Human Genetics, 2012, 13: 151-170. doi: 10.1146/annurev-genom-090711-163814 pmid: 22703178
[4]	CHEN L, BAO Y, WANG D D, TIAN Y, ZENG T, GU T T, XU W W, LU L Z. Integrated omics analysis reveals the differentiation of intestinal microbiota and metabolites between Pekin ducks and Shaoxing ducks. Poultry Science, 2024, 103(9): 103976.
[5]	HUANG Y, LV H J, SONG Y C, SUN C J, ZHANG Z F, CHEN S R. Community composition of cecal microbiota in commercial yellow broilers with high and low feed efficiencies. Poultry Science, 2021, 100(4): 100996.
[6]	DU W Y, DENG J X, YANG Z L, ZENG L H, YANG X R. Metagenomic analysis reveals linkages between cecal microbiota and feed efficiency in Xiayan chickens. Poultry Science, 2020, 99(12): 7066-7075. doi: 10.1016/j.psj.2020.09.076 pmid: 33248623
[7]	MCCORMACK U M, CURIÃO T, METZLER-ZEBELI B U, MAGOWAN E, BERRY D P, REYER H, PRIETO M L, BUZOIANU S G, HARRISON M, REBEIZ N, CRISPIE F, COTTER P D, O’SULLIVAN O, GARDINER G E, LAWLOR P G. Porcine feed efficiency-associated intestinal microbiota and physiological traits: finding consistent cross-locational biomarkers for residual feed intake. mSystems, 2019, 4(4): e00324-18.
[8]	SIEGERSTETTER S C, SCHMITZ-ESSER S, MAGOWAN E, WETZELS S U, ZEBELI Q, LAWLOR P G, O’CONNELL N E, METZLER-ZEBELI B U. Intestinal microbiota profiles associated with low and high residual feed intake in chickens across two geographical locations. PLoS ONE, 2017, 12(11): e0187766.
[9]	BAE Y, KOO B, LEE S, MO J, OH K, MO I P. Bacterial diversity and its relationship to growth performance of broilers. Korean Journal of Veterinary Research, 2017, 57(3): 159-167.
[10]	STANLEY D, GEIER M S, DENMAN S E, HARING V R, CROWLEY T M, HUGHES R J, MOORE R J. Identification of chicken intestinal microbiota correlated with the efficiency of energy extraction from feed. Veterinary Microbiology, 2013, 164(1/2): 85-92.
[11]	YAN W, SUN C J, YUAN J W, YANG N. Gut metagenomic analysis reveals prominent roles of Lactobacillus and cecal microbiota in chicken feed efficiency. Scientific Reports, 2017, 7: 45308.
[12]	TOROK V A, HUGHES R J, MIKKELSEN L L, PEREZ- MALDONADO R, BALDING K, MACALPINE R, PERCY N J, OPHEL-KELLER K. Identification and characterization of potential performance-related gut microbiotas in broiler chickens across various feeding trials. Applied and Environmental Microbiology, 2011, 77(17): 5868-5878. doi: 10.1128/AEM.00165-11 pmid: 21742925
[13]	STANLEY D, DENMAN S E, HUGHES R J, GEIER M S, CROWLEY T M, CHEN H L, HARING V R, MOORE R J. Intestinal microbiota associated with differential feed conversion efficiency in chickens. Applied Microbiology and Biotechnology, 2012, 96(5): 1361-1369. doi: 10.1007/s00253-011-3847-5 pmid: 22249719
[14]	SINGH K M, SHAH T, DESHPANDE S, JAKHESARA S J, KORINGA P G, RANK D N, JOSHI C G. High through put 16S rRNA gene-based pyrosequencing analysis of the fecal microbiota of high FCR and low FCR broiler growers. Molecular Biology Reports, 2012, 39(12): 10595-10602. doi: 10.1007/s11033-012-1947-7 pmid: 23053958
[15]	LEESON S, SUMMERS J J, LEESON S, SUMMERS J D. Scott's nutrition of the chicken. India: CBS Publishers & Distributors Pvt Ltd. 2001.
[16]	CHAMORRO S, ROMERO C, BRENES A, SÁNCHEZ-PATÁN F, BARTOLOMÉ B, VIVEROS A, ARIJA I. Impact of a sustained consumption of grape extract on digestion, gut microbial metabolism and intestinal barrier in broiler chickens. Food & Function, 2019, 10(3): 1444-1454.
[17]	全耀东, 尚秀国, 陈伟, 郑春田. 谷氨酰胺的生理功能及其对断奶仔猪肠道健康的影响. 动物营养学报, 2024: 1-10. (2024-07- 13). https://kns.cnki.net/kcms/detail/11.5461.S.20240709.2238.017.html
	QUAN Y D, SHANG X G, CHEN W, ZHENG C T. Physiological functions of glutamine and its effects on intestinal health of weaned piglets. Chinese Journal of Animal Nutrition, 2024: 1-10. (2024-07- 13). https://kns.cnki.net/kcms/detail/11.5461.S.20240709.2238.017.html. (in Chinese)
[18]	VIGORS S, SWEENEY T, O’SHEA C J, KELLY A K, O’DOHERTY J V. Pigs that are divergent in feed efficiency, differ in intestinal enzyme and nutrient transporter gene expression, nutrient digestibility and microbial activity. Animal, 2016, 10(11): 1848-1855. pmid: 27173889
[19]	HAO Y S, JI Z Q, SHEN Z J, XUE Y J, ZHANG B, YU D X, LIU T, LUO D W, XING G N, TANG J, et al. Increase dietary fiber intake ameliorates cecal morphology and drives cecal species-specific of short-chain fatty acids in white Pekin ducks. Frontiers in Microbiology, 2022, 13: 853797.
[20]	YAN X X, ZHANG H, LIN A L, SU Y. Antagonization of ghrelin suppresses muscle protein deposition by altering gut microbiota and serum amino acid composition in a pig model. Biology, 2022, 11(6): 840.
[21]	YASOOB T B, YU D F, KHALID A R, ZHANG Z, ZHU X F, SAAD H M, HANG S Q. Oral administration of Moringa oleifera leaf powder relieves oxidative stress, modulates mucosal immune response and cecal microbiota after exposure to heat stress in New Zealand White rabbits. Journal of Animal Science and Biotechnology, 2021, 12(1): 66.
[22]	李巧艳. 美沙酮维持治疗与强制隔离戒毒对肠道微生物多样性的影响[D]. 兰州: 兰州大学, 2021.
	LI Q Y. Effects of methadone maintenance treatment and compulsory isolation detoxification on gut microbial diversity[D]. Lanzhou: Lanzhou University, 2021. (in Chinese)
[23]	OTTOSSON F, BRUNKWALL L, ERICSON U, NILSSON P M, ALMGREN P, FERNANDEZ C, MELANDER O, ORHO-MELANDER M. Connection between BMI-related plasma metabolite profile and gut microbiota. The Journal of Clinical Endocrinology & Metabolism, 2018, 103(4): 1491-1501.
[24]	袁维维. 多阶段生酮饮食对肥胖人群肠道菌群结构特征的影响[D]. 无锡: 江南大学, 2021.
	YUAN W W. Effect of multi-stage ketogenic diet on the structural characteristics of gut microbiota[D]. Wuxi: Jiangnan University, 2021. (in Chinese)
[25]	FLUITMAN K S, DAVIDS M, OLOFSSON L E, WIJDEVELD M, TREMAROLI V, KEIJSER B J F, VISSER M, BÄCKHED F, NIEUWDORP M, IJZERMAN R G. Gut microbial characteristics in poor appetite and undernutrition: A cohort of older adults and microbiota transfer in germ-free mice. Journal of Cachexia, Sarcopenia and Muscle, 2022, 13(4): 2188-2201.
[26]	LUO S H, MAO R, LI Y. Mendelian randomization highlights gut microbiota of short-chain FattyAcids’ producer as protective factor of cerebrovascular disease. Current Neurovascular Research, 2024, 21(1): 32-40.
[27]	EVERARD A, LAZAREVIC V, DERRIEN M, GIRARD M, MUCCIOLI G G, NEYRINCK A M, POSSEMIERS S, VAN HOLLE A, FRANÇOIS P, DE VOS W M, et al. Responses of gut microbiota and glucose and lipid metabolism to prebiotics in genetic obese and diet-induced leptin-resistant mice. Diabetes, 2011, 60(11): 2775-2786. doi: 10.2337/db11-0227 pmid: 21933985
[28]	FIELDING R A, REEVES A R, JASUJA R, LIU C, BARRETT B B, LUSTGARTEN M S. Muscle strength is increased in mice that are colonized with microbiota from high-functioning older adults. Experimental Gerontology, 2019, 127: 110722.
[29]	MAHMUD M R, SALONEN A, PELTONIEMI O, VENHORANTA H, OLIVIERO C. Impact of intestinal microbiota on growth performance of suckling and weaned piglets. Microbiology Spectrum, 2023, 11(3): e03744-22.
[30]	TAN Z, WANG Y, YANG T, AO H, CHEN S K, XING K, ZHANG F X, ZHAO X T, LIU J F, WANG C D. Differences in gut microbiota composition in finishing Landrace pigs with low and high feed conversion ratios. Antonie Van Leeuwenhoek, 2018, 111(9): 1673-1685.
[31]	BJERRUM L, ENGBERG R M, LESER T D, JENSEN B B, FINSTER K, PEDERSEN K. Microbial community composition of the ileum and cecum of broiler chickens as revealed by molecular and culture-based techniques. Poultry Science, 2006, 85(7): 1151-1164. pmid: 16830854
[32]	STANLEY D, HUGHES R J, MOORE R J. Microbiota of the chicken gastrointestinal tract: influence on health, productivity and disease. Applied Microbiology and Biotechnology, 2014, 98(10): 4301-4310. doi: 10.1007/s00253-014-5646-2 pmid: 24643736
[33]	SUN L L, XIE C, WANG G, WU Y, WU Q, WANG X M, LIU J, DENG Y Y, XIA J L, CHEN B, et al. Gut microbiota and intestinal FXR mediate the clinical benefits of metformin. Nature Medicine, 2018, 24: 1919-1929. doi: 10.1038/s41591-018-0222-4 pmid: 30397356
[34]	MICHAEL O S, DIBIA C L, SOETAN O A, ADEYANJU O A, OYEWOLE A L, BADMUS O O, ADETUNJI C O, SOLADOYE A O. Sodium acetate prevents nicotine-induced cardiorenal dysmetabolism through uric acid/creatine kinase-dependent pathway. Life Sciences, 2020, 257: 118127.
[35]	WEI Y X, GAO J, KOU Y B, LIU M N, MENG L Y, ZHENG X P, XU S H, LIANG M, SUN H X, LIU Z Z, WANG Y G. The intestinal microbial metabolite desaminotyrosine is an anti-inflammatory molecule that modulates local and systemic immune homeostasis. The FASEB Journal, 2020, 34(12): 16117-16128.
[36]	SHETE V, QUADRO L. Mammalian metabolism of β-carotene: Gaps in knowledge. Nutrients, 2013, 5(12): 4849-4868. doi: 10.3390/nu5124849 pmid: 24288025
[37]	REBOUL E. Absorption of vitamin A and carotenoids by the enterocyte: Focus on transport proteins. Nutrients, 2013, 5(9): 3563-3581. doi: 10.3390/nu5093563 pmid: 24036530
[38]	AHMED S A, SARMA P, BARGE S R, SWARGIARY D, DEVI G S, BORAH J C. Xanthosine, a purine glycoside mediates hepatic glucose homeostasis through inhibition of gluconeogenesis and activation of glycogenesis via regulating the AMPK/FoxO1/AKT/ GSK3β signaling cascade. Chemico-Biological Interactions, 2023, 371: 110347.
[39]	ZHOU J, LIU J, LIN Q Q, SHI L H, ZENG Z G, GUAN L C, MA Y Z, ZENG Y T, ZHONG S L, XU L S. Characteristics of the gut microbiome and metabolic profile in elderly patients with sarcopenia. Frontiers in Pharmacology, 2023, 14: 1279448.
[40]	REN Y L, SHI X Y, MU J, LIU S Y, QIAN X, PEI W L, NI S H, ZHANG Z D, LI L, ZHANG Z. Chronic exposure to parabens promotes non-alcoholic fatty liver disease in association with the changes of the gut microbiota and lipid metabolism. Food & Function, 2024, 15(3): 1562-1574.
[41]	ZHU J X, LIU W B, BIAN Z, MA Y M, KANG Z X, JIN J H, LI X Y, GE S Y, HAO Y L, ZHANG H X, XIE Y H. Lactobacillus plantarum Zhang-LL inhibits colitis-related tumorigenesis by regulating arachidonic acid metabolism and CD22-mediated B-cell receptor regulation. Nutrients, 2023, 15(21): 4512.
[42]	KRAUTKRAMER K A, FAN J, BÄCKHED F. Gut microbial metabolites as multi-Kingdom intermediates. Nature Reviews Microbiology, 2021, 19: 77-94.
[43]	PESSOA J, BELEW G D, BARROSO C, EGAS C, JONES J G. The gut microbiome responds progressively to fat and/or sugar-rich diets and is differentially modified by dietary fat and sugar. Nutrients, 2023, 15(9): 2097.

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原料 Ingredient	含量 Content (%)		营养水平 Nutrient levels²⁾	1-21日龄 1 to 21 days of age	22-78日龄 22 to 78 days of age
原料 Ingredient	1-21日龄 1 to 21 days of age	22-78日龄 22 to 78 days of age	营养水平 Nutrient levels²⁾	1-21日龄 1 to 21 days of age	22-78日龄 22 to 78 days of age
玉米 Corn	55.00	58.00	代谢能 ME/(MJ·kg^-1)	12.32	12.77
豆粕 Soybean meal	34.00	30.00	粗蛋白质 CP (%)	21.56	20.48
玉米蛋白粉 Corn gluten meal	4.80	5.00	赖氨酸 Lys (%)	1.20	1.13
大豆油 Soybean oil	2.40	3.00	蛋氨酸 Met (%)	0.48	0.51
氯化钠 NaCl	0.30	0.30	蛋氨酸+半胱氨酸 Met+Cys (%)	0.88	0.84
石粉 Limestone	1.20	1.50	钙 Ca (%)	1.12	1.02
磷酸氢钙 CaHPO₄	1.30	1.20	总磷 TP (%)	0.49	0.64
预混料 Premix¹⁾	1.00	1.00
合计 Total	100.00	100.00

项目 Item	组别Group		P值 P value
项目 Item	LF	HF	P值 P value
日采食量ADFI (g.d^-1)	128.33±8.61	103.28±8.04	P=0.059
日增重ADG (g.d^-1)	60.98±4.17a	21.03±0.55b	P≤0.001
FCR	2.11±0.02b	4.92±0.37a	P≤0.001

项目 Item	组别Group		P值 P value
项目 Item	LF	HF	P值 P value
十二指肠Duodenum	34±3.81	33.33±2.42	P=0.732
空肠Jejunum	73.5±5.75a	67±4.05b	P=0.047
回肠Ileum	69.08±8.13	66.75±7.49	P=0.616
直肠Rectum	10.58±1.11b	12±0.84a	P=0.032
盲肠Cecum	43.33±6.89	42.67±6.41	P=0.866

项目 Item		绒毛高度 Villus height (mm)	隐窝深度 Crypt depth (mm)	绒毛高度/隐窝深度 Villus height/Crypt depth	肠壁厚度 Intestinal thickness (mm)
十二指肠 Duodenum	LF	1919.65±206.42	363.21±53.05	5.08±0.50	608.12±64.75
	HF	1937.71±320.27	418.5±61.18	4.45±0.87	634.28±101.47
	P值P value	P=0.924	P=0.142	P=0.236	P=0.616
空肠 Jejunum	LF	1817.54±99.70	380.02±37.13	4.71±0.32	636.03±59.70
	HF	1924.00±198.19	394.17±78.02	4.77±0.86	589.17±32.29
	P值P value	P=0.315	P=0.700	P=0.892	P=0.152
回肠 Ileum	LF	1579.28±380.43	376.09±37.55	4.75±0.92^a	675.41±41.54
	HF	1441.44±219.86	379.97±93.38	3.59±0.59^b	622.15±59.22
	P值P value	P=0.460	P=0.933	P=0.039	P=0.114

Integration of Intestinal Flora and Small Molecule Metabolite to Analyze the Role of Factors Regulating Feed Conversion in Broiler Chickens

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