动物营养学报    2022, Vol. 34 Issue (8): 4829-4835    PDF    
代谢组学在筛选奶牛酮病生物标志物中的应用
安彦昊1 , 马学虎1 , 孙奕烁1 , 马燕芬1,2     
1. 宁夏大学农学院, 宁夏回族自治区反刍动物分子细胞育种重点实验室, 银川 750021;
2. 内蒙古自治区 农牧业科学院, 动物营养与饲料研究所, 呼和浩特 010031
摘要: 酮病作为围产期奶牛最为常见的一种营养代谢病, 严重损害奶牛健康、生产性能、繁殖性能, 其病因学的一些问题至今尚未解决。代谢组学作为系统生物学的重要组成部分, 不仅可以对某一生物或细胞在某一特定生理时期内小分子量的代谢产物进行定性、定量分析, 还可以为动物营养代谢疾病研究提供一定的理论基础和技术平台。本文就代谢组学在奶牛酮病生物标志物筛选上的应用进行综述。
关键词: 生物标志物    代谢组学    奶牛    酮病    
Application of Metabonomics in Screening Biomarkers of Ketosis in Dairy Cows
AN Yanhao1 , MA Xuehu1 , SUN Yishuo1 , MA Yanfen1,2     
1. Key Laboratory of Ruminant Molecular Cell Breeding in Ningxia, School of Agriculture, Ningxia University, Yinchuan 750021, China;
2. Institute of Animal Nutrition and Feed, Inner Mongolia Academy of Agriculture and Animal Husbandry Sciences, Huhhot 010031, China
Abstract: As one of the most common nutritional and metabolic diseases in perinatal dairy cows, ketosis seriously damages the health, performance and reproductive performance of dairy cows. Some problems about its etiology have not been solved yet. Metabolomics is an important part of system biology, which can not only conduct qualitative and quantitative analysis of the small molecular weight metabolites of a certain organism or cell in a specific physiological period, but also provide a certain theoretical basis and technical platform for the study of nutritional and metabolic diseases in animals. This review summarized the application of metabolomics in marker screening of ketosis in dairy cows.
Key words: biomarkers    metabonomics    dairy cows    ketosis    

近年来,随着我国奶牛存栏量的增多以及畜牧业集约化、标准化和现代化水平的不断提高,奶牛群体中营养代谢疾病的发病率在逐年增高,尤其是在高产奶牛。高产奶牛群体中酮病的发病率极高,酮病不仅会造成产奶量的下降,还会引发产后奶牛的一系列疾病,严重时会导致奶牛死亡。据报道,美国围产后期奶牛的临床型酮病发病率为43%[1],我国围产后期奶牛的临床型酮病发病率为2%~20%,亚临床型酮病发病率为10%~30%[2]。酮病作为一种高风险疾病,其发病机理复杂,特别是亚临床型酮病,由于其没有明显的病理变化和临床症状,在诊断和预防过程中困难相对较大,会对奶牛健康、生产性能和繁殖性能产生负面影响[3-4],给养殖业造成严重的经济损失和危害[5]。截止到目前,奶牛酮病的发病机理还没有完全了解清楚,之前有很多研究主要是通过对酮病奶牛的某一代谢途径进行研究[6-7],得到的结果只能单方面作为奶牛代谢状况的依据,仍然缺乏对奶牛酮病代谢的全景式研究。代谢组学技术在动物生产中的广泛应用[8],使人们将病理学的表观认识和代谢物分子之间的动态变化结合在一起,可以从分子水平层面更好地、更全面地、更系统地揭示疾病的病理变化和发病机理[9]。代谢组学作为基因表达的最下游,逐渐被用于分析奶牛酮病的发生,因为它可以反映生物系统的现状,而且应用起来相对便宜。代谢组学技术可以全面系统地呈现出动物不同组织和体液中所有差异性代谢物数量上的动态变化[10]。组织和体液中差异性代谢物不仅可以对奶牛酮病的发病机理研究提供一定的理论基础,还可作为奶牛酮病早期诊断的生物标志物。本文综述了应用代谢组学技术筛选酮病奶牛体液中生物标志物的相关研究,以期为生产实践中快速精准诊断奶牛酮病提供技术支撑。

1 奶牛酮病

酮病是由于奶牛围产期能量供应不足和泌乳期间大量的能量需求,导致奶牛进入能量负平衡(negative energy balance,NEB)状态而引起的以能量代谢障碍为基础的代谢性疾病[11],临床表现为食欲减退、昏睡、产奶量下降,呼出气体、乳汁、尿液中有酮味,消化代谢发生紊乱,血液、乳汁、尿中酮体浓度增高,并且会出现低血糖症、酮血症、酮尿症和酮乳症等临床病理特征。酮体的主要成分为乙酰乙酸、β-羟丁酸(β-hydroxybutyric acid,BHBA)和丙酮。酮病多见于分娩后6周内的高产奶牛和日产奶量高于30 kg的高产奶牛[12]。酮病通常分为临床型酮病和亚临床型酮病。正常情况下健康奶牛血液中葡萄糖(glucose,GLU)浓度低于500 mg/L,BHBA浓度低于1.2 mmol/L,尿酮浓度为3~30 mg/L;如果血液中BHBA浓度在1.2~3.0 mmol/L,尿酮浓度高于100 mg/L,可诊断为亚临床型酮病;若血液中BHBA浓度≥3.0 mmol/L,则诊断为临床型酮病[13-14],其中高浓度的BHBA、非酯化脂肪酸(non esterified fatty acid,NEFA)和低浓度的GLU是诊断酮病发生的黄金指标[15]

奶牛酮病发病机制很复杂,目前对其发病机制尚不完全清楚,但基本可以确定是糖脂代谢紊乱导致的。当围产期奶牛摄入的能量无法满足泌乳需求时,机体就会动员脂质产生甘油和脂肪酸来弥补GLU的缺乏[16-18],但脂质的大量动员增加了血液中NEFA的浓度[19]。被动员的脂质首先被分解为NEFA,然后经β-氧化分解生成乙酰辅酶A,乙酰辅酶A最终被草酰乙酸氧化产生能量来满足机体能量的需求。如果GLU和草酰乙酸的含量不能够满足机体能量的需求,生酮氨基酸经丙酮酸的氧化脱羧产生大量的乙酰辅酶A聚集在肝脏,在酶的作用下生成丙酮[20],进一步生成酮体[21]。此外,肝脏从血浆中吸收的过量脂肪酸也可以在肝细胞中代谢生成酮体,从而诱发酮病[11, 22]。由此可以看出,奶牛酮病的发生与奶牛饲粮的配制、机体自身的体况有很大关系。酮病作为高产奶牛围产期极度易发、危害性最大的一种营养代谢性疾病,虽然死亡率很低,但诱发的炎性疾病和较高的治疗成本会给养殖户带来一定的经济损失和危害,在一定程度上制约着我国奶牛业的发展。

2 代谢组学

代谢组学是对细胞、组织或器官中所有代谢物进行系统地研究的一门科学,它可以直接揭示生物机体中的化学过程和代谢状态[23]。代谢组学已被广泛应用在人和动物研究中,如毒理学、药理学、先天代谢缺陷疾病、食品科学等。当机体受到病理生理刺激或环境因素影响,体内糖类、脂质、核苷酸和氨基酸等内源性小分子代谢物(相对分子质量小于1 000)种类和数量就会发生变化[24],通过分析其代谢途径就能明确代谢物之间的相互关系,从而确定疾病之间潜在的途径。机体体液中代谢产物成分的变化可以很好反映出机体的健康状态或疾病情况[25],通过代谢组学可以发现在疾病诊断、疾病发病机制、疾病治疗中存在的潜在生物标志物,结合多组学技术平台(如蛋白质组学、基因组学、转录组学等)提供最全面的生物学状态图谱[26]。目前,基于核磁共振(nuclear magnetic resonance,NMR)、气相色谱-质谱(gas chromatography/mass spectrometry,GC-MS)和液相色谱-质谱(liquid chromatography/mass spectrometry,LC-MS)等代谢组学技术被广泛应用[27],代谢组学主要研究的对象包括血液[28]、乳液[29]、尿液[30]等。

3 代谢组学技术在奶牛上的应用

代谢组学技术已被广泛应用到毒理学、药理学、先天性代谢缺陷以及营养学的研究中[31]。近年来,代谢组学技术在奶牛营养代谢疾病、繁殖障碍疾病、肢蹄病等方面的应用报道逐渐增多[32-35]。Xu等[36]基于NMR技术鉴定出脂肪肝奶牛血浆代谢物的变化,共鉴定出31种代谢产物,采用1H-NMR技术结合多变量统计分析发现总蛋白(total protein,TP)、谷草转氨酶(aspartate aminotransferase,AST)可作为诊断奶牛脂肪肝的潜在生物标志物。Luo等[37]使用靶向代谢组学技术鉴定奶牛分娩期间血浆代谢物的变化,鉴定出67种差异性代谢物,包括氨基酸、脂质、糖类和核苷酸,进一步分析发现,围产前期到泌乳期间增加的代谢物主要参与脂质代谢和能量代谢,而围产前期到泌乳期间减少的代谢物则与氨基酸代谢有关。Zhang等[38]应用代谢组学技术研究了跛行前、跛行和瘸后奶牛血清代谢物的变化,筛选出跛行奶牛血清中的潜在生物标志物二酰基磷脂酰胆碱、磷脂酰胆碱酰基烷基、鞘磷脂、异亮氨酸、亮氨酸和赖氨酸,通过检测这些代谢物即可区分出跛行奶牛和健康奶牛。

3.1 血液代谢组学分析

近几年来,应用代谢组学技术筛选奶牛血浆差异性代谢物来判定奶牛疾病发病机制的研究越来越多。通过对健康、亚临床型酮病及临床型酮病奶牛血浆差异性代谢物的分析发现,发生亚临床型酮病时,血液中胆碱含量的升高促进了肝脏中脂肪酸以卵磷脂的形式被运输和氧化利用[39],而胆碱含量的降低与脂类物质含量的升高呈负相关[40],说明奶牛发生亚临床型酮病时能量负平衡不是很严重,但肌酸间接提供的能量不仅减少体内糖原利用,而且满足机体能量的需求,同时也能缓解能量负平衡[41]。能量负平衡严重时,肌酸的含量显著升高,此时需要更多的肌酸来缓解这种负平衡[42],肌酸含量的变化为缓解酮病奶牛能量负平衡的研究提供了一定的技术依据,可以进一步验证将胆碱和肌酸作为诊断亚临床型酮病的特异性生物标志物。此外,碳水化合物、脂肪酸、氨基酸、谷甾醇和维生素E异构体等也可作为判定奶牛酮病的潜在生物标志物,其中2-哌啶羧酸和顺-9-十六烯酸与酮病代谢紊乱密切相关,GLU、BHBA和NEFA在临床实践中用于检测酮病代谢紊乱[43]。Sun等[44]发现,亚临床型酮病和临床型酮病奶牛血浆中乳酸盐、谷氨酸盐、谷氨酰胺和苯丙氨酸的含量高于健康奶牛,说明奶牛酮病的发生与糖异生底物的缺乏有很大关系,推测三羧酸循环发生异常可能是导致奶牛酮病发生的重要原因。此外,多项研究发现奶牛发生酮病时糖类代谢、脂类代谢、氨基酸代谢、维生素和微量元素代谢等多个代谢途径均发生了代谢紊乱[45-47]。李影[48]应用1H-NMR技术验证了Ⅰ型酮病和Ⅱ型酮病发生过程中奶牛糖类代谢、脂类代谢和氨基酸代谢发生紊乱,并发现柠檬酸的形成是三羧酸循环发生异常的主要原因,乳酸可以作为区分Ⅰ型酮病和Ⅱ型酮病奶牛的参考指标,胆碱磷酸甘油酯可作为诊断酮病奶牛脂类代谢发生紊乱的生物标志物,其中酪氨酸、亮氨酸、异亮氨酸、丙氨酸等可为奶牛酮病的治疗提供新的策略。

采用1H-NMR和GC/MS技术分析临床型酮病奶牛和亚临床型酮病奶牛血浆差异性代谢物,发现脂类代谢发生异常时十六酸、十七酸、十八酸、十四酸、油酸、9-十六碳烯酸和氨基丙二酸含量升高[39],通过对比组间差异性代谢物发现BHBA、GLU、乳酸、亮氨酸、异亮氨酸、丙氨酸、柠檬酸为共同代谢物,其中BHBA和GLU是奶牛酮病监测的首选指标,同时胎次、泌乳量、体况和干物质摄入量也可作为奶牛酮病群体监测的辅助参数[49],因此,可以考虑将奶牛血浆中部分脂肪酸作为奶牛酮病诊断的生物标志物。Hailemariam等[50]采用液相色谱质谱靶向技术分析了6头健康奶牛和6头酮病奶牛分娩前4周、分娩前1周、分娩后1周和分娩后4周4个时间点血浆代谢物,结果发现健康奶牛和酮病奶牛在以上4个时间点的血浆代谢物均有明显差异,并发现肉毒碱、丙酰肉毒碱和溶血磷脂酰胆碱可以作为奶牛围产期酮病预防的生物标志物,其敏感性高达87%,特异性高达85%。

奶牛体内GLU极度缺乏的状态下,与健康奶牛对比,临床型酮病奶牛血浆中生糖氨基酸含量降低,机体才将生糖氨基酸作为生糖物质来补充GLU的缺乏,可以考虑将其作为诊断临床型酮病与亚临床型酮病的生物标志物[40]。临床型酮病奶牛血液中极低密度脂蛋白(very low-density lipoprotein,VLDL)与低密度脂蛋白(low-density lipoprotein,LDL)的含量是降低的,之前也有研究指出酮病奶牛体内脂肪动员增多会造成LDL和VLDL含量下降[16, 39],说明在临床型酮病奶牛血液中脂蛋白代谢异常,可以考虑将LDL和VLDL作为奶牛临床型酮病诊断的生物标志物。此外,Wu等[19]采用代谢组学和蛋白质组学技术全面揭示了围产期奶牛酮病的发生和发展过程,并发现4-羟基-6-甲基吡喃-2-酮和肉桂酰甘氨酸的代谢产物可作为诊断奶牛酮病的潜在生物标志物。

3.2 乳汁代谢组学分析

牛乳中的差异性代谢物也可以作为判定奶牛酮病的生物标志物。Lu等[51]采用非靶向代谢组学和蛋白质组学技术对泌乳期奶牛乳汁的能量平衡状态进行了分析,发现奶牛重度能量负平衡状态下牛乳中的急性期反应蛋白、不饱和脂肪酸和半乳糖含量较高,能量正平衡状态下牛乳中的胆固醇及相关蛋白和胃抑素含量较高,这为评估能量负平衡奠定了理论基础。利用NMR技术对酮病奶牛的牛乳差异性代谢物进行分析发现,泌乳前4周甘油磷酸胆碱与磷酸胆碱的比率以及泌乳中期甘油磷酸胆碱的含量可作为奶牛应对代谢应激的生物标志物[52]。众所周知,血浆中酮体(丙酮、乙酰乙酸和BHBA)与被动员的体脂肪的不完全β-氧化有关[53],从而引发酮病。因此,血浆和牛乳中的酮体都可作为诊断泌乳期奶牛临床型酮病或亚临床型酮病的生物标志物[54-55]。Xu等[29]采用代谢组学技术分析了牛乳中差异性代谢物,发现牛乳中高含量的甘氨酸和低含量的胆碱与泌乳奶牛的能量负平衡有一定关系,可以作为诊断奶牛酮病的生物标志物。

3.3 尿液代谢组学分析

采用代谢组学技术分析尿液差异性代谢物可以反映出动物机体代谢与饲粮之间的相互关系。为了区分酮病奶牛与健康奶牛,采用NMR和质谱(mass spectrometry,MS)技术识别尿液中的代谢特征物,发现某些脂肪酸(C5-M-DC、C18:2、C14:1-OH、C16:2)和甘油磷脂(PC aa C36:4、PC ae C42:1、PC ae C30:2、PC aa C40:4、PC aa C38:3)可作为酮病发病早期的生物标志物,这些生物标志物有可能便于更早地诊断出分娩后易患酮病的奶牛,也可作为早期酮病生物标志物来区分酮病奶牛和健康奶牛[56]。在肝组织的蛋白质组学研究中,精氨酸酶活性在酮病奶牛中降低[57]。Xu等[57]研究发现,酮病奶牛肝脏中精氨酸酶活性的下调抑制了精氨酸的降解,Zhang等[56]的研究结果与此研究结果一致。此外,Zhang等[56]还发现尿液中不对称二甲基精氨酸(asymmetric dimethylarginine,ADMA)和对称二甲基精氨酸(symmetric dimethylarginine,SDMA)的含量在1个或多个时间点上有所增加,特别是在分娩前8周和分娩前4周,ADMA和SDMA都是精氨酸的类似物。目前,SDMA已被用作肾功能和肾小球过滤的诊断生物标志物[58],而奶牛尿液中的ADMA/SDMA和L-精氨酸/ADMA的比值被用于区分健康奶牛和酮病奶牛[56]

4 小结与展望

随着畜牧业的不断发展,基于代谢组学的动物营养代谢病研究还有待进一步探究,采用动物体液代谢生物标志物解析营养代谢病的发病机制或许是揭示动物代谢病发病机制的一种有效途径。奶牛酮病的发病机制涉及多种致病因素(病理生理学、基因组学、内分泌系统、营养调控等),以往的研究也涉及了多个方面,但一般只针对酮病某一代谢途径或致病因素研究,尽管结果为奶牛酮病的代谢提供了一定的研究思路,但也存在一定的不足之处。

近年来,随着代谢组学技术在阐明疾病发病机理、临床疾病诊断和检测疾病的治疗情况等方面的研究报道逐渐增多,代谢组学技术的应用和临床科研已成为各国科研人员关注的焦点。利用1H-NMR和GC/MS技术检测临床型酮病奶牛、亚临床型酮病奶牛和健康奶牛的血浆、奶样和尿样,结合多元统计分析,能够全景式地展现出酮病奶牛体液特征代谢谱,获得组间差异性代谢物,为研究奶牛酮病的发病机理,探索酮病奶牛的病理状态和环境因素之间的关系提供了新的理论基础和技术支撑,并具有极高的科学研究价值。

参考文献
[1]
MCART J A A, NYDAM D V, OSPINA P A, et al. A field trial on the effect of propylene glycol on milk yield and resolution of ketosis in fresh cows diagnosed with subclinical ketosis[J]. Journal of Dairy Science, 2011, 94(12): 6011-6020. DOI:10.3168/jds.2011-4463
[2]
刘宗平. 现代动物营养代谢病学[M]. 北京: 化学工业出版社, 2003.
LIU Z P. Modern animal nutrition and metabolism[M]. Beijing: Chemical Industry Press, 2003 (in Chinese).
[3]
GRUMMER R R, MASHEK D G, HAYIRLI A. Dry matter intake and energy balance in the transition period[J]. The Veterinary Clinics of North America: Food Animal Practice, 2004, 20(3): 447-470. DOI:10.1016/j.cvfa.2004.06.013
[4]
ZHANG G S, AMETAJ B N. Ketosis an old story under a new approach[J]. Dairy, 2020, 1(1): 42-60. DOI:10.3390/dairy1010005
[5]
BAIRD G D. Primary ketosis in the high-producing dairy cow: clinical and subclinical disorders, treatment, prevention, and outlook[J]. Journal of Dairy Science, 1982, 65(1): 1-10. DOI:10.3168/jds.S0022-0302(82)82146-2
[6]
JAAKSON H, KARIS P, LING K, et al. Adipose tissue insulin receptor and glucose transporter 4 expression, and blood glucose and insulin responses during glucose tolerance tests in transition Holstein cows with different body condition[J]. Journal of Dairy Science, 2018, 101(1): 752-766. DOI:10.3168/jds.2017-12877
[7]
HA N T, DRÖGEMVLLER C, REIMER C, et al. Liver transcriptome analysis reveals important factors involved in the metabolic adaptation of the transition cow[J]. Journal of Dairy Science, 2017, 100(11): 9311-9323. DOI:10.3168/jds.2016-12454
[8]
WU X H, SUN H Z, XUE M Y, et al. Serum metabolome profiling revealed potential biomarkers for milk protein yield in dairy cows[J]. Journal of Proteomics, 2018, 184: 54-61. DOI:10.1016/j.jprot.2018.06.005
[9]
YANG J R, CHEN C X, CHEN W, et al. Proteomics and metabonomics analyses of covid-19 complications in patients with pulmonary fibrosis[J]. Scientific Reports, 2021, 11(1): 14601. DOI:10.1038/s41598-021-94256-8
[10]
SUN X J, ZHAO B S, QU H C, et al. Sera and lungs metabonomics reveals key metabolites of resveratrol protecting against PAH in rats[J]. Biomedicine & Pharmacotherapy, 2021, 133: 110910.
[11]
WHITE H M. The role of TCA cycle anaplerosis in ketosis and fatty liver in periparturient dairy cows[J]. Animals, 2015, 5(3): 793-802. DOI:10.3390/ani5030384
[12]
SHAW J C. Studies on ketosis in dairy cattle.Ⅶ.The efficacy of B vitamins and methionine in the treatment of ketosis[J]. Journal of Dairy Science, 1946, 29(3): 131-139. DOI:10.3168/jds.S0022-0302(46)92457-5
[13]
DUFFIELD T F, LISSEMORE K D, MCBRIDE B W, et al. Impact of hyperketonemia in early lactation dairy cows on health and production[J]. Journal of Dairy Science, 2009, 92(2): 571-580. DOI:10.3168/jds.2008-1507
[14]
OSPINA P A, NYDAM D V, STOKOL T, et al. Evaluation of nonesterified fatty acids and beta-hydroxybutyrate in transition dairy cattle in the northeastern United States: critical thresholds for prediction of clinical diseases[J]. Journal of Dairy Science, 2010, 93(2): 546-554. DOI:10.3168/jds.2009-2277
[15]
ABUELO A, HERNÁNDEZ J, BENEDITO J L, et al. A comparative study of the metabolic profile, insulin sensitivity and inflammatory response between organically and conventionally managed dairy cattle during the periparturient period[J]. Animal, 2014, 8(9): 1516-1525. DOI:10.1017/S1751731114001311
[16]
XU C, WANG Z, LIU G W, et al. Metabolic characteristic of the liver of dairy cows during ketosis based on comparative proteomics[J]. Asian-Australasian Journal of Animal Sciences, 2008, 21(7): 1003-1010. DOI:10.5713/ajas.2008.70392
[17]
GROSS J J, SCHWARZ F J, EDER K, et al. Liver fat content and lipid metabolism in dairy cows during early lactation and during a mid-lactation feed restriction[J]. Journal of Dairy Science, 2013, 96(8): 5008-5017. DOI:10.3168/jds.2012-6245
[18]
FARID A S, HONKAWA K, FATH E M, et al. Serum paraoxonase-1 as biomarker for improved diagnosis of fatty liver in dairy cows[J]. BMC Veterinary Research, 2013, 9: 73. DOI:10.1186/1746-6148-9-73
[19]
WU Z L, CHEN S Y, HU S Q, et al. Metabolomic and proteomic profiles associated with ketosis in dairy cows[J]. Frontiers in Genetics, 2020, 11: 551587. DOI:10.3389/fgene.2020.551587
[20]
GERICH J E, MEYER C, WOERLE H J, et al. Renal gluconeogenesis: its importance in human glucose homeostasis[J]. Diabetes Care, 2001, 24(2): 382-391. DOI:10.2337/diacare.24.2.382
[21]
SUNDRUM A. Metabolic disorders in the transition period indicate that the dairy cows' ability to adapt is overstressed[J]. Animals, 2015, 5(4): 978-1020. DOI:10.3390/ani5040395
[22]
OSPINA P A, NYDAM D V, STOKOL T, et al. Associations of elevated nonesterified fatty acids and beta-hydroxybutyrate concentrations with early lactation reproductive performance and milk production in transition dairy cattle in the northeastern United States[J]. Journal of Dairy Science, 2010, 93(4): 1596-1603. DOI:10.3168/jds.2009-2852
[23]
ZHOU J, YUE S M, PENG Q H, et al. Metabonomic responses of grazing yak to different concentrate supplementations in cold season[J]. Animals, 2020, 10(9): 1595. DOI:10.3390/ani10091595
[24]
FIEHN O, KOPKA J, DÖRMANN P, et al. Metabolite profiling for plant functional genomics[J]. Nature Biotechnology, 2000, 18(11): 1157-1161. DOI:10.1038/81137
[25]
AMETAJ B N. A systems veterinary approach in understanding transition cow diseases: metabolomics[C]//Proceedings of the 4th International Symposium on Dairy Cow Nutrition and Milk Quality, Session 1, Advances in Fundamental Research. Beijing: [s. n. ], 2015: 78-85.
[26]
CHEN Q, FRANCIS E, HU G, et al. Metabolomic profiling of women with gestational diabetes mellitus and their offspring: review of metabolomics studies[J]. Journal of Diabetes and Its Complications, 2018, 32(5): 512-523. DOI:10.1016/j.jdiacomp.2018.01.007
[27]
YU M Q, ZHU Y, CONG Q W, et al. Metabonomics research progress on liver diseases[J]. Canadian Journal of Gastroenterology & Hepatology, 2017, 2017: 8467192.
[28]
FEST J, VIJFHUIZEN L S, GOEMAN J J, et al. Search for early pancreatic cancer blood biomarkers in five European prospective population biobanks using metabolomics[J]. Endocrinology, 2019, 160(7): 1731-1742. DOI:10.1210/en.2019-00165
[29]
XU W, VERVOORT J, SACCENTI E, et al. Milk metabolomics data reveal the energy balance of individual dairy cows in early lactation[J]. Scientific Reports, 2018, 8(1): 15828. DOI:10.1038/s41598-018-34190-4
[30]
SACCENTI E, TENORI L, VERBRUGGEN P, et al. Of monkeys and men: a metabolomic analysis of static and dynamic urinary metabolic phenotypes in two species[J]. PLoS One, 2014, 9(9): e106077. DOI:10.1371/journal.pone.0106077
[31]
SPRATLIN J L, SERKOVA N J, ECKHARDT S G. Clinical applications of metabolomics in oncology: a review[J]. Clinical Cancer Research, 2009, 15(2): 431-440. DOI:10.1158/1078-0432.CCR-08-1059
[32]
ZHAO S, ZHAO J, BU D, et al. Metabolomics analysis reveals large effect of roughage types on rumen microbial metabolic profile in dairy cows[J]. Letters in Applied Microbiology, 2014, 59(1): 79-85. DOI:10.1111/lam.12247
[33]
DERVISHI E, ZHANG G, MANDAL R, et al. Targeted metabolomics: new insights into pathobiology of retained placenta in dairy cows and potential risk biomarkers[J]. Animal, 2018, 12(5): 1050-1059. DOI:10.1017/S1751731117002506
[34]
ZHANG G S, DENG Q L, MANDAL R, et al. DI/LC-MS/MS-based metabolic profiling for identification of early predictive serum biomarkers of metritis in transition dairy cows[J]. Journal of Agricultural and Food Chemistry, 2017, 65(38): 8510-8521. DOI:10.1021/acs.jafc.7b02000
[35]
ZHENG J S, SUN L W, SHU S, et al. Nuclear magnetic resonance-based serum metabolic profiling of dairy cows with footrot[J]. The Journal of Veterinary Medical Science, 2016, 78(9): 1421-1428. DOI:10.1292/jvms.15-0720
[36]
XU C, SUN L W, XIA C, et al. 1H-nuclear magnetic resonance-based plasma metabolic profiling of dairy cows with fatty liver[J]. Asian-Australasian Journal of Animal Sciences, 2016, 29(2): 219-229.
[37]
LUO Z Z, SHEN L H, JIANG J, et al. Plasma metabolite changes in dairy cows during parturition identified using untargeted metabolomics[J]. Journal of Dairy Science, 2019, 102(5): 4639-4650. DOI:10.3168/jds.2018-15601
[38]
ZHANG G S, ZWIERZCHOWSKI G, MANDAL R, et al. Serum metabolomics identifies metabolite panels that differentiate lame dairy cows from healthy ones[J]. Metabolomics, 2020, 16(6): 73. DOI:10.1007/s11306-020-01693-z
[39]
孙玲伟, 张洪友, 夏成, 等. 奶牛酮病的血浆代谢组学分析[J]. 畜牧兽医学报, 2013, 44(10): 1667-1674.
SUN L W, ZHANG H Y, XIA C, et al. Serum metabonomic studies of ketosis in dairy cows[J]. Acta Veterinaria et Zootechnica Sinica, 2013, 44(10): 1667-1674 (in Chinese). DOI:10.11843/j.issn.0366-6964.2013.10.022
[40]
GAO H C, DONG B J, LIU X, et al. Metabonomic profiling of renal cell carcinoma: high-resolution proton nuclear magnetic resonance spectroscopy of human serum with multivariate data analysis[J]. Analytica Chimica Acta, 2008, 624(2): 269-277. DOI:10.1016/j.aca.2008.06.051
[41]
FRANCAUX M, DEMEURE R, GOUDEMANT J F, et al. Effect of exogenous creatine supplementation on muscle PCr metabolism[J]. International Journal of Sports Medicine, 2000, 21(2): 139-145. DOI:10.1055/s-2000-11065
[42]
高阳, 李影, 夏成, 等. 奶牛酮病血浆差异代谢物及其代谢途径分析[J]. 中国畜牧杂志, 2015, 51(3): 51-56.
GAO Y, LI Y, XIA C, et al. Plasma different metabolites and their metabolic pathway analysis for ketosis in dairy cow[J]. Chinese Journal of Animal Science, 2015, 51(3): 51-56 (in Chinese). DOI:10.3969/j.issn.0258-7033.2015.03.013
[43]
ZHANG H Y, WU L, XU C, et al. Plasma metabolomic profiling of dairy cows affected with ketosis using gas chromatography/mass spectrometry[J]. BMC Veterinary Research, 2013, 9: 186. DOI:10.1186/1746-6148-9-186
[44]
SUN L W, ZHANG H Y, WU L, et al. 1H-nuclear magnetic resonance-based plasma metabolic profiling of dairy cows with clinical and subclinical ketosis[J]. Journal of Dairy Science, 2014, 97(3): 1552-1562. DOI:10.3168/jds.2013-6757
[45]
LAPIERRE H, LOBLEY G E, DOEPEL L, et al. Triennial lactation symposium: mammary metabolism of amino acids in dairy cows[J]. Journal of Animal Science, 2012, 90(5): 1708-1721. DOI:10.2527/jas.2011-4645
[46]
PAN Y H, ZHANG Y J, CUI J, et al. Adaptation of phenylalanine and tyrosine catabolic pathway to hibernation in bats[J]. PLoS One, 2013, 8(4): e62039. DOI:10.1371/journal.pone.0062039
[47]
GRUMMER R R. Nutritional and management strategies for the prevention of fatty liver in dairy cattle[J]. Veterinary Journal, 2008, 176(1): 10-20. DOI:10.1016/j.tvjl.2007.12.033
[48]
李影. 基于1H NMR技术的奶牛Ⅰ型和Ⅱ型酮病血浆代谢组学分析[D]. 硕士学位论文. 大庆: 黑龙江八一农垦大学, 2015.
LI Y. Plasma metabolomics analysis of type Ⅰ and type Ⅱ ketosis in dairy cows based on 1H NMR technique[D]. Master's Thesis. Daqing: Heilongjiang Bayi Agricultural University, 2015. (in Chinese)
[49]
孙玲伟. 基于1H NMR和GC/MS技术的奶牛酮病血浆代谢组学分析[D]. 硕士学位论文. 大庆: 黑龙江八一农垦大学, 2014.
SUN L W. 1H NMR and GC/MS based plasma metabolic profiling of dairy cows with ketosis[D]. Master's Thesis. Daqing: Heilongjiang Bayi Agricultural University, 2014. (in Chinese)
[50]
HAILEMARIAM D, MANDAL R, SALEEM F, et al. Identification of predictive biomarkers of disease state in transition dairy cows[J]. Journal of Dairy Science, 2014, 97(5): 2680-2693. DOI:10.3168/jds.2013-6803
[51]
LU J, ANTUNES FERNANDES E, PÁEZ CANO A E, et al. Changes in milk proteome and metabolome associated with dry period length, energy balance, and lactation stage in postparturient dairy cows[J]. Journal of Proteome Research, 2013, 12(7): 3288-3296. DOI:10.1021/pr4001306
[52]
KLEIN M S, BUTTCHEREIT N, MIEMCZYK S P, et al. NMR metabolomic analysis of dairy cows reveals milk glycerophosphocholine to phosphocholine ratio as prognostic biomarker for risk of ketosis[J]. Journal of Proteome Research, 2012, 11(2): 1373-1381. DOI:10.1021/pr201017n
[53]
ZHOU Z, LOOR J J, PICCIOLI-CAPPELLI F, et al. Circulating amino acids in blood plasma during the peripartal period in dairy cows with different liver functionality index[J]. Journal of Dairy Science, 2016, 99(3): 2257-2267. DOI:10.3168/jds.2015-9805
[54]
LEBLANC S. Monitoring metabolic health of dairy cattle in the transition period[J]. The Journal of Reproduction and Development, 2010, 56(Suppl): S29-S35.
[55]
ENJALBERT F, NICOT M C, BAYOURTHE C, et al. Ketone bodies in milk and blood of dairy cows: relationship between concentrations and utilization for detection of subclinical ketosis[J]. Journal of Dairy Science, 2001, 84(3): 583-589. DOI:10.3168/jds.S0022-0302(01)74511-0
[56]
ZHANG G S, MANDAL R, WISHART D S, et al. A multi-platform metabolomics approach identifies urinary metabolite signatures that differentiate ketotic from healthy dairy cows[J]. Frontiers in Veterinary Science, 2021, 8: 595983. DOI:10.3389/fvets.2021.595983
[57]
XU C, WANG Z. Comparative proteomic analysis of livers from ketotic cows[J]. Veterinary Research Communications, 2008, 32(3): 263-273. DOI:10.1007/s11259-007-9028-4
[58]
KIELSTEIN J T, SALPETER S R, BODE-BOEGER S M, et al. Symmetric dimethylarginine (SDMA) as endogenous marker of renal function—a Meta-analysis[J]. Nephrology, Dialysis, Transplantation, 2006, 21(9): 2446-2451. DOI:10.1093/ndt/gfl292