A multiplatform metabolomic approach to characterize fecal signatures of negative postnatal events in chicks: a pilot study
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摘要
Background: Negative experiences in early life can induce long-lasting effects on the welfare, health, and performance of farm animals. A delayed placement of chicks in rearing houses has negative effects on their performance, and results in fecal-specific odors detectable by rats. Based on this observation, the volatile organic compounds(VOCs) and metabolites from the feces of 12-day-old chickens were screened for early markers of response to negative events using gas-chromatography and liquid-chromatography coupled with mass spectrometry(GC-MS, LC-HRMS).Results: The low reproducibility of solid-phase micro-extraction of the VOCs followed by GC-MS was not suitable for marker discovery, in contrast to liquid extraction of metabolites from freeze-dried feces followed by GC-MS or LC-HRMS analysis. Therefore, the fecal metabolome from 12-day-old chicks having experienced a normal or delayed placement were recorded by GC-MS and LC-HRMS in two genotypes from two experiments. From both experiments, 25 and 35 metabolites, respectively explaining 81% and 45% of the difference between delayed and control chickens, were identified by orthogonal partial least-squares discriminant analysis from LC-HRMS and GC-MS profiling.Conclusion: The sets of molecules identified will be useful to better understand the chicks' response to negative events over time and will contribute to define stress or welfare biomarkers.
Background: Negative experiences in early life can induce long-lasting effects on the welfare, health, and performance of farm animals. A delayed placement of chicks in rearing houses has negative effects on their performance, and results in fecal-specific odors detectable by rats. Based on this observation, the volatile organic compounds(VOCs) and metabolites from the feces of 12-day-old chickens were screened for early markers of response to negative events using gas-chromatography and liquid-chromatography coupled with mass spectrometry(GC-MS, LC-HRMS).Results: The low reproducibility of solid-phase micro-extraction of the VOCs followed by GC-MS was not suitable for marker discovery, in contrast to liquid extraction of metabolites from freeze-dried feces followed by GC-MS or LC-HRMS analysis. Therefore, the fecal metabolome from 12-day-old chicks having experienced a normal or delayed placement were recorded by GC-MS and LC-HRMS in two genotypes from two experiments. From both experiments, 25 and 35 metabolites, respectively explaining 81% and 45% of the difference between delayed and control chickens, were identified by orthogonal partial least-squares discriminant analysis from LC-HRMS and GC-MS profiling.Conclusion: The sets of molecules identified will be useful to better understand the chicks' response to negative events over time and will contribute to define stress or welfare biomarkers.
Background: Negative experiences in early life can induce long-lasting effects on the welfare, health, and performance of farm animals. A delayed placement of chicks in rearing houses has negative effects on their performance, and results in fecal-specific odors detectable by rats. Based on this observation, the volatile organic compounds(VOCs) and metabolites from the feces of 12-day-old chickens were screened for early markers of response to negative events using gas-chromatography and liquid-chromatography coupled with mass spectrometry(GC-MS, LC-HRMS).Results: The low reproducibility of solid-phase micro-extraction of the VOCs followed by GC-MS was not suitable for marker discovery, in contrast to liquid extraction of metabolites from freeze-dried feces followed by GC-MS or LC-HRMS analysis. Therefore, the fecal metabolome from 12-day-old chicks having experienced a normal or delayed placement were recorded by GC-MS and LC-HRMS in two genotypes from two experiments. From both experiments, 25 and 35 metabolites, respectively explaining 81% and 45% of the difference between delayed and control chickens, were identified by orthogonal partial least-squares discriminant analysis from LC-HRMS and GC-MS profiling.Conclusion: The sets of molecules identified will be useful to better understand the chicks' response to negative events over time and will contribute to define stress or welfare biomarkers.
引文
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19.Guilloteau L,Collin A,Koch A,Leterrier C.Spontaneous intake and longterm effects of essential oils after a negative postnatal experience in chicks.bioRxiv.2018:452136.https://doi.org/10.1101/452136.
20.Rahimi S,Esmaeilzadeh L,Karimi Torshizi MA.Comparison of growth performance of six commercial broiler hybrids in Iran.Shiraz Univ.2006;7:38-44.https://doi.org/10.22099/IJVR.2006.2661.
21.Vas G,Vékey K.Solid-phase microextraction:a powerful sample preparation tool prior to mass spectrometric.analysis.J Mass Spectrom.2004;39:233-54.
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23.Emond P,Mavel S,A?doud N,Nadal-Desbarats L,Montigny F,Bonnet-Brilhault F,et al.GC-MS-based urine metabolic profiling of autism spectrum disorders.Anal Bioanal Chem.2013;405:5291-300.https://doi.org/10.1007/s00216-013-6934-x.
24.Want EJ,Masson P,Michopoulos F,Wilson ID,Theodoridis G,Plumb RS,et al.Global metabolic profiling of animal and human tissues via UPLC-MS.Nat Protoc.2012;8:17-32.https://doi.org/10.1038/nprot.2012.135.
25.Wishart DS,Feunang YD,Marcu A,Guo AC,Liang K,Vázquez-Fresno R,et al.HMDB 4.0:the human metabolome database for 2018.Nucleic Acids Res.2018;46:D608-17.https://doi.org/10.1093/nar/gkx1089.
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28.Jackson JE.A user’s guide to principal components.Hoboken:Wiley;1991.
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31.Kanehisa M,Goto S.KEGG:Kyoto encyclopedia of genes and genomes.Nucleic Acids Res.2000;28:27-30.https://doi.org/10.1093/nar/28.1.27.
32.Reade S,Mayor A,Aggio R,Khalid T,Pritchard D,Ewer A,et al.Optimisation of sample preparation for direct SPME-GC-MS analysis of murine and human Faecal volatile organic compounds for Metabolomic studies.J Anal Bioanal Tech.2014;5.https://doi.org/10.4172/2155-9872.1000184.
33.Burton RA,Fincher GB.Evolution and development of cell walls in cereal grains.Front Plant Sci.2014;5:456..
35.Sánchez-Hernández L,Puchalska P,García-Ruiz C,Crego AL,Marina ML.Determination of Trigonelline in seeds and vegetable oils by capillary electrophoresis as a novel marker for the detection of adulterations in olive oils.J Agric Food Chem.2010;58:7489-96.https://doi.org/10.1021/jf100550b.
36.Vernocchi P,Del Chierico F,Putignani L.Gut microbiota profiling:metabolomics based approach to unravel compounds affecting human health.Front Microbiol.2016;7:1144.https://doi.org/10.3389/fmicb.2016.01144.
37.Dai Z,Wu Z,Hang S,Zhu W,Wu G.Amino acid metabolism in intestinal bacteria and its potential implications for mammalian reproduction.Mol Hum.Reprod.2015;21:389-409.
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41.Cheng H,Singleton P,Muir W.Social stress in laying hens:differential effect of stress on plasma dopamine concentrations and adrenal function in genetically selected chickens.Poult Sci.2003;82:192-8..
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43.L?tvedt P,Fallahshahroudi A,Bektic L,Altimiras J,Jensen P.Chicken domestication changes expression of stress-related genes in brain,pituitary and adrenals.Neurobiol Stress.2017;7:113-21.
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2.Goerlich VC,N?tt D,Elfwing M,Macdonald B,Jensen P.Transgenerational effects of early experience on behavioral,hormonal and gene expression responses to acute stress in the precocial chicken.Horm Behav.2012;61:711-8.https://doi.org/10.1016/J.YHBEH.2012.03.006.
3.Bergoug H,Guinebretiere M,Tong Q,Roulston N,Romanini CEB,Exadaktylos V,et al.Effect of transportation duration of 1-day-old chicks on postplacement production performances and pododermatitis of broilers up to slaughter age.Poult Sci.2013;92:3300-9.https://doi.org/10.3382/ps.2013-03118.
4.Bigot K,Mignon-Grasteau S,Picard M,Tesseraud S.Effects of delayed feed intake on body,intestine,and muscle development in neonate broilers.Poult Sci.2003;82:781-8.https://doi.org/10.1093/ps/82.5.781.
5.Shakeel I,Khan AA,Qureshi S,Adil S,Wani BM,Din MM,et al.Stress levels,mortality,intestinal morphometry and Histomorphology of Chabro broiler birds subjected to varying degrees of post hatch delay in feeding.Pakistan J Biol Sci.2016;19:331-7.https://doi.org/10.3923/pjbs.2016.331.337.
6.Di Lena M,Porcelli F,Altomare DF.Volatile organic compounds as new biomarkers for colorectal cancer:a review.Color Dis.2016;18:654-63.https://doi.org/10.1111/codi.13271.
7.Baranska A,Mujagic Z,Smolinska A,Dallinga JW,Jonkers DMAE,Tigchelaar EF,et al.Volatile organic compounds in breath as markers for irritable bowel syndrome:a metabolomic approach.Aliment Pharmacol Ther.2016;44:45-56.https://doi.org/10.1111/apt.13654.
8.Martin HJ,Turner MA,Bandelow S,Edwards L,Riazanskaia S,Thomas CLP.Volatile organic compound markers of psychological stress in skin:a pilot study.J Breath Res.2016;10:046012.https://doi.org/10.1088/1752-7155/10/4/046012.
9.Garner CE,Smith S,Elviss NC,Humphrey TJ,White P,Ratcliffe NM,et al.Identification of Campylobacter infection in chickens from volatile faecal emissions.Biomarkers.2008;13:413-21.https://doi.org/10.1080/13547500801966443.
10.Goldansaz SA,Guo AC,Sajed T,Steele MA,Plastow GS,Wishart DS.Livestock metabolomics and the livestock metabolome:a systematic review.PLoS One.2017;12:e0177675.https://doi.org/10.1371/journal.pone.0177675.
11.Beauclercq S,Nadal-Desbarats L,Hennequet-Antier C,Collin A,Tesseraud S,Bourin M,et al.Serum and muscle metabolomics for the prediction of ultimate pH,a key factor for chicken-meat quality.J Proteome Res.2016;15:1168-78.https://doi.org/10.1021/acs.jproteome.5b01050.
12.Beauclercq S,Nadal-Desbarats L,Hennequet-Antier C,Gabriel I,Tesseraud S,Calenge F,et al.Relationships between digestive efficiency and metabolomic profiles of serum and intestinal contents in chickens.Sci Rep.2018;8:6678.https://doi.org/10.1038/s41598-018-24978-9.
13.Zampiga M,Flees J,Meluzzi A,Dridi S,Sirri F.Application of omics technologies for a deeper insight into quali-quantitative production traits in broiler chickens:a review.J Anim Sci Biotechnol.2018;9:61.https://doi.org/10.1186/s40104-018-0278-5.
14.Bombail V,Barret B,Raynaud A,Jer?me N,Saint-Albin A,Ridder C,et al.In search of stress odours across species:behavioural responses of rats to faeces from chickens and rats subjected to various types of stressful events.Appl Anim Behav Sci.2017.https://doi.org/10.1016/J.APPLANIM.2017.10.013.
15.Sakuma K,Hayashi S,Yasaka Y,Nishijima H,Funabashi H,Hayashi M,et al.Analysis of odor compounds in feces of mice that were exposed to various stresses during.breeding.Exp Anim.2013;62:101-7.
16.Bijland LR,Bomers MK,Smulders YM.Smelling the diagnosis:a review on the use of scent in diagnosing disease.Neth J Med.2013;71:300-7.
17.Sheriff MJ,Krebs CJ,Boonstra R.Assessing stress in animal populations:do fecal and plasma glucocorticoids tell the same story?Gen Comp Endocrinol.2010;166:614-9..
18.Dehnhard M,Schreer A,Krone O,Jewgenow K,Krause M,Grossmann R.Measurement of plasma corticosterone and fecal glucocorticoid metabolites in the chicken(Gallus domesticus),the great cormorant(Phalacrocorax carbo),and the goshawk(Accipiter gentilis).Gen Comp Endocrinol.2003;131:345-52.https://doi.org/10.1016/S0016-6480(03)00033-9.
19.Guilloteau L,Collin A,Koch A,Leterrier C.Spontaneous intake and longterm effects of essential oils after a negative postnatal experience in chicks.bioRxiv.2018:452136.https://doi.org/10.1101/452136.
20.Rahimi S,Esmaeilzadeh L,Karimi Torshizi MA.Comparison of growth performance of six commercial broiler hybrids in Iran.Shiraz Univ.2006;7:38-44.https://doi.org/10.22099/IJVR.2006.2661.
21.Vas G,Vékey K.Solid-phase microextraction:a powerful sample preparation tool prior to mass spectrometric.analysis.J Mass Spectrom.2004;39:233-54.
22.DiéméB,Lefèvre A,Nadal-Desbarats L,Galineau L,Madji Hounoum B,Montigny F,et al.Workflow methodology for rat brain metabolome exploration using NMR,LC-MS and GC-MS analytical platforms.J Pharm Biomed Anal.2017;142:270-8.https://doi.org/10.1016/j.jpba.2017.03.068.
23.Emond P,Mavel S,A?doud N,Nadal-Desbarats L,Montigny F,Bonnet-Brilhault F,et al.GC-MS-based urine metabolic profiling of autism spectrum disorders.Anal Bioanal Chem.2013;405:5291-300.https://doi.org/10.1007/s00216-013-6934-x.
24.Want EJ,Masson P,Michopoulos F,Wilson ID,Theodoridis G,Plumb RS,et al.Global metabolic profiling of animal and human tissues via UPLC-MS.Nat Protoc.2012;8:17-32.https://doi.org/10.1038/nprot.2012.135.
25.Wishart DS,Feunang YD,Marcu A,Guo AC,Liang K,Vázquez-Fresno R,et al.HMDB 4.0:the human metabolome database for 2018.Nucleic Acids Res.2018;46:D608-17.https://doi.org/10.1093/nar/gkx1089.
26.Sumner LW,Amberg A,Barrett D,Beale MH,Beger R,Daykin CA,et al.Proposed minimum reporting standards for chemical analysis.Metabolomics.2007;3:211-21.https://doi.org/10.1007/s11306-007-0082-2.
27.Bitar T,Mavel S,Emond P,Nadal-Desbarats L,Lefèvre A,Mattar H,et al.Identification of metabolic pathway disturbances using multimodal metabolomics in autistic disorders in a middle eastern population.J Pharm Biomed Anal.2018;152:57-65.https://doi.org/10.1016/J.JPBA.2018.01.007.
28.Jackson JE.A user’s guide to principal components.Hoboken:Wiley;1991.
29.Trygg J,Wold S.Orthogonal projections to latent structures(O-PLS).JChemom.2002;16:119-28.https://doi.org/10.1002/cem.695.
30.Eriksson L,Trygg J,Wold S.CV-ANOVA for significance testing of PLS and OPLSmodels.J Chemom.2008;22:594-600.https://doi.org/10.1002/cem.1187.
31.Kanehisa M,Goto S.KEGG:Kyoto encyclopedia of genes and genomes.Nucleic Acids Res.2000;28:27-30.https://doi.org/10.1093/nar/28.1.27.
32.Reade S,Mayor A,Aggio R,Khalid T,Pritchard D,Ewer A,et al.Optimisation of sample preparation for direct SPME-GC-MS analysis of murine and human Faecal volatile organic compounds for Metabolomic studies.J Anal Bioanal Tech.2014;5.https://doi.org/10.4172/2155-9872.1000184.
33.Burton RA,Fincher GB.Evolution and development of cell walls in cereal grains.Front Plant Sci.2014;5:456..
35.Sánchez-Hernández L,Puchalska P,García-Ruiz C,Crego AL,Marina ML.Determination of Trigonelline in seeds and vegetable oils by capillary electrophoresis as a novel marker for the detection of adulterations in olive oils.J Agric Food Chem.2010;58:7489-96.https://doi.org/10.1021/jf100550b.
36.Vernocchi P,Del Chierico F,Putignani L.Gut microbiota profiling:metabolomics based approach to unravel compounds affecting human health.Front Microbiol.2016;7:1144.https://doi.org/10.3389/fmicb.2016.01144.
37.Dai Z,Wu Z,Hang S,Zhu W,Wu G.Amino acid metabolism in intestinal bacteria and its potential implications for mammalian reproduction.Mol Hum.Reprod.2015;21:389-409.
38.Galland L.The gut microbiome and the brain.J Med Food.2014;17:1261-
72.https://doi.org/10.1089/jmf.2014.7000.
39.Dennis RL.Adrenergic and noradrenergic regulation of poultry behavior and production.Domest Anim Endocrinol.2016;56:S94-100.https://doi.org/10.1016/J.DOMANIEND.2016.02.007.
40.Pruessner JC,Champagne F,Meaney MJ,Dagher A.Dopamine release in response to a psychological stress in humans and its relationship to early life maternal care:a positron emission tomography study using[11C]Raclopride.J Neurosci.2004;24:2825-31.
41.Cheng H,Singleton P,Muir W.Social stress in laying hens:differential effect of stress on plasma dopamine concentrations and adrenal function in genetically selected chickens.Poult Sci.2003;82:192-8..
42.Bureau C,Hennequet-Antier C,Couty M,GuémenéD.Gene array analysis of adrenal glands in broiler chickens following ACTH treatment.BMCGenomics.2009;10:430.https://doi.org/10.1186/1471-2164-10-430.
43.L?tvedt P,Fallahshahroudi A,Bektic L,Altimiras J,Jensen P.Chicken domestication changes expression of stress-related genes in brain,pituitary and adrenals.Neurobiol Stress.2017;7:113-21.
44.Wu G..Amino acids:metabolism,functions,and nutrition.Amino Acids.2009;37:1-17.https://doi.org/10.1007/s00726-009-0269-0.
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