MetaboLights

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hts/studies/public/MTBLS14861/FILES/plasma_62.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_293.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_161.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_137.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_269.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_323.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_28.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_196.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_110.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_153.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_253.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_11.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_125.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_210.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_24.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_54.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_302.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_60.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_172.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_334.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_155.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_168.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_198.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_317.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_43.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_206.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_112.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_296.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_236.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_249.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_279.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_39.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_309.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_221.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_3.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_264.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_310.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_176.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_281.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_326.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_32.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_15.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_191.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_140.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_272.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_58.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_183.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_63.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_121.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_20.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_288.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_342.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_152.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_245.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_337.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_195.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_159.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_116.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_292.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_47.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_148.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_202.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_164.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_51.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_261.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_35.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_225.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_268.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_136.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_180.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_6.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_27.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_306.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_187.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_241.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_144.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_209.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_330.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_179.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_233.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_322.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_217.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_128.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/plasma_314.rawftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/transfer_12853225_files_19c37a58.zipftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/transfer_12851866_files_79513d89.zipftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861/FILES/transfer_12851482_files_e85ca246.zipprimaryOK200ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14861 Metabolite identification was performed on raw chromatograms using AMDIS software (version 2.72), a home-made target library consisting of 121 compounds (NIST14 spectral library), and a n-alkane library (C7-C40) to calculate the retention index of each of the identified compounds. The final identification and quantification methodology were performed on AMDIS results report file using R scripts (version 4.3.1). Each compound with a retention index greater than 2% was considered unacceptable [50].MetaboLightsPublicGas Chromatography MS - negative - medium-polarityAnimal ethics statementThis study has received approval from the Animal Ethical Committee 'SCIENCE ET SANTE ANIMALES N°115' in Toulouse, France, under the reference number SSA_2022_014.Animals and study designThe present study was conducted on a cohort of dogs that has been previously described in detail for its epidemiological and clinical characteristics [19]. Briefly, client-owned dogs were recruited on a voluntary basis and allocated to one of two age groups: young adult dogs (2-5 years) and senior dogs (≥ 8 years). Only dogs weighing more than 20 kg and considered clinically healthy, with no ongoing medical treatments or diagnosed chronic diseases, were included.To limit potential confounding effects of breed-related metabolic variability, young adult dogs were selected to match as closely as possible the breed distribution of the senior dog group.All dogs underwent a standardized study design comprising two veterinary consultations. During the first visit, eligibility was confirmed through a general clinical examination. Eligible dogs were then transitioned over one week to a standardized commercial diet (PURINA PRO PLAN® All Sizes Light Adult) and maintained exclusively on this diet for at least three weeks.Following the dietary standardization period, a second consultation was performed after an overnight fast of at least eight hours. This visit included a standardized clinical examination and biological sample collection, as described below.SamplingAt the second visit, owners were instructed to bring a fecal sample collected from their dog within the previous four hours and stored at +4°C. This sample was intended for short-chain fatty acid (SCFA) analysis and dry matter determination and was kept on ice throughout the consultation. In the laboratory, 1 g of feces was mixed with 2 mL of a 25% (v/v) sulfuric acid solution for SCFA analysis. The remaining fecal material, used for dry matter determination, as well as the acidified sample, were subsequently stored at −20°C until analysis.At the beginning of the consultation, a blood sample was collected from the cephalic vein into a 3 mL lithium heparin tube. The heparinized blood was directly centrifuged at 2,000 × g for 10 min at room temperature (22°C) to obtain plasma for metabolomic analysis. At the end of the consultation, an additional fecal sample was collected directly from the fecal ampulla by rectal palpation using nitrile gloves for microbiota analysis. Those biological samples were kept on ice during the consultation and subsequently stored at −80 °C until analysis.Plasma metabolome analysisPlasma metabolomic profiling was performed using gas chromatography–mass spectrometry (GC–MS) with electron ionization (EI) at 70 eV. The analytical method was adapted from the protocol described by [49]. Polar metabolites were extracted at 4°C using a cold methanol/water mixture (8:1, v/v) containing ribitol as internal standard (200 µg/mL), followed by centrifugation. After evaporation under vacuum, samples underwent methoximation (methoxyamine hydrochloride, overnight at room temperature) and trimethylsilylation (MSTFA with 1% TMCS, 1 h at 37°C) prior to injection. GC separation was performed on a DB-5MS UI capillary column (30 m × 0.25 mm × 0.25 µm; Thermo Scientific) using helium as carrier gas (1 mL/min) on a Trace 1300 gas chromatograph coupled to an ISQ-LT mass spectrometer (Thermo Scientific, Waltham, MA, USA). The oven temperature was programmed from 70°C (2 min), increased to 80°C at 1°C/min, then to 310°C at 10°C/min (held 30 min). Mass spectra were acquired in full scan mode (m/z 40–500) with Chromeleon software (version 7.2.10 ES). Metabolite identification was performed on raw chromatograms using AMDIS software (version 2.72), a home-made target library consisting of 121 compounds (NIST14 spectral library), and a n-alkane library (C7-C40) to calculate the retention index of each of the identified compounds. The final identification and quantification methodology were performed on AMDIS results report file using R scripts (version 4.3.1). Each compound with a retention index greater than 2% was considered unacceptable [50].Fecal microbiota analysisGenomic DNA was extracted using the Quick-DNATM Fecal/Soil Microbe 96 Kit (ZYMO RESEARCH, Orange, CA, USA) according to the manufacturer’s instructions. The V3–V4 hypervariable region of the 16S rRNA gene was amplified by PCR using the forward primer 5′-ACGGGAGGCAGCAG-3′ and the reverse primer 5′-AGGATTAGATACCCTGGTA-3′. Amplicon libraries were sequenced using Illumina high-throughput sequencing technology at the GeT-PlaGe core facility (INRAE, Toulouse, France). Raw paired-end reads (2 × 250 bp) were processed using the FROGS pipeline on the Galaxy platform [51]. Reads were merged with VSEARCH using a maximum mismatch rate of 0.1 in the overlap region, and unmerged reads were discarded. Amplicons outside the expected size range (380-500 bp, including primers) were removed. ASVs (Amplicon Sequence Variant) were clustered using the Swarm algorithm (aggregation distance d = 1) with the fastidious refinement option enabled. Chimeric sequences were detected and removed. ASVs representing less than 0.005% of the total sequence abundance (5 × 10^-5) were discarded following the recommendation of Bokulich et al., (2013), and PhiX control sequences introduced during Illumina sequencing were removed [52]. Taxonomic affiliation was performed using BLAST against the SILVA database (release 138.1), with classification assigned from domain to species level.Age-related differences in the gut microbiota and plasma metabolome in dogs: a prospective cross-sectional study under standardized dietary conditions.Tiphaine Blanchardenvtplasmamass spectrometry assayAnimal ethics statementThis study has received approval from the Animal Ethical Committee 'SCIENCE ET SANTE ANIMALES N°115' in Toulouse, France, under the reference number SSA_2022_014.Animals and study designThe present study was conducted on a cohort of dogs that has been previously described in detail for its epidemiological and clinical characteristics [19]. Briefly, client-owned dogs were recruited on a voluntary basis and allocated to one of two age groups: young adult dogs (2-5 years) and senior dogs (≥ 8 years). Only dogs weighing more than 20 kg and considered clinically healthy, with no ongoing medical treatments or diagnosed chronic diseases, were included.To limit potential confounding effects of breed-related metabolic variability, young adult dogs were selected to match as closely as possible the breed distribution of the senior dog group.All dogs underwent a standardized study design comprising two veterinary consultations. During the first visit, eligibility was confirmed through a general clinical examination. Eligible dogs were then transitioned over one week to a standardized commercial diet (PURINA PRO PLAN® All Sizes Light Adult) and maintained exclusively on this diet for at least three weeks.Following the dietary standardization period, a second consultation was performed after an overnight fast of at least eight hours. This visit included a standardized clinical examination and biological sample collection, as described below.SamplingAt the second visit, owners were instructed to bring a fecal sample collected from their dog within the previous four hours and stored at +4°C. This sample was intended for short-chain fatty acid (SCFA) analysis and dry matter determination and was kept on ice throughout the consultation. In the laboratory, 1 g of feces was mixed with 2 mL of a 25% (v/v) sulfuric acid solution for SCFA analysis. The remaining fecal material, used for dry matter determination, as well as the acidified sample, were subsequently stored at −20°C until analysis.At the beginning of the consultation, a blood sample was collected from the cephalic vein into a 3 mL lithium heparin tube. The heparinized blood was directly centrifuged at 2,000 × g for 10 min at room temperature (22°C) to obtain plasma for metabolomic analysis. At the end of the consultation, an additional fecal sample was collected directly from the fecal ampulla by rectal palpation using nitrile gloves for microbiota analysis. Those biological samples were kept on ice during the consultation and subsequently stored at −80 °C until analysis.Plasma metabolome analysisPlasma metabolomic profiling was performed using gas chromatography–mass spectrometry (GC–MS) with electron ionization (EI) at 70 eV. The analytical method was adapted from the protocol described by [49]. Polar metabolites were extracted at 4°C using a cold methanol/water mixture (8:1, v/v) containing ribitol as internal standard (200 µg/mL), followed by centrifugation. After evaporation under vacuum, samples underwent methoximation (methoxyamine hydrochloride, overnight at room temperature) and trimethylsilylation (MSTFA with 1% TMCS, 1 h at 37°C) prior to injection. GC separation was performed on a DB-5MS UI capillary column (30 m × 0.25 mm × 0.25 µm; Thermo Scientific) using helium as carrier gas (1 mL/min) on a Trace 1300 gas chromatograph coupled to an ISQ-LT mass spectrometer (Thermo Scientific, Waltham, MA, USA). The oven temperature was programmed from 70°C (2 min), increased to 80°C at 1°C/min, then to 310°C at 10°C/min (held 30 min). Mass spectra were acquired in full scan mode (m/z 40–500) with Chromeleon software (version 7.2.10 ES). Metabolite identification was performed on raw chromatograms using AMDIS software (version 2.72), a home-made target library consisting of 121 compounds (NIST14 spectral library), and a n-alkane library (C7-C40) to calculate the retention index of each of the identified compounds. The final identification and quantification methodology were performed on AMDIS results report file using R scripts (version 4.3.1). Each compound with a retention index greater than 2% was considered unacceptable [50].Fecal microbiota analysisGenomic DNA was extracted using the Quick-DNATM Fecal/Soil Microbe 96 Kit (ZYMO RESEARCH, Orange, CA, USA) according to the manufacturer’s instructions. The V3–V4 hypervariable region of the 16S rRNA gene was amplified by PCR using the forward primer 5′-ACGGGAGGCAGCAG-3′ and the reverse primer 5′-AGGATTAGATACCCTGGTA-3′. Amplicon libraries were sequenced using Illumina high-throughput sequencing technology at the GeT-PlaGe core facility (INRAE, Toulouse, France). Raw paired-end reads (2 × 250 bp) were processed using the FROGS pipeline on the Galaxy platform [51]. Reads were merged with VSEARCH using a maximum mismatch rate of 0.1 in the overlap region, and unmerged reads were discarded. Amplicons outside the expected size range (380-500 bp, including primers) were removed. ASVs (Amplicon Sequence Variant) were clustered using the Swarm algorithm (aggregation distance d = 1) with the fastidious refinement option enabled. Chimeric sequences were detected and removed. ASVs representing less than 0.005% of the total sequence abundance (5 × 10^-5) were discarded following the recommendation of Bokulich et al., (2013), and PhiX control sequences introduced during Illumina sequencing were removed [52]. Taxonomic affiliation was performed using BLAST against the SILVA database (release 138.1), with classification assigned from domain to species level.Canis lupus familiarishttps://www.ebi.ac.uk/metabolights/MTBLS14861Annabelle Meynadier. GenPhySE, Universite ́ de Toulouse, INRAE, ENVT, Castanet Tolosan 31326, France. annabelle.meynadier@envt.fr.Tiphaine Blanchard. GenPhySE, Universite ́ de Toulouse, INRAE, ENVT, Castanet Tolosan 31326, France. tiphaine.blanchard@envt.fr.Statistical analysisAll statistical analyses were performed using R software (version 4.2.2) [56].Fecal microbiota count data and relative proportions of fecal SCFA were first subjected to zero replacement using the Geometric Bayesian Multiplicative (GBM) method, followed by a centered log-ratio (CLR) transformation, which is appropriate for compositional data. For microbiota analyses, only bacterial genera with a prevalence greater than 10% across samples were retained.For plasma metabolomic data, metabolite concentrations were corrected for injection batch effects related to the chromatographic sequence. This correction was performed by extracting the residuals from a linear regression model. Only metabolites detected in more than 10% of samples were included in subsequent analyses to avoid spurious results driven by rare or sporadically detected features [20].SexCollection time pointAgetiphaine.blanchard@envt.frAnimal ethics statementThis study has received approval from the Animal Ethical Committee 'SCIENCE ET SANTE ANIMALES N°115' in Toulouse, France, under the reference number SSA_2022_014.Animals and study designThe present study was conducted on a cohort of dogs that has been previously described in detail for its epidemiological and clinical characteristics [19]. Briefly, client-owned dogs were recruited on a voluntary basis and allocated to one of two age groups: young adult dogs (2-5 years) and senior dogs (≥ 8 years). Only dogs weighing more than 20 kg and considered clinically healthy, with no ongoing medical treatments or diagnosed chronic diseases, were included.To limit potential confounding effects of breed-related metabolic variability, young adult dogs were selected to match as closely as possible the breed distribution of the senior dog group.All dogs underwent a standardized study design comprising two veterinary consultations. During the first visit, eligibility was confirmed through a general clinical examination. Eligible dogs were then transitioned over one week to a standardized commercial diet (PURINA PRO PLAN® All Sizes Light Adult) and maintained exclusively on this diet for at least three weeks.Following the dietary standardization period, a second consultation was performed after an overnight fast of at least eight hours. This visit included a standardized clinical examination and biological sample collection, as described below.SamplingAt the second visit, owners were instructed to bring a fecal sample collected from their dog within the previous four hours and stored at +4°C. This sample was intended for short-chain fatty acid (SCFA) analysis and dry matter determination and was kept on ice throughout the consultation. In the laboratory, 1 g of feces was mixed with 2 mL of a 25% (v/v) sulfuric acid solution for SCFA analysis. The remaining fecal material, used for dry matter determination, as well as the acidified sample, were subsequently stored at −20°C until analysis.At the beginning of the consultation, a blood sample was collected from the cephalic vein into a 3 mL lithium heparin tube. The heparinized blood was directly centrifuged at 2,000 × g for 10 min at room temperature (22°C) to obtain plasma for metabolomic analysis. At the end of the consultation, an additional fecal sample was collected directly from the fecal ampulla by rectal palpation using nitrile gloves for microbiota analysis. Those biological samples were kept on ice during the consultation and subsequently stored at −80 °C until analysis.Plasma metabolome analysisPlasma metabolomic profiling was performed using gas chromatography–mass spectrometry (GC–MS) with electron ionization (EI) at 70 eV. The analytical method was adapted from the protocol described by [49]. Polar metabolites were extracted at 4°C using a cold methanol/water mixture (8:1, v/v) containing ribitol as internal standard (200 µg/mL), followed by centrifugation. After evaporation under vacuum, samples underwent methoximation (methoxyamine hydrochloride, overnight at room temperature) and trimethylsilylation (MSTFA with 1% TMCS, 1 h at 37°C) prior to injection. GC separation was performed on a DB-5MS UI capillary column (30 m × 0.25 mm × 0.25 µm; Thermo Scientific) using helium as carrier gas (1 mL/min) on a Trace 1300 gas chromatograph coupled to an ISQ-LT mass spectrometer (Thermo Scientific, Waltham, MA, USA). The oven temperature was programmed from 70°C (2 min), increased to 80°C at 1°C/min, then to 310°C at 10°C/min (held 30 min). Mass spectra were acquired in full scan mode (m/z 40–500) with Chromeleon software (version 7.2.10 ES). Metabolite identification was performed on raw chromatograms using AMDIS software (version 2.72), a home-made target library consisting of 121 compounds (NIST14 spectral library), and a n-alkane library (C7-C40) to calculate the retention index of each of the identified compounds. The final identification and quantification methodology were performed on AMDIS results report file using R scripts (version 4.3.1). Each compound with a retention index greater than 2% was considered unacceptable [50].Fecal microbiota analysisGenomic DNA was extracted using the Quick-DNATM Fecal/Soil Microbe 96 Kit (ZYMO RESEARCH, Orange, CA, USA) according to the manufacturer’s instructions. The V3–V4 hypervariable region of the 16S rRNA gene was amplified by PCR using the forward primer 5′-ACGGGAGGCAGCAG-3′ and the reverse primer 5′-AGGATTAGATACCCTGGTA-3′. Amplicon libraries were sequenced using Illumina high-throughput sequencing technology at the GeT-PlaGe core facility (INRAE, Toulouse, France). Raw paired-end reads (2 × 250 bp) were processed using the FROGS pipeline on the Galaxy platform [51]. Reads were merged with VSEARCH using a maximum mismatch rate of 0.1 in the overlap region, and unmerged reads were discarded. Amplicons outside the expected size range (380-500 bp, including primers) were removed. ASVs (Amplicon Sequence Variant) were clustered using the Swarm algorithm (aggregation distance d = 1) with the fastidious refinement option enabled. Chimeric sequences were detected and removed. ASVs representing less than 0.005% of the total sequence abundance (5 × 10^-5) were discarded following the recommendation of Bokulich et al., (2013), and PhiX control sequences introduced during Illumina sequencing were removed [52]. Taxonomic affiliation was performed using BLAST against the SILVA database (release 138.1), with classification assigned from domain to species level.MetabolomicsTrace 1336Trace 1337Trace 1338Trace 1339AgingTrace 1340Trace 1341Trace 1342Trace 1343Trace 1344DogTrace 1345Trace 1346Trace 1325Trace 1326Trace 1327Trace 1328Trace 1329Trace 1330Trace 1331Trace 1332Trace 1333Trace 1334Trace 1335Trace 1358Trace 1359Trace 1360Trace 1361Trace 1362Trace 1363Trace 1364Trace 1365Trace 1366Trace 1367Trace 1400Trace 1368Trace 1401Trace 1347Trace 1348Trace 1349Trace 1350Trace 1351Trace 1352Trace 1353Trace 1354Trace 1355Trace 1356Trace 1357Trace 1413Trace 1414Trace 1415Trace 1416Trace 1417Trace 1418Trace 1419Trace 1380Trace 1381Trace 1382Trace 1383Trace 1384Trace 1385Trace 1386Trace 1387Trace 1420Trace 1388Trace 1421Trace 1300Trace 1389Trace 1422Trace 1301Trace 1302Trace 1369Trace 1402Trace 1403Trace 1404Trace 1405Trace 1406Trace 1407Trace 1408Trace 1409Trace 1370Trace 1371Trace 1372Trace 1373Trace 1374Trace 1375Trace 1376Trace 1377Trace 1410Trace 1378Trace 1411Trace 1379Trace 1412Trace 1314PlasmaTrace 1315Trace 1316Trace 1317Trace 1318Trace 1319Trace 1320Trace 1321Trace 1322Trace 1323Trace 1324Trace 1303Trace 1304Trace 1305Trace 1306Trace 1307Trace 1308Trace 1309untargeted metabolite profilingTrace 1390Trace 1391Trace 1392Trace 1393Trace 1394Trace 1395Trace 1396Trace 1397Trace 1398Trace 1310Trace 1399Trace 1311Trace 1312Trace 1313Trace 1336Trace 1337Trace 1338Trace 1339AgingTrace 1340Trace 1341Trace 1342Trace 1343Trace 1344DogTrace 1345Trace 1346Trace 1325Trace 1326Trace 1327Trace 1328Trace 1329Trace 1330Trace 1331Trace 1332Trace 1333Trace 1334Trace 1335Trace 1358Trace 1359Trace 1360Trace 1361Trace 1362Trace 1363Trace 1364Trace 1365Trace 1366Trace 1367Trace 1400Trace 1368Trace 1401Trace 1347Trace 1348Trace 1349Trace 1350Trace 1351Trace 1352Trace 1353Trace 1354Trace 1355Trace 1356Trace 1357Trace 1413Trace 1414Trace 1415Trace 1416Trace 1417Trace 1418Trace 1419Trace 1380Trace 1381Trace 1382Trace 1383Trace 1384Trace 1385Trace 1386Trace 1387Trace 1420Trace 1388Trace 1421Trace 1300Trace 1389Trace 1422Trace 1301Trace 1302Trace 1369Trace 1402Trace 1403Trace 1404Trace 1405Trace 1406Trace 1407Trace 1408Trace 1409Trace 1370Trace 1371Trace 1372Trace 1373Trace 1374Trace 1375Trace 1376Trace 1377Trace 1410Trace 1378Trace 1411Trace 1379Trace 1412Trace 1314PlasmaTrace 1315Trace 1316Trace 1317Trace 1318Trace 1319Trace 1320Trace 1321Trace 1322Trace 1323Trace 1324Trace 1303Trace 1304Trace 1305Trace 1306Trace 1307Trace 1308Trace 1309untargeted metabolite profilingTrace 1390Trace 1391Trace 1392Trace 1393Trace 1394Trace 1395Trace 1396Trace 1397Trace 1398Trace 1310Trace 1399Trace 1311Trace 1312Trace 1313Animal ethics statementThis study has received approval from the Animal Ethical Committee 'SCIENCE ET SANTE ANIMALES N°115' in Toulouse, France, under the reference number SSA_2022_014.Animals and study designThe present study was conducted on a cohort of dogs that has been previously described in detail for its epidemiological and clinical characteristics [19]. Briefly, client-owned dogs were recruited on a voluntary basis and allocated to one of two age groups: young adult dogs (2-5 years) and senior dogs (≥ 8 years). Only dogs weighing more than 20 kg and considered clinically healthy, with no ongoing medical treatments or diagnosed chronic diseases, were included.To limit potential confounding effects of breed-related metabolic variability, young adult dogs were selected to match as closely as possible the breed distribution of the senior dog group.All dogs underwent a standardized study design comprising two veterinary consultations. During the first visit, eligibility was confirmed through a general clinical examination. Eligible dogs were then transitioned over one week to a standardized commercial diet (PURINA PRO PLAN® All Sizes Light Adult) and maintained exclusively on this diet for at least three weeks.Following the dietary standardization period, a second consultation was performed after an overnight fast of at least eight hours. This visit included a standardized clinical examination and biological sample collection, as described below.SamplingAt the second visit, owners were instructed to bring a fecal sample collected from their dog within the previous four hours and stored at +4°C. This sample was intended for short-chain fatty acid (SCFA) analysis and dry matter determination and was kept on ice throughout the consultation. In the laboratory, 1 g of feces was mixed with 2 mL of a 25% (v/v) sulfuric acid solution for SCFA analysis. The remaining fecal material, used for dry matter determination, as well as the acidified sample, were subsequently stored at −20°C until analysis.At the beginning of the consultation, a blood sample was collected from the cephalic vein into a 3 mL lithium heparin tube. The heparinized blood was directly centrifuged at 2,000 × g for 10 min at room temperature (22°C) to obtain plasma for metabolomic analysis. At the end of the consultation, an additional fecal sample was collected directly from the fecal ampulla by rectal palpation using nitrile gloves for microbiota analysis. Those biological samples were kept on ice during the consultation and subsequently stored at −80 °C until analysis.Plasma metabolome analysisPlasma metabolomic profiling was performed using gas chromatography–mass spectrometry (GC–MS) with electron ionization (EI) at 70 eV. The analytical method was adapted from the protocol described by [49]. Polar metabolites were extracted at 4°C using a cold methanol/water mixture (8:1, v/v) containing ribitol as internal standard (200 µg/mL), followed by centrifugation. After evaporation under vacuum, samples underwent methoximation (methoxyamine hydrochloride, overnight at room temperature) and trimethylsilylation (MSTFA with 1% TMCS, 1 h at 37°C) prior to injection. GC separation was performed on a DB-5MS UI capillary column (30 m × 0.25 mm × 0.25 µm; Thermo Scientific) using helium as carrier gas (1 mL/min) on a Trace 1300 gas chromatograph coupled to an ISQ-LT mass spectrometer (Thermo Scientific, Waltham, MA, USA). The oven temperature was programmed from 70°C (2 min), increased to 80°C at 1°C/min, then to 310°C at 10°C/min (held 30 min). Mass spectra were acquired in full scan mode (m/z 40–500) with Chromeleon software (version 7.2.10 ES). Metabolite identification was performed on raw chromatograms using AMDIS software (version 2.72), a home-made target library consisting of 121 compounds (NIST14 spectral library), and a n-alkane library (C7-C40) to calculate the retention index of each of the identified compounds. The final identification and quantification methodology were performed on AMDIS results report file using R scripts (version 4.3.1). Each compound with a retention index greater than 2% was considered unacceptable [50].Fecal microbiota analysisGenomic DNA was extracted using the Quick-DNATM Fecal/Soil Microbe 96 Kit (ZYMO RESEARCH, Orange, CA, USA) according to the manufacturer’s instructions. The V3–V4 hypervariable region of the 16S rRNA gene was amplified by PCR using the forward primer 5′-ACGGGAGGCAGCAG-3′ and the reverse primer 5′-AGGATTAGATACCCTGGTA-3′. Amplicon libraries were sequenced using Illumina high-throughput sequencing technology at the GeT-PlaGe core facility (INRAE, Toulouse, France). Raw paired-end reads (2 × 250 bp) were processed using the FROGS pipeline on the Galaxy platform [51]. Reads were merged with VSEARCH using a maximum mismatch rate of 0.1 in the overlap region, and unmerged reads were discarded. Amplicons outside the expected size range (380-500 bp, including primers) were removed. ASVs (Amplicon Sequence Variant) were clustered using the Swarm algorithm (aggregation distance d = 1) with the fastidious refinement option enabled. Chimeric sequences were detected and removed. ASVs representing less than 0.005% of the total sequence abundance (5 × 10^-5) were discarded following the recommendation of Bokulich et al., (2013), and PhiX control sequences introduced during Illumina sequencing were removed [52]. Taxonomic affiliation was performed using BLAST against the SILVA database (release 138.1), with classification assigned from domain to species level.falseAge-related differences in the gut microbiota and plasma metabolome in dogs: a prospective cross-sectional study under standardized dietary conditionsBackground: Aging is associated with progressive metabolic and physiological changes that influence health span in humans and may similarly impact dogs. This study aimed to characterize in the gut microbiota and their associations with host metabolism. Client-owned young adult (2-5 years, > 20 kg, n = 50) and senior (≥ 8 years, > 20 kg, n = 56) dogs were prospectively enrolled and fed a standardized diet for four weeks. Results: Multivariate analyses identified distinct age-related metabolic and microbial signatures, likely linked to oxidative stress, inflammation, and alterations in protein metabolism. Senior dogs exhibited lower plasma concentrations of glycine, tyrosine, and methionine, together with higher variability of plasma succinic acid concentrations. Compared with young adult dogs, senior dogs showed higher relative abundances of Collinsella and Streptococcus, lower relative abundances of Agathobacter, which was positively correlated with plasma vitamin E status, and higher fecal isovalerate concentrations. Conclusions: These findings highlight coordinated age-related changes in host metabolism and gut microbiota and underscore the potential of integrated multi-omics approaches to identify biological markers and nutritional targets relevant to canine aging.2026-06-262026-06-26MTBLS14861