Age-related changes in gut microbiota composition from newborn to centenarian: a cross-sectional study View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2016-05-25

AUTHORS

Toshitaka Odamaki, Kumiko Kato, Hirosuke Sugahara, Nanami Hashikura, Sachiko Takahashi, Jin-zhong Xiao, Fumiaki Abe, Ro Osawa

ABSTRACT

BackgroundIt has been reported that the composition of human gut microbiota changes with age; however, few studies have used molecular techniques to investigate the long-term, sequential changes in gut microbiota composition. In this study, we investigated the sequential changes in gut microbiota composition in newborn to centenarian Japanese subjects.ResultsFecal samples from 367 healthy Japanese subjects between the ages of 0 and 104 years were analyzed by high-throughput sequencing of amplicons derived from the V3-V4 region of the 16S rRNA gene. Analysis based on bacterial co-abundance groups (CAGs) defined by Kendall correlations between genera revealed that certain transition types of microbiota were enriched in infants, adults, elderly individuals and both infant and elderly subjects. More positive correlations between the relative abundances of genera were observed in the elderly-associated CAGs compared with the infant- and adult-associated CAGs. Hierarchical Ward’s linkage clustering based on the abundance of genera indicated five clusters, with median (interquartile range) ages of 3 (0–35), 33 (24–45), 42 (32–62), 77 (36–84) and 94 (86–98) years. Subjects were predominantly clustered with their matched age; however, some of them fell into mismatched age clusters. Furthermore, clustering based on the proportion of transporters predicted by phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt) showed that subjects were divided into two age-related groups, the adult-enriched and infant/elderly-enriched clusters. Notably, all the drug transporters based on Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthology groups were found in the infant/elderly-enriched cluster.ConclusionOur results indicate some patterns and transition points in the compositional changes in gut microbiota with age. In addition, the transporter property prediction results suggest that nutrients in the gut might play an important role in changing the gut microbiota composition with age. More... »

PAGES

90

References to SciGraph publications

  • 2013-08-25. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences in NATURE BIOTECHNOLOGY
  • 2012-05-09. Human gut microbiome viewed across age and geography in NATURE
  • 2013-11-13. Commensal microbe-derived butyrate induces the differentiation of colonic regulatory T cells in NATURE
  • 2014-12-14. The gut microbiota of Colombians differs from that of Americans, Europeans and Asians in BMC MICROBIOLOGY
  • 2014-06-05. Choice of bacterial DNA extraction method from fecal material influences community structure as evaluated by metagenomic analysis in MICROBIOME
  • 2013-08-05. The interaction between gut microbiota and age-related changes in immune function and inflammation in IMMUNITY & AGEING
  • 2011-12-01. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea in THE ISME JOURNAL: MULTIDISCIPLINARY JOURNAL OF MICROBIAL ECOLOGY
  • 2015-02-23. Diversity in gut bacterial community of school-age children in Asia in SCIENTIFIC REPORTS
  • 2013-08-28. Richness of human gut microbiome correlates with metabolic markers in NATURE
  • 2012-07-13. Gut microbiota composition correlates with diet and health in the elderly in NATURE
  • 2014-04-15. Gut microbiome of the Hadza hunter-gatherers in NATURE COMMUNICATIONS
  • 2011-06-24. Metagenomic biomarker discovery and explanation in GENOME BIOLOGY
  • 2015-04-06. Effects of Bifidobacterium supplementation on intestinal microbiota composition and the immune response in healthy infants in WORLD JOURNAL OF PEDIATRICS
  • 2009-06-09. The Firmicutes/Bacteroidetes ratio of the human microbiota changes with age in BMC MICROBIOLOGY
  • 2010-04-11. QIIME allows analysis of high-throughput community sequencing data in NATURE METHODS
  • 2015-10-02. Discordant temporal development of bacterial phyla and the emergence of core in the fecal microbiota of young children in THE ISME JOURNAL: MULTIDISCIPLINARY JOURNAL OF MICROBIAL ECOLOGY
  • 2012-06-14. Host genetic and environmental effects on mouse intestinal microbiota in THE ISME JOURNAL: MULTIDISCIPLINARY JOURNAL OF MICROBIAL ECOLOGY
  • 2015-08-28. Probiotic Bifidobacterium longum alters gut luminal metabolism through modification of the gut microbial community in SCIENTIFIC REPORTS
  • 2015-02-23. Metabolic cross-feeding via intercellular nanotubes among bacteria in NATURE COMMUNICATIONS
  • Journal

    TITLE

    BMC Microbiology

    ISSUE

    1

    VOLUME

    16

    Related Patents

  • Compositions Comprising Bacterial Strains
  • Compositions Comprising Bacterial Strains
  • Compositions Comprising Bacterial Strains
  • Compositions Comprising Bacterial Strains
  • Compositions Comprising Bacterial Strains
  • Pirin Polypeptide And Immune Modulation
  • Compositions Comprising Bacterial Strains
  • Composition Comprising Bacterial Strains
  • Compositions Comprising Bacterial Strains
  • Compositions Comprising Bacterial Strains
  • Compositions Comprising Bacterial Strains
  • Compositions Comprising Bacterial Strains
  • Polypeptide And Immune Modulation
  • Compositions Comprising Bacterial Strains
  • Compositions Comprising Bacterial Strains
  • Compositions Comprising Bacterial Strains
  • Bacterium For Use As A Probiotic For Nutritional And Medical Applications
  • Compositions Comprising Bacterial Strains
  • Compositions Comprising Bacterial Strains
  • Compositions Comprising Bacterial Strains
  • Pirin Polypeptide And Immune Modulation
  • Compositions Comprising Bacterial Strains
  • Compositions Comprising Bacterial Strains
  • Compositions Comprising Bacterial Strains
  • Compositions Comprising Bacterial Strains
  • Compositions Comprising Bacterial Strains
  • Compositions Comprising Bacterial Strains
  • Polypeptide And Immune Modulation
  • Compositions Comprising Bacterial Strains
  • Compositions Comprising A Bacterial Strain Of The Genus Megasphera And Uses Thereof
  • Compositions Comprising Bacterial Strains
  • Compositions Comprising Bacterial Strains
  • Compositions Comprising Bacterial Strains
  • Compositions Comprising Bacterial Strains
  • Compositions Comprising Bacterial Strains
  • Compositions Comprising Bacterial Strain
  • Lactic Acid Bacterial Strains
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/s12866-016-0708-5

    DOI

    http://dx.doi.org/10.1186/s12866-016-0708-5

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1004553266

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/27220822


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/06", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Biological Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0604", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Genetics", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Adolescent", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Adult", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Age Factors", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Aged", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Aged, 80 and over", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Bacteria", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Child", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Child, Preschool", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Cluster Analysis", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Cross-Sectional Studies", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Feces", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Female", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Gastrointestinal Microbiome", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "High-Throughput Nucleotide Sequencing", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Humans", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Infant", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Infant, Newborn", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Male", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Middle Aged", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "RNA, Ribosomal, 16S", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Sequence Analysis, DNA", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Young Adult", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Next Generation Science Institute, Morinaga Milk Industry Co., Ltd., Zama, Kanagawa, Japan", 
              "id": "http://www.grid.ac/institutes/grid.419972.0", 
              "name": [
                "Next Generation Science Institute, Morinaga Milk Industry Co., Ltd., Zama, Kanagawa, Japan"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Odamaki", 
            "givenName": "Toshitaka", 
            "id": "sg:person.01331322535.15", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01331322535.15"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Next Generation Science Institute, Morinaga Milk Industry Co., Ltd., Zama, Kanagawa, Japan", 
              "id": "http://www.grid.ac/institutes/grid.419972.0", 
              "name": [
                "Next Generation Science Institute, Morinaga Milk Industry Co., Ltd., Zama, Kanagawa, Japan"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Kato", 
            "givenName": "Kumiko", 
            "id": "sg:person.01306417773.37", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01306417773.37"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Next Generation Science Institute, Morinaga Milk Industry Co., Ltd., Zama, Kanagawa, Japan", 
              "id": "http://www.grid.ac/institutes/grid.419972.0", 
              "name": [
                "Next Generation Science Institute, Morinaga Milk Industry Co., Ltd., Zama, Kanagawa, Japan"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Sugahara", 
            "givenName": "Hirosuke", 
            "id": "sg:person.01345137415.31", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01345137415.31"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Next Generation Science Institute, Morinaga Milk Industry Co., Ltd., Zama, Kanagawa, Japan", 
              "id": "http://www.grid.ac/institutes/grid.419972.0", 
              "name": [
                "Next Generation Science Institute, Morinaga Milk Industry Co., Ltd., Zama, Kanagawa, Japan"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Hashikura", 
            "givenName": "Nanami", 
            "id": "sg:person.0641503625.34", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0641503625.34"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Food Ingredients & Technology Institute, Morinaga Milk Industry Co., Ltd., Zama, Kanagawa, Japan", 
              "id": "http://www.grid.ac/institutes/grid.419972.0", 
              "name": [
                "Food Ingredients & Technology Institute, Morinaga Milk Industry Co., Ltd., Zama, Kanagawa, Japan"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Takahashi", 
            "givenName": "Sachiko", 
            "id": "sg:person.0723151153.71", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0723151153.71"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Next Generation Science Institute, Morinaga Milk Industry Co., Ltd., Zama, Kanagawa, Japan", 
              "id": "http://www.grid.ac/institutes/grid.419972.0", 
              "name": [
                "Next Generation Science Institute, Morinaga Milk Industry Co., Ltd., Zama, Kanagawa, Japan"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Xiao", 
            "givenName": "Jin-zhong", 
            "id": "sg:person.0765610722.27", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0765610722.27"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Food Ingredients & Technology Institute, Morinaga Milk Industry Co., Ltd., Zama, Kanagawa, Japan", 
              "id": "http://www.grid.ac/institutes/grid.419972.0", 
              "name": [
                "Food Ingredients & Technology Institute, Morinaga Milk Industry Co., Ltd., Zama, Kanagawa, Japan"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Abe", 
            "givenName": "Fumiaki", 
            "id": "sg:person.01300772050.26", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01300772050.26"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Bioresource Science, Graduate School of Agricultural Science, Kobe University, Kobe, Hyogo, Japan", 
              "id": "http://www.grid.ac/institutes/grid.31432.37", 
              "name": [
                "Department of Bioresource Science, Graduate School of Agricultural Science, Kobe University, Kobe, Hyogo, Japan"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Osawa", 
            "givenName": "Ro", 
            "id": "sg:person.01136102115.58", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01136102115.58"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1038/nbt.2676", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1034019934", 
              "https://doi.org/10.1038/nbt.2676"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ismej.2011.139", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1051863807", 
              "https://doi.org/10.1038/ismej.2011.139"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/srep13548", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1010482194", 
              "https://doi.org/10.1038/srep13548"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/2049-2618-2-19", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1026297002", 
              "https://doi.org/10.1186/2049-2618-2-19"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature11319", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1032682406", 
              "https://doi.org/10.1038/nature11319"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ncomms4654", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1000325507", 
              "https://doi.org/10.1038/ncomms4654"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nmeth.f.303", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1009032055", 
              "https://doi.org/10.1038/nmeth.f.303"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature12506", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003823032", 
              "https://doi.org/10.1038/nature12506"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ncomms7238", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052025669", 
              "https://doi.org/10.1038/ncomms7238"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2180-9-123", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1049309371", 
              "https://doi.org/10.1186/1471-2180-9-123"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s12519-015-0025-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004724905", 
              "https://doi.org/10.1007/s12519-015-0025-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ismej.2015.177", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1026945768", 
              "https://doi.org/10.1038/ismej.2015.177"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ismej.2012.54", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1038641995", 
              "https://doi.org/10.1038/ismej.2012.54"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature12721", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1021135023", 
              "https://doi.org/10.1038/nature12721"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1742-4933-10-31", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1039753171", 
              "https://doi.org/10.1186/1742-4933-10-31"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/gb-2011-12-6-r60", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1000243423", 
              "https://doi.org/10.1186/gb-2011-12-6-r60"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/srep08397", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006452375", 
              "https://doi.org/10.1038/srep08397"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature11053", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052378845", 
              "https://doi.org/10.1038/nature11053"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s12866-014-0311-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001548821", 
              "https://doi.org/10.1186/s12866-014-0311-6"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2016-05-25", 
        "datePublishedReg": "2016-05-25", 
        "description": "BackgroundIt has been reported that the composition of human gut microbiota changes with age; however, few studies have used molecular techniques to investigate the long-term, sequential changes in gut microbiota composition. In this study, we investigated the sequential changes in gut microbiota composition in newborn to centenarian Japanese subjects.ResultsFecal samples from 367 healthy Japanese subjects between the ages of 0 and 104\u00a0years were analyzed by high-throughput sequencing of amplicons derived from the V3-V4 region of the 16S rRNA gene. Analysis based on bacterial co-abundance groups (CAGs) defined by Kendall correlations between genera revealed that certain transition types of microbiota were enriched in infants, adults, elderly individuals and both infant and elderly subjects. More positive correlations between the relative abundances of genera were observed in the elderly-associated CAGs compared with the infant- and adult-associated CAGs. Hierarchical Ward\u2019s linkage clustering based on the abundance of genera indicated five clusters, with median (interquartile range) ages of 3 (0\u201335), 33 (24\u201345), 42 (32\u201362), 77 (36\u201384) and 94 (86\u201398) years. Subjects were predominantly clustered with their matched age; however, some of them fell into mismatched age clusters. Furthermore, clustering based on the proportion of transporters predicted by phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt) showed that subjects were divided into two age-related groups, the adult-enriched and infant/elderly-enriched clusters. Notably, all the drug transporters based on Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthology groups were found in the infant/elderly-enriched cluster.ConclusionOur results indicate some patterns and transition points in the compositional changes in gut microbiota with age. In addition, the transporter property prediction results suggest that nutrients in the gut might play an important role in changing the gut microbiota composition with age.", 
        "genre": "article", 
        "id": "sg:pub.10.1186/s12866-016-0708-5", 
        "isAccessibleForFree": true, 
        "isPartOf": [
          {
            "id": "sg:journal.1024253", 
            "issn": [
              "1471-2180"
            ], 
            "name": "BMC Microbiology", 
            "publisher": "Springer Nature", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "1", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "16"
          }
        ], 
        "keywords": [
          "co-abundance groups", 
          "gut microbiota composition", 
          "microbiota composition", 
          "gut microbiota", 
          "Japanese subjects", 
          "infant/", 
          "cross-sectional study", 
          "sequential changes", 
          "healthy Japanese subjects", 
          "age-related groups", 
          "age-related changes", 
          "median age", 
          "human gut microbiota", 
          "elderly subjects", 
          "elderly individuals", 
          "ConclusionOur results", 
          "drug transporters", 
          "infants", 
          "abundance of genera", 
          "age", 
          "V3-V4 region", 
          "microbiota", 
          "subjects", 
          "positive correlation", 
          "group", 
          "Kyoto Encyclopedia", 
          "molecular techniques", 
          "BackgroundIt", 
          "years", 
          "transporters", 
          "study", 
          "important role", 
          "adults", 
          "gut", 
          "centenarians", 
          "changes", 
          "genes", 
          "Kendall correlation", 
          "correlation", 
          "proportion", 
          "individuals", 
          "high-throughput sequencing", 
          "sequencing", 
          "role", 
          "unobserved states", 
          "phylogenetic investigation", 
          "results", 
          "Encyclopedia", 
          "reconstruction", 
          "amplicons", 
          "patterns", 
          "samples", 
          "relative abundance", 
          "addition", 
          "rRNA gene", 
          "types", 
          "investigation", 
          "analysis", 
          "proportion of transporters", 
          "clusters", 
          "nutrients", 
          "region", 
          "community", 
          "technique", 
          "point", 
          "composition", 
          "compositional changes", 
          "abundance", 
          "state", 
          "genus", 
          "clustering", 
          "transition type", 
          "age clusters", 
          "transition point", 
          "orthology groups", 
          "prediction results"
        ], 
        "name": "Age-related changes in gut microbiota composition from newborn to centenarian: a cross-sectional study", 
        "pagination": "90", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1004553266"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1186/s12866-016-0708-5"
            ]
          }, 
          {
            "name": "pubmed_id", 
            "type": "PropertyValue", 
            "value": [
              "27220822"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1186/s12866-016-0708-5", 
          "https://app.dimensions.ai/details/publication/pub.1004553266"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2022-12-01T06:35", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-springernature-scigraph/baseset/20221201/entities/gbq_results/article/article_714.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://doi.org/10.1186/s12866-016-0708-5"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1186/s12866-016-0708-5'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/s12866-016-0708-5'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s12866-016-0708-5'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s12866-016-0708-5'


     

    This table displays all metadata directly associated to this object as RDF triples.

    354 TRIPLES      21 PREDICATES      142 URIs      115 LITERALS      29 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1186/s12866-016-0708-5 schema:about N03a4bdd0ae594cf6860680395b25671d
    2 N10150ef7955c4112b0bfd786ad2132aa
    3 N4b146d78b1e34db08af4468042a0ba30
    4 N5a408d11dc264cfc91d27d09ea61e3d2
    5 N5b8bfca6e6744a17b5675ad2663202db
    6 N5e1892582601421e9a72f349d9da99bd
    7 N6583d67e881843f18bff39975bbf16a2
    8 N678e13ec2d6b4ad2a256cd149636aaaf
    9 N7525cfb967574778833603c7b730a227
    10 N7bd446993101498bae7cca115a07b3ea
    11 N7f6392a57c37499a87d28732cf3ddd2b
    12 N8608ec7e0db045fb9156bfd8e9651743
    13 N9e3c9e4048fe4dfab8373d5785350ea0
    14 Naa7324ff61b24b5cae3cdd39874d6c41
    15 Nae1c18510daf41c5a3b8bfe59c1a046e
    16 Naf1fcd98867042508b0c5d1d5cc5c65d
    17 Nb3765493544e40a38e35a6c8c66481f4
    18 Nc3155575d0ce4183ac428945fc53f321
    19 Nd7fc163759104c4db28821799e735aa4
    20 Nda317c5c2dc443f7b2212f51416d35ad
    21 Ne5f0b22cfaa74775acb97065749669c2
    22 Nfe84d1cf723a42f7a80f654d120431c6
    23 anzsrc-for:06
    24 anzsrc-for:0604
    25 schema:author Na288f9265a4a4b20baabf0730e36e358
    26 schema:citation sg:pub.10.1007/s12519-015-0025-3
    27 sg:pub.10.1038/ismej.2011.139
    28 sg:pub.10.1038/ismej.2012.54
    29 sg:pub.10.1038/ismej.2015.177
    30 sg:pub.10.1038/nature11053
    31 sg:pub.10.1038/nature11319
    32 sg:pub.10.1038/nature12506
    33 sg:pub.10.1038/nature12721
    34 sg:pub.10.1038/nbt.2676
    35 sg:pub.10.1038/ncomms4654
    36 sg:pub.10.1038/ncomms7238
    37 sg:pub.10.1038/nmeth.f.303
    38 sg:pub.10.1038/srep08397
    39 sg:pub.10.1038/srep13548
    40 sg:pub.10.1186/1471-2180-9-123
    41 sg:pub.10.1186/1742-4933-10-31
    42 sg:pub.10.1186/2049-2618-2-19
    43 sg:pub.10.1186/gb-2011-12-6-r60
    44 sg:pub.10.1186/s12866-014-0311-6
    45 schema:datePublished 2016-05-25
    46 schema:datePublishedReg 2016-05-25
    47 schema:description BackgroundIt has been reported that the composition of human gut microbiota changes with age; however, few studies have used molecular techniques to investigate the long-term, sequential changes in gut microbiota composition. In this study, we investigated the sequential changes in gut microbiota composition in newborn to centenarian Japanese subjects.ResultsFecal samples from 367 healthy Japanese subjects between the ages of 0 and 104 years were analyzed by high-throughput sequencing of amplicons derived from the V3-V4 region of the 16S rRNA gene. Analysis based on bacterial co-abundance groups (CAGs) defined by Kendall correlations between genera revealed that certain transition types of microbiota were enriched in infants, adults, elderly individuals and both infant and elderly subjects. More positive correlations between the relative abundances of genera were observed in the elderly-associated CAGs compared with the infant- and adult-associated CAGs. Hierarchical Ward’s linkage clustering based on the abundance of genera indicated five clusters, with median (interquartile range) ages of 3 (0–35), 33 (24–45), 42 (32–62), 77 (36–84) and 94 (86–98) years. Subjects were predominantly clustered with their matched age; however, some of them fell into mismatched age clusters. Furthermore, clustering based on the proportion of transporters predicted by phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt) showed that subjects were divided into two age-related groups, the adult-enriched and infant/elderly-enriched clusters. Notably, all the drug transporters based on Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthology groups were found in the infant/elderly-enriched cluster.ConclusionOur results indicate some patterns and transition points in the compositional changes in gut microbiota with age. In addition, the transporter property prediction results suggest that nutrients in the gut might play an important role in changing the gut microbiota composition with age.
    48 schema:genre article
    49 schema:isAccessibleForFree true
    50 schema:isPartOf N14f7343557924baf899c77c6b3ce4fdb
    51 N3e4bf63d02cb4b8a854d5ebbad7759db
    52 sg:journal.1024253
    53 schema:keywords BackgroundIt
    54 ConclusionOur results
    55 Encyclopedia
    56 Japanese subjects
    57 Kendall correlation
    58 Kyoto Encyclopedia
    59 V3-V4 region
    60 abundance
    61 abundance of genera
    62 addition
    63 adults
    64 age
    65 age clusters
    66 age-related changes
    67 age-related groups
    68 amplicons
    69 analysis
    70 centenarians
    71 changes
    72 clustering
    73 clusters
    74 co-abundance groups
    75 community
    76 composition
    77 compositional changes
    78 correlation
    79 cross-sectional study
    80 drug transporters
    81 elderly individuals
    82 elderly subjects
    83 genes
    84 genus
    85 group
    86 gut
    87 gut microbiota
    88 gut microbiota composition
    89 healthy Japanese subjects
    90 high-throughput sequencing
    91 human gut microbiota
    92 important role
    93 individuals
    94 infant/
    95 infants
    96 investigation
    97 median age
    98 microbiota
    99 microbiota composition
    100 molecular techniques
    101 nutrients
    102 orthology groups
    103 patterns
    104 phylogenetic investigation
    105 point
    106 positive correlation
    107 prediction results
    108 proportion
    109 proportion of transporters
    110 rRNA gene
    111 reconstruction
    112 region
    113 relative abundance
    114 results
    115 role
    116 samples
    117 sequencing
    118 sequential changes
    119 state
    120 study
    121 subjects
    122 technique
    123 transition point
    124 transition type
    125 transporters
    126 types
    127 unobserved states
    128 years
    129 schema:name Age-related changes in gut microbiota composition from newborn to centenarian: a cross-sectional study
    130 schema:pagination 90
    131 schema:productId N18155e3151da42dc91f236ae4a9ac38d
    132 N31f444e009a048d5842f8a0f793c0350
    133 N7236d506a1c44b979cb5def79f2a3450
    134 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004553266
    135 https://doi.org/10.1186/s12866-016-0708-5
    136 schema:sdDatePublished 2022-12-01T06:35
    137 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    138 schema:sdPublisher N0f01cfdd217a456297573f620eef850e
    139 schema:url https://doi.org/10.1186/s12866-016-0708-5
    140 sgo:license sg:explorer/license/
    141 sgo:sdDataset articles
    142 rdf:type schema:ScholarlyArticle
    143 N03a4bdd0ae594cf6860680395b25671d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    144 schema:name Adult
    145 rdf:type schema:DefinedTerm
    146 N09dc9c24ed234076a4ee76049c812361 rdf:first sg:person.01136102115.58
    147 rdf:rest rdf:nil
    148 N0f01cfdd217a456297573f620eef850e schema:name Springer Nature - SN SciGraph project
    149 rdf:type schema:Organization
    150 N10150ef7955c4112b0bfd786ad2132aa schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    151 schema:name Child, Preschool
    152 rdf:type schema:DefinedTerm
    153 N14f7343557924baf899c77c6b3ce4fdb schema:issueNumber 1
    154 rdf:type schema:PublicationIssue
    155 N18155e3151da42dc91f236ae4a9ac38d schema:name pubmed_id
    156 schema:value 27220822
    157 rdf:type schema:PropertyValue
    158 N31f444e009a048d5842f8a0f793c0350 schema:name dimensions_id
    159 schema:value pub.1004553266
    160 rdf:type schema:PropertyValue
    161 N3e4bf63d02cb4b8a854d5ebbad7759db schema:volumeNumber 16
    162 rdf:type schema:PublicationVolume
    163 N44ed939a21fb4204a372d130bea3e5c1 rdf:first sg:person.01306417773.37
    164 rdf:rest N464d3a18f0344587988ad095a9769ca7
    165 N464d3a18f0344587988ad095a9769ca7 rdf:first sg:person.01345137415.31
    166 rdf:rest N97616139b5084bb785a2d6d4cea567c3
    167 N4b146d78b1e34db08af4468042a0ba30 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    168 schema:name Female
    169 rdf:type schema:DefinedTerm
    170 N4e2990ba633d4416b5321ce9099f5471 rdf:first sg:person.0765610722.27
    171 rdf:rest Nb25ec3e35eb347aabb70c925a5dd2406
    172 N5a408d11dc264cfc91d27d09ea61e3d2 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    173 schema:name Gastrointestinal Microbiome
    174 rdf:type schema:DefinedTerm
    175 N5b8bfca6e6744a17b5675ad2663202db schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    176 schema:name Adolescent
    177 rdf:type schema:DefinedTerm
    178 N5e1892582601421e9a72f349d9da99bd schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    179 schema:name Humans
    180 rdf:type schema:DefinedTerm
    181 N6583d67e881843f18bff39975bbf16a2 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    182 schema:name Cross-Sectional Studies
    183 rdf:type schema:DefinedTerm
    184 N678e13ec2d6b4ad2a256cd149636aaaf schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    185 schema:name Sequence Analysis, DNA
    186 rdf:type schema:DefinedTerm
    187 N7236d506a1c44b979cb5def79f2a3450 schema:name doi
    188 schema:value 10.1186/s12866-016-0708-5
    189 rdf:type schema:PropertyValue
    190 N7525cfb967574778833603c7b730a227 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    191 schema:name Feces
    192 rdf:type schema:DefinedTerm
    193 N75a75dc85b574d35b3c771c7d6d3d7ab rdf:first sg:person.0723151153.71
    194 rdf:rest N4e2990ba633d4416b5321ce9099f5471
    195 N7bd446993101498bae7cca115a07b3ea schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    196 schema:name Aged
    197 rdf:type schema:DefinedTerm
    198 N7f6392a57c37499a87d28732cf3ddd2b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    199 schema:name Aged, 80 and over
    200 rdf:type schema:DefinedTerm
    201 N8608ec7e0db045fb9156bfd8e9651743 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    202 schema:name Child
    203 rdf:type schema:DefinedTerm
    204 N97616139b5084bb785a2d6d4cea567c3 rdf:first sg:person.0641503625.34
    205 rdf:rest N75a75dc85b574d35b3c771c7d6d3d7ab
    206 N9e3c9e4048fe4dfab8373d5785350ea0 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    207 schema:name RNA, Ribosomal, 16S
    208 rdf:type schema:DefinedTerm
    209 Na288f9265a4a4b20baabf0730e36e358 rdf:first sg:person.01331322535.15
    210 rdf:rest N44ed939a21fb4204a372d130bea3e5c1
    211 Naa7324ff61b24b5cae3cdd39874d6c41 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    212 schema:name Infant, Newborn
    213 rdf:type schema:DefinedTerm
    214 Nae1c18510daf41c5a3b8bfe59c1a046e schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    215 schema:name High-Throughput Nucleotide Sequencing
    216 rdf:type schema:DefinedTerm
    217 Naf1fcd98867042508b0c5d1d5cc5c65d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    218 schema:name Infant
    219 rdf:type schema:DefinedTerm
    220 Nb25ec3e35eb347aabb70c925a5dd2406 rdf:first sg:person.01300772050.26
    221 rdf:rest N09dc9c24ed234076a4ee76049c812361
    222 Nb3765493544e40a38e35a6c8c66481f4 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    223 schema:name Middle Aged
    224 rdf:type schema:DefinedTerm
    225 Nc3155575d0ce4183ac428945fc53f321 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    226 schema:name Age Factors
    227 rdf:type schema:DefinedTerm
    228 Nd7fc163759104c4db28821799e735aa4 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    229 schema:name Cluster Analysis
    230 rdf:type schema:DefinedTerm
    231 Nda317c5c2dc443f7b2212f51416d35ad schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    232 schema:name Young Adult
    233 rdf:type schema:DefinedTerm
    234 Ne5f0b22cfaa74775acb97065749669c2 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    235 schema:name Male
    236 rdf:type schema:DefinedTerm
    237 Nfe84d1cf723a42f7a80f654d120431c6 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    238 schema:name Bacteria
    239 rdf:type schema:DefinedTerm
    240 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
    241 schema:name Biological Sciences
    242 rdf:type schema:DefinedTerm
    243 anzsrc-for:0604 schema:inDefinedTermSet anzsrc-for:
    244 schema:name Genetics
    245 rdf:type schema:DefinedTerm
    246 sg:journal.1024253 schema:issn 1471-2180
    247 schema:name BMC Microbiology
    248 schema:publisher Springer Nature
    249 rdf:type schema:Periodical
    250 sg:person.01136102115.58 schema:affiliation grid-institutes:grid.31432.37
    251 schema:familyName Osawa
    252 schema:givenName Ro
    253 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01136102115.58
    254 rdf:type schema:Person
    255 sg:person.01300772050.26 schema:affiliation grid-institutes:grid.419972.0
    256 schema:familyName Abe
    257 schema:givenName Fumiaki
    258 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01300772050.26
    259 rdf:type schema:Person
    260 sg:person.01306417773.37 schema:affiliation grid-institutes:grid.419972.0
    261 schema:familyName Kato
    262 schema:givenName Kumiko
    263 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01306417773.37
    264 rdf:type schema:Person
    265 sg:person.01331322535.15 schema:affiliation grid-institutes:grid.419972.0
    266 schema:familyName Odamaki
    267 schema:givenName Toshitaka
    268 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01331322535.15
    269 rdf:type schema:Person
    270 sg:person.01345137415.31 schema:affiliation grid-institutes:grid.419972.0
    271 schema:familyName Sugahara
    272 schema:givenName Hirosuke
    273 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01345137415.31
    274 rdf:type schema:Person
    275 sg:person.0641503625.34 schema:affiliation grid-institutes:grid.419972.0
    276 schema:familyName Hashikura
    277 schema:givenName Nanami
    278 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0641503625.34
    279 rdf:type schema:Person
    280 sg:person.0723151153.71 schema:affiliation grid-institutes:grid.419972.0
    281 schema:familyName Takahashi
    282 schema:givenName Sachiko
    283 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0723151153.71
    284 rdf:type schema:Person
    285 sg:person.0765610722.27 schema:affiliation grid-institutes:grid.419972.0
    286 schema:familyName Xiao
    287 schema:givenName Jin-zhong
    288 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0765610722.27
    289 rdf:type schema:Person
    290 sg:pub.10.1007/s12519-015-0025-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004724905
    291 https://doi.org/10.1007/s12519-015-0025-3
    292 rdf:type schema:CreativeWork
    293 sg:pub.10.1038/ismej.2011.139 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051863807
    294 https://doi.org/10.1038/ismej.2011.139
    295 rdf:type schema:CreativeWork
    296 sg:pub.10.1038/ismej.2012.54 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038641995
    297 https://doi.org/10.1038/ismej.2012.54
    298 rdf:type schema:CreativeWork
    299 sg:pub.10.1038/ismej.2015.177 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026945768
    300 https://doi.org/10.1038/ismej.2015.177
    301 rdf:type schema:CreativeWork
    302 sg:pub.10.1038/nature11053 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052378845
    303 https://doi.org/10.1038/nature11053
    304 rdf:type schema:CreativeWork
    305 sg:pub.10.1038/nature11319 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032682406
    306 https://doi.org/10.1038/nature11319
    307 rdf:type schema:CreativeWork
    308 sg:pub.10.1038/nature12506 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003823032
    309 https://doi.org/10.1038/nature12506
    310 rdf:type schema:CreativeWork
    311 sg:pub.10.1038/nature12721 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021135023
    312 https://doi.org/10.1038/nature12721
    313 rdf:type schema:CreativeWork
    314 sg:pub.10.1038/nbt.2676 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034019934
    315 https://doi.org/10.1038/nbt.2676
    316 rdf:type schema:CreativeWork
    317 sg:pub.10.1038/ncomms4654 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000325507
    318 https://doi.org/10.1038/ncomms4654
    319 rdf:type schema:CreativeWork
    320 sg:pub.10.1038/ncomms7238 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052025669
    321 https://doi.org/10.1038/ncomms7238
    322 rdf:type schema:CreativeWork
    323 sg:pub.10.1038/nmeth.f.303 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009032055
    324 https://doi.org/10.1038/nmeth.f.303
    325 rdf:type schema:CreativeWork
    326 sg:pub.10.1038/srep08397 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006452375
    327 https://doi.org/10.1038/srep08397
    328 rdf:type schema:CreativeWork
    329 sg:pub.10.1038/srep13548 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010482194
    330 https://doi.org/10.1038/srep13548
    331 rdf:type schema:CreativeWork
    332 sg:pub.10.1186/1471-2180-9-123 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049309371
    333 https://doi.org/10.1186/1471-2180-9-123
    334 rdf:type schema:CreativeWork
    335 sg:pub.10.1186/1742-4933-10-31 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039753171
    336 https://doi.org/10.1186/1742-4933-10-31
    337 rdf:type schema:CreativeWork
    338 sg:pub.10.1186/2049-2618-2-19 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026297002
    339 https://doi.org/10.1186/2049-2618-2-19
    340 rdf:type schema:CreativeWork
    341 sg:pub.10.1186/gb-2011-12-6-r60 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000243423
    342 https://doi.org/10.1186/gb-2011-12-6-r60
    343 rdf:type schema:CreativeWork
    344 sg:pub.10.1186/s12866-014-0311-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001548821
    345 https://doi.org/10.1186/s12866-014-0311-6
    346 rdf:type schema:CreativeWork
    347 grid-institutes:grid.31432.37 schema:alternateName Department of Bioresource Science, Graduate School of Agricultural Science, Kobe University, Kobe, Hyogo, Japan
    348 schema:name Department of Bioresource Science, Graduate School of Agricultural Science, Kobe University, Kobe, Hyogo, Japan
    349 rdf:type schema:Organization
    350 grid-institutes:grid.419972.0 schema:alternateName Food Ingredients & Technology Institute, Morinaga Milk Industry Co., Ltd., Zama, Kanagawa, Japan
    351 Next Generation Science Institute, Morinaga Milk Industry Co., Ltd., Zama, Kanagawa, Japan
    352 schema:name Food Ingredients & Technology Institute, Morinaga Milk Industry Co., Ltd., Zama, Kanagawa, Japan
    353 Next Generation Science Institute, Morinaga Milk Industry Co., Ltd., Zama, Kanagawa, Japan
    354 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...