Microbiome of prebiotic-treated mice reveals novel targets involved in host response during obesity
Identifieur interne : 000026 ( Pmc/Curation ); précédent : 000025; suivant : 000027Microbiome of prebiotic-treated mice reveals novel targets involved in host response during obesity
Auteurs : Amandine Everard [Belgique] ; Vladimir Lazarevic [Suisse] ; Nadia Gaïa [Suisse] ; Maria Johansson [Suède] ; Marcus St Hlman [Suède] ; Fredrik Backhed [Suède] ; Nathalie M. Delzenne [Belgique] ; Jacques Schrenzel [Suisse] ; Patrice François [Suisse] ; Patrice D. Cani [Belgique]Source :
- The ISME Journal [ 1751-7362 ] ; 2014.
Abstract
The gut microbiota is involved in metabolic and immune disorders associated with obesity and type 2 diabetes. We previously demonstrated that prebiotic treatment may significantly improve host health by modulating bacterial species related to the improvement of gut endocrine, barrier and immune functions. An analysis of the gut metagenome is needed to determine which bacterial functions and taxa are responsible for beneficial microbiota–host interactions upon nutritional intervention. We subjected mice to prebiotic (Pre) treatment under physiological (control diet: CT) and pathological conditions (high-fat diet: HFD) for 8 weeks and investigated the production of intestinal antimicrobial peptides and the gut microbiome. HFD feeding significantly decreased the expression of regenerating islet-derived 3-gamma (Reg3g) and phospholipase A2 group-II (PLA2g2) in the jejunum. Prebiotic treatment increased Reg3g expression (by ∼50-fold) and improved intestinal homeostasis as suggested by the increase in the expression of intectin, a key protein involved in intestinal epithelial cell turnover. Deep metagenomic sequencing analysis revealed that HFD and prebiotic treatment significantly affected the gut microbiome at different taxonomic levels. Functional analyses based on the occurrence of clusters of orthologous groups (COGs) of proteins also revealed distinct profiles for the HFD, Pre, HFD-Pre and CT groups. Finally, the gut microbiota modulations induced by prebiotics counteracted HFD-induced inflammation and related metabolic disorders. Thus, we identified novel putative taxa and metabolic functions that may contribute to the development of or protection against the metabolic alterations observed during HFD feeding and HFD-Pre feeding.
Url:
DOI: 10.1038/ismej.2014.45
PubMed: 24694712
PubMed Central: 4163056
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Molecular and Clinical Medicine, University of Gothenburg</institution>, Gothenburg,<country>Sweden</country></nlm:aff><country xml:lang="fr">Suède</country><wicri:regionArea># see nlm:aff country strict</wicri:regionArea></affiliation></author><author><name sortKey="Backhed, Fredrik" sort="Backhed, Fredrik" uniqKey="Backhed F" first="Fredrik" last="Backhed">Fredrik Backhed</name><affiliation wicri:level="1"><nlm:aff id="aff3"><institution>Wallenberg Laboratory/Sahlgrenska Center for Cardiovascular and Metabolic Research, Sahlgrenska University Hospital</institution>, Gothenburg,<country>Sweden</country></nlm:aff><country xml:lang="fr">Suède</country><wicri:regionArea># see nlm:aff country strict</wicri:regionArea></affiliation><affiliation wicri:level="1"><nlm:aff id="aff4"><institution>Department of Molecular and Clinical Medicine, University of Gothenburg</institution>, Gothenburg,<country>Sweden</country></nlm:aff><country xml:lang="fr">Suède</country><wicri:regionArea># see 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Delzenne</name><affiliation wicri:level="1"><nlm:aff id="aff1"><institution>Université catholique de Louvain, Louvain Drug Research Institute, WELBIO (Walloon Excellence in Life sciences and BIOtechnology), Metabolism and Nutrition Research Group</institution>, Brussels,<country>Belgium</country></nlm:aff><country xml:lang="fr">Belgique</country><wicri:regionArea># see nlm:aff country strict</wicri:regionArea></affiliation></author><author><name sortKey="Schrenzel, Jacques" sort="Schrenzel, Jacques" uniqKey="Schrenzel J" first="Jacques" last="Schrenzel">Jacques Schrenzel</name><affiliation wicri:level="1"><nlm:aff id="aff2"><institution>Geneva University Hospitals, Division of Infectious Diseases, Genomic Research Lab</institution>, Geneva,<country>Switzerland</country></nlm:aff><country xml:lang="fr">Suisse</country><wicri:regionArea># see nlm:aff country strict</wicri:regionArea></affiliation><affiliation wicri:level="1"><nlm:aff id="aff5"><institution>Geneva University Hospitals, Laboratory of Bacteriology</institution>, Geneva,<country>Switzerland</country></nlm:aff><country xml:lang="fr">Suisse</country><wicri:regionArea># see nlm:aff country strict</wicri:regionArea></affiliation></author><author><name sortKey="Francois, Patrice" sort="Francois, Patrice" uniqKey="Francois P" first="Patrice" last="François">Patrice François</name><affiliation wicri:level="1"><nlm:aff id="aff2"><institution>Geneva University Hospitals, Division of Infectious Diseases, Genomic Research Lab</institution>, Geneva,<country>Switzerland</country></nlm:aff><country xml:lang="fr">Suisse</country><wicri:regionArea># see nlm:aff country strict</wicri:regionArea></affiliation></author><author><name sortKey="Cani, Patrice D" sort="Cani, Patrice D" uniqKey="Cani P" first="Patrice D" last="Cani">Patrice D. Cani</name><affiliation wicri:level="1"><nlm:aff id="aff1"><institution>Université catholique de Louvain, Louvain Drug Research Institute, WELBIO (Walloon Excellence in Life sciences and BIOtechnology), Metabolism and Nutrition Research Group</institution>, Brussels,<country>Belgium</country></nlm:aff><country xml:lang="fr">Belgique</country><wicri:regionArea># see nlm:aff country strict</wicri:regionArea></affiliation></author></analytic><series><title level="j">The ISME Journal</title><idno type="ISSN">1751-7362</idno><idno type="eISSN">1751-7370</idno><imprint><date when="2014">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>The gut microbiota is involved in metabolic and immune disorders associated with obesity and type 2 diabetes. We previously demonstrated that prebiotic treatment may significantly improve host health by modulating bacterial species related to the improvement of gut endocrine, barrier and immune functions. An analysis of the gut metagenome is needed to determine which bacterial functions and taxa are responsible for beneficial microbiota–host interactions upon nutritional intervention. We subjected mice to prebiotic (Pre) treatment under physiological (control diet: CT) and pathological conditions (high-fat diet: HFD) for 8 weeks and investigated the production of intestinal antimicrobial peptides and the gut microbiome. HFD feeding significantly decreased the expression of regenerating islet-derived 3-gamma (Reg3g) and phospholipase A2 group-II (PLA2g2) in the jejunum. Prebiotic treatment increased Reg3g expression (by ∼50-fold) and improved intestinal homeostasis as suggested by the increase in the expression of intectin, a key protein involved in intestinal epithelial cell turnover. Deep metagenomic sequencing analysis revealed that HFD and prebiotic treatment significantly affected the gut microbiome at different taxonomic levels. Functional analyses based on the occurrence of clusters of orthologous groups (COGs) of proteins also revealed distinct profiles for the HFD, Pre, HFD-Pre and CT groups. Finally, the gut microbiota modulations induced by prebiotics counteracted HFD-induced inflammation and related metabolic disorders. Thus, we identified novel putative taxa and metabolic functions that may contribute to the development of or protection against the metabolic alterations observed during HFD feeding and HFD-Pre feeding.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Altschul, Sf" uniqKey="Altschul S">SF Altschul</name></author><author><name sortKey="Gish, W" uniqKey="Gish W">W Gish</name></author><author><name sortKey="Miller, W" uniqKey="Miller W">W Miller</name></author><author><name sortKey="Myers, Ew" uniqKey="Myers E">EW Myers</name></author><author><name sortKey="Lipman, Dj" uniqKey="Lipman D">DJ Lipman</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Bevins, Cl" uniqKey="Bevins C">CL Bevins</name></author><author><name sortKey="Salzman, Nh" uniqKey="Salzman N">NH Salzman</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Cani, Pd" uniqKey="Cani P">PD 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<front><journal-meta><journal-id journal-id-type="nlm-ta">ISME J</journal-id><journal-id journal-id-type="iso-abbrev">ISME J</journal-id><journal-title-group><journal-title>The ISME Journal</journal-title></journal-title-group><issn pub-type="ppub">1751-7362</issn><issn pub-type="epub">1751-7370</issn><publisher><publisher-name>Nature Publishing Group</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">24694712</article-id><article-id pub-id-type="pmc">4163056</article-id><article-id pub-id-type="pii">ismej201445</article-id><article-id pub-id-type="doi">10.1038/ismej.2014.45</article-id><article-categories><subj-group subj-group-type="heading"><subject>Original Article</subject></subj-group></article-categories><title-group><article-title>Microbiome of prebiotic-treated mice reveals novel targets involved in host response during obesity</article-title><alt-title alt-title-type="running">Prebiotic feeding induces gut metagenome changes</alt-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Everard</surname><given-names>Amandine</given-names></name><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="author-notes" rid="note1"><sup>6</sup></xref></contrib><contrib contrib-type="author"><name><surname>Lazarevic</surname><given-names>Vladimir</given-names></name><xref ref-type="aff" rid="aff2">2</xref><xref ref-type="author-notes" rid="note1"><sup>6</sup></xref></contrib><contrib contrib-type="author"><name><surname>Gaïa</surname><given-names>Nadia</given-names></name><xref ref-type="aff" rid="aff2">2</xref></contrib><contrib contrib-type="author"><name><surname>Johansson</surname><given-names>Maria</given-names></name><xref ref-type="aff" rid="aff3">3</xref><xref ref-type="aff" rid="aff4">4</xref></contrib><contrib contrib-type="author"><name><surname>Ståhlman</surname><given-names>Marcus</given-names></name><xref ref-type="aff" rid="aff3">3</xref><xref ref-type="aff" rid="aff4">4</xref></contrib><contrib contrib-type="author"><name><surname>Backhed</surname><given-names>Fredrik</given-names></name><xref ref-type="aff" rid="aff3">3</xref><xref ref-type="aff" rid="aff4">4</xref></contrib><contrib contrib-type="author"><name><surname>Delzenne</surname><given-names>Nathalie M</given-names></name><xref ref-type="aff" rid="aff1">1</xref></contrib><contrib contrib-type="author"><name><surname>Schrenzel</surname><given-names>Jacques</given-names></name><xref ref-type="aff" rid="aff2">2</xref><xref ref-type="aff" rid="aff5">5</xref></contrib><contrib contrib-type="author"><name><surname>François</surname><given-names>Patrice</given-names></name><xref ref-type="aff" rid="aff2">2</xref></contrib><contrib contrib-type="author"><name><surname>Cani</surname><given-names>Patrice D</given-names></name><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="corresp" rid="caf1">*</xref></contrib><aff id="aff1"><label>1</label><institution>Université catholique de Louvain, Louvain Drug Research Institute, WELBIO (Walloon Excellence in Life sciences and BIOtechnology), Metabolism and Nutrition Research Group</institution>, Brussels,<country>Belgium</country></aff><aff id="aff2"><label>2</label><institution>Geneva University Hospitals, Division of Infectious Diseases, Genomic Research Lab</institution>, Geneva,<country>Switzerland</country></aff><aff id="aff3"><label>3</label><institution>Wallenberg Laboratory/Sahlgrenska Center for Cardiovascular and Metabolic Research, Sahlgrenska University Hospital</institution>, Gothenburg,<country>Sweden</country></aff><aff id="aff4"><label>4</label><institution>Department of Molecular and Clinical Medicine, University of Gothenburg</institution>, Gothenburg,<country>Sweden</country></aff><aff id="aff5"><label>5</label><institution>Geneva University Hospitals, Laboratory of Bacteriology</institution>, Geneva,<country>Switzerland</country></aff></contrib-group><author-notes><corresp id="caf1"><label>*</label><institution>Université catholique de Louvain, LDRI, Metabolism and Nutrition Research Group</institution>, Av. E. Mounier, 73 Box B1.73.11, 1200 Brussels, <country>Belgium</country>. E-mail: <email>patrice.cani@uclouvain.be</email></corresp><fn fn-type="present-address" id="note1"><label>6</label><p>These authors contributed equally to this work.</p></fn></author-notes><pub-date pub-type="ppub"><month>10</month><year>2014</year></pub-date><pub-date pub-type="epub"><day>03</day><month>04</month><year>2014</year></pub-date><pub-date pub-type="pmc-release"><day>1</day><month>10</month><year>2014</year></pub-date><volume>8</volume><issue>10</issue><fpage>2116</fpage><lpage>2130</lpage><history><date date-type="received"><day>27</day><month>01</month><year>2014</year></date><date date-type="accepted"><day>27</day><month>02</month><year>2014</year></date></history><permissions><copyright-statement>Copyright © 2014 International Society for Microbial Ecology</copyright-statement><copyright-year>2014</copyright-year><copyright-holder>International Society for Microbial Ecology</copyright-holder><license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by-nc-nd/3.0/"><pmc-comment>author-paid</pmc-comment>
<license-p>This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit <ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by-nc-nd/3.0/">http://creativecommons.org/licenses/by-nc-nd/3.0/</ext-link></license-p></license></permissions><abstract><p>The gut microbiota is involved in metabolic and immune disorders associated with obesity and type 2 diabetes. We previously demonstrated that prebiotic treatment may significantly improve host health by modulating bacterial species related to the improvement of gut endocrine, barrier and immune functions. An analysis of the gut metagenome is needed to determine which bacterial functions and taxa are responsible for beneficial microbiota–host interactions upon nutritional intervention. We subjected mice to prebiotic (Pre) treatment under physiological (control diet: CT) and pathological conditions (high-fat diet: HFD) for 8 weeks and investigated the production of intestinal antimicrobial peptides and the gut microbiome. HFD feeding significantly decreased the expression of regenerating islet-derived 3-gamma (Reg3g) and phospholipase A2 group-II (PLA2g2) in the jejunum. Prebiotic treatment increased Reg3g expression (by ∼50-fold) and improved intestinal homeostasis as suggested by the increase in the expression of intectin, a key protein involved in intestinal epithelial cell turnover. Deep metagenomic sequencing analysis revealed that HFD and prebiotic treatment significantly affected the gut microbiome at different taxonomic levels. Functional analyses based on the occurrence of clusters of orthologous groups (COGs) of proteins also revealed distinct profiles for the HFD, Pre, HFD-Pre and CT groups. Finally, the gut microbiota modulations induced by prebiotics counteracted HFD-induced inflammation and related metabolic disorders. Thus, we identified novel putative taxa and metabolic functions that may contribute to the development of or protection against the metabolic alterations observed during HFD feeding and HFD-Pre feeding.</p></abstract><kwd-group><kwd>metagenomic</kwd><kwd>gut microbiota</kwd><kwd>type 2 diabetes</kwd><kwd>antimicrobial peptides</kwd><kwd>Reg3g</kwd><kwd>prebiotics</kwd></kwd-group></article-meta></front><floats-group><fig id="fig1"><label>Figure 1</label><caption><p>HFD feeding and prebiotic treatment affect antimicrobial peptides in the intestine. Antibacterial peptide mRNA expression: (<bold>a</bold>) Regenerating islet-derived 3-gamma (RegIIIγ, encoded by Reg3g); (<bold>b</bold>) Phospholipase A2 group II (encoded by Pla2g2); (<bold>c</bold>) Lysozyme C (encoded by Lyz1); (<bold>d</bold>) α-defensins (encoded by Defa); and (<bold>e</bold>) Angiogenin 4 (encoded by Ang4) measured in the jejunum of control diet-fed mice (CT) (<italic>n</italic>=9), CT diet-fed mice treated with prebiotics (CT-Pre) (<italic>n</italic>=10), HFD-fed mice (HFD) (<italic>n</italic>=10) and HFD-fed mice treated with prebiotics (HFD-Pre) (<italic>n</italic>=10). Data are means±s.e.m. Data with different superscript letters are significantly different (<italic>P<</italic>0.05) according to a <italic>post hoc</italic> ANOVA one-way statistical analysis.</p></caption><graphic xlink:href="ismej201445f1"></graphic></fig><fig id="fig2"><label>Figure 2</label><caption><p>Prebiotic treatment increases colon intectin expression, a protein involved in the turnover of intestinal mucosa. Intectin mRNA expression measured in the colon of control diet-fed mice (CT) (<italic>n</italic>=9), CT diet-fed mice treated with prebiotics (CT-Pre) (<italic>n</italic>=10), HFD-fed mice (HFD) (<italic>n</italic>=10) and HFD-fed mice treated with prebiotics (HFD-Pre) (<italic>n</italic>=10). Data are means±s.e.m. Data with different superscript letters are significantly different (<italic>P<</italic>0.05) according to a <italic>post hoc</italic> ANOVA one-way statistical analysis.</p></caption><graphic xlink:href="ismej201445f2"></graphic></fig><fig id="fig3"><label>Figure 3</label><caption><p>HFD feeding and prebiotic treatment affect the proportions of different phyla. The composition of abundant bacterial phyla identified in the gut microbiota of (<bold>a</bold>) control diet-fed mice (CT) (<italic>n</italic>=9), (<bold>b</bold>) CT diet-fed mice treated with prebiotics (CT-Pre) (<italic>n</italic>=9), (<bold>c</bold>) HFD-fed mice (HFD) (<italic>n</italic>=7) and (<bold>d</bold>) HFD-fed mice treated with prebiotics (HFD-Pre) (<italic>n</italic>=10). Undetected phyla are not represented on the pie chart. The significant changes in specific phyla are shown in <xref ref-type="supplementary-material" rid="sup1">Supplementary Table S1</xref>.</p></caption><graphic xlink:href="ismej201445f3"></graphic></fig><fig id="fig4"><label>Figure 4</label><caption><p>HFD feeding and prebiotic treatment affect the proportions of different genera. The composition of the bacterial genera significantly affected by the treatment and identified in the gut microbiota of control diet-fed mice (CT) (<italic>n</italic>=9), CT diet-fed mice treated with prebiotics (CT-Pre) (<italic>n</italic>=9), HFD-fed mice (HFD) (<italic>n</italic>=7) and HFD-fed mice treated with prebiotics (HFD-Pre) (<italic>n</italic>=10). Each column is set at 100% to illustrate the proportion of each genus among the different groups; the absence of any colour indicates that the genus was not detected in this group of mice. The statistically significant changes observed between different groups are shown in <xref ref-type="supplementary-material" rid="sup1">Supplementary Table S2</xref>.</p></caption><graphic xlink:href="ismej201445f4"></graphic></fig><fig id="fig5"><label>Figure 5</label><caption><p>PCoA of the gut bacterial communities and COG abundance. The analysis was based on the Bray–Curtis similarity matrix constructed using the square-root-transformed OTU (<bold>a</bold>) or COG abundances (<bold>b</bold>). The per cent of variation explained is given in brackets. OTU, operational taxonomic unit.</p></caption><graphic xlink:href="ismej201445f5"></graphic></fig><fig id="fig6"><label>Figure 6</label><caption><p>Orthologous groups of proteins (COGs) affected by the dietary treatments. (<bold>a</bold>) Occurrence of identified orthologous groups of proteins (COGs) according to COG functional categories: (A) RNA processing and modification are not shown on the figure since the abundance (%) was under 0.0025% in all four diet groups; (C) Energy production and conversion; (D) Cell-cycle control, cell division and chromosome partitioning; (E) Amino acid transport and metabolism; (F) Nucleotide transport and metabolism; (G) Carbohydrate transport and metabolism; (H) Coenzyme transport and metabolism; (I) Lipid transport and metabolism; (J) Translation, ribosomal structure and biogenesis; (K) Transcription; (L) Replication, recombination and repair; (M) Cell wall/membrane/envelope biogenesis; (N) Cell motility; (O) Posttranslational modification, protein turnover, chaperones; (P) Inorganic ion transport and metabolism; (Q) Secondary metabolites biosynthesis, transport and catabolism; (R) General function prediction only; (S) function unknown; (T), Signal transduction mechanisms; (U) Intracellular trafficking, secretion and vesicular transport; (V), defence mechanisms. Data are means±s.e.m. *<italic>P<</italic>0.05, **<italic>P<</italic>0.01, ***<italic>P<</italic>0.001 vs CT. (<bold>b</bold>) Changes in the occurrence of COGs according to the metabolic pathways affected following the different treatments. COGs with a median of ⩾4 (∼0.005% of the total number of COGs in normalised data sets) in at least one of the four diet groups were compared. Red and blue fields correspond to statistically significant increases and decreases in the COG relative abundances, respectively. The details of each COG number are shown in <xref ref-type="supplementary-material" rid="sup1">Supplementary Table S3</xref>.</p></caption><graphic xlink:href="ismej201445f6"></graphic></fig><fig id="fig7"><label>Figure 7</label><caption><p>Prebiotic treatment increases caecal short chain fatty acids content. (<bold>a</bold>) Actetate, (<bold>b</bold>) Propionate, (<bold>c</bold>) Butyrate, (<bold>d</bold>) Lactate and (<bold>e</bold>) Succinate concentrations (μmol g<sup>−1</sup> of dry caecal content) measured in the caecal content of control diet-fed mice (CT) (<italic>n</italic>=9), CT diet-fed mice treated with prebiotics (CT-Pre) (<italic>n</italic>=10), HFD-fed mice (HFD) (<italic>n</italic>=10) and HFD-fed mice treated with prebiotics (HFD-Pre) (<italic>n</italic>=10). Data are means±s.e.m. Data with different superscript letters are significantly different (<italic>P<</italic>0.05) according to a <italic>post hoc</italic> ANOVA one-way statistical analysis.</p></caption><graphic xlink:href="ismej201445f7"></graphic></fig><fig id="fig8"><label>Figure 8</label><caption><p>Prebiotic treatment decreased inflammation, fat mass development and insulin resistance associated with HFD-induced obesity. Inflammatory markers mRNA expression: (<bold>a</bold>) LPS binding protein (encoded by LBP) and (<bold>b</bold>) Myeloid differentiation primary response gene (88) (encoded by Myd88) in the liver; (<bold>c</bold>) LPS binding protein (encoded by LBP); (<bold>d</bold>) Monocyte chemoattractant protein 1 (encoded by MCP1) and (<bold>e</bold>) Cluster of differentiation 11c (encoded by CD11c) in the epididymal adipose tissue (EAT); (<bold>f</bold>) Plasma leptin (ng μl<sup>−1</sup>) in cava vein and (<bold>g</bold>) Insulin resistance index determined by multiplying the area under the curve (from 0 to 15 min) of blood glucose and plasma insulin following an oral glucose load (2 g glucose per kg of body weight) measured in control diet-fed mice (CT) (<italic>n</italic>=9), CT diet-fed mice treated with prebiotics (CT-Pre) (<italic>n</italic>=10), HFD-fed mice (HFD) (<italic>n</italic>=10) and HFD-fed mice treated with prebiotics (HFD-Pre) (<italic>n</italic>=10). Data are means±s.e.m. Data with different superscript letters are significantly different (<italic>P<</italic>0.05) according to a <italic>post hoc</italic> ANOVA one-way statistical analysis.</p></caption><graphic xlink:href="ismej201445f8"></graphic></fig><table-wrap id="tbl1"><label>Table 1</label><caption><title>Ecological indices based on the relative abundance of OTUs and COG families</title></caption><table frame="hsides" rules="groups" border="1"><colgroup><col align="left"></col><col align="char" char="("></col><col align="char" char="("></col><col align="char" char="("></col><col align="char" char="("></col><col align="center"></col><col align="char" char="("></col><col align="char" char="("></col><col align="char" char="("></col><col align="char" char="("></col><col align="center"></col></colgroup><thead valign="bottom"><tr><th align="left" valign="top" charoff="50"> </th><th colspan="4" align="center" valign="top" char="(" charoff="50"><italic>Median (IQR)</italic><hr></hr></th><th colspan="6" align="center" valign="top" charoff="50"><italic>Change (%)</italic><hr></hr></th></tr><tr><th align="left" valign="top" charoff="50"> </th><th align="center" valign="top" char="(" charoff="50"><italic>CT</italic></th><th align="center" valign="top" char="(" charoff="50"><italic>CT-Pre</italic></th><th align="center" valign="top" char="(" charoff="50"><italic>HFD</italic></th><th align="center" valign="top" char="(" charoff="50"><italic>HFD-Pre</italic></th><th align="center" valign="top" charoff="50"><italic>CT-Pre vs CT</italic></th><th align="center" valign="top" char="(" charoff="50"><italic>HFD vs CT</italic></th><th align="center" valign="top" char="(" charoff="50"><italic>HFD-Pre vs CT</italic></th><th align="center" valign="top" char="(" charoff="50"><italic>HFD vs CT-Pre</italic></th><th align="center" valign="top" char="(" charoff="50"><italic>HFD-Pre vs CT-Pre</italic></th><th align="center" valign="top" charoff="50"><italic>HFD-Pre vs HFD</italic></th></tr></thead><tbody valign="top"><tr><td colspan="11" align="left" valign="top" charoff="50"><italic>16S rDNA sequences normalised to 870</italic></td></tr><tr><td align="left" valign="top" charoff="50"> Number of OTUs</td><td align="char" valign="top" char="(" charoff="50">319 (308–339)</td><td align="char" valign="top" char="(" charoff="50">291 (286–299)</td><td align="char" valign="top" char="(" charoff="50">236 (231–247)</td><td align="char" valign="top" char="(" charoff="50">259 (244–268)</td><td align="center" valign="top" charoff="50">ns</td><td align="char" valign="top" char="(" charoff="50">−26 (**)</td><td align="char" valign="top" char="(" charoff="50">−19 (***)</td><td align="char" valign="top" char="(" charoff="50">−19 (***)</td><td align="char" valign="top" char="(" charoff="50">−11 (**)</td><td align="center" valign="top" charoff="50">ns</td></tr><tr><td align="left" valign="top" charoff="50"> Shannon diversity H' (loge)</td><td align="char" valign="top" char="(" charoff="50">5.222 (5.053–5.309)</td><td align="char" valign="top" char="(" charoff="50">5.051 (4.963–5.110)</td><td align="char" valign="top" char="(" charoff="50">4.250 (4.200–4.470)</td><td align="char" valign="top" char="(" charoff="50">4.485 (4.345–4.651)</td><td align="center" valign="top" charoff="50">ns</td><td align="char" valign="top" char="(" charoff="50">−19 (***)</td><td align="char" valign="top" char="(" charoff="50">−14 (***)</td><td align="char" valign="top" char="(" charoff="50">−16 (***)</td><td align="char" valign="top" char="(" charoff="50">−11 (***)</td><td align="center" valign="top" charoff="50">ns</td></tr><tr><td align="left" valign="top" charoff="50"> Chao1</td><td align="char" valign="top" char="(" charoff="50">685 (550–755)</td><td align="char" valign="top" char="(" charoff="50">606 (521–612)</td><td align="char" valign="top" char="(" charoff="50">548 (465–596)</td><td align="char" valign="top" char="(" charoff="50">555 (530–660)</td><td align="center" valign="top" charoff="50">ns</td><td align="char" valign="top" char="(" charoff="50">−20 (*)</td><td align="center" valign="top" char="(" charoff="50">ns</td><td align="center" valign="top" char="(" charoff="50">ns</td><td align="center" valign="top" char="(" charoff="50">ns</td><td align="center" valign="top" charoff="50">ns</td></tr><tr><td align="left" valign="top" charoff="50"> </td><td align="char" valign="top" char="(" charoff="50"> </td><td align="char" valign="top" char="(" charoff="50"> </td><td align="char" valign="top" char="(" charoff="50"> </td><td align="char" valign="top" char="(" charoff="50"> </td><td align="center" valign="top" charoff="50"> </td><td align="char" valign="top" char="(" charoff="50"> </td><td align="char" valign="top" char="(" charoff="50"> </td><td align="char" valign="top" char="(" charoff="50"> </td><td align="char" valign="top" char="(" charoff="50"> </td><td align="center" valign="top" charoff="50"> </td></tr><tr><td colspan="11" align="left" valign="top" charoff="50"><italic>COGs normalised to 82 417</italic></td></tr><tr><td align="left" valign="top" charoff="50"> Number of COG families</td><td align="char" valign="top" char="(" charoff="50">2188 (2177–2208)</td><td align="char" valign="top" char="(" charoff="50">2194 (2190–2213)</td><td align="char" valign="top" char="(" charoff="50">2190 (2176–2204)</td><td align="char" valign="top" char="(" charoff="50">2244 (2227–2280)</td><td align="center" valign="top" charoff="50">ns</td><td align="center" valign="top" char="(" charoff="50">ns</td><td align="char" valign="top" char="(" charoff="50">2.4 (***)</td><td align="center" valign="top" char="(" charoff="50">ns</td><td align="char" valign="top" char="(" charoff="50">2.3 (**)</td><td align="char" valign="top" char="(" charoff="50">2.4 (**)</td></tr><tr><td align="left" valign="top" charoff="50"> Shannon diversity H' (loge)</td><td align="char" valign="top" char="(" charoff="50">6.826 (6.807–6.827)</td><td align="char" valign="top" char="(" charoff="50">6.827 (6.814–6.835)</td><td align="char" valign="top" char="(" charoff="50">6.840 (6.834–6.846)</td><td align="char" valign="top" char="(" charoff="50">6.876 (6.861–6.883)</td><td align="center" valign="top" charoff="50">ns</td><td align="char" valign="top" char="(" charoff="50">0.34 (*)</td><td align="char" valign="top" char="(" charoff="50">0.86 (***)</td><td align="center" valign="top" char="(" charoff="50">ns</td><td align="char" valign="top" char="(" charoff="50">0.71 (***)</td><td align="char" valign="top" char="(" charoff="50">0.52 (**)</td></tr></tbody></table><table-wrap-foot><fn id="t1-fn1"><p>Abbreviations: COG, clusters of orthologous group; CT, control diet-fed mice; CT-Pre, CT diet-fed mice treated with prebiotics; HFD, HFD-fed mice; HFD-Pre, HFD-fed mice treated with prebiotics; IQR, interquartile range; OTU, operational taxonomic unit.</p></fn><fn id="t1-fn2"><p>Mean of OTU and COG richness and the Shannon diversity index for the different treatment groups and percentage of changes.</p></fn><fn id="t1-fn3"><p>Only significant changes (*<italic>P</italic><0.05; **<italic>P</italic><0.01; ***<italic>P</italic><0.001 using Mann–Whitney test) are presented. ns, non-significant (<italic>P</italic>>0.05) changes.</p></fn></table-wrap-foot></table-wrap></floats-group></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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