Co-occurrence network of significantly abundant species.

<p>A. Spearman correlation method. B. SparCC method. The nodes represent microbial species from the genus <i>Bacteroides</i> (green), <i>Bifidobacterium</i> (blue), <i>Faecalibacterium</i> (orange) and <i>Roseburia</i> (purple). The thickness of the edges represents the level of association depending on the value of Spearman’s correlation coefficient or the SparCC score respectively. Positive correlations are predicted to exist between species of Faecalibacterium and Roseburia, as shown in gray lines. Negative correlations are predicted to exist between species of Bacteroides and Bifidobacterium, as shown in red lines, except for (<i>Bacteroides pectinophilus</i>).</p

Profiles of log₁₀ 16S rRNA copies, extracellular metabolite concentrations, and pH of <i>Bifidobacterium adolescentis</i> and <i>Bacteroides thetaiotaomicron</i> in mono- and co-cultures in YCGMS (Yeast Casitone glucose maltose starch) medium for 56 h.

<p>A and B. Log₁₀ 16S rRNA copies per ml of culture, C-E. Starch, maltose and glucose consumption. F-H. Acetate, lactate and formate production. I-J. Succinate and propionate production. K. pH profile of mono- and co-cultures of <i>B</i>. <i>adolescentis</i> and <i>B</i>. <i>thetaiotaomicron</i>. BB = co-culture of <i>Bifidobacterium adolescentis</i> and <i>Bacteroides thetaiotaomicron</i>, Bad = mono-culture of <i>B</i>. <i>adolescentis</i>, Bth = mono-culture of <i>B</i>. <i>thetaiotaomicron</i>. Experiments were performed in triplicates and error bars represent the standard deviation between each biological replicate. P-values less than and greater than 0.01 are summarized with two asterisks and ‘non-significant (ns)’ respectively.</p

Co-occurrence network of significantly abundant species.

<p>A. Spearman correlation method. B. SparCC method. The nodes represent microbial species from the genus <i>Bacteroides</i> (green), <i>Bifidobacterium</i> (blue), <i>Faecalibacterium</i> (orange) and <i>Roseburia</i> (purple). The thickness of the edges represents the level of association depending on the value of Spearman’s correlation coefficient or the SparCC score respectively. Positive correlations are predicted to exist between species of Faecalibacterium and Roseburia, as shown in gray lines. Negative correlations are predicted to exist between species of Bacteroides and Bifidobacterium, as shown in red lines, except for (<i>Bacteroides pectinophilus</i>).</p

<i>In vitro</i> co-cultures of human gut bacterial species as predicted from co-occurrence network analysis

<div><p>Network analysis of large metagenomic datasets generated by current sequencing technologies can reveal significant co-occurrence patterns between microbial species of a biological community. These patterns can be analyzed in terms of pairwise combinations between all species comprising a community. Here, we construct a co-occurrence network for abundant microbial species encompassing the three dominant phyla found in human gut. This was followed by an <i>in vitro</i> evaluation of the predicted microbe-microbe co-occurrences, where we chose species pairs <i>Bifidobacterium adolescentis</i> and <i>Bacteroides thetaiotaomicron</i>, as well as <i>Faecalibacterium prausnitzii</i> and <i>Roseburia inulinivorans</i> as model organisms for our study. We then delineate the outcome of the co-cultures when equal distributions of resources were provided. The growth behavior of the co-culture was found to be dependent on the types of microbial species present, their specific metabolic activities, and resulting changes in the culture environment. Through this reductionist approach and using novel <i>in vitro</i> combinations of microbial species under anaerobic conditions, the results of this work will aid in the understanding and design of synthetic community formulations.</p></div

Profiles of log₁₀ 16S rRNA copies and extracellular metabolite concentrations, of <i>Faecalibacterium prausnitzii</i> and <i>Roseburia inulinivorans</i> mono- and co-cultures in YCFAGD (Yeast Casitone free acetate glucose disaccharide) and YCGD (Yeast Casitone glucose disaccharide) medium for 50h.

<p>A-B Log₁₀ 16S rRNA gene copies per ml culture, C-F. Acetate and butyrate concentration of <i>F</i>. <i>prausnitzii</i> and <i>R</i>. <i>inulinivorans</i> mono- and co-cultures in YCFAGD and YCGD medium. FR = co-culture of <i>Faecalibacterium prausnitzii</i> and <i>Roseburia inulinivorans</i>, Fpr = mono-culture of <i>Faecalibacterium prausnitzii</i>, Rin = mono-culture of <i>Roseburia inulinivorans</i>. Experiments were performed in triplicates and error bars represent the standard deviation between each biological replicate. P-values less than and greater than 0.01 are summarized with two asterisks and ‘non-significant (ns)’ respectively.</p

<i>Ffar2</i>^+/- mice exhibit normal glucose tolerance at gestational day 15 (G15).

<p>(<b>a</b>) Plasma glucose concentrations from WT, <i>Ffar2</i>^+/- and <i>Ffar2</i>^-/- female mice during an IPGTT at G15. (<b>b</b>) The corresponding area under the curve (AUC) for the IPGTT. WT circles and white bars; <i>Ffar2</i>^+/-, squares and gray bars; <i>Ffar2</i>^-/-, triangles and black bars. Data in (a) were compared by 2-way ANOVA with Bonferroni post-hoc analyses. Data in (b) were compared by Student’s t-test (*, p<0.05; **, p<0.01; ***p<0.001), n = 4–5 mice/ group.</p

Effects of antibiotic treatment on gestational glucose tolerance before and during pregnancy.

<p>(<b>a</b>) Timeline of antibiotic treatment, pregnancy and reconstitution of the gut microbiota of female mice, where the control group was WT (<i>Ffar2</i>^+/+) mice and the experimental group was <i>Ffar2</i>^-/- mice. (<b>b-c</b>) Plasma glucose concentrations during an IPGTT in antibiotic-treated mice at G0 (<b>b</b>) and at G15 (<b>c</b>). (<b>d-e</b>) Plasma glucose concentrations during an IPGTT in mice after gut microbiome reconstitution at G0 (<b>d</b>) and G15 (<b>e</b>). (<b>f-g</b>) Comparison of AUC values for plasma glucose concentrations during an IPGTT administered to control, antibiotic-treated and gut microbiota reconstituted WT and <i>Ffar2</i>^-/- mice prior to pregnancy (<b>f</b>) and at G15 (<b>g</b>). (<b>h</b>) Area under the curve (AUC) values for plasma glucose concentrations during an IPGTT in WT and <i>Ffar2</i>^-/- mice on G15 of their first and second pregnancy. WT, circles and white bars; <i>Ffar2</i>^-/-, triangles and black bars. Data in (<b>b-e</b>) were compared by 2-way ANOVA with Bonferroni post-hoc analyses. Data in (<b>f-h</b>) were compared by Student’s t-test. (*, p<0.05; **, p<0.01; ***p<0.001), n = 7–16, mice/group.</p

FFA2 Contribution to Gestational Glucose Tolerance Is Not Disrupted by Antibiotics

<div><p>During the insulin resistant phase of pregnancy, the mRNA expression of free fatty acid 2 receptor (<i>Ffar2</i>) is upregulated and as we recently reported, this receptor contributes to insulin secretion and pancreatic beta cell mass expansion in order to maintain normal glucose homeostasis during pregnancy. As impaired gestational glucose levels can affect metabolic health of offspring, we aimed to explore the role of maternal <i>Ffar2</i> expression during pregnancy on the metabolic health of offspring and also the effects of antibiotics, which have been shown to disrupt gut microbiota fermentative activity (the source of the FFA2 ligands) on gestational glucose homeostasis. We found that maternal <i>Ffar2</i> expression and impaired glucose tolerance during pregnancy had no effect on the growth rates, <i>ad lib</i> glucose and glucose tolerance in the offspring between 3 and 6 weeks of age. To disrupt short chain fatty acid production, we chronically treated WT mice and <i>Ffar2</i>^<i>-/-</i> mice with broad range antibiotics and further compared their glucose tolerance prior to pregnancy and at gestational day 15, and also quantified cecum and plasma SCFAs. We found that during pregnancy antibiotic treatment reduced the levels of SCFAs in the cecum of the mice, but resulted in elevated levels of plasma SCFAs and altered concentrations of individual SCFAs. Along with these changes, gestational glucose tolerance in WT mice, but not <i>Ffar2</i>^<i>-/-</i> mice improved while on antibiotics. Additional data showed that gestational glucose tolerance worsened in <i>Ffar2</i>^<i>-/-</i> mice during a second pregnancy. Together, these results indicate that antibiotic treatment alone is inadequate to deplete plasma SCFA concentrations, and that modulation of gut microbiota by antibiotics does not disrupt the contribution of FFA2 to gestational glucose tolerance.</p></div

Antibiotics alter the relative abundance of individual SCFAs in circulation during pregnancy.

<p>Total plasma SCFA levels which includes acetate, propionate, and butyrate measured in WT and <i>Ffar2</i>^-/- mice at G0 <b>(a)</b> and G15 <b>(b)</b> under control vs antibiotic-treated conditions. (<b>b-h</b>) Relative abundance of individual SCFAs (acetate, <b>c-d</b>; propionate, <b>e-f</b>; and butyrate, <b>g-h</b>) in WT and <i>Ffar2</i>^-/- mice at G0 (<b>c, e</b> and <b>g</b>) and G15 (<b>d, f</b> and <b>h</b>) under control vs antibiotic-treated conditions. WT, white bars; <i>Ffar2</i>^-/-, black bars. Data are represented as mean ± SEM n = 6–15, and were analyzed by Student’s t-test (*p ≤ 0.05).</p

Antibiotic treatment substantially alters GLP-1 secretion independent of the mouse genotype.

<p>Insulin sensitivity, as measured by the insulin tolerance test in control female WT (<b>a</b>) and female <i>Ffar2</i>^-/- mice (<b>b</b>), treated with antibiotics (open circles) or untreated (filled circles), where the y axis shows the relative glucose level at each time point as compared to the glucose level at 0 min. (<b>c-d</b>) The serum insulin response to a glucose challenge in antibiotic treated mice (open symbols) compared with control mice (filled symbols) in both WT mice (<b>c</b>) and <i>Ffar2</i>^-/- mice (<b>d</b>). Plasma GLP-1 levels in antibiotic treated mice (white bars) compared to control mice (black bars) at 0 min and 30 min for the WT (<b>e</b>) and <i>Ffar2</i>^-/- mice (<b>f</b>). Data are represented as mean ± SEM. Data in (<b>a-d</b>) were compared by 2-way ANOVA with Bonferroni post-hoc analyses. Data in (<b>e, f</b>) were compared by Student’s t-test. (*, p<0.05; **, p<0.01; ***p<0.001), n = 5–8 mice/group.</p