常在細菌叢の普遍的性質と、その数理的解釈

学位の種別: 課程博士審査委員会委員 : （主査）東京大学教授 森下 真一, 東京大学教授 浅井 潔, 東京大学教授 津田 宏治, 東京大学教授 鈴木 穣, 東京大学准教授 岩崎 渉, 早稲田大学教授 服部 正平, お茶の水女子大学准教授 郡 宏University of Tokyo(東京大学

The influences of low protein diet on the intestinal microbiota of mice

Recent research suggests that protein deficiency symptoms are influenced by the intestinal microbiota. We investigated the influence of low protein diet on composition of the intestinal microbiota through animal experiments. Specific pathogen-free (SPF) mice were fed one of four diets (3, 6, 9, or 12% protein) for 4 weeks (n = 5 per diet). Mice fed the 3% protein diet showed protein deficiency symptoms such as weight loss and low level of blood urea nitrogen concentration in their serum. The intestinal microbiota of mice in the 3% and 12% protein diet groups at day 0, 7, 14, 21 and 28 were investigated by 16S rRNA gene sequencing, which revealed differences in the microbiota. In the 3% protein diet group, a greater abundance of urease producing bacterial species was detected across the duration of the study. In the 12% diet protein group, increases of abundance of Streptococcaceae and Clostridiales families was detected. These results suggest that protein deficiency may be associated with shifts in intestinal microbiota

A 3-dimensional mathematical model of microbial proliferation that generates the characteristic cumulative relative abundance distributions in gut microbiomes.

The gut microbiome is highly variable among individuals, largely due to differences in host lifestyle and physiology. However, little is known about the underlying processes or rules that shape the complex microbial community. In this paper, we show that the cumulative relative abundance distribution (CRAD) of microbial species can be approximated by a power law function, and found that the power exponent of CRADs generated from 16S rRNA gene and metagenomic data for normal gut microbiomes of humans and mice was similar consistently with ∼0.9. A similarly robust power exponent was observed in CRADs of gut microbiomes during dietary interventions and several diseases. However, the power exponent was found to be ∼0.6 in CRADs from gut microbiomes characterized by lower species richness, such as those of human infants and the small intestine of mice. In addition, the CRAD of gut microbiomes of mice treated with antibiotics differed slightly from those of infants and the small intestines of mice. Based on these observations, in addition to data on the spatial distribution of microbes in the digestive tract, we developed a 3-dimensional mathematical model of microbial proliferation that reproduced the experimentally observed CRAD patterns. Our model indicated that the CRAD may be determined by the ratio of emerging to pre-existing species during non-uniform spatially competitive proliferation, independent of species composition

CRADs of the human infant gut microbiome.

<p>A: Clustering of the 13 human infant samples at the genus level. Hierarchal clustering is performed at the genus-level abundance of the human infant samples. Each genus is shown in a different color. B: Individual CRADs of gut microbiomes of 13 human infants based on the OTU-level composition. The CRADs of the gut microbiomes of the 13 infants (red) and the 104 healthy Japanese adults (black) are shown. C: Median CRADs of the same samples as those in (B). The CRADs of the gut microbiomes of the 13 infants (red) and the 104 healthy Japanese adults (black) are shown in the boxplot.</p

Concept of the mathematical model.

<p>To determine the species in the blue-shaded lattice (top), a species in the black shaded area (underneath) is randomly selected with probability <i>p</i>. A new additional species joins with probability 1-<i>p</i>.</p

CRADs of murine gut microbiomes.

<p>A: Clustering of the bacterial composition at the OTU level in mice. Hierarchal clustering is performed at the OTU-level abundance of the samples. Each OTU is shown in a different color. B: Individual CRADs of murine gut microbiomes based on the OTU-level composition. C: Median CRADs of the same samples as those in (B).</p

CRADs of the dissected mouse gut contents.

<p>S1-S4, C, and L indicate the contents of the small intestine, the cecum, and the large intestine, respectively.</p

Simulation of CRADs of mice microbiomes.

<p>The boxplot shows the observed CRADs of the combined samples, small and large intestine and cecum. Red and blue lines represent the average CRADs in the simulation with <i>p</i> = 1.0 and <i>p</i> = 0.9975, respectively, with 200 initial species. Lattice size in the following simulations was (<i>x</i>, <i>y</i>, <i>z</i>) = (40, 40, 2000). The simulation results are obtained from the average of 100 repetitions.</p

Simulation of CRADs of gut microbiomes in antibiotic-treated mice.

<p>The red and blue lines represent the CRADs of samples from two mice treated with antibiotics. Black boxplots represent the CRADs in the simulation with <i>p</i> = 1, where the growth rate of 197 of the 200 species was set to half that of the other 3 species. Lattice size in the following simulations was (<i>x</i>, <i>y</i>, <i>z</i>) = (40, 40, 2000). The simulation results are obtained from the average of 100 repetitions.</p

Simulation of CRADs of infant and adult gut microbiomes.

<p>Red and blue boxes represent the CRADs of the gut microbiomes of the 13 infants and the 104 Japanese adults, respectively. The solid line and the dashed line represent the simulation results for <i>p</i> = 1.0 with 100 initial species and <i>p</i> = 0.999 with 200 initial species, respectively. Lattice size in the following simulations was (<i>x</i>, <i>y</i>, <i>z</i>) = (40, 40, 2000). The simulation results are obtained from the average of 100 repetitions.</p