24 research outputs found

    Genetic determinants of gut microbiota composition and bile acid profiles in mice.

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    The microbial communities that inhabit the distal gut of humans and other mammals exhibit large inter-individual variation. While host genetics is a known factor that influences gut microbiota composition, the mechanisms underlying this variation remain largely unknown. Bile acids (BAs) are hormones that are produced by the host and chemically modified by gut bacteria. BAs serve as environmental cues and nutrients to microbes, but they can also have antibacterial effects. We hypothesized that host genetic variation in BA metabolism and homeostasis influence gut microbiota composition. To address this, we used the Diversity Outbred (DO) stock, a population of genetically distinct mice derived from eight founder strains. We characterized the fecal microbiota composition and plasma and cecal BA profiles from 400 DO mice maintained on a high-fat high-sucrose diet for ~22 weeks. Using quantitative trait locus (QTL) analysis, we identified several genomic regions associated with variations in both bacterial and BA profiles. Notably, we found overlapping QTL for Turicibacter sp. and plasma cholic acid, which mapped to a locus containing the gene for the ileal bile acid transporter, Slc10a2. Mediation analysis and subsequent follow-up validation experiments suggest that differences in Slc10a2 gene expression associated with the different strains influences levels of both traits and revealed novel interactions between Turicibacter and BAs. This work illustrates how systems genetics can be utilized to generate testable hypotheses and provide insight into host-microbe interactions

    Genetic mapping of microbial and host traits reveals production of immunomodulatory lipids by Akkermansia muciniphila in the murine gut.

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    The molecular bases of how host genetic variation impacts the gut microbiome remain largely unknown. Here we used a genetically diverse mouse population and applied systems genetics strategies to identify interactions between host and microbe phenotypes including microbial functions, using faecal metagenomics, small intestinal transcripts and caecal lipids that influence microbe-host dynamics. Quantitative trait locus (QTL) mapping identified murine genomic regions associated with variations in bacterial taxa; bacterial functions including motility, sporulation and lipopolysaccharide production and levels of bacterial- and host-derived lipids. We found overlapping QTL for the abundance of Akkermansia muciniphila and caecal levels of ornithine lipids. Follow-up in vitro and in vivo studies revealed that A. muciniphila is a major source of these lipids in the gut, provided evidence that ornithine lipids have immunomodulatory effects and identified intestinal transcripts co-regulated with these traits including Atf3, which encodes for a transcription factor that plays vital roles in modulating metabolism and immunity. Collectively, these results suggest that ornithine lipids are potentially important for A. muciniphila-host interactions and support the role of host genetics as a determinant of responses to gut microbes

    Accelerated Evolution of the Prdm9 Speciation Gene across Diverse Metazoan Taxa

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    The onset of prezygotic and postzygotic barriers to gene flow between populations is a hallmark of speciation. One of the earliest postzygotic isolating barriers to arise between incipient species is the sterility of the heterogametic sex in interspecies' hybrids. Four genes that underlie hybrid sterility have been identified in animals: Odysseus, JYalpha, and Overdrive in Drosophila and Prdm9 (Meisetz) in mice. Mouse Prdm9 encodes a protein with a KRAB motif, a histone methyltransferase domain and several zinc fingers. The difference of a single zinc finger distinguishes Prdm9 alleles that cause hybrid sterility from those that do not. We find that concerted evolution and positive selection have rapidly altered the number and sequence of Prdm9 zinc fingers across 13 rodent genomes. The patterns of positive selection in Prdm9 zinc fingers imply that rapid evolution has acted on the interface between the Prdm9 protein and the DNA sequences to which it binds. Similar patterns are apparent for Prdm9 zinc fingers for diverse metazoans, including primates. Indeed, allelic variation at the DNA–binding positions of human PRDM9 zinc fingers show significant association with decreased risk of infertility. Prdm9 thus plays a role in determining male sterility both between species (mouse) and within species (human). The recurrent episodes of positive selection acting on Prdm9 suggest that the DNA sequences to which it binds must also be evolving rapidly. Our findings do not identify the nature of the underlying DNA sequences, but argue against the proposed role of Prdm9 as an essential transcription factor in mouse meiosis. We propose a hypothetical model in which incompatibilities between Prdm9-binding specificity and satellite DNAs provide the molecular basis for Prdm9-mediated hybrid sterility. We suggest that Prdm9 should be investigated as a candidate gene in other instances of hybrid sterility in metazoans

    Taxonomy based on science is necessary for global conservation

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    Can States Take Over and Turn Around School Districts? Evidence from Lawrence, Massachusetts

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    The Federal government has spent billions of dollars to support turnarounds of low-achieving schools, yet most evidence on the impact of such turnarounds comes from high-profile, exceptional settings and not from examples driven by state policy decisions at scale. In this paper, we study the impact of state takeover and district-level turnaround in Lawrence, Massachusetts. Takeover of the Lawrence Public School (LPS) district was driven by the state’s accountability system, which increases state control in response to chronic underperformance. We find that the first two years of the LPS turnaround produced large achievement gains in math and modest gains in reading. Our preferred estimates compare LPS to other low income school districts in a differences-in-differences framework, although the results are robust to a wide variety of specifications, including student fixed effects. While the LPS turnaround was a package of interventions that cannot be fully separated, we find evidence that intensive small-group instruction led to particularly large achievement gains for participating students

    The effect of Relacin on Rel-ribosomes interaction.

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    <p>(<b>A</b>) Relacin inhibits dissociation of Rel/Spo from the ribosome. The relative amount of Rel/Spo (<i>D. radiodurans</i>) bound to purified ribosomes was quantified following the addition of increasing levels of Relacin. Rel/Spo molecules associated with 70S complexes were detected by Western blot analysis (see <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1002925#s4" target="_blank">Materials and Methods</a>). Histogram indicates the average of two independent biological repeats. Error bars represent the range. (<b>B</b>) Ribosome independent inhibition of (p)ppGpp synthesis. The constitutively active, ribosome-independent RelAC638F (<i>E. coli</i>) protein was treated with increasing concentrations of Relacin, as indicated (see <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1002925#s4" target="_blank">Materials and Methods</a>) in the presence or absence of isolated ribosomes. Shown is the average of duplicates of a representative experiment. Error bars represent the range.</p

    Relacin affects bacterial growth and survival.

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    <p>(<b>A</b>) Relacin influences entry into stationary phase. Shown are growth curves of wild type <i>B. subtilis</i> (PY79) cells grown in CH medium at 37°C in the absence or presence of increasing concentrations of Relacin added at OD<sub>600</sub> 0.2. (<b>B</b>) Relacin exerts a toxic effect. The viability of <i>B. subtilis</i> (PY79) cells was evaluated by counting colony forming units (CFU) after 24 hours of incubation in CH medium at 37°C in the absence or presence of increasing concentrations of Relacin added at OD<sub>600</sub> 0.2. Shown is a representative experiment, in which SD was calculated from at least three repeats for each concentration. (<b>C</b>) Long term effect of Relacin treatment. The effect of Relacin (2 mM) on the viability of wild type <i>B. subtilis</i> (PY79) cells or <i>ΔrelA</i> (ME215) cells was measured. Cells were incubated in CH medium at 37°C, and viability was determined by counting colony forming units (CFU). Relacin was added at OD<sub>600</sub> 0.2. Shown is a representative experiment, in which SD was calculated from at least three repeats for each point. (<b>D</b>) The toxic effect of Relacin on GAS. The effect of Relacin (2 mM) on the viability of wild type GAS (JRS4) cells, incubated in THY medium at 37°C, was evaluated as in (C).</p

    Relacin influences the sporulation process in <i>Bacilli</i>.

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    <p>(<b>A</b>) Relacin inhibits sporulation. Microscopy images of sporulating wild type <i>B. subtilis</i> (PY79) cells in the absence or presence of Relacin, added at time 0 of sporulation at the indicated concentrations. Upper panels: cells at t = 2 hr of sporulation stained with the fluorescent membrane dye FM1–43. Arrows indicate position of polar septa. Lower panels: phase contrast images of cells at t = 24 hr of sporulation. Scale bars correspond to 1 µm. (<b>B</b>) Relacin inhibits expression of the mid-sporulation protein SpoIIQ. Fluorescence microscopy images of <i>B. subtilis</i> (PE128) cells harboring <i>spoIIQ-gfp</i> at t = 4 hr of sporulation, in the absence (upper panels) or presence (lower panels) of Relacin (1 mM), added at time 0 of sporulation. Shown are phase contrast (red), GFP fluorescence (green) and overlay images. Scale bar corresponds to 1 µm. (<b>C–D</b>) Relacin inhibits <i>Bacilli</i> spore formation. The formation of heat resistant <i>B. subtilis</i> (PY79) (C) and <i>B. anthracis</i> (Sterne) (D) spores was monitored in the absence or presence of Relacin, added at the indicated concentrations at time 0 of sporulation (see <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1002925#ppat.1002925.s007" target="_blank">Text <i>S1</i></a>). Shown are representative experiments, in which SD was calculated from at least three repeats for each concentration. (<b>E</b>) Relacin added at different time points during sporulation inhibits spore formation. Inhibition of spore formation by wild type <i>B. subtilis</i> (PY79) cells was evaluated after addition of Relacin (1 mM) at the indicated time points of sporulation. Inhibition was determined using a heat resistance assay (see <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1002925#ppat.1002925.s007" target="_blank">Text <i>S1</i></a>) and is expressed relative to untreated cultures. Shown is a representative experiment, in which SD was calculated from at least three repeats for each time point.</p

    Relacin inhibits the activity of Rel proteins.

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    <p>(<b>A</b>) Chemical structure of Relacin. (<b>B–C</b>) Relacin inhibits (p)ppGpp synthesis <i>in vitro</i>. Representative autoradiograms of PEI thin-layer chromatography showing a decrease in labeled (p)ppGpp synthesized from α-<sup>32</sup>P-GTP precursor by purified RelA (<i>E. coli</i>) (B) or Rel/Spo (<i>D. radiodurans</i>) (C) with increasing concentrations of Relacin, as indicated (see <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1002925#s4" target="_blank">Materials and Methods</a>). Shown is the average of duplicates of a representative experiment. Error bars represent the range. (<b>D</b>) Relacin inhibits (p)ppGpp synthesis in living <i>B. subtilis</i> (PY79) cells. The accumulation of (p)ppGpp in response to amino acid starvation, induced by the addition of SHX, was monitored in the absence or presence of increasing concentrations of Relacin, as indicated. The (p)ppGpp level was determined using PEI thin-layer chromatography as in (B–C) (see <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1002925#s4" target="_blank">Materials and Methods</a>). Shown is the average of duplicates of a representative experiment. Error bars represent the range.</p
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