Interactions of diet, gut microbiota, and host genetics play essential roles in the development of metabolic diseases. A/J and C57BL/6J (C57) are two mouse strains known to display different susceptibilities to metabolic disorders. In this context, we analyzed gut microbiota composition in A/J and C57 mice, and assessed its responses to high-fat diet (HFD) and antibiotic (AB) treatment. We also exchanged the gut microbiota between the two strains following AB treatment to evaluate its impact on the metabolism. We showed that A/J and C57 mice have different microbiome structure and composition at baseline. Moreover, A/J and C57 microbiomes responded differently to HFD and AB treatments. Exchange of the gut microbiota between the two strains was successful as recipients’ microbiota resembled donor-strain microbiota. Seven weeks after inoculation, the differences between recipients persisted and were still closer from the donor-strain microbiota. Despite effective microbiota transplants, the response to HFD was not markedly modified in C57 and A/J mice. Particularly, body weight gain and glucose intolerance in response to HFD remained different in the two mouse strains whatever the changes in microbiome composition. This indicated that genetic background has a much stronger impact on metabolic responses to HFD than gut microbiome composition. View

Interactions of diet, gut microbiota, and host genetics play essential roles in the development of metabolic diseases. A/J and C57BL/6J (C57) are two mouse strains known to display different susceptibilities to metabolic disorders. In this context, we analyzed gut microbiota composition in A/J and C57 mice, and assessed its responses to high-fat diet (HFD) and antibiotic (AB) treatment. We also exchanged the gut microbiota between the two strains following AB treatment to evaluate its impact on the metabolism. We showed that A/J and C57 mice have different microbiome structure and composition at baseline. Moreover, A/J and C57 microbiomes responded differently to HFD and AB treatments. Exchange of the gut microbiota between the two strains was successful as recipients’ microbiota resembled donor-strain microbiota. Seven weeks after inoculation, the differences between recipients persisted and were still closer from the donor-strain microbiota. Despite effective microbiota transplants, the response to HFD was not markedly modified in C57 and A/J mice. Particularly, body weight gain and glucose intolerance in response to HFD remained different in the two mouse strains whatever the changes in microbiome composition. This indicated that genetic background has a much stronger impact on metabolic responses to HFD than gut microbiome composition. View

We have previously provided thefirst evidence that the microbiota modulates the physiology of the olfactoryepithelium using germfree mice. The extent to which changes to the olfactory system depend on the microbiotais still unknown. In the present work, we explored if different microbiota would differentially impact olfaction.We therefore studied the olfactory function of three groups of mice of the same genetic background, whoseparents had been conventionalized before mating with microbiota from three different mouse strains. Caecalshort chain fatty acids profiles and 16S rRNA gene sequencing ascertained that gut microbiota differed betweenthe three groups. We then used a behavioural test to measure the attractiveness of various odorants and observedthat the three groups of mice differed in their attraction towards odorants. Their olfactory epithelium properties,including electrophysiological responses recorded by electro-olfactograms and expression of genes related to theolfactory transduction pathway, also showed several differences. Overall, our data demonstrate that differencesin gut microbiota profiles are associated with differences in olfactory preferences and in olfactory epitheliumfunctioning

We have previously provided thefirst evidence that the microbiota modulates the physiology of the olfactoryepithelium using germfree mice. The extent to which changes to the olfactory system depend on the microbiotais still unknown. In the present work, we explored if different microbiota would differentially impact olfaction.We therefore studied the olfactory function of three groups of mice of the same genetic background, whoseparents had been conventionalized before mating with microbiota from three different mouse strains. Caecalshort chain fatty acids profiles and 16S rRNA gene sequencing ascertained that gut microbiota differed betweenthe three groups. We then used a behavioural test to measure the attractiveness of various odorants and observedthat the three groups of mice differed in their attraction towards odorants. Their olfactory epithelium properties,including electrophysiological responses recorded by electro-olfactograms and expression of genes related to theolfactory transduction pathway, also showed several differences. Overall, our data demonstrate that differencesin gut microbiota profiles are associated with differences in olfactory preferences and in olfactory epitheliumfunctioning

We have previously provided thefirst evidence that the microbiota modulates the physiology of the olfactoryepithelium using germfree mice. The extent to which changes to the olfactory system depend on the microbiotais still unknown. In the present work, we explored if different microbiota would differentially impact olfaction.We therefore studied the olfactory function of three groups of mice of the same genetic background, whoseparents had been conventionalized before mating with microbiota from three different mouse strains. Caecalshort chain fatty acids profiles and 16S rRNA gene sequencing ascertained that gut microbiota differed betweenthe three groups. We then used a behavioural test to measure the attractiveness of various odorants and observedthat the three groups of mice differed in their attraction towards odorants. Their olfactory epithelium properties,including electrophysiological responses recorded by electro-olfactograms and expression of genes related to theolfactory transduction pathway, also showed several differences. Overall, our data demonstrate that differencesin gut microbiota profiles are associated with differences in olfactory preferences and in olfactory epitheliumfunctioning

Latent Block Model (LBM) is a model-based method to cluster simultaneously the d columns and n rows of a data matrix. Parameter estimation in LBM is a difficult and multifaceted problem. Although various estimation strategies have been proposed and are now well understood empirically, theoretical guarantees about their asymptotic behavior is rather sparse. We show here that under some mild conditions on the parameter space, and in an asymptotic regime where log(d)/n and log(n)/d tend to 0 when n and d tend to +∞, (1) the maximum-likelihood estimate of the complete model (with known labels) is consistent and (2) the log-likelihood ratios are equivalent under the complete and observed (with unknown labels) models. This equivalence allows us to transfer the asymptotic consistency to the maximum likelihood estimate under the observed model. Moreover, the variational estimator is also consistent.

Latent Block Model (LBM) is a model-based method to cluster simultaneously the d columns and n rows of a data matrix. Parameter estimation in LBM is a difficult and multifaceted problem. Although various estimation strategies have been proposed and are now well understood empirically, theoretical guarantees about their asymptotic behavior is rather sparse. We show here that under some mild conditions on the parameter space, and in an asymptotic regime where log(d)/n and log(n)/d tend to 0 when n and d tend to +∞, (1) the maximum-likelihood estimate of the complete model (with known labels) is consistent and (2) the log-likelihood ratios are equivalent under the complete and observed (with unknown labels) models. This equivalence allows us to transfer the asymptotic consistency to the maximum likelihood estimate under the observed model. Moreover, the variational estimator is also consistent.

Motivation: De novo comparative metagenomics is one of the most straightforward ways to analyze large sets of metagenomic data. Latest methods use the fraction of shared k-mers to estimate genomic similarity between read sets. However, those methods, while extremely efficient, are still limited by computational needs for practical usage outside of large computing facilities. Results: We present SimkaMin, a quick comparative metagenomics tool with low disk and memory footprints, thanks to an efficient data subsampling scheme used to estimate Bray-Curtis and Jaccard dissimilarities. One billion metagenomic reads can be analyzed in <3 min, with tiny memory (1.09 GB) and disk (approximate to 0.3 GB) requirements and without altering the quality of the downstream comparative analyses, making of SimkaMin a tool perfectly tailored for very large-scale metagenomic projects.

Motivation: De novo comparative metagenomics is one of the most straightforward ways to analyze large sets of metagenomic data. Latest methods use the fraction of shared k-mers to estimate genomic similarity between read sets. However, those methods, while extremely efficient, are still limited by computational needs for practical usage outside of large computing facilities. Results: We present SimkaMin, a quick comparative metagenomics tool with low disk and memory footprints, thanks to an efficient data subsampling scheme used to estimate Bray-Curtis and Jaccard dissimilarities. One billion metagenomic reads can be analyzed in <3 min, with tiny memory (1.09 GB) and disk (approximate to 0.3 GB) requirements and without altering the quality of the downstream comparative analyses, making of SimkaMin a tool perfectly tailored for very large-scale metagenomic projects.

Motivation: De novo comparative metagenomics is one of the most straightforward ways to analyze large sets of metagenomic data. Latest methods use the fraction of shared k-mers to estimate genomic similarity between read sets. However, those methods, while extremely efficient, are still limited by computational needs for practical usage outside of large computing facilities. Results: We present SimkaMin, a quick comparative metagenomics tool with low disk and memory footprints, thanks to an efficient data subsampling scheme used to estimate Bray-Curtis and Jaccard dissimilarities. One billion metagenomic reads can be analyzed in <3 min, with tiny memory (1.09 GB) and disk (approximate to 0.3 GB) requirements and without altering the quality of the downstream comparative analyses, making of SimkaMin a tool perfectly tailored for very large-scale metagenomic projects.