Genetic architecture of heart mitochondrial proteome influencing cardiac hypertrophy.

Mitochondria play an important role in both normal heart function and disease etiology. We report analysis of common genetic variations contributing to mitochondrial and heart functions using an integrative proteomics approach in a panel of inbred mouse strains called the Hybrid Mouse Diversity Panel (HMDP). We performed a whole heart proteome study in the HMDP (72 strains, n=2-3 mice) and retrieved 848 mitochondrial proteins (quantified in ≥50 strains). High- resolution association mapping on their relative abundance levels revealed three trans-acting genetic loci on chromosomes (chr) 7, 13 and 17 that regulate distinct classes of mitochondrial proteins as well as cardiac hypertrophy. DAVID enrichment analyses of genes regulated by each of the loci revealed that the chr13 locus was highly enriched for complex-I proteins (24 proteins, P=2.2E-61), the chr17 locus for mitochondrial ribonucleoprotein complex (17 proteins, P=3.1E-25) and the chr7 locus for ubiquinone biosynthesis (3 proteins, P=6.9E-05). Follow-up high resolution regional mapping identified NDUFS4, LRPPRC and COQ7 as the candidate genes for chr13, chr17 and chr7 loci, respectively, and both experimental and statistical analyses supported their causal roles. Furthermore, a large cohort of Diversity Outbred mice was used to corroborate Lrpprc gene as a driver of mitochondrial DNA (mtDNA)-encoded gene regulation, and to show that the chr17 locus is specific to heart. Variations in all three loci were associated with heart mass in at least one of two independent heart stress models, namely, isoproterenol-induced heart failure and diet-induced obesity. These findings suggest that common variations in certain mitochondrial proteins can act in trans to influence tissue-specific mitochondrial functions and contribute to heart hypertrophy, eluci- dating mechanisms that may underlie genetic susceptibility to heart failure in human populations

Méthodes statistiques pour la stratification de patients dans le contexte de l'obésité : application aux données du microbiote intestinal et cytométrie en flux

The prevalence of obesity and type II diabetes has experienced a significant surge in recent times, underscoring the urgent need for public health research in this domain. Concurrently, the advent of high-throughput technologies has enabled the collection of extensive and diverse data from patients as well as the intestinal microbiota. This high-dimensional data has a structure that is unique to each patient. So, in order to identify different patterns between patients and to be able to stratify (i.e. classify them into different homogeneous subgroups based on their biological characteristics) them, it is necessary to develop new computational methods. This thesis presents the concept of Double Clustering, which involves the task of simultaneously grouping cell types and patients. To address this challenge, we propose a novel algorithmic approach called LDA-DC (Latent Dirichlet Allocation for Double Clustering). This method aims to identify clusters of cells associated with patient phenotypes, facilitating effective patient stratification. Through the utilization of publicly available patient data, we demonstrate the efficacy of our methodology. Furthermore, we apply our approach on metagenomic data from patients of the NutriOmics laboratory and clustered them into a network structure that reveals groups of patients with shared clinical, biological, and nutritional characteristics. Additionally, we have developed an artificial neural network-based methodology to predict the metabolic age of patients suffering from obesity and/or diabetes, allowing for a comparison with non-obese patients and enabling further patient stratification. This thesis is therefore in line with the concept of precision and predictive medicine, proposing a computational framework for stratifying patients and identifying different variables that can be modulated as part of preventive health strategies.La prévalence de l'obésité et du diabète de type II a fortement augmenté ces dernières années, faisant de la recherche dans ces domaines une priorité de santé publique. Parallèlement à cela, le développement des technologies à haut débit ont permis d’obtenir un grand nombre de données hétérogènes provenant des patients et de leur microbiote intestinal. Ces données de grandes dimensions ont une structure qui est propre à chacun d'eux. Ainsi dans le but d’identifier différents schémas entre les patients et pouvoir les stratifier (c’est-à-dire les classifier en différents sous-groupes homogènes en fonction de leurs caractéristiques biologiques), il est nécessaire de développer de nouvelles méthodes computationnelles. Cette thèse présente le concept de « Double Clustering », qui implique la tâche de regrouper simultanément les types de cellules et les patients. Pour cela, nous proposons une nouvelle approche algorithmique appelée LDA-DC (Latent Dirichlet Allocation for Double Clustering) dont le but est d’identifier les groupes de cellules associés aux phénotypes des patients, facilitant ainsi une stratification efficace de ceux-ci. Nous démontrons l'efficacité de notre méthodologie en utilisant des données de patients disponibles publiquement. De plus, nous appliquons notre approche aux données métagénomiques des patients du laboratoire NutriOmics et stratifions les patients sous forme de réseau révélant des groupes de patients ayant des caractéristiques cliniques, biologiques et nutritionnelles communes. D’autre part, nous avons développé une méthodologie basée sur un réseau de neurones artificiel dans le but de prédire l’âge métabolique des patients atteints d’obésité et/ou de diabète de type II en comparaison des patients non-obèses, permettant une stratification des patients. Ainsi, cette thèse s'aligne avec les principes de la médecine de précision et de la médecine prédictive en proposant une approche computationnelle permettant la stratification des patients et l'identification de variables pouvant être modulées dans le cadre d'une stratégie de médecine préventive

Méthodes statistiques pour la stratification de patients dans le contexte de l'obésité : application aux données du microbiote intestinal et cytométrie en flux

La prévalence de l'obésité et du diabète de type II a fortement augmenté ces dernières années, faisant de la recherche dans ces domaines une priorité de santé publique. Parallèlement à cela, le développement des technologies à haut débit ont permis d’obtenir un grand nombre de données hétérogènes provenant des patients et de leur microbiote intestinal. Ces données de grandes dimensions ont une structure qui est propre à chacun d'eux. Ainsi dans le but d’identifier différents schémas entre les patients et pouvoir les stratifier (c’est-à-dire les classifier en différents sous-groupes homogènes en fonction de leurs caractéristiques biologiques), il est nécessaire de développer de nouvelles méthodes computationnelles. Cette thèse présente le concept de « Double Clustering », qui implique la tâche de regrouper simultanément les types de cellules et les patients. Pour cela, nous proposons une nouvelle approche algorithmique appelée LDA-DC (Latent Dirichlet Allocation for Double Clustering) dont le but est d’identifier les groupes de cellules associés aux phénotypes des patients, facilitant ainsi une stratification efficace de ceux-ci. Nous démontrons l'efficacité de notre méthodologie en utilisant des données de patients disponibles publiquement. De plus, nous appliquons notre approche aux données métagénomiques des patients du laboratoire NutriOmics et stratifions les patients sous forme de réseau révélant des groupes de patients ayant des caractéristiques cliniques, biologiques et nutritionnelles communes. D’autre part, nous avons développé une méthodologie basée sur un réseau de neurones artificiel dans le but de prédire l’âge métabolique des patients atteints d’obésité et/ou de diabète de type II en comparaison des patients non-obèses, permettant une stratification des patients. Ainsi, cette thèse s'aligne avec les principes de la médecine de précision et de la médecine prédictive en proposant une approche computationnelle permettant la stratification des patients et l'identification de variables pouvant être modulées dans le cadre d'une stratégie de médecine préventive.The prevalence of obesity and type II diabetes has experienced a significant surge in recent times, underscoring the urgent need for public health research in this domain. Concurrently, the advent of high-throughput technologies has enabled the collection of extensive and diverse data from patients as well as the intestinal microbiota. This high-dimensional data has a structure that is unique to each patient. So, in order to identify different patterns between patients and to be able to stratify (i.e. classify them into different homogeneous subgroups based on their biological characteristics) them, it is necessary to develop new computational methods. This thesis presents the concept of Double Clustering, which involves the task of simultaneously grouping cell types and patients. To address this challenge, we propose a novel algorithmic approach called LDA-DC (Latent Dirichlet Allocation for Double Clustering). This method aims to identify clusters of cells associated with patient phenotypes, facilitating effective patient stratification. Through the utilization of publicly available patient data, we demonstrate the efficacy of our methodology. Furthermore, we apply our approach on metagenomic data from patients of the NutriOmics laboratory and clustered them into a network structure that reveals groups of patients with shared clinical, biological, and nutritional characteristics. Additionally, we have developed an artificial neural network-based methodology to predict the metabolic age of patients suffering from obesity and/or diabetes, allowing for a comparison with non-obese patients and enabling further patient stratification. This thesis is therefore in line with the concept of precision and predictive medicine, proposing a computational framework for stratifying patients and identifying different variables that can be modulated as part of preventive health strategies

Beta-hydroxybutyrate dampens adipose progenitors’ profibrotic activation through canonical Tgfβ signaling and non-canonical ZFP36-dependent mechanisms

International audienceAdipose tissue contains progenitor cells that contribute to beneficial tissue expansion when needed by de novo adipocyte formation (classical white or beige fat cells with thermogenic potential). However, in chronic obesity, they can exhibit an activated pro-fibrotic, extracellular matrix (ECM)-depositing phenotype that highly aggravates obesity-related adipose tissue dysfunction.Objective: Given that progenitors' fibrotic activation and fat cell browning appear to be antagonistic cell fates, we have examined the anti-fibrotic potential of pro-browning agents in an obesogenic condition.Results: In obese mice fed a high fat diet, thermoneutral housing, which induces brown fat cell dormancy, increases the expression of ECM gene programs compared to conventionally raised animals, indicating aggravation of obesity-related tissue fibrosis at thermoneutrality. In a model of primary cultured murine adipose progenitors, we found that exposure to b-hydroxybutyrate selectively reduced Tgfb-dependent profibrotic responses of ECM genes like Ctgf, Loxl2 and Fn1. This effect is observed in both subcutaneous and visceral-derived adipose progenitors, as well as in 3T3-L1 fibroblasts. In 30 patients with obesity eligible for bariatric surgery, those with higher circulating b-hydroxybutyrate levels have lower subcutaneous adipose tissue fibrotic scores. Mechanistically, b-hydroxybutyrate limits Tgfb-dependent collagen accumulation and reduces Smad2-3 protein expression and phosphorylation in visceral progenitors. Moreover, b-hydroxybutyrate induces the expression of the ZFP36 gene, encoding a post-transcriptional regulator that promotes the degradation of mRNA by binding to AU-rich sites within 3 0 UTRs. Importantly, complete ZFP36 deficiency in a mouse embryonic fibroblast line from null mice, or siRNA knock-down in primary progenitors, indicate that ZFP36 is required for b-hydroxybutyrate anti-fibrotic effects.Conclusion: These data unravel the potential of b-hydroxybutyrate to limit adipose tissue matrix deposition, a finding that might exploited in an obesogenic context

Genetic architecture of heart mitochondrial proteome influencing cardiac hypertrophy.

Mitochondria play an important role in both normal heart function and disease etiology. We report analysis of common genetic variations contributing to mitochondrial and heart functions using an integrative proteomics approach in a panel of inbred mouse strains called the Hybrid Mouse Diversity Panel (HMDP). We performed a whole heart proteome study in the HMDP (72 strains, n=2-3 mice) and retrieved 848 mitochondrial proteins (quantified in ≥50 strains). High-resolution association mapping on their relative abundance levels revealed three trans-acting genetic loci on chromosomes (chr) 7, 13 and 17 that regulate distinct classes of mitochondrial proteins as well as cardiac hypertrophy. DAVID enrichment analyses of genes regulated by each of the loci revealed that the chr13 locus was highly enriched for complex-I proteins (24 proteins, P=2.2E-61), the chr17 locus for mitochondrial ribonucleoprotein complex (17 proteins, P=3.1E-25) and the chr7 locus for ubiquinone biosynthesis (3 proteins, P=6.9E-05). Follow-up high resolution regional mapping identified NDUFS4, LRPPRC and COQ7 as the candidate genes for chr13, chr17 and chr7 loci, respectively, and both experimental and statistical analyses supported their causal roles. Furthermore, a large cohort of Diversity Outbred mice was used to corroborate Lrpprc gene as a driver of mitochondrial DNA (mtDNA)-encoded gene regulation, and to show that the chr17 locus is specific to heart. Variations in all three loci were associated with heart mass in at least one of two independent heart stress models, namely, isoproterenol-induced heart failure and diet-induced obesity. These findings suggest that common variations in certain mitochondrial proteins can act in trans to influence tissue-specific mitochondrial functions and contribute to heart hypertrophy, elucidating mechanisms that may underlie genetic susceptibility to heart failure in human populations