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.

Latent dirichlet allocation for double clustering (LDA-DC): discovering patients phenotypes and cell populations within a single Bayesian framework.

BACKGROUND: Current clinical routines rely more and more on "omics" data such as flow cytometry data from host and microbiota. Cohorts variability in addition to patients' heterogeneity and huge dimensions make it difficult to understand underlying structure of the data and decipher pathologies. Patients stratification and diagnostics from such complex data are extremely challenging. There is an acute need to develop novel statistical machine learning methods that are robust with respect to the data heterogeneity, efficient from the computational viewpoint, and can be understood by human experts. RESULTS: We propose a novel approach to stratify cell-based observations within a single probabilistic framework, i.e., to extract meaningful phenotypes from both patients and cells data simultaneously. We define this problem as a double clustering problem that we tackle with the proposed approach. Our method is a practical extension of the Latent Dirichlet Allocation and is used for the Double Clustering task (LDA-DC). We first validate the method on artificial datasets, then we apply our method to two real problems of patients stratification based on cytometry and microbiota data. We observe that the LDA-DC returns clusters of patients and also clusters of cells related to patients' conditions. We also construct a graphical representation of the results that can be easily understood by humans and are, therefore, of a big help for experts involved in pre-clinical research.

Beta-hydroxybutyrate dampens adipose progenitors' profibrotic activation through canonical Tgfß signaling and non-canonical ZFP36-dependent mechanisms.

BACKGROUND/PURPOSE: Adipose 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. METHODS: 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 ß-hydroxybutyrate selectively reduced Tgfß-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 ß-hydroxybutyrate levels have lower subcutaneous adipose tissue fibrotic scores. Mechanistically, ß-hydroxybutyrate limits Tgfß-dependent collagen accumulation and reduces Smad2-3 protein expression and phosphorylation in visceral progenitors. Moreover, ß-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'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 ß-hydroxybutyrate anti-fibrotic effects. CONCLUSION: These data unravel the potential of ß-hydroxybutyrate to limit adipose tissue matrix deposition, a finding that might exploited in an obesogenic context.