Biology subjects

Lusis, A. J.

Publications and source records attributed to Lusis, A. J..

25 records · Page 2Linked to original sources

bioRxiv2020

Machine learning reveals time-varying microbial predictors with complex effects on glucose regulation

The incidence of type 2 diabetes (T2D) has been increasing globally and a growing body of evidence links type 2 diabetes with altered microbiota composition. Type 2 diabetes is preceded by a long pre-diabetic state characterized by changes in various metabolic parameters. We tested whether the gut microbiome could have predictive potential for T2D development during the healthy and pre-diabetic disease stages. We used prospective data of 608 well-phenotyped Finnish men collected from the population-based Metabolic Syndrome In Men (METSIM) study to build machine learning models for predicting continuous glucose and insulin measures in a shorter (1.5 year) and longer (4.5 year) period. Our results show that the inclusion of gut microbiome improves prediction accuracy for modelling T2D associated parameters such as glycosylated hemoglobin and insulin measures. We identified novel microbial biomarkers and described their effects on the predictions using interpretable machine learning techniques, which revealed complex linear and non-linear associations. Additionally, the modelling strategy carried out allowed us to compare the stability of model performances and biomarker selection, also revealing differences in short-term and long-term predictions. The identified microbiome biomarkers provide a predictive measure for various metabolic traits related to T2D, thus providing an additional parameter for personal risk assessment. Our work also highlights the need for robust modelling strategies and the value of interpretable machine learning. ImportanceRecent studies have shown a clear link between gut microbiota and type 2 diabetes. However, current results are based on cross-sectional studies that aim to determine the microbial dysbiosis when the disease is already prevalent. In order to consider microbiome as a factor in disease risk assessment, prospective studies are needed. Our study is the first study that assesses the gut microbiome as a predictive measure for several type 2 diabetes associated parameters in a longitudinal study setting. Our results revealed a number of novel microbial biomarkers that can improve the prediction accuracy for continuous insulin measures and glycosylated hemoglobin levels. These results make the prospect of using microbiome in personalized medicine promising.

microbiology↗

bioRxiv2019

The Lands cycle modulates plasma membrane lipid organization and insulin sensitivity in skeletal muscle

Aberrant lipid metabolism promotes the development of skeletal muscle insulin resistance, but the exact identity of lipid-mediated mechanisms relevant to human obesity remains unclear. A comprehensive lipidomic analyses of primary myocytes from lean insulin-sensitive (LN) and obese insulin-resistant (OB) individuals revealed several species of lysophospholipids (lyso-PL) that were differentially-abundant. These changes coincided with greater expression of lysophosphatidylcholine acyltransferase 3 (LPCAT3), an enzyme involved in phospholipid transacylation (Lands cycle). Strikingly, mice with skeletal muscle-specific knockout of LPCAT3 (LPCAT3-MKO) exhibited greater muscle lyso-PC/PC, concomitant with greater insulin sensitivity in vivo and insulin-stimulated skeletal muscle glucose uptake ex vivo. Absence of LPCAT3 reduced phospholipid packing of the cellular membranes and increased plasma membrane lipid clustering, suggesting that LPCAT3 affects insulin receptor phosphorylation by modulating plasma membrane lipid organization. In conclusion, obesity accelerates the skeletal muscle Lands cycle, whose consequence might induce the disruption of plasma membrane organization that suppresses muscle insulin action.

physiology↗

bioRxiv2019

The Effects of Mutations are Modified by Genetic Background in Mice

BackgroundThe majority of quantitative genetic models used to map complex traits assume that alleles have similar effects across all individuals. Significant evidence suggests, however, that epistatic interactions modulate the impact of many alleles. Nevertheless, identifying epistatic interactions remains computationally and statistically challenging. In this work, we address some of these challenges by developing a statistical test for polygenic epistasis that determines whether the effect of an allele is altered by the global genetic ancestry proportion from distinct progenitors. ResultsWe applied our method to data from mice and yeast. For the mice, we observed 49 significant genotype-by-ancestry interaction associations across 14 phenotypes as well as over 1,400 Bonferroni-corrected genotype-by-ancestry interaction associations for mouse gene expression data. For the yeast, we observed 92 significant genotype-by-ancestry interactions across 38 phenotypes. Given this evidence of epistasis, we test for and observe evidence of rapid selection pressure on ancestry specific polymorphisms within one of the cohorts, consistent with epistatic selection. ConclusionsUnlike our prior work in human populations, we observe widespread evidence of ancestry-modified SNP effects, perhaps reflecting the greater divergence present in crosses using mice and yeast. Author SummaryMany statistical tests which link genetic markers in the genome to differences in traits rely on the assumption that the same polymorphism will have identical effects in different individuals. However, there is substantial evidence indicating that this is not the case. Epistasis is the phenomenon in which multiple polymorphisms interact with one another to amplify or negate each others effects on a trait. We hypothesized that individual SNP effects could be changed in a polygenic manner, such that the proportion of as genetic ancestry, rather than specific markers, might be used to capture epistatic interactions. Motivated by this possibility, we develop a new statistical test that allowed us to examine the genome to identify polymorphisms which have different effects depending on the ancestral makeup of each individual. We use our test in two different populations of inbred mice and a yeast panel and demonstrate that these sorts of variable effect polymorphisms exist in 14 different physical traits in mice and 38 phenotypes in yeast as well as in murine gene expression. We use the term "polygenic epistasis" to distinguish these interactions from the more conventional two- or multi-locus interactions.

genomics↗

bioRxiv2018

Host Genetic Background and Gut Microbiota Contribute to Differential Metabolic Responses to High Fructose Consumption in Mice

BackgroundIt is unclear how high fructose consumption induces disparate metabolic responses in genetically diverse mouse strains. ObjectiveWe aim to investigate whether the gut microbiota contributes to differential metabolic responses to fructose. MethodsEight-week-old male C57BL/6J (B6), DBA/2J (DBA), and FVB/NJ (FVB) mice were given 8% fructose solution or regular water (control) for 12 weeks. The gut microbiota composition in cecum and feces was analyzed using 16S rDNA sequencing, and PERMANOVA was used to compare community across mouse strains, treatments, and time points. Microbiota abundance was correlated with metabolic phenotypes and host gene expression in hypothalamus, liver and adipose tissues using Biweight midcorrelation. To test the causal role of the gut microbiota in determining fructose response, we conducted fecal transplants from B6 to DBA mice and vice versa for 4 weeks, as well as gavaged antibiotic-treated DBA mice with Akkermansia for 9 weeks, accompanied with or without fructose treatment. ResultsCompared to B6 and FVB, DBA mice had significantly higher Firmicutes/Bacteroidetes ratio and lower baseline levels of Akkermansia and S24-7 (P < 0.05), accompanied by metabolic dysregulation after fructose consumption. Fructose altered specific microbial taxa in individual mouse strains, such as a 7.27-fold increase in Akkermansia in B6 and 0.374-fold change in Rikenellaceae in DBA (FDR < 5%), which demonstrated strain-specific correlations with host metabolic and transcriptomic phenotypes. Fecal transplant experiments indicated that B6 microbes conferred resistance to fructose-induced weight gain in DBA mice (F = 43.1, P < 0.001), and Akkermansia colonization abrogated the fructose-induced weight gain (F = 17.8, P <0.001) and glycemic dysfunctions (F = 11.8, P = 0.004) in DBA mice. ConclusionsOur findings support that differential microbiota composition between mouse strains is partially responsible for host metabolic sensitivity to fructose, and that Akkermansia is a key bacterium that confers resistance to fructose-induced metabolic dysregulation.

systems biology↗

bioRxiv2018

Genetic control of the HDL proteome

High-density lipoproteins (HDL) are nanoparticles with >80 associated proteins, phospholipids, cholesterol and cholesteryl esters. A comprehensive genetic analysis of the regulation of proteome of HDL isolated from a panel of 100 diverse inbred strains of mice, Hybrid Mouse Diversity Panel (HMDP), revealed widely varied HDL protein levels across the strains. Some of this variation was explained by local, cis-acting regulation, termed cis-protein quantitative trait loci. Variations in apolipoprotein A-II and apolipoprotein C-3 affected the abundance of multiple HDL proteins indicating a coordinated regulation. We identified modules of co-varying proteins and define a protein-protein interaction network describing the protein composition of the naturally occurring subspecies of HDL in mice. Sterol efflux capacity varied up to 3-fold across the strains and HDL proteins displayed distinct correlation patterns with macrophage and ABCA1 specific cholesterol efflux capacity and cholesterol exchange, suggesting that subspecies of HDL participate in discrete functions. The baseline and stimulated sterol efflux capacity phenotypes associated with distinct QTLs with smaller effect size suggesting a multi genetic regulation. Our results highlight the complexity of HDL particles by revealing high degree of heterogeneity and intercorrelation, some of which is associated with functional variation, supporting the concept that HDL-cholesterol alone is not an accurate measure of HDLs properties such as protection against CAD.

systems biology↗

bioRxiv2018

Variability and compensation of cardiomycoyte ionic conductances at the population level

Conductances of ion channels and transporters controlling cardiac excitation may vary in a population of subjects with different cardiac gene expression patterns. However, the amount of variability and its origin are not quantitatively known. We propose a new computational method to predict this variability that consists of finding combinations of conductances generating a normal intracellular Ca2+ transient without any constraint on the action potential. Furthermore, we validate experimentally its predictions using the Hybrid Mouse Diversity Panel, a model system of genetically diverse mouse strains that allows us to quantify inter-subject versus intra-subject variability. The method predicts that conductances of inward Ca2+ and outward K+ currents compensate each other to generate a normal Ca2+ transient in good quantitative agreement with current measurements in ventricular myocytes from hearts of different isogenic strains. Our results suggest that a feedback mechanism sensing the aggregate Ca2+ transient of the heart suffices to regulate ionic conductances.

biophysics↗

bioRxiv2017

A personalized, multi-omics approach identifies genes involved in cardiac hypertrophy and heart failure

Identifying genes underlying complex diseases remains a major challenge. Biomarkers are typically identified by comparing average levels of gene expression in populations of healthy and diseased individuals. However, genetic diversities may undermine the effort to uncover genes with significant but individual contribution to the spectrum of disease phenotypes within a population. Here we leverage the Hybrid Mouse Diversity Panel (HMDP), a model system of 100+ genetically diverse strains of mice exhibiting different complex disease traits, to develop a personalized differential gene expression analysis that is able to identify disease-associated genes missed by traditional population-wide methods. The population-level and personalized approaches are compared for isoproterenol(ISO)-induced cardiac hypertrophy and heart failure using pre- and post-ISO gene expression and phenotypic data. The personalized approach identifies 36 Fold-Change (FC) genes predictive of the severity of cardiac hypertrophy, and enriched in genes previously associated with cardiac diseases in human. Strikingly, these genes are either up- or down-regulated at the individual strain level, and are therefore missed when averaging at the population level. Using insights from the gene regulatory network and protein-protein interactome, we identify Hes1 as a strong candidate FC gene. We validate its role by showing that even a mild knockdown of 20-40% of Hes1 can induce a dramatic reduction of hypertrophy by 80-90% in rat neonatal cardiac cells. These findings emphasize the importance of a personalized approach to identify causal genes underlying complex diseases as well as to develop personalized therapies.\n\nSignificanceA traditional approach to investigate the genetic basis of complex diseases is to look for genes with a global change in expression between diseased and healthy individuals. Here, we investigate individual changes of gene expression by inducing heart failure in 100 strains of genetically distinct mice. We find that genes associated to the severity of the disease are either up- or down-regulated across individuals and are therefore missed by a traditional population level approach. However, they are enriched in human cardiac disease genes and form a coregulated module strongly interacting with a cardiac hypertrophic signaling network in the human interactome. Our analysis demonstrates that individualized approaches are crucial to reveal all genes involved in the development of complex diseases.

systems biology↗