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Identification of genetic modulators of lysosomal enzyme activities and glycosphingolipids (GSLs) may facilitate the development of therapeutics for diseases in which they participate, including Lysosomal Storage Disorders (LSDs). To this end, we used a systems genetics approach: we measured 11 hepatic lysosomal enzymes and many of their natural substrates (GSLs), followed by modifier gene mapping by GWAS and transcriptomics associations in a panel of inbred strains. Unexpectedly, most GSLs showed no association between their levels and the enzyme activity that catabolizes them. Genomic mapping identified 30 shared predicted modifier genes between the enzymes and GSLs, which are clustered in three pathways and are associated with other diseases. Surprisingly, they are regulated by ten common transcription factors, and their majority by miRNA-340p. In conclusion, we have identified novel regulators of GSL metabolism, which may serve as therapeutic targets for LSDs and may suggest the involvement of GSL metabolism in other pathologies.

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We are all similar but a bit different. These differences are partially due to variations in our genomes and are related to the heterogeneity of symptoms and responses to treatments that patients exhibit. Most animal studies are performed in one single strain with one manipulation. However, due to the lack of variability, therapies are not always reproducible when treatments are translated to humans. Panels of already sequenced organisms are valuable tools for mimicking human phenotypic heterogeneities and gene mapping. This review summarizes the current knowledge of mouse, fly, and yeast panels with insightful applications for translational research.

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The acid ß-glucocerebrosidase (GCase) enzyme cleaves glucosylceramide into glucose and ceramide. Loss of function variants in the gene encoding for GCase can lead to Gaucher disease and Parkinson's disease. Therapeutic strategies aimed at increasing GCase activity by targeting a modulating factor are attractive and poorly explored. To identify genetic modifiers, we measured hepatic GCase activity in 27 inbred mouse strains. A genome-wide association study (GWAS) using GCase activity as a trait identified several candidate modifier genes, including Dmrtc2 and Arhgef1 (p=2.1x10-7), and Grik5 (p=2.1x10-7). Bayesian integration of the gene mapping with transcriptomics was used to build integrative networks. The analysis uncovered additional candidate GCase regulators, highlighting modules of the acute phase response (p=1.01x10-8), acute inflammatory response (p=1.01x10-8), fatty acid beta-oxidation (p=7.43x10-5), among others. Our study revealed previously unknown candidate modulators of GCase activity, which may facilitate the design of therapies for diseases with GCase dysfunction.

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Cardiovascular disease (CVD) is a broad definition for diseases of the heart and blood vessels with high mortality and morbidity worldwide. Atherosclerosis and hypertension are the most common causes of CVD, and multiple factors confer the susceptibility. Some of the predisposing factors are modifiable such as diet, smoking, and exercise, whereas others, including age, sex, and individual's genetic variations contributing to the CVD composition traits, are non-modifiable. This latter group includes serum lipid traits. High serum lipid levels, specifically high levels of serum low-density lipoprotein cholesterol and triglycerides, are well-established key risk factors of atherosclerosis. This review will discuss genomics and systems biology approaches in the study of common dyslipidemias. The non-Mendelian forms of dyslipidemias are highly complex, and the molecular mechanisms underlying these polygenic lipid disorders are estimated to involve hundreds of genes. Interactions between the different genes and environmental factors also contribute to the clinical outcomes; however, very little is known about these interactions and their molecular mechanisms. To better address the complex genetic architecture and multiple properties leading to high serum lipid levels, networks and systems approach combining information at genomic, transcriptomics, methylomics, proteomics, metabolomics, and phenome level are being developed, with the ultimate goal to elucidate the cascade of dynamic changes leading to CVD in humans. (REV INVEST CLIN. 2018;70:217-23).

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A single gene can partake in several biological processes, and therefore gene deletions can lead to different-sometimes unexpected-phenotypes. However, it is not always clear whether such pleiotropy reflects the loss of a unique molecular activity involved in different processes or the loss of a multifunctional protein. Here, using Saccharomyces cerevisiae metabolism as a model, we systematically test the null hypothesis that enzyme phenotypes depend on a single annotated molecular function, namely their catalysis. We screened a set of carefully selected genes by quantifying the contribution of catalysis to gene deletion phenotypes under different environmental conditions. While most phenotypes were explained by loss of catalysis, slow growth was readily rescued by a catalytically inactive protein in about one-third of the enzymes tested. Such noncatalytic phenotypes were frequent in the Alt1 and Bat2 transaminases and in the isoleucine/valine biosynthetic enzymes Ilv1 and Ilv2, suggesting novel "moonlighting" activities in these proteins. Furthermore, differential genetic interaction profiles of gene deletion and catalytic mutants indicated that ILV1 is functionally associated with regulatory processes, specifically to chromatin modification. Our systematic study shows that gene loss phenotypes and their genetic interactions are frequently not driven by the loss of an annotated catalytic function, underscoring the moonlighting nature of cellular metabolism.

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This chapter describes methods currently available for visualizing results from systems genetics experiments. Here, we abstract from the statistical methods used for genetic mapping, which are dependent on the specific resource being used, i.e. F2, RILs, or outbred populations among others. We use a public dataset with results from a mouse eQTL experiment for three examples of visualization: genome-wide dot plots of marker-by-gene association, karyotype-like plots, and circos plots. Dot plots give a first overview of the results from eQTL mapping, allowing detecting genome-wide patterns of cis- and trans-genetic association to transcription level. Karyotype-like plots provide chromosomal context and allow integrating multiple tracks of information in a single plot. Circos plots can, in addition, display long-range interactions to provide an overview of genetic connectivity at the genome level. All examples are developed and explained using R code, an open-source language with powerful statistical and graphical capabilities. The principles reviewed here, however, can be applied with other software options, organisms, and to any type of molecular phenotype that can be assigned to a genomic position.

Assuntos