Quantitative genomics of locomotor behavior in Drosophila melanogaster

The locomotor behavior of Drosophila melanogaster was quantified in a large population of inbred lines derived from a single natural population, showing that many pleiotropic genes show correlated transcriptional responses to multiple behaviors

A transcriptional network associated with natural variation in Drosophila aggressive behavior

A genome-wide screen of inbred Drosophila lines together with transcriptional network modeling reveals insights into the genetic bases of heritable aggression

Genes of the Unfolded Protein Response Pathway Harbor Risk Alleles for Primary Open Angle Glaucoma

The statistical power of genome-wide association (GWA) studies to detect risk alleles for human diseases is limited by the unfavorable ratio of SNPs to study subjects. This multiple testing problem can be surmounted with very large population sizes when common alleles of large effects give rise to disease status. However, GWA approaches fall short when many rare alleles may give rise to a common disease, or when the number of subjects that can be recruited is limited. Here, we demonstrate that this multiple testing problem can be overcome by a comparative genomics approach in which an initial genome-wide screen in a genetically amenable model organism is used to identify human orthologues that may harbor risk alleles for adult-onset primary open angle glaucoma (POAG). Glaucoma is a major cause of blindness, which affects over 60 million people worldwide. Several genes have been associated with juvenile onset glaucoma, but genetic factors that predispose to adult onset primary open angle glaucoma (POAG) remain largely unknown. Previous genome-wide analysis in a Drosophila ocular hypertension model identified transcripts with altered regulation and showed induction of the unfolded protein response (UPR) upon overexpression of transgenic human glaucoma-associated myocilin (MYOC). We selected 16 orthologous genes with 62 polymorphic markers and identified in two independent human populations two genes of the UPR that harbor POAG risk alleles, BIRC6 and PDIA5. Thus, effectiveness of the UPR in response to accumulation of misfolded or aggregated proteins may contribute to the pathogenesis of POAG and provide targets for early therapeutic intervention

Genetic basis of transcriptome diversity in Drosophila melanogaster

Understanding how DNA sequence variation is translated into variation for complex phenotypes has remained elusive but is essential for predicting adaptive evolution, for selecting agriculturally important animals and crops, and for personalized medicine. Gene expression may provide a link between variation in DNA sequence and organismal phenotypes, and its abundance can be measured efficiently and accurately. Here we quantified genomewide variation in gene expression in the sequenced inbred lines of the Drosophila melanogaster Genetic Reference Panel (DGRP), increasing the annotated Drosophila transcriptome by 11%, including thousands of novel transcribed regions (NTRs). We found that 42%of the Drosophila transcriptome is genetically variable in males and females, including the NTRs, and is organized into modules of genetically correlated transcripts. We found that NTRs often were negatively correlated with the expression of protein-coding genes, which we exploited to annotate NTRs functionally. We identified regulatory variants for the mean and variance of gene expression, which have largely independent genetic control. Expression quantitative trait loci (eQTLs) for the mean, but not for the variance, of gene expression were concentrated near genes. Notably, the variance eQTLs often interacted epistatically with local variants in these genes to regulate gene expression. This comprehensive characterization of population-scale diversity of transcriptomes and its genetic basis in the DGRP is critically important for a systems understanding of quantitative trait variation

Epistasis dominates the genetic architecture of Drosophila quantitative traits

Epistasis-nonlinear genetic interactions between polymorphic loci-is the genetic basis of canalization and speciation, and epistatic interactions can be used to infer genetic networks affecting quantitative traits. However, the role that epistasis plays in the genetic architecture of quantitative traits is controversial. Here, we compared the genetic architecture of three Drosophila life history traits in the sequenced inbred lines of the Drosophila melanogaster Genetic Reference Panel (DGRP) and a large outbred, advanced intercross population derived from 40 DGRP lines (Flyland). We assessed allele frequency changes between pools of individuals at the extremes of the distribution for each trait in the Flyland population by deep DNA sequencing. The genetic architecture of all traits was highly polygenic in both analyses. Surprisingly, none of the SNPs associated with the traits in Flyland replicated in the DGRP and vice versa. However, the majority of these SNPs participated in at least one epistatic interaction in the DGRP. Despite apparent additive effects at largely distinct loci in the two populations, the epistatic interactions perturbed common, biologically plausible, and highly connected genetic networks. Our analysis underscores the importance of epistasis as a principal factor that determines variation for quantitative traits and provides a means to uncover genetic networks affecting these traits. Knowledge of epistatic networks will contribute to our understanding of the genetic basis of evolutionarily and clinically important traits and enhance predictive ability at an individualized level in medicine and agricultur

Diabetic cardiomiopathy progression is triggered by miR122-5p and involves extracellular matrix: a 5-year prospective study

The purpose of this study was to follow the long-term progression of diabetic cardiomyopathy by combining cardiac magnetic resonance (CMR) and molecular analysis

Overexpression of Myocilin in the Drosophila Eye Activates the Unfolded Protein Response: Implications for Glaucoma

Glaucoma is the world's second leading cause of bilateral blindness with progressive loss of vision due to retinal ganglion cell death. Myocilin has been associated with congenital glaucoma and 2-4% of primary open angle glaucoma (POAG) cases, but the pathogenic mechanisms remain largely unknown. Among several hypotheses, activation of the unfolded protein response (UPR) has emerged as a possible disease mechanism.We used a transgenic Drosophila model to analyze whole-genome transcriptional profiles in flies that express human wild-type or mutant MYOC in their eyes. The transgenic flies display ocular fluid discharge, reflecting ocular hypertension, and a progressive decline in their behavioral responses to light. Transcriptional analysis shows that genes associated with the UPR, ubiquitination, and proteolysis, as well as metabolism of reactive oxygen species and photoreceptor activity undergo altered transcriptional regulation. Following up on the results from these transcriptional analyses, we used immunoblots to demonstrate the formation of MYOC aggregates and showed that the formation of such aggregates leads to induction of the UPR, as evident from activation of the fluorescent UPR marker, xbp1-EGFP. CONCLUSIONS / SIGNIFICANCE: Our results show that aggregation of MYOC in the endoplasmic reticulum activates the UPR, an evolutionarily conserved stress pathway that culminates in apoptosis. We infer from the Drosophila model that MYOC-associated ocular hypertension in the human eye may result from aggregation of MYOC and induction of the UPR in trabecular meshwork cells. This process could occur at a late age with wild-type MYOC, but might be accelerated by MYOC mutants to account for juvenile onset glaucoma

Quantitative and Molecular Genetic Analyses of Mutations Increasing Drosophila Life Span

Understanding the genetic and environmental factors that affect variation in life span and senescence is of major interest for human health and evolutionary biology. Multiple mechanisms affect longevity, many of which are conserved across species, but the genetic networks underlying each mechanism and cross-talk between networks are unknown. We report the results of a screen for mutations affecting Drosophila life span. One third of the 1,332 homozygous P–element insertion lines assessed had quantitative effects on life span; mutations reducing life span were twice as common as mutations increasing life span. We confirmed 58 mutations with increased longevity, only one of which is in a gene previously associated with life span. The effects of the mutations increasing life span were highly sex-specific, with a trend towards opposite effects in males and females. Mutations in the same gene were associated with both increased and decreased life span, depending on the location and orientation of the P–element insertion, and genetic background. We observed substantial—and sex-specific—epistasis among a sample of ten mutations with increased life span. All mutations increasing life span had at least one deleterious pleiotropic effect on stress resistance or general health, with different patterns of pleiotropy for males and females. Whole-genome transcript profiles of seven of the mutant lines and the wild type revealed 4,488 differentially expressed transcripts, 553 of which were common to four or more of the mutant lines, which include genes previously associated with life span and novel genes implicated by this study. Therefore longevity has a large mutational target size; genes affecting life span have variable allelic effects; alleles affecting life span exhibit antagonistic pleiotropy and form epistatic networks; and sex-specific mutational effects are ubiquitous. Comparison of transcript profiles of long-lived mutations and the control line reveals a transcriptional signature of increased life span

A machine-learning based bio-psycho-social model for the prediction of non-obstructive and obstructive coronary artery disease

Background: Mechanisms of myocardial ischemia in obstructive and non-obstructive coronary artery disease (CAD), and the interplay between clinical, functional, biological and psycho-social features, are still far to be fully elucidated. Objectives: To develop a machine-learning (ML) model for the supervised prediction of obstructive versus non-obstructive CAD. Methods: From the EVA study, we analysed adults hospitalized for IHD undergoing conventional coronary angiography (CCA). Non-obstructive CAD was defined by a stenosis < 50% in one or more vessels. Baseline clinical and psycho-socio-cultural characteristics were used for computing a Rockwood and Mitnitski frailty index, and a gender score according to GENESIS-PRAXY methodology. Serum concentration of inflammatory cytokines was measured with a multiplex flow cytometry assay. Through an XGBoost classifier combined with an explainable artificial intelligence tool (SHAP), we identified the most influential features in discriminating obstructive versus non-obstructive CAD. Results: Among the overall EVA cohort (n = 509), 311 individuals (mean age 67 ± 11 years, 38% females; 67% obstructive CAD) with complete data were analysed. The ML-based model (83% accuracy and 87% precision) showed that while obstructive CAD was associated with higher frailty index, older age and a cytokine signature characterized by IL-1β, IL-12p70 and IL-33, non-obstructive CAD was associated with a higher gender score (i.e., social characteristics traditionally ascribed to women) and with a cytokine signature characterized by IL-18, IL-8, IL-23. Conclusions: Integrating clinical, biological, and psycho-social features, we have optimized a sex- and gender-unbiased model that discriminates obstructive and non-obstructive CAD. Further mechanistic studies will shed light on the biological plausibility of these associations. Clinical trial registration: NCT02737982