Larval density can be used to predict genetic modifiers of glucagon signaling in Drosophila melanogaster.

Obesity is a growing concern. 42.3% of people in the U.S were considered obese between 2017-2018. Much is still unknown about the genetic components that contribute to weight gain. In humans, the hormone glucagon is a major contributor to the body's energy regulation as it signals for the breakdown of lipids. Treatments targeting the glucagon pathway have helped patients with both weight loss and appetite suppression. Understanding the genetic modifiers of glucagon signaling and its downstream pathways could enable the development of a wider variety of effective therapeutics. In this study, we blocked the glucagon pathway in Drosophila melanogaster by reducing the expression of the fly ortholog of the glucagon receptor (AKHR). We then crossed our model to the Drosophila Genetic Reference Panel (DGRP) and looked for natural variation in fat content. We used variation in larval density to identify candidate modifier genes through a genome-wide association study. We then tested these modifier genes by increasing or decreasing their expression in the AKHR model. We screened these candidates initially with the same density assay used in the original study to narrow down to four candidate genes that substantially impacted the density of the larvae: THADA, AmyD, GluRIIC, and CG9826. We further characterized these candidates using biochemical assays to analyze stored metabolites such as triglycerides, glucose, glycogen, and protein under control, high sugar, and high fat conditions to see if the larvae are resistant to environmental changes. Our results indicate consistency between the results of the density assay and direct measurement of metabolite levels. In particular, THADA and AmyD are highlighted as interesting genes for additional study. We hope to improve our understanding of the glucagon signaling pathway, obesity, and lipid metabolism. We also aim to provide candidate genes that can be regarded as future therapeutic targets.

A natural genetic variation screen identifies insulin signaling, neuronal communication, and innate immunity as modifiers of hyperglycemia in the absence of Sirt1.

Variation in the onset, progression, and severity of symptoms associated with metabolic disorders such as diabetes impairs the diagnosis and treatment of at-risk patients. Diabetes symptoms, and patient variation in these symptoms, are attributed to a combination of genetic and environmental factors, but identifying the genes and pathways that modify diabetes in humans has proven difficult. A greater understanding of genetic modifiers and the ways in which they interact with metabolic pathways could improve the ability to predict a patient's risk for severe symptoms, as well as enhance the development of individualized therapeutic approaches. In this study, we use the Drosophila Genetic Reference Panel to identify genetic variation influencing hyperglycemia associated with loss of Sirt1 function. Through analysis of individual candidate functions, physical interaction networks, and gene set enrichment analysis, we identify not only modifiers involved in canonical glucose metabolism and insulin signaling, but also genes important for neuronal signaling and the innate immune response. Furthermore, reducing the expression of several of these candidates suppressed hyperglycemia, making them potential candidate therapeutic targets. These analyses showcase the diverse processes contributing to glucose homeostasis and open up several avenues of future investigation.