Prediction of complex phenotypes using the Drosophila melanogaster metabolome

Comparing statistical learning methods for complex trait prediction from gene expression

Accurate prediction of complex traits is an important task in quantitative genetics that has become increasingly relevant for personalized medicine. Genotypes have traditionally been used for trait prediction using a variety of methods such as mixed models, Bayesian methods, penalized regressions, dimension reductions, and machine learning methods. Recent studies have shown that gene expression levels can produce higher prediction accuracy than genotypes. However, only a few prediction methods were used in these studies. Thus, a comprehensive assessment of methods is needed to fully evaluate the potential of gene expression as a predictor of complex trait phenotypes. Here, we used data from the Drosophila Genetic Reference Panel (DGRP) to compare the ability of several existing statistical learning methods to predict starvation resistance from gene expression in the two sexes separately. The methods considered differ in assumptions about the distribution of gene effect sizes - ranging from models that assume that every gene affects the trait to more sparse models - and their ability to capture gene-gene interactions. We also used functional annotation (i.e., Gene Ontology (GO)) as an external source of biological information to inform prediction models. The results show that differences in prediction accuracy between methods exist, although they are generally not large. Methods performing variable selection gave higher accuracy in females while methods assuming a more polygenic architecture performed better in males. Incorporating GO annotations further improved prediction accuracy for a few GO terms of biological significance. Biological significance extended to the genes underlying highly predictive GO terms with different genes emerging between sexes. Notably, the Insulin-like Receptor (InR) was prevalent across methods and sexes. Our results confirmed the potential of transcriptomic prediction and highlighted the importance of selecting appropriate methods and strategies in order to achieve accurate predictions.

Heavy metal exposure and metabolomics analysis: an emerging frontier in environmental health

Exposure to heavy metals in various populations can lead to extensive damage to different organs, as these metals infiltrate and bioaccumulate in the human body, causing metabolic disruptions in various organs. To comprehensively understand the metal homeostasis, inter-organ "traffic," and extensive metabolic alterations resulting from heavy metal exposure, employing complementary analytical methods is crucial. Metabolomics is pivotal in unraveling the intricacies of disease vulnerability by furnishing thorough understandings of metabolic changes linked to different metabolic diseases. This field offers exciting prospects for enhancing the disease prevention, early detection, and tailoring treatment approaches to individual needs. This article consolidates the existing knowledge on disease-linked metabolic pathways affected by the exposure of diverse heavy metals providing concise overview of the underlying impact mechanisms. The main aim is to investigate the connection between the altered metabolic pathways and long-term complex health conditions induced by heavy metals such as diabetes mellitus, cardiovascular diseases, renal disorders, inflammation, neurodegenerative diseases, reproductive risks, and organ damage. Further exploration of common pathways may unveil the shared targets for treating associated pathological conditions. In this article, the role of metabolomics in disease susceptibility is emphasized that metabolomics is expected to be routinely utilized for the diagnosis and monitoring of diseases and practical value of biomarkers derived from metabolomics, as well as determining their appropriate integration into extensive clinical settings.

Perspectives on the Drosophila melanogaster Model for Advances in Toxicological Science

The use of Drosophila melanogaster for studies of toxicology has grown considerably in the last decade. The Drosophila model has long been appreciated as a versatile and powerful model for developmental biology and genetics because of its ease of handling, short life cycle, low cost of maintenance, molecular genetic accessibility, and availability of a wide range of publicly available strains and data resources. These features, together with recent unique developments in genomics and metabolomics, make the fly model especially relevant and timely for the development of new approach methodologies and movements toward precision toxicology. Here, we offer a perspective on how flies can be leveraged to identify risk factors relevant to environmental exposures and human health. First, we review and discuss fundamental toxicologic principles for experimental design with Drosophila. Next, we describe quantitative and systems genetics approaches to resolve the genetic architecture and candidate pathways controlling susceptibility to toxicants. Finally, we summarize the current state and future promise of the emerging field of Drosophila metabolomics for elaborating toxic mechanisms. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC.

Multi-omics to predict changes during cold pressor test

Background

The cold pressor test (CPT) is a widely used pain provocation test to investigate both pain tolerance and cardiovascular responses. We hypothesize, that performing multi-omic analyses during CPT gives the opportunity to home in on molecular mechanisms involved. Twenty-two females were phenotypically assessed before and after a CPT, and blood samples were taken. RNA-Sequencing, steroid profiling and untargeted metabolomics were performed. Each ‘omic level was analyzed separately at both single-feature and systems-level (principal component [PCA] and partial least squares [PLS] regression analysis) and all ‘omic levels were combined using an integrative multi-omics approach, all using the paired-sample design.

Results

We showed that PCA was not able to discriminate time points, while PLS did significantly distinguish time points using metabolomics and/or transcriptomic data, but not using conventional physiological measures. Transcriptomic and metabolomic data revealed at feature-, systems- and integrative- level biologically relevant processes involved during CPT, e.g. lipid metabolism and stress response.

Conclusion

Multi-omics strategies have a great potential in pain research, both at feature- and systems- level. Therefore, they should be exploited in intervention studies, such as pain provocation tests, to gain knowledge on the biological mechanisms involved in complex traits.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08981-z.

A Drosophila melanogaster model for TMEM43-related arrhythmogenic right ventricular cardiomyopathy type 5

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a severe cardiac disease that leads to heart failure or sudden cardiac death (SCD). For the pathogenesis of ARVC, various mutations in at least eight different genes have been identified. A rare form of ARVC is associated with the mutation TMEM43 p.S358L, which is a fully penetrant variant in male carriers. TMEM43 p.S358 is homologous to CG8111 p.S333 in Drosophila melanogaster. We established CRISPR/Cas9-mediated CG8111 knock-out mutants in Drosophila, as well as transgenic fly lines carrying an overexpression construct of the CG8111 p.S333L substitution. Knock-out flies developed normally, whereas the overexpression of CG8111 p.S333L caused growth defects, loss of body weight, cardiac arrhythmias, and premature death. An evaluation of a series of model mutants that replaced S333 by selected amino acids proved that the conserved serine is critical for the physiological function of CG8111. Metabolomic and proteomic analyses revealed that the S333 in CG8111 is essential to proper energy homeostasis and lipid metabolism in the fly. Of note, metabolic impairments were also found in the murine Tmem43 disease model, and fibrofatty replacement is a hallmark of human ARVC5. These findings contribute to a more comprehensive understanding of the molecular functions of CG8111 in Drosophila, and can represent a valuable basis to assess the aetiology of the human TMEM43 p.S358L variant in more detail.

Rapid Adjustments in Thermal Tolerance and the Metabolome to Daily Environmental Changes - A Field Study on the Arctic Seed Bug Nysius groenlandicus

Laboratory investigations on terrestrial model-species, typically of temperate origin, have demonstrated that terrestrial ectotherms can cope with daily temperature variations through rapid hardening responses. However, few studies have investigated this ability and its physiological basis in the field. Especially in polar regions, where the temporal and spatial temperature variations can be extreme, are hardening responses expected to be important. Here, we examined diurnal adjustments in heat and cold tolerance in the Greenlandic seed bug Nysius groenlandicus by collecting individuals for thermal assessment at different time points within and across days. We found a significant correlation between observed heat or cold tolerance and the ambient microhabitat temperatures at the time of capture, indicating that N. groenlandicus continuously and within short time-windows respond physiologically to thermal changes and/or other environmental variables in their microhabitats. Secondly, we assessed underlying metabolomic fingerprints using GC-MS metabolomics in a subset of individuals collected during days with either low or high temperature variation. Concentrations of metabolites, including sugars, polyols, and free amino acids varied significantly with time of collection. For instance, we detected elevated sugar levels in animals caught at the lowest daily field temperatures. Polyol concentrations were lower in individuals collected in the morning and evening and higher at midday and afternoon, possibly reflecting changes in temperature. Additionally, changes in concentrations of metabolites associated with energetic metabolism were observed across collection times. Our findings suggest that in these extreme polar environments hardening responses are marked and likely play a crucial role for coping with microhabitat temperature variation on a daily scale, and that metabolite levels are actively altered on a daily basis.