Metabolomics: State-of-the-Art Technologies and Applications on Drosophila melanogaster

Aqueous extract from Astragalus membranaceus can improve the function degradation and delay aging on Drosophila melanogaster through antioxidant mechanism

Astragali radix is the dry root of the leguminous plants Astragalus membranaceus (Fisch.) Bge. Var. mongholicus (Bge.) Hsiao and Astragalus membranaceus (Fisch.) Bge. Astragali radix is mostly used clinically as a decoction. A number of pharmacological studies shows that Astragalus extract can increase telomerase activity, and has anti-oxidation, anti-inflammatory, immune regulation, anti-cancer, lowering blood lipid, lowering blood sugar and other effects. However, the anti-aging mechanism of aqueous extract from Astragali Radix (ARE) is still unclear. In this study, we evaluated the anti-aging effect of ARE on Drosophila melanogaster (D. melanogaster) and investigated the underlying mechanism. The results of lifespan assay showed that 1.25 mg/mL of ARE can significantly prolong the lifespan of D. melanogaster in a natural aging model, and protect against H2O2 and paraquat. Meanwhile, ARE can improve flies climbing ability and food intake. Metabolomics and the glutamate content assay suggested that ARE prevented an age-dependent increase in glutamate levels in D. melanogaster. Furthermore, ARE showed a dose-dependent effect on the scavenging ability of DPPH in vitro. Superoxide dismutase and catalase activities in the aging group also increased after the intervention of ARE. The data and the findings described here support the notion that ARE may play a preventive role in aging by improving the climbing ability, eliminating harmful free radicals accumulated in D. melanogaster and triggering antioxidant responses.

Bacterial challenge-associated metabolic phenotypes in Hermetia illucens defining nutritional and functional benefits

Black soldier fly (BSF, Hermetia illucens) is popular for its applications in animal feed, waste management and antimicrobial peptide source. The major advantages of BSF larva include their robust immune system and high nutritional content that can be further developed into more potential agricultural and medical applications. Several strategies are now being developed to exploit their fullest capabilities and one of these is the immunity modulation using bacterial challenges. The mechanism underlying metabolic responses of BSF to different bacteria has, however, remained unclear. In the current study, entometabolomics was employed to investigate the metabolic phenoconversion in response to either Escherichia coli, Staphylococcus aureus, or combined challenges in BSF larva. We have, thus far, characterised 37 metabolites in BSF larva challenged with different bacteria with the major biochemical groups consisting of amino acids, organic acids, and sugars. The distinct defense mechanism-specific metabolic phenotypes were clearly observed. The combined challenge contributed to the most significant metabolic phenoconversion in BSF larva with the dominant metabolic phenotypes induced by S. aureus. Our study suggested that the accumulation of energy-related metabolites provided by amino acid catabolism is the principal metabolic pathway regulating the defense mechanism. Therefore, combined challenge is strongly recommended for raising BSF immunity as it remarkably triggered amino acid metabolisms including arginine and proline metabolism and alanine, aspartate and glutamate metabolism along with purine metabolism and pyruvate metabolism that potentially result in the production of various nutritional and functional metabolites.

Exploiting the Potential of Drosophila Models in Lysosomal Storage Disorders: Pathological Mechanisms and Drug Discovery

Lysosomal storage disorders (LSDs) represent a complex and heterogeneous group of rare genetic diseases due to mutations in genes coding for lysosomal enzymes, membrane proteins or transporters. This leads to the accumulation of undegraded materials within lysosomes and a broad range of severe clinical features, often including the impairment of central nervous system (CNS). When available, enzyme replacement therapy slows the disease progression although it is not curative; also, most recombinant enzymes cannot cross the blood-brain barrier, leaving the CNS untreated. The inefficient degradative capability of the lysosomes has a negative impact on the flux through the endolysosomal and autophagic pathways; therefore, dysregulation of these pathways is increasingly emerging as a relevant disease mechanism in LSDs. In the last twenty years, different LSD Drosophila models have been generated, mainly for diseases presenting with neurological involvement. The fruit fly provides a large selection of tools to investigate lysosomes, autophagy and endocytic pathways in vivo, as well as to analyse neuronal and glial cells. The possibility to use Drosophila in drug repurposing and discovery makes it an attractive model for LSDs lacking effective therapies. Here, ee describe the major cellular pathways implicated in LSDs pathogenesis, the approaches available for their study and the Drosophila models developed for these diseases. Finally, we highlight a possible use of LSDs Drosophila models for drug screening studies.

Machine Learning Applications for Mass Spectrometry-Based Metabolomics

The metabolome of an organism depends on environmental factors and intracellular regulation and provides information about the physiological conditions. Metabolomics helps to understand disease progression in clinical settings or estimate metabolite overproduction for metabolic engineering. The most popular analytical metabolomics platform is mass spectrometry (MS). However, MS metabolome data analysis is complicated, since metabolites interact nonlinearly, and the data structures themselves are complex. Machine learning methods have become immensely popular for statistical analysis due to the inherent nonlinear data representation and the ability to process large and heterogeneous data rapidly. In this review, we address recent developments in using machine learning for processing MS spectra and show how machine learning generates new biological insights. In particular, supervised machine learning has great potential in metabolomics research because of the ability to supply quantitative predictions. We review here commonly used tools, such as random forest, support vector machines, artificial neural networks, and genetic algorithms. During processing steps, the supervised machine learning methods help peak picking, normalization, and missing data imputation. For knowledge-driven analysis, machine learning contributes to biomarker detection, classification and regression, biochemical pathway identification, and carbon flux determination. Of important relevance is the combination of different omics data to identify the contributions of the various regulatory levels. Our overview of the recent publications also highlights that data quality determines analysis quality, but also adds to the challenge of choosing the right model for the data. Machine learning methods applied to MS-based metabolomics ease data analysis and can support clinical decisions, guide metabolic engineering, and stimulate fundamental biological discoveries.

Drosophila melanogaster as an alternative model organism in nutrigenomics

Nutrigenomics explains the interaction between the genome, the proteome, the epigenome, the metabolome, and the microbiome with the nutritional environment of an organism. It is therefore situated at the interface between an organism's health, its diet, and the genome. The diet and/or specific dietary compounds are able to affect not only the gene expression patterns, but also the epigenetic mechanisms as well as the production of metabolites and the bacterial composition of the microbiota. Drosophila melanogaster provides a well-suited model organism to unravel these interactions in the context of nutrigenomics as it combines several advantages including an affordable maintenance, a short generation time, a high fecundity, a relatively short life expectancy, a well-characterized genome, and the availability of several mutant fly lines. Furthermore, it hosts a mammalian-like intestinal system with a clear microbiota and a fat body resembling the adipose tissue with liver-equivalent oenocytes, supporting the fly as an excellent model organism not only in nutrigenomics but also in nutritional research. Experimental approaches that are essentially needed in nutrigenomic research, including several sequencing technologies, have already been established in the fruit fly. However, studies investigating the interaction of a specific diet and/or dietary compounds in the fly are currently very limited. The present review provides an overview of the fly's morphology including the intestinal microbiome and antimicrobial peptides as modulators of the immune system. Additionally, it summarizes nutrigenomic approaches in the fruit fly helping to elucidate host-genome interactions with the nutritional environment in the model organism Drosophila melanogaster.