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.

The Drosophila melanogaster embryo has been widely utilized as a model for genetics and developmental biology due to its small size, short generation time, and large brood size. Information on embryonic metabolism during developmental progression is important for further understanding the mechanisms of Drosophila embryogenesis. Therefore, the aim of this study is to assess the changes in embryos’ metabolome that occur at different stages of the Drosophila embryonic development. Time course samples of Drosophila embryos were subjected to GC/MS-based metabolome analysis for profiling of low molecular weight hydrophilic metabolites, including sugars, amino acids, and organic acids. The results showed that the metabolic profiles of Drosophila embryo varied during the course of development and there was a strong correlation between the metabolome and different embryonic stages. Using the metabolome information, we were able to establish a prediction model for developmental stages of embryos starting from their high-resolution quantitative metabolite composition. Among the important metabolites revealed from our model, we suggest that different amino acids appear to play distinct roles in different developmental stages and an appropriate balance in trehalose-glucose ratio is crucial to supply the carbohydrate source for the development of Drosophila embryo.

Epigenetics is now emerging as a key regulation in response to various stresses. We herein identified the Drosophila histone methyltransferase G9a (dG9a) as a key factor to acquire tolerance to starvation stress. The depletion of dG9a led to high sensitivity to starvation stress in adult flies, while its overexpression induced starvation stress resistance. The catalytic domain of dG9a was not required for starvation stress resistance. dG9a plays no apparent role in tolerance to other stresses including heat and oxidative stresses. Metabolomic approaches were applied to investigate global changes in the metabolome due to the loss of dG9a during starvation stress. The results obtained indicated that dG9a plays an important role in maintaining energy reservoirs including amino acid, trehalose, glycogen, and triacylglycerol levels during starvation. Further investigations on the underlying mechanisms showed that the depletion of dG9a repressed starvation-induced autophagy by controlling the expression level of Atg8a, a critical gene for the progression of autophagy, in a different manner to that in cancer cells. These results indicate a positive role for dG9a in starvation-induced autophagy.

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The family Ustilaginaceae (belonging to the smut fungi) are known for their plant pathogenicity. Despite the fact that these plant diseases cause agricultural yield reduction, smut fungi attracted special attention in the field of industrial biotechnology. Ustilaginaceae show a versatile product spectrum such as organic acids (e.g., itaconate, malate, succinate), polyols (e.g., erythritol, mannitol), and extracellular glycolipids, which are considered value-added chemicals with potential applications in the pharmaceutical, food, and chemical industries. This study focused on itaconate as a platform chemical for the production of resins, plastics, adhesives, and biofuels. During this work, 72 different Ustilaginaceae strains from 36 species were investigated for their ability to (co-) consume the CO₂-derived substrates acetate and formate, potentially contributing toward a carbon-neutral itaconate production. The fungal growth and product spectrum with special interest in itaconate was characterized. Ustilago maydis MB215 and Ustilago rabenhorstiana NBRC 8995 were identified as promising candidates for acetate metabolization whereas Ustilago cynodontis NBRC 7530 was identified as a potential production host using formate as a co-substrate enhancing the itaconate production. Selected strains with the best itaconate production were characterized in more detail in controlled-batch bioreactor experiments confirming the co-substrate utilization. Thus, a proof-of-principle study was performed resulting in the identification and characterization of three promising Ustilaginaceae biocatalyst candidates for carbon-neutral itaconate production contributing to the biotechnological relevance of Ustilaginaceae.