Biorefinery employing fungi can be a strategy for valorizing low-cost rest materials, by-products and wastes into several valuable bioproducts through the fungal fermentation. Mucoromycota fungi are soil fungi with a highly versatile metabolic system that positions them as powerful microbial cell factories for biorefinery applications. Lipids, pigments, chitin/chitosan, polyphosphates, ethanol, organic acids and enzymes are main Mucoromycota products that can be refined from the fermentation process and applied in nutrition, chemical or biofuel industries. In addition, Mucoromycota biomass can be used as it is for specific purposes, such as feed. Mucoromycota fungi can be employed in developing co-production processes, whereby several intra-and extracellular products are simultaneously formed in a single fermentation process, and, thus, economic viability of the process can be improved. This mini review provides a comprehensive overview over the recent advances in the production of valuable metabolites by Mucoromycota fungi and fermentation strategies which could be potentially applied in the industrial biorefinery settings. Key points• Biorefineries utilizing Mucoromycota fungi as production cell factories can provide a wide range of bioproducts.• Mucoromycota fungi are able to perform co-production of various metabolites in a single fermentation process.• Versatile metabolism of Mucoromycota allows valorization of a various low-cost substrates such as wastes and rest materials.

Calcium controls important processes in fungal metabolism, such as hyphae growth, cell wall synthesis, and stress tolerance. Recently, it was reported that calcium affects polyphosphate and lipid accumulation in fungi. The purpose of this study was to assess the effect of calcium on the accumulation of lipids and polyphosphate for six oleaginous Mucoromycota fungi grown under different phosphorus/pH conditions. A Duetz microtiter plate system (Duetz MTPS) was used for the cultivation. The compositional profile of the microbial biomass was recorded using Fourier-transform infrared spectroscopy, the high throughput screening extension (FTIR-HTS). Lipid content and fatty acid profiles were determined using gas chromatography (GC). Cellular phosphorus was determined using assay-based UV-Vis spectroscopy, and accumulated phosphates were characterized using solid-state 31P nuclear magnetic resonance spectroscopy. Glucose consumption was estimated by FTIR-attenuated total reflection (FTIR-ATR). Overall, the data indicated that calcium availability enhances polyphosphate accumulation in Mucoromycota fungi, while calcium deficiency increases lipid production, especially under acidic conditions (pH 2–3) caused by the phosphorus limitation. In addition, it was observed that under acidic conditions, calcium deficiency leads to increase in carotenoid production. It can be concluded that calcium availability can be used as an optimization parameter in fungal fermentation processes to enhance the production of lipids or polyphosphates.

Infrared spectroscopy delivers abundant information about the chemical composition, as well as the structural and optical properties of intact samples in a non-destructive manner. We present a deep convolutional neural network which exploits all of this information and solves full-wave inverse scattering problems and thereby obtains the 3D optical, structural and chemical properties from infrared spectroscopic measurements of intact micro-samples. The proposed model encodes scatter-distorted infrared spectra and infers the distribution of the complex refractive index function of concentrically spherical samples, such as many biological cells. The approach delivers simultaneously the molecular absorption, sample morphology and effective refractive index in both the cell wall and interior from a single measured spectrum. The model is trained on simulated scatter-distorted spectra, where absorption in the distinct layers is simulated and the scatter-distorted spectra are estimated by analytic solutions of Maxwell’s equations for samples of different sizes. This allows for essentially real-time deep learning-enabled infrared diffraction micro-tomography, for a large subset of biological cells.