Assessment of Biotechnologically Important Filamentous Fungal Biomass by Fourier Transform Raman Spectroscopy

Transcriptome analysis reveals diverse Curvularia tsudae strategies in response to cadmium stress

Cadmium (Cd) is a highly toxic heavy metal that poses significant threats to living organisms. Curvularia tsudae has demonstrated remarkable survival capabilities in the presence of high Cd concentrations, exhibiting its exceptional Cd tolerance. Although some physiological studies have been conducted, the molecular mechanisms underlying Cd tolerance in C. tsudae is largely unknown. In this study, a comparative transcriptome analysis was performed to explore the molecular mechanisms of C. tsudae under Cd stress. Among the 10,498 identified unigenes, 2526 differentially expressed genes (DEGs) were identified between the Cd-free and Cd-treated samples. Functional annotation and enrichment analysis of these DEGs identified several key biological processes involved in coping with Cd stress. Genes related to cell wall modification and organic acid metabolism contributes to Cd binding or chelation. Fourier transform infrared spectroscopy (FTIR) analysis further highlighted the modifications in functional groups with the cell wall under Cd stress. Furthermore, the transporters tended to be modulated in response to Cd stress, and up-regulated genes involved in antioxidants likely contributes to high Cd tolerance. The processes from DNA to protein metabolism appeared to responsive to the presence of Cd stress as well. These results contribute to the advance of the current knowledge about the response of C. tsudae to Cd stress and lay the foundation for further advancements in using fungi for the remediation of Cd-polluted environments.

Raman spectroscopy online monitoring of biomass production, intracellular metabolites and carbon substrates during submerged fermentation of oleaginous and carotenogenic microorganisms

BACKGROUND

Monitoring and control of both growth media and microbial biomass is extremely important for the development of economical bioprocesses. Unfortunately, process monitoring is still dependent on a limited number of standard parameters (pH, temperature, gasses etc.), while the critical process parameters, such as biomass, product and substrate concentrations, are rarely assessable in-line. Bioprocess optimization and monitoring will greatly benefit from advanced spectroscopy-based sensors that enable real-time monitoring and control. Here, Fourier transform (FT) Raman spectroscopy measurement via flow cell in a recirculatory loop, in combination with predictive data modeling, was assessed as a fast, low-cost, and highly sensitive process analytical technology (PAT) system for online monitoring of critical process parameters. To show the general applicability of the method, submerged fermentation was monitored using two different oleaginous and carotenogenic microorganisms grown on two different carbon substrates: glucose fermentation by yeast Rhodotorula toruloides and glycerol fermentation by marine thraustochytrid Schizochytrium sp. Additionally, the online FT-Raman spectroscopy approach was compared with two at-line spectroscopic methods, namely FT-Raman and FT-infrared spectroscopies in high throughput screening (HTS) setups.

RESULTS

The system can provide real-time concentration data on carbon substrate (glucose and glycerol) utilization, and production of biomass, carotenoid pigments, and lipids (triglycerides and free fatty acids). Robust multivariate regression models were developed and showed high level of correlation between the online FT-Raman spectral data and reference measurements, with coefficients of determination (R2) in the 0.94-0.99 and 0.89-0.99 range for all concentration parameters of Rhodotorula and Schizochytrium fermentation, respectively. The online FT-Raman spectroscopy approach was superior to the at-line methods since the obtained information was more comprehensive, timely and provided more precise concentration profiles.

CONCLUSIONS

The FT-Raman spectroscopy system with a flow measurement cell in a recirculatory loop, in combination with prediction models, can simultaneously provide real-time concentration data on carbon substrate utilization, and production of biomass, carotenoid pigments, and lipids. This data enables monitoring of dynamic behaviour of oleaginous and carotenogenic microorganisms, and thus can provide critical process parameters for process optimization and control. Overall, this study demonstrated the feasibility of using FT-Raman spectroscopy for online monitoring of fermentation processes.

High-throughput vibrational spectroscopy methods for determination of degree of acetylation for chitin and chitosan

The rising demand for chitin and chitosan in chemical, agro-food, and healthcare industries is creating a need for rapid and high-throughput analysis. The physicochemical properties of these biopolymers are greatly dependent on the degree of acetylation (DA). Conventional methods for DA determination, such as LC-MS and ¹H NMR, are time-consuming when performed on many samples, and therefore efficient methods are needed. Here, high-throughput microplate-based FTIR and FT-Raman methods were compared with their manual counterparts. Partial least squares regression models were based on 30 samples of chitin and chitosan with reference DA values obtained by LC-MS and ¹H NMR, and the models were validated on an independent test set of 16 samples. The overall predictive accuracy of the high-throughput methods was at the same level as the manual methods and the well-established LC-MS and ¹H NMR methods. Therefore, high-throughput FTIR and FT-Raman DA determination methods have great potential to serve as fast and economical substitutes for traditional methods.

Carboxymethylcellulose reinforced starch films and rapid detection of spoiled beverages

The integrity of the packaging of a liquid foodstuff makes it difficult to detect spoilage. Therefore, it is important to develop a sensitive, fast and real-time material for liquid food detection. CMC, as lignocellulose derivatives and starch are widely used in the food industry. In this study, starch films with pH-responsive properties are successfully prepared from full-component starch and corn amylopectin (CA) by adding CMC. The effects of CMC on the mechanical properties, morphology characteristics, physical and chemical structures, stability and pH responsiveness of the starch films are analyzed. The starch/CMC-1.0 g composite films display good electrical conductivity and reduce the resistance of the composite film by two orders of magnitude. The composite films have pH response ability; in the simulation of orange juice spoilage experiment, the CA/CMC composite film has a more sensitive current response and was more suitable for the application to liquid food quality detection. Additionally, the starch/CMC composite films have potential applications for rapid detection and real-time monitoring of the safety of liquid food.

Deep learning-enabled Inference of 3D molecular absorption distribution of biological cells from IR spectra

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.

Characterizing viral samples using machine learning for Raman and absorption spectroscopy

Machine learning methods can be used as robust techniques to provide invaluable information for analyzing biological samples in pharmaceutical industries, such as predicting the concentration of viral particles of interest in biological samples. Here, we utilized both convolutional neural networks (CNNs) and random forests (RFs) to predict the concentration of the samples containing measles, mumps, rubella, and varicella-zoster viruses (ProQuad®) based on Raman and absorption spectroscopy. We prepared Raman and absorption spectra data sets with known concentration values, then used the Raman and absorption signals individually and together to train RFs and CNNs. We demonstrated that both RFs and CNNs can make predictions with R² values as high as 95%. We proposed two different networks to jointly use the Raman and absorption spectra, where our results demonstrated that concatenating the Raman and absorption data increases the prediction accuracy compared to using either Raman or absorption spectrum alone. Additionally, we further verified the advantage of using joint Raman-absorption with principal component analysis. Furthermore, our method can be extended to characterize properties other than concentration, such as the type of viral particles.

Ion-Modified Starch Film Enables Rapid Detection of Spoiled Fruit Juices

Juice, as a liquid foodstuff, is subject to spoilage and damage due to complications during transport and storage. The appearance of intact outer packaging often makes spoilage and damage difficult to detect. Therefore, it of particular importance to develop a fast, real-time material to evaluate liquid foodstuffs. In this paper, starch films with pH response characteristics are successfully prepared by inorganic ion modification by utilizing whole starch and amylopectin as raw materials. The mechanical properties, stability properties, hydrophilic properties and pH electrical signal response indices of the films are analyzed and measured. The films exhibit good electrical conductivity values with 1.0 mL of ion addition (10 mmol/L), causing the composite film to respond sensitively to solutions with varying pH values. In the test of spoiled orange juice, the full-component corn starch (CS) film has more sensitive resistance and current responses, which is more conducive for applications in the quality monitoring of juice. The results indicate that modified starch films can potentially be applied in the real-time monitoring of the safety of liquid foodstuffs.

The Use of Constituent Spectra and Weighting in Extended Multiplicative Signal Correction in Infrared Spectroscopy

Extended multiplicative signal correction (EMSC) is a widely used preprocessing technique in infrared spectroscopy. EMSC is a model-based method favored for its flexibility and versatility. The model can be extended by adding constituent spectra to explicitly model-known analytes or interferents. This paper addresses the use of constituent spectra and demonstrates common pitfalls. It clarifies the difference between analyte and interferent spectra, and the importance of orthogonality between model spectra. Different normalization approaches are discussed, and the importance of weighting in the EMSC is demonstrated. The paper illustrates how constituent analyte spectra can be estimated, and how they can be used to extract additional information from spectral features. It is shown that the EMSC parameters can be used in both regression tasks and segmentation tasks.

Mucoromycota fungi as powerful cell factories for modern biorefinery

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.