Interactions between fungal growth, substrate utilization, and enzyme production during solid substrate cultivation of Phanerochaete chrysosporium on cotton stalks

Artificial Intelligence Aided Lipase Production and Engineering for Enzymatic Performance Improvement

With the development of artificial intelligence (AI), tailoring methods for enzyme engineering have been widely expanded. Additional protocols based on optimized network models have been used to predict and optimize lipase production as well as properties, namely, catalytic activity, stability, and substrate specificity. Here, different network models and algorithms for the prediction and reforming of lipase, focusing on its modification methods and cases based on AI, are reviewed in terms of both their advantages and disadvantages. Different neural networks coupled with various algorithms are usually applied to predict the maximum yield of lipase by optimizing the external cultivations for lipase production, while one part is used to predict the molecule variations affecting the properties of lipase. However, few studies have directly utilized AI to engineer lipase by affecting the structure of the enzyme, and a set of research gaps needs to be explored. Additionally, future perspectives of AI application in enzymes, including lipase engineering, are deduced to help the redesign of enzymes and the reform of new functional biocatalysts. This review provides a new horizon for developing effective and innovative AI tools for lipase production and engineering and facilitating lipase applications in the food industry and biomass conversion.

Modeling the microbial pretreatment of camelina straw and switchgrass by Trametes versicolor and Phanerochaete chrysosporium via solid-state fermentation process: A growth kinetic sub-model in the context of biomass-based biorefineries

Advancing microbial pretreatment of lignocellulose has the potential not only to reduce the carbon footprint and environmental impacts of the pretreatment processes from cradle-to-grave, but also increase biomass valorization, support agricultural growers, and boost the bioeconomy. Mathematical modeling of microbial pretreatment of lignocellulose provides insights into the metabolic activities of the microorganisms as responses to substrate and environment and provides baseline targets for the design, development, and optimization of solid-state-fermentation (SSF) bioreactors, including substrate concentrations, heat and mass transfer. In this study, the growth of Trametes versicolor 52J (TV52J), Trametes versicolor m4D (TVm4D), and Phanerochaete chrysosporium (PC) on camelina straw (CS) and switchgrass (SG) during an SSF process was examined. While TV52J illustrated the highest specific growth rate and maximum cell concentration, a mutant strain deficient in cellulose catabolism, TVm4D, performed best in terms of holocellulose preservation and delignification. The hybrid logistic-Monod equation along with holocellulose consumption and delignification models described well the growth kinetics. The oxygen uptake rate and carbon dioxide production rate were directly correlated to the fungal biomass concentration; however, a more sophisticated non-linear relationship might explain those correlations better than a linear model. This study provides an informative baseline for developing SSF systems to integrate fungal pretreatment into a large-scale, on-farm, wet-storage process for the utilization of agricultural residues as feedstocks for biofuel production.

Machine learning aided experimental approach for evaluating the growth kinetics of Candida antarctica for lipase production

A hybrid machine learning (ML) aided experimental approach was proposed in this study to evaluate the growth kinetics of Candida antarctica for lipase production. Different ML models were trained and optimized to predict the growth curves at various substrate concentrations. Results on comparison demonstrate the superior performance of the Gradient boosting regression (GBR) model in growth curves prediction. GBR-based growth kinetics was found to be matching well with the results of the conventional experimental approach while significantly reducing the experimental effort, time, and resources by ∼ 50%. Further, the activity and enzyme kinetics of lipase produced in this study was investigated on hydrolysis of p-nitrophenyl butyrate resulting in a maximum lipase activity of 24.07 U at 44 h. The robustness and significance of developed kinetic models were ensured through detailed statistical analysis. The application of the proposed hybrid approach can be extended to any other microbial process.

Optimization of White-Rot Fungi Mycelial Culture Components for Bioremediation of Pharmaceutical-Derived Pollutants

White-rot fungi can degrade a wide spectrum of environmental pollutants, including pharmaceuticals, which are not efficiently removed from wastewater by conventional methods, e.g., the activated sludge method. However, the treatment of wastewater with the use of fungal cultures (mycoremediation) also has significant limitations: among others, the need to use appropriate, often-expensive culture media. We aimed to screen 18 media ingredients, including seven agrifood byproducts for Armillaria mellea, Phanerochaete chrysosporium and Pleurotus ostreatus in submerged cultures to select the low-cost medium optimal for biomass production and laccase activity. We screened nine mathematic models to describe the relation of fungal growth and the amount of the selected byproduct in media. Finally, we tested the ability of the strain with the highest mycelial growth and enzyme-producing ability in the selected medium to degrade eight drug contaminants. Three media variants composed of byproducts provided both efficient growth and laccase production: corn steep liquor + poplar, dried distillers grains with solubles + poplar and corn steep liquor 50%. Among the investigated growth models, the Han–Levenspiel equation described well the specific growth rate in function of the nominal substrate concentration in one-component media. Pleurotus ostreatus, the fungus with the highest ligninolytic enzyme activity, cultured in medium composed of corn steep liquor, removed six of eight drug contaminants with a removal degree of 20–90% in 48 h. The obtained data on the optimal culture media consisting of insoluble components provide initial data for upscaling the process and designing an appropriate type of bioreactor for the process of removing drug contaminants from water.

Protein improvement of banana peel through sequential solid state fermentation using mixed-culture of Phanerochaete chrysosporium and Candida utilis

Banana peel (BP) is a major waste produced by fruit processing industries. Pre-treatment of BP at different temperatures led to 40% reduction in saponin at 100 °C (from 9.5 to 5.7 mg/g). Sequential mixed culture of Phanerochaete chrysosporium (P. chrysosporium) and Candida utilis (C. utilis) gave highest protein enrichment (88.93 mg/g). There is 26% increase in protein synthesis (from 88.93 to 111.78 mg/g) after media screening. Inclusion of KH₂PO₄, FeSO₄·7H₂O, wheat flour and sucrose in the media contributed positively to protein synthesis, while elevated concentration of urea, peptone, K₂HPO₄, KCl, NH₄H₂PO_4, and MgSO₄.7H₂O are required to reach optimum protein synthesis. Total soluble sugar (TSS), total reducing sugar (TRS) and total carbohydrate (CHO) consumption varied with respect to protein synthesis in all experimental runs. Optimum protein synthesis required 6 days and inclusion of 5% sucrose, 0.6% NH₄H₂PO₄, 0.4% KCl, and 0.5% MgSO₄·7H₂O as concentration media constituents to reach 140.95 mg/g protein synthesis equivalent to 300% increase over the raw banana peel protein content (35.0 mg/g).

Growth, substrate consumption, and product formation kinetics of Phanerochaete chrysosporium and Schizophyllum commune mixed culture under solid-state fermentation of fruit peels

Kinetic analysis of solid-state fermentation (SSF) of fruit peels with Phanerochaete chrysosporium and Schizophyllum commune mixed culture was studied in flask and 7 kg capacity reactor. Modified Monod kinetic model suggested by Haldane sufficiently described microbial growth with co-efficient of determination (R ²) reaching 0.908 at increased substrate concentration than the classical Monod model (R ² = 0.932). Leudeking-Piret model adequately described product synthesis in non-growth-dependent manner (R ² = 0.989), while substrate consumption by P. chrysosporium and S. commune fungal mixed culture was growth-dependent (R ² = 0.938). Hanes-Woolf model sufficiently represented α-amylase and cellulase enzymes synthesis (R ² = 0.911 and 0.988); α-amylase had enzyme maximum velocity (V _max) of 25.19 IU/gds/day and rate constant (K _m) of 11.55 IU/gds/day, while cellulase enzyme had V _max of 3.05 IU/gds/day and K _m of 57.47 IU/gds/day. Product yield in the reactor increased to 32.65 mg/g/day compared with 28.15 mg/g/day in shake flask. 2.5 cm media thickness was adequate for product formation within a 6 day SSF in the tray reactor.

Genome Editing in Cotton with the CRISPR/Cas9 System

Genome editing is an important tool for gene functional studies as well as crop improvement. The recent development of the CRISPR/Cas9 system using single guide RNA molecules (sgRNAs) to direct precise double strand breaks in the genome has the potential to revolutionize agriculture. Unfortunately, not all sgRNAs are equally efficient and it is difficult to predict their efficiency by bioinformatics. In crops such as cotton (Gossypium hirsutum L.), with labor-intensive and lengthy transformation procedures, it is essential to minimize the risk of using an ineffective sgRNA that could result in the production of transgenic plants without the desired CRISPR-induced mutations. In this study, we have developed a fast and efficient method to validate the functionality of sgRNAs in cotton using a transient expression system. We have used this method to validate target sites for three different genes GhPDS, GhCLA1, and GhEF1 and analyzed the nature of the CRISPR/Cas9-induced mutations. In our experiments, the most frequent type of mutations observed in cotton cotyledons were deletions (∼64%). We prove that the CRISPR/Cas9 system can effectively produce mutations in homeologous cotton genes, an important requisite in this allotetraploid crop. We also show that multiple gene targeting can be achieved in cotton with the simultaneous expression of several sgRNAs and have generated mutations in GhPDS and GhEF1 at two target sites. Additionally, we have used the CRISPR/Cas9 system to produce targeted gene fragment deletions in the GhPDS locus. Finally, we obtained transgenic cotton plants containing CRISPR/Cas9-induced gene editing mutations in the GhCLA1 gene. The mutation efficiency was very high, with 80.6% of the transgenic lines containing mutations in the GhCLA1 target site resulting in an intense albino phenotype due to interference with chloroplast biogenesis.

Genome Editing in Cotton with the CRISPR/Cas9 System

Genome editing is an important tool for gene functional studies as well as crop improvement. The recent development of the CRISPR/Cas9 system using single guide RNA molecules (sgRNAs) to direct precise double strand breaks in the genome has the potential to revolutionize agriculture. Unfortunately, not all sgRNAs are equally efficient and it is difficult to predict their efficiency by bioinformatics. In crops such as cotton (Gossypium hirsutum L.), with labor-intensive and lengthy transformation procedures, it is essential to minimize the risk of using an ineffective sgRNA that could result in the production of transgenic plants without the desired CRISPR-induced mutations. In this study, we have developed a fast and efficient method to validate the functionality of sgRNAs in cotton using a transient expression system. We have used this method to validate target sites for three different genes GhPDS, GhCLA1, and GhEF1 and analyzed the nature of the CRISPR/Cas9-induced mutations. In our experiments, the most frequent type of mutations observed in cotton cotyledons were deletions (∼64%). We prove that the CRISPR/Cas9 system can effectively produce mutations in homeologous cotton genes, an important requisite in this allotetraploid crop. We also show that multiple gene targeting can be achieved in cotton with the simultaneous expression of several sgRNAs and have generated mutations in GhPDS and GhEF1 at two target sites. Additionally, we have used the CRISPR/Cas9 system to produce targeted gene fragment deletions in the GhPDS locus. Finally, we obtained transgenic cotton plants containing CRISPR/Cas9-induced gene editing mutations in the GhCLA1 gene. The mutation efficiency was very high, with 80.6% of the transgenic lines containing mutations in the GhCLA1 target site resulting in an intense albino phenotype due to interference with chloroplast biogenesis.

Modelling of different enzyme productions by solid-state fermentation on several agro-industrial residues

A simple kinetic model, with only three fitting parameters, for several enzyme productions in Petri dishes by solid-state fermentation is proposed in this paper, which may be a valuable tool for simulation of this type of processes. Basically, the model is able to predict temporal fungal enzyme production by solid-state fermentation on complex substrates, maximum enzyme activity expected and time at which these maxima are reached. In this work, several fermentations in solid state were performed in Petri dishes, using four filamentous fungi grown on different agro-industrial residues, measuring xylanase, exo-polygalacturonase, cellulose and laccase activities over time. Regression coefficients after fitting experimental data to the proposed model turned out to be quite high in all cases. In fact, these results are very interesting considering, on the one hand, the simplicity of the model and, on the other hand, that enzyme activities correspond to different enzymes, produced by different fungi on different substrates.