ABSTRACT
BACKGROUND: The exact mechanism by which fungal strains sense insoluble cellulose is unknown, but research points to the importance of transglycosylation products generated by fungi during cellulose breakdown. Here, we used multi-omics approach to identify the transglycosylation metabolites and determine their function in cellulase induction in a model strain, Talaromyces cellulolyticus MTCC25456. RESULTS: Talaromyces sp. is a novel hypercellulolytic fungal strain. Based on genome scrutiny and biochemical analysis, we predicted the presence of cellulases on the surface of its spores. We performed metabolome analysis to show that these membrane-bound cellulases act on polysaccharides to form a mixture of disaccharides and their transglycosylated derivatives. Inevitably, a high correlation existed between metabolite data and the KEGG enrichment analysis of differentially expressed genes in the carbohydrate metabolic pathway. Analysis of the contribution of the transglycosylation product mixtures to cellulase induction revealed a 57% increase in total cellulase. Further research into the metabolites, using in vitro induction tests and response surface methodology, revealed that Talaromyces sp. produces cell wall-breaking enzymes in response to cellobiose and gentiobiose as a stimulant. Precisely, a 2.5:1 stoichiometric ratio of cellobiose to gentiobiose led to a 2.4-fold increase in cellulase synthesis. The application of the optimized inducers in cre knockout strain significantly increased the enzyme output. CONCLUSION: This is the first study on the objective evaluation and enhancement of cellulase production using optimized inducers. Inducer identification and genetic engineering boosted the cellulase production in the cellulolytic fungus Talaromyces sp.
ABSTRACT
Background and Objectives: Application of artificial intelligence/machine learning (AI/ML) for automation of diabetes management can enhance equitable access to care and ensure delivery of minimum standards of care. Objective of the current study was to create a clinical decision support system using machine learning approach for diabetes drug management in people living with Type 2 diabetes. Methodology: Study was conducted at an Endocrinology clinic and data collected from the electronic clinic management system. 15485 diabetes prescriptions of 4974 patients were accessed. A data subset of 1671 diabetes prescriptions of 940 patients with information on diabetes drugs, demographics (age, gender, body mass index), biochemical parameters (HbA1c, fasting blood glucose, creatinine) and patient clinical parameters (diabetes duration, compliance to diet/exercise/medications, hypoglycemia, contraindication to any drug, summary of patient self monitoring of blood glucose data, diabetes complications) was used in analysis. An input of patient variables were used to predict all diabetes drug classes to be prescribed. Random forest algorithms were used to create decision trees for all diabetes drugs. Results and Conclusion: Accuracy for predicting use of each individual drug class varied from 85% to 99.4%. Multi-drug accuracy, indicating that all drug predictions in a prescription are correct, stands at 72%. Multi drug class accuracy in clinical application may be higher than this result, as in a lot of clinical scenarios, two or more diabetes drugs may be used interchangeably. This report presents a first positive step in developing a robust clinical decision support system to transform access and quality of diabetes care.
