Application of sequential design for enhanced L-asparaginase synthesis from Ganoderma australe GPC191.

With an increasing demand for L-asparaginase in pharmaceutical and food sectors for its cytostatic and acrylamide-reducing qualities, there's a need to discover novel, highly productive enzyme sources with improved pharmacokinetic profiles. Keeping this in mind, the present study aimed at maximizing the potential of Ganoderma australe GPC191 to produce L-asparaginase by fermentation medium optimization using statistical validation. Of the 11 physicochemical parameters evaluated under submerged fermentation conditions through one-factor-at-a-time approach and Plackett-Burman design, only four parameters (inoculum load, L-asparagine, soybean meal, and initial pH) influenced L-asparaginase production, significantly (p < 0.001). The optimal levels and interaction effects of these on the overall production were further evaluated by the central composite rotatable design of response surface methodology. Post-optimization, 27.34 U/mL was predicted as the maximum activity at pH 7 with 5n inoculum load and 15 g/L each of L-asparagine and soybean meal. Experimental validation yielded an activity of 28.52 U/mL, indicating an overall 18.17-fold increase from the unoptimized stage. To our knowledge, this is the first report signifying the L-asparaginase production aptitude of G. australe with sequential statistical validation using agricultural waste, which can serve as a model to enhance its yields, offering a sustainable and cost-effective solution for industrial application.

Analysis and Prediction of Human Mobility in the United States during the Early Stages of the COVID-19 Pandemic using Regularized Linear Models.

Since the United States started grappling with the COVID-19 pandemic, with the highest number of confirmed cases and deaths in the world as of August 2020, most states have enforced travel restrictions resulting in drastic reductions in mobility and travel. However, the long-term implications of this crisis to mobility still remain uncertain. To this end, this study proposes an analytical framework that determines the most significant factors affecting human mobility in the United States during the early days of the pandemic. Particularly, the study uses least absolute shrinkage and selection operator (LASSO) regularization to identify the most significant variables influencing human mobility and uses linear regularization algorithms, including ridge, LASSO, and elastic net modeling techniques, to predict human mobility. State-level data were obtained from various sources from January 1, 2020 to June 13, 2020. The entire data set was divided into a training and a test data set, and the variables selected by LASSO were used to train models by the linear regularization algorithms, using the training data set. Finally, the prediction accuracy of the developed models was examined on the test data. The results indicate that several factors, including the number of new cases, social distancing, stay-at-home orders, domestic travel restrictions, mask-wearing policy, socioeconomic status, unemployment rate, transit mode share, percent of population working from home, and percent of older (60+ years) and African and Hispanic American populations, among others, significantly influence daily trips. Moreover, among all models, ridge regression provides the most superior performance with the least error, whereas both LASSO and elastic net performed better than the ordinary linear model.

Bioprospecting of the agaricomycete Ganoderma australe GPC191 as novel source for L-asparaginase production.

L-Asparaginase is a therapeutically and industrially-competent enzyme, acting predominantly as an anti-neoplastic and anti-cancerous agent. The existing formulations of prokaryotic L-asparaginase are often toxic and contain L-glutaminase and urease residues, thereby increasing the purification steps. Production of L-glutaminase and urease free L-asparaginase is thus desired. In this research, bioprospecting of isolates from the less explored class Agaricomycetes was undertaken for L-asparaginase production. Plate assay (using phenol red and bromothymol blue dyes) was performed followed by estimation of L-asparaginase, L-glutaminase and urease activities by Nesslerization reaction for all the isolates. The isolate displaying the desired enzyme production was subjected to morphological, molecular identification, and phylogenetic analysis with statistical validation using Jukes-Cantor by Neighbour-joining tree of Maximum Likelihood statistical method. Among the isolates, Ganoderma australe GPC191 with significantly high zone index value (5.581 ± 0.045 at 120 h) and enzyme activity (1.57 ± 0.006 U/mL), devoid of L-glutaminase and urease activity was selected. The present study for the first-time reported G. australe as the potential source of L-glutaminase and urease-free L-asparaginase and also is one of the few studies contributing to the literature of G. australe in India. Hence, it can be postulated that it may find its future application in pharmaceutical and food industries.