Current genetic strategies to investigate gene functions in Trichoderma reesei.

The filamentous fungus Trichoderma reesei (teleomorph Hypocrea jecorina, Ascomycota) is a well-known lignocellulolytic enzymes-producing strain in industry. To increase the fermentation titer of lignocellulolytic enzymes, random mutagenesis and rational genetic engineering in T. reesei were carried out since it was initially found in the Solomon Islands during the Second World War. Especially the continuous exploration of the underlying regulatory network during (hemi)cellulase gene expression in the post-genome era provided various strategies to develop an efficient fungal cell factory for these enzymes' production. Meanwhile, T. reesei emerges competitiveness potential as a filamentous fungal chassis to produce proteins from other species (e.g., human albumin and interferon α-2b, SARS-CoV-2 N antigen) in virtue of the excellent expression and secretion system acquired during the studies about (hemi)cellulase production. However, all the achievements in high yield of (hemi)cellulases are impossible to finish without high-efficiency genetic strategies to analyze the proper functions of those genes involved in (hemi)cellulase gene expression or secretion. Here, we in detail summarize the current strategies employed to investigate gene functions in T. reesei. These strategies are supposed to be beneficial for extending the potential of T. reesei in prospective strain engineering.

Machine learning approach for estimating the human-related VOC emissions in a university classroom

Indoor air quality becomes increasingly important, partly because the COVID-19 pandemic increases the time people spend indoors. Research into the prediction of indoor volatile organic compounds (VOCs) is traditionally confined to building materials and furniture. Relatively little research focuses on estimation of human-related VOCs, which have been shown to contribute significantly to indoor air quality, especially in densely-occupied environments. This study applies a machine learning approach to accurately estimate the human-related VOC emissions in a university classroom. The time-resolved concentrations of two typical human-related (ozone-related) VOCs in the classroom over a five-day period were analyzed, i.e., 6-methyl-5-hepten-2-one (6-MHO), 4-oxopentanal (4-OPA). By comparing the results for 6-MHO concentration predicted via five machine learning approaches including the random forest regression (RFR), adaptive boosting (Adaboost), gradient boosting regression tree (GBRT), extreme gradient boosting (XGboost), and least squares support vector machine (LSSVM), we find that the LSSVM approach achieves the best performance, by using multi-feature parameters (number of occupants, ozone concentration, temperature, relative humidity) as the input. The LSSVM approach is then used to predict the 4-OPA concentration, with mean absolute percentage error (MAPE) less than 5%, indicating high accuracy. By combining the LSSVM with a kernel density estimation (KDE) method, we further establish an interval prediction model, which can provide uncertainty information and viable option for decision-makers. The machine learning approach in this study can easily incorporate the impact of various factors on VOC emission behaviors, making it especially suitable for concentration prediction and exposure assessment in realistic indoor settings.

Machine learning approach for estimating the human-related VOC emissions in a university classroom.

Indoor air quality becomes increasingly important, partly because the COVID-19 pandemic increases the time people spend indoors. Research into the prediction of indoor volatile organic compounds (VOCs) is traditionally confined to building materials and furniture. Relatively little research focuses on estimation of human-related VOCs, which have been shown to contribute significantly to indoor air quality, especially in densely-occupied environments. This study applies a machine learning approach to accurately estimate the human-related VOC emissions in a university classroom. The time-resolved concentrations of two typical human-related (ozone-related) VOCs in the classroom over a five-day period were analyzed, i.e., 6-methyl-5-hepten-2-one (6-MHO), 4-oxopentanal (4-OPA). By comparing the results for 6-MHO concentration predicted via five machine learning approaches including the random forest regression (RFR), adaptive boosting (Adaboost), gradient boosting regression tree (GBRT), extreme gradient boosting (XGboost), and least squares support vector machine (LSSVM), we find that the LSSVM approach achieves the best performance, by using multi-feature parameters (number of occupants, ozone concentration, temperature, relative humidity) as the input. The LSSVM approach is then used to predict the 4-OPA concentration, with mean absolute percentage error (MAPE) less than 5%, indicating high accuracy. By combining the LSSVM with a kernel density estimation (KDE) method, we further establish an interval prediction model, which can provide uncertainty information and viable option for decision-makers. The machine learning approach in this study can easily incorporate the impact of various factors on VOC emission behaviors, making it especially suitable for concentration prediction and exposure assessment in realistic indoor settings.

GP73 is a glucogenic hormone contributing to SARS-CoV-2-induced hyperglycemia.

Severe cases of infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are associated with elevated blood glucose levels and metabolic complications. However, the molecular mechanisms for how SARS-CoV-2 infection alters glycometabolic control are incompletely understood. Here, we connect the circulating protein GP73 with enhanced hepatic gluconeogenesis during SARS-CoV-2 infection. We first demonstrate that GP73 secretion is induced in multiple tissues upon fasting and that GP73 stimulates hepatic gluconeogenesis through the cAMP/PKA signaling pathway. We further show that GP73 secretion is increased in cultured cells infected with SARS-CoV-2, after overexpression of SARS-CoV-2 nucleocapsid and spike proteins and in lungs and livers of mice infected with a mouse-adapted SARS-CoV-2 strain. GP73 blockade with an antibody inhibits excessive glucogenesis stimulated by SARS-CoV-2 in vitro and lowers elevated fasting blood glucose levels in infected mice. In patients with COVID-19, plasma GP73 levels are elevated and positively correlate with blood glucose levels. Our data suggest that GP73 is a glucogenic hormone that likely contributes to SARS-CoV-2-induced abnormalities in systemic glucose metabolism.

The effect of corticosteroid treatment on patients with coronavirus infection: a systematic review and meta-analysis.

OBJECTIVES: An outbreak of novel coronavirus in 2019 threatens the health of people, and there is no proven pharmacological treatment. Although corticosteroids were widely used during outbreaks of severe acute respiratory syndrome and Middle East respiratory syndrome, their efficacy remainedhighly controversial. We aimed to further evaluate the influence of corticosteroids on patients with coronavirus infection. METHODS: We conducted a comprehensive search of literature published in PubMed, Embase, Cochrane library, and China National Knowledge Infrastructure (CNKI) from January 1, 2002 to March 15, 2020. All statistical analyses in this study were performed on stata14.0. RESULTS: A total of 5270 patients from 15 studies were included in this meta-analysis. The result indicated that critical patients were more likely to require corticosteroids therapy (risk ratio [RR] = 1.56, 95% confidence interval [CI] = 1.28-1.90, P<0.001). However, corticosteroid treatment was associated with higher mortality (RR = 2.11, 95%CI = 1.13-3.94, P = 0.019), longer length of stay (weighted mean difference [WMD] = 6.31, 95%CI = 5.26-7.37, P<0.001), a higher rate of bacterial infection (RR = 2.08, 95%CI = 1.54-2.81, P<0.001), and hypokalemia (RR = 2.21, 95%CI = 1.07-4.55, P = 0.032) but not hyperglycemia (RR = 1.37, 95%CI=0.68-2.76, P = 0.376) or hypocalcemia (RR = 1.35, 95%CI = 0.77-2.37, P = 0.302). CONCLUSIONS: Patients with severe conditions are more likely to require corticosteroids. Corticosteroid use is associated with increased mortality in patients with coronavirus pneumonia.