Genome Assembly and Analyses of the Macrofungus Macrocybe gigantea

Enhancing Protease and Amylase Activities in Bacillus licheniformis XS-4 for Traditional Soy Sauce Fermentation Using ARTP Mutagenesis

This study was conducted to increase the enzymatic activity of Bacillus licheniformis XS-4, which was isolated from the traditional fermented mash of Xianshi soy sauce. The mutation was induced by atmospheric and room-temperature plasma (ARTP), and a mutant strain, mut80, was obtained. mut80 exhibited significant increases in protease and amylase activity by 90.54% and 143.10%, respectively, and the enhanced enzymatic activities were stably maintained after 20 consecutive incubations. Re-sequencing analysis of mut80 revealed that the mutation sites were located in 1518447(AT-T) and 4253106(G-A) in its genome, which was involved in the metabolic pathways of amino acids. The expression of the protease synthetic gene (aprX) increased 1.54 times, while that of the amylase gene (amyA) increased 11.26 times, as confirmed via RT-qPCR. Using ARTP mutagenesis, the present study proposes a highly efficient microbial resource with enhanced protease and amylase activity provided by B. licheniformis, which can potentially be used to improve the efficiency of traditional soy sauce fermentation.

Construction of a prognostic immune signature for lower grade glioma that can be recognized by MRI radiomics features to predict survival in LGG patients

BACKGROUND

This study aimed to identify a series of prognostically relevant immune features by immunophenoscore. Immune features were explored using MRI radiomics features to prediction the overall survival (OS) of lower-grade glioma (LGG) patients and their response to immune checkpoints.

METHOD

LGG data were retrieved from TCGA and categorized into training and internal validation datasets. Patients attending the First Affiliated Hospital of Harbin Medical University were included in an external validation cohort. An immunophenoscore-based signature was built to predict malignant potential and response to immune checkpoint inhibitors in LGG patients. In addition, a deep learning neural network prediction model was built for validation of the immunophenoscore-based signature.

RESULTS

Immunophenotype-associated mRNA signatures (IMriskScore) for outcome prediction and ICB therapeutic effects in LGG patients were constructed. Deep learning of neural networks based on radiomics showed that MRI radiomic features determined IMriskScore. Enrichment analysis and ssGSEA correlation analysis were performed. Mutations in CIC significantly improved the prognosis of patients in the high IMriskScore group. Therefore, CIC is a potential therapeutic target for patients in the high IMriskScore group. Moreover, IMriskScore is an independent risk factor that can be used clinically to predict LGG patient outcomes.

CONCLUSIONS

The IMriskScore model consisting of a sets of biomarkers, can independently predict the prognosis of LGG patients and provides a basis for the development of personalized immunotherapy strategies. In addition, IMriskScore features were predicted by MRI radiomics using a deep learning approach using neural networks. Therefore, they can be used for the prognosis of LGG patients.