Transcriptional Analysis Revealing the Improvement of ε-Poly-L-lysine Production from Intracellular ROS Elevation after Botrytis cinerea Induction.

Gray mold, caused by Botrytis cinerea, poses significant threats to various crops, while it can be remarkably inhibited by ε-poly-L-lysine (ε-PL). A previous study found that B. cinerea extracts could stimulate the ε-PL biosynthesis of Streptomyces albulus, while it is unclear whether the impact of the B. cinerea signal on ε-PL biosynthesis is direct or indirect. This study evaluated the role of elevated reactive oxygen species (ROS) in efficient ε-PL biosynthesis after B. cinerea induction, and its underlying mechanism was disclosed with a transcriptome analysis. The microbial call from B. cinerea could arouse ROS elevation in cells, which fall in a proper level that positively influenced the ε-PL biosynthesis. A systematic transcriptional analysis revealed that this proper dose of intracellular ROS could induce a global transcriptional promotion on key pathways in ε-PL biosynthesis, including the embden-meyerhof-parnas pathway, the pentose phosphate pathway, the tricarboxylic acid cycle, the diaminopimelic acid pathway, ε-PL accumulation, cell respiration, and energy synthesis, in which sigma factor HrdD and the transcriptional regulators of TcrA, TetR, FurA, and MerR might be involved. In addition, the intracellular ROS elevation also resulted in a global modification of secondary metabolite biosynthesis, highlighting the secondary signaling role of intracellular ROS in ε-PL production. This work disclosed the transcriptional mechanism of efficient ε-PL production that resulted from an intracellular ROS elevation after B. cinerea elicitors' induction, which was of great significance in industrial ε-PL production as well as the biocontrol of gray mold disease.

Macrophage mannose receptor 1 and S100A9 were identified as serum diagnostic biomarkers for colorectal cancer through a label-free quantitative proteomic analysis.

BACKGROUND: Simple blood tests that could be used for early detection are crucial for the ultimate control and prevention of colorectal cancer (CRC). In this study, we performed a serum proteomic analysis of CRC and health volunteers to identify the novel biomarkers involved in CRC. METHOD: A shotgun proteomic method was applied to identify serum proteins in the serum samples of three CRC and three health volunteers using a combination of high-performance liquid chromatography and mass spectrometry. Label-free protein profiling was conducted to quantify the proteins and compare the profiles of the CRC and health volunteers. Two differentially expressed proteins were further validated by western blot analysis. Quantity analysis was performed through enzyme linked immunosorbent assay (ELISA) in serum from 96 healthy and 118 CRC volunteers. RESULTS: Among of the 373 identified proteins, 69 were linked to CRC (33 upregulated and 36 downregulated). The Gene Ontology and DAVID databases were used to identify the location and function of the different proteins. Among the 69 proteins linked to CRC, two proteins, namely, macrophage mannose receptor 1 (MRC1) and S100A9, were verified to be upregulated in CRC by western blot analysis and could be used to identify CRC from healthy volunteers with high accuracy through ELISA analysis. CONCLUSION: MRC1 and S100A9 may contribute to the determination of the mechanisms and screening involved in CRC.

Label-free quantitative mass spectrometry reveals a panel of differentially expressed proteins in colorectal cancer.

To identify potential biomarkers involved in CRC, a shotgun proteomic method was applied to identify soluble proteins in three CRCs and matched normal mucosal tissues using high-performance liquid chromatography and mass spectrometry. Label-free protein profiling of three CRCs and matched normal mucosal tissues were then conducted to quantify and compare proteins. Results showed that 67 of the 784 identified proteins were linked to CRC (28 upregulated and 39 downregulated). Gene Ontology and DAVID databases were searched to identify the location and function of differential proteins that were related to the biological processes of binding, cell structure, signal transduction, cell adhesion, and so on. Among the differentially expressed proteins, tropomyosin-3 (TPM3), endoplasmic reticulum resident protein 29 (ERp29), 18 kDa cationic antimicrobial protein (CAMP), and heat shock 70 kDa protein 8 (HSPA8) were verified to be upregulated in CRC tissue and seven cell lines through western blot analysis. Furthermore, the upregulation of TPM3, ERp29, CAMP, and HSPA8 was validated in 69 CRCs byimmunohistochemistry (IHC) analysis. Combination of TPM3, ERp29, CAMP, and HSPA8 can identify CRC from matched normal mucosal achieving an accuracy of 73.2% using IHC score. These results suggest that TPM3, ERp29, CAMP, and HSPA8 are great potential IHC diagnostic biomarkers for CRC.

Using machine learning algorithms to guide rehabilitation planning for home care clients.

BACKGROUND: Targeting older clients for rehabilitation is a clinical challenge and a research priority. We investigate the potential of machine learning algorithms - Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) - to guide rehabilitation planning for home care clients. METHODS: This study is a secondary analysis of data on 24,724 longer-term clients from eight home care programs in Ontario. Data were collected with the RAI-HC assessment system, in which the Activities of Daily Living Clinical Assessment Protocol (ADLCAP) is used to identify clients with rehabilitation potential. For study purposes, a client is defined as having rehabilitation potential if there was: i) improvement in ADL functioning, or ii) discharge home. SVM and KNN results are compared with those obtained using the ADLCAP. For comparison, the machine learning algorithms use the same functional and health status indicators as the ADLCAP. RESULTS: The KNN and SVM algorithms achieved similar substantially improved performance over the ADLCAP, although false positive and false negative rates were still fairly high (FP > .18, FN > .34 versus FP > .29, FN. > .58 for ADLCAP). Results are used to suggest potential revisions to the ADLCAP. CONCLUSION: Machine learning algorithms achieved superior predictions than the current protocol. Machine learning results are less readily interpretable, but can also be used to guide development of improved clinical protocols.