Mass spectrometry-based proteomics for abiotic stress studies

Plant synthetic biology innovations for biofuels and bioproducts

Plant-based biosynthesis of fuels, chemicals, and materials promotes environmental sustainability, which includes decreases in greenhouse gas emissions, water pollution, and loss of biodiversity. Advances in plant synthetic biology (synbio) should improve precision and efficacy of genetic engineering for sustainability. Applicable synbio innovations include genome editing, gene circuit design, synthetic promoter development, gene stacking technologies, and the design of environmental sensors. Moreover, recent advancements in developing spatially resolved and single-cell omics contribute to the discovery and characterization of cell-type-specific mechanisms and spatiotemporal gene regulations in distinct plant tissues for the expression of cell- and tissue-specific genes, resulting in improved bioproduction. This review highlights recent plant synbio progress and new single-cell molecular profiling towards sustainable biofuel and biomaterial production.

Advances in mass spectrometry-based phosphoproteomics for elucidating abscisic acid signaling and plant responses to abiotic stress

Abiotic stresses have significant impacts on crop yield and quality. Even though significant efforts during the past decade have been devoted to uncovering the core signaling pathways associated with the phytohormone abscisic acid (ABA) and abiotic stress in plants, abiotic stress signaling mechanisms in most crops remain largely unclear. The core components of the ABA signaling pathway, including early events in the osmotic stress-induced phosphorylation network, have recently been elucidated in Arabidopsis with the aid of phosphoproteomics technologies. We now know that SNF1-related kinases 2 (SnRK2s) are not only inhibited by the clade A type 2C protein phosphatases (PP2Cs) through dephosphorylation, but also phosphorylated and activated by upstream mitogen-activated protein kinase kinase kinases (MAP3Ks). Through describing the course of studies to elucidate abiotic stress and ABA signaling, we will discuss how we can take advantage of the latest innovations in mass-spectrometry-based phosphoproteomics and structural proteomics to boost our investigation of plant regulation and responses to ABA and abiotic stress.

Machine learning algorithms based on proteomic data mining accurately predicting the recurrence of hepatitis B-related hepatocellular carcinoma

BACKGROUND AND AIM

Over 10% of hepatocellular carcinoma (HCC) cases recur each year, even after surgical resection. Currently, there is a lack of knowledge about the causes of recurrence and the effective prevention. Prediction of HCC recurrence requires diagnostic markers endowed with high sensitivity and specificity. This study aims to identify new key proteins for HCC recurrence and to build machine learning algorithms for predicting HCC recurrence.

METHODS

The proteomics data for analysis in this study were obtained from the Clinical Proteomics Tumor Analysis Consortium (CPTAC) database. We analyzed different proteins based on cases with or without recurrence of HCC. Survival analysis, Cox regression analysis, and area under the ROC curves (AUROC > 0.7) were used to screen for more significant differential proteins. Predictive models for HCC recurrence were developed using four machine learning algorithms.

RESULTS

A total of 690 differentially expressed proteins between 50 relapsed and 77 non-relapsed hepatitis B-related HCC patients were identified. Seven of these proteins had an AUROC > 0.7 for 5-year survival in HCC, including BAHCC1, ESF1, RAP1GAP, RUFY1, SCAMP3, STK3, and TMEM230. Among the machine learning algorithms, the random forest algorithm showed the highest AUROC values (AUROC: 0.991, 95% CI 0.962-0.999) for identifying HCC recurrence, followed by the support vector machine (AUROC: 0.893, 95% Cl 0.824-0.956), the logistic regression (AUROC: 0.774, 95% Cl 0.672-0.868), and the multi-layer perceptron algorithm (AUROC: 0.571, 95% Cl 0.459-0.682).

CONCLUSIONS

Our study identifies seven novel proteins for predicting HCC recurrence and the random forest algorithm as the most suitable predictive model for HCC recurrence.