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WRKY transcription factors (TFs) can participate in plant biological stress responses and play important roles. SlWRKY80 was found to be differentially expressed in the Mi-1- and Mi-3-resistant tomato lines by RNA-seq and may serve as a key node for disease resistance regulation. This study used RNAi to determine whether SlWRKY80 silencing could influence the sensitivity of 'M82' (mi-1/mi-1)-susceptible lines to M. incognita. Further overexpression of this gene revealed a significant increase in tomato disease resistance, ranging from highly susceptible to susceptible, combined with the identification of growth (plant height, stem diameter, and leaf area) and physiological (soluble sugars and proteins; root activity) indicators, clarifying the role of SlWRKY80 as a positive regulatory factor in tomato defense against M. incognita. Based on this phenomenon, a preliminary exploration of its metabolic signals revealed that SlWRKY80 stimulates different degrees of signaling, such as salicylic acid (SA), jasmonic acid (JA), and ethylene (ETH), and may synergistically regulate reactive oxygen species (ROS) accumulation and scavenging enzyme activity, hindering the formation of feeding sites and ultimately leading to the reduction of root gall growth. To our knowledge, SlWRKY80 has an extremely high utilization value for improving tomato resistance to root-knot nematodes and breeding. [ABSTRACT FROM AUTHOR]

Background: Imatinib is the most widely used tyrosine kinase inhibitor (TKI) in patients with newly diagnosed chronic-phase chronic myeloid leukemia(CML-CP). However, failure to achieve optimal response after imatinib administration, and subsequent switch to second-generation TKI therapy results in poor efficacy and induces drug resistance. In the present study, we developed and validated a nomogram to predict the efficacy of imatinib in the treatment of patients newly diagnosed with CML-CP in order to help clinicians truly select patients who need 2 nd generation TKI during initial therapy and to supplement the risk score system., Methods: We retrospectively analyzed 156 patients newly diagnosed with CML-CP who met the inclusion criteria and were treated with imatinib at the Second Affiliated Hospital of Xi'an Jiao Tong University from January 2012 to June 2022. The patients were divided into a poor-response cohort (N = 60)and an optimal-response cohort (N = 43) based on whether they achieved major molecular remission (MMR) after 12 months of imatinib treatment. Using univariate and multivariate logistic regression analyses, we developed a chronic myeloid leukemia imatinib-poor treatment (CML-IMP) prognostic model using a nomogram considering characteristics like age, sex, HBG, splenic size, and ALP. The CML-IMP model was internally validated and compared with Sokal, Euro, EUTOS, and ELTS scores., Results: The area under the curve of the receiver operator characteristic curve (AUC)of 0.851 (95% CI 0.778-0.925) indicated satisfactory discriminatory ability of the nomogram. The calibration plot shows good consistency between the predicted and actual observations. The net reclassification index (NRI), continuous NRI value, and the integrated discrimination improvement (IDI) showed that the nomogram exhibited superior predictive performance compared to the Sokal, EUTOS, Euro, and ELTS scores ( P < 0.05). In addition, the clinical decision curve analysis (DCA) showed that the nomogram was useful for clinical decision-making. In predicting treatment response, only Sokal and CML-IMP risk stratification can effectively predict the cumulative acquisition rates of CCyR, MMR, and DMR ( P <0.05)., Conclusion: We constructed a nomogram that can be effectively used to predict the efficacy of imatinib in patients with newly diagnosed CML-CP based on a single center, 10-year retrospective cohort study., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Li, Zhang, Wang, He, Zhang, Cao, Chen, Liu, Zhang, Wang, Zhao, Yang, Meng, Chen, Zhang, Wang, Wang and Ma.)