Evaluation of drought-tolerant varieties based on root system architecture in cotton (Gossypium hirsutum L.).

BACKGROUND: Root system architecture (RSA) exhibits significant genetic variability and is closely associated with drought tolerance. However, the evaluation of drought-tolerant cotton cultivars based on RSA in the field conditions is still underexplored. RESULTS: So, this study conducted a comprehensive analysis of drought tolerance based on physiological and morphological traits (i.e., aboveground and RSA, and yield) within a rain-out shelter, with two water treatments: well-watered (75 ± 5% soil relative water content) and drought stress (50 ± 5% soil relative water content). The results showed that principal component analysis identified six principal components, including highlighting the importance of root traits and canopy parameters in influencing drought tolerance. Moreover, the systematic cluster analysis was used to classify 80 cultivars into 5 categories, including drought-tolerant cultivars, relatively drought-tolerant cultivars, intermediate cultivars, relatively drought-sensitive cultivars, and drought-sensitive cultivars. Further validation of the drought tolerance index showed that the yield drought tolerance index and biomass drought tolerance index of the drought-tolerant cultivars were 8.97 and 5.05 times higher than those of the drought-sensitive cultivars, respectively. CONCLUSIONS: The RSA of drought-tolerant cultivars was characterised by a significant increase in average length-all lateral roots, a significant decrease in average lateral root emergence angle and a moderate root/shoot ratio. In contrast, the drought-sensitive cultivars showed a significant decrease in average length-all lateral roots and a significant increase in both average lateral root emergence angle and root/shoot ratio. It is therefore more comprehensive and accurate to assess field crop drought tolerance by considering root performance.

Bufalin stimulates antitumor immune response by driving tumor-infiltrating macrophage toward M1 phenotype in hepatocellular carcinoma.

BACKGROUND: Immunotherapy for hepatocellular carcinoma (HCC) exhibits limited clinical efficacy due to immunosuppressive tumor microenvironment (TME). Tumor-infiltrating macrophages (TIMs) account for the major component in the TME, and the dominance of M2 phenotype over M1 phenotype in the TIMs plays the pivotal role in sustaining the immunosuppressive character. We thus investigate the effect of bufalin on promoting TIMs polarization toward M1 phenotype to improve HCC immunotherapy. METHODS: The impact of bufalin on evoking antitumor immune response was evaluated in the immunocompetent mouse HCC model. The expression profiling of macrophage-associated genes, surface markers and cytokines on bufalin treatment in vitro and in vivo were detected using flow cytometry, immunofluorescence, western blot analysis, ELISA and RT-qPCR. Cell signaling involved in M1 macrophage polarization was identified via the analysis of gene sequencing, and bufalin-governed target was explored by immunoprecipitation, western blot analysis and gain-and-loss of antitumor immune response. The combination of bufalin and antiprogrammed cell death protein 1 (anti-PD-1) antibody was also assessed in orthotopic HCC mouse model. RESULTS: In this study, we showed that bufalin can function as an antitumor immune modulator that governs the polarization of TIMs from tumor-promoting M2 toward tumor-inhibitory M1, which induces HCC suppression through the activation of effector T cell immune response. Mechanistically, bufalin inhibits overexpression of p50 nuclear factor kappa B (NF-κB) factor, leading to the predominance of p65-p50 heterodimers over p50 homodimers in the nuclei. The accumulation of p65-p50 heterodimers activates NF-κB signaling, which is responsible for the production of immunostimulatory cytokines, thus resulting in the activation of antitumor T cell immune response. Moreover, bufalin enhances the antitumor activity of anti-PD-1 antibody, and the combination exerts synergistic effect on HCC suppression. CONCLUSIONS: These data expound a novel antitumor mechanism of bufalin, and facilitate exploitation of a new potential macrophage-based HCC immunotherapeutic modality.

Bioinformatic Analysis to Identify a Multi-mRNA Signature for the Prediction of Metastasis in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) with metastasis indicates worse prognosis for patients. However, the current methods are insufficient to accurately predict HCC metastasis at early stage. Based on the expression profiles of three Gene Expression Omnibus datasets, the differentially expressed genes associated with HCC metastasis were screened by online analytical tool GEO2R and weighted gene co-expression network analysis. Second, a risk score model including 27-mRNA was established by univariate Cox regression analyses, time-dependent ROC curves and least absolute shrinkage and selection operator Cox regression analysis. Then, we validated the model in cohort The Cancer Genome Atlas-liver hepatocellular carcinoma and analyzed the functions and key signaling pathways of the genes associated with the risk score model. According to the risk score model, patients were divided into two subgroups (high risk and low risk groups). The metastasis rate between two subgroups was significantly different in training cohort (p < 0.0001, hazard ratio [HR]: 10.3, confidence interval [95% CI]: 6.827-15.55) and external validation cohort (p = 0.0008, HR: 1.768, 95% CI: 1.267-2.467). Multivariable analysis showed that the risk score model was superior to and independent of other clinical factors (such as tumor stage, tumor size, and other parameters) in predicting early HCC metastasis. Moreover, the risk score model could predict the overall survival of patients with HCC. Finally, most of 27-mRNA were enriched in exosome and membrane bounded organelle, and these were involved in transportation and metabolic biological process. Protein-protein interaction network analysis showed most of these genes might be key genes affecting the progression of HCC. In addition, 3 genes of 27-mRNA were also differentially expressed in peripheral blood mononuclear cell. In conclusion, by using two combined methods and a broader of HCC datasets, our study provided reliable and superior predictive model for HCC metastases, which will facilitate individual medical management for these high metastatic risk HCC patients.

Validation of a criteria-specific long-term survival prediction model for hepatocellular carcinoma patients after liver transplantation.

The aim of this study was to validate a criteria-specific long-term survival prediction model (MHCAT) in a large cohort of hepatocellular carcinoma (HCC) patients after liver transplantation (LT) in China. Independent risk factors in MHCAT were retrospectively analysed for HCC patients recorded in the China Liver Transplant Registry. Survival predictions for each patient were calculated using MHCAT scores and the Metroticket formula separately, and the prediction efficacy of MHCAT and Metroticket was compared using the area under ROC curve (c-statistic). A total of 1371 LTs for HCC were analysed in the study, with a median follow-up of 22.2 months (IQR 6.1-72.4 months). The proportions meeting the Milan, UCSF, Fudan and Hangzhou criteria were 34.4%, 39.7%, 44.2% and 51.9%, respectively. The c-statistics for MHCAT predictions of 3- and 5-year survival rates of HCC recipients were 0.712-0.727 and 0.726-0.741, respectively. Among these patients, 1298 LTs for HCC were ultimately selected for the comparison analysis for prediction efficacy. The c-statistic of MHCAT for predictions of 3-year survival with reference to the Milan, UCSF and Fudan criteria was significantly increased compared with that for Metroticket (p < 0.05). In conclusion, MHCAT can effectively predict long-term survival for HCC recipients after LT.