Ethylene is the key phytohormone to enhance arsenic resistance in Arabidopsis thaliana.

The toxic metalloid arsenic is prevalent in the environment and poses a threat to nearly all organisms. However, the mechanism by which phytohormones modulate arsenic resistance is not well-understood. Therefore, we analyzed multiple phytohormones based on the results of transcriptome sequencing, content changes, and related mutant growth under arsenic stress. We found that ethylene was the key phytohormone in Arabidopsis thaliana response to arsenic. Further investigation showed the ethylene-overproducing mutant eto1-1 generated less malondialdehyde (MDA), H2O2, and O2•- under arsenic stress compared to wild-type, while the ethylene-insensitive mutant ein2-5 displayed opposite patterns. Compared to wild-type, eto1-1 accumulated a smaller amount of arsenic and a larger amount of non-protein thiols. Additionally, the immediate ethylene precursor, 1-aminocyclopropane-1-carboxylic acid (ACC), enhanced resistance to arsenic in wide-type, but not in mutants with impaired detoxification capability (i.e., cad1-3, pad2-1, abcc1abcc2), which confirmed that ethylene regulated arsenic detoxification by enhancing arsenic chelation. ACC also upregulated the expression of gene(s) involved in arsenic detoxification, among which ABCC2 was directly transcriptionally activated by the ethylene master transcription factor ethylene-insensitive 3 (EIN3). Overall, our study shows that ethylene is the key phytohormone to enhance arsenic resistance by reducing arsenic accumulation and promoting arsenic detoxification at both physiological and molecular levels.

Co-applied biochar and PGPB promote maize growth and reduce CO2 emission by modifying microbial communities in coal mining degraded soils.

Coal mining is one of the human activities that has the greatest impact on the global carbon (C) cycle and biodiversity. Biochar and plant growth-promoting bacteria (PGPB) have been both used to improve coal mining degraded soils; however, it is uncertain whether the effects of biochar application on soil respiration and microbial communities are influenced by the presence or absence of PGPB and soil nitrogen (N) level in coal mining degraded soils. A pot experiment was carried out to examine whether the effects of biochar addition (0, 1, 2 and 4% of soil mass) on soil properties, soil respiration, maize growth, and microbial communities were altered by the presence or absence of PGPB (i.e. Sphingobium yanoikuyae BJ1) (0, 200 mL suspension (2 × 106 colony forming unit (CFU) mL-1)) and two soil N levels (N0 and N1 at 0 and 0.2 g kg-1 urea- N, respectively). The results showed the presence of BJ1 enhanced the maize biomass relative to the absence of BJ1, particularly in N1 soils, which was related to the discovery of Lysobacter and Nocardioides that favor plant growth in N1 soils. This indicates a conversion in soil microbial communities to beneficial ones. The application of biochar at a rate of 1% decreased the cumulative CO2 regardless of the presence or absence of BJ1; BJ1 increased the ß-glucosidase (BG) activities, and BG activities were also positively correlated with RB41 strain with high C turnover in N1 soils, which indicates that the presence of BJ1 improves the C utilization rates of RB41, decreasing soil C mineralization. Our results highlight that biochar addition provided environmental benefits in degraded coal mining soils, and the direction and magnitude of these effects are highly dependent on the presence of PGPB and the soil N level.

A prognostic metabolism-related gene signature associated with the tumor immune microenvironment in neuroblastoma.

Neuroblastoma (NB) is the most prevalent malignant solid tumor in children. Tumor metabolism, including lipid, amino acid, and glucose metabolism, is intricately linked to the genesis and progression of tumors. This study aimed to establish a prognostic gene signature for NB patients, based on metabolism-related genes, and to investigate a treatment approach that could enhance the survival rate of high-risk NB patients. From the NB dataset GSE49710, we identified metabolism-related gene markers utilizing the "limma" R package and univariate Cox analysis combined with least absolute shrinkage and selection operator (LASSO) regression analysis. We explored the correlation between these gene markers and the overall survival of NB patients. Gene set enrichment analysis (GSEA) and single-sample GSEA algorithms were used to assess the differences in metabolism and immune status. Furthermore, we examined the association between metabolic subgroups and drug responsiveness. Concurrently, data downloaded from TARGET and MTAB were used for external verification. Using multicolor immunofluorescence and immunohistochemistry, we investigated the relationship between the lipid metabolism-related gene ELOVL6 with both the International Neuroblastoma Staging System classification of NB and survival rate. Finally, we explored the effect of high ELOVL6 expression on the immune microenvironment in NB using flow cytometry. We identified an eight-gene signature comprising metabolism-related genes in NB: ELOVL6, OSBPL9, RPL27A, HSD17B3, ACHE, AKR1C1, PIK3R1, and EPHX2. This panel effectively predicted disease-free survival, and was validated using an internal dataset from GSE49710 and two external datasets from the TARGET and MTAB databases. Moreover, our findings confirmed that ELOVL6 fosters an immunosuppressive microenvironment and contributes to the malignant progression in NB. The eight-gene signature is significant in predicting the prognosis of NB, effectively classifying patients into high- and low-risk groups. This classification may guide the development of innovative treatment strategies for these patients. Notably, the signature gene ELOVL6 markedly encourages an immunosuppressive microenvironment and malignant progression in NB.

Dynamic changes and correlation analysis of microorganisms and flavonoids/ amino acids during white tea storage.

White tea stored for various times have different flavors. However, the mechanism of flavor conversion remains elusive. Flavonoids and amino acids are two typical flavor components in tea. Herein, the contents of 46 flavonoids and 40 amino acids were measured in white tea (Shoumei) stored for 1, 3, 5 and 7 years, respectively. L-tryptophan, L-ornithine and L-theanine contribute to the refreshing taste of Shoumei 1 and 3. Quercetin, rutin and hesperidin contribute to aging charm and grain aroma of Shoumei 5 and 7. 306 bacterial OTUs and 268 fungal OTUs core microbiota existed in all samples. Interestingly, white teas contained higher richness of fungi than bacteria. The correlation analysis showed that the cooperation with bacteria and fungi may result in the flavonoids and amino acids composition changes in white teas during storage. Overall, this study provides new insights into flavor conversion of white tea during storage.

Molecular Mechanisms of Chlorophyll Deficiency in Ilex × attenuata 'Sunny Foster' Mutant.

Ilex × attenuata 'Sunny Foster' represents a yellow leaf mutant originating from I. × attenuata 'Foster#2', a popular ornamental woody cultivar. However, the molecular mechanisms underlying this leaf color mutation remain unclear. Using a comprehensive approach encompassing cytological, physiological, and transcriptomic methodologies, notable distinctions were discerned between the mutant specimen and its wild type. The mutant phenotype displayed aberrant chloroplast morphology, diminished chlorophyll content, heightened carotenoid/chlorophyll ratios, and a decelerated rate of plant development. Transcriptome analysis identified differentially expressed genes (DEGs) related to chlorophyll metabolism, carotenoid biosynthesis and photosynthesis. The up-regulation of CHLD and CHLI subunits leads to decreased magnesium chelatase activity, while the up-regulation of COX10 increases heme biosynthesis-both impair chlorophyll synthesis. Conversely, the down-regulation of HEMD hindered chlorophyll synthesis, and the up-regulation of SGR enhanced chlorophyll degradation, resulting in reduced chlorophyll content. Additionally, genes linked to carotenoid biosynthesis, flavonoid metabolism, and photosynthesis were significantly down-regulated. We also identified 311 putative differentially expressed transcription factors, including bHLHs and GLKs. These findings shed light on the molecular mechanisms underlying leaf color mutation in I. × attenuata 'Sunny Foster' and provide a substantial gene reservoir for enhancing leaf color through breeding techniques.

Prognostic significance of grade of malignancy based on histopathological differentiation and Ki-67 in pancreatic ductal adenocarcinoma.

OBJECTIVE: Tumor cell malignancy is indicated by histopathological differentiation and cell proliferation. Ki-67, an indicator of cellular proliferation, has been used for tumor grading and classification in breast cancer and neuroendocrine tumors. However, its prognostic significance in pancreatic ductal adenocarcinoma (PDAC) remains uncertain. METHODS: Patients who underwent radical pancreatectomy for PDAC were retrospectively enrolled, and relevant prognostic factors were examined. Grade of malignancy (GOM), a novel index based on histopathological differentiation and Ki-67, is proposed, and its clinical significance was evaluated. RESULTS: The optimal threshold for Ki-67 was determined to be 30%. Patients with a Ki-67 expression level > 30% rather than ≤ 30% had significantly shorter 5-year overall survival (OS) and recurrence-free survival (RFS). In multivariate analysis, both histopathological differentiation and Ki-67 were identified as independent prognostic factors for OS and RFS. The GOM was used to independently stratify OS and RFS into 3 tiers, regardless of TNM stage and other established prognostic factors. The tumor-node-metastasis-GOM stage was used to stratify survival into 5 distinct tiers, and surpassed the predictive performance of TNM stage for OS and RFS. CONCLUSIONS: Ki-67 is a valuable prognostic indicator for PDAC. Inclusion of the GOM in the TNM staging system may potentially enhance prognostic accuracy for PDAC.

A novel clinical model for risk prediction and stratification of new-onset diabetes mellitus after distal pancreatectomy.

Background: The incidence of new-onset diabetes mellitus (NODM) after distal pancreatectomy (DP) remains high. Few studies have focused on NODM in patients with pancreatic benign or low-grade malignant lesions (PBLML). This study aimed to develop and validate an effective clinical model for risk prediction and stratification of NODM after DP in patients with PBLML. Methods: A follow-up survey was conducted to investigate NODM in patients without preoperative DM who underwent DP. Four hundred and forty-eight patients from Peking Union Medical College Hospital (PUMCH) and 178 from Guangdong Provincial People's Hospital (GDPH) met the inclusion criteria. They constituted the training cohort and the validation cohort, respectively. Univariate and multivariate Cox regression, as well as least absolute shrinkage and selection operator (LASSO) analyses, were used to identify the independent risk factors. The nomogram was constructed and verified. Concordance index (C-index), receiver operating characteristic (ROC) curve, calibration curves, and decision curve analysis (DCA) were applied to assess its predictive performance and clinical utility. Accordingly, the optimal cut-off point was determined by maximally selected rank statistics method, and the cumulative risk curves for the high- and low-risk populations were plotted to evaluate the discrimination ability of the nomogram. Results: The median follow-up duration was 42.8 months in the PUMCH cohort and 42.9 months in the GDPH cohort. The postoperative cumulative 5-year incidences of DM were 29.1% and 22.1%, respectively. Age, body mass index (BMI), length of pancreatic resection, intraoperative blood loss, and concomitant splenectomy were significant risk factors. The nomogram demonstrated significant predictive utility for post-pancreatectomy DM. The C-indexes of the nomogram were 0.739 and 0.719 in the training and validation cohorts, respectively. ROC curves demonstrated the predictive accuracy of the nomogram, and the calibration curves revealed that prediction results were in general agreement with the actual results. The considerable clinical applicability of the nomogram was certified by DCA. The optimal cut-off point for risk prediction value was 2.88, and the cumulative risk curves of each cohort showed significant differences between the high- and low-risk groups. Conclusions: The nomogram could predict and identify the NODM risk population, and provide guidance to physicians in monitoring and controlling blood glucose levels in PBLML patients after DP.