Data_Sheet_1_Aztreonam is a novel chemical inducer that promotes Agrobacteium transformation and lateral root development in soybean.pdf

Agrobacterium-mediated soybean transformation is the simplest method of gene transfer. However, the low transformation due to the intractable nature of soybean genotypes hinders this process. The use of biochemicals (acetosyringone, cinnamic acid, flavonoids, etc.) plays an important role in increasing soybean transformation. These biochemicals induce chemotaxis and virulence gene activation during the infection process. Here we identified a biochemical, aztreonam (a monobactam), for high agrobacterium-mediated transformation in soybean. The soybean explants from three genotypes were inoculated with A. tumefaciens (GV3101) harboring the pMDC32 vector containing hpt or the GmUbi-35S-GUS vector containing the GUS gene during two separate events. High transient GUS expression was obtained during cotyledon explant culture on MS media supplemented with 2.5 mg/L aztreonam. The aztreonam-treated explants showed high efficiency in transient and stable transformation as compared to the untreated control. The transformation of aztreonam-treated explants during seed imbibition resulted in an average of 21.1% as compared to 13.2% in control by using the pMDC32 vector and 28.5 and 20.7% while using the GUS gene cassette, respectively. Based on these findings, the metabolic analysis of the explant after aztreonam treatment was assessed. The high accumulation of flavonoids was identified during an untargeted metabolic analysis. The quantification results showed a significantly high accumulation of the four compounds, i.e., genistein, apigenin, naringenin, and genistin, in cotyledon explants after 18 hours of aztreonam treatment. Alongside this, aztreonam also had some surprising effects on root elongation and lateral root formation when compared to indole-3-butyric acid (IBA). Our findings were limited to soybeans. However, the discovery of aztreonam and its effect on triggering flavonoids could lead to the potential role of aztreonam in the agrobacterium-mediated transformation of different crops.</p

Mean self-perception ratings of human traits across conditions in Experiment 3.

<p>Mean self-perception ratings of human traits across conditions in Experiment 3.</p

Mean self-perception ratings of human traits across conditions in Experiment 1.

<p>Mean self-perception ratings of human traits across conditions in Experiment 1.</p

Table2_Cellular Senescence-Related Genes: Predicting Prognosis in Gastric Cancer.XLS

Our study aimed to explore the effect of cellular senescence and to find potential therapeutic strategies for gastric cancer. Cellular senescence-related genes were acquired from the CellAge database, while gastric cancer data were obtained from GEO and TCGA databases. SMARCA4 had the highest mutation frequency (6%), and it was linked to higher overall survival (OS) and progression-free survival (PFS). The gastric cancer data in TCGA database served as a training set to construct a prognostic risk score signature, and GEO data were used as a testing set to validate the accuracy of the signature. Patients with the low-risk score group had a longer survival time, while the high-risk score group is the opposite. Patients with low-risk scores had higher immune infiltration and active immune-related pathways. The results of drug sensitivity analysis and the TIDE algorithm showed that the low-risk score group was more susceptible to chemotherapy and immunotherapy. Most patients with mutation genes had a lower risk score than the wild type. Therefore, the risk score signature with cellular senescence-related genes can predict gastric cancer prognosis and identify gastric cancer patients who are sensitive to chemotherapy and immunotherapy.</p

Table5_Cellular Senescence-Related Genes: Predicting Prognosis in Gastric Cancer.XLS

Our study aimed to explore the effect of cellular senescence and to find potential therapeutic strategies for gastric cancer. Cellular senescence-related genes were acquired from the CellAge database, while gastric cancer data were obtained from GEO and TCGA databases. SMARCA4 had the highest mutation frequency (6%), and it was linked to higher overall survival (OS) and progression-free survival (PFS). The gastric cancer data in TCGA database served as a training set to construct a prognostic risk score signature, and GEO data were used as a testing set to validate the accuracy of the signature. Patients with the low-risk score group had a longer survival time, while the high-risk score group is the opposite. Patients with low-risk scores had higher immune infiltration and active immune-related pathways. The results of drug sensitivity analysis and the TIDE algorithm showed that the low-risk score group was more susceptible to chemotherapy and immunotherapy. Most patients with mutation genes had a lower risk score than the wild type. Therefore, the risk score signature with cellular senescence-related genes can predict gastric cancer prognosis and identify gastric cancer patients who are sensitive to chemotherapy and immunotherapy.</p

Mean meta-perception ratings of human traits across conditions in Experiment 3.

<p>Mean meta-perception ratings of human traits across conditions in Experiment 3.</p

Table1_Cellular Senescence-Related Genes: Predicting Prognosis in Gastric Cancer.DOCX

Our study aimed to explore the effect of cellular senescence and to find potential therapeutic strategies for gastric cancer. Cellular senescence-related genes were acquired from the CellAge database, while gastric cancer data were obtained from GEO and TCGA databases. SMARCA4 had the highest mutation frequency (6%), and it was linked to higher overall survival (OS) and progression-free survival (PFS). The gastric cancer data in TCGA database served as a training set to construct a prognostic risk score signature, and GEO data were used as a testing set to validate the accuracy of the signature. Patients with the low-risk score group had a longer survival time, while the high-risk score group is the opposite. Patients with low-risk scores had higher immune infiltration and active immune-related pathways. The results of drug sensitivity analysis and the TIDE algorithm showed that the low-risk score group was more susceptible to chemotherapy and immunotherapy. Most patients with mutation genes had a lower risk score than the wild type. Therefore, the risk score signature with cellular senescence-related genes can predict gastric cancer prognosis and identify gastric cancer patients who are sensitive to chemotherapy and immunotherapy.</p

Table6_Cellular Senescence-Related Genes: Predicting Prognosis in Gastric Cancer.DOCX

Our study aimed to explore the effect of cellular senescence and to find potential therapeutic strategies for gastric cancer. Cellular senescence-related genes were acquired from the CellAge database, while gastric cancer data were obtained from GEO and TCGA databases. SMARCA4 had the highest mutation frequency (6%), and it was linked to higher overall survival (OS) and progression-free survival (PFS). The gastric cancer data in TCGA database served as a training set to construct a prognostic risk score signature, and GEO data were used as a testing set to validate the accuracy of the signature. Patients with the low-risk score group had a longer survival time, while the high-risk score group is the opposite. Patients with low-risk scores had higher immune infiltration and active immune-related pathways. The results of drug sensitivity analysis and the TIDE algorithm showed that the low-risk score group was more susceptible to chemotherapy and immunotherapy. Most patients with mutation genes had a lower risk score than the wild type. Therefore, the risk score signature with cellular senescence-related genes can predict gastric cancer prognosis and identify gastric cancer patients who are sensitive to chemotherapy and immunotherapy.</p

Additional file 16: of Correction to: SKIP controls flowering time via the alternative splicing of SEF pre-mRNA in Arabidopsis

Table S10. Primers used in the studies (DOCX 18 kb

Datasheet1_Identification of a novel cellular senescence-related signature for the prediction of prognosis and immunotherapy response in colon cancer.zip

The study was conducted to construct a cellular senescence-related risk score signature to predict prognosis and immunotherapy response in colon cancer. Colon cancer data were acquired from the Gene Expression Omnibus and The Cancer Genome Atlas databases. And cellular senescence-related genes were obtained from the CellAge database. The colon cancer data were classified into different clusters based on cellular senescence-related gene expression. Next, prognostic differential genes among clusters were identified with survival analysis. A cellular senescence-related risk score signature was developed by performing the LASSO regression analysis. Finally, PCA analysis, t-SNE analysis, Kaplan-Meier survival analysis, ROC analysis, univariate Cox regression analysis, multivariate Cox regression analysis, C-index analysis, meta-analysis, immune infiltration analysis, and IPS score analysis were used to evaluate the significance of the risk signature for predicting prognosis and immunotherapy response in colon cancer. The colon cancer data were classified into three clusters. The patients in cluster A and cluster B had longer survival. A cellular senescence-related risk score signature was developed. Patients in the low-risk score group showed a better prognosis. The risk score signature could predict colon cancer patients’ prognosis independently of other clinical characteristics. The risk score signature predicted the prognosis of colon cancer patients more accurately than other signatures. Patients in the low-risk score group showed a better response to immunotherapy. The opposite was true for the high-risk score group. In conclusion, the cellular senescence-related risk score signature could be used for the prediction of prognosis and immunotherapy response in colon cancer.</p