Biofortified Rice Provides Rich Sakuranetin in Endosperm.

Sakuranetin plays a key role as a phytoalexin in plant resistance to biotic and abiotic stresses, and possesses diverse health-promoting benefits. However, mature rice seeds do not contain detectable levels of sakuranetin. In the present study, a transgenic rice plant was developed in which the promoter of an endosperm-specific glutelin gene OsGluD-1 drives the expression of a specific enzyme naringenin 7-O-methyltransferase (NOMT) for sakuranetin biosynthesis. The presence of naringenin, which serves as the biosynthetic precursor of sakuranetin made this modification feasible in theory. Liquid chromatography tandem mass spectrometry (LC-MS/MS) validated that the seeds of transgenic rice accumulated remarkable sakuranetin at the mature stage, and higher at the filling stage. In addition, the panicle blast resistance of transgenic rice was significantly higher than that of the wild type. Specially, the matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) imaging was performed to detect the content and spatial distribution of sakuranetin and other nutritional metabolites in transgenic rice seeds. Notably, this genetic modification also did not change the nutritional and quality indicators such as soluble sugars, total amino acids, total flavonoids, amylose, total protein, and free amino acid content in rice. Meanwhile, the phenotypes of the transgenic plant during the whole growth and developmental periods and agricultural traits such as grain width, grain length, and 1000-grain weight exhibited no significant differences from the wild type. Collectively, the study provides a conceptual advance on cultivating sakuranetin-rich biofortified rice by metabolic engineering. This new breeding idea may not only enhance the disease resistance of cereal crop seeds but also improve the nutritional value of grains for human health benefits.

Searching for the Virulence-contributing Genes of the Magnaporthe oryzae by Transcriptome Analysis.

Magnaporthe oryzae is a fungal pathogen that causes rice blast. Plant metabolites such as plant hormones and phytoalexin can promote or inhibit the rice blast infection. To study the effect of plant metabolites on M. oryzae, we selected salicylic acid (SA), abscisic acid (ABA), and a phytoalexin sakuranetin to treat M. oryzae grown on the medium. Through the analysis of transcriptome data, 185 and 38 genes, 803 and 156 genes, and 1525 and 428 genes were up- or down-regulated after SA, ABA, or sakuranetin treatment. Among these differentially expressed genes (DEGs), most of them were annotated to the cellular process and metabolic process in the biological process category and binding and catalytic activity in the molecular function category by GO analysis. According to KEGG pathway analysis, metabolism is the pathway with the highest number of DEGs, and the main enriched pathway is carbohydrate, lipid, and amino acid metabolism. In addition, we also found two ABA-induced up-regulated genes that may contribute to M. oryzae infection from the transcriptome data. We verified their expressions in M. oryzae that infected rice.

The OsBZR1-OsSPX1/2 module fine-tunes the growth-immunity trade-off in adaptation to phosphate availability in rice.

The growth-promoting hormones brassinosteroids (BRs) and their key signaling component BZR1 play a vital role in balancing normal growth and defense reactions. Here, we discovered that BRs and OsBZR1 upregulated sakuranetin accumulation and conferred basal defense against Magnaporthe oryzae infection under normal conditions. Resource shortages, including phosphate (Pi) deficiency, potentially disrupt this growth-defense balance. OsSPX1 and OsSPX2 have been reported to sense Pi concentration and interact with the Pi signal mediator OsPHR2, thus regulating Pi starvation responses. In this study, we discovered that OsSPX1/2 interacts with OsBZR1 in both Pi-sufficient and Pi-deficient conditions, inhibiting BR-responsive genes. When Pi is sufficient, OsSPX1/2 is captured by OsPHR2, enabling most of OsBZR1 to promote plant growth and maintain basal resistance. In response to Pi starvation, more OsSPX1/2 is released from OsPHR2 to inhibit OsBZR1 activity, resulting in slower growth. Collectively, our study reveals that the OsBZR1-SPX1/2 module balances the plant growth-immunity trade-off in response to Pi availability.

Unveiling targeted spatial metabolome of rice seed at the dough stage using Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry imaging.

Rice (Oryza sativa) seeds contain a variety of metabolites, which not only provide energy for their own growth and development, but also are an important source of nutrition for humans. It is crucial to study the distribution of metabolites in rice seeds, but the spatial metabolome of rice seeds is rarely investigated. In this study, Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry (MALDI-MS) imaging was used to reveal the spatial distribution of free soluble sugars (glucose, fructose, sucrose, and maltose), amino acids (9 essential amino acids and 2 amino acids affecting rice eating quality: L-aspartic acid and L-glutamic acid), and 4 metabolites in the flavonoids synthesis pathway (cinnamic acid, naringenin chalcone, naringenin, and dihydrokaempferol) in rice seed at the dough stage. It was found that the 4 free soluble sugars present similar spatial distribution, mainly distributed in the seed cortex and embryo with high abundance. The majority of amino acids are also concentrated in the rice cortex and embryo, while the others are abundant in the whole seed. Besides cinnamic acid distributed in the seed cortex and embryo, the naringenin chalcone, naringenin, and dihydrokaempferol were also found in the endosperm and had lower content. Furthermore, a colocalization phylogenetic tree according to the spatial distribution imaging of each metabolite was constructed. This study revealed the distribution diversity of metabolites in different segmentations of rice seed at the dough stage, providing clues for the nutritional differences between brown rice and white rice, and serving as a reference for people to target a healthy diet.

Corrigendum: Involvement of PtPHR1 in phosphates starvation-induced alkaloid biosynthesis in Pinellia ternata (Thunb.) Breit.

[This corrects the article DOI: 10.3389/fpls.2022.914648.].

Construction and validation of a progression prediction model for locally advanced rectal cancer patients received neoadjuvant chemoradiotherapy followed by total mesorectal excision based on machine learning.

Background: We attempted to develop a progression prediction model for local advanced rectal cancer(LARC) patients who received preoperative neoadjuvant chemoradiotherapy(NCRT) and operative treatment to identify high-risk patients in advance. Methods: Data from 272 LARC patients who received NCRT and total mesorectal excision(TME) from 2011 to 2018 at the Fourth Hospital of Hebei Medical University were collected. Data from 161 patients with rectal cancer (each sample with one target variable (progression) and 145 characteristic variables) were included. One Hot Encoding was applied to numerically represent some characteristics. The K-Nearest Neighbor (KNN) filling method was used to determine the missing values, and SmoteTomek comprehensive sampling was used to solve the data imbalance. Eventually, data from 135 patients with 45 characteristic clinical variables were obtained. Random forest, decision tree, support vector machine (SVM), and XGBoost were used to predict whether patients with rectal cancer will exhibit progression. LASSO regression was used to further filter the variables and narrow down the list of variables using a Venn diagram. Eventually, the prediction model was constructed by multivariate logistic regression, and the performance of the model was confirmed in the validation set. Results: Eventually, data from 135 patients including 45 clinical characteristic variables were included in the study. Data were randomly divided in an 8:2 ratio into a data set and a validation set, respectively. Area Under Curve (AUC) values of 0.72 for the decision tree, 0.97 for the random forest, 0.89 for SVM, and 0.94 for XGBoost were obtained from the data set. Similar results were obtained from the validation set. Twenty-three variables were obtained from LASSO regression, and eight variables were obtained by considering the intersection of the variables obtained using the previous four machine learning methods. Furthermore, a multivariate logistic regression model was constructed using the data set; the ROC indicated its good performance. The ROC curve also verified the good predictive performance in the validation set. Conclusions: We constructed a logistic regression model with good predictive performance, which allowed us to accurately predict whether patients who received NCRT and TME will exhibit disease progression.

Involvement of PtPHR1 in phosphates starvation-induced alkaloid biosynthesis in Pinellia ternata (Thunb.) Breit.

Nowadays, because of the great benefit to human health, more and more efforts have been made to increase the production of alkaloids in Pinellia ternata (Thunb.) Breit. Phosphate (Pi) plays a critical role in plant growth and development, as well as secondary metabolism. However, its effect and regulation mechanism of Pi signaling on alkaloid biosynthesis call for further exploration. Here, we reported that Pi starvation could induce alkaloid accumulation in P. ternata. We cloned a cDNA sequence encoding PtPHR1 from P. ternata, which was further identified by nuclear localization, transcription activity, and binding ability to the PHR1-binding sequence. We found that the transformation of PtPHR1 into the Arabidopsis phr1 mutant (designated as PtPHR1OE/phr1) led to the rescue of the phenotype of the phr1 mutant to that of the wild-type, including the expression level of Pi starvation-induced genes and anthocyanin accumulation. The combination of these biochemical and genetic experiments indicated that PtPHR1 was intended to have a role similar to that of AtPHR1 in Pi signaling and metabolic responses. Interestingly, we found that Pi starvation also induced the production of benzoic acid, an intermediate in the biosynthetic pathway of phenylpropylamino alkaloids. Furthermore, this induction effect was impaired in the phr1 mutant but partly recovered in PtPHR1OE/phr1 plants. Together, our data suggest that Pi starvation promoted benzoic acid-derived alkaloid biosynthesis in P. ternata under the control of PtPHR1. Our finding that PtPHR1 is involved in the regulation of Pi signaling on alkaloid biosynthesis shows a direct link between the Pi nutrient supply and secondary metabolism.

A systematic and comprehensive analysis of colorectal squamous cell carcinoma: Implication for diagnosis and treatment.

BACKGROUND: This study was aimed at establishing a nomogram for survival prediction of Colorectal squamous cell carcinoma (CSCC), understanding the molecular pathogenesis, exploring a better treatment, and predicting the potential therapeutic agents. METHODS: Surveillance, Epidemiology, and End Results (SEER) database was used to obtained CSCC patients and the nomogram was performed. Propensity score matching (PSM), Kaplan-Meier analysis, subgroup analysis, and interaction test were used to explore the better treatment strategy for CSCC. Bioinformatics were used to explore the molecular mechanism and potential therapeutic drugs of CSCC. RESULTS: A total of 3949 CSCC patients were studied. The nomogram was constructed and evaluated to have a good performance. We found that the radiotherapy had a better effect than surgery, and the difference between radiotherapy and combined therapy was not significant. 821 differentially expressed genes in CSCC were obtained from GSE6988 dataset. DNA damage repair, mismatch repair, and cell cycle pathways might contribute to CSCC occurrence as indicated by KEGGpathway and GSEA analysis. Transcription factors analysis revealed that TP63 and STAT1 may have an important role in occurrence and development of CSCC. 1607 potential drugs against CSCC were found using the CMAP database, and molecular docking was carried out to show the binding energy between TP63 and drugs. CONCLUSIONS: A good prognosis nomogram was constructed for CSCC. We also have a better understanding of the underlying molecular mechanisms of occurrence and development of CSCC and predicted potential therapeutic drugs, providing a theoretical basis for the treatment of CSCC.

Survival Analysis and Risk Factors in COVID-19 Patients.

OBJECTIVE: The aim of this study is to evaluate the clinical characteristics and outcomes in 2019 coronavirus disease (COVID-19) patients and to help clinicians perform correct treatment and evaluate prognosis and guide the treatment. METHODS: Patients totaling 239 were diagnosed with COVID-19 and were included in this study. Patients were divided into the improvement group and the death group according to their outcome (improvement or death). Clinical characteristics and laboratory parameters were collected from medical records. Continuous variables were tested by an independent sample T test, and categorical variables were analyzed by the chi-square test or Fisher's exact test. The Cox proportional hazard regression model was used for survival analysis in death patients. The time-dependent area under curves (AUC) based on white blood cell count, lymphocyte count, neutrophil count by age, blood urea nitrogen, and C-reactive protein were plotted. RESULTS: Efficacy evaluation indicated that 99 (41.4%) patients had deteriorated, and 140 (58.6%) patients had improved. Oxygen saturation, hemoglobin levels, infection-related indicators, lymphocyte and platelet counts, C-reactive protein, serum albumin, liver and kidney function, and lactate dehydrogenase in improvement group were statistically significant between the improvement and death groups. A survival analysis revealed that comorbidities, lymphocyte counts, platelet count, serum albumin, C-reactive protein level, and renal dysfunction may be risk factors in patients with COVID-19. CONCLUSION: Patients with comorbidities, lower lymphocyte counts in hemogram, platelet count and serum albumin, high C-reactive protein level, and renal dysfunction may have higher risk for death. More attention should be given to risk management in the progression of COVID-19.