Computational Pathology for Prediction of Isocitrate Dehydrogenase Gene Mutation from Whole Slide Images in Adult Patients with Diffuse Glioma.

Isocitrate dehydrogenase gene (IDH) mutation is one of the most important molecular markers of glioma. Accurate detection of IDH status is a crucial step for integrated diagnosis of adult-type diffuse gliomas. Herein, a clustering-based hybrid of a convolutional neural network and a vision transformer deep learning model was developed to detect IDH mutation status from annotation-free hematoxylin and eosin-stained whole slide pathologic images of 2275 adult patients with diffuse gliomas. For comparison, a pure convolutional neural network, a pure vision transformer, and a classic multiple-instance learning model were also assessed. The hybrid model achieved an area under the receiver operating characteristic curve of 0.973 in the validation set and 0.953 in the external test set, outperforming the other models. The hybrid model's ability in IDH detection between difficult subgroups with different IDH status but shared histologic features, achieving areas under the receiver operating characteristic curve ranging from 0.850 to 0.985 in validation and test sets. These data suggest that the proposed hybrid model has a potential to be used as a computational pathology tool for preliminary rapid detection of IDH mutation from whole slide images in adult patients with diffuse gliomas.

Radiomic profiling for insular diffuse glioma stratification with distinct biologic pathway activities.

Current literature emphasizes surgical complexities and customized resection for managing insular gliomas; however, radiogenomic investigations into prognostic radiomic traits remain limited. We aimed to develop and validate a radiomic model using multiparametric magnetic resonance imaging (MRI) for prognostic prediction and to reveal the underlying biological mechanisms. Radiomic features from preoperative MRI were utilized to develop and validate a radiomic risk signature (RRS) for insular gliomas, validated through paired MRI and RNA-seq data (N = 39), to identify core pathways underlying the RRS and individual prognostic radiomic features. An 18-feature-based RRS was established for overall survival (OS) prediction. Gene set enrichment analysis (GSEA) and weighted gene coexpression network analysis (WGCNA) were used to identify intersectional pathways. In total, 364 patients with insular gliomas (training set, N = 295; validation set, N = 69) were enrolled. RRS was significantly associated with insular glioma OS (log-rank p = 0.00058; HR = 3.595, 95% CI:1.636-7.898) in the validation set. The radiomic-pathological-clinical model (R-P-CM) displayed enhanced reliability and accuracy in prognostic prediction. The radiogenomic analysis revealed 322 intersectional pathways through GSEA and WGCNA fusion; 13 prognostic radiomic features were significantly correlated with these intersectional pathways. The RRS demonstrated independent predictive value for insular glioma prognosis compared with established clinical and pathological profiles. The biological basis for prognostic radiomic indicators includes immune, proliferative, migratory, metabolic, and cellular biological function-related pathways.

Characterization of Pancreatic Fistula after Post-pancreatectomy Acute Pancreatitis.

OBJECTIVE: This study aimed to investigate the clinical significance and risk factors of postoperative pancreatic fistula (POPF) after post-pancreatectomy acute pancreatitis (PPAP) in patients who underwent pancreaticoduodenectomy (PD). SUMMARY BACKGROUND DATA: PPAP has been recognized as a critical factor in the pathophysiology of POPF after PD. METHODS: A total of 817 consecutive patients who underwent elective PD between January 2020 and June 2022 were included. PPAP and POPF were defined in accordance with the International Study Group for Pancreatic Surgery (ISGPS) definitions. Multivariate logistic analyses were performed to investigate the risk factors for POPF. Comparisons between PPAP-associated POPF and non-PPAP-associated POPF were made to further characterize this intriguing complication. RESULTS: Overall, 159 (19.5%) patients developed POPF after PD, of which 73 (45.9%) occurred following PPAP, and the remaining 86 (54.1%) had non-PPAP-associated POPF. Patients with PPAP-associated POPF experienced significantly higher morbidity than patients without POPF. Multivariate analyses revealed distinct risk factors for each POPF type. For PPAP-associated POPF, independent risk factors included estimated blood loss >200 mL (OR 1.93), MPD ≤3 cm (OR 2.88), and soft pancreatic texture (OR 2.01), largely overlapping with FRS (Fistula Risk Score) elements. On the other hand, non-PPAP-associated POPF was associated with age >65 years (OR 1.95), male (OR 2.10), and MPD ≤3 cm (OR 2.57). Notably, among patients with PPAP, the incidence of POPF consistently hovered around 50% regardless of the FRS stratification. CONCLUSIONS: PPAP-associated POPF presents as a distinct pathophysiology in the development of POPF after PD, potentially opening doors for future prevention strategies targeting the early postoperative period.

Robotic versus open pancreatoduodenectomy in patients with pancreatic duct adenocarcinoma after the learning curve: a propensity score-matched analysis.

BACKGROUND: Studies have demonstrated that the learning curve plays an important role in robotic pancreatoduodenectomy (RPD). Although improved short-term outcomes of RPD after the learning curve have been reported compared to open pancreatoduodenectomy (OPD), there is a lack of long-term survival analyses. METHODS: Patients who underwent curative intended RPD and OPD for pancreatic duct adenocarcinoma (PDAC) between January 2017 and June 2020 were retrospectively reviewed. A 1:2 propensity score matching (PSM) analysis was performed to balance the baseline characteristics between the RPD and OPD groups. RESULTS: Of the 548 patients (108 RPD and 440 OPD), 103 RPD patients were matched with 206 OPD patients after PSM. There were 194 (62.8%) men and 115 (37.2%) women, with a median age of 64 (58-69) years. The median overall survival (OS) in the RPD group was 33.2 months compared with 25.7 months in the OPD group (p = 0.058, log-rank). The median disease-free survival (DFS) following RPD was longer than the OPD (18.5 vs. 14.0 months, p = 0.011, log-rank). The RPD group has a lower incidence of local recurrence compared the OPD group (36.9% vs. 51.2%, p = 0.071). Multivariate Cox analysis demonstrated that RPD was independently associated with improved OS (HR 0.70, 95% CI 0.52-0.94, p = 0.019) and DFS (HR 0.66, 95% CI 0.50-0.88, p = 0.005). CONCLUSION: After the learning curve, RPD had improved oncologic outcomes in PDAC patients compared to OPD. Future prospective randomized clinical trials will be required to validate these findings.

Nanoscale Ultrafine Zinc Metal Anodes for High Stability Aqueous Zinc Ion Batteries.

Aqueous Zn batteries (AZBs) are a promising energy storage technology, due to their high theoretical capacity, low redox potential, and safety. However, dendrite growth and parasitic reactions occurring at the surface of metallic Zn result in severe instability. Here we report a new method to achieve ultrafine Zn nanograin anodes by using ethylene glycol monomethyl ether (EGME) molecules to manipulate zinc nucleation and growth processes. It is demonstrated that EGME complexes with Zn2+ to moderately increase the driving force for nucleation, as well as adsorbs on the Zn surface to prevent H-corrosion and dendritic protuberances by refining the grains. As a result, the nanoscale anode delivers high Coulombic efficiency (ca. 99.5%), long-term cycle life (over 366 days and 8800 cycles), and outstanding compatibility with state-of-the-art cathodes (ZnVO and AC) in full cells. This work offers a new route for interfacial engineering in aqueous metal-ion batteries, with significant implications for the commercial future of AZBs.

Revealing the Role of Active Fillers in Li-ion Conduction of Composite Solid Electrolytes.

Composite solid electrolytes (CSEs) consisting of polyethylene oxide (PEO) matrix and active inorganic fillers have shown great potential for practical applications. However, mechanisms of how different active fillers enhance ion transport in CSEs still remain inconclusive. In this work, the component dependencies of ionic conductivity of PEO-based CSEs are investigated by comparing two widely investigated active fillers: NASICON-type (LATP) and garnet-type (LLZTO). In terms of ionic conductivity, the optimum ratios are strikingly different for LLZTO (10 wt%) and LATP (50 wt%). Through experimental and computational studies, it is demonstrated that the high affinity between LATP and PEO facilitates unhindered interfacial Li+ transfer so that LATP functions as a bulk-active filler to provide additional inorganic ion pathways. By contrast, Li+ transfer between LLZTO and PEO is found to be sluggish. Instead, LLZTO mainly improves ionic conductivity by dissociating lithium salt, making it a surface-active filler. Through categorizing active fillers based on their Li+ conductive mechanisms, this work provides new understanding and guidelines for componential design and optimization of solid composite electrolytes.

TP53 missense mutation reveals gain-of-function properties in small-sized KRAS transformed pancreatic ductal adenocarcinoma.

BACKGROUND: Although the molecular features of pancreatic ductal adenocarcinoma (PDAC) have been well described, the impact of detailed gene mutation subtypes on disease progression remained unclear. This study aimed to evaluate the impact of different TP53 mutation subtypes on clinical characteristics and outcomes of patients with PDAC. METHODS: We included 639 patients treated with PDAC in Ruijin Hospital affiliated to Shanghai Jiaotong University School of Medicine between Jan 2019 and Jun 2021. The genomic alterations of PDAC were analyzed, and the association of TP53 mutation subtypes and other core gene pathway alterations with patients' clinical characteristics were evaluated by Chi-squared test, Kaplan-Meier method and Cox regression model. RESULTS: TP53 missense mutation was significantly associated with poor differentiation in KRASmut PDAC (50.7% vs. 36.1%, P = 0.001). In small-sized (≤ 2 cm) KRASmut tumors, significantly higher LNs involvement (54.8% vs. 23.5%, P = 0.010) and distal metastic rate (20.5% vs. 2.9%, P = 0.030) were observed in those with TP53 missense mutation instead of truncating mutation. Compared with TP53 truncating mutation, missense mutation was significantly associated with reduced DFS (6.6 [5.6-7.6] vs. 9.2 [5.2-13.3] months, HR 0.368 [0.200-0.677], P = 0.005) and OS (9.6 [8.0-11.1] vs. 18.3 [6.7-30.0] months, HR 0.457 [0.248-0.842], P = 0.012) in patients who failed to receive chemotherapy, while higher OS (24.2 [20.8-27.7] vs. 23.8 [19.0-28.5] months, HR 1.461 [1.005-2.124], P = 0.047) was observed in TP53missense cases after chemotherapy. CONCLUSIONS: TP53 missense mutation was associated with poor tumor differentiation, and revealed gain-of-function properties in small-sized KRAS transformed PDAC. Nonetheless, it was not associated with insensitivity to chemotherapy, highlighting the neoadjuvant therapy before surgery as the potential optimized strategy for the treatment of a subset of patients.

Biological underpinnings of radiomic magnetic resonance imaging phenotypes for risk stratification in IDH wild-type glioblastoma.

BACKGROUND: To develop and validate a conventional MRI-based radiomic model for predicting prognosis in patients with IDH wild-type glioblastoma (GBM) and reveal the biological underpinning of the radiomic phenotypes. METHODS: A total of 801 adult patients (training set, N = 471; internal validation set, N = 239; external validation set, N = 91) diagnosed with IDH wild-type GBM were included. A 20-feature radiomic risk score (Radscore) was built for overall survival (OS) prediction by univariate prognostic analysis and least absolute shrinkage and selection operator (LASSO) Cox regression in the training set. GSEA and WGCNA were applied to identify the intersectional pathways underlying the prognostic radiomic features in a radiogenomic analysis set with paired MRI and RNA-seq data (N = 132). The biological meaning of the conventional MRI sequences was revealed using a Mantel test. RESULTS: Radscore was demonstrated to be an independent prognostic factor (P < 0.001). Incorporating the Radscore into a clinical model resulted in a radiomic-clinical nomogram predicting survival better than either the Radscore model or the clinical model alone, with better calibration and classification accuracy (a total net reclassification improvement of 0.403, P < 0.001). Three pathway categories (proliferation, DNA damage response, and immune response) were significantly correlated with the prognostic radiomic phenotypes. CONCLUSION: Our findings indicated that the prognostic radiomic phenotypes derived from conventional MRI are driven by distinct pathways involved in proliferation, DNA damage response, and immunity of IDH wild-type GBM.

The Impact of Additional Para-aortic Dissection During Pancreaticoduodenectomy for Resectable Pancreatic Cancer.

BACKGROUND: The short-term outcome and long-term survival of pancreaticoduodenectomy with additional para-aortic dissection (PAD) for patients with resectable pancreatic cancer remain obscure. PATIENTS AND METHODS: Consecutive patients who underwent radical pancreaticoduodenectomy for resectable pancreatic cancer in a single high-volume center during a 7-year period were included retrospectively. Both short- and long-term effects of PAD were compared between the PAD group and the no PAD group. Then, the PAD group was divided into the non-metastatic para-aortic lymph node (PALN-) group and the metastatic PALN (PALN+) group to further analyze the prognosis of PALN+. RESULTS: Of the 909 included patients, 280 (30.8%) underwent PAD during pancreaticoduodenectomy. The PAD group had a higher rate of intra-abdominal infection compared with the no PAD group (28.6% vs. 20.7%, P = 0.009) but no differences were found in the incidence of other complications. The overall survival (OS) and recurrence-free survival (RFS) were also comparable between the two groups. Subgroup analysis showed that patients with PALN+ had a worse OS than patients in the PALN- group (median of 14 vs. 20 months, P = 0.048). Multivariate Cox regression analysis further revealed that PALN+ was an independent adverse predictor of OS (hazard ratio: 1.70, P = 0.007). CONCLUSIONS: This study suggests that the addition of PAD during pancreaticoduodenectomy does not improve the prognosis of patients with resectable pancreatic cancer and may lead to an increased risk of infection. However, the accurate preoperative assessment and appropriate treatment strategy for patients with PALN+ need further investigation due to the poor prognosis.

Controlling the Zero Hall Plateau in a Quantum Anomalous Hall Insulator by In-Plane Magnetic Field.

We investigate the quantum anomalous Hall plateau transition in the presence of independent out-of-plane and in-plane magnetic fields. The perpendicular coercive field, zero Hall plateau width, and peak resistance value can all be systematically controlled by the in-plane magnetic field. The traces taken at various fields almost collapse into a single curve when the field vector is renormalized to an angle as a geometric parameter. These results can be explained consistently by the competition between magnetic anisotropy and in-plane Zeeman field, and the close relationship between quantum transport and magnetic domain structure. The accurate control of zero Hall plateau facilitates the search for chiral Majorana modes based on the quantum anomalous Hall system in proximity to a superconductor.

CRISPR-Cas9 identifies growth-related subtypes of glioblastoma with therapeutical significance through cell line knockdown.

BACKGROUND: Glioblastoma (GBM) is a type of highly malignant brain tumor that is known for its significant intratumoral heterogeneity, meaning that there can be a high degree of variability within the tumor tissue. Despite the identification of several subtypes of GBM in recent years, there remains to explore a classification based on genes related to proliferation and growth. METHODS: The growth-related genes of GBM were identified by CRISPR-Cas9 and univariate Cox regression analysis. The expression of these genes in the Cancer Genome Atlas cohort (TCGA) was used to construct growth-related genes subtypes (GGSs) via consensus clustering. Validation of this subtyping was performed using the nearest template prediction (NTP) algorithm in two independent Gene Expression Omnibus (GEO) cohorts and the ZZ cohort. Additionally, copy number variations, biological functions, and potential drugs were analyzed for each of the different subtypes separately. RESULTS: Our research established multicenter-validated GGSs. GGS1 exhibits the poorest prognosis, with the highest frequency of chr 7 gain & chr 10 loss, and the lowest frequency of chr 19 & 20 co-gain. Additionally, GGS1 displays the highest expression of EGFR. Furthermore, it is significantly enriched in metabolic, stemness, proliferation, and signaling pathways. Besides we showed that Foretinib may be a potential therapeutic agent for GGS1, the worst prognostic subtype, through data screening and in vitro experiments. GGS2 has a moderate prognosis, with a slightly higher proportion of chr 7 gain & chr 10 loss, and the highest proportion of chr 19 & 20 co-gain. The prognosis of GGS3 is the best, with the least chr 7 gain & 10 loss and EGFR expression. CONCLUSIONS: These results enhance our understanding of the heterogeneity of GBM and offer insights for stratified management and precise treatment of GBM patients.

Radiomic features from multiparametric magnetic resonance imaging predict molecular subgroups of pediatric low-grade gliomas.

BACKGROUND: We aimed to develop machine learning models for prediction of molecular subgroups (low-risk group and intermediate/high-risk group) and molecular marker (KIAA1549-BRAF fusion) of pediatric low-grade gliomas (PLGGs) based on radiomic features extracted from multiparametric MRI. METHODS: 61 patients with PLGGs were included in this retrospective study, which were divided into a training set and an internal validation set at a ratio of 2:1 based on the molecular subgroups or the molecular marker. The patients were classified into low-risk and intermediate/high-risk groups, BRAF fusion positive and negative groups, respectively. We extracted 5929 radiomic features from multiparametric MRI. Thereafter, we removed redundant features, trained random forest models on the training set for predicting the molecular subgroups or the molecular marker, and validated their performance on the internal validation set. The performance of the prediction model was verified by 3-fold cross-validation. RESULTS: We constructed the classification model differentiating low-risk PLGGs from intermediate/high-risk PLGGs using 4 relevant features, with an AUC of 0.833 and an accuracy of 76.2% in the internal validation set. In the prediction model for predicting KIAA1549-BRAF fusion using 4 relevant features, an AUC of 0.818 and an accuracy of 81.0% were achieved in the internal validation set. CONCLUSIONS: The current study demonstrates that MRI radiomics is able to predict molecular subgroups of PLGGs and KIAA1549-BRAF fusion with satisfying sensitivity. TRIAL REGISTRATION: This study was retrospectively registered at clinicaltrials.gov (NCT04217018).

Improving Noninvasive Classification of Molecular Subtypes of Adult Gliomas With Diffusion-Weighted MR Imaging: An Externally Validated Machine Learning Algorithm.

BACKGROUND: Genetic testing for molecular markers of gliomas sometimes is unavailable because of time-consuming and expensive, even limited tumor specimens or nonsurgery cases. PURPOSE: To train a three-class radiomic model classifying three molecular subtypes including isocitrate dehydrogenase (IDH) mutations and 1p/19q-noncodeleted (IDHmut-noncodel), IDH wild-type (IDHwt), IDH-mutant and 1p/19q-codeleted (IDHmut-codel) of adult gliomas and investigate whether radiomic features from diffusion-weighted imaging (DWI) could bring additive value. STUDY TYPE: Retrospective. POPULATION: A total of 755 patients including 111 IDHmut-noncodel, 571 IDHwt, and 73 IDHmut-codel cases were divided into training (n = 480) and internal validation set (n = 275); 139 patients including 21 IDHmut-noncodel, 104 IDHwt, and 14 IDHmut-codel cases were utilized as external validation set. FIELD STRENGTH/SEQUENCE: A 1.5 T or 3.0 T/multiparametric MRI, including T1-weighted (T1), T1-weighted gadolinium contrast-enhanced (T1c), T2-weighted (T2), fluid attenuated inversion recovery (FLAIR), and DWI. ASSESSMENT: The performance of multiparametric radiomic model (random-forest model) using 22 selected features from T1, T2, FLAIR, T1c images and apparent diffusion coefficient (ADC) maps, and conventional radiomic model using 20 selected features from T1, T2, FLAIR, and T1c images was assessed in internal and external validation sets by comparing probability values and actual incidence. STATISTICAL TESTS: Mann-Whitney U test, Chi-Squared test, Wilcoxon test, receiver operating curve (ROC), and area under the curve (AUC); DeLong analysis. P < 0.05 was statistically significant. RESULTS: The multiparametric radiomic model achieved AUC values for IDHmut-noncodel, IDHwt, and IDHmut-codel of 0.8181, 0.8524, and 0.8502 in internal validation set and 0.7571, 0.7779, and 0.7491 in external validation set, respectively. Multiparametric radiomic model showed significantly better diagnostic performance after DeLong analysis, especially in classifying IDHwt and IDHmut-noncodel subtypes. DATA CONCLUSION: Radiomic features from DWI could bring additive value and improve the performance of conventional MRI-based radiomic model for classifying the molecular subtypes especially IDHmut-noncodel and IDHwt of adult gliomas. TECHNICAL EFFICACY: Stage 2.

Drain fluid volume combined with amylase level predicts clinically relevant postoperative pancreatic fistula after pancreaticoduodenectomy: A retrospective clinical study.

BACKGROUND AND AIM: Several indicators are recognized in the development of clinically relevant postoperative pancreatic fistula (CR-POPF) after pancreaticoduodenectomy (PD). However, drain fluid volume (DFV) remains poorly studied. We aimed to discover the predictive effects of DFV and guide clinical management. METHODS: We retrospectively reviewed the clinical data of patients that received PD between January 2015 and December 2019 in a high-volume center. DFV was analyzed as a potential risk factor and postoperative short-term outcomes as well as drain removal time were compared stratified by different DFV levels. Receiver operating characteristic curves and area under curves (AUC) were compared for DFV alone and DFV combined with drain fluid amylase (DFA). Subgroup analysis of DFV stratified by DFA evaluated the predictability of CR-POPF. RESULTS: CR-POPF occurred in 19.7% of 841 patients. Hypertension, postoperative day 3 (POD3) DFA ≥ 300 U/L, and POD3 DFV ≥ 30 mL were independent risk factors, while pancreatic main duct diameter ≥ 3 mm was a protective factor. POD3 DFV ≥ 30 mL increased the overall occurrences of CR-POPF and major complications (P = 0.017; P = 0.029). POD3 DFV alone presented a low predictive value (AUC 0.602), while POD3 DFV combined with DFA had a high predictive value (AUC 0.759) for CR-POPF. Subgroup analysis showed that the combination of POD3 DFV ≥ 30 mL and DFA ≥ 300 U/L led to higher incidences of CR-POPF (P = 0.003). CONCLUSION: CR-POPF is common after PD, and high DFV combined with DFA may predict its occurrence and facilitate appropriate management.

Epidemiological analysis of varicella in Dalian from 2009 to 2019 and application of three kinds of model in prediction prevalence of varicella.

OBJECTIVE: This study described the epidemic characteristics of varicella in Dalian from 2009 to 2019, explored the fitting effect of Grey model first-order one variable( GM(1,1)), Markov model, and GM(1,1)-Markov model on varicella data, and found the best fitting method for this type of data, to better predict the incidence trend. METHODS: For this Cross-sectional study, this article was completed in 2020, and the data collection is up to 2019. Due to the global epidemic, the infectious disease data of Dalian in 2020 itself does not conform to the normal changes of varicella and is not included. The epidemiological characteristics of varicella from 2009 to 2019 were analyzed by epidemiological descriptive methods. Using the varicella prevalence data from 2009 to 2018, predicted 2019 and compared with actual value. First made GM (1,1) prediction and Markov prediction. Then according to the relative error of the GM (1,1), made GM (1,1)-Markov prediction. RESULTS: This study collected 37,223 cases from China Information System for Disease Control and Prevention's "Disease Prevention and Control Information System" and the cumulative population was 73,618,235 from 2009 to 2019. The average annual prevalence was 50.56/100000. Varicella occurred all year round, it had a bimodal distribution. The number of cases had two peaks from April to June and November to January of the following year. The ratio of males to females was 1.17:1. The 4 to 25 accounted for 60.36% of the total population. The age of varicella appeared to shift backward. Students, kindergarten children, scattered children accounted for about 64% of all cases. The GM(1,1) model prediction result of 2019 would be 53.64, the relative error would be 14.42%, the Markov prediction result would be 56.21, the relative error would be 10.33%, and the Gray(1,1)-Markov prediction result would be 59.51. The relative error would be 5.06%. CONCLUSIONS: Varicella data had its unique development characteristics. The accuracy of GM (1,1)-Markov model is higher than GM(1.1) model and Markov model. The model can be used for prediction and decision guidance.

From bulk to interface: electrochemical phenomena and mechanism studies in batteries via electrochemical quartz crystal microbalance.

Understanding the bulk and interfacial behaviors during the operation of batteries (e.g., Li-ion, Na-ion, Li-O2 batteries, etc.) is of great significance for the continuing improvement of the performance. Electrochemical quartz crystal microbalance (EQCM) is a powerful tool to this end, as it enables in situ investigation into various phenomena, including ion insertion/deinsertion within electrodes, solid nucleation from the electrolyte, interphasial formation/evolution and solid-liquid coordination. As such, EQCM analysis helps to decipher the underlying mechanisms both in the bulk and at the interface. This tutorial review will present the recent progress in mechanistic studies of batteries achieved by the EQCM technology. The fundamentals and unique capability of EQCM are first discussed and compared with other techniques, and then the combination of EQCM with other in situ techniques is also covered. In addition, the recent studies utilizing EQCM technologies in revealing phenomena and mechanisms of various batteries are reviewed. Perspectives regarding the future application of EQCM in battery studies are given at the end.

Dimethylarsinic acid is the causal agent inducing rice straighthead disease.

Straighthead disease is a physiological disorder in rice with symptoms of sterile spikelets, distorted husks, and erect panicles. Methylated arsenic species have been implicated as the causal agent of the disease, but direct evidence is lacking. Here, we investigated whether dimethylarsinic acid (DMA) causes straighthead disease and its effect on the transcriptome of young panicles. DMA addition caused typical straighthead symptoms in hydroponic culture, which were alleviated by silicon addition. DMA addition to soil at the tillering to flowering stages induced straighthead disease. Transgenic rice expressing a bacterial arsenite methyltransferase gene gained the ability to methylate arsenic to mainly DMA, with the consequence of inducing straighthead disease. Field surveys showed that seed setting rate decreased with increasing DMA concentration in the husk, with an EC50 of 0.18 mg kg-1. Transcriptomic analysis showed that 364 and 856 genes were significantly up- and down-regulated, respectively, in the young panicles of DMA-treated plants compared with control, whereas Si addition markedly reduced the number of genes affected. Among the differentially expressed genes, genes related to cell wall modification and oxidative stress responses were the most prominent, suggesting that cell wall metabolism is a sensitive target of DMA toxicity and silicon protects against this toxicity.

OsPTR7 (OsNPF8.1), a Putative Peptide Transporter in Rice, is Involved in Dimethylarsenate Accumulation in Rice Grain.

Rice (Oryza sativa) is a major dietary source of arsenic (As) for the population consuming rice as their staple food. Rice grain contains both inorganic As and methylated As species, especially dimethyarsinate (DMA). DMA is highly mobile in long-distance translocation in plants, but the underlying mechanism remains unknown. In the present study, we showed that OsPTR7 (OsNPF8.1), a putative peptide transporter in rice, was permeable to DMA in Xenopus laevis oocytes. Transient expression of the OsPTR7-green fluorescent protein (GFP) in tobacco protoplasts showed that OsPTR7 was localized in the cell plasma membrane. Quantitative real-time PCR analysis showed that OsPTR7 was more highly expressed in the shoots than in the roots at the seedling stage. At the flowering and grain-filling stage, the OsPTR7 transcript was abundant in the leaves, node I and roots. Knockout or knockdown mutants of OsPTR7 had significantly decreased root to shoot translocation of DMA compared with wild-type plants and accumulated less As in the brown rice. In field-grown plants, DMA accounted for 35% of the total As in the brown rice of wild-type plants but was undetectable in the knockout mutant. Our study demonstrates that OsPTR7 is involved in the long-distance translocation of DMA and contributes to the accumulation of DMA in rice grain.