Personalized surgical recommendations and quantitative therapeutic insights for patients with metastatic breast cancer: Insights from deep learning.

Background: The role of surgery in metastatic breast cancer (MBC) is currently controversial. Several novel statistical and deep learning (DL) methods promise to infer the suitability of surgery at the individual level. Objective: The objective of this study was to identify the most applicable DL model for determining patients with MBC who could benefit from surgery and the type of surgery required. Methods: We introduced the deep survival regression with mixture effects (DSME), a semi-parametric DL model integrating three causal inference methods. Six models were trained to make individualized treatment recommendations. Patients who received treatments in line with the DL models' recommendations were compared with those who underwent treatments divergent from the recommendations. Inverse probability weighting (IPW) was used to minimize bias. The effects of various features on surgery selection were visualized and quantified using multivariate linear regression and causal inference. Results: In total, 5269 female patients with MBC were included. DSME was an independent protective factor, outperforming other models in recommending surgery (IPW-adjusted hazard ratio [HR] = 0.39, 95% confidence interval [CI]: 0.19-0.78) and type of surgery (IPW-adjusted HR = 0.66, 95% CI: 0.48-0.93). DSME was superior to other models and traditional guidelines, suggesting a higher proportion of patients benefiting from surgery, especially breast-conserving surgery. The debiased effect of patient characteristics, including age, tumor size, metastatic sites, lymph node status, and breast cancer subtypes, on surgery decision was also quantified. Conclusions: Our findings suggested that DSME could effectively identify patients with MBC likely to benefit from surgery and the specific type of surgery needed. This method can facilitate the development of efficient, reliable treatment recommendation systems and provide quantifiable evidence for decision-making.

Time-gated imaging through dense fog via physics-driven Swin transformer.

Imaging through the fog is valuable for many areas, such as autonomous driving and cosmic exploration. However, due to the influence of strong backscattering and diffuse reflection generated by the dense fog on the temporal-spatial correlations of photons returning from the target object, the reconstruction quality of most existing methods is significantly reduced under dense fog conditions. In this study, we describe the optical scatter imaging process and propose a physics-driven Swin Transformer method utilizing Time-of-Flight (ToF) and Deep Learning principles to mitigate scattering effects and reconstruct targets in conditions of heterogeneous dense fog. The results suggest that, despite the exponential decrease in the number of ballistic photons as the optical thickness of fog increases, the Physics-Driven Swin Transformer method demonstrates satisfactory performance in imaging targets obscured by dense fog. Importantly, this article highlights that even in dense fog imaging experiments with optical thickness reaching up to 3.0, which exceeds previous studies, commonly utilized quantitative evaluation metrics like PSNR and SSIM indicate that our method is cutting-edge in imaging through dense fog.

Individualized survival prediction and surgery recommendation for patients with glioblastoma.

Background: There is a lack of individualized evidence on surgical choices for glioblastoma (GBM) patients. Aim: This study aimed to make individualized treatment recommendations for patients with GBM and to determine the importance of demographic and tumor characteristic variables in the selection of extent of resection. Methods: We proposed Balanced Decision Ensembles (BDE) to make survival predictions and individualized treatment recommendations. We developed several DL models to counterfactually predict the individual treatment effect (ITE) of patients with GBM. We divided the patients into the recommended (Rec.) and anti-recommended groups based on whether their actual treatment was consistent with the model recommendation. Results: The BDE achieved the best recommendation effects (difference in restricted mean survival time (dRMST): 5.90; 95% confidence interval (CI), 4.40-7.39; hazard ratio (HR): 0.71; 95% CI, 0.65-0.77), followed by BITES and DeepSurv. Inverse probability treatment weighting (IPTW)-adjusted HR, IPTW-adjusted OR, natural direct effect, and control direct effect demonstrated better survival outcomes of the Rec. group. Conclusion: The ITE calculation method is crucial, as it may result in better or worse recommendations. Furthermore, the significant protective effects of machine recommendations on survival time and mortality indicate the superiority of the model for application in patients with GBM. Overall, the model identifies patients with tumors located in the right and left frontal and middle temporal lobes, as well as those with larger tumor sizes, as optimal candidates for SpTR.

Application of a circular-shaped pulsed field ablation catheter with magnetic sensors for pulmonary vein isolation: a multi-centre clinical study report.

AIMS: A few studies have reported the effect and safety of pulsed field ablation (PFA) catheters for ablating atrial fibrillation (AF), which were mainly based on basket-shaped or flower-shaped designs. However, the clinical application of a circular-shaped multi-electrode catheter with magnetic sensors is very limited. To study the efficacy and safety of a PFA system in patients with paroxysmal AF using a circular-shaped multi-electrode catheter equipped with magnetic sensors for pulmonary vein isolation (PVI). METHODS AND RESULTS: A novel proprietary bipolar PFA system was used for PVI, which utilized a circular-shaped multi-electrode catheter with magnetic sensors and allowed for three-dimensional model reconstruction, mapping, and ablation in one map. To evaluate the efficacy, efficiency, and safety of this PFA system, a prospective, multi-centre, single-armed, pre-market clinical study was performed. From July 2021 to December 2022, 151 patients with paroxysmal AF were included and underwent PVI. The study examined procedure time, immediate success rate, procedural success rate at 12 months, and relevant complications. In all 151 patients, all the pulmonary veins were acutely isolated using the studied system. Pulsed field ablation delivery was 78.4 ± 41.8 times and 31.3 ± 16.7 ms per patient. Skin-to-skin procedure time was 74.2 ± 29.8 min, and fluoroscopy time was 13.1 ± 7.6 min. The initial 11 (7.2%) cases underwent procedures with deep sedation anaesthesia, and the following cases underwent local anaesthesia. In the initial 11 cases, 4 cases (36.4%) presented transient vagal responses, and the rest were all successfully preventatively treated with atropine injection and rapid fluid infusion. No severe complications were found during or after the procedure. During follow-up, 3 cases experienced atrial flutter, and 11 cases had AF recurrence. The estimated 12-month Kaplan-Meier of freedom from arrhythmia was 88.4%. CONCLUSION: The PFA system, comprised of a circular PFA catheter with magnetic sensors, could rapidly achieve PVI under three-dimensional guidance and demonstrated excellent safety with comparable effects.

Quantified treatment effect at the individual level is more indicative for personalized radical prostatectomy recommendation: implications for prostate cancer treatment using deep learning.

BACKGROUND: There are potential uncertainties and overtreatment existing in radical prostatectomy (RP) for prostate cancer (PCa) patients, thus identifying optimal candidates is quite important. PURPOSE: This study aims to establish a novel causal inference deep learning (DL) model to discern whether a patient can benefit more from RP and to identify heterogeneity in treatment responses among PCa patients. METHODS: We introduce the Self-Normalizing Balanced individual treatment effect for survival data (SNB). Six models were trained to make individualized treatment recommendations for PCa patients. Inverse probability treatment weighting (IPTW) was used to avoid treatment selection bias. RESULTS: 35,236 patients were included. Patients whose actual treatment was consistent with SNB recommendations had better survival outcomes than those who were inconsistent (multivariate hazard ratio (HR): 0.76, 95% confidence interval (CI), 0.64-0.92; IPTW-adjusted HR: 0.77, 95% CI, 0.61-0.95; risk difference (RD): 3.80, 95% CI, 2.48-5.11; IPTW-adjusted RD: 2.17, 95% CI, 0.92-3.35; the difference in restricted mean survival time (dRMST): 3.81, 95% CI, 2.66-4.85; IPTW-adjusted dRMST: 3.23, 95% CI, 2.06-4.45). Keeping other covariates unchanged, patients with 1 ng/mL increase in PSA levels received RP caused 1.77 months increase in the time to 90% mortality, and the similar results could be found in age, Gleason score, tumor size, TNM stages, and metastasis status. CONCLUSIONS: Our highly interpretable and reliable DL model (SNB) may identify patients with PCa who could benefit from RP, outperforming other models and clinical guidelines. Additionally, the DL-based treatment guidelines obtained can provide priori evidence for subsequent studies.

Deep learning-guided adjuvant chemotherapy selection for elderly patients with breast cancer.

PURPOSE: The efficacy of adjuvant chemotherapy in elderly breast cancer patients is currently controversial. This study aims to provide personalized adjuvant chemotherapy recommendations using deep learning (DL). METHODS: Six models with various causal inference approaches were trained to make individualized chemotherapy recommendations. Patients who received actual treatment recommended by DL models were compared with those who did not. Inverse probability treatment weighting (IPTW) was used to reduce bias. Linear regression, IPTW-adjusted risk difference (RD), and SurvSHAP(t) were used to interpret the best model. RESULTS: A total of 5352 elderly breast cancer patients were included. The median (interquartile range) follow-up time was 52 (30-80) months. Among all models, the balanced individual treatment effect for survival data (BITES) performed best. Treatment according to following BITES recommendations was associated with survival benefit, with a multivariate hazard ratio (HR) of 0.78 (95% confidence interval (CI): 0.64-0.94), IPTW-adjusted HR of 0.74 (95% CI: 0.59-0.93), RD of 12.40% (95% CI: 8.01-16.90%), IPTW-adjusted RD of 11.50% (95% CI: 7.16-15.80%), difference in restricted mean survival time (dRMST) of 12.44 (95% CI: 8.28-16.60) months, IPTW-adjusted dRMST of 7.81 (95% CI: 2.93-11.93) months, and p value of the IPTW-adjusted Log-rank test of 0.033. By interpreting BITES, the debiased impact of patient characteristics on adjuvant chemotherapy was quantified, which mainly included breast cancer subtype, tumor size, number of positive lymph nodes, TNM stages, histological grades, and surgical type. CONCLUSION: Our results emphasize the potential of DL models in guiding adjuvant chemotherapy decisions for elderly breast cancer patients.

IiAGL6 participates in the regulation of stamen development and pollen formation in Isatis indigotica.

The AGL6 (AGMOUSE LIKE 6) gene is a member of the SEP subfamily and functions as an E-class floral homeotic gene in the development of floral organs. In this study, we cloned IiAGL6, the orthologous gene of AGL6 in Isatis indigotica. The constitutive expression of IiAGL6 in Arabidopsis thaliana resulted in a late-flowering phenotype and the development of curly leaves during the vegetative growth period. Abnormal changes in floral organ development were observed during the reproductive stage. In woad plants, suppression of IiAGL6 using TRV-VIGS (tobacco rattle virus-mediated virus-induced gene silencing) decreased the number of stamens and led to the formation of aberrant anthers. Similar changes in stamen development were also observed in miRNA-AGL6 transgenic Arabidopsis plants. Yeast two-hybrid and BiFC tests showed that IiAGL6 can interact with other MADS-box proteins in woad; thus, playing a key role in defining the identities of floral organs, particularly during stamen formation. These findings might provide novel insights and help investigate the biological roles of MADS transcription factors in I. indigotica.

Reasoning and causal inference regarding surgical options for patients with low-grade gliomas using machine learning: A SEER-based study.

BACKGROUND: Due to the heterogeneity of low-grade gliomas (LGGs), the lack of randomized control trials, and strong clinical evidence, the effect of the extent of resection (EOR) is currently controversial. AIM: To determine the best choice between subtotal resection (STR) and gross-total resection (GTR) for individual patients and to identify features that are potentially relevant to treatment heterogeneity. METHODS: Patients were enrolled from the SEER database. We used a novel DL approach to make treatment recommendations for patients with LGG. We also made causal inference of the average treatment effect (ATE) of GTR compared with STR. RESULTS: The patients were divided into the Consis. and In-consis. groups based on whether their actual treatment and model recommendations were consistent. Better brain cancer-specific survival (BCSS) outcomes in the Consis. group was observed. Overall, we also identified two subgroups that showed strong heterogeneity in response to GTR. By interpreting the models, we identified numerous variables that may be related to treatment heterogeneity. CONCLUSIONS: This is the first study to infer the individual treatment effect, make treatment recommendation, and guide surgical options through deep learning approach in LGG research. Through causal inference, we found that heterogeneous responses to STR and GTR exist in patients with LGG. Visualization of the model yielded several factors that contribute to treatment heterogeneity, which are worthy of further discussion.

Analyzing and predicting the risk of death in stroke patients using machine learning.

Background: Stroke is an acute disorder and dysfunction of the focal neurological system that has long been recognized as one of the leading causes of death and severe disability in most regions globally. This study aimed to supplement and exploit multiple comorbidities, laboratory tests and demographic factors to more accurately predict death related to stroke, and furthermore, to make inferences about the heterogeneity of treatment in stroke patients to guide better treatment planning. Methods: We extracted data from the Medical Information Mart from the Intensive Care (MIMIC)-IV database. We compared the distribution of the demographic factors between the control and death groups. Subsequently, we also developed machine learning (ML) models to predict mortality among stroke patients. Furthermore, we used meta-learner to recognize the heterogeneity effects of warfarin and human albumin. We comprehensively evaluated and interpreted these models using Shapley Additive Explanation (SHAP) analysis. Results: We included 7,483 patients with MIMIC-IV in this study. Of these, 1,414 (18.9%) patients died during hospitalization or 30 days after discharge. We found that the distributions of age, marital status, insurance type, and BMI differed between the two groups. Our machine learning model achieved the highest level of accuracy to date in predicting mortality in stroke patients. We also observed that patients who were consistent with the model determination had significantly better survival outcomes than the inconsistent population and were better than the overall treatment group. Conclusion: We used several highly interpretive machine learning models to predict stroke prognosis with the highest accuracy to date and to identify heterogeneous treatment effects of warfarin and human albumin in stroke patients. Our interpretation of the model yielded a number of findings that are consistent with clinical knowledge and warrant further study and verification.

IiSVP of Isatis indigotica can reduce the size and repress the development of floral organs.

KEY MESSAGE: IiSVP of Isatis indigotica was cloned and its expression pattern was analyzed. Ectopic expression of IiSVP in Arabidopsis could delay the flowering time and reduce the size of the floral organs. SVP (SHORT VEGETATIVE PHASE) can negatively regulate the flowering time of Arabidopsis. In the present work, the cDNA of IiSVP, an orthologous gene of AtSVP in I. indigotica, was cloned. IiSVP was highly expressed in rosette leaves, inflorescences and petals, but weakly expressed in sepals, pistils and young silicles. The results of subcellular localization showed that IiSVP was localized in nucleus. Bioinformatics analysis indicated that this protein was a MADS-box transcription factor. Constitutive expression of IiSVP in Arabidopsis thaliana resulted in decrease of the number of petals and stamens, and curly sepals were formed. In IiSVP transgenic Arabidopsis plants, obvious phenotypic variations in flowers could be observed, especially the size of the floral organs. In comparison with the wild-type plants, the size of petals, stamens and pistil in IiSVP transgenic Arabidopsis plants was decreased significantly. In some transgenic plants, the petals were wrapped by the sepals. Yeast two-hybrid experiments showed that IiSVP could form higher-order complexes with other MADS proteins, including IiSEP1, IiSEP3, IiAP1 and IiSEP4, but could not interact with IiSEP2. In this work, it was proved that the flowering process and the floral development in Arabidopsis could be affected by IiSVP from I. indigotica Fortune.

PI-NLOS: polarized infrared non-line-of-sight imaging.

Passive non-line-of-sight (NLOS) imaging is a promising technique to enhance visual perception for the occluded object hidden behind the wall. Here we present a data-driven NLOS imaging framework by using polarization cue and long-wavelength infrared (LWIR) images. We design a dual-channel input deep neural network to fuse the intensity features from polarized LWIR images and contour features from polarization degree images for NLOS scene reconstruction. To train the model, we create a polarized LWIR NLOS dataset which contains over ten thousand images. The paper demonstrates the passive NLOS imaging experiment in which the hidden people is approximate 6 meters away from the relay wall. It is an exciting finding that even the range is further than that in the prior works. The quantitative evaluation metric of PSNR and SSIM show that our method as an advance over state-of-the-art in passive NLOS imaging.

Development and Validation of a Nomogram Based on Geriatric Nutritional Risk Index to Predict Surgical Site Infection Among Gynecologic Oncology Patients.

Background: The geriatric nutritional risk index (GNRI) is a commonly used method to assess nutritional risk for predicting potential surgical site infections (SSI) in cancer patients. This study aims to create and verify a simple nomogram and a dynamic web-based calculator for predicting the risk of SSI among gynecologic oncology patients. Methods: A retrospective evaluation was conducted on patients who were admitted into a tertiary hospital in China with confirmed diagnosis of gynecologic cancer between 01 August 2017 and 30 November 2021. A two-piecewise linear regression model with a smoothing function was used to investigate the non-linear association between GNRI and SSI to determine the ideal cut-off point. Three models were developed on the basis of different variables to predict SSI in gynecologic oncology patients. Through a nomogram the concordance index (C-index), the Akaike information criterion (AIC), and the integrated discrimination index (IDI) were used to determine the final model. Finally, the performance of the nomogram was validated using the 1,000-bootstrap resamples method and analyzed using C-index, GiViTI calibration belts, and decision curve. Also, a user-friendly dynamic web-based calculator was developed. Results: A total of 1,221 patients were included in the analysis. A non-linear association could be observed between GNRI and SSI risk with a GNRI cut-off value of 101.7. After adding GNRI to Model 2 (which comprised Morse Fall Scale score, preoperative length of stay, operation time, and estimated blood loss), the AIC value decreased, the C-index value increased and IDI increased significantly. The nomogram C-index in the development cohort and internal validation cohort demonstrates a moderate-high degree of discrimination. The GiViTI calibrated belt showed a good agreement between the observed and predicted probabilities of SSI. The decision curve validates the clinical feasibility of the nomogram with a threshold value between 0 and 49%. Conclusion: The GNRI cut-off value of 101.7 allowed for appropriate stratification of patients into distinct SSI risk groups. This study found that including GNRI in the above nomogram (Model 2) would enhance its potential to predict SSI in gynecologic oncology patients.

Functional identification of the different regions in B-class floral homeotic MADS-box proteins IiAP3 and IiPI from Isatis indigotica.

APETALA3 (AP3) and PISTILLATA (PI) are B-class MADS-box floral homeotic genes of Arabidopsis and are involved in specifying the identity of petals and stamens. In the present work, IiAP3 and IiPI, the respective orthologous genes of AP3 and PI, were cloned from Isatis indigotica. By expressing in ap3-6 and pi-1 homozygous mutant and in wild-type Arabidopsis under the control of AP3 promoter or CaMV 35S promoter, we demonstrated that IiAP3 and IiPI were functionally equivalent to AP3 and PI of Arabidopsis. Referring to previous reports and the research results in the present work, expression patterns of AP3 and PI homologs are not the same in different angiosperms possessing diverse floral structures. It suggests that the alterations in expression may contribute to the changing morphology of flowers. To further determine the relationship between IiAP3 and IiPI, the coding sequences of the different structural regions in these two proteins were swapped with each other, and the data collected from transgenic Arabidopsis plants of the chimeric constructs suggested that MADS domain was irreplaceable for the function of IiAP3, K domain of IiAP3 was involved in specifying the identity of stamens, K domain of IiPI was mainly related to the formation of petals, and C-terminal region of IiPI was involved in characterization of stamens. In addition, a complete KC region of these two proteins was more effective in phenotypic complementation of the mutants.

Derivation and validation of a nomogram for predicting nonventilator hospital-acquired pneumonia among older hospitalized patients.

BACKGROUND: Currently, there is no effective tool for predicting the risk of nonventilator hospital-acquired pneumonia (NV-HAP) in older hospitalized patients. The current study aimed to develop and validate a simple nomogram and a dynamic web-based calculator for predicting the risk of NV-HAP among older hospitalized patients. METHODS: A retrospective evaluation was conducted on 15,420 consecutive older hospitalized patients admitted to a tertiary hospital in China between September 2017 and June 2020. The patients were randomly divided into training (n = 10,796) and validation (n = 4624) cohorts at a ratio of 7:3. Predictors of NV-HAP were screened using the least absolute shrinkage and selection operator method and multivariate logistic regression. The identified predictors were integrated to construct a nomogram using R software. Furthermore, the optimum cut-off value for the clinical application of the model was calculated using the Youden index. The concordance index (C-index), GiViTI calibration belts, and decision curve were analysed to validate the discrimination, calibration, and clinical utility of the model, respectively. Finally, a dynamic web-based calculator was developed to facilitate utilization of the nomogram. RESULTS: Predictors included in the nomogram were the Charlson comorbidity index, NRS-2002, enteral tube feeding, Barthel Index, use of sedatives, use of NSAIDs, use of inhaled steroids, and "time at risk". The C-index of the nomogram for the training and validation cohorts was 0.813 and 0.821, respectively. The 95% CI region of the GiViTI calibration belt in the training (P = 0.694) and validation (P = 0.614) cohorts did not cross the diagonal bisector line, suggesting that the prediction model had good discrimination and calibration. Furthermore, the optimal cut-off values for the training and validation cohorts were 1.58 and 1.74%, respectively. Analysis of the decision curve showed that the nomogram had good clinical value when the threshold likelihood was between 0 and 49%. CONCLUSION: The developed nomogram can be used to predict the risk of NV-HAP among older hospitalized patients. It can, therefore, help healthcare providers initiate targeted medical interventions in a timely manner for high-risk groups.

SEPALLATA--like genes of Isatis indigotica can affect the architecture of the inflorescences and the development of the floral organs.

BACKGROUND: The architecture of inflorescence and the development of floral organs can influence the yield of seeds and have a significant impact on plant propagation. E-class floral homeotic MADS-box genes exhibit important roles in regulation of floral transition and differentiation of floral organs. Woad (Isatis indigotica) possesses unique inflorescence, floral organs and fruit. However, very little research has been carried out to determine the function of MADS-box genes in this medicinal cruciferous plant species. RESULTS: SEPALLATA orthologs in I. indigotica were cloned by degenerate PCR. The sequence possessing the highest identity with SEP2 and SEP4 of Arabidopsis were named as IiSEP2 and IiSEP4, respectively. Constitutive expression of IiSEP2 in Columbia (Col-0) ecotype of Arabidopsis led to early flowering, and the number of the flowers and the lateral branches was reduced, indicating an alteration in architecture of the inflorescences. Moreover, the number of the floral organs was declined, the sepals were turned into carpelloid tissues bearing stigmatic papillae and ovules, and secondary flower could be produced in apetalous terminal flowers. In 35S::IiSEP4-GFP transgenic Arabidopsis plants in Landsberg erecta (Ler) genetic background, the number of the floral organs was decreased, sepals were converted into curly carpelloid structures, accompanied by generation of ovules. Simultaneously, the size of petals, stamens and siliques was diminished. In 35S::IiSEP4-GFP transgenic plants of apetalous ap1 cal double mutant in Ler genetic background, the cauliflower phenotype was attenuated significantly, and the petal formation could be rescued. Occasionally, chimeric organs composed of petaloid and sepaloid tissues, or petaloid and stamineous tissues, were produced in IiSEP4 transgenic plants of apl cal double mutant. It suggested that overexpression of IiSEP4 could restore the capacity in petal differentiation. Silencing of IiSEP4 by Virus-Induced Gene Silencing (VIGS) can delay the flowering time, and reduce the number and size of the floral organs in woad flowers. CONCLUSION: All the results showed that SEPALLATA-like genes could influence the architecture of the inflorescence and the determinacy of the floral meristems, and was also related to development of the floral organs.

Improved Zero-Charge Storage Performance of LiCoO2/Mesocarbon Microbead Lithium-Ion Batteries by Li5FeO4 Cathode Additive.

Discharging lithium-ion batteries to zero-charge state is one of the most reliable ways to avoid the thermal runaway during their transportation and storage. However, the zero-charge state causes the degradation or even complete failure of lithium-ion batteries. Specialized solutions are required to endow lithium-ion batteries with improved zero-charge storage performance, namely, the ability to tolerate zero-charge state for a long time without unacceptable capacity loss. Here, we report that a Li5FeO4 cathode additive can improve the zero-charge storage performance of LiCoO2/mesocarbon microbead (MCMB) batteries. The irreversible charge capacity of the Li5FeO4 additive results in the downregulation of anode and cathode potentials when the battery is at zero-charge state. More importantly, the Li5FeO4 additive offers a small discharge plateau below 2.9 V versus Li/Li+, which can hold the anode potential at zero-charge battery state (APZBS) in a potential range of 2.4∼2.5 V versus Li/Li+ during storage for 10 days. Such a precise control on APZBS not only suppresses the decomposition of the solid electrolyte interface film on the MCMB anode and inhibits the dissolution of the copper current collector occurring at high potentials but also avoids the excessive decrease of the cathode potential at the zero-charge battery state and consequently protects the LiCoO2 cathode from overlithiation occurring at low potentials. As a result, the Li5FeO4 additive with a charge capacity percentage of 23% in the cathode increases the capacity recovery ratio of the LiCoO2/MCMB battery from 37.6 to 95.5% after being stored at the zero-charge state for 10 days.

Protein Phosphorylation Changes During Systemic Acquired Resistance in Arabidopsis thaliana.

Systemic acquired resistance (SAR) in plants is a defense response that provides resistance against a wide range of pathogens at the whole-plant level following primary infection. Although the molecular mechanisms of SAR have been extensively studied in recent years, the role of phosphorylation that occurs in systemic leaves of SAR-induced plants is poorly understood. We used a data-independent acquisition (DIA) phosphoproteomics platform based on high-resolution mass spectrometry in an Arabidopsis thaliana model to identify phosphoproteins related to SAR establishment. A total of 8011 phosphorylation sites from 3234 proteins were identified in systemic leaves of Pseudomonas syringae pv. maculicola ES4326 (Psm ES4326) and mock locally inoculated plants. A total of 859 significantly changed phosphoproteins from 1119 significantly changed phosphopeptides were detected in systemic leaves of Psm ES4326 locally inoculated plants, including numerous transcription factors and kinases. A variety of defense response-related proteins were found to be differentially phosphorylated in systemic leaves of Psm ES4326 locally inoculated leaves, suggesting that these proteins may be functionally involved in SAR through phosphorylation or dephosphorylation. Significantly changed phosphoproteins were enriched mainly in categories related to response to abscisic acid, regulation of stomatal movement, plant-pathogen interaction, MAPK signaling pathway, purine metabolism, photosynthesis-antenna proteins, and flavonoid biosynthesis. A total of 28 proteins were regulated at both protein and phosphorylation levels during SAR. RT-qPCR analysis revealed that changes in phosphorylation levels of proteins during SAR did not result from changes in transcript abundance. This study provides comprehensive details of key phosphoproteins associated with SAR, which will facilitate further research on the molecular mechanisms of SAR.

Signals in systemic acquired resistance of plants against microbial pathogens.

After a local infection by the microbial pathogens, plants will produce strong resistance in distal tissues to cope with the subsequent biotic attacks. This type of the resistance in the whole plant is termed as systemic acquired resistance (SAR). The priming of SAR can confer the robust defense responses and the broad-spectrum disease resistances in plants. In general, SAR is activated by the signal substances generated at the local sites of infection, and these small signaling molecules can be rapidly transported to the systemic tissues through the phloem. In the last two decades, numerous endogenous metabolites were proved to be the potential elicitors of SAR, including methyl salicylate (MeSA), azelaic acid (AzA), glycerol-3-phosphate (G3P), free radicals (NO and ROS), pipecolic acid (Pip), N-hydroxy-pipecolic acid (NHP), dehydroabietinal (DA), monoterpenes (α-pinene and ß-pinene) and NAD(P). In the meantime, the proteins associated with the transport of these signaling molecules were also identified, such as DIR1 (DEFECTIVE IN INDUCED RESISTANCE 1) and AZI1 (AZELAIC ACID INDUCED 1). This review summarizes the recent findings related to synthesis, transport and interaction of the different signal substances in SAR.

Role of melatonin in UV-B signaling pathway and UV-B stress resistance in Arabidopsis thaliana.

Melatonin (N-acetyl-5-methoxytryptamine) plays important roles in plant defences against a variety of biotic and abiotic stresses, including UV-B stress. Molecular mechanisms underlying functions of melatonin in plant UV-B responses are poorly understood. Here, we show that melatonin effect on molecular signalling pathways, physiological changes and UV-B stress resistance in Arabidopsis. Both exogenous and endogenous melatonin affected expression of UV-B signal transduction pathway genes. Experiments using UV-B signalling component mutants cop1-4 and hy5-215 revealed that melatonin not only acts as an antioxidant to promote UV-B stress resistance, but also regulates expression of several key components of UV-B signalling pathway, including ubiquitin-degrading enzyme (COP1), transcription factors (HY5, HYH) and RUP1/2. Our findings indicate that melatonin delays and subsequently enhances expression of COP1, HY5, HYH and RUP1/2, which act as central effectors in UV-B signalling pathway, thus regulating their effects on antioxidant systems to protect the plant from UV-B stress.

Metabolomics analysis identifies metabolites associated with systemic acquired resistance in Arabidopsis.

BACKGROUND: Systemic acquired resistance (SAR) is a type of plant defense response that provides a long-lasting resistance to broad-spectrum pathogens in uninfected distal tissues following an initial localized infection. However, little information is available at present on the biological basis of SAR at the molecular level, especially in uninfected distal leaves. METHODS: In the present work, we used two SAR-inducing pathogens, avirulent Pseudomonas syringae pv. maculicola ES4326 harboring avrRpm1 (Psm avrRpm1) and virulent P. syringae pv. maculicola ES4326 (Psm ES4326), to induce SAR in Arabidopsis ecotype Col-0. A metabolomics approach based on ultra-high-performance liquid chromatography (UPLC) coupled with mass spectrometry (MS) was used to identify SAR-related metabolites in infected local leaves, and in uninfected distal leaves. RESULTS: Differentially accumulated metabolites were distinguished by statistical analyses. The results showed that both the primary metabolism and the secondary metabolism were significantly altered in infected local leaves and in uninfected distal leaves, including phenolic compounds, amino acids, nucleotides, organic acids, and many other metabolites. CONCLUSIONS: The content of amino acids and phenolic compounds increased in uninfected distal leaves, suggesting their contribution to the establishment of SAR. In addition, 2'-hydroxy-4, 4', 6'-trimethoxychalcone, phenylalanine, and p-coumaric acid were identified as potential components which may play important roles both in basic resistance and in SAR. This work provides a reference for understanding of the metabolic mechanism associated with SAR in plants, which will be useful for further investigation of the molecular basis of the systemic immunity.