DNA-functionalized MOF fluorescent probes for the enzyme-free and pretreatment-free detection of MicroRNA in serum.

MicroRNA (miRNA) is a promising biomarker that plays an important role in various biomedical applications, especially in cancer diagnosis. However, the current miRNA detection technology has inherent limitations such as complex operation, expensive testing cost and excessive detection time. In this study, a dual signal amplification biosensor based on DNA-functionalized metal-organic frameworks (MOFs) fluorescent probes, MFPBiosensor, was established for the enzyme-free and pretreatment-free detection of the colon cancer (CC) marker miR-23a. DNA-functionalized MOFs NH2-MIL-53(Al) (DNA@MOFs) were synthesized as fluorescent probes with specific recognition functions. A single DNA@MOF carries a large number of fluorescent ligands 2-aminoterephthalic acid (NH2-H2BDC), which can generate strong fluorescence signals after alkaline hydrolysis. Combined with catalyzed hairpin assembly (CHA), an efficient isothermal amplification technique, the dual signal enhancement strategy reduced matrix interference and sensitized the signal response. The established MFPBiosensor successfully detected extremely low levels of miRNA in complex biological samples with acceptable sensitivity and specificity. With a single detection cost of $0.583 and a test time of 50 min, the excellent inexpensive and rapid advantage of the MFPBiosensor is highlighted. More importantly, the subtle design enables the MFPBiosensor to achieve convenient batch detection, where miRNA in serum can be directly detected without any pretreatment process or enzyme. In conclusion, MFPBiosensor is a promising biosensor with substantial potential for commercial miRNA detection and clinical diagnostic applications of CC.

Development of double-layer poly (amino acid) modified electrochemical sensor for sensitive and direct detection of betamethasone in cosmetics.

Screening for illegal use of glucocorticoids (GCs) in cosmetics by electrochemical methods is extremely challenging due to the poor electrochemical activity of GCs. In this study, poly-L-Serine/poly-Taurine modified electrode (P(Tau)/P(L-Ser)/GCE) was prepared for sensitive and direct determination of betamethasone in cosmetics by a simple two-step in situ electropolymerization reaction. The relevant parameters of preparation and electroanalytical conditions were respectively studied, including the concentration of polymerization solution, the number of scanning circles and the scanning rate. The SEM and EDS mapping demonstrated successful preparation of P(Tau)/P(L-Ser)/GCE. The electro-catalytic properties of the obtained electrodes were investigated using cyclic voltammetry and differential pulse voltammetry methods, showing a remarkable improvement of sensitivity for the detection of betamethasone due to the synergic effect of both P(L-Ser) and P(Tau). In addition, we investigated the electrochemical reduction of betamethasone on the surface of modified electrode. It was found that the process was controlled by diffusion effect and involved the transfer of two electrons and two protons. Then the electrochemical sensor method based on P(Tau)/P(L-Ser)/GCE was established and delivered a linear response to betamethasone concentration from 0.5 to 20 µg mL-1 with a limit of detection of 32.2 ng mL-1, with excellent recoveries (98.1%-106.8%) and relative standard deviations (＜4.8%). Furthermore, the established electrochemical sensor method was compared with conventional HPLC method. The results showed that both of them were comparable. Moreover, the established electrochemical sensor method was with the merits of short analysis time, environmentally friendly, low cost and easy to achieve in-site detection.

The identification of blood pressure variation with hypovolemia based on the volume compensation method.

Objective: The purpose of this study is to identify the blood pressure variation, which is important in continuous blood pressure monitoring, especially in the case of low blood volume, which is critical for survival. Methods: A pilot study was conducted to identify blood pressure variation with hypovolemia using five Landrace pigs. New multi-dimensional morphological features of Photoplethysmography (PPG) were proposed based on experimental study of hemorrhagic shock in pigs, which were strongly correlated with blood pressure changes. Five machine learning methods were compared to develop the blood pressure variation identification model. Results: Compared with the traditional blood pressure variation identification model with single characteristic based on single period area of PPG, the identification accuracy of mean blood pressure variation based on the proposed multi-feature random forest model in this paper was up to 90%, which was 17% higher than that of the traditional blood pressure variation identification model. Conclusion: By the proposed multi-dimensional features and the identification method, it is more accurate to detect the rapid variation in blood pressure and to adopt corresponding measures. Significance: Rapid and accurate identification of blood pressure variation under low blood volume ultimately has the potential to effectively avoid complications caused by abnormal blood pressure in patients with clinical bleeding trauma.

A machine learning method for predicting the probability of MODS using only non-invasive parameters.

OBJECTIVES: Timely and accurate prediction of multiple organ dysfunction syndrome (MODS) is essential for the rescue and treatment of trauma patients However, existing methods are invasive, easily affected by artifacts and can be difficult to perform in a pre-hospital setting. We aim to develop prediction models for patients with MODS using only non-invasive parameters. METHOD: In this study, records from 2319 patients were extracted from the Multiparameter Intelligent Monitoring in Intensive Care Ⅲ database (MIMIC Ⅲ), based on the sequential organ failure assessment (SOFA) score. Seven commonly used machine learning (ML) methods were selected and applied to develop a real-time prediction method for MODS based on full parameters (laboratory parameter. drug and non-invasive parameters, 57 parameters in total) and non-invasive parameters only (17 parameters) and compared with four traditional scoring systems. RESULTS: The prediction results using LightGBM (LGBM) and Adaboost based on the full parameter modeling were 0.959 for area under receiver operating characteristic curve (AUC), outperforming four traditional scoring systems. The removal of 40 parameters and retaining of 17 non-invasive parameters decreased the AUC value of LGBM by 0.015, which still outperformed all traditional scoring systems. CONCLUSIONS: A real-time and accurate MODS prediction method was developed in this paper based on non-invasive parameters by comparing the performance of four ML methods, which proved to be superior to the traditional scoring systems. This method can help medical staff to diagnose MODS as soon as possible and can improve the survival rate of patients in a pre-hospital setting.

Highly efficient amino-functionalized aluminum-based metal organic frameworks mesoporous nanorods for selective extraction of hydrocortisone in pharmaceutical wastewater.

Hydrocortisone (HC), as a common steroid hormone drug, is also one of the key intermediates involved in the synthesis of multiple steroid hormone drugs. Residual HC in pharmaceutical wastewater frequently pollutes environmental water as steroid hormone contaminant and possesses great threat to human health as well as sustainable development of the ecosystem. Herein, in order to develop a highly efficient adsorbent system for selective enrichment and detection of HC in pharmaceutical wastewater, a novel amino-functionalized aluminum-based metal organic frameworks (Al-MOFs@NH2) mesoporous nanorod is fabricated, in which 2-aminoterephthalic acid plays a dual role as organic linker and functional modification unit. The resultant Al-MOFs@NH2 not only exhibits stable mesoporous structure but also has large specific surface area (849.76 m2 g-1) and plentiful binding sites, which significantly increases the adsorption capacity for HC. Under the promotion of hydrogen bonding and hydrophobic interaction together, Al-MOFs@NH2 possesses high adsorption capacity (218.53 mg g-1) for HC, as well as shows satisfactory selectivity for HC and other steroid hormones. Moreover, a method using Al-MOFs@NH2 as solid phase extraction adsorbents combined with high performance liquid chromatography (HPLC) has been developed to specifically enrich and detect trace amount of HC in pharmaceutical wastewater. The developed method has a low limit of detection (LOD) (0.5×10-3 µg mL-1) and shows satisfactory recoveries for HC (75.9%-102.5%) with an acceptable relative standard deviation (RSD). These results demonstrate that the facile one-step preparation and excellent adsorption capacity makes Al-MOFs@NH2 attractive to capture and remove environmental steroid hormone pollutants. More importantly, the method proposed in this work is expected to provide a prospective solution for analysis of strong bioactive contaminants in pharmaceutical wastewater.

Enzyme Encapsulation by Facile Self-Assembly Silica-Modified Magnetic Nanoparticles for Glucose Monitoring in Urine.

Silica nanoparticles hold tremendous potential for the encapsulation of enzymes. However, aqueous alcohol solutions and catalysts are prerequisites for the production of silica nanoparticles, which are too harsh for maintaining the enzyme activity. Herein, a procedure without any organic solvents and catalysts (acidic or alkaline) is developed for the synthesis of silica-encapsulated glucose-oxidase-coated magnetic nanoparticles by a facile self-assembly route, avoiding damage of the enzyme structure in the reaction system. The encapsulated enzyme was characterized by scanning electron microscopy, transmission electron microscopy, energy-dispersive spectrometry, and a vibrating sample magnetometer. Finally, a colorimetric sensing method was developed for the detection of glucose in urine samples based on the encapsulated glucose oxidase and a hydrogen peroxide test strip. The method exhibited a good linear performance in the concentration range of 20~160 µg mL-1 and good recoveries ranging from 94.3 to 118.0%. This work proves that the self-assembly method could be employed to encapsulate glucose oxidase into silica-coated magnetic particles. The developed colorimetric sensing method shows high sensitivity, which will provide a promising tool for the detection of glucose and the monitoring of diabetes.

Two-photon and Three-photon Fluorescence of Triton X-100 in the Ultraviolet Region.

We report, to our best knowledge, the first observation of two-photon and three-photon fluorescence of Triton X-100 (TX-100) in water and cyclohexane. The observed multiphoton fluorescence (MF) falls in the ultraviolet region 280-340nm as its one photon fluorescence does. Effects of excitation wavelengths and solution concentrations on the fluorescence spectra are investigated. We found the optimal excitation wavelength and solution concentration to obtain the strongest MF. For relatively weaker three-photon fluorescence, there exists fluctuation in its spectrum due to its small SNR. The peak wavelength is around 300nm and only varies slightly with the solution concentration, solvent type, and excitation wavelength, which is quite different from those of other luminophors. This work has extended the wave band of MF to the purple and ultraviolet regions of 280-340nm and study of TX-100 to nonlinear optics field. The results may be potentially applied in ultraviolet MF detection and in manufacturing ultraviolet multiphoton laser in the future. Although for the latter case, there is still a long way to go to enhance its fluorescence efficiency and cross section of stimulated emission beforehand.

A Novel Nanomedicine Ameliorates Acute Inflammatory Bowel Disease by Regulating Macrophages and T-Cells.

Ramulus mori polysaccharide (RMP), one of the most important active components of R. mori, has been attracting increasing interest because of its potent bioactive properties, including anti-inflammatory, antitumor, and antidiabetic effects. Despite the great therapeutic potential of RMP, its inherent properties of low bioavailability and brief biological half-life have limited its applications to the clinic. Thus, RMP was packaged by poly(lactic-co-glycolic acid) (PLGA) nanoparticles to develop a novel anti-inflammatory nanomedicine (PLGA-RMP) in this study. The nanoparticles were synthesized via a double-emulsion solvent evaporation technique, and the average diameter of PLGA-RMP was about 202 nm. PLGA-RMP nanoparticles reduced the expression of inflammatory cytokines while promoting the production of IL-10, and boosted the phenotypic shift in macrophages in vitro. Furthermore, lipopolysaccharide (LPS)-induced inflammatory bowel disease (IBD) in mouse was used to examine the anti-inflammatory effect of PLGA-RMP in vivo. Oral administration of PLGA-RMP in LPS-induced IBD mice substantially mitigated the intestinal inflammation compared to treatment with LPS alone, as evidenced by attenuation of disease activity index scores and inflammatory damage in the intestine. Meanwhile, PLGA-RMP suppressed the expression and secretion of specific inflammatory cytokines including TNF-α, IL-6, IL-1ß, and PGE2 in the inflamed intestine while inhibiting the activation of CD3+CD8+ T-cells and increasing the number of activated Tregs in the intestine. These results indicated that PLGA-RMP deserves further consideration as a potential therapeutic nanomedicine to treat various inflammatory diseases, including IBD.

Prognostic Signature for Lung Adenocarcinoma Patients Based on Cell-Cycle-Related Genes.

OBJECTIVE: To screen lung adenocarcinoma (LUAC)-specific cell-cycle-related genes (CCRGs) and develop a prognostic signature for patients with LUAC. METHODS: The GSE68465, GSE42127, and GSE30219 data sets were downloaded from the GEO database. Single-sample gene set enrichment analysis was used to calculate the cell cycle enrichment of each sample in GSE68465 to identify CCRGs in LUAC. The differential CCRGs compared with LUAC data from The Cancer Genome Atlas were determined. The genetic data from GSE68465 were divided into an internal training group and a test group at a ratio of 1:1, and GSE42127 and GSE30219 were defined as external test groups. In addition, we combined LASSO (least absolute shrinkage and selection operator) and Cox regression analysis with the clinical information of the internal training group to construct a CCRG risk scoring model. Samples were divided into high- and low-risk groups according to the resulting risk values, and internal and external test sets were used to prove the validity of the signature. A nomogram evaluation model was used to predict prognosis. The CPTAC and HPA databases were chosen to verify the protein expression of CCRGs. RESULTS: We identified 10 LUAC-specific CCRGs (PKMYT1, ETF1, ECT2, BUB1B, RECQL4, TFRC, COCH, TUBB2B, PITX1, and CDC6) and constructed a model using the internal training group. Based on this model, LUAC patients were divided into high- and low-risk groups for further validation. Time-dependent receiver operating characteristic and Cox regression analyses suggested that the signature could precisely predict the prognosis of LUAC patients. Results obtained with CPTAC, HPA, and IHC supported significant dysregulation of these CCRGs in LUAC tissues. CONCLUSION: This prognostic prediction signature based on CCRGs could help to evaluate the prognosis of LUAC patients. The 10 LUAC-specific CCRGs could be used as prognostic markers of LUAC.

SIZ1 negatively regulates aluminum resistance by mediating the STOP1-ALMT1 pathway in Arabidopsis.

Sensitive to proton rhizotoxicity 1 (STOP1) functions as a crucial regulator of root growth during aluminum (Al) stress. However, how this transcription factor is regulated by Al stress to affect downstream genes expression is not well understood. To explore the underlying mechanisms of the function and regulation of STOP1, we employed a yeast two hybrid screen to identify STOP1-interacting proteins. The SUMO E3 ligase SIZ1, was found to interact with STOP1 and mainly facilitate its SUMO modification at K40 and K212 residues. Simultaneous introduction of K40R and K212R substitutions in STOP1 enhances its transactivation activity to upregulate the expression of aluminum-activated malate transporter 1 (ALMT1) via increasing the association with mediator 16 (MED16) transcriptional co-activator. Loss of function of SIZ1 causes highly increased expression of ALMT1, thus enhancing Al-induced malate exudation and Al tolerance. Also, we found that the protein level of SIZ1 is reduced in response to Al stress. Genetic evidence demonstrates that STOP1/ALMT1 is epistatic to SIZ1 in regulating root growth response to Al stress. This study suggests a mechanism about how the SIZ1-STOP1-ALMT1 signaling module is involved in root growth response to Al stress.

Two-Photon Fluorescence Study of Olive Oils at Different Excitation Wavelengths.

Two-photon fluorescence (TPF) of olive oils is discovered and observed experimentally for the first time. Variations of the single-photon fluorescence (SPF) and TPF with the excitation wavelength are investigated for four different olive oils. The results show that fluorescence of the cosmetic olive oils (COO) is very weak and exhibits only one spectral peak around 490 nm. While for the ordinary edible oils (OEO) whether they are during their shelf life or not, their fluorescence spectra may exhibit multiple peak structures. The short-term natural expiration only slightly weakens TPF of OEO. Moreover, the excitation wavelength affects the OEO spectra considerably in terms of the spectral peak number, the spectral peak position, and spectral shapes. When the excitation wavelength decreases from 700 nm, the whole TPF of the OEO also decreases. Relatively, however, the short wave band will decrease and disappear more quickly. While for the SPF, the long wave band will decrease and disappear first. The optimal excitation wavelengths to make the TPF strongest are around 700 nm and 640 nm for OEOs and COO, respectively. And effects of temperature on SPF and TPF of extra virgin olive oil are also explored. This work may be of significance for its potential applications in TPF detection and two-photon laser.

Exciting Wavelength and Concentration Related Two-Photon Fluorescence of Single and Mixed Laser Dyes.

Two-photon nonlinear process induced fluorescence of Rhodamine 6G (R6G), Rhodamine B (RB), and their mixed aqueous solutions in mass proportion of 1:1, is experimentally observed by different exciting wavelengths. It shows that, for each sample, the exciting wavelength can influence the fluorescence intensity considerably but only slightly influence the peak wavelength of the spectrum. The optimal exciting wavelengths of R6G and the mixed dyes are around 700 nm. While for RB, the optimal exciting wavelengths can be 700 nm and 620 nm. For each dye sample, the spectral red-shift will occur as increase of the solution concentration. The mixing of the two dyes will cause the spectral red-shift with regard to the single dye under our experimental conditions. Moreover, in comparison, at lower concentrations, the mixed dye has relatively intense fluorescence. This work is of significance for determining the optimal exciting wavelength and developing the tunable two-photon dye lasers.

A machine learning approach for mortality prediction only using non-invasive parameters.

At present, the traditional scoring methods generally utilize laboratory measurements to predict mortality. It results in difficulties of early mortality prediction in the rural areas lack of professional laboratorians and medical laboratory equipment. To improve the efficiency, accuracy, and applicability of mortality prediction in the remote areas, a novel mortality prediction method based on machine learning algorithms is proposed, which only uses non-invasive parameters readily available from ordinary monitors and manual measurement. A new feature selection method based on the Bayes error rate is developed to select valuable features. Based on non-invasive parameters, four machine learning models were trained for early mortality prediction. The subjects contained in this study suffered from general critical diseases including but not limited to cancer, bone fracture, and diarrhea. Comparison tests among five traditional scoring methods and these four machine learning models with and without laboratory measurement variables are performed. Only using the non-invasive parameters, the LightGBM algorithms have an excellent performance with the largest accuracy of 0.797 and AUC of 0.879. There is no apparent difference between the mortality prediction performance with and without laboratory measurement variables for the four machine learning methods. After reducing the number of feature variables to no more than 50, the machine learning models still outperform the traditional scoring systems, with AUC higher than 0.83. The machine learning approaches only using non-invasive parameters achieved an excellent mortality prediction performance and can equal those using extra laboratory measurements, which can be applied in rural areas and remote battlefield for mortality risk evaluation. Graphical abstract.

AtHB7/12 Regulate Root Growth in Response to Aluminum Stress.

Aluminum (Al) stress is a major limiting factor for plant growth and crop production in acid soils. At present, only a few transcription factors involved in the regulation of Al resistance have been characterized. Here, we used reversed genetic approach through phenotype analysis of overexpressors and mutants to demonstrate that AtHB7 and AtHB12, two HD-Zip I transcription factors, participate in Al resistance. In response to Al stress, AtHB7 and AtHB12 displayed different dynamic expression patterns. Although both AtHB7 and AtHB12 positively regulate root growth in the absence of Al stress, our results showed that AtHB7 antagonizes with AtHB12 to control root growth in response to Al stress. The athb7/12 double mutant displayed a wild-type phenotype under Al stress. Consistently, our physiological analysis showed that AtHB7 and AtHB12 oppositely regulate the capacity of cell wall to bind Al. Yeast two hybrid assays showed that AtHB7 and AtHB12 could form homo-dimers and hetero-dimers in vitro, suggesting the interaction between AtHB7 and AtHB12 in the regulation of root growth. The conclusion was that AtHB7 and AtHB12 oppositely regulate Al resistance by affecting Al accumulation in root cell wall.

A new method for identifying the acute respiratory distress syndrome disease based on noninvasive physiological parameters.

Early diagnosis and prevention play a crucial role in the treatment of patients with ARDS. The definition of ARDS requires an arterial blood gas to define the ratio of partial pressure of arterial oxygen to fraction of inspired oxygen (PaO2/FiO2 ratio). However, many patients with ARDS do not have a blood gas measured, which may result in under-diagnosis of the condition. Using data from MIMIC-III Database, we propose an algorithm based on patient non-invasive physiological parameters to estimate P/F levels to aid in the diagnosis of ARDS disease. The machine learning algorithm was combined with the filter feature selection method to study the correlation of various noninvasive parameters from patients to identify the ARDS disease. Cross-validation techniques are used to verify the performance of algorithms for different feature subsets. XGBoost using the optimal feature subset had the best performance of ARDS identification with the sensitivity of 84.03%, the specificity of 87.75% and the AUC of 0.9128. For the four machine learning algorithms, reducing a certain number of features, AUC can still above 0.8. Compared to Rice Linear Model, this method has the advantages of high reliability and continually monitoring the development of patients with ARDS.

A matter of time: grappling with everyday ethical tensions at the confluence between policy and practice in a psychiatric unit.

OBJECTIVE: To provide insights on emergent ethical tensions experienced by mental health practitioners during system re-organisation, which is sufficiently grounded in empirical data at the local level to inform policy on recovery at institutional and provincial levels. METHOD: Ethnographic methods using narrative and critical phenomenological resources over 24 months. FINDINGS: Everyday ethical tensions emerged at the confluence of different experiences of time, for example, how a context of increasing pressure to decrease patients' length of stay at the hospital (service-defined time) challenged efforts to listen to and advocate for what mattered to patients (personal time) and maintain the integrity of interventions (clinical time). In this context, practitioners drew on clinical language and that of personal recovery to strategically 'push back', 'play with' or 'take back' time. DISCUSSION: Examining everyday practices through ethnographic methods can illuminate the everyday ethical tensions that arise when mental health professionals and psychiatrists grapple with, often competing, goods. Critical phenomenological resources can help expand the structural considerations in empirical ethics, excavate underground practices and raise questions about the conceptual categories undergirding normative ethics. Experiencing-with practitioners in clinical contexts as they encounter and creatively resolve ethical tensions also propose a normative ethics of possibility, to help bridge the gap between empirical and normative ethics. CONCLUSION: Focus on the relationship between policy, temporal practices and ethics suggests a reconfiguration of time and re-imagination of ethics in institutional settings in ways that can ultimately benefit patients and professionals alike.

Dye-enhanced third upconversion Raman overtone in the ultraviolet region under intense excitation of a 532 nm laser.

We report, to our best knowledge, the first observation of enhanced third upconversion Raman overtone of water in the ultraviolet region 240-380 nm by Rhodamine 6G (R6G) under intense excitation of a 532 nm laser. Its spectral peak is mostly around 288 nm. This ultraviolet spectrum has also been obtained for purified water on the Raman spectrometer LRS-3 and fluorescence spectrometer RF-5301PC, which have photomultiplier tubes. The spectral profile is approximately a triangle in most cases. Moreover, an intense and broad background is also observed to appear in the spectrum. Additionally, the conventional visible luminescence spectrum of R6G is discovered to coexist with the ultraviolet one. In comparison, the visible spectrum is much more intense.

Inference of time-delayed gene regulatory networks based on dynamic Bayesian network hybrid learning method.

Gene regulatory networks (GRNs) research reveals complex life phenomena from the perspective of gene interaction, which is an important research field in systems biology. Traditional Bayesian networks have a high computational complexity, and the network structure scoring model has a single feature. Information-based approaches cannot identify the direction of regulation. In order to make up for the shortcomings of the above methods, this paper presents a novel hybrid learning method (DBNCS) based on dynamic Bayesian network (DBN) to construct the multiple time-delayed GRNs for the first time, combining the comprehensive score (CS) with the DBN model. DBNCS algorithm first uses CMI2NI (conditional mutual inclusive information-based network inference) algorithm for network structure profiles learning, namely the construction of search space. Then the redundant regulations are removed by using the recursive optimization algorithm (RO), thereby reduce the false positive rate. Secondly, the network structure profiles are decomposed into a set of cliques without loss, which can significantly reduce the computational complexity. Finally, DBN model is used to identify the direction of gene regulation within the cliques and search for the optimal network structure. The performance of DBNCS algorithm is evaluated by the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in Escherichia coli, and compared with other state-of-the-art methods. The experimental results show the rationality of the algorithm design and the outstanding performance of the GRNs.

Characterization of VuMATE1 Expression in Response to Iron Nutrition and Aluminum Stress Reveals Adaptation of Rice Bean (Vigna umbellata) to Acid Soils through Cis Regulation.

Rice bean (Vigna umbellata) VuMATE1 appears to be constitutively expressed at vascular system but root apex, and Al stress extends its expression to root apex. Whether VuMATE1 participates in both Al tolerance and Fe nutrition, and how VuMATE1 expression is regulated is of great interest. In this study, the role of VuMATE1 in Fe nutrition was characterized through in planta complementation assays. The transcriptional regulation of VuMATE1 was investigated through promoter analysis and promoter-GUS reporter assays. The results showed that the expression of VuMATE1 was regulated by Al stress but not Fe status. Complementation of frd3-1 with VuMATE1 under VuMATE1 promoter could not restore phenotype, but restored with 35SCaMV promoter. Immunostaining of VuMATE1 revealed abnormal localization of VuMATE1 in vasculature. In planta GUS reporter assay identified Al-responsive cis-acting elements resided between -1228 and -574 bp. Promoter analysis revealed several cis-acting elements, but transcription is not simply regulated by one of these elements. We demonstrated that cis regulation of VuMATE1 expression is involved in Al tolerance mechanism, while not involved in Fe nutrition. These results reveal the evolution of VuMATE1 expression for better adaptation of rice bean to acid soils where Al stress imposed but Fe deficiency pressure released.

Physiological and Molecular Analysis of Aluminium-Induced Organic Acid Anion Secretion from Grain Amaranth (Amaranthus hypochondriacus L.) Roots.

Grain amaranth (Amaranthus hypochondriacus L.) is abundant in oxalate and can secrete oxalate under aluminium (Al) stress. However, the features of Al-induced secretion of organic acid anions (OA) and potential genes responsible for OA secretion are poorly understood. Here, Al-induced OA secretion in grain amaranth roots was characterized by ion charomatography and enzymology methods, and suppression subtractive hybridization (SSH) together with quantitative real-time PCR (qRT-PCR) was used to identify up-regulated genes that are potentially involved in OA secretion. The results showed that grain amaranth roots secrete both oxalate and citrate in response to Al stress. The secretion pattern, however, differs between oxalate and citrate. Neither lanthanum chloride (La) nor cadmium chloride (Cd) induced OA secretion. A total of 84 genes were identified as up-regulated by Al, in which six genes were considered as being potentially involved in OA secretion. The expression pattern of a gene belonging to multidrug and toxic compound extrusion (MATE) family, AhMATE1, was in close agreement with that of citrate secretion. The expression of a gene encoding tonoplast dicarboxylate transporter and four genes encoding ATP-binding cassette transporters was differentially regulated by Al stress, but the expression pattern was not correlated well with that of oxalate secretion. Our results not only reveal the secretion pattern of oxalate and citrate from grain amaranth roots under Al stress, but also provide some genetic information that will be useful for further characterization of genes involved in Al toxicity and tolerance mechanisms.