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Clarifying current situation of farmers' fertilization and yield in citrus producing areas and the effects of different fertilization measures can provide a scientific basis for improving the yield and quality of citrus in China. We retrieved 92 literatures on citrus fertilization from the CNKI and Web of Science to examine the impacts of nitrogen (N), phosphorus (P or P2O5), and potassium (K or K2O) fertilizer dosage and partial productivity under farmers' conventional fertilization and experts' optimized fertilization, as well as the effects of optimized fertilization measures on citrus yield and quality by using meta-analysis approach. The average conventional application rates of N, P2O5, and K2O were 507.3, 262.2, and 369.3 kg·hm-2 in citrus production in China. Compared with conventional fertilization, optimized fertilization resulted in a reduction of N and P2O5 by 14.7% and 8.3%, an increase in K2O application by 6.6%, which promoted partial productivity of N, P2O5, and K2O fertilizers by 7.8%, 18.4%, and 14.7%, correspondingly. The optimized fertilization resulted in 11.9% and 2.8% increase in fruit yield and single fruit weight, while improved vitamin C content (Vc, 3.1%), total soluble solids (TSS, 5.9%) and total sugar content (TSC, 8.6%). Additionally, it also led to a reduction in titratable acid (TA, -3.4%) and total acid content (TAC, -3.6%), and consequently elevated the TSS/TA (14.0%) and TSC/TAC (9.5%). Among different optimized fertilization methods, the effect of optimized NPK + medium and/or micro element fertilizer on citrus yield and fruit quality was the best, especially NPK decrement ≤25% between optimized NPK measures. The effect of conventional NPK + organic fertilizer was higher than conventional NPK + medium and/or micro element fertilizer. However, different citrus varieties, including mandarins, pomelos, and oranges, showed different responses to optimized fertilization. Optimized fertilization management could synergistically improve citrus yield, fertilizer use efficiency, and fruit quality. Therefore, the strategy of integrated nutrient management1 with reducing NPK fertilizer, balancing medium and/or micro nutrient fertilizer and improving soil fertility by organic fertilizer should be adopted according to local conditions in citrus producing areas of China.

Development of a convenient and effective hypertension risk prediction model and exploration of the relationship between Serum Ferritin and Hypertension Risk: a study based on NHANES 2017-March 2020.

Background: Hypertension is a major public health problem, and its resulting other cardiovascular diseases are the leading cause of death worldwide. In this study, we constructed a convenient and high-performance hypertension risk prediction model to assist in clinical diagnosis and explore other important influencing factors. Methods: We included 8,073 people from NHANES (2017-March 2020), using their 120 features to form the original dataset. After data pre-processing, we removed several redundant features through LASSO regression and correlation analysis. Thirteen commonly used machine learning methods were used to construct prediction models, and then, the methods with better performance were coupled with recursive feature elimination to determine the optimal feature subset. After data balancing through SMOTE, we integrated these better-performing learners to construct a fusion model based for predicting hypertension risk on stacking strategy. In addition, to explore the relationship between serum ferritin and the risk of hypertension, we performed a univariate analysis and divided it into four level groups (Q1 to Q4) by quartiles, with the lowest level group (Q1) as the reference, and performed multiple logistic regression analysis and trend analysis. Results: The optimal feature subsets were: age, BMI, waist, SBP, DBP, Cre, UACR, serum ferritin, HbA1C, and doctors recommend reducing salt intake. Compared to other machine learning models, the constructed fusion model showed better predictive performance with precision, accuracy, recall, F1 value and AUC of 0.871, 0.873, 0.871, 0.869 and 0.966, respectively. For the analysis of the relationship between serum ferritin and hypertension, after controlling for all co-variates, OR and 95% CI from Q2 to Q4, compared to Q1, were 1.396 (1.176-1.658), 1.499 (1.254-1.791), and 1.645 (1.360-1.989), respectively, with P < 0.01 and P for trend <0.001. Conclusion: The hypertension risk prediction model developed in this study is efficient in predicting hypertension with only 10 low-cost and easily accessible features, which is cost-effective in assisting clinical diagnosis. We also found a trend correlation between serum ferritin levels and the risk of hypertension.

Adaptive responses of carbon and nitrogen metabolisms to nitrogen-deficiency in Citrus sinensis seedlings.

BACKGROUND: In China, nitrogen (N)-deficiency often occurs in Citrus orchards, which is one of the main causes of yield loss and fruit quality decline. Little information is known about the adaptive responses of Citrus carbon (C) and N metabolisms to N-deficiency. Seedlings of 'Xuegan' (Citrus sinensis (L.) Osbeck) were supplied with nutrient solution at an N concentration of 0 (N-deficiency), 5, 10, 15 or 20 mM for 10 weeks. Thereafter, we examined the effects of N supply on the levels of C and N in roots, stems and leaves, and the levels of organic acids, nonstructural carbohydrates, NH4+-N, NO3--N, total soluble proteins, free amino acids (FAAs) and derivatives (FAADs), and the activities of key enzymes related to N assimilation and organic acid metabolism in roots and leaves. RESULTS: N-deficiency elevated sucrose export from leaves to roots, C and N distributions in roots and C/N ratio in roots, stems and leaves, thus enhancing root dry weight/shoot dry weight ratio and N use efficiency. N-deficient leaves displayed decreased accumulation of starch and total nonstructural carbohydrates (TNC) and increased sucrose/starch ratio as well as a partitioning trend of assimilated C toward to sucrose, but N-deficient roots displayed elevated accumulation of starch and TNC and reduced sucrose/starch ratio as well as a partitioning trend of assimilated C toward to starch. N-deficiency reduced the concentrations of most FAADs and the ratios of total FAADs (TFAADs)/N in leaves and roots. N-deficiency reduced the demand for C skeleton precursors for amino acid biosynthesis, thus lowering TFAADs/C ratio in leaves and roots. N-deficiency increased (decreased) the relative amounts of C-rich (N-rich) FAADs, thus increasing the molar ratio of C/N in TFAADs in leaves and roots. CONCLUSIONS: Our findings corroborated our hypothesis that C and N metabolisms displayed adaptive responses to N-deficiency in C. sinensis seedlings, and that some differences existed between roots and leaves in N-deficiency-induced alterations of and C and N metabolisms.

[Rapid analysis of serum components and metabolites of Sanzi San by high performance liguid chromatography-quadrupole/ electrostatic field orbitrap high resolution mass spectrometry].

Sanzi San, a Mongolian medicine, comprises three herbs: Terminalia chebula, Melia toosendan, and Gardenia jasminoides. Clinically, Sanzi San is administered orally and distributed via blood to the action site, which implies that the absorption, distribution, metabolism, and excretion (ADME) are closely related to the pharmacological action and curative effect. Therefore, possible explanations for the material basis of Sanzi San were explored in this study preliminarily. A strategy based on serum pharmacochemistry was first applied to explore the absorbed bioactive components and metabolites of Sanzi San. Wistar rats were randomly divided into normal and dosing groups, which were provided with the Sanzi San's water extract for three days. Then, the rat's blood samples were obtained from their abdomiral aorta using a sterile blood collection tube after administering the medicine. The blood samples were then centrifuged at 3500 r/min for 10 min to obtain the serum samples. A practical method based on high performance liquid chromatography coupled with quadrupole and electrostatic field orbitrap high resolution mass spectrometry (HPLC-Q/Orbitrap HRMS) was developed to screen and analyze numerous bioactive components and metabolites adsorbed in the serum of the dosing rats after oral administration of the Sanzi San's water extract. Chromatographic separation was achieved on a SHIMADZU GIST C18 chromatographic column (150 mm×4.6 mm, 5 µm). The temperature of the column was maintained at 30 ℃. The flow rate was 0.5 mL/min, and the injection volume was 10 µL. The mobile phase comprised an aqueous solution of 0.1% formic acid and methanol under gradient elution. A heated electrospray ion (HESI) source was used with positive and negative ion scanning modes. To rapidly screen out and identify the absorbed bioactive components and metabolites of Sanzi San in the rat serum samples, a simple three-step approach was developed. First, the known components in Sanzi San were listed systematically by exploring various databases, such as the Web of Science, PubMed, and Chinese National Knowledge Infrastructure. In addition, relevant information on drug biotransformation and the characteristic fragmentation patterns of parent compounds were summarized. Second, the absorbed components and metabolites were ascertained using the Xcalibur 3.0 software. Based on the information related to the parent compound's structure, the software could be used to identify the unique peaks by comparing the chromatograms of the normal and dosing samples. Consequently, the total ion chromatograms of serum samples were established. Finally, the Compound Discover 3.0 software was used to predict the metabolic pathways and fragmentation of the absorbed compounds. Using this approach, 55 compounds were characterized, including 41 prototype components and 14 metabolites. The main prototype components in the serum sample were tannins, iridoids, and phenolic acids. The details of these compounds have been summarized and presented. Regarding the absorbed bioactive components and metabolites in the serum samples of rats administered with Sanzi San, phase Ⅰ and phase Ⅱ biochemical reactions were involved in the biotransformation pathways. The phase Ⅰ reaction modified the components and created sites for the phase Ⅱ reaction, involving reduction and hydrolysis. The phase Ⅱ reaction coupled groups to existing conjugation sites, including glucuronide to glucuronic acid, sulfate, and methyl. MS/MS spectra indicated that methylation, demethylation, and dehydroxylation are the metabolic pathways of procyanidins. Additionally, glucuronidation, deglucosidation, hydration, and demethylation are the metabolic pathways of iridoids in Sanzi San. This study comprehensively analyzed the components of the Sanzi San's water extract absorbed in the rat's serum. Our results revealed information regarding the pharmacodynamic substances and the major pathways involved in the ADME of Sanzi San. Further, potential medicinal ingredients for the pharmacological effects and clinical use of Sanzi San were explored at the serum pharmacochemistry level.

Molecular mechanisms for magnesium-deficiency-induced leaf vein lignification, enlargement and cracking in Citrus sinensis revealed by RNA-Seq.

Citrus sinensis (L.) Osbeck seedlings were fertigated with nutrient solution containing 2 [magnesium (Mg)-sufficiency] or 0 mM (Mg-deficiency) Mg(NO3)2 for 16 weeks. Thereafter, RNA-Seq was used to investigate Mg-deficiency-responsive genes in the veins of upper and lower leaves in order to understand the molecular mechanisms for Mg-deficiency-induced vein lignification, enlargement and cracking, which appeared only in the lower leaves. In this study, 3065 upregulated and 1220 downregulated, and 1390 upregulated and 375 downregulated genes were identified in Mg-deficiency veins of lower leaves (MDVLL) vs Mg-sufficiency veins of lower leaves (MSVLL) and Mg-deficiency veins of upper leaves (MDVUL) vs Mg-sufficiency veins of upper leaves (MSVUL), respectively. There were 1473 common differentially expressed genes (DEGs) between MDVLL vs MSVLL and MDVUL vs MSVUL, 1463 of which displayed the same expression trend. Magnesium-deficiency-induced lignification, enlargement and cracking in veins of lower leaves might be related to the following factors: (i) numerous transciption factors and genes involved in lignin biosynthesis pathways, regulation of cell cycle and cell wall metabolism were upregulated; and (ii) reactive oxygen species, phytohormone and cell wall integrity signalings were activated. Conjoint analysis of proteome and transcriptome indicated that there were 287 and 56 common elements between DEGs and differentially abundant proteins (DAPs) identified in MDVLL vs MSVLL and MDVUL vs MSVUL, respectively, and that among these common elements, the abundances of 198 and 55 DAPs matched well with the transcript levels of the corresponding DEGs in MDVLL vs MSVLL and MDVUL vs MSVUL, respectively, indicating the existence of concordances between protein and transcript levels.

Cardiac manifestations of COVID-19 in Shenzhen, China.

PURPOSE: The coronavirus disease 2019 (COVID-19) outbreak has become a global public health concern; however, relatively few detailed reports of related cardiac injury are available. The aims of this study were to compare the clinical and echocardiographic characteristics of inpatients in the intensive-care unit (ICU) and non-ICU patients. METHODS: We recruited 416 patients diagnosed with COVID-19 and divided them into two groups: ICU (n = 35) and non-ICU (n = 381). Medical histories, laboratory findings, and echocardiography data were compared. RESULTS: The levels of myocardial injury markers in ICU vs non-ICU patients were as follows: troponin I (0.029 ng/mL [0.007-0.063] vs 0.006 ng/mL [0.006-0.006]) and myoglobin (65.45 µg/L [39.77-130.57] vs 37.00 µg/L [26.40-53.54]). Echocardiographic findings included ventricular wall thickening (12 [39%] vs 1 [4%]), pulmonary hypertension (9 [29%] vs 0 [0%]), and reduced left-ventricular ejection fraction (5 [16%] vs 0 [0%]). Overall, 10% of the ICU patients presented with right heart enlargement, thickened right-ventricular wall, decreased right heart function, and pericardial effusion. Cardiac complications were more common in ICU patients, including acute cardiac injury (21 [60%] vs 13 [3%]) (including 2 cases of fulminant myocarditis), atrial or ventricular tachyarrhythmia (3 [9%] vs 3 [1%]), and acute heart failure (5 [14%] vs 0 [0%]). CONCLUSION: Myocardial injury marker elevation, ventricular wall thickening, pulmonary artery hypertension, and cardiac complications including acute myocardial injury, arrhythmia, and acute heart failure are more common in ICU patients with COVID-19. Cardiac injury in COVID-19 patients may be related more to the systemic response after infection rather than direct damage by coronavirus.

Curcumin-Loaded Poly(L-lactide-co-glycolide) Microbubble-Mediated Sono-photodynamic Therapy in Liver Cancer Cells.

Sono-photodynamic therapy (SPDT) activates the same photo-/sonosensitizer and exerts more marked antitumor effects than sonodynamic therapy or photodynamic therapy. We aimed to explore the utilization of curcumin (CUR)-loaded poly(L-lactide-co-glycolide) microbubble (MB)-mediated SPDT (CUR-PLGA-MB-SPDT) in HepG2 liver cancer cells. The cytotoxicity and intracellular accumulation of CUR were determined. We used 40 µM CUR as the photo-/sonosensitizer for 3 h. In a comparison of CUR-SDT or CUR-PDT, HepG2 cell viability decreased and apoptotic rate increased in CUR-SPDT. The CUR-PLGA MBs had round spheres with smooth surfaces and an average size of 3.7 µm. In CUR-PLGA MBs, drug entrapment efficiency and drug-loading capacity were 74.29 ± 2.60% and 17.14 ± 0.60%, respectively. CUR-loaded PLGA MBs (CUR-PLGA MBs) had good biocompatibility with normal L02 cells and were almost non-cytotoxic to HepG2 cells. Among CUR-SDT, CUR-PDT, CUR-SPDT or CUR-PLGA-MB-SDT, the cell CUR-PLGA-MB-SPDT had the lowest viability. Transmission electron microscopy revealed pyroptosis and apoptosis in the CUR-PLGA-MB-SPDT group; the potential mechanism was related to the mitochondrial membrane potential loss and increased production of intracellular reactive oxygen species. These findings suggested that CUR-PLGA-MB-SPDT may be a promising treatment for liver cancer.

Early Diagnosis of Hepatocellular Carcinoma Using Machine Learning Method.

Hepatocellular carcinoma (HCC) is a serious cancer which ranked the fourth in cancer-related death worldwide. Hence, more accurate diagnostic models are urgently needed to aid the early HCC diagnosis under clinical scenarios and thus improve HCC treatment and survival. Several conventional methods have been used for discriminating HCC from cirrhosis tissues in patients without HCC (CwoHCC). However, the recognition successful rates are still far from satisfactory. In this study, we applied a computational approach that based on machine learning method to a set of microarray data generated from 1091 HCC samples and 242 CwoHCC samples. The within-sample relative expression orderings (REOs) method was used to extract numerical descriptors from gene expression profiles datasets. After removing the unrelated features by using maximum redundancy minimum relevance (mRMR) with incremental feature selection, we achieved "11-gene-pair" which could produce outstanding results. We further investigated the discriminate capability of the "11-gene-pair" for HCC recognition on several independent datasets. The wonderful results were obtained, demonstrating that the selected gene pairs can be signature for HCC. The proposed computational model can discriminate HCC and adjacent non-cancerous tissues from CwoHCC even for minimum biopsy specimens and inaccurately sampled specimens, which can be practical and effective for aiding the early HCC diagnosis at individual level.

Molecular and physiological mechanisms underlying magnesium-deficiency-induced enlargement, cracking and lignification of Citrus sinensis leaf veins.

Little is known about the physiological and molecular mechanisms underlying magnesium (Mg)-deficiency-induced enlargement, cracking and lignification of midribs and main lateral veins of Citrus leaves. Citrus sinensis (L.) Osbeck seedlings were irrigated with nutrient solution at a concentration of 0 (Mg-deficiency) or 2 (Mg-sufficiency) mM Mg(NO3)2 for 16 weeks. Enlargement, cracking and lignification of veins occurred only in lower leaves, but not in upper leaves. Total soluble sugars (glucose + fructose + sucrose), starch and cellulose concentrations were less in Mg-deficiency veins of lower leaves (MDVLL) than those in Mg-sufficiency veins of lower leaves (MSVLL), but lignin concentration was higher in MDVLL than that in MSVLL. However, all four parameters were similar between Mg-deficiency veins of upper leaves (MDVUL) and Mg-sufficiency veins of upper leaves (MSVUL). Using label-free, liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis, we identified 1229 and 492 differentially abundant proteins (DAPs) in MDVLL vs MSVLL and MDVUL vs MSVUL, respectively. Magnesium-deficiency-induced alterations of Mg, nonstructural carbohydrates, cell wall components, and protein profiles were greater in veins of lower leaves than those in veins of upper leaves. The increased concentration of lignin in MDVLL vs MSVLL might be caused by the following factors: (i) repression of cellulose and starch accumulation promoted lignin biosynthesis; (ii) abundances of proteins involved in phenylpropanoid biosynthesis pathway, hormone biosynthesis and glutathione metabolism were increased; and (iii) the abundances of the other DAPs [viz., copper/zinc-superoxide dismutase, ascorbate oxidase (AO) and ABC transporters] involved in lignin biosynthesis were elevated. Also, the abundances of several proteins involved in cell wall metabolism (viz., expansins, Rho GTPase-activating protein gacA, AO, monocopper oxidase-like protein and xyloglucan endotransglucosylase/hydrolase) were increased in MDVLL vs MSVLL, which might be responsible for the enlargement and cracking of leaf veins.

Evaluation of different computational methods on 5-methylcytosine sites identification.

5-Methylcytosine (m5C) plays an extremely important role in the basic biochemical process. With the great increase of identified m5C sites in a wide variety of organisms, their epigenetic roles become largely unknown. Hence, accurate identification of m5C site is a key step in understanding its biological functions. Over the past several years, more attentions have been paid on the identification of m5C sites in multiple species. In this work, we firstly summarized the current progresses in computational prediction of m5C sites and then constructed a more powerful and reliable model for identifying m5C sites. To train the model, we collected experimentally confirmed m5C data from Homo sapiens, Mus musculus, Saccharomyces cerevisiae and Arabidopsis thaliana, and compared the performances of different feature extraction methods and classification algorithms for optimizing prediction model. Based on the optimal model, a novel predictor called iRNA-m5C was developed for the recognition of m5C sites. Finally, we critically evaluated the performance of iRNA-m5C and compared it with existing methods. The result showed that iRNA-m5C could produce the best prediction performance. We hope that this paper could provide a guide on the computational identification of m5C site and also anticipate that the proposed iRNA-m5C will become a powerful tool for large scale identification of m5C sites.

Low pH effects on reactive oxygen species and methylglyoxal metabolisms in Citrus roots and leaves.

BACKGROUND: Limited data are available on the responses of reactive oxygen species (ROS) and methylglyoxal (MG) metabolisms to low pH in roots and leaves. In China, quite a few of Citrus are cultivated in acidic soils (pH < 5.0). 'Xuegan' (Citrus sinensis) and 'Sour pummelo' (Citrus grandis) (C. sinensis were more tolerant to low pH than C. grandis) seedlings were irrigated daily with nutrient solution at a pH of 2.5, 3 or 5 for nine months. Thereafter, we examined low pH effects on growth, and superoxide anion production rate (SAP), malondialdehyde (MDA), MG, antioxidants, and enzymes related to ROS and MG detoxification in roots and leaves in order to (a) test the hypothesis that low pH affected ROS and MG metabolisms more in roots than those of leaves, and (b) understand the roles of ROS and MG metabolisms in Citrus low pH-tolerance and -toxicity. RESULTS: Compared with control, most of the physiological parameters related to ROS and MG metabolisms were greatly altered at pH 2.5, but almost unaffected at pH 3. In addition to decreased root growth, many fibrous roots became rotten and died at pH 2.5. pH 2.5-induced changes in SAP, the levels of MDA, MG and antioxidants, and the activities of most enzymes related to ROS and MG metabolisms were greater in roots than those of leaves. Impairment of root ascorbate metabolism was the most serious, especially in C. grandis roots. pH 2.5-induced increases in MDA and MG levels in roots and leaves, decreases in the ratios of ascorbate/(ascorbate+dehydroascorbate) in roots and leaves and of reduced glutathione/(reduced+oxidized glutathione) in roots were greater in C. grandis than those in C. sinensis. CONCLUSIONS: Low pH affected MG and ROS metabolisms more in roots than those in leaves. The most seriously impaired ascorbate metabolism in roots was suggested to play a role in low pH-induced root death and growth inhibition. Low pH-treated C. sinensis roots and leaves had higher capacity to maintain a balance between ROS and MG production and their removal via detoxification systems than low pH-treated C. grandis ones, thus contribute to the higher acid-tolerance of C. sinensis.

Magnesium-Deficiency Effects on Pigments, Photosynthesis and Photosynthetic Electron Transport of Leaves, and Nutrients of Leaf Blades and Veins in Citrus sinensis Seedlings.

Citrus sinensis seedlings were irrigated with nutrient solution at a concentration of 0 (Mg-deficiency) or 2 (Mg-sufficiency) mM Mg (NO3)2 for 16 weeks. Mg-deficiency-induced interveinal chlorosis, vein enlargement and corkiness, and alterations of gas exchange, pigments, chlorophyll a fluorescence (OJIP) transients and related parameters were observed in middle and lower leaves, especially in the latter, but not in upper leaves. Mg-deficiency might impair the whole photosynthetic electron transport, including structural damage to thylakoids, ungrouping of photosystem II (PSII), inactivation of oxygen-evolving complex (OEC) and reaction centers (RCs), increased reduction of primary quinone electron acceptor (QA) and plastoquinone pool at PSII acceptor side and oxidation of PSI end-electron acceptors, thus lowering energy transfer and absorption efficiency and the transfer of electrons to the dark reactions, hence, the rate of CO2 assimilation in Mg-deficiency middle and lower leaves. Although potassium, Mg, manganese and zinc concentration in blades displayed a significant and positive relationship with the corresponding element concentration in veins, respectively, great differences existed in Mg-deficiency-induced alterations of nutrient concentrations between leaf blades and veins. For example, Mg-deficiency increased boron level in the blades of upper leaves, decreased boron level in the blades of lower leaves, but did not affect boron level in the blades of middle leaves and veins of upper, middle and lower leaves. To conclude, Mg-deficiency-induced interveinal chlorosis, vein enlargement, and corkiness, and alterations to photosynthesis and related parameters increased with increasing leaf age. Mg-deficiency-induced enlargement and corkiness of veins were not caused by Mg-deficiency-induced boron-starvation.

iDNA6mA-Rice: A Computational Tool for Detecting N6-Methyladenine Sites in Rice.

DNA N6-methyladenine (6mA) is a dominant DNA modification form and involved in many biological functions. The accurate genome-wide identification of 6mA sites may increase understanding of its biological functions. Experimental methods for 6mA detection in eukaryotes genome are laborious and expensive. Therefore, it is necessary to develop computational methods to identify 6mA sites on a genomic scale, especially for plant genomes. Based on this consideration, the study aims to develop a machine learning-based method of predicting 6mA sites in the rice genome. We initially used mono-nucleotide binary encoding to formulate positive and negative samples. Subsequently, the machine learning algorithm named Random Forest was utilized to perform the classification for identifying 6mA sites. Our proposed method could produce an area under the receiver operating characteristic curve of 0.964 with an overall accuracy of 0.917, as indicated by the fivefold cross-validation test. Furthermore, an independent dataset was established to assess the generalization ability of our method. Finally, an area under the receiver operating characteristic curve of 0.981 was obtained, suggesting that the proposed method had good performance of predicting 6mA sites in the rice genome. For the convenience of retrieving 6mA sites, on the basis of the computational method, we built a freely accessible web server named iDNA6mA-Rice at http://lin-group.cn/server/iDNA6mA-Rice.

Magnesium Deficiency Induced Global Transcriptome Change in Citrus sinensis Leaves Revealed by RNA-Seq.

Magnesium (Mg) deficiency is one of the major constraining factors that limit the yield and quality of agricultural products. Uniform seedlings of the Citrus sinensis were irrigated with Mg deficient (0 mM MgSO4) and Mg sufficient (1 mM MgSO4) nutrient solutions for 16 weeks. CO2 assimilation, starch, soluble carbohydrates, TBARS content and H2O2 production were measured. Transcriptomic analysis of C. sinensis leaves was performed by Illumina sequencing. Our results showed that Mg deficiency decreased CO2 assimilation, but increased starch, sucrose, TBARS content and H2O2 production in C. sinensis leaves. A total of 4864 genes showed differential expression in response to Mg deficiency revealed by RNA-Seq and the transcriptomic data were further validated by real-time quantitative PCR (RT-qPCR). Gene ontology (GO) enrichment analysis indicated that the mechanisms underlying Mg deficiency tolerance in C. sinensis may be attributed to the following aspects: a) enhanced microtubule-based movement and cell cycle regulation; b) elevated signal transduction in response to biotic and abiotic stimuli; c) alteration of biological processes by tightly controlling phosphorylation especially protein phosphorylation; d) down-regulation of light harvesting and photosynthesis due to the accumulation of carbohydrates; e) up-regulation of cell wall remodeling and antioxidant system. Our results provide a comprehensive insight into the transcriptomic profile of key components involved in the Mg deficiency tolerance in C. sinensis and enrich our understanding of the molecular mechanisms by which plants adapted to a Mg deficient condition.

Identification of hormone binding proteins based on machine learning methods.

The soluble carrier hormone binding protein (HBP) plays an important role in the growth of human and other animals. HBP can also selectively and non-covalently interact with hormone. Therefore, accurate identification of HBP is an important prerequisite for understanding its biological functions and molecular mechanisms. Since experimental methods are still labor intensive and cost ineffective to identify HBP, it's necessary to develop computational methods to accurately and efficiently identify HBP. In this paper, a machine learning-based method was proposed to identify HBP, in which the samples were encoded by using the optimal tripeptide composition obtained based on the binomial distribution method. In the 5-fold cross-validation test, the proposed method yielded an overall accuracy of 97.15%. For the convenience of scientific community, a user-friendly webserver called HBPred2.0 was built, which could be freely accessed at http://lin-group.cn/server/HBPred2.0/.

Responses of reactive oxygen species and methylglyoxal metabolisms to magnesium-deficiency differ greatly among the roots, upper and lower leaves of Citrus sinensis.

BACKGROUND: Magnesium (Mg)-deficiency is one of the most prevalent physiological disorders causing a reduction in Citrus yield and quality. 'Xuegan' (Citrus sinensis) seedlings were irrigated for 16 weeks with nutrient solution containing 2 mM (Mg-sufficiency) or 0 mM (Mg-deficiency) Mg(NO3)2. Thereafter, we investigated the Mg-deficient effects on gas exchange and chlorophyll a fluorescence in the upper and lower leaves, and Mg, reactive oxygen species (ROS) and methylglyoxal (MG) metabolisms in the roots, lower and upper leaves. The specific objectives were to corroborate the hypothesis that the responses of ROS and MG metabolisms to Mg-deficiency were greater in the lower leaves than those in the upper leaves, and different between the leaves and roots. RESULTS: Mg level was higher in the Mg-deficient upper leaves than that in the Mg-deficient lower leaves. This might be responsible for the Mg-deficiency-induced larger alterations of all the measured parameters in the lower leaves than those in the upper leaves, but they showed similar change patterns between the Mg-deficient lower and upper leaves. Accordingly, Mg-deficiency increased greatly their differences between the lower and upper leaves. Most of parameters involved in ROS and MG metabolisms had similar variation trends and degrees between the Mg-deficient lower leaves and roots, but several parameters (namely glutathione S-transferase, sulfite reductase, ascorbate and dehydroascorbate) displayed the opposite variation trends. Obviously, differences existed in the Mg-deficiency-induced alterations of ROS and MG metabolisms between the lower leaves and roots. Although the activities of most antioxidant and sulfur metabolism-related enzymes and glyoxalase I and the level of reduced glutathione in the Mg-deficient leaves and roots and the level of ascorbate in the leaves were kept in higher levels, the levels of malonaldehyde and MG and/or electrolyte leakage were increased in the Mg-deficient lower and upper leaves and roots, especially in the Mg-deficient lower leaves and roots. CONCLUSIONS: The ROS and MG detoxification systems as a whole did not provide sufficient detoxification capacity to prevent the Mg-deficiency-induced production and accumulation of ROS and MG, thus leading to lipid peroxidation and the loss of plasma membrane integrity, especially in the lower leaves and roots.

Preparative separation of four isomers of synthetic anisodamine by HPLC and diastereomer crystallization.

Anisodamine (654-1), a well-known cholinergic antagonist, is marketed as synthetic anisodamine (mixture of four isomers, 654-2) in China. To preparative resolution and comparison of the bioactivities of the four isomers of synthetic anisodamine, current work explores an economic and effective separation method by using preparative high performance liquid chromatography (HPLC) and diastereomer crystallization. Their absolute configurations were established by single-crystal X-ray diffraction and circular dichroism method. The purities of each isomer were more than 95%. Among them, 654-2-A2 (6R, 2'S configuration) exhibited better effect on cabachol preconditioned small intestine tension more than 654-2 and other isomers. The direct separation method without using HPLC was tried as well, which was still on progress. This is the first report of the method for preparative separation of four isomers of synthetic anisodamine which could be used for large-scale production in industry.

DNA physical properties outperform sequence compositional information in classifying nucleosome-enriched and -depleted regions.

The nucleosome is the fundamental structural unit of eukaryotic chromatin and plays an essential role in the epigenetic regulation of cellular processes, such as DNA replication, recombination, and transcription. Hence, it is important to identify nucleosome positions in the genome. Our previous model based on DNA deformation energy, in which a set of DNA physical descriptors was used, performed well in predicting nucleosome dyad positions and occupancy. In this study, we established a machine-learning model for predicting nucleosome occupancy in order to further verify the physical descriptors. Results showed that (1) our model outperformed several other sequence compositional information-based models, indicating a stronger dependence of nucleosome positioning on DNA physical properties; (2) nucleosome-enriched and -depleted regions have distinct features in terms of DNA physical descriptors like sequence-dependent flexibility and equilibrium structure parameters; (3) gene transcription start sites and termination sites can be well characterized with the distribution patterns of the physical descriptors, indicating the regulatory role of DNA physical properties in gene transcription. In addition, we developed a web server for the model, which is freely accessible at http://lin-group.cn/server/iNuc-force/.

A Survey for Predicting Enzyme Family Classes Using Machine Learning Methods.

Enzymes are proteins that act as biological catalysts to speed up cellular biochemical processes. According to their main Enzyme Commission (EC) numbers, enzymes are divided into six categories: EC-1: oxidoreductase; EC-2: transferase; EC-3: hydrolase; EC-4: lyase; EC-5: isomerase and EC-6: synthetase. Different enzymes have different biological functions and acting objects. Therefore, knowing which family an enzyme belongs to can help infer its catalytic mechanism and provide information about the relevant biological function. With the large amount of protein sequences influxing into databanks in the post-genomics age, the annotation of the family for an enzyme is very important. Since the experimental methods are cost ineffective, bioinformatics tool will be a great help for accurately classifying the family of the enzymes. In this review, we summarized the application of machine learning methods in the prediction of enzyme family from different aspects. We hope that this review will provide insights and inspirations for the researches on enzyme family classification.

Identifying Phage Virion Proteins by Using Two-Step Feature Selection Methods.

Accurate identification of phage virion protein is not only a key step for understanding the function of the phage virion protein but also helpful for further understanding the lysis mechanism of the bacterial cell. Since traditional experimental methods are time-consuming and costly for identifying phage virion proteins, it is extremely urgent to apply machine learning methods to accurately and efficiently identify phage virion proteins. In this work, a support vector machine (SVM) based method was proposed by mixing multiple sets of optimal g-gap dipeptide compositions. The analysis of variance (ANOVA) and the minimal-redundancy-maximal-relevance (mRMR) with an increment feature selection (IFS) were applied to single out the optimal feature set. In the five-fold cross-validation test, the proposed method achieved an overall accuracy of 87.95%. We believe that the proposed method will become an efficient and powerful method for scientists concerning phage virion proteins.