Synergism of Metabolite Action in Plant Responses to Stresses

A diversity of unfavorable environmental factors determines complex interactions between phytohormones and signal elements and also the formation and combined action of metabolites in plants. This permits the plant to overcome stresses and to realize its potential capacities. Synergism between plant chemical constituents during ontogeny was revealed in the case of their responses to various abiotic stressors, such as pathogens or pests, and also during competition between plants. Jasmonic, salicylic, and abscisic acids, ethylene, hydrogen peroxide, NO, antioxidants, defensive proteins, and enzymes can manifest synergistic plant responses to unfavorable factors.__________Translated from Fiziologiya Rastenii, Vol. 52, No. 4, 2005, pp. 614–621.Original Russian Text Copyright © 2005 by Ryabushkina.     

植物促生菌提高植物重金属耐受性研究进展

近年来植物修复技术因其独特的优势而被广泛关注。许多植物被认为是有价值的利用资源, 然而, 最有实际使用价值的植物对重金属的耐受性有限, 实际应用中变得越来越困难。植物促生菌资源对环境无污染, 具有独特的多样性和巨大的潜力。随着资源的开发和技术的发展, 微生物调控将会使植物修复技术变得更加可行和更有价值。回顾近年来新兴的微生物调控技术, 主要是植物促生菌的筛选、鉴定和应用价值。     

Plant Phenomics: Fundamental Bases,Software and Hardware Platforms,and Machine Learning

Russian Journal of Plant Physiology - In recent years, a new branch of plant physiology, plant phenomics, which focuses on identifying patterns of organization and changes in plant Phenomes, i.e.,...     

Machine Learning in Healthcare

Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) technology have brought on substantial strides in predicting and identifying health emergencies, disease populations, and disease state and immune response, amongst a few. Although, skepticism remains regarding the practical application and interpretation of results from ML-based approaches in healthcare settings, the inclusion of these approaches is increasing at a rapid pace. Here we provide a brief overview of machine learning-based approaches and learning algorithms including supervised, unsupervised, and reinforcement learning along with examples. Second, we discuss the application of ML in several healthcare fields, including radiology, genetics, electronic health records, and neuroimaging. We also briefly discuss the risks and challenges of ML application to healthcare such as system privacy and ethical concerns and provide suggestions for future applications.     

Classifying Drugs by their Arrhythmogenic Risk Using Machine Learning

All medications have adverse effects. Among the most serious of these are cardiac arrhythmias. Current paradigms for drug safety evaluation are costly, lengthy, conservative, and impede efficient drug development. Here, we combine multiscale experiment and simulation, high-performance computing, and machine learning to create a risk estimator to stratify new and existing drugs according to their proarrhythmic potential. We capitalize on recent developments in machine learning and integrate information across 10 orders of magnitude in space and time to provide a holistic picture of the effects of drugs, either individually or in combination with other drugs. We show, both experimentally and computationally, that drug-induced arrhythmias are dominated by the interplay between two currents with opposing effects: the rapid delayed rectifier potassium current and the L-type calcium current. Using Gaussian process classification, we create a classifier that stratifies drugs into safe and arrhythmic domains for any combinations of these two currents. We demonstrate that our classifier correctly identifies the risk categories of 22 common drugs exclusively on the basis of their concentrations at 50% current block. Our new risk assessment tool explains under which conditions blocking the L-type calcium current can delay or even entirely suppress arrhythmogenic events. Using machine learning in drug safety evaluation can provide a more accurate and comprehensive mechanistic assessment of the proarrhythmic potential of new drugs. Our study paves the way toward establishing science-based criteria to accelerate drug development, design safer drugs, and reduce heart rhythm disorders.     

A Latex Metabolite Benefits Plant Fitness under Root Herbivore Attack

Plants produce large amounts of secondary metabolites in their shoots and roots and store them in specialized secretory structures. Although secondary metabolites and their secretory structures are commonly assumed to have a defensive function, evidence that they benefit plant fitness under herbivore attack is scarce, especially below ground. Here, we tested whether latex secondary metabolites produced by the common dandelion (Taraxacum officinale agg.) decrease the performance of its major native insect root herbivore, the larvae of the common cockchafer (Melolontha melolontha), and benefit plant vegetative and reproductive fitness under M. melolontha attack. Across 17 T. officinale genotypes screened by gas and liquid chromatography, latex concentrations of the sesquiterpene lactone taraxinic acid β-D-glucopyranosyl ester (TA-G) were negatively associated with M. melolontha larval growth. Adding purified TA-G to artificial diet at ecologically relevant concentrations reduced larval feeding. Silencing the germacrene A synthase ToGAS1, an enzyme that was identified to catalyze the first committed step of TA-G biosynthesis, resulted in a 90% reduction of TA-G levels and a pronounced increase in M. melolontha feeding. Transgenic, TA-G-deficient lines were preferred by M. melolontha and suffered three times more root biomass reduction than control lines. In a common garden experiment involving over 2,000 T. officinale individuals belonging to 17 different genotypes, high TA-G concentrations were associated with the maintenance of high vegetative and reproductive fitness under M. melolontha attack. Taken together, our study demonstrates that a latex secondary metabolite benefits plants under herbivore attack, a result that provides a mechanistic framework for root herbivore driven natural selection and evolution of plant defenses below ground.     

Comparative Metabolite Fingerprinting of the Rumen System during Colonisation of Three Forage Grass (Lolium perenne L.) Varieties

The rumen microbiota enable ruminants to degrade complex ligno-cellulosic compounds to produce high quality protein for human consumption. However, enteric fermentation by domestic ruminants generates negative by-products: greenhouse gases (methane) and environmental nitrogen pollution. The current lack of cultured isolates representative of the totality of rumen microbial species creates an information gap about the in vivo function of the rumen microbiota and limits our ability to apply predictive biology for improvement of feed for ruminants. In this work we took a whole ecosystem approach to understanding how the metabolism of the microbial population responds to introduction of its substrate. Fourier Transform Infra Red (FTIR) spectroscopy-based metabolite fingerprinting was used to discriminate differences in the plant-microbial interactome of the rumen when using three forage grass varieties (Lolium perenne L. cv AberDart, AberMagic and Premium) as substrates for microbial colonisation and fermentation. Specific examination of spectral regions associated with fatty acids, amides, sugars and alkanes indicated that although the three forages were apparently similar by traditional nutritional analysis, patterns of metabolite flux within the plant-microbial interactome were distinct and plant genotype dependent. Thus, the utilisation pattern of forage nutrients by the rumen microbiota can be influenced by subtleties determined by forage genotypes. These data suggest that our interactomic approach represents an important means to improve forages and ultimately the livestock environment.     

SRFA法构建水稻DNA指纹图谱

水稻基因组DNA用PstⅠ酶切同时与人工接头连接后,使用选择性引物进行PCR扩增,琼脂糖凝胶电泳检测所构建的水稻DNA指纹图谱。结果表明在JX17和ZYQ8间以及5种野生稻间均存在DNA多态性片段。     

Amplified Fragment Length Polymorphism Fingerprinting of Pseudomonas Strains from a Poultry Processing Plant

Molecular typing has been used previously to identify and trace dissemination of pathogenic and spoilage bacteria associated with food processing. Amplified fragment length polymorphism (AFLP) is a novel DNA fingerprinting technique which is considered highly reproducible and has high discriminatory power. This technique was used to fingerprint 88 Pseudomonas fluorescens and Pseudomonas putida strains that were previously isolated from plate counts of carcasses at six processing stages and various equipment surfaces and environmental sources of a poultry abattoir. Clustering of the AFLP patterns revealed a high level of diversity among the strains. Six clusters (clusters I through VI) were delineated at an arbitrary Dice coefficient level of 0.65; clusters III (31 strains) and IV (28 strains) were the largest clusters. More than one-half (52.3%) of the strains obtained from carcass samples, which may have represented the resident carcass population, grouped together in cluster III. By contrast, 43.2% of the strains from most of the equipment surfaces and environmental sources grouped together in cluster IV. In most cases, the clusters in which carcass strains from processing stages grouped corresponded to the clusters in which strains from the associated equipment surfaces and/or environmental sources were found. This provided evidence that there was cross-contamination between carcasses and the abattoir environment at the DNA level. The AFLP data also showed that strains were being disseminated from the beginning to the end of the poultry processing operation, since many strains associated with carcasses at the packaging stage were members of the same clusters as strains obtained from carcasses after the defeathering stage.     

Comparison of Prediction Results Obtained by the Learning Machine and Experienced Microbiologists

Based on three measures of evaluation, it was concluded that with respect to pseudomonads the learning machine was superior to experienced microbiologists in predicting the presence or absence of the following four features; the production of phenazine compounds, the utilizations of d-sorbitol, of n-hexadecane, and of histamine.     

Prediction of Side Chain Orientations in Proteins by Statistical Machine Learning Methods

Abstract

We develop ways to predict the side chain orientations of residues within a protein structure by using several different statistical machine learning methods. Here side chain orientation of a given residue i is measured by an angle Ω_i between the vector pointing from the center of the protein structure to the C^α _i atom and the vector pointing from the C^α _i atom to the center of its side chain atoms. To predict the Ω_i angles, we construct statistical models by using several different methods such as general linear regression, a regression tree and bagging, a neural network, and a support vector machine. The root mean square errors for the different models range only from 36.67 to 37.60 degrees and the correlation coefficients are all between 30% and 34%. The performances of different models in the test set are, thus, quite similar, and show the relative predictive power of these models to be significant in comparison with random side chain orientations.     

Machine Learning for Detecting Gene-Gene Interactions

Complex interactions among genes and environmental factors are known to play a role in common human disease aetiology. There is a growing body of evidence to suggest that complex interactions are 'the norm' and, rather than amounting to a small perturbation to classical Mendelian genetics, interactions may be the predominant effect. Traditional statistical methods are not well suited for detecting such interactions, especially when the data are high dimensional (many attributes or independent variables) or when interactions occur between more than two polymorphisms. In this review, we discuss machine-learning models and algorithms for identifying and characterising susceptibility genes in common, complex, multifactorial human diseases. We focus on the following machine-learning methods that have been used to detect gene-gene interactions: neural networks, cellular automata, random forests, and multifactor dimensionality reduction. We conclude with some ideas about how these methods and others can be integrated into a comprehensive and flexible framework for data mining and knowledge discovery in human genetics.     

Machine Learning for Biomedical Literature Triage

This paper presents a machine learning system for supporting the first task of the biological literature manual curation process, called triage. We compare the performance of various classification models, by experimenting with dataset sampling factors and a set of features, as well as three different machine learning algorithms (Naive Bayes, Support Vector Machine and Logistic Model Trees). The results show that the most fitting model to handle the imbalanced datasets of the triage classification task is obtained by using domain relevant features, an under-sampling technique, and the Logistic Model Trees algorithm.     

Toxic Metabolite Produced by Aspergillus wentii

Mycelial extracts of an Aspergillus wentii strain grown on yeast-extract sucrose medium and initially isolated from country-cured ham were highly toxic when inoculated into chicken embryos or fed to mice. Moldy corn and rice were less toxic when fed to mice. Water extracts of moldy corn or rice or culture filtrates from yeast-extract sucrose medium were not toxic. Purification by thin-layer chromatography followed by crystallization yielded orange-red crystals that showed high toxicity and had a melting point of 285 to 286 C. Chloroform solutions of the crystals had absorption maxima at 270, 295, and 452 nm. The smallest amount of this component necessary to have zero hatchability of fertile eggs was 50 mug/egg.     

Plant Uptake and Distribution of Endosulfan and Its Sulfate Metabolite Persisted in Soil

The distributions of endosulfan (ED) residues (α-, β-isomers, and sulfate-metabolite) in cucumbers grown in soils treated with ED at concentrations of 20 and 40 mg kg^-1 were assessed using indoor and outdoor experiments. In all treatments, degradation rates of the α-isomer in soils were higher than that of the β-isomer. In the indoor tests, uptake amounts of total ED by cucumbers, after 15 d of growth, were 7.8 and 14.5 mg kg^-1 in 20 and 40 mg kg^-1-treated pots, respectively. For growth time from 15 to 30 d, uptake amounts in 20 and 40 mg kg^-1-treated pots were 3.8 and 7.9 mg kg^-1, respectively. Outdoor tests resulted in smaller ED residues in cucumbers than those in indoor tests. In both indoor and outdoor tests, ED residues absorbed were highest in roots, and the α-isomer was the more frequently absorbed isomer. These results will be useful for determining management criteria for soil persistent pesticides.     

RAPD重建的大豆属植物的亲缘关系

利用８种ＲＡＰＤ引物（ＯＰＨ－２、ＯＰＨ－３、ＯＰＨ－５、ＯＰＨ－１２、ＯＰＨ－１５、ＯＰＨ－１６、ＯＰＨ－１８和ＯＰＨ－２０）对大豆属的２１份植物材料,其中包括Ｇｌｙｃｉｎｅ亚属的１０个种和Ｓｏｊａ亚属的３个种,进行了基因组指纹图谱构建。通过对获得的基因组指纹图谱的量化分析,利用Ｕｎｗｅｉｇｈｔｅｄｐａｉｒｇｒｏｕｐｗｉｔｈｍａｔｈｅｍａｔｉｃａｖｅｒａｇｅ（ＵＰＧＭＡ）对大豆属中的各个种进行了亲缘关系重建。重建的亲缘关系表明：Ｇ．ｔｏｍｅｎｔｅｌｌａ种中存在３种不同的进化类型,其分化距离已大于某些种种间的分化距离,它们可能是被形态遮蔽的３个种。Ｓｏｊａ亚属内３个种的分化关系同前人的研究推断相同,其亲缘关系很近,这一结果支持将这３个种归并为一个种的观点。但是重建的亲缘关系未能显示出大豆属两个亚属的划分格局。     

复合茶树接穗与接穗母本的代谢物差异分析

以两个通过群体选育获得的云南普洱茶优良品种‘云瑰’和‘福云6号’为接穗,均以茶树品种‘短节白毫’为砧木所形成的嫁接复合茶树为研究对象,通过代谢组学技术,对两个品种的接穗与接穗母本的代谢物进行分析,考察嫁接前后茶树品种‘云瑰’和‘福云6号’的差异代谢物和共同代谢物的种类、数量变化特征。结果发现:(1)两品种茶树嫁接后比嫁接前的农艺性状均有所改善,嫁接优势明显。(2)相对于‘云瑰’,‘福云6号’的接穗与接穗母本茶叶中差异代谢物的数量与种类都比较少,说明‘福云6号’嫁接在‘短节白毫’上时,接穗能最大程度地保留母本的特点。(3)‘云瑰’的接穗和接穗母本茶叶中检测和定量了115个差异代谢物,且含量表现上调的代谢物数量低于下调的数量;‘福云6号’的接穗和接穗母本茶叶中共检测和定量了47个差异代谢物,且含量表现上调的代谢物数量高于下调的数量。(4)‘云瑰’的接穗和接穗母本茶叶中差异代谢物在38条KEGG通路中富集,大多数已经鉴定的差异代谢物存在于苯丙氨酸代谢等相关途径中;‘福云6号’的接穗和接穗母本茶叶中差异代谢物在21条KEGG通路中富集,大多数已经鉴定的差异代谢物存在于与嘌呤代谢等相关途径中。该研究结果对充分发挥茶树嫁接技术在茶叶生产中的作用提供了理论依据。     

PCR-SSCP技术在假丝酵母属菌种鉴别中的应用

选取中国工业微生物菌种保藏管理中心(CICC)保藏的假丝酵母属的7个种30株菌, 对其rDNA的ITS1区及ITS2区进行了PCR-SSCP指纹图谱分析, 结果表明在假丝酵母属种水平的区分鉴定中, ITS1区与ITS2区的PCR-SSCP图谱均能对本研究所选7个种的菌株进行显著区分, 比较两个区段的PCR-SSCP图谱及鉴别效果, 发现ITS2区的应用效果要优于ITS1区。