Modeling optimal mineral nutrition for hazelnut micropropagation

Micropropagation of hazelnut (Corylus avellana L.) is typically difficult because of the wide variation in response among cultivars. This study was designed to determine the required mineral nutrient concentrations for micropropagation of C. avellana cultivars using a response surface design analysis. Driver and Kuniyuki Walnut (DKW) medium mineral nutrients were separated into five factors: NH₄NO₃, Ca(NO₃)₂, mesos (MgSO₄ and KH₂PO₄), K₂SO₄, and minor nutrients (boron, copper, manganese, molybdenum, and zinc) ranging from 0.5× to 2× the standard DKW medium concentrations with 33 treatments for use in modeling. Overall quality and shoot length for all cultivars were influenced by ammonium and nitrate nitrogen, mesos and minors. Reduced Ca(NO₃)₂ improved multiplication while higher amounts increased shoot length for most cultivars. Uptake of nutrients varied among the cultivars. Calcium and magnesium concentrations were greater in the shoots that grew well compared to poorly-growing and control treatments. All five cultivars showed improved growth on some treatments and the models indicated that shoots grown on an optimized medium would be even better. This model indicates that NH₄NO₃, Ca(NO₃)₂, mesos, and minors all had significant effects on hazelnut growth and multiplication and should be optimized in future experiments.     

Use of gene expression data for predicting continuous phenotypes for animal production and breeding

Traits such as disease resistance are costly to evaluate and slow to improve using current methods. Analysis of gene expression profiles (e.g. DNA microarrays) has potential for predicting such phenotypes and has been used in an analogous way to classify cancer types in human patients. However, doubts have been raised regarding the use of classification methods with microarray data for this purpose. Here we propose a method using random regression with cross validation, which accounts for the distribution of variation in the trait and utilises different subsets of patients or animals to perform a complete validation of predictive ability. Published breast tumour data were used to test the method. Despite the small dataset (n < 100), the new approach resulted in a moderate but significant correlation between the predicted and actual phenotypes (0.32). Binary classification of the predicted phenotypes yielded similar classification error rates to those found by other authors (35%). Unlike other methods, the new method gave a quantitative estimate of phenotype that could be used to rank animals and select those with extreme phenotypic performance. Use of the method in an optimal way using larger sample sizes, and combining DNA microarrays and other testing platforms, is recommended.     

Screening Arabidopsis for mutants in mineral nutrition

Arabidopsis thaliana is a small herbaceous plant which is used as a model plant for defining the molecular basis of many plant processes. The advantages of this plant for genetic studies are its small, well-characterized genome, a short life cycle, large seed set and small seed size. The analysis of mutants of this plant has proved useful in understanding basic plant processes. To isolate Arabidopsis mutants in mineral nutrition, we have devised a method of screening based on X-ray fluorescence spectrometry (XRFS) analysis of leaves. We have identified three mutants in P and Mn nutrition after screening over 100 000 seedlings. These mutants show either excessive accumulation of P or Mn in shoots or an inabilty to accumulate normal concentrations of P.     

Use of metabotyping for optimal nutrition

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Use of reconstituted metabolic networks to assist in metabolomic data visualization and mining

Metabolomics experiments seldom achieve their aim of comprehensively covering the entire metabolome. However, important information can be gleaned even from sparse datasets, which can be facilitated by placing the results within the context of known metabolic networks. Here we present a method that allows the automatic assignment of identified metabolites to positions within known metabolic networks, and, furthermore, allows automated extraction of sub-networks of biological significance. This latter feature is possible by use of a gap-filling algorithm. The utility of the algorithm in reconstructing and mining of metabolomics data is shown on two independent datasets generated with LC–MS LTQ-Orbitrap mass spectrometry. Biologically relevant metabolic sub-networks were extracted from both datasets. Moreover, a number of metabolites, whose presence eluded automatic selection within mass spectra, could be identified retrospectively by virtue of their inferred presence through gap filling.     

Biotechnological development of effective phytases for mineral nutrition and environmental protection

Phytases are hydrolytic enzymes that initiate the release of phosphate from phytate (myo-inositol hexakisphosphate), the major phosphorus (P) form in animal feeds of plant origin. These enzymes can be supplemented in diets for food animals to improve P nutrition and to reduce P pollution of animal excreta. This mini-review provides a synopsis of the concept of "ideal phytase" and the biotechnological approaches for developing such an enzyme. Examples of Escherichia coli AppA and Aspergillus fumigatus PhyA are presented to illustrate how new phytases are identified from microorganisms and developed by genetic engineering based on the gene sequences and protein structures of these enzymes. We also discuss the characteristics of different heterologous phytase expression systems, including those of plants, bacteria, fungi, and yeast.     

Bayesian data mining of protein domains gives an efficient predictive algorithm and new insight

Identification of structural domains in uncharacterized protein sequences is important in the prediction of protein tertiary folds and functional sites, and hence in designing biologically active molecules. We present a new predictive computational method of classifying a protein into single, two continuous or two discontinuous domains using Bayesian Data Mining. The algorithm requires only the primary sequence and computer-predicted secondary structure. It incorporates correlation patterns between certain 3-dimensional motifs and some local helical folds found conserved in the vicinity of protein domains with high statistical confidence. The prediction of domain-class by this computationally simple and fast method shows good accuracy of prediction-average accuracies 83.3% for single domain, 60% for two continuous and 65.7% for two discontinuous domain proteins. Experiments on the large validation sample show its performance to be significantly better than that of DGS and DomSSEA. Computations of Bayesian probabilities show important features in terms of correlation of certain conserved patterns of secondary folds and tertiary motifs and give new insight. Applications for improved accuracy of predicting domain boundary points relevant to protein structural and functional modeling are also highlighted.     

Growth and mineral nutrition of two Chlorococcalean algae

The effect of composition of the medium and pH on the growth of Pediastrum duplex Meyen and Dictyosphaerium pulchellum Wood was studied. Both species showed preference to alkaline conditions. The ammonium nitrate grown colonies of D. pulchellum lacked mucilage and showed a more compact form, resembling D. pulchellum var. minitum Deflandre. From this it appears that D. pulchellum var. minitum is a nutritional variant of the species and not a stable variety.     

Improving in vitro mineral nutrition for diverse pear germplasm

Mineral nutrition in the media used for growth of in vitro plants is often difficult to optimize due to complex chemical interactions of required nutrients. The response of plant tissue to standard growth media varies widely due to the genetic diversity of the plant species studied. This study was designed as the initial step in determining the optimal mineral nutrient requirements for micropropagation of shoot tips from a collection of genetically diverse pear germplasm. Five mineral nutrient factors were defined from Murashige and Skoog salts: NH₄NO₃, KNO₃, mesos (CaCl₂·2H₂0–KH₂PO₄–MgSO₄), micronutrients (B, Cu, Co, I, Mn, Mo, and Zn), and Fe-EDTA. Each factor was varied over a range of concentrations. Treatment combinations were selected using response surface methods. Five pears in three species (Pyrus communis ‘Horner 51,’ ‘Old Home?×?Farmingdale 87,’ ‘Winter Nelis,’ Pyrus dimorphophylla, and Pyrus ussuriensis ‘Hang Pa Li’) were grown on each treatment combination, responses were measured, and each response was analyzed by analysis of variance. The analyses resulted in the identification of the following factors with the single largest effects on plant response: shoot quality (mesos), leaf spotting/necrosis (mesos), leaf size (mesos), leaf color (mesos, NH₄NO₃, and KNO₃), shoot number (NH₄NO₃ and Fe), nodes (NH₄NO₃ and KNO₃), and shoot length (mesos and Fe). Factors with the largest effects (mesos and Fe) were similar among the genotypes. This approach was very successful for defining the appropriate types and concentrations of mineral nutrients for micropropagation of diverse pear genotypes.     

A network partition algorithm for mining gene functional modules of colon cancer from DNA microarray data

Computational analysis is essential for transforming the masses of microarray data into a mechanistic understanding of cancer. Here we present a method for finding gene functional modules of cancer from microarray data and have applied it to colon cancer. First, a colon cancer gene network and a normal colon tissue gene network were constructed using correlations between the genes. Then the modules that tended to have a homogeneous functional composition were identified by splitting up the network. Analysis of both networks revealed that they are scale-free. Comparison of the gene functional modules for colon cancer and normal tissues showed that the modules＇ functions changed with their structures.     

Knowledge discovery of Middle East dust sources using Apriori spatial data mining algorithm

Identifying the areas susceptible to dust storm formation is one effective way of dealing with this destructive environmental phenomenon. This study is the first attempt to employ the Apriori spatial data mining algorithm to dust source susceptibility mapping (DSSM). The research process was based on extracting association rules between spatial-temporal patterns of dust drivers (including soil, vegetation, and climate parameters) in the Middle East's hotspots dust sources (HDSs). For this purpose, HDSs were identified using visual interpretation of sub-daily MODIS-Terra/Aqua RGB images from 2000 to 2021. The Middle East's HDSs mainly correspond to desert areas with poor vegetation cover and ephemeral/dried-up water bodies. A total of three million rules were extracted by running the Apriori algorithm. Accordingly, bare and non-vegetated lands, high soil thickness, low soil moisture, very high wind speed, and high temperature were identified as the most common features of HDSs. Using three measures including support, confidence, and lift, 54 frequent, reliable, and logical rules were selected, and the related maps were generated. Then, the susceptible dust sources (SDSs) map of the Middle East was produced in five classes of extreme (13% of the areas), high (14%), moderate (16%), low (17%), and no (40%) susceptibility through the weighted linear combination of the rule maps. The accuracy of the identified SDSs was estimated at 83.7% using the verification points. A sensitivity analysis was performed using the leave-one-out method to determine the isolated effect of the selected rules on the produced SDSs map. The model uncertainty varied between 15.7% and 16.8% for different rules. The variation range of uncertainty was 1.1%, demonstrating that a single rule does not significantly affect the model's performance; however, some rules have a more influential role. Our results revealed that Apriori's ability to provide generalizable association rules is a robust algorithm for DSSM.     

Automatic rule generation for protein annotation with the C4.5 data mining algorithm applied on SWISS-PROT.

MOTIVATION: The gap between the amount of newly submitted protein data and reliable functional annotation in public databases is growing. Traditional manual annotation by literature curation and sequence analysis tools without the use of automated annotation systems is not able to keep up with the ever increasing quantity of data that is submitted. Automated supplements to manually curated databases such as TrEMBL or GenPept cover raw data but provide only limited annotation. To improve this situation automatic tools are needed that support manual annotation, automatically increase the amount of reliable information and help to detect inconsistencies in manually generated annotations. RESULTS: A standard data mining algorithm was successfully applied to gain knowledge about the Keyword annotation in SWISS-PROT. 11 306 rules were generated, which are provided in a database and can be applied to yet unannotated protein sequences and viewed using a web browser. They rely on the taxonomy of the organism, in which the protein was found and on signature matches of its sequence. The statistical evaluation of the generated rules by cross-validation suggests that by applying them on arbitrary proteins 33% of their keyword annotation can be generated with an error rate of 1.5%. The coverage rate of the keyword annotation can be increased to 60% by tolerating a higher error rate of 5%. AVAILABILITY: The results of the automatic data mining process can be browsed on http://golgi.ebi.ac.uk:8080/Spearmint/ Source code is available upon request. CONTACT: kretsch@ebi.ac.uk.