Unity in diversity, a systems approach to regulating plant cell physiology by 2-oxoglutarate-dependent dioxygenases

Elevating Ascorbate in Arabidopsis Stimulates the Production of Abscisic Acid, Phaseic Acid, and to a Lesser Extent Auxin (IAA) and Jasmonates, Resulting in Increased Expression of DHAR1 and Multiple Transcription Factors Associated with Abiotic Stress Tolerance

Gene expression and phytohormone contents were measured in response to elevating ascorbate in the absence of other confounding stimuli such as high light and abiotic stresses. Young Arabidopsis plants were treated with 25 mM solutions of l-galactose pathway intermediates l-galactose (l-gal) or l-galactono-1,4-lactone (l-galL), as well as L-ascorbic acid (AsA), with 25 mM glucose used as control. Feeding increased rosette AsA 2- to 4-fold but there was little change in AsA biosynthetic gene transcripts. Of the ascorbate recycling genes, only Dehydroascorbate reductase 1 expression was increased. Some known regulatory genes displayed increased expression and included ANAC019, ANAC072, ATHB12, ZAT10 and ZAT12. Investigation of the ANAC019/ANAC072/ATHB12 gene regulatory network revealed a high proportion of ABA regulated genes. Measurement of a subset of jasmonate, ABA, auxin (IAA) and salicylic acid compounds revealed consistent increases in ABA (up to 4.2-fold) and phaseic acid (PA; up to 5-fold), and less consistently certain jasmonates, IAA, but no change in salicylic acid levels. Increased ABA is likely due to increased transcripts for the ABA biosynthetic gene NCED3. There were also smaller increases in transcripts for transcription factors ATHB7, ERD1, and ABF3. These results provide insights into how increasing AsA content can mediate increased abiotic stress tolerance.

Elevating Ascorbate in Arabidopsis Stimulates the Production of Abscisic Acid, Phaseic Acid, and to a Lesser Extent Auxin (IAA) and Jasmonates, Resulting in Increased Expression of DHAR1 and Multiple Transcription Factors Associated with Abiotic Stress Tolerance

Gene expression and phytohormone contents were measured in response to elevating ascorbate in the absence of other confounding stimuli such as high light and abiotic stresses. Young Arabidopsis plants were treated with 25 mM solutions of l-galactose pathway intermediates l-galactose (l-gal) or l-galactono-1,4-lactone (l-galL), as well as L-ascorbic acid (AsA), with 25 mM glucose used as control. Feeding increased rosette AsA 2- to 4-fold but there was little change in AsA biosynthetic gene transcripts. Of the ascorbate recycling genes, only Dehydroascorbate reductase 1 expression was increased. Some known regulatory genes displayed increased expression and included ANAC019, ANAC072, ATHB12, ZAT10 and ZAT12. Investigation of the ANAC019/ANAC072/ATHB12 gene regulatory network revealed a high proportion of ABA regulated genes. Measurement of a subset of jasmonate, ABA, auxin (IAA) and salicylic acid compounds revealed consistent increases in ABA (up to 4.2-fold) and phaseic acid (PA; up to 5-fold), and less consistently certain jasmonates, IAA, but no change in salicylic acid levels. Increased ABA is likely due to increased transcripts for the ABA biosynthetic gene NCED3. There were also smaller increases in transcripts for transcription factors ATHB7, ERD1, and ABF3. These results provide insights into how increasing AsA content can mediate increased abiotic stress tolerance.

Fe(2)OG: an integrated HMM profile-based web server to predict and analyze putative non-haem iron(II)- and 2-oxoglutarate-dependent dioxygenase function in protein sequences

Objective

Non-haem iron(II)- and 2-oxoglutarate-dependent dioxygenases (i2OGdd), are a taxonomically and functionally diverse group of enzymes. The active site comprises ferrous iron in a hexa-coordinated distorted octahedron with the apoenzyme, 2-oxoglutarate and a displaceable water molecule. Current information on novel i2OGdd members is sparse and relies on computationally-derived annotation schema. The dissimilar amino acid composition and variable active site geometry thereof, results in differing reaction chemistries amongst i2OGdd members. An additional need of researchers is a curated list of sequences with putative i2OGdd function which can be probed further for empirical data.

Results

This work reports the implementation of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Fe\left(2\right)OG$$\end{document}Fe2OG, a web server with dual functionality and an extension of previous work on i2OGdd enzymes \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\left(Fe\left(2\right)OG\equiv \{H2OGpred,DB2OG\}\right)$$\end{document}Fe2OG≡{H2OGpred,DB2OG}. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Fe\left(2\right)OG$$\end{document}Fe2OG, in this form is completely revised, updated (URL, scripts, repository) and will strengthen the knowledge base of investigators on i2OGdd biochemistry and function. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Fe\left(2\right)OG$$\end{document}Fe2OG, utilizes the superior predictive propensity of HMM-profiles of laboratory validated i2OGdd members to predict probable active site geometries in user-defined protein sequences. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Fe\left(2\right)OG$$\end{document}Fe2OG, also provides researchers with a pre-compiled list of analyzed and searchable i2OGdd-like sequences, many of which may be clinically relevant. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Fe(2)OG$$\end{document}Fe(2)OG, is freely available (http://204.152.217.16/Fe2OG.html) and supersedes all previous versions, i.e., H2OGpred, DB2OG.

Mathematical Basis of Predicting Dominant Function in Protein Sequences by a Generic HMM-ANN Algorithm

The accurate annotation of an unknown protein sequence depends on extant data of template sequences. This could be empirical or sets of reference sequences, and provides an exhaustive pool of probable functions. Individual methods of predicting dominant function possess shortcomings such as varying degrees of inter-sequence redundancy, arbitrary domain inclusion thresholds, heterogeneous parameterization protocols, and ill-conditioned input channels. Here, I present a rigorous theoretical derivation of various steps of a generic algorithm that integrates and utilizes several statistical methods to predict the dominant function in unknown protein sequences. The accompanying mathematical proofs, interval definitions, analysis, and numerical computations presented are meant to offer insights not only into the specificity and accuracy of predictions, but also provide details of the operatic mechanisms involved in the integration and its ensuing rigor. The algorithm uses numerically modified raw hidden markov model scores of well defined sets of training sequences and clusters them on the basis of known function. The results are then fed into an artificial neural network, the predictions of which can be refined using the available data. This pipeline is trained recursively and can be used to discern the dominant principal function, and thereby, annotate an unknown protein sequence. Whilst, the approach is complex, the specificity of the final predictions can benefit laboratory workers design their experiments with greater confidence.

In silico Identification and Taxonomic Distribution of Plant Class C GH9 Endoglucanases

The glycoside hydrolase 9 superfamily, mainly comprising the endoglucanases, is represented in all three domains of life. The current division of GH9 enzymes, into three subclasses, namely A, B, and C, is centered on parameters derived from sequence information alone. However, this classification is ambiguous, and is limited by the paralogous ancestry of classes B and C endoglucanases, and paucity of biochemical and structural data. Here, we extend this classification schema to putative GH9 endoglucanases present in green plants, with an emphasis on identifying novel members of the class C subset. These enzymes cleave the β(1 → 4) linkage between non-terminal adjacent D-glucopyranose residues, in both, amorphous and crystalline regions of cellulose. We utilized non redundant plant GH9 enzymes with characterized molecular data, as the training set to construct Hidden Markov Models (HMMs) and train an Artificial Neural Network (ANN). The parameters that were used for predicting dominant enzyme function, were derived from this training set, and subsequently refined on 147 sequences with available expression data. Our knowledge-based approach, can ascribe differential endoglucanase activity (A, B, or C) to a query sequence with high confidence, and was used to construct a local repository of class C GH9 endoglucanases (GH9C = 241) from 32 sequenced green plants.

Co-operative intermolecular kinetics of 2-oxoglutarate dependent dioxygenases may be essential for system-level regulation of plant cell physiology

Can the stimulus-driven synergistic association of 2-oxoglutarate dependent dioxygenases be influenced by the kinetic parameters of binding and catalysis?In this manuscript, I posit that these indices are necessary and specific for a particular stimulus, and are key determinants of a dynamic clustering that may function to mitigate the effects of this trigger. The protein(s)/sequence(s) that comprise this group are representative of all major kingdoms of life, and catalyze a generic hydroxylation, which is, in most cases accompanied by a specialized conversion of the substrate molecule. Iron is an essential co-factor for this transformation and the response to waning levels is systemic, and mandates the simultaneous participation of molecular sensors, transporters, and signal transducers. Here, I present a proof-of-concept model, that an evolving molecular network of 2OG-dependent enzymes can maintain iron homeostasis in the cytosol of root hair cells of members of the family Gramineae by actuating a non-reductive compensatory chelation by the phytosiderophores. Regression models of empirically available kinetic data (iron and alpha-ketoglutarate) were formulated, analyzed, and compared. The results, when viewed in context of the superfamily responding as a unit, suggest that members can indeed, work together to accomplish system-level function. This is achieved by the establishment of transient metabolic conduits, wherein the flux is dictated by kinetic compatibility of the participating enzymes. The approach adopted, i.e., predictive mathematical modeling, is integral to the hypothesis-driven acquisition of experimental data points and, in association with suitable visualization aids may be utilized for exploring complex plant biochemical systems.