Neuro-fuzzy model of homocysteine metabolism

In view of well-documented association of hyperhomocysteinaemia with a wide spectrum of diseases and higher incidence of vitamin deficiencies in Indians, we proposed a mathematical model to forecast the role of demographic and genetic variables in influencing homocysteine metabolism and investigated the influence of life style modulations in controlling homocysteine levels. Total plasma homocysteine levels were measured in fasting samples using reverse phase HPLC. Multiple linear regression (MLR) and neuro-fuzzy models were developed. The MLR model explained 64% variability in homocysteine, while the neuro-fuzzy model showed higher accuracy in predicting homocysteine with a mean absolute error of 0.00002 \(\mu \hbox {mol}/\hbox {L}\). Methylene tetrahydrofolate reductase (MTHFR) C677T, 5-methyltetrahydrofolate homocysteine methyltransferase (MTR) A2756G and 5-methyltetrahydrofolate homocysteine methyltransferase reductase (MTRR) A66G were shown to be positively associatiated with homocysteine, while nonvegetarian diet, serine hydroxymethyltransferase 1 (SHMT1) C1420T and TYMS \(5^\prime \)-UTR 28 bp tandem repeat exhibited negative association with homocysteine. The protective role of SHMT1 C1420T was attributed to more H-bonding interactions in the mutant modelled compared to the wild type, as shown through in silico analysis. To conclude, polymorphisms in genes regulating remethylation of homocysteine strongly influence homocysteine levels. The restoration of one-carbon homeostasis by SHMT1 C1420T or increased flux of folate towards remethylation due to TYMS \(5^\prime \)-UTR 28 bp tandem repeat or nonvegetarian diet can lower homocysteine levels.     

Role of calcium–calmodulin in auxin-induced somatic embryogenesis in leaf base cultures of wheat (<Emphasis Type="Italic">Triticum aestivum</Emphasis> var. HD 2329)

Wheat leaf bases cultured for 1 day on 2,4-d (10 μM) display the induction of somatic embryogenesis. The induction of somatic embryogenesis by 2,4-d appears to be calcium-mediated as treatment of leaf bases with the calcium chelator, EGTA, prior to 2,4-d treatment, inhibited the induction of somatic embryogenesis. This sensitivity of auxin to reduced calcium levels can be reversed by calcium ions alone and not any other divalent cation like magnesium or zinc. Additionally, the expression of the three calcium-regulated genes, Triticum aestivum calmodulin binding protein kinase, calcium-dependent protein kinase, and putative calcium binding protein was analyzed in wheat leaf bases which suggest a specific role for Ca²⁺ in somatic embryogenesis. Application of the calcium ionophore, A23187, either alone or along with 2,4-d, induced somatic embryogenesis. This specificity for calcium was verified both by treatment with the calcium antagonist TMB8, and the elimination of calcium from the medium, resulting in reduction of somatic embryogenesis by 80%. Treatment with calcium channel blockers like verapamil and nifedipine, calcium antagonist, lanthanum, and calmodulin inhibitors chlorpromazine and fluphenazine, prior to the 2,4-d treatment, inhibited induction of somatic embryogenesis. The present study thus provides evidence for the involvement of calcium–calmodulin in the stimulus–response coupling of auxin-induced somatic embryogenesis in wheat leaf base system.     

X-ray structure of Paramecium bursaria Chlorella virus arginine decarboxylase: insight into the structural basis for substrate specificity

The group IV pyridoxal-5'-phosphate (PLP)-dependent decarboxylases belong to the beta/alpha barrel structural family, and include enzymes with substrate specificity for a range of basic amino acids. A unique homolog of this family, the Paramecium bursaria Chlorella virus arginine decarboxylase (cvADC), shares about 40% amino acid sequence identity with the eukaryotic ornithine decarboxylases (ODCs). The X-ray structure of cvADC has been solved to 1.95 and 1.8 A resolution for the free and agmatine (product)-bound enzymes. The global structural differences between cvADC and eukaryotic ODC are minimal (rmsd of 1.2-1.4 A); however, the active site has significant structural rearrangements. The key "specificity element," is identified as the 310-helix that contains and positions substrate-binding residues such as E296 cvADC (D332 in T. brucei ODC). In comparison to the ODC structures, the 310-helix in cvADC is shifted over 2 A away from the PLP cofactor, thus accommodating the larger arginine substrate. Within the context of this conserved fold, the protein is designed to be flexible in the positioning and amino acid sequence of the 310-helix, providing a mechanism to evolve different substrate preferences within the family without large structural rearrangements. Also, in the structure, the "K148-loop" (homologous to the "K169-loop" of ODC) is observed in a closed, substrate-bound conformation for the first time. Apparently the K148 loop is a mobile loop, analogous to those observed in triose phosphate isomerase and tryptophan synthetase. In conjunction with prior structural studies these data predict that this loop adopts different conformations throughout the catalytic cycle, and that loop movement may be kinetically linked to the rate-limiting step of product release.     

Osmosensing by WNK Kinases

With No Lysine (K) WNK kinases regulate electro-neutral cotransporters that are controlled by osmotic stress and chloride. We showed previously that autophosphorylation of WNK1 is inhibited by chloride, raising the possibility that WNKs are activated by osmotic stress. Here we demonstrate that unphosphorylated WNK isoforms 3 and 1 autophosphorylate in response to osmotic pressure in vitro, applied with the crowding agent polyethylene glycol (PEG)400 or osmolyte ethylene glycol (EG), and that this activation is opposed by chloride. Small angle x-ray scattering of WNK3 in the presence and absence of PEG400, static light scattering in EG, and crystallography of WNK1 were used to understand the mechanism. Osmosensing in WNK3 and WNK1 appears to occur through a conformational equilibrium between an inactive, unphosphorylated, chloride-binding dimer and an autophosphorylation-competent monomer. An improved structure of the inactive kinase domain of WNK1, and a comparison with the structure of a monophosphorylated form of WNK1, suggests that large cavities, greater hydration, and specific bound water may participate in the osmosensing mechanism. Our prior work showed that osmolytes have effects on the structure of phosphorylated WNK1, suggestive of multiple stages of osmotic regulation in WNKs.     

The third conformation of p38α MAP kinase observed in phosphorylated p38α and in solution

MAPKs engage substrates, MAP2Ks, and phosphatases via a docking groove in the C-terminal domain of the kinase. Prior crystallographic studies on the unphosphorylated MAPKs p38α and ERK2 defined the docking groove and revealed long-range conformational changes affecting the activation loop and active site of the kinase induced by peptide. Solution NMR data presented here for unphosphorylated p38α with a MEK3b-derived peptide (p38α/pepMEK3b) validate these findings. Crystallograhic data from doubly phosphorylated active p38α (p38α/T?GY?/pepMEK3b) reveal a structure similar to unphosphorylated p38α/MEK3b, and distinct from phosphorylated p38γ (p38γ/T?GY?) and ERK2 (ERK2/T?EY?). The structure supports the idea that MAP kinases adopt three distinct conformations: unphosphorylated, phosphorylated, and a docking peptide-induced form.     

Multi-enzyme logic network architectures for assessing injuries: digital processing of biomarkers

A multi-enzyme biocatalytic cascade processing simultaneously five biomarkers characteristic of traumatic brain injury (TBI) and soft tissue injury (STI) was developed. The system operates as a digital biosensor based on concerted function of 8 Boolean AND logic gates, resulting in the decision about the physiological conditions based on the logic analysis of complex patterns of the biomarkers. The system represents the first example of a multi-step/multi-enzyme biosensor with the built-in logic for the analysis of complex combinations of biochemical inputs. The approach is based on recent advances in enzyme-based biocomputing systems and the present paper demonstrates the potential applicability of biocomputing for developing novel digital biosensor networks.     

Thermal decomposition studies of cis-bis(1-alkenyl)platinum(II) complexes

Thermal decomposition studies of bis(1-alkenyl)platinum(II) complexes of the type Pt(L₂)R₂ (where L = PPh₃ or L₂ = dppe or dppp; R = 1-alkenyl) are reported. The results revealed interesting organic product distributions depending on the length of the alkenyl chains, the nature of the supporting ligands and the metal centers. It is believed that the major decomposition pathways for the title complexes involve β-hydride elimination and/or reductive elimination as well as alkene isomerization. Mechanisms for the major and minor decomposition pathways are proposed. The amount of isomeric products obtained, i.e., 2-alkenes and 1,(n ? 2)-diene, increases with increasing chain length.     

Critical role for polar residues in coupling leukotriene B4 binding to signal transduction in BLT1

Leukotriene B(4) (LTB(4)) mediates a variety of inflammatory diseases such as asthma, arthritis, atherosclerosis, and cancer through activation of the G-protein-coupled receptor, BLT1. Using in silico molecular dynamics simulations combined with site-directed mutagenesis we characterized the ligand binding site and activation mechanism for BLT1. Mutation of residues predicted as potential ligand contact points in transmembrane domains (TMs) III (H94A and Y102A), V (E185A), and VI (N241A) resulted in reduced binding affinity. Analysis of arginines in extracellular loop 2 revealed that mutating arginine 156 but not arginine 171 or 178 to alanine resulted in complete loss of LTB(4) binding to BLT1. Structural models for the ligand-free and ligand-bound states of BLT1 revealed an activation core formed around Asp-64, displaying multiple dynamic interactions with Asn-36, Ser-100, and Asn-281 and a triad of serines, Ser-276, Ser-277, and Ser-278. Mutagenesis of many of these residues in BLT1 resulted in loss of signaling capacity while retaining normal LTB(4) binding function. Thus, polar residues within TMs III, V, and VI and extracellular loop 2 are critical for ligand binding, whereas polar residues in TMs II, III, and VII play a central role in transducing the ligand-induced conformational change to activation. The delineation of a validated binding site and activation mechanism should facilitate structure-based design of inhibitors targeting BLT1.     

Data Mining Strategies to Improve Multiplex Microbead Immunoassay Tolerance in a Mouse Model of Infectious Diseases

Multiplex methodologies, especially those with high-throughput capabilities generate large volumes of data. Accumulation of such data (e.g., genomics, proteomics, metabolomics etc.) is fast becoming more common and thus requires the development and implementation of effective data mining strategies designed for biological and clinical applications. Multiplex microbead immunoassay (MMIA), on xMAP or MagPix platform (Luminex), which is amenable to automation, offers a major advantage over conventional methods such as Western blot or ELISA, for increasing the efficiencies in serodiagnosis of infectious diseases. MMIA allows detection of antibodies and/or antigens efficiently for a wide range of infectious agents simultaneously in host blood samples, in one reaction vessel. In the process, MMIA generates large volumes of data. In this report we demonstrate the application of data mining tools on how the inherent large volume data can improve the assay tolerance (measured in terms of sensitivity and specificity) by analysis of experimental data accumulated over a span of two years. The combination of prior knowledge with machine learning tools provides an efficient approach to improve the diagnostic power of the assay in a continuous basis. Furthermore, this study provides an in-depth knowledge base to study pathological trends of infectious agents in mouse colonies on a multivariate scale. Data mining techniques using serodetection of infections in mice, developed in this study, can be used as a general model for more complex applications in epidemiology and clinical translational research.     

NetiNeti: discovery of scientific names from text using machine learning methods

ABSTRACT: BACKGROUND: A scientific name for an organism can be associated with almost all biological data. Name identification is an important step in many text mining tasks aiming to extract useful information from biological, biomedical and biodiversity text sources. A scientific name acts as an important metadata element to link biological information. RESULTS: We present NetiNeti (Name Extraction from Textual Information-Name Extraction for Taxonomic Indexing), a machine learning based approach for recognition of scientific names including the discovery of new species names from text that will also handle misspellings, OCR errors and other variations in names. The system generates candidate names using rules for scientific names and applies probabilistic machine learning methods to classify names based on structural features of candidate names and features derived from their contexts. NetiNeti can also disambiguate scientific names from other names using the contextual information. We evaluated NetiNeti on legacy biodiversity texts and biomedical literature (MEDLINE). NetiNeti performs better (precision = 98.9 % and recall = 70.5 %) compared to a popular dictionary based approach (precision = 97.5 % and recall = 54.3 %) on a 600-page biodiversity book that was manually marked by an annotator. On a small set of PubMed Central's full text articles annotated with scientific names, the precision and recall values are 98.5 % and 96.2 % respectively. NetiNeti found more than 190,000 unique binomial and trinomial names in more than 1,880,000 PubMed records when used on the full MEDLINE database. NetiNeti also successfully identifies almost all of the new species names mentioned within web pages. Additionally, we present the comparison results of various machine learning algorithms on our annotated corpus. Naive Bayes and Maximum Entropy with Generalized Iterative Scaling (GIS) parameter estimation are the top two performing algorithms. CONCLUSIONS: We present NetiNeti, a machine learning based approach for identification and discovery of scientific names. The system implementing the approach can be accessed at http://namefinding.ubio.org.