Kyrylo Bessonov

The Effects of Threonine Phosphorylation on the Stability and Dynamics of the Central Molecular Switch Region of 18.5-kDa Myelin Basic Protein

The classic isoforms of myelin basic protein (MBP) are essential for the formation and maintenance of myelin in the central nervous system of higher vertebrates. The protein is involved in all facets of the development, compaction, and stabilization of the multilamellar myelin sheath, and also interacts with cytoskeletal and signaling proteins. The predominant 18.5-kDa isoform of MBP is an intrinsically-disordered protein that is a candidate auto-antigen in the human demyelinating disease multiple sclerosis. A highly-conserved central segment within classic MBP consists of a proline-rich region (murine 18.5-kDa sequence –T92-P93-R94-T95-P96-P97-P98-S99–) containing a putative SH3-ligand, adjacent to a region that forms an amphipathic α-helix (P82-I90) upon interaction with membranes, or under membrane-mimetic conditions. The T92 and T95 residues within the proline-rich region can be post-translationally modified through phosphorylation by mitogen-activated protein (MAP) kinases. Here, we have investigated the structure of the α-helical and proline-rich regions in dilute aqueous buffer, and have evaluated the effects of phosphorylation at T92 and T95 on the stability and dynamics of the α-helical region, by utilizing four 36-residue peptides (S72–S107) with differing phosphorylation status. Nuclear magnetic resonance spectroscopy reveals that both the α-helical as well as the proline-rich regions are disordered in aqueous buffer, whereas they are both structured in a lipid environment (cf., Ahmed et al., Biochemistry 51, 7475-9487, 2012). Thermodynamic analysis of trifluoroethanol-titration curves monitored by circular dichroism spectroscopy reveals that phosphorylation, especially at residue T92, impedes formation of the amphipathic α-helix. This conclusion is supported by molecular dynamics simulations, which further illustrate that phosphorylation reduces the folding reversibility of the α-helix upon temperature perturbation and affect the global structure of the peptides through altered electrostatic interactions. The results support the hypothesis that the central conserved segment of MBP constitutes a molecular switch in which the conformation and/or intermolecular interactions are mediated by phosphorylation/dephosphorylation at T92 and T95.

Misincorporation of the proline homologue Aze (azetidine-2-carboxylic acid) into recombinant myelin basic protein

We have evaluated the effects of the proline homologue Aze (1) (azetidine-2-carboxylic acid) on growth of Escherichia coli strains used to over-express recombinant forms of murine myelin basic protein (rmMBP), and on the degree of misincorporation. Addition of Aze to minimal media resulted in severe diminution of growth rate, but rmMBP could still be produced and purified. Mass spectrometry indicated that a detectable proportion of the rmMBP produced had incorporated Aze instead of proline (Pro), to a maximum of three of eleven possible sites. Molecular modelling of a proline-rich region of rmMBP illustrated that the misincorporation of Aze at any site would cause a severe bend in the polypeptide chain, and that multiple Pro-->Aze substitutions would completely disrupt a poly-proline type II structure that has been conjectured to be functionally significant.

Parameterization of the proline analogue Aze (azetidine-2-carboxylic acid) for molecular dynamics simulations and evaluation of its effect on homo-pentapeptide conformations.

We have parameterized and evaluated the proline homologue Aze (azetidine-2-carboxylic acid) for the gromos56a3 force-field for use in molecular dynamics simulations using GROMACS. Using bi-phasic cyclohexane/water simulation systems and homo-pentapeptides, we measured the Aze solute interaction potential energies, ability to hydrogen bond with water, and overall compaction, for comparison to Pro, Gly, and Lys. Compared to Pro, Aze has a slightly higher H-bonding potential, and stronger electrostatic but weaker non-electrostatic interactions with water. The 20-ns simulations revealed the preferential positioning of Aze and Pro at the interface of the water and cyclohexane layers, with Aze spending more time in the aqueous layer. We also demonstrated through simulations of the homo-pentapeptides that Aze has a greater propensity than Pro to undergo trans→cis peptide bond isomerization, which results in a severe 180° bend in the polypeptide chain. The results provide evidence for the hypothesis that the misincorporation of Aze within proline-rich regions of proteins could disrupt the formation of poly-proline type II structures and compromise events such as recognition and binding by SH3-domains.

Classifying microarray data with association rules

In this paper we investigate a method for classifying microarray data using association rules. Associative classifiers, classification systems based on association rules, show good performance level while being easy to read and understand. This feature is especially attractive for biological data where experts can read and validate the association rules. Relevant features are selected using Support Vector Machines with Recursive Feature Elimination. These features are discretized according to their relative expression levels (upregulated, downregulated or no change) and then they are used to build an associative classifier. The proposed combination proves highly accurate for the studied microarray data collection. In addition the classification rules discovered and employed in the classification process prove to be biologically relevant.

Functional Analyses of NSF1 in Wine Yeast Using Interconnected Correlation Clustering and Molecular Analyses

Analyzing time-course expression data captured in microarray datasets is a complex undertaking as the vast and complex data space is represented by a relatively low number of samples as compared to thousands of available genes. Here, we developed the Interdependent Correlation Clustering (ICC) method to analyze relationships that exist among genes conditioned on the expression of a specific target gene in microarray data. Based on Correlation Clustering, the ICC method analyzes a large set of correlation values related to gene expression profiles extracted from given microarray datasets. ICC can be applied to any microarray dataset and any target gene. We applied this method to microarray data generated from wine fermentations and selected NSF1, which encodes a C2H2 zinc finger-type transcription factor, as the target gene. The validity of the method was verified by accurate identifications of the previously known functional roles of NSF1. In addition, we identified and verified potential new functions for this gene; specifically, NSF1 is a negative regulator for the expression of sulfur metabolism genes, the nuclear localization of Nsf1 protein (Nsf1p) is controlled in a sulfur-dependent manner, and the transcription of NSF1 is regulated by Met4p, an important transcriptional activator of sulfur metabolism genes. The inter-disciplinary approach adopted here highlighted the accuracy and relevancy of the ICC method in mining for novel gene functions using complex microarray datasets with a limited number of samples.

Biologically relevant association rules for classification of microarray data

In this paper we investigate a method for classifying microarray data using association rules. Associative classifiers, classification systems based on association rules, show good performance level while being easy to read and understand. This feature is especially attractive for biological data where experts can read and validate the association rules. Relevant features are selected using Support Vector Machines with Recursive Feature Elimination. These features are discretized according to their relative expression levels (upregulated, downregulated or no change) and then they are used to build an associative classifier. The proposed combination proves highly accurate for the studied microarray data collection. In addition, the classification rules discovered and employed in the classification process prove to be biologically relevant.

Using eDNA to biomonitor the fish community in a tropical oligotrophic lake

Environmental DNA (eDNA) is an effective approach for detecting vertebrates and plants, especially in aquatic ecosystems, but prior studies have largely examined eDNA in cool temperate settings. By contrast, this study employs eDNA to survey the fish fauna in tropical Lake Bacalar (Mexico) with the additional goal of assessing the possible presence of invasive fishes, such as Amazon sailfin catfish. Sediment and water samples were collected from eight stations in Lake Bacalar on three occasions over a 4-month interval. Each sample was stored in the presence or absence of lysis buffer to compare eDNA recovery. Short fragments (184-187 bp) of the cytochrome c oxidase I (COI) gene were amplified using fusion primers and then sequenced on Ion Torrent PGM and S5 before their source species were determined using a custom reference sequence database constructed on BOLD. In total, eDNA sequences were recovered from 75 species of vertebrates including 47 fishes, 15 birds, 7 mammals, 5 reptiles, and 1 amphibian. Although all species are known from this region, 6 fish species represent new records for the study area, while 2 require verification. Sequences for five species (2 birds, 2 mammals, 1 reptile) were only detected from sediments, while sequences from 52 species were only recovered from water. Because DNA from the Amazon sailfin catfish was not detected, we used a mock eDNA experiment to confirm our methods were appropriate for its detection. We developed protocols that enabled the recovery of eDNA from tropical oligotrophic aquatic ecosystems, and confirmed their effectiveness in detecting diverse species of vertebrates including an invasive species of Amazon catfish.

Association network modeling from microarray data around fermentation stress response gene NSF1 (YPL230W) using significantly co-expressed gene set

NSF1 is one of the newly discovered fermentation stress response proteins that play a crucial role in the adaptation of the yeast Saccharomyces cerevisiae to fermentation stress conditions. Using time course microarray gene expression profiles of Saccharomyces cerevisiae (DBY7286) grown in YPD media, we identified and mapped genes significantly correlated to the NSF1 expression, hence producing a framework of analysis conditioned on the NSF1 gene function. From the analysis, we developed a novel approach using clustering on the correlated variable that constructed a core set of co-expressed genes. The result is an inter-associated gene network conditioned on the expressed NSF1 gene. The complete-link clustering algorithm grouped the NSF1 associated genes into functional clusters with a high degree correspondence to known metabolic pathways. Co-expressed genes to formed distinct functional chromosomal neighborhoods.