Jeerayut Chaijaruwanich

Differences in Global Gene Expression in Peripheral Blood Mononuclear Cells Indicate a Significant Role of the Innate Responses in Progression of Dengue Fever but Not Dengue Hemorrhagic Fever

BACKGROUND Dengue virus infection causes an array of symptoms ranging from dengue fever (DF) to dengue hemorrhagic fever (DHF). The pathophysiological processes behind these 2 clinical manifestations are unclear. METHOD In the present study, genomewide transcriptomes of peripheral blood mononuclear cells (PBMCs) collected from children with acute-phase DF (i.e., DF PBMCs) or acute-phase DHF (i.e., DHF PBMCs) were compared using microarray analysis. Results of genome screening were validated at the genomic and proteomics levels. RESULTS DHF had stronger influences on the gene expression profile than did DF. Of the affected genes, metabolic gene expression was influenced the most. For the immune response category, 17 genes were more strongly up-regulated in DF PBMCs than in DHF PBMCs. Eight of the these 17 genes were categorized as belonging to the interferon (IFN) system. The up-regulation of IFN-related genes was accompanied by strong expression of CD59, a complement inhibitor. DHF PBMCs expressed genes involved in T and B cell activation, cytokine production, complement activation, and T cell apoptosis more strongly than did DF PBMCs. CONCLUSION We hypothesize that, during DF, genes in the IFN system and complement inhibitor play a role in lowering virus production and reducing tissue damage. In patients with DHF, the dysfunction of immune cells, complement, and cytokines increases viral load and tissue damage.

Draft genome sequence of Arthrospira platensis C1 (PCC9438)

Arthrospira platensis is a cyanobacterium that is extensively cultivated outdoors on a large commercial scale for consumption as a food for humans and animals. It can be grown in monoculture under highly alkaline conditions, making it attractive for industrial production. Here we describe the complete genome sequence of A. platensis C1 strain and its annotation. The A. platensis C1 genome contains 6,089,210 bp including 6,108 protein-coding genes and 45 RNA genes, and no plasmids. The genome information has been used for further comparative analysis, particularly of metabolic pathways, photosynthetic efficiency and barriers to gene transfer.

Inferring transcriptional gene regulation network of starch metabolism in Arabidopsis thaliana leaves using graphical Gaussian model

BackgroundStarch serves as a temporal storage of carbohydrates in plant leaves during day/night cycles. To study transcriptional regulatory modules of this dynamic metabolic process, we conducted gene regulation network analysis based on small-sample inference of graphical Gaussian model (GGM).ResultsTime-series significant analysis was applied for Arabidopsis leaf transcriptome data to obtain a set of genes that are highly regulated under a diurnal cycle. A total of 1,480 diurnally regulated genes included 21 starch metabolic enzymes, 6 clock-associated genes, and 106 transcription factors (TF). A starch-clock-TF gene regulation network comprising 117 nodes and 266 edges was constructed by GGM from these 133 significant genes that are potentially related to the diurnal control of starch metabolism. From this network, we found that β-amylase 3 (b-amy3: At4g17090), which participates in starch degradation in chloroplast, is the most frequently connected gene (a hub gene). The robustness of gene-to-gene regulatory network was further analyzed by TF binding site prediction and by evaluating global co-expression of TFs and target starch metabolic enzymes. As a result, two TFs, indeterminate domain 5 (AtIDD5: At2g02070) and constans-like (COL: At2g21320), were identified as positive regulators of starch synthase 4 (SS4: At4g18240). The inference model of AtIDD5-dependent positive regulation of SS4 gene expression was experimentally supported by decreased SS4 mRNA accumulation in Atidd5 mutant plants during the light period of both short and long day conditions. COL was also shown to positively control SS4 mRNA accumulation. Furthermore, the knockout of AtIDD5 and COL led to deformation of chloroplast and its contained starch granules. This deformity also affected the number of starch granules per chloroplast, which increased significantly in both knockout mutant lines.ConclusionsIn this study, we utilized a systematic approach of microarray analysis to discover the transcriptional regulatory network of starch metabolism in Arabidopsis leaves. With this inference method, the starch regulatory network of Arabidopsis was found to be strongly associated with clock genes and TFs, of which AtIDD5 and COL were evidenced to control SS4 gene expression and starch granule formation in chloroplasts.

SCMCRYS: Predicting Protein Crystallization Using an Ensemble Scoring Card Method with Estimating Propensity Scores of P-Collocated Amino Acid Pairs

Existing methods for predicting protein crystallization obtain high accuracy using various types of complemented features and complex ensemble classifiers, such as support vector machine (SVM) and Random Forest classifiers. It is desirable to develop a simple and easily interpretable prediction method with informative sequence features to provide insights into protein crystallization. This study proposes an ensemble method, SCMCRYS, to predict protein crystallization, for which each classifier is built by using a scoring card method (SCM) with estimating propensity scores of p-collocated amino acid (AA) pairs (p = 0 for a dipeptide). The SCM classifier determines the crystallization of a sequence according to a weighted-sum score. The weights are the composition of the p-collocated AA pairs, and the propensity scores of these AA pairs are estimated using a statistic with optimization approach. SCMCRYS predicts the crystallization using a simple voting method from a number of SCM classifiers. The experimental results show that the single SCM classifier utilizing dipeptide composition with accuracy of 73.90% is comparable to the best previously-developed SVM-based classifier, SVM_POLY (74.6%), and our proposed SVM-based classifier utilizing the same dipeptide composition (77.55%). The SCMCRYS method with accuracy of 76.1% is comparable to the state-of-the-art ensemble methods PPCpred (76.8%) and RFCRYS (80.0%), which used the SVM and Random Forest classifiers, respectively. This study also investigates mutagenesis analysis based on SCM and the result reveals the hypothesis that the mutagenesis of surface residues Ala and Cys has large and small probabilities of enhancing protein crystallizability considering the estimated scores of crystallizability and solubility, melting point, molecular weight and conformational entropy of amino acids in a generalized condition. The propensity scores of amino acids and dipeptides for estimating the protein crystallizability can aid biologists in designing mutation of surface residues to enhance protein crystallizability. The source code of SCMCRYS is available at http://iclab.life.nctu.edu.tw/SCMCRYS/.

HIV-1 CRF01_AE coreceptor usage prediction using kernel methods based logistic model trees

The determination of HIV-1 coreceptor usage plays a major role in HIV treatment. Since Maraviroc has been used in a treatment for patients those exclusively harbor R5-tropic strains, the efficient performance of classifying HIV-1 coreceptor usage can help choose the most advantaged HIV treatment. In general, HIV-1 variants are classified as R5-tropic and X4-tropic or dual/mixed tropic based on their coreceptor usages. The classification of the coreceptor usage has been developed by using the various computational methods or genotypic algorithms based on V3 amino acid sequences. Most genotypic tools have been designed based on a data set of the HIV-1 subtype B that seemed to be reliable only for this subtype. However, the performance of these tools decreases in non-B subtypes. In this study, the support vector machine (SVM) method has been used to classify the HIV-1 coreceptor. To develop an efficient SVM classifier, we present a feature selector using the logistic model tree (LMT) method to select the most relevant positions from the V3 amino acid sequences. Our approach achieves as high as 97.8% accuracy, 97.7% specificity, and 97.9% sensitivity measured by ten-fold cross-validation on 273 sequences.

Physiological and transcriptional responses to high temperature in Arthrospira (Spirulina) platensis C1.

Arthrospira (Spirulina) platensis is a well-known commercial cyanobacterium that is used as a food and in feed supplements. In this study, we examined the physiological changes and whole-genome expression in A. platensis C1 exposed to high temperature. We found that photosynthetic activity was significantly decreased after the temperature was shifted from 35°C to 42°C for 2 h. A reduction in biomass production and protein content, concomitant with the accumulation of carbohydrate content, was observed after prolonged exposure to high temperatures for 24 h. Moreover, the results of the expression profiling in response to high temperature at the designated time points (8 h) revealed two distinct phases of the responses. The first was the immediate response phase, in which the transcript levels of genes involved in different mechanisms, including genes for heat shock proteins; genes involved in signal transduction and carbon and nitrogen metabolism; and genes encoding inorganic ion transporters for magnesium, nitrite and nitrate, were either transiently induced or repressed by the high temperature. In the second phase, the long-term response phase, both the induction and repression of the expression of genes with important roles in translation and photosynthesis were observed. Taken together, the results of our physiological and transcriptional studies suggest that dynamic changes in the transcriptional profiles of these thermal-responsive genes might play a role in maintaining cell homeostasis under high temperatures, as reflected in the growth and biochemical composition, particularly the protein and carbohydrate content, of A. platensis C1.

Lanna Dharma handwritten character recognition on palm leaves manuscript based on Wavelet transform

Lanna Dharma alphabet is used in the past in the North of Thailand, mainly for religious communication. Most of handwritten Lanna Dharma is found in form of old palm leaves manuscripts. These documents have not been properly preserved, still unprotected and damaged by the time. To preserve these valuable documents, handwritten optical character recognition is one of the first choices. This paper proposes an efficient method for Lanna Dharma handwritten character recognition from palm leaves manuscript image. In recent years, research towards Dharma Lanna character recognition from printed document is proposed. However, the proposed method cannot be applied to handwritten documents. This research aims to compare the different feature extraction methods for Lanna Dharma handwritten recognition. The first step in the proposed method is image preprocessing that binarized, enhanced, line segmented, level segmented and character segmented. The next step, each character image was extracted as feature vector using various feature extraction method based on Wavelet transform. Then several alternative feature extraction methods were compared by evaluating their effect on character recognition performance using K-Nearest Neighbor algorithm. The experimental results show that the best feature extraction is 2D, 1D wavelet transform and region properties feature extraction. The recognition rates of 10-fold crosses validation are 93.22 % for upper level, 95.48% for middle level, and 97.77% for lower level.

Prediction of the disulphide bonding state of cysteines in proteins using Conditional Random Fields

The formation of disulphide bonds between cysteines plays a major role in protein folding, structure, function and evolution. Many computational approaches have been used to predict the disulphide bonding state ofcysteines. In our work, we developed a novel method based on Conditional Random Fields (CRFs) to predict the disulphide bonding state from protein primary sequence, predicted secondary structures and predicted relative solvent accessibilities (all-state information). Our experiments obtain 84% accuracy, 88% precision and 94% recall, using all-state information. However, our results show essentially identical results when using protein sequence and predicted relative solvent accessibilities in the absence of secondary structure.

Sequence based human leukocyte antigen gene prediction using informative physicochemical properties

Prediction of different classes within the human leukocyte antigen (HLA) gene family can provide insight into the human immune system and its response to viral pathogens. Therefore, it is desirable to develop an efficient and easily interpretable method for predicting HLA gene class compared to existing methods. We investigated the HLA gene prediction problem as follows: (a) establishing a dataset (HLA262) such that the sequence identity of the complete HLA dataset was reduced to 30%; (b) proposing a feature set of informative physicochemical properties that cooperate with SVM (named HLAPred) to achieve high accuracy and sensitivity (90.04% and 82.99%, respectively) compared with existing methods; and (c) analysing the informative physicochemical properties to understand the physicochemical properties and molecular mechanisms of the HLA gene family.

Predicting protein crystallization using a simple scoring card method

Many computational methods have been developed to predict protein crystallization. Most methods use amino acid and dipeptide compositions as part of the informative features. To advance the prediction accuracy, the support vector machine (SVM) based classifiers and ensemble approaches were effective and commonly-used techniques. However, these techniques suffer from the low interpretation ability of insight into crystallization. In this study, we utilize a newly-developed scoring card method (SCM) with a dipeptide composition feature to predict protein crystallization. This SCM classifier obtains prediction results 74%, 0.55 and 0.83 for accuracy, sensitivity and specificity, respectively, which is comparable to the SVM classifier using the same benchmarks. The experimental results show that the SCM classifier has advantages of simplicity, high interpretability, and high accuracy in predicting protein crystallization, compared with existing SVM-basedensemble classifiers.