Sukon Prasitwattanaseree

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.

Determination of PM10 and its ion composition emitted from biomass burning in the chamber for estimation of open burning emissions

Biomass samples including agricultural waste (rice straw and maize residue) and forest leaf litter were collected from Chiang Mai Province, Thailand for the burning experiment in the self-designed stainless steel chamber to simulate the emissions of PM10. The burning of leaf litter emitted the highest PM10 (1.52±0.65 g kg(-1)). The PM10-bound ions emitted from the burning of rice straw and maize residue showed the same trend, which was K(+)>Cl(-)>SO4(2-)>NH4(+)>NO3(-). However, the emissions from maize residue burning were ~1.5-2.0 times higher than those from the rice straw burning. The ion content emitted from leaf litter burning was almost the same for all ion species. Noticeably, K(+) and Cl(-) concentrations were ~2-4 times lower than those emitted from agricultural waste burning. It can be deduced that K(+) and Cl(-) were highly emitted from agricultural waste burning due to the use of fertilizer and herbicides in the field, respectively. Based on emission values obtained from the chamber, the pollutant emission rate from open burning was calculated. Burned areas in Chiang Mai Province were 3510 and 866 km(2) in 2010 and 2011, respectively. Forest burning was 71-88%, while agricultural land burning accounted for 12-29% (rice field: crop field=1:3) of total burned area. Therefore, emissions of PM10 from open burning in Chiang Mai were 3051 ton (2010) and 705 ton (2011). Major ions emitted from agricultural waste burning were found to be K(+) and Cl(-), while those from forest burning were SO4(2-) and K(+).

New prediction models for dental age estimation in Thai children and adolescents

UNLABELLED The aims of this study were to develop new prediction models for dental age estimation and to test the accuracy of the resulting models in comparison with the Demirjian et al. and the Willems et al. methods in Thai children and adolescents. Digital panoramic radiographs of 1,134 Thai individuals (487 males and 647 females) aged from 6 to 15 years were selected and evaluated for dental age estimation. Quadratic regression was used to generate new models. The results showed that the new prediction models indicated a strong correlation coefficient between the dental maturity score and the chronological age in both sexes (r=0.951 for males, r=0.945 for females). The new age prediction models were: y=0.006297x(2) - 0.804930x+32.591843 for males and y=0.010677x(2) - 1.538823x+61.955056 for females, where y is the dental age, x is the dental maturity score according to Demirjian et al. METHOD Moreover, these new models were tested showing the greatest accuracy for estimating the age in Thai samples using the mean difference values between the dental and the chronological ages (-0.04 years for males, 0.02 years for females) when compared with the Demirjian et al. and the Willems et al. METHODS In addition, the new models revealed a high percentage of accuracy in the absolute difference values between the dental and the chronological ages within 1 year (76.26% and 74.49% for males and females, respectively). Furthermore, our results in mean difference values indicated that the Demirjian et al. method (0.11 and 0.10 years for males and females, respectively) was more accurate than the Willems et al. method (-0.37 and -0.39 years for males and females, respectively) in Thai samples. In conclusion, the new age prediction models in this study provide accurate age estimation in both sexes, suggesting that these models be applied for forensic age estimation, especially in Thai children and adolescents.

Inference of Gene Regulatory Network by Bayesian Network Using Metropolis-Hastings Algorithm

Bayesian networks are widely used to infer genes regulatory network from their transcriptional expression data. Bayesian network of the best score is usually chosen as genes regulatory model. However, without the hint from biological ground truth, and given a small number of transcriptional expression observations, the resulting Bayesian networks might not correspond to the real one. To deal with these two constrains, this paper proposes a stochastic approach to fit an existing hypothetical gene regulatory network, derived from biological evidence, with few available amount of transcriptional expression levels of the genes. The hypothetical gene regulatory network is set as an initial model of Bayesian network and fitted with transcriptional expression data by using Metropolis-Hastings algorithm. In this work, the transcriptional regulation of gene CYC1 by co-regulators HAP2 HAP3 HAP4 of yeast (Saccharomyces Cerevisiae) is considered as example. Due to the simulation results, ten probable gene regulatory networks which are similar to the given hypothetical model are obtained. This shows that Metropolis-Hastings algorithm can be used as a simulation model for gene regulatory network.

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.

Application of factor analysis on mycobacterium tuberculosis transcriptional responses for drug clustering, drug target, and pathway detections

Recently, the differential transcriptional responses of Mycobacterium tuberculosis to drug and growth-inhibitory conditions were monitored to generate a data set of 436 microarray profiles. These profiles were valuably used for grouping drugs, identifying drug targets and detecting related pathways, based on various conventional methods; such as Pearson correlation, hierarchical clustering, and statistical tests. These conventional clustering methods used the high dimensionality of gene space to reveal drug groups basing on the similarity of expression levels of all genes. In this study, we applied the factor analysis with these conventional methods for drug clustering, drug target detection and pathway detection. The latent variables or factors of gene expression levels in loading space from factor analysis allowed the hierarchical clustering to discover true drug groups. The t-test method was applied to identify drug targets which most significantly associated with each drug cluster. Then, gene ontology was used to detect pathway associations for each group of drug targets.

Age estimation by chest plate radiographs in a Thai male population

Costal cartilage ossification on chest plate radiographs is one of the useful methods in age estimation for adults. This study was performed in 136 remains yielding a regression formula for age estimation in Thai male population. Eight features on chest plate imaging were scored based on Garvins method. Composite scores were calculated by summation of all scores and analyzed to generate the regression of age as: Age=16.664×e0.161 (composite score) with a 95% prediction interval. The predicted age intervals in all composite scores were overlapping except for score 0 and 7. It could be concluded that if all features are absent/present, the person is likely to be less/more than 29years old.

Estimation of Retention Time in GC/MS of Volatile Metabolites in Fragrant Rice Using Principle Components of Molecular Descriptors

A quantitative structure-retention relationship (QSRR) study was applied for an estimation of retention times of secondary volatile metabolites in Thai jasmine rice. In this study, chemical components in rice seed were extracted using solvent extraction, then separated and identified by gas chromatography-mass spectrometry (GC-MS). A set of molecular descriptors was generated for these substances obtained from GC-MS analysis to numerically represent the molecular structure of such compounds. Principal component analysis (PCA) and principal component regression analysis (PCR) were used to model the retention times of these compounds as a function of the theoretically derived descriptors. The best fitted regression model was obtained with R-squared of 0.900. The informative chemical properties related to retention time were elucidated. The results of this study demonstrate clearly that the combination of molecular weight and autocorrelation functions of two dimensional interatomic distance, which are molecular polarizability, atom identity, sigma charge, sigma electronegativity and polarizability, can be considered as comprehensive factors for predicting the retention times of volatile compounds in rice.

Estimation of stature using fragmentary femur and tibia lengths in a Thai population

Stature estimation is one of the most important parameters for human identification. The current study aimed to investigate the relationship between stature and femur and tibia measurements other than length in modern Thai skeletons, and to derive regression equations that can be used for living stature estimation when complete bones are unavailable. The sample consisted of 159 males and 96 females. We used two analytical methods, i.e. direct 1-step and indirect 2-step. Results showed that the upper breadth of the femur from males and the maximum anteroposterior diameter of the lateral condyle from females were the best estimators of stature. Maximum proximal breadth of the tibia showed the lowest standard error values in males and females. The derived equations were tested on a holdout sample in which the distal breadth of the tibia exhibited the lowest mean absolute error in both males and females, while in the femur measurements, the maximum anteroposterior diameter of the lateral condyle produced the lowest mean absolute error in males, and an absolute mean value from vertical head diameter in females. The present study suggests that measurements taken from damaged bones can be used for stature estimation.

Spatial and Temporal Analyses of Cervical Cancer Patients in Upper Northern Thailand

Background: Cervical cancer is a major public health problem worldwide. There have been several studies indicating that risk is associated with geographic location and that the incidence of cervical cancer has changed over time. In Thailand, incidence rates have also been found to be different in each region. Methods: Participants were women living or having lived in upper Northern Thailand and subjected to cervical screening at Maharaj Nakorn Chiang Mai Hospital between January 2010 and December 2014. Generalized additive models with Loess smooth curve fitting were applied to estimate the risk of cervical cancer. For the spatial analysis, Google Maps were employed to find the geographical locations of the participants’ addresses. The Quantum Geographic Information System was used to make a map of cervical cancer risk. Two univariate smooths: x equal to the residency duration was used in the temporal analysis of residency duration, and x equal to the calendar year that participants moved to upper Northern Thailand or birth year for participants already living there, were used in the temporal analysis of the earliest year. The spatial-temporal analysis was conducted in the same way as the spatial analysis except that the data were split into overlapping calendar years. Results: In the spatial analysis, the risk of cervical cancer was shown to be highest in the Eastern sector of upper Northern Thailand (p-value <0.001). In the temporal analysis of residency duration, the risk was shown to be steadily increasing (p-value =0.008), and in the temporal analysis of the earliest year, the risk was observed to be steadily decreasing (p-value=0.016). In the spatial-temporal analysis, the risk was stably higher in Chiang Rai and Nan provinces compared to Chiang Mai province. According to the display movement over time, the odds of developing cervical cancer declined in all provinces. Conclusions: The risk of cervical cancer has decreased over time but, in some areas, there is a higher risk than in the major province of Chiang Mai. Therefore, we should promote cervical cancer screening coverage in all areas, especially where access is difficult and/or to women of lower socioeconomic status.