[Cloning of transcription factor PcFBA-1 in Pogostemon cabin and its interaction with FPPS promoter].

Farnesyl diphosphate synthase(FPPS) is a key enzyme at the branch point of the sesquiterpene biosynthetic pathway, but there are no reports on the transcriptional regulation of FPPS promoter in Pogostemon cabin. In the early stage of this study, we obtained the binding protein PcFBA-1 of FPPS gene promoter in P. cabin. In order to explore the possible mechanism of PcFBA-1 involved in the regulation of patchouli alcohol biosynthesis, this study performed PCR-based cloning and sequencing analysis of PcFBA-1, analyzed the expression patterns of PcFBA-1 in different tissues by fluorescence quantitative PCR and its subcellular localization using the protoplast transformation system, detected the binding of PcFBA-1 protein to the FPPS promoter in vitro with the yeast one-hybrid system, and verified its transcriptional regulatory function by dual-luciferase reporter gene assay. The findings demonstrated that the cloned PcFBA-1 had an open reading frame(ORF) of 1 131 bp, encoding a protein of 376 amino acids, containing two conserved domains named F-box-like superfamily and FBA-1 superfamily, and belonging to the F-box family. Moreover, neither signal peptide nor transmembrane domain was contained, implying that it was an unstable hydrophilic protein. In addition, as revealed by fluorescence quantitative PCR results, PcFBA-1 had the highest expression in leaves, and there was no significant difference in expression in roots or stems. PcFBA-1 protein was proved mainly located in the cytoplasm. Furthermore, yeast one-hybrid screening and dual-luciferase reporter gene assay showed that PcFBA-1 was able to bind to FPPS promoter both in vitro and in vivo to enhance the activity of FPPS promoter. In summary, this study identifies a new transcription factor PcFBA-1 in P. cabin, which directly binds to the FPPS gene promoter to enhance the promoter activity. This had laid a foundation for the biosynthesis of patchouli alcohol and other active ingre-dients and provided a basis for metabolic engineering and genetic improvement of P. cabin.

Cadmium induces ovarian granulosa cell damage by activating PERK-eIF2α-ATF4 through endoplasmic reticulum stress.

This study aimed to investigate whether cadmium induces ovarian granulosa cell damage by activating protein kinase R-like endoplasmic reticulum kinase (PERK)-eIF2α-ATF4 through endoplasmic reticulum (ER) stress and to elucidate the underlying regulation mechanism. Two models of cadmium exposure were established. In one model, ovarian granulosa cells isolated from 21-day-old female Sprague Dawley rats were cultured in vitro for 36 h and exposed to CdCl2 (0, 5, 10, and 20 µM), and in another model, a human ovarian granulosa tumor cell line (COV434) was used to construct the binding immunoglobulin protein (BIP)-knockdown cell line sh-BIP and exposed to 0 and 20 µM CdCl2. After exposure to cadmium for 12 h, the expression mRNA and protein levels of BIP, p-PERK, and p-eIF2α were determined in the two models. miRNAs related to BIP were also detected in granulosa cells after cadmium exposure. We found that mRNA and protein levels of all factors were upregulated in each cadmium-dose group, except for BIP mRNA expression in the 5 µM Cd group. The BIP gene was knocked down in COV434 cells before exposure to cadmium. All factors were upregulated in COV434 cells exposed to Cd, and the expression of the p-eIF2α protein was downregulated in sh-BIP cells exposed to Cd. In addition, no differences in BIP-related miRNAs were detected in cadmium-exposed rat ovarian granulosa cells versus the control group. Cadmium induces ovarian granulosa cell damage by inducing ER stress.

ESA-UbiSite: accurate prediction of human ubiquitination sites by identifying a set of effective negatives.

Motivation: Numerous ubiquitination sites remain undiscovered because of the limitations of mass spectrometry-based methods. Existing prediction methods use randomly selected non-validated sites as non-ubiquitination sites to train ubiquitination site prediction models. Results: We propose an evolutionary screening algorithm (ESA) to select effective negatives among non-validated sites and an ESA-based prediction method, ESA-UbiSite, to identify human ubiquitination sites. The ESA selects non-validated sites least likely to be ubiquitination sites as training negatives. Moreover, the ESA and ESA-UbiSite use a set of well-selected physicochemical properties together with a support vector machine for accurate prediction. Experimental results show that ESA-UbiSite with effective negatives achieved 0.92 test accuracy and a Matthews's correlation coefficient of 0.48, better than existing prediction methods. The ESA increased ESA-UbiSite's test accuracy from 0.75 to 0.92 and can improve other post-translational modification site prediction methods. Availability and Implementation: An ESA-UbiSite-based web server has been established at http://iclab.life.nctu.edu.tw/iclab_webtools/ESAUbiSite/ . Contact: syho@mail.nctu.edu.tw. Supplementary information: Supplementary data are available at Bioinformatics online.

[Effects of aqueous extracts of gecko on stemness of hepatocellular carcinoma].

To explore the effects and mechanism of aqueous extracts of gecko on cancer stem cells properties of hepatocellular carcinoma. In vitro, MTT assay was used to detect the cells growth in Huh7 and Hep3B. Spheroid-forming assay and flow cytometry were performed to observe the the stemness of Huh7 and Hep3B cells. The protein expressions of ß-catenin, CD44, c-Myc, CCND1, Sox2, Oct4, Nanog and ABCG2 were detected by Western blot. Interacting proteins were detected by co-immunoprecipitation; and a subcutaneous xenograft model was used to detect the stemness of hepatoma carcinoma cells. The results indicated that aqueous extracts of gecko induced cell growth inhibition in a dose- and time-dependent manner, with the IC50 of (0.750±0.112) g•mL⁻¹ for Huh7 and (0.454±0.039) g•mL⁻¹ for Hep3B, respectively. The number and size of tumor spheres formed by hepatoma carcinoma cells were decreased after treatment by aqueous extracts of gecko(P<0.05); the proportions of cells staining with putative markers for cancer stem cells, such as CD133 and CD44, were decreased(P<0.05). After treatment with aqueous extracts of gecko, the expression levels of ß-catenin, CD44, c-Myc, CCND1, Sox2, Oct4, Nanog and ABCG2 were decreased. Co-immunoprecipitation results showed that the aqueous extracts of gecko could inhibit the interaction between LRP6 and Frizzled6, indicating that the aqueous extracts of gecko could inhibit the proliferation of hepatoma cells, the formation of tumor spheres and the proportion of tumor stem cells, and inhibit the Wnt signaling pathway by targeting LRP6 to prevent the formation of LRP6 and Frizzled6 complexes.

A hydrophobic spine stabilizes a surface-exposed α-helix according to analysis of the solvent-accessible surface area.

BACKGROUND: Most of hydrophilic and hydrophobic residues are thought to be exposed and buried in proteins, respectively. In contrast to the majority of the existing studies on protein folding characteristics using protein structures, in this study, our aim was to design predictors for estimating relative solvent accessibility (RSA) of amino acid residues to discover protein folding characteristics from sequences. METHODS: The proposed 20 real-value RSA predictors were designed on the basis of the support vector regression method with a set of informative physicochemical properties (PCPs) obtained by means of an optimal feature selection algorithm. Then, molecular dynamics simulations were performed for validating the knowledge discovered by analysis of the selected PCPs. RESULTS: The RSA predictors had the mean absolute error of 14.11% and a correlation coefficient of 0.69, better than the existing predictors. The hydrophilic-residue predictors preferred PCPs of buried amino acid residues to PCPs of exposed ones as prediction features. A hydrophobic spine composed of exposed hydrophobic residues of an α-helix was discovered by analyzing the PCPs of RSA predictors corresponding to hydrophobic residues. For example, the results of a molecular dynamics simulation of wild-type sequences and their mutants showed that proteins 1MOF and 2WRP_H16I (Protein Data Bank IDs), which have a perfectly hydrophobic spine, have more stable structures than 1MOF_I54D and 2WRP do (which do not have a perfectly hydrophobic spine). CONCLUSIONS: We identified informative PCPs to design high-performance RSA predictors and to analyze these PCPs for identification of novel protein folding characteristics. A hydrophobic spine in a protein can help to stabilize exposed α-helices.

SCMBYK: prediction and characterization of bacterial tyrosine-kinases based on propensity scores of dipeptides.

BACKGROUND: Bacterial tyrosine-kinases (BY-kinases), which play an important role in numerous cellular processes, are characterized as a separate class of enzymes and share no structural similarity with their eukaryotic counterparts. However, in silico methods for predicting BY-kinases have not been developed yet. Since these enzymes are involved in key regulatory processes, and are promising targets for anti-bacterial drug design, it is desirable to develop a simple and easily interpretable predictor to gain new insights into bacterial tyrosine phosphorylation. This study proposes a novel SCMBYK method for predicting and characterizing BY-kinases. RESULTS: A dataset consisting of 797 BY-kinases and 783 non-BY-kinases was established to design the SCMBYK predictor, which achieved training and test accuracies of 97.55 and 96.73%, respectively. Furthermore, the leave-one-phylum-out method was used to predict specific bacterial phyla hosts of target sequences, gaining 97.39% average test accuracy. After analyzing SCMBYK-derived propensity scores, four characteristics of BY-kinases were determined: 1) BY-kinases tend to be composed of α-helices; 2) the amino-acid content of extracellular regions of BY-kinases is expected to be dominated by residues such as Val, Ile, Phe and Tyr; 3) BY-kinases structurally resemble nuclear proteins; 4) different domains play different roles in triggering BY-kinase activity. CONCLUSIONS: The SCMBYK predictor is an effective method for identification of possible BY-kinases. Furthermore, it can be used as a part of a novel drug repurposing method, which recognizes putative BY-kinases and matches them to approved drugs. Among other results, our analysis revealed that azathioprine could suppress the virulence of M. tuberculosis, and thus be considered as a potential antibiotic for tuberculosis treatment.

Estimating survival time of patients with glioblastoma multiforme and characterization of the identified microRNA signatures.

BACKGROUND: Though glioblastoma multiforme (GBM) is the most frequently occurring brain malignancy in adults, clinical treatment still faces challenges due to poor prognoses and tumor relapses. Recently, microRNAs (miRNAs) have been extensively used with the aim of developing accurate molecular therapies, because of their emerging role in the regulation of cancer-related genes. This work aims to identify the miRNA signatures related to survival of GBM patients for developing molecular therapies. RESULTS: This work proposes a support vector regression (SVR)-based estimator, called SVR-GBM, to estimate the survival time in patients with GBM using their miRNA expression profiles. SVR-GBM identified 24 out of 470 miRNAs that were significantly associated with survival of GBM patients. SVR-GBM had a mean absolute error of 0.63 years and a correlation coefficient of 0.76 between the real and predicted survival time. The 10 top-ranked miRNAs according to prediction contribution are as follows: hsa-miR-222, hsa-miR-345, hsa-miR-587, hsa-miR-526a, hsa-miR-335, hsa-miR-122, hsa-miR-24, hsa-miR-433, hsa-miR-574 and hsa-miR-320. Biological analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway on the identified miRNAs revealed their influence in GBM cancer. CONCLUSION: The proposed SVR-GBM using an optimal feature selection algorithm and an optimized SVR to identify the 24 miRNA signatures associated with survival of GBM patients. These miRNA signatures are helpful to uncover the individual role of miRNAs in GBM prognosis and develop miRNA-based therapies.

SCMPSP: Prediction and characterization of photosynthetic proteins based on a scoring card method.

BACKGROUND: Photosynthetic proteins (PSPs) greatly differ in their structure and function as they are involved in numerous subprocesses that take place inside an organelle called a chloroplast. Few studies predict PSPs from sequences due to their high variety of sequences and structues. This work aims to predict and characterize PSPs by establishing the datasets of PSP and non-PSP sequences and developing prediction methods. RESULTS: A novel bioinformatics method of predicting and characterizing PSPs based on scoring card method (SCMPSP) was used. First, a dataset consisting of 649 PSPs was established by using a Gene Ontology term GO:0015979 and 649 non-PSPs from the SwissProt database with sequence identity <= 25%.- Several prediction methods are presented based on support vector machine (SVM), decision tree J48, Bayes, BLAST, and SCM. The SVM method using dipeptide features-performed well and yielded - a test accuracy of 72.31%. The SCMPSP method uses the estimated propensity scores of 400 dipeptides - as PSPs and has a test accuracy of 71.54%, which is comparable to that of the SVM method. The derived propensity scores of 20 amino acids were further used to identify informative physicochemical properties for characterizing PSPs. The analytical results reveal the following four characteristics of PSPs: 1) PSPs favour hydrophobic side chain amino acids; 2) PSPs are composed of the amino acids prone to form helices in membrane environments; 3) PSPs have low interaction with water; and 4) PSPs prefer to be composed of the amino acids of electron-reactive side chains. CONCLUSIONS: The SCMPSP method not only estimates the propensity of a sequence to be PSPs, it also discovers characteristics that further improve understanding of PSPs. The SCMPSP source code and the datasets used in this study are available at http://iclab.life.nctu.edu.tw/SCMPSP/.

Characterizing informative sequence descriptors and predicting binding affinities of heterodimeric protein complexes.

BACKGROUND: Protein-protein interactions (PPIs) are involved in various biological processes, and underlying mechanism of the interactions plays a crucial role in therapeutics and protein engineering. Most machine learning approaches have been developed for predicting the binding affinity of protein-protein complexes based on structure and functional information. This work aims to predict the binding affinity of heterodimeric protein complexes from sequences only. RESULTS: This work proposes a support vector machine (SVM) based binding affinity classifier, called SVM-BAC, to classify heterodimeric protein complexes based on the prediction of their binding affinity. SVM-BAC identified 14 of 580 sequence descriptors (physicochemical, energetic and conformational properties of the 20 amino acids) to classify 216 heterodimeric protein complexes into low and high binding affinity. SVM-BAC yielded the training accuracy, sensitivity, specificity, AUC and test accuracy of 85.80%, 0.89, 0.83, 0.86 and 83.33%, respectively, better than existing machine learning algorithms. The 14 features and support vector regression were further used to estimate the binding affinities (Pkd) of 200 heterodimeric protein complexes. Prediction performance of a Jackknife test was the correlation coefficient of 0.34 and mean absolute error of 1.4. We further analyze three informative physicochemical properties according to their contribution to prediction performance. Results reveal that the following properties are effective in predicting the binding affinity of heterodimeric protein complexes: apparent partition energy based on buried molar fractions, relations between chemical structure and biological activity in principal component analysis IV, and normalized frequency of beta turn. CONCLUSIONS: The proposed sequence-based prediction method SVM-BAC uses an optimal feature selection method to identify 14 informative features to classify and predict binding affinity of heterodimeric protein complexes. The characterization analysis revealed that the average numbers of beta turns and hydrogen bonds at protein-protein interfaces in high binding affinity complexes are more than those in low binding affinity complexes.

Discovery of prognostic biomarkers for predicting lung cancer metastasis using microarray and survival data.

BACKGROUND: Few studies have investigated prognostic biomarkers of distant metastases of lung cancer. One of the central difficulties in identifying biomarkers from microarray data is the availability of only a small number of samples, which results overtraining. Recently obtained evidence reveals that epithelial-mesenchymal transition (EMT) of tumor cells causes metastasis, which is detrimental to patients' survival. RESULTS: This work proposes a novel optimization approach to discovering EMT-related prognostic biomarkers to predict the distant metastasis of lung cancer using both microarray and survival data. This weighted objective function maximizes both the accuracy of prediction of distant metastasis and the area between the disease-free survival curves of the non-distant and distant metastases. Seventy-eight patients with lung cancer and a follow-up time of 120 months are used to identify a set of gene markers and an independent cohort of 26 patients is used to evaluate the identified biomarkers. The medical records of the 78 patients show a significant difference between the disease-free survival times of the 37 non-distant- and the 41 distant-metastasis patients. The experimental results thus obtained are as follows. 1) The use of disease-free survival curves can compensate for the shortcoming of insufficient samples and greatly increase the test accuracy by 11.10%; and 2) the support vector machine with a set of 17 transcripts, such as CCL16 and CDKN2AIP, can yield a leave-one-out cross-validation accuracy of 93.59%, a test accuracy of 76.92%, a large disease-free survival area of 74.81%, and a mean survival prediction error of 3.99 months. The identified putative biomarkers are examined using related studies and signaling pathways to reveal the potential effectiveness of the biomarkers in prospective confirmatory studies. CONCLUSIONS: The proposed new optimization approach to identifying prognostic biomarkers by combining multiple sources of data (microarray and survival) can facilitate the accurate selection of biomarkers that are most relevant to the disease while solving the problem of insufficient samples.

SCMMTP: identifying and characterizing membrane transport proteins using propensity scores of dipeptides.

BACKGROUND: Identifying putative membrane transport proteins (MTPs) and understanding the transport mechanisms involved remain important challenges for the advancement of structural and functional genomics. However, the transporter characters are mainly acquired from MTP crystal structures which are hard to crystalize. Therefore, it is desirable to develop bioinformatics tools for the effective large-scale analysis of available sequences to identify novel transporters and characterize such transporters. RESULTS: This work proposes a novel method (SCMMTP) based on the scoring card method (SCM) using dipeptide composition to identify and characterize MTPs from an existing dataset containing 900 MTPs and 660 non-MTPs which are separated into a training dataset consisting 1,380 proteins and an independent dataset consisting 180 proteins. The SCMMTP produced estimating propensity scores for amino acids and dipeptides as MTPs. The SCMMTP training and test accuracy levels respectively reached 83.81% and 76.11%. The test accuracy of support vector machine (SVM) using a complicated classification method with a low possibility for biological interpretation and position-specific substitution matrix (PSSM) as a protein feature is 80.56%, thus SCMMTP is comparable to SVM-PSSM. To identify MTPs, SCMMTP is applied to three datasets including: 1) human transmembrane proteins, 2) a photosynthetic protein dataset, and 3) a human protein database. MTPs showing α-helix rich structure is agreed with previous studies. The MTPs used residues with low hydration energy. It is hypothesized that, after filtering substrates, the hydrated water molecules need to be released from the pore regions. CONCLUSIONS: SCMMTP yields estimating propensity scores for amino acids and dipeptides as MTPs, which can be used to identify novel MTPs and characterize transport mechanisms for use in further experiments. AVAILABILITY: http://iclab.life.nctu.edu.tw/iclab_webtools/SCMMTP/.

SCMHBP: prediction and analysis of heme binding proteins using propensity scores of dipeptides.

BACKGROUND: Heme binding proteins (HBPs) are metalloproteins that contain a heme ligand (an iron-porphyrin complex) as the prosthetic group. Several computational methods have been proposed to predict heme binding residues and thereby to understand the interactions between heme and its host proteins. However, few in silico methods for identifying HBPs have been proposed. RESULTS: This work proposes a scoring card method (SCM) based method (named SCMHBP) for predicting and analyzing HBPs from sequences. A balanced dataset of 747 HBPs (selected using a Gene Ontology term GO:0020037) and 747 non-HBPs (selected from 91,414 putative non-HBPs) with an identity of 25% was firstly established. Consequently, a set of scores that quantified the propensity of amino acids and dipeptides to be HBPs is estimated using SCM to maximize the predictive accuracy of SCMHBP. Finally, the informative physicochemical properties of 20 amino acids are identified by utilizing the estimated propensity scores to be used to categorize HBPs. The training and mean test accuracies of SCMHBP applied to three independent test datasets are 85.90% and 71.57%, respectively. SCMHBP performs well relative to comparison with such methods as support vector machine (SVM), decision tree J48, and Bayes classifiers. The putative non-HBPs with high sequence propensity scores are potential HBPs, which can be further validated by experimental confirmation. The propensity scores of individual amino acids and dipeptides are examined to elucidate the interactions between heme and its host proteins. The following characteristics of HBPs are derived from the propensity scores: 1) aromatic side chains are important to the effectiveness of specific HBP functions; 2) a hydrophobic environment is important in the interaction between heme and binding sites; and 3) the whole HBP has low flexibility whereas the heme binding residues are relatively flexible. CONCLUSIONS: SCMHBP yields knowledge that improves our understanding of HBPs rather than merely improves the prediction accuracy in predicting HBPs.

Short-term glutamine supplementation decreases lung inflammation and the receptor for advanced glycation end-products expression in direct acute lung injury in mice.

BACKGROUND: Glutamine (GLN) has been reported to improve clinical and experimental sepsis outcomes. However, the mechanisms underlying the actions of GLN remain unclear, and may depend upon the route of GLN administration and the model of acute lung injury (ALI) used. The aim of this study was to investigate whether short-term GLN supplementation had an ameliorative effect on the inflammation induced by direct acid and lipopolysaccharide (LPS) challenge in mice. METHODS: Female BALB/c mice were divided into two groups, a control group and a GLN group (4.17% GLN supplementation). After a 10-day feeding period, ALI was induced by intratracheal administration of hydrochloric acid (pH 1.0; 2 mL/kg of body weight [BW]) and LPS (5 mg/kg BW). Mice were sacrificed 3 h after ALI challenge. In this early phase of ALI, serum, lungs, and bronchoalveolar lavage fluid (BALF) from the mice were collected for further analysis. RESULTS: The results of this study showed that ALI-challenged mice had a significant increase in myeloperoxidase activity and expression of interleukin (IL)-1ß, IL-6, and tumor necrosis factor-α in the lung compared with unchallenged mice. Compared with the control group, GLN pretreatment in ALI-challenged mice reduced the levels of receptor for advanced glycation end-products (RAGE) and IL-1ß production in BALF, with a corresponding decrease in their mRNA expression. The GLN group also had markedly lower in mRNA expression of cyclooxygenase-2 and NADPH oxidase-1. CONCLUSIONS: These results suggest that the benefit of dietary GLN may be partly contributed to an inhibitory effect on RAGE expression and pro-inflammatory cytokines production at an early stage in direct acid and LPS-induced ALI in mice.

Bronchial asthma is associated with increased risk of chronic kidney disease.

BACKGROUND: Bronchial asthma influences some chronic diseases such as coronary heart disease, diabetes mellitus, and hypertension, but the impact of asthma on vital diseases such as chronic kidney disease is not yet verified. This study aims to clarify the association between bronchial asthma and the risk of developing chronic kidney disease. METHODS: The National Health Research Institute provided a database of one million random subjects for the study. A random sample of 141 064 patients aged ≥18 years without a history of kidney disease was obtained from the database. Among them, there were 35 086 with bronchial asthma and 105 258 without asthma matched for sex and age for a ration of 1:3. After adjusting for confounding risk factors, a Cox proportional hazards model was used to compare the risk of developing chronic kidney disease during a three-year follow-up period. RESULTS: Of the subjects with asthma, 2 196 (6.26%) developed chronic kidney disease compared to 4 120 (3.91%) of the control subjects. Cox proportional hazards regression analysis revealed that subjects with asthma were more likely to develop chronic kidney disease (hazard ratio [HR]: 1.56; 95% CI: 1.48-1.64; p < 0.001). After adjusting for sex, age, monthly income, urbanization level, geographic region, diabetes mellitus, hypertension, hyperlipidemia, and steroid use, the HR for asthma patients was 1.40 (95% CI: 1.33-1.48; p = 0.040). There was decreased HRs in steroid use (HR: 0.56; 95% CI: 0.62-0.61; p < 0.001) in the development of chronic kidney disease. Expectorants, bronchodilators, anti-muscarinic agents, airway smooth muscle relaxants, and leukotriene receptor antagonists may also be beneficial in attenuating the risk of chronic kidney disease. CONCLUSIONS: Patients with bronchial asthma may have increased risk of developing chronic kidney disease. The use of steroids or non-steroidal drugs in the treatment of asthma may attenuate this risk.

Increased risk of major depression subsequent to a first-attack and non-infection caused urticaria in adolescence: a nationwide population-based study.

BACKGROUND: Non-infection caused urticaria is a common ailment in adolescents. Its symptoms (e.g., unusual rash appearance, limitation of daily activities, and recurrent itching) may contribute to the development of depressive stress in adolescents; the potential link has not been well studied. This study aimed to investigate the risk of major depression after a first-attack and non-infection caused urticaria. METHODS: This study used the Taiwan Longitudinal Health Insurance Database. A total of 5,755 adolescents hospitalized for a first-attack and non-infection caused urticaria from 2005 to 2009 were recruited as the study group, together with 17,265 matched non-urticarial enrollees who comprised the control group. Patients who had any history of urticaria or depression prior to the evaluation period were excluded. Each patient was followed for one year to identify the occurrence of depression. Cox proportional hazards models were generated to compute the risk of major depression, adjusting for the subjects' sociodemographic characteristics. Depression-free survival curves were also analyzed. RESULTS: Thirty-four (0.6%) adolescents with non-infection caused urticaria and 59 (0.3%) non-urticarial control subjects suffered a new-onset episode of major depression during the study period. The stratified Cox proportional analysis showed that the crude hazard ratio (HR) of depression among adolescents with urticaria was 1.73 times (95% CI, 1.13-2.64) than that of the control subjects without urticaria. Moreover, the HR were higher in physical (HR: 3.39, 95% CI 2.77-11.52) and allergy chronic urticaria (HR: 2.43, 95% CI 3.18-9.78). CONCLUSION: Individuals who have a non-infection caused urticaria during adolescence are at a higher risk of developing major depression.

Rule-based knowledge acquisition method for promoter prediction in human and Drosophila species.

The rapid and reliable identification of promoter regions is important when the number of genomes to be sequenced is increasing very speedily. Various methods have been developed but few methods investigate the effectiveness of sequence-based features in promoter prediction. This study proposes a knowledge acquisition method (named PromHD) based on if-then rules for promoter prediction in human and Drosophila species. PromHD utilizes an effective feature-mining algorithm and a reference feature set of 167 DNA sequence descriptors (DNASDs), comprising three descriptors of physicochemical properties (absorption maxima, molecular weight, and molar absorption coefficient), 128 top-ranked descriptors of 4-mer motifs, and 36 global sequence descriptors. PromHD identifies two feature subsets with 99 and 74 DNASDs and yields test accuracies of 96.4% and 97.5% in human and Drosophila species, respectively. Based on the 99- and 74-dimensional feature vectors, PromHD generates several if-then rules by using the decision tree mechanism for promoter prediction. The top-ranked informative rules with high certainty grades reveal that the global sequence descriptor, the length of nucleotide A at the first position of the sequence, and two physicochemical properties, absorption maxima and molecular weight, are effective in distinguishing promoters from non-promoters in human and Drosophila species, respectively.

Role of reimbursement and Physicians' awareness in the survival of sorafenib-eligible advanced hepatocellular carcinoma patients.

In 2008, sorafenib became the first approved systemic therapeutic agent for advanced HCC. Although its pharmacological efficacy has been established, reimbursement for such a new, high-cost drug, as well as physicians' awareness and prescription practice, likewise contribute to its clinical effectiveness. We therefore conducted a retrospective study using 38 sorafenib-eligible, advanced HCC patients when sorafenib was approved but not yet reimbursed as a control and 216 patients during the reimbursed era. Study group showed longer survival at 8.2 months versus the control's 4.9 months (p = 0.0063 hazard ratio: 0.612 [0.431 ~ 0.868], p = 0.0059). Among the 42 (19.4%) patients who survived more than 2 years, 50% had tumor rupture, and all 32 patients with portal vein tumor thrombus and/or extrahepatic metastasis received sorafenib (p = 0.003). Furthermore, during their first 2 years of HCC management, sorafenib had been given in 29.1% of the treatment courses among survivors between 2 and 5 years while it was prescribed in 55.8% among the more than 5 years survivor group (p < 0.001). In conclusion, survival of sorafenib-eligible HCC patients significantly improved after reimbursement. Patients who underwent longer sorafenib treatment had a survival advantage, except for those with tumor rupture. Reimbursement and awareness of prescriptions for a newly introduced medication therefore improve clinical effectiveness.

HCS-Neurons: identifying phenotypic changes in multi-neuron images upon drug treatments of high-content screening.

BACKGROUND: High-content screening (HCS) has become a powerful tool for drug discovery. However, the discovery of drugs targeting neurons is still hampered by the inability to accurately identify and quantify the phenotypic changes of multiple neurons in a single image (named multi-neuron image) of a high-content screen. Therefore, it is desirable to develop an automated image analysis method for analyzing multi-neuron images. RESULTS: We propose an automated analysis method with novel descriptors of neuromorphology features for analyzing HCS-based multi-neuron images, called HCS-neurons. To observe multiple phenotypic changes of neurons, we propose two kinds of descriptors which are neuron feature descriptor (NFD) of 13 neuromorphology features, e.g., neurite length, and generic feature descriptors (GFDs), e.g., Haralick texture. HCS-neurons can 1) automatically extract all quantitative phenotype features in both NFD and GFDs, 2) identify statistically significant phenotypic changes upon drug treatments using ANOVA and regression analysis, and 3) generate an accurate classifier to group neurons treated by different drug concentrations using support vector machine and an intelligent feature selection method. To evaluate HCS-neurons, we treated P19 neurons with nocodazole (a microtubule depolymerizing drug which has been shown to impair neurite development) at six concentrations ranging from 0 to 1000 ng/mL. The experimental results show that all the 13 features of NFD have statistically significant difference with respect to changes in various levels of nocodazole drug concentrations (NDC) and the phenotypic changes of neurites were consistent to the known effect of nocodazole in promoting neurite retraction. Three identified features, total neurite length, average neurite length, and average neurite area were able to achieve an independent test accuracy of 90.28% for the six-dosage classification problem. This NFD module and neuron image datasets are provided as a freely downloadable MatLab project at http://iclab.life.nctu.edu.tw/HCS-Neurons. CONCLUSIONS: Few automatic methods focus on analyzing multi-neuron images collected from HCS used in drug discovery. We provided an automatic HCS-based method for generating accurate classifiers to classify neurons based on their phenotypic changes upon drug treatments. The proposed HCS-neurons method is helpful in identifying and classifying chemical or biological molecules that alter the morphology of a group of neurons in HCS.

Association of Kinase-Insert-Domain-Containing Receptor Polymorphisms with Glioma Susceptibility in a Chinese Population: A Hospital-Based Case-Control Study.

Background: Gliomas are the most common malignant tumors of the central nervous system. However, the inherited genetic variation in gliomas is presently unclear. Therefore, this study investigated the association of the rs2071559 and rs2239702 gene polymorphisms with glioma susceptibility in Chinese patients. Methods: In this study, a case-control approach was used to compare and analyze whether two genes, rs2071559 and rs2239702, were associated with the risk of glioma formation. Results: The cases and controls were matched for sex, smoking status, and family history of cancer using single nucleotide polymorphisms. Specific rs2071559 and rs2239702 alleles were found much more frequently in the glioma group than in the control group (P < 0.001 and P = 0.014, respectively). Conclusions: These findings suggest that specific rs2071559 and rs2239702 polymorphisms are associated with a higher risk of glioma development; the risk allele is C in rs2071559 or A in rs2239702. Moreover, the kinase-insert-domain-containing receptor may act as a suppressor of tumor progression.

ThermalProGAN: A sequence-based thermally stable protein generator trained using unpaired data.

MOTIVATION: The synthesis of proteins with novel desired properties is challenging but sought after by the industry and academia. The dominating approach is based on trial-and-error inducing point mutations, assisted by structural information or predictive models built with paired data that are difficult to collect. This study proposes a sequence-based unpaired-sample of novel protein inventor (SUNI) to build ThermalProGAN for generating thermally stable proteins based on sequence information. RESULTS: The ThermalProGAN can strongly mutate the input sequence with a median number of 32 residues. A known normal protein, 1RG0, was used to generate a thermally stable form by mutating 51 residues. After superimposing the two structures, high similarity is shown, indicating that the basic function would be conserved. Eighty four molecular dynamics simulation results of 1RG0 and the COVID-19 vaccine candidates with a total simulation time of 840[Formula: see text]ns indicate that the thermal stability increased. CONCLUSION: This proof of concept demonstrated that transfer of a desired protein property from one set of proteins is feasible. Availability and implementation: The source code of ThermalProGAN can be freely accessed at https://github.com/markliou/ThermalProGAN/ with an MIT license. The website is https://thermalprogan.markliou.tw:433. Supplementary information: Supplementary data are available on Github.