Identification of Novel Biomarkers for Pre-diabetic Diagnosis Using a Combinational Approach.

Reliable protein markers for pre-diabetes in humans are not clinically available. In order to identify novel and reliable protein markers for pre-diabetes in humans, healthy volunteers and patients diagnosed with pre-diabetes and stroke were recruited for blood collection. Blood samples were collected from healthy and pre-diabetic subjects 12 h after fasting. BMI was calculated from body weight and height. Fasting blood glucose (FBG), glycated hemoglobin (HbA1C), triglyceride (TG), total cholesterol, high-density lipoprotein, low-density lipoprotein (LDL), insulin and albumin were assayed by automated clinical laboratory methods. We used a quantitative proteomics approach to identify 1074 proteins from the sera of pre-diabetic and healthy subjects. Among them, 500 proteins were then selected using Mascot analysis scores. Further, 70 out of 500 proteins were selected via volcano plot analysis according to their statistical significance and average relative protein ratio. Eventually, 7 serum proteins were singled out as candidate markers for pre-diabetes due to their diabetic relevance and statistical significance. Immunoblotting data demonstrated that laminin subunit alpha 2 (LAMA2), mixed-lineage leukemia 4 (MLL4), and plexin domain containing 2 (PLXDC2) were expressed in pre-diabetic patients but not healthy volunteers. Receiver operating characteristic curve analysis indicated that the combination of the three proteins has greater diagnostic efficacy than any individual protein. Thus, LAMA2, MLL4 and PLXDC2 are novel and reliable serum protein markers for pre-diabetic diagnosis in humans.

Covariate-adjusted heatmaps for visualizing biological data via correlation decomposition.

Motivation: Heatmap is a popular visualization technique in biology and related fields. In this study, we extend heatmaps within the framework of matrix visualization (MV) by incorporating a covariate adjustment process through the estimation of conditional correlations. MV can explore the embedded information structure of high-dimensional large-scale datasets effectively without dimension reduction. The benefit of the proposed covariate-adjusted heatmap is in the exploration of conditional association structures among the subjects or variables that cannot be done with conventional MV. Results: For adjustment of a discrete covariate, the conditional correlation is estimated by the within and between analysis. This procedure decomposes a correlation matrix into the within- and between-component matrices. The contribution of the covariate effects can then be assessed through the relative structure of the between-component to the original correlation matrix while the within-component acts as a residual. When a covariate is of continuous nature, the conditional correlation is equivalent to the partial correlation under the assumption of a joint normal distribution. A test is then employed to identify the variable pairs which possess the most significant differences at varying levels of correlation before and after a covariate adjustment. In addition, a z-score significance map is constructed to visualize these results. A simulation and three biological datasets are employed to illustrate the power and versatility of our proposed method. Availability and implementation: GAP is available to readers and is free to non-commercial applications. The installation instructions, the user's manual, and the detailed tutorials can be found at http://gap.stat.sinica.edu.tw/Software/GAP. Supplementary information: Supplementary Data are available at Bioinformatics online.

Symptom patterns and phenotypic subgrouping of women with polycystic ovary syndrome: association between endocrine characteristics and metabolic aberrations.

STUDY QUESTION: What are the potential endocrine characteristics related to risk and severity of metabolic disturbances in women with polycystic ovary syndrome (PCOS)? SUMMARY ANSWER: Women with PCOS could be subtyped into four subgroups according to heterogeneous endocrine characteristics and the major predictive endocrine factors for metabolic aberrations among different subgroups were free androgen index (FAI) and luteinizing hormone (LH) levels. WHAT IS KNOWN ALREADY: Women diagnosed with PCOS present with highly heterogeneous phenotypes, including endocrine and metabolic aberrations. Different strategies have been proposed to predict the metabolic outcomes but whether the endocrine factors can solely predict the metabolic aberrations is still inconclusive. STUDY DESIGN, SIZE, DURATION: A cross-sectional study including 460 patients recruited from a reproductive endocrinology outpatient clinic of a tertiary medical center. PARTICIPANTS/MATERIALS, SETTING, METHODS: Patients with PCOS diagnosed according to the 2003 Rotterdam criteria were studied. Clinical history recorded by questionnaires, anthropometric measurements, biochemistry tests after an overnight fast, and pelvic ultrasonography were collected from all patients. MAIN RESULTS AND THE ROLE OF CHANCE: Applying a matrix visualization and clustering approach (generalized association plots), the patients were divided into four distinct clusters according to the correlation with four endocrine parameters. Each cluster exhibited specific endocrine characteristics and the prevalence of metabolic syndrome (MS) was significantly different among the clusters (P < 0.0001). The high-risk subgroups for MS included one cluster with higher mean (SD) FAI (39.6 (14.7) in cluster 4), and another one with lower mean (SD) FAI (10 (6.4) in cluster 2). A common endocrine characteristic of these two metabolically unhealthy clusters was relatively lower LH level. Contrarily, higher LH level (≧15 mIU/ml) during early follicular phase was found to be the best indicator of the metabolically healthy cluster (cluster 1). While high FAI level did correlate with more severe metabolic aberrations, high LH level showed better predictive value than low FAI level to become a metabolically healthy cluster. LIMITATIONS, REASONS FOR CAUTION: The results should be applied to other populations with caution due to racial or environmental differences. Another limitation is a lack of normal non-PCOS control in our study. WIDER IMPLICATIONS OF THE FINDINGS: Stratifying women with PCOS into meaningful subtypes could provide a better understanding of related risk factors and potentially enable the design and delivery of more effective screening and treatment intervention. STUDY FUNDING/COMPETING INTERESTS: This study was supported by grant NSC 100-2314-B002-027-MY3 from the National Science Council of Taiwan. TRIAL REGISTRATION NUMBER: Nil.

Identification of bicluster regions in a binary matrix and its applications.

Biclustering has emerged as an important approach to the analysis of large-scale datasets. A biclustering technique identifies a subset of rows that exhibit similar patterns on a subset of columns in a data matrix. Many biclustering methods have been proposed, and most, if not all, algorithms are developed to detect regions of "coherence" patterns. These methods perform unsatisfactorily if the purpose is to identify biclusters of a constant level. This paper presents a two-step biclustering method to identify constant level biclusters for binary or quantitative data. This algorithm identifies the maximal dimensional submatrix such that the proportion of non-signals is less than a pre-specified tolerance δ. The proposed method has much higher sensitivity and slightly lower specificity than several prominent biclustering methods from the analysis of two synthetic datasets. It was further compared with the Bimax method for two real datasets. The proposed method was shown to perform the most robust in terms of sensitivity, number of biclusters and number of serotype-specific biclusters identified. However, dichotomization using different signal level thresholds usually leads to different sets of biclusters; this also occurs in the present analysis.

Morus alba and active compound oxyresveratrol exert anti-inflammatory activity via inhibition of leukocyte migration involving MEK/ERK signaling.

BACKGROUND: Morus alba has long been used in traditional Chinese medicine to treat inflammatory diseases; however, the scientific basis for such usage and the mechanism of action are not well understood. This study investigated the action of M. alba on leukocyte migration, one key step in inflammation. METHODS: Gas chromatography-mass spectrometry (GC-MS) and cluster analyses of supercritical CO2 extracts of three Morus species were performed for chemotaxonomy-aided plant authentication. Phytochemistry and CXCR4-mediated chemotaxis assays were used to characterize the chemical and biological properties of M. alba and its active compound, oxyresveratrol. fluorescence-activated cell sorting (FACS) and Western blot analyses were conducted to determine the mode of action of oxyresveratrol. RESULTS: Chemotaxonomy was used to help authenticate M. alba. Chemotaxis-based isolation identified oxyresveratrol as an active component in M. alba. Phytochemical and chemotaxis assays showed that the crude extract, ethyl acetate fraction and oxyresveratrol from M. alba suppressed cell migration of Jurkat T cells in response to SDF-1. Mechanistic study indicated that oxyresveratrol diminished CXCR4-mediated T-cell migration via inhibition of the MEK/ERK signaling cascade. CONCLUSIONS: A combination of GC-MS and cluster analysis techniques are applicable for authentication of the Morus species. Anti-inflammatory benefits of M. alba and its active compound, oxyresveratrol, may involve the inhibition of CXCR-4-mediated chemotaxis and MEK/ERK pathway in T and other immune cells.

Development of a brief self-report questionnaire for screening putative pre-psychotic states.

BACKGROUND: Several self-report instruments were developed to capture psychotic prodrome, and were claimed to have good predictive validity. The feasibility of screening is questionable considering the heterogeneity of the targeted populations and the negative ramifications of false positive identification. This study developed a questionnaire using data covering a wide range of clinical characteristics. METHODS: One hundred and eleven putative pre-psychotic participants, 129 normal comparison subjects, and 95 non-psychotic psychiatric outpatients completed a 231-item questionnaire comprising a 110-item Wisconsin psychotic prone scale, 74-item schizotypal personality questionnaire, 33-item basic symptoms, and 14-item cognitive symptoms. Items showing the best discriminating power, estimated using chi-square statistics with Bonferroni correction, were extracted to create a brief version. A two-stage cut-off approach emphasizing specific items was applied to maximize sensitivity and specificity. The concurrent validity of the proposed approach was estimated using a ten-fold cross-validation procedure. RESULTS: A 15-item self-report questionnaire was developed. Respondents checking at least eight items, or those checking three to seven items including any of the three referring to feeling stress in crowds, aloofness, and perceptual disturbance, would be considered putatively pre-psychotic with the largest sensitivity+specificity (0.784+0.705=1.489). This cut-off selection was the best estimate by calculating 1000 permutations in the cross-validation procedure. CONCLUSIONS: This investigation proposes a different orientation for applying questionnaires to screen putative pre-psychotic states, with less emphasis on attenuated psychotic symptoms and predictive values. Besides providing a handy tool for increasing awareness and referral, the instructions of such a screening questionnaire should be carefully worded.

MicroRNA-214 downregulation contributes to tumor angiogenesis by inducing secretion of the hepatoma-derived growth factor in human hepatoma.

BACKGROUND & AIMS: Unusual hypervascularity is a hallmark of human hepatocellular carcinoma (HCC). Although microRNA-214 (miR-214) is upregulated in other human cancers, it is downregulated in HCC. We elucidated the biological and clinical significance of miR-214 downregulation in HCC. METHODS: MicroRNAs deregulated in HCC were identified using array-based microRNA profiling. A luciferase reporter assay confirmed target association between miR-214 and the hepatoma-derived growth factor (HDGF). Tube formation and in vivo angiogenesis assays validated the roles of miR-214/HDGF in angiogenesis. RESULTS: miR-214 downregulation was associated with higher tumor recurrence and worse clinical outcomes. Ectopic expression of miR-214 suppressed xenograft tumor growth and microvascularity of the tumors and their surrounding tissues. The genes downregulated by ectopic expression of miR-214 were involved in the regulation of apoptosis, cell cycle, and angiogenesis. Integrated analysis disclosed HDGF as a downstream target of miR-214. Conditioned medium of HCC cells contained bioactivity to stimulate tube formation of human umbilical vein endothelial cells, which was abolished by pretreatment of the conditioned media with HDGF antibodies, suppression of HDGF expression or ectopic expression of miR-214 in the donor HCC cells. The angiogenic activity of the conditioned media, lost by ectopic expression of miR-214 in the donor cells, was restored by supplementation with recombinant HDGF. In vivo tumor angiogenesis assays showed significant suppression of tumor vascularity by ectopic expression of miR-214. CONCLUSIONS: A novel role of microRNA in tumorigenesis is identified. Downregulation of miR-214 contributes to the unusual hypervascularity of HCC via activation of the HDGF paracrine pathway for tumor angiogenesis.

Meta-analytical biomarker search of EST expression data reveals three differentially expressed candidates.

BACKGROUND: Researches have been conducted for the identification of differentially expressed genes (DEGs) by generating and mining of cDNA expressed sequence tags (ESTs) for more than a decade. Although the availability of public databases make possible the comprehensive mining of DEGs among the ESTs from multiple tissue types, existing studies usually employed statistics suitable only for two categories. Multi-class test has been developed to enable the finding of tissue specific genes, but subsequent search for cancer genes involves separate two-category test only on the ESTs of the tissue of interest. This constricts the amount of data used. On the other hand, simple pooling of cancer and normal genes from multiple tissue types runs the risk of Simpson's paradox. Here we presented a different approach which searched for multi-cancer DEG candidates by analyzing all pertinent ESTs in all categories and narrowing down the cancer biomarker candidates via integrative analysis with microarray data and selection of secretory and membrane protein genes as well as incorporation of network analysis. Finally, the differential expression patterns of three selected cancer biomarker candidates were confirmed by real-time qPCR analysis. RESULTS: Seven hundred and twenty three primary DEG candidates (p-value < 0.05 and lower bound of confidence interval of odds ratio ≥ 1.65) were selected from a curated EST database with the application of Cochran-Mantel-Haenszel statistic (CMH). GeneGO analysis results indicated this set as neoplasm enriched. Cross-examination with microarray data further narrowed the list down to 235 genes, among which 96 had membrane or secretory annotations. After examined the candidates in protein interaction network, public tissue expression databases, and literatures, we selected three genes for further evaluation by real-time qPCR with eight major normal and cancer tissues. The higher-than-normal tissue expression of COL3A1, DLG3, and RNF43 in some of the cancer tissues is in agreement with our in silico predictions. CONCLUSIONS: Searching digitized transcriptome using CMH enabled us to identify multi-cancer differentially expressed gene candidates. Our methodology demonstrated simultaneously analysis for cancer biomarkers of multiple tissue types with the EST data. With the revived interest in digitizing the transcriptomes by NGS, cancer biomarkers could be more precisely detected from the ESTs. The three candidates identified in this study, COL3A1, DLG3, and RNF43, are valuable targets for further evaluation with a larger sample size of normal and cancer tissue or serum samples.

Different efficacies of erlotinib and gefitinib in taiwanese patients with advanced non-small cell lung cancer: a retrospective multicenter study.

INTRODUCTION: Epidermal growth factor receptor-tyrosine kinase inhibitors are used as effective first-line and salvage therapy in the treatment of advanced non-small cell lung cancer (NSCLC) patients in East Asia. The objective of this study was to compare the efficacy of gefitinib and erlotinib in Taiwanese patients with advanced NSCLC. METHODS: Clinical data of NSCLC patients treated with gefitinib or erlotinib from January 2004 to December 2008 were collected retrospectively. Five tertiary referral centers in Taiwan participated in the study. RESULTS: Of the 1122 patients enrolled, 506 (45%) were female, 594 (53%) were never smokers or former light smokers, and 867 (77%) were diagnosed with adenocarcinoma. Epidermal growth factor receptor-tyrosine kinase inhibitors were prescribed as first-line treatment in 465 (41%) patients and as second-line or salvage therapy in 657 patients (59%). The objective response rate was similar between the gefitinib and erlotinib treatment groups, while disease control rate was 58.9 and 65.8% (p = 0.025), respectively. Median progression-free survival of gefitinib and erlotinib groups was 3.6 and 4.6 months, respectively (p = 0.027). Median overall survival of gefitinib and erlotinib groups was 9.6 and 10.7 months, respectively (p = 0.013). CONCLUSION: Taiwanese patients with advanced NSCLC treated with erlotinib reported higher disease control rate, longer progression-free survival, and overall survival compared with patients treated with gefitinib.

Comparative metabolomics approach coupled with cell- and gene-based assays for species classification and anti-inflammatory bioactivity validation of Echinacea plants.

Echinacea preparations were the top-selling herbal supplements or medicines in the past decade; however, there is still frequent misidentification or substitution of the Echinacea plant species in the commercial Echinacea products with not well chemically defined compositions in a specific preparation. In this report, a comparative metabolomics study, integrating supercritical fluid extraction, gas chromatography/mass spectrometry and data mining, demonstrates that the three most used medicinal Echinacea species, Echinacea purpurea, E. pallida, and E. angustifolia, can be easily classified by the distribution and relative content of metabolites. A mitogen-induced murine skin inflammation study suggested that alkamides were the active anti-inflammatory components present in Echinacea plants. Mixed alkamides and the major component, dodeca-2E,4E,8Z,10Z(E)-tetraenoic acid isobutylamides, were then isolated from E. purpurea root extracts for further bioactivity elucidation. In macrophages, the alkamides significantly inhibited cyclooxygenase 2 (COX-2) activity and the lipopolysaccharide-induced expression of COX-2, inducible nitric oxide synthase and specific cytokines or chemokines [i.e., TNF-α, interleukin (IL)-1α, IL-6, MCP-1, MIP-1ß] but elevated heme oxygenase-1 protein expression. Cichoric acid, however, exhibited little or no effect. The results of high-performance liquid chromatography/electron spray ionization/mass spectrometry metabolite profiling of alkamides and phenolic compounds in E. purpurea roots showed that specific phytocompound (i.e., alkamides, cichoric acid and rutin) contents were subject to change under certain post-harvest or abiotic treatment. This study provides new insight in using the emerging metabolomics approach coupled with bioactivity assays for medicinal/nutritional plant species classification, quality control and the identification of novel botanical agents for inflammatory disorders.

Anti-diabetic properties of three common Bidens pilosa variants in Taiwan.

Bidens pilosa L. var. radiata (BPR), B. pilosa L. var. pilosa (BPP), and B. pilosa L. var. minor (BPM) are common variants of a plant often used as a folk remedy for diabetes in Taiwan. However, the three variants are often misidentified and little is known about their relative anti-diabetic efficacy and chemical composition. In this paper, we have first developed a method based on GC-MS and cluster analysis with visualization to assist in rapidly determining the taxonomy of these three Bidens variants. GC-MS was used to determine the chemical compositions of supercritical extracts, and differences and similarities in the variants were determined by hierarchical cluster analysis. Next, the HPLC profiles of the methanol crude extracts in the Bidens plants and evaluated anti-diabetic effects of methanol crude extracts were compared, as well as three polyacetylenic compounds of the Bidens plants using db/db mice. Single-dose and long-term experiments showed that the BPR extract had higher glucose-lowering and insulin-releasing activities than extracts from the other two variants, and that cytopiloyne was the most effective pure compound among the three polyacetylenic compounds. BPR extract and cytopiloyne also significantly reduced the percentage of the glycosylated hemoglobin A1c in db/db mice. Besides, both animal studies and HPLC analysis demonstrated a good correlation between anti-diabetic efficacy of the Bidens extracts and the particular polyacetylenes present.

Methods for simultaneously identifying coherent local clusters with smooth global patterns in gene expression profiles.

BACKGROUND: The hierarchical clustering tree (HCT) with a dendrogram 1 and the singular value decomposition (SVD) with a dimension-reduced representative map 2 are popular methods for two-way sorting the gene-by-array matrix map employed in gene expression profiling. While HCT dendrograms tend to optimize local coherent clustering patterns, SVD leading eigenvectors usually identify better global grouping and transitional structures. RESULTS: This study proposes a flipping mechanism for a conventional agglomerative HCT using a rank-two ellipse (R2E, an improved SVD algorithm for sorting purpose) seriation by Chen 3 as an external reference. While HCTs always produce permutations with good local behaviour, the rank-two ellipse seriation gives the best global grouping patterns and smooth transitional trends. The resulting algorithm automatically integrates the desirable properties of each method so that users have access to a clustering and visualization environment for gene expression profiles that preserves coherent local clusters and identifies global grouping trends. CONCLUSION: We demonstrate, through four examples, that the proposed method not only possesses better numerical and statistical properties, it also provides more meaningful biomedical insights than other sorting algorithms. We suggest that sorted proximity matrices for genes and arrays, in addition to the gene-by-array expression matrix, can greatly aid in the search for comprehensive understanding of gene expression structures. Software for the proposed methods can be obtained at http://gap.stat.sinica.edu.tw/Software/GAP.

Molecular signature of clinical severity in recovering patients with severe acute respiratory syndrome coronavirus (SARS-CoV).

BACKGROUND: Severe acute respiratory syndrome (SARS), a recent epidemic human disease, is caused by a novel coronavirus (SARS-CoV). First reported in Asia, SARS quickly spread worldwide through international travelling. As of July 2003, the World Health Organization reported a total of 8,437 people afflicted with SARS with a 9.6% mortality rate. Although immunopathological damages may account for the severity of respiratory distress, little is known about how the genome-wide gene expression of the host changes under the attack of SARS-CoV. RESULTS: Based on changes in gene expression of peripheral blood, we identified 52 signature genes that accurately discriminated acute SARS patients from non-SARS controls. While a general suppression of gene expression predominated in SARS-infected blood, several genes including those involved in innate immunity, such as defensins and eosinophil-derived neurotoxin, were upregulated. Instead of employing clustering methods, we ranked the severity of recovering SARS patients by generalized associate plots (GAP) according to the expression profiles of 52 signature genes. Through this method, we discovered a smooth transition pattern of severity from normal controls to acute SARS patients. The rank of SARS severity was significantly correlated with the recovery period (in days) and with the clinical pulmonary infection score. CONCLUSION: The use of the GAP approach has proved useful in analyzing the complexity and continuity of biological systems. The severity rank derived from the global expression profile of significantly regulated genes in patients may be useful for further elucidating the pathophysiology of their disease.