Biomedical subjects

Jason H Moore

Publications and source records attributed to Jason H Moore.

At least 19 recordsLinked to original sources

PubMed2025

A decentralized future for the open-science databases.

The continuous and reliable open access to curated biological data repositories is indispensable for accelerating rigorous scientific inquiry and fostering reproducible research outcomes. However, the current paradigm, which relies heavily on centralized infrastructure for the storage and distribution of foundational biomedical datasets, inherently introduces significant vulnerabilities. This centralized model is susceptible to single points of failure, including cyberattacks, technical malfunctions, natural disasters, and even political or funding uncertainties. Such disruptions can lead to widespread data unavailability, data loss, integrity compromises, and substantial delays in critical research, ultimately impeding scientific progress. The downstream effect of such interruptions can be the widespread paralysis of diverse research activities, including computational, clinical, molecular, and climate studies. This scenario vividly illustrates the inherent dangers of consolidating essential scientific resources within a single geopolitical or institutional locus. As data generation is accelerating and the global landscape continues to fluctuate, the sustainability of centralized models must be critically re-evaluated. A shift toward federated and decentralized architectures may offer a robust and forward-looking approach to enhancing the resilience of scientific data infrastructures by reducing exposure to governance instability, infrastructural fragility, and funding volatility, while also promoting equity and global accessibility. Inspired by established models such as ELIXIR's federated infrastructure and the policy and funding frameworks developed by CODATA and the Global Biodata Coalition (GBC), emerging Decentralized Science (DeSci) initiatives can contribute to building more resilient, fair, and incentive-aligned data ecosystems. The future of open science depends on integrating these complementary approaches to establish a globally distributed, economically sustainable, and institutionally robust infrastructure that safeguards scientific data as a public good, further ensuring continued accessibility, interoperability, and preservation for generations to come. Here, we examine the structural limitations of centralized repositories, evaluate federated and decentralized models, and propose a hybrid framework for resilient, fair, and sustainable scientific data stewardship.

data accessibility↗

PubMed2007

Epistatic effects of polymorphisms in genes from the renin-angiotensin, bradykinin, and fibrinolytic systems on plasma t-PA and PAI-1 levels.

Tissue plasminogen activator (t-PA) and plasminogen activator inhibitor 1 (PAI-1) directly influence thrombus formation and degradation and thereby risk for arterial thrombosis. Activation of the renin-angiotensin system has been linked to the production of PAI-1 expression via the angiotensin II type 1 receptor (AT1R). In addition, bradykinin can induce the release of t-PA through a B2 receptor mechanism. In the present study, we aimed to investigate the epistatic effects of polymorphisms in genes from the renin-angiotensin, bradykinin, and fibrinolytic systems on plasma t-PA and PAI-1 levels in a large population-based sample (n=2527). We demonstrated a strong significant interaction within genetic variations of the bradykinin B2 gene (P=0.002) and between ACE and bradykinin B2 (p=0.003) polymorphisms on t-PA levels in females. In males, polymorphisms in the bradykinin B2 and AT1R gene showed the most strong effect on t-PA levels (P=0.006). In both females and males, the bradykinin B2 gene interacted with AT1R gene on plasma PAI-1 levels (P=0.026 and P=0.039, respectively). In addition, the current study found a borderline significant interaction between PAI 4G5G and ACE I/D on plasma t-PA and PAI-1 levels. These results support the idea that the interplay between the renin-angiotensin, bradykinin, and fibrinolytic systems might play an important role in t-PA and PAI-1 biology.

Bradykinin↗

PubMed2007

Evidence for epistasis between SLC6A4 and ITGB3 in autism etiology and in the determination of platelet serotonin levels.

Autism is a neurodevelopmental disorder of unclear etiology. The consistent finding of platelet hyperserotonemia in a proportion of patients and its heritability within affected families suggest that genes involved in the serotonin system play a role in this disorder. The role in autism etiology of seven candidate genes in the serotonin metabolic and neurotransmission pathways and mapping to autism linkage regions (SLC6A4, HTR1A, HTR1D, HTR2A, HTR5A, TPH1 and ITGB3) was analyzed in a sample of 186 nuclear families. The impact of interactions among these genes in autism was assessed using the multifactor-dimensionality reduction (MDR) method in 186 patients and 181 controls. We further evaluated whether the effect of specific gene variants or gene interactions associated with autism etiology might be mediated by their influence on serotonin levels, using the quantitative transmission disequilibrium test (QTDT) and the restricted partition method (RPM), in a sample of 109 autistic children. We report a significant main effect of the HTR5A gene in autism (P = 0.0088), and a significant three-locus model comprising a synergistic interaction between the ITGB3 and SLC6A4 genes with an additive effect of HTR5A (P < 0.0010). In addition to the previously reported contribution of SLC6A4, we found significant associations of ITGB3 haplotypes with serotonin level distribution (P = 0.0163). The most significant models contributing to serotonin distribution were found for interactions between TPH1 rs4537731 and SLC6A4 haplotypes (P = 0.002) and between HTR1D rs6300 and SLC6A4 haplotypes (P = 0.013). In addition to the significant independent effects, evidence for interaction between SLC6A4 and ITGB3 markers was also found. The overall results implicate SLC6A4 and ITGB3 gene interactions in autism etiology and in serotonin level determination, providing evidence for a common underlying genetic mechanism and a molecular explanation for the association of platelet hyperserotonemia with autism.

Adolescent↗

PubMed2006

Renin-angiotensin system gene polymorphisms and coronary artery disease in a large angiographic cohort: detection of high order gene-gene interaction.

There have been many reports regarding the association between renin-angiotensin system (RAS) gene polymorphisms and coronary artery disease (CAD) or acute myocardial infarction (AMI), but the results are inconsistent. In the present study, we used several new approaches with multilocus data to reappraise this issue in a large and relatively homogeneous Taiwanese population. A total of 1254 consecutive patients who underwent cardiac catheterization (735 with documented coronary artery disease and 519 without) between 1996 and 2003 were recruited. Angiotensin-converting enzyme gene insertion/deletion (I/D) polymorphism; T174M, M235T, G-6A, A-20C, G-152A and G-217A polymorphisms of the angiotensinogen gene; and A1166C polymorphism of the angiotensin II type I receptor gene were genotyped. In single-locus analyses, no locus was associated with CAD, history of AMI and three-vessel CAD, either with or without adjustment for conventional CAD risk factors. For multilocus analyses, we recreated a balanced population, with the controls individually matched to the cases regarding the conventional CAD risk factors. We found that the angiotensinogen gene haplotype profile was significantly different between the cases and controls (chi2=31.6, P=0.030) in haplotype analyses. Furthermore, significant three-locus (G-217A, M235T and I/D) gene-gene interactions were detected by multifactor-dimensionality reduction method (highest cross-validation consistency 10.0, lowest prediction error 40.56%, P=0.017) and many even higher order gene-gene interactions by multilocus genotype disequilibrium tests (16 genotype disequilibria exclusively found in the controls, all of which included at least two genes among AGT, ACE and AT1R genes). Our study is the first to demonstrate epistatic, high-order, gene-gene interactions between RAS gene polymorphisms and CAD. These results are compatible with the concept of multilocus and multi-gene effects in complex diseases that would be missed with conventional approaches.

Aged↗

PubMed2006

Transcriptional profiling in coronary artery disease: indications for novel markers of coronary collateralization.

BACKGROUND: The development of collateral circulation plays an important role in protecting tissues from ischemic damage, and its stimulation has emerged as one of principal approaches to therapeutic angiogenesis. Clinical observations have documented substantial differences in the extent of collateralization among patients with coronary artery disease (CAD), with some individuals demonstrating marked abundance and others showing nearly complete absence of these vessels. Recent studies have suggested that circulating monocytes play a major role in collateral growth. The present study was undertaken to determine transcriptional profiles of circulating monocytes in CAD patients with different extents of collateral growth. METHODS AND RESULTS: Monocyte transcriptomes from CAD patients with and without collateral vessels were obtained by use of high-throughput expression profiling. Using a newly developed redundancy-based data mining method, we have identified a set of molecular markers characteristic of a "noncollateralgenic" phenotype. Moreover, we show that these transcriptional abnormalities are independent of the severity of CAD or any other known clinical parameter thought to affect collateral development and correlated with protein expression levels in monocytes and plasma. CONCLUSIONS: Monocyte transcription profiling identifies sets of patients with extensive versus poorly developed collateral circulation. Thus, genetic factors may heavily influence coronary collateral vessel growth in CAD and affect prognosis and response to therapeutic interventions.

Aged↗

PubMed2006

Elevated male European and female African contributions to the genomes of African American individuals.

The differential relative contribution of males and females from Africa and Europe to individual African American genomes is relevant to mapping genes utilizing admixture analysis. The assessment of ancestral population contributions to the four types of genomic DNA (autosomes, X and Y chromosomes, and mitochondrial) with their differing modes of inheritance is most easily addressed in males. A thorough evaluation of 93 African American males for 2,018 autosomal single nucleotide polymorphic (SNP) markers, 121 X chromosome SNPs, 10 Y chromosome haplogroups specified by SNPs, and six haplogroup defining mtDNA SNPs is presented. A distinct lack of correlation observed between the X chromosome and the autosomal admixture fractions supports separate treatment of these chromosomes in admixture-based gene mapping applications. The European genetic contributions were highest (and African lowest) for the Y chromosome (28.46%), followed by the autosomes (19.99%), then the X chromosome (12.11%), and the mtDNA (8.51%). The relative order of admixture fractions in the genomic compartments validates previous studies that suggested sex-biased gene flow with elevated European male and African female contributions. There is a threefold higher European male contribution compared with European females (Y chromosome vs. mtDNA) to the genomes of African American individuals meaning that admixture-based gene discovery will have the most power for the autosomes and will be more limited for X chromosome analysis.

Black or African American↗

PubMed2006

Cytokine expression patterns associated with systemic adverse events following smallpox immunization.

Vaccinia virus is reactogenic in a significant number of vaccinees, with the most common adverse events being fever, lymphadenopathy, and rash. Although the inoculation is given in the skin, these adverse events suggest a robust systemic inflammatory response. To elucidate the cytokine response signature of systemic adverse events, we used a protein microarray technique to precisely quantitate 108 serum cytokines and chemokines in vaccine recipients before and 1 week after primary immunization with Aventis Pasteur smallpox vaccine. We studied 74 individuals after vaccination, of whom 22 experienced a systemic adverse event and 52 did not. The soluble factors most associated with adverse events were selected on the basis of voting among a committee of machine-learning methods and statistical procedures, and the selected cytokines were used to build a final decision-tree model. On the basis of changes in protein expression, we identified 6 cytokines that accurately discriminate between individuals on the basis of adverse event status: granulocyte colony-stimulating factor, stem cell factor, monokine induced by interferon-gamma (CXCL9), intercellular adhesion molecule-1, eotaxin, and tissue inhibitor of metalloproteinases-2. This cytokine signature is characteristic of particular inflammatory response pathways and suggests that the secretion of cytokines by fibroblasts plays a central role in systemic adverse events.

Adolescent↗

PubMed2006

A role for CETP TaqIB polymorphism in determining susceptibility to atrial fibrillation: a nested case control study.

BACKGROUND: Studies investigating the genetic and environmental characteristics of atrial fibrillation (AF) may provide new insights in the complex development of AF. We aimed to investigate the association between several environmental factors and loci of candidate genes, which might be related to the presence of AF. METHODS: A nested case-control study within the PREVEND cohort was conducted. Standard 12 lead electrocardiograms were recorded and AF was defined according to Minnesota codes. For every case, an age and gender matched control was selected from the same population (n = 194). In addition to logistic regression analyses, the multifactor-dimensionality reduction (MDR) method and interaction entropy graphs were used for the evaluation of gene-gene and gene-environment interactions. Polymorphisms in genes from the Renin-angiotensin, Bradykinin and CETP systems were included. RESULTS: Subjects with AF had a higher prevalence of electrocardiographic left ventricular hypertrophy, ischemic heart disease, hypertension, renal dysfunction, elevated levels of C-reactive protein (CRP) and increased urinary albumin excretion as compared to controls. The polymorphisms of the Renin-angiotensin system and Bradykinin gene did not show a significant association with AF (p > 0.05). The TaqIB polymorphism of the CETP gene was significantly associated with the presence of AF (p < 0.05). Using the MDR method, the best genotype-phenotype models included the combination of micro- or macroalbuminuria and CETP TaqIB polymorphism, CRP >3 mg/L and CETP TaqIB polymorphism, renal dysfunction and the CETP TaqIB polymorphism, and ischemic heart disease and CETP TaqIB polymorphism (1000 fold permutation testing, P < 0.05). Interaction entropy graph showed that the combination of albuminuria and CETP TaqIB polymorphism removed the most entropy. CONCLUSION: CETP TaqIB polymorphism is significantly associated with the presence of AF in the context of micro- or macroalbuminuria, elevated C-reactive protein, renal dysfunction, and ischemic heart disease.

Atrial Fibrillation↗

PubMed2006

Dissecting trait heterogeneity: a comparison of three clustering methods applied to genotypic data.

BACKGROUND: Trait heterogeneity, which exists when a trait has been defined with insufficient specificity such that it is actually two or more distinct traits, has been implicated as a confounding factor in traditional statistical genetics of complex human disease. In the absence of detailed phenotypic data collected consistently in combination with genetic data, unsupervised computational methodologies offer the potential for discovering underlying trait heterogeneity. The performance of three such methods--Bayesian Classification, Hypergraph-Based Clustering, and Fuzzy k-Modes Clustering--appropriate for categorical data were compared. Also tested was the ability of these methods to detect trait heterogeneity in the presence of locus heterogeneity and/or gene-gene interaction, which are two other complicating factors in discovering genetic models of complex human disease. To determine the efficacy of applying the Bayesian Classification method to real data, the reliability of its internal clustering metrics at finding good clusterings was evaluated using permutation testing. RESULTS: Bayesian Classification outperformed the other two methods, with the exception that the Fuzzy k-Modes Clustering performed best on the most complex genetic model. Bayesian Classification achieved excellent recovery for 75% of the datasets simulated under the simplest genetic model, while it achieved moderate recovery for 56% of datasets with a sample size of 500 or more (across all simulated models) and for 86% of datasets with 10 or fewer nonfunctional loci (across all simulated models). Neither Hypergraph Clustering nor Fuzzy k-Modes Clustering achieved good or excellent cluster recovery for a majority of datasets even under a restricted set of conditions. When using the average log of class strength as the internal clustering metric, the false positive rate was controlled very well, at three percent or less for all three significance levels (0.01, 0.05, 0.10), and the false negative rate was acceptably low (18 percent) for the least stringent significance level of 0.10. CONCLUSION: Bayesian Classification shows promise as an unsupervised computational method for dissecting trait heterogeneity in genotypic data. Its control of false positive and false negative rates lends confidence to the validity of its results. Further investigation of how different parameter settings may improve the performance of Bayesian Classification, especially under more complex genetic models, is ongoing.

Algorithms↗

PubMed2006

A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility.

Detecting, characterizing, and interpreting gene-gene interactions or epistasis in studies of human disease susceptibility is both a mathematical and a computational challenge. To address this problem, we have previously developed a multifactor dimensionality reduction (MDR) method for collapsing high-dimensional genetic data into a single dimension (i.e. constructive induction) thus permitting interactions to be detected in relatively small sample sizes. In this paper, we describe a comprehensive and flexible framework for detecting and interpreting gene-gene interactions that utilizes advances in information theory for selecting interesting single-nucleotide polymorphisms (SNPs), MDR for constructive induction, machine learning methods for classification, and finally graphical models for interpretation. We illustrate the usefulness of this strategy using artificial datasets simulated from several different two-locus and three-locus epistasis models. We show that the accuracy, sensitivity, specificity, and precision of a naïve Bayes classifier are significantly improved when SNPs are selected based on their information gain (i.e. class entropy removed) and reduced to a single attribute using MDR. We then apply this strategy to detecting, characterizing, and interpreting epistatic models in a genetic study (n = 500) of atrial fibrillation and show that both classification and model interpretation are significantly improved.

Atrial Fibrillation↗

PubMed2006

Single-nucleotide polymorphisms for diagnosis of salt-sensitive hypertension.

BACKGROUND: Salt-sensitive (SS) hypertension affects >30 million Americans and is often associated with low plasma renin activity. We tested the diagnostic validity of several candidate genes for SS and low-renin hypertension. METHODS: In Japanese patients with newly diagnosed, untreated hypertension (n = 184), we studied polymorphisms in 10 genes, including G protein-coupled receptor kinase type 4 (GRK4), some variations of which are associated with hypertension and impair D1 receptor (D1R)-inhibited renal sodium transport. We used the multifactor dimensionality reduction method to determine the genotype associated with salt sensitivity (> or =10% increase in blood pressure with high sodium intake) or low renin. To determine whether the GRK4 genotype is associated with impaired D1R function, we tested the natriuretic effect of docarpamine, a dopamine prodrug, in normotensive individuals with or without GRK4 polymorphisms (n = 18). RESULTS: A genetic model based on GRK4 R65L, GRK4 A142V, and GRK4 A486V was 94.4% predictive of SS hypertension, whereas the single-locus model with only GRK4 A142V was 78.4% predictive, and a 2-locus model of GRK4 A142V and CYP11B2 C-344T was 77.8% predictive of low-renin hypertension. Sodium excretion was inversely related to the number of GRK4 variants in hypertensive persons, and the natriuretic response to dopaminergic stimulation was impaired in normotensive persons having > or =3 GRK4 gene variants. CONCLUSIONS: GRK4 gene variants are associated with SS and low-renin hypertension. However, the genetic model predicting SS hypertension is different from the model for low renin, suggesting genetic differences in these 2 phenotypes. Like low-renin testing, screening for GRK4 variants may be a useful diagnostic adjunct for detection of SS hypertension.

Asian People↗

PubMed2006

Machine learning for detecting gene-gene interactions: a review.

Complex interactions among genes and environmental factors are known to play a role in common human disease aetiology. There is a growing body of evidence to suggest that complex interactions are 'the norm' and, rather than amounting to a small perturbation to classical Mendelian genetics, interactions may be the predominant effect. Traditional statistical methods are not well suited for detecting such interactions, especially when the data are high dimensional (many attributes or independent variables) or when interactions occur between more than two polymorphisms. In this review, we discuss machine-learning models and algorithms for identifying and characterising susceptibility genes in common, complex, multifactorial human diseases. We focus on the following machine-learning methods that have been used to detect gene-gene interactions: neural networks, cellular automata, random forests, and multifactor dimensionality reduction. We conclude with some ideas about how these methods and others can be integrated into a comprehensive and flexible framework for data mining and knowledge discovery in human genetics.

Algorithms↗

PubMed2006

Diabetic nephropathy is associated with gene expression levels of oxidative phosphorylation and related pathways.

The in vitro behavior of skin fibroblasts from patients with or without diabetic nephropathy is associated with diabetic nephropathy risk. Here we compared skin fibroblast gene expression profiles from two groups of type 1 diabetic patients: 20 with very fast ("fast-track") versus 20 with very slow ("slow-track") rates of development of diabetic nephropathy lesions. Gene expression profiles of skin fibroblasts grown in 25 mmol/l glucose for 36 h were assessed by Affymetrix HG-U133A GeneChips to determine the proportion of genes in a given biological pathway that were directionally consistent in their group differences. Five pathways reached statistical significance. All had significantly greater proportions of genes with higher expression levels in the fast-track group. These pathways, the first four of which are closely related and have overlapping genes, included oxidative phosphorylation (P<0.001), electron transport system complex III (P=0.017), citrate cycle (P=0.037), propanoate metabolism (P=0.044), and transcription factors (P=0.046). These results support the concept that oxidative phosphorylation and related upstream pathways may be important in the pathogenesis of diabetic nephropathy. Whether these findings reflect inherent genetic cellular characteristics, "cell memory," or both requires further study.

Adult↗

PubMed2006

Visual analysis of statistical results from microarray studies of human breast cancer.

Computational and statistical analysis of microarray data is a daunting challenge. Perhaps even more daunting is the biological interpretation of microarray data analysis results. We have previously developed the exploratory visual analysis (EVA) software and database for exploring data analysis results in the context of biological information on each gene available in public databases such as Entrez Gene. EVA brings a flexible combination of statistics and biological annotation to the user's desktop in a straightforward visual interface. Using a publicly available microarray dataset of gene expression response to chemotherapeutic agents in human breast cancer cell lines, we demonstrate the usefulness of the EVA system for interpreting statistical results. EVA can extend previous analyses as well as aid in making novel discoveries. Thus, we anticipate EVA will prove a useful addition to the repertoire of computational methods for microarray data analysis. The EVA software is freely available to academic users.

Antineoplastic Agents↗

PubMed2006

The gender-specific role of polymorphisms from the fibrinolytic, renin-angiotensin, and bradykinin systems in determining plasma t-PA and PAI-1 levels.

Tissue plasminogen activator (t-PA) and plasminogen activator inhibitor 1 (PAI-1) directly influence thrombus formation and degradation and thus risk for arterial thrombosis. We report here results from a genetic analysis of plasma t-PA and PAI-1 levels in a large population-based sample from the PREVEND study in Groningen, the Netherlands (n = 2,527). We measured polymorphisms from genes of the fibrinolytic system, the renin-angiotensin system (RAS), and the bradykinin system. We found that males had higher levels of natural-log transformed t-PA, and PAI-1 (P < 0.01) compared to females. When stratifying females by menopausal status, PAI-1 levels were only significantly different between pre-menopausal females and males (p < 0.001). Furthermore, we found that age, body mass index, and waist-to-hip ratio were significant predictors of t-PA and PAI-1 in both females and males, and that the regression relationships between these factors and plasma t-PA and PAI-1 were dependent on gender. In addition, we found that the PAI-1 4G/5G polymorphism was a significant predictor of PAI-1 levels in both females and males, that the angiotensin II type I receptor A1166C was a significant predictor of t-PA and PAI-1 levels in females, and that the bradykinin receptor B2 58CT polymorphism was a significant predictor of t-PA levels in females. In conclusion, this large population-based study showed that t-PA and PAI-1 levels are determined by several demographic and genetic factors involved in the fibrinolytic, RAS and bradykinin system. In addition, the results support the idea that the biology of t-PA and PAI-1 is different between females and males.

Adult↗

PubMed2005

Concordance of multiple analytical approaches demonstrates a complex relationship between DNA repair gene SNPs, smoking and bladder cancer susceptibility.

Study results of single nucleotide polymorphisms (SNPs) and cancer susceptibility are often conflicting, possibly because of the analytic challenges of testing for multiple genetic and environmental risk factors using traditional analytic tools. We investigated the relationship between DNA repair gene SNPs, smoking, and bladder cancer susceptibility in 355 cases and 559 controls enrolled in a population-based study of bladder cancer in the US. Our multifaceted analytical approach included logistic regression, multifactor dimensionality reduction, and hierarchical interaction graphs for the analysis of gene-gene and gene-environment interactions followed by linkage disequilibrium and haplotype analysis. Overall, we did not find an association between any single DNA repair gene SNP and bladder cancer risk. We did find a marginally significant elevated risk of the XPD codon 751 homozygote variant among never smokers [adjusted odds ratio (OR) 2.5, 95% confidence interval (CI) 1.0-6.2]. In addition, the XRCC1 194 variant allele was associated with a reduced bladder cancer risk among heavy smokers [adjusted OR 0.4, 95% CI 0.2-0.9)]. The best predictors of bladder cancer included the XPD codon 751 and 312 SNPs along with smoking. Interpretation of this multifactor model revealed that the relationship between the XPD SNPs and bladder cancer is mostly non-additive while the effect of smoking is mostly additive. Since the two XPD SNPs are in significant linkage disequilibrium (D' = 0.52, P = 0.0001), we estimated XPD haplotypes. Individuals with variant XPD haplotypes were more susceptible to bladder cancer [e.g. adjusted OR 2.5, 95% CI 1.7-3.6] and the effect was magnified when smoking was considered. These results support the hypothesis that common polymorphisms in DNA repair genes modify bladder cancer risk and emphasize the need for a multifaceted statistical approach to identify gene-gene and gene-environment interactions.

Adult↗

PubMed2005

Proteomic-based prognosis of brain tumor patients using direct-tissue matrix-assisted laser desorption ionization mass spectrometry.

Clinical diagnosis and treatment decisions for a subset of primary human brain tumors, gliomas, are based almost exclusively on tissue histology. Approaches for glioma diagnosis can be highly subjective due to the heterogeneity and infiltrative nature of these tumors and depend on the skill of the neuropathologist. There is therefore a critical need to develop more precise, non-subjective, and systematic methods to classify human gliomas. To this end, mass spectrometric analysis has been applied to these tumors to determine glioma-specific protein patterns. Protein profiles have been obtained from human gliomas of various grades through direct analysis of tissue samples using matrix-assisted laser desorption ionization mass spectrometry (MS). Statistical algorithms applied to the MS profiles from tissue sections identified protein patterns that correlated with tumor histology and patient survival. Using a data set of 108 glioma patients, two patient populations, a short-term and a long-term survival group, were identified based on the tissue protein profiles. In addition, a subset of 57 patients diagnosed with high-grade, grade IV, malignant gliomas were analyzed and a novel classification scheme that segregated short-term and long-term survival patients based on the proteomic profiles was developed. The protein patterns described served as an independent indicator of patient survival. These results show that this new molecular approach to monitoring gliomas can provide clinically relevant information on tumor malignancy and is suitable for high-throughput clinical screening.

Biomarkers, Tumor↗

PubMed2005

A gene expression fingerprint of C. elegans embryonic motor neurons.

BACKGROUND: Differential gene expression specifies the highly diverse cell types that constitute the nervous system. With its sequenced genome and simple, well-defined neuroanatomy, the nematode C. elegans is a useful model system in which to correlate gene expression with neuron identity. The UNC-4 transcription factor is expressed in thirteen embryonic motor neurons where it specifies axonal morphology and synaptic function. These cells can be marked with an unc-4::GFP reporter transgene. Here we describe a powerful strategy, Micro-Array Profiling of C. elegans cells (MAPCeL), and confirm that this approach provides a comprehensive gene expression profile of unc-4::GFP motor neurons in vivo. RESULTS: Fluorescence Activated Cell Sorting (FACS) was used to isolate unc-4::GFP neurons from primary cultures of C. elegans embryonic cells. Microarray experiments detected 6,217 unique transcripts of which approximately 1,000 are enriched in unc-4::GFP neurons relative to the average nematode embryonic cell. The reliability of these data was validated by the detection of known cell-specific transcripts and by expression in UNC-4 motor neurons of GFP reporters derived from the enriched data set. In addition to genes involved in neurotransmitter packaging and release, the microarray data include transcripts for receptors to a remarkably wide variety of signaling molecules. The added presence of a robust array of G-protein pathway components is indicative of complex and highly integrated mechanisms for modulating motor neuron activity. Over half of the enriched genes (537) have human homologs, a finding that could reflect substantial overlap with the gene expression repertoire of mammalian motor neurons. CONCLUSION: We have described a microarray-based method, MAPCeL, for profiling gene expression in specific C. elegans motor neurons and provide evidence that this approach can reveal candidate genes for key roles in the differentiation and function of these cells. These methods can now be applied to generate a gene expression map of the C. elegans nervous system.

Animals↗