Genetic dissection and prognostic modeling of overt stroke in sickle cell anemia.

Sickle cell anemia (SCA) is a paradigmatic single gene disorder caused by homozygosity with respect to a unique mutation at the beta-globin locus. SCA is phenotypically complex, with different clinical courses ranging from early childhood mortality to a virtually unrecognized condition. Overt stroke is a severe complication affecting 6-8% of individuals with SCA. Modifier genes might interact to determine the susceptibility to stroke, but such genes have not yet been identified. Using Bayesian networks, we analyzed 108 SNPs in 39 candidate genes in 1,398 individuals with SCA. We found that 31 SNPs in 12 genes interact with fetal hemoglobin to modulate the risk of stroke. This network of interactions includes three genes in the TGF-beta pathway and SELP, which is associated with stroke in the general population. We validated this model in a different population by predicting the occurrence of stroke in 114 individuals with 98.2% accuracy.

Glycosaminoglycans and glucose prevent apoptosis in 4-methylumbelliferone-treated human aortic smooth muscle cells.

Smooth muscle cells (SMCs) have a pivotal role in cardiovascular diseases and are responsible for hyaluronan (HA) deposition in thickening vessel walls. HA regulates SMC proliferation, migration, and inflammation, which accelerates neointima formation. We used the HA synthesis inhibitor 4-methylumbelliferone (4-MU) to reduce HA production in human aortic SMCs and found a significant increase of apoptotic cells. Interestingly, the exogenous addition of HA together with 4-MU reduced apoptosis. A similar anti-apoptotic effect was observed also by adding other glycosaminoglycans and glucose to 4-MU-treated cells. Furthermore, the anti-apoptotic effect of HA was mediated by Toll-like receptor 4, CD44, and PI3K but not by ERK1/2.

How accurate can genetic predictions be?

BACKGROUND: Pre-symptomatic prediction of disease and drug response based on genetic testing is a critical component of personalized medicine. Previous work has demonstrated that the predictive capacity of genetic testing is constrained by the heritability and prevalence of the tested trait, although these constraints have only been approximated under the assumption of a normally distributed genetic risk distribution. RESULTS: Here, we mathematically derive the absolute limits that these factors impose on test accuracy in the absence of any distributional assumptions on risk. We present these limits in terms of the best-case receiver-operating characteristic (ROC) curve, consisting of the best-case test sensitivities and specificities, and the AUC (area under the curve) measure of accuracy. We apply our method to genetic prediction of type 2 diabetes and breast cancer, and we additionally show the best possible accuracy that can be obtained from integrated predictors, which can incorporate non-genetic features. CONCLUSION: Knowledge of such limits is valuable in understanding the implications of genetic testing even before additional associations are identified.

The challenges of informatics in synthetic biology: from biomolecular networks to artificial organisms.

The field of synthetic biology holds an inspiring vision for the future; it integrates computational analysis, biological data and the systems engineering paradigm in the design of new biological machines and systems. These biological machines are built from basic biomolecular components analogous to electrical devices, and the information flow among these components requires the augmentation of biological insight with the power of a formal approach to information management. Here we review the informatics challenges in synthetic biology along three dimensions: in silico, in vitro and in vivo. First, we describe state of the art of the in silico support of synthetic biology, from the specific data exchange formats, to the most popular software platforms and algorithms. Next, we cast in vitro synthetic biology in terms of information flow, and discuss genetic fidelity in DNA manipulation, development strategies of biological parts and the regulation of biomolecular networks. Finally, we explore how the engineering chassis can manipulate biological circuitries in vivo to give rise to future artificial organisms.

Regulation of myogenic progenitor proliferation in human fetal skeletal muscle by BMP4 and its antagonist Gremlin.

Skeletal muscle side population (SP) cells are thought to be "stem"-like cells. Despite reports confirming the ability of muscle SP cells to give rise to differentiated progeny in vitro and in vivo, the molecular mechanisms defining their phenotype remain unclear. In this study, gene expression analyses of human fetal skeletal muscle demonstrate that bone morphogenetic protein 4 (BMP4) is highly expressed in SP cells but not in main population (MP) mononuclear muscle-derived cells. Functional studies revealed that BMP4 specifically induces proliferation of BMP receptor 1a-positive MP cells but has no effect on SP cells, which are BMPR1a-negative. In contrast, the BMP4 antagonist Gremlin, specifically up-regulated in MP cells, counteracts the stimulatory effects of BMP4 and inhibits proliferation of BMPR1a-positive muscle cells. In vivo, BMP4-positive cells can be found in the proximity of BMPR1a-positive cells in the interstitial spaces between myofibers. Gremlin is expressed by mature myofibers and interstitial cells, which are separate from BMP4-expressing cells. Together, these studies propose that BMP4 and Gremlin, which are highly expressed by human fetal skeletal muscle SP and MP cells, respectively, are regulators of myogenic progenitor proliferation.

Inferring cell cycle feedback regulation from gene expression data.

Feedback control is an important regulatory process in biological systems, which confers robustness against external and internal disturbances. Genes involved in feedback structures are therefore likely to have a major role in regulating cellular processes. Here we rely on a dynamic Bayesian network approach to identify feedback loops in cell cycle regulation. We analyzed the transcriptional profile of the cell cycle in HeLa cancer cells and identified a feedback loop structure composed of 10 genes. In silico analyses showed that these genes hold important roles in system's dynamics. The results of published experimental assays confirmed the central role of 8 of the identified feedback loop genes in cell cycle regulation. In conclusion, we provide a novel approach to identify critical genes for the dynamics of biological processes. This may lead to the identification of therapeutic targets in diseases that involve perturbations of these dynamics.

A systems biology approach to modeling vibrio cholerae gene expression under virulence-inducing conditions.

Vibrio cholerae is a Gram-negative bacillus that is the causative agent of cholera. Pathogenesis in vivo occurs through a series of spatiotemporally controlled events under the control of a gene cascade termed the ToxR regulon. Major genes in the ToxR regulon include the master regulators toxRS and tcpPH, the downstream regulator toxT, and virulence factors, the ctxAB and tcpA operons. Our current understanding of the dynamics of virulence gene expression is limited to microarray analyses of expression at selected time points. To better understand this process, we utilized a systems biology approach to examine the temporal regulation of gene expression in El Tor V. cholerae grown under virulence-inducing conditions in vitro (AKI medium), using high-resolution time series genomic profiling. Results showed that overall gene expression in AKI medium mimics that of in vivo studies but with less clear temporal separation between upstream regulators and downstream targets. Expression of toxRS was unaffected by growth under virulence-inducing conditions, but expression of toxT was activated shortly after switching from stationary to aerating conditions. The tcpA operon was also activated early during mid-exponential-phase growth, while the ctxAB operon was turned on later, after the rise in toxT expression. Expression of ctxAB continued to rise despite an eventual decrease in toxT. Cluster analysis of gene expression highlighted 15 hypothetical genes and six genes related to environmental information processing that represent potential new members of the ToxR regulon. This study applies systems biology tools to analysis of gene expression of V. cholerae in vitro and provides an important comparator for future studies done in vivo.

Mapping transcription mechanisms from multimodal genomic data.

BACKGROUND: Identification of expression quantitative trait loci (eQTLs) is an emerging area in genomic study. The task requires an integrated analysis of genome-wide single nucleotide polymorphism (SNP) data and gene expression data, raising a new computational challenge due to the tremendous size of data. RESULTS: We develop a method to identify eQTLs. The method represents eQTLs as information flux between genetic variants and transcripts. We use information theory to simultaneously interrogate SNP and gene expression data, resulting in a Transcriptional Information Map (TIM) which captures the network of transcriptional information that links genetic variations, gene expression and regulatory mechanisms. These maps are able to identify both cis- and trans- regulating eQTLs. The application on a dataset of leukemia patients identifies eQTLs in the regions of the GART, PCP4, DSCAM, and RIPK4 genes that regulate ADAMTS1, a known leukemia correlate. CONCLUSIONS: The information theory approach presented in this paper is able to infer the dependence networks between SNPs and transcripts, which in turn can identify cis- and trans-eQTLs. The application of our method to the leukemia study explains how genetic variants and gene expression are linked to leukemia.

Integrative predictive model of coronary artery calcification in atherosclerosis.

BACKGROUND: Many different genetic and clinical factors have been identified as causes or contributors to atherosclerosis. We present a model of preclinical atherosclerosis based on genetic and clinical data that predicts the presence of coronary artery calcification in healthy Americans of European descent 45 to 84 years of age in the Multi-Ethnic Study of Atherosclerosis (MESA). METHODS AND RESULTS: We assessed 712 individuals for the presence or absence of coronary artery calcification and assessed their genotypes for 2882 single-nucleotide polymorphisms. With the use of these single-nucleotide polymorphisms and relevant clinical data, a Bayesian network that predicts the presence of coronary calcification was constructed. The model contained 13 single-nucleotide polymorphisms (from genes AGTR1, ALOX15, INSR, PRKAB1, IL1R2, ESR2, KCNK1, FBLN5, PPARA, VEGFA, PON1, TDRD6, PLA2G7, and 1 ancestry informative marker) and 5 clinical variables (sex, age, weight, smoking, and diabetes mellitus) and achieved 85% predictive accuracy, as measured by area under the receiver operating characteristic curve. This is a significant (P<0.001) improvement on models that use just the single-nucleotide polymorphism data or just the clinical variables. CONCLUSIONS: We present an investigation of joint genetic and clinical factors associated with atherosclerosis that shows predictive results for both cases, as well as enhanced performance for their combination.

Association of linear growth impairment in pediatric Crohn's disease and a known height locus: a pilot study.

The etiology of growth impairment in Crohn's disease (CD) has been inadequately explained by nutritional, hormonal, and/or disease-related factors, suggesting that genetics may be an additional contributor. The aim of this cross-sectional study was to investigate genetic variants associated with linear growth in pediatric-onset CD. We genotyped 951 subjects (317 CD patient-parent trios) for 64 polymorphisms within 14 CD-susceptibility and 23 stature-associated loci. Patient height-for-age Z-score < -1.64 was used to dichotomize probands into growth-impaired and nongrowth-impaired groups. The transmission disequilibrium test (TDT) was used to study association to growth impairment. There was a significant association between growth impairment in CD (height-for-age Z-score < -1.64) and a stature-related polymorphism in the dymeclin gene DYM (rs8099594) (OR = 3.2, CI [1.57-6.51], p = 0.0007). In addition, there was nominal over-transmission of two CD-susceptibility alleles, 10q21.1 intergenic region (rs10761659) and ATG16L1 (rs10210302), in growth-impaired CD children (OR = 2.36, CI [1.26-4.41] p = 0.0056 and OR = 2.45, CI [1.22-4.95] p = 0.0094, respectively). Our data indicate that genetic influences due to stature-associated and possibly CD risk alleles may predispose CD patients to alterations in linear growth. This is the first report of a link between a stature-associated locus and growth impairment in CD.

Genome-wide association for smoking cessation success in a trial of precessation nicotine replacement.

Abilities to successfully quit smoking display substantial evidence for heritability in classic and molecular genetic studies. Genome-wide association (GWA) studies have demonstrated single-nucleotide polymorphisms (SNPs) and haplotypes that distinguish successful quitters from individuals who were unable to quit smoking in clinical trial participants and in community samples. Many of the subjects in these clinical trial samples were aided by nicotine replacement therapy (NRT). We now report novel GWA results from participants in a clinical trial that sought dose/response relationships for "precessation" NRT. In this trial, 369 European-American smokers were randomized to 21 or 42 mg NRT, initiated 2 wks before target quit dates. Ten-week continuous smoking abstinence was assessed on the basis of self-reports and carbon monoxide levels. SNP genotyping used Affymetrix 6.0 arrays. GWA results for smoking cessation success provided no P value that reached "genome-wide" significance. Compared with chance, these results do identify (a) more clustering of nominally positive results within small genomic regions, (b) more overlap between these genomic regions and those identified in six prior successful smoking cessation GWA studies and (c) sets of genes that fall into gene ontology categories that appear to be biologically relevant. The 1,000 SNPs with the strongest associations form a plausible Bayesian network; no such network is formed by randomly selected sets of SNPs. The data provide independent support, based on individual genotyping, for many loci previously nominated on the basis of data from genotyping in pooled DNA samples. These results provide further support for the idea that aid for smoking cessation may be personalized on the basis of genetic predictors of outcome.

Transcriptional network classifiers.

BACKGROUND: Gene interactions play a central role in transcriptional networks. Many studies have performed genome-wide expression analysis to reconstruct regulatory networks to investigate disease processes. Since biological processes are outcomes of regulatory gene interactions, this paper develops a system biology approach to infer function-dependent transcriptional networks modulating phenotypic traits, which serve as a classifier to identify tissue states. Due to gene interactions taken into account in the analysis, we can achieve higher classification accuracy than existing methods. RESULTS: Our system biology approach is carried out by the Bayesian networks framework. The algorithm consists of two steps: gene filtering by Bayes factor followed by collinearity elimination via network learning. We validate our approach with two clinical data. In the study of lung cancer subtypes discrimination, we obtain a 25-gene classifier from 111 training samples, and the test on 422 independent samples achieves 95% classification accuracy. In the study of thoracic aortic aneurysm (TAA) diagnosis, 61 samples determine a 34-gene classifier, whose diagnosis accuracy on 33 independent samples achieves 82%. The performance comparisons with three other popular methods, PCA/LDA, PAM, and Weighted Voting, confirm that our approach yields superior classification accuracy and a more compact signature. CONCLUSIONS: The system biology approach presented in this paper is able to infer function-dependent transcriptional networks, which in turn can classify biological samples with high accuracy. The validation of our classifier using clinical data demonstrates the promising value of our proposed approach for disease diagnosis.

Predictive genomics of cardioembolic stroke.

Cardioembolic stroke is a complex disease resulting from the interaction of numerous factors. Using data from Genes Affecting Stroke Risk and Outcome Study (GASROS), we show that a multivariate predictive model built using Bayesian networks is able to achieve a predictive accuracy of 86% on the fitted values as computed by the area under the receiver operating characteristic curve relative to that of the individual single nucleotide polymorphism with the highest prognostic performance (area under the receiver operating characteristic curve=60%).

Automated programming for bioinformatics algorithm deployment.

UNLABELLED: Many bioinformatics solutions suffer from the lack of usable interface/platform from which results can be analyzed and visualized. Overcoming this hurdle would allow for more widespread dissemination of bioinformatics algorithms within the biological and medical communities. The algorithms should be accessible without extensive technical support or programming knowledge. Here, we propose a dynamic wizard platform that provides users with a Graphical User Interface (GUI) for most Java bioinformatics library toolkits. The application interface is generated in real-time based on the original source code. This platform lets developers focus on designing algorithms and biologists/physicians on testing hypotheses and analyzing results. AVAILABILITY: The open source code can be downloaded from: http://bcl.med.harvard.edu/proteomics/proj/APBA/.

A testable prognostic model of nicotine dependence.

Individuals' dependence on nicotine, primarily through cigarette smoking, is a major source of morbidity and mortality worldwide. Many smokers attempt but fail to quit smoking, motivating researchers to identify the origins of this dependence. Because of the known heritability of nicotine-dependence phenotypes, considerable interest has been focused on discovering the genetic factors underpinning the trait. This goal, however, is not easily attained: no single factor is likely to explain any great proportion of dependence because nicotine dependence is thought to be a complex trait (i.e., the result of many interacting factors). Genomewide association studies are powerful tools in the search for the genomic bases of complex traits, and in this context, novel candidate genes have been identified through single nucleotide polymorphism (SNP) association analyses. Beyond association, however, genetic data can be used to generate predictive models of nicotine dependence. As expected in the context of a complex trait, individual SNPs fail to accurately predict nicotine dependence, demanding the use of multivariate models. Standard approaches, such as logistic regression, are unable to consider large numbers of SNPs given existing sample sizes. However, using Bayesian networks, one can overcome these limitations to generate a multivariate predictive model, which has markedly enhanced predictive accuracy on fitted values relative to that of individual SNPs. This approach, combined with the data being generated by genomewide association studies, promises to shed new light on the common, complex trait nicotine dependence.

Prediction of chronic obstructive pulmonary disease (COPD) in asthma patients using electronic medical records.

OBJECTIVE: Identify clinical factors that modulate the risk of progression to COPD among asthma patients using data extracted from electronic medical records. DESIGN: Demographic information and comorbidities from adult asthma patients who were observed for at least 5 years with initial observation dates between 1988 and 1998, were extracted from electronic medical records of the Partners Healthcare System using tools of the National Center for Biomedical Computing "Informatics for Integrating Biology to the Bedside" (i2b2). MEASUREMENTS: A predictive model of COPD was constructed from a set of 9,349 patients (843 cases, 8,506 controls) using Bayesian networks. The model's predictive accuracy was tested using it to predict COPD in a future independent set of asthma patients (992 patients; 46 cases, 946 controls), who had initial observation dates between 1999 and 2002. RESULTS: A Bayesian network model composed of age, sex, race, smoking history, and 8 comorbidity variables is able to predict COPD in the independent set of patients with an accuracy of 83.3%, computed as the area under the Receiver Operating Characteristic curve (AUROC). CONCLUSIONS: Our results demonstrate that data extracted from electronic medical records can be used to create predictive models. With improvements in data extraction and inclusion of more variables, such models may prove to be clinically useful.

Use of Bayesian networks to probabilistically model and improve the likelihood of validation of microarray findings by RT-PCR.

Though genome-wide technologies, such as microarrays, are widely used, data from these methods are considered noisy; there is still varied success in downstream biological validation. We report a method that increases the likelihood of successfully validating microarray findings using real time RT-PCR, including genes at low expression levels and with small differences. We use a Bayesian network to identify the most relevant sources of noise based on the successes and failures in validation for an initial set of selected genes, and then improve our subsequent selection of genes for validation based on eliminating these sources of noise. The network displays the significant sources of noise in an experiment, and scores the likelihood of validation for every gene. We show how the method can significantly increase validation success rates. In conclusion, in this study, we have successfully added a new automated step to determine the contributory sources of noise that determine successful or unsuccessful downstream biological validation.

GO PaD: the Gene Ontology Partition Database.

Gene Ontology (GO) has been widely used to infer functional significance associated with sets of genes in order to automate discoveries within large-scale genetic studies. A level in GO's direct acyclic graph structure is often assumed to be indicative of its terms' specificities, although other work has suggested this assumption does not hold. Unfortunately, quantitative analysis of biological functions based on nodes at the same level (as is common in gene enrichment analysis tools) can lead to incorrect conclusions as well as missed discoveries due to inefficient use of available information. This paper addresses these using an informational theoretic approach encoded in the GO Partition Database that guarantees to maximize information for gene enrichment analysis. The GO Partition Database was designed to feature ontology partitions with GO terms of similar specificity. The GO partitions comprise varying numbers of nodes and present relevant information theoretic statistics, so researchers can choose to analyze datasets at arbitrary levels of specificity. The GO Partition Database, featuring GO partition sets for functional analysis of genes from human and 10 other commonly studied organisms with a total of 131,972 genes, is available on the internet at: bcl.med.harvard.edu/proj/gopart. The site also includes an online tutorial.

Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks.

BACKGROUND: Reverse engineering cellular networks is currently one of the most challenging problems in systems biology. Dynamic Bayesian networks (DBNs) seem to be particularly suitable for inferring relationships between cellular variables from the analysis of time series measurements of mRNA or protein concentrations. As evaluating inference results on a real dataset is controversial, the use of simulated data has been proposed. However, DBN approaches that use continuous variables, thus avoiding the information loss associated with discretization, have not yet been extensively assessed, and most of the proposed approaches have dealt with linear Gaussian models. RESULTS: We propose a generalization of dynamic Gaussian networks to accommodate nonlinear dependencies between variables. As a benchmark dataset to test the new approach, we used data from a mathematical model of cell cycle control in budding yeast that realistically reproduces the complexity of a cellular system. We evaluated the ability of the networks to describe the dynamics of cellular systems and their precision in reconstructing the true underlying causal relationships between variables. We also tested the robustness of the results by analyzing the effect of noise on the data, and the impact of a different sampling time. CONCLUSION: The results confirmed that DBNs with Gaussian models can be effectively exploited for a first level analysis of data from complex cellular systems. The inferred models are parsimonious and have a satisfying goodness of fit. Furthermore, the networks not only offer a phenomenological description of the dynamics of cellular systems, but are also able to suggest hypotheses concerning the causal interactions between variables. The proposed nonlinear generalization of Gaussian models yielded models characterized by a slightly lower goodness of fit than the linear model, but a better ability to recover the true underlying connections between variables.

Transcription factor CHF1/Hey2 regulates the global transcriptional response to platelet-derived growth factor in vascular smooth muscle cells.

The cardiovascular restricted transcription factor CHF1/Hey2 has been previously shown to regulate the smooth muscle response to growth factors. To determine how CHF1/Hey2 affects the smooth muscle response to growth factors, we performed a genomic screen for transcripts that are differentially expressed in wild-type and knockout smooth muscle cells after stimulation with platelet-derived growth factor. We screened 45,101 probes representing >39,000 transcripts derived from at least 34,000 genes, at eight different time points. We analyzed the expression data utilizing an algorithm based on Bayesian statistics to derive the best polynomial clustering model to fit the expression data. We found that in a total of 9,827 transcripts the normalized ratio of knockout to wild-type expression diverged more than threefold from baseline in at least one time point, and these transcripts separated into 17 distinct clusters. Further analysis of each cluster revealed distinct alterations in gene expression patterns for immediate early genes, transcription factors, matrix metalloproteinases, signaling molecules, and other molecules important in vascular biology. Our findings demonstrate that CHF1/Hey2 profoundly affects vascular smooth muscle phenotype by altering both the absolute expression level of a variety of genes and the kinetics of growth factor-induced gene expression.