Search

Mathematical models that utilize network representations have proven to be valuable tools for investigating biological systems. Often dynamic models are not feasible due to their complex functional forms that rely on unknown rate parameters. Network propagation has been shown to accurately capture the sensitivity of nodes to changes in other nodes; without the need for dynamic systems and parameter estimation. Node sensitivity measures rely solely on network structure and encode a sensitivity matrix that serves as a good approximation to the Jacobian matrix. The use of a propagation-based sensitivity matrix as a Jacobian has important implications for network optimization. This work develops Integrated Graph Propagation and OptimizatioN (IGPON), which aims to identify optimal perturbation patterns that can drive networks to desired target states. IGPON embeds propagation into an objective function that aims to minimize the distance between a current observed state and a target state. Optimization is performed using Broyden's method with the propagationbased sensitivity matrix as the Jacobian. IGPON is applied to simulated random networks, DREAM4 in silico networks, and over-represented pathways from STAT6 knockout data and YBX1 knockdown data. Results demonstrate that IGPON is an effective way to optimize directed and undirected networks that are robust to uncertainty in the network structure.

The BayesNetBP package has been developed for probabilistic reasoning and visualization in Bayesian networks with nodes that are purely discrete, continuous or mixed (discrete and continuous). Probabilistic reasoning enables a user to absorb information into a Bayesian network and make queries about how the probabilities within the network change in light of new information. The package was developed in the R programming language and is freely available from the Comprehensive R Archive Network. A shiny app with Cytoscape widgets provides an interactive interface for evidence absorption, queries, and visualizations.

Background Heart failure (HF) is a leading cause of cardiac morbidity among women, whose risk factors differ from those in men. We used machine-learning approaches to develop risk- prediction models for incident HF in a cohort of postmenopausal women from the Women’s Health Initiative (WHI). Methods We used 2 machine-learning methods—Least Absolute Shrinkage and Selection Operator (LASSO) and Classification and Regression Trees (CART)—to perform variable selection on 1227 baseline WHI variables for the primary outcome of incident HF. These variables were then used to construct separate Cox proportional hazard models, and we compared these results, using receiver-operating characteristic (ROC) curve analysis, against a comparator model built using variables from the Atherosclerosis Risk in Communities (ARIC) HF prediction model. We analyzed 43,709 women who had 2222 incident HF events; median follow-up was 14.3 years. Results LASSO selected 10 predictors, and CART selected 11 predictors. The highest correlation between selected variables was 0.46. In addition to selecting well-established predictors such as age, myocardial infarction, and smoking, novel predictors included physical function, number of pregnancies, number of previous live births and age at menopause. In ROC analysis, the CART-derived model had the highest C-statistic of 0.83 (95% confidence interval [CI], 0.81-0.85), followed by LASSO 0.82 (95% CI, 0.81-0.84) and ARIC 0.73 (95% CI, 0.70-0.76). Conclusions Machine-learning approaches can be used to develop HF risk-prediction models that can have better discrimination compared with an established HF risk model and may provide a basis for investigating novel HF predictors.

The objective of network module detection is to identify groups of nodes within a network structure that are tightly connected. Nodes in a network often have attributes (aka metadata) associated with them. It is often desirable to identify groups of nodes that are tightly connected in the network structure, but also have strong similarity in their attributes. Utilizing attribute information in module detection is a major challenge because it requires bridging the structural network with attribute data. A Weighted Fast Greedy (WFG) algorithm for attribute-based module detection is proposed. WFG utilizes logistic regression to bridge the structural and attribute spaces. The logistic function naturally emphasizes associations between attributes and network structure accordingly, and can be easily interpreted. A breast cancer application is presented that connects a protein���protein interaction network gene expression data and a survival outcome. This application demonstrates the importance of embedding attribute information into the community detection framework on a breast cancer dataset. Five modules were significant for survival and they contained known pathways and markers for cancer, including cell cycle, p53 pathway, BRCA1, BRCA2, and AURKB, among others. Whereas, neither the gene expression data nor the network structure alone gave rise to these cancer biomarkers and signatures.

The ERBB2 gene encodes a transmembrane tyrosine kinase receptor that belongs to the epidermal growth factor receptor (EGFR) family. ERBB2 plays a pivotal role during heart development and is essential for normal cardiac function, particularly during episodes of cardiac stress. The monoclonal antibody drug trastuzumab is used for the therapy of breast cancers that overexpress ERBB2. The clinical use of trastuzumab is limited by the development of cardiotoxicity in some patients. Inter-individual differences in the expression of ERBB2 in cardiac tissue may impact the risk of cardiotoxicity. In this study, we examined whether DNA methylation status in the proximal promoter region of ERBB2 is associated to variable ERBB2 mRNA and ERBB2 protein expression in human myocardium. Complementary studies with ERBB2 gene reporter constructs and chromatin immunoprecipitation suggest that differential methylation in specific CpG sites modify the binding of Sp1 to the promoter of ERBB2. DNA methylation in the ERBB2 locus may contribute to the variable expression of ERBB2 in human myocardium.

Telomere length is a heritable marker of cellular age that is associated with morbidity and mortality. Poor sleep behaviors, which are also associated with adverse health events, may be related to leukocyte telomere length (LTL). We studied a subpopulation of 3,145 postmenopausal women (1,796 European-American (EA) and 1,349 African-American (AA)) enrolled in the Women’s Health Initiative in 1993–1998 with data on Southern blot-measured LTL and self-reported usual sleep duration and sleep disturbance. LTL-sleep associations were analyzed separately for duration and disturbance using weighted and confounder-adjusted linear regression models in the entire sample (AAs + EAs; adjusted for race/ethnicity) and in racial/ethnic strata, since LTL differs by ancestry. After adjustment for covariates, each additional daily hour of sleep beyond 5 hours, approximately, was associated with a 27-base-pair (95% confidence interval (CI): 6, 48) longer LTL in the entire sample. Associations between sleep duration and LTL were strongest among AAs (adjusted β = 37, 95% CI: 4, 70); a similar, nonsignificant association was observed for EAs (adjusted β = 20, 95% CI: −7, 48). Sleep disturbance was not associated with LTL in our study. Our models did not show departure from linearity (quadratic sleep terms: P ≥ 0.55). Our results suggest that longer sleep duration is associated with longer LTL in postmenopausal women.

Background: The metabolome is a collection of exogenous chemicals and metabolites from cellular processes that may reflect the body’s response to environmental exposures. Studies of air pollution and metabolomics are limited. Objectives: To explore changes in the human metabolome before, during, and after the 2008 Beijing Olympics Games, when air pollution was high, low, and high, respectively. Methods: Serum samples were collected before, during, and after the Olympics from 26 participants in an existing panel study. Gas and ultra-high performance liquid chromatography/mass spectrometry were used in metabolomics analysis. Repeated measures ANOVA, network analysis, and enrichment analysis methods were employed to identify metabolites and classes associated with air pollution changes. Results: A total of 886 molecules were measured in our metabolomics analysis. Network partitioning identified four modules with 65 known metabolites that significantly changed across the three time points. All known molecules in the first module (n=33) were lipids (e.g., eicosapentaenoic acid, stearic acid). The second module consisted primarily of dipeptides (n=24, e.g., isoleucylglycine) plus 8 metabolites from four other classes (e.g., hypoxanthine, 12-hydroxyeicosatetraenoic acid). Most of the metabolites in Modules 3 (19 of 23) and 4 (5 of 5) were unknown. Enrichment analysis of module-identified metabolites indicted significantly overrepresented pathways, including long- and medium-chain fatty acids, polyunsaturated fatty acids (n3 and n6), eicosanoids, lysolipid, dipeptides, fatty acid metabolism, and purine metabolism [(hypo) xanthine/inosine–containing pathways]. Conclusions: We identified two major metabolic signatures: one consisting of lipids, and a second that included dipeptides, polyunsaturated fatty acids, taurine, and xanthine. Metabolites in both groups decreased during the 2008 Beijing Olympics, when air pollution was low, and increased after the Olympics, when air pollution returned to normal (high) levels. https://doi.org/10.1289/EHP3705

Clustering is a challenging problem in unsupervised learning. In lieu of a gold standard, stability has become a valuable surrogate to performance and robustness. In this work, we propose a non-parametric bootstrapping approach to estimating the stability of a clustering method, which also captures stability of the individual clusters and observations. This flexible framework enables different types of comparisons between clusterings and can be used in connection with two possible bootstrap approaches for stability. The first approach, scheme 1, can be used to assess confidence (stability) around clustering from the original dataset based on bootstrap replications. A second approach, scheme 2, searches over the bootstrap clusterings for an optimally stable partitioning of the data. The two schemes accommodate different model assumptions that can be motivated by an investigator’s trust (or lack thereof) in the original data and additional computational considerations. We propose a hierarchical visualization extrapolated from the stability profiles that give insights into the separation of groups, and projected visualizations for the inspection of the stability of individual operations. Our approaches show good performance in simulation and on real data. These approaches can be implemented using the R package bootcluster that is available on the Comprehensive R Archive Network (CRAN).

Down syndrome (DS, trisomy 21) is the most common major chromosomal aneuploidy compatible with life. The additional whole or partial copy of chromosome 21 results in genome-wide imbalances that drive the complex pathobiology of DS. Differential DNA methylation in the context of trisomy 21 may contribute to the variable architecture of the DS phenotype. The goal of this study was to examine the genomic DNA methylation landscape in myocardial tissue from non-fetal individuals with DS. More than 480,000 unique CpG sites were interrogated in myocardial DNA samples from individuals with (n = 12) and without DS (n = 12) using DNA methylation arrays. A total of 93 highly differentially methylated CpG sites and 16 differentially methylated regions were identified in myocardial DNA from subjects with DS. There were 18 differentially methylated CpG sites in chromosome 21, including 5 highly differentially methylated sites. A CpG site in the RUNX1 locus was differentially methylated in DS myocardium, and linear regression suggests that donors’ age, gender, DS status, and RUNX1 methylation may contribute up to ~51% of the variability in RUNX1 mRNA expression. In DS myocardium, only 58% of the genes overlapping with differentially methylated regions codify for proteins with known functions and 24% are non-coding RNAs. This study provides an initial snapshot on the extent of genome-wide differential methylation in myocardial tissue from persons with DS.

The intracardiac synthesis of anthracycline alcohol metabolites (e.g., daunorubicinol) contributes to the pathogenesis of anthracycline-related cardiotoxicity. Cancer patients with Down syndrome (DS) are at increased risk for anthracycline-related cardiotoxicity. We profiled the expression of anthracycline metabolizing enzymes in hearts from donors with- and without- DS.Cardiac expression of CBR1, CBR3, AKR1A1, AKR1C3 and AKR7A2 was examined by quantitative real time PCR, quantitative immunoblotting, and enzyme activity assays using daunorubicin. The CBR1 polymorphism rs9024 was investigated by allelic discrimination with fluorescent probes. The contribution of CBRs/AKRs proteins to daunorubicin reductase activity was examined by multiple linear regression.CBR1 was the most abundant transcript (average relative expression; DS: 81%, non-DS: 58%), and AKR7A2 was the most abundant protein (average relative expression; DS: 38%, non-DS: 35%). Positive associations between cardiac CBR1 protein levels and daunorubicin reductase activity were found for samples from donors with- and without- DS. Regression analysis suggests that sex, CBR1, AKR1A1, and AKR7A2 protein levels were significant contributors to cardiac daunorubicin reductase activity. CBR1 rs9024 genotype status impacts on cardiac CBR1 expression in non-DS hearts.CBR1, AKR1A1, and AKR7A2 protein levels point to be important determinants for predicting the synthesis of cardiotoxic daunorubicinol in heart.

A higher incidence of coronary artery disease is associated with a lower level of HDL-cholesterol. We searched for genetic loci influencing HDL-cholesterol in F2 mice from a cross between MRL/MpJ and SM/J mice. Quantitative trait loci (QTL) mapping revealed one significant HDL QTL (Apoa2 locus), four suggestive QTL on chromosomes 10, 11, 13, and 18 and four additional QTL on chromosomes 1 proximal, 3, 4, and 7 after adjusting HDL for the strong Apoa2 locus. A novel nonsynonymous polymorphism supports Lipg as the QTL gene for the chromosome 18 QTL, and a difference in Abca1 expression in liver tissue supports it as the QTL gene for the chromosome 4 QTL. Using weighted gene co-expression network analysis, we identified a module that after adjustment for Apoa2, correlated with HDL, was genetically determined by a QTL on chromosome 11, and overlapped with the HDL QTL. A combination of bioinformatics tools and systems genetics helped identify several candidate genes for both the chromosome 11 HDL and module QTL based on differential expression between the parental strains, cis regulation of expression, and causality modeling. We conclude that integrating systems genetics to a more-traditional genetics approach improves the power of complex trait gene identification.

Daunorubicin (DAUN) and doxorubicin (DOX) are used to treat a variety of cancers. The use of DAUN and DOX is hampered by the development of cardiotoxicity. Clinical evidence suggests that patients with leukemia and Down syndrome are at increased risk for anthracycline-related cardiotoxicity. Carbonyl reductases and aldo-keto reductases (AKRs) catalyze the reduction of DAUN and DOX into cardiotoxic C-13 alcohol metabolites. Anthracyclines also exert cardiotoxicity by triggering mitochondrial dysfunction. In recent studies, a collection of heart samples from donors with and without Down syndrome was used to investigate determinants for anthracycline-related cardiotoxicity including cardiac daunorubicin reductase activity (DA), carbonyl reductase/AKRs protein expression, mitochondrial DNA content (mtDNA), and AKR7A2 DNA methylation status. In this study, the available demographic, biochemical, genetic, and epigenetic data were integrated through classification and regression trees analysis with the aim of pinpointing the most relevant variables for the synthesis of cardiotoxic daunorubicinol (i.e., DA). Seventeen variables were considered as potential predictors. Leave-one-out-cross-validation was performed for model selection and to estimate the generalization error. The classification and regression trees analysis model and variable importance measures suggest that cardiac mtDNA content, mtDNA(4977) deletion frequency, and AKR7A2 protein content are the most important variables in determining DA.

Understanding community structure in networks has received considerable attention in recent years. Detecting and leveraging community structure holds promise for understanding and potentially intervening with the spread of influence. Network features of this type have important implications in a number of research areas, including, marketing, social networks, and biology. However, an overwhelming majority of traditional approaches to community detection cannot readily incorporate information of node attributes. Integrating structural and attribute information is a major challenge. We propose a exible iterative method; inverse regularized Markov Clustering (irMCL), to network clustering via the manipulation of the transition probability matrix (aka stochastic flow) corresponding to a graph. Similar to traditional Markov Clustering, irMCL iterates between "expand" and "inflate" operations, which aim to strengthen the intra-cluster flow, while weakening the inter-cluster flow. Attribute information is directly incorporated into the iterative method through a sigmoid (logistic function) that naturally dampens attribute influence that is contradictory to the stochastic flow through the network. We demonstrate advantages and the exibility of our approach using simulations and real data. We highlight an application that integrates breast cancer gene expression data set and a functional network defined via KEGG pathways reveal significant modules for survival.

Graphical models have proven to be a valuable tool for connecting genotypes and phenotypes. Structural learning of phenotype-genotype networks has received considerable attention in the post-genome era. In recent years, a dozen different methods have emerged for network inference, which leverage natural variation that arises in certain genetic populations. The structure of the network itself can be used to form hypotheses based on the inferred direct and indirect network relationships, but represents a premature endpoint to the graphical analyses. In this work, we extend this endpoint. We examine the unexplored problem of perturbing a given network structure, and quantifying the system-wide effects on the network in a node-wise manner. The perturbation is achieved through the setting of values of phenotype node(s), which may reflect an inhibition or activation, and propagating this information through the entire network. We leverage belief propagation methods in Conditional Gaussian Bayesian Networks (CG-BNs), in order to absorb and propagate phenotypic evidence through the network. We show that the modeling assumptions adopted for genotype-phenotype networks represent an important sub-class of CG-BNs, which possess properties that ensure exact inference in the propagation scheme. The system-wide effects of the perturbation are quantified in a node-wise manner through the comparison of perturbed and unperturbed marginal distributions using a symmetric Kullback-Leibler divergence. Applications to kidney and skin cancer expression quantitative trait loci (eQTL) data from different mus musculus populations are presented. System-wide effects in the network were predicted and visualized across a spectrum of evidence. Sub-pathways and regions of the network responded in concert, suggesting co-regulation and coordination throughout the network in response to phenotypic changes. We demonstrate how these predicted system-wide effects can be examined in connection with estimated class probabilities for covariates of interest, e.g. cancer status. Despite the uncertainty in the network structure, we demonstrate the system-wide predictions are stable across an ensemble of highly likely networks. A software package, geneNetBP, which implements our approach, was developed in the R programming language.

The intracardiac synthesis of anthracycline alcohol metabolites by aldo–keto reductases (AKRs) contributes to the pathogenesis of anthracycline-related cardiotoxicity. AKR7A2 is the most abundant anthracycline reductase in hearts from donors with and without Down syndrome (DS), and its expression varies between individuals (≈tenfold). We investigated whether DNA methylation impacts AKR7A2 expression in hearts from donors with (n = 11) and without DS (n = 30). Linear models were used to test for associations between methylation status and cardiac AKR7A2 expression. In hearts from donors without DS, DNA methylation status at CpG site −865 correlated with AKR7A2 mRNA (Pearson’s regression coefficient, r = −0.4051, P = 0.0264) and AKR7A2 protein expression (r = −0.5818, P = 0.0071). In heart tissue from donors with DS, DNA methylation status at CpG site −232 correlated with AKR7A2 protein expression (r = 0.8659, P = 0.0025). Multiple linear regression modeling revealed that methylation at several CpG sites is associated with the synthesis of cardiotoxic daunorubicinol. AKR7A2 methylation status in lymphoblastoid cell lines from donors with and without DS was examined to explore potential parallelisms between cardiac tissue and lymphoid cells. These results suggest that DNA methylation impacts AKR7A2 expression and the synthesis of cardiotoxic daunorubicinol.

Background: Genome-wide association studies (GWAS) have identified associations of telomere maintenance genes with breast cancer risk. This, coupled with the recognized role of telomere dysfunction as a cancer hallmark, has motivated the need for further study of telomeres and breast cancer. Further, the meta-GWAS discovery of seven single nucleotide polymorphisms (SNPs) associated with telomere length (TL) enables the use of an aggregated genetic risk score (GRS) for TL. We hypothesized that a higher GRS (representing shorter TL) would be associated with increased risk of breast cancer, and that a strengthened association may exist between higher TL GRS and aggressive breast cancer risk. Methods: In a race/ethnically diverse sample of invasive breast cancer cases (N=1,108) and non-cases (N=20,023) from the Women’s Health Initiative, we derived an unweighted TL GRS using seven previously identified TL-associated SNPs. Women were postmenopausal and the average age at enrollment was 63.9 years. We tested TL GRS associations with overall breast cancer risk and for estrogen receptor (ER), progesterone receptor (PR), and her2/neu (HER2) status-specific breast cancer risk using Cox proportional hazards models adjusted for age and race/ethnicity (in the entire sample) and age in strata of European American (EA; N=9,796), African American (AA; N=7,504), and Hispanic American (HA; N=3,229) women. We also considered reproductive risk factors, family history of breast cancer, hormone therapy, tumor characteristics, BMI, physical activity, smoking, alcohol, and US region as potential confounders. Results: We observed a small but statistically significant association between higher TL GRS (shorter TL) and reduced risk of overall breast cancer in the entire sample (HR=0.96, 95% CI: 0.93-1.00). Results of race/ethnicity-stratified analyses for TL GRS and overall breast cancer risk were not significant. The TL GRS was independently associated with decreased risk of ER-negative, PR-positive, and HER2-positive breast cancer risk in the entire sample (HR=0.82, 95% CI: 0.71-0.94, HR=0.94, 95% CI: 0.89-0.99, and HR=0.88, 95% CI: 0.79-0.99, respectively). Similarly, the TL GRS was associated with a reduced risk of ER-negative breast cancer in AA (HR=0.79, 95% CI: 0.64-0.97) and independently with PR-positive and HER2-positive breast cancer risk in HA (HR=0.85, 95% CI: 0.75-0.97 and HR=0.79, 95% CI: 0.58-0.99, respectively). Adjustment for other covariates did not influence hazards. Conclusions: Our results suggest that higher TL GRS is associated with reduced risk of overall breast cancer and with the development of specific breast cancer subtypes, such as ER negativity and/or PR and HER2 positivity. While our focus on GWAS-implicated SNPs may not encompass the full spectrum of genes involved in TL, further studies into the potential mechanism for shorter TL and reduced breast cancer risk are needed. Citation Format: Laurie Grieshober, Jean Wactawski-Wende, Rachael Hageman Blair, Lina Mu, Leah Preus, Jing Nie, Jiali Han, Jaymie R. Meliker, Thomas Rohan, Heather M. Ochs-Balcom. Telomere length genetic risk score is associated with breast cancer risk [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1288. doi:10.1158/1538-7445.AM2017-1288

Cancer is a major health problem with high mortality rates. In the post-genome era, investigators have access to massive amounts of rapidly accumulating high-throughput data in publicly available databases, some of which are exclusively devoted to housing Cancer data. However, data interpretation efforts have not kept pace with data collection, and gained knowledge is not necessarily translating into better diagnoses and treatments. A fundamental problem is to integrate and interpret data to further our understanding in Cancer Systems Biology. Viewing cancer as a network provides insights into the complex mechanisms underlying the disease. Mathematical and statistical models provide an avenue for cancer network modeling. In this article, we review two widely used modeling paradigms: deterministic metabolic models and statistical graphical models. The strength of these approaches lies in their flexibility and predictive power. Once a model has been validated, it can be used to make predictions and generate hypotheses. We describe a number of diverse applications to Cancer Biology, including, the system-wide effects of drug-treatments, disease prognosis, tumor classification, forecasting treatment outcomes, and survival predictions.