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Garlic, an Allium vegetable, contains rich flavonoids organosulfur compounds (OSCs) that have potent anticancer properties. The aim of the review is to provide an overview of the different types of garlic, their active compounds, and the potential anticancer benefits with a focus on antioxidant activity. Animal and cell line studies have provided convincing evidence that garlic and its organosulfur compounds inhibit carcinogenesis through a number of events including induction of apoptosis, inhibiting cellular proliferation, scavenging radical oxygen species (ROS), increasing the activities of enzymes such as glutathione S-transferase, and reducing tumor size. Epidemiological studies showed compelling evidence that garlic consumption is associated with decreased risk of colorectal cancer, but inconsistent evidence for stomach, breast, and prostate cancers. Studies also suggest that the presence and potency of garlic OSCs varies with respect to the preparation and form of garlic. Further epidemiological studies with information on garlic form consumed or preparation methods and molecular studies regarding its antioxidant mechanisms, such as increasing enzymatic and nonenzymatic antioxidants levels, are warranted.

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.

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.

Introduction Metabolites are the closest representative of the phenotype and the most proximal reporters of the body's responses to exogenous exposure. The study aims to examine whether changes in ...

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.

Graphical models describe the linear correlation structure of data and have been used to establish causal relationships among phenotypes in genetic mapping populations. Data are typically collected at a single point in time. Biological processes on the other hand are often non-linear and display time varying dynamics. The extent to which graphical models can recapitulate the architecture of an underlying biological processes is not well understood. We consider metabolic networks with known stoichiometry to address the fundamental question: “What can causal networks tell us about metabolic pathways?”. Using data from an Arabidopsis BaySha population and simulated data from dynamic models of pathway motifs, we assess our ability to reconstruct metabolic pathways using graphical models. Our results highlight the necessity of non-genetic residual biological variation for reliable inference. Recovery of the ordering within a pathway is possible, but should not be expected. Causal inference is sensitive to subtle patterns in the correlation structure that may be driven by a variety of factors, which may not emphasize the substrate-product relationship. We illustrate the effects of metabolic pathway architecture, epistasis and stochastic variation on correlation structure and graphical model-derived networks. We conclude that graphical models should be interpreted cautiously, especially if the implied causal relationships are to be used in the design of intervention strategies., Author Summary High-throughput profiling data are pervasive in modern genetic studies. The large-scale nature of the data can make interpretation challenging. Methods that estimate networks or graphs have become popular tools for proposing causal relationships among traits. However, it is not obvious that these methods are able to capture causal biological mechanisms. Here we address the power and limitations of causal inference methods in biological systems. We examine metabolic data from simulation and from a well-characterized metabolic pathway in plants. We show that variation has to propagate through the pathway for reliable network inference. While it is possible for causal inference methods to recover the ordering of the biological pathway, it should not be expected. Causal relationships create subtle patterns in correlation, which may be dominated by other biological factors that do not reflect the ordering of the underlying pathway. Our results shape expectations about these methods and explain some of the successes and failures of causal graphical models for network inference.

Despite considerable progress understanding genes that affect the HDL particle, its function, and cholesterol content, genes identified to date explain only a small percentage of the genetic variation. We used N-ethyl-N-nitrosourea mutagenesis in mice to discover novel genes that affect HDL cholesterol levels. Two mutant lines (Hlb218 and Hlb320) with low HDL cholesterol levels were established. Causal mutations in these lines were mapped using linkage analysis: for line Hlb218 within a 12 Mbp region on Chr 10; and for line Hlb320 within a 21 Mbp region on Chr 7. High-throughput sequencing of Hlb218 liver RNA identified a mutation in Pla2g12b. The transition of G to A leads to a cysteine to tyrosine change and most likely causes a loss of a disulfide bridge. Microarray analysis of Hlb320 liver RNA showed a 7-fold downregulation of Hpn; sequencing identified a mutation in the 3′ splice site of exon 8. Northern blot confirmed lower mRNA expression level in Hlb320 and did not show a difference in splicing, suggesting that the mutation only affects the splicing rate. In addition to affecting HDL cholesterol, the mutated genes also lead to reduction in serum non-HDL cholesterol and triglyceride levels. Despite low HDL cholesterol levels, the mice from both mutant lines show similar atherosclerotic lesion sizes compared to control mice. These new mutant mouse models are valuable tools to further study the role of these genes, their affect on HDL cholesterol levels, and metabolism.