Genetic variants in IGF-I, IGF-II, IGFBP-3, and adiponectin genes and colon cancer risk in African Americans and Whites.

PURPOSE: Evaluating genetic susceptibility may clarify effects of known environmental factors and also identify individuals at high risk. We evaluated the association of four insulin-related pathway gene polymorphisms in insulin-like growth factor-1 (IGF-I) (CA)( n ) repeat, insulin-like growth factor-2 (IGF-II) (rs680), insulin-like growth factor-binding protein-3 (IGFBP-3) (rs2854744), and adiponectin (APM1 rs1501299) with colon cancer risk, as well as relationships with circulating IGF-I, IGF-II, IGFBP-3, and C-peptide in a population-based study. METHODS: Participants were African Americans (231 cases and 306 controls) and Whites (297 cases, 530 controls). Consenting subjects provided blood specimens and lifestyle/diet information. Genotyping for all genes except IGF-I was performed by the 5'-exonuclease (Taqman) assay. The IGF-I (CA)(n) repeat was assayed by PCR and fragment analysis. Circulating proteins were measured by enzyme immunoassays. Odds ratios (ORs) and 95 % confidence intervals (CIs) were calculated by logistic regression. RESULTS: The IGF-I (CA)( 19 ) repeat was higher in White controls (50 %) than African American controls (31 %). Whites homozygous for the IGF-I (CA)(19) repeat had a nearly twofold increase in risk of colon cancer (OR = 1.77; 95 % CI = 1.15-2.73), but not African Americans (OR = 0.73, 95 % CI 0.50-1.51). We observed an inverse association between the IGF-II Apa1 A-variant and colon cancer risk (OR = 0.49, 95 % CI 0.28-0.88) in Whites only. Carrying the IGFBP-3 variant alleles was associated with lower IGFBP-3 protein levels, a difference most pronounced in Whites (p-trend <0.05). CONCLUSIONS: These results support an association between insulin pathway-related genes and elevated colon cancer risk in Whites but not in African Americans.

Overexpression of the cardiac beta(2)-adrenergic receptor and expression of a beta-adrenergic receptor kinase-1 (betaARK1) inhibitor both increase myocardial contractility but have differential effects on susceptibility to ischemic injury.

Cardiac beta(2)-adrenergic receptor (beta(2)AR) overexpression is a potential contractile therapy for heart failure. Cardiac contractility was elevated in mice overexpressing beta(2)ARs (TG4s) with no adverse effects under normal conditions. To assess the consequences of beta(2)AR overexpression during ischemia, perfused hearts from TG4 and wild-type mice were subjected to 20-minute ischemia and 40-minute reperfusion. During ischemia, ATP and pH fell lower in TG4 hearts than wild type. Ischemic injury was greater in TG4 hearts, as indicated by lower postischemic recoveries of contractile function, ATP, and phosphocreatine. Because beta(2)ARs, unlike beta(1)ARs, couple to G(i) as well as G(s), we pretreated mice with the G(i) inhibitor pertussis toxin (PTX). PTX treatment increased basal contractility in TG4 hearts and abolished the contractile resistance to isoproterenol. During ischemia, ATP fell lower in TG4+PTX than in TG4 hearts. Recoveries of contractile function and ATP were lower in TG4+PTX than in TG4 hearts. We also studied mice that overexpressed either betaARK1 (TGbetaARK1) or a betaARK1 inhibitor (TGbetaARKct). Recoveries of function, ATP, and phosphocreatine were higher in TGbetaARK1 hearts than in wild-type hearts. Despite basal contractility being elevated in TGbetaARKct hearts to the same level as that of TG4s, ischemic injury was not increased. In summary, beta(2)AR overexpression increased ischemic injury, whereas betaARK1 overexpression was protective. Ischemic injury in the beta(2)AR overexpressors was exacerbated by PTX treatment, implying that it was G(s) not G(i) activity that enhanced injury. Unlike beta(2)AR overexpression, basal contractility was increased by betaARK1 inhibitor expression without increasing ischemic injury, thus implicating a safer potential therapy for heart failure.

Induced pluripotent stem cell-derived cardiac progenitors differentiate to cardiomyocytes and form biosynthetic tissues.

The mammalian heart has little capacity to regenerate, and following injury the myocardium is replaced by non-contractile scar tissue. Consequently, increased wall stress and workload on the remaining myocardium leads to chamber dilation, dysfunction, and heart failure. Cell-based therapy with an autologous, epigenetically reprogrammed, and cardiac-committed progenitor cell source could potentially reverse this process by replacing the damaged myocardium with functional tissue. However, it is unclear whether cardiac progenitor cell-derived cardiomyocytes are capable of attaining levels of structural and functional maturity comparable to that of terminally-fated cardiomyocytes. Here, we first describe the derivation of mouse induced pluripotent stem (iPS) cells, which once differentiated allow for the enrichment of Nkx2-5(+) cardiac progenitors, and the cardiomyocyte-specific expression of the red fluorescent protein. We show that the cardiac progenitors are multipotent and capable of differentiating into endothelial cells, smooth muscle cells and cardiomyocytes. Moreover, cardiac progenitor selection corresponds to cKit(+) cell enrichment, while cardiomyocyte cell-lineage commitment is concomitant with dual expression of either cKit/Flk1 or cKit/Sca-1. We proceed to show that the cardiac progenitor-derived cardiomyocytes are capable of forming electrically and mechanically coupled large-scale 2D cell cultures with mature electrophysiological properties. Finally, we examine the cell progenitors' ability to form electromechanically coherent macroscopic tissues, using a physiologically relevant 3D culture model and demonstrate that following long-term culture the cardiomyocytes align, and form robust electromechanical connections throughout the volume of the biosynthetic tissue construct. We conclude that the iPS cell-derived cardiac progenitors are a robust cell source for tissue engineering applications and a 3D culture platform for pharmacological screening and drug development studies.

The Effects of Geography, Environment and Phylogeny on Community Assembly and Gene Flow Dynamics

It is increasingly evident that evolutionary processes play a role in how ecological communities are assembled. However the extend to which evolution influences how plants respond to spatial and environmental gradients and interact with each other is less clear. In this dissertation I leverage evolutionary tools and thinking to understand how space and environment affect community composition and patterns of gene flow in a unique system of Atlantic rainforest and restinga (sandy coastal plains) habitats in Southeastern Brazil.

In chapter one I investigate how space and environment affect the population genetic structure and gene flow of Aechmea nudicaulis, a bromeliad species that co-occurs in forest and restinga habitats. I genotyped seven microsatellite loci and sequenced one chloroplast DNA region for individuals collected in 7 pairs of forest / restinga sites. Bayesian genetic clustering analyses show that populations of A. nudicaulis are geographically structured in northern and southern populations, a pattern consistent with broader scale phylogeographic dynamics of the Atlantic rainforest. On the other hand, explicit migration models based on the coalescent estimate that inter-habitat gene flow is less common than gene flow between populations in the same habitat type, despite their geographic discontinuity. I conclude that there is evidence for repeated colonization of the restingas from forest populations even though the steep environmental gradient between habitats is a stronger barrier to gene flow than geographic distance.

In chapter two I use data on 2800 individual plants finely mapped in a restinga plot and on first-year survival of 500 seedlings to understand the roles of phylogeny, functional traits and abiotic conditions in the spatial structuring of that community. I demonstrate that phylogeny is a poor predictor of functional traits in and that convergence in these traits is pervasive. In general, the community is not phylogenetically structured, with at best 14% of the plots deviating significantly from the null model. The functional traits SLA, leaf dry matter content (LDMC), and maximum height also showed no clear pattern of spatial structuring. On the other hand, leaf area is strongly overdispersed across all spatial scales. Although leaf area overdispersion would be generally taken as evidence of competition, I argue that interpretation is probably misleading. Finally, I show that seedling survival is dramatically increased when they grow shaded by an adult individual, suggesting that seedlings are being facilitated. Phylogenetic distance to their adult neighbor has no influence on rates of survival though. Taken together, these results indicate that phylogeny has very limited influence on the fine scale assembly of restinga communities.

Can Genetics Predict Response to Complex Behavioral Interventions? Evidence from a Genetic Analysis of the Fast Track Randomized Control Trial.

Early interventions are a preferred method for addressing behavioral problems in high-risk children, but often have only modest effects. Identifying sources of variation in intervention effects can suggest means to improve efficiency. One potential source of such variation is the genome. We conducted a genetic analysis of the Fast Track randomized control trial, a 10-year-long intervention to prevent high-risk kindergarteners from developing adult externalizing problems including substance abuse and antisocial behavior. We tested whether variants of the glucocorticoid receptor gene NR3C1 were associated with differences in response to the Fast Track intervention. We found that in European-American children, a variant of NR3C1 identified by the single-nucleotide polymorphism rs10482672 was associated with increased risk for externalizing psychopathology in control group children and decreased risk for externalizing psychopathology in intervention group children. Variation in NR3C1 measured in this study was not associated with differential intervention response in African-American children. We discuss implications for efforts to prevent externalizing problems in high-risk children and for public policy in the genomic era.

Transgenerational Effects of Early Life Starvation on Growth, Reproduction, and Stress Resistance in Caenorhabditis elegans.

Starvation during early development can have lasting effects that influence organismal fitness and disease risk. We characterized the long-term phenotypic consequences of starvation during early larval development in Caenorhabditis elegans to determine potential fitness effects and develop it as a model for mechanistic studies. We varied the amount of time that larvae were developmentally arrested by starvation after hatching ("L1 arrest"). Worms recovering from extended starvation grew slowly, taking longer to become reproductive, and were smaller as adults. Fecundity was also reduced, with the smallest individuals most severely affected. Feeding behavior was impaired, possibly contributing to deficits in growth and reproduction. Previously starved larvae were more sensitive to subsequent starvation, suggesting decreased fitness even in poor conditions. We discovered that smaller larvae are more resistant to heat, but this correlation does not require passage through L1 arrest. The progeny of starved animals were also adversely affected: Embryo quality was diminished, incidence of males was increased, progeny were smaller, and their brood size was reduced. However, the progeny and grandprogeny of starved larvae were more resistant to starvation. In addition, the progeny, grandprogeny, and great-grandprogeny were more resistant to heat, suggesting epigenetic inheritance of acquired resistance to starvation and heat. Notably, such resistance was inherited exclusively from individuals most severely affected by starvation in the first generation, suggesting an evolutionary bet-hedging strategy. In summary, our results demonstrate that starvation affects a variety of life-history traits in the exposed animals and their descendants, some presumably reflecting fitness costs but others potentially adaptive.

Integrated pathway and epistasis analysis reveals interactive effect of genetic variants at TERF1 and AFAP1L2 loci on melanoma risk.

Genome-wide association studies (GWASs) have characterized 13 loci associated with melanoma, which only account for a small part of melanoma risk. To identify new genes with too small an effect to be detected individually but which collectively influence melanoma risk and/or show interactive effects, we used a two-step analysis strategy including pathway analysis of genome-wide SNP data, in a first step, and epistasis analysis within significant pathways, in a second step. Pathway analysis, using the gene-set enrichment analysis (GSEA) approach and the gene ontology (GO) database, was applied to the outcomes of MELARISK (3,976 subjects) and MDACC (2,827 subjects) GWASs. Cross-gene SNP-SNP interaction analysis within melanoma-associated GOs was performed using the INTERSNP software. Five GO categories were significantly enriched in genes associated with melanoma (false discovery rate ≤ 5% in both studies): response to light stimulus, regulation of mitotic cell cycle, induction of programmed cell death, cytokine activity and oxidative phosphorylation. Epistasis analysis, within each of the five significant GOs, showed significant evidence for interaction for one SNP pair at TERF1 and AFAP1L2 loci (pmeta-int  = 2.0 × 10(-7) , which met both the pathway and overall multiple-testing corrected thresholds that are equal to 9.8 × 10(-7) and 2.0 × 10(-7) , respectively) and suggestive evidence for another pair involving correlated SNPs at the same loci (pmeta-int  = 3.6 × 10(-6) ). This interaction has important biological relevance given the key role of TERF1 in telomere biology and the reported physical interaction between TERF1 and AFAP1L2 proteins. This finding brings a novel piece of evidence for the emerging role of telomere dysfunction into melanoma development.

Explicit DNase sequence bias modeling enables high-resolution transcription factor footprint detection.

DNaseI footprinting is an established assay for identifying transcription factor (TF)-DNA interactions with single base pair resolution. High-throughput DNase-seq assays have recently been used to detect in vivo DNase footprints across the genome. Multiple computational approaches have been developed to identify DNase-seq footprints as predictors of TF binding. However, recent studies have pointed to a substantial cleavage bias of DNase and its negative impact on predictive performance of footprinting. To assess the potential for using DNase-seq to identify individual binding sites, we performed DNase-seq on deproteinized genomic DNA and determined sequence cleavage bias. This allowed us to build bias corrected and TF-specific footprint models. The predictive performance of these models demonstrated that predicted footprints corresponded to high-confidence TF-DNA interactions. DNase-seq footprints were absent under a fraction of ChIP-seq peaks, which we show to be indicative of weaker binding, indirect TF-DNA interactions or possible ChIP artifacts. The modeling approach was also able to detect variation in the consensus motifs that TFs bind to. Finally, cell type specific footprints were detected within DNase hypersensitive sites that are present in multiple cell types, further supporting that footprints can identify changes in TF binding that are not detectable using other strategies.

Chromatin accessibility mapping identifies mediators of basal transcription and retinoid-induced repression of OTX2 in medulloblastoma.

Despite an emerging understanding of the genetic alterations giving rise to various tumors, the mechanisms whereby most oncogenes are overexpressed remain unclear. Here we have utilized an integrated approach of genomewide regulatory element mapping via DNase-seq followed by conventional reporter assays and transcription factor binding site discovery to characterize the transcriptional regulation of the medulloblastoma oncogene Orthodenticle Homeobox 2 (OTX2). Through these studies we have revealed that OTX2 is differentially regulated in medulloblastoma at the level of chromatin accessibility, which is in part mediated by DNA methylation. In cell lines exhibiting chromatin accessibility of OTX2 regulatory regions, we found that autoregulation maintains OTX2 expression. Comparison of medulloblastoma regulatory elements with those of the developing brain reveals that these tumors engage a developmental regulatory program to drive OTX2 transcription. Finally, we have identified a transcriptional regulatory element mediating retinoid-induced OTX2 repression in these tumors. This work characterizes for the first time the mechanisms of OTX2 overexpression in medulloblastoma. Furthermore, this study establishes proof of principle for applying ENCODE datasets towards the characterization of upstream trans-acting factors mediating expression of individual genes.

Evaluating marker-guided treatment selection strategies

© 2014, The International Biometric Society.A potential venue to improve healthcare efficiency is to effectively tailor individualized treatment strategies by incorporating patient level predictor information such as environmental exposure, biological, and genetic marker measurements. Many useful statistical methods for deriving individualized treatment rules (ITR) have become available in recent years. Prior to adopting any ITR in clinical practice, it is crucial to evaluate its value in improving patient outcomes. Existing methods for quantifying such values mainly consider either a single marker or semi-parametric methods that are subject to bias under model misspecification. In this article, we consider a general setting with multiple markers and propose a two-step robust method to derive ITRs and evaluate their values. We also propose procedures for comparing different ITRs, which can be used to quantify the incremental value of new markers in improving treatment selection. While working models are used in step I to approximate optimal ITRs, we add a layer of calibration to guard against model misspecification and further assess the value of the ITR non-parametrically, which ensures the validity of the inference. To account for the sampling variability of the estimated rules and their corresponding values, we propose a resampling procedure to provide valid confidence intervals for the value functions as well as for the incremental value of new markers for treatment selection. Our proposals are examined through extensive simulation studies and illustrated with the data from a clinical trial that studies the effects of two drug combinations on HIV-1 infected patients.

Drug-drug interaction studies of cardiovascular drugs involving P-glycoprotein, an efflux transporter, on the pharmacokinetics of edoxaban, an oral factor Xa inhibitor.

BACKGROUND: Edoxaban, an oral direct factor Xa inhibitor, is in development for thromboprophylaxis, including prevention of stroke and systemic embolism in patients with atrial fibrillation (AF). P-glycoprotein (P-gp), an efflux transporter, modulates absorption and excretion of xenobiotics. Edoxaban is a P-gp substrate, and several cardiovascular (CV) drugs have the potential to inhibit P-gp and increase drug exposure. OBJECTIVE: To assess the potential pharmacokinetic interactions of edoxaban and 6 cardiovascular drugs used in the management of AF and known P-gp substrates/inhibitors. METHODS: Drug-drug interaction studies with edoxaban and CV drugs with known P-gp substrate/inhibitor potential were conducted in healthy subjects. In 4 crossover, 2-period, 2-treatment studies, subjects received edoxaban 60 mg alone and coadministered with quinidine 300 mg (n = 42), verapamil 240 mg (n = 34), atorvastatin 80 mg (n = 32), or dronedarone 400 mg (n = 34). Additionally, edoxaban 60 mg alone and coadministered with amiodarone 400 mg (n = 30) or digoxin 0.25 mg (n = 48) was evaluated in a single-sequence study and 2-cohort study, respectively. RESULTS: Edoxaban exposure measured as area under the curve increased for concomitant administration of edoxaban with quinidine (76.7 %), verapamil (52.7 %), amiodarone (39.8 %), and dronedarone (84.5 %), and exposure measured as 24-h concentrations for quinidine (11.8 %), verapamil (29.1 %), and dronedarone (157.6 %) also increased. Administration of edoxaban with amiodarone decreased the 24-h concentration for edoxaban by 25.7 %. Concomitant administration with digoxin or atorvastatin had minimal effects on edoxaban exposure. CONCLUSION: Coadministration of the P-gp inhibitors quinidine, verapamil, and dronedarone increased edoxaban exposure. Modest/minimal effects were observed for amiodarone, atorvastatin, and digoxin.