Gene-by-Environment Expression and Calculation of the Frailty Index

Background: Frailty can be described as a phenotype (e.g., sarcopenia, reduced grip strength, decreased VO2 max) or as a ratio of deficits, i.e., a Frailty Index (FI). FI predicts survival, death, cognitive impairment, falls, and hospitalizations. Frailty is influenced by both genes and environment. We calculated the FI as the sum of measured deficits divided by the total number of items assessed in a pedigree-based sample of 1,029 Mexican Americans participants in the San Antonio Family Heart Study. We performed a novel search for genotype-by-environment interactions (GXE) influencing FI. Such interactions lead to heritable differences between individuals in their responses to the environment. Methods: We investigated a panel of 34 measured environmental factors to look for GXE influencing frailty. We employed a powerful polygenic approach to genotype-by-environment modeling, allowing for both dichotomous and continuous environmental measures. We performed likelihood-based estimation of parameters and tests for the presence of GXE. Results: GXE interactions influencing frailty were observed for the following environments: obesity (P=7.9E-10), hypertriglyceridemia (P=2.74E-09), low HDL (P=2.15E-06), impaired glucose status (P=.002), hypertension (P=0.01), and diabetes (P=0.02), Additionally, GXE interactions were detected for a number of quantitative dietary components: carbohydrates (P=5.73E-07), fats (P=2.01E-06), fiber (P=2.76E-05), dietary cholesterol (P=0.01), and protein ( P=0.006). These results document substantial statistical evidence for the interactive effects of genes and environmental factors on frailty. Conclusion: Our results support the presence of substantive gene-by-environmental interactions influencing frailty. This finding documents the presence of heritable differences between individuals that lead to differential response to environmental challenges

Non-alcoholic fatty liver disease and hepatocellular carcinoma risk associated gene expression phenotypes in Hispanics

Background: Non-alcoholic fatty liver disease (NAFLD) is a state of metabolic dysregulation characterized by excessive lipid accumulation into the hepatocytes (hepatic steatosis). It is a major determinant of risk for hepatocellular carcinoma (HCC). Hispanics in south Texas exhibit one of the highest incidences of NAFLD and HCC in the United States. Methods: We used an induced pluripotent stem cell (iPSC) based hepatocyte (HEP) model to identify high lipid stress induced transcriptomic changes in HEPs to better understand hepatic steatosis associated HCC risk. Well-characterized, iPSC differentiated functional HEPs generated from six participants in our San Antonio Mexican American family study were challenged with high lipid conditions in in-vitro culture. The lipid challenged and vehicle treated HEPs were then analyzed for cellular lipid accumulation, fibrosis, and genome wide gene expression by mRNA-sequencing. Results: Quantitative measures of cellular neutral lipids and fibrosis marker (COL1A1) were significantly increased in lipid challenged HEPs. These measures also showed a high correlation (r2 ≥70%) with individual’s in-vivo liver fat. Genome wide differential gene expression analysis identified 78 genes that were significantly differentially expressed (DE) between lipid challenged and vehicle treated HEPs. Functional annotation analysis showed significant enrichment of DE genes in liver hyperplasia/hyperproliferation functions (27 genes; p-value 2.0x10-2 to 9.2x10-2), and included several genes (PDRG1, PLIN2, CFHR3, ANXA2P3, HBA1, HBA2, HBB) whose altered expression was shown to be associated with HCC risk. Conclusions: We have identified several genes associated with risk for HCC for which expression was significantly dysregulated by a high lipid stress challenge in HEPs

Utility of Lymphoblastoid Cell Lines for Induced Pluripotent Stem Cell Generation

A large number of EBV immortalized LCLs have been generated and maintained in genetic/epidemiological studies as a perpetual source of DNA and as a surrogate in vitro cell model. Recent successes in reprograming LCLs into iPSCs have paved the way for generating more relevant in vitro disease models using this existing bioresource. However, the overall reprogramming efficiency and success rate remain poor and very little is known about the mechanistic changes that take place at the transcriptome and cellular functional level during LCL-to-iPSC reprogramming. Here, we report a new optimized LCL-to-iPSC reprogramming protocol using episomal plasmids encoding pluripotency transcription factors and mouse p53DD (p53 carboxy-terminal dominant-negative fragment) and commercially available reprogramming media. We achieved a consistently high reprogramming efficiency and 100% success rate using this optimized protocol. Further, we investigated the transcriptional changes in mRNA and miRNA levels, using FC-abs ≥ 2.0 and FDR ≤ 0.05 cutoffs; 5,228 mRNAs and 77 miRNAs were differentially expressed during LCL-to-iPSC reprogramming. The functional enrichment analysis of the upregulated genes and activation of human pluripotency pathways in the reprogrammed iPSCs showed that the generated iPSCs possess transcriptional and functional profiles very similar to those of human ESCs

Disease Modeling and Disease Gene Discovery in Cardiomyopathies: A Molecular Study of Induced Pluripotent Stem Cell Generated Cardiomyocytes

The in vitro modeling of cardiac development and cardiomyopathies in human induced pluripotent stem cell (iPSC)-derived cardiomyocytes (CMs) provides opportunities to aid the discovery of genetic, molecular, and developmental changes that are causal to, or influence, cardiomyopathies and related diseases. To better understand the functional and disease modeling potential of iPSC-differentiated CMs and to provide a proof of principle for large, epidemiological-scale disease gene discovery approaches into cardiomyopathies, well-characterized CMs, generated from validated iPSCs of 12 individuals who belong to four sibships, and one of whom reported a major adverse cardiac event (MACE), were analyzed by genome-wide mRNA sequencing. The generated CMs expressed CM-specific genes and were highly concordant in their total expressed transcriptome across the 12 samples (correlation coefficient at 95% CI =0.92 ± 0.02). The functional annotation and enrichment analysis of the 2116 genes that were significantly upregulated in CMs suggest that generated CMs have a transcriptomic and functional profile of immature atrial-like CMs; however, the CMs-upregulated transcriptome also showed high overlap and significant enrichment in primary cardiomyocyte (p-value = 4.36 × 10−9), primary heart tissue (p-value = 1.37 × 10−41) and cardiomyopathy (p-value = 1.13 × 10−21) associated gene sets. Modeling the effect of MACE in the generated CMs-upregulated transcriptome identified gene expression phenotypes consistent with the predisposition of the MACE-affected sibship to arrhythmia, prothrombotic, and atherosclerosis risk

Highly efficient induced pluripotent stem cell reprogramming of cryopreserved lymphoblastoid cell lines

Tissue culture based in-vitro experimental modeling of human inherited disorders provides insight into the cellular and molecular mechanisms involved and the underlying genetic component influencing the disease phenotype. The breakthrough development of induced pluripotent stem cell (iPSC) technology represents a quantum leap in experimental modeling of human diseases, providing investigators with a self-renewing and thus unlimited source of pluripotent cells for targeted differentiation into functionally relevant disease specific tissue/cell types. The existing rich bio-resource of Epstein-Barr virus (EBV) immortalized lymphoblastoid cell line (LCL) repositories generated from a wide array of patients in genetic and epidemiological studies worldwide, many of them with extensive genotypic, genomic and phenotypic data already existing, provides a great opportunity to reprogram iPSCs from any of these LCL donors in the context of their own genetic identity for disease modeling and disease gene identification. However, due to the low reprogramming efficiency and poor success rate of LCL to iPSC reprogramming, these LCL resources remain severely underused for this purpose. Here, we detailed step-by-step instructions to perform our highly efficient LCL-to-iPSC reprogramming protocol using EBNA1/OriP episomal plasmids encoding pluripotency transcription factors (i.e., OCT3/4, SOX2, KLF4, L-MYC, and LIN28), mouse p53DD (p53 carboxy-terminal dominant-negative fragment) and commercially available reprogramming media. We achieved a consistently high reprogramming efficiency and 100% success rate (\u3e 200 reprogrammed iPSC lines) using this protocol

Role of miRNA-mRNA Interaction in Neural Stem Cell Differentiation of Induced Pluripotent Stem Cells

miRNA regulates the expression of protein coding genes and plays a regulatory role in human development and disease. The human iPSCs and their differentiated progenies provide a unique opportunity to identify these miRNA-mediated regulatory mechanisms. To identify miRNA–mRNA regulatory interactions in human nervous system development, well characterized NSCs were differentiated from six validated iPSC lines and analyzed for differentially expressed (DE) miRNome and transcriptome by RNA sequencing. Following the criteria, moderated t statistics, FDR-corrected p-value ≤ 0.05 and fold change—absolute (FC-abs) ≥2.0, 51 miRNAs and 4033 mRNAs were found to be significantly DE between iPSCs and NSCs. The miRNA target prediction analysis identified 513 interactions between 30 miRNA families (mapped to 51 DE miRNAs) and 456 DE mRNAs that were paradoxically oppositely expressed. These 513 interactions were highly enriched in nervous system development functions (154 mRNAs; FDR-adjusted p-value range: 8.06 × 10−15–1.44 × 10−4). Furthermore, we have shown that the upregulated miR-10a-5p, miR-30c-5p, miR23-3p, miR130a-3p and miR-17-5p miRNA families were predicted to down-regulate several genes associated with the differentiation of neurons, neurite outgrowth and synapse formation, suggesting their role in promoting the self-renewal of undifferentiated NSCs. This study also provides a comprehensive characterization of iPSC-generated NSCs as dorsal neuroepithelium, important for their potential use in in vitro modeling of human brain development and disease

microRNA and mRNA interactions in induced pluripotent stem cell reprogramming of lymphoblastoid cell lines

A large number of Epstein Barr virus (EBV) immortalized lymphoblastoid cell lines (LCLs) have been generated and maintained in genetic/epidemiological studies as a perpetual source of DNA and as a surrogate in vitro cell model. Recent successes in reprograming LCLs into induced pluripotent stem cells (iPSCs) has paved the way to generate more relevant in vitro disease models using this existing bioresource. However, the latent EBV infection in the LCLs make them a unique cell type by altering expression of many cellular genes and miRNAs. These EBV induced changes in the LCL miRNome and transcriptome are reversed upon reprogramming into iPSCs, which allows a unique opportunity to better understand the miRNA and mRNA interactions that are EBV induced in LCLs and the changes that takes place during iPSC reprogramming. To identify the potential miRNA-mRNA interactions and better understand their role in regulating the cellular transitions in LCLs and their reprogrammed iPSCs, we performed a parallel genome-wide miRNA and mRNA expression analysis in six LCLs and their reprogrammed iPSCs. A total of 85 miRNAs and 5,228 mRNAs were significantly differentially expressed (DE). The target prediction of the DE miRNAs using TargetScan-Human, TarBase and miRecords databases identified 1,842 mRNA targets that were DE between LCLs and their reprogrammed iPSCs. The functional annotation, upstream regulator and gene expression analysis of the predicted DE mRNA targets suggest the role of DE miRNAs in regulating EBV induced changes in LCLs and self-renewal, pluripotency and differentiation in iPSCs

Genome-wide linkage scan for loci influencing plasma triglyceride levels

We conducted a genome-wide linkage scan to detect loci that influence the levels of fasting triglycerides in plasma. Fasting triglyceride levels were available at 4 time points (visits), 2 pre- and 2 post-fenofibrate intervention. Multipoint identity-by-descent (MIBD) matrices were derived from genotypes using IBDLD. Variance-component linkage analyses were then conducted using SOLAR (Sequential Oligogenic Linkage Analysis Routines). We found evidence of linkage (logarithm of odds [LOD] ≥3) at 5 chromosomal regions with triglyceride levels in plasma. The highest LOD scores were observed for linkage to the estimated genetic value (additive genetic component) of the log-normalized triglyceride levels in plasma. Our results suggest that a chromosome 10 locus at 37 cM (LODpre = 3.01, LODpost = 3.72) influences fasting triglyceride levels in plasma regardless of the fenofibrate intervention, and that loci in chromosomes 1 at 170 cM and 4 at 24 cM ceases to affect the triglyceride levels when fenofibrate is present, while the regions in chromosomes 6 at 136 to 162 cM and 11 at 39 to 40 cM appear to influence triglyceride levels in response to fenofibrate

Fast and powerful heritability inference for family-based neuroimaging studies.

Heritability estimation has become an important tool for imaging genetics studies. The large number of voxel- and vertex-wise measurements in imaging genetics studies presents a challenge both in terms of computational intensity and the need to account for elevated false positive risk because of the multiple testing problem. There is a gap in existing tools, as standard neuroimaging software cannot estimate heritability, and yet standard quantitative genetics tools cannot provide essential neuroimaging inferences, like family-wise error corrected voxel-wise or cluster-wise P-values. Moreover, available heritability tools rely on P-values that can be inaccurate with usual parametric inference methods. In this work we develop fast estimation and inference procedures for voxel-wise heritability, drawing on recent methodological results that simplify heritability likelihood computations (Blangero et al., 2013). We review the family of score and Wald tests and propose novel inference methods based on explained sum of squares of an auxiliary linear model. To address problems with inaccuracies with the standard results used to find P-values, we propose four different permutation schemes to allow semi-parametric inference (parametric likelihood-based estimation, non-parametric sampling distribution). In total, we evaluate 5 different significance tests for heritability, with either asymptotic parametric or permutation-based P-value computations. We identify a number of tests that are both computationally efficient and powerful, making them ideal candidates for heritability studies in the massive data setting. We illustrate our method on fractional anisotropy measures in 859 subjects from the Genetics of Brain Structure study