Nicholas E. Hardison

Characterization of Conserved and Nonconserved Imprinted Genes in Swine

To increase our understanding of imprinted genes in swine, we carried out a comprehensive analysis of this gene family using two complementary approaches: expression and phenotypic profiling of parthenogenetic fetuses, and analysis of imprinting by pyrosequencing. The parthenote placenta and fetus were smaller than those of controls but had no obvious morphological differences at Day 28 of gestation. By Day 30, however, the parthenote placentas had decreased chorioallantoic folding, decreased chorionic ruggae, and reduction of fetal-maternal interface surface in comparison with stage-matched control fetuses. Using Affymetrix Porcine GeneChip microarrays and/or semiquantitative PCR, brain, fibroblast, liver, and placenta of Day 30 fetuses were profiled, and 25 imprinted genes were identified as differentially expressed in at least one of the four tissue types: AMPD3, CDKN1C, COPG2, DHCR7, DIRAS3, IGF2 (isoform specific), IGF2AS, IGF2R, MEG3, MEST, NAP1L5, NDN, NNAT, OSBPL1A, PEG3, APEG3, PEG10, PLAGL1, PON2, PPP1R9A, SGCE, SLC38A4, SNORD107, SNRPN, and TFPI2. For DIRAS3, PLAGL1, SGCE, and SLC38A4, tissue-specific differences were detected. In addition, we examined the imprinting status of candidate genes by quantitative allelic pyrosequencing. Samples were collected from Day 30 pregnancies generated from reciprocal crosses of Meishan and White Composite breeds, and single-nucleotide polymorphisms were identified in candidate genes. Imprinting was confirmed for DIRAS3, DLK1, H19, IGF2AS, NNAT, MEST, PEG10, PHLDA2, PLAGL1, SGCE, and SNORD107. We also found no evidence of imprinting in ASB4, ASCL2, CD81, COMMD1, DCN, DLX5, and H13. Combined, these results represent the most comprehensive survey of imprinted genes in swine to date.

Transcriptional profiling of human placentas from pregnancies complicated by preeclampsia reveals disregulation of sialic acid acetylesterase and immune signalling pathways.

The placenta plays an important role as a regulator of fetal nutrition and growth throughout development and placental factors contribute to gestational abnormalities such as preeclampsia. This study describes the genome-wide gene expression profiles of a large (n = 60) set of human placentas in order to uncover gene expression patterns associated with preeclampsia. In addition to confirming changes in expression of soluble factors associated with preeclampsia such as sFLT1 (soluble fms-like tyrosine kinase-1), sENG (soluble endoglin), and INHA (inhibin alpha), we also find changes in immune-associated signaling pathways, offering a potential upstream explanation for the shallow trophoblast invasion and inadequate uterine remodeling typically observed in pathogenesis of preeclampsia. Notably, we also find evidence of preeclampsia-associated placental upregulation of sialic acid acetylesterase (SIAE), a gene functionally associated with autoimmune diseases.

Pharmacogenomic characterization of US FDA-approved cytotoxic drugs

AIMS Individualization of cancer chemotherapy based on the patients genetic makeup holds promise for reducing side effects and improving efficacy. However, the relative contribution of genetics to drug response is unknown. MATERIALS & METHODS In this study, we investigated the cytotoxic effect of 29 commonly prescribed chemotherapeutic agents from diverse drug classes on 125 lymphoblastoid cell lines derived from 14 extended families. RESULTS The results of this systematic study highlight the variable role that genetics plays in response to cytotoxic drugs, ranging from a heritability of <0.15 for gemcitabine to >0.60 for epirubicin. CONCLUSION Putative quantitative trait loci for cytotoxic response were identified, as well as drug class-specific signatures, which could indicate possible shared genetic mechanisms. In addition to the identification of putative quantitative trait locis, the results of this study inform the prioritization of chemotherapeutic drugs with a sizable genetic response component for future investigation.

Differentially expressed microRNAs and affected biological pathways revealed by modulated modularity clustering (MMC) analysis of human preeclamptic and IUGR placentas

INTRODUCTION This study focuses on the implementation of modulated modularity clustering (MMC) a new cluster algorithm for the identification of molecular signatures of preeclampsia and intrauterine growth restriction (IUGR), and the identification of affected microRNAs METHODS Eighty-six human placentas from normal (40), growth-restricted (27), and preeclamptic (19) term pregnancies were profiled using Illumina Human-6 Beadarrays. MMC was utilized to generate modules based on similarities in placental transcriptome. Gene Set Enrichment Analysis (GSEA) was used to predict affected microRNAs. Expression levels of these candidate microRNAs were investigated in seventy-one human term placentas as follows: control (29); IUGR (26); and preeclampsia (16). RESULTS MMC identified two modules, one representing IUGR placentas and one representing preeclamptic placentas. 326 differentially expressed genes in the module representing IUGR and 889 differentially expressed genes in a module representing preeclampsia were identified. Functional analysis of molecular signatures associated with IUGR identified P13K/AKT, mTOR, p70S6K, apoptosis and IGF-1 signaling as being affected. Analysis of variance of GSEA-predicted microRNAs indicated that miR-194 was significantly down-regulated both in preeclampsia (p = 0.0001) and IUGR (p = 0.0304), and miR-149 was significantly down-regulated in preeclampsia (p = 0.0168). DISCUSSION Implementation of MMC, allowed identification of genes disregulated in IUGR and preeclampsia. The reliability of MMC was validated by comparing to previous linear modeling analysis of preeclamptic placentas. CONCLUSION MMC allowed the elucidation of a molecular signature associated with preeclampsia and a subset of IUGR samples. This allowed the identification of genes, pathways, and microRNAs affected in these diseases.

Differences in X-Chromosome Transcriptional Activity and Cholesterol Metabolism between Placentae from Swine Breeds from Asian and Western Origins

To gain insight into differences in placental physiology between two swine breeds noted for their dissimilar reproductive performance, that is, the Chinese Meishan and white composite (WC), we examined gene expression profiles of placental tissues collected at 25, 45, 65, 85, and 105 days of gestation by microarrays. Using a linear mixed model, a total of 1,595 differentially expressed genes were identified between the two pig breeds using a false-discovery rate q-value ≤0.05. Among these genes, we identified breed-specific isoforms of XIST, a long non-coding RNA responsible X-chromosome dosage compensation in females. Additionally, we explored the interaction of placental gene expression and chromosomal location by DIGMAP and identified three Sus scrofa X chromosomal bands (Xq13, Xq21, Xp11) that represent transcriptionally active clusters that differ between Meishan and WC during placental development. Also, pathway analysis identified fundamental breed differences in placental cholesterol trafficking and its synthesis. Direct measurement of cholesterol confirmed that the cholesterol content was significantly higher in the Meishan versus WC placentae. Taken together, this work identifies key metabolic pathways that differ in the placentae of two swine breeds noted for differences in reproductive prolificacy.

Identification of SNPs and INDELS in swine transcribed sequences using short oligonucleotide microarrays

BackgroundGenome-wide detection of single feature polymorphisms (SFP) in swine using transcriptome profiling of day 25 placental RNA by contrasting probe intensities from either Meishan or an occidental composite breed with Affymetrix porcine microarrays is presented. A linear mixed model analysis was used to identify significant breed-by-probe interactions.ResultsGene specific linear mixed models were fit to each of the log2 transformed probe intensities on these arrays, using fixed effects for breed, probe, breed-by-probe interaction, and a random effect for array. After surveying the day 25 placental transcriptome, 857 probes with a q-value ≤ 0.05 and |fold change| ≥ 2 for the breed-by-probe interaction were identified as candidates containing SFP. To address the quality of the bioinformatics approach, universal pyrosequencing assays were designed from Affymetrix exemplar sequences to independently assess polymorphisms within a subset of probes for validation. Additionally probes were randomly selected for sequencing to determine an unbiased confirmation rate. In most cases, the 25-mer probe sequence printed on the microarray diverged from Meishan, not occidental crosses. This analysis was used to define a set of highly reliable predicted SFPs according to their probability scores.ConclusionBy applying a SFP detection method to two mammalian breeds for the first time, we detected transition and transversion single nucleotide polymorphisms, as well as insertions/deletions which can be used to rapidly develop markers for genetic mapping and association analysis in species where high density genotyping platforms are otherwise unavailable.SNPs and INDELS discovered by this approach have been publicly deposited in NCBIs SNP repository dbSNP. This method is an attractive bioinformatics tool for uncovering breed-by-probe interactions, for rapidly identifying expressed SNPs, for investigating potential functional correlations between gene expression and breed polymorphisms, and is robust enough to be used on any Affymetrix gene expression platform.

The power of quantitative grammatical evolution neural networks to detect gene-gene interactions

Applying grammatical evolution to evolve neural networks (GENN) has been increasing used in genetic epidemiology to detect gene-gene or gene-environment interactions, also known as epistasis, in high dimensional data. GENN approaches have previously been shown to be highly successful in a range of simulated and real case-control studies, and has recently been applied to quantitative traits. In the current study, we evaluate the potential of an application of GENN to quantitative traits (QTGENN) to a range of simulated genetic models. We demonstrate the power of the approach, and compare this power to more traditional linear regression analysis approaches. We find that the QTGENN approach has relatively high power to detect both single-locus models as well as several completely epistatic two-locus models, and favorably compares to the regression methods.

Optimization of grammatical evolution decision trees

The detection of gene-gene and gene-environment interactions in genetic association studies presents a difficult computational and statistical challenge, especially as advances in genotyping technology have rapidly expanded the number of potential genetic predictors in such studies. The scale of these studies makes exhaustive search approaches infeasible, inspiring the application of evolutionary computation algorithms to perform variable selection and build classification models. Recently, an application of grammatical evolution to evolve decision trees (GEDT) has been introduced for detecting interaction models. Initial results were promising, but relied on arbitrary parameter choices for the evolutionary process. In the current study, we present the results of a parameter sweep evaluating the power of GEDT and show that improved parameter choices improves the performance of the method. The results of these experiments are important for the continued optimization, evaluation, and comparison of this and related methods, and for proper application in real data.

A balanced accuracy fitness function leads to robust analysis using grammatical evolution neural networks in the case of class imbalance

Grammatical Evolution Neural Networks (GENN) is a computational method designed to detect gene-gene interactions in genetic epidemiology, but has so far only been evaluated in situations with balanced numbers of cases and controls. Real data, however, rarely has such perfectly balanced classes. In the current study, we test the power of GENN to detect interactions in data with a range of class imbalance using two fitness functions (classification error and balanced error), as well as data re-sampling. We show that when using classification error, class imbalance greatly decreases the power of GENN. Re-sampling methods demonstrated improved power, but using balanced accuracy resulted in the highest power. Based on the results of this study, balanced error has replaced classification error in the GENN algorithm