Additive,Epistatic, and Environmental Effects Through the Lens of Expression Variability QTL in a Twin Cohort

The expression of a gene can vary across individuals in the general population, as well as between monozygotic twins. This variable expression is assumed to be due to the influence of both genetic and nongenetic factors. Yet little evidence supporting this assumption has been obtained from empirical data. In this study, we used expression data from a large twin cohort to investigate the influences of genetic and nongenetic factors on variable gene expression. We focused on a set of expression variability QTL (evQTL)—i.e., genetic loci associated with the variance, as opposed to the mean, of gene expression. We identified evQTL for 99, 56, and 79 genes in lymphoblastoid cell lines, skin, and fat, respectively. The differences in gene expression, measured by the relative mean difference (RMD), tended to be larger between pairs of dizygotic (DZ) twins than between pairs of monozygotic (MZ) twins, showing that genetic background influenced the expression variability. Furthermore, a more profound RMD was observed between pairs of MZ twins whose genotypes were associated with greater expression variability than the RMD found between pairs of MZ twins whose genotypes were associated with smaller expression variability. This suggests that nongenetic (e.g., environmental) factors contribute to the variable expression. Lastly, we demonstrated that the formation of evQTL is likely due to partial linkages between eQTL SNPs that are additively associated with the mean of gene expression; in most cases, no epistatic effect is involved. Our findings have implications for understanding divergent sources of gene expression variability.     

Multifactor dimensionality reduction-phenomics: a novel method to capture genetic heterogeneity with use of phenotypic variables

Complex human diseases do not have a clear inheritance pattern, and it is expected that risk involves multiple genes with modest effects acting independently or interacting. Major challenges for the identification of genetic effects are genetic heterogeneity and difficulty in analyzing high-order interactions. To address these challenges, we present MDR-Phenomics, a novel approach based on the multifactor dimensionality reduction (MDR) method, to detect genetic effects in pedigree data by integration of phenotypic covariates (PCs) that may reflect genetic heterogeneity. The P value of the test is calculated using a permutation test adjusted for multiple tests. To validate MDR-Phenomics, we compared it with two MDR-based methods: (1) traditional MDR pedigree disequilibrium test (PDT) without consideration of PCs (MDR-PDT) and (2) stratified phenotype (SP) analysis based on PCs, with use of MDR-PDT with a Bonferroni adjustment (SP-MDR). Using computer simulations, we examined the statistical power and type I error of the different approaches under several genetic models and sampling scenarios. We conclude that MDR-Phenomics is more powerful than MDR-PDT and SP-MDR when there is genetic heterogeneity, and the statistical power is affected by sample size and the number of PC levels. We further compared MDR-Phenomics with conditional logistic regression (CLR) for testing interactions across single or multiple loci with consideration of PC. The results show that CLR with PC has only slightly smaller power than does MDR-Phenomics for single-locus analysis but has considerably smaller power for multiple loci. Finally, by applying MDR-Phenomics to autism, a complex disease in which multiple genes are believed to confer risk, we attempted to identify multiple gene effects in two candidate genes of interest—the serotonin transporter gene (SLC6A4) and the integrin beta 3 gene (ITGB3) on chromosome 17. Analyzing four markers in SLC6A4 and four markers in ITGB3 in 117 white family triads with autism and using sex of the proband as a PC, we found significant interaction between two markers—rs1042173 in SLC6A4 and rs3809865 in ITGB3.     

Principal component analysis for clustering gene expression data

MOTIVATION: There is a great need to develop analytical methodology to analyze and to exploit the information contained in gene expression data. Because of the large number of genes and the complexity of biological networks, clustering is a useful exploratory technique for analysis of gene expression data. Other classical techniques, such as principal component analysis (PCA), have also been applied to analyze gene expression data. Using different data analysis techniques and different clustering algorithms to analyze the same data set can lead to very different conclusions. Our goal is to study the effectiveness of principal components (PCs) in capturing cluster structure. Specifically, using both real and synthetic gene expression data sets, we compared the quality of clusters obtained from the original data to the quality of clusters obtained after projecting onto subsets of the principal component axes. RESULTS: Our empirical study showed that clustering with the PCs instead of the original variables does not necessarily improve, and often degrades, cluster quality. In particular, the first few PCs (which contain most of the variation in the data) do not necessarily capture most of the cluster structure. We also showed that clustering with PCs has different impact on different algorithms and different similarity metrics. Overall, we would not recommend PCA before clustering except in special circumstances.     

Genetic and physiological bases for phenological responses to current and predicted climates

We are now reaching the stage at which specific genetic factors with known physiological effects can be tied directly and quantitatively to variation in phenology. With such a mechanistic understanding, scientists can better predict phenological responses to novel seasonal climates. Using the widespread model species Arabidopsis thaliana, we explore how variation in different genetic pathways can be linked to phenology and life-history variation across geographical regions and seasons. We show that the expression of phenological traits including flowering depends critically on the growth season, and we outline an integrated life-history approach to phenology in which the timing of later life-history events can be contingent on the environmental cues regulating earlier life stages. As flowering time in many plants is determined by the integration of multiple environmentally sensitive gene pathways, the novel combinations of important seasonal cues in projected future climates will alter how phenology responds to variation in the flowering time gene network with important consequences for plant life history. We discuss how phenology models in other systems—both natural and agricultural—could employ a similar framework to explore the potential contribution of genetic variation to the physiological integration of cues determining phenology.     

The CSF-1 receptor fashions the intestinal stem cell niche

Gastrointestinal (GI) homeostasis requires the action of multiple pathways. There is some controversy regarding whether small intestine (SI) Paneth cells (PCs) play a central role in orchestrating crypt architecture and their relationship with Lgr5 + ve stem cells. Nevertheless, we previously showed that germline CSF-1 receptor (Csf1r) knock out (KO) or Csf1 mutation is associated with an absence of mature PC, reduced crypt proliferation and lowered stem cell gene, Lgr5 expression. Here we show the additional loss of CD24, Bmi1 and Olfm4 expression in the KO crypts and a high resolution 3D localization of CSF-1R mainly to PC. The induction of GI-specific Csf1r deletion in young adult mice also led to PC loss over a period of weeks, in accord with the anticipated long life span of PC, changed distribution of proliferating cells and this was with a commensurate loss of Lgr5 and other stem cell marker gene expression. By culturing SI organoids, we further show that the Csf1r^?/? defect in PC production is intrinsic to epithelial cells as well as definitively affecting stem cell activity. These results show that CSF-1R directly supports PC maturation and that in turn PCs fashion the intestinal stem cell niche.     

Implications of Proprotein Convertases in Ovarian Cancer Cell Proliferation and Tumor Progression: Insights for PACE4 as a Therapeutic Target

Proprotein convertases are a family of kexin-like serine proteases that process proteins at single and multiple basic residues. Among the predicted and identified PC substrates, an increasing number of proteins having functions in cancer progression indicate that PCs may be potential targets for antineoplastic drugs. In support of this notion, we identified PACE4 as a vital PC involved in prostate cancer proliferation and progression, contrasting with the other co-expressed PCs. The aim of the present study was to test the importance of PCs in ovarian cancer cell proliferation and tumor progression. Based on tissue-expression profiles, furin, PACE4, PC5/6 and PC7 all displayed increased expression in primary tumor, ascites cells and metastases. These PCs were also expressed in variable levels in three model ovarian cell lines tested, namely SKOV3, CAOV3 and OVCAR3 cells. Since SKOV3 cells closely represented the PC expression profile of ovarian cancer cells, we chose them to test the effects of PC silencing using stable gene-silencing shRNA strategy to generate knockdown SKOV3 cells for each expressed PC. In vitro and in vivo assays confirmed the role of PACE4 in the sustainment of SKOV3 cell proliferation, which was not observed with the other three PCs. We also tested PACE4 peptide inhibitors on all three cell lines and observed consequent reduced cell proliferation which was correlated with PACE4 expression. Overall, these data support a role of PACE4 in promoting cell proliferation in ovarian cancer and provides further evidence for PACE4 as a potential therapeutic target.     

Transcriptome Profiling of Human Ulcerative Colitis Mucosa Reveals Altered Expression of Pathways Enriched in Genetic Susceptibility Loci

Human colonic mucosa altered by inflammation due to ulcerative colitis (UC) displays a drastically altered pattern of gene expression compared with healthy tissue. We aimed to understand the underlying molecular pathways influencing these differences by analyzing three publically-available, independently-generated microarray datasets of gene expression from endoscopic biopsies of the colon. Gene set enrichment analysis (GSEA) revealed that all three datasets share 87 gene sets upregulated in UC lesions and 8 gene sets downregulated (false discovery rate <0.05). The upregulated pathways were dominated by gene sets involved in immune function and signaling, as well as the control of mitosis. We applied pathway analysis to genotype data derived from genome-wide association studies (GWAS) of UC, consisting of 5,584 cases and 11,587 controls assembled from eight European-ancestry cohorts. The upregulated pathways derived from the gene expression data showed a highly significant overlap with pathways derived from the genotype data (33 of 56 gene sets, hypergeometric P = 1.49×10^–19). This study supports the hypothesis that heritable variation in gene expression as measured by GWAS signals can influence key pathways in the development of disease, and that comparison of genetic susceptibility loci with gene expression signatures can differentiate key drivers of inflammation from secondary effects on gene expression of the inflammatory process.     

Genome-wide Insights into the Patterns and Determinants of Fine-Scale Population Structure in Humans

Studying genomic patterns of human population structure provides important insights into human evolutionary history and the relationship among populations, and it has significant practical implications for disease-gene mapping. Here we describe a principal component (PC)-based approach to studying intracontinental population structure in humans, identify the underlying markers mediating the observed patterns of fine-scale population structure, and infer the predominating evolutionary forces shaping local population structure. We applied this methodology to a data set of 650K SNPs genotyped in 944 unrelated individuals from 52 populations and demonstrate that, although typical PC analyses focus on the top axes of variation, substantial information about population structure is contained in lower-ranked PCs. We identified 18 significant PCs, some of which distinguish individual populations. In addition to visually representing sample clusters in PC biplots, we estimated the set of all SNPs significantly correlated with each of the most informative axes of variation. These polymorphisms, unlike ancestry-informative markers (AIMs), constitute a much larger set of loci that drive genomic signatures of population structure. The genome-wide distribution of these significantly correlated markers can largely be accounted for by the stochastic effects of genetic drift, although significant clustering does occur in genomic regions that have been previously implicated as targets of recent adaptive evolution.     

Genetic Control of Individual Differences in Gene-Specific Methylation in Human Brain

We have observed extensive interindividual differences in DNA methylation of 8590 CpG sites of 6229 genes in 153 human adult cerebellum samples, enriched in CpG island “shores” and at further distances from CpG islands. To search for genetic factors that regulate this variation, we performed a genome-wide association study (GWAS) mapping of methylation quantitative trait loci (mQTLs) for the 8590 testable CpG sites. cis association refers to correlation of methylation with SNPs within 1 Mb of a CpG site. 736 CpG sites showed phenotype-wide significant cis association with 2878 SNPs (after permutation correction for all tested markers and methylation phenotypes). In trans analysis of methylation, which tests for distant regulation effects, associations of 12 CpG sites and 38 SNPs remained significant after phenotype-wide correction. To examine the functional effects of mQTLs, we analyzed 85 genes that were with genetically regulated methylation we observed and for which we had quality gene expression data. Ten genes showed SNP-methylation-expression three-way associations—the same SNP simultaneously showed significant association with both DNA methylation and gene expression, while DNA methylation was significantly correlated with gene expression. Thus, we demonstrated that DNA methylation is frequently a heritable continuous quantitatively variable trait in human brain. Unlike allele-specific methylation, genetic polymorphisms mark both cis- and trans-regulatory genetic sites at measurable distances from their CpG sites. Some of the genetically regulated DNA methylation is directly connected with genetically regulated gene expression variation.     

European American stratification in ovarian cancer case control data: the utility of genome-wide data for inferring ancestry

We investigated the ability of several principal components analysis (PCA)-based strategies to detect and control for population stratification using data from a multi-center study of epithelial ovarian cancer among women of European-American ethnicity. These include a correction based on an ancestry informative markers (AIMs) panel designed to capture European ancestral variation and corrections utilizing un-thinned genome-wide SNP data; case-control samples were drawn from four geographically distinct North-American sites. The AIMs-only and genome-wide first principal components (PC1) both corresponded to the previously described North or Northwest-Southeast axis of European variation. We found that the genome-wide PCA captured this primary dimension of variation more precisely and identified additional axes of genome-wide variation of relevance to epithelial ovarian cancer. Associations evident between the genome-wide PCs and study site corroborate North American immigration history and suggest that undiscovered dimensions of variation lie within Northern Europe. The structure captured by the genome-wide PCA was also found within control individuals and did not reflect the case-control variation present in the data. The genome-wide PCA highlighted three regions of local LD, corresponding to the lactase (LCT) gene on chromosome 2, the human leukocyte antigen system (HLA) on chromosome 6 and to a common inversion polymorphism on chromosome 8. These features did not compromise the efficacy of PCs from this analysis for ancestry control. This study concludes that although AIMs panels are a cost-effective way of capturing population structure, genome-wide data should preferably be used when available.     

Neuroinflammation-Induced Interactions between Protease-Activated Receptor 1 and Proprotein Convertases in HIV-Associated Neurocognitive Disorder

The proprotein convertases (PCs) furin, PC5, PACE4, and PC7 cleave secretory proteins after basic residues, including the HIV envelope glycoprotein (gp160) and Vpr. We evaluated the abundance of PC mRNAs in postmortem brains of individuals exhibiting HIV-associated neurocognitive disorder (HAND), likely driven by neuroinflammation and neurotoxic HIV proteins (e.g., envelope and Vpr). Concomitant with increased inflammation-related gene expression (interleukin-1β [IL-1β]), the mRNA levels of the above PCs are significantly increased, together with those of the proteinase-activated receptor 1 (PAR1), an inflammation-associated receptor that is cleaved by thrombin at ProArg₄₁↓ (where the down arrow indicates the cleavage location), and potentially by PCs at Arg₄₁XXXXArg₄₆↓. The latter motif in PAR1, but not its R46A mutant, drives its interactions with PCs. Indeed, PAR1 upregulation leads to the inhibition of membrane-bound furin, PC5B, and PC7 and inhibits gp160 processing and HIV infectivity. Additionally, a proximity ligation assay revealed that furin and PC7 interact with PAR1. Reciprocally, increased furin expression reduces the plasma membrane abundance of PAR1 by trapping it in the trans-Golgi network. Furthermore, soluble PC5A/PACE4 can target/disarm cell surface PAR1 through cleavage at Arg₄₆↓. PACE4/PC5A decreased calcium mobilization induced by thrombin stimulation. Our data reveal a new PC-PAR1-interaction pathway, which offsets the effects of HIV-induced neuroinflammation, viral infection, and potentially the development of HAND.     

A Comprehensive Genetic Approach for Improving Prediction of Skin Cancer Risk in Humans

Prediction of genetic risk for disease is needed for preventive and personalized medicine. Genome-wide association studies have found unprecedented numbers of variants associated with complex human traits and diseases. However, these variants explain only a small proportion of genetic risk. Mounting evidence suggests that many traits, relevant to public health, are affected by large numbers of small-effect genes and that prediction of genetic risk to those traits and diseases could be improved by incorporating large numbers of markers into whole-genome prediction (WGP) models. We developed a WGP model incorporating thousands of markers for prediction of skin cancer risk in humans. We also considered other ways of incorporating genetic information into prediction models, such as family history or ancestry (using principal components, PCs, of informative markers). Prediction accuracy was evaluated using the area under the receiver operating characteristic curve (AUC) estimated in a cross-validation. Incorporation of genetic information (i.e., familial relationships, PCs, or WGP) yielded a significant increase in prediction accuracy: from an AUC of 0.53 for a baseline model that accounted for nongenetic covariates to AUCs of 0.58 (pedigree), 0.62 (PCs), and 0.64 (WGP). In summary, prediction of skin cancer risk could be improved by considering genetic information and using a large number of single-nucleotide polymorphisms (SNPs) in a WGP model, which allows for the detection of patterns of genetic risk that are above and beyond those that can be captured using family history. We discuss avenues for improving prediction accuracy and speculate on the possible use of WGP to prospectively identify individuals at high risk.     

Unifying measures of gene function and evolution

Recent genome analyses revealed intriguing correlations between variables characterizing the functioning of a gene, such as expression level (EL), connectivity of genetic and protein-protein interaction networks, and knockout effect, and variables describing gene evolution, such as sequence evolution rate (ER) and propensity for gene loss. Typically, variables within each of these classes are positively correlated, e.g. products of highly expressed genes also have a propensity to be involved in many protein-protein interactions, whereas variables between classes are negatively correlated, e.g. highly expressed genes, on average, evolve slower than weakly expressed genes. Here, we describe principal component (PC) analysis of seven genome-related variables and propose biological interpretations for the first three PCs. The first PC reflects a gene's 'importance', or the 'status' of a gene in the genomic community, with positive contributions from knockout lethality, EL, number of protein-protein interaction partners and the number of paralogues, and negative contributions from sequence ER and gene loss propensity. The next two PCs define a plane that seems to reflect the functional and evolutionary plasticity of a gene. Specifically, PC2 can be interpreted as a gene's 'adaptability' whereby genes with high adaptability readily duplicate, have many genetic interaction partners and tend to be non-essential. PC3 also might reflect the role of a gene in organismal adaptation albeit with a negative rather than a positive contribution of genetic interactions; we provisionally designate this PC 'reactivity'. The interpretation of PC2 and PC3 as measures of a gene's plasticity is compatible with the observation that genes with high values of these PCs tend to be expressed in a condition- or tissue-specific manner. Functional classes of genes substantially vary in status, adaptability and reactivity, with the highest status characteristic of the translation system and cytoskeletal proteins, highest adaptability seen in cellular processes and signalling genes, and top reactivity characteristic of metabolic enzymes.     

The roles and functions of Paneth cells in Crohn’s disease: A critical review

Paneth cells (PCs) are located at the base of small intestinal crypts and secrete the α‐defensins, human α‐defensin 5 (HD‐5) and human α‐defensin 6 (HD‐6) in response to bacterial, cholinergic and other stimuli. The α‐defensins are broad‐spectrum microbicides that play critical roles in controlling gut microbiota and maintaining intestinal homeostasis. Inflammatory bowel disease, including ulcerative colitis and Crohn's disease (CD), is a complicated autoimmune disorder. The pathogenesis of CD involves genetic factors, environmental factors and microflora. Surprisingly, with regard to genetic factors, many susceptible genes and pathogenic pathways of CD, including nucleotide‐binding oligomerization domain 2 (NOD2), autophagy‐related 16‐like 1 (ATG16L1), immunity‐related guanosine triphosphatase family M (IRGM), wingless‐related integration site (Wnt), leucine‐rich repeat kinase 2 (LRRK2), histone deacetylases (HDACs), caspase‐8 (Casp8) and X‐box‐binding protein‐1 (XBP1), are relevant to PCs. As the underlying mechanisms are being unravelled, PCs are identified as the central element of CD pathogenesis, integrating factors among microbiota, intestinal epithelial barrier dysfunction and the immune system. In the present review, we demonstrate how these genes and pathways regulate CD pathogenesis via their action on PCs and what treatment modalities can be applied to deal with these PC‐mediated pathogenic processes.     

Optogenetic Modulation and Multi-Electrode Analysis of Cerebellar Networks In Vivo

The firing patterns of cerebellar Purkinje cells (PCs), as the sole output of the cerebellar cortex, determine and tune motor behavior. PC firing is modulated by various inputs from different brain regions and by cell-types including granule cells (GCs), climbing fibers and inhibitory interneurons. To understand how signal integration in PCs occurs and how subtle changes in the modulation of PC firing lead to adjustment of motor behaviors, it is important to precisely record PC firing in vivo and to control modulatory pathways in a spatio-temporal manner. Combining optogenetic and multi-electrode approaches, we established a new method to integrate light-guides into a multi-electrode system. With this method we are able to variably position the light-guide in defined regions relative to the recording electrode with micrometer precision. We show that PC firing can be precisely monitored and modulated by light-activation of channelrhodopsin-2 (ChR2) expressed in PCs, GCs and interneurons. Thus, this method is ideally suited to investigate the spatio/temporal modulation of PCs in anesthetized and in behaving mice.     

Gene expression analysis in post-embryonic pericardial cells of Drosophila

Increasing evidence suggests conservation of cardiovascular molecules between vertebrates and invertebrates. Vertebrate Rudhira, an evolutionary conserved WD40 protein is expressed during primitive erythropoiesis, neoangiogenesis and tumors. We report here the expression profile of the Drosophila ortholog of Rudhira (DRudh) in the fly life cycle. DRudh is expressed specifically in all post-embryonic pericardial cells (PCs) and garland cells (GCs). This is the first report of a cytoplasmic marker highly specific to post-embryonic PCs. Embryonic PCs belong to three distinct genetic classes based on Odd-skipped (Odd), Even-skipped (Eve) and Tinman (Tin) expression. To identify which among these three classes of PCs expresses DRudh in post-embryonic stages, we analyzed expression of embryonic PC markers in the post-embryonic stages. Unlike in the embryo all larval PCs show an identical gene expression profile. While Odd and Eve expression is mutually exclusive in the embryonic PCs, these two markers are co-expressed in larval PCs but show a distinct subcellular localization. Tin is not expressed in any post-embryonic PC. Additionally larval PCs also express the GATA factor, Serpent (Srp) and the extracellular matrix protein, Pericardin (Prc). While PC number is known to decrease post-embryogenesis, which of the Odd or Eve lineage embryonic PCs persists is not known. Co-expression of the two distinct lineage markers only in post-embryonic stages indicates a complex temporal regulation of gene expression in PCs.     

Morphometrics within dog breeds are highly reproducible and dispute Rensch’s rule

Using 27 body measurements, we have identified 13 breed-defining metrics for 109 of 159 domestic dog breeds, most of which are recognized by the American Kennel Club (AKC). The data set included 1,155 dogs at least 1 year old (average 5.4 years), and for 53 breed populations, complete measurement data were collected from at least three males and three females. We demonstrate, first, that AKC breed standards are rigorously adhered to for most domestic breeds with little variation observed within breeds. Second, Rensch’s rule, which describes a scaling among taxa such that sexual dimorphism is greater among larger species if males are the larger sex, with less pronounced differences in male versus female body size in smaller species, is not maintained in domestic dog breeds because the proportional size difference between males and females of small and large breeds is essentially the same. Finally, principal components (PCs) analysis describes both the overall body size (PC1) and the shape (length versus width) of the skeleton (PC2). That the integrity of the data set is sufficiently rich to discern PCs has strong implications for mapping studies, suggesting that individual measurements may not be needed for genetic studies of morphologic traits, particularly in the case of breed-defining traits that are typically under strong selection. Rather, phenotypes derived from data sets such as these, collected at a fraction of the effort and cost, may be used to direct whole-genome association studies aimed at understanding the genetic basis of fixed morphologic phenotypes defining distinct dog breeds.

     

A Method for Multiplex Gene Synthesis Employing Error Correction Based on Expression

Our ability to engineer organisms with new biosynthetic pathways and genetic circuits is limited by the availability of protein characterization data and the cost of synthetic DNA. With new tools for reading and writing DNA, there are opportunities for scalable assays that more efficiently and cost effectively mine for biochemical protein characteristics. To that end, we have developed the Multiplex Library Synthesis and Expression Correction (MuLSEC) method for rapid assembly, error correction, and expression characterization of many genes as a pooled library. This methodology enables gene synthesis from microarray-synthesized oligonucleotide pools with a one-pot technique, eliminating the need for robotic liquid handling. Post assembly, the gene library is subjected to an ampicillin based quality control selection, which serves as both an error correction step and a selection for proteins that are properly expressed and folded in E. coli. Next generation sequencing of post selection DNA enables quantitative analysis of gene expression characteristics. We demonstrate the feasibility of this approach by building and testing over 90 genes for empirical evidence of soluble expression. This technique reduces the problem of part characterization to multiplex oligonucleotide synthesis and deep sequencing, two technologies under extensive development with projected cost reduction.