Dalia Arafat

Blood-Informative Transcripts Define Nine Common Axes of Peripheral Blood Gene Expression

We describe a novel approach to capturing the covariance structure of peripheral blood gene expression that relies on the identification of highly conserved Axes of variation. Starting with a comparison of microarray transcriptome profiles for a new dataset of 189 healthy adult participants in the Emory-Georgia Tech Center for Health Discovery and Well-Being (CHDWB) cohort, with a previously published study of 208 adult Moroccans, we identify nine Axes each with between 99 and 1,028 strongly co-regulated transcripts in common. Each axis is enriched for gene ontology categories related to sub-classes of blood and immune function, including T-cell and B-cell physiology and innate, adaptive, and anti-viral responses. Conservation of the Axes is demonstrated in each of five additional population-based gene expression profiling studies, one of which is robustly associated with Body Mass Index in the CHDWB as well as Finnish and Australian cohorts. Furthermore, ten tightly co-regulated genes can be used to define each Axis as “Blood Informative Transcripts” (BITs), generating scores that define an individual with respect to the represented immune activity and blood physiology. We show that environmental factors, including lifestyle differences in Morocco and infection leading to active or latent tuberculosis, significantly impact specific axes, but that there is also significant heritability for the Axis scores. In the context of personalized medicine, reanalysis of the longitudinal profile of one individual during and after infection with two respiratory viruses demonstrates that specific axes also characterize clinical incidents. This mode of analysis suggests the view that, rather than unique subsets of genes marking each class of disease, differential expression reflects movement along the major normal Axes in response to environmental and genetic stimuli.

Whole genome sequencing in support of wellness and health maintenance

BackgroundWhole genome sequencing is poised to revolutionize personalized medicine, providing the capacity to classify individuals into risk categories for a wide range of diseases. Here we begin to explore how whole genome sequencing (WGS) might be incorporated alongside traditional clinical evaluation as a part of preventive medicine. The present study illustrates novel approaches for integrating genotypic and clinical information for assessment of generalized health risks and to assist individuals in the promotion of wellness and maintenance of good health.MethodsWhole genome sequences and longitudinal clinical profiles are described for eight middle-aged Caucasian participants (four men and four women) from the Center for Health Discovery and Well Being (CHDWB) at Emory University in Atlanta. We report multivariate genotypic risk assessments derived from common variants reported by genome-wide association studies (GWAS), as well as clinical measures in the domains of immune, metabolic, cardiovascular, musculoskeletal, respiratory, and mental health.ResultsPolygenic risk is assessed for each participant for over 100 diseases and reported relative to baseline population prevalence. Two approaches for combining clinical and genetic profiles for the purposes of health assessment are then presented. First we propose conditioning individual disease risk assessments on observed clinical status for type 2 diabetes, coronary artery disease, hypertriglyceridemia and hypertension, and obesity. An approximate 2:1 ratio of concordance between genetic prediction and observed sub-clinical disease is observed. Subsequently, we show how more holistic combination of genetic, clinical and family history data can be achieved by visualizing risk in eight sub-classes of disease. Having identified where their profiles are broadly concordant or discordant, an individual can focus on individual clinical results or genotypes as they develop personalized health action plans in consultation with a health partner or coach.ConclusionThe CHDWB will facilitate longitudinal evaluation of wellness-focused medical care based on comprehensive self-knowledge of medical risks.

Using Blood Informative Transcripts in Geographical Genomics: Impact of Lifestyle on Gene Expression in Fijians

In previous geographical genomics studies of the impact of lifestyle on gene expression inferred from microarray analysis of peripheral blood samples, we described the complex influences of culture, ethnicity, and gender in Morocco, and of pregnancy in Brisbane. Here we describe the use of nanofluidic Fluidigm quantitative RT-PCR arrays targeted at a set of 96 transcripts that are broadly informative of the major axes of immune gene expression, to explore the population structure of transcription in Fiji. As in Morocco, major differences are seen between the peripheral blood transcriptomes of rural villagers and residents of the capital city, Suva. The effect is much greater in Indian villages than in Melanesian highlanders and appears to be similar with respect to the nature of at least two axes of variation. Gender differences are much smaller than ethnicity or lifestyle effects. Body mass index is shown to associate with one of the axes as it does in Atlanta and Brisbane, establishing a link between the epidemiological transition of human metabolic disease, and gene expression profiles.

Effect of normalization on statistical and biological interpretation of gene expression profiles

An under-appreciated aspect of the genetic analysis of gene expression is the impact of post-probe level normalization on biological inference. Here we contrast nine different methods for normalization of an Illumina bead-array gene expression profiling dataset consisting of peripheral blood samples from 189 individual participants in the Center for Health Discovery and Well Being study in Atlanta, quantifying differences in the inference of global variance components and covariance of gene expression, as well as the detection of variants that affect transcript abundance (eSNPs). The normalization strategies, all relative to raw log2 measures, include simple mean centering, two modes of transcript-level linear adjustment for technical factors, and for differential immune cell counts, variance normalization by interquartile range and by quantile, fitting the first 16 Principal Components, and supervised normalization using the SNM procedure with adjustment for cell counts. Robustness of genetic associations as a consequence of Pearson and Spearman rank correlation is also reported for each method, and it is shown that the normalization strategy has a far greater impact than correlation method. We describe similarities among methods, discuss the impact on biological interpretation, and make recommendations regarding appropriate strategies.

A Burden of Rare Variants Associated with Extremes of Gene Expression in Human Peripheral Blood

In order to evaluate whether rare regulatory variants in the vicinity of promoters are likely to impact gene expression, we conducted a novel burden test for enrichment of rare variants at the extremes of expression. After sequencing 2-kb promoter regions of 472 genes in 410 healthy adults, we performed a quadratic regression of rare variant count on bins of peripheral blood transcript abundance from microarrays, summing over ranks of all genes. After adjusting for common eQTLs and the major axes of gene expression covariance, a highly significant excess of variants with minor allele frequency less than 0.05 at both high and low extremes across individuals was observed. Further enrichment was seen in sites annotated as potentially regulatory by RegulomeDB, but a deficit of effects was associated with known metabolic disease genes. The main result replicates in an independent sample of 75 individuals with RNA-seq and whole-genome sequence information. Three of four predicted large-effect sites were validated by CRISPR/Cas9 knockdown in K562 cells, but simulations indicate that effect sizes need not be unusually large to produce the observed burden. Unusually divergent low-frequency promoter haplotypes were observed at 31 loci, at least 9 of which appear to be derived from Neandertal admixture, but these were not associated with divergent gene expression in blood. The overall burden test results are consistent with rare and private regulatory variants driving high or low transcription at specific loci, potentially contributing to disease.

Mature Cystic Fibrosis Airway Neutrophils Suppress T Cell Function: Evidence for a Role of Arginase 1 but Not Programmed Death-Ligand 1

Bacteria colonize cystic fibrosis (CF) airways, and although T cells with appropriate Ag specificity are present in draining lymph nodes, they are conspicuously absent from the lumen. To account for this absence, we hypothesized that polymorphonuclear neutrophils (PMNs), recruited massively into the CF airway lumen and actively exocytosing primary granules, also suppress T cell function therein. Programmed death–ligand 1 (PD-L1), which exerts T cell suppression at a late step, was expressed bimodally on CF airway PMNs, delineating PD-L1hi and PD-L1lo subsets, whereas healthy control (HC) airway PMNs were uniformly PD-L1hi. Blood PMNs incubated in CF airway fluid lost PD-L1 over time; in coculture, Ab blockade of PD-L1 failed to inhibit the suppression of T cell proliferation by CF airway PMNs. In contrast with PD-L1, arginase 1 (Arg1), which exerts T cell suppression at an early step, was uniformly high on CF and HC airway PMNs. However, arginase activity was high in CF airway fluid and minimal in HC airway fluid, consistent with the fact that Arg1 activation requires primary granule exocytosis, which occurs in CF, but not HC, airway PMNs. In addition, Arg1 expression on CF airway PMNs correlated negatively with lung function and positively with arginase activity in CF airway fluid. Finally, combined treatment with arginase inhibitor and arginine rescued the suppression of T cell proliferation by CF airway fluid. Thus, Arg1 and PD-L1 are dynamically modulated upon PMN migration into human airways, and, Arg1, but not PD-L1, contributes to early PMN-driven T cell suppression in CF, likely hampering resolution of infection and inflammation.

Mesenchymal Stromal Cells Derived From Crohn's Patients Deploy Indoleamine 2,3-dioxygenase-mediated Immune Suppression, Independent of Autophagy.

Autologous bone marrow-derived mesenchymal stromal cells (MSCs) for adoptive cell therapy of luminal Crohns disease (CD) are being tested in clinical trials. However, CD is associated with dysregulation of autophagy and its effect on MSCs immunobiology is unknown. Here, we demonstrate no quantitative difference in phenotype, in vitro growth kinetics and molecular signatures to IFNγ between MSCs derived from CD and healthy individuals. CD MSCs were indistinguishable from those derived from healthy controls at inhibiting T-cell proliferation through an indoleamine 2,3-dioxygenase (IDO)-dependent mechanism. Upon IFNγ prelicensing, both MSC populations inhibit T-cell effector functions. Neither a single-nucleotide polymorphism (SNP) rs7820268 in the IDO gene, nor a widely reported CD predisposing SNP ATG16L1rs2241880 modulated the suppressive function of MSCs carrying these haplotypes. IFNγ stimulation or coculture with activated T cells upregulated the expression of autophagy genes and/or vacuoles on MSCs. Pharmacological blockade of autophagy pathway did not reverse the immunosuppressive properties and IFNγ responsiveness of MSCs confirming the absence of a functional link between these two cell biochemical properties. We conclude that autophagy, but not IDO and IFNγ responsiveness, is dispensable for MSCs immunosuppressive properties. MSCs from CD subjects are functionally analogous to those of healthy individuals.

Comparative transcriptomics and metabolomics in a rhesus macaque drug administration study

We describe a multi-omic approach to understanding the effects that the anti-malarial drug pyrimethamine has on immune physiology in rhesus macaques (Macaca mulatta). Whole blood and bone marrow (BM) RNA-Seq and plasma metabolome profiles (each with over 15,000 features) have been generated for five naïve individuals at up to seven timepoints before, during and after three rounds of drug administration. Linear modeling and Bayesian network analyses are both considered, alongside investigations of the impact of statistical modeling strategies on biological inference. Individual macaques were found to be a major source of variance for both omic data types, and factoring individuals into subsequent modeling increases power to detect temporal effects. A major component of the whole blood transcriptome follows the BM with a time-delay, while other components of variation are unique to each compartment. We demonstrate that pyrimethamine administration does impact both compartments throughout the experiment, but very limited perturbation of transcript or metabolite abundance was observed following each round of drug exposure. New insights into the mode of action of the drug are presented in the context of pyrimethamines predicted effect on suppression of cell division and metabolism in the immune system.

Genetic risk prediction in a small cohort of healthy adults in Atlanta

Compared with single markers, polygenic scores that evaluate the joint effects of multiple trait-associated variants are more effective in explaining the variance of traits and risk of diseases. In total, 182 CHDWB (Emory-Georgia Tech Center for Health Discovery and Well Being study) adults were genotyped to investigate the common variant contributions to three traits (height, BMI, serum triglycerides) and three diseases (coronary artery disease (CAD), type 2 diabetes (T2D) and asthma). Association was contrasted between weighted and simple allelic sum polygenic scores with quantitative traits, and with the Framingham risk scores for CAD and T2D. Although the cohort size is two or three orders of magnitude smaller than typical discovery cohorts, we were able to detect significant associations and to explain up to 5% of the traits by the genetic risk scores, despite a strong influence of outliers. An unexpected finding was that CAD-associated single nucleotide polymorphisms (SNPs) explain a significant amount of the variation for total serum cholesterol. Forward step-wise sequential addition of SNPs into the regression model showed that the top-ranked SNPs explain a large proportion of variance, whereas inclusion of gender and ethnicity also affect the performance of polygenic scores.

Characterization of distinct classes of differential gene expression in osteoblast cultures from non-syndromic craniosynostosis bone.

Craniosynostosis, the premature fusion of one or more skull sutures, occurs in approximately 1 in 2500 infants, with the majority of cases non-syndromic and of unknown etiology. Two common reasons proposed for premature suture fusion are abnormal compression forces on the skull and rare genetic abnormalities. Our goal was to evaluate whether different sub-classes of disease can be identified based on total gene expression profiles. RNA-Seq data were obtained from 31 human osteoblast cultures derived from bone biopsy samples collected between 2009 and 2011, representing 23 craniosynostosis fusions and 8 normal cranial bones or long bones. No differentiation between regions of the skull was detected, but variance component analysis of gene expression patterns nevertheless supports transcriptome-based classification of craniosynostosis. Cluster analysis showed 4 distinct groups of samples; 1 predominantly normal and 3 craniosynostosis subtypes. Similar constellations of sub-types were also observed upon re-analysis of a similar dataset of 199 calvarial osteoblast cultures. Annotation of gene function of differentially expressed transcripts strongly implicates physiological differences with respect to cell cycle and cell death, stromal cell differentiation, extracellular matrix (ECM) components, and ribosomal activity. Based on these results, we propose non-syndromic craniosynostosis cases can be classified by differences in their gene expression patterns and that these may provide targets for future clinical intervention.