Mixed effects modeling of proliferation rates in cell-based models: consequence for pharmacogenomics and cancer

Physiological hypoxia improves growth and functional differentiation of human intestinal epithelial organoids

Background

The epithelium in the colonic mucosa is implicated in the pathophysiology of various diseases, including inflammatory bowel diseases and colorectal cancer. Intestinal epithelial organoids from the colon (colonoids) can be used for disease modeling and personalized drug screening. Colonoids are usually cultured at 18-21% oxygen without accounting for the physiological hypoxia in the colonic epithelium (3% to <1% oxygen). We hypothesize that recapitulating the in vivo physiological oxygen environment (i.e., physioxia) will enhance the translational value of colonoids as pre-clinical models. Here we evaluate whether human colonoids can be established and cultured in physioxia and compare growth, differentiation, and immunological responses at 2% and 20% oxygen.

Methods

Growth from single cells to differentiated colonoids was monitored by brightfield images and evaluated with a linear mixed model. Cell composition was identified by immunofluorescence staining of cell markers and single-cell RNA-sequencing (scRNA-seq). Enrichment analysis was used to identify transcriptomic differences within cell populations. Pro-inflammatory stimuli induced chemokines and Neutrophil gelatinase-associated lipocalin (NGAL) release were analyzed by Multiplex profiling and ELISA. Direct response to a lower oxygen level was analyzed by enrichment analysis of bulk RNA sequencing data.

Results

Colonoids established in a 2% oxygen environment acquired a significantly larger cell mass compared to a 20% oxygen environment. No differences in expression of cell markers for cells with proliferation potential (KI67 positive), goblet cells (MUC2 positive), absorptive cells (MUC2 negative, CK20 positive) and enteroendocrine cells (CGA positive) were found between colonoids cultured in 2% and 20% oxygen. However, the scRNA-seq analysis identified differences in the transcriptome within stem-, progenitor- and differentiated cell clusters. Both colonoids grown at 2% and 20% oxygen secreted CXCL2, CXCL5, CXCL10, CXCL12, CX3CL1 and CCL25, and NGAL upon TNF + poly(I:C) treatment, but there appeared to be a tendency towards lower pro-inflammatory response in 2% oxygen. Reducing the oxygen environment from 20% to 2% in differentiated colonoids altered the expression of genes related to differentiation, metabolism, mucus lining, and immune networks.

Conclusions

Our results suggest that colonoids studies can and should be performed in physioxia when the resemblance to in vivo conditions is important.

Genetic and transcriptomic analyses support a switch to lytic phase in Epstein Barr virus infection as an important driver in developing Systemic Lupus Erythematosus

To investigate the molecular mechanisms through which Epstein-Barr virus (EBV) may contribute to Systemic Lupus Erythematosus (SLE) pathogenesis, we interrogated SLE genetic risk loci for signatures of EBV infection. We first compared the gene expression profile of SLE risk genes across 459 different cell/tissue types. EBV-infected B cells (LCLs) had the strongest representation of highly expressed SLE risk genes. By determining an SLE risk allele effect on gene expression (expression quantitative trait loci, eQTL) in LCLs and 16 other immune cell types, we identified 79 SLE risk locus:gene pairs putatively interacting with EBV infection. A total of 10 SLE risk genes from this list (CD40, LYST, JAZF1, IRF5, BLK, IKZF2, IL12RB2, FAM167A, PTPRC and SLC15A) were targeted by the EBV transcription factor, EBNA2, differentially expressed between LCLs and B cells, and the majority were also associated with EBV DNA copy number, and expression level of EBV encoded genes. Our final gene network model based on these genes is suggestive of a nexus involving SLE risk loci and EBV latency III and B cell proliferation signalling pathways. Collectively, our findings provide further evidence to support the interaction between SLE risk loci and EBV infection that is in part mediated by EBNA2. This interplay may increase the tendency towards EBV lytic switching dependent on the presence of SLE risk alleles. These results support further investigation into targeting EBV as a therapeutic strategy for SLE.

A conserved expression signature predicts growth rate and reveals cell & lineage-specific differences

Isogenic cells cultured together show heterogeneity in their proliferation rate. To determine the differences between fast and slow-proliferating cells, we developed a method to sort cells by proliferation rate, and performed RNA-seq on slow and fast proliferating subpopulations of pluripotent mouse embryonic stem cells (mESCs) and mouse fibroblasts. We found that slowly proliferating mESCs have a more naïve pluripotent character. We identified an evolutionarily conserved proliferation-correlated transcriptomic signature that is common to all eukaryotes: fast cells have higher expression of genes for protein synthesis and protein degradation. This signature accurately predicted growth rate in yeast and cancer cells, and identified lineage-specific proliferation dynamics during development, using C. elegans scRNA-seq data. In contrast, sorting by mitochondria membrane potential revealed a highly cell-type specific mitochondria-state related transcriptome. mESCs with hyperpolarized mitochondria are fast proliferating, while the opposite is true for fibroblasts. The mitochondrial electron transport chain inhibitor antimycin affected slow and fast subpopulations differently. While a major transcriptional-signature associated with cell-to-cell heterogeneity in proliferation is conserved, the metabolic and energetic dependency of cell proliferation is cell-type specific.

By performing RNA sequencing on cells sorted by their proliferation rate, this study identifies a gene expression signature capable of predicting proliferation rates in diverse eukaryotic cell types and species. This signature, applied to single-cell RNA sequencing data from embryos of the roundworm C. elegans, reveals lineage-specific proliferation differences during development. In contrast to the universality of the proliferation signature, mitochondria and metabolism related genes show a high degree of cell-type specificity; mouse pluripotent stem cells (mESCs) and differentiated cells (fibroblasts) exhibit opposite relations between mitochondria state and proliferation. Furthermore, we identified a slow proliferating subpopulation of mESCs with higher expression of pluripotency genes. Finally, we show that fast and slow proliferating subpopulations are differentially sensitive to mitochondria inhibitory drugs in different cell types.

Highlights:

A FACS-based method to determine the transcriptomes of fast and slow proliferating subpopulations.

A universal proliferation-correlated transcriptional signature indicates high protein synthesis and degradation in fast proliferating cells across cell types and species.

Applied to scRNA-seq, the expression signature predicts the global proliferation slowdown during C. elegans development.

Mitochondria membrane potential predicts proliferation rate in a cell-type specific manner, with ETC complex III inhibitor having distinct effects on fibroblasts vs mESCs.

Gender and the Sex Hormone Estradiol Affect Multiple Sclerosis Risk Gene Expression in Epstein-Barr Virus-Infected B Cells

Multiple Sclerosis (MS) is a complex immune-mediated disease of the central nervous system. Treatment is based on immunomodulation, including specifically targeting B cells. B cells are the main host for the Epstein-Barr Virus (EBV), which has been described as necessary for MS development. Over 200 genetic loci have been identified as increasing susceptibility to MS. Many MS risk genes have altered expression in EBV infected B cells, dependent on the risk genotype, and are themselves regulated by the EBV transcription factor EBNA2. Females are 2-3 times more likely to develop MS than males. We investigated if MS risk loci might mediate the gender imbalance in MS. From a large public dataset, we identified gender-specific associations with EBV traits, and MS risk SNP/gene pairs with gender differences in their associations with gene expression. Some of these genes also showed gender differences in correlation of gene expression level with Estrogen Receptor 2. To test if estrogens may drive these gender specific differences, we cultured EBV infected B cells (lymphoblastoid cell lines, LCLs), in medium depleted of serum to remove the effects of sex hormones as well as the estrogenic effect of phenol red, and then supplemented with estrogen (100 nM estradiol). Estradiol treatment altered MS risk gene expression, LCL proliferation rate, EBV DNA copy number and EBNA2 expression in a sex-dependent manner. Together, these data indicate that there are estrogen-mediated gender-specific differences in MS risk gene expression and EBV functions. This may in turn contribute to gender differences in host response to EBV and to MS susceptibility.

The interaction of Multiple Sclerosis risk loci with Epstein-Barr virus phenotypes implicates the virus in pathogenesis

Translating the findings of genome wide association studies (GWAS) to new therapies requires identification of the relevant immunological contexts to interrogate for genetic effects. In one of the largest GWAS, more than 200 risk loci have been identified for Multiple Sclerosis (MS) susceptibility. Infection with Epstein-Barr virus (EBV) appears to be necessary for the development of Multiple Sclerosis (MS). Many MS risk loci are associated with altered gene expression in EBV infected B cells (LCLs). We have interrogated this immunological context to identify interaction between MS risk loci and EBV DNA copy number, intrinsic growth rate and EBV encoded miRNA expression. The EBV DNA copy number was associated with significantly more risk alleles for MS than for other diseases or traits. EBV miRNAs BART4-3p and BART3-5p were highly associated with EBV DNA copy number and MS risk loci. The poliovirus receptor (PVR) risk SNP was associated with EBV DNA copy number, PVR and miRNA expression. Targeting EBV miRNAs BART4-3p and BART3-5p, and the gene PVR, may provide therapeutic benefit in MS. This study also indicates how immunological context and risk loci interactions can be exploited to validate and develop novel therapeutic approaches.

Evidence from genome wide association studies implicates reduced control of Epstein-Barr virus infection in multiple sclerosis susceptibility

BACKGROUND

Genome wide association studies have identified > 200 susceptibility loci accounting for much of the heritability of multiple sclerosis (MS). Epstein-Barr virus (EBV), a memory B cell tropic virus, has been identified as necessary but not sufficient for development of MS. The molecular and immunological basis for this has not been established. Infected B cell proliferation is driven by signalling through the EBV produced cell surface protein LMP1, a homologue of the MS risk gene CD40.

METHODS

We have investigated transcriptomes of B cells and EBV-infected B cells at Latency III (LCLs) and identified MS risk genes with altered expression on infection and with expression levels associated with the MS risk genotype (LCLeQTLs). The association of LCLeQTL genomic burden with EBV phenotypes in vitro and in vivo was examined. The risk genotype effect on LCL proliferation with CD40 stimulation was assessed.

RESULTS

These LCLeQTL MS risk SNP:gene pairs (47 identified) were over-represented in genes dysregulated between B and LCLs (p < 1.53 × 10^-4), and as target loci of the EBV transcription factor EBNA2 (p < 3.17 × 10^-16). Overall genetic burden of LCLeQTLs was associated with some EBV phenotypes but not others. Stimulation of the CD40 pathway by CD40L reduced LCL proliferation (p < 0.001), dependent on CD40 and TRAF3 MS risk genotypes. Both CD40 and TRAF3 risk SNPs are in binding sites for the EBV transcription factor EBNA2, with expression of each correlated with EBNA2 expression dependent on genotype.

CONCLUSIONS

These data indicate targeting EBV may be of therapeutic benefit in MS.

Genetic variations associated with gemcitabine treatment outcome in pancreatic cancer

BACKGROUND

Pancreatic cancer is a rapidly fatal disease with gemcitabine remaining the first-line therapy. We performed a genotype-phenotype association study to identify biomarkers for predicting gemcitabine treatment outcome.

MATERIALS AND METHODS

We selected the top 200 single nucleotide polymorphisms (SNPs) identified from our previous genome-wide association study to associate with overall survival using 400 patients treated with/or without gemcitabine, followed by imputation analysis for regions around the identified SNPs and a replication study using an additional 537 patients by the TaqMan genotyping assay. Functional validation was performed using quantitative reverse transcription-PCR for gemcitabine-induced expression in genotyped lymphoblastoid cell lines and siRNA knockdown for candidate genes in pancreatic cancer cell lines.

RESULTS

Four SNPs in chromosome 1, 3, 9, and 20 showed an interaction with gemcitabine from the discovery cohort of 400 patients (P<0.01). Subsequently, we selected those four genotyped plus four imputed SNPs for SNP×gemcitabine interaction analysis using the secondary validation cohort. Two imputed SNPs in CDH4 and KRT8P35 showed a trend in interaction with gemcitabine treatment. The lymphoblastoid cell lines with the variant sequences showed increased CDH4 expression compared with the wild-type cells after gemcitabine exposure. Knockdown of CDH4 significantly desensitized pancreatic cancer cells to gemcitabine cytotoxicity. The CDH4 SNPs that interacted with treatment are more predictive than prognostic.

CONCLUSION

We identified SNPs with gemcitabine-dependent effects on overall survival. CDH4 might contribute to variations in gemcitabine response. These results might help us to better predict gemcitabine response in pancreatic cancer.

Impacts of human activity modes and climate on heavy metal "spread" in groundwater are biased

Groundwater quality deterioration has attracted world-wide concerns due to its importance for human water supply. Although more and more studies have shown that human activities and climate are changing the groundwater status, an investigation on how different groundwater heavy metals respond to human activity modes (e.g. mining, waste disposal, agriculture, sewage effluent and complex activity) in a varying climate has been lacking. Here, for each of six heavy metals (i.e. Fe, Zn, Mn, Pb, Cd and Cu) in groundwater, we use >330 data points together with mixed-effect models to indicate that (i) human activity modes significantly influence the Cu and Mn but not Zn, Fe, Pb and Cd levels, and (ii) annual mean temperature (AMT) only significantly influences Cu and Pb levels, while annual precipitation (AP) only significantly affects Fe, Cu and Mn levels. Given these differences, we suggest that the impacts of human activity modes and climate on heavy metal "spread" in groundwater are biased.

MicroRNA biogenesis and cellular proliferation

Given the fundamental roles of microRNAs (miRNAs) in physiological, developmental, and pathologic processes, we hypothesized that genes involved in miRNA biogenesis contribute to human complex traits. For 13 such genes, we evaluated the relationship between transcription and 2 classes of complex traits, namely cellular growth and sensitivity to various chemotherapeutic agents in a set of lymphoblastoid cell lines. We found a highly significant correlation between argonaute RNA-induced silencing complex catalytic component 2 (AGO2) expression and cellular growth rate (Bonferroni-adjusted P < 0.05), and report additional miRNA biogenesis genes with suggestive associations with either cellular growth rate or chemotherapeutic sensitivity. AGO2 expression was found to be correlated with multiple drug sensitivity phenotypes. Furthermore, small interfering RNA knockdown of AGO2 resulted in cellular growth inhibition in an ovarian cancer cell line (OVCAR-3), supporting the role of this miRNA biogenesis gene in cell proliferation in cancer cells. Expression quantitative trait loci mapping indicated that genetic variation (in the form of both single-nucleotide polymorphisms and copy number variations) that may regulate the expression of AGO2 can have downstream effects on cellular growth-dependent complex phenotypes.

Genetic variation in human DNA replication timing

Genomic DNA replicates in a choreographed temporal order that impacts the distribution of mutations along the genome. We show here that DNA replication timing is shaped by genetic polymorphisms that act in cis upon megabase-scale DNA segments. In genome sequences from proliferating cells, read depth along chromosomes reflected DNA replication activity in those cells. We used this relationship to analyze variation in replication timing among 161 individuals sequenced by the 1000 Genomes Project. Genome-wide association of replication timing with genetic variation identified 16 loci at which inherited alleles associate with replication timing. We call these "replication timing quantitative trait loci" (rtQTLs). rtQTLs involved the differential use of replication origins, exhibited allele-specific effects on replication timing, and associated with gene expression variation at megabase scales. Our results show replication timing to be shaped by genetic polymorphism and identify a means by which inherited polymorphism regulates the mutability of nearby sequences.

Host genetic variants and gene expression patterns associated with Epstein-Barr virus copy number in lymphoblastoid cell lines

Lymphoblastoid cell lines (LCLs) are commonly used in molecular genetics, supplying DNA for the HapMap and 1000 Genomes Projects, used to test chemotherapeutic agents, and informing the basis of a number of population genetics studies of gene expression. The process of transforming human B cells into LCLs requires the presence of Epstein-Barr virus (EBV), a double-stranded DNA virus which through B-cell immortalisation maintains an episomal virus genome in every cell of an LCL at variable copy numbers. Previous studies have reported that EBV alters host-gene expression and EBV copy number may be under host genetic control. We performed a genome-wide association study of EBV genome copy number in LCLs and found the phenotype to be highly heritable, although no individual SNPs achieved a significant association with EBV copy number. The expression of two host genes (CXCL16 and AGL) was positively correlated and expression of ADARB2 was negatively correlated with EBV copy number in a genotype-independent manner. This study shows an association between EBV copy number and the gene expression profile of LCLs, and suggests that EBV copy number should be considered as a covariate in future studies of host gene expression in LCLs.

Identification and validation of genetic variants that influence transcription factor and cell signaling protein levels

Many genetic variants associated with human disease have been found to be associated with alterations in mRNA expression. Although it is commonly assumed that mRNA expression changes will lead to consequent changes in protein levels, methodological challenges have limited our ability to test the degree to which this assumption holds true. Here, we further developed the micro-western array approach and globally examined relationships between human genetic variation and cellular protein levels. We collected more than 250,000 protein level measurements comprising 441 transcription factor and signaling protein isoforms across 68 Yoruba (YRI) HapMap lymphoblastoid cell lines (LCLs) and identified 12 cis and 160 trans protein level QTLs (pQTLs) at a false discovery rate (FDR) of 20%. Whereas up to two thirds of cis mRNA expression QTLs (eQTLs) were also pQTLs, many pQTLs were not associated with mRNA expression. Notably, we replicated and functionally validated a trans pQTL relationship between the KARS lysyl-tRNA synthetase locus and levels of the DIDO1 protein. This study demonstrates proof of concept in applying an antibody-based microarray approach to iteratively measure the levels of human proteins and relate these levels to human genome variation and other genomic data sets. Our results suggest that protein-based mechanisms might functionally buffer genetic alterations that influence mRNA expression levels and that pQTLs might contribute phenotypic diversity to a human population independently of influences on mRNA expression.

The impact of microRNA expression on cellular proliferation

As an important class of non-coding regulatory RNAs, microRNAs (miRNAs) play a key role in a range of biological processes. These molecules serve as post-transcriptional regulators of gene expression and their regulatory activity has been implicated in disease pathophysiology and pharmacological traits. We sought to investigate the impact of miRNAs on cellular proliferation to gain insight into the molecular basis of complex traits that depend on cellular growth, including, most prominently, cancer. We examined the relationship between miRNA expression and intrinsic cellular growth (iGrowth) in the HapMap lymphoblastoid cell lines derived from individuals of different ethnic backgrounds. We found a substantial enrichment for miRNAs (53 miRNAs, FDR < 0.05) correlated with cellular proliferation in pooled CEU (Caucasian of northern and western European descent) and YRI (individuals from Ibadan, Nigeria) samples. Specifically, 119 miRNAs (59 %) were significantly correlated with iGrowth in YRI; of these miRNAs, 18 were correlated with iGrowth in CEU. To gain further insight into the effect of miRNAs on cellular proliferation in cancer, we showed that over-expression of miR-22, one of the top iGrowth-associated miRNAs, leads to growth inhibition in an ovarian cancer cell line (SKOV3). Furthermore, over-expression of miR-22 down-regulates the expression of its target genes (MXI1 and SLC25A37) in this ovarian cancer cell line, highlighting an miRNA-mediated regulatory network potentially important for cellular proliferation. Importantly, our study identified miRNAs that can be used as molecular targets in cancer therapy.

Poly-omic prediction of complex traits: OmicKriging

High-confidence prediction of complex traits such as disease risk or drug response is an ultimate goal of personalized medicine. Although genome-wide association studies have discovered thousands of well-replicated polymorphisms associated with a broad spectrum of complex traits, the combined predictive power of these associations for any given trait is generally too low to be of clinical relevance. We propose a novel systems approach to complex trait prediction, which leverages and integrates similarity in genetic, transcriptomic, or other omics-level data. We translate the omic similarity into phenotypic similarity using a method called Kriging, commonly used in geostatistics and machine learning. Our method called OmicKriging emphasizes the use of a wide variety of systems-level data, such as those increasingly made available by comprehensive surveys of the genome, transcriptome, and epigenome, for complex trait prediction. Furthermore, our OmicKriging framework allows easy integration of prior information on the function of subsets of omics-level data from heterogeneous sources without the sometimes heavy computational burden of Bayesian approaches. Using seven disease datasets from the Wellcome Trust Case Control Consortium (WTCCC), we show that OmicKriging allows simple integration of sparse and highly polygenic components yielding comparable performance at a fraction of the computing time of a recently published Bayesian sparse linear mixed model method. Using a cellular growth phenotype, we show that integrating mRNA and microRNA expression data substantially increases performance over either dataset alone. Using clinical statin response, we show improved prediction over existing methods. We provide an R package to implement OmicKriging (http://www.scandb.org/newinterface/tools/OmicKriging.html).

Integrative analyses of genetic variation, epigenetic regulation, and the transcriptome to elucidate the biology of platinum sensitivity

BACKGROUND

Using genome-wide genetic, gene expression, and microRNA expression (miRNA) data, we developed an integrative approach to investigate the genetic and epigenetic basis of chemotherapeutic sensitivity.

RESULTS

Through a sequential multi-stage framework, we identified genes and miRNAs whose expression correlated with platinum sensitivity, mapped these to genomic loci as quantitative trait loci (QTLs), and evaluated the associations between these QTLs and platinum sensitivity. A permutation analysis showed that top findings from our approach have a much lower false discovery rate compared to those from a traditional GWAS of drug sensitivity. Our approach identified five SNPs associated with 10 miRNAs and the expression level of 15 genes, all of which were associated with carboplatin sensitivity. Of particular interest was one SNP (rs11138019), which was associated with the expression of both miR-30d and the gene ABCD2, which were themselves correlated with both carboplatin and cisplatin drug-specific phenotype in the HapMap samples. Functional study found that knocking down ABCD2 in vitro led to increased apoptosis in ovarian cancer cell line SKOV3 after cisplatin treatment. Over-expression of miR-30d in vitro caused a decrease in ABCD2 expression, suggesting a functional relationship between the two.

CONCLUSIONS

We developed an integrative approach to the investigation of the genetic and epigenetic basis of human complex traits. Our approach outperformed standard GWAS and provided hints at potential biological function. The relationships between ABCD2 and miR-30d, and ABCD2 and platin sensitivity were experimentally validated, suggesting a functional role of ABCD2 and miR-30d in sensitivity to platinating agents.

Protein quantitative trait loci identify novel candidates modulating cellular response to chemotherapy

Annotating and interpreting the results of genome-wide association studies (GWAS) remains challenging. Assigning function to genetic variants as expression quantitative trait loci is an expanding and useful approach, but focuses exclusively on mRNA rather than protein levels. Many variants remain without annotation. To address this problem, we measured the steady state abundance of 441 human signaling and transcription factor proteins from 68 Yoruba HapMap lymphoblastoid cell lines to identify novel relationships between inter-individual protein levels, genetic variants, and sensitivity to chemotherapeutic agents. Proteins were measured using micro-western and reverse phase protein arrays from three independent cell line thaws to permit mixed effect modeling of protein biological replicates. We observed enrichment of protein quantitative trait loci (pQTLs) for cellular sensitivity to two commonly used chemotherapeutics: cisplatin and paclitaxel. We functionally validated the target protein of a genome-wide significant trans-pQTL for its relevance in paclitaxel-induced apoptosis. GWAS overlap results of drug-induced apoptosis and cytotoxicity for paclitaxel and cisplatin revealed unique SNPs associated with the pharmacologic traits (at p<0.001). Interestingly, GWAS SNPs from various regions of the genome implicated the same target protein (p<0.0001) that correlated with drug induced cytotoxicity or apoptosis (p≤0.05). Two genes were functionally validated for association with drug response using siRNA: SMC1A with cisplatin response and ZNF569 with paclitaxel response. This work allows pharmacogenomic discovery to progress from the transcriptome to the proteome and offers potential for identification of new therapeutic targets. This approach, linking targeted proteomic data to variation in pharmacologic response, can be generalized to other studies evaluating genotype-phenotype relationships and provide insight into chemotherapeutic mechanisms.

The central dogma of biology explains that DNA is transcribed to mRNA that is further translated into protein. Many genome-wide studies have implicated genetic variation that influences gene expression and that ultimately affect downstream complex traits including response to drugs. However, because of technical limitations, few studies have evaluated the contribution of genetic variation on protein expression and ensuing effects on downstream phenotypes. To overcome this challenge, we used a novel technology to simultaneously measure the baseline expression of 441 proteins in lymphoblastoid cell lines and compared them with publicly available genetic data. To further illustrate the utility of this approach, we compared protein-level measurements with chemotherapeutic induced apoptosis and cell-growth inhibition data. This study demonstrates the importance of using protein information to understand the functional consequences of genetic variants identified in genome-wide association studies. This protein data set will also have broad utility for understanding the relationship between other genome-wide studies of complex traits.

Genetic variation is the major determinant of individual differences in leukocyte endothelial adhesion

Objective

To determine the genetic contribution to leukocyte endothelial adhesion.

Methods

Leukocyte endothelial adhesion was assessed through a novel cell-based assay using human lymphoblastoid cell lines. A high-throughput screening method was developed to evaluate the inter-individual variability in leukocyte endothelial adhesion using lymphoblastoid cell lines derived from different donors. To assess heritability, ninety-two lymphoblastoid cell lines derived from twenty-three monozygotic twin pairs and twenty-three sibling pairs were compared. These lymphoblastoid cell lines were plated with the endothelial cell line EA.hy926 and labeled with Calcein AM dye. Fluorescence was assessed to determine endothelial cell adhesion to each lymphoblastoid cell line. Intra-pair similarity was determined for monozygotic twins and siblings using Pearson pairwise correlation coefficients.

Results

A leukocyte endothelial adhesion assay for lymphoblastoid cell lines was developed and optimized (CV = 8.68, Z′-factor = 0.67, SNR = 18.41). A higher adhesion correlation was found between the twins than that between the siblings. Intra-pair similarity for leukocyte endothelial adhesion in monozygotic twins was 0.60 compared to 0.25 in the siblings. The extent to which these differences are attributable to underlying genetic factors was quantified and the heritability of leukocyte endothelial adhesion was calculated to be 69.66% (p-value<0.0001).

Conclusions

There is a heritable component to leukocyte endothelial adhesion. Underlying genetic predisposition plays a significant role in inter-individual variability of leukocyte endothelial adhesion.

A genome-wide systematic analysis reveals different and predictive proliferation expression signatures of cancerous vs. non-cancerous cells

Understanding cell proliferation mechanisms has been a long-lasting goal of the scientific community and specifically of cancer researchers. Previous genome-scale studies of cancer proliferation determinants have mainly relied on knockdown screens aimed to gauge their effects on cancer growth. This powerful approach has several limitations such as off-target effects, partial knockdown, and masking effects due to functional backups. Here we employ a complementary approach and assign each gene a cancer Proliferation Index (cPI) that quantifies the association between its expression levels and growth rate measurements across 60 cancer cell lines. Reassuringly, genes found essential in cancer gene knockdown screens exhibit significant positive cPI values, while tumor suppressors exhibit significant negative cPI values. Cell cycle, DNA replication, splicing and protein production related processes are positively associated with cancer proliferation, while cellular migration is negatively associated with it – in accordance with the well known “go or grow” dichotomy. A parallel analysis of genes' non-cancerous proliferation indices (nPI) across 224 lymphoblastoid cell lines reveals surprisingly marked differences between cancerous and non-cancerous proliferation. These differences highlight genes in the translation and spliceosome machineries as selective cancer proliferation-associated proteins. A cross species comparison reveals that cancer proliferation resembles that of microorganisms while non-cancerous proliferation does not. Furthermore, combining cancerous and non-cancerous proliferation signatures leads to enhanced prediction of patient outcome and gene essentiality in cancer. Overall, these results point to an inherent difference between cancerous and non-cancerous proliferation determinants, whose understanding may contribute to the future development of novel cancer-specific anti-proliferative drugs.

One of the hallmarks of cancer is uncontrolled cellular proliferation, and therefore many anti-cancer drugs aim to disrupt cancer proliferation. However, some of these drugs (e.g., chemotherapeutic agents) affect normal proliferating cells as well, resulting in undesirable side effects. Understanding the differences between cancerous and non-cancerous proliferation can help us design new selective drugs that kill cancer cells without harming normal cells. In this work, we use genome scale gene expression and growth rate measurements across 60 cancer cell lines (NCI-60) to uncover genetic determinants of cancerous proliferation. In parallel, gene expression and growth rate measurements of non-cancerous cell lines allow us to uncover determinants of non-cancerous proliferation. Notably, we find marked differences between the cancerous and non-cancerous proliferation. The two proliferation signatures can be used jointly to enhance the prediction of patient outcome in cancer. Notably, we find that certain genes in the translation and spliceosome machineries are involved in cancerous proliferation but not in non-cancerous proliferation, highlighting them as putative selective anti-cancer drug targets.

Single-cell gene expression analysis reveals genetic associations masked in whole-tissue experiments

Gene expression in multiple individual cells from a tissue or culture sample varies according to cell-cycle, genetic, epigenetic and stochastic differences between the cells. However, single-cell differences have been largely neglected in the analysis of the functional consequences of genetic variation. Here we measure the expression of 92 genes affected by Wnt signaling in 1,440 single cells from 15 individuals to associate single-nucleotide polymorphisms (SNPs) with gene-expression phenotypes, while accounting for stochastic and cell-cycle differences between cells. We provide evidence that many heritable variations in gene function--such as burst size, burst frequency, cell cycle-specific expression and expression correlation/noise between cells--are masked when expression is averaged over many cells. Our results demonstrate how single-cell analyses provide insights into the mechanistic and network effects of genetic variability, with improved statistical power to model these effects on gene expression.

Genome-wide variation of cytosine modifications between European and African populations and the implications for complex traits

Elucidating cytosine modification differences between human populations can enhance our understanding of ethnic specificity in complex traits. In this study, cytosine modification levels in 133 HapMap lymphoblastoid cell lines derived from individuals of European or African ancestry were profiled using the Illumina HumanMethylation450 BeadChip. Approximately 13% of the analyzed CpG sites showed differential modification between the two populations at a false discovery rate of 1%. The CpG sites with greater modification levels in European descent were enriched in the proximal regulatory regions, while those greater in African descent were biased toward gene bodies. More than half of the detected population-specific cytosine modifications could be explained primarily by local genetic variation. In addition, a substantial proportion of local modification quantitative trait loci exhibited population-specific effects, suggesting that genetic epistasis and/or genotype × environment interactions could be common. Distinct correlations were observed between gene expression levels and cytosine modifications in proximal regions and gene bodies, suggesting epigenetic regulation of interindividual expression variation. Furthermore, quantitative trait loci associated with population-specific modifications can be colocalized with expression quantitative trait loci and single nucleotide polymorphisms previously identified for complex traits with known racial disparities. Our findings revealed abundant population-specific cytosine modifications and the underlying genetic basis, as well as the relatively independent contribution of genetic and epigenetic variations to population differences in gene expression.

Genetic and epigenetic variants contributing to clofarabine cytotoxicity

2-chloro-2-fluoro-deoxy-9-D-arabinofuranosyladenine (Clofarabine), a purine nucleoside analog, is used in the treatment of hematologic malignancies and as induction therapy for stem cell transplantation. The discovery of pharmacogenomic markers associated with chemotherapeutic efficacy and toxicity would greatly benefit the utility of this drug. Our objective was to identify genetic and epigenetic variants associated with clofarabine toxicity using an unbiased, whole genome approach. To this end, we employed International HapMap lymphoblastoid cell lines (190 LCLs) of European (CEU) or African (YRI) ancestry with known genetic information to evaluate cellular sensitivity to clofarabine. We measured modified cytosine levels to ascertain the contribution of genetic and epigenetic factors influencing clofarabine-mediated cytotoxicity. Association studies revealed 182 single nucleotide polymorphisms (SNPs) and 143 modified cytosines associated with cytotoxicity in both populations at the threshold P ≤ 0.0001. Correlation between cytotoxicity and baseline gene expression revealed 234 genes at P ≤ 3.98 × 10(-6). Six genes were implicated as: (i) their expression was directly correlated to cytotoxicity, (ii) they had a targeting SNP associated with cytotoxicity, and (iii) they had local modified cytosines associated with gene expression and cytotoxicity. We identified a set of three SNPs and three CpG sites targeting these six genes explaining 43.1% of the observed variation in phenotype. siRNA knockdown of the top three genes (SETBP1, BAG3, KLHL6) in LCLs revealed altered susceptibility to clofarabine, confirming relevance. As clofarabine's toxicity profile includes acute kidney injury, we examined the effect of siRNA knockdown in HEK293 cells. siSETBP1 led to a significant change in HEK293 cell susceptibility to clofarabine.

Comprehensive genetic analysis of cytarabine sensitivity in a cell-based model identifies polymorphisms associated with outcome in AML patients

A whole-genome approach was used to investigate the genetic determinants of cytarabine-induced cytotoxicity. We performed a meta-analysis of genome-wide association studies involving 523 lymphoblastoid cell lines (LCLs) from individuals of European, African, Asian, and African American ancestry. Several of the highest-ranked single-nucleotide polymorphisms (SNPs) were within the mutated in colorectal cancers (MCC) gene. MCC expression was induced by cytarabine treatment from 1.7- to 26.6-fold in LCLs. A total of 33 SNPs ranked at the top of the meta-analysis (P < 10(-5)) were successfully tested in a clinical trial of patients randomized to receive low-dose or high-dose cytarabine plus daunorubicin and etoposide; of these, 18 showed association (P < .05) with either cytarabine 50% inhibitory concentration in leukemia cells or clinical response parameters (minimal residual disease, overall survival (OS), and treatment-related mortality). This count (n = 18) was significantly greater than expected by chance (P = .016). For rs1203633, LCLs with AA genotype were more sensitive to cytarabine-induced cytotoxicity (P = 1.31 × 10(-6)) and AA (vs GA or GG) genotype was associated with poorer OS (P = .015), likely as a result of greater treatment-related mortality (P = .0037) in patients with acute myeloid leukemia (AML). This multicenter AML02 study trial was registered at www.clinicaltrials.gov as #NCT00136084.

Genetic architecture of microRNA expression: implications for the transcriptome and complex traits

We sought to comprehensively and systematically characterize the relationship between genetic variation, miRNA expression, and mRNA expression. Genome-wide expression profiling of samples of European and African ancestry identified in each population hundreds of miRNAs whose increased expression is correlated with correspondingly reduced expression of target mRNAs. We scanned 3' UTR SNPs with a potential functional effect on miRNA binding for cis-acting expression quantitative trait loci (eQTLs) for the corresponding proximal target genes. To extend sequence-based, localized analyses of SNP effect on miRNA binding, we proceeded to dissect the genetic basis of miRNA expression variation; we mapped miRNA expression levels-as quantitative traits-to loci in the genome as miRNA eQTLs, demonstrating that miRNA expression is under significant genetic control. We found that SNPs associated with miRNA expression are significantly enriched with those SNPs already shown to be associated with mRNA. Moreover, we discovered that many of the miRNA-associated genetic variations identified in our study are associated with a broad spectrum of human complex traits from the National Human Genome Research Institute catalog of published genome-wide association studies. Experimentally, we replicated miRNA-induced mRNA expression inhibition and the cis-eQTL relationship to the target gene for several identified relationships among SNPs, miRNAs, and mRNAs in an independent set of samples; furthermore, we conducted miRNA overexpression and inhibition experiments to functionally validate the miRNA-mRNA relationships. This study extends our understanding of the genetic regulation of the transcriptome and suggests that genetic variation might underlie observed relationships between miRNAs and mRNAs more commonly than has previously been appreciated.