Sarcoidosis is a multisystem immuno-inflammatory disorder of unknown etiology that most commonly involves the lungs. We hypothesized that an unbiased approach to identify pathways activated in bronchoalveolar lavage (BAL) cells can shed light on the pathogenesis of this complex disease.We recruited 15 patients with various stages of sarcoidosis and 12 healthy controls. All subjects underwent bronchoscopy with lavage. For each subject, total RNA was extracted from BAL cells and hybridized to an Affymetrix U133A microarray. Rigorous statistical methods were applied to identify differential gene expression between subjects with sarcoidosis vs. controls. To better elucidate pathways differentially activated between these groups, we integrated network and gene set enrichment analyses of BAL cell transcriptional profiles.Sarcoidosis patients were either non-smokers or former smokers, all had lung involvement and only two were on systemic prednisone. Healthy controls were all non-smokers. Comparison of BAL cell gene expression between sarcoidosis and healthy subjects revealed over 1500 differentially expressed genes. Several previously described immune mediators, such as interferon gamma, were upregulated in the sarcoidosis subjects. Using an integrative computational approach we constructed a modular network of over 80 gene sets that were highly enriched in patients with sarcoidosis. Many of these pathways mapped to inflammatory and immune-related processes including adaptive immunity, T-cell signaling, graft vs. host disease, interleukin 12, 23 and 17 signaling. Additionally, we uncovered a close association between the proteasome machinery and adaptive immunity, highlighting a potentially important and targetable relationship in the pathobiology of sarcoidosis.BAL cells in sarcoidosis are characterized by enrichment of distinct transcriptional programs involved in immunity and proteasomal processes. Our findings add to the growing evidence implicating alveolar resident immune effector cells in the pathogenesis of sarcoidosis and identify specific pathways whose activation may modulate disease progression.The online version of this article (doi:10.1186/s12931-016-0411-y) contains supplementary material, which is available to authorized users. Sarcoidosis is a heterogeneous disease characterized by noncaseating granulomatous inflammation affecting multiple organs. The lung is the most common site involved in this systemic disease with over 90 % of patients developing pulmonary sarcoidosis . AlthougTo our knowledge, the transcriptional state of immune effecter cells residing within lung airspaces has not been systematically surveyed in sarcoidosis. We hypothesized that an unbiased approach to identify pathways differentially activated in bronchoalveolar lavage (BAL) cells of sarcoidosis patients could shed light on the pathogenesis of this complex disease and reveal previously unrecognized targets for future therapeutic intervention.N = 25) were recruited from patients undergoing routine clinical evaluation for diagnosis of sarcoidosis without acute pulmonary symptoms (Table N = 12) composed of individuals with no history of lung disease and no medication usage at time of bronchoscopy was recruited as controls. These healthy individuals volunteered to undergo bronchoscopy as part of an Institutional Review Board-approved research program. The entire protocol was approved by the East Carolina University & Medical Center Institutional Review Board (#05-0581) and written informed consent was obtained from all participants.Sarcoidosis subjects .Transcriptional profiling was performed on a subset of the study cohort: 15 subjects with sarcoidosis and the 12 controls. For each subject, total RNA from BAL cells was extracted using the RNeasy mini kit  and its integrity verified by the Agilent 2100 Bioanalyzer . Amplified biotin-labeled cRNA was generated from 2 mg of total RNA and hybridized to Affymetrix Human GeneChip U133A 2.0 microarrays  and processed according to the manufacturer’s instructions. Background adjustment and quantile normalization of log-transformed intensities was performed using RMA software [t-test [Initially, we used a form of multidimensional scaling known as correspondence analysis  to detert-test  coupled t-test . An FDR t-test .P-values from this analysis were corrected for multiple testing using an FDR threshold < 1 %. We then undertook a more unbiased and comprehensive approach by applying Gene Set Enrichment Analysis (GSEA) to the entire microarray dataset using over 1300 canonical pathways derived from multiple resources including KEGG, Reactome, Pathway Interaction Database, and Biocarta among others [We applied two levels of functional enrichment analysis to the expression dataset. First we focused on the subset of differentially expressed genes FDR < 1 %) and used Webgestalt software  to identg others . These pg others . To dispg others , 16. A s % and usT) values for genes of interest were normalized to a housekeeping gene, glyceraldehyde 3-phosphate dehydrogenase (GAPDH), as previously described [P-values were calculated using two-tailed Student’s t-test of log2-transformed data .We confirmed differential expression of selected genes by qPCR using the ABI Prism 7300 Detection System . RNA from BAL cell samples were analyzed in duplicate using primer sets for proteasome subunit beta type 8 (PSMB8) , proteasome subunit beta type 9 (PSMB9)  and interferon gamma (IFNG) . Threshold cycle (Cescribed  and relaescribed . Data arN = 15); 2) a validation group of patients that were not part of the original microarray study . P-values were calculated using the nonparametric, two-tailed Mann–Whitney test because the cytokine data were not normally distributed based on Shapiro-Wilk’s normality test .We evaluated the protein expression of two candidate genes identified from the microarray experiments, IL6 and CCL5 (RANTES), in BAL fluid of sarcoidosis and control subjects using ultrasensitive kits  as per manufacturer’s instructions. The plates were read using the MSD Discovery workbench analyzer and software package. Two groups of sarcoidosis patients were studied: 1) subjects who also underwent microarray analysis of their BAL cells , but found no significant enrichment in the control group : 1) lung biopsy tissue from patients with sarcoidosis (N = 6) and control subjects (N = 6) as reported by Crouser et al. [N = 38) and healthy subjects (N = 20) from Koth et al. [To extend and compare our findings to previous important work in this field, we obtained publicly available microarray data from two published studies  periN = 86 gene sets) compared to lung tissue (N = 32) or PBLs (N = 47). This finding suggests that airspace immune effector cells may provide a more robust and consistent signal compared to the more heterogeneous cell types obtained from lung tissue biopsies or the lack of proximal association with involved end organ as is the case with circulating leukocytes. Secondly, we found that in both BAL cells and lung tissue biopsies of sarcoidosis patients, T cell-associated gene sets were significantly upregulated but in contrast, these pathways were downregulated in the PBL dataset. While the implications of this observation are not clear, the known critical role played by of activation of Th1 and T cell receptor signaling in the pathobiology of sarcoidosis highlights the importance of sampling cells from affected target organs to understand mechanisms of disease pathogenesis. Finally, the enrichment of the proteasome pathway in sarcoidosis was found only in our BAL cell analysis. Given the importance of this molecular machinery in modulating adaptive immunity and thereby having a potentially novel role in sarcoidosis, our approach of interrogating immune effector cells in the airspaces shows promise for helping unravel previously unrecognized processes in this complex disease.Using an FDR < 1 % cutoff to designate significant enrichment, we identified 32 gene sets upregulated in lung tissue and 47 gene sets upregulated in PBLs of patients with sarcoidosis  as well as an independent group of subjects with sarcoidosis . Subject characteristics of the independent sarcoidosis group are available at Additional file We assessed whether the immuno-inflammatory signal observed in BAL cells of patients with sarcoidosis was also present in their BAL fluid by measuring protein levels of two differentially upregulated genes: IL6 and CCL5. As shown in Fig. The etiology of sarcoidosis continues to be debated, with much investigation into potential instigators of the observed exaggerated and aberrant immune response. Since the lung is the most common site of organ involvement, we hypothesized that investigating the airspace immune cell transcriptome would provide insight into disease pathogenesis and reveal putative novel therapeutic targets.We compared the transcriptional profiles of lung BAL cells in sarcoidosis patients versus control subjects and found significant differences between the two groups, most prominently the selective enrichment of distinct immune-related pathways. As confirmation of our unbiased approach, we identified a number of immune modulators previously implicated in sarcoidosis, including IFNG , IL12 2, and IL1Our data are consistent with studies of whole blood and lung tissue transcriptome which demonstrated upregulation of interferon signaling and activation of immuno-inflammatory pathways –32, 35. By integrating pathway and network analysis of BAL cell expression profiles, we constructed a modular overview of differentially activated processes in sarcoidosis that linked key putative drivers of disease. In particular, our analysis highlighted a potentially important connection between adaptive immunity and the proteasomal apparatus. The proteasome is part of the ubiquitin-proteasome system, which controls intracellular protein degradation and turnover. Activation of proteasome complex components suggests that sarcoidosis induces a proteolytic environment for substrate degradation. We found upregulation of constitutive proteasome catalytic subunits and the inducible proteasome subunits, PSMB8, PSMB9 and PSMB10. The inducible subunits form the immunoproteasome, which is upregulated by oxidative stress and proinflammatory cytokines, particularly IFNG . Key funOur findings are consistent with a recent report by Sixt et al. , who fouOur study has a number of limitations. Since the number of subjects was relatively small, our analyses should be considered exploratory and require confirmation in larger cohorts. However, our sample size was similar to previous reports leveraging tissue gene expression profiling to elucidate disease mechanisms in sarcoidosis , 36. SinWe have outlined a systematic framework to investigate the role of airspace immune cells in the pathogenesis of sarcoidosis. We found that this complex disease is associated with the activation of diverse yet distinct pathways and identified key members of the proteasome apparatus as potential modulators of host immune response. Future work is required to further delineate these relationships and determine their influence in disease progression.


Bacillus subtilis. This synthetic QS system allowed us to uncouple and independently investigate ECM production and QS in both biofilms and single cells. Results revealed that ECM production directly enhances the response to QS signals, which may offset the cost of ECM production.Bacteria predominantly reside in microbial communities known as biofilms, where cells are encapsulated and protected by the extracellular matrix (ECM). While all biofilm cells benefit from the ECM, only a subgroup of cells carries the burden of producing this public good. This dilemma provokes the question of how these cells balance the cost of ECM production. Here we show that ECM producing cells have a higher gene expression response to quorum sensing (QS) signals, which can lead to a private benefit. Specifically, we constructed a synthetic quorum-sensing system with designated “Sender” and “Receiver” cells in  Bacillus subtilis, are polysaccharides and amyloid-like protein filaments [B. subtilis biofilms [Fig 1A). Those ECM producer cells are clearly burdened with a cost at a time of environmental stress and high cell density. This cost is evident by the observation that an ECM-deficient strain can outcompete the ECM producing strain in a mixed culture [Fig 1B). Here we investigated whether ECM producers could also enjoy a private benefit, countering the cost of this public good production .Even though bacteria are unicellular organisms, they predominantly reside in structured communities known as biofilms ,2. One oilaments –8. It habiofilms ,10 . Therefore, we constructed an orthogonal (inert) synthetic QS system in B. subtilis to study how the response to QS signals may be affected by ECM production . This system consists of dedicated “Sender” and “Receiver” cells that can secrete or respond to QS signals, respectively. This synthetic QS system provides the unique opportunity to study QS without interfering with native B. subtilis QS or ECM production . Investigating synthetic QS in the background of ECM gene deletion strains revealed that ECM expressing cells are more responsive to QS signals. Additionally, we find that the ECM produced by cells retains QS peptides in vivo, enhancing the QS response. Finally, we confirm that results from our synthetic QS system also apply to the native QS response of B. subtilis. Our findings thus support a model where the cellular cost of ECM production can be compensated by an enhanced QS response.ECM expression and QS are tightly coupled and cannot be perturbed independently, since native QS regulates ECM expression –31 , an und2 (Fisher BioReagents), 700 μM CaCl2 (Fisher BioReagents),50 μM MnCl2 (Sigma-Aldrich), 100 μM FeCl3 (Sigma-Aldrich), 1 μM ZnCl2 (Sigma-Aldrich),2 μM thiamine (Fisher BioReagents), 0.5% glycerol (Sigma-Aldrich) and 0.5% glutamate (Sigma-Aldrich)[MSgg medium was used when testing cells’ response to AIP. MSgg medium containing 5 mM potassium phosphate , 100 mM MOPS , 2mM MgCl-Aldrich).agr system or fluorescent proteins fused to PrpsD were integrated into B. subtilis using a standard chromosomal integration method. First, target genes were generated through fusion PCR and cloned into chromosomal integration vectors. Then the resulted constructs were confirmed by direct sequencing. The agr system was PCR amplified using Phusion high-fidelity DNA polymerase (NEB) and appropriate primers from Staphylococcus epidermidis ATTC 14990. Vectors designed for integration into the gltA, amyE and sacA loci were pGlt-Kan , pDL30 , pSac-Cm [B. subtilis using a standard one-step transformation procedure [The  pSac-Cm   [epsH deletion were constructed based on this ΔepsH strain. To construct the ΔtapA operon strain, upstream and downstream regions of the tapA operon were cloned into per449, a generic integration vector constructed for integration into the gene of interest . The resulting vector was then transformed into B. subtilis.The dge, MA) . All othS. epidermidis AIP was synthesized by Protein Chemistry Technology Core in University of Texas Southwestern Medical Center. The synthesis method was previously described by Otto M, et al. [, et al. .B. subtilis strains were grown on LB agar plates with appropriate antibiotics at 37°C for overnight. For each strain, a single colony was picked and grown in 1mL LB liquid culture at 37°C for 6h until saturation. When needed, cultures from different strains were mixed together at a certain ratio and diluted 5000-fold with MSgg medium. Then 1μL culture was spotted on MSgg solid plate . During the imaging process, cells were incubated at 30°C.The biofilm formation assay is adapted from previous protocols . B. subtMovies and snapshots were acquired with an IX71 inverted fluorescence microscope (Olympus), IX83 inverted fluorescence microscope (Olympus) or DeltaVision microscopy imaging system . Biofilm movies were taken from the bottom with a 2.5x objective lens  to film the whole biofilm or a 10x objective lens  to zoom in on certain regions. Images were taken every 40 min. Single cell movies were taken with a 100x objective lens  while growing 10-fold diluted cell cultures on an MSgg pad at 30°C. Top-view of biofilms was acquired by a Retiga 2000R digital camera (QImaging) via an SZX10 fluorescent stereomicroscope (Olympus).http://imagej.nih.gov/ij/) and MATLAB (MathWorks) were used for image processing and analysis. The detailed image analysis methods for biofilms and single cells are described below respectively.ImageJ  were integrated into each strain except one. For strains containing PrpsD driven fluorescent proteins, a fluorescent image was taken accordingly. Then this fluorescent image was processed using ImageJ to determine the region of the strain in two steps. First, the edge was detected using the Otsu method in auto local threshold function. Second, the fluorescence positive region was determined by the analyze particle function. After the region of each fluorescently marked strain was determined, the rest of the area was assigned to the one strain without a fluorescent marker.In a mixed biofilm, regions of each strain were segmented out based on fluorescence images of constitutive markers. P3-yfp fluorescent intensity of all pixels within every 12.9μm (10 pixels) distance was calculated and plotted against its distance from the edge of the Sender microcolony for WT Receiver and ΔepsH Receiver, respectively. The average P3-yfp fluorescence intensity of WT Receiver and ΔepsH Receiver within 12.9μm from the edge of Sender microcolony , where I is the average image intensity in the region of a certain strain and I0 is the average image intensity in regions outside of the biofilm.Cell density was determined using bright field images as previously described and validated by A. Seminara et al. . Relativagr system) from Staphylococcus epidermidis into the biofilm forming B. subtilis NCIB 3610 strain . We chose the S. epidermidis QS system because of the following four main reasons: (1) S. epidermidis is a gram-positive bacteria, which increases the likelihood that this QS system could be functional in gram-positive B. subtilis cells. (2) The S. epidermidis QS system is well characterized and simple [agrD gene encodes the AIP peptide and AgrB is responsible for posttranslational modification and export of AIP [agr operon driven by the P2 promoter, as well as gene expression of downstream targets driven by P3 promoters [S. epidermidis AIP is a short peptide containing 8 amino acids [S1 Fig) and is similar in size to the major phr encoded QS peptides in B. subtilis that range in size from 5–6 amino acids [agr system in B. subtilis, suggesting that the transplanted QS system would not interfere with native B. subtilis QS. Therefore, transplantation of the S. epidermidis QS system into B. subtilis provides a unique opportunity to directly investigate how ECM expression may affect the QS response without interfering with native ECM production or QS.To investigate how ECM expression may affect the response to QS signals, we constructed a synthetic QS system. In particular, we transplanted the Auto-Inducing-Peptide (AIP) based QS system  no acids  no acids ,43. (4) agr system after it was transformed into B. subtilis. Specifically, we constructed two types of synthetic QS systems in B. subtilis, namely “Receiver” and “Sender” cells . The Sender cells contain the entire agr operon  and are capable of sensing and secreting AIP. In contrast, Receiver cells can only respond to AIP, but not synthesize it as they lack the necessary genes. Receiver cells only contain the AgrC receptor and downstream AgrA transcription factor that regulates expression of the P3 promoter. Therefore, we can use Receiver cells to measure the response to AIP by monitoring expression of a fluorescent protein reporter YFP from the P3 promoter (P3-yfp).We began by testing the modularity and functionality of the B. subtilis Receiver cells containing the synthetic QS system could indeed respond to extracellular AIP . In particular, we find that addition of 100 nM chemically synthesized AIP induced a nearly 5-fold increase in P3 expression . We also obtained the AIP dose response curve for P3 expression in Receiver cells . Receiver cells begin to elicit a detectable response to approximately 70 nM extracellular AIP. The response increased with increasing AIP concentrations and surprisingly did not saturate even with addition of 10 μM AIP, a concentration 3 orders higher than the minimal activating concentration. We also performed two control experiments to determine adverse effects of S. epidermidis gene integrations or AIP peptide addition to B. subtilis biofilm morphology. Results show that chromosomal integration of agr genes into B. subtilis does not affect biofilm formation . Furthermore, extracellular addition of 10 μM AIP does not alter B. subtilis biofilm development or morphology, suggesting that native ECM production is not affected by the presence of S. epidermidis AIP . Together, these data show that the S. epidermidis agr system in Receiver cells is functional and does not interfere with native B. subtilis biofilm development.Quantitative fluorescence microscopy measurements confirmed that Fig 2E). Exposure of Receiver cells to supernatant collected from Sender cells triggered P3 expression in Receiver cells . The ECM deficient Receiver strain was obtained by deleting the B. subtilis epsH gene (S1 Table). The epsH gene encodes a critical enzyme necessary for extracellular polysaccharide synthesis and it has been shown that ΔepsH cells are deficient in ECM production and biofilm development [Fig 3B). We then measured the QS response of Receiver cells with and without the epsH gene to AIP secreted by an adjacent cluster of Sender cells . Results show that the ΔepsH strain has approximately four-fold lower QS response (P3-yfp) compared to the Receiver strain with an intact epsH gene . Control experiments show that despite the reduced response to AIP, the ΔepsH Receiver strain was still functional and capable of exhibiting an AIP dependent dose response curve . Furthermore, we confirmed that the difference in the response between Receiver strains was not due to differences in cell density . These findings suggest that ECM deficient cells have a reduced response to AIP-mediated QS and are therefore less effective in sensing signals from distant cells.Next, we investigated directly in biofilm communities whether QS signals would be affected by ECM expression. Specifically, we grew mixed biofilms that contained Sender cells together with two types of Receiver cells, where one Receiver strain was deficient in ECM production . We find that the ΔepsH strain had approximately two-fold lower QS response compared to the wild type Receiver strain . Results show that the QS response of WT Receiver cells, capable of ECM production, is clearly higher compared to the response measured in the nearest physically adjacent ΔepsH Receiver cells . Furthermore, the low QS response of ΔepsH Receiver cells did not decrease further as a function of increasing distance from the WT Receiver cells . Together, these results indicate that enhanced QS response in ECM producing cells is indeed a private property at the single cell level that does not extend to adjacent cells.To directly test the finding that enhanced QS appears to be limited exclusively to ECM producing cells, we performed experiments at the single cell level. In particular, we wanted to determine if ECM expression could enhance the QS response of directly adjacent cells. We thus turned to experiments in mixed microcolonies that enabled quantitative measurement of the QS response in single cells . We then imaged single cells sampled from this liquid culture. Consistent with our previous results, quantitative fluorescence microscopy showed that ECM deficient ΔepsH Receiver cells have a lower response to AIP than wild type Receiver cells, even when grown together in shaking liquid cultures .Finally we investigated whether the reduced QS response observed in ECM deficient strains was limited to structured communities such as biofilms, or could even be observed in liquid cultures. Accordingly, we grew an equal mixture of wild type and epsH gene, or perhaps be a general consequence of ECM deficiency? To address this question, we repeated the same experiment using an equal mixture of wild type and a tapA operon deletion strain (S1 Table). The tapA operon encodes another critical component of the ECM, namely proteins that can form amyloid-like protein filaments [ΔepsH Receiver cells, the tapA operon deleted Receiver cells also exhibit a reduced QS response compared to wild type Receiver cells . Therefore, regardless of whether the ECM deficiency is caused by lack of exopolysaccharides or amyloid-like protein fibers, the QS response to AIP is reduced in both types of ECM deficient cells. We also ruled out the possibility that the reduced QS response observed in ECM deficient cells was due to a general problem with protein expression by measuring the activity of a ribosomal gene promoter (PrpsD) in wild type and ECM deficient cells . Collectively, these results show that the reduced QS response is not tied to the deletion of any particular gene, but appears to be caused by an overall deficiency in ECM production. Furthermore, the reduced QS response in ECM deficient strains not only arises in the context of the biofilm community or microcolonies, but appears to be a single cell level property that even applies in liquid cultures.Could the reduced QS response be specifically linked to the deletion of the ilaments . We findin vitro study that indicated transient physical binding of QS molecules to the ECM [ΔepsH cells to the same protocol to serve as a control. The supernatant collected after sonication from both WT and ΔepsH cells was added to WT Receiver cells and their QS response was measured . We found that WT Receiver cells elicited a higher QS response when exposed to the supernatant from WT cells compared to that obtained from ΔepsH cells . This result provides a mechanistic explanation for the enhanced QS response of ECM producing cells, by indicating that the ECM concentrates AIP in the vicinity of cells and therefore increases the likelihood of binding to receptors on the membrane .Motivated by the recent  the ECM , we wanttapA expression (PtapA-cfp). Single cell analysis of the two-color reporter strain for P3 and PtapA expression shows that the QS response in Receiver cells positively correlates with the natural variation in B. subtilis ECM expression . In contrast, the control experiment showed that variation in PtapA-cfp expression did not correlate with expression of the PrpsD promoter for a ribosomal gene . Therefore, in addition to the reduced QS response observed in ECM gene deletion strains, response to the QS signal is also affected by the natural variation in ECM expression that is inherent to B. subtilis.The amount of ECM produced by individual cells can vary ,44. TherB. subtilis. Accordingly, we used what is known as conditioned, or spent media that naturally contains QS signals expressed by B. subtilis cells, such as ComX, and then monitored expression of Psrf, a B. subtilis native QS responsive gene promoter [srf-lacZ compared to ΔepsH cells . This result shows that the amplitude of the native QS response also depends on ECM production, indicating that results obtained with our synthetic QS system may be generally applicable to native QS systems in B. subtilis.We asked whether our results obtained using a synthetic QS system would also be relevant to the native QS response of promoter . We findFig 5C). As the name implies, QS refers to a collective process by which individual cells can sense global population density. While QS contains information about the population, the response to a QS signal is an intracellular process that can be considered “private” to the cell. In fact, recent studies have shown that response to QS can give rise to an advantageous metabolic response in individual cells [The results presented in this study indicate that the cost of ECM expression may be balanced by an enhanced private response to QS signaling Click here for additional data file.S2 FigΔepsH cells grown on MSgg plates for 3 days.WT cells were grown on MSgg plates with 0 or 10 μM AIP for 3 days. Then the bright field image was taken from the top and compared to (TIF)Click here for additional data file.S3 Figagr system integrated) and Sender strain (with P3-yfp).Four-day-old biofilm structure of control strain (no (TIF)Click here for additional data file.S4 Fig(TIF)Click here for additional data file.S5 FigΔepsH Receiver cells to AIP Dose-response curve of (TIF)Click here for additional data file.S6 FigΔepsH Receiver cells and WT Receiver cells merge. Sender cells are marked by PrpsD-mCherry (red) and WT Receiver cells are marked by PrpsD-cfp (blue), where PrpsD is a constitutive promoter. This is the same figure as 0)) of WT Receiver cell clusters and ΔepsH Receiver cell clusters .(A) A local region in a mixed biofilm where clusters of Sender cells, (TIF)Click here for additional data file.S7 FigA) ΔepsH Receiver cells grown nearby WT cells  on the MSgg agar pad containing 100nM AIP. ΔepsH Receiver cells with different distance from the border of WT cells are false colored with magenta (layer 1), cyan (layer 2) and yellow (layer 3). (B) Response (P3-yfp) of these three layers of ΔepsH Receiver cells .((TIF)Click here for additional data file.S8 FigA) Snapshot of WT and ΔepsH cells grow in MSgg liquid culture. PrpsD-cfp is shown in green. (B) P3-yfp fluorescence intensity in WT and ΔepsH cells .((TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S1 Text(DOCX)Click here for additional data file.


This imaging system was used for longitudinal analysis of the density, distribution, and evolution of intratumoral infectious centers after systemic administration of oncolytic vesicular stomatitis virus in tumor-bearing mice and revealed heterogeneous delivery of virus particles both within and between tumors in animals receiving identical therapy. This study provides compelling validation of high resolution in vivo reporter gene mapping as a convenient method for serial monitoring of intratumoral virus spread that will be necessary to address critical barriers to systemic oncolytic virus efficacy such as intratumoral delivery.Systemically administered oncolytic viruses have the ability to cause tumor destruction through the expansion and coalescence of intratumoral infectious centers. Efficacy is therefore dependent upon both the density and intratumoral distribution of virus-infected cells achieved after initial virus infusion, and delivery methods are being developed to enhance these critical parameters. However, the three-dimensional (3D) mapping of intratumoral infectious centers is difficult using conventional immunohistochemical methodology, requiring painstaking 3D reconstruction of numerous sequential stained tumor sections, with no ability to study the temporal evolution of spreading infection in a single animal. We therefore developed a system using very high-resolution noninvasive  It has been predicted mathematically and confirmed experimentally that there is a virus dose threshold necessary to establish infection in the tumor.3 Reduction in virus bioavailability limits the amount of virus that reaches the tumor to initiate infection. Virus bioavailability is rapidly reduced by antibody and complement neutralization in the blood, sequestration in the mononuclear phagocytic system of the liver and spleen, off-target extravasation at sites other than the targeted tumors and failure of extravasation in the tumors.1 Approaches are being developed to limit the influence of these barriers, thereby improving virus delivery to the tumor microvessels,5 but antitumor efficacy ultimately depends on its homogeneous dissemination throughout the tumor parenchyma. In the systemic oncolytic virotherapy trials reported to date, detectable levels of intratumoral virus delivery have been highly variable and only a small number of complete tumor responses have been documented.7 We are therefore working to optimize the potency of systemic oncolytic virotherapy by developing new protocols for virus infusion that will increase the efficiency and uniformity of intratumoral virus delivery.Oncolytic viruses, such as vesicular stomatitis virus (VSV), are novel, experimental cancer therapeutics that selectively extravasate into tumors to infect and kill cancer cells.To evaluate the relative merits of different virus infusion protocols, it is necessary to determine how the virus distributes throughout tumors in time and space under different experimental conditions. The traditional approach to this question is to harvest tumors at multiple timepoints after virus administration and section and stain them with antiviral antibodies. This approach, however, gives only two-dimensional (2D) data, and while painstaking three-dimensional (3D) reconstruction of the data from multiple serial sections is theoretically possible, the approach is too time consuming to be of real practical value. An additional serious limitation of this traditional approach is that the tumor-bearing animal must be killed at the time of tumor harvest making it impossible to follow the evolution of oncolytic infection in a single animal over time.8 One such reporter gene currently used in preclinical and clinical applications is the sodium–iodide symporter (NIS) gene which codes for a protein that concentrates iodide and other anionic radiotracers in virus-infected cells of live animals that is detected by nuclear imaging techniques including positron emission tomography imaging and single-photon emitted computed tomography (SPECT) imaging.9 Indeed, the NIS reporter gene has been used extensively to track the intratumoral propagation of recombinant oncolytic viruses such as measles, vesicular stomatitis, vaccinia, herpes simplex, and adenoviruses, although the resolution of the imaging data reported in these studies was relatively limited.10–19 Resolution is the ability to distinguish between two closely spaced objects on an image. Since positrons travels a short distance before annihilation, the theoretical maximum resolution of positron emission tomography is inferior to SPECT, a fact that is (at last) reflected in the performance of the newest microSPECT instruments. However, resolution comes at the expense of sensitivity in SPECT imaging; so, there is an important tradeoff between these two critical parameters. Advances in imaging technology now allow for not only considerably higher sensitivity of γ-ray detection at the body surface but also for much higher resolution of adjacent foci of NIS radiotracer uptake within a given tumor.Recombinant viruses engineered to encode reporter proteins whose expression can be detected by noninvasive radioisotopic imaging methods have been used extensively to enable the noninvasive quantitative monitoring of viral infection.21 Here, we report the initial findings of these studies, which provide a strong validation for the use of high-resolution in vivo reporter gene mapping to serially monitor the intratumoral spread of an oncolytic virus infection. The data also highlight the problem of heterogeneous delivery of virus particles both within and between tumors in animals receiving identical therapy.We therefore decided to assess the ability of high-resolution micro single-photon emitted computed tomography/computed tomography (microSPECT/CT), using our recently acquired USPECT-II/CT imaging instrument (MILabs) with theoretical resolution <0.35 mm compared to the resolution of our Siemens Inveon positron emission tomography/CT with theoretical resolution <1.4 mm, to track the evolution of intratumoral infectious centers after systemic administration of a NIS-expressing oncolytic VSV (VSV-mIFNβ-NIS) in tumor-bearing mice.2 The model was based on immunohistochemical analysis showing that virus seeds intratumoral infection that spreads outward to neighboring cells with infected cells remaining viable before becoming apoptotic. Functional NIS expression has previously been shown to correlate with these stages of infection as infected cells express the NIS transgene for ~20 hours before becoming apoptotic and losing their ability to concentrate radioiodine. Autoradiography of explanted tumors from multiple timepoints after infection showed that radiotracer accumulation was maximal at the periphery of expanding infectious centers, corresponding to the leading edge of the advancing infection where the tumor cells had only recently been infected and were therefore still viable  and imaged 24 hours later. The microSPECT/CT data were collected over a period of 25 minutes, starting 1 hour after intraperitoneal administration of 500 µCi 99mTc as the pertechnetate anion (99mTcO4−). The results of these imaging studies show that microSPECT/CT imaging is able to distinguish multiple independent foci of radiotracer uptake by VSV-mIFNβ-NIS-infected cells within a single tumor  were given a single intravenous dose of VSV-mIFNβ-NIS , VSV-mIFNβ-NIS was again administered to immunocompetent BALB/c mice bearing subcutaneous MPC-11 plasmacytomas and microSPECT/CT images were collected from each mouse at multiple timepoints thereafter , always over a period of 25 minutes starting 1 hour after intraperitoneal administration of 500 µCi 99mTcO4−. The results of these imaging studies for two mice administered low (5 × 106 TCID50) and high (1 × 108 TCID50) doses of VSV-mIFNβ-NIS are shown in To determine whether high-resolution microSPECT/CT imaging could be used to follow the evolution of an intratumoral VSV-mIFNβ-NIS infection  using both NIS-specific and HSV1-TK-specific radiotracers.24This NIS imaging technology will be beneficial to compare changes in intratumoral infection distribution and overall tumor infection burden as a result of different infusion protocols. However, this technology has not yet been used to differentiate between infection resulting from sequential oncolytic virus administration. For differentiation between infectious centers from multiple doses, noninvasive reporter genes with specificity for different radioisotopes will be necessary. The potential to use SPECT imaging to detect different radiotracers, dual-tracer imaging, is being developed and could be used in the future with similar analysis techniques.26 The use of noninvasive imaging to observe intratumoral infection is unable to portray cell death not associated with direct virus infection. Therefore, immunohistochemical analysis remains a valuable tool to investigate roles of immune-mediated and bystander cell killing.The use of noninvasive imaging offers the benefits of spatial and temporal observation of intratumoral infection. This imaging technique is however limited in the ability to portray cellular states independent of infection status. The MPC-11tumor model used here may be a special case for observing intratumoral spread of oncolytic VSV leading to wholesale tumor destruction. In other tumor models, immune-mediated cell death or vascular shutdown resulting in bystander killing are additional sources of tumor cell death subsequent to oncolytic virus administration.The heterogeneity of infectious center distribution throughout a tumor as well as the variability of distribution across multiple tumors requires the use of 3D, live-animal imaging beyond standard immunohistochemical techniques in order to analyze spatial or temporal virus–tumor interactions. The work presented here validates the use of noninvasive microSPECT/CT imaging to monitor VSV-mIFNβ-NIS infection distribution and evolution through time and space in tumor-bearing animals. The use of noninvasive imaging to track intratumoral infection is a convenient and accurate method of collecting anatomic and functional data of analytical value for optimizing and standardizing systemic oncolytic virotherapy. Importantly, the heterogeneity of oncolytic virus infection within a tumor hinders the comparison of infection distribution across multiple tumors and ultimately confirms the presence of tumor infection voids as a detriment to therapeutic efficacy. The heterogeneity of infection distribution identified here is a serious issue in the field of oncolytics. In order to advance oncolytic therapies, it will be necessary to optimize the predictability and homogeneity of virus delivery throughout every tumor, a pursuit in which 3D noninvasive imaging will be invaluable.2 atmosphere. MPC-11 murine myeloma cells and BHK-21 cells were obtained from and American Type Culture Collection  and cultured in Dulbecco’s modified Eagles medium  supplemented with 10% FBS. All media contained penicillin (100 U/ml) and streptomycin (100 mg/ml) antibiotics. MPC-11 cells are syngeneic to the immunocompetent BALB/c mouse strain and successful tumor growth confirms cell line identity. These cell lines were not otherwise authenticated.All cells were cultured at 37 °C in 5% CO19 was amplified in BHK-21 cells as previously described.19 Viral titer was quantified by measuring 50% tissue culture infective dose (TCID50) on BHK-21 cells calculated using the Spearman–Karber equation as described previously.27 Virus was stored at −80 °C until use.VSV-mIFNβ-hNIS6 cells in PBS, total volume 100 µl) into the right hind flank of 5-to-6-week-old female immunocompetent BALB/c  mice. Tumor burden was measured by serial caliper measurements. When the tumors reached 0.5 cm in diameter, a single dose of recombinant VSV in saline  was administered intravenously via the tail vein.Animals were maintained and cared for in accordance with Mayo Clinic Institutional Animal Care and Use Committee (A57712 and A23810). MPC-11 syngeneic murine myeloma tumors were established in mice by subcutaneously implanting washed tumor cells . During imaging, animals were maintained under general anesthesia with isoflurane in O2 supplied from a veterinary vaporizer and delivered through mouse-specific nose cones. The resulting Neuroimaging Informatics Technology Initiative (NIFTI) image files were analyzed using PMOD 3.5 software . Data analysis, region of interest quantitation and video files were generated by Imanis Life Sciences .A high-resolution microSPECT/CT system  was used for coregistered microSPECT/CT imaging 24–72 hours postvirus administration. A dose of 0.5mCi


The primary aim of this pilot study was to determine whether DN variation contributes to OCD risk. To this aim, we evaluated whether there is an elevated rate of DN mutations in OCD, which would justify this approach toward gene discovery in larger studies of the disorder. Furthermore, to explore functional molecular correlations among genes with nonsynonymous DN SNVs in OCD probands, a protein–protein interaction (PPI) network was generated based on databases of direct molecular interactions. We applied Degree-Aware Disease Gene Prioritization (DADA) to rank the PPI network genes based on their relatedness to a set of OCD candidate genes from two OCD genome-wide association studies . In addition, we performed a pathway analysis with genes from the PPI network. The rate of DN SNVs in OCD was 2.51 × 10−8 per base per generation, significantly higher than a previous estimated rate in unaffected subjects using the same sequencing platform and analytic pipeline. Several genes harboring DN SNVs in OCD were highly interconnected in the PPI network and ranked high in the DADA analysis. Nearly all the DN SNVs in this study are in genes expressed in the human brain, and a pathway analysis revealed enrichment in immunological and central nervous system functioning and development. The results of this pilot study indicate that further investigation of DN variation in larger OCD cohorts is warranted to identify specific risk genes and to confirm our preliminary finding with regard to PPI network enrichment for particular biological pathways and functions.Studies of rare genetic variation have identified molecular pathways conferring risk for developmental neuropsychiatric disorders. To date, no published whole-exome sequencing studies have been reported in obsessive-compulsive disorder (OCD). We sequenced all the genome coding regions in 20 sporadic OCD cases and their unaffected parents to identify rare  A substantial portion may indeed reflect gene–gene interactions and gene–environmental interactions that have not been taken into consideration in estimates of narrow sense heritability.3 In addition, understanding the heritability of genetic diseases requires a more comprehensive assessment of human genetic variation, including rare variation, throughout the genome.4The etiology of common neuropsychiatric disorders is believed to be multifactorial, with contributions from both environmental and genetic factors.5 Although protein-coding genes constitute only about 1% of the human genome, they are estimated to harbor 85% of mutations with large effects in disease-related traits.6The emergence of next-generation sequencing platforms is facilitating comprehensive searches for both rare and common single-nucleotide variants (SNVs) across all genes in the genome via whole-exome sequencing (WES).8 schizophrenia10 and autism14 by applying WES in families with no previous history of these disorders or their related phenotypes—so-called sporadic or simplex families—and identifying recurrent de novo (DN) SNVs. The DN SNVs tend to be more common in patients than in controls or unaffected siblings, mainly when such variations are nonsynonymous and located in brain-expressed genes.15Several groups have identified genes conferring risk for intellectual disability,17 Family, twin, segregation and linkage studies suggest a complex genetic etiology, complicating the confirmation of specific risk variants; consequently, the cellular and molecular mechanisms underlying OCD pathophysiology remain uncertain.18Obsessive-compulsive disorder (OCD) is a severe neuropsychiatric disorder, commonly having an early age of onset, and is characterized by the presence of obsessions  and compulsions (repetitive behaviors) that can become incapacitating.19 Significant variants were located in serotonergic genes, and, in males only, catecholamine modulation genes.19 The first published genome-wide association study (GWAS), querying common polymorphisms in a large cohort of OCD patients, did not find any variants reaching the genome-wide significance threshold, but top signals in several common variants were related to transcriptional regulation, cytoskeleton dynamics, ion channel assembly and gating, protein ubiquitination and degradation, and glutamate signaling.20 The latest GWAS in OCD21 also found no variants reaching genome-wide significance but showed significant overlap with top signals from the first GWAS. Furthermore, network analysis of top signals in these GWAS studies support the idea that genetic risk for OCD may cluster in certain biological networks or systems.22A meta-analysis of common variant genetic association studies of OCD found multiple polymorphisms with significant association.31 The first genome-wide investigation of rare copy number variation (CNV) in OCD and Tourette syndrome reported a 3.3-fold increase in large (>500 kb) deletions that overlap with CNVs reported in other neurodevelopmental disorders. An overall DN CNV rate of 1.4% was reported in OCD, slightly higher than the estimated frequency for controls, suggesting that rare DN variation may have a role in OCD pathogenesis.32 To date, there have been no published studies examining rare coding SNVs across the genome in OCD.There have been few studies examining rare variants in OCD.33 and that topological properties of PPI networks represent molecular functional correlations among genes that are important for understanding disease biology. We then performed Degree-Aware Disease Gene Prioritization (DADA), ranking our SNVs against ‘seed' genes identified from two OCD GWAS, predicting that top-ranked genes in this analysis would achieve greater connectivity in our PPI network. Finally, we asked whether all the genes in our PPI network were enriched in certain canonical biological pathways, in an attempt to enhance our understanding of OCD pathophysiology.In the current pilot study, we examined 20 simplex OCD parent–child trios using WES to detect SNVs. Given the recent success in other neuropsychiatric disorders, we focused on detection of DN SNVs in OCD, aiming to advance our understanding of the contribution of rare nonsynonymous DN SNVs to the disorder. Next, we mapped these SNVs onto a protein–protein interaction (PPI) network, hypothesizing that perturbations of integrated molecular networks through genomic and environmental influences can increase the risk for complex diseases such as OCDThis study was approved by the Research Ethics Committees of the University of São Paulo School of Medicine, as well as by the Brazilian National Commission of Research Ethics . All the participating subjects gave written informed consent.34 The parents were directly screened with the Structured Clinical Interview for DSM-IV Axis I Disorders; those with any Axis I psychiatric diagnosis were excluded.The OCD patients, meeting DSM-IV criteria for the diagnosis, and their unaffected parents, were recruited at the Outpatient Clinic of the Obsessive-Compulsive Spectrum Disorders Program of the Institute of Psychiatry, at the University of São Paulo School of Medicine Hospital das Clínicas. The probands were evaluated by semi-structured and structured interviews included in the Brazilian Research Consortium on Obsessive-Compulsive Spectrum Disorders instruments, administered by trained clinicians .34 The p11 and summarized below.Exome capture, sequencing and variant detection were performed at the Yale Center for Genomic Analysis, as described previouslyThe DNA samples from whole blood were enriched for exonic sequence with the NimbleGen SeqCap EZ Exome v2 capture library . The samples were sequenced using the Illumina HiSeq 2000 platform . We multiplexed four samples during each capture reaction and sequencing lane, pooling parents and probands when possible.http://bio-bwa.sourceforge.net/).35 The aligned reads were trimmed to the exome target using an in-house script. The trimmed and aligned data were converted to a sorted binary format, and duplicates were removed using SAMtools (http://samtools.sourceforge.net/),36 which was also used to identify the SNVs. SAMtools was selected for initial variant calling in order to replicate the bioinformatic pipeline used to analyze control subjects in the literature, which served as our comparison cohort for the overall rate of DN SNVs.11 For subsequent analyses of PPI networks, pathways and disease gene prioritization, we also considered confirmed variants from a second alignment and variant calling pipeline which followed the GATK v3 best practices guidelines (http://genome.ucsc.edu/) for impact on the encoded protein  and for population allele frequency using ExAC v0.3.37Short-read sequences were aligned to the human reference genome (hg19/NCBI 37) using the Burrows–Wheeler Aligner (idelines . The purhttp://pngu.mgh.harvard.edu/~purcell/plink/).38 Families were discarded if expected relationships did not confirm.A panel of informative genotypes was used to perform identity-by-descent in each study subject using the PLINK software package  the variant was not predicted in either parent, (2) at least eight unique reads supported the variant in the proband and (3) the locus had ⩾20 × coverage in both parents and the proband. We validated all the DN SNVs by PCR and Sanger sequencing . We calc39 and the iRefScape plugin40 to create a PPI network. This network is based on databases of direct physical interactions between genes, with proteins as nodes and interactions as undirected edges.41 We included PPIs that were present in any of the 10 databases consolidated within iRefIndex40  DN SNVs by constructing a PPI network. A network is a set of elements interacting with each other through pairwise interactions. In the case of biological PPI networks, the components are the gene proteins (nodes) that are connected to each other by links (edges) representing known physical interactions between two components. We used CytoscapefIndex40 . We applhttp://compbio.case.edu/dada/), a tool for performing network-based prioritization of candidate disease genes.43 DADA analysis of our candidate genes harboring validated DN SNVs detected by WES were performed with the ‘seed' genes (curated from other studies as likely to be associated with the disorder) from two OCD GWAS reported to date21  to map genes to canonical pathways  based on a hypergeometric distribution.To determine whether the list of all genes identified in our PPI network showed enrichment for specific biological pathways, we used Ingenuity Pathway Analysis  years and the mean Y-BOCS score was 26.1 (±5.7). Other clinical and demographic characteristics of included subjects are shown in de novo events in 92.8% of all targeted bases , 19 DN SNVs  were confirmed by Sanger sequencing . Among n−8 per base pair for human haploid genome and 1.12 events per trio. A two-tailed Poisson rate ratio test (R package rateratio.test) indicated that the rate of DN SNVs observed in our study differs significantly from that observed in unaffected siblings of autism probands, sequenced on the same platform and analyzed with the same bioinformatics pipeline11  DN SNVs, which we included in downstream analyses, for a total of 20 nonsynonymous DN SNVs . There wFAM5B, CCDC108, VCX2, MUC5B, ARHGAP6, SLC35G5) were not present in the PPI databases. Therefore, 14 genes served as the input for construction of our PPI network  were found to be highly and independently interconnected with other non-neighboring genes , displaying 40 and 187 interactions (edges), respectively; six genes were found to be ‘bottleneck' genes  that connect different complexes or pathways in the network; and three were found to be ‘bridge' genes  with high information flow, located between highly connected modules.  DN SNVs detected by our GATK and SAMtools pipelines , Table 1modules. . Network21 as seed genes  in our PPI network, three are considered bottleneck genes , and one is considered a bridge gene  signaling, bone morphogenic protein signaling and glucocorticoid receptor signaling  using all 320 nodes in the PPI network , as thesignaling . Narrowiignaling . There wling  in larger patient cohorts. Furthermore, the network analyses in this pilot study integrate prior findings from GWAS−8) differs significantly from a rate previously reported in unaffected subjects  in this analysis, suggesting that they may be more relevant for disease (52WWP1 (WW domain containing E3 ubiquitin protein ligase 1) inhibits transcriptional activity induced by TGF-β, a member of a highly pleiotropic cytokine family that maintains immune homeostasis, directs lymphocyte differentiation and orchestrates aspects of embryonic development including neuronal migration and synapse formation.54SMAD4 (SMAD family member 4) codes for a signal transduction protein that is activated by TGF-β signaling during proliferation and differentiation of the central nervous system.55 Furthermore, some of our candidate genes were classified as ‘bottlenecks' (key connectors) in the PPI network  receptor 1 [Knops blood group]), an important member of the family of regulators of complement activation and a crucial multifunctional mediator of innate immunity; MYO10 (myosin X), which may have a role in neurite outgrowth and axon guidance;57 and AP1G1 , important for the formation of clathrin-coated vesicles to transport ligand–receptor complexes from the plasma membrane or from the trans-Golgi network to lysosomes.58 Although AP1G1 has not been associated with any psychiatric disorder, it is noteworthy that the first genome-wide investigation of large, rare CNVs in OCD found a de novo CNV encompassing AP1GBP1, a related gene that also acts on clathrin-coated vesicles and may have a role in endocytosis.32In addition to the variants conformed using SAMtools, we confirmed eight additional nonsynonymous DN SNVs, predicted using a GATK best practices pipeline. We used all 20 nonsynonymous DN SNVs detected by both pipelines for downstream analyses. Given that protein gene products associated with disease have a higher likelihood of physically interacting, disease .52WWP1  network , believe21 two of the three highest ranked genes (WWP1 and SMAD4) were found to be brokers in the PPI network (BAMBI (bone morphogenetic protein and activin membrane-bound inhibitor), coding for a pseudoreceptor that negatively modulates TGF-β.59BAMBI is classified as a bridge gene in our PPI network and is among the genes regulated by top-ranking SNPs in the OCD GWAS I secondary analysis;20 its protein product has been found to be selectively and significantly enriched in white matter progenitor cells, and can modulate their differentiation in oligodendrocytes or astrocytes.60To investigate the potential relevance of our PPI network to OCD, we conducted a DADA analysis to see whether prioritized genes would have more connectivity in our PPI network. In this analysis, using a seed gene list curated from the two GWAS in OCD, network . The hig network  was BAMB have negative Residual Variation Intolerance Scores,61 indicating that in large exome-sequencing databases, they are found to contain less common functional genetic variation relative to the genome-wide expectation. Genes with negative Residual Variation Intolerance Scores are referred to as relatively ‘intolerant' to genetic variation and more likely to be involved with neurodevelopmental disease61  sporadic OCD families may have higher rates of DN coding SNVs, suggesting that further study of this type of variation in larger cohorts holds potential to identify risk genes; (2) a PPI network can be constructed on the basis of DN SNVs that appears to have relevance to OCD, judged against common variant findings from other genetic studies of OCD; and (3) PPI network genes show enrichment for biological pathways and functions involved with neurodevelopmental and immunological processes. These findings hold great promise for elucidating our understanding of OCD neurobiology and potential treatments, and deserve further scrutiny in larger cohorts.


However, genetic variation in this pathway has not been examined with human alcohol use phenotypes. Among a sample of adults living in Detroit, Michigan , 206 genetic variants across the mGluR–eEF2–AMPAR pathway  were found to predict number of drinking days per month  when considered as a set (set-based linear regression conducted in PLINK). In addition, a CpG site located in the 3′-untranslated region on the north shore of EEF2 (cg12255298) was hypermethylated in those who drank more frequently (P<0.05). Importantly, the association between several genetic variants within the mGluR–eEF2–AMPAR pathway and alcohol use behavior  replicated in the Grady Trauma Project (GTP), an independent sample of adults living in Atlanta, Georgia , including individual variants in GRM1, GRM5, EEF2, MTOR, GRIA1, GRIA4 and HOMER2 (P<0.05). Gene-based analyses conducted in the GTP indicated that GRM1  and EEF2  withstood multiple test corrections and predicted increased alcohol consumption and related problems. In conclusion, insights from rodent studies enabled the identification of novel human alcohol candidate genes within the mGluR–eEF2–AMPAR pathway.Rodent models implicate metabotropic glutamate receptors (mGluRs) and downstream signaling pathways in addictive behaviors through metaplasticity. One way mGluRs can influence synaptic plasticity is by regulating the local translation of AMPA receptor trafficking proteins via eukaryotic elongation factor 2 ( In this respect, the wealth of potential therapeutic targets collected from validated animal models of alcohol drinking could contribute to advances in the identification of mechanisms underlying genotype–phenotype associations in humans.6Advances in genomic technology have enabled progress toward identifying genes involved in alcohol use disorders. This has led to a growing recognition of the need to understand the mechanisms underlying genotype–phenotype associations for continued progress in the translational sciences.7 Metabotropic glutamate receptors (mGluRs) are G-protein-coupled receptors that are expressed throughout the central nervous system that can be found in both the pre- and postsynaptic membrane of glutamatergic synapses. MGluRs and downstream signaling pathways are known to regulate several alcohol-related behaviors in animal studies. For example, pharmacological inhibition and genetic targeting of Group 1 mGluRs, mGlu1 and 5  significantly reduces alcohol self-administration and relapse-like behavior in rodents.15 Furthermore, downstream pathways including signaling through the mGlu1/5 scaffolding proteins and downstream protein kinases influence excessive alcohol consumption.22 As a result of this compelling data, mGluRs are currently being explored as biomarkers for alcohol abuse and as pharmacologic targets for therapeutic treatment of alcohol use disorders.24Chronic alcohol abuse is thought to lead to adaptations of the glutamate system that result in a ‘hyper-glutamatergic' state.26 One particular Group 1 mGluR (mGlu1 and 5) pathway that has gained interest in addictive disorders is the mechanism by which mGluR activation controls rapid local translation of proteins involved in long-term synaptic plasticity,27 which can occur through the phosphorylation of eukaryotic translation elongation factor 2 ), and signaling proteins , and eEF2-kinase (eEF2K)), influence eEF2 to act as a switch from promoting global protein synthesis to the local translation of a set of proteins, such as calcium/calmodulin-dependent protein kinase II alpha, (CaMK2α) and activity-related cytoskeleton protein Arc (ARC). These proteins  regulate the trafficking of α-amino-3-hydroxy-methyl-4-isoxasolepropionic acid receptors (AMPA) glutamate receptors , thereby affecting synaptic strength. Further, AMPA receptors (AMPARs), ARC and CaMK2α have independently been implicated in alcohol consumption in rodent models.32 Recent evidence has shown that regulating AMPAR trafficking through mGluRs in specific neural circuits can reverse addictive behaviors in rodents.34 There is an abundance of evidence to suggest a link between variants in the mGluR–eEF2–AMPAR pathway and alcohol use behaviors; however, this pathway has not previously been examined in humans. Therefore, the primary goal of this study is to examine genotypic variation in the mGluR–eEF2–AMPAR pathway as it relates to alcohol use behavior in humans.Alterations in synaptic strength are one potential physiological mechanism underlying the maladaptive learning processes associated with addiction. 2 eEF2; .28, 29 S35 showed a higher peripheral blood DNA methylation level in the α-synuclein gene (SNCA) in alcoholic patients than in controls. Since that time, subsequent studies using peripheral blood indicated that several other genes38 were more highly methylated in subjects with alcoholism than in controls. Only one of these studies examined methylation effects in an African American sample, and found that CpG sites in multiple genes were hypermethylated in alcoholic cases,39 which is consistent with findings in other populations.38Although there is a large literature examining genetic influences on alcohol use behaviors, comparatively fewer studies have examined epigenetic profiles in alcohol consumption. Epigenetic modifications are regulatory mechanisms that alter gene expression without changing DNA sequence. One of the best-studied epigenetic mechanisms is DNA methylation, which may change activity in the promoter region, as well as other genic regions, and thus regulate gene transcription. Accumulating data indicate that heavy alcohol consumption can alter the methylation status of specific genes. One of the first studiesmGluR–eEF2–AMPAR using gene-based and pathway-based analyses.40 Given the likely multifactorial nature of alcohol consumption, we hypothesize that genetic variants in the mGluR–eEF2–AMPAR pathway will have a combined effect on risk for heavy alcohol consumption and related problems . In the DNHS, we explore one potential mechanism underlying the genotype–phenotype association, by testing the association between alcohol consumption and methylation sites in the eEF2 region, a functional target of the mGluR–eEF2–AMPAR pathway.Drawing on samples from two community-based majority African American cohorts, the Detroit Neighborhood Health Study (DNHS) and the Grady Trauma Project (GTP), we assessed the association between frequency of alcohol consumption and genotypic variants in the et al.41Participants include 788 individuals with available genetic data who participated in the DNHS, a longitudinal population-based cohort of male and female (57.7%) adults  living in Detroit, MI, USA. Participants were administered a 40-min assessment, which included demographic variables including self-identified race, sex, age, as well as questions on alcohol use, and other behavioral characteristics. Assessments were administered using structured interviews, and each participant received $25 for their participation in the survey. Written and verbal informed consent was obtained for all the participants, and all procedures in this study were approved by the institutional review boards of the University of Michigan. When asked how they would describe their racial background, 82.5% endorsed ‘Black/African American', with the remainder of the sample endorsing ‘Asian', ‘American Indian', or ‘Alaska Native', paralleling census-based estimates on individuals living in Detroit, MI, USA. Further details regarding the DNHS study background and collection can be found in Uddin 42 35.7% of the full sample had never consumed alcohol in their lifetime and were excluded from further analyses.The following question was used to assess alcohol consumption: ‘Thinking about the past 30 days on how many days did you drink any alcoholic beverages?' Only individuals who had evidence of alcohol exposure were included in analyses so that genetic and environmental influences on the decision to initiate alcohol drinking are not confounded with genetic and environmental influences on current frequency of consumption.n=125), and received an additional $25 if they elected to do so. A total 802 samples were collected from consenting participants for genetic analysis. DNA samples were sent to the Applied Genomics Technology Facility  for genotyping using the HumanOmniExpress BeadChips . Further details regarding genome-wide association procedures have been published previously.43 Samples were removed due to low call rate (<95%) and duplicate issues, with a remaining sample of 795 individuals (418 women and 367 men). A total of 688 890 single-nucleotide polymorphisms (SNPs) passed quality control filters .Respondents were also asked to provide blood specimens by way of venipuncture . All 31 MDS components were used to adjust for any remaining population stratification in the association analyses.We used multi-dimensional scaling (MDS) analysis of genome-wide identity-by-state data implemented in PLINK version 1.07 ref.  to deterP>1 × 10−6) within 100 kb upstream and downstream of each of the genes of interest in the mGluR–eEF2–AMPAR pathway  were included as the trafficking of these proteins are the functional consequence targeted by this pathway. Genotypic analyses included 15 SNPs in glutamate receptor metabotropic 1 (GRM1), 38 SNPs in glutamate receptor metabotropic 5 (GRM5), 31 SNPs in Homer homolog 1 (HOMER1), 38 SNPs in Homer homolog 2 (HOMER2), 15 SNPs in MTOR, 19 SNPs in eukaryotic elongation factor 2 kinase (EEF2K), 8 SNPs in Eukaryotic Translation Initiation Factor 4E (EIF4E), 4 SNPs in Eukaryotic Translation Elongation Factor 2 (EEF2), 13 SNPs in calcium/calmodulin-dependent protein kinase II (CAMK2A), 3 SNPs in activity-related cytoskeleton protein Arc (ARC), 9 SNPs in glutamate receptor, ionotropic, AMPA 1 (GRIA1) and 13 SNPs in glutamate receptor, ionotropic, AMPA 4 (GRIA4). Note that GRIA2 and GRIA3 were excluded from these analyses due to exclusions based on quality control criteria .In the following analyses, we analyzed 206 SNPs available and meeting quality control criteria  during the in-home visits when the biologic specimens were collected.A total 179 blood samples were obtained from consenting participants via venipuncture and whole-blood-derived genomic DNA was isolated from 400 ml of whole blood using the DNA Mini Kit  following the manufacturer's recommended protocol. One microgram of the resulting genomic DNA was then submitted for DNA methylation profiling by the genomics core facility, AGTC, at Wayne State University using the HumanMethylation450 BeadChip (Illumina). Genomic DNA was denatured and bisulfite converted using the Zymo EZ-96 DNA Methylation kit deep well format , following the alternative incubation protocol. Bisulfite converted DNA was amplified, fragmented and hybridized to the HumanMethylation450 Beadchip using standard Illumina Infinium Methylation HD protocol. Arrays were then scanned on the Illumina iScan using standard Illumina scanner settings. The resulting raw DNA methylation data were imported from IDAT files, Illumina's proprietary file format, using the R package ChAMPEEF2) detailed in In accordance with this study's hypothesis, and to reduce the number of tests run (and the related need for multiple test corrections), we analyzed eight CpG sites available within 100 kb upstream and downstream of eukaryotic translation elongation factor 2  adults  approached while in the waiting rooms of primary care or obstetrical–gynecological clinics of Grady Memorial Hospital in Atlanta, GA, USA. This population predominantly (95.2%) self-identifies as ‘African American/Black', the remainder of the sample self-identifies as ‘White' (3.3%) or ‘Other.' Participants were paid $15 for this phase of the study. Written and verbal informed consent was obtained for all participants, and all procedures in this study were approved by the institutional review boards of Emory University School of Medicine and Grady Memorial Hospital, Atlanta, GA, USA. Participants completed a battery of self-report measures that took 45 to 75 min to complete.50). Each of the 10 AUDIT items is rated on a scale of 0–4, with higher scores reflecting more problematic alcohol drinking. The first question of the AUDIT was used to examine alcohol consumption: ‘During the last year, on average, how many drinks containing alcohol do you have on a typical drinking day?' Individuals who had never consumed alcohol in their lifetime  were excluded from analyses. Other AUDIT items address problematic behavior or consequences accompanying heavy alcohol consumption, such as ‘How often during the last year have you failed to do what was normally expected of you because of drinking?' and ‘How often during the last year have you been unable to remember what happened the night before because of drinking?'. Thus, the GTP measure of alcohol consumption differs from that obtained in the DNHS in its time period  and that it also includes alcohol consumption-related hazardous behaviors. Twin and molecular genetic studies have shown that there is evidence for shared genetic influences across different phenotypes.52 Therefore, convergent findings from these two alcohol phenotypes (frequency of consumption in past 30 days vs AUDIT scores) would increase support for findings. Full data were available on 1034 participants.Measures of alcohol use were obtained by verbal interview using the Alcohol Use Disorders Identification Test , and genotyping was conducted using Illumina's Omni1-Quad BeadChip. The Omni1-Quad BeadChip interrogates 1 011 219 individual SNPs. Genotypes were called using Illumina's GenomeStudio software. PLINK version 1.07 ref.  was usedMDS analysis conducted in PLINK was run on 1 011 219 SNPs to infer axes of ancestry. On the basis of MDS, African American subjects who fell within three standard deviations of the medians of the first and second MDS component in this sample were retained (92%). To reduce population stratification, we removed all individuals who described themselves as ‘White' in the association analyses (3.3%). All MDS components were used to adjust for any remaining population stratification in the association analyses.n=676; methylation n=154), descriptive statistics on key variables, as well as comparisons between the subset of individuals with methylation data to the genotypic sample are provided in z-scored) and modeled as a continuous variable. We assessed main effects of all 206 SNPs and eight CpG sites available using the SNP or methylation beta value as predictors, controlling for demographic characteristics , behavioral characteristics  and peripheral blood mononuclear cell count. Methylation beta values were centered to the mean in all models.For the DNHS samples  within that pathway set. In the main effects model, SNP coefficients were accepted as significant if the 100 000 permutations of the set-based regression analysis  produced an empirical P-value <0.05. Coefficients for gene methylation value were accepted as significant if P<0.006, based on the Bonferroni correction for multiple testing . Finally, post hoc analyses of individual SNPs included in the mGluR–eEf2–AMPAR pathway were conducted to provide further information on each individual variant. These analyses were conducted in PLINK using linear regression for a quantitative phenotype.Gene-based and pathway-based tests were conducted in PLINK  using set-based analyses (‘—set-test'). Gene-based tests included all available SNPs in a given gene, corrected for the number of independent signals  within that gene set. Pathway-based tests included all available SNPs within each of the genes considered in the n=1352). In the GTP, AUDIT scores were modeled as continuous variables. Using the same parameters as described above , gene-based and pathway-based tests were conducted in PLINK using set-based analyses. As described above, post hoc analyses of individual SNPs included in the mGluR–eEf2–AMPAR pathway were conducted to provide further information on each individual variant in the GTP.In an effort to replicate the genotypic analyses conducted in the DNHS, parallel analyses were conducted in the GTP , MTOR , EIF4E , EEF2  and CAMK2A  withstood multiple test corrections and predicted increased alcohol consumption in the DNHS. Pathway-based analyses indicated that 206 variants across each of these genes considered as a single set significantly predicted alcohol consumption in the DNHS . This association withstood correction for the number of independent signals tested across these 206 variants (seven linkage disequilibrium blocks). Several individual SNPs across GRM1, GRM5, HOMER1, HOMER2, MTOR, EIF4E, EEF2, CAMK2A and GRIA4 were associated with increased alcohol consumption in the DNHS .Gene-based analyses indicated that the DNHS . HoweverGRM1, GRM5, HOMER1, HOMER2, EEF2K, MTOR, EIF4E, EEF2, CAMK2A, ARC, GRIA1, GRIA4 and alcohol use behavior (AUDIT) in the GTP. Although the association between this pathway (174 SNPs) and alcohol consumption only trended towards significance in the GTP , several variants within this pathway were associated with alcohol consumption in the GTP, including variants in GRM1, GRM5, HOMER4, MTOR, EEF2, GRIA and GRIA4 , and EEF2 , withstood multiple test corrections and, as in the DNHS, predicted increased alcohol use behavior in the GTP , located in the 3'-untranslated region on the north shore of eEF2 , was significantly and positively related to alcohol consumption  and withstood a multiple test correction (0.05/8=0.006). For individuals with higher drinking days per month (1 s.d. above the mean), cg12255298 was hypermethylated as compared with individuals with fewer drinking days per month (1 s.d. below the mean). This relationship is depicted in Of the eight CpG sites examined across the mGluR–eEf2–AMPAR pathway  predicted number of drinking days per month when considered as a set. Further, a CpG site in the 3′-untranslated region on the north shore of EEF2 (cg12255298) was hypermethylated in those who drank more frequently. Importantly, the association between alcohol use behavior and several SNPs within this pathway  were replicated in an independent sample, the GTP. When considered as a set in the GTP, the mGluR–eEf2–AMPAR pathway only approached significance , with significant gene-based effects observed for GRM1 and EEF2. Taken together, these results suggest that insights gained from rodent studies enabled the examination of novel alcohol candidate genes within the mGluR–eEf2–AMPAR pathway. Such pathway-based approaches,40 combined with functional genomic data, can aid the field in identifying mechanisms underlying phenotype–genotype associations.We believe this is the first study to examine the association between human alcohol use and variants in the  pathway , which hmGluR–eEF2–AMPAR related genes  to alcohol use behaviors in rodents.58 However, relatively few studies have examined these genetic variants in human genetic association studies. Exceptions include variants in GRM5, CAMK2A and GRIA2, which have independently been identified in association studies of alcohol use phenotypes in humans,59 with preclinical rodent models supporting their role in functional regulation of alcohol self-administration.61 To our knowledge, the mGluR–eEF2–AMPAR pathway variants implicated in this study have not been previously associated (at the genome-wide level) with alcohol consumption behavior in large genome-wide association studies (GWAS). This includes the largest GWAS of alcohol consumption behavior conducted on 26 316 individuals  is implicated in this study's findings. Notably, the participants in the Schumann et al. GWAS, as well as other previously published large GWAS of alcohol use behavior  are largely of European ancestry. This study's findings may indicate the potential to identify novel genetic risk variants for alcohol use behavior in populations of African ancestry. Importantly, these findings should be replicated in other admixed populations to determine whether mGluR–eEF2–AMPAR effects are more relevant to alcohol use behavior in specific populations.Several studies have linked GRM1 variant rs2235875, which is significantly associated with alcohol use behavior across the DNHS and GTP, is an intronic variant and rs3170368, which is also associated with alcohol use behavior across both samples, is a variant located in the 3′-untranslated region of EEF2.Although this study emphasizes a systems-based approach to genetic association , the individual SNPs most robustly associated with alcohol use behavior in this study are detailed in 39 The methylation patterns observed in this study are consistent with previous studies of other genes,39 in that CpG sites located within EEF2 are hypermethylated in heavier drinkers  as compared with lighter drinkers . Given the relatively small sample sizes in the present study, particularly for the epigenetic subsample, these genetic and epigenetic effects should be replicated in future studies with greater sample size and ancestral diversity.Previous studies examining methylation patterns in alcohol use phenotypes have found that CpG sites are typically hypermethylated among alcoholic cases as compared with controls.63 For example, there has been an increased focus on how drugs of abuse  affect glutamate synaptic plasticity in mesocorticolimbic circuitry that may explain the selective and enduring nature of addiction.64 It is now well supported that acute and long-term alcohol exposure alters glutamatergic synaptic plasticity in preclinical animal models.68 Signaling through mGluRs can modulate synaptic plasticity by regulating the insertion of AMPARs in the postsynaptic membrane. AMPAR trafficking underlies multiple mechanisms of neuroplasticity implicated in drug addiction including unsilencing of synapses, long-term potentiation, long-term depression and synaptic scaling.69 Genotypic variation in this pathway may lead to individual differences in the response to alcohol consumption. For instance, SNPs detected here may influence the expression of proteins in this pathway and evidence from genetic manipulations in preclinical models support their role in alcohol consumption.55 Furthermore, long-term alterations in glutamate transmission that occur following drug/alcohol exposure have subsequently been shown to functionally regulate addictive behaviors such as drug sensitization, motivation and relapse-like behavior.72 Therefore, the variants in the mGluR–eEF2–AMPAR pathway may not only influence behavioral components of alcohol consumption but also the susceptibility to developing an alcohol use disorder. This has led to the interest in AMPAR and mGluR modulators as potential pharmacotherapies for alcohol use disorders.64 Given that GRM1 (and not GRM5) was found to be associated with alcohol use behavior, these findings emphasize its role regulating alcohol consumption in humans.Several mechanisms linking altered excitatory neuroplasticity to substance abuse have been hypothesized.mGluR–eEF2–AMPAR pathway in human alcohol use behavior, and adds support to the cross-species evidence for this pathway in alcohol use behavior. Furthermore, this study is the first to find a significant association of GRM1 and/or EEF2 with alcohol use behavior. However, the results of this study should be interpreted in light of several key limitations. First, sample sizes in both the DNHS and the GTP were limited; this study only had adequate power to detect 78% and 92% of main effects with risk ratios >1, given the parameters of the DNHS and GTP, respectively. However, the cross-species consilience, as well as the replication of variants (GRM1 and EEF2) in an independent sample lessens this concern.6 Second, the present analysis uses data available at a single time point, using retrospective reports of alcohol behavior measured differently in two population-based samples. Twin and molecular genetic studies have shown that different measures of alcohol phenotypes can yield significantly different findings.52 However, there is also evidence for shared genetic influences across different phenotypes.51 Therefore, convergent findings from these two alcohol phenotypes (frequency of consumption in past 30 days vs AUDIT scores) increase support for these findings. In addition, previous studies have found that individuals typically underreport their drinking consumption,74 which would likely diminish the association effects observed in this study. However, recent studies directly comparing retrospective reports to observational data conclude that this bias is significantly less pronounced in regular drinkers and when less time has passed since the drinking days being recalled.74 Therefore, the past recent drinking period examined in this study somewhat mitigates this concern. Third, the variants included in this pathway analysis were limited by availability of genotypes in the DNHS and GTP. For example, GRIA2 and GRIA3 were excluded from this study due to exclusions based on quality control criteria . Finally, in addition to genetic and epigenetic influences on alcohol consumption, key aspects of the contextual environment have a large impact on alcohol use behaviors. Due to the focus on a novel pathway, and limited power to examine gene–environment interactions in this statistical paradigm, environmental influences were not examined in the present study and should be included in future studies.This study indicates a role for the 3 In this study, we utilized two established approaches to delve further into the associations observed between genotypic variation and alcohol consumption in two human samples. This study used insights gained from rodent studies to identify novel alcohol candidate genes within the mGluR–eEf2–AMPAR pathway and to elucidate mechanisms related to altered glutamatergic transmission and synaptic plasticity. Given the findings from the present study, there is growing support for the role of GRM1 and EEF2 in alcohol use behaviors across species and across methodologies. Such translationally informed pathway approaches offer promise in propelling the field towards understanding mechanisms underlying phenotype–genotype associations.Although advances in genomic technology and bioinformatics have enabled progress toward identifying some genes involved in alcohol use disorders, few studies conducted in human samples have identified mechanisms underlying genotype–phenotype associations. These mechanisms are important for downstream use in the translational sciences including pharmacologic, clinical and public health interventions.


CYP2D6 and CYP2C19 by comparison with MiSeq® amplicon sequencing data (n = 36). This analysis revealed that the concordance rate between WES and MiSeq® was high, achieving 99.60% for variants that were called without exceeding the truth-sensitivity threshold (99%), defined during variant quality score recalibration (VQSR). Beyond this threshold, the proportion of discordant calls increased markedly. Subsequently, we expanded our findings beyond CYP2D6 and CYP2C19 to include more genes genotyped by the iPLEX® ADME PGx Panel in the subset of twelve samples. WES performed well, agreeing with the genotyping panel in approximately 99% of the selected pass-filter variant calls. Overall, our results have demonstrated WES to be a promising approach for pharmacogenomic profiling, with an estimated error rate of lower than 1%. Quality filters, particularly VQSR, are important for reducing the number of false variants. Future studies may benefit from examining the role of WES in the clinical setting for guiding drug therapy.Whole-exome sequencing (WES) has been widely used for analysis of human genetic diseases, but its value for the pharmacogenomic profiling of individuals is not well studied. Initially, we performed an in-depth evaluation of the accuracy of WES variant calling in the pharmacogenes  CYP2D6 gene is confounded by the presence of closely related pseudogenes, CYP2D7 and CYP2D8, such that pre-amplification with long-range PCRs is usually applied to avoid undesired sequence contamination (Whole-exome sequencing (WES) is an increasingly important technology in rare-disease  and drugmination .CYP2D6 and CYP2C19 for 36 samples by amplicon sequencing on the MiSeq® platform. Then, we expanded our findings and evaluated the more general applicability of WES to pharmacogenomic profiling by cross-comparison with the iPLEX® ADME PGx Panel. The iPLEX®ADME PGx panel uses the MassARRAY® system  to simultaneously analyze 184 single nucleotide polymorphisms (SNP), insertions and deletions (INDELs) and 16 copy number variations (CNV) across 36 genes relevant to drug absorption, distribution, metabolism, and excretion.The work described in this paper had two primary aims. First, we carried out an in-depth cross-validation of WES variant calls in A total of 36 samples were included in this study. These sequenced samples comprised various research samples referred to our laboratory for pharmacogenomic investigation. This study was approved by the Southern Health and Disability Ethics Committee, New Zealand. Potential participants were contacted first by mail and were required to indicate interest to participate by filling in and returning an enclosed form. Face-to-face interviews were subsequently conducted to obtain written consent and collect relevant medical history. The study information sheet and consent form included procedures for handling of incidental findings, which would be followed up in consultation with a clinical geneticist. DNA was extracted from peripheral blood leukocytes using a KingFisher Flex Magnetic Particle Processor, as per the manufacturer’s instructions .CYP2D6 and CYP2C19 genes were performed. For WES, paired-end 100-bp sequence reads were generated on HiSeq® 2000 and aligned by BWA v0.74 (http://picard.sourceforge.net). Reads originating from PCR duplicates were removed with Picard before and after local realignment around potential indels with GATK v2.7.1 . DNAs from these samples were standardized to 10 ng/μL in a final volume of 200 μL. This was followed by genotyping on the MassARRAY® System (Agena Bioscience) using iPLEX® Gold Biochemistry and Typer v4.0 Software (Agena Bioscience).A subset of twelve samples were then selected for multiplexed genotyping by the iPLEXTM capture kit, were apt to be poorly covered. Variant calls generated by MiSeq® amplicon sequencing were required to have an approximate read depth of at least 10-fold to be considered sufficiently confident. A false-positive event was defined as the calling of the alternate allele that was determined to be absent by MiSeq® amplicon or follow-up Sanger sequencing. A false-negative event was defined as the failure to detect the alternate allele(s).Whole-exome sequencing genotype calls having a read depth < 4 or a genotype quality score < 10 were designated “not evaluable.” GATK defines genotype quality score as “the Phred-scaled confidence that the genotype assignment is correct.” Further, to assess the effectiveness of VQSR at improving variant calling accuracy, we divided all variants into two sets, namely those that were called below the 99% truth sensitivity threshold, also designated “pass-filter,” and those that were called with surplus sensitivity (< 99.9%). These values represent varied levels of variant detection sensitivity relative to a set of known, true variants; and 99% is a commonly adopted threshold. Then, because the call-sets comprised a mix of on- and off-target variants, those sites that were distant from the target segments, defined for the TruSeq® ADME PGx Panel. Again, only pass-filter WES genotype calls, i.e., having a read depth ≥ 4 and a genotype quality score ≥ 10, were considered. For the iPLEX® data set, only genotype calls that had a call rate > 85%, indicating good quality, were selected for validation. The call rates were determined by the Typer 4 (Agena Bioscience) software, having assessed spectra quality related to the reported haplotype. Sanger sequencing was performed to resolve disagreement between the two platforms.Comparison was carried out for variant calls emitted by both WES and the iPLEXCYP2D6 and CYP2C19, with data generated by an amplicon sequencing assay we developed on the MiSeq® (Illumina) platform, for the same 36 samples ; of these, 27 were called without violating the 99% truth-sensitivity threshold. WES variant calling was highly accurate for the 27 pass-filter variant sites, achieving a concordance rate of 99.60% (Table 2). For the 43 variant sites identified by WES a total of 943 individual WES genotype calls were generated across the 36 samples, but 202 calls were excluded for being of insufficient quality  or because MiSeq® amplicon sequencing data were not available. Of the 741 verifiable WES genotype calls, only three calls were found to be discordant with MiSeq® data. None were false-positive. Sanger sequencing revealed that two of the three WES genotype calls that were discordant with MiSeq® data (rs17885098 and rs3758581) were actually accurate (Table 3).Whole-exome sequencing was carried out on 36 subjects referred to our laboratory for PGx analysis. We initially compared the WES data quality for two key pharmacogenes, ® data dropped considerably to 89.11% (Table 2). A total of 1512 calls were generated for the 43 variant positions; but quality filtering and a dearth of sufficiently confident MiSeq® data (read depth < 10) for a number of sites resulted in a final comparison set of 1249 WES genotype calls. Of these, 136 mismatches were identified, representing a large increase of 133 discordant calls in relation to the more stringent call-set (truth-sensitivity threshold < 99%). The majority of these discrepant calls were false-positives.In contrast, when the truth sensitivity threshold was relaxed to include an additional collection of 16 variant sites (< 99.9%), the rate of concordance with MiSeq® ADME PGx Panel (Agena Bioscience). Of all 192 polymorphisms covered by the iPLEX® ADME PGx panel, 184 are SNPs and INDELs. Notably, 16 variant sites were not captured by the Nextera exome capture kit; hence, these variants could not be screened by WES (Table 4). Only pass-filter WES genotype calls  were considered for comparison, which resulted in a final set of 64 variant sites that were called by both WES and the iPLEX® ADME PGx panel (Table 5).We next sought to examine the concordance between WES data and a broad PGx profile of 192 nucleotide variations in 36 genes, generated on a subset of 12 subjects using the iPLEX® ADME PGx Panel (Table 6), yielding a concordance rate of 98.89%. By Sanger sequencing, we confirmed that all these discordant calls were correctly genotyped based on the WES data.After eliminating all low-quality (described above) and missed calls from both panels, 719 genotype calls were included in our final analysis. Of these, eight calls at four variant sites were found to be discordant with the iPLEXPrevious studies have established WES to be an effective, high-throughput variant detection tool that has been successfully used in the analysis of Mendelian disorders . This seCYP2D6 and CYP2C19 were highly accurate, yielding a near-perfect degree of concordance with the MiSeq® amplicon sequencing data, despite previous concerns that WES is likely to underperform in genes with closely related homologs , is located outside the capture intervals of the TruSeqTM kit. Nevertheless, 100% agreement was found between WES and MiSeq® amplicon sequencing data for the variant. Had strict target definitions been adopted with no interval extension or “padding,” this variant would be overlooked. Given the intrinsic mechanism of TruSeqTM capture, labeling the off-target sequences as such is slightly misleading. The kit employs contiguous probes to target regions of interest; thus capture of overhanging sequences in fragmented genomic DNA is unavoidable. The current GATK practice supports a certain extent of flexibility in WES data processing to encompass variants located within the exon-intron boundaries. Off-target variants represent a valuable subset that gives added information, and this has already been reported by other investigators  with respect to their approximate read depth and distance from the nearest target segment. Coverage appeared to range from poor to good even for variants located at similar distances from TruSeq® ADME PGx Panel in a subset of 12 samples. The panel successfully detected 181 single-nucleotide variants per sample but of these, only 64 were also called by WES without violating the truth-sensitivity threshold. The majority of the WES-derived variant calls were consistent with those obtained from the iPLEX®ADME PGx Panel, giving a concordance rate of 98.89%. Eight discordant WES genotype calls were observed and were subsequently verified by Sanger sequencing, again confirming the accuracy of WES variants.Using the truth-sensitivity threshold pre-defined above, we then assessed the broader applicability of WES to pharmacogenomic profiling by cross-comparison with the iPLEXOverall, our results have demonstrated that as a pharmacogenomic screening tool, WES has an estimated error rate of lower than 1% for VQSR-filtered variants. This accuracy of the WES dataset reflects the increased reliability and quality of data now available using longer reads for WES analysis. The error rate would probably decrease further in the future with improved analysis software or sequencing workflows. Applying VQSR alone appears unlikely to remove all false-positive variant calls and additional filters are required. GATK Best Practice currently implements the Genotype Refinement workflow to achieve higher data quality. In this pipeline, genotypes with quality score < 20 are filtered out following VQSR. A recent study has also found that read depth ≥ 8 and genotype quality ≥ 20 are good thresholds for removing unreliable genotype calls . Our resCYP2C19 gene, was not detected by WES. This variant is common in the Caucasian population with an allele frequency of 18% . Various practical aspects of reporting WES results should be considered, such as obtaining patient consent for storing and utilizing this piece of information, translating the WES data into an easy-to-understand format, and determining the actionability of novel reported variants.We have demonstrated that WES is a promising tool in detecting pharmacogenomic variants, even for complex loci such as the EWC, SC, KT, and PS performed the experiments; EWC, SC, KT, and KL analyzed the sequence data; EWC and KT drafted the manuscript; SC, KL, PS, NH, and MK critically reviewed the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.