Aggregate invasion in gradient hydrogels occurred bi-directionally with sprout alignment observed in the direction parallel to the gradient while control hydrogels with homogeneous properties resulted in uniform invasion. In PBFP gradient hydrogels, aggregate sprout length was found to be twice as long in the direction parallel to the gradient as compared to the perpendicular direction after three weeks in culture. This directionality was found to be more prominent in gradient regions of increased stiffness, crosslinked MMP-sensitive peptide presentation, and immobilized YRGDS concentration.The spatial presentation of immobilized extracellular matrix (ECM) cues and matrix mechanical properties play an important role in directed and guided cell behavior and neovascularization. The goal of this work was to explore whether gradients of elastic modulus, immobilized matrix metalloproteinase (MMP)-sensitivity, and YRGDS cell adhesion ligands are capable of directing 3D vascular sprout formation in tissue engineered scaffolds. PEGDA hydrogels were engineered with mechanical and biofunctional gradients using perfusion-based frontal photopolymerization (PBFP). Bulk photopolymerized hydrogels with uniform mechanical properties, degradation, and immobilized biofunctionality served as controls. Gradient hydrogels exhibited an 80.4% decrease in elastic modulus and a 56.2% decrease in immobilized YRGDS. PBFP hydrogels also demonstrated gradients in hydrogel degradation with degradation times ranging from 10–12 hours in the more crosslinked regions to 4–6 hours in less crosslinked regions. An  Tissue engineering seeks to replace or restore damaged and/or diseased tissues using a combination of biocompatible, biodegradable polymer scaffolds, biofunctional molecules and cells. Although significant progress has been made with respect to engineering thin or avascular tissues, the regeneration of thicker and more metabolically demanding tissues has been hindered by the inability to promote rapid and stable neovascularization resulting in insufficient oxygen and nutrient delivery. Therefore, the continued enhancement of tissue engineered scaffolds is highly dependent on the ability to stimulate rapid and stable neovascularization within a biomaterial In vivo studies of the retina have demonstrated that gradients of secreted vascular endothelial growth factor (VEGF) help shape vascular patterns during angiogenic sprouting by guiding endothelial tip cell formation and subsequent migration In vitro studies using rigid substrates have shown that endothelial cells align with laminin A variety of growth factors have been shown to stimulate neovascularization, however, the spatial presentation of these as well as other extracellular matrix (ECM) signals play a critical role in this process. Gradients of immobilized ECM molecules While the use of rigid substrates has provided significant inight on the role of specific gradients on endothelial cell behavior and neovascularization on two-dimensional (2D) surfaces, the creation of gradients within three-dimensional (3D) scaffolds more accurately mimics the native physiological environment. Limited studies have investigated the effect of gradients on 3D cell behavior. The effects of gradients of VEGF and matrix composition on 3D endothelial cell invasion have been investigated in natural scaffolds such as collagen Gradients of signals and matrix properties have also been embedded within synthetic polymeric hydrogels While the above mentioned PEGDA hydrogel studies have previously reported directed cellular behavior in response to an individual type of gradient of biofunctionality or matrix stiffness, it is not entirely clear whether these targeted gradients induced spatial variations in other biomaterial properties upon hydrogel crosslinking. In a recent study, we quantified the presence of multiple types of gradients in PEGDA scaffolds where the gradients were generated using a novel photopolymerization technique ). Specifically, the use of PBFP resulted in the generation of PEGDA hydrogels with tunable gradients of immobilized YRGDS and crosslink density and/or elastic modulus. This previous study demonstrated that simultaneous gradients in YRGDS and elastic modulus result in bi-directional fibroblast aggregate outgrowth on the surface of PEGDA hydrogels with cells spreading twice as far in the direction parallel to the gradient as compared to the perpendicular direction in vivo microenvironment. To our knowledge the effects of gradients for directing 3D cell behavior within synthetic PEG hydrogel scaffolds has not been previously investigated and would provide significant insight for applications in regenerative medicine as well as physiologic processes such as neovascularization where the presence of gradients play an important role. In this study we evaluate and quantify the influence of simultaneous gradients on 3D directed cell invasion and vascularization within synthetic PEGDA hydrogels formed by PBFP. While the combination of mechanical and biochemical gradients may not be necessary for all tissue types, inclusion of these gradients holds immense potential for complex tissue regeneration in vitro neovascularization assay While PEGDA hydrogels with embedded gradients offer a 3D environment for investigating and directing cell behavior, studies to date have exploited the use of these scaffolds to explore the effects of gradients on 2D cell behavior, which is not representative of the 3D Dimethylformamide (DMF), O-benzotriazole-N,N,N’,N’-tetramethyl-uronium-hexafluoro-phosphate (HBTU), trifluoroacetic acid (TFA), Fmoc-amino acids and the Wang resin were obtained from AAPPTec . N,N-Diisopropylethylamine (DIEA), thioanisole, triisopropylsilane (TIS) and diethyl ether were obtained from Fisher Scientific . Piperidine, phenol, N-vinylpyrrolidone (NVP), triethanolamine (TEA) and eosin Y were obtained from Sigma Aldrich .Hydrogels were rendered sensitive to cell-mediated proteolysis by the inclusion of the MMP-sensitive peptide sequence VPMS↓MRGG 2000-GGVPMS↓MRGGK-PEG2000-acryl (MW ∼ 5200 Da), which will henceforth be referred to as degradable PEGDA5200, was achieved by reacting both the terminal amine as well as the amine side group of the c-terminal lysine with the succinimidyl valerate ester (SVA) of an acryl-PEG2000-SVA  in 50 mM sodium bicarbonate (pH 8) at a 1∶2 mole ratio for four hours. The reaction mixture was then dialyzed for 24 hours to remove unreacted reagents, lyophilized and stored at −80°C until use.Synthesis of acryl-PEG3400-YRGDS was synthesized by reacting the terminal amine of the cell adhesion peptide YRGDS with the SVA of an acryl-PEG3400-SVA  in 50 mM sodium bicarbonate (pH 8) at a 1.05∶1 mole ratio for four hours. The reaction mixture was then dialyzed for 24 hours to remove unreacted peptide, lyophilized and stored at −80°C until use.Acryl-PEG5200, 17.5% (w/v) PEG3400, 225 mM TEA, 37 mM NVP and 10 mg/mL acryl-PEG3400-YRGDS dissolved in deionized water (pH 8.3). The unacrylated PEG3400 was included to increase the density of the precursor solution and ensure that the perfused eosin solution remained buoyant at lower precursor concentrations of PEGDA to initiate the frontal polymerization process as previously described 3400.by diffusion. For cell invasion experiments, the precursor solution was sterile filtered using a 0.2 µm Teflon syringe filter.PEGDA hydrogels were generated from a precursor solution consisting of 7.5% (w/v) degradable PEGDA2 visible light (λ = 514 nm) using an Argon Ion Laser . The buoyant eosin Y enters the chamber and rises to the surface of the precursor solution. Once a threshold eosin concentration has been reached, polymerization commences forming a polymer reaction front that propagates through the precursor solution and is fed from below by the continuous addition of eosin. Polymerization was carried out for 20 minutes to ensure full propagation of the reaction front. Control hydrogels were formed under similar conditions via bulk photopolymerization using a modified version of the chamber in which the glass frit filter disk was replaced by a solid base. Bulk hydrogels were formed through the addition of 400 µL of 0.1 mM eosin Y to the precursor solution prior to polymerization. This volume of eosin Y was equivalent to the time integral of the eosin volumetric flow rate and the precursor/eosin solution was well mixed to ensure uniform photopolymerization.PEGDA hydrogels were generated by PBFP as has been previously described Following polymerization, PBFP hydrogels and their bulk equivalents were extracted from the chamber and sectioned in approximately 2 mm increments along the x–z plane using an array of razor blades . To neglvs. strain curve. The elastic modulus of the hydrogel was taken as the slope of the linear region  of the stress vs. strain curve as has been previously reported Mechanical properties of PBFP hydrogels, as well as their bulk equivalents, were quantified using compression experiments. Prior to mechanical testing, hydrogel sections were swollen for 48 hours with several bath water changes. Hydrogel sections were then loaded onto a TA RSA3 mechanical tester  controlled by TA Orchestrator software. Samples were compressed at a rate of −0.5 mm/min to obtain a stress 3400-YRGDS into the hydrogel network was quantified by radiolabeling the tyrosine (Y) residue with 125I prior to gel formation 125I according to previously reported techniques 3400-YRGDS was added to the precursor in an amount not exceeding 20 wt % of the total peptide. Hydrogels were then polymerized and sectioned as outlined above. Hydrogel sections were swollen in deionized water for 48 hours with a minimum of two bath water changes to allow for diffusion of unreacted acryl-PEG3400-YRGDS. Following washing, the radioactivity of each section was determined using a Packard Cobra II gamma counter  and the concentration of immobilized YRGDS in each sectioned quantified based on the specific activity of the radiolabelled YRGDS and the final hydrogel volume.Spatial incorporation of acryl-PEG2 (pH 7.4) overnight. Collagenase-1A (Sigma Aldrich) at a concentration of 1 µg/mL was added to the hydrogel sections and the swollen weight of each section was recorded every two hours until complete hydrogel degradation was achieved. Fresh enzyme suspended in 10 mM HBS with 1 mM CaCl2 (pH 7.4) was added following each measurement. In order to ensure that hydrogel degradation was due to the specific action of MMP-1, control studies were performed with bulk hydrogels placed in a buffer solution lacking enzyme.Spatial variations in degradation time as a function of the gradient in MMP-sensitive crosslinks was evaluated by incubating the hydrogel sections in a fixed concentration of an interstitial collagenase . PBFP gradient hydrogels and their bulk equivalents were sectioned as outlined above and swollen in deionized water for 36 hours before being transferred to pre-weighed tubes and incubated in 10 mM HEPES buffered saline  with 1 mM CaCl2. Once the aggregates had formed, they were removed from the wells and washed in culture media prior to monitoring cell behavior within PEGDA hydrogels as described below. In order to visualize the presence of endothelial and smooth muscle cell interactions within the vascular sprouts, each cell type was fluorescently labeled using different stains prior to aggregate formation. HUASMCs and HUVECS were fluorescently labeled using PKH26 and CellVue® Claret cell membrane labels (Sigma Aldrich), respectively. Following aggregate formation and placement within PEGDA hydrogels, fluorescently labeled sprout invasion was imaged daily using dual channel confocal microscopy  until the fluorescent signal diminished below detectable levels.Human umbilical vein endothelial cells (HUVECs) were obtained from Lonza  and maintained in endothelial growth media  containing 2% fetal bovine serum (FBS), human epidermal growth factor (hEGF), hydrocortisone, bovine brain extract (BBE), ascorbic acid and gentamicin/amphotericin (GA). Human umbilical artery smooth muscle cells (HUASMCs) were obtained from Lonza and maintained in smooth muscle growth media  supplemented with 2% FBS, hEGF, human fibroblastic growth factor (hFGF), insulin and GA. Cells were grown to 80–90% confluency and collected by incubation with Trypsin for 5 minutes followed by centrifugation for 10 minutes at 200 g. Cell aggregates composed of 50% HUVEC and 50% HUASMC were formed by suspending both cells types (5000 cells/well) in EGM culture media containing 0.24% (w/v) methyl cellulose (Sigma-Aldrich) in round-bottom, low binding 96 well-plates (Fisher Scientific). Cells were incubated overnight at 37°C and 5% COFollowing polymerization, hydrogels were swollen for 36 hours in 10 mM HBS (pH 7.4) followed by 12 hours in endothelial basal media in preparation for aggregate implantation. The schematic in Comparisons between various conditions are expressed as mean ± standard deviation. Statistical analysis was evaluated using either one- or two-way ANOVA followed by a Tukey’s post-test  with p<0.05 considered significant. Normality was confirmed prior to running ANOVA by a Kolmogorov-Smirnov normality test with normality confirmed by p>0.05.Spatial variations in hydrogel elastic modulus and immobilized concentrations of the YRGDS cell adhesion ligand were evaluated using compression testing and radiolabeling experiments, respectively. As shown in PBFP has been previously shown to result in gradients of crosslinking In vivo, vascular sprout formation and vessel stabilization within the extracellular matrix involves coordinated cellular interactions between endothelial and mural cells such as smooth muscle cells which are critical to vessel assembly in vitro co-culture model was chosen to more accurately recapitulate the coordinated cell processes of in vivo vascular sprout invasion. Previous results have shown that both cell types contribute to formation and stabilization of networks in this model Vascular sprout formation within MMP-sensitive PEGDA bulk and gradient hydrogels was achieved using a previously established co-culture sprouting model of neovascularization composed of HUVECs and HUASMCs In order to quantify cell invasion and sprout formation as a function of gradient properties, co-culture aggregates of endothelial and smooth muscle cells were seeded within the top (0–4 mm), middle (4–6 mm), and bottom (6–10 mm) regions of PBFP gradient hydrogels as well as within bulk polymerized control gels. Aggregates seeded in the top region of PBFP gradient gels were exposed to an elastic modulus ranging from 3.17 to 2.16 kPa and a YRGDS concentration of 152.2 to 139.4 µM while aggregates in the middle and bottom regions of the gradient gels were subjected to an elastic modulus ranging from 2.16 to 1.15 kPa and 1.15 to 0.62 kPa, respectively, as well as a YRGDS concentration of 139.4 to 99.9 µM and 99.9 to 66.7 µM, respectively. Aggregates seeded in the top hydrogel regions were also exposed to a higher concentration of MMP-sensitive crosslinks than those seeded in lower less crosslinked regions. In bulk hydrogel controls, aggregates were exposed to uniform concentrations of YRGDS (124.9 µM) and MMP-sensitive crosslinks as well as a homogeneous elastic modulus (2.13 kPa). The properties of bulk hydrogel controls were similar to those of the middle region of the PBFP gradient hydrogels. Sprout invasion and directionality was visualized by F-actin staining after 3 weeks in culture . As showCell invasion within the various regions of the gradient hydrogels as well as within the isotropic bulk controls was monitored over time by quantifying the total projected area of the aggregate and resulting sprout formation. As shown in Aggregate sprout orientation in response to the gradients generated was evaluated by quantifying the x- and y-direction aggregate diameters  as well To further characterize the directionality of vascular sprouting, sprout length was quantified as a function of angle over time. Similar to the results observed for anisotropy index, the average sprout length for aggregates seeded in bulk control gels  was equiThe dimensions of tissue engineered constructs are dependent on the extent to which vessels can be stimulated to form within the implant. Therefore, the continued enhancement of tissue engineered scaffolds relies on the ability of the construct to stimulate rapid and stable neovascularization ex vivo chick aortic outgrowth assay as compared to other MMP-sensitive substrates The use of crosslinked MMP-sensitive sequences in PEG hydrogels with homogeneously distributed biofunctionality and mechanical properties has been previously explored due to the important role of MMP enzymes during neovascularization in vitro model of neovascularization based on the co-culture of endothelial and smooth muscle cells The effects of the presented gradients on vascular sprout formation in MMP-sensitive PEGDA hydrogels were evaluated using a previously established Previous studies of endothelial cell motility on rigid surfaces have shown that simultaneous gradients of signals, more specifically VEGF and fibronectin, have resulted in enhanced and directional 2D EC motility as compared to surfaces with either factor alone Based on the data shown in Vascular sprout invasion and alignment were found to be dependent on the gradient hydrogel properties to which cells were exposed. Aggregates located in the upper, more crosslinked region of the gradient hydrogels exhibited a much greater degree of directionality  and 9B tTo the best of our knowledge, this is the first instance investigating the effects of gradients of matrix mechanical properties and immobilized ECM molecules on directed 3D cellular behavior and neovascularization in proteolytically degradable PEGDA hydrogels. While the use of simultaneous gradients to direct 3D vascular sprout formation may potentially lead to more rapid and enhanced neovascularization of synthetic tissue engineered constructs, the specific role of each gradient in this process needs to be further evaluated. In this study gradients of crosslink density were inherently linked to gradients of MMP-sensitivity and degradation since the MMP-sensitive domains were crosslinked within the gel in order to enable 3D cell invasion into the matrix. Gradients of crosslink density (and stiffness) were also linked to gradients of YRGDS since regions exposed to a higher degree of crosslinking also resulted in a higher incorporation of pendant YRGDS moieties. Future experiments will attempt to isolate the effects of individual gradients of elastic modulus, immobilized concentrations of YRGDS, or MMP-sensitivity on the observed directional response by the manipulation of the composition of specific precursor components in the feed stream to the perfusion system. Furthermore, to gain a better understanding of the distance that cells are able to sense a particular gradient, additional control groups with uniform properties similar to those in different regions of the gradient hydrogels will be investigated in future studies of scaffolds presenting individual gradients. Finally, future efforts will address the effects of variations in the magnitude of the gradients on directed neovascularization.in vitro neovascularization assay. In scaffolds containing these gradients, bi-directional sprout invasion was observed with vascular sprouts oriented both up and down the gradient. Vascular sprout directionality was more prominent in gradient regions of increased stiffness, as well as increased presentation of crosslinked MMP-sensitive domains, and immobilized YRGDS concentration. The presented study offers insight in designing synthetic scaffolds that can be used to promote directed and guided neovascularization within engineered tissues.In this study, we quantified vascular sprout formation in PEGDA scaffolds that presented simultaneous gradients of elastic modulus, immobilized concentrations of YRGDS and MMP-sensitivity using an


The alveolar epithelium serves as a barrier between organism and environment and functions as the first line of protection against potential respiratory pathogens. Alveolar type II (TII) cells have traditionally been considered the immune cells of the alveolar epithelium, as they possess immunomodulatory functions; however, the precise role of alveolar type I (TI) cells, which comprise ∼95% of the alveolar epithelial surface area, in lung immunity is not clear. We sought to determine if there was a difference in the response of TI and TII cells to lung injury and if TI cells could actively participate in the alveolar immune response. TI cells isolated via fluorescence activated cell sorting (FACS) from LPS-injured rats demonstrated greater fold-induction of multiple inflammatory mediators than TII cells isolated in the same manner from the same animals. Levels of the cytokines TNF-α, IL-6 and IL-1β from cultured primary rat TI cells after LPS stimulation were significantly increased compared to similarly studied primary rat TII cells. We found that contrary to published reports, cultured TII cells produce relatively small amounts of TNF-α, IL-6 and IL-1β after LPS treatment; the higher levels of cytokine expression from cultured TII cells reported in the literature were likely from macrophage contamination due to traditional non-FACS TII cell isolation methods. Co-culture of TII cells with macrophages prior to LPS stimulation increased TNF-α and IL-6 production to levels reported by other investigators for TII cells, however, co-culture of TI cells and macrophages prior to LPS treatment resulted in marked increases in TNF-α and IL-6 production. Finally, exogenous surfactant blunted the IL-6 response to LPS in cultured TI cells. Taken together, these findings advocate a role for TI cells in the innate immune response and suggest that both TI and TII cells are active players in host defense mechanisms in the lung. The alveolar epithelium is an important component of the innate immune response of the lung. By providing an anatomic barrier that separates the organism from the external environment, the alveolar epithelium serves as a first line of defense against potential inhaled pathogens. While the cells of the innate immune system, such as alveolar macrophages and dendritic cells, harbor the bulk of the responsibility for prompting an immune reaction upon encountering inhaled pathogens, the cells that comprise the alveolar epithelium have also been implicated in aiding to trigger an inflammatory response.The alveolar epithelium is comprised of two morphologically different cell types – alveolar type I (TI) and alveolar type II (TII) cells. TII cells, which cover 3–5% of the alveolar surface area, are cuboidal epithelial cells with diameters of ∼10 µm. TII cells have been extensively studied and have been labeled defenders of the alveolus for their immunomodulatory functions Newer data led us to reconsider the current thoughts surrounding the role of TI cells in alveolar inflammation. TI cells contain toll-like receptor 4 (TLR4), a receptor for lipopolysaccharide (LPS), a cell wall protein found on gram negative bacteria, and primary rat TI cells have been shown to produce the pro-inflammatory cytokines TNF-α, IL-6 and IL-1β in response to LPS stimulation We used fluorescence-activated cell sorting (FACS) to isolate relatively pure populations of TI and TII cells for our studies. We performed PCR array analysis on primary rat TI and TII cells isolated by FACS from LPS-injured and non-injured control animals to compare their inflammatory profiles. To isolate the specific responses to LPS treatment from each cell type, we cultured FACS-isolated TI and TII cells, treated the cells with LPS, and then measured pro-inflammatory cytokine secretion using ELISA. We discovered that TI cells produce more cytokines per cell after LPS treatment than TII cells, but this effect was magnified by the fact that TII cells produced relatively small amounts of TNF-α, IL-6 or IL-1β in response to LPS, which is contrary to previously published reports The studies contained in this report not only suggest that TI cells are capable of mounting an inflammatory response after LPS exposure, but that the microenvironment surrounding both TI and TII cells may contribute to their ability to secrete cytokines and participate in the innate immune response. Determining how TI cells respond to respiratory pathogens and clarifying their role in the immune response in the lung may alter how lung immunity is viewed and possibly produce new therapeutic targets for addressing inflammation in the distal lung.All animal experiments were performed in accordance with, and with the approval of, protocols sanctioned by the IACUC committees at the University of California, San Francisco and the San Francisco VA Medical Center .ad libitum access to food and water. Rats were anesthetized by brief inhalation of 4% isoflurane prior to intratracheal instillation of normal saline (control) or LPS 10 mg/kg ) Pathogen-free, adult male Sprague-Dawley rats from Charles River Laboratories  weighing ∼150–200 g were used in all experiments. All animals were housed and provided 6 TI cells or 8×106 TII cells per animal. Cell purity was determined via immunocytochemical staining of samples using RTI40 to label TI cells and RTII70 to label TII cells. Cell preparations containing >2% of contaminating cells were discarded; >95% of all cells were viable as measured by a Live/Dead kit .Adult Sprague Dawley rats were euthanized with an intraperitoneal injection of pentobarbital (100 mg/kg). Alveolar TI and TII cells were isolated from the lungs by FACS using methods described by Gonzalez et al TII cells were isolated using a conventional method of enzymatic digestion with porcine elastase followed by panning over plates coated with IgG to remove macrophages and leukocytes, as described previously +2/Mg+2-free PBS containing 5 mM EDTA and 5 mM EGTA at 37°C. The aliquots for each rat were pooled and centrifuged at 500 g for 12 minutes. The cells were then collected, counted, and resuspended in DMEM containing 1% penicillin-streptomycin and 10% fetal bovine serum . In order to assess purity of the preparations, cells were stained with a modified Wright Giemsa stain, and reached purities of >95% macrophages.The lungs of adult Sprague-Dawley rats were lavaged 5 times with 7 ml aliquots of Ca2/air incubator at 37°C. TII cells were resuspended in DMEM-H16 with 10% fetal bovine serum, plated at the same density, and cultured in 5% CO2 at 37°C Using the methods of Gonzales et al Macrophages collected from the bronchoalveolar lavage were added to cultured TI or TII cells  in increasing numbers to represent cultures containing 5% , 10% , 15%  or 20%  macrophages. Similar numbers of macrophages were cultured alone as a control. For co-cultures of alveolar epithelial cells and macrophages, 10% macrophages, equaling 50,000 macrophages, were added.2 First Strand Kit . The resulting cDNA reaction was mixed with SYBR green qPCR master mix and transferred into the 96-well RT2 Profiler PCR Array for Rat Inflammatory Cytokines and Receptors (SABiosciences), containing appropriate primers for genes associated with the chemokines, cytokines, and interleukins involved in the inflammatory response as well as their receptors and primers for housekeeping genes. Real-time PCR utilizing the RT2 Profiler PCR Array System was performed according to manufacturer's instructions utilizing an ABI Prism 7500 RT-PCR thermocycler . Normalized threshold cycle data from the real-time instrument were calculated and interpreted using the PCR Array Data Analysis software tool (SABiosciences) which utilizes the ΔΔCT method of data analysis and normalizes all results to five housekeeping genes. The relative expression of each gene in either the TI or TII cells of the LPS-treated animals was compared with the expression in the TI or TII cells of control animals; significance was assessed by an unpaired Student's t-test, with a p value of ≤0.05 considered statistically significant. Validation of expression for select genes was accomplished using quantitative real-time PCR with gene expression assays purchased from Applied Biosystems. Relative amounts of mRNA for the validation studies were calculated using the comparative threshold method with 18S RNA as the internal control. Significance was again assessed by an unpaired Student's t-test, with a p value of ≤0.05 being considered significant.Approximately 2 µg total RNA were mixed with genomic DNA elimination buffer prior to synthesis of cDNA using the RTwww.NIH.gov).After instillation of LPS or saline vehicle (for uninjured control) into the lungs of rats, TI and TII cells were isolated via FACS 18 hours afterwards as described above. Protein lysates from injured and uninjured TI and TII cells were studied using standard Western blotting techniques. Antibodies used were CCR5, SPP1  and β-actin (Sigma). Densitometric quantitation was performed using ImageJ software  and the lungs were removed, digested with porcine grade IV elastase (Sigma) and minced and then filtered to form a single cell suspension, from which aliquots were taken and spun down onto slides. These cytocentrifuged mixed lung cell preparations were then fixed in 4% paraformaldehyde prior to immunohistochemical staining. Antibodies used were phospho-IKKα/β , RTI40 and RTII70. Secondary antibodies were conjugated to Alexa Fluor 488 or 594 (Invitrogen). Control slides utilized a non-specific IgG primary antibody. All slides were mounted with Prolong Antifade Reagent (Invitrogen) and images were captured with a Leica DC500 camera on a Leica Orthoplan microscope.Cell culture supernatants were assayed using ELISA for TNF-α, IL-6  or IL-1β  per manufacturer's instructions.2+ and Mg2+ and then flushing the solution back and forth into the lungs three times. The procedure was repeated with another 3 ml three additional times and the BAL from a single animal was pooled. The BAL was centrifuged at 500 g for 5 minutes to remove cells and cellular debris. The supernatant was collected and protease inhibitors (Sigma) were added prior to centrifugation at 27,000 g for 60 minutes. The pellet, containing large aggregate surfactant, was collected and resuspended in surfactant buffer  Adult Sprague Dawley rats were euthanized as described above. Bronchoalveolar lavage was collected by first instilling 3 ml of 10 mM PBS with Cat-test was employed. For comparisons of multiple groups, a one-way analysis of variance (ANOVA) followed by post-hoc analysis using the Bonferroni t-test was conducted using GraphPad Prism 5.0 software . A p value of <0.05 was considered significant.All experiments were performed a minimum of three times unless otherwise stated. For comparisons between the control and treated groups, unpaired Student's p value was <0.05. TI cells from control uninjured rats demonstrated a statistically significant higher level of expression of multiple genes (43 of 84 genes assayed) when compared to control uninjured TII cells, with CCL3, CCL4, CCR1, CCR2, CCR5, IL10ra, IL1β, IL2rb, SPP1 (secreted phosphoprotein 1 or osteopontin) and TNF being the ten most differentially expressed , and IL10ra, interleukin 10 receptor alpha, because its ligand, IL-10, is considered an anti-inflammatory cytokine Since many transcripts undergo post-transcriptional modification, we examined whether our mRNA data translated into similar differences in protein expression. We chose to measure the protein expression of SPP1 and CCR5 as well-characterized antibodies were available for these proteins. SPP1 is an important participant in both innate and acquired immunity in vitro model.We anticipated that a higher number of genes in either LPS-injured TI or TII cells would have been up-regulated compared to their non-injured controls, given that prior data from our lab demonstrated that TI cells in culture produce significantly more TNF-α, IL-6 and IL-1β via ELISA analysis than controls after LPS stimulation TII cells are known to possess immunomodulatory functions, including the ability to produce various cytokines upon stimulation Recent reports of TNF-α levels in primary TII cell cultures range from ∼800 to 1000 pg/ml 24 hours after LPS stimulation at doses ranging from 0.01 to 10 µg/ml The conventional method of rat TII cell isolation typically produces TII cell purities that are reported to be between 85–97%, resulting in a 3–15% contamination rate, with macrophages most often implicated as the contaminating cell type The addition of macrophages to TII cells resulted in an increase in cytokine production after treatment with LPS, albeit to levels less than that for macrophages alone. Given that TI cells also share a microenvironment with macrophages, we performed similar experiments with TI cells. The addition of escalating numbers of macrophages to cultured TI cells prior to stimulation of the co-culture with LPS resulted in a dramatic dose-dependent increase in TNF-α  and IL-6  expression that was significantly greater than the addition of the same number of macrophages to cultured FACS-isolated TII cells. These results not only reinforce our findings that TI cells produce more cytokines per cell than TII cells both at baseline and upon LPS exposure, but also reveal that macrophages have a more profound effect on TNF-α and IL-6 secretion on TI cells than TII cells in response to LPS . FurtherSoluble factors present in conditioned media could be one explanation for the sizeable cytokine response of TI cells co-cultured with macrophages prior to LPS stimulation. In an attempt to gain insight into how macrophages may promote such a heightened cytokine response in TI cells, we collected the supernatant from cultured macrophages (10% Mac) that had been stimulated with LPS and then added the conditioned media to cultured TI cells. Conditioned media from LPS-stimulated macrophages (10% Mac) increased TI cell TNF-α production (4111.2±195.6 pg/ml), but not nearly to the levels seen after LPS addition to co-cultures of TI cells and macrophages. This same macrophage conditioned media, however, blunted the degree of TI cell IL-6 production (337.0±31.2 pg/ml) when compared to LPS-treated TI cell IL-6 expression, which is in contrast to the high levels of IL-6 measured in LPS-stimulated co-cultures of TI cells and macrophages. Conditioned media from LPS-stimulated macrophages did not significantly increase TII cell cytokine production over TII cells stimulated with LPS, nor did conditioned media from LPS-stimulated TII cells alter macrophage cytokine production over LPS treatment alone (results not shown). The addition of conditioned media from LPS-stimulated TI cells to cultured macrophages did increase TNF-α (4956.7±654.1 pg/ml) and IL-6 (446.3±13.8 pg/ml) levels above those seen with the addition of LPS to macrophages (10% Macs) alone , but agaIt has been long reported that surfactant can decrease the degree of lung inflammation in response to endotoxin stimulation The NF-κB/IκB pathway has been implicated in the inflammatory cascade caused by a variety of stimuli, including LPS, in TII cells The alveolar epithelium has a number of very important functions, primary among them gas exchange and existence as a tight barrier to separate organism from environment. Alveolar TII cells have traditionally been considered the immune cell of the alveolar epithelium as this cell produces surfactant, cytokines and chemokines, all of which have been implicated as being important factors in lung immunity. TII cells, however, only cover approximately 5% of the internal surface area of the lung, while TI cells cover the remaining 95% To gain a broader view of the potential for TI cells to participate in lung inflammation, we performed PCR array analysis comparing control and LPS-stimulated TI and TII cells. There was a large number of inflammatory mediators more highly expressed in TI cells compared to TII cells at baseline, with 43 of the 84 genes demonstrating fold-increases >2 that were statistically significant. Similar widespread gene induction was seen in LPS-injured TI cells, with TI cells having much higher-fold gene expression than TII cells from the same injured animals. With >50% of the genes assayed exhibiting higher expression in TI cells from either control or LPS-injured animals when compared to TII cells, we expected a larger number of genes to be up-regulated upon comparison between either control TI and LPS-treated TI cells or control TII and LPS-treated TII cells. When comparing control and LPS-treated TI cells, only three genes were significantly induced in LPS-treated TI cells versus controls. Similarly, in our comparison of control and LPS-treated TII cells, only five genes were more highly expressed in LPS-treated TII cells than controls. The relatively high expression levels of the majority of the genes at baseline in both sets of control cells may have rendered the up-regulation of genes after LPS treatment less striking by comparison, leaving us to underestimate the true number of mediators that may have been enhanced by injury.in vivo. Such results led us to use cultured primary cells that had been given at least 24 hours to recover from the isolation process before stimulation with LPS or other agents. Furthermore, this method allowed the assay of cytokine secretion via ELISA analysis of the conditioned media. Despite the fact that working with cells from LPS-injured animals is the more valid in vivo model, we used in vitro studies of cultured cells to isolate the specific responses of each cell type to various agents and conditions.Cell isolation, in and of itself, can trigger the immune response by promoting cellular stress during the processes of enzymatic digestion, tissue mincing, and filtering. Studies have shown that TNF-α production from freshly isolated rat alveolar epithelial cells can decrease dramatically, from 1200 pg/ml immediately after isolation to 50 pg/ml 48 hours later We were very surprised to find that primary cultured FACS TII cells produced significantly lower levels of selected cytokines than previously published reports for LPS-stimulated TII cells 6 cells/rat) and the need to standardize our co-culture experiments led to our use of cell number in plating our cultures.One may contend that our method of comparison of cytokine levels in cultured TI and TII cells, plating by cell number, may not be the ideal manner in which to contrast the inflammatory responses of the two cell types, and that the greater surface area of TI cells may explain why they produce more cytokines. The surface area ratio of TI∶TII cells is 43∶1 Since the method of TII cell isolation was the most obvious difference between our experiments and those published by other investigators, we hypothesized that the bulk of the measured cytokines seen with LPS treatment of TII cells comes from the contaminating cells within the TII cell preparation. This concern of cell contamination contributing to the overall cytokine response of TII cells has been expressed by other investigators One of the most striking findings in this study was the dramatic increase in TNF-α and IL-6 production in the co-cultures of TI cells and macrophages. Exposing cultured TI cells to conditioned media from LPS-treated macrophages suggested that while TI cells do respond to inflammatory mediators liberated by activated macrophages by increasing TNF-α secretion, cell-cell interaction between macrophages and TI cells is a much more potent factor in modulating TI cell cytokine release. Conditioned media from activated macrophages decreased the levels of TI cell IL-6 compared to LPS-stimulated TI cells alone, while LPS treatment of co-cultures of TI cells and macrophages significantly increased IL-6 production. The IL-6 conditioned media experiments reinforce the notion that cell-cell interaction between TI cells and macrophages is powerful, as co-culture of these cells overcomes the inhibition of IL-6 production in TI cells by inhibitory mediators released by activated macrophages. While the exact cell of origin of the IL-6 in this instance is unclear, these results not only make a strong case for TI cells as key participants in the immune response of the lung, but they also suggest that the interaction between macrophages and TI cells may be equally important.Studies of pulmonary surfactant in the immune response have suggested that surfactant dampens inflammation, particularly in the face of endotoxin-stimulated lung injury The NF-κB pathway is important in stimulating pro-inflammatory cytokines and activating genes that regulate the inflammatory response in multiple different organs, tissue and cells Streptococci pneumoniae exposure upon comparison of wild type to RelAΔ/Δ mice. While these are very interesting results, comparisons between their study and ours must be made with caution. As is often the case with transgenic mouse models, the relative presence, or absence, of certain targeted genes can vary, even between genotypically similar littermates Δ/Δ mice to PCR array analysis, whereas we focused on isolated rat TI and TII cells. They did study isolated mouse TI cells, but only measured production of CCL20 and CXCL5 mRNA, whereas we measured both transcript and protein expression patterns of various pro-inflammatory cytokines. Nevertheless, despite the differences in study design and methods, the final message culled from both of our reports is largely the same: alveolar TI cells may play an active role in the innate immune response. While the differences in cell-specific cytokine production that we observed are intriguing, there was never the expectation that the alveolar epithelium would produce levels of cytokines that would rival that of immune cells such as alveolar macrophages or dendritic cells. The more relevant question to us was whether or not TI cells could produce cytokines, and thus participate in the innate immune response of the lung.Recently, Yamamoto et al. Our data has shown that TI and TII cells not only respond differently to LPS stimulation, but there exists the possibility that the two alveolar cell types work in concert within their microenvironment to provide a balanced inflammatory response to infection. One could postulate that TI cells may serve to heighten the pro-inflammatory response with enhanced cytokine production, particularly in the presence of alveolar macrophages, while TII cells, through the production of surfactant and relatively modest cytokine expression, provide more of an anti-inflammatory response by trying to contain inflammation. Future investigation into possible mechanisms contributing to the differential inflammatory response of TI and TII cells to LPS, along with more characterization of the alveolar microenvironment during injury, are essential to better understanding the role of the alveolar epithelium and its component cells in the innate immune response of the distal lung. Studies to determine if the responses reported here are stimulus-specific or if the alveolar epithelial cells respond similarly to other respiratory insults such as viruses, gram-positive bacteria or tobacco smoke are also warranted. Given that diseases such as pneumonia and acute respiratory distress syndrome all heavily impact diverse patient populations, elucidating the role of TI and TII cells in the innate immune response could help develop new therapies and treatment options to improve outcomes for all affected patients.


High coverage whole genome sequencing provides near complete information about genetic variation. However, other technologies can be more efficient in some settings by (a) reducing redundant coverage within samples and (b) exploiting patterns of genetic variation across samples. To characterize as many samples as possible, many genetic studies therefore employ lower coverage sequencing or SNP array genotyping coupled to statistical imputation. To compare these approaches individually and in conjunction, we developed a statistical framework to estimate genotypes jointly from sequence reads, array intensities, and imputation. In European samples, we find similar sensitivity (89%) and specificity (99.6%) from imputation with either 1× sequencing or 1 M SNP arrays. Sensitivity is increased, particularly for low-frequency polymorphisms  Projects, we show that 1 M SNP arrays used for genome wide association studies perform similarly to 1× sequencing. We find that adding low coverage sequence reads to dense array data significantly increases rare variant sensitivity, but adding dense array data to low coverage sequencing has only a small impact. Finally, we describe an improved SNP calling algorithm used in the 1000 G project, inspired by a novel next-generation sequencing error mode identified through analysis of disputed SNPs. These results inform the use of next-generation sequencing in genetic studies and model an approach to further improve genotype calling methods. High coverage whole genome sequencing gives maximum information about individual-level genetic variation for use in disease gene mapping The literature on analytical methods for low coverage sequencing and SNP arrays has until now been largely non-overlapping Specifically, we asked four questions: (1) as a baseline, how does power  vary based on sequence read depth and SNP array density, with and without imputation; (2) do combinations of different technologies have improved performance relative to each data type alone; (3) when sequencing and arrays disagree, is it possible to identify the incorrect technology; and (4) can we learn and ultimately resolve new error modes based on these disagreements?We first developed a statistical framework that can jointly estimate genotypes from array intensities, sequence reads, and imputation . The fraThis framework produces an integrated estimate of the sequence variation in each individual, enabling comparisons among technologies. We used it to perform a series of experiments, modeling different array or sequence data collection strategies. Using raw array intensities and sequence reads from the Hapmap To evaluate performance (relative to high coverage whole genome sequencing), we compared the resulting genotype calls for a single test sample to “gold-standard” genotypes, published from multiple high coverage sequencing technologies by the 1000 Project Pilot, for the same sample. We focused our metrics only on non-reference alleles. To measure sensitivity  we defined We first assessed the sensitivity of each technology. Based on the clear value and wide availability of methods and data for statistical imputation, from this point forward we discuss only calls made using imputation.We next assessed the specificity of each technology. Using a 90% posterior probability no-call threshold, ased to  , S11. WhOur analysis allowed direct comparison of low coverage sequencing and SNP arrays within a common framework . For exa classes . Thus, sNotably, performance is different for coding variants — which constitute variants . In contWe observe that different methods, even those with similar sensitivities across all variants, perform differently across the frequency spectrum . In partBased on the different sensitivities of SNP arrays and sequencing across the frequency spectrum, we asked if calls made jointly from the two technologies might have higher sensitivity and specificity than calls from either technology alone. We find that, regardless of the array density or sequence coverage, joint calls uniformly have higher  greater . In contreater  and formatted for input with the Affymetrix Power Tools (APT) Software Package. To obtain intensity data for the Affy 6, Ilmn 1 M, Omni 2.5, and Metabochip arrays, we genotyped 180 Hapmap samples on each array using standard lab protocols; for the Omni 2.5 array, we also genotyped an additional 381 European samples from the 1000 G June 2011 Integrated Phase 1 Variant Release. For the Illumina arrays, raw data (in the form of IDAT files) was converted to normalized intensity data using a custom implementation IDATConverter of the Illumina normalization algorithm . Illumina normalized intensity data were then scaled by a factor of 2000 to be within the range expected for Birdseed allele intensity input files. Affymetrix raw intensity data were normalized with the quantile normalization method in the APT program apt-probeset-summarize.Intensity data for the Affy 100 k and Affy 500 k arrays were downloaded from the International Hapmap Project website (http://www.1000genomes.org). Sequence data for each sample was roughly 4× coverage, and to approximate .5×, 1×, or 2× coverage, was down-sampled with the GATK Sequence data for the 180 Hapmap samples were downloaded from the 1000 genomes project Pilot 1 release, and data for the 381 European samples were downloaded from the Integrated Phase 1 Variant Release (We downloaded sequence and genotype data for our test sample (Hapmap sample NA12878) from the 1000 G Pilot 2 release. The genotype data was used as gold-standard data. We used the sequence data to approximate 4× coverage for our test sample: we kept reads from a sufficient number of read groups such that the average depth was as close as possible to 4× genome-wide, with read groups preferentially chosen to include those with the latest sequencing technology. We then further down-sampled this data with the GATK to approximate .5× (12.5%), 1× (25%), or 2× (50%) sequence coverage for our test sample.Sequence likelihoods For each experiment, we jointly called genotypes for our test sample together with the 381 European samples. For all experiments we used 4× sequence coverage and Omni 2.5 intensity data for the 381 samples, while we varied the sequence and intensity data used for the test sample.Because only our test sample had intensity data for the Affy 100 k, Affy 500 k, Affy 6, or Ilmn 1 M arrays, we needed to use additional samples to learn cluster locations for experiments with these arrays (a single sample cannot be clustered). These additional samples were 41 unrelated European and 41 unrelated African samples from among the 180 Hapmap samples genotyped on the arrays. For experiments that solely evaluated calls at sites on each array, we used joint calls from these 82 samples together with our test sample. For experiments that evaluated overall sensitivity and specificity, we used these 82 samples to obtain cluster locations that we then input to the joint calling framework.Sensitivity and specificity were computed for each experiment as described in the main text. We report numbers computed only from SNPs on chromosome 20. We also omitted A/T or G/C SNPs when we computed our metrics to avoid possible DNA strand ambiguities.For our analysis of the improvement in imputation due to joint calls with array data, we analyzed We defined 18 potential error modes  and tested whether apparent sequence errors or apparent array errors were enriched for each. For the 5 binary error modes, we computed the fraction of apparent sequence errors and apparent array errors with the error mode and then assessed differences between the groups with Fisher's exact test; for the 13 continuous error modes, we used a Kruskal-Wallis one-way analysis of variance to test for differences between the apparent sequence error and apparent array error distributions. Nominally significant p-values  be caused by an error mode. We considered a SNP as characterized by a binary error mode  if it had value 1 for the error mode. For continuous error modes, we used kernel density estimation as implemented in the R software package to fit separate probability density functions to SNPs not in dispute : ovals represent cluster locations based on array calls, the outline of each circle represents the sequence calls, and the fill of each circle represents the joint call. (a) Cluster locations given only array data; no genotypes can be called. (b) Genotypes given only sequence data; most genotypes can be weakly called. (c) Genotypes obtained by multiplying SNP array and sequence genotype likelihoods; some genotypes can be called but the cluster locations do not change. (d) Genotypes given sequence and array data for this SNP only; most genotypes can be called and cluster locations begin to resolve. (e) Genotypes given sequence and array data for all SNPs; all genotypes can be called and cluster locations mostly resolve.(PDF)Click here for additional data file.Figure S2Algorithm overview. We implemented our framework in a Python program. The program accepts a set of partially overlapping intensity files, with SNP array data, and VCF files, with sequence genotype likelihoods. It initializes cluster locations and sample genotypes using the Birdseed algorithm, and then iteratively re-estimates sample genotypes and cluster locations. It uses the Beagle algorithm for phasing and imputation and a modified version of Birdseed to estimate cluster locations conditional on current genotype estimates, intensity data, and sequence data. After a number of iterations, the program produces a VCF file with posterior probabilities of all genotypes for all input samples at all input sites.(PDF)Click here for additional data file.Figure S3Experimental procedure. Schematic of the procedure used for our experiments. Details are given in (PDF)Click here for additional data file.Figure S4Sensitivity and specificity of data collection strategies: 41 sample European reference panel. Shown is data analogous to a) Sensitivity of calls. (b) Specificity of calls. (c) SensI by variant frequency. (d) SensI for four sequence coverages. (e) SensI for four array densities.(PDF)Click here for additional data file.Figure S5Sensitivity and specificity of data collection strategies: 41 sample African reference panel. Shown is data analogous to a) Sensitivity of calls. (b) Specificity of calls. (c) SensI by variant frequency. (d) SensI for four sequence coverages. (e) SensI for four array densities.(PDF)Click here for additional data file.Figure S6Sensitivity and specificity of data collection strategies: no reference panel. Shown is data analogous to a) 42 European samples sequenced. (b) 42 African samples sequenced.(PDF)Click here for additional data file.Figure S7Impact of reference panel on sensitivity and specificity. We constructed a reference panel in three different ways: from 4× sequence data (Seq panel), from 4× sequence data and the array data used to genotype the test sample (Seq and array panel), and from 4× sequence data and Omni 2.5 data (Seq and Omni panel). We then assessed sensitivity and specificity when the test sample was called with all combinations of sequence and array data. (a) a 381 European sample reference panel; (b) a 41 European sample reference panel; and (c) a 41 African sample reference panel. We find that the use of array data on top of 4× sequence data to build the reference panel has a small but significant effect on sensitivity for low coverage sequencing or small reference panels.(PDF)Click here for additional data file.Figure S8Sensitivity gains from investment. Shown are SensI values for all combinations of array and sequence data. Points are colored according to the array data collected and labeled with the sequence data collected. The x-axis plots SensD, a measure of genotyping investment intrinsic to a technology. SensD correlates, though not strictly, with cost. (a) 381 European sample reference panel. (b) 41 European sample reference panel. (c) 41 African sample reference panel.(PDF)Click here for additional data file.Figure S9Specificity with and without imputation. Shown is data analogous to a) 381 European sample reference panel. (b) 41 European sample reference panel. (c) 41 African sample reference panel.(PDF)Click here for additional data file.Figure S10Impact of no-call threshold on sensitivity and specificity without imputation. Shown are SensD and SpecD values, as computed in −X. For our main analysis, we called genotypes only at sites where the genotype quality exceeded 10. (a) No genotype quality thresholds; calls at all sites where most likely genotype probability exceeds 33.3%. (b) Genotype quality threshold of 10: calls at sites where the most likely genotype probability exceeds 90%. (c) Genotype quality threshold of 20: calls at sites where the most likely genotype probability exceeds 99%.(PDF)Click here for additional data file.Figure S11Impact of no-call threshold on sensitivity and specificity with imputation. Shown are numbers analogous to a) No genotype quality thresholds. (b) Genotype quality threshold of 10. (c) Genotype quality threshold of 20.(PDF)Click here for additional data file.Figure S12Sensitivity and specificity at heterozygous and homozygous variants. Shown are data analogous to a) Heterozygous genotypes. (b) Homozygous non-reference genotypes.(PDF)Click here for additional data file.Figure S13Sensitivity and specificity in coding regions. Shown are data analogous to a) Coding variants. (b) Noncoding variants.(PDF)Click here for additional data file.Figure S14Sensitivity and specificity by minor allele frequency: 381 European sample reference panel. Shown are data analogous to (PDF)Click here for additional data file.Figure S15Impact of prior array data on sensitivity: 381 sample European reference panel. Shown are data analogous to a) All variants. (b) Variants with minor allele frequency (MAF) between .5 and 5%. (c) Variants with MAF>5%.(PDF)Click here for additional data file.Figure S16Sensitivity and specificity at sites not on the array: actual values. Shown is data analogous to a) 381 European sample reference panel. (b) 41 European sample reference panel. (c) 41 African sample reference panel.(PDF)Click here for additional data file.Figure S17Impact of additional sequence data on sensitivity and specificity at sites on the array. We evaluated joint calls from sequence and array data when different fractions of samples had sequence data available; as in (PDF)Click here for additional data file.Figure S18Sensitivity and specificity at sites on the array by allele frequency. Sensitivity and specificity at sites on each SNP array as a function of minor allele frequency. Joint calls were made in the same manner as described in a) Sensitivity. (b) Specificity.(PDF)Click here for additional data file.Figure S19Impact of sequence data and phasing on sensitivity and specificity for sites on the array. We computed joint calls for 83 samples (as in es as in  but in f(PDF)Click here for additional data file.Figure S20Impact of new clusters and joint likelihoods on sensitivity and specificity at sites on the chip. At sites on the SNP array, joint calls have higher sensitivity and specificity than array calls for two reasons: genotype likelihoods for the test-sample are computed from both sequence and array data, and genotype cluster locations are computed based on sequence data and haplotype phasing ( phasing . To quan(PDF)Click here for additional data file.Figure S21Analysis of SNPs initially classified as “false positive”. We identified 639 SNPs on the Metabochip polymorphic based on sequence data but monomorphic based on array data. Based on the joint calls, 127 SNPs are no-called and therefore unresolved, 331 SNPs are polymorphic, and 181 SNPs are monomorphic. The left plots show calls based on array data  and sequence data (outlines of circles represent genotype classes); the right plots show joint calls with colors and symbols as defined in (PDF)Click here for additional data file.Figure S22Genotype likelihoods of fragment-based calling. (a) Mathematical formalism for fragment-based (rather than read-based) SNP calling. The likelihood of a read pair given a hypothesized genotype GTAB with alleles A and B is calculated via a two-stage inference that weights the probability of each read independently by the probability of a PCR (or other) error occurring in the sequenced DNA fragment. In the above equation, p refers to error rate of the fragment, f refers to the base in the fragment, e refers to the error rate in the read, and b refers to the base in the read. Thus, errors that occur during fragment construction are counted only once, while errors that occur during sequencing are counted independently. (b) Comparison of the SNP genotype likelihood quality for GATK (PDF)Click here for additional data file.Figure S23Analysis of disputed SNPs. As described in the text, we used our framework to resolve SNPs where array data widely disagreed with 4× sequencing data. We classify disputes as apparent sequence (or array) errors if the joint genotype calls disagree with calls based on sequence (or array) data. (a) Example disputed SNPs. The left plots show calls based on array data  and sequence data (outlines of circles represent genotype classes); the right plots show joint calls with colors and symbols as defined in bc) We asked which disputed SNPs were due to known or predicted error modes. Plotted are values for six potential error modes, each stratified across four classes of SNPs: agreed , disputed , apparent array errors, and apparent sequence errors. (b) Shown are the fraction of SNPs in each class (1) with a DNA flanking region that occurs at multiple genomic locations, (2) that lie within low complexity regions, and (3) that lie within 50 bp of another SNP. Apparent array errors have higher values for each of these metrics than apparent sequence errors. (c) Shown are distributions over SNPs for (1) the likelihood of forward/reverse DNA strand bias in sequence calls, (2) the length of a neighboring homopolymer run, and (3) the average  error rate of the reads supporting the sequence call . Apparent sequence errors have higher values for each of these metrics than apparent array errors.(PDF)Click here for additional data file.Figure S24Properties of resolved SNPs with greater than 10% discordant genotypes. Distributions for all potential error modes assessed on disputed SNPs, as in Figure 23bc.(PDF)Click here for additional data file.Figure S25Properties of resolved SNPs with greater than 25% discordant genotypes. The same data as in (PDF)Click here for additional data file.Figure S26Characterized disputed SNPs. We fit separate distributions to each potential error mode for disputed and non-disputed SNPs, as well as for each class of apparent error mode. We considered a SNPs as characterized by an error mode according to one of two criteria: if the error mode was binary, it characterized a SNP if the SNP had value 1 for the error mode; if the error mode was continuous, it characterized a SNP if the log of the likelihood ratio (LOD) of the error mode distribution to the non-disputed distribution exceeded two. (a) SNPs with greater than 25% discordant genotypes. (b) SNPs with greater than 10% discordant genotypes.(PDF)Click here for additional data file.Text S1Available as supporting supporting information are: (1) calculations that show how previously described genotype calling algorithms are similar in principle to the joint calling framework when one or more of sequence reads, SNP array intensities, or linkage disequilibrium is omitted; (2) sensitivity and specificity calculations with a 41 sample European reference panel and a 41 sample African reference panel; and (3) investigation of the increase of sensitivity with additional genotyping investment.(PDF)Click here for additional data file.


At almost all expressed genes we observed Pol II occupancy downstream of the EAGs suggesting that Pol II pausing 3′ from the transcription units is a rather common phenomenon. Downstream of EAGs Pol II transcripts can also be detected by global run-on and sequencing, suggesting the presence of functionally active Pol II. Based on Pol II occupancy downstream of EAGs we could distinguish distinct clusters of Pol II pause patterns. On core histone genes, coding for non-polyadenylated transcripts, Pol II occupancy is quickly dropping after the EAG. In contrast, on genes, whose transcripts undergo polyA tail addition [poly(A)+], Pol II occupancy downstream of the EAGs can be detected up to 4–6 kb. Inhibition of polyadenylation significantly increased Pol II occupancy downstream of EAGs at poly(A)+ genes, but not at the EAGs of core histone genes. The differential genome-wide Pol II occupancy profiles 3′ of the EAGs have also been confirmed in mouse embryonic stem (mES) cells, indicating that Pol II pauses genome-wide downstream of the EAGs in mammalian cells. Moreover, in mES cells the sharp drop of Pol II signal at the EAG of core histone genes seems to be independent of the phosphorylation status of the C-terminal domain of the large subunit of Pol II. Thus, our study uncovers a potential link between different mRNA 3′ end processing mechanisms and consequent Pol II transcription termination processes.Recent genome-wide chromatin immunoprecipitation coupled high throughput sequencing (ChIP-seq) analyses performed in various eukaryotic organisms, analysed RNA Polymerase II (Pol II) pausing around the transcription start sites of genes. In this study we have further investigated genome-wide binding of Pol II downstream of the 3′  RNA polymerase II (Pol II) transcription is a highly regulated process that requires coordinated action of numerous transcription factors. It can be divided into initiation, promoter escape, elongation and termination phases. Transcription initiation is a complex series of ordered multistep process that involves the recruitment of Pol II to a promoter, local melting of the DNA around the transcription start site (TSS), and formation of the first few phosphodiester bonds of mRNA. Recognition of promoters begins with the assembly of a large protein complex, called the preinitiation complex (PIC), containing Pol II and multiple general transcription factors (GTFs) on the promoter Drosophila heat shock gene (HSP70) promoters, and shown that Pol II stalls 20–50 nucleotides downstream from the TSS Several studies analysed Pol II binding at the promoter and its entry in transcription genome-wide by using chromatin immunoprecipitation (ChIP) assay coupled either to genomic DNA microarrays (ChIP-chip) or to high throughput sequencing (ChIP-seq). These reports uncovered a significant fraction of genes that have high levels of Pol II density at or around their TSSs when compared to the transcribed region of the genes + genes, but only inferred for histone genes It is long established that 3′-end processing is absolutely required for transcription termination Besides the existence of a link between pre-mRNA 3′ end processing and termination, Pol II pausing has also been implicated in promoting transcription termination. Studies in mammalian cells indicate that termination can be separated into two steps: pausing and polymerase release To characterize Pol II pausing downstream of the EAG of the transcription units in human MCF7 cells genome-wide, we used high-resolution occupancy profiling by ChIP-seq of Pol II in human cells. Comparison of our ChIP-seq results with (i) recently published Gro-seq data To better understand Pol II behavior at the 3′ end of the transcription units and in transcription termination, we have analysed the patterns and profiles of Pol II binding downstream of EAGs in differentiated human cells. To this end, we have generated ChIP-seq data for Pol II from the MCF7 human breast cancer cell line (GSE34001) by using an antibody that binds to the N-terminus of the largest subunit of Pol II . This antibody allows the detection of Pol II independently of the phosphorylation status of the C-terminal domain (CTD) of its largest subunit. To test for non-specific binding, we carried out a control ChIP-seq using an antibody raised against a yeast factor that does not recognize any human proteins . Sequencing reads were mapped to human genome, and uniquely mapped reads were considered for further analyses. To avoid the overlapping of Pol II occupancy signals downstream from the EAGs with signals coming from neighboring transcription units, only those human refseq genes (13787 genes) were analysed, which were at least 4 kb away from the neighboring transcription units. Pol II tag density −/+4 kb around the gene body and in the transcribed regions was then calculated. The average tag density calculated on 13787 genes, which do not have genes in −/+4 kb neighboring region, shows that Pol II binding profiles are relatively low in the transcribed regions, but higher at both, around the TSSs and 3′ from the EAGs . These dAs in our initial analysis , both exTo validate the bioinformatically isolated Pol II pause patterns downstream of the EAG shown in , we have+ genes (HEPA), independently from the distance of the next neighboring gene(s), and found that on the majority of highly expressed genes Pol II pausing downstream of the EAG sites is similar to that described above in Cluster PA1 To validate Pol II occupancy on genes in Cluster HEPA1 and HEPA2 we have carried out ChIP-qPCR. To this end we have compared Pol II occupancy downstream of the EAGs on two randomly chosen highly expressed genes with high 3′ peak with those which have no or only very low 3′ peaks . For gen+ genes. Instead of polyA tail addition, the cleavage-only mRNA 3′-end formation of core histone genes involves stem-loop formation, the U7 snRNP, hairpin-binding protein and specific components of the cleavage/poly(A) complex Replication dependent core histone genes are intronless and coding for mRNAs that have a different 3′ processing mechanism than mRNAs transcribed from poly(A)As core histone genes are intronless, we have analysed Pol II occupancy on all intronless genes from the human genome to test whether the narrow Pol II pause profile downstream of the EAG was characteristic of core histone genes or, rather, a common feature of genes producing transcripts that do not undergo splicing. Interestingly, the sharp drop of Pol II occupancy occurred only downstream of EAGs of core histone genes suggesting that the narrow Pol II pausing pattern 3′ of EAGs is the characteristic of core histone genes . Taken t+ genes in differentiated human cells and to analyse the state of the phosphorylation of the C-terminal domain (CTD) of the largest subunit of Pol II, we analysed four different published Pol II ChIP-seq data sets from pluripotent mouse embryonic stem cells (mESs) + genes and core histone genes + genes further suggest a possible differential link between Pol II pausing and the 3′ end processing of the corresponding transcripts.In order to further investigate the differential pattern observed for Pol II occupancy downstream of the EAGs on core histone and poly(A)ne genes . In mES s tested .Various RNA-processing events have been shown to occur cotranscriptionally + genes have significant Pol II occupancy throughout a long region after the 3′ of the genes and that core histone genes have a very sharp drop of Pol II occupancy at 3′ of their EAGs, suggests that the different 3′ processing regulatory mechanisms influence the residency time of Pol II downstream of the EAGs. These differential Pol II occupancy mechanisms 3′ of the distinct gene categories seem to be conserved in vertebrates and not influenced by the differentiation state of the cells.By analyzing genome-wide Pol II occupancy together with Gro-seq data + genes belonging to the broad category are higher expressed than those belonging to the very broad category + genes, where polyadenylation would be defective. The increase of Pol II residency time 3′ of poly(A)+ genes in turn may reduce the amount of Pol II that can be released from each gene to enter in a new reinitiation cycle. This hypothesis is in good agreement with our findings showing that inhibition of polyadenylation reduces Pol II occupancy at the TSSs of the tested genes  genes a very efficient Pol II recycling is required.Our genome-wide results also suggest that the pausing step downstream of the EAGs reflects a slowing down of the Pol II elongation complex. It seems that on genes that produce polyadenylated transcripts Pol II binding downstream of EAGs is in general different than during transcription in the gene body in terms of detectable Pol II occupancy. Moreover, in most of the cases after the cleavage event at the polyadenylation site, transcriptionally active Pol II can be detected for a long time and distance on the DNA template. In good agreement, all the transcripts identified by Gro-seq are mapping in the sense orientation 3′ from the EAGs, suggesting that they are produced by Pol II molecules that continue to transcribe after having finished the pre-mRNAs. In this respect it is interesting that inhibition of polyadenylation increased Pol II occupancy downstream of EAGs on poly(A)ed genes . Thus, P+ genes, at core histone genes Pol II occupancy downstream of the EAGs quickly declines + genes represents a differential interaction between Pol II and the two different 3′ processing machineries. This idea is also corroborated by the functional studies, which showed that inhibition of polyadenylation increased Pol II occupancy 3′ of the EAGs of poly(A)+ genes, while it had no significant effect on Pol II drop at the 3′ end of the core histone genes.Core histone genes are generally small in size, intronless and encoding for transcripts that do not undergo polyadenylation. The 3′ end of core histone mRNAs is formed by a cleavage reaction between the stem–loop and the purine-rich sequence declines  and 4. SOur findings are in good agreement with previous reports describing a connection between polyadenylation and Pol II pause followed by transcription termination . Cells were broken with a Dounce homogenizer, and the nuclear fraction was resuspended in sonication buffer . The chromatin was sonicated with a Bioruptor (Diagenode) sonicator into 100–500-bp fragments and centrifuged to avoid any remaining cell debris.Human MCF7 cells  were grown up to 85–90% confluence, washed with PBS, and cross-linked with 1% formaldehyde for 20 min at room temperature. The reaction was stopped with 0.5 M glycine, and cells were washed three times with ice-cold PBS supplemented with 0.5 mM phenylmethylsulfonyl fluoride (PMSF), scraped, and resuspended in swelling buffer  up to 1 ml (0.05% SDS concentration) was used for one IP. For ChIP-seq 5 samples were added together. Protein G Sepharose beads were washed and blocked with sonication buffer containing cold-water fish skin gelatin (SIGMA) and yeast tRNA. Chromatin samples were pre-cleared with unblocked beads at 4°C, for 2 hours. The precleared chromatin samples were rotated overnight at 4°C with the Pol II antibody , and then blocked beads were added for 2 hours to the samples to pull down specific protein-DNA complexes. After immunoprecipitation, samples were washed two times at 4°C with the following buffers: twice with Sonication buffer (without SDS), twice with buffer A , twice with buffer B , and finally twice with Tris-EDTA buffer . Bound fraction of the chromatin was eluted with 2×100 µl of elution buffer  at 65°C for 2×10 min and elutions were pooled. RNase A treatment (5 µg/ml), and reverse cross-linking (125 mM NaCl) was carried out at 65°C overnight. Elutions were finally incubated with proteinase K. DNA was phenol-chloroform extracted and precipitated by ethanol. Validation of the ChIP was performed by quantitative PCR (qPCR) analysis using a Roche LightCycler 480 with Sybr green (Roche) master mix.As a negative control, we immunoprecipitated the cross-linked material with a yeast antibody. The ChIP experiments were repeated at least twice, and all the qPCR reactions were done in triplicates.To inhibit polyadenylation human MCF7 cells were grown up to 85–90% confluence and the medium was changed with fresh one containing cordycepin  www.ucsc.org), tags were mapped onto the human genome hg19. The obtained Pol II ChIP-seq data from human MCF7 cells was deposited in GEO under GSE34001number.Sample preparation was performed as described by the manufacturer. The 32 base pair tags generated from Illumina/Solexa were mapped to the human genome Build 36.1 (UCSC hg18) using the eland program allowing two mismatches. Only sequences that mapped uniquely to the genome with maximum of two mismatches were used for further analysis. We obtained 10.9 and 4.5 millions uniquely mapped reads for Pol II and Mock, respectively. Using the liftover tool from UCSC (qPCR was performed by Roche LightCycler 480 with Sybr green (Roche) master mix. The sequences of the oligonucleotides are available upon request.www.ucsc.org), human genome Build 37 (hg19 assembly). Gene definitions were given by the refseq genes Genome annotations were downloaded from the UCSC Genome Browser . In this experiment, MCF7 cells were tested as five replicates using Affymetrix U133 Plus 2.0 arrays. The cel files were normalized by gcrma method http://www.r-project.org/). The expression level for each probeset was calculated as the mean of the five replicates. While processing the data, we applied the following filters. Firstly, we excluded probesets assigned to several genes localized on different places on the genome to avoid annotation artifacts. Secondly, we eliminated all low intensity probesets where signal cannot be distinguished from noise. These filters allowed us to obtain curated list of expressed probesets. A single relative expression value was computed for each gene and based upon relative expression values we selected lists of highly expressed genes.Cel files of MCF7 cells were downloaded from Gene Expression Omnibus Click here for additional data file.Table S1List containing 500 highly expressed genes from MCF7 cells used in the analysis in (DOC)Click here for additional data file.Table S2List containing 100 highly expressed genes from MCF7 cells used in (DOC)Click here for additional data file.


The 6p21 locus previously linked to reduced FEV1 is associated with COPD only. Larger studies will be needed to distinguish whether these COPD-related effects may reflect, in part, associations specific to different lung cancer histology. We demonstrate that when the “risk genotypes” derived from the univariate analysis are incorporated into an algorithm with clinical variables, independently associated with lung cancer in multivariate analysis, modest discrimination is possible on receiver operator curve analysis (AUC = 0.70). We suggest that genetic susceptibility to lung cancer includes genes conferring susceptibility to COPD and that sub-phenotyping with spirometry is critical to identifying genes underlying the development of lung cancer.Epidemiological studies show that approximately 20–30% of chronic smokers develop chronic obstructive pulmonary disease (COPD) while 10–15% develop lung cancer. COPD pre-exists lung cancer in 50–90% of cases and has a heritability of 40–77%, much greater than for lung cancer with heritability of 15–25%. These data suggest that smokers susceptible to COPD may also be susceptible to lung cancer. This study examines the association of several overlapping chromosomal loci, recently implicated by GWA studies in COPD, lung function and lung cancer, in (n = 1400) subjects sub-phenotyped for the presence of COPD and matched for smoking exposure. Using this approach we show; the 15q25 locus confers susceptibility to lung cancer and COPD, the 4q31 and 4q22 loci both confer a reduced risk to both COPD and lung cancer, the 6p21 locus confers susceptibility to lung cancer in smokers with pre-existing COPD, the 5p15 and 1q23 loci both confer susceptibility to lung cancer in those with no pre-existing COPD. We also show the 5q33 locus, previously associated with reduced FEV The question that then arises is “Given the possible overlap in genetic susceptibility between COPD and lung cancer, is there an alternative study design to current approaches that might better identify susceptibility genes in lung cancer?”Recent genome-wide association (GWA) studies in lung cancer, COPD and lung function , with the highest value of the best three acceptable blows used for classification of COPD status. COPD was defined according to Global Initiative for Chronic Obstructive Lung Diseases (GOLD) stage 2 or more criteria (FEV1/FVC<70% and FEV1% predicted ≤80%) using pre-bronchodilator spirometric measurements [www.goldcopd.com]. A modified ATS respiratory questionnaire  as those serving the lung cancer and COPD hospital clinics through either (a) a community postal advertisement or (b) while attending community-based retired military/servicemen's clubs. Controls with COPD, based on pre-bronchodilator spirometry (GOLD stage 1 or more), who constituted 35% of the smoking volunteers, were excluded from further analysis.The study was approved by the Multi Centre Ethics Committee .The present cross-sectional case–control study compared smokers of the same ethnicity with comparable demographic variables . The controls in the current study were carefully chosen to best represent the majority of smokers who have maintained normal or near-normal lung function despite decades of smoking (“resistant smoker”) as shown by many studies –1 concentration) into 96-well or 384-well plates. Samples were genotyped using either the Sequenom™ system (Sequenom™ Autoflex Mass Spectrometer and Samsung 24 pin nanodispenser) by the Australian Genome Research Facility (www.agrf.com.au) or by our university lab using Taqman® SNP genotyping assays  utilising minor groove-binder probes. The Sequenom™ sequences were designed in house by AGRF with amplification and separation methods  as previously described Genomic DNA was extracted from whole blood samples using standard salt-based methods and purified genomic DNA was aliquoted  gene on 15q25, the rs1052486 SNP, situated within the HLA-B associated transcript (BAT3) gene on 6p21, and the rs402710 SNP, situated within the cisplatin-resistance regulated gene 9 (CRR9) gene on 5p15, were genotyped using the Sequenom™ system, whilst the remaining eight SNPs, the rs7671167 SNP, situated within the Family with sequence similarity 13A (FAM13A) gene on 4q22, the rs1489759 SNP, situated near the hedgehog-interacting protein (HHIP) gene on 4q31, the rs2202507 SNP, situated near the glycophorin A (GYPA) gene on 4q31, the rs2808630 SNP, situated near the C-reactive protein (CRP) gene on 1q21, the rs10516526 SNP, situated within the glutathione S-transferase C-terminal domain (GSTCD) gene on 4q42, the rs1422795 SNP, situated within the A Disintegrin and Metalloproteinase 19 (ADAM19) gene on 5q33, the rs2070600 SNP, situated within the receptor for advanced glycation end-products (AGER) gene on 6p21, and the rs11155242 SNP, situated within the G-protein receptor 126 (GPR126) gene on 6q24, were genotyped by Taqman® SNP genotyping assays. Failed samples were repeated until call rates of ≥95% for each SNP in each cohort were achieved. Genotype frequencies for each SNP were compared between the 3 primary groups  and with sub-phenotyping the lung cancer cohort according to the presence or absence of COPD based on GOLD 2 criteria.The cumulative effect of those SNP genotypes identified as susceptible  or protective (OR<1), based on significant distortions in frequency (P<0.05) between the cases or sub-phenotypes and the control smokers, was examined using a previously published algorithm a) the frequency of lung cancer and (b) the floating absolute risk  across the combined smoker/ex-smoker cohort The algorithmic approach used here involved deriving an overall “susceptibility score” for each subject (from the control and lung cancer cohorts) by combining genetic data (cumulative SNP scores) and clinical variables, identified in a multivariate analysis as previously described WWW.NCSS.COM)Patient characteristics in the cases and controls were compared by ANOVA for continuous variables and Chi-squared test for discrete variables ). Genotype and allele frequencies were checked for each SNP by Hardy–Weinberg Equilibrium (HWE). Population admixture across cohorts was performed using structure analysis on genotyping data from 40 unrelated SNPs Genotype data (9-SNP panel) and the clinical variables were combined in a stepwise logistic regression to assess their relative effects on discriminating low and high risk (by point estimate and receiver operating characteristic (ROC) curve) by score quintile. The frequency distribution of the lung cancer susceptibility score was compared across the cases and controls. Its clinical utility was assessed using ROC analysis, which assesses how well the model predicts risk across the score .Characteristics of the lung cancer cases, COPD cases and healthy control smokers are summarized in www.hapmap.org). The observed genotypes for the two Chr 4q31 SNPs (HHIP and GYPA) in this study were 65% concordant, in accordance with the reported degree of LD between these SNPs. The concordance for the other SNPs in “close” proximity (BAT3 and AGER on 6p21) showed very poor concordance as expected. As all SNPs were in Hardy-Weinberg equilibrium and amplification plots were used to ensure correct genotype calls, significant genotyping error is unlikely. We found no evidence for population stratification between the cohorts using 40 unlinked SNPs from unrelated genes  The genotyping results for the 12 SNPs are shown in The results below describe individual SNP associations between resistant smokers and those with COPD or lung cancer . We found no effects from gender, height or smoking status (current vs former) on any of these associations. A relationship between SNP variants and lung function was only found for rs 16969968 in the lung cancer cases as previously published (26) but not for the other SNP variants (unpublished data). The numbers were considered too small to look at lung cancer sub-grouped by histology. The genotype results below are summarised in As previously reported Consistent with previous studies, the CC genotype was found more frequently in control subjects compared to those with COPD  (63), lung cancer   lung canThe GG genotype was 23% in the controls group compared to 26% in the lung cancer group  and 21% in the COPD group  . CompareWe found no difference in the GG genotype frequency in controls and COPD cases  or lung cancer cases  . CompareThe GG genotype of the HHIP (rs 1489759) SNP was found to be more prevalent in the control group compared to COPD  and lung cancer  groups . SimilarCompared to controls, the frequency of the CC genotype was marginally increased lung cancer cases  and COPD cases  groups . When thCompared to controls, the TT/TC genotype frequency was significantly decreased in COPD patients  but not in lung cancer . Sub-grouping lung cancer cases according to COPD phenotype did not identify any other associations. The TT/TC genotypes of the AGER SNP appeared to confer a protective effect for COPD G1 in .ompared to controls, the CC genotype was slightly less frequent in lung cancer  and COPD groups  but significantly lower in the lung cancer only group . The frequency of the CC genotype was also significantly lower in the lung cancer only cohort compared to lung cancer with COPD despite the modest numbers . This suggests the CC genotype of the CRP SNP was associated with susceptibility to lung cancer only G0 in .Using the results of the uni-variate analysis above, nine “risk genotypes” were identified as either protective or susceptible . For eac1) with a strong genetic basis 1) GWA studies, the results of this current study suggest the genetic effects from these loci confer specific protective or susceptibility effects on COPD, Lung cancer or both . Both the rs169968 SNP (CHRNA3/5 gene on the Chr 15q25 locus) and the rs1052486 SNP (BAT3 gene on the Chr 6p21 locus) appear to confer susceptibility to lung cancer, but the latter only in conjunction with COPD (G2). The rs402710 SNP (CRR9 (TERT) on the Chr 5p15 locus) appears to confer susceptibility to lung cancer in those with no pre-existing COPD, in keeping with other studies (G3) Epidemiological studies suggest COPD is an important sub-phenotype of lung cancer. The results of this study suggest genetic associations broadly define three disease groups: smokers primarily susceptible to COPD (G1), smokers susceptible to both COPD and lung cancer (G2), and smokers susceptible to lung cancer only (G3) . More imThe pathologic link between COPD and lung cancer may stem in part from the overlapping inflammatory, apoptotic and matrix remodelling/repair processes 1 the biomarker of responsiveness. The latter is based on the epidemiological studies showing that FEV1 is the most important risk factor for lung cancer among smokers 1 is estimated to be 40–77% compared to only 15–25% for lung cancer 1), might provide an alternate control group to the non-random (and unscreened) smokers used in case-controls to date 1 be routinely measured in genetic epidemiology studies of lung cancer to better understand the role of “COPD genes” in lung cancer The use of healthy smokers as controls in this study represents a novel though possibly controversial approach It is possible that the specific associations reported in this study reflect in part, small sample size and chance findings. This represents an important limitation of the current study requiring replication in a larger study. It is also possible that the findings reflect true associations that have been better identified, despite small sample sizes, by more precise phenotyping of subjects. Minimising misclassification has been shown to improve the power of a study to identify true associations The authors have previously reported a lung cancer susceptibility model whereby genotype data is combined with non-genetic data