HudsonAlpha Institute

Huntsville, AL, United States

HudsonAlpha Institute

Huntsville, AL, United States
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Vogel J.P.,U.S. Department of Agriculture | Garvin D.F.,University of Minnesota | Garvin D.F.,Oregon State University | Mockler T.C.,Oregon State University | And 148 more authors.
Nature | Year: 2010

Three subfamilies of grasses, the Ehrhartoideae, Panicoideae and Pooideae, provide the bulk of human nutrition and are poised to become major sources of renewable energy. Here we describe the genome sequence of the wild grass Brachypodium distachyon (Brachypodium), which is, to our knowledge, the first member of the Pooideae subfamily to be sequenced. Comparison of the Brachypodium, rice and sorghum genomes shows a precise history of genome evolution across a broad diversity of the grasses, and establishes a template for analysis of the large genomes of economically important pooid grasses such as wheat. The high-quality genome sequence, coupled with ease of cultivation and transformation, small size and rapid life cycle, will help Brachypodium reach its potential as an important model system for developing new energy and food crops. © 2010 Macmillan Publishers Limited. All rights reserved.

News Article | November 23, 2016

F10 descendants of wild-derived striped mice (R. pumilio, originating from Goegap Nature Reserve, South Africa, S 29° 41.56′, E 18° 1.60′) were obtained from a captive colony at the University of Zurich (Switzerland) and are now maintained at Harvard University. They are kept at a 16:8 light-dark cycle and given food ad libitum. Developmental stages were inferred from morphological similarities with M. musculus embryos. Harvard University’s IACUC committee approved all experiments. Adults. We identified three main hair types based on their individual pigment pattern: black, banded and light. To characterize the pigment pattern along the dorsoventral axis, we quantified the proportion of each of these hair types in 1 mm hair plugs taken from each dorsal stripe, the flank and the ventrum of five adult mice. In addition, we scored the number of guard, awl and zigzag hair found in each region. To determine hair length, we placed hairs from the hair plugs on microscope slides, mounted them with glycerol and measured their length using Axiovision Microscopy Software (Zeiss). Embryos and pups. We fixed embryos with 4% paraformaldehyde, dissected the skin and mounted it on glass slides (dermal side up). For estimating hair follicle density in pups, we detached a portion of each stripe from the muscle, while leaving the ends attached, embedded samples in OCT (Fisher Scientific), cryosectioned them transversally and stained them with haematoxylin and eosin. This technique allowed us to count individual hair follicles and assign them to the specific region to which they belonged (light stripes, dark stripes or flank). We counted the number of hair follicles and estimated the surface area of the tissue using ImageJ35. Since our phenotypic characterization (Fig. 1b and Extended Data Figs 1b, 3b) and gene expression patterns determined by qPCR (Figs 1j and 2b) showed no differences between the two dark stripes, we carried out most of our analyses with dark stripe 1 (closest to the midline). We manually quantified the number of MITF+ cells per hair follicle, as detected with the antibody. For quantification of MITF fluorescence, we obtained images from hair follicles in the light and dark stripes, outlined stained cells, and measured the integrated density using ImageJ35. To obtain the corrected total cell fluorescence (CTCF), we multiplied the area of each selected cell by the mean fluorescence of the background readings and subtracted that value from the integrated density of stained cells36. To quantify the extent of KIT staining, we obtained images from hair follicles in the light and dark stripes, delineated the hair bulb area and measured the proportion of the area that was stained with KIT+ using ImageJ. Data were obtained from three pups and three embryos. All counts were done blind and samples were randomized. Statistical differences were determined using two-tailed t-tests or ANOVA (sample sizes and statistical tests used are indicated in figure legends). We injected the peritoneum of striped mouse pregnant females with 10 μg g−1 of EdU (5-ethynyl-2′-deoxyuridine) (ThermoFisher Scientific) two days before birth and collected pups at P2. To measure proliferating cells, we microdissected dark and light stripes, embedded them in OCT for posterior cryosectioning and used the Click-iT EdU Imaging kit (ThermoFisher Scientific), following the manufacturers’ protocol. We counted proliferating cells in the epidermis and hair follicles from pictures of light and dark stripes. Data were obtained from three individuals. All counts were done blind and samples were randomized. Statistical differences were established using two-tailed t-tests (sample sizes are provided in the figure legend). Striped mouse embryos were fixed in 4% paraformaldehyde, embedded in OCT/sucrose, and sectioned using a cryostat (CM 3050S, Leica). We performed immunohistochemistry using anti-MITF (Abcam 80651; 1:100), anti-ALX3 (Abcam 64985; 1:500), anti-KIT (DAKO A4502; 1:1,000), anti-E-cadherin (Millipore ECCD-2; 1:200), anti-S100 (Abcam 4066; 1:200), and anti-SOX10 (Abcam 27655; 1:100). We visualized reactions with Alexa-dye-conjugated secondary antibodies (Molecular Probes) at 1:500 dilution in 3% bovine serum albumin (BSA), PBS and Tween or with biotinylated goat anti-rabbit (Jackson Labs) and tyramide signal amplification (Perkin Elmer). For controls, we incubated sections with PBS instead of primary antibodies, but no specific cellular staining was observed. Cell nuclei were stained with DAPI (Southern Biotech). We examined sections using a LSM 700 confocal microscope and an A1 Imager (Zeiss). All pictures are representative of at least three individuals. We separated the skin from the muscle and microdissected skin tissue corresponding to different regions (that is, dark stripe 1, light stripe, dark stripe 2, flank and ventrum) at the different time points indicated throughout the text. We then extracted total RNA using the fibrous tissue RNeasy kit (Qiagen), which included a DNase on-column treatment. Using qScript cDNA SuperMix (Quanta BioSciences), we generated complementary DNA (cDNA) and then performed qPCR using PerfeCTa SYBR Green FastMix (Quanta BioSciences). We used 40 cycles of amplification on an Eppendorf Mastercycler. For analysis of striped mice, we designed primers along sites that were conserved across mice and rats. For chipmunk samples, we designed primers along sites that were conserved across mice and thirteen-lined ground squirrels (all primers sequences are listed in Supplementary Table 2). For measurements of Mitf expression in M. musculus samples, we used validated qPCR primers from the PrimerBank database37. We assayed gene expression in triplicate for each sample and normalized the data using the housekeeping gene β-actin. Samples used for qPCR correspond to different individuals than those used for RNA-seq analysis. We analysed data from all qPCR experiments using the comparative C method38, and established statistical significance of expression differences using either ANOVA followed by a Tukey–Kramer test or two-tailed t-tests (sample sizes and specific statistical tests used are given in each figure legend). For each of the time points described in the text, we dissected skin tissue (dark stripe 1, light stripe, flank) and extracted RNA as indicated for qPCR. We used RNA from three different regions (light stripe, dark stripe and flank) from each of three individuals for four different stages (E19, E22, P0, P2; n = 12; 36 libraries in total). We prepared cDNA libraries for each sample using Illumina’s TruSeq RNA Library Preparation Kit v2. We multiplexed individual libraries (six per lane) and sequenced them as paired-end 50-bp reads on an Illumina HiSeq 2000 instrument at the Genome Sequencing Laboratory of the HudsonAlpha Institute. We used Cutadapt software (version 1.8.1) to trim RNA-seq reads for residual adaptors and low quality sequences. Since a good quality reference genome is not currently available for the striped mouse, we used a dual exploratory strategy to assess differential expression between skin regions across various developmental stages. First, we aligned the trimmed RNA-seq reads against the M. musculus reference genome version using genomic sequence and transcript annotations obtained from Ensembl (release 80) and the STAR aligner software (version 2.5.0b). In parallel, we assembled trimmed RNA-seq reads into a de novo transcriptome using the Trinity suite of tools (version 2.1.0). The resulting de novo transcriptome assembly was subsequently annotated using an in-house annotation procedure supported by the human reference exon sequences retrieved from the Ref–Seq sequence database (GRCh37/hg19, release 55; Briefly, to associate a specific gene entity to each de novo assembled transcriptomic contig, we mapped our de novo assembly against a comprehensive database of reference human exon sequences using the Blast software (version 2.2.22). We retained only alignments with a significant Blast Expected Value < 10−4 for subsequent annotation purposes. Based on these alignments, we subsequently computed an ad hoc mapping score for each pair {assembly contig, gene entity} for which at least one significant exon alignment was identified. The mapping score was computed as the sum of the highest Blast alignment bit-scores at each position within a particular contig, associated with at least one significant alignment against an exon of the considered gene entity. Ultimately, the annotation procedure associated to each mapped contig the gene entity with the highest ad hoc mapping score. We then used the annotated de novo transcriptome assembly as reference for aligning trimmed RNA-seq reads using the Bowtie2 software (version 2.2.5). We computed gene counts from read alignments, obtained using either the M. musculus reference or the de novo transcriptome assembly, with three software tools included in the Trinity suite: eXpress (version 1.5.1), Kallisto (0.42.4) and RSEM (version 1.2.23). We then used the individual sets of gene counts computed for each transcriptome reference and each abundance estimation tool to test for differential gene expression between samples from various skin regions with the DESeq2 package (version 1.10.1) from Bioconductor. The entire dataset (3 individuals, 3 regions, 4 stages; n = 36 libraries) was analysed under the DESeq2 negative binomial generalized linear model, which is a powerful and robust approach for identifying genes that are differentially expressed, either between stages or between regions. In all analyses, we used a FDR < 0.1 as a statistical significance threshold. Results presented here depict region-specific two-way comparisons across all four stages, dark versus light (Fig. 2a), light versus flank (Extended Data Fig. 4e) and dark versus flank (Extended Data Fig. 4f), in which genes assessed as significant represent the intersection between the three abundance estimation approaches implemented in the Trinity suite. There are major changes in cell composition and skin development across the four stages we examined by RNA-seq (E19, E22, P0, P2), associated with large changes in gene expression profiles. Therefore to examine the relationship between light and dark stripes at different stages, we developed supervised learning models in which the gene expression profile at one stage was tested as a predictor of stripe identity, light versus dark, and subsequent stages. After normalization and variance stabilization using R functions implemented in DESeq2, we carried out a principal component analysis (PCA), using R functions implemented in the FactoMineR (version 1.33) package, to identify variance components associated with light versus dark phenotype across all stages. The PCA results then were used to develop supervised learning models using the R functions implemented in the randomForest package (version 4.6-12), including optimization steps based on the top 5% of the most informative gene expression profiles associated with each stage. The results demonstrate the ability of learning models based on a specific region and stage to predict region identity, light versus dark, in other stages, in which the accuracy of the models is evaluated by averaging over 30 independent iterations (Extended Data Fig. 2f). For in situ hybridization, we generated species-specific riboprobes by cloning a 545-bp fragment of Alx3 from M. musculus and the striped mouse. We carried out section in situ hybridizations following protocols described previously39, 40 and visualized samples using an A1 Imager (Zeiss). All pictures are representative of at least three individuals. We purchased B16-F1 melanoma cells from ATCC and maintained them in DMEM with 10% fetal bovine serum (FBS, Sigma-Aldrich), 100 U ml−1 penicillin, and 100 μg ml−1 streptomycin in a 37 °C incubator with 5% CO at physiological pH 7.4. We grew cells to 70–80% confluency and performed all experiments within 10 passages. B16-F1 cells tested negative for Mycoplasma contamination. We cultured mouse keratinocytes and maintained them in 0.05 mM Ca2+ E-medium with 10% FBS serum, following previously established protocols18. For gain-of-function experiments, we used the LV–Alx3:GFP and LV–GFP constructs described above. For loss-of-function experiments, we used five constructs (four specific to Alx3 and one scrambled sequence) from existing RNAi lentiviral libraries41 (details and clone numbers listed in Supplementary Table 3). For viral infections, we plated cells in 6-well dishes at 300,000 cells per well and incubated with lentivirus in the presence of polybrene (100 μg m−1). All infections were carried out in triplicate. After two days in culture, we selected infected cells using either puromycin (2 μg ml−1; shRNA constructs) or FACS (gain-of-function), and processed samples for mRNA analyses. For cell-culture-insert experiments, we plated wild-type cells on 0.4 μm transwell inserts (Falcon, BD) at 200,000 cells per ml and incubated them in plates containing a bottom layer of transduced cells (keratinocytes or melanocytes) for three days (see Extended Data Fig. 7a, c for an illustration of the experimental design). For construction of LV–Alx3:GFP, we replaced the puromycin cassette of PLKO.1, a generic lentiviral vector containing the PKG promoter41, with a fragment containing Alx3 cDNA cloned from M. musculus (NM_007441.3), a P2A sequence and a histone-fluorescent protein gene fusion (Hist2h2be–eGFP). LV–GFP, which contains only the sequence coding for Hist2h2be–eGFP, was originally designed from PLKO.1 using a similar strategy18 (Addgene plasmid 25999). We carried out large-scale production of VSV-G pseudotyped lentivirus using calcium phosphate transfections of 293FT cells and helper plasmids, pMD2.G and psPAX2 (Addgene plasmids 12259 and 12260). Transfection conditions, subsequent viral concentration and titration followed established guidelines18. For injections, we anaesthetized C57Bl/6 females at E8.5 of gestation and injected embryos with 1.5 μl of a constant viral titre. We collected transduced skin from P4 pups, which we used for immunohistochemistry, following procedures outlined above, and for isolation of virus-infected melanocytes via FACS. For FACS, we used a KIT antibody (ebioscience 14-1171-81; 1:1,000), sorted KIT+ GFP+ cells directly in TRIzol LS (Invitrogen), and extracted RNA following the protocol outlined in the TRIzol LS manual. We manually quantified the number of MITF+ GFP+ and SOX10+ GFP+ cells in hair follicles, as detected with our antibodies. To determine the number of keratinocytes per follicular area, we divided the number of K14+ GFP+ cells in hair follicles by the hair bulb area, using ImageJ35. To determine hair follicle density, we obtained pictures of skin sections stained with DAPI and counted the number of hair follicles per tissue section area, using ImageJ35. Data were obtained from three individuals per lentiviral construct injected. All counts were done blind and samples were randomized. Statistical differences were established using two-tailed t-tests (sample sizes for each analysis are indicated in figure legends). We obtained comparative sequence data from publicly available nucleotide databases at NCBI ( Evolutionarily conserved non-coding sequences were identified using the global sequence alignment tool incorporated in the UCSC genome browser (, PipMaker (, and LAGAN ( We conducted EMSA using nuclear extracts45 of melanoma B16-F1 cells prepared in the presence of protease inhibitors (complete protease inhibitor cocktail; Roche) and determined protein concentrations using the Bio-Rad protein assay. Synthetic complementary oligonucleotides were annealed and labelled using [γ-32P]-ATP and T4 kinase. We performed binding reactions at room temperature in the presence of 20,000 counts per min (c.p.m.) of radiolabelled probe (approximately 6–10 fmol) in a volume of 20 μl containing 2 μg poly (dI-dC), 20 mM HEPES (pH 7.9), 70 mM KCl, 2.5 mM MgCl , 1 mM dithiothreitol, 0.3 mM EDTA and 10% glycerol. We then added competitor oligonucleotides of identical (specific) or unrelated (non-specific) sequences to the probe at the indicated fold molar excess. The sequences of the oligonucleotides used are listed in Supplementary Table 2. When indicated, we added specific antiserum24 or control non-immune rabbit serum (NRS) to the binding reaction. The reaction mixtures were resolved using 5% non-denaturing polyacrylamide gels, which were subsequently dried and autoradiographed. We prepared cell lysates from B16-F1 cells, resolved by SDS–PAGE, and blotted them onto a BioTrace PVDF membrane (Pall Corporation). We detected ALX3 immunoreactivity with a rabbit polyclonal primary antiserum (1:4,000 dilution)24 and a horseradish peroxidase-conjugated goat anti-rabbit secondary antibody (1:10,000 dilution; Bio-Rad Laboratories). To detect ACTIN we used a mouse monoclonal antibody (1:10,000 dilution, clone AC-15; Sigma) and a horseradish peroxidase-conjugated goat anti-mouse antibody (1:5,000 dilution; Bio-Rad Laboratories). We visualized immunoreactive bands using an ECL detection system (GE Healthcare). We performed ChIP assays as previously described46 using B16-F1 cells treated with 1% formaldehyde. We isolated the cross-linked protein–DNA complexes and, after sonication, we incubated chromatin with an ALX3 antiserum24 or with control NRS. Next, we isolated antibody–protein–DNA complexes by incubation with protein A-sepharose. To detect bound DNA, we carried out qPCR on triplicate samples using oligonucleotide primers that amplify fragments of the Mitf gene corresponding to the regions containing sites 3, 5 or 10. As a control, we used promoter sequences from the Tyr gene as described47. Oligonucleotides used in ChIP assays are listed in Supplementary Table 2. We amplified a 1.5-kb region of the Mitf M promoter from M. musculus and subsequently cloned it into the SacI–HindIII sites of the pLightSwitch_Prom luciferase reporter vector (Switchgear Genomics, Active Motif). We then generated additional luciferase constructs from the wild-type construct by mutating the different Alx3 binding motifs (TAAT to GCCG) using the Q5 site-directed mutagenesis kit (New England Biolabs). We verified all constructs by sequencing. We next transfected B16-F1 melanocytes with LV–Alx3:GFP and LV–GFP by using FuGENE HD (Active Motif). Using FACS, we selected the stable transfected clones and confirmed overexpression of Alx3 by qPCR. The day before the transfection, we seeded cells at a density of 1 × 104 cells per well and 16 h later transfected them with the different Mitf constructs using a FuGENE HD to plasmid DNA ratio of 3:1 (300 nl FuGENE HD to 100 ng plasmid DNA per well). We then harvested cells and processed them using the LightSwitch luciferase assay kit (Switchgear Genomics) following the protocol guidelines and measured luciferase using a SpectraMax L luminometer (Molecular Devices). We normalized luciferase activity relative to luminescence from cells transfected with the pLightSwitch_Prom luciferase reporter vector (empty vector). We did not observe a difference in luciferase activity when we transfected our two stable cell lines (LV–Alx3:GFP and LV–GFP) with an empty vector (pLightSwitch_Prom) or a vector containing the promoter for a housekeeping gene (ACTB_PROM). We performed all luciferase experiments using five replicates per construct and established the statistical significance of luminescence differences using two-tailed t-tests (sample sizes for each experiment are indicated in the figure legends). We collected T. striatus at Harvard University’s Concord Field Station (Concord) using Sherman live traps (Massachusetts state permit: 027.14SCM). Chipmunks were euthanized, skin punches were taken from the different body regions, and samples were processed for qPCR as indicated above. The striped mouse de novo transcriptome assembly has been deposited in under accession number doi:10.5061/dryad.7v222.

Guo Y.,Vanderbilt Ingram Cancer Center | Cai Q.,Vanderbilt University | Samuels D.C.,Vanderbilt University | Ye F.,Vanderbilt Ingram Cancer Center | And 13 more authors.
Mutation Research - Genetic Toxicology and Environmental Mutagenesis | Year: 2012

The human mitochondrial genome has an exclusively maternal mode of inheritance. Mitochondrial DNA (mtDNA) is particularly vulnerable to environmental insults due in part to an underdeveloped DNA repair system, limited to base excision and homologous recombination repair. Radiation exposure to the ovaries may cause mtDNA mutations in oocytes, which may in turn be transmitted to offspring. We hypothesized that the children of female cancer survivors who received radiation therapy may have an increased rate of mtDNA heteroplasmy mutations, which conceivably could increase their risk of developing cancer and other diseases. We evaluated 44 DNA blood samples from 17 Danish and 1 Finnish families (18 mothers and 26 children). All mothers had been treated for cancer as children and radiation doses to their ovaries were determined based on medical records and computational models. DNA samples were sequenced for the entire mitochondrial genome using the Illumina GAII system. Mother's age at sample collection was positively correlated with mtDNA heteroplasmy mutations. There was evidence of heteroplasmy inheritance in that 9 of the 18 families had at least one child who inherited at least one heteroplasmy site from his or her mother. No significant difference in single nucleotide polymorphisms between mother and offspring, however, was observed. Radiation therapy dose to ovaries also was not significantly associated with the heteroplasmy mutation rate among mothers and children. No evidence was found that radiotherapy for pediatric cancer is associated with the mitochondrial genome mutation rate in female cancer survivors and their children. © 2012 Elsevier B.V..

Barnes S.,University of Alabama at Birmingham | Benton H.P.,Scripps Research Institute | Casazza K.,University of Alabama at Birmingham | Cooper S.J.,HudsonAlpha Institute | And 9 more authors.
Journal of Mass Spectrometry | Year: 2016

The study of metabolism has had a long history. Metabolomics, a systems biology discipline representing analysis of known and unknown pathways of metabolism, has grown tremendously over the past 20 years. Because of its comprehensive nature, metabolomics requires careful consideration of the question(s) being asked, the scale needed to answer the question(s), collection and storage of the sample specimens, methods for extraction of the metabolites from biological matrices, the analytical method(s) to be employed and the quality control of the analyses, how collected data are correlated, the statistical methods to determine metabolites undergoing significant change, putative identification of metabolites and the use of stable isotopes to aid in verifying metabolite identity and establishing pathway connections and fluxes. The National Institutes of Health Common Fund Metabolomics Program was established in 2012 to stimulate interest in the approaches and technologies of metabolomics. To deliver one of the program's goals, the University of Alabama at Birmingham has hosted an annual 4-day short course in metabolomics for faculty, postdoctoral fellows and graduate students from national and international institutions. This paper is the first part of a summary of the training materials presented in the course to be used as a resource for all those embarking on metabolomics research. The complete set of training materials including slide sets and videos can be viewed at Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Schmidt M.D.,Cornell University | Vallabhajosyula R.R.,CFD Research Corporation | Jenkins J.W.,HudsonAlpha Institute | Hood J.E.,CFD Research Corporation | And 3 more authors.
Physical Biology | Year: 2011

The reverse engineering of metabolic networks from experimental data is traditionally a labor-intensive task requiring a priori systems knowledge. Using a proven model as a test system, we demonstrate an automated method to simplify this process by modifying an existing or related model - suggesting nonlinear terms and structural modifications - or even constructing a new model that agrees with the system's time series observations. In certain cases, this method can identify the full dynamical model from scratch without prior knowledge or structural assumptions. The algorithm selects between multiple candidate models by designing experiments to make their predictions disagree. We performed computational experiments to analyze a nonlinear seven-dimensional model of yeast glycolytic oscillations. This approach corrected mistakes reliably in both approximated and overspecified models. The method performed well to high levels of noise for most states, could identify the correct model de novo, and make better predictions than ordinary parametric regression and neural network models. We identified an invariant quantity in the model, which accurately derived kinetics and the numerical sensitivity coefficients of the system. Finally, we compared the system to dynamic flux estimation and discussed the scaling and application of this methodology to automated experiment design and control in biological systems in real time. © 2011 IOP Publishing Ltd.

Beane J.,Boston University | Vick J.,Boston University | Schembri F.,Boston University | Anderlind C.,Boston University | And 12 more authors.
Cancer Prevention Research | Year: 2011

Cigarette smoke creates a molecular field of injury in epithelial cells that line the respiratory tract. We hypothesized that transcriptome sequencing (RNA-Seq) will enhance our understanding of the field of molecular injury in response to tobacco smoke exposure and lung cancer pathogenesis by identifying gene expression differences not interrogated or accurately measured by microarrays. We sequenced the highmolecular-weight fraction of total RNA (>200 nt) from pooled bronchial airway epithelial cell brushings (n = 3 patients per pool) obtained during bronchoscopy from healthy never smoker (NS) and current smoker (S) volunteers and smokers with (C) and without (NC) lung cancer undergoing lung nodule resection surgery. RNA-Seq libraries were prepared using 2 distinct approaches, one capable of capturing non-polyadenylated RNA (the prototype NuGEN Ovation RNA-Seq protocol) and the other designed to measure only polyadenylated RNA (the standard Illumina mRNA-Seq protocol) followed by sequencing generating approximately 29 million 36 nt reads per pool and approximately 22 million 75 nt paired-end reads per pool, respectively. The NuGEN protocol captured additional transcripts not detected by the Illumina protocol at the expense of reduced coverage of polyadenylated transcripts, while longer read lengths and a paired-end sequencing strategy significantly improved the number of reads that could be aligned to the genome. The aligned reads derived from the two complementary protocols were used to define the compendium of genes expressed in the airway epithelium (n = 20,573 genes). Pathways related to the metabolism of xenobiotics by cytochrome P450, retinol metabolism, and oxidoreductase activity were enriched among genes differentially expressed in smokers, whereas chemokine signaling pathways, cytokine-cytokine receptor interactions, and cell adhesion molecules were enriched among genes differentially expressed in smokers with lung cancer. There was a significant correlation between the RNA-Seq gene expression data and Affymetrix microarray data generated from the same samples (P < 0.001); however, the RNA-Seq data detected additional smoking- and cancer-related transcripts whose expression was were either not interrogated by or was not found to be significantly altered when using microarrays, including smokingrelated changes in the inflammatory genes S100A8 and S100A9 and cancer-related changes in MUC5AC and secretoglobin (SCGB3A1). Quantitative real-time PCR confirmed differential expression of select genes and non-coding RNAs within individual samples. These results demonstrate that transcriptome sequencing has the potential to provide new insights into the biology of the airway field of injury associated with smoking and lung cancer. The measurement of both coding and non-coding transcripts by RNA-Seq has the potential to help elucidate mechanisms of response to tobacco smoke and to identify additional biomarkers of lung cancer risk and novel targets for chemoprevention. ©2011 AACR.

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