Browsing by Subject "Oligonucleotide Array Sequence Analysis"

Now showing 1 - 20 of 43
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    A host transcriptional signature for presymptomatic detection of infection in humans exposed to influenza H1N1 or H3N2.
    (PLoS One, 2013) Woods, Christopher W; McClain, Micah T; Chen, Minhua; Zaas, Aimee K; Nicholson, Bradly P; Varkey, Jay; Veldman, Timothy; Kingsmore, Stephen F; Kingsmore, Stephen F; Huang, Yongsheng; Lambkin-Williams, Robert; Gilbert, Anthony G; Hero, Alfred O; Ramsburg, Elizabeth; Glickman, Seth; Lucas, Joseph E; Carin, Lawrence; Ginsburg, Geoffrey S
    There is great potential for host-based gene expression analysis to impact the early diagnosis of infectious diseases. In particular, the influenza pandemic of 2009 highlighted the challenges and limitations of traditional pathogen-based testing for suspected upper respiratory viral infection. We inoculated human volunteers with either influenza A (A/Brisbane/59/2007 (H1N1) or A/Wisconsin/67/2005 (H3N2)), and assayed the peripheral blood transcriptome every 8 hours for 7 days. Of 41 inoculated volunteers, 18 (44%) developed symptomatic infection. Using unbiased sparse latent factor regression analysis, we generated a gene signature (or factor) for symptomatic influenza capable of detecting 94% of infected cases. This gene signature is detectable as early as 29 hours post-exposure and achieves maximal accuracy on average 43 hours (p = 0.003, H1N1) and 38 hours (p-value = 0.005, H3N2) before peak clinical symptoms. In order to test the relevance of these findings in naturally acquired disease, a composite influenza A signature built from these challenge studies was applied to Emergency Department patients where it discriminates between swine-origin influenza A/H1N1 (2009) infected and non-infected individuals with 92% accuracy. The host genomic response to Influenza infection is robust and may provide the means for detection before typical clinical symptoms are apparent.
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    A nucleosome-guided map of transcription factor binding sites in yeast.
    (PLoS Comput Biol, 2007-11) Narlikar, Leelavati; Gordân, Raluca; Hartemink, Alexander J
    Finding functional DNA binding sites of transcription factors (TFs) throughout the genome is a crucial step in understanding transcriptional regulation. Unfortunately, these binding sites are typically short and degenerate, posing a significant statistical challenge: many more matches to known TF motifs occur in the genome than are actually functional. However, information about chromatin structure may help to identify the functional sites. In particular, it has been shown that active regulatory regions are usually depleted of nucleosomes, thereby enabling TFs to bind DNA in those regions. Here, we describe a novel motif discovery algorithm that employs an informative prior over DNA sequence positions based on a discriminative view of nucleosome occupancy. When a Gibbs sampling algorithm is applied to yeast sequence-sets identified by ChIP-chip, the correct motif is found in 52% more cases with our informative prior than with the commonly used uniform prior. This is the first demonstration that nucleosome occupancy information can be used to improve motif discovery. The improvement is dramatic, even though we are using only a statistical model to predict nucleosome occupancy; we expect our results to improve further as high-resolution genome-wide experimental nucleosome occupancy data becomes increasingly available.
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    A robotics platform for automated batch fabrication of high density, microfluidics-based DNA microarrays, with applications to single cell, multiplex assays of secreted proteins.
    (The Review of scientific instruments, 2011-09) Ahmad, Habib; Sutherland, Alex; Shin, Young Shik; Hwang, Kiwook; Qin, Lidong; Krom, Russell-John; Heath, James R
    Microfluidics flow-patterning has been utilized for the construction of chip-scale miniaturized DNA and protein barcode arrays. Such arrays have been used for specific clinical and fundamental investigations in which many proteins are assayed from single cells or other small sample sizes. However, flow-patterned arrays are hand-prepared, and so are impractical for broad applications. We describe an integrated robotics/microfluidics platform for the automated preparation of such arrays, and we apply it to the batch fabrication of up to eighteen chips of flow-patterned DNA barcodes. The resulting substrates are comparable in quality with hand-made arrays and exhibit excellent substrate-to-substrate consistency. We demonstrate the utility and reproducibility of robotics-patterned barcodes by utilizing two flow-patterned chips for highly parallel assays of a panel of secreted proteins from single macrophage cells.
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    A Transcriptional Signature Identifies LKB1 Functional Status as a Novel Determinant of MEK Sensitivity in Lung Adenocarcinoma.
    (Cancer research, 2017-01) Kaufman, Jacob M; Yamada, Tadaaki; Park, Kyungho; Timmers, Cynthia D; Amann, Joseph M; Carbone, David P
    LKB1 is a commonly mutated tumor suppressor in non-small cell lung cancer that exerts complex effects on signal transduction and transcriptional regulation. To better understand the downstream impact of loss of functional LKB1, we developed a transcriptional fingerprint assay representing this phenotype. This assay was predictive of LKB1 functional loss in cell lines and clinical specimens, even those without detected sequence alterations in the gene. In silico screening of drug sensitivity data identified putative LKB1-selective drug candidates, revealing novel associations not apparent from analysis of LKB1 mutations alone. Among the candidates, MEK inhibitors showed robust association with signature expression in both training and testing datasets independent of RAS/RAF mutations. This susceptibility phenotype is directly altered by RNA interference-mediated LKB1 knockdown or by LKB1 re-expression into mutant cell lines and is readily observed in vivo using a xenograft model. MEK sensitivity is dependent on LKB1-induced changes in AKT and FOXO3 activation, consistent with genomic and proteomic analyses of LKB1-deficient lung adenocarcinomas. Our findings implicate the MEK pathway as a potential therapeutic target for LKB1-deficient cancers and define a practical NanoString biomarker to identify functional LKB1 loss. Cancer Res; 77(1); 153-63. ©2016 AACR.
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    Advances to Bayesian network inference for generating causal networks from observational biological data.
    (Bioinformatics, 2004-12-12) Yu, Jing; Smith, V Anne; Wang, Paul P; Hartemink, Alexander J; Jarvis, Erich D
    MOTIVATION: Network inference algorithms are powerful computational tools for identifying putative causal interactions among variables from observational data. Bayesian network inference algorithms hold particular promise in that they can capture linear, non-linear, combinatorial, stochastic and other types of relationships among variables across multiple levels of biological organization. However, challenges remain when applying these algorithms to limited quantities of experimental data collected from biological systems. Here, we use a simulation approach to make advances in our dynamic Bayesian network (DBN) inference algorithm, especially in the context of limited quantities of biological data. RESULTS: We test a range of scoring metrics and search heuristics to find an effective algorithm configuration for evaluating our methodological advances. We also identify sampling intervals and levels of data discretization that allow the best recovery of the simulated networks. We develop a novel influence score for DBNs that attempts to estimate both the sign (activation or repression) and relative magnitude of interactions among variables. When faced with limited quantities of observational data, combining our influence score with moderate data interpolation reduces a significant portion of false positive interactions in the recovered networks. Together, our advances allow DBN inference algorithms to be more effective in recovering biological networks from experimentally collected data. AVAILABILITY: Source code and simulated data are available upon request. SUPPLEMENTARY INFORMATION: http://www.jarvislab.net/Bioinformatics/BNAdvances/
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    An epigenome-wide association study of posttraumatic stress disorder in US veterans implicates several new DNA methylation loci.
    (Clinical epigenetics, 2020-03) Logue, Mark W; Miller, Mark W; Wolf, Erika J; Huber, Bertrand Russ; Morrison, Filomene G; Zhou, Zhenwei; Zheng, Yuanchao; Smith, Alicia K; Daskalakis, Nikolaos P; Ratanatharathorn, Andrew; Uddin, Monica; Nievergelt, Caroline M; Ashley-Koch, Allison E; Baker, Dewleen G; Beckham, Jean C; Garrett, Melanie E; Boks, Marco P; Geuze, Elbert; Grant, Gerald A; Hauser, Michael A; Kessler, Ronald C; Kimbrel, Nathan A; Maihofer, Adam X; Marx, Christine E; Qin, Xue-Jun; Risbrough, Victoria B; Rutten, Bart PF; Stein, Murray B; Ursano, Robert J; Vermetten, Eric; Vinkers, Christiaan H; Ware, Erin B; Stone, Annjanette; Schichman, Steven A; McGlinchey, Regina E; Milberg, William P; Hayes, Jasmeet P; Verfaellie, Mieke; Traumatic Stress Brain Study Group

    Background

    Previous studies using candidate gene and genome-wide approaches have identified epigenetic changes in DNA methylation (DNAm) associated with posttraumatic stress disorder (PTSD).

    Methods

    In this study, we performed an EWAS of PTSD in a cohort of Veterans (n = 378 lifetime PTSD cases and 135 controls) from the Translational Research Center for TBI and Stress Disorders (TRACTS) cohort assessed using the Illumina EPIC Methylation BeadChip which assesses DNAm at more than 850,000 sites throughout the genome. Our model included covariates for ancestry, cell heterogeneity, sex, age, and a smoking score based on DNAm at 39 smoking-associated CpGs. We also examined in EPIC-based DNAm data generated from pre-frontal cortex (PFC) tissue from the National PTSD Brain Bank (n = 72).

    Results

    The analysis of blood samples yielded one genome-wide significant association with PTSD at cg19534438 in the gene G0S2 (p = 1.19 × 10-7, padj = 0.048). This association was replicated in an independent PGC-PTSD-EWAS consortium meta-analysis of military cohorts (p = 0.0024). We also observed association with the smoking-related locus cg05575921 in AHRR despite inclusion of a methylation-based smoking score covariate (p = 9.16 × 10-6), which replicates a previously observed PGC-PTSD-EWAS association (Smith et al. 2019), and yields evidence consistent with a smoking-independent effect. The top 100 EWAS loci were then examined in the PFC data. One of the blood-based PTSD loci, cg04130728 in CHST11, which was in the top 10 loci in blood, but which was not genome-wide significant, was significantly associated with PTSD in brain tissue (in blood p = 1.19 × 10-5, padj = 0.60, in brain, p = 0.00032 with the same direction of effect). Gene set enrichment analysis of the top 500 EWAS loci yielded several significant overlapping GO terms involved in pathogen response, including "Response to lipopolysaccharide" (p = 6.97 × 10-6, padj = 0.042).

    Conclusions

    The cross replication observed in independent cohorts is evidence that DNA methylation in peripheral tissue can yield consistent and replicable PTSD associations, and our results also suggest that that some PTSD associations observed in peripheral tissue may mirror associations in the brain.
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    Automatic annotation of spatial expression patterns via sparse Bayesian factor models.
    (PLoS Comput Biol, 2011-07) Pruteanu-Malinici, Iulian; Mace, Daniel L; Ohler, Uwe
    Advances in reporters for gene expression have made it possible to document and quantify expression patterns in 2D-4D. In contrast to microarrays, which provide data for many genes but averaged and/or at low resolution, images reveal the high spatial dynamics of gene expression. Developing computational methods to compare, annotate, and model gene expression based on images is imperative, considering that available data are rapidly increasing. We have developed a sparse Bayesian factor analysis model in which the observed expression diversity of among a large set of high-dimensional images is modeled by a small number of hidden common factors. We apply this approach on embryonic expression patterns from a Drosophila RNA in situ image database, and show that the automatically inferred factors provide for a meaningful decomposition and represent common co-regulation or biological functions. The low-dimensional set of factor mixing weights is further used as features by a classifier to annotate expression patterns with functional categories. On human-curated annotations, our sparse approach reaches similar or better classification of expression patterns at different developmental stages, when compared to other automatic image annotation methods using thousands of hard-to-interpret features. Our study therefore outlines a general framework for large microscopy data sets, in which both the generative model itself, as well as its application for analysis tasks such as automated annotation, can provide insight into biological questions.
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    Comparative analyses of seven algorithms for copy number variant identification from single nucleotide polymorphism arrays.
    (Nucleic Acids Res, 2010-05) Dellinger, AE; Saw, SM; Goh, LK; Seielstad, M; Young, TL; Li, YJ
    Determination of copy number variants (CNVs) inferred in genome wide single nucleotide polymorphism arrays has shown increasing utility in genetic variant disease associations. Several CNV detection methods are available, but differences in CNV call thresholds and characteristics exist. We evaluated the relative performance of seven methods: circular binary segmentation, CNVFinder, cnvPartition, gain and loss of DNA, Nexus algorithms, PennCNV and QuantiSNP. Tested data included real and simulated Illumina HumHap 550 data from the Singapore cohort study of the risk factors for Myopia (SCORM) and simulated data from Affymetrix 6.0 and platform-independent distributions. The normalized singleton ratio (NSR) is proposed as a metric for parameter optimization before enacting full analysis. We used 10 SCORM samples for optimizing parameter settings for each method and then evaluated method performance at optimal parameters using 100 SCORM samples. The statistical power, false positive rates, and receiver operating characteristic (ROC) curve residuals were evaluated by simulation studies. Optimal parameters, as determined by NSR and ROC curve residuals, were consistent across datasets. QuantiSNP outperformed other methods based on ROC curve residuals over most datasets. Nexus Rank and SNPRank have low specificity and high power. Nexus Rank calls oversized CNVs. PennCNV detects one of the fewest numbers of CNVs.
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    Comparison of pattern detection methods in microarray time series of the segmentation clock.
    (PLoS One, 2008-08-06) Dequéant, Mary-Lee; Ahnert, Sebastian; Edelsbrunner, Herbert; Fink, Thomas MA; Glynn, Earl F; Hattem, Gaye; Kudlicki, Andrzej; Mileyko, Yuriy; Morton, Jason; Mushegian, Arcady R; Pachter, Lior; Rowicka, Maga; Shiu, Anne; Sturmfels, Bernd; Pourquié, Olivier
    While genome-wide gene expression data are generated at an increasing rate, the repertoire of approaches for pattern discovery in these data is still limited. Identifying subtle patterns of interest in large amounts of data (tens of thousands of profiles) associated with a certain level of noise remains a challenge. A microarray time series was recently generated to study the transcriptional program of the mouse segmentation clock, a biological oscillator associated with the periodic formation of the segments of the body axis. A method related to Fourier analysis, the Lomb-Scargle periodogram, was used to detect periodic profiles in the dataset, leading to the identification of a novel set of cyclic genes associated with the segmentation clock. Here, we applied to the same microarray time series dataset four distinct mathematical methods to identify significant patterns in gene expression profiles. These methods are called: Phase consistency, Address reduction, Cyclohedron test and Stable persistence, and are based on different conceptual frameworks that are either hypothesis- or data-driven. Some of the methods, unlike Fourier transforms, are not dependent on the assumption of periodicity of the pattern of interest. Remarkably, these methods identified blindly the expression profiles of known cyclic genes as the most significant patterns in the dataset. Many candidate genes predicted by more than one approach appeared to be true positive cyclic genes and will be of particular interest for future research. In addition, these methods predicted novel candidate cyclic genes that were consistent with previous biological knowledge and experimental validation in mouse embryos. Our results demonstrate the utility of these novel pattern detection strategies, notably for detection of periodic profiles, and suggest that combining several distinct mathematical approaches to analyze microarray datasets is a valuable strategy for identifying genes that exhibit novel, interesting transcriptional patterns.
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    Comprehensive gene expression profiling and immunohistochemical studies support application of immunophenotypic algorithm for molecular subtype classification in diffuse large B-cell lymphoma: a report from the International DLBCL Rituximab-CHOP Consortium Program Study.
    (Leukemia, 2012-09) Visco, C; Li, Y; Xu-Monette, ZY; Miranda, RN; Green, TM; Li, Y; Tzankov, A; Wen, W; Liu, W-M; Kahl, BS; d'Amore, ESG; Montes-Moreno, S; Dybkær, K; Chiu, A; Tam, W; Orazi, A; Zu, Y; Bhagat, G; Winter, JN; Wang, H-Y; O'Neill, S; Dunphy, CH; Hsi, ED; Zhao, XF; Go, RS; Choi, WWL; Zhou, F; Czader, M; Tong, J; Zhao, X; van Krieken, JH; Huang, Q; Ai, W; Etzell, J; Ponzoni, M; Ferreri, AJM; Piris, MA; Møller, MB; Bueso-Ramos, CE; Medeiros, LJ; Wu, L; Young, KH
    Gene expression profiling (GEP) has stratified diffuse large B-cell lymphoma (DLBCL) into molecular subgroups that correspond to different stages of lymphocyte development-namely germinal center B-cell like and activated B-cell like. This classification has prognostic significance, but GEP is expensive and not readily applicable into daily practice, which has lead to immunohistochemical algorithms proposed as a surrogate for GEP analysis. We assembled tissue microarrays from 475 de novo DLBCL patients who were treated with rituximab-CHOP chemotherapy. All cases were successfully profiled by GEP on formalin-fixed, paraffin-embedded tissue samples. Sections were stained with antibodies reactive with CD10, GCET1, FOXP1, MUM1 and BCL6 and cases were classified following a rationale of sequential steps of differentiation of B cells. Cutoffs for each marker were obtained using receiver-operating characteristic curves, obviating the need for any arbitrary method. An algorithm based on the expression of CD10, FOXP1 and BCL6 was developed that had a simpler structure than other recently proposed algorithms and 92.6% concordance with GEP. In multivariate analysis, both the International Prognostic Index and our proposed algorithm were significant independent predictors of progression-free and overall survival. In conclusion, this algorithm effectively predicts prognosis of DLBCL patients matching GEP subgroups in the era of rituximab therapy.
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    Diagnosis of partial body radiation exposure in mice using peripheral blood gene expression profiles.
    (PLoS One, 2010-07-12) Meadows, Sarah K; Dressman, Holly K; Daher, Pamela; Himburg, Heather; Russell, J Lauren; Doan, Phuong; Chao, Nelson J; Lucas, Joseph; Nevins, Joseph R; Chute, John P
    In the event of a terrorist-mediated attack in the United States using radiological or improvised nuclear weapons, it is expected that hundreds of thousands of people could be exposed to life-threatening levels of ionizing radiation. We have recently shown that genome-wide expression analysis of the peripheral blood (PB) can generate gene expression profiles that can predict radiation exposure and distinguish the dose level of exposure following total body irradiation (TBI). However, in the event a radiation-mass casualty scenario, many victims will have heterogeneous exposure due to partial shielding and it is unknown whether PB gene expression profiles would be useful in predicting the status of partially irradiated individuals. Here, we identified gene expression profiles in the PB that were characteristic of anterior hemibody-, posterior hemibody- and single limb-irradiation at 0.5 Gy, 2 Gy and 10 Gy in C57Bl6 mice. These PB signatures predicted the radiation status of partially irradiated mice with a high level of accuracy (range 79-100%) compared to non-irradiated mice. Interestingly, PB signatures of partial body irradiation were poorly predictive of radiation status by site of injury (range 16-43%), suggesting that the PB molecular response to partial body irradiation was anatomic site specific. Importantly, PB gene signatures generated from TBI-treated mice failed completely to predict the radiation status of partially irradiated animals or non-irradiated controls. These data demonstrate that partial body irradiation, even to a single limb, generates a characteristic PB signature of radiation injury and thus may necessitate the use of multiple signatures, both partial body and total body, to accurately assess the status of an individual exposed to radiation.
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    Differential Apaf-1 levels allow cytochrome c to induce apoptosis in brain tumors but not in normal neural tissues.
    (Proc Natl Acad Sci U S A, 2007-12-26) Johnson, Carrie E; Huang, Yolanda Y; Parrish, Amanda B; Smith, Michelle I; Vaughn, Allyson E; Zhang, Qian; Wright, Kevin M; Van Dyke, Terry; Wechsler-Reya, Robert J; Kornbluth, Sally; Deshmukh, Mohanish
    Brain tumors are typically resistant to conventional chemotherapeutics, most of which initiate apoptosis upstream of mitochondrial cytochrome c release. In this study, we demonstrate that directly activating apoptosis downstream of the mitochondria, with cytosolic cytochrome c, kills brain tumor cells but not normal brain tissue. Specifically, cytosolic cytochrome c is sufficient to induce apoptosis in glioblastoma and medulloblastoma cell lines. In contrast, primary neurons from the cerebellum and cortex are remarkably resistant to cytosolic cytochrome c. Importantly, tumor tissue from mouse models of both high-grade astrocytoma and medulloblastoma display hypersensitivity to cytochrome c when compared with surrounding brain tissue. This differential sensitivity to cytochrome c is attributed to high Apaf-1 levels in the tumor tissue compared with low Apaf-1 levels in the adjacent brain tissue. These differences in Apaf-1 abundance correlate with differences in the levels of E2F1, a previously identified activator of Apaf-1 transcription. ChIP assays reveal that E2F1 binds the Apaf-1 promoter specifically in tumor tissue, suggesting that E2F1 contributes to the expression of Apaf-1 in brain tumors. Together, these results demonstrate an unexpected sensitivity of brain tumors to postmitochondrial induction of apoptosis. Moreover, they raise the possibility that this phenomenon could be exploited therapeutically to selectively kill brain cancer cells while sparing the surrounding brain parenchyma.
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    Discrimination of vanadium from zinc using gene profiling in human bronchial epithelial cells.
    (Environmental health perspectives, 2005-12) Li, Zhuowei; Stonehuerner, Jackie; Devlin, Robert B; Huang, Yuh-Chin T
    We hypothesized that gene expression profiling may discriminate vanadium from zinc in human bronchial epithelial cells (HBECs). RNA from HBECs exposed to vehicle, V (50 microM), or Zn (50 microM) for 4 hr (n = 4 paired experiments) was hybridized to Affymetrix Hu133A chips. Using one-class t-test with p < 0.01, we identified 140 and 76 genes with treatment:control ratios > or = 2.0 or < or = 0.5 for V and Zn, respectively. We then categorized these genes into functional pathways and compared the number of genes in each pathway between V and Zn using Fisher's exact test. Three pathways regulating gene transcription, inflammatory response, and cell proliferation distinguished V from Zn. When genes in these three pathways were matched with the 163 genes flagged by the same statistical filtration for V:Zn ratios, 12 genes were identified. The hierarchical clustering analysis showed that these 12 genes discriminated V from Zn and consisted of two clusters. Cluster 1 genes (ZBTB1, PML, ZNF44, SIX1, BCL6, ZNF450) were down-regulated by V and involved in gene transcription, whereas cluster 2 genes (IL8, IL1A, PTGS2, DTR, TNFAIP3, CXCL3) were up-regulated and linked to inflammatory response and cell proliferation. Also, metallothionein 1 genes (MT1F, MT1G, MT1K) were up-regulated by Zn only. Thus, using microarray analysis, we identified a small set of genes that may be used as biomarkers for discriminating V from Zn. The novel genes and pathways identified by the microarray may help us understand the pathogenesis of health effects caused by environmental V and Zn exposure.
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    Epstein-Barr virus induces global changes in cellular mRNA isoform usage that are important for the maintenance of latency.
    (Journal of virology, 2013-11) Homa, Nicholas J; Salinas, Raul; Forte, Eleonora; Robinson, Timothy J; Garcia-Blanco, Mariano A; Luftig, Micah A
    Oncogenic viruses promote cell proliferation through the dramatic reorganization of host transcriptomes. In addition to regulating mRNA abundance, changes in mRNA isoform usage can have a profound impact on the protein output of the transcriptome. Using Epstein-Barr virus (EBV) transformation of primary B cells, we have studied the ability of an oncogenic virus to alter the mRNA isoform profile of its host. Using the algorithm called SplicerEX with two complementary Affymetrix microarray platforms, we uncovered 433 mRNA isoform changes regulated by EBV during B-cell transformation. These changes were largely orthogonal with the 2,163 mRNA abundance changes observed during transformation, such that less than one-third of mRNAs changing at the level of isoform also changed in overall abundance. While we observed no preference for a mechanistic class of mRNA isoform change, we detected a significant shortening of 3' untranslated regions and exclusion of cassette exons in EBV-transformed cells relative to uninfected B cells. Gene ontology analysis of the mRNA isoform changes revealed significant enrichment in nucleic acid binding proteins. We validated several of these isoform changes and were intrigued by those in two mRNAs encoding the proteins XBP1 and TCF4, which have both been shown to bind and activate the promoter of the major EBV lytic trans-activator BZLF1. Our studies indicate that EBV latent infection promotes the usage of mRNA isoforms of XBP1 and TCF4 that restrict BZLF1 activation. Therefore, characterization of global changes in mRNA isoform usage during EBV infection identifies a new mechanism for the maintenance of latent infection.
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    Escherichia coli global gene expression in urine from women with urinary tract infection.
    (PLoS pathogens, 2010-11) Hagan, Erin C; Lloyd, Amanda L; Rasko, David A; Faerber, Gary J; Mobley, Harry LT
    Murine models of urinary tract infection (UTI) have provided substantial data identifying uropathogenic E. coli (UPEC) virulence factors and assessing their expression in vivo. However, it is unclear how gene expression in these animal models compares to UPEC gene expression during UTI in humans. To address this, we used a UPEC strain CFT073-specific microarray to measure global gene expression in eight E. coli isolates monitored directly from the urine of eight women presenting at a clinic with bacteriuria. The resulting gene expression profiles were compared to those of the same E. coli isolates cultured statically to exponential phase in pooled, sterilized human urine ex vivo. Known fitness factors, including iron acquisition and peptide transport systems, were highly expressed during human UTI and support a model in which UPEC replicates rapidly in vivo. While these findings were often consistent with previous data obtained from the murine UTI model, host-specific differences were observed. Most strikingly, expression of type 1 fimbrial genes, which are among the most highly expressed genes during murine experimental UTI and encode an essential virulence factor for this experimental model, was undetectable in six of the eight E. coli strains from women with UTI. Despite the lack of type 1 fimbrial expression in the urine samples, these E. coli isolates were generally capable of expressing type 1 fimbriae in vitro and highly upregulated fimA upon experimental murine infection. The findings presented here provide insight into the metabolic and pathogenic profile of UPEC in urine from women with UTI and represent the first transcriptome analysis for any pathogenic E. coli during a naturally occurring infection in humans.
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    Evidence-ranked motif identification.
    (Genome Biol, 2010) Georgiev, S; Boyle, AP; Jayasurya, K; Ding, X; Mukherjee, Sayan; Ohler, Uwe
    cERMIT is a computationally efficient motif discovery tool based on analyzing genome-wide quantitative regulatory evidence. Instead of pre-selecting promising candidate sequences, it utilizes information across all sequence regions to search for high-scoring motifs. We apply cERMIT on a range of direct binding and overexpression datasets; it substantially outperforms state-of-the-art approaches on curated ChIP-chip datasets, and easily scales to current mammalian ChIP-seq experiments with data on thousands of non-coding regions.
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    Expression in aneuploid Drosophila S2 cells.
    (PLoS Biol, 2010-02-23) Zhang, Yu; Malone, John H; Powell, Sara K; Periwal, Vipul; Spana, Eric; Macalpine, David M; Oliver, Brian
    Extensive departures from balanced gene dose in aneuploids are highly deleterious. However, we know very little about the relationship between gene copy number and expression in aneuploid cells. We determined copy number and transcript abundance (expression) genome-wide in Drosophila S2 cells by DNA-Seq and RNA-Seq. We found that S2 cells are aneuploid for >43 Mb of the genome, primarily in the range of one to five copies, and show a male genotype ( approximately two X chromosomes and four sets of autosomes, or 2X;4A). Both X chromosomes and autosomes showed expression dosage compensation. X chromosome expression was elevated in a fixed-fold manner regardless of actual gene dose. In engineering terms, the system "anticipates" the perturbation caused by X dose, rather than responding to an error caused by the perturbation. This feed-forward regulation resulted in precise dosage compensation only when X dose was half of the autosome dose. Insufficient compensation occurred at lower X chromosome dose and excessive expression occurred at higher doses. RNAi knockdown of the Male Specific Lethal complex abolished feed-forward regulation. Both autosome and X chromosome genes show Male Specific Lethal-independent compensation that fits a first order dose-response curve. Our data indicate that expression dosage compensation dampens the effect of altered DNA copy number genome-wide. For the X chromosome, compensation includes fixed and dose-dependent components.
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    Expression signatures of TP53 mutations in serous ovarian cancers.
    (BMC Cancer, 2010-05-26) Bernardini, Marcus Q; Baba, Tsukasa; Lee, Paula S; Barnett, Jason C; Sfakianos, Gregory P; Secord, Angeles Alvarez; Murphy, Susan K; Iversen, Edwin; Marks, Jeffrey R; Berchuck, Andrew
    BACKGROUND: Mutations in the TP53 gene are extremely common and occur very early in the progression of serous ovarian cancers. Gene expression patterns that relate to mutational status may provide insight into the etiology and biology of the disease. METHODS: The TP53 coding region was sequenced in 89 frozen serous ovarian cancers, 40 early stage (I/II) and 49 advanced stage (III/IV). Affymetrix U133A expression data was used to define gene expression patterns by mutation, type of mutation, and cancer stage. RESULTS: Missense or chain terminating (null) mutations in TP53 were found in 59/89 (66%) ovarian cancers. Early stage cancers had a significantly higher rate of null mutations than late stage disease (38% vs. 8%, p < 0.03). In advanced stage cases, mutations were more prevalent in short term survivors than long term survivors (81% vs. 30%, p = 0.0004). Gene expression patterns had a robust ability to predict TP53 status within training data. By using early versus late stage disease for out of sample predictions, the signature derived from early stage cancers could accurately (86%) predict mutation status of late stage cancers. CONCLUSIONS: This represents the first attempt to define a genomic signature of TP53 mutation in ovarian cancer. Patterns of gene expression characteristic of TP53 mutation could be discerned and included several genes that are known p53 targets or have been described in the context of expression signatures of TP53 mutation in breast cancer.
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    Ferrochelatase is a conserved downstream target of the blue light-sensing White collar complex in fungi.
    (Microbiology, 2010-08) Idnurm, Alexander; Heitman, Joseph
    Light is a universal signal perceived by organisms, including fungi, in which light regulates common and unique biological processes depending on the species. Previous research has established that conserved proteins, originally called White collar 1 and 2 from the ascomycete Neurospora crassa, regulate UV/blue light sensing. Homologous proteins function in distant relatives of N. crassa, including the basidiomycetes and zygomycetes, which diverged as long as a billion years ago. Here we conducted microarray experiments on the basidiomycete fungus Cryptococcus neoformans to identify light-regulated genes. Surprisingly, only a single gene was induced by light above the commonly used twofold threshold. This gene, HEM15, is predicted to encode a ferrochelatase that catalyses the final step in haem biosynthesis from highly photoreactive porphyrins. The C. neoformans gene complements a Saccharomyces cerevisiae hem15Delta strain and is essential for viability, and the Hem15 protein localizes to mitochondria, three lines of evidence that the gene encodes ferrochelatase. Regulation of HEM15 by light suggests a mechanism by which bwc1/bwc2 mutants are photosensitive and exhibit reduced virulence. We show that ferrochelatase is also light-regulated in a white collar-dependent fashion in N. crassa and the zygomycete Phycomyces blakesleeanus, indicating that ferrochelatase is an ancient target of photoregulation in the fungal kingdom.
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    Gene expression signatures that predict radiation exposure in mice and humans.
    (PLoS Med, 2007-04) Dressman, Holly K; Muramoto, Garrett G; Chao, Nelson J; Meadows, Sarah; Marshall, Dawn; Ginsburg, Geoffrey S; Nevins, Joseph R; Chute, John P
    BACKGROUND: The capacity to assess environmental inputs to biological phenotypes is limited by methods that can accurately and quantitatively measure these contributions. One such example can be seen in the context of exposure to ionizing radiation. METHODS AND FINDINGS: We have made use of gene expression analysis of peripheral blood (PB) mononuclear cells to develop expression profiles that accurately reflect prior radiation exposure. We demonstrate that expression profiles can be developed that not only predict radiation exposure in mice but also distinguish the level of radiation exposure, ranging from 50 cGy to 1,000 cGy. Likewise, a molecular signature of radiation response developed solely from irradiated human patient samples can predict and distinguish irradiated human PB samples from nonirradiated samples with an accuracy of 90%, sensitivity of 85%, and specificity of 94%. We further demonstrate that a radiation profile developed in the mouse can correctly distinguish PB samples from irradiated and nonirradiated human patients with an accuracy of 77%, sensitivity of 82%, and specificity of 75%. Taken together, these data demonstrate that molecular profiles can be generated that are highly predictive of different levels of radiation exposure in mice and humans. CONCLUSIONS: We suggest that this approach, with additional refinement, could provide a method to assess the effects of various environmental inputs into biological phenotypes as well as providing a more practical application of a rapid molecular screening test for the diagnosis of radiation exposure.