Biomedical subjects

Jared C Roach

Publications and source records attributed to Jared C Roach.

9 recordsLinked to original sources

PubMed2006

Genetic mapping at 3-kilobase resolution reveals inositol 1,4,5-triphosphate receptor 3 as a risk factor for type 1 diabetes in Sweden.

We mapped the genetic influences for type 1 diabetes (T1D), using 2,360 single-nucleotide polymorphism (SNP) markers in the 4.4-Mb human major histocompatibility complex (MHC) locus and the adjacent 493 kb centromeric to the MHC, initially in a survey of 363 Swedish T1D cases and controls. We confirmed prior studies showing association with T1D in the MHC, most significantly near HLA-DR/DQ. In the region centromeric to the MHC, we identified a peak of association within the inositol 1,4,5-triphosphate receptor 3 gene (ITPR3; formerly IP3R3). The most significant single SNP in this region was at the center of the ITPR3 peak of association (P=1.7 x 10(-4) for the survey study). For validation, we typed an additional 761 Swedish individuals. The P value for association computed from all 1,124 individuals was 1.30 x 10(-6) (recessive odds ratio 2.5; 95% confidence interval [CI] 1.7-3.9). The estimated population-attributable risk of 21.6% (95% CI 10.0%-31.0%) suggests that variation within ITPR3 reflects an important contribution to T1D in Sweden. Two-locus regression analysis supports an influence of ITPR3 variation on T1D that is distinct from that of any MHC class II gene.

Adolescent↗

PubMed2006

Systems biology approaches identify ATF3 as a negative regulator of Toll-like receptor 4.

The innate immune system is absolutely required for host defence, but, uncontrolled, it leads to inflammatory disease. This control is mediated, in part, by cytokines that are secreted by macrophages. Immune regulation is extraordinarily complex, and can be best investigated with systems approaches (that is, using computational tools to predict regulatory networks arising from global, high-throughput data sets). Here we use cluster analysis of a comprehensive set of transcriptomic data derived from Toll-like receptor (TLR)-activated macrophages to identify a prominent group of genes that appear to be regulated by activating transcription factor 3 (ATF3), a member of the CREB/ATF family of transcription factors. Network analysis predicted that ATF3 is part of a transcriptional complex that also contains members of the nuclear factor (NF)-kappaB family of transcription factors. Promoter analysis of the putative ATF3-regulated gene cluster demonstrated an over-representation of closely apposed ATF3 and NF-kappaB binding sites, which was verified by chromatin immunoprecipitation and hybridization to a DNA microarray. This cluster included important cytokines such as interleukin (IL)-6 and IL-12b. ATF3 and Rel (a component of NF-kappaB) were shown to bind to the regulatory regions of these genes upon macrophage activation. A kinetic model of Il6 and Il12b messenger RNA expression as a function of ATF3 and NF-kappaB promoter binding predicted that ATF3 is a negative regulator of Il6 and Il12b transcription, and this hypothesis was validated using Atf3-null mice. ATF3 seems to inhibit Il6 and Il12b transcription by altering chromatin structure, thereby restricting access to transcription factors. Because ATF3 is itself induced by lipopolysaccharide, it seems to regulate TLR-stimulated inflammatory responses as part of a negative-feedback loop.

Activating Transcription Factor 3↗

PubMed2006

A third approach to gene prediction suggests thousands of additional human transcribed regions.

The identification and characterization of the complete ensemble of genes is a main goal of deciphering the digital information stored in the human genome. Many algorithms for computational gene prediction have been described, ultimately derived from two basic concepts: (1) modeling gene structure and (2) recognizing sequence similarity. Successful hybrid methods combining these two concepts have also been developed. We present a third orthogonal approach to gene prediction, based on detecting the genomic signatures of transcription, accumulated over evolutionary time. We discuss four algorithms based on this third concept: Greens and CHOWDER, which quantify mutational strand biases caused by transcription-coupled DNA repair, and ROAST and PASTA, which are based on strand-specific selection against polyadenylation signals. We combined these algorithms into an integrated method called FEAST, which we used to predict the location and orientation of thousands of putative transcription units not overlapping known genes. Many of the newly predicted transcriptional units do not appear to code for proteins. The new algorithms are particularly apt at detecting genes with long introns and lacking sequence conservation. They therefore complement existing gene prediction methods and will help identify functional transcripts within many apparent "genomic deserts."

Algorithms↗

PubMed2005

Endotoxin recognition: in fish or not in fish?

The interaction between pathogens and their multicellular hosts is initiated by activation of pathogen recognition receptors (PRRs). These receptors, that include most notably members of the toll-like receptor (TLR) family, recognize specific pathogen-associated molecular patterns (PAMPs). TLR4 is a central part of the receptor complex that is involved in the activation of the immune system by lipopolysaccharide (LPS) through the specific recognition of its endotoxic moiety (Lipid A). This is a critical event that is essential for the immune response to Gram-negative bacteria as well as the etiology of endotoxic shock. Interestingly, compared to mammals, fish are resistant to endotoxic shock. This in vivo resistance concurs with in vitro studies demonstrating significantly lowered sensitivity of fish leukocytes to LPS activation. Further, our in vitro analyses demonstrate that in trout mononuclear phagocytes, LPS fails to induce antiviral genes, an event that occurs downstream of TLR4 and is required for the development of endotoxic shock. Finally, an in silico approach that includes mining of different piscine genomic and EST databases, reveals the presence in fish of all of the major TLR signaling elements except for the molecules specifically involved in TLR4-mediated endotoxin recognition and signaling in mammals. Collectively, our analysis questions the existence of TLR4-mediated cellular responses to LPS in fish. We further speculate that other receptors, in particular beta-2 integrins, may play a primary role in the activation of piscine leukocytes by LPS.

Animals↗

PubMed2005

Application of Affymetrix array and Massively Parallel Signature Sequencing for identification of genes involved in prostate cancer progression.

BACKGROUND: Affymetrix GeneChip Array and Massively Parallel Signature Sequencing (MPSS) are two high throughput methodologies used to profile transcriptomes. Each method has certain strengths and weaknesses; however, no comparison has been made between the data derived from Affymetrix arrays and MPSS. In this study, two lineage-related prostate cancer cell lines, LNCaP and C4-2, were used for transcriptome analysis with the aim of identifying genes associated with prostate cancer progression. METHODS: Affymetrix GeneChip array and MPSS analyses were performed. Data was analyzed with GeneSpring 6.2 and in-house perl scripts. Expression array results were verified with RT-PCR. RESULTS: Comparison of the data revealed that both technologies detected genes the other did not. In LNCaP, 3,180 genes were only detected by Affymetrix and 1,169 genes were only detected by MPSS. Similarly, in C4-2, 4,121 genes were only detected by Affymetrix and 1,014 genes were only detected by MPSS. Analysis of the combined transcriptomes identified 66 genes unique to LNCaP cells and 33 genes unique to C4-2 cells. Expression analysis of these genes in prostate cancer specimens showed CA1 to be highly expressed in bone metastasis but not expressed in primary tumor and EPHA7 to be expressed in normal prostate and primary tumor but not bone metastasis. CONCLUSION: Our data indicates that transcriptome profiling with a single methodology will not fully assess the expression of all genes in a cell line. A combination of transcription profiling technologies such as DNA array and MPSS provides a more robust means to assess the expression profile of an RNA sample. Finally, genes that were differentially expressed in cell lines were also differentially expressed in primary prostate cancer and its metastases.

Cell Line, Tumor↗

PubMed2005

The evolution of vertebrate Toll-like receptors.

The complete sequences of Takifugu Toll-like receptor (TLR) loci and gene predictions from many draft genomes enable comprehensive molecular phylogenetic analysis. Strong selective pressure for recognition of and response to pathogen-associated molecular patterns has maintained a largely unchanging TLR recognition in all vertebrates. There are six major families of vertebrate TLRs. This repertoire is distinct from that of invertebrates. TLRs within a family recognize a general class of pathogen-associated molecular patterns. Most vertebrates have exactly one gene ortholog for each TLR family. The family including TLR1 has more species-specific adaptations than other families. A major family including TLR11 is represented in humans only by a pseudogene. Coincidental evolution plays a minor role in TLR evolution. The sequencing phase of this study produced finished genomic sequences for the 12 Takifugu rubripes TLRs. In addition, we have produced >70 gene models, including sequences from the opossum, chicken, frog, dog, sea urchin, and sea squirt.

Animals↗

PubMed2003

Evolutionary algorithms for the selection of single nucleotide polymorphisms.

BACKGROUND: Large databases of single nucleotide polymorphisms (SNPs) are available for use in genomics studies. Typically, investigators must choose a subset of SNPs from these databases to employ in their studies. The choice of subset is influenced by many factors, including estimated or known reliability of the SNP, biochemical factors, intellectual property, cost, and effectiveness of the subset for mapping genes or identifying disease loci. We present an evolutionary algorithm for multiobjective SNP selection. RESULTS: We implemented a modified version of the Strength-Pareto Evolutionary Algorithm (SPEA2) in Java. Our implementation, Multiobjective Analyzer for Genetic Marker Acquisition (MAGMA), approximates the set of optimal trade-off solutions for large problems in minutes. This set is very useful for the design of large studies, including those oriented towards disease identification, genetic mapping, population studies, and haplotype-block elucidation. CONCLUSION: Evolutionary algorithms are particularly suited for optimization problems that involve multiple objectives and a complex search space on which exact methods such as exhaustive enumeration cannot be applied. They provide flexibility with respect to the problem formulation if a problem description evolves or changes. Results are produced as a trade-off front, allowing the user to make informed decisions when prioritizing factors. MAGMA is open source and available at http://snp-magma.sourceforge.net. Evolutionary algorithms are well suited for many other applications in genomics.

Algorithms↗

PubMed2002

A clade of trypsins found in cold-adapted fish.

A clade of trypsins, known as group III, is identified by phylogenetic analysis. These trypsins occur in fish that spend all or part of their lives at temperatures near 0 degrees C and may represent extreme psychrophilic enzymes. A principal component analysis of amino acid compositions distinguishes group III from mesophilic trypsins, as do molecular trees and multidimensional scaling of molecular sequence distances. The primary sequences of group III trypsins, in conjunction with the known structures of mesophilic trypsins, permit insight into function and mechanisms of cold adaption. The techniques employed are broadly applicable to phylogenies characterized by a markedly different, or "fast-evolving," clade. An updated lactate dehydrogenase molecular tree illustrates an additional fast-evolving clade.

Adaptation, Physiological↗

PubMed2002

Genomic analysis of the olfactory receptor region of the mouse and human T-cell receptor alpha/delta loci.

We have conducted a comparative genomic analysis of several olfactory receptor (OR) genes that lie immediately 5' to the V-alpha gene segments at the mouse and human T-cell receptor (TCR) alpha/delta loci. Five OR genes are identified in the human cluster. The murine cluster has at least six OR genes; the first five are orthologous to the human genes. The sixth mouse gene has arisen since mouse-human divergence by a duplication of a approximately 10-kb block. One pair of OR paralogs found at the mouse and human loci are more similar to each other than to their corresponding orthologs. This paralogous "twinning" appears to be under selection, perhaps to increase sensitivity to particular odorants or to resolve structurally-similar odorants. The promoter regions of the mouse OR genes were identified by RACE-PCR. Orthologs share extensive 5' UTR homology, but we find no significant similarity among paralogs. These findings extend previous observations that suggest that OR genes do not share local significant regulatory homology despite having a common regulatory agenda. We also identified a diverged TCR-alpha gene segment that uses a divergent recombination signal sequence (RSS) to initiate recombination in T-cells from within the OR region. We explored the hypothesis that OR genes may use DNA recombination in expressing neurons, e.g., to recombine ORs into a transcriptionally active locus. We searched the mouse sequence for OR-flanking RSS motifs, but did not find evidence to suggest that these OR genes use TCR-like recombination target sequences.

Animals↗