1 / 49

Genomic Analysis of Stress and Inflammation

Genomic Analysis of Stress and Inflammation. Massachusetts General Hospital Departments of Medicine and Genetics Harvard Medical School Boston University. Genetic dissection of signal transduction Host-pathogen interactions: Pseudomonas and CF Definition of Protein networks

albert
Télécharger la présentation

Genomic Analysis of Stress and Inflammation

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Genomic Analysis of Stress and Inflammation Massachusetts General Hospital Departments of Medicine and Genetics Harvard Medical School Boston University

  2. Genetic dissection of signal transduction Host-pathogen interactions: Pseudomonas and CF Definition of Protein networks Macrophage activation by metabolic and pathogen stresses Microarray and sequencing Brian Seed, PhD Education and Training Fred Ausubel, PhD Proteomics Jack Szostak, PhD Human Tissue and Animal Models Mason Freeman, MD Bioinformatics Temple Smith, PhD George Church, PhD PGA components and projects Projects Component Centers

  3. Components : Microarrays • Microarray generation • Human and mouse cDNA arrays • Bacterial arrays • Specialty arrays (e.g., inflammatory gene subsets) • Sequencing and cDNA library generation • Array verification and generation • Identification of genes isolated by RNA display • cDNA library production

  4. Education and Training • Genomics training course - hands-on lab experience • Web based genomics training • Visiting scientist project advice • Seminar series in genomics • Bioinformatics training, undergrad work study

  5. Proteomics • Development of RNA display technology for identification of protein-protein interactions • PDZ domains • Kinase substrate identification

  6. Human Tissue / Animal Models • Acquisition and process of human tissues for gene expression profiling and immunohistochemistry • Atherosclerotic lesions (carotid, coronary, periph.) • Hearts (idiopathic cardiomyopathy, CAD) • Lungs (cystic fibrosis, emphysematous) • Mouse and Cell Models • CD14 and toll receptor null (endotoxin/bacterial signaling) • CD36 null (lipid uptake) • SR-A null (lipid uptake and bacterial interactions) • Cystic Fibrosis (with Gerry Pier and Fred Ausubel)

  7. Animal Models • Conditional KO mice • Based on homologous recombination in bacteria • Conditional alleles generated by site-specific recombinase action • Flexible, medium throughput technology • In vivo imaging of transgenic reporter mice • Fluorescence imaging of cells in living animals • Ear, dorsal skin chamber and cranial windows • Provides information about the activation of genes in the organismic environment

  8. Generation of Mutant Collections • Pseudomonas Transposon Insertion Collection • High-quality non-redundant collection of multiple insertions in all non-essential genes • Somatic cell mutant cell lines • Reporter cell lines that facilitate the rapid identification of mammalian somatic cell mutations affecting signal transduction • Mutant progeny derived from those lines

  9. Bioinformatics • Database design and management • Software development (data entry and tracking software) • Web access of data for internal and external users • Data analysis software • Bioinformatics education

  10. Genetic dissection of signal transduction • To create reporter cell lines that allow identification of genes promoting activation of stress and inflammation pathways. • To develop and exploit automated sib selection strategies to identify new molecules that activate stress and inflammation pathways. • To use microarray analysis to understand the genotypes of mutant cell lines bearing lesions in stress and inflammation signal transduction pathways.

  11. .

  12. Stages in the enrichment of an NF-kB inducer

  13. .

  14. .

  15. .

  16. In Vivo Imaging • Direct visualization of reporter gene output in transgenic animals • Can be performed using the same reporters used in high-throughput discovery screens • Allows responding cell populations to be identified in vivo • Responding cells can be culled and phenotyped

  17. .

  18. .

  19. Host/Pathogen interactions • The mucoid derivatives of PA14 from Aim 2 will be used to infect CF mice. The P. aeruginosa microarray (Aim 1) and a murine microarray consisting of the currently available UniGene clusters will be used to determine gene expression profiles for the pathogen and the host, respectively, before and during the infection process. • Mucoid derivatives of P. aeruginosa PA14 mutants attenuated for pathogenicity in model non-vertebrate hosts will be introduced into CF mice to identify virulence-related factors required for infection of the CF lung. • P. aeruginosa PA14 mutants identified in Aim 4 that display defects in CF lung pathogenesis will be used to infect CF mice and expression of both P. aeruginosa and mouse genes will be analyzed using the P. aeruginosa and mouse DNA microarrays.

  20. . Bacterial Pathogen

  21. . P. aeruginosa E. coli P. aeruginosa Kills C. elegans and Colonizes the C. elegans Intestine 100 P. aeruginosa E. coli 80 60 % nematodes killed 40 20 0 0 20 40 60 80 Hours of Feeding on P. aeruginosa

  22. P. aeruginosa Pathogenicity-Related Genes Identified by Screening in Model Hosts • 32 Pathogenicity genes identified out of 8,000 random transposon insertions screened. • Need to screen 30,000 random insertions to reach saturation. • Obtain full set of virulence-related genes by screening in several model invertebrate hosts. • Because screening for mutant phenotypes is rate limiting, construct non-redundant insertion library (4800 nonessential genes) to screen the rest of the genome.

  23. Advantages of a Non-Redundant Library • Simplifies screening in multiple hosts. • Multiple insertion alleles of certain genes will be useful for confirming the phenotypes of insertion mutations. • > 80% savings in time for each screen in a model host. • The library can be expanded until it is saturated. • Obtain information about genes that are NOT required for pathogenesis as well as genes that are. • The PCR products used to construct the library can be used to synthesize a micro-array of (non-essential) ORFs for PA14 expression studies.

  24. new sequences Processing: - transposon clipping - N-stripping processed new sequences PA14 genome IST sequences trash sequences public sequences patent sequences PA14 -specific Sequence PAO1 Raw Data Archive - sequences - trace files archive raw data BLAST/FASTA present? present? present? present? y y y y n n n n mutant priority definition 1 annotated ORF 2 not annot. ORF, putative ORF 3 annotated ORF, put. Promoter 4 not annot. ORF, not put. ORF, put. Promoter 5 not annot. ORF, not put. ORF, not put. Promoter contaminant? y n novel PA14 specific

  25. PA14 genome IST sequences. PA14 non-genome project sequences • annotated ORF • putative ORF • put. Promoter for annotated ORF • not annot. ORF, put. Promoter • not put. ORF, not put. Promoter • homology to pathogenicity factors • homology to regulatory proteins • homology to house keeping genes • assignment to a pathway containing pathogenicity factors • coordinate of the mutation along the 5’-3’ axis compared to other mutants targeted in the same gene • PA14-specific? library of NR1PA14 redundancies Building the non-redundant PA14 mutant library select seq. for inclusion in NR1PA14 PA14 redundant intermediate library tested in any host? Sibling present in NR1PA14? n n combine these criteria for mutant priority definition: y y PA14 non-redundant library (NR1PA14)

  26. Definition of protein networks by RNA display • To create a cellular protein-RNA fusion library from pooled mRNA from normal human and mouse tissues • To use isolated domains from proteins transducing stress and inflammation pathway signals to identify interactionpartners of those proteins • To automate the detection of interactions between signal transduction proteins and their target proteins. • To make slick slides for presentations

  27. RNA-Protein Fusions

  28. P P P P P mRNA Display

  29. Decoding Protein-Protein Interactions Challenge:to identify all binding partners of a given “bait” protein Solution: pass a library of cellular mRNA fusions over the protein & identify which fusions bind to the bait protein

  30. Deconvoluting mRNA-Protein Fusion Targets with Microarrays

  31. Cellular Library Features • No cloning • Randomly primed cDNA • Direct assembly of library in vitro • Libraries are large enough to contain all possible start and end points for every protein fragment

  32. Cellular Libraries: Selections Cellular protein domain library Target (bait) Select: 1-4 rounds PCR

  33. Identification of Kinase Substrates Library of fusions prepared from cellular RNA Phosphorylate fusions with kinase in vitro Immunoprecipitate phosphorylated fusions with anti-phosphotyrosine Ab PCR RNA from phosphorylated fusions

  34. Other Selections in Progress- PDZ domains binding known targets- coiled-coil partners- calmodulin binding proteins

  35. Macrophage Activation • To perform comparative gene expression studies assessing the impact of key proteins in inflammatory and stress response pathways, using macrophages taken from wild type and knock-out mice. • To explore concordances between murine and human macrophage expression, and to establish baseline profiles revealing the consequences of various sample collection practices. • To analyze gene expression in aortas taken from normal and apo E null mice and from coronary arteries of mice following allogeneic heart transplantation • To conduct parallel investigations on the gene expression profiles of human carotid endarterectomy, coronary endarterectomy, and heart transplant specimens, and to establish, if possible, the characteristic gene clustering features of these conditions.

  36. Fig. 2 LPS signaling pathways LPS signal transduction pathway

  37. Fig. 4

  38. I-OxLDL degradation PPAR null g 125 + OxLDL + LDL A. B. 800 600 400 200 0 wt cl 3 cl 4 cl 5 P388D1

  39. RNA display

More Related