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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

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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


Pga components and projects

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


Components microarrays
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


Education and training
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


Proteomics
Proteomics

  • Development of RNA display technology for identification of protein-protein interactions

    • PDZ domains

    • Kinase substrate identification


Human tissue animal models
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)


Animal models
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


Generation of mutant collections
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


Bioinformatics
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


Genetic dissection of signal transduction
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.


.



.


.


.


In vivo imaging
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


.


.


Host pathogen interactions
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.


.

Bacterial Pathogen


.

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


P aeruginosa pathogenicity related genes identified by screening in model hosts
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.


Advantages of a non redundant library
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.


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


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)


Definition of protein networks by rna display
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



P

P

P

P

P

mRNA Display


Decoding protein protein interactions
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



Cellular library features
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


Cellular libraries selections
Cellular Libraries: Selections

Cellular protein

domain library

Target

(bait)

Select:

1-4 rounds

PCR


Identification of kinase substrates
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


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


Macrophage activation
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.


Fig. 2 LPS signaling pathways

LPS signal transduction pathway



I-OxLDL degradation

PPAR null

g

125

+ OxLDL

+ LDL

A.

B.

800

600

400

200

0

wt

cl 3

cl 4

cl 5

P388D1



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