Immune tolerance from gene expression to drug discovery
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Immune Tolerance: from Gene Expression to Drug Discovery. Therapeutic Immunology Group (Prof. H. Waldmann) Therapeutic Antibody Centre (Prof. G. Hale). Immune Tolerance and Therapy. Therapy to reverse breakdown of self tolerance in autoimmune diseases

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Immune Tolerance: from Gene Expression to Drug Discovery

Therapeutic Immunology Group

(Prof. H. Waldmann)

Therapeutic Antibody Centre

(Prof. G. Hale)


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Immune Tolerance and Therapy

  • Therapy to reverse breakdown of self tolerance in autoimmune diseases

  • Tolerance induction rather than non-specific immunosuppression to avoid rejection of transplanted organs

  • Reversal of acquired tolerance to tumour antigens and latent viral infections


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Why Start from Gene Expression?

What are the approaches to find new therapeutics:

Random screening of chemical libraries in a “surrogate assay” (eg. suppression of antigen specific proliferation in vitro).

Look for monoclonal antibodies that modulate a function (eg. same assay).

Targeted chemical design/antibodies against specific protein structures.

But:

How to identify the most relevant/specific target proteins on possibly rare cells? Ideally we want targets expressed ONLY on target cells to avoid potential toxicity against other tissues.

An answer:

Look for genes that are specifically expressed in the functional cell type of interest – in our example, Th1 but NOT T regulatory (Treg) cells.


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Methods for Analysing Gene Expression

Analysis of known genes (RT-PCR/Antibodies/Protein Gels):

There are >1000 interesting “immunological” genes and probably many more important but unidentified genes. How to choose?

Differential Display and Gene Cloning:

Clones genes over-expressed in one cell compared to one other, but these may be shared with other cells and you don’t know what you are working with until you have it cloned and sequenced. How to choose?

Gene Chips/Arrays

Can identify patterns of expression from many (10,000+) genes and multiple samples. Genes must already have been cloned (<1/3 of genome?), it is quick, but not very sensitive (or reliable?), and currently expensive.

SAGE (Serial Analysis of Gene Expression)

Can identify almost the entire pattern of gene expression (the “transcriptome”) with no a priori knowledge of the gene sequences. Multiple samples are directly comparable as a database. Sensitivity depends on the number of tags sequenced: this can be labour intensive.


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CD4+ T cell clones/lines against DBY-Ek male antigen

Clone Source Polarised in Type Cytokines

R2.2 A1(M)xRAG-1-/- IL-2 Th1 IFN-g

(graft primed)

R2.4 A1(M)xRAG-1-/- IL-4 Th2 IL-4, IL-10

(graft primed)

Tr1D1 A1(M)xRAG-1-/- IL-10 Tr1/Treg IL-10 (IL-4)

(naïve) (aCD3 cloned)

A1MP A1(M) naïve Anti-CTLA4 Treg IL-10 (IL-4)

DBY-Ek peptide

SKA A1(M) + male DBY-Ek peptide Tskin/Treg IL-10

line skin graft CD4+

sorted IELs


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SAGE details

AE = Nla-III

TE = BsmF1



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Analysis of SAGE data

  • Use SAGEv3.01 software (Velculescu et al) to extract numbers of tags from raw sequence files.

  • Use Access to link tags to known genes, Unigene clusters, and ESTs (from NCBI “reliable tag” list).

  • Use Excel to manipulate data tables and calculate statistics (custom written function for Beysian stats).

  • Use custom written cluster analysis and presentation program (running on Acorn RISC-PC).


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DC cluster

T cell clone cluster

Spleen CD4 cell cluster



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A close up of a T antigens, cytokines and receptors)reg

cluster of known genes


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Real Time PCR Machine antigens, cytokines and receptors)


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TM4 antigens, cytokines and receptors)

HPRT

TM4

HPRT

Th1

Th1

Treg

Treg


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Quantitative RT-PCR from rejecting, syngeneic and tolerant skin grafts

Ratio of tolerant to rejecting skin graft expression



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Th2 skin grafts

Th1

Tr1D1

A1MP

Tskin

Chandra/Ly116 ->

Live cells

(7AAD neg)

Permeabilized

Chandra/Ly116 ->

CD25-

Peptide

inhibition

CD4+CD25+

Chandra/Ly116 ->


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CD25 -> skin grafts

CD103 ->

Chandra/Ly116 ->

CD4 ->

Th2 (R2.4)

Th1 (R2.2)

CD103 ->

CD103 ->

Tskin(SkA)

Treg(Tr1D1)

CD103 ->

CD103 ->



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Humanized monoclonal antibodies against cell surface molecules

expressed by effector but not regulatory T cells


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Monoclonal molecules

Antibody

Production

Bioreactor

Schematic





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For more information see the TIG Web site: molecules

www.molbiol.ox.ac.uk/pathology/tig/

Or go to the Pathology Web site:

www.path.ox.ac.uk

And click on “Herman Waldmann”


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