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 Tolerance induction rather than non- specific immunosuppression to avoid rejection of transplanted organs Reversal of acquired tolerance to tumour antigens and latent viral infections

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.

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.

CD4 + T cell clones/lines against DBY-E k male antigen CloneSourcePolarised in TypeCytokines R2.2A1(M)xRAG-1 -/- IL-2 Th1IFN-  (graft primed) R2.4A1(M)xRAG-1 -/- IL-4 Th2IL-4, IL-10 (graft primed) Tr1D1 A1(M)xRAG-1 -/- IL-10 Tr1/TregIL-10 (IL-4) (naïve) (  CD3 cloned) A1MPA1(M) naïve Anti-CTLA4 TregIL-10 (IL-4) DBY-E k peptide SKAA1(M) + maleDBY-E k peptide Tskin/TregIL-10 lineskin graft CD4 + sorted IELs

AE = Nla-III TE = BsmF1 SAGE details

Automated DNA Sequencing Machine

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).

DC cluster T cell clone cluster Spleen CD4 cell cluster

Clustered Expression Chart of approx. 300 known genes (CD antigens, cytokines and receptors)

A close up of a Treg cluster of known genes

Real Time PCR Machine

HPRT TM4 Th1 Treg Th1 Treg

Ratio of tolerant to rejecting skin graft expression Quantitative RT-PCR from rejecting, syngeneic and tolerant skin grafts

Fluorescence Activated Cell Scanner

Th1 Th2 Tr1D1 A1MP Tskin Permeabilized Live cells (7AAD neg) Peptide inhibition CD25- CD4+CD25+ Chandra/Ly116 ->

CD103 -> CD4 -> Chandra/Ly116 -> CD103 -> CD25 -> Th1 (R2.2) Th2 (R2.4) Treg(Tr1D1) Tskin(SkA)

The Therapeutic Antibody Centre

Humanized monoclonal antibodies against cell surface molecules expressed by effector but not regulatory T cells

Monoclonal Antibody Production Bioreactor Schematic

Hollow Fibre Bioreactor for Antibody Production

Therapeutic antibody purification

Clinical Trials of Therapeutic Antibody

For more information see the TIG Web site: Or go to the Pathology Web site: And click on “Herman Waldmann”