Litterature for Phd course in Immunological Bioinformatics

 

Below is a description of each of the articles in the notes for the course. The articles are arranged according to the lectures given in the course. It is recommended that you familiarize yourself with the articles corresponding to each lecture before it is held. We think you should spend approximately 80 hours reading these articles. The articles that are easiest to read, and that we suggest to read first are marked with italics.   

 

Introduction to bioinformatics and immunology

The first article in the collection by Plotkin (1999) gives an overview of the history of vaccination and is good to start with. The article by Luscombe et al. (2001) gives a brief introduction to the field of Bioinformatics.

 

The immune system

Some of the articles marked with italics below can serve as an introduction/refresher in immunology.

 

Exercises: Databases and web resources in Immunological bioinformatics, OL

The article by Lund et al. give an overview of the different web tools that are available for vaccine design.

 

Cellular immunity

The paper by Yewdell and Bennink (1999) gives a comprehensive review of why only a few of the thousands of peptides produced by a pathogen induce a measurable immune response, i.e., become immunodominant. The review focuses on the limitations caused by MHC binding, antigen processing, and the CD8+ repertoire.  

 

Antibody mediated immunity

The excerpt from Janeway et al. (2001) gives an introduction to the humoral immune response, in which antibodies produced by B cells inhibit infections. The review by Burton et al. (2001) gives a comprehensive review of how viruses are neutralized by antibodies.

 

Exercises: Phylogeny of immunological sequences

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Infectious diseases in the new millennium

In the article by Jha et al. (2002) they describe which measures could be taken to reduce the mortality among the poor.

 

Anti retroviral resistance

Garcia-Lerma and Heneine (2001) describe different methods for measuring HIV-1 drug resistance.

 

Exercises: Viral evolution

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Vaccine design in the 21st century

The article by Ellis (1999) gives a good overview over the different technologies for making vaccines.

 

Modeling the immune system in the genome era

Lauemøller et al. (2001) describes how the data generated by the ongoing genomics projects can be used in rational vaccine design. The article also gives a good introduction to the fields of immunology and immunological bioinformatics.

 

Exercises: Intellectual property rights in bioinformatics

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Experimental and theoretical description of peptide-MHC binding

A perquisite for making accurate prediction tools is the availability of abundant and accurate experimental data. Sylvester-Hvid at al. (2002) describes a simple and sensitive new ELISA based assay for measuring peptide-HLA binding affinity.

 

Prediction of proteasome processing, and TAP binding

For a peptide to be presented by the MHC molecules it must first be generated by the proteasome and then transported in to the endoplasmatic reticulum (ER) by the TAP transporter. The article by Kesmir et al. (2002) presents a new neural network method for predicting where the proteasome is most likely to cleave, and the article by Uebel and Tampe (1999) describes the specificity of the proteasome and the TAP transporter.

 

Exercises: Simulation models of the immune system, CK

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Prediction of T-cell epitopes using neural networks

The binding of a peptide to the MHC molecule is the most restrictive step in determining whether a peptide will be recognized by the immune system and much attention has therefore been given to develop accurate methods for predicting which peptides can bind to which MHC molecules. Hammer (1995) gives a good introduction to this field and the more recent article by Gulokota and DeLisi (2001) describes how to make neural network based methods. Nielsen et al. (2003) compares the accuracy of different methods for prediction of peptide binding to MHC.

 

Selection of epitopes using bioinformatics tools

The proteome for a pathogenic organism will typically contain thousands of possible epitopes and selecting the best ones to use in a vaccine is a major challenge. The articles by Schultze and Vonderheide (2001) and Hagmann (2000) describes how genomics and bioinformatics can be used to select epitopes for (cancer) vaccines, and Gaschen et al. (2002) discusses the added complications of genetic variability in HIV-1 vaccine design.

 

Exercises: Neural network prediction of T cell epitopes

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Discovery of disease related genes using analysis of DNA array data; Example: HIV

Microarray experiments are increasingly being used in studies of the immune system and how to handle the large amounts of data generated by such experiments is an important challenge. Quackenbush (2001) contains a good introduction to the area of microarray data.

 

Modeling of structural epitopes

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Exercises: Modeling of epitopes

van Regenmortel (1996) describes the different methods that have been used to predict which stretches of amino can induce an antibody response (continuous/linear B cell epitopes).

 

DNA vaccines

The review by Wiener and Kennedy (1999) gives a good introduction not only to the field of DNA vaccines, but also to vaccines and immunology in general.

 

 

Plasmid design, Sylvia Corbet

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Exercises: Plasmid design, Vaccine design, Gene Atlases, PW

The article by Friis et al. (2000) describes how to use visualizations of genomes to identify pathogenic regions.

 

NOVO

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Vaccination against allergy

Durham and Till (1998) describes the changes associated with allergen immunotherapy. Yazdanbakhsh et al. (2001) dicsuss the possible link between allergy and lack of helminth infections. The article by Mirza et al. (2000), show how to use structure information in allergy vaccine design.

 

Exercises: Use of gene expression data, OL

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Information theory of the immune system

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Immune systems and systems biology

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Exercises: Information content of immune processing

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References

 

Burton DR, Saphire EO, Parren PW. A model for neutralization of viruses based on antibody coating of the virion surface. Curr Top Microbiol Immunol. 2001;260:109-43.

 

Cover TM, Thomas JA, Elements of information theory, 1991, Wiley, New York.

 

Durham SR, Till SJ. Immunologic changes associated with allergen immunotherapy. J Allergy Clin Immunol. 1998 Aug;102(2):157-64.

 

Ellis RW. New technologies for making vaccines. Vaccine. 1999 Mar 26;17(13-14):1596-604.

 

Gaschen B, Taylor J, Yusim K, Foley B, Gao F, Lang D, Novitsky V, Haynes B, Hahn BH, Bhattacharya T, Korber B. Diversity considerations in HIV-1 vaccine selection. Science. 2002 Jun 28;296(5577):2354-60.

 

Garcia-Lerma JG, Heneine W. Resistance of human immunodeficiency virus type 1 to reverse transcriptase and protease inhibitors: genotypic and phenotypic testing. J Clin Virol. 2001 Jun;21(3):197-212.

 

Gulukota K, DeLisi C. Neural network method for predicting peptides that bind major histocompatibility complex molecules. Methods Mol Biol. 2001;156:201-9.

 

Hagmann M. Computers aid vaccine design. Science. 2000 Oct 6;290(5489):80-2.

 

Hagmann M. Doing immunology on a chip. Science. 2000 Oct 6;290(5489):82-3.

 

Hammer J. New methods to predict MHC-binding sequences within protein antigens. Curr Opin Immunol. 1995 Apr;7(2):263-9.

 

Janeway CA, et al Immuno Biology, Garland, 2001.

 

Jha P, Mills A, Hanson K, Kumaranayake L, Conteh L, Kurowski C, Nguyen SN, Cruz VO, Ranson K, Vaz LM, Yu S, Morton O, Sachs JD. Improving the health of the global poor. Science. 2002 Mar 15;295(5562):2036-9.

 

Kitano H., Foundations of systems biology, MIT Press, Cambridge MA.

 

Lauemøller SL, Kesmir C, Corbet SL, Fomsgaard A, Claesson MH, Brunak S, Buus, S. Identifying cytotoxic T cell epitopes from genomic and proteomic information The human MHC project”. Rev Immunogenetics, 2001.

 

Lund O, Nielsen M, Kesmir C, Christensen JK, Lundegaard C, Worning P,and Brunak S. Web based tools for vaccine designin HIV Molecular Immunology Database 2003. Edited by: Korber B Published by: Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM.

 

Luscombe NM, Greenbaum D, Gerstein M. What is bioinformatics? A proposed definition and overview of the field. Methods Inf Med. 2001;40(4):346-58.

 

Mirza O, Henriksen A, Ipsen H, Larsen JN, Wissenbach M, Spangfort MD, Gajhede M. Dominant epitopes and allergic cross-reactivity: complex formation between a Fab fragment of a monoclonal murine IgG antibody and the major allergen from birch pollen Bet v 1. J Immunol. 2000 Jul 1;165(1):331-8.

 

Nielsen M, Lundegaard C, Worning P, Lauemøller SL, Lamberth K, Buus S, Brunak S and Lund O. Reliable prediction of T Cell epitopes using neural networks with novel sequence representations. Protein Science 2003 12:1007-1017.

 

Plotkin SA, Orenstein WA, Vaccines, W.B. Saunders Company, Philadelphia, 1999.

 

Quackenbush J. Computational analysis of microarray data. Nat Rev Genet. 2001 Jun;2(6):418-27.

 

Schultze JL, Vonderheide RH. From cancer genomics to cancer immunotherapy: toward second-generation tumor antigens. Trends Immunol. 2001 Sep;22(9):516-23.

 

Sylvester-Hvid C, Kristensen N, Blicher T, Ferre H, Lauemoller SL, Wolf XA, Lamberth K, Nissen MH, Pedersen LO, Buus S. Establishment of a quantitative ELISA capable of determining peptide - MHC class I interaction. Tissue Antigens. 2002 Apr;59(4):251-8.

 

Van Regenmortel MHV. Mapping Epitope Structure and Activity: From One-Dimensional Prediction to Four-Dimensional Description of Antigenic Specificity Methods. 1996 Jun;9(3):465-72.

 

Weiner DB, Kennedy RC. Genetic vaccines. Sci Am. 1999 Jul;281(1):50-7.

Yazdanbakhsh M, van den Biggelaar A, Maizels RM. Th2 responses without atopy: immunoregulation in chronic helminth infections and reduced allergic disease. Trends Immunol. 2001 Jul;22(7):372-7.

 

Yewdell JW, Bennink JR. Immunodominance in major histocompatibility complex class I-restricted T lymphocyte responses. Annu Rev Immunol. 1999;17:51-88.