From Functional Genomics to Functional Immunomics: New Challenges, Old Problems, Big Rewards

The development of DNA microarray technology a decade ago led to the establishment of functional genomics as one of the most active and successful scientific disciplines today. With the ongoing development of immunomic microarray technology—a spatially addressable, large-scale technology for measurement of specific immunological response—the new challenge of functional immunomics is emerging, which bears similarities to but is also significantly different from functional genomics. Immunonic data has been successfully used to identify biological markers involved in autoimmune diseases, allergies, viral infections such as human immunodeficiency virus (HIV), influenza, diabetes, and responses to cancer vaccines. This review intends to provide a coherent vision of this nascent scientific field, and speculate on future research directions. We discuss at some length issues such as epitope prediction, immunomic microarray technology and its applications, and computation and statistical challenges related to functional immunomics. Based on the recent discovery of regulation mechanisms in T cell responses, we envision the use of immunomic microarrays as a tool for advances in systems biology of cellular immune responses, by means of immunomic regulatory network models

Novel strategies for characterizing T Cell responses in SIV-infected rhesus monkeys

Thesis (S.M.)--Harvard-MIT Division of Health Sciences and Technology, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 103-114).Human Immunodeficiency Virus (HIV) is the cause of Acquired Immune Deficiency Syndrome (AIDS) and has killed over 25 million people since the disease was first recognized in 1981. As of 2007, 33 million people globally are infected with HIV and this number is growing. HIV infects and depletes CD4+ helper T cells, affecting the ability of the immune system to defend the host against common infections. While anti-retroviral therapy has decreased morbidity and mortality, these drugs are not curative. In addition, they are beyond the financial reach of many HIV infected patients. Thus, the development of strategies to control HIV spread is a high priority. The most relevant animal model for studying HIV is the Simian Immunodeficiency Virus (SIV) - infected rhesus monkey. While HIV research has focused on studying peripheral blood specimens, mucosal sites have recently been identified as a focal point for HIV replication and tissue destruction. They are usually the sites of primary infection in the setting of sexual transmission and they are also important sites of immune depletion. If methods for controlling the replication of the virus early after infection in mucosal sites are available, it may be possible to eliminate the virus prior to systemic spread. While strategies for generating strong neutralizing antibody responses have not yet been developed, emerging data suggest that CD8+ cytotoxic T cells can contribute substantially to early virus control. It is important to study CD8+ T cells in the setting of SIV infection in rhesus monkeys, particularly in mucosal sites, using functional as well as transcriptional assays.(cont.) One of the challenges in studying mucosal cellular immunity is the limited number of cells available in biopsies, making traditional assay systems such as flow cytometry very difficult to employ. Here, technologies for isolating rare cell populations and extracting RNA from these cells for gene expression analysis were developed. These technologies were then applied to peripheral blood specimens, looking at gene expression differences between virus-specific CD8+ T cells in Mamu-A*01+ and Mamu-A*02+ monkeys. The ultimate goal of these studies is to gain a better understanding of SIV immunopathogenesis (as a model for HIV immunopathogenesis) and to find a way to control or eliminate the virus.by Amy Shi.S.M

Databases and computational interaction models of Toll-like receptors

Toll-like receptors (TLRs) recognize pathogen-associated molecular patterns (PAMPs) on invading organisms and are the first line of defense in innate immunity. To date, much has been learned about TLRs and their roles in autoimmune diseases are being unraveled. The autoimmune disease systemic lupus erythematosus (SLE) progresses as a consequence of the inappropriate recognition of self nucleic acids by TLRs. For the development of therapeutic approaches of SLE it is necessary to understand possible negative regulation mechanisms of TLR. Single immunoglobulin interleukin-1 receptor-related molecule (SIGIRR) is the best characterized TLR signaling inhibitor. It can interfere with the receptor complexes and attenuate the recruitment of downstream adaptors to the receptors. So far, the mechanisms of structural interactions between SIGIRR, TLRs and adaptor molecules are unknown. To develop a working hypothesis for these interactions, we constructed three- dimensional models for these single molecules based on computational predictions. Then, models of essential complexes involved in the TLR signaling and the SIGIRR inhibiting processes were yielded through protein-protein docking analysis. With the high-throughput genome sequencing projects, a central repository for the growing amount of TLR sequence information has been created. However, subsequent annotations for these TLR sequences are incomplete. For example, the indicated numbers and positions of leucine-rich repeat (LRR) motifs contained in individual TLR ectodomains are greatly distinct or missing in established databases. In this vein, we have developed a database of TLR structural motifs called TollML (http://tollml.lrz.de). It integrates all TLR protein sequences that have been identified to date. These sequences were semi-automatically partitioned into three levels of structural motif categories. The manual motif identification procedure provided TollML with the most complete and accurate database of LRR motifs compared with other databases that contain TLR data. LRR motifs are present not only in TLRs, but also in many other proteins. To date, more than 6,000 LRR protein sequences and more than 130 crystal structures of them have been determined. This knowledge has increased our ability to use individual LRR structures extracted from the crystal structures as building blocks to model LRR proteins with unknown structures. Because the individual LRR structures are not directly available from any protein structure database, we have developed a conformational LRR database called LRRML (http://lrrml.lrz.de). It collects three- dimensional LRR structures manually identified from all determined crystal structures of LRR-containing proteins and thus provides a source for the structural modeling and analysis of LRR proteins. With the help of TollML and LRRML, we constructed models of the human/mouse TLR5-13 ectodomains and suggested some potential receptor-ligand interaction residues based on these models