Many areas of research and medicine rely critically upon knowing a person’s individual immune system proteins, as they determine an individual’s ability to fight disease or mistakenly attack their own tissues. However, obtaining this information is costly and difficult.
A new study, published February 29 in the open-access journal PLoS Computational Biology, Listgarten et al demonstrate how statistical modeling can help researchers obtain this information more easily and cost effectively.
At the core of the human immune response is the train-to-kill mechanism in which specialized immune cells are sensitized to recognize small pieces of foreign pathogens (e.g., HIV). Following this sensitization, these cells are then activated to kill cells that display this same piece of pathogen. However, for sensitization and killing to occur, the pathogen must be “paired up” with one of the infected person’s specialized immune proteins—an HLA (human leukocyte antigen).
The way in which pathogen peptides interact with these HLA proteins defines if and how an immune response will be generated. Therefore, knowing which HLA proteins a person has is vital in transplant medicine, finding immunogenetic risk factors for disease, and understanding the way viruses like HIV mutate inside their host and evade the immune system.
The model uses a large set of previously measured, high-quality HLA data to find statistical patterns in this type of data. Using these patterns, the team from Microsoft Research, the National Cancer Institute, Massachusetts General, and the University of Oxford is able to take low-quality HLA data and clean it up so that it is of higher quality than that originally measured in the laboratory. With this publication, Listgarten and co-authors have made a public tool available to the research community so that others can improve the quality of their HLA data and thus study individual immune systems more effectively.
PLEASE ADD THIS LINK TO THE PUBLISHED ARTICLE IN ONLINE VERSIONS OF YOUR REPORT: http://www.ploscompbiol.org/doi/pcbi.1000016 (link will go live on Friday, February 29)
CITATION: Listgarten J, Brumme Z, Kadie C, Xiaojiang G, Walker B, et al. (2008) Statistical Resolution of Ambiguous HLA Typing Data. PLoS Comput Biol 4(2): e1000016. doi:10.1371/journal.pcbi.1000016
Jennifer Listgarten, David Heckerman
This press release refers to an upcoming article in PLoS Computational Biology. The release is provided by the article authors. Any opinions expressed in this release or article are the personal views of the journal staff and/or article contributors, and do not necessarily represent the views or policies of PLoS. PLoS expressly disclaims any and all warranties and liability in connection with the information found in the releases and articles and your use of such information.
About PLoS Computational Biology
PLoS Computational Biology (www.ploscompbiol.org) features works of exceptional significance that further our understanding of living systems at all scales through the application of computational methods. All works published in PLoS Computational Biology are open access. Everything is immediately available subject only to the condition that the original authorship and source are properly attributed. Copyright is retained by the authors. The Public Library of Science uses the Creative Commons Attribution License.
About the Public Library of Science
The Public Library of Science (PLoS) is a non-profit organization of scientists and physicians committed to making the world's scientific and medical literature a freely available public resource. For more information, visit http://www.plos.org.
PLEASE MENTION THE OPEN ACCESS JOURNAL PLoS COMPUTATIONAL BIOLOGY (www.ploscompbiol.org) AS THE SOURCE FOR THIS ARTICLE AND PROVIDE A LINK TO THE FREELY AVAILABLE TEXT. THANK YOU.
PLoS Computational Biology is an open-access, peer-reviewed journal published weekly by the Public Library of Science (PLoS) as the official journal of the International Society for Computational Biology (ISCB).
EMBARGO in place until: Thursday, February 28, 2008, 5pm PST/8pm EST
AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert! system.