ARTICLE pubs.acs.org/jpr Systems Biology Approach Predicts Antibody Signature Associated with Brucella melitensis Infection in Humans Li Liang,*,† Xiaolin Tan,† Silvia Juarez,† Homarh Villaverde,‡ Jozelyn Pablo,† Rie Nakajima-Sasaki,† Eduardo Gotuzzo,‡ Mayuko Saito,#,r Gary Hermanson,§ Douglas Molina,§ Scott Felgner,§ W. John W. Morrow,§ Xiaowu Liang,§ Robert H. Gilman,||,z,r D. Huw Davies,† Renee M. Tsolis,^ Joseph M. Vinetz,#,z and Philip L. Felgner †, * †Department of Medicine, Division of Infectious Diseases, University of California, Irvine, California 92697, United States ‡Alexander von Humboldt Institute of Tropical Medicine, Universidad Peruana Cayetano Heredia, Lima, Peru §Antigen Discovery, Inc., Irvine, California 92618, United States D)epartment of International Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland 21205, United States ^Department of Medical Microbiology and Immunology, University of California, Davis, Davis, California 95616, United States #Division of Infectious Diseases, Department of Medicine, University of California San Diego School of Medicine, La Jolla, California 92093-0741, United States rAsociacion Beneﬁca PRISMA, Lima, Peru zDepartment of Microbiology, Universidad Peruana Cayetano Heredia, Lima, Peru bS Supporting Information ABSTRACT: A complete understanding of the factors that determine selection of antigens recognized by the humoral immune response following infectious agent challenge is lacking. Here we illustrate a systems biology approach to identify the antibody signature associated with Brucella melitensis (Bm) infection in humans and predict proteomic features of serodiagnostic antigens. By taking advantage of a full proteome microarray expressing previously cloned 1406 and newly cloned 1640 Bm genes, we were able to identify 122 immunodominant antigens and 33 serodiagnostic antigens. The reactive antigens were then classiﬁed according to annotated functional features (COGs), computationally predicted features (e.g., subcellular localization, physical properties), and protein expression estimated by mass spectrometry (MS). Enrichment analyses indicated that membrane associa- tion and secretion were signiﬁcant enriching features of the reactive antigens, as were proteins predicted to have a signal peptide, a single transmembrane domain, and outer membrane or periplasmic location. These features accounted for 67% of the serodiagnostic antigens. An overlay of the seroreactive antigen set with proteomic data sets generated by MS identiﬁed an additional 24%, suggesting that protein expression in bacteria is an additional determinant in the induction of Brucella-speciﬁc antibodies. This analysis indicates that one-third of the proteome contains enriching features that account for 91% of the antigens recognized, and after B. melitensis infection the immune system develops signiﬁcant antibody titers against 10% of the proteins with these enriching features. This systems biology approach provides an empirical basis for understanding the breadth and speciﬁcity of the immune response to B. melitensis and a new framework for comparing the humoral responses against other microorganisms. KEYWORDS: Brucella melitensis, enriching features, immune response, protein microarray ’ INTRODUCTION A major component of the adaptive immune response to infection is the generation of protective and long-lasting humoral immunity, but factors governing selection of the particular antigens recognized are unclear.1,2 It is not uncommon for viruses encoding a small number of proteins to generate antibodies against each encoded protein. But for infectious agents containing hundreds or thousands of proteins only a subset of the proteome is recognized and little is known about the extent or the characterisitics of this subset of antigens. Methods for making a complete empirical accounting of the immunoproteome have limitations, particularly when the genome of the organism is large. Here we describe a B. melitensis proteome microarray that enables this problem to be directly addressed by applying an unbiased systems biology approach to identify immunodominant Received: June 30, 2011 Published: August 24, 2011 r 2011 American Chemical Society 4813 dx.doi.org/10.1021/pr200619r | J. Proteome Res. 2011, 10, 4813–4824