A rapid and efficient way to obtain modified chemokines for functional and biophysical studies

Chemokines and their receptors control cell migration associated with routine immune surveillance, inflammation and development. They are also implicated in a large number of inflammatory diseases, cancer and HIV. Here we describe a rapid and efficient way to express and purify milligram quantities of multiple chemokine ligands (CCL7/MCP-3, CCL14/HCC-1, CCL3/MIP-1α and CXCL8/IL-8) containing C-terminal modifications to enable coupling to fluorescent dyes or small molecules such as biotin, in vitro. These labeled chemokines display wild-type behavior in both receptor binding and calcium mobilization assays. The ability to rapidly and inexpensively produce labeled chemokines opens the way for their use in many applications, including non-traditional chemokine-receptor interaction studies, both on intact cells and with purified receptor reconstituted in artificial membranes in vitro. Furthermore, the ability to immobilize chemokines to obtain ligand affinity columns aids in efforts to purify chemokine receptors for structural and biophysical studies, by facilitating the separation of functional proteins from their non-functional counterparts.     

MCP-1 binds to oxidized LDL and is carried by lipoprotein(a) in human plasma

Lipoprotein oxidation plays an important role in pathogenesis of atherosclerosis. Oxidized low density lipoprotein (OxLDL) induces profound inflammatory responses in vascular cells, such as production of monocyte chemoattractant protein-1 (MCP-1) [chemokine (C-C motif) ligand 2], a key chemokine in the initiation and progression of vascular inflammation. Here we demonstrate that OxLDL also binds MCP-1 and that the OxLDL-bound MCP-1 retains its ability to recruit monocytes. A human MCP-1 mutant in which basic amino acids Arg-18 and Lys-19 were replaced with Ala did not bind to OxLDL. The MCP-1 binding to OxLDL was inhibited by the monoclonal antibody E06, which binds oxidized phospholipids (OxPLs) in OxLDL. Because OxPLs are carried by lipoprotein(a) [Lp(a)] in human plasma, we tested to determine whether Lp(a) binds MCP-1. Recombinant wild-type but not mutant MCP-1 added to human plasma bound to Lp(a), and its binding was inhibited by E06. Lp(a) captured from human plasma contained MCP-1 and the Lp(a)-associated endogenous MCP-1 induced monocyte migration. These results demonstrate that OxLDL and Lp(a) bind MCP-1 in vitro and in vivo and that OxPLs are major determinants of the MCP-1 binding. The association of MCP-1 with OxLDL and Lp(a) may play a role in modulating monocyte trafficking during atherogenesis.     

What is the best control strategy for multiple infectious disease outbreaks?

Effective control of infectious disease outbreaks is an important public health goal. In a number of recent studies, it has been shown how different intervention measures like travel restrictions, school closures, treatment and prophylaxis might allow us to control outbreaks of diseases, such as SARS, pandemic influenza and others. In these studies, control of a single outbreak is considered. It is, however, not clear how one should handle a situation where multiple outbreaks are likely to occur. Here, we identify the best control strategy for such a situation. We further discuss ways in which such a strategy can be implemented to achieve additional public health objectives.     

Tissue-Specific Functional Networks for Prioritizing Phenotype and Disease Genes

Integrated analyses of functional genomics data have enormous potential for identifying phenotype-associated genes. Tissue-specificity is an important aspect of many genetic diseases, reflecting the potentially different roles of proteins and pathways in diverse cell lineages. Accounting for tissue specificity in global integration of functional genomics data is challenging, as “functionality” and “functional relationships” are often not resolved for specific tissue types. We address this challenge by generating tissue-specific functional networks, which can effectively represent the diversity of protein function for more accurate identification of phenotype-associated genes in the laboratory mouse. Specifically, we created 107 tissue-specific functional relationship networks through integration of genomic data utilizing knowledge of tissue-specific gene expression patterns. Cross-network comparison revealed significantly changed genes enriched for functions related to specific tissue development. We then utilized these tissue-specific networks to predict genes associated with different phenotypes. Our results demonstrate that prediction performance is significantly improved through using the tissue-specific networks as compared to the global functional network. We used a testis-specific functional relationship network to predict genes associated with male fertility and spermatogenesis phenotypes, and experimentally confirmed one top prediction, Mbyl1. We then focused on a less-common genetic disease, ataxia, and identified candidates uniquely predicted by the cerebellum network, which are supported by both literature and experimental evidence. Our systems-level, tissue-specific scheme advances over traditional global integration and analyses and establishes a prototype to address the tissue-specific effects of genetic perturbations, diseases and drugs.     

Molecular and antigenic characterization of reassortant H3N2 viruses from turkeys with a unique constellation of pandemic H1N1 internal genes

Triple reassortant (TR) H3N2 influenza viruses cause varying degrees of loss in egg production in breeder turkeys. In this study we characterized TR H3N2 viruses isolated from three breeder turkey farms diagnosed with a drop in egg production. The eight gene segments of the virus isolated from the first case submission (FAV-003) were all of TR H3N2 lineage. However, viruses from the two subsequent case submissions (FAV-009 and FAV-010) were unique reassortants with PB2, PA, nucleoprotein (NP) and matrix (M) gene segments from 2009 pandemic H1N1 and the remaining gene segments from TR H3N2. Phylogenetic analysis of the HA and NA genes placed the 3 virus isolates in 2 separate clades within cluster IV of TR H3N2 viruses. Birds from the latter two affected farms had been vaccinated with a H3N4 oil emulsion vaccine prior to the outbreak. The HAl subunit of the H3N4 vaccine strain had only a predicted amino acid identity of 79% with the isolate from FAV-003 and 80% for the isolates from FAV-009 and FAV-0010. By comparison, the predicted amino acid sequence identity between a prototype TR H3N2 cluster IV virus A/Sw/ON/33853/2005 and the three turkey isolates from this study was 95% while the identity between FAV-003 and FAV-009/10 isolates was 91%. When the previously identified antigenic sites A, B, C, D and E of HA1 were examined, isolates from FAV-003 and FAV-009/10 had a total of 19 and 16 amino acid substitutions respectively when compared with the H3N4 vaccine strain. These changes corresponded with the failure of the sera collected from turkeys that received this vaccine to neutralize any of the above three isolates in vitro.     

A simple mathematical model helps to explain the immunodominance of CD8 T cells in influenza A virus infections

Understanding immunodominance, the phenomenon of epitope-specific T cells expanding in an often distinctly hierarchical fashion, is important for the design of T-cell-based intervention strategies. Several recent studies have investigated immunodominance of H-2D(b)-restricted CD8(+) T cells specific for the nucleoprotein NP366 and acid polymerase PA224 epitopes during influenza A virus infection of C57BL/6 mice. CD8(+) T cells specific for these two epitopes are codominant during primary infection; NP366 dominates during secondary infection. While a number of explanations for this observation have been proposed, none of them can fully account for all the observed data. In this article, we use a simple mathematical model to explain the seemingly inconsistent data. We show that the dynamic interactions between CD8(+) T cells and antigen presentation lead to a situation where CD8(+) T cells are limiting during the initial response whereas antigen is limiting in the secondary response. This "numbers game" between antigen and CD8(+) T cells can reproduce the observed immunodominance of the NP336- and PA224-specific CD8(+) T cells, thereby explaining the reported experimental data.