Peptide Antagonism and T Cell Receptor Interactions with Peptide-MHC Complexes

AbstractWe describe antagonist peptides that specifically inhibit cytolytic activity of T cell clones and lines that express the antigen-specific receptor of CD8+ T lymphocyte clone 2C, which recognizes peptides in association with syngeneic (Kb) and allogeneic (Ld) MHC proteins. Addition of an antagonist peptide that can bind to Kb on 2C cells decreased the tyrosine phosphorylation of CD3 ζ chains elicited by prior exposure of the cells to an agonist peptide-Kb complex. Contrary to previous agonist-antagonist comparisons, the 2C T cell receptor had higher affinity for an antagonist peptide-Kb complex than for a weak agonist peptide-Kb complex. This difference is considered in light of evidence that antigen-specific receptor affinity values can be substantially higher when determined with the receptor on live cells than with the receptor in cell-free systems

Affinity Inequality among Serum Antibodies That Originate in Lymphoid Germinal Centers

Upon natural infection with pathogens or vaccination, antibodies are produced by a process called affinity maturation. As affinity maturation ensues, average affinity values between an antibody and ligand increase with time. Purified antibodies isolated from serum are invariably heterogeneous with respect to their affinity for the ligands they bind, whether macromolecular antigens or haptens (low molecular weight approximations of epitopes on antigens). However, less is known about how the extent of this heterogeneity evolves with time during affinity maturation. To shed light on this issue, we have taken advantage of previously published data from Eisen and Siskind (1964). Using the ratio of the strongest to the weakest binding subsets as a metric of heterogeneity (or affinity inequality), we analyzed antibodies isolated from individual serum samples. The ratios were initially as high as 50-fold, and decreased over a few weeks after a single injection of small antigen doses to around unity. This decrease in the effective heterogeneity of antibody affinities with time is consistent with Darwinian evolution in the strong selection limit. By contrast, neither the average affinity nor the heterogeneity evolves much with time for high doses of antigen, as competition between clones of the same affinity is minimal.Ragon Institute of MGH, MIT and HarvardSamsung Scholarship FoundationNational Science Foundation (U.S.). Graduate Research Fellowship (Grant 1122374

Gene Function Classification Using Bayesian Models with Hierarchy-Based Priors

We investigate the application of hierarchical classification schemes to the annotation of gene function based on several characteristics of protein sequences including phylogenic descriptors, sequence based attributes, and predicted secondary structure. We discuss three Bayesian models and compare their performance in terms of predictive accuracy. These models are the ordinary multinomial logit (MNL) model, a hierarchical model based on a set of nested MNL models, and a MNL model with a prior that introduces correlations between the parameters for classes that are nearby in the hierarchy. We also provide a new scheme for combining different sources of information. We use these models to predict the functional class of Open Reading Frames (ORFs) from the E. coli genome. The results from all three models show substantial improvement over previous methods, which were based on the C5 algorithm. The MNL model using a prior based on the hierarchy outperforms both the non-hierarchical MNL model and the nested MNL model. In contrast to previous attempts at combining these sources of information, our approach results in a higher accuracy rate when compared to models that use each data source alone. Together, these results show that gene function can be predicted with higher accuracy than previously achieved, using Bayesian models that incorporate suitable prior information

The effects of weather and climate change on dengue

There is much uncertainty about the future impact of climate change on vector-borne diseases. Such uncertainty reflects the difficulties in modelling the complex interactions between disease, climatic and socioeconomic determinants. We used a comprehensive panel dataset from Mexico covering 23 years of province-specific dengue reports across nine climatic regions to estimate the impact of weather on dengue, accounting for the effects of non-climatic factors

Landscape and Residential Variables Associated with Plague-Endemic Villages in the West Nile Region of Uganda

Plague, caused by the bacteria Yersinia pestis , is a severe, often fatal disease. This study focuses on the plagueendemic West Nile region of Uganda, where limited information is available regarding environmental and behavioral risk factors associated with plague infection. We conducted observational surveys of 10 randomly selected huts within historically classified case and control villages (four each) two times during the dry season of 2006 ( N = 78 case huts and N = 80 control huts), which immediately preceded a large plague outbreak. By coupling a previously published landscape-level statistical model of plague risk with this observational survey, we were able to identify potential residence-based risk factors for plague associated with huts within historic case or control villages (e.g., distance to neighboring homestead and presence of pigs near the home) and huts within areas previously predicted as elevated risk or low risk (e.g., corn and other annual crops grown near the home, water storage in the home, and processed commercial foods stored in the home). The identified variables are consistent with current ecologic theories on plague transmission dynamics. This preliminary study serves as a foundation for future case control studies in the area

Weak pairwise correlations imply strongly correlated network states in a neural population

Biological networks have so many possible states that exhaustive sampling is impossible. Successful analysis thus depends on simplifying hypotheses, but experiments on many systems hint that complicated, higher order interactions among large groups of elements play an important role. In the vertebrate retina, we show that weak correlations between pairs of neurons coexist with strongly collective behavior in the responses of ten or more neurons. Surprisingly, we find that this collective behavior is described quantitatively by models that capture the observed pairwise correlations but assume no higher order interactions. These maximum entropy models are equivalent to Ising models, and predict that larger networks are completely dominated by correlation effects. This suggests that the neural code has associative or error-correcting properties, and we provide preliminary evidence for such behavior. As a first test for the generality of these ideas, we show that similar results are obtained from networks of cultured cortical neurons.Comment: Full account of work presented at the conference on Computational and Systems Neuroscience (COSYNE), 17-20 March 2005, in Salt Lake City, Utah (http://cosyne.org