A classification of tasks for the systematic study of immune response using functional genomics data

A novel and divergent role of granzyme A and B in resistance to helminth infection

Granzyme (gzm) A and B, proteases of NK cells and T killer cells, mediate cell death, but also cleave extracellular matrices, inactivate intracellular pathogens, and induce cytokines. Moreover, macrophages, Th2 cells, regulatory T cells, mast cells, and B cells can express gzms. We recently reported gzm induction in human filarial infection. In this study, we show that in rodent filarial infection with Litomosoides sigmodontis, worm loads were significantly reduced in gzmA × B and gzmB knockout mice during the whole course of infection, but enhanced only early in gzmA knockout compared with wild-type mice. GzmA/B deficiency was associated with a defense-promoting Th2 cytokine and Ab shift, enhanced early inflammatory gene expression, and a trend of reduced alternatively activated macrophage induction, whereas gzmA deficiency was linked with reduced inflammation and a trend toward increased alternatively activated macrophages. This suggests a novel and divergent role for gzms in helminth infection, with gzmA contributing to resistance and gzmB promoting susceptibility.

A guide to modern statistical analysis of immunological data

BACKGROUND

The number of subjects that can be recruited in immunological studies and the number of immunological parameters that can be measured has increased rapidly over the past decade and is likely to continue to expand. Large and complex immunological datasets can now be used to investigate complex scientific questions, but to make the most of the potential in such data and to get the right answers sophisticated statistical approaches are necessary. Such approaches are used in many other scientific disciplines, but immunological studies on the whole still use simple statistical techniques for data analysis.

RESULTS

The paper provides an overview of the range of statistical methods that can be used to answer different immunological study questions. We discuss specific aspects of immunological studies and give examples of typical scientific questions related to immunological data. We review classical bivariate and multivariate statistical techniques (factor analysis, cluster analysis, discriminant analysis) and more advanced methods aimed to explore causal relationships (path analysis/structural equation modelling) and illustrate their application to immunological data. We show the main features of each method, the type of study question they can answer, the type of data they can be applied to, the assumptions required for each method and the software that can be used.

CONCLUSION

This paper will help the immunologist to choose the correct statistical approach for a particular research question.

A systematic strategy for large-scale analysis of genotype phenotype correlations: identification of candidate genes involved in African trypanosomiasis

It is increasingly common to combine Microarray and Quantitative Trait Loci data to aid the search for candidate genes responsible for phenotypic variation. Workflows provide a means of systematically processing these large datasets and also represent a framework for the re-use and the explicit declaration of experimental methods. In this article, we highlight the issues facing the manual analysis of microarray and QTL data for the discovery of candidate genes underlying complex phenotypes. We show how automated approaches provide a systematic means to investigate genotype-phenotype correlations. This methodology was applied to a use case of resistance to African trypanosomiasis in the mouse. Pathways represented in the results identified Daxx as one of the candidate genes within the Tir1 QTL region. Subsequent re-sequencing in Daxx identified a deletion of an amino acid, identified in susceptible mouse strains, in the Daxx-p53 protein-binding region. This supports recent experimental evidence that apoptosis could be playing a role in the trypanosomiasis resistance phenotype. Workflows developed in this investigation, including a guide to loading and executing them with example data, are available at http://workflows.mygrid.org.uk/repository/myGrid/PaulFisher/.