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Eickhoff, C.

Publications and source records attributed to Eickhoff, C..

2 recordsLinked to original sources

Mycobacteria-specific CD4+IFN-γ+ cell expresses naïve-surface markers and confers superior protection against tuberculosis infection compared to central and effector memory CD4+ T cell subsets

Failure of the most recent tuberculosis (TB) vaccine trial to boost BCG mediated anti-TB immunity despite highly durable Th1-specific central (TCM) and effector (TEM) memory cell responses, highlights the importance of identifying optimal T cell targets for protective vaccines. Here we describe a novel, Mycobacterium tuberculosis (Mtb)-specific IFN-{gamma}+CD4+ T cell population expressing surface markers characteristic of naive T cells (TNLM), that were induced in both human (CD45RA+CCR7+CD27+CD95-) and murine (CD62L+CD44-Sca-1+CD122-) systems in response to mycobacteria. In BCG vaccinated subjects and those with latent TB infection, TNLM cells, compared to bonafide naive CD4+ T cells were identified by absence of CD95 expression and had increased expression CCR7 and CD27, the activation markers T-bet, CD69 and PD-1 and the survival marker CD74. Increased TNLM frequencies were noted in the lung and spleen of wild type C57BL6 mice at 2 weeks after infection with Mtb, and progressively decreased at later time points, a pattern not seen in TNF-+CD4+ T cells expressing naive cell surface markers. Importantly, adoptive transfer of highly purified TNLM from vaccinated ESAT-61-20-specific TCR transgenic mice conferred superior protection against Mtb infection in Rag-/- mice when compared with total meory populations (central and effector memory cells). Thus, TNLM cells may represent a memory T cell population that if optimally targeted may significantly improve future TB vaccine responses.

immunology

Efficient graph-color compression with neighborhood-informed Bloom filters

MotivationTechnological advancements in high-throughput DNA sequencing have led to an exponential growth of sequencing data being produced and stored as a byproduct of biomedical research. Despite its public availability, a majority of this data remains hard to query to the research community due to a lack of efficient data representation and indexing solutions. One of the available techniques to represent read data is a condensed form as an assembly graph. Such a representation contains all sequence information but does not store contextual information and metadata.\n\nResultsWe present two new approaches for a compressed representation of a graph coloring: a lossless compression scheme based on a novel application of wavelet tries as well as a highly accurate lossy compression based on a set of Bloom filters. Both strategies retain a coloring with dynamically changing graph topology. We present construction and merge procedures for both methods and evaluate their performance on a wide range of different datasets. By dropping the requirement of a fully lossless compression and using the topological information of the underlying graph, we can reduce memory requirements by up to three orders of magnitude. Representing individual colors as independently stored modules, our approaches are fully dynamic and can be efficiently parallelized. These properties allow for an easy upscaling to the problem sizes common to the biomedical domain.\n\nAvailabilityWe provide prototype implementations in C++, summaries of our experiments as well as links to all datasets publicly at https://github.com/ratschlab/graph_annotation.\n\nContactandre.kahles@inf.ethz.ch, carsten@brown.edu, Gunnar.Ratsch@ratschlab.org

bioinformatics