We investigated the expression pattern of the breast cancer associated gene LIV‐1 on mRNA and protein level in 111 human breast cancer patients by in situ hybridization as well as immunohistochemistry and focused on the unknown potential of LIV‐1 expression levels as a prognostic marker. To our knowledge, this is the first study on endogenous LIV‐1 protein expression. Results of our study indicate that LIV‐1 mRNA and protein expression levels are only weakly correlated, suggesting posttranscriptional regulatory mechanisms. Furthermore, LIV‐1 mRNA quantity in combination with a positive ER status seem to represent a better marker than the progesterone receptor status according to the prognostic significance for relapse free survival (RFS). A negative correlation of LIV‐1 protein levels with tumor size, grade and stage reflects an association of LIV‐1 protein expression with less aggressive tumors. High LIV‐1 protein expression seems to be associated with a longer relapse free and overall survival in breast cancer patients with invasive ductal carcinoma. This association, however, seems to be dependent from other prognostic markers. Our data suggest that LIV‐1 is a promising candidate for a novel marker for breast cancer patients with better outcome. Furthermore, our study presents a revised cDNA sequence of LIV‐1 and demonstrates the localization of endogenous LIV‐1 in the endoplasmic reticulum. (Supplementary material for this article can be found on the International Journal of Cancer website at http://www.interscience.wiley.com/jpages/0020-7136/suppmat/index.html). © 2005 Wiley‐Liss, Inc.

Germinal centers (GCs) are complex, multicell-type, transient structures that form in secondary lymphatic tissues in response to T cell-dependent stimulation. This process is crucial to the adaptive immune response because it is the source of affinity maturation and long-lived B cell memory. Our previous studies showed that the growth of murine splenic GCs is nonsynchronized, involving broad-volume distributions of individual GCs at any time. This raises the question whether such a thing as a typical GC exists. To address this matter, we acquired large-scale confocal data on GCs throughout the course of the 2-phenyl-5-oxazolone chicken serum albumin-driven primary immune response in BALB/c mice. Semiautomated image analysis of 3457 GC sections revealed that, although there is no typical GC in terms of size, GCs have a typical cellular composition in that the cell ratios of resident T cells, macrophages, proliferating cells, and apoptotic nuclei are maintained during the established phase of the response. Moreover, our data provide evidence that the dark zone (DZ) and light zone (LZ) compartments of GCs are about the same size and led us to estimate that the minimal cell loss rate in GCs is 3% per hour. Furthermore, we found that the population of GC macrophages is larger and more heterogeneous than previously thought, and that despite enrichment of T cells in the LZ, the DZ of murine splenic GCs is not poor in T cells. DZ and LZ differ in the T cell-to-macrophage ratio rather than in the density of T cells.

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Optimization of antibody affinity is a hallmark of the humoral immune response. It takes place in hundreds of transient microstructures called germinal centers (GCs). Their function and time-dependent behavior are subjects of active investigation. According to a generally accepted notion, their individual kinetics follows the average kinetics of all GCs present in the observed lymphatic tissue. In this review, we challenge this view and point out, with the help of mathematical simulations, that inferring the kinetics of individual GCs from cross-sectional evaluation of GC kinetics is virtually impossible. Thus, the time course of individual GCs is open to conjecture. For instance, one possible interpretation is that GCs exist for a time span considerably shorter than that of the observed average kinetics. We explore the implications of different temporal organizations of GCs in the light of the hypothesis that GC B-cell emigrants recolonize GC niches. This assumption leads to a view where GCs work in parallel but are linked by recirculation of B-cell emigrants. In this view, interleaved global and local competition provide for an implementation of multiple levels of B-cell selection in affinity maturation. The concepts of iteration, all-or-none behavior, and phasic mutation schedule are discussed in the light of this hypothesis.

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Material

FoodChain-Lab is modular open-source software for trace-back and trace-forward analysis in food-borne disease outbreak investigations. Development of FoodChain-Lab has been driven by a need for appropriate software in several food-related outbreaks in Germany since 2011. The software allows integrated data management, data linkage, enrichment and visualization as well as interactive supply chain analyses. Identification of possible outbreak sources or vehicles is facilitated by calculation of tracing scores for food-handling stations (companies or persons) and food products under investigation. The software also supports consideration of station-specific cross-contamination, analysis of geographical relationships, and topological clustering of the tracing network structure. FoodChain-Lab has been applied successfully in previous outbreak investigations, for example during the 2011 EHEC outbreak and the 2013/14 European hepatitis A outbreak. The software is most useful in complex, multi-area outbreak investigations where epidemiological evidence may be insufficient to discriminate between multiple implicated food products. The automated analysis and visualization components would be of greater value if trading information on food ingredients and compound products was more easily available.

The Shiga toxin-producing Escherichia coli O104:H4 outbreak in Germany in 2011 required the development of appropriate tools in real-time for tracing suspicious foods along the supply chain, namely salad ingredients, sprouts, and seeds. Food commodities consumed at locations identified as most probable site of infection (outbreak clusters) were traced back in order to identify connections between different disease clusters via the supply chain of the foods. A newly developed relational database with integrated consistency and plausibility checks was used to collate these data for further analysis. Connections between suppliers, distributors, and producers were visualized in network graphs and geographic projections. Finally, this trace-back and trace-forward analysis led to the identification of sprouts produced by a horticultural farm in Lower Saxony as vehicle for the pathogen, and a specific lot of fenugreek seeds imported from Egypt as the most likely source of contamination. Network graphs have proven to be a powerful tool for summarizing and communicating complex trade relationships to various stake holders. The present article gives a detailed description of the newly developed tracing tools and recommendations for necessary requirements and improvements for future foodborne outbreak investigations.