Neurotropic Lineage III Strains of \u3cem\u3eListeria monocytogenes\u3c/em\u3e Disseminate to the Brain without Reaching High Titer in the Blood

Listeria monocytogenes is thought to colonize the brain using one of three mechanisms: direct invasion of the blood-brain barrier, transportation across the barrier by infected monocytes, and axonal migration to the brain stem. The first two pathways seem to occur following unrestricted bacterial growth in the blood and thus have been linked to immunocompromise. In contrast, cell-to-cell spread within nerves is thought to be mediated by a particular subset of neurotropic L. monocytogenes strains. In this study, we used a mouse model of foodborne transmission to evaluate the neurotropism of several L. monocytogenes isolates. Two strains preferentially colonized the brain stems of BALB/cByJ mice 5 days postinfection and were not detectable in blood at that time point. In contrast, infection with other strains resulted in robust systemic infection of the viscera but no dissemination to the brain. Both neurotropic strains (L2010-2198, a human rhombencephalitis isolate, and UKVDL9, a sheep brain isolate) typed as phylogenetic lineage III, the least characterized group of L. monocytogenes. Neither of these strains encodes InlF, an internalin-like protein that was recently shown to promote invasion of the blood-brain barrier. Acute neurologic deficits were observed in mice infected with the neurotropic strains, and milder symptoms persisted for up to 16 days in some animals. These results demonstrate that neurotropic L. monocytogenes strains are not restricted to any one particular lineage and suggest that the foodborne mouse model of listeriosis can be used to investigate the pathogenic mechanisms that allow L. monocytogenes to invade the brain stem. IMPORTANCE Progress in understanding the two naturally occurring central nervous system (CNS) manifestations of listeriosis (meningitis/meningoencephalitis and rhombencephalitis) has been limited by the lack of small animal models that can readily distinguish between these distinct infections. We report here that certain neurotropic strains of Listeria monocytogenes can spread to the brains of young otherwise healthy mice and cause neurological deficits without causing a fatal bacteremia. The novel strains described here fall within phylogenetic lineage III, a small collection of L. monocytogenes isolates that have not been well characterized to date. The animal model reported here mimics many features of human rhombencephalitis and will be useful for studying the mechanisms that allow L. monocytogenes to disseminate to the brain stem following natural foodborne transmission

DeBi: Discovering Differentially Expressed Biclusters using a Frequent Itemset Approach

<p>Abstract</p> <p>Background</p> <p>The analysis of massive high throughput data via clustering algorithms is very important for elucidating gene functions in biological systems. However, traditional clustering methods have several drawbacks. Biclustering overcomes these limitations by grouping genes and samples simultaneously. It discovers subsets of genes that are co-expressed in certain samples. Recent studies showed that biclustering has a great potential in detecting marker genes that are associated with certain tissues or diseases. Several biclustering algorithms have been proposed. However, it is still a challenge to find biclusters that are significant based on biological validation measures. Besides that, there is a need for a biclustering algorithm that is capable of analyzing very large datasets in reasonable time.</p> <p>Results</p> <p>Here we present a fast biclustering algorithm called DeBi (Differentially Expressed BIclusters). The algorithm is based on a well known data mining approach called frequent itemset. It discovers maximum size homogeneous biclusters in which each gene is strongly associated with a subset of samples. We evaluate the performance of DeBi on a yeast dataset, on synthetic datasets and on human datasets.</p> <p>Conclusions</p> <p>We demonstrate that the DeBi algorithm provides functionally more coherent gene sets compared to standard clustering or biclustering algorithms using biological validation measures such as Gene Ontology term and Transcription Factor Binding Site enrichment. We show that DeBi is a computationally efficient and powerful tool in analyzing large datasets. The method is also applicable on multiple gene expression datasets coming from different labs or platforms.</p

Biclustering via optimal re-ordering of data matrices in systems biology: rigorous methods and comparative studies

<p>Abstract</p> <p>Background</p> <p>The analysis of large-scale data sets via clustering techniques is utilized in a number of applications. Biclustering in particular has emerged as an important problem in the analysis of gene expression data since genes may only jointly respond over a subset of conditions. Biclustering algorithms also have important applications in sample classification where, for instance, tissue samples can be classified as cancerous or normal. Many of the methods for biclustering, and clustering algorithms in general, utilize simplified models or heuristic strategies for identifying the "best" grouping of elements according to some metric and cluster definition and thus result in suboptimal clusters.</p> <p>Results</p> <p>In this article, we present a rigorous approach to biclustering, OREO, which is based on the Optimal RE-Ordering of the rows and columns of a data matrix so as to globally minimize the dissimilarity metric. The physical permutations of the rows and columns of the data matrix can be modeled as either a network flow problem or a traveling salesman problem. Cluster boundaries in one dimension are used to partition and re-order the other dimensions of the corresponding submatrices to generate biclusters. The performance of OREO is tested on (a) metabolite concentration data, (b) an image reconstruction matrix, (c) synthetic data with implanted biclusters, and gene expression data for (d) colon cancer data, (e) breast cancer data, as well as (f) yeast segregant data to validate the ability of the proposed method and compare it to existing biclustering and clustering methods.</p> <p>Conclusion</p> <p>We demonstrate that this rigorous global optimization method for biclustering produces clusters with more insightful groupings of similar entities, such as genes or metabolites sharing common functions, than other clustering and biclustering algorithms and can reconstruct underlying fundamental patterns in the data for several distinct sets of data matrices arising in important biological applications.</p