Central Skull Base Anatomy and Pathology: A Review.

The central skull base is an anatomically complex region of the head and neck which hosts a variety of neoplastic, vascular, infectious, inflammatory, and developmental pathologies. Evaluation of its intricate anatomy requires dedicated and complementary imaging modalities of MRI and CT. This article will provide a brief review of the anatomy of the central skull base, followed by an overview of common pathologies encountered in this region and their characteristic radiological characteristics.

Mitochondrial autophagosomes as a mechanism of drug resistance in breast carcinoma.

We have previously described the process by which mitochondria donate their membranes for the formation of autophagosomes, and in this study we show that the same process could be involved in drug sequestration and exocytosis resulting in multidrug-resistant cancerous cells. We examine the implications of mitochondrial vesicle formation of mitoautophagosomes (MAPS) in response to the cytotoxic drug MKT-077, which targets mortalin, in a drug-resistant breast carcinoma cell line overexpressing P-glycoprotein (P-gp). The breast cancer cell line MCF-7Adr is derived from MCF-7, but differs from its ancestral line in tolerance of MKT-077-induced mitochondrial toxicity. Our ultrastructural observations suggest that autophagy in the MCF-7Adr cells entails regional sequestration of MKT077 in multilamellar LC3-labeled MAPS, which then separate from their mitochondria, and fuse with or engulf each other. MAPS appeared to be migrating through the cytoplasm and fusing with the plasma membrane, thus carrying out exocytotic secretion. This mechanism, which seems ineffective in the ancestral cell line, provides a resistance mechanism for MKT-077 by enhancing the efflux process of the cells. After 8 hr of MKT-077 exposure, a fraction of the resistant cells appeared viable and contained larger number of smaller sized mitochondria. Mitoautophagosomes, therefore, provide a potentially novel model for multidrug resistance in cancerous cells and may contribute to the P-gp efflux process.

A Systems Biology Interpretation of Array Comparative Genomic Hybridization (aCGH) Data through Phylogenetics.

Array Comparative Genomic Hybridization (aCGH) is a rapid screening technique to detect gene deletions and duplications, providing an overview of chromosomal aberrations throughout the entire genome of a tumor, without the need for cell culturing. However, the heterogeneity of aCGH data obfuscates existing methods of data analysis. Analysis of aCGH data from a systems biology perspective or in the context of total aberrations is largely absent in the published literature. We present here a novel alternative to the functional analysis of aCGH data using the phylogenetic paradigm that is well-suited to high dimensional datasets of heterogeneous nature, but has not been widely adapted to aCGH data. Maximum parsimony phylogenetic analysis sorts out genetic data through the simplest presentation of the data on a cladogram, a graphical evolutionary tree, thus providing a powerful and efficient method for aCGH data analysis. For example, the cladogram models the multiphasic changes in the cancer genome and identifies shared early mutations in the disease progression, providing a simple yet powerful means of aCGH data interpretation. As such, applying maximum parsimony phylogenetic analysis to aCGH results allows for the differentiation between drivers and passenger genes aberrations in cancer specimens. In addition to offering a novel methodology to analyze aCGH results, we present here a crucial software suite that we wrote to carry out the analysis. In a broader context, we wish to underscore that phylogenetic analysis of aCGH data is a non-parametric method that circumvents the pitfalls and frustrations of standard analytical techniques that rely on parametric statistics. Organizing the data in a cladogram as explained in this research article provides insights into the disease common aberrations, as well as the disease subtypes and their shared aberrations (the synapomorphies) of each subtype. Hence, we report the method and make the software suite publicly and freely available at http://software.phylomcs.com so that researchers can test alternative and innovative approaches to the analysis of aCGH data.

Single-nucleotide variations in cardiac arrhythmias: prospects for genomics and proteomics based biomarker discovery and diagnostics.

Cardiovascular diseases are a large contributor to causes of early death in developed countries. Some of these conditions, such as sudden cardiac death and atrial fibrillation, stem from arrhythmias-a spectrum of conditions with abnormal electrical activity in the heart. Genome-wide association studies can identify single nucleotide variations (SNVs) that may predispose individuals to developing acquired forms of arrhythmias. Through manual curation of published genome-wide association studies, we have collected a comprehensive list of 75 SNVs associated with cardiac arrhythmias. Ten of the SNVs result in amino acid changes and can be used in proteomic-based detection methods. In an effort to identify additional non-synonymous mutations that affect the proteome, we analyzed the post-translational modification S-nitrosylation, which is known to affect cardiac arrhythmias. We identified loss of seven known S-nitrosylation sites due to non-synonymous single nucleotide variations (nsSNVs). For predicted nitrosylation sites we found 1429 proteins where the sites are modified due to nsSNV. Analysis of the predicted S-nitrosylation dataset for over- or under-representation (compared to the complete human proteome) of pathways and functional elements shows significant statistical over-representation of the blood coagulation pathway. Gene Ontology (GO) analysis displays statistically over-represented terms related to muscle contraction, receptor activity, motor activity, cystoskeleton components, and microtubule activity. Through the genomic and proteomic context of SNVs and S-nitrosylation sites presented in this study, researchers can look for variation that can predispose individuals to cardiac arrhythmias. Such attempts to elucidate mechanisms of arrhythmia thereby add yet another useful parameter in predicting susceptibility for cardiac diseases.