Identification and characterization of cvHsp. A novel human small stress protein selectively expressed in cardiovascular and insulin-sensitive tissues.

Starting with computational tools that search for tissue-selective expression of assembled expressed sequenced tags, we have identified by focusing on heart libraries a novel small stress protein of 170 amino acids that we named cvHsp. cvHsp was found as being computationally selectively and highly (0.3% of the total RNA) expressed in human heart. The cvHsp gene mapped to 1p36.23-p34.3 between markers D1S434 and D1S507. The expression of cvHsp was analyzed with RNA dot, Northern blots, or reverse transcription-polymerase chain reaction: expression was high in heart, medium in skeletal muscle, and low in aorta or adipose tissues. In the heart of rat models of cardiac pathologies, cvHsp mRNA expression was either unchanged (spontaneous hypertension), up-regulated (right ventricular hypertrophy induced by monocrotaline treatment), or down-regulated (left ventricular hypertrophy following aortic banding). In obese Zucker rats, cvHsp mRNA was increased in skeletal muscle, brown, and white adipose tissues but remained unchanged in the heart. Western blot analysis using antipeptide polyclonal antibodies revealed two specific bands at 23 and 25 kDa for cvHsp in human heart. cvHsp interacted in both yeast two-hybrid and immunoprecipitation experiments with alpha-filamin or actin-binding protein 280. Within cvHsp, amino acid residues 56-119 were shown to be important for its specific interaction with the C-terminal tail of alpha-filamin.

Detecting selective expression of genes and proteins.

Selective expression of a gene product (mRNA or protein) is a pattern in which the expression is markedly high, or markedly low, in one particular tissue compared with its level in other tissues or sources. We present a computational method for the identification of such patterns. The method combines assessments of the reliability of expression quantitation with a statistical test of expression distribution patterns. The method is applicable to small studies or to data mining of abundance data from expression databases, whether mRNA or protein. Though the method was developed originally for gene-expression analyses, the computational method is, in fact, rather general. It is well suited for the identification of exceptional values in many sorts of intensity data, even noisy data, for which assessments of confidences in the sources of the intensities are available. Moreover, the method is indifferent as to whether the intensities are experimentally or computationally derived. We show details of the general method and examples of computational results on gene abundance data.

Tumor heterogeneity and progression: conceptual foundations for modeling.

A conceptual foundation for modeling tumor progression, growth, and heterogeneity is presented. The purpose of such models is to aid understanding, test ideas, formulate experiments, and to model cancer 'in machina' to address the dynamic features of tumor cell heterogeneity, progression, and growth. The descriptive capabilities of such an approach provides a consistent language for qualitatively reasoning about tumor behavior. This approach provides a schema for building conceptual models that combine three key phenomenological driving elements: growth, progression, and genetic instability. The growth element encompasses processes contributing to changes in tumor bulk and is distinct from progression per se. The progression element subsumes a broad collection of processes underlying phenotypic progression. The genetics elements represents heritable changes which potentially affect tumor character and behavior. Models, conceptual and mathematical, can be built for different tumor situations by drawing upon the interaction of these three distinct driving elements. These models can be used as tools to explore a diversity of hypotheses concerning dynamic changes in cellular populations during tumor progression, including the generation of intratumor heterogeneity. Such models can also serve to guide experimentation and to gain insight into dynamic aspects of complex tumor behavior.