The capillary supply of human skeletal muscle in health and disease

BACKGROUND: Capillaries function to provide a surface area for nutrient and waste exchange with cells. The capillary supply of skeletal muscle is highly organized, and therefore, represents an excellent choice to study factors regulating diffusion. Muscle is comprised of three specific fibre types, each with specific contractile and metabolic characteristics, which influence the capillary supply of a given muscle; in addition, both environmental and genetic factors influence the capillary supply, including aging, physical training, and various disease processes. OBJECTIVE: The present study was undertaken to develop and assess the functionality of a data base, from which virtual experiments can be conducted on the capillary supply of human muscle, and the adaptations of the capillary bed in muscle to various perturbations. METHODS: To create the database, an extensive search of the literature was conducted using various search engines, and the three key words - "capillary, muscle, and human". This search yielded 169 papers from which the data for the 46 variables on the capillary supply and fibre characteristics of muscle were extracted for inclusion in the database. A series of statistical analyses (ANOVA) were done on the capillary database to examine differences in skeletal muscle capillarization and fibre characteristics between young and old individuals, between healthy and diseased individuals, and between untrained, endurance trained, endurance welltrained, and resistance trained individuals, using SAS. RESULTS: There was a significantly higher capillarization in the young compared to the old individuals, in the healthy compared to the diseased individuals, and in the endurance-trained and endurance well-trained compared to the untrained individuals. CONCLUSIONS: The results of this study support the conclusion that the capillary supply of skeletal muscle is closely regulated by factors aimed at optimizing oxygen and nutrient supply and/or waste removal in response to changes in muscle mass and/or metabolic activity.

A Survey of hMLH1 mRNA Splicing Profiles in Human Cell Lines: Comparing Primary Cultured Fibroblasts Before and After Oxidative Stress and Transiently versus Stably Demethylated Colon Cancer Derived Cell Lines

Most human genes undergo alternative splicing and loss of splicing fidelity is associated with disease. Epigenetic silencing of hMLH 1 via promoter cytosine methylation is causally linked to a subset of sporadic non-polyposis colon cancer and is reversible by 5-aza-2' -deoxycytidine treatment. Here I investigated changes in hMLHI mRNA splicing profiles in normal fibroblasts and colon cancer-derived human cell lines. I established the types and frequencies of hMLHI mRNA transcripts generated under baseline conditions, after hydrogen peroxide induced oxidative stress, and in acutely 5-aza-2' -deoxycytidine-treated and stably derepressed cancer cell lines. I found that hMLHI is extensively spliced under all conditions including baseline (50% splice variants), the splice variant distribution changes in response to oxidative stress, and certain splice variants are sensitive to 5- aza-2' -deoxycytidine treatment: Splice variant diversity and frequency of exon 17 skipping correlates with the level of hMLHI promoter methylation suggesting a link between promoter methylation and mRNA splicing.

Disease-Gene Association Using a Genetic Algorithm

Understanding the relationship between genetic diseases and the genes associated with them is an important problem regarding human health. The vast amount of data created from a large number of high-throughput experiments performed in the last few years has resulted in an unprecedented growth in computational methods to tackle the disease gene association problem. Nowadays, it is clear that a genetic disease is not a consequence of a defect in a single gene. Instead, the disease phenotype is a reflection of various genetic components interacting in a complex network. In fact, genetic diseases, like any other phenotype, occur as a result of various genes working in sync with each other in a single or several biological module(s). Using a genetic algorithm, our method tries to evolve communities containing the set of potential disease genes likely to be involved in a given genetic disease. Having a set of known disease genes, we first obtain a protein-protein interaction (PPI) network containing all the known disease genes. All the other genes inside the procured PPI network are then considered as candidate disease genes as they lie in the vicinity of the known disease genes in the network. Our method attempts to find communities of potential disease genes strongly working with one another and with the set of known disease genes. As a proof of concept, we tested our approach on 16 breast cancer genes and 15 Parkinson's Disease genes. We obtained comparable or better results than CIPHER, ENDEAVOUR and GPEC, three of the most reliable and frequently used disease-gene ranking frameworks.

Disease-Gene Association Using Genetic Programming

As a result of mutation in genes, which is a simple change in our DNA, we will have undesirable phenotypes which are known as genetic diseases or disorders. These small changes, which happen frequently, can have extreme results. Understanding and identifying these changes and associating these mutated genes with genetic diseases can play an important role in our health, by making us able to find better diagnosis and therapeutic strategies for these genetic diseases. As a result of years of experiments, there is a vast amount of data regarding human genome and different genetic diseases that they still need to be processed properly to extract useful information. This work is an effort to analyze some useful datasets and to apply different techniques to associate genes with genetic diseases. Two genetic diseases were studied here: Parkinson’s disease and breast cancer. Using genetic programming, we analyzed the complex network around known disease genes of the aforementioned diseases, and based on that we generated a ranking for genes, based on their relevance to these diseases. In order to generate these rankings, centrality measures of all nodes in the complex network surrounding the known disease genes of the given genetic disease were calculated. Using genetic programming, all the nodes were assigned scores based on the similarity of their centrality measures to those of the known disease genes. Obtained results showed that this method is successful at finding these patterns in centrality measures and the highly ranked genes are worthy as good candidate disease genes for being studied. Using standard benchmark tests, we tested our approach against ENDEAVOUR and CIPHER - two well known disease gene ranking frameworks - and we obtained comparable results.