A network medicine approach to quantify distance between hereditary disease modules on the interactome

We introduce a MeSH-based method that accurately quantifies similarity between heritable diseases at molecular level. This method effectively brings together the existing information about diseases that is scattered across the vast corpus of biomedical literature. We prove that sets of MeSH terms provide a highly descriptive representation of heritable disease and that the structure of MeSH provides a natural way of combining individual MeSH vocabularies. We show that our measure can be used effectively in the prediction of candidate disease genes. We developed a web application to query more than 28.5 million relationships between 7,574 hereditary diseases (96% of OMIM) based on our similarity measure

Prediction of Human Disease Genes by Human-Mouse Conserved Coexpression Analysis

One of the most limiting aspects of biological research in the post-genomic era is the capability to integrate massive datasets on gene structure and function for producing useful biological knowledge. In this report we have applied an integrative approach to address the problem of identifying likely candidate genes within loci associated with human genetic diseases. Despite the recent progress in sequencing technologies, approaching this problem from an experimental perspective still represents a very demanding task, because the critical region may typically contain hundreds of positional candidates. We found that by concentrating only on genes sharing similar expression profiles in both human and mouse, massive microarray datasets can be used to reliably identify disease-relevant relationships among genes. Moreover, we found that integrating the coexpression criterion with systematic phenome analysis allows efficient identification of disease genes in large genomic regions. Using this approach on 850 OMIM loci characterized by unknown molecular basis, we propose high-probability candidates for 81 genetic diseases

A Network of Genes, Genetic Disorders, and Brain Areas

The network-based approach has been used to describe the relationship among genes and various phenotypes, producing a network describing complex biological relationships. Such networks can be constructed by aggregating previously reported associations in the literature from various databases. In this work, we applied the network-based approach to investigate how different brain areas are associated to genetic disorders and genes. In particular, a tripartite network with genes, genetic diseases, and brain areas was constructed based on the associations among them reported in the literature through text mining. In the resulting network, a disproportionately large number of gene-disease and disease-brain associations were attributed to a small subset of genes, diseases, and brain areas. Furthermore, a small number of brain areas were found to be associated with a large number of the same genes and diseases. These core brain regions encompassed the areas identified by the previous genome-wide association studies, and suggest potential areas of focus in the future imaging genetics research. The approach outlined in this work demonstrates the utility of the network-based approach in studying genetic effects on the brain

Molecular mechanistic associations of human diseases

<p>Abstract</p> <p>Background</p> <p>The study of relationships between human diseases provides new possibilities for biomedical research. Recent achievements on human genetic diseases have stimulated interest to derive methods to identify disease associations in order to gain further insight into the network of human diseases and to predict disease genes.</p> <p>Results</p> <p>Using about 10000 manually collected causal disease/gene associations, we developed a statistical approach to infer meaningful associations between human morbidities. The derived method clustered cardiometabolic and endocrine disorders, immune system-related diseases, solid tissue neoplasms and neurodegenerative pathologies into prominent disease groups. Analysis of biological functions confirmed characteristic features of corresponding disease clusters. Inference of disease associations was further employed as a starting point for prediction of disease genes. Efforts were made to underpin the validity of results by relevant literature evidence. Interestingly, many inferred disease relationships correspond to known clinical associations and comorbidities, and several predicted disease genes were subjects of therapeutic target research.</p> <p>Conclusions</p> <p>Causal molecular mechanisms present a unifying principle to derive methods for disease classification, analysis of clinical disorder associations, and prediction of disease genes. According to the definition of causal disease genes applied in this study, these results are not restricted to genetic disease/gene relationships. This may be particularly useful for the study of long-term or chronic illnesses, where pathological derangement due to environmental or as part of sequel conditions is of importance and may not be fully explained by genetic background.</p

Genome-wide profiling of p63 DNA-binding sites identifies an element that regulates gene expression during limb development in the 7q21 SHFM1 locus.

Contains fulltext : 88501.pdf (publisher's version ) (Open Access)Heterozygous mutations in p63 are associated with split hand/foot malformations (SHFM), orofacial clefting, and ectodermal abnormalities. Elucidation of the p63 gene network that includes target genes and regulatory elements may reveal new genes for other malformation disorders. We performed genome-wide DNA-binding profiling by chromatin immunoprecipitation (ChIP), followed by deep sequencing (ChIP-seq) in primary human keratinocytes, and identified potential target genes and regulatory elements controlled by p63. We show that p63 binds to an enhancer element in the SHFM1 locus on chromosome 7q and that this element controls expression of DLX6 and possibly DLX5, both of which are important for limb development. A unique micro-deletion including this enhancer element, but not the DLX5/DLX6 genes, was identified in a patient with SHFM. Our study strongly indicates disruption of a non-coding cis-regulatory element located more than 250 kb from the DLX5/DLX6 genes as a novel disease mechanism in SHFM1. These data provide a proof-of-concept that the catalogue of p63 binding sites identified in this study may be of relevance to the studies of SHFM and other congenital malformations that resemble the p63-associated phenotypes

Seaweed biopolymers as additives for unfired clay bricks

Unfired clay bricks are an environmentally friendly alternative to conventional masonry materials such as fired bricks and concrete blocks but their use is currently limited by their relatively poor mechanical and durability properties. While products like cement and lime are commonly added to earthen materials in an effort to improve their physical performance, these additives can also have a negative influence on the overall environmental impact. The purpose of this research is to investigate the use of alginate, a natural and renewable biopolymer obtained from brown seaweeds, as an admixture for unfired clay blocks. A total of 5 different alginates have been investigated and combined with 3 soil compositions to create prototype specimens which have then been characterised and compared in relation to flexural and compressive strength, microstructure, abrasive strength and hygroscopic behaviour. The results demonstrate that improvements in mechanical strength are dependent on the type of alginate used and the composition of the soil. The greatest increase in compressive strength is achieved using an alginate sourced from the Laminaria Hyperborea seaweed and offers a value more than double that of the equivalent control specimen. Increases in the alginate dosage do not necessarily lead to an increase in strength suggesting that there is an optimum concentration at which strength improvement is most effective