7 research outputs found
Analyzing the Chinese landscape in anti-diabetic drug research: leading knowledge production institutions and thematic communities
Analysis of gene expression in the nervous system identifies key genes and novel candidates for health and disease
The incidence of neurodegenerative diseases in the developed world has risen over the last century, concomitant with an increase in average human lifespan. A major challenge is therefore to identify genes that control neuronal health and viability with a view to enhancing neuronal health during ageing and reducing the burden of neurodegeneration. Analysis of gene expression data has recently been used to infer gene functions for a range of tissues from co-expression networks. We have now applied this approach to transcriptomic datasets from the mammalian nervous system available in the public domain. We have defined the genes critical for influencing neuronal health and disease in different neurological cell types and brain regions. The functional contribution of genes in each co-expression cluster was validated using human disease and knockout mouse phenotypes, pathways and gene ontology term annotation. Additionally a number of poorly annotated genes were implicated by this approach in nervous system function. Exploiting gene expression data available in the public domain allowed us to validate key nervous system genes and, importantly, to identify additional genes with minimal functional annotation but with the same expression pattern. These genes are thus novel candidates for a role in neurological health and disease and could now be further investigated to confirm their function and regulation during ageing and neurodegeneration. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10048-017-0509-5) contains supplementary material, which is available to authorized users
Plasmodium malariae and P. ovale genomes provide insights into malaria parasite evolution
Elucidation of the evolutionary history and interrelatedness of Plasmodium species that infect humans has been hampered by a lack of genetic information for three human-infective species: P. malariae and two P. ovale species (P. o. curtisi and P. o. wallikeri)(1). These species are prevalent across most regions in which malaria is endemic(2,3) and are often undetectable by light microscopy(4), rendering their study in human populations difficult(5). The exact evolutionary relationship of these species to the other human-infective species has been contested(6,7). Using a new reference genome for P. malariae and a manually curated draft P. o. curtisi genome, we are now able to accurately place these species within the Plasmodium phylogeny. Sequencing of a P. malariae relative that infects chimpanzees reveals similar signatures of selection in the P. malariae lineage to another Plasmodium lineage shown to be capable of colonization of both human and chimpanzee hosts. Molecular dating suggests that these host adaptations occurred over similar evolutionary timescales. In addition to the core genome that is conserved between species, differences in gene content can be linked to their specific biology. The genome suggests that P. malariae expresses a family of heterodimeric proteins on its surface that have structural similarities to a protein crucial for invasion of red blood cells. The data presented here provide insight into the evolution of the Plasmodium genus as a whole
Plasmodium malariae and P. ovale genomes provide insights into malaria parasite evolution
Changing characteristics of the empathic communication network after empathy-enhancement program for medical students
Visualization of omics data for systems biology
Gehlenborg N, O'Donoghue SI, Baliga NS, et al. Visualization of omics data for systems biology. Nat Methods. 2010;7(3s):S56-S68.High-throughput studies of biological systems are rapidly accumulating a wealth of 'omics'-scale data. Visualization is a key aspect of both the analysis and understanding of these data, and users now have many visualization methods and tools to choose from. The challenge is to create clear, meaningful and integrated visualizations that give biological insight, without being overwhelmed by the intrinsic complexity of the data. In this review, we discuss how visualization tools are being used to help interpret protein interaction, gene expression and metabolic profile data, and we highlight emerging new directions