dc.contributor.author	Xiao, Hui
dc.contributor.author	Bartoszek, Krzysztof
dc.contributor.author	Lio’, Pietro
dc.date.accessioned	2018-11-30T07:02:29Z
dc.date.available	2018-11-30T07:02:29Z
dc.date.issued	2018-11-30
dc.date.updated	2018-11-30T07:02:26Z
dc.description.abstract	Abstract Background Inflammation is a core element of many different, systemic and chronic diseases that usually involve an important autoimmune component. The clinical phase of inflammatory diseases is often the culmination of a long series of pathologic events that started years before. The systemic characteristics and related mechanisms could be investigated through the multi–omic comparative analysis of many inflammatory diseases. Therefore, it is important to use molecular data to study the genesis of the diseases. Here we propose a new methodology to study the relationships between inflammatory diseases and signalling molecules whose dysregulation at molecular levels could lead to systemic pathological events observed in inflammatory diseases. Results We first perform an exploratory analysis of gene expression data of a number of diseases that involve a strong inflammatory component. The comparison of gene expression between disease and healthy samples reveals the importance of members of gene families coding for signalling factors. Next, we focus on interested signalling gene families and a subset of inflammation related diseases with multi–omic features including both gene expression and DNA methylation. We introduce a phylogenetic–based multi–omic method to study the relationships between multi–omic features of inflammation related diseases by integrating gene expression, DNA methylation through sequence based phylogeny of the signalling gene families. The models of adaptations between gene expression and DNA methylation can be inferred from pre–estimated evolutionary relationship of a gene family. Members of the gene family whose expression or methylation levels significantly deviate from the model are considered as the potential disease associated genes. Conclusions Applying the methodology to four gene families (the chemokine receptor family, the TNF receptor family, the TGF– β gene family, the IL–17 gene family) in nine inflammation related diseases, we identify disease associated genes which exhibit significant dysregulation in gene expression or DNA methylation in the inflammation related diseases, which provides clues for functional associations between the diseases.
dc.identifier.citation	BMC Bioinformatics. 2018 Nov 30;19(Suppl 15):439
dc.identifier.doi	10.17863/CAM.33463
dc.identifier.uri	https://www.repository.cam.ac.uk/handle/1810/286150
dc.language.iso	eng
dc.language.rfc3066	en
dc.rights.holder	The Author(s)
dc.title	Multi–omic analysis of signalling factors in inflammatory comorbidities
dc.type	Journal Article
rioxxterms.versionofrecord	10.1186/s12859-018-2413-x

Multi–omic analysis of signalling factors in inflammatory comorbidities

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