Monday 20 June 2016

Transcriptome analysis of human brain tissue identifies reduced expression of complement complex C1Q Genes in Rett syndrome

Lin P, Nicholls L, Assareh H, Fang Z, Amos TG, Edwards RJ, Assareh AA, Voineagu I (2016): Transcriptome analysis of human brain tissue identifies reduced expression of complement complex C1Q Genes in Rett syndrome. BMC Genomics 17(1):427. doi: 10.1186/s12864-016-2746-7.


BACKGROUND: MECP2, the gene mutated in the majority of Rett syndrome cases, is a transcriptional regulator that can activate or repress transcription. Although the transcription regulatory function of MECP2 has been known for over a decade, it remains unclear how transcriptional dysregulation leads to the neurodevelopmental disorder. Notably, little convergence was previously observed between the genes abnormally expressed in the brain of Rett syndrome mouse models and those identified in human studies.

METHODS: Here we carried out a comprehensive transcriptome analysis of human brain tissue from Rett syndrome brain using both RNA-seq and microarrays.

RESULTS: We identified over two hundred differentially expressed genes, and identified the complement C1Q complex genes (C1QA, C1QB and C1QC) as a point of convergence between gene expression changes in human and mouse Rett syndrome brain.

CONCLUSIONS: The results of our study support a role for alterations in the expression level of C1Q complex genes in RTT pathogenesis.

PMID: 27267200

Wednesday 15 June 2016

Research snapshot: June 2016

Research interests in the Edwards lab stem from a fascination with the molecular basis of evolutionary change and how we can harness the genetic sequence patterns left behind to make useful predictions about contemporary biological systems.

The core research in the lab is the study of Short Linear Motifs (SLiMs), which are short regions of proteins that mediate interactions with other proteins. This research originated with Rich’s postdoctoral research, during which he developed a bioinformatics (sequence analysis) method for rational design of biologically active short peptides. He subsequently developed SLiMDisc, one of the first algorithms for successfully predicting novel SLiMs from sequence data - and coined the term “SLiM” into the bargain - before developing the first SLiM prediction algorithm able to estimate the statistical significance of motif predictions (SLiMFinder), which greatly increased the reliability of predictions. SLiMFinder has since spawned a number of motif discovery tools and webservers and is still arguably the most successful SLiM prediction tool on benchmarking data.

Current research is looking to develop these SLiM prediction tools further and apply them to important biological questions. Of particular interest is the molecular mimicry employed by viruses to interact with host proteins and the role of SLiMs in other diseases, such as cancer. Other work is concerned with the evolutionary dynamics of SLiMs within protein interaction networks.

A new and exciting area of research in the lab is functional genomics with PacBio long-read sequencing. We are collaborating with industrial and academic partners to de novo sequence, assemble, annotate and interrogate the genomes of a selection of microbes with interesting metabolic abilities.

Finally, the lab has a number of interdisciplinary collaborative projects applying bioinformatics tools and molecular evolution theory to experimental biology, often using large genomic, transcriptomic and/or proteomic datasets. These projects often involve the development of bespoke bioinformatics pipelines and a number of open source bioinformatics tools have been generated as a result.