Biology subjects

Laing, E.

Publications and source records attributed to Laing, E..

3 recordsLinked to original sources

bioRxiv2019

A systematic evaluation of Mycobacterium tuberculosis Genome-Scale Metabolic Networks

The metabolism of the causative agent of TB, Mycobacterium tuberculosis (Mtb) has recently re-emerged as an attractive drug target. A powerful approach to study Mtb metabolism is to use a systems biology framework, such as a Genome-Scale Metabolic Network (GSMN) that allows the dynamic interactions of the many individual components of metabolism to be interrogated together. Several GSMNs networks have been constructed for Mtb and used to study the complex relationship between Mtb genotype and phenotype. However, their utility is hampered by the existence of multiple models of varying properties and performances. Here we systematically evaluate eight recently published metabolic models of Mtb-H37Rv to facilitate model choice. The best performing models, sMtb2018 and iEK1011, were refined and improved for use in future studies by the TB research community.

systems biology↗

bioRxiv2019

Transcriptional signatures of progressive neuropathology in transgenic tau and amyloid mouse models.

The onset and progression of Alzheimers disease (AD) is characterized by increasing intracellular aggregation of hyperphosphorylated tau protein and accumulation of {beta}-amyloid (A{beta}) in the neocortex. Despite recent success in identifying genetic risk factors for AD the transcriptional mechanisms involved in disease progression are not fully understood. We used transgenic mice harbouring human tau (rTg4510) and amyloid precursor protein (J20) mutations to investigate transcriptional changes associated with the development of both tau and amyloid pathology. Using highly-parallel RNA sequencing we profiled transcriptional variation in the entorhinal cortex at four time points identifying robust genotype-associated differences in entorhinal cortex gene expression in both models. We quantified neuropathological burden across multiple brain regions in the same individual mice, identifying widespread changes in gene expression paralleling the development of tau pathology in rTg4510 mice. Differentially expressed transcripts included genes associated with familial AD from genetic studies of human patients, and genes annotated to both common and rare variants identified in GWAS and exome-sequencing studies of late-onset sporadic AD. Systems-level analyses identified discrete co-expression networks associated with the progressive accumulation of tau, with these enriched for genes and pathways previously implicated in the neuro-immunological and neurodegenerative processes driving AD pathology. Finally, we report considerable overlap between tau-associated networks and AD-associated co-expression modules identified in the human cortex. Our data provide further support for an immune-response component in the accumulation of tau, and reveal novel molecular pathways associated with the progression of AD neuropathology.

genomics↗

bioRxiv2018

PREMONition: An algorithm for predicting the circadian clock-regulated molecular network

A transcriptional feedback loop is central to clock function in animals, plants and fungi. The clock genes involved in its regulation are specific to - and highly conserved within - the kingdoms of life. However, other shared clock mechanisms, such as phosphorylation, are mediated by proteins found broadly among living organisms, performing functions in many cellular sub-systems. Use of homology to directly infer involvement/association with the clock mechanism in new, developing model systems, is therefore of limited use. Here we describe the approach PREMONition, PREdicting Molecular Networks, that uses functional relationships to predict molecular circadian clock associations. PREMONition is based on the incorporation of proteins encoded by known clock genes (when available), rhythmically expressed clock-controlled genes and non-rhythmically expressed but interacting genes into a cohesive network. After tuning PREMONition on the networks derived for human, fly and fungal circadian clocks, we deployed the approach to predict a molecular clock network for Saccharomyces cerevisiae, for which there are no readily-identifiable clock gene homologs. The predicted network was validated using gene expression data and a growth assay for sensitivity to light, a zeitgeber of circadian clocks of most organisms. PREMONition may be used to identify candidate clock-regulated processes and thus candidate clock genes in other organisms.

bioinformatics↗