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Sarah Langley

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Nanyang Assistant Professor Sarah Langley 
Nanyang Assistant Professor
Principal Investigator, Integrative Biology of Disease


Assistant Professor Sarah Raye Langley is a Nanyang Assistant Professor in LKCMedicine, and an awardee of the 2018 Nanyang Assistant Professorship. She obtained her PhD in Bioinformatics and Statistical Genetics and her MSc in Bioinformatics and Theoretical Systems Biology from Imperial College London and her BA in Physics and Mathematical Sciences from Colby College. Prior to joining LKCMedicine, she worked in the areas of systems genetics and genomics at Duke-NUS Medical School and Imperial College London and in the area of vascular proteomics at the BHF Centre of Excellence, King’s College London. 

Asst Prof Langley is a computational biologist whose research focuses on using large-scale omics data to understand dysregulated transcriptional and translational processes in human disease, with a focus on neurological and cardiometabolic disorders. She has contributed to more than 30 publications in journals such as JCI, Nature Neuroscience, and Nature and is involved in several international and domestic collaborations.

Research Focus 

We use a combination of high-throughput omics data to study dysregulated transcriptional and translational processes in human disease. The lab has a focus on neurological disorders (e.g. Huntington disease, Alzheimer’s disease, epilepsy) but also works in the areas of cardiovascular and metabolic disease (atherosclerosis, hypertension, diabetes). There are three broad areas of investigation - disease mechanisms, pharmaco -genomics and -transcriptomics, and computational methods development.

Disease mechanisms

To investigate disease mechanisms and dysregulated molecular processes, we utilize large scale omics datasets – primarily DNA- and RNA-sequencing and mass spectrometry proteomics –coupled with cutting edge analytical techniques. By integrating these different omics datasets in systems-level approach, we aim to interrogate the role that molecular processes, such as alternative splicing or ribosomal frameshifting, play in the development and progression of human disease. 

Pharmaco-genomics and -transcriptomics 

We investigate the interactions between molecular phenotypes and small molecule/drug perturbations through the use of genetic-drug response association studies and large-scale transcriptomic screens. By elucidating these interactions, our goal is to understand the effect of genetic backgrounds on drug response in diseases with current treatment options as well as to identify putative compounds for repurposing efforts for those diseases with poor or no therapeutic options. 

Computational Methods Development

We also develop computational methodologies and pipelines for elucidating insight into large biological datasets. The selection of these methodologies for development is determined by the biological questions of interest and our current ability to answer them. This involves a combination of statistics, programming, machine learning and the use of high-performance computing resources.

Selected Publications

Tan ALM, Langley SR, et al. Ethnicity-specific skeletal muscle transcriptional signatures and their relevance to insulin resistance in Singapore. JCEM. 2018. Advance Access. doi:10.1210/jc.2018-00309.

Langley SR, Willeit K, Didangelos A, et al. Extracellular matrix proteomics identifies molecular signature of symptomatic carotid plaques. JCI. 2017. 127(4):1546. doi:10.1172/JCI86924 

Delahaye-Duriez A, Shkura K, Langley SR, et al. Rare and common epilepsies converge to a single gene regulatory network: opportunities for novel antiepileptic drug discovery. Genome Biol. 2016. 17(1): 245. doi:10.1186/s13059-016-1097-7 

Johnson MR, Shkura K, Langley SR, et al. Systems genetics identifies a convergent gene network for cognition and neurodevelopmental disease. Nat. Neurosci. 2016. 19(2): 223-232. doi:10.1038/nn.4205 

Langley SR and Mayr M. Comparative analysis of statistical methods used for detecting differential expression in label-free mass spectrometry proteomics. J. Proteomics. 2015. 129:83-92. doi:10.1016/j.jprot.2015.07.012 

Heinig M, Petretto E, Wallace C, Bottolo L, Rotival M, Lu H, Li Y, Sarwar R, Langley SR, et al. A trans-acting locus regulates an anti-viral expression network and type 1 diabetes risk. Nature. 2010. 467(7314): 460-464. doi:10.1038/nature09386 

Nishihara E, Tsaih S, Tsukahara C, Langley S, et al. Quantitative trait loci associated with blood pressure of metabolic syndrome in the progeny of NZO/HILtJ x C3H/HeJ intercrosses. Mamm. Genome. 2007. 18(8): 573-583. doi:10.1007/s00335-007-9033-5  

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