A Distance-Based Test of Association Between Paired Heterogeneous Genomic Data

Due to rapid technological advances, a wide range of different measurements can be obtained from a given biological sample including single nucleotide polymorphisms, copy number variation, gene expression levels, DNA methylation and proteomic profiles. Each of these distinct measurements provides the means to characterize a certain aspect of biological diversity, and a fundamental problem of broad interest concerns the discovery of shared patterns of variation across different data types. Such data types are heterogeneous in the sense that they represent measurements taken at very different scales or described by very different data structures. We propose a distance-based statistical test, the generalized RV (GRV) test, to assess whether there is a common and non-random pattern of variability between paired biological measurements obtained from the same random sample. The measurements enter the test through distance measures which can be chosen to capture particular aspects of the data. An approximate null distribution is proposed to compute p-values in closed-form and without the need to perform costly Monte Carlo permutation procedures. Compared to the classical Mantel test for association between distance matrices, the GRV test has been found to be more powerful in a number of simulation settings. We also report on an application of the GRV test to detect biological pathways in which genetic variability is associated to variation in gene expression levels in ovarian cancer samples, and present results obtained from two independent cohorts

The coordinate actions of calcineurin and Hog1 mediate the stress response through multiple nodes of the cell cycle network

Upon exposure to environmental stressors, cells transiently arrest the cell cycle while they adapt and restore homeostasis. A challenge for all cells is to distinguish between stress signals and coordinate the appropriate adaptive response with cell cycle arrest. Here we investigate the role of the phosphatase calcineurin (CN) in the stress response and demonstrate that CN activates the Hog1/p38 pathway in both yeast and human cells. In yeast, the MAPK Hog1 is transiently activated in response to several well-studied osmostressors. We show that when a stressor simultaneously activates CN and Hog1, CN disrupts Hog1-stimulated negative feedback to prolong Hog1 activation and the period of cell cycle arrest. Regulation of Hog1 by CN also contributes to inactivation of multiple cell cycle-regulatory transcription factors (TFs) and the decreased expression of cell cycle-regulated genes. CN-dependent downregulation of G1/S genes is dependent upon Hog1 activation, whereas CN inactivates G2/M TFs through a combination of Hog1-dependent and -independent mechanisms. These findings demonstrate that CN and Hog1 act in a coordinated manner to inhibit multiple nodes of the cell cycle-regulatory network. Our results suggest that crosstalk between CN and stress-activated MAPKs helps cells tailor their adaptive responses to specific stressors

Modeling complex flow structures and drag around a submerged plant of varied posture

Although vegetation is present in many rivers, the bulk of past work concerned with modeling the influence of vegetation on flow has considered vegetation to be morphologically simple and has generally neglected the complexity of natural plants. Here we report on a combined flume and numerical model experiment which incorporates time-averaged plant posture, collected through terrestrial laser scanning, into a computational fluid dynamics model to predict flow around a submerged riparian plant. For three depth-limited flow conditions (Reynolds number = 65,000–110,000), plant dynamics were recorded through high-definition video imagery, and the numerical model was validated against flow velocities collected with an acoustic Doppler velocimeter. The plant morphology shows an 18% reduction in plant height and a 14% increase in plant length, compressing and reducing the volumetric canopy morphology as the Reynolds number increases. Plant shear layer turbulence is dominated by Kelvin-Helmholtz type vortices generated through shear instability, the frequency of which is estimated to be between 0.20 and 0.30 Hz, increasing with Reynolds number. These results demonstrate the significant effect that the complex morphology of natural plants has on in-stream drag, and allow a physically determined, species-dependent drag coefficient to be calculated. Given the importance of vegetation in river corridor management, the approach developed here demonstrates the necessity to account for plant motion when calculating vegetative resistance

Flow-vegetation interactions at the plant-scale: the importance of volumetric canopy morphology on flow field dynamics

Vegetation is abundant in rivers, and has a significant influence on their hydraulic, geomorphological, and ecological functioning. However, past modelling of the influence of vegetation has generally neglected the complexity of natural plants. This thesis develops a novel numerical representation of flow through and around floodplain and riparian vegetation, focusing on flow-vegetation interactions at the plant-scale. The plant volumetric canopy morphology, which comprises the distribution of vegetal elements over the three-dimensional plant structure, is accurately captured at the millimetre scale spatial resolution using Terrestrial Laser Scanning (TLS), and incorporated into a Computational Fluid Dynamics (CFD) model used to predict flow. Numerical modelling, with vegetation conceptualised as a porous blockage, is used to improve the process-understanding of flow-vegetation interactions. Model predictions are validated against flume experiments, with plant motion dynamics investigated, and analysis extended to consider turbulent flow structures and the plant drag response. Results demonstrate the spatially heterogeneous velocity fields associated with plant volumetric canopy morphology. The presence of leaves, in addition to the posture and aspect of the plant, significantly modifies flow field dynamics. New insights into flow-vegetation interactions include the control of plant porosity, influencing ‘bleed-flow’ through the plant body. As the porosity of the plant reduces, and bleed-flow is prevented, the volume of flow acceleration increases by up to ~150%, with more sub-canopy flow diverted beneath the impermeable plant blockage. Species-dependent drag coefficients are quantified; these are shown to be dynamic as the plant reconfigures, differing from the commonly assigned value of unity, and for the species’ investigated in this thesis range between 0.95 and 2.92. The newly quantified drag coefficients are used to re-evaluate vegetative flow resistance, and the physically-determined Manning’s n values calculated are highly applicable to conveyance estimators and industry standard hydraulic models used in the management of the river corridor

DNA methylation as a biomarker for age-related cognitive impairment

PhD ThesisDue to the ageing population, the number of patients diagnosed with age-related diseases such as stroke and Parkinson’s disease are on the rise. In both post-stroke dementia (PSD) and mild cognitive impairment in Parkinson’s disease (PD-MCI), the mechanisms resulting in cognitive decline are unknown. This project aims to identify a biomarker which could predict those patients most at risk of developing cognitive decline, which would subsequently assist healthcare professionals in recommending early treatment and care. Epigenetics is an emerging field in which biomarkers have previously been useful in prognostication of cancers and prediction of cardiovascular disease. In this study, 30 patients from a PSD cohort (COGFAST) and 48 patients from a PD-MCI cohort (ICICLE) were analysed using the Illumina HumanMethylation450 BeadChip to identify differentially methylated positions which could predict patients who would later develop cognitive decline. Top hits were validated using Pyrosequencing to confirm DNA methylation differences in a replication cohort. Individual CpG sites within APOB and NGF were identified as potential blood-based biomarkers for PSD and one CpG site within CHCHD5 was highlighted as a potential blood-based biomarker for PD-MCI. In addition, methylation at one CpG site within NGF and a CpG site (cg18837178) within a non-coding RNA, were found to be associated with Braak staging (degree of brain pathology) using DNA from two brain regions. NGF deregulation has previously been associated with Alzheimer’s disease, and this finding indicates it may also have a role in the development of PSD. These novel findings represent the first steps towards the identification of blood-based biomarkers to assist with diagnosis of PSD and PD-MCI, but require further validation in a larger independent cohort. The differentially methylated genes identified may also give insight into some of the mechanisms involved in these complex diseases, potentially leading to the future development of targeted preventative treatments.Medical Research Council and Newcastle Universit

Distance-based differential analysis of gene curves

Motivation: Time course gene expression experiments are performed to study time-varying changes in mRNA levels of thousands of genes. Statistical methods from functional data analysis (FDA) have recently gained popularity for modelling and exploring such time courses. Each temporal profile is treated as the realization of a smooth function of time, or curve, and the inferred curve becomes the basic unit of statistical analysis. The task of identifying genes with differential temporal profiles then consists of detecting statistically significant differences between curves, where such differences are commonly quantified by computing the area between the curves or the l2 distance. Results: We propose a general test statistic for detecting differences between gene curves, which only depends on a suitably chosen distance measure between them. The test makes use of a distance-based variance decomposition and generalizes traditional MANOVA tests commonly used for vectorial observations. We also introduce the visual l2 distance, which is shown to capture shape-related differences in gene curves and is robust against time shifts, which would otherwise inflate the traditional l2 distance. Other shape-related distances, such as the curvature, may carry biological significance. We have assessed the comparative performance of the test on realistically simulated datasets and applied it to human immune cell responses to bacterial infection over time