Biology subjects

Kindalova, P.

Publications and source records attributed to Kindalova, P..

5 recordsLinked to original sources

bioRxiv2025

A CRISPR-CAS9 high throughput machine-learning platform for modulation of genes involved in Parkinson's disease-associated PINK1-mitophagy in iPSC-derived dopaminergic neurons

Parkinsons disease (PD) is a progressive neurodegenerative disorder characterised by the loss of dopaminergic neurons, driven by complex molecular mechanisms that are not fully understood. To address this issue, we have developed a novel high-content phenotypic screening platform using human induced pluripotent stem cell-derived dopaminergic neurons to investigate the PINK1-PARKIN mitophagy pathway, a critical process in PD pathogenesis. Utilising high throughput, 384 well arrayed CRISPR-CAS9 genetic manipulation and high-content immunofluorescence imaging complemented with machine learning analysis, we examined ubiquitin (Ub) pSer65 levels. Ub pSer65, a potential PD clinical biomarker, is a key marker of mitophagy initiation in dopaminergic neurons upon mitophagy initiation using exogenous stimuli to mimic the disease relevant environment. The CRISPR-CAS9 knockout (KO) screen revealed two distinct phenotypic classes: essential genes causing cell death upon deletion, and genes modulating Ub pSer65 levels. Notably, KO of PINK1, PARKIN, and TOM7 genes decreased Ub pSer65 upregulation during mitophagy activation, confirming their established roles in the pathway and validating the suitability of the platform for target identification. This innovative platform provides a precise tool to further interrogate PD-associated genes, offering insights into mitophagy-related pathogenic mechanisms and identification of potential therapeutic targets. By bridging functional genomics with disease-specific neuronal models, this approach presents a promising strategy for advancing PD research and developing targeted interventions. To our knowledge, this is the first reported use of a human, translationally relevant cell model to study genetic perturbation within a disease relevant phenotype.

neuroscience↗

bioRxiv2021

Penalized generalized estimating equations for relative risk regression with applications to brain lesion data

Motivated by a brain lesion application, we introduce penalized generalized estimating equations for relative risk regression for modelling correlated binary data. Brain lesions can have varying incidence across the brain and result in both rare and high incidence outcomes. As a result, odds ratios estimated from generalized estimating equations with logistic regression structures are not necessarily directly interpretable as relative risks. On the other hand, use of log-link regression structures with the binomial variance function may lead to estimation instabilities when event probabilities are close to 1. To circumvent such issues, we use generalized estimating equations with log-link regression structures with identity variance function and unknown dispersion parameter. Even in this setting, parameter estimates can be infinite, which we address by penalizing the generalized estimating functions with the gradient of the Jeffreys prior. Our findings from extensive simulation studies show significant improvement over the standard log-link generalized estimating equations by providing finite estimates and achieving convergence when boundary estimates occur. The real data application on UK Biobank brain lesion maps further reveals the instabilities of the standard log-link generalized estimating equations for a large-scale data set and demonstrates the clear interpretation of relative risk in clinical applications.

neuroscience↗

bioRxiv2021

Voxel-wise and spatial modelling of binary lesion masks: A review and comparison of methods

ObjectivesWhite matter lesions are a very common finding on MRI in older adults and their presence increases the risk of stroke and dementia. Accurate and computationally efficient modelling methods are necessary to map the association of lesion incidence with risk factors, such as hypertension. However, there is no consensus in the brain mapping literature whether a voxel-wise modelling approach is better for binary lesion data than a more computationally intensive spatial modelling approach that accounts for voxel dependence. MethodsWe review three regression approaches for modelling binary lesion masks including massunivariate probit regression modelling with either maximum likelihood estimates, or mean bias-reduced estimates, and spatial Bayesian modelling, where the regression coefficients have a conditional autoregressive model prior to account for local spatial dependence. We design a novel simulation framework of artificial lesion maps to compare the three alternative lesion mapping methods. The age effect on lesion probability estimated from a reference data set (13,680 individuals from the UK Biobank) is used to simulate a realistic voxel-wise distribution of lesions across age. To mimic the real features of lesion masks, we propose matching brain lesion summaries (total lesion volume, average lesion size and lesion count) across the reference data set and the simulated data sets. Thus, we allow for a fair comparison between the modelling approaches, under a realistic simulation setting. ResultsOur findings suggest that bias-reduced estimates for voxel-wise binary-response generalized linear models (GLMs) overcome the drawbacks of infinite and biased maximum likelihood estimates and scale well for large data sets because voxel-wise estimation can be performed in parallel across voxels. Contrary to the assumption of spatial dependence being key in lesion mapping, our results show that voxel-wise bias-reduction and spatial modelling result in largely similar estimates. ConclusionBias-reduced estimates for voxel-wise GLMs are not only accurate but also computationally efficient, which will become increasingly important as more biobank-scale neuroimaging data sets become available.

neuroscience↗

bioRxiv2020

Spatial distribution and cognitive impact of cerebrovascular risk-related white matter hyperintensities

ObjectivesWhite matter hyperintensities (WMHs) are considered macroscale markers of cerebrovascular burden and are associated with increased risk of vascular cognitive impairment and dementia. However, the spatial location of WMHs has typically been considered in broad categories of periventricular versus deep white matter. The spatial distribution of WHMs associated with individual cerebrovascular risk factors (CVR), controlling for frequently comorbid risk factors, has not been systematically investigated at the population level in a healthy ageing cohort. Furthermore, there is an inconsistent relationship between total white matter hyperintensity load and cognition, which may be due to the confounding of several simultaneous risk factors in models based on smaller cohorts. MethodsWe examined trends in individual CVR factors on total WMH burden in 13,680 individuals (aged 45-80) using data from the UK Biobank. We estimated the spatial distribution of white matter hyperintensities associated with each risk factor and their contribution to explaining total WMH load using voxel-wise probit regression and univariate linear regression. Finally, we explored the impact of CVR-related WMHs on speed of processing using regression and mediation analysis. ResultsContrary to the assumed dominance of hypertension as the biggest predictor of WMH burden, we show associations with a number of risk factors including diabetes, heavy smoking, APOE{varepsilon} 4/{varepsilon}4 status and high waist-to-hip ratio of similar, or greater magnitude to hypertension. The spatial distribution of WMHs varied considerably with individual cerebrovascular risk factors. There were independent effects of visceral adiposity, as measured by waist-to-hip ratio, and carriage of the APOE{varepsilon} 4 allele in terms of the unique spatial distribution of CVR-related WMHs. Importantly, the relationship between total WMH load and speed of processing was mediated by waist-to-hip ratio suggesting cognitive consequences to WMHs associated with excessive visceral fat deposition. ConclusionWaist-to-hip ratio, diabetes, heavy smoking, hypercholesterolemia and homozygous APOE{varepsilon} 4 status are important risk factors, beyond hypertension, associated with WMH total burden and warrant careful control across ageing. The spatial distribution associated with different risk factors may provide important clues as to the pathogenesis and cognitive consequences of WMHs. High waist-to-hip ratio is a key risk factor associated with slowing in speed of processing. With global obesity levels rising, focused management of visceral adiposity may present a useful strategy for the mitigation of cognitive decline in ageing.

neuroscience↗

bioRxiv2018

Modelling the distribution of white matter hyperintensities due to ageing on MRI images using Bayesian inference

White matter hyperintensities (WMH), also known as white matter lesions, are localised white matter areas that appear hyperintense on MRI scans. WMH commonly occur in the ageing population, and are often associated with several factors such as cognitive disorders, cardiovascular risk factors, cerebrovascular and neurodegenerative diseases. Despite the fact that some links between lesion location and parametric factors such as age have already been established, the relationship between voxel-wise spatial distribution of lesions and these factors is not yet well understood. Hence, it would be of clinical importance to model the distribution of lesions at the population-level and quantitatively analyse the effect of various factors on the lesion distribution model.\n\nIn this work we compare various methods, including our proposed method, to generate voxel-wise distributions of WMH within a population with respect to various factors. Our proposed Bayesian spline method models the spatio-temporal distribution of WMH with respect to a parametric factor of interest, in this case age, within a population. Our probabilistic model takes as input the lesion segmentation binary maps of subjects belonging to various age groups and provides a population-level parametric lesion probability map as output. We used a spline representation to ensure a degree of smoothness in space and the dimension associated with the parameter, and formulated our model using a Bayesian framework.\n\nWe tested our algorithm output on simulated data and compared our results with those obtained using various existing methods with different levels of algorithmic and computational complexity. We then compared the better performing methods on a real dataset, consisting of 1000 subjects of the UK Biobank, divided in two groups based on hypertension diagnosis. Finally, we applied our method on a clinical dataset of patients with vascular disease.\n\nOn simulated dataset, the results from our algorithm showed a mean square error (MSE) value of 7.27 x 10-5, which was lower than the MSE value reported in the literature, with the advantage of being robust and computationally efficient. In the UK Biobank data, we found that the lesion probabilities are higher for the hypertension group compared to the non-hypertension group and further verified this finding using a statistical t-test. Finally, when applying our method on patients with vascular disease, we observed that the overall probability of lesions is significantly higher in later age groups, which is in line with the current literature.

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