Biology subjects

Fox, N. C.

Publications and source records attributed to Fox, N. C..

11 recordsLinked to original sources

bioRxiv2024

Unbiased data-driven analysis of five amyloid-beta peptides for biomarker investigations in familial Alzheimer's disease

Structured AbstractO_ST_ABSINTRODUCTIONC_ST_ABSChanges to the relative abundance of amyloid-beta (A{beta}) peptides are hallmarks of Alzheimers disease (AD). iPSC-derived neurons offer a physiological model of A{beta} production. We employed unbiased, data-driven analyses to investigate combinations of A{beta} peptides as AD biomarkers and the relative contribution of peptides to AD pathogenesis. METHODSWe measured A{beta}37, A{beta}38, A{beta}40, A{beta}42 and A{beta}43 in ten iPSC-neuronal cultures from PSEN1 mutation carriers. We combined these data with published cell model data and used linear weighted combinations to 1) distinguish AD from controls, and 2) predict age-at-onset for PSEN1 mutations. RESULTSData-driven approaches distinguished A{beta}42 and A{beta}43 from shorter peptides, providing unbiased evidence for their contribution to disease. Weighted linear combinations of A{beta} peptides outperform A{beta}42/40 and provide insights into relative peptide contribution as biomarkers; the optimal ratio for all data is represented as (21 {middle dot} A{beta}37 + 10 {middle dot} A{beta}38 + 69 {middle dot} A{beta}40)/(94 {middle dot} A{beta}42 + 6 {middle dot} A{beta}43). DISCUSSIONThe algorithm discovered herein can be further refined to improve biomarkers for AD.

neuroscience↗

bioRxiv2024

Spectrum of gamma-Secretase dysfunction as a unifying predictor of ADAD age at onset across PSEN1, PSEN2 and APP causal genes

Autosomal Dominant Alzheimers Disease (ADAD), caused by mutations in Presenilins (PSEN1/2) and Amyloid Precursor Protein (APP) genes, typically manifests before age 65. Age at symptom onset (AAO) is relatively consistent among carriers of the same PSEN1 mutation, but more variable for PSEN2 and APP variants, with these mutations associated with later AAOs than PSEN1. Understanding this clinical variability is crucial for developing predictive models and tailored interventions in ADAD. Biochemical in vitro assessment of {gamma}-secretase function is valuable in evaluating PSEN1 variant pathogenicity, disease onset and progression. Here, we examined A{beta} profiles relationships to AAO across causal genes. Our analysis showed linear correlations between mutation-induced shifts in A{beta} profiles and AAO for PSEN2 and APP mutations. Integration with PSEN1 data revealed parallel but shifted correlations, indicating a common pathogenic mechanism with gene-specific onset timing shifts. Our data support a unified model of ADAD pathogenesis wherein {gamma}-secretase dysfunction and shifts in A{beta} profiles define disease onset. This biochemical analysis of ADAD causality and established quantitative relationships deepen our understanding of ADAD pathogenesis, offering potential for predictive AAO modelling with implications for clinical practice, genetic research and development of therapeutic strategies modulating {gamma}-secretase across ADAD forms and potentially more broadly in AD. SummaryWe examined the relationships between (full) A{beta} peptide profiles and age at symptom onset (AAO) across all Alzheimers disease causal genes. Our findings establish a quantitative framework for mutations pathogenicity assessment and AAO prediction; with implications for clinical practice, genetic counselling, fundamental and translational research.

neuroscience↗

bioRxiv2024

Mutations in PSEN1 predispose inflammation in an astrocyte model of familial Alzheimer's disease through disrupted regulated intramembrane proteolysis

Mutations in PSEN1 cause familial Alzheimers disease with almost complete penetrance. Age at onset is highly variable between different PSEN1 mutations and even within families with the same mutation. Current research into late onset Alzheimers disease implicates inflammation in both disease onset and progression. PSEN1 is the catalytic subunit of {gamma}-secretase, responsible for regulated intramembrane proteolysis of numerous substrates that include cytokine receptors. For this reason, we tested the hypothesis that mutations in PSEN1 impact inflammatory responses in astrocytes, thereby contributing to disease progression. Here, using iPSC-astrocytes, we show that PSEN1 is upregulated in response to inflammatory stimuli, and this upregulation is disrupted by pathological PSEN1 mutations. Using transcriptomic analyses, we demonstrate that PSEN1 mutant astrocytes have an augmented inflammatory profile in their basal state, concomitant with an upregulation of genes coding for regulated intramembrane proteolytic and robust activation of JAK-STAT signalling. Using JAK-STAT2 as an example signalling pathway, we show altered phosphorylation cascades in PSEN1 mutant astrocytes, reinforcing the notion of altered cytokine signalling cascades. Finally, we use small molecule modulators of {gamma}-secretase to confirm a role for PSEN1/{gamma}-secretase in regulating the astrocytic response to inflammatory stimuli. Together, these data suggest that mutations in PSEN1 enhance cytokine signalling via impaired regulated intramembrane proteolysis, thereby predisposing astrocytic inflammatory profiles. These findings support a two-hit contribution of PSEN1 mutations to fAD pathogenesis, not only impacting APP and A{beta} processing but also altering the cellular response to inflammation.

neuroscience↗

bioRxiv2023

Neuroimaging and plasma marker evidence for white matter macrostructure loss in Parkinsons disease

Parkinsons disease (PD) is the second commonest neurodegenerative disorder and over half of patients progress to postural instability, dementia or death within 10 years of diagnosis. However, onset and rate of progression to poor outcomes is highly variable, underpinned by heterogeneity in the underlying pathological process. Improved biomarkers of poor outcomes would be helpful for targeted treatment, but most studies to-date have been limited to a single modality or the assessment of patients with established cognitive impairment. Here, we use multimodal neuroimaging and plasma biomarkers in 98 patients with PD and 28 age-matched controls followed-up over 3 years, including: gray matter (cortical thickness), white matter (macrostructure: fibre-cross section and microstructure: fibre density) at whole-brain and tract level, structural and functional connectivity and plasma levels of neurofilament light chain (NFL) and phosphorylated tau (p-tau) 181. We show extensive reductions in fibre cross-section and structural connectivity in PD with poor outcomes, with preserved gray matter and functional connectivity. NFL, but not p-tau181 levels was increased in PD with poor outcomes and correlated with white matter loss. These findings suggest that imaging sensitive to white matter macrostructure and plasma NFL may be useful biomarkers of poor outcomes in PD. As new targeted treatments are emerging, these biomarkers show important potential to aid patient selection for treatments and improve stratification to clinical trials.

neuroscience↗

bioRxiv2022

Neuroimaging within the Dominantly Inherited Alzheimer's Network (DIAN): PET and MRI

The Dominantly Inherited Alzheimer Network (DIAN) Observational Study is an international collaboration studying autosomal dominant Alzheimer disease (ADAD). This rare form of Alzheimer disease (AD) is caused by mutations in the presenilin 1 (PSEN1), presenilin 2 (PSEN2), or amyloid precursor protein (APP) genes. As individuals from these families have a 50% chance of inheriting the familial mutation, this provides researchers with a well-matched cohort of carriers vs non-carriers for case-control studies. An important trait of ADAD is that the age at symptom onset is highly predictable and consistent for each specific mutation, allowing researchers to estimate an individuals point in their disease time course prior to symptom onset. Although ADAD represents only a small proportion (approximately 0.1%) of all AD cases, studying this form of AD allows researchers to investigate preclinical AD and the progression of changes that occur within the brain prior to AD symptom onset. Furthermore, the young age at symptom onset (typically 30-60 years) means age-related comorbidities are much less prevalent than in sporadic AD, thereby allowing AD pathophysiology to be studied independent of these confounds. A major goal of the DIAN Observational Study is to create a global resource for AD researchers. To that end, the current manuscript provides an overview of the DIAN magnetic resonance imaging (MRI) and positron emission tomography (PET) protocols and highlights the key imaging results of this study to date.

neuroscience↗

bioRxiv2021

The European Prevention of Alzheimer's Dementia (EPAD) MRI Dataset and Processing Workflow

The European Prevention of Alzheimer Dementia (EPAD) is a multi-center study that aims to characterize the preclinical and prodromal stages of Alzheimers Disease. The EPAD imaging dataset includes core (3D T1w, 3D FLAIR) and advanced (ASL, diffusion MRI, and resting-state fMRI) MRI sequences. Here, we give an overview of the semi-automatic multimodal and multisite pipeline that we developed to curate, preprocess, quality control (QC), and compute image-derived phenotypes (IDPs) from the EPAD MRI dataset. This pipeline harmonizes DICOM data structure across sites and performs standardized MRI preprocessing steps. A semi-automated MRI QC procedure was implemented to visualize and flag MRI images next to site-specific distributions of QC features -- i.e. metrics that represent image quality. The value of each of these QC features was evaluated through comparison with visual assessment and step-wise parameter selection based on logistic regression. IDPs were computed from 5 different MRI modalities and their sanity and potential clinical relevance were ascertained by assessing their relationship with biological markers of aging and dementia. The EPAD v1500.0 data release encompassed core structural scans from 1356 participants 842 fMRI, 831 dMRI, and 858 ASL scans. From 1356 3D T1w images, we identified 17 images with poor quality and 61 with moderate quality. Five QC features -- Signal to Noise Ratio (SNR), Contrast to Noise Ratio (CNR), Coefficient of Joint Variation (CJV), Foreground-Background energy Ratio (FBER), and Image Quality Rate (IQR) -- were selected as the most informative on image quality by comparison with visual assessment. The multimodal IDPs showed greater impairment in associations with age and dementia biomarkers, demonstrating the potential of the dataset for future clinical analyses.

neuroscience↗

bioRxiv2021

Plasma amyloid beta ratios in autosomal dominant Alzheimers disease: the influence of genotype

In-vitro studies of autosomal dominant Alzheimers disease (ADAD) implicate longer A{beta} peptides in pathogenesis, however less is known about the behaviour of ADAD mutations in-vivo. In this cross-sectional cohort study, we used liquid chromatography-tandem mass spectrometry to analyse 66 plasma samples from ADAD family members who were at-risk of inheriting a mutation or were already symptomatic. We tested for differences in plasma A{beta}42:38, 38:40 and 42:40 ratios between Presenilin1 (PSEN1) and Amyloid Precursor Protein (APP) carriers. We examined the relationship between plasma and in-vitro models of A{beta} processing and, among PSEN1 carriers, tested for associations with parental age at onset (AAO). 39 participants were mutation carriers (28 PSEN1 and 11 APP). Age- and sex-adjusted models showed marked differences in plasma A{beta} between APP and PSEN1: higher A{beta}42:38 in PSEN1 versus APP (p<0.001) and non-carriers (p<0.001); higher A{beta}38:40 in APP versus PSEN1 (p<0.001) and non-carriers (p<0.001), while A{beta}42:40 was higher in APP and PSEN1 compared to non-carriers (both p<0.001). A{beta} profiles were reasonably consistent in plasma and cell lines. Within PSEN1, sex-adjusted models demonstrated negative associations between (i)A{beta}42:40 (ii)A{beta}42:38 and parental AAO. In-vivo differences in A{beta} processing between APP and PSEN1 provide insights into ADAD pathophysiology which can inform therapy development.

neuroscience↗

bioRxiv2020

Attack behaviour in naïve Gyrfalcons is modelled by the same guidance law as in Peregrines, but at a lower guidance gain

The aerial hunting behaviours of birds are strongly influenced by their flight morphology and ecology, but little is known of how this variation relates to the behavioural algorithms guiding flight. Here we use onboard GPS loggers to record the attack trajectories of captive-bred Gyrfalcons (Falco rusticolus) during their maiden flights against robotic aerial targets, which we compare to existing flight data from Peregrines (Falco peregrinus) The attack trajectories of both species are modelled most economically by a proportional navigation guidance law, which commands turning in proportion to the angular rate of the line-of-sight to target, at a guidance gain N. However, Gyrfalcons operate at significantly lower values of N than Peregrines, producing slower turning and a longer path to intercept. Gyrfalcons are less agile and less manoeuvrable than Peregrines, but this physical constraint is insufficient to explain their lower guidance gain. On the other hand, lower values of N promote the tail-chasing behaviour that is typical of wild Gyrfalcons, and which apparently serves to tire their prey in a prolonged high-speed pursuit. Moreover, during close pursuit of fast evasive prey such as Ptarmigan (Lagopus spp.), proportional navigation will be less prone to being thrown off by erratic target manoeuvres if N is low. The fact that low-gain proportional navigation successfully models the maiden attack flights of Gyrfalcons suggests that this behavioural algorithm is embedded in a hardwired guidance loop, which we hypothesise is ancestral to the clade containing Gyrfalcons and Peregrines. SUMMARY STATEMENTNaive Gyrfalcons attacking aerial targets are modelled by the same proportional navigation guidance law as Peregrines, but with a lower navigation constant that promotes tail-chasing rather than efficient interception.

zoology↗

bioRxiv2018

Integrative pathway enrichment analysis of multivariate omics data

Multi-omics datasets quantify complementary aspects of molecular biology and thus pose challenges to data interpretation and hypothesis generation. ActivePathways is an integrative method that discovers significantly enriched pathways across multiple omics datasets using a statistical data fusion approach, rationalizes contributing evidence and highlights associated genes. We demonstrate its utility by analyzing coding and non-coding mutations from 2,583 whole cancer genomes, revealing frequently mutated hallmark pathways and a long tail of known and putative cancer driver genes. We also studied prognostic molecular pathways in breast cancer subtypes by integrating genomic and transcriptomic features of tumors and tumor-adjacent cells and found significant associations with immune response processes and anti-apoptotic signaling pathways. ActivePathways is a versatile method that improves systems-level understanding of cellular organization in health and disease through integration of multiple molecular datasets and pathway annotations.

bioinformatics↗

bioRxiv2018

Data-driven models of dominantly-inherited Alzheimer’s disease progression

Dominantly-inherited Alzheimers disease is widely hoped to hold the key to developing interventions for sporadic late onset Alzheimers disease. We use emerging techniques in generative data-driven disease-progression modelling to characterise dominantly-inherited Alzheimers disease progression with unprecedented resolution, and without relying upon familial estimates of years until symptom onset (EYO). We retrospectively analysed biomarker data from the sixth data freeze of the Dominantly Inherited Alzheimer Network observational study, including measures of amyloid proteins and neurofibrillary tangles in the brain, regional brain volumes and cortical thicknesses, brain glucose hypometabolism, and cognitive performance from the Mini-Mental State Examination (all adjusted for age, years of education, sex, and head size, as appropriate). Data included 338 participants with known mutation status (211 mutation carriers: 163 PSEN1; 17 PSEN2; and 31 APP) and a baseline visit (age 19-66; up to four visits each, 1{middle dot}1 {+/-} 1{middle dot}9 years in duration; spanning 30 years before, to 21 years after, parental age of symptom onset). We used an event-based model to estimate sequences of biomarker changes from baseline data across disease subtypes (mutation groups), and a differential-equation model to estimate biomarker trajectories from longitudinal data (up to 66 mutation carriers, all subtypes combined). The two models concur that biomarker abnormality proceeds as follows: amyloid deposition in cortical then sub-cortical regions (approximately 24{+/-}11 years before onset); CSF p-tau (17{+/-}8 years), tau and A{beta}42 changes; neurodegeneration first in the putamen and nucleus accumbens (up to 6 {+/-} 2 years); then cognitive decline (7 {+/-} 6 years), cerebral hypometabolism (4 {+/-} 4 years), and further regional neurodegeneration. Our models predicted symptom onset more accurately than EYO: root-mean-squared error of 1{middle dot}35 years versus 5{middle dot}54 years. The models reveal hidden detail on dominantly-inherited Alzheimers disease progression, as well as providing data-driven systems for fine-grained patient staging and prediction of symptom onset with great potential utility in clinical trials.

neuroscience↗

bioRxiv2017

Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference

The heterogeneity of neurodegenerative diseases is a key confound to disease understanding and treatment development, as study cohorts typically include multiple phenotypes on distinct disease trajectories. Here we present a new machine learning technique - Subtype and Stage Inference (SuStaIn) - able to uncover data-driven disease phenotypes with distinct temporal progression patterns, from widely available crosssectional patient studies. Results from imaging studies in two neurodegenerative diseases reveal new subgroups and their distinct trajectories of regional neurodegeneration. In genetic frontotemporal dementia, SuStaIn identifies genotypes from imaging alone, validating its ability to identify subtypes, and characterises within-group heterogeneity for the first time. In Alzheimers disease, SuStaIn uncovers three subtypes, uniquely revealing their temporal complexity. SuStaIn provides fine-grained patient stratification, which substantially enhances the ability to predict conversion between diagnostic categories over standard models that ignore subtype (p=7.18x10--4) or temporal stage (p=3.96x10-5). SuStaIn thus offers new promise for enabling disease subtype discovery and precision medicine.

neuroscience↗