Biology subjects

Young, A. L.

Publications and source records attributed to Young, A. L..

14 recordsLinked to original sources

bioRxiv2025

JAK2V617F Myeloproliferative Neoplasms Support Parallel Evolution of Independent Leukemic Clones

Myeloproliferative neoplasms (MPNs) are hematological diseases predominantly driven by the JAK2V617F mutation. Progression from chronic-phase MPN to secondary acute myeloid leukemia (sAML) is a severe complication that dramatically worsens disease prognosis. While progression to sAML is classically linked to MPN clones acquiring additional mutations, the absence of JAK2V617F in some cases of post-MPN sAML cases suggests alternative mechanisms of transformation. Utilizing patient samples and in vivo modeling, we establish that leukemic clones can emerge independently of JAK2-mutant cells and undergo positive selection in the pro-inflammatory MPN environment, leading to parallel disease evolution. Genetic and pharmacological inhibition of IL-12 and TNF mitigates this competitive advantage. Our data establish a new paradigm and show that disease progression in MPN can arise from parallel acute myeloid leukemia (pAML) clones.

cancer biology↗

bioRxiv2025

Infant CD4 T-cell response to SARS-CoV-2 mRNA vaccination is restricted in cytokine production and modified by vaccine manufacturer

BACKGROUNDSafe and effective vaccines are a key preventative measure to protect infants from SARS-CoV-2 infection and disease. Although mRNA vaccines induce robust antibody titers in infants, little is known about the quality of CD4 T-cell responses induced by vaccination. CD4 T-cell responses are important in orchestrating coordinated immune responses during infection and may help to limit disease severity. METHODS: To characterize the CD4 T-cell response to SARS-CoV-2 mRNA vaccination in infants, we sampled blood from 13 infants before and after primary SARS-CoV-2 mRNA vaccine series; samples from 12 historical vaccinated adults were used for comparisons. PBMC were stimulated with Spike peptide pools and the ability of CD4 T-cells to secrete Th1, Th2, and Th17 cytokines was quantified. A measure of polyfunctionality was generated using the COMPASS algorithm. RESULTS: We observed a significant increase in CD4 T-cells producing IL-2 (0.01% vs. 0.08%, p=0.04) and TNF- (0.007% vs. 0.07%, p=0.007) following vaccination in infants but a more muted induction of IFN-{gamma} production (0.01% vs 0.04%, p=0.08). This contrasted with adults, in whom vaccination induced robust production of IFN-{gamma}, IL-2, and TNF-. Th2 and Th17 responses were limited in both infants and adults. In infants, CD4 T-cell responses post-vaccination were greater in those who received mRNA-1273 versus BNT162b. In contrast to CD4 T-cell responses, Spike-specific IgG titers were similar in infants and adults. CONCLUSIONS: These data suggest that infants have restricted induction of cytokine producing CD4 T-cells following SARS-CoV-2 mRNA vaccination relative to adults.

immunology↗

bioRxiv2024

POLYGENIC DETERMINANTS OF H5N1 ADAPTATION TO BOVINE CELLS

Avian influenza H5N1 clade 2.3.4.4b viruses caused a global panzootic and, unexpectedly, widespread outbreaks in dairy cattle, therefore representing a pandemic threat. To inform effective control strategies, it is critical to determine whether the potential to adapt to bovine cells is a generalised feature of H5N1 viruses, or is specific to clade 2.3.4.4b, or even more restricted to specific genotypes within this clade (e.g., B3.13 and D1.1). Using a large panel of H5N1 viruses representing >60 years of their natural history and other IAV for comparative purposes, we demonstrate that virus adaptation to bovine cells is: (i) highly variable across 2.3.4.4b genotypes, (ii) limited in viruses predating the global expansion of this clade, (iii) determined by the viral internal gene cassette, and (iv) not restricted to udder epithelial cells. Mutations in the PB2 polymerase subunit, particularly M631L, emerge as key determinants of adaptation, although their phenotypic effects are context dependent and have limited enhanced viral polymerase activity in human cells. Bovine B3.13 and some avian genotypes also exhibit enhanced modulation of bovine interferon-induced antiviral responses, determined by at least the viral PB2, nucleoprotein, and the non-structural protein NS1. Our results highlight the polygenic nature of IAV host range and reveal that the potential to cross the species barrier varies during the evolutionary trajectory of H5N1, with some avian viruses more predisposed to spillover than others.

microbiology↗

bioRxiv2023

Disease progression modelling reveals heterogeneity in trajectories of Lewy-type alpha-synuclein pathology

Lewy body (LB) disorders, characterized by the aggregation of misfolded -synuclein proteins, exhibit notable clinical heterogeneity. This may be due to variations in accumulation patterns of LB neuropathology. By applying data-driven disease progression modelling to regional neuropathological LB density scores from 814 brain donors, we describe three inferred trajectories of LB pathology that were characterized by differing clinicopathological presentation and longitudinal antemortem clinical progression. Most donors (81.9%) showed earliest pathology in the olfactory bulb, followed by accumulation in either limbic (60.8%) or brainstem (21.1%) regions. The remaining donors (18.1%) exhibited the first abnormalities in brainstem regions. Early limbic pathology was associated with Alzheimers disease-associated characteristics. Meanwhile, brainstem-first pathology was associated with progressive motor impairment and substantial LB pathology outside of the brain. Our data provides evidence for heterogeneity in the temporal spread of LB pathology, possibly explaining some of the clinical disparities observed in LBDs.

pathology↗

bioRxiv2023

Surveillance of Vermont wildlife in 2021-2022 reveals no detected SARS-CoV-2 viral RNA

Previous studies have documented natural infections of SARS-CoV-2 in various domestic and wild animals. More recently, studies have been published noting the susceptibility of members of the Cervidae family, and infections in both wild and captive cervid populations. In this study, we investigated the presence of SARS-CoV-2 in mammalian wildlife within the state of Vermont. 739 nasal or throat samples were collected from wildlife throughout the state during the 2021 and 2022 harvest season. Data was collected from red and gray foxes (Vulpes vulples and Urocyon cineroargentus, respectively), fishers (Martes pennati), river otters (Lutra canadensis), coyotes (Canis lantrans), bobcats (Lynx rufus rufus), black bears (Ursus americanus), and white-tailed deer (Odocoileus virginianus). Samples were tested for the presence of SARS-CoV-2 via quantitative RT-qPCR using the CDC N1/N2 primer set and/or the WHO-E gene primer set. Our results indicate that no sampled wildlife were positive for SARS-CoV-2. This finding is surprising, given that most published North America studies have found SARS-CoV-2 within their deer populations. The absence of SARS-CoV-2 RNA in populations sampled here may provide insights in to the various environmental and anthropogenic factors that reduce spillover and spread in North Americans wildlife populations.

microbiology↗

bioRxiv2023

Tumour mitochondrial DNA mutations drive aerobic glycolysis to enhance checkpoint blockade

The mitochondrial genome encodes essential machinery for respiration and metabolic homeostasis but is paradoxically among the most common targets of somatic mutation in the cancer genome, with truncating mutations in respiratory complex I genes being most over-represented1. While mitochondrial DNA (mtDNA) mutations have been associated with both improved and worsened prognoses in several tumour lineages1-,3, whether these mutations are drivers or exert any functional effect on tumour biology remains controversial. Here we discovered that complex I-encoding mtDNA mutations are sufficient to remodel the tumour immune landscape and therapeutic resistance to immune checkpoint blockade. Using mtDNA base editing technology4 we engineered recurrent truncating mutations in the mtDNA-encoded complex I gene, Mt-Nd5, into murine models of melanoma. Mechanistically, these mutations promoted utilisation of pyruvate as a terminal electron acceptor and increased glycolytic flux without major effects on oxygen consumption, driven by an over-reduced NAD pool and NADH shuttling between GAPDH and MDH1, mediating a Warburg-like metabolic shift. In turn, without modifying tumour growth, this altered cancer cell-intrinsic metabolism reshaped the tumour microenvironment in both mice and humans, promoting an anti- tumour immune response characterised by loss of resident neutrophils. This subsequently sensitised tumours bearing high mtDNA mutant heteroplasmy to immune checkpoint blockade, with phenocopy of key metabolic changes being sufficient to mediate this effect. Strikingly, patient lesions bearing >50% mtDNA mutation heteroplasmy also demonstrated a >2.5-fold improved response rate to checkpoint inhibitor blockade. Taken together these data nominate mtDNA mutations as functional regulators of cancer metabolism and tumour biology, with potential for therapeutic exploitation and treatment stratification.

cancer biology↗

bioRxiv2021

pySuStaIn: a Python implementation of the Subtype and Stage Inference algorithm

Progressive disorders are highly heterogeneous. Symptom-based clinical classification of these disorders may not reflect the underlying pathobiology. Data-driven subtyping and staging of patients has the potential to disentangle the complex spatiotemporal patterns of disease progression. Tools that enable this are in high demand from clinical and treatment-development communities. Here we describe the pySuStaIn software package, a Python-based implementation of the Subtype and Stage Inference (SuStaIn) algorithm. SuStaIn unravels the complexity of heterogeneous diseases by inferring multiple disease progression patterns (subtypes) and individual severity (stages) from cross-sectional data. The primary aims of pySuStaIn are to enable widespread application and translation of SuStaIn via an accessible Python package that supports simple extension and generalization to novel modelling situations within a single, consistent architecture. Code metadataCurrent code version v1.0 Permanent link to code/repository used of this code version https://github.com/ucl-pond/pySuStaIn Legal Code License MIT Code versioning system used git Software code languages, tools, and services used Python Compilation requirements, operating environments & dependencies Linux, Mac, Windows Support email for questions leon.aksman@loni.usc.edu, p.wijeratne@ucl.ac.uk, alexandra.young@kcl.ac.uk

neuroscience↗

bioRxiv2020

Tau-first subtype of Alzheimer's disease consistently identified across in vivo and post mortem studies

Alzheimers disease (AD) is marked by the spread of misfolded amyloid-{beta} and tau proteins throughout the brain. While it is commonly believed that amyloid-{beta} abnormality drives the cascade of AD pathogenesis, several in vivo and post mortem studies indicate that in some subjects localized tau-based neurofibrillary tangles precede amyloid-{beta} pathology. This suggests that there may be multiple distinct subtypes of protein aggregation pathways within AD, with potentially different demographic, cognitive and comorbidity profiles. We investigated this hypothesis, applying data-driven disease progression subtyping models to post mortem immunohistochemistry and in vivo positron emission tomography (PET) and cerebrospinal fluid (CSF) based measures of protein pathologies in two large observational cohorts. We consistently identified both amyloid-first and tau-first AD subtypes, where tau-first subjects had higher levels of soluble TREM2 compared to amyloid-first subjects. Our work provides insight into AD progression that may be valuable for interventional trials targeting amyloid-{beta} and tau.

neuroscience↗

bioRxiv2019

Oncologic Therapy Shapes the Fitness Landscape of ClonalHematopoiesis

Clonal hematopoiesis (CH) is frequent in cancer patients and associated with increased risk of therapy related myeloid neoplasms (tMN). To define the relationship between CH, oncologic therapy, and tMN progression, we studied 24,439 cancer patients. We show that previously treated patients have increased rates of CH, with enrichment of mutations in DNA Damage Response (DDR) genes (TP53, PPM1D, CHEK2). Exposure to radiation, platinum and topoisomerase II inhibitors have the strongest association with CH with evidence of dose-dependence and gene-treatment interactions. We validate these associations in serial sampling from 525 patients and show that exposure to cytotoxic and radiation therapy imparts a selective advantage specifically in hematopoietic cells with DDR mutations. In patients who progressed to tMN, the clone at CH demarcated the dominant clone at tMN diagnosis. CH mutational features predict risk of therapy-related myeloid neoplasm in solid tumor patients with clinical implications for early detection and treatment decisions.

genetics↗

bioRxiv2019

The Evolutionary Dynamics and Fitness Landscape of Clonal Haematopoiesis

Somatic mutations acquired in healthy tissues as we age are major determinants of cancer risk. Whether variants confer a fitness advantage or rise to detectable frequencies by chance, however, remains largely unknown. Here, by combining blood sequencing data from [~]50,000 individuals, we reveal how mutation, genetic drift and fitness differences combine to shape the genetic diversity of healthy blood ( clonal haematopoiesis). By analysing the spectrum of variant allele frequencies we quantify fitness advantages for key pathogenic variants and genes and provide bounds on the number of haematopoietic stem cells. Positive selection, not drift, is the major force shaping clonal haematopoiesis. The remarkably wide variation in variant allele frequencies observed across individuals is driven by chance differences in the timing of mutation acquisition combined with differences in the cell-intrinsic fitness effect of variants. Contrary to the widely held view that clonal haematopoiesis is driven by ageing-related alterations in the stem cell niche, the data are consistent with the age dependence being driven simply by continuing risk of mutations and subsequent clonal expansions that lead to increased detectability at older ages.

genomics↗

bioRxiv2018

Non-Parametric Mixture Modelling and its Application to Disease Progression Modelling

Dementia is characterised by its progressive degeneration of cognitive abilities. In research cohorts, detailed neuropsychological test batteries are often administered to better understand how cognition changes over time. Understanding cognitive changes in dementia is of great importance, particularly in determining how structural changes in the brain may affect cognition and in facilitating earlier detection of symptomatic changes. Disease progression models are often applied to these data to understand how a disease changes over time from cross-sectional data or to disease trajectories from large numbers of individuals. Previous disease progression models used to build longitudinal models from cross-sectional data have focused on brain imaging data; however, these models are not directly applicable to cognitive data. Here we use the novel, non-parametric, Kernel Density Estimation Mixture Modelling (KDEMM) approach and demonstrate accurate modelling of the progression of cognitive test data. We found that using KDEMM resulted in more accurate models of disease progression in simulated data compared to Gaussian Mixture Models (GMMs) for the majority of parameters used to simulate the data. When comparing KDEMM and GMM to cognitive data collected in different Alzheimers Disease subtypes, we found the KDEMM resulted in a model much more in line with clinical phenotype. We anticipate that the KDEMM will be used to integrate cognitive test data, and other non-normally distributed datasets into complex disease progression models.

neuroscience↗

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↗

bioRxiv2017

Progression of regional grey matter atrophy in multiple sclerosis

Grey matter atrophy is present from the earliest clinical stages of multiple sclerosis (MS), but the temporal ordering is poorly understood. We aimed to determine the sequence in which grey matter regions become atrophic in MS, and its association with disability accumulation.\n\nIn this longitudinal study, we included 1,417 subjects: 253 with clinically-isolated syndrome (CIS), 708 relapsing-remitting MS (RRMS), 128 secondary-progressive MS (SPMS), 125 primary-progressive MS (PPMS), and 203 healthy controls from 7 European centres. Subjects underwent repeated MRI scanning (total number of scans 3,604); the mean follow-up for patients was 2.41yrs (SD{+/-}1.97). Disability was scored using the Expanded Disability Status Scale (EDSS). We calculated the volume of brain grey matter regions and brainstem using an unbiased within-subject template. We used an established data-driven event-based model (EBM) to determine the sequence of occurrence of atrophy and its uncertainty. We assigned each subject to a specific EBM stage, based on the number of their atrophic regions. We used nested linear mixed-effects regression models to explore the associations between the rate of increase in the EBM stages over time, disease duration and annual rate of EDSS gain.\n\nThe first regions to become atrophic in CIS and relapse-onset MS patients (RRMS and SPMS) were the posterior cingulate cortex and precuneus, followed by the middle cingulate cortex, brainstem and thalamus. The sequence of atrophy in PPMS showed a similar involvement of the thalamus, cuneus, precuneus, and pallidum, followed by the brainstem and posterior cingulate cortex. The cerebellum, caudate and putamen showed early atrophy in relapse-onset MS and late atrophy in PPMS. Patients with SPMS showed the highest EBM stages (highest number of atrophic regions, all p<0.001) at study entry. Rates of increase in EBM stages were significantly different from healthy controls in all MS phenotypes, except for CIS. The increase in the number of atrophic regions (EBM stage) was associated with disease duration in all patients. EBM stage was associated with disability accumulation in RRMS independent of disease duration (p<0.0001).\n\nThis data-driven staging of atrophy progression in a large MS sample demonstrates that grey matter atrophy spreads to involve more regions over time. The sequence in which regions become atrophic is reasonably consistent across MS phenotypes. The spread of atrophy was associated with disease duration, and disability accumulation in RRMS.\n\nAbbreviations

neuroscience↗