Biology subjects

Andrews, T. D.

Publications and source records attributed to Andrews, T. D..

7 recordsLinked to original sources

bioRxiv2026

A propensity to hyperinsulinaemia underpins diet-induced diabetes in NODk mice

Aims/hypothesisHeterogeneity in the pathophysiology of type 2 diabetes is increasingly being realised. The currently available rodent models of type 2 diabetes all have limitations and do not accurately reflect all human type 2 diabetes subtypes. NOD.BR-H2k /Wicker mice (NODk), derived from the non-obese diabetic (NOD) mouse, are type 1 diabetes resistant. However, transgene induced beta-cell stress in male NODk mice induces hyperinsulinaemia followed by diabetes. Here we have investigated the propensity of NODk mice to develop a Western-diet (WD) induced hyperinsulinaemic subtype of type 2 diabetes. Comparator mouse strains used were BALB/c and B10.BR-H2k /SgSnJ mice (B10k). MethodsIn the longer-term studies (14-24 weeks), NODk, B10k and BALB/c mice were randomised to receive Chow or WD from 4 weeks of age, followed by serial measurement of body weight and fed-state blood glucose. IPGTT and IPITT tests were conducted at 13 weeks of age. Blood and pancreas were harvested for further analyses at 14 and 24 weeks of age, or sooner if diabetes developed (blood glucose concentrations [≥]20 mmol/l on two consecutive days). In the acute studies, metabolic characteristics of the three strains at 8 weeks of age, continued on Chow or after a 5-day WD challenge (WDC) were assessed, along with harvesting pancreas on day 5 for ex vivo islet insulin secretion, electron microscopy, and bulk islet transcriptomics analyses. ResultsMale WD-fed NODk mice became markedly hyperinsulinaemic, gained excess weight and developed a severe type 2 diabetes phenotype. Emergence of diabetes was associated with islet endocrine cell apoptosis and loss of beta-cell mass, without evidence of insulitis. Insulin resistance on IPITT testing, however, was not evident in Chow-fed NODk mice. In contrast, male B10k mice already had poor glucose tolerance on Chow diet and, despite having a hypoinsulinaemic phenotype, were resistant to WD-induced diabetes. BALB/c mice developed very mild glucose intolerance and hyperinsulinaemia in response to the WD. Female NODk mice were diabetes resistant. At 8 weeks of age, male Chow-fed NODk mice were mildly hyperinsulinaemic despite relative hypoglycaemia compared to the other strains. The acute 5-day WDC markedly increased hyperinsulinaemia in NODk mice. Transcriptomics analyses identified robust strain-specific differences, including altered islet cell differentiation, energy metabolism, endoplasmic reticulum to golgi vesicle transport and insulin processing. Conclusions/interpretationNODk mice, which exhibit mild hyperinsulinaemic hypoglycaemia on Chow diet and rapidly develop marked hyperinsulinaemia on WD, are type 2 diabetes prone. In contrast, B10k mice have poor glucose tolerance on Chow diet and no or limited capacity to increase insulinaemia in response to WD, are diabetes resistant. These findings support the hypothesis that hyperinsulinaemia is upstream to insulin resistance in the pathogenesis of severe insulin resistant subset of type 2 diabetes for which the WD-fed NODk mouse is a suitable new mouse model. Research in ContextO_ST_ABSWhat is already known about this subject?C_ST_ABSO_LIWhich of insulin hypersecretion and insulin resistance are upstream in the pathogenesis of the severe insulin resistant subtype of type 2 diabetes continues to be debated C_LIO_LIRodent models of type 2 diabetes do not accurately reflect all human subtypes of type 2 diabetes C_LIO_LINODk mice, derived from the non-obese diabetic (NOD) mouse, are type 1 diabetes resistant, but with transgene induction of islet beta-cell stress develop hyperinsulinaemia, followed by type 2 diabetes C_LI What is the key question?O_LICould Western-diet fed NODk mice be developed as a model of severe insulin resistant type 2 diabetes and shed light on its upstream pathogenesis? C_LI What are the new findings?O_LIMale NODk mice tend to hyperinsulinaemic hypoglycaemia on Chow diet, rapidly develop marked hyperinsulinaemia on Western-diet feeding, and then develop type 2 diabetes C_LIO_LIMale B10k mice (one of two comparator strains (B10k and BALB/c)) have poor glucose tolerance on Chow diet, limited capacity to increase insulinaemia in response to Western-diet feeding, but are resistant to develop Western-diet induced type 2 diabetes C_LIO_LIIsolated islet findings show strain differences that favour intrinsic hyper-responsiveness and hypo-responsiveness of islet beta-cells of NODk and B10k mice, underpinning their respective metabolic phenotypes C_LI How might this impact on clinical practice in the foreseeable future? O_LIThe findings are in support of the insulin hypersecretion hypothesis for severe insulin resistant type 2 diabetes, such that therapies to limit islet beta-cell hyperresponsiveness to prevent and treat this subtype of diabetes warrant investigation C_LI

physiology↗

bioRxiv2026

Towards a Cytometry Foundation Model: Interpretable Sample-level Predictive Modelling via Pretrained Transformers

Foundation models have transformed scientific data modelling across domains, yet flow cytometry has lacked one. Despite the abundance of high-dimensional cellular data, automated analysis remains bottlenecked by marker variability: prior studies are typically confined to fixed marker panels and homogeneous data, limiting scalability and generalisation due to architectural constraints. We present the Generalised Pretrained Cytometry Transformer (GPCT), an interpretable framework designed to learn from heterogeneous marker panels for sample-level predictive modelling. Through a novel cytometry-specific pretraining regime, GPCT learns transferable cellular representations that achieve high classification accuracy across diverse datasets. Notably, pretraining significantly boosts performance on data-scarce downstream tasks, marking a pivotal step towards a cytometry foundation model. Furthermore, GPCT maintains interpretability and identifies the specific cell subsets most influential to its predictions. This enables direct biological validation of learned patterns and provides a data-driven basis for refining traditional gating strategies.

bioinformatics↗

bioRxiv2025

Superior batch alignment and hyper-dimensional cytometry representations allow ultra-sensitive classification of disease phenotypes

Analysis of cytometry data predominantly relies on clustering and dimensionality reduction approaches for computational tractability. This is particularly relevant for modern spectral flow cytometers, which can simultaneously measure an increasingly large number of antibody marker channels. While dimensionality reduction provides for more efficient data processing, this comes at the expense of data loss that may miss subtle patterns among rare cell types that may be critical for disease detection. Maintaining analysis at full dimensions presents opportunities to preserve resolution and provides complete downstream data interpretability. However, a significant obstacle to analysis of cytometry data without dimensionality reduction is the significant batch effects observed in this data between experiment days, operators and equipment types. Here we show a new strategy to denoise batch variation from both flow- and mass-cytometry datasets using an autoencoder neural network architecture. We generated a benchmark flow cytometry dataset in mice to compare this approach to current toolsets and find our approach shows superior preservation of biological signals, whilst also performing batch correction equal to current best methodology. Our batch alignment approach works to such an extent that it becomes practically possible to project batch aligned data into multidimensional space to generate a novel representation of cellular phenotype for downstream model building. This hyperdimensional approach maintains original data resolution without requiring dimensionality reduction, and thus any resultant cell populations that differentiate phenotypes remain fully interpretable. We show with two large clinical datasets that our batch-alignment approach coupled with the multi-dimensional representation successfully detects meaningful patterns in cases where the original analysis methods struggled. This new framework removes some of the inherent technical limitations encountered in the integration of large, multi-batch cytometry datasets and provides a framework for machine learning model building from this modality. An implementation of this framework and an associated web application accompanies this manuscript at http://voxelcoder.cloud. Significance StatementThis work addresses a critical bottleneck in cytometry analysis by introducing a neural network-based approach that effectively removes technical batch variation while preserving biological signals. The novel hyperdimensional representation maintains full data resolution without dimensionality reduction, enabling more sensitive detection of disease-associated cellular signatures than current methods. Importantly, this framework enables reliable batch normalization of fresh samples processed at different times and locations, overcoming the practical constraints of clinical sample collection where simultaneous processing is often impossible. The enhanced sensitivity for identifying pathogenic cellular patterns has immediate implications for biomarker discovery and personalized medicine applications.

systems biology↗

bioRxiv2025

Benchmark of Wide Range of Pairwise Distance Metrics for Automated Classification of Mouse Mutant Phenotypes from Flow Cytometry Data

Precision medicine requires a comprehensive mapping of genotype to phenotype to provide patients with individually tailored treatment. However, when using flow cytometry to identify phenotypes, such as the quantity of various immune cell populations in tissue and blood used to identify autoimmune disorders, it is often unclear which cellular phenotypes are from healthy and disease individuals, especially when including the effects of population diversity, due to the high-dimensional nature of the data. To identify and segregate healthy phenotype from various disease phenotypes, we use pairwise distance metrics between each samples cell populations. By comparing distance metrics between C57BL/6 clone mice with mutations of known phenotype, we find that cosine similarity is best suited for segregating wildtype from mutant samples while respecting minute differences in already small cell populations, and that standardised Euclidean distance is best suited for machine-learning input due to its sensitivity. Both metrics outperform other tested metrics (including Aitchison, Euclidean, Manhattan, Earth-Movers Distance, and squared Euclidean). We demonstrate the utility of these different pairwise metrics through their application to a classification task of known mutant phenotypes: using an existing FACS phenotype dataset derived from X000 inbred C57BL/6 mice that harbour potentially phenotypic genetic variation introduced through ENU mutagenesis of individual pedigree-founding G0 male mice.

bioinformatics↗

bioRxiv2024

Rare and novel RELA variants are common in systemic autoimmunity

ObjectivePhenotypic diversity of autoimmune diseases presents an ongoing diagnostic and drug development challenge for clinicians and scientists. Recent discovery of mutations in RELA (encoding RELA) in patients with different diagnoses has highlighted that different pathogenic molecular mechanisms are at play and may explain the observed phenotypic diversity. We identified seven novel/rare RELA variants in patients with autoimmune diseases and examine the functional consequences on immune signalling. MethodsWild type and mutant RELA proteins were ectopically expressed in HEK293 cells. Western blot and NF-{kappa}B/IFN{beta} luciferase reporter assays were used to determine RELA expression and transcriptional activity, respectively. In patients (n=3), B and T cell populations were examined via flow cytometry and NF-{kappa}B and interferon stimulated genes in PBMCs were assessed via qPCR following toll-like receptor activation. ResultsRELAI250V, RELAR295H and RELAE3* displayed a loss in NF-{kappa}B transcriptional activity. Comparative to RELAWT, RELAI250V protein expression was reduced. Two variants, RELAI250V and RELAR295H, induced hyperactivation of the IFN{beta} promoter. An elevated IFN gene signature was not detected in patient PBMCs following toll-like receptor activation, however the patient heterozygous for I250V had elevated IFN{beta} transcripts at baseline and after TLR7/8 activation. A reduction in transitional, unswitched memory and memory B cell and cTfh (CCR6-CXCR3-) T cell subsets was shared by the patient group. ConclusionWe expand upon the clinical syndromes linked to RELA dysfunction and uncover rare and novel variants that have distinct functional effects on gene transcription downstream of NF-{kappa}B and IFN{beta} promoter elements. These findings reinforce an important role for RELA in a range of autoimmune and autoinflammatory diseases.

immunology↗

bioRxiv2019

Pharmacogenetic variants associated with off-target adverse drug reactions are mostly predicted to be benign

Tools that predict the functional importance of genetic variation almost always rely on sequence conservation across deep evolutionary divergences as a primary discriminator. However, sequence conservation information is misleading when predicting the functional importance of pharmacogenetic variants related to off-target adverse drug reactions. Sequence conservation is largely maintained by evolutionary purifying selection, which has not been relevant for most drugs until very recently, especially for off-target effects. Here, we use a simple classification criteria to identify variants with off-target pharmacogenetic effects from the PharmGKB database. We show that off-target pharmacogenetic variation is predicted mostly to be benign by all state-of-the-art prediction tools we tested. Hence, off-target pharmacogenetic variants are overwhelmingly invisible to all predictive methodologies currently employed. Very different analytical approaches will be needed to address this important problem.\n\nAuthor SummaryWhen a personal genome sequence is obtained for a given person, the sequence is compared to the human reference sequence to identify where it differs from the genome of that person. One application of this information is that it may identify how a specific person may react to particular drugs. However, when computationally predicting the functional importance of a genetic variant, the tools used rely heavily on sequence conservation information to make their prediction. From an evolutionary point of view, the use of drugs to treat diseases is a very recent activity - and one that has not had time to cause certain variants to either be selected for or removed from the population. This produces a blind-spot for tools that predict variant functional effects, especially for drugs with off-target interactions that may produce unanticipated effects.

bioinformatics↗

bioRxiv2017

Cleaning clinical genomic data: Simple identification and removal of recurrently miscalled variants in single genomes

Identification of sequence variation from short-read sequence data is subject to common-yet-intermittent miscalling that occurs in a sequence intrinsic manner. We identify that recurrent false positive single nucleotide variants are strongly present in databases of human sequence variation and demonstrate how each individual sample generates a unique set of recurrent false positive variants. These recurrent miscalls result from known difficulties aligning short-read sequence data between redundant genomic regions. We could replicate, catalogue and remove three quarters of these recurrent miscalls for any given exome with as little as ten rounds of read resampling, realignment and recalling. The removal of such misleading variants reduces the search space for identification of disease causing variants.\n\nList of Abbreviations

genomics↗