Biology subjects

Edwards, L.

Publications and source records attributed to Edwards, L..

10 recordsLinked to original sources

bioRxiv2026

Evolution of the motor cortex microstructure and its lateralization: a comparative study of chimpanzees and humans

Human hand coordination exceeds that of other species, including great apes, and is marked by pronounced right-hand dominance. This specialization parallels an expansion of its cortical representation, forming the hand-knob in the motor cortex. In humans, this region shows high myelination on quantitative MRI (qMRI), but whether this feature is shared with great apes remains unclear. It is also unknown whether increased right-hand dominance in humans is mirrored by greater hemispheric asymmetry in cortical microstructure. Using high-resolution qMRI, we compared motor cortex subdivisions controlling the leg, hand, and face in humans and chimpanzees. We found consistently higher myelin and iron content in the hand-knob in both species, suggesting an evolutionarily conserved role. However, only humans showed enhanced rightward lateralization. These results highlight both conserved and species-specific features of the motor cortex, offering insights into the evolution of manual dexterity and handedness.

neuroscience↗

bioRxiv2026

A DNA foundation model predicts osteoporosis risk genes without proximity bias

Targets supported by human genetic associations are more than twice as likely to progress from clinical development to approval. Genome-wide association studies are the largest source of genetic evidence for disease risk but linking non-coding variants to effector genes remains a significant barrier to identifying causal targets. Current gene-mapping approaches suffer from proximity bias, largely ignoring distal genes. Here we introduce Rosalind, a DNA foundation model fine-tuned on human genetic variation from GTEx, that directly predicts variant-gene regulatory relationships from sequence without relying on nearest-gene heuristics. We demonstrate Rosalinds accuracy through extensive benchmarking, apply it to multiple complex traits to establish broad utility, and provide experimental validation in osteoporosis using a translational osteoblast assay. We demonstrate that genes distal to osteoporosis risk variants were significantly more likely to alter a bone formation phenotype than nearest genes. Together, these results highlight deep learning-based regulatory models as a general and scalable framework for translating novel genetic insights to drug discovery.

genetics↗

bioRxiv2026

Mechanistic Language Modeling and Oxygenated 3D Screening Reveal Berberine and Enzalutamide Synergy in Resistant Prostate Cancer

Resistance to androgen receptor inhibitors remains a primary challenge in prostate cancer treatment, yet identifying synergis-tic co-therapies is hindered by immense combinatorial search spaces and the limited interpretability of predictive computation models. Here, we developed an integrated discovery-validation axis coupling knowledge-augmented large language models with oxygen-supplemented 3D spheroid assays. By leveraging inherent model stochasticity, our framework measures the degree of consensus across independent predictions to establish a formal metric for predictive accuracy. This principle enables high-throughput assessment of complex signaling crosstalk, yielding mechanistic rationales for all predictions and defining a high-confidence zone that minimizes experimental attrition. Utilizing this approach to screen 3,592 natural products, we identified a previously unrecognized synergy between berberine and enzalutamide that re-sensitizes resistant cells. Validation confirms that berberine perturbs the PI3K/AKT/mTOR and AMPK axes, a finding consistent with the mechanistic rationales computationally derived by the framework. Integrating interpretable AI with physiologically relevant 3D screening provides a scalable methodology for the rational discovery of synergistic therapies. SignificanceIntegrating mechanistic AI with oxygenated 3D screening, we identify a novel berberine-enzalutamide synergy. This framework resolves complex signaling dependencies, providing a scalable, transparent methodology for the rational discovery of effective combination therapies.

bioengineering↗

bioRxiv2025

PatchDNA: A Flexible and Biologically-Informed Alternative to Tokenization for DNA

AO_SCPLOWBSTRACTC_SCPLOWDNA language models are emerging as powerful tools for representing genomic sequences, with recent progress driven by self-supervised learning. However, performance on downstream tasks is sensitive to tokenization strategies reflecting the complex encodings in DNA, where both regulatory elements and single-nucleotide changes can be functionally significant. Yet existing models are fixed to their initial tokenization strategy; single-nucleotide encodings result in long sequences that challenge transformer architectures, while fixed multi-nucleotide schemes like byte pair encoding struggle with character level modeling. Drawing inspiration from the Byte Latent Transformers combining of bytes into patches, we propose that patching provides a competitive and more efficient alternative to tokenization for DNA sequences. Furthermore, patching eliminates the need for a fixed vocabulary, which offers unique advantages to DNA. Leveraging this, we propose a biologically informed strategy, using evolutionary conservation scores as a guide for patch boundaries. By prioritizing conserved regions, our approach directs computational resources to the most functionally relevant parts of the DNA sequence. We show that models up to an order of magnitude smaller surpass current state-of-the-art performance in existing DNA benchmarks. Importantly, our approach provides the flexibility to change patching without retraining, overcoming a fundamental limitation of current tokenization methods.

genomics↗

bioRxiv2025

Local resource heterogeneity drives density-dependent dispersal expression in a phoretic mite

O_LIDispersal is favoured in highly heterogenous and ephemeral environments where it can increase fitness. However, whilst we generally assume that unfavourable local conditions cue dispersal to elsewhere, little is known about how heterogeneity in local conditions influences dispersal expression. This is an important element of dispersal, since dispersal characteristics rely on such local cues to signal their development. C_LIO_LIWe assessed the role of local environmental heterogeneity on the expression of a plastic dispersal phenotype in experimental populations of the bulb mite Rhizoglyphus robini through a multigenerational, 18-week experiment. Populations were kept under either heterogenous, homogenous, or intermediate food distribution treatments, and at low or high population density, via culling. This allowed us to test for the effect of local environmental heterogeneity and competition on dispersal expression, but also on population size and growth. C_LIO_LIWe found that heterogenous environments induce greater expression of dispersal in high density environments, but not in low-density ones. Additionally, whilst culled treatments have on average larger population sizes than non-culled treatments, non-culled and heterogenous environments have similar sizes to culled treatments. Treatments did not impact population growth rate. C_LIO_LIOur findings that heterogenous treatments only induce greater dispersal expression under non-culled treatments suggests that heterogeneity may exacerbate competition around feeding patches, causing infrequent access to food. We also infer that culled populations become larger than non-culled, at least in the short term, due to rapid compensatory growth causing an overrepresentation of juveniles in the population. It is unclear why unculled heterogenous environments exhibit a similarly large population size to culled environment but could be due to food being hard to access but easy to locate. C_LIO_LIOverall, we find an ecologically significant impact of local heterogeneity on population size and dispersal expression, much of which may arise due to the influences of heterogeneity on competition around foraging spaces. Such findings highlight the need to distinguish between local and regional heterogeneity when modelling the evolution of dispersal, since these two alternate levels may perform significantly different roles in influencing the expression or adaptive value of dispersal. C_LI

evolutionary biology↗

bioRxiv2023

Itaconate stabilizes CPT1a to enhance lipid utilization during inflammation

One primary metabolic manifestation of inflammation is the diversion of cis-aconitate within the tricarboxylic acid (TCA) cycle to synthesize the immunometabolite itaconate. Itaconate is well established to possess immunomodulatory and metabolic effects within myeloid cells and lymphocytes, however, its effects in other organ systems during sepsis remain less clear. Utilizing Irg1 knockout mice that are deficient in synthesizing itaconate, we aimed at understanding the metabolic role of itaconate in the liver and systemically during sepsis. We find itaconate aids in lipid metabolism during sepsis. Specifically, Irg1 KO mice develop a heightened level of hepatic steatosis when induced with polymicrobial sepsis. Proteomics analysis reveal enhanced expression of enzymes involved in fatty acid oxidation in following 4-ocytl itaconate (4-OI) treatment in vitro. Downstream analysis reveals itaconate stabilizes the expression of the mitochondrial fatty acid uptake enzyme CPT1a, mediated by its hypoubiquitination. Chemoproteomic analysis revealed itaconate interacts with proteins involved in protein ubiquitination as a potential mechanism underlying its stabilizing effect on CPT1a. From a systemic perspective, we find itaconate deficiency triggers a hypothermic response following endotoxin stimulation, potentially mediated by brown adipose tissue (BAT) dysfunction. Finally, by use of metabolic cage studies, we demonstrate Irg1 KO mice rely more heavily on carbohydrates versus fatty acid sources for systemic fuel utilization in response to endotoxin treatment. Our data reveal a novel metabolic role of itaconate in modulating fatty acid oxidation during polymicrobial sepsis.

physiology↗

bioRxiv2022

Transgenic porcine model reveals two roles for LGR5 during lung development and homeostasis

Cells expressing LGR5 play a pivotal role in homeostasis, repair, and regeneration in multiple organs including skin and gastrointestinal tract, yet little is known about their role in the lung. Findings from mice, a widely used animal model, suggest that lung LGR5 expression differs from that of humans. In this work, using a new transgenic pig model, we identify two main populations of LGR5+ cells in the lung that are conserved in human, but not mouse lungs. Using RNA sequencing, 3D imaging and organoid models, we determine that in the fetal lung, epithelial LGR5 expression is transient in a subpopulation of SOX9+/ETV+/SFTPC+ progenitor lung tip cells. In contrast, epithelial LGR5 expression is absent from postnatal lung, but is reactivated in bronchioalveolar organoids derived from basal airway cells. We also describe a separate population of mesenchymal LGR5+ cells that surrounds developing and mature airways, lies adjacent to airway basal cells, and is closely associated with nerve fibers. Transcriptionally, mesenchymal LGR5+ cells include a subset of peribronchial fibroblasts (PBF) that express unique patterns of SHH, FGF, WNT and TGF-{beta} signaling pathway genes. These results support distinct roles for LGR5+ cells in the lung and describe a physiologically relevant animal model for further studies on the function of these cells in repair and regeneration.

cell biology↗

bioRxiv2022

Error quantification in multi-parameter mapping facilitates robust estimation and enhanced group level sensitivity

Multi-Parameter Mapping (MPM) is a comprehensive quantitative neuroimaging protocol that enables estimation of four physical parameters (longitudinal and effective transverse relaxation rates R1 and [Formula], proton density PD, and magnetization transfer saturation MTsat) that are sensitive to microstructural tissue properties such as iron and myelin content. Their capability to reveal microstructural brain differences, however, is tightly bound to controlling random noise and artefacts (e.g. caused by head motion) in the signal. Here, we introduced a method to estimate the local error of PD, R1 and MTsat maps that captures both noise and artefacts on a routine basis without requiring additional data. To investigate the methods sensitivity to random noise, we calculated the model-based signal-to-noise ratio (mSNR) and showed in measurements and simulations that it correlated linearly with an experimental raw-image-based SNR map. We found that the mSNR varied with MPM protocols, magnetic field strength (3T vs. 7T) and MPM parameters: it halved from PD to R1 and decreased from PD to MTsat by a factor of 3-4. Exploring the artefact-sensitivity of the error maps, we generated robust MPM parameters using two successive acquisitions of each contrast and the acquisition-specific errors to down-weight erroneous regions. The resulting robust MPM parameters showed reduced variability at the group level as compared to their single-repeat or averaged counterparts. The error and mSNR maps may better inform power-calculations by accounting for local data quality variations across measurements. Code to compute the mSNR maps and robustly combined MPM maps is available in the open-source hMRI toolbox.

neuroscience↗

bioRxiv2021

The Reactobiome Unravels a New Paradigm in Human Gut Microbiome Metabolism

Changes in microbial metabolism have been used as the main approach to assess function and elucidate environmental and host-microbiome interactions. This can be hampered by uncharacterised metagenome species and lack of metabolic annotation. To address this, we present a comprehensive computational platform for population stratification based on microbiome composition, the underlying metabolic potential and generation of metagenome species and community level metabolic models. We revisit the concepts of enterotype and microbiome richness introducing the reactobiome as a stratification method to unravel the metabolic features of the human gut microbiome. The reactobiome encapsulates resilience and microbiome dysbiosis at a functional level. We describe five reactotypes in healthy populations from 16 countries, with specific amino acid, carbohydrate and xenobiotic metabolic features. The validity of the approach was tested to unravel host-microbiome and environmental interactions by applying the reactobiome analysis on a one-year Swedish longitudinal cohort, integrating gut metagenomics, plasma metabolomics and clinical data.

systems biology↗

bioRxiv2020

Transcriptome analysis of IPF fibroblastic foci identifies key pathways involved in fibrogenesis

Fibroblastic foci (FF) represent the cardinal pathogenic lesion in idiopathic pulmonary fibrosis (IPF) and comprise activated fibroblasts and myofibroblasts, the key effector cells responsible for dysregulated extracellular matrix deposition in multiple fibrotic conditions. The aim of this study was to define the major transcriptional programmes involved in fibrogenesis in IPF by profiling un-manipulated myo/fibroblasts within FF in situ by laser capture microdissection. The challenges associated with deriving gene calls from low amounts of RNA and the absence of a meaningful comparator cell type were overcome by adopting novel data mining strategies and by using weighted gene co-expression network analysis (WGCNA), as well as an eigengene-based approach to identify transcriptional signatures which correlate with fibrillar collagen gene expression. WGCNA identified prominent clusters of genes associated with cell cycle, inflammation/differentiation, translation and cytoskeleton/cell adhesion. Collagen eigengene analysis revealed that TGF-{beta}1, RhoA kinase and the TSC2/RHEB axis formed major signalling clusters associated with collagen gene expression. Functional studies using CRISPR-Cas9 gene edited cells demonstrated a key role for the TSC2/RHEB axis in regulating TGF-{beta}1-induced mTORC1 activation and collagen I deposition in mesenchymal cells reflecting IPF and other disease settings, including cancer-associated fibroblasts. These data provide strong support for the human tissue-based and bioinformatics approaches adopted to identify critical transcriptional nodes associated with the key pathogenic cell responsible for fibrogenesis in situ and further identifies the TSC2/RHEB axis as a potential novel target for interfering with excessive matrix deposition in IPF and other fibrotic conditions. What is the key question?Can we identify a transcriptional signature associated with collagen gene expression in the fibrotic focus, the cardinal fibrotic lesion in IPF? What is the bottom line?We herein define the major transcriptional programmes involved in fibrogenesis in IPF by profiling myo/fibroblasts within FF in situ by laser capture microdissection. Why read on?The data provide strong support for a human tissue-based approach to identify critical transcriptional nodes associated with fibrogenesis in situ and further identifies the TSC2/RHEB axis as a potential novel target for interfering with excessive matrix deposition in IPF and other fibrotic conditions.

cell biology↗