Biology subjects

Grant, S.

Publications and source records attributed to Grant, S..

18 recordsLinked to original sources

bioRxiv2026

Phenotypic inference from sparse tumor genomes informs an explainable deep-learning model for cancer prognosis

Somatic genomic alterations are widely profiled in cancer and remain the primary source for personalized therapy, yet their clinical utility is limited to few actionable targets. AI/ML models offer opportunities to capture genome-wide complexities, but clinical translation is hindered by poor interpretability, often limited to single-gene effects, and overlooks higher-order phenotypic interactions. To address this, we developed PhenoMap, a machine-learning framework that infers tumor phenotypic states from somatic variants. Trained on 9,000 pan-cancer genomes and transcriptomes, PhenoMap accurately reconstructs expression-based pathway enrichment scores and consolidated hallmark cancer phenotypes, enabling multilevel interpretation at phenotype, pathway, and gene scales. PhenoMap captured molecular subtypes and key resistance pathways across breast, lung, and brain cancers. We leveraged these features in PhenoSurv, a deep survival model integrating phenotypic reconstruction loss, Kullback-Leibler divergence, and survival loss to learn biologically-grounded predictors. PhenoSurv outperformed state-of-the-art survival models while providing robust mechanistic explanations. NOTCH1 signaling and SMARCA4 mutations emerged as a major prognostic factor in hormone receptor-positive breast cancer. TGF-{beta} signaling and inflammasomes, potentially modulated by FAT1, predicted lung adenocarcinoma outcomes, while inositol metabolism and PI3K signaling were key drivers in brain cancer. Together, PhenoMap and PhenoSurv provide accurate, interpretable, and clinically actionable models for precision oncology. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=191 SRC="FIGDIR/small/734894v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@190f157org.highwire.dtl.DTLVardef@d496f1org.highwire.dtl.DTLVardef@101dbeeorg.highwire.dtl.DTLVardef@10e0799_HPS_FORMAT_FIGEXP M_FIG C_FIG PhenoMap framework leverages genomic data and explainable deep learning to identify phenotype, pathway, and gene-level prognostic markers for precision oncology.

bioinformatics↗

bioRxiv2026

Quantifying Exposure of Pacific Salmon and Steelhead to Climate Change in the Fraser River basin

Climate change can affect salmon and steelhead (Oncorhynchus spp.) throughout their anadromous life cycles, yet there have been no assessments of which Canadian populations face the greatest exposure. We developed a framework to quantify relative climate change exposure of salmon and steelhead populations based on the spatial and temporal distribution of different life stages. Exposure was calculated from climate model projections for freshwater and marine climate variables considering unique impact thresholds for each population and life stage. We applied this framework to 60 Conservation Units of Pacific salmon and steelhead in the Fraser River basin, British Columbia. Lake-type sockeye had the highest exposure, driven by elevated stream temperatures during adult freshwater migration and spawning stages and relatively low thermal tolerance of marine stages. Chinook salmon were the next most exposed, while coho, pink, and chum salmon had relatively low exposure. Uniquely, steelhead exposure was driven by high stream temperatures during incubation. Our framework is broadly applicable, and our findings provide critical input for climate change vulnerability assessments and forward-looking resilience planning for Pacific salmon.

ecology↗

bioRxiv2026

Streptomyces enrichment in roots during drought is uncoupled from plant benefit and is driven by host suppression of iron uptake and immunity

Drought reshapes plant root microbiota, yet the mechanistic drivers and consequences of this observation remain unclear. We discovered that suppression of host immunity and iron homeostasis is required for Streptomyces enrichment in roots during drought across diverse soils. Genetic and physiological manipulation of these host pathways confirmed their requirement in modulating Streptomyces root enrichment. Drought-induced suppression of iron uptake was conserved across the [~]160 My monocot-eudicot divergence. Some Streptomyces strains enhanced plant growth and rescued iron uptake under drought. These benefits were uncoupled from Streptomyces root enrichment. They were instead shaped by intra-Streptomyces antagonism. We propose a two-step model: drought-driven down regulation of host iron and immune pathways enriches Streptomyces, while intra-genus dynamics fine-tune strain-level assembly and functional outcomes. Our data refine the idea that Streptomyces are enriched in roots during drought in response to a plant cry for help and consequently contribute to alleviation of this abiotic stress.

plant biology↗

bioRxiv2025

LCI9 is required for normal pyrenoid starch sheath formation and efficiency of the CO2-concentrating mechanism in Chlamydomonas reinhardtii

O_LIPyrenoid -based CO2-concentrating mechanisms (pCCMs) boost photosynthesis by delivering elevated levels of CO2 to Rubisco. Pyrenoids are often surrounded by a starch sheath thought to enhance pCCM efficiency, but little is known about how the sheath is formed. Here we have assessed the role of the pyrenoid-associate protein LCI9 in starch sheath formation in Chlamydomonas reinhardtii. C_LIO_LIUsing an lci9 mutant we assessed the role of LCI9 in starch sheath morphology by fluorescence and electron microscopy, and its impact on pCCM function by CO2 uptake and growth assays. We determined potential interactors of LCI9 via TurboID proximity labeling. C_LIO_LIIn the absence of LCI9, cells fail to correctly assemble the starch sheath, have lower affinity for inorganic carbon and grow slower under CO2-limiting conditions. LCI9 localises to the starch sheath and is physically close to a range of starch metabolism enzymes and other potential scaffolding proteins. Finally, we show that LCI9 associates with starch in the land plant Arabidopsis thaliana. C_LIO_LILCI9 likely acts as a pyrenoid specific scaffold for starch metabolism enzymes. LCI9 could contribute to ongoing efforts to build a functional pyrenoid in a land plant. C_LI

plant biology↗

bioRxiv2025

Evolutionary convergence of a Pyroptosis-Apoptosis crosstalk drives non-canonical inflammasome signalling.

The coordination of efficient immune responses to infections is essential to enable pathogen clearance and host survival. Cell death modalities are a crucial component of the innate immune system and are key to controlling intracellular and extracellular infections. Human caspase-4 and -5 are cysteine proteases activated upon intracellular detection of bacterial lipopolysaccharide (LPS) and triggers pyroptosis, a lytic and pro-inflammatory form of cell death. However, the interaction between these caspases and other cell death modalities remains poorly understood. Here, we build on evidence that caspase-4 can engage in crosstalk with the apoptotic executioner caspases-3 and -7. Our work validated caspase-3 and -7 as direct substrates of caspase-4 and -5, and demonstrated that this specificity was regulated by exosite interactions as opposed to the tetrapeptide. Using AlphaFold, we generated an interaction model between caspase-4 and executioner caspases-3 and -7. From this, we experimentally validated the importance of a hydrophobic interface on caspase-3 and -7 that engaged the caspase-4 exosite, enabling their recognition and cleavage. Importantly, this interface is not used by the apoptotic initiator caspase-8, which cleaved caspase-3 in a tetrapeptide-dependent manner. Our work highlights that inflammatory caspases have evolved a novel mechanism to coordinate crosstalk between pyroptotic and apoptotic signalling, and suggests that these pathways may synergise for the efficient amplification of cell death pathways.

biochemistry↗

bioRxiv2025

Proteasome Inhibition Enhances Lysosome-mediated Targeted Protein Degradation

Proteasome inhibitor drugs are currently used in the clinic to treat multiple myeloma and mantle cell lymphoma. These inhibitors cause accumulation of undegraded proteins, thus inducing proteotoxic stress and consequent cell death. However, cancer cells counteract this effect by activating an adaptive response through the transcription factor Nuclear factor erythroid 2-related factor 1 (NRF1, also known as NFE2L1). NRF1 induces transcriptional upregulation of proteasome and autophagy/lysosomal genes, thereby reducing proteotoxic stress and diminishing the effectiveness of proteasome inhibition. While suppressing this protective autophagy response is one potential strategy, here we investigated whether this heightened autophagy could instead be leveraged therapeutically. To this end, we designed an autophagy-targeting chimera (AUTAC) compound to selectively degrade the anti-apoptotic protein Mcl1 via the lysosome. Our results show that this lysosome-mediated targeted degradation is significantly amplified in the presence of proteasome inhibition, in a NRF1-dependent manner. Mechanistically, AUTAC-driven Mcl1 clearance requires K63-linked ubiquitination by UBC13 and TRAF6 and recognition by the cargo receptor p62/SQSTM1. The combination of the proteasome inhibitor carfilzomib and Mcl1 AUTAC synergistically promoted cell death in both in vitro models, including wild-type and proteasome inhibitor-resistant multiple myeloma and lung cancer cells, and in mouse tumor xenografts. Thus, our work offers a novel strategy for enhancing proteasome inhibitor efficacy by exploiting the adaptive autophagy response. More broadly, our study establishes a framework for amplifying lysosome-mediated targeted protein degradation, with potential applications in cancer therapeutics and beyond.

biochemistry↗

bioRxiv2025

A synaptic-astrocytic proteomic signature associated with synaptopathy in Alzheimer's Disease

Synapse loss is the greatest correlate of cognitive impairment in Alzheimers Disease (AD) and offers a therapeutic avenue alongside disease-modifying therapies. However, the events preceding synapse loss in the human condition have not been well characterised. In this study, we describe a pseudotemporal profile of alterations in the synaptic proteome prior to excitatory synapse loss in human post-mortem brain AD tissue using synapse proteomics and synaptome mapping techniques. In a region with early-stage disease pathology, the most predominant changes were pre-synaptic and featured changes in metabolism and exocytosis. In a mid-stage disease state, alongside initial synapse loss, there was a dominance of inhibitory synaptic changes. In a region with late-stage disease pathology and profound synapse loss, post-synaptic changes were most prevalent with a range of canonical synaptic transmission pathways reduced and differential excitatory synapse subtype pathology. Synapse loss was associated with changes in astrocytic proteins which were enriched for those at peri-synaptic astrocytic processes, including an upregulation of complement activation and endocytosis; a signature that differed from the astrocyte cytosolic proteome. Taken together, this provides evidence of a cascade of events leading to synapse loss with multiple points for therapeutic intervention to alleviate cognitive decline in AD. Data are available via ProteomeXchange with identifier PXD056052.

neuroscience↗

bioRxiv2024

Cancer-Specific Alterations in Nuclear Matrix Proteins Determined by Multi-omics Analyses of Ductal Carcinoma in Situ

Breast cancer (BC) is the most common cancer affecting women in the United States. Ductal carcinoma in situ (DCIS) is the earliest identifiable pre-invasive BC lesion. Estimates show that 14 to 50% of DCIS cases progress to invasive BC. Our objective was to identify nuclear matrix proteins (NMP) with specifically altered expression in DCIS and later stages of BC compared to non-diseased breast reduction mammoplasty and a contralateral breast explant using mass spectrometry and RNA sequencing to accurately identify aggressive DCIS. Sixty NMPs were significantly differentially expressed between the DCIS and non-diseased breast epithelium in an isogenic contralateral pair of patient-derived extended explants. Ten of the sixty showed significant mRNA expression level differences that matched the protein expression. These 10 proteins were similarly expressed in non-diseased breast reduction cells. Three NMPs (RPL7A, RPL11, RPL31) were significantly upregulated in DCIS and all other BC stages compared to the matching contralateral breast culture and an unrelated non-diseased breast reduction culture. RNA sequencing analyses showed that these three genes were upregulated increasingly with BC progression. Finally, we identified three NMPs (AHNAK, CDC37 and DNAJB1) that were significantly downregulated in DCIS and all other BC stages compared to the isogenically matched contralateral culture and the non-diseased breast reduction culture using both proteomics and RNA sequencing techniques.

cancer biology↗

bioRxiv2023

Spatial Patterning Analysis of Cellular Ensembles (SPACE) enables statistically robust discovery of complex spatial organization at the cell and tissue level

Spatial patterns of cells and other biological elements drive both physiologic and pathologic processes within tissues. While many imaging and transcriptomic methods document tissue organization, discerning these patterns is challenging, especially when they involve multiple elements in complex arrangements. To address this challenge, we present Spatial Patterning Analysis of Cellular Ensembles (SPACE), an R package for analysis of high-plex spatial data. SPACE is compatible with any data collection modality that records values (i.e., categorical cell/structure types or quantitative expression levels) at fixed spatial coordinates (i.e., 2d pixels or 3d voxels). SPACE detects not only broad patterns of co-occurrence but also context-dependent associations, quantitative gradients and orientations, and other organizational complexities. Via a robust information theoretic framework, SPACE explores all possible ensembles of tissue elements - single elements, pairs, triplets, and so on - and ranks the most strongly patterned ensembles. For single images, rankings reflect patterns that differ from random assortment. For sets of images, rankings reflect patterns that differ across sample groups (e.g., genotypes, treatments, timepoints, etc.). Further tools then thoroughly characterize the nature of each pattern for intuitive interpretation. We validate SPACE and demonstrate its advantages using murine lymph node images for which ground truth has been defined. We then use SPACE to detect new patterns across varied datasets, including tumors and tuberculosis granulomas.

bioinformatics↗

bioRxiv2023

An RNA foundation model enables discovery of disease mechanisms and candidate therapeutics

Accurately modeling and predicting RNA biology has been a long-standing challenge, bearing significant clinical ramifications for variant interpretation and the formulation of tailored therapeutics. We describe a foundation model for RNA biology, "BigRNA", which was trained on thousands of genome-matched datasets to predict tissue-specific RNA expression, splicing, microRNA sites, and RNA binding protein specificity from DNA sequence. Unlike approaches that are restricted to missense variants, BigRNA can identify pathogenic non-coding variant effects across diverse mechanisms, including polyadenylation, exon skipping and intron retention. BigRNA accurately predicted the effects of steric blocking oligonucleotides (SBOs) on increasing the expression of 4 out of 4 genes, and on splicing for 18 out of 18 exons across 14 genes, including those involved in Wilson disease and spinal muscular atrophy. We anticipate that BigRNA and foundation models like it will have widespread applications in the field of personalized RNA therapeutics.

bioinformatics↗

bioRxiv2022

Activity of the manganese efflux transporter SLC30A10 in dopaminergic but not GABAergic neurons protects against neurotoxicity

Metals such as copper, iron, and manganese (Mn) are essential for life, but induce neurotoxicity at elevated levels. Yet, the neuronal mechanisms of metal-induced neurological disease are largely unclear. A primary limitation has been an inability to selectively alter metal levels in specific neurons, so that the role of the targeted neurons in disease biology can be isolated. Here, we show that neuron-specific depletion of metal efflux transporters provides the feasibility to overcome this limitation by focusing on Mn, which accumulates in the basal ganglia and induces motor disease, and the Mn-specific efflux transporter SLC30A10. Pan-neuronal/glial Slc30a10 knockout mice exhibited increased basal ganglia Mn levels and hypolocomotor deficits in early-life (pre-adulthood), which were exacerbated by Mn exposure. The locomotor deficits of the pan-neuronal/glial strain was associated with a reduction in evoked striatal dopamine release without dopaminergic (DAergic) neurodegeneration or changes in striatal tissue dopamine levels. Furthermore, DAergic-specific, but not GABAergic-specific, Slc30a10 knockout mice recapitulated the hypolocomotor phenotype of the pan-neuronal/glial knockouts in early-life although Mn levels were elevated in the targeted basal ganglia regions of both the neuron-specific strains. Put together, our results imply that (1) activity of SLC30A10 in DAergic neurons is necessary to protect against early-life Mn neurotoxicity; (2) increasing Mn levels in DAergic neurons is sufficient to induce early-life motor disease, suggesting that Mn targets DAergic neurons in the early-life period to induce motor deficits; and (3) neuron-specific knockout of metal efflux transporters may be a widely applicable strategy to elucidate mechanisms of metal-induced neurotoxicity.

neuroscience↗

bioRxiv2021

A mechanistically interpretable model of the retinal neural code for natural scenes with multiscale adaptive dynamics

The visual system processes stimuli over a wide range of spatiotemporal scales, with individual neurons receiving input from tens of thousands of neurons whose dynamics range from milliseconds to tens of seconds. This poses a challenge to create models that both accurately capture visual computations and are mechanistically interpretable. Here we present a model of salamander retinal ganglion cell spiking responses recorded with a multielectrode array that captures natural scene responses and slow adaptive dynamics. The model consists of a three-layer convolutional neural network (CNN) modified to include local recurrent synaptic dynamics taken from a linear-nonlinear-kinetic (LNK) model [1]. We presented alternating natural scenes and uniform field white noise stimuli designed to engage slow contrast adaptation. To overcome difficulties fitting slow and fast dynamics together, we first optimized all fast spatiotemporal parameters, then separately optimized recurrent slow synaptic parameters. The resulting full model reproduces a wide range of retinal computations and is mechanistically interpretable, having internal units that correspond to retinal interneurons with biophysically modeled synapses. This model allows us to study the contribution of model units to any retinal computation, and examine how long-term adaptation changes the retinal neural code for natural scenes through selective adaptation of retinal pathways.

neuroscience↗

bioRxiv2020

A configurable model of the synaptic proteome reveals the molecular mechanisms of disease co-morbidity.

Synapses contain highly complex proteomes which control synaptic transmission, cognition and behaviour. Genes encoding synaptic proteins are associated with neuronal disorders many of which show clinical co-morbidity. Our hypothesis is that there is mechanistic overlap that is emergent from the network properties of the molecular complex. To test this requires a detailed and comprehensive molecular network model. We integrated 57 published synaptic proteomic datasets obtained between 2000 and 2019 that describe over 7000 proteins. The complexity of the postsynaptic proteome is reaching an asymptote with a core set of ~3000 proteins, with less data on the presynaptic terminal, where each new study reveals new components in its landscape. To complete the network, we added direct protein-protein interaction data and functional metadata including disease association. The resulting amalgamated molecular interaction network model is embedded into a SQLite database. The database is highly flexible allowing the widest range of queries to derive custom network models based on meta-data including species, disease association, synaptic compartment, brain region, and method of extraction. This network model enables us to perform in-depth analyses that dissect molecular pathways of multiple diseases revealing shared and unique protein components. We can clearly identify common and unique molecular profiles for co-morbid neurological disorders such as Schizophrenia and Bipolar Disorder and even disease comorbidities which span biological systems such as the intersection of Alzheimers Disease with Hypertension.

systems biology↗

bioRxiv2020

Meaningful Effects in the Adolescent Brain Cognitive Development Study

The Adolescent Brain Cognitive Development (ABCD) Study is the largest single-cohort prospective longitudinal study of neurodevelopment and childrens health in the United States. A cohort of n= 11,880 children aged 9-10 years (and their parents/guardians) were recruited across 22 sites and are being followed with in-person visits on an annual basis for at least 10 years. The study approximates the US population on several key sociodemographic variables, including sex, race, ethnicity, household income, and parental education. Data collected include assessments of health, mental health, substance use, culture and environment and neurocognition, as well as geocoded exposures, structural and functional magnetic resonance imaging (MRI), and whole-genome genotyping. Here, we describe the ABCD Study aims and design, as well as issues surrounding estimation of meaningful associations using its data, including population inferences, hypothesis testing, power and precision, control of covariates, interpretation of associations, and recommended best practices for reproducible research, analytical procedures and reporting of results.

neuroscience↗

bioRxiv2020

The human and mouse synaptome architecture of excitatory synapses show conserved features.

Large-scale mapping of the location of synapses and their molecular properties in the mouse has shown that diverse synapse types are spatially distributed across the brain. The diversity of synapses is known as the synaptome and the spatial distribution as the synaptome architecture. Synaptome maps in the mouse show each brain region has a characteristic compositional signature. The signature can store behavioral representations and is modified in mouse genetic models of human disease. The human synaptome remains unexplored and whether it has any conserved features with the mouse synaptome is unknown. As a first step toward creating a human synaptome atlas, we have labelled and imaged synapses expressing the excitatory synapse protein PSD95 in twenty human brain regions in four phenotypically normal individuals. We quantified the number, size and intensity of approximately a billion individual synaptic puncta and compared their regional distributions. We found that each region showed a distinct signature of synaptic puncta parameters. Comparison of brain regions showed the synaptome of cortical and hippocampal structures were similar but distinct to the synaptome of cerebellum and brainstem. Comparison of human and mouse synaptome revealed conservation of synaptic puncta parameters, hierarchical organization of brain regions and network architecture. These data show that the synaptome of humans and mouse share conserved features despite the 1000-fold difference in brain size and 90 million years since a common ancestor. This first draft human synaptome atlas illustrates the feasibility of generating a systematic atlas of the human synaptome in health and disease.

neuroscience↗

bioRxiv2019

A lifespan program of mouse synaptome architecture

How synapses change molecularly during the lifespan and across all brain circuits is unknown. We analyzed the protein composition of billions of individual synapses from birth to old age on a brain-wide scale in the mouse, revealing a program of changes in the lifespan synaptome architecture spanning individual dendrites to the systems level. Three major phases were uncovered, corresponding to human childhood, adulthood and old age. An arching trajectory of synaptome architecture drives the differentiation and specialization of brain regions to a peak in young adults before dedifferentiation returns the brain to a juvenile state. This trajectory underscores changing network organization and hippocampal physiology that may account for lifespan transitions in intellectual ability and memory, and the onset of behavioral disorders. One sentence summaryThe synaptome architecture of the mouse brain undergoes continuous changes that organize brain circuitry across the lifespan.

neuroscience↗

bioRxiv2019

ATP7B Variant c.1934T>G p.Met645Arg Causes Wilson Disease by Promoting Exon 6 Skipping

Wilson Disease is a recessive genetic disorder caused by pathogenic loss-of-function variants in the ATP7B gene. It is characterized by disrupted copper homeostasis resulting in liver disease and/or neurological abnormalities. The variant NM_000053.3:c.1934T>G (Met645Arg) has been reported as compound heterozygous and is highly prevalent among Wilson Disease patients of Spanish descent. Accordingly, it is classified as pathogenic by leading molecular diagnostic centers. However, functional studies suggest that the amino acid change does not alter protein function, leading one ClinVar submitter to question its pathogenicity. Here we used a minigene system and gene-edited HepG2 cells to demonstrate that c.1934T>G causes approximately 70% skipping of exon 6. Exon 6 skipping results in frameshift and stop gain, which is expected to cause loss of ATP7B function. The elucidation of the mechanistic effect for this variant resolves any doubt about its pathogenicity and enables the development of genetic medicines for restoring correct splicing.

genetics↗

bioRxiv2017

A dementia-associated risk variant near TMEM106B alters chromatin architecture and gene expression

Neurodegenerative diseases pose an extraordinary threat to the worlds aging population, yet no disease-modifying therapies are available. While genome-wide association studies (GWAS) have identified hundreds of novel risk loci for neurodegeneration, the mechanisms by which these loci influence disease risk are largely unknown. Indeed, of the many thousands of SNP-trait associations identified by GWAS over the past ~10 years, very few are understood mechanistically. Here, we investigate the association of common genetic variants at the 7p21 locus with risk for the neurodegenerative disease frontotemporal lobar degeneration. We show that variants associated with disease risk correlate with increased brain expression of the 7p21 gene TMEM106B, and no other genes. Furthermore, incremental increases in TMEM106B levels result in incremental increases in lysosomal phenotypes and cell toxicity. We then combine fine-mapping, bioinformatics, and bench-based approaches to functionally characterize all candidate causal variants at this locus. This approach identified a noncoding variant, rs1990620, which differentially recruits CTCF, influencing CTCF-mediated long-range chromatin looping interactions between multiple cis-regulatory elements, including the TMEM106B promoter. Our findings thus provide an in-depth analysis of the 7p21 locus linked by GWAS to frontotemporal lobar degeneration, nominating a causal variant and a causal mechanism for allele-specific expression and disease association at this locus. Finally, we show that genetic variants associated with risk for neurodegenerative diseases beyond frontotemporal lobar degeneration are enriched in brain CTCF-binding sites genome-wide, implicating CTCF-mediated gene regulation in risk for neurodegeneration more generally.

genetics↗