Biology subjects

Moore, R.

Publications and source records attributed to Moore, R..

At least 37 records · Page 2Linked to original sources

bioRxiv2022

The autism-associated gene SYNGAP1 regulates human cortical neurogenesis

Autism spectrum disorder (ASD) is a genetically heterogeneous disorder linked with rare, inherited and de novo mutations occurring in two main functional gene categories: gene expression regulation and synaptic function1. Accumulating evidence points to dysregulation in cortical neurogenesis as a convergent mechanism in ASD pathophysiology2-8. While asynchronous development has been identified as a shared feature among ASD-risk genes in the category of gene expression regulation, it remains unknown whether this phenotype is also associated with ASD-risk genes in the synaptic function category. Here we show for the first time the expression of the synaptic Ras GTP-ase activating protein 1 (SYNGAP1), one of the top ASD risk genes9, in human cortical progenitors (hCPs). Interestingly, we found that multiple components of the postsynaptic density (PSD) of excitatory synapses, of which SYNGAP1 is one of the most abundant components 10,11, are enriched in the proteome of hCPs. Specifically, we discover that SYNGAP1 is expressed within the apical domain of human radial glia cells (hRGCs) where it lines the wall of the developing cortical ventricular zone colocalizing with the tight junction-associated protein and MAGUK family member TJP1. In a cortical organoid model of SYNGAP1 haploinsufficiency, we show dysregulated cytoskeletal dynamics that impair the scaffolding and division plane of hRGCs, resulting in disrupted lamination of the cortical plate and accelerated maturation of cortical projection neurons. Overall, the discovery of the expression and function of SYNGAP1 in cortical progenitor cells reframes our understanding of the pathophysiology of SYNGAP1-related disorders and, more broadly, underscores the importance of dissecting the role of synaptic genes associated with neurodevelopmental disorders in distinct cell types across developmental stages.

developmental biology↗

bioRxiv2021

A simple and direct method to define clonal selection in somatic mosaicism

As DNA variants accumulate in somatic stem cells, become selected or evolve neutrally, they may ultimately alter tissue function. When, and how, selection occurs in homeostatic tissues is incompletely understood. Here, we introduce SCIFER, a scalable method that identifies selection in an individual tissue, without requiring knowledge of the underlying driver event. Moreover, SCIFER infers the self-renewal and mutation dynamics of the tissues stem cells, and, if selection is present, the size and growth rate of the largest selected clone. We benchmark SCIFER with published data and then probe bone marrow of 22 non-leukemic individuals for clonal hematopoiesis (CH), identifying CH with known and unknown driver events. Unexpectedly, we find accelerated division of all stem cells in CH, compared to age-matched non-CH individuals, suggesting that the bone marrow environment alters stem cell dynamics in individuals with CH. SCIFER is broadly applicable to renewing somatic tissues to detect and quantify selection.

cancer biology↗

bioRxiv2021

The pink salmon genome: uncovering the genomic consequences of a strict two-year life-cycle

Pink salmon (Oncorhynchus gorbuscha) adults are the smallest of the five Pacific salmon native to the western Pacific Ocean. Pink salmon are also the most abundant of these species and account for a large proportion of the commercial value of the salmon fishery worldwide. A strict two-year life-history of most pink salmon generates temporally isolated populations that spawn either in even-years or odd-years. To uncover the influence of this genetic isolation, reference genome assemblies were generated for each year-class and whole genome re-sequencing data was collected from salmon of both year-classes. The salmon were sampled from six Canadian rivers and one Japanese river. At multiple centromeres we identified peaks of Fst between year-classes that were millions of base-pairs long. The largest Fst peak was also associated with a million base-pair chromosomal polymorphism found in the odd-year genome near a centromere. These Fst peaks may be the result of centromere drive or a combination or reduced recombination and genetic drift, and they could influence speciation. Other regions of the genome influenced by odd-year and even-year temporal isolation and tentatively under selection were mostly associated with genes related to immune function, organ development/maintenance, and behaviour.

genomics↗

bioRxiv2021

Genome-Wide Detection of Imprinted Differentially Methylated Regions Using Nanopore Sequencing

Imprinting is a critical part of normal embryonic development in mammals, controlled by defined parent-of-origin (PofO) differentially methylated regions (DMRs) known as imprinting control regions. As we and others have shown, direct nanopore sequencing of DNA provides a mean to detect allelic methylation and to overcome the drawbacks of methylation array and short-read technologies. Here we leverage publicly-available nanopore sequence data for 12 standard B-lymphocyte cell lines to present the first genome-wide mapping of imprinted intervals in humans using this technology. We were able to phase 95% of the human methylome and detect 94% of the well-characterized imprinted DMRs. In addition, we found 28 novel imprinted DMRs (12 germline and 16 somatic), which we confirmed using whole-genome bisulfite sequencing (WGBS) data. Analysis of WGBS data in mus musculus, rhesus macaque, and chimpanzee suggested that 12 of these are conserved. We also detected subtle parental methylation bias spanning several kilobases at seven known imprinted clusters. These results expand the current state of knowledge of imprinting, with potential applications in the clinic. We have also demonstrated that nanopore long reads, can reveal imprinting using only parent-offspring trios, as opposed to the large multi - generational pedigrees that have previously been required.

genomics↗

bioRxiv2021

Statistical and machine learning models for classification of human wear and delivery days in accelerometry data

PurposeAccelerometers are increasingly utilized in healthcare research to assess human activity. Accelerometry data are often collected by mailing accelerometers to participants, who wear the accelerometers to collect data on their activity. The devices are then mailed back to the laboratory for analysis. We develop models to classify days in accelerometry data as activity from actual human wear or the delivery process. These models can be used to automate the cleaning of accelerometry datasets that are adulterated with activity from delivery. MethodsFor the classification of delivery days in accelerometry data, we developed statistical and machine learning models in a supervised learning context using a large human activity and delivery labeled accelerometry dataset. We extracted several features, which were included to develop random forest, logistic regression, mixed effects regression, and multilayer perceptron models, while convolutional neural network, recurrent neural network, and hybrid convolutional recurrent neural network models were developed without feature extraction. Model performances were assessed using Monte Carlo cross-validation. ResultsWe found that a hybrid convolutional recurrent neural network performed best in the classification task with an F1 score of 0.960 but simpler models such as logistic regression and random forest also had excellent performance with F1 scores of 0.951 and 0.957, respectively. ConclusionThe models developed in this study can be used to classify days in accelerometry data as either human or delivery activity. An analyst can weigh the larger computational cost and greater performance of the convolutional recurrent neural network against the faster but slightly less powerful random forest or logistic regression. The best performing models for classification of delivery data are publicly available on the open source R package, PhysicalActivity.

bioinformatics↗

bioRxiv2020

Targeting Vitamin-D receptor (VDR) by a small molecule antagonist MeTC7 inhibits PD-L1 but controls THMYCN neuroblastoma growth PD-L1 independently

Vitamin-D receptor (VDR) mRNA is enriched in malignant lung, ovarian and pancreatic tissues and showed poor prognoses. Calcitriol and stable or CRISPR-directed VDR upregulation increased PD-L1mRNA and protein expression in cancer cells in-vitro. A ChIP assay showed the binding of VDR with VDREPD-L1. Stattic, a STAT3 phosphorylation inhibitor blocked calcitriol or VDR overexpression induced PD-L1 upregulation. MeTC7, a VDR antagonist developed by us, reduced PD-L1 expression on macrophages, ovarian, lung, breast, and pancreatic cancer cells in-vitro. In radiotherapy inducible PD-L1 model of orthotopic MC38 murine colon cancer, MeTC7 decreased PD-L1 surface expression, suppressed inflammatory monocytes (IMs) population and increased intra-tumoral CD69+PD1+CD8+T-cells. Intriguingly, MeTC7 reduced TH-MYCN transgenic neuroblastoma tumor growth without affecting PD-L1 and tumor immune milieu. In summary, Vitamin-D/VDR drives PD-L1 expression on cancer cells via STAT-3. Inhibiting VDR exhibited anti-checkpoint effects in orthotopic colon tumors, whereas PDL1-independent and anti-VDR/MYCN effects controlled growth of transgenic neuroblastoma and xenografted tumors. SummaryVitamin-D/VDR induces PD-L1 expression on cancer cells via STAT-3; and targeting VDR by a novel small molecule antagonist MeTC7 exhibits both anti-PD-L1 and anti-VDR/MYCN effects in tumor models.

cancer biology↗

bioRxiv2020

PD-1 blockade exacerbates Mycobacterium tuberculosis infection in rhesus macaques

Boosting immune cell function by targeting the co-inhibitory receptor PD-1 may have applications in the treatment of chronic infections. Here we examine the role of PD-1 during Mycobacterium tuberculosis (Mtb) infection of rhesus macaques. Animals treated with PD-1 mAb developed worse disease and higher granuloma bacterial loads compared to isotype control treated monkeys. PD-1 blockade increased the number and functionality of granuloma Mtb-specific CD8 T cells. In contrast, Mtb-specific CD4 T cells in PD-1 treated macaques were not increased in number or function in granulomas, upregulated high levels of CTLA-4 and exhibited reduced intralesional trafficking in live imaging studies. In granulomas of PD-1 treated animals, multiple pro-inflammatory cytokines were elevated, and more cytokines correlated with bacterial loads, leading to the identification of a role for caspase 1 in the exacerbation of tuberculosis after PD-1 blockade. Lastly, increased Mtb bacterial loads after PD-1 blockade were found to associate with the composition of the intestinal microbiota prior to infection in individual macaques. Therefore, PD-1-mediated co-inhibition is required for control of Mtb infection in macaques, perhaps due to its role in dampening detrimental inflammation as well as allowing for normal CD4 T cell responses.

immunology↗

bioRxiv2020

A comprehensive logic-based model of the human immune system to study the dynamics responses to mono- and coinfections

The immune system is a complex and dynamic network, crucial for combating infections and maintaining health. Developing a comprehensive digital twin of the immune system requires incorporating essential cellular components and their interactions. This study presents the first blueprint for an immune system digital twin, consisting of a comprehensive and simulatable mechanistic model. It integrates 51 innate and adaptive immune cells, 37 secretory factors, and 11 disease conditions, providing the foundation for developing a multi-scale model. The cellular-level model demonstrates its potential in characterizing immune responses to various single and combinatorial disease conditions. By making the model available in easy-to-use formats directly in the Cell Collective platform, the community can easily and further expand it. This blueprint represents a significant step towards developing general-purpose immune digital twins, with far-reaching implications for the future of digital twin technology in life sciences and healthcare, advancing patient care, and accelerating precision medicine.

systems biology↗

bioRxiv2020

Septins disruption controls tumor growth and enhances efficacy of Herceptin

Septin expressions are altered in cancer cells and exhibit poor prognoses in malignancies. As the first approach to develop a septin filament targeting agent, we optimized the structure of Forchlorfenuron (FCF), a known plant cytokinin to generate UR214-9, which contrary to FCF, causes septin-2/9 filamental structural catastrophe in cancer cells without altering cellular septin protein levels. In-silico docking using septin-2/septin-2 dimer complex showed that UR214-9 displaced the guanine carbonyl oxygen from the GDP binding domain and showed increased binding energy than FCF(-8.59vs-7.21). UR214-9 reduced cancer cell growth, downregulated HER2/STAT-3 axis and controlled growth of HER2+ pancreatic, breast and ovarian cancer xenografts in NSG mice and enhanced response of Herceptin against HER2+breast cancer xenograft. Transcriptome analysis of UR214-9 exposed cells demonstrated significant perturbation of <20 genes compared to afatinib which impacted >1200 genes in JIMT-1 breast cancer cells indicating target specificity and non-transcriptional functions of UR214-9. In summary, disrupting septins via UR214-9 is a new approach to control the growth of HER2+ malignancies.

cancer biology↗

bioRxiv2020

Genome-wide scale analyses identify novel BMI genotype-environment interactions using a conditional false discovery rate

Genotype-environment interaction (GxE) studies typically focus on variants with previously known marginal associations. While such two-step filtering greatly reduces the multiple testing burden, it can miss loci with pronounced GxE effects, which tend to have weaker marginal associations. To test for GxE effects on a genome-wide scale whilst leveraging information from marginal associations in a flexible manner, we combine the conditional false discovery rate with interaction test results obtained from StructLMM. After validating our approach, we applied this strategy to UK Biobank (UKBB) data to probe for GxE effects on BMI. Using 126,077 UKBB individuals for discovery, we identified known (FTO, MC4R, SEC16B) and novel GxE signals, many of which replicated (FAM150B/ALKAL2,TMEM18, EFR3B, ZNF596-FAM87A, LIN7C-BDNF, FAIM2, UNC79, LAT) in an independent subset of UKBB (n=126,076). Finally, when analysing the full UKBB cohort, we identified 140 candidate loci with GxE effects, highlighting the advantages of our approach.

genetics↗

bioRxiv2020

Integrative computational approach identifies new targets in CD4+ T cell-mediated immune disorders

CD4+ T cells provide adaptive immunity against pathogens and abnormal cells, and they are also associated with various immune related diseases. CD4+ T cells metabolism is dysregulated in these pathologies and represents an opportunity for drug discovery and development. Genome-scale metabolic modeling offers an opportunity to accelerate drug discovery by providing high-quality information about possible target space in the context of a modeled disease. Here, we develop genome-scale models of naive, Th1, Th2 and Th17 CD4+ T cell subtypes to map metabolic perturbations in rheumatoid arthritis, multiple sclerosis, and primary biliary cholangitis. We subjected these models to in silico simulations for drug response analysis of existing FDA-approved drugs, and compounds. Integration of disease-specific differentially expressed genes with altered reactions in response to metabolic perturbations identified 68 drug targets for the three autoimmune diseases. In vitro experimental validations together with literature-based evidence showed that modulation of fifty percent of identified drug targets has been observed to lead to suppression of CD4+ T cells, further increasing their potential impact as therapeutic interventions. The used approach can be generalized in the context of other diseases, and novel metabolic models can be further used to dissect CD4+ T cell metabolism.

systems biology↗

bioRxiv2019

Novel functional insights revealed by distinct protein-protein interactions of the residual SWI/SNF complex in SMARCA4-deficient small cell carcinoma of the ovary, hypercalcemic type

Chromatin remodeling plays a critical role in tumor suppression as demonstrated by 20% of human cancers bearing inactivating mutations in SWI/SNF chromatin remodeling complex members. Mutations in different SWI/SNF subunits drive a variety of adult and pediatric tumor types, including non-small cell lung cancers, rhabdoid tumors, medulloblastomas, and ovarian cancers. Small cell carcinoma of the ovary hypercalcemic type (SCCOHT) is an aggressive subtype of ovarian cancer occurring in young women. Nearly all (>98%) SCCOHTs have inactivating mutations in SMARCA4, which encodes 1 of 2 mutually exclusive catalytic subunits of the SWI/SNF complex. Less than half of SCCOHT patients survive 5 years despite aggressive surgery and multimodal chemotherapy. Empirical support for effective SCCOHT treatments is scarce, in part because of the poor understanding of SCCOHT tumorigenesis. To gain insight into the functional consequences of SWI/SNF subunit loss, we defined SWI/SNF composition and its protein-protein interactions (PPIs) by immunoprecipitation and mass spectrometry (IP-MS) of SWI/SNF subunits in 3 SCCOHT cell lines. Comparing these results to a cell line containing a wild-type SWI/SNF complex, the interaction of most canonical core SWI/SNF subunits was observed in all SCCOHT cell lines at a lower abundance. The SCCOHT SWI/SNF also lacked ATPase module subunits and showed a drastic reduction in PBAF-specific subunit interactions. The wild-type and SCCOHT SWI/SNF subunits immunoprecipitated a shared set of 26 proteins, including core SWI/SNF subunits and RNA processing proteins. We observed 131 proteins exclusively interacting with the wild-type SWI/SNF complex including isoform-specific SWI/SNF subunits, members of the NuRD complex, and members of the MLL3/4 complex. We observed 60 PPIs exclusive to the SCCOHT residual SWI/SNF shared in at least 2 of the 3 SCCOHT cell lines, including many proteins involved in RNA processing. Differential interactions with the residual SWI/SNF complex in SCCOHT may further elucidate altered functional consequences of SMARCA4 mutations in these tumors as well as identify synthetic lethal targets that translate to other SWI/SNF-deficient tumors.

cancer biology↗

bioRxiv2019

Dissociation of solid tumour tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase associated stress responses

BackgroundSingle-cell RNA sequencing (scRNAseq) is a powerful tool for studying complex biological systems, such as tumour heterogeneity and tissue microenvironments. However, the sources of technical and biological variation in primary solid tumour tissues and patient-derived mouse xenografts for scRNAseq, are not well understood. Here, we used low temperature (6{degrees}C) protease and collagenase (37{degrees}C) to identify the transcriptional signatures associated with tissue dissociation across a diverse scRNAseq dataset comprising 128,481 cells from patient cancer tissues, patient-derived breast cancer xenografts and cancer cell lines.\n\nResultsWe observe substantial variation in standard quality control (QC) metrics of cell viability across conditions and tissues. From FACS sorted populations gated for cell viability, we identify a sub-population of dead cells that would pass standard data filtering practices, and quantify the extent to which their transcriptomes differ from live cells. We identify a further subpopulation of transcriptomically \"dying\" cells that exhibit up-regulation of MHC class I transcripts, in contrast with live and fully dead cells. From the contrast between tissue protease dissociation at 37{degrees}C or 6{degrees}C, we observe that collagenase digestion results in a stress response. We derive a core gene set of 512 heat shock and stress response genes, including FOS and JUN, induced by collagenase (37{degrees}C), which are minimized by dissociation with a cold active protease (6{degrees}C). While induction of these genes was highly conserved across all cell types, cell type-specific responses to collagenase digestion were observed in patient tissues. We observe that the yield of cancer and non-cancer cell types varies between tissues and dissociation methods.\n\nConclusionsThe method and conditions of tumour dissociation influence cell yield and transcriptome state and are both tissue and cell type dependent. Interpretation of stress pathway expression differences in cancer single cell studies, including components of surface immune recognition such as MHC class I, may be especially confounded. We define a core set of 512 genes that can assist with identification of such effects in dissociated scRNA-seq experiments.

genomics↗

bioRxiv2019

Basal protrusions mediate spatiotemporal patterns of spinal neuron differentiation

During early spinal cord development, neurons of particular subtypes differentiate with a sparse periodic pattern while later neurons differentiate in the intervening space to eventually produce continuous columns of similar neurons. The mechanisms that regulate this spatiotemporal pattern are unknown. In vivo imaging of zebrafish reveals differentiating spinal neurons transiently extend two long protrusions along the basal surface of the spinal cord prior to axon initiation. These protrusions express Delta protein consistent with the possibility they influence Notch signalling at a distance of several cell diameters. Experimental reduction of laminin expression leads to smaller protrusions and shorter distances between differentiating neurons. The experimental data and a theoretical model support the proposal that the pattern of neuronal differentiation is regulated by transient basal protrusions that deliver temporally controlled lateral inhibition mediated at a distance. This work uncovers novel, stereotyped protrusive activity of new-born neurons that organizes long distance spatiotemporal patterning of differentiation.

developmental biology↗

bioRxiv2018

High-resolution structural genomics reveals new therapeutic vulnerabilities in glioblastoma

We investigated the role of 3D genome architecture in instructing functional properties of glioblastoma stem cells (GSCs) by generating the highest-resolution 3D genome maps to-date for this cancer. Integration of DNA contact maps with chromatin and transcriptional profiles identified specific mechanisms of gene regulation, including individual physical interactions between regulatory regions and their target genes. Residing in structurally conserved regions in GSCs was CD276, a gene known to play a role in immuno-modulation. We show that, unexpectedly, CD276 is part of a stemness network in GSCs and can be targeted with an antibody-drug conjugate to curb self-renewal, a key stemness property. Our results demonstrate that integrated structural genomics datasets can be employed to rationally identify therapeutic vulnerabilities in self-renewing cells.\n\nSIGNIFICANCEIn adult GBM, GSCs act as therapy-resistant reservoirs to nucleate tumor recurrence. New therapeutic approaches that target these cell populations hold the potential of significantly improving patient care and overall prognosis for this always-lethal cancer. Our work describes new links between 3D genome architecture and stemness properties in GSCs. In particular, through integration of multiple genomics and structural genomics datasets, we found an unexpected connection between immune-related genes and self-renewal programs in GBM. Among these, we show that targeting CD276 with knockdown strategies or specific antibody-drug conjugates achieve suppression of self-renewal. Strategies to target CD276+ cells are currently in clinical trials for solid tumors. Our results indicate that CD276-targeting agents could be deployed in GBM to specifically target GSC populations.\n\nHIGHLIGHTSO_LIWe generated high (sub-5 kb) resolution Hi-C maps for stem-like cells from GBM patients.\nC_LIO_LIIntegration of Hi-C and genomics datasets dissects mechanisms of gene regulation.\nC_LIO_LI3D genomes poise immune-related genes, including CD276, for expression.\nC_LIO_LITargeting CD276 curbs self-renewal properties of GBM cells.\nC_LI

cancer biology↗

bioRxiv2018

Subclonal architecture, evolutionary trajectories and patterns of inheritance of germline variants in pediatric glioblastoma

Pediatric glioblastoma (pGBM) is a lethal cancer with no effective therapies. Intratumoral genetic heterogeneity and mode of tumor evolution have not been systematically addressed for this cancer. Whole-genome sequencing of germline-tumor pairs showed that pGBM is characterized by intratumoral genetic heterogeneity and consequent subclonal architecture. We found that pGBM undergoes extreme evolutionary trajectories, with primary and recurrent tumors having different subclonal compositions. Analysis of variant allele frequencies supported a model of tumor growth involving slow-cycling cancer stem cells that give rise to fast-proliferating progenitor-like cells and to non-dividing cells. pGBM patients germlines had subclonal structural variants, some of which underwent dynamic frequency fluctuations during tumor evolution. By sequencing germlines of mother-father-patient trios, we found that inheritance of deleterious germline variants from healthy parents cooperate with de novo germline and somatic events to the tumorigenic process. Our studies therefore challenge the current notion that pGBM is a relatively homogeneous molecular entity.

cancer biology↗

bioRxiv2018

Resource: Scalable whole genome sequencing of 40,000 single cells identifies stochastic aneuploidies, genome replication states and clonal repertoires

Essential features of cancer tissue cellular heterogeneity such as negatively selected genome topologies, sub-clonal mutation patterns and genome replication states can only effectively be studied by sequencing single-cell genomes at scale and high fidelity. Using an amplification-free single-cell genome sequencing approach implemented on commodity hardware (DLP+) coupled with a cloud-based computational platform, we define a resource of 40,000 single-cell genomes characterized by their genome states, across a wide range of tissue types and conditions. We show that shallow sequencing across thousands of genomes permits reconstruction of clonal genomes to single nucleotide resolution through aggregation analysis of cells sharing higher order genome structure. From large-scale population analysis over thousands of cells, we identify rare cells exhibiting mitotic mis-segregation of whole chromosomes. We observe that tissue derived scWGS libraries exhibit lower rates of whole chromosome anueploidy than cell lines, and loss of p53 results in a shift in event type, but not overall prevalence in breast epithelium. Finally, we demonstrate that the replication states of genomes can be identified, allowing the number and proportion of replicating cells, as well as the chromosomal pattern of replication to be unambiguously identified in single-cell genome sequencing experiments. The combined annotated resource and approach provide a re-implementable large scale platform for studying lineages and tissue heterogeneity.

genomics↗

bioRxiv2018

A linear mixed model approach to study multivariate gene-environment interactions

Different environmental factors, including diet, physical activity, or external conditions can contribute to genotype-environment interactions (GxE). Although high-dimensional environmental data are increasingly available, and multiple environments have been implicated with GxE at the same loci, multi-environment tests for GxE are not established. Such joint analyses can increase power to detect GxE and improve the interpretation of these effects. Here, we propose the structured linear mixed model (StructLMM), a computationally efficient method to test for and characterize loci that interact with multiple environments. After validating our model using simulations, we apply StructLMM to body mass index in UK Biobank, where our method detects previously known and novel GxE signals. Finally, in an application to a large blood eQTL dataset, we demonstrate that StructLMM can be used to study interactions with hundreds of environmental variables.

genetics↗