Biology subjects

Ryten, M.

Publications and source records attributed to Ryten, M..

At least 55 records · Page 3Linked to original sources

bioRxiv2022

Pseudogenes limit the identification of novel common transcripts generated by their parent genes

The human genome contains numerous duplicated regions, such as parent-pseudogene pairs, causing sequencing reads to align equally well to either gene. The extent to which this ambiguity complicates transcriptomic analyses is currently unknown. This is concerning as many parent genes have been linked to disease, including GBA1, causally linked to both Parkinsons and Gaucher disease. We find that most of the short sequencing reads that map to GBA1, also map to its pseudogene, GBAP1. Using long-read RNA-sequencing in human brain, where all reads mapped uniquely, we demonstrate significant differences in expression compared to short-read data. We identify novel transcripts from both GBA1 and GBAP1, including protein-coding transcripts that are translated in vitro and detected in proteomic data, but that lack GCase activity. By combining long-read with single-nuclear RNA-sequencing to analyse brain-relevant cell types we demonstrate that transcript expression varies by brain region with cell-type-selectivity. Taken together, these results suggest a non-lysosomal function for both GBA1 and GBAP1 in brain. Finally, we demonstrate that inaccuracies in annotation are widespread among parent genes, with implications for many human diseases.

genetics↗

bioRxiv2022

Genome-wide analysis of Structural Variants in Parkinson's Disease using Short-Read Sequencing data

Parkinsons disease is a complex neurodegenerative disorder, affecting approximately one million individuals in the USA alone. A significant proportion of risk for Parkinsons disease is driven by genetics. Despite this, the majority of the common genetic variation that contributes to disease risk is unknown, in-part because previous genetic studies have focussed solely on the contribution of single nucleotide variants. Structural variants represent a significant source of genetic variation in the human genome. However, because assay of this variability is challenging, structural variants have not been cataloged on a genome-wide scale, and their contribution to the risk of Parkinsons disease remains unknown. In this study, we 1) leveraged the GATK-SV pipeline to detect and genotype structural variants in 7,772 short-read sequencing data and 2) generated a subset of matched whole-genome Oxford Nanopore Technologies long-read sequencing data from the PPMI cohort to allow for comprehensive structural variant confirmation. We detected, genotyped, and tested 3,154 "high-confidence" common structural variant loci, representing over 412 million nucleotides of non-reference genetic variation. Using the long-read sequencing data, we validated three structural variants that may drive the association signals at known Parkinsons disease risk loci, including a 2kb intronic deletion within the gene LRRN4. Further, we confirm that the majority of structural variants in the human genome cannot be detected using short-read sequencing alone, encompassing on average around 4 million nucleotides of inaccessible sequence per genome. Therefore, although these data provide the most comprehensive survey of the contribution of structural variants to the genetic risk of Parkinsons disease to date, this study highlights the need for large-scale long-read datasets to fully elucidate the role of structural variants in Parkinsons disease.

genetics↗

bioRxiv2022

Analysis of nucleus and cytoplasm-specific RNA fractions demonstrates that a significant proportion of the genetic regulation of gene expression across the human brain occurs post-transcriptionally

Gaining insight into the genetic regulation of gene expression in human brain is key to the interpretation of genome-wide association studies for major neurological and neuropsychiatric diseases. Expression quantitative trait loci (eQTL) analyses have largely been used to achieve this, providing valuable insights into the genetic regulation of steady-state RNA in human brain, but not distinguishing between molecular processes regulating transcription and stability. RNA quantification within cellular fractions can disentangle these processes in cell types and tissues which are challenging to model in vitro. We investigated the underlying molecular processes driving the genetic regulation of gene expression specific to a cellular fraction using allele-specific expression (ASE). Applying ASE analysis to genomic and transcriptomic data from paired nuclear and cytoplasmic fractions of anterior prefrontal cortex, cerebellar cortex and putamen tissues from 4 post-mortem neuropathologically-confirmed control human brains, we demonstrate that a significant proportion of genetic regulation of gene expression occurs post-transcriptionally in the cytoplasm, with genes undergoing this form of regulation more likely to be synaptic. These findings have implications for understanding the structure of gene expression regulation in human brain, and importantly the interpretation of rapidly growing single-nucleus brain RNA-sequencing and eQTL datasets, where cytoplasm-specific regulatory events could be missed.

genomics↗

bioRxiv2022

The contribution of Neanderthal introgression and natural selection to neurodegenerative diseases

Humans are thought to be more susceptible to neurodegeneration than equivalently-aged primates. It is not known whether this vulnerability is specific to anatomically-modern humans or shared with other hominids. The contribution of introgressed Neanderthal DNA to neurodegenerative disorders remains uncertain. It is also unclear how common variants associated with neurodegenerative disease risk are maintained by natural selection in the population despite their deleterious effects. In this study, we aimed to quantify the genome-wide contribution of Neanderthal introgression and positive selection to the heritability of complex neurodegenerative disorders to address these questions. We used stratified-linkage disequilibrium score regression to investigate the relationship between five SNP-based signatures of natural selection, reflecting different timepoints of evolution, and genome-wide associated variants of the three most prevalent neurodegenerative disorders: Alzheimers disease, amyotrophic lateral sclerosis and Parkinsons disease. We found no evidence for enrichment of positively-selected SNPs in the heritability of Alzheimers disease, amyotrophic lateral sclerosis and Parkinsons disease,, suggesting that common deleterious disease variants are unlikely to be maintained by positive selection. There was no enrichment of Neanderthal introgression in the SNP-heritability of these disorders, suggesting that Neanderthal admixture is unlikely to have contributed to disease risk. These findings provide insight into the origins of neurodegenerative disorders within the evolution of Homo sapiens and addresses a long-standing debate, showing that Neanderthal admixture is unlikely to have contributed to common genetic risk of neurodegeneration in anatomically-modern humans.

neuroscience↗

bioRxiv2022

ggtranscript: an R package for the visualization and interpretation of transcript isoforms using ggplot2

MotivationThe advent of long-read sequencing technologies has increased demand for the visualisation and interpretation of transcripts. However, tools that perform such visualizations remain inflexible and lack the ability to easily identify differences between transcript structures. Here, we introduce ggtranscript, an R package that provides a fast and flexible method to visualize and compare transcripts. As a ggplot2 extension, ggtranscript inherits the functionality and familiarity of ggplot2 making it easy to use. Availability and implementationggtranscript is available at https://github.com/dzhang32/ggtranscript, DOI: https://doi.org/10.5281/zenodo.6374061

bioinformatics↗

bioRxiv2021

Huntington's disease age at motor onset is modified by the tandem hexamer repeat in TCERG1

BackgroundHuntingtons disease is caused by an expanded CAG tract in HTT. The length of the CAG tract accounts for over half the variance in age at onset of disease, and is influenced by other genetic factors, mostly implicating the DNA maintenance machinery. We examined a single nucleotide variant, rs79727797, on chromosome 5 in the TCERG1 gene, previously reported to be associated with Huntingtons disease and a quasi-tandem repeat (QTR) hexamer in exon 4 of TCERG1 with a central pure repeat. MethodsWe developed a novel method for calling perfect and imperfect repeats from exome sequencing data, and tested association between the QTR in TCERG1 and residual age at motor onset (after correcting for the effects of CAG length in the HTT gene) in 610 individuals with Huntingtons disease via regression analysis. ResultsWe found a significant association between age at onset and the sum of the repeat lengths from both alleles of the QTR (p = 2.1x10-9), with each added repeat hexamer reducing age at onset by one year (95% confidence interval [0.7, 1.4]). This association explained that previously observed with rs79727797. ConclusionsThe association with age at onset in the genome-wide association study is due to a QTR hexamer in TCERG1, translated to a glutamine/alanine tract in the protein. We could not distinguish whether this was due to cis-effects of the hexamer repeat on gene expression or of the encoded glutamine/alanine tract in the protein. These results motivate further study of the mechanisms by which TCERG1 modifies onset of HD.

genetics↗

bioRxiv2021

PhenoExam: an R package and Web application for the examination of phenotypes linked to genes and gene sets

Gene set based phenotype enrichment analysis (detecting phenotypic terms that emerge as significant in a set of genes) can improve the rate of genetic diagnoses amongst other research purposes. To facilitate diverse phenotype analysis, we developed PhenoExam, a freely available R package for tool developers and a web interface for users, which performs: (1) phenotype and disease enrichment analysis on a gene set; (2) measures statistically significant phenotype similarities between gene sets and (3) detects significant differential phenotypes or disease terms across different databases. PhenoExam achieves these tasks by integrating databases or resources such as the HPO, MGD, CRISPRbrain, CTD, ClinGen, CGI, OrphaNET, UniProt, PsyGeNET, and Genomics England Panel App. PhenoExam accepts both human and mouse genes as input. We developed PhenoExam to assist a variety of users, including clinicians, computational biologists and geneticists. It can be used to support the validation of new gene-to-disease discoveries, and in the detection of differential phenotypes between two gene sets (a phenotype linked to one of the gene set but no to the other) that are useful for differential diagnosis and to improve genetic panels. We validated PhenoExam performance through simulations and its application to real cases. We demonstrate that PhenoExam is effective in distinguishing gene sets or Mendelian diseases with very similar phenotypes through projecting the disease-causing genes into their annotation-based phenotypic spaces. We also tested the tool with early onset Parkinsons disease and dystonia genes, to show phenotype-level similarities but also potentially interesting differences. More specifically, we used PhenoExam to validate computationally predicted new genes potentially associated with epilepsy. Therefore, PhenoExam effectively discovers links between phenotypic terms across annotation databases through effective integration. The R package is available at https://github.com/alexcis95/PhenoExam and the Web tool is accessible at https://snca.atica.um.es/PhenoExamWeb/.

bioinformatics↗

bioRxiv2021

Cross-platform transcriptional profiling identifies common and distinct molecular pathologies in Lewy Body diseases

Parkinsons disease (PD), Parkinsons disease with dementia (PDD) and dementia with Lewy bodies (DLB) are three clinically, genetically and neuropathologically overlapping neurodegenerative diseases collectively known as the Lewy body diseases (LBDs). A variety of molecular mechanisms have been implicated in PD pathogenesis, but the mechanisms underlying PDD and DLB remain largely unknown, a knowledge gap that presents an impediment to the discovery of disease-modifying therapies. Transcriptomic profiling can contribute to addressing this gap, but remains limited in the LBDs. Here, we applied paired bulk-tissue and single-nucleus RNA-sequencing to anterior cingulate cortex samples derived from 28 individuals, including healthy controls, PD, PDD and DLB cases (n = 7 per group), to transcriptomically profile the LBDs. Using this approach, we (i) found transcriptional alterations in multiple cell types across the LBDs; (ii) discovered evidence for widespread dysregulation of RNA splicing, particularly in PDD and DLB; (iii) identified potential splicing factors, with links to other dementia-related neurodegenerative diseases, coordinating this dysregulation; and (iv) identified transcriptomic commonalities and distinctions between the LBDs that inform understanding of the relationships between these three clinical disorders. Together, these findings have important implications for the design of RNA-targeted therapies for these diseases and highlight a potential molecular "window" of therapeutic opportunity between the initial onset of PD and subsequent development of Lewy body dementia.

neuroscience↗

bioRxiv2021

Detection of pathogenic splicing events from RNA-sequencing data using dasper

Although next-generation sequencing technologies have accelerated the discovery of novel gene-to-disease associations, many patients with suspected Mendelian diseases still leave the clinic without a genetic diagnosis. An estimated one third of these patients will have disorders caused by mutations impacting splicing. RNA-sequencing has been shown to be a promising diagnostic tool, however few methods have been developed to integrate RNA-sequencing data into the diagnostic pipeline. Here, we introduce dasper, an R/Bioconductor package that improves upon existing tools for detecting aberrant splicing by using machine learning to incorporate disruptions in exon-exon junction counts as well as coverage. dasper is designed for diagnostics, providing a rank-based report of how aberrant each splicing event looks, as well as including visualization functionality to facilitate interpretation. We validate dasper using 16 patient-derived fibroblast cell lines harbouring pathogenic variants known to impact splicing. We find that dasper is able to detect pathogenic splicing events with greater accuracy than existing LeafCutterMD or z-score approaches. Furthermore, by only applying a broad OMIM gene filter (without any variant-level filters), dasper is able to detect pathogenic splicing events within the top 10 most aberrant identified for each patient. Since using publicly available control data minimises costs associated with incorporating RNA-sequencing into diagnostic pipelines, we also investigate the use of 504 GTEx fibroblast samples as controls. We find that dasper leverages publicly available data effectively, ranking pathogenic splicing events in the top 25. Thus, we believe dasper can increase diagnostic yield for a pathogenic splicing variants and enable the efficient implementation of RNA-sequencing for diagnostics in clinical laboratories.

bioinformatics↗

bioRxiv2021

Genetic variability associated with OAS1 expression in myeloid cells increases the risk of Alzheimer's disease and severe COVID-19 outcomes

Genome-wide association studies of late-onset Alzheimers disease (AD) have highlighted the importance of variants associated with genes expressed by the innate immune system in determining risk for AD. Recently, we and others have shown that genes associated with variants that confer risk for AD are significantly enriched in transcriptional networks expressed by amyloid-responsive microglia. This allowed us to predict new risk genes for AD, including the interferon-responsive oligoadenylate synthetase 1 (OAS1). However, the function of OAS1 within microglia and its genetic pathway are not known. Using genotyping from 1,313 individuals with sporadic AD and 1,234 control individuals, we confirm that the OAS1 variant, rs1131454, is associated with increased risk for AD and decreased OAS1 expression. Moreover, we note that the same locus was recently associated with critical illness in response to COVID-19, linking variants that are associated with AD and a severe response to COVID-19. By analysing single-cell RNA-sequencing (scRNA-seq) data of isolated microglia from APPNL-G-F knock-in and wild-type C57BL/6J mice, we identify a transcriptional network that is significantly upregulated with age and amyloid deposition, and contains the mouse orthologue Oas1a, providing evidence that Oas1a plays an age-dependent function in the innate immune system. We identify a similar interferon-related transcriptional network containing OAS1 by analysing scRNA-seq data from human microglia isolated from individuals with AD. Finally, using human iPSC-derived microglial cells (h-iPSC-Mg), we see that OAS1 is required to limit the pro-inflammatory response of microglia. When stimulated with interferon-gamma (IFN-{gamma}), we note that cells with lower OAS1 expression show an exaggerated pro-inflammatory response, with increased expression and secretion of TNF-. Collectively, our data support a link between genetic risk for AD and susceptibility to critical illness with COVID-19 centred on OAS1 and interferon signalling, a finding with potential implications for future treatments of both AD and COVID-19, and the development of biomarkers to track disease progression.

genetics↗

bioRxiv2021

Leveraging omic features with F3UTER enables identification of unannotated 3'UTRs for synaptic genes

There is growing evidence for the importance of 3 untranslated region (3UTR) dependent regulatory processes. However, our current human 3UTR catalogue is incomplete. Here, we developed a machine learning-based framework, leveraging both genomic and tissue-specific transcriptomic features to predict previously unannotated 3UTRs. We identify unannotated 3UTRs associated with 1,513 genes across 39 human tissues, with the greatest abundance found in brain. These unannotated 3UTRs were significantly enriched for RNA binding protein (RBP) motifs and exhibited high human lineage-specificity. We found that brain-specific unannotated 3UTRs were enriched for the binding motifs of important neuronal RBPs such as TARDBP and RBFOX1, and their associated genes were involved in synaptic function and brain- related disorders. Our data is shared through an online resource F3UTER (https://astx.shinyapps.io/F3UTER/). Overall, our data improves 3UTR annotation and provides novel insights into the mRNA-RBP interactome in the human brain, with implications for our understanding of neurological and neurodevelopmental diseases.

bioinformatics↗

bioRxiv2021

Human brain mitochondrial-nuclear cross-talk is cell-type specific and is perturbed by neurodegeneration

Mitochondrial dysfunction contributes to the pathogenesis of many neurodegenerative diseases as mitochondria are essential to neuronal function. The mitochondrial genome encodes a small number of core respiratory chain proteins, whereas the vast majority of mitochondrial proteins are encoded by the nuclear genome. Here we focus on establishing a profile of nuclear-mitochondrial transcriptional relationships in healthy human central nervous system tissue data, before examining perturbations of these processes in Alzheimer&#8217s disease using transcriptomic data originating from affected human brain tissue. Through cross-central nervous system analysis of mitochondrial-nuclear gene pair relationships, we find that the cell type composition underlies regional variation, and variation is driven at the subcellular level by heterogeneity of nuclear-mitochondrial coordination in post-synaptic regions. We show that nuclear genes causally implicated in sporadic Parkinson&#8217s disease and Alzheimer&#8217s disease show much stronger relationships with the mitochondrial genome than expected by chance, and that nuclear-mitochondrial relationships are significantly perturbed in Alzheimer&#8217s disease cases, particularly amongst genes involved in synaptic and lysosomal pathways. Finally, we present MitoNuclearCOEXPlorer, a web tool designed to allow users to interrogate and visualise key mitochondrial-nuclear relationships in multi-dimensional brain data. We conclude that mitochondrial-nuclear relationships differ significantly across regions of the healthy brain, which appears to be driven by the functional specialisation of different cell types. We also find that mitochondrial-nuclear co-expression in critical pathways is disrupted in Alzheimer&#8217s disease, potentially implicating the regulation of energy balance and removal of dysfunctional mitochondria in the etiology or progression of the disease and making the case for the relevance of bi-genomic co-ordination in the pathogenesis of neurodegenerative diseases.

genomics↗

bioRxiv2020

Modeling multifunctionality of genes with secondary gene co-expression networks in human brain provides novel disease insights

MotivationCo-expression networks are a powerful gene expression analysis method to study how genes co-express together in clusters with functional coherence that usually resemble specific cell type behaviour for the genes involved. They can be applied to bulk-tissue gene expression profiling and assign function, and usually cell type specificity, to a high percentage of the gene pool used to construct the network. One of the limitations of this method is that each gene is predicted to play a role in a specific set of coherent functions in a single cell type (i.e. at most we get a single for each gene). We present here GMSCA (Gene Multifunctionality Secondary Co-expression Analysis), a software tool that exploits the co-expression paradigm to increase the number of functions and cell types ascribed to a gene in bulk-tissue co-expression networks. ResultsWe applied GMSCA to 27 co-expression networks derived from bulk-tissue gene expression profiling of a variety of brain tissues. Neurons and glial cells (microglia, astrocytes and oligodendrocytes) were considered the main cell types. Applying this approach, we increase the overall number of predicted triplets by 46.73%. Moreover, GMSCA predicts that the SNCA gene, traditionally associated to work mainly in neurons, also plays a relevant function in oligodendrocytes. AvailabilityThe tool is available at GitHub,https://github.com/drlaguna/GMSCA as open source software. ImplementationGSMCA is implemented in R.

bioinformatics↗

bioRxiv2020

Genetic analysis of amyotrophic lateral sclerosis identifies contributing pathways and cell types.

Despite the considerable progress in unraveling the genetic causes of amyotrophic lateral sclerosis (ALS), we do not fully understand the molecular mechanisms underlying the disease. We analyzed genome-wide data involving 78,500 individuals using a polygenic risk score approach to identify the biological pathways and cell types involved in ALS. This data-driven approach identified multiple aspects of the biology underlying the disease that resolved into broader themes, namely neuron projection morphogenesis, membrane trafficking, and signal transduction mediated by ribonucleotides. We also found that genomic risk in ALS maps consistently to GABAergic cortical interneurons and oligodendrocytes, as confirmed in human single-nucleus RNA-seq data. Using two-sample Mendelian randomization, we nominated five differentially expressed genes (ATG16L2, ACSL5, MAP1LC3A, PLXNB2, and SCFD1) within the significant pathways as relevant to ALS. We conclude that the disparate genetic etiologies of this fatal neurological disease converge on a smaller number of final common pathways and cell types.

genetics↗

bioRxiv2020

CoExp Web, a web tool for the exploitation of co-expression networks

SummaryThis paper presents the CoExp Web tool. CoExp is a software tool and a Web resource that fosters research on co-expression network models by providing a highly usable means to exploit and share multiple network models. Availability and Implementationthe Web resource is accessible at https://snca.atica.um.es/coexp/. The software at the front and back-ends is accessible at https://github.com/SoniaRuiz/CoExp_Web. Contactskgtsg0@ucl.ac.uk (Sonia Garcia-Ruiz)

bioinformatics↗

bioRxiv2020

Large-scale pathway-specific polygenic risk, transcriptomic community networks and functional inferences in Parkinson disease

Polygenic inheritance plays a central role in Parkinson disease (PD). A priority in elucidating PD etiology lies in defining the biological basis of genetic risk. Unraveling how risk leads to disruption will yield disease-modifying therapeutic targets that may be effective. Here, we utilized a high-throughput and hypothesis-free approach to determine biological pathways underlying PD using the largest currently available cohorts of genetic data and gene expression data from International Parkinsons Disease Genetics Consortium (IPDGC) and the Accelerating Medicines Partnership - Parkinsons disease initiative (AMP-PD), among other sources. We placed these insights into a cellular context. We applied large-scale pathway-specific polygenic risk score (PRS) analyses to assess the role of common variation on PD risk in a cohort of 457,110 individuals by focusing on a compilation of 2,199 publicly annotated gene sets representative of curated pathways, of which we nominate 46 pathways associated with PD risk. We assessed the impact of rare variation on PD risk in an independent cohort of whole-genome sequencing data, including 4,331 individuals. We explored enrichment linked to expression cell specificity patterns using single-cell gene expression data and demonstrated a significant risk pattern for adult dopaminergic neurons, serotonergic neurons, and radial glia. Subsequently, we created a novel way of building de novo pathways by constructing a network expression community map using transcriptomic data derived from the blood of 1,612 PD patients, which revealed 54 connecting networks associated with PD. Our analyses highlight several promising pathways and genes for functional prioritization and provide a cellular context in which such work should be done.

genetics↗

bioRxiv2020

Human-lineage-specific genomic elements: relevance to neurodegenerative disease and APOE transcript usage

Knowledge of genomic features specific to the human lineage may provide insights into brain-related diseases. We leverage high-depth whole genome sequencing data to generate a combined annotation identifying regions simultaneously depleted for genetic variation (constrained regions) and poorly conserved across primates. We propose that these constrained, non-conserved regions (CNCRs) have been subject to human-specific purifying selection and are enriched for brain-specific elements. We find that CNCRs are depleted from protein-coding genes but enriched within lncRNAs. We demonstrate that per-SNP heritability of a range of brain-relevant phenotypes are enriched within CNCRs. We find that genes implicated in neurological diseases have high CNCR density, including APOE, highlighting an unannotated intron-3 retention event. Using human brain RNA-sequencing data, we show the intron-3-retaining transcript/s to be more abundant in Alzheimers disease with more severe tau and amyloid pathological burden. Thus, we demonstrate the importance of human-lineage-specific sequences in brain development and neurological disease. We release our annotation through vizER (https://snca.atica.um.es/browser/app/vizER).

neuroscience↗

bioRxiv2020

Common variation at the LRRK2 locus is associated with survival in the primary tauopathy progressive supranuclear palsy

The genetic basis of variation in the rate of disease progression of primary tauopathies has not been determined. In two independent progressive supranuclear palsy cohorts, we show that common variation at the LRRK2 locus determines survival from motor symptom onset to death, possibly through regulation of gene expression. This links together genetic risk in alpha-synuclein and tau disorders, and suggests that modulation of proteostasis and neuro-inflammation by LRRK2 inhibitors may have a therapeutic role across disorders.

genetics↗