Biology subjects

Luscombe, N. M.

Publications and source records attributed to Luscombe, N. M..

At least 37 records · Page 2Linked to original sources

bioRxiv2020

SPACE exploration of chromatin proteome to reveal associated RNA-binding proteins

RNA-binding proteins (RBPs) play diverse roles in regulating co-transcriptional RNA-processing and chromatin functions, but our knowledge of the repertoire of chromatin-associated RBPs (caRBPs) and their interactions with chromatin remains limited. Here, we developed SPACE (Silica Particle Assisted Chromatin Enrichment) to isolate global and regional chromatin components with high specificity and sensitivity, and SPACEmap to identify the chromatin-contact regions in proteins. Applied to mouse embryonic stem cells, SPACE identified 1,459 chromatin-associated proteins, [~]48% of which are annotated as RBPs, indicating their dual roles in chromatin and RNA-binding. Additionally, SPACEmap stringently verified chromatin-binding of 404 RBPs and identified their chromatin-contact regions. Notably, SPACEmap showed that about half of the caRBPs bind to chromatin by intrinsically disordered regions (IDRs). Studying SPACE and total proteome dynamics from mES cells grown in 2iL and serum medium indicates significant correlation (R = 0.62). One of the most dynamic caRBPs is Dazl, which we find co-localized with PRC2 at transcription start sites of genes that are distinct from Dazl mRNA binding. Dazl and other PRC2-colocalised caRBPs are rich in intrinsically disordered regions (IDRs), which could contribute to the formation and regulation of phase-separated PRC condensates. Together, our approach provides an unprecedented insight into IDR-mediated interactions and caRBPs with moonlighting functions in native chromatin.

molecular biology↗

bioRxiv2020

Centromere-specific antibody-mediated karyotyping of Okinawan Oikopleura dioica suggests the presence of three chromosomes.

Oikopleura dioica is a ubiquitous marine tunicate of biological interest due to features that include dioecious reproduction, short life cycle, and vertebrate-like dorsal notochord while possessing a relatively compact genome. The use of tunicates as model organisms, particularly with these characteristics, offers the advantage of facilitating studies in evolutionary development and furthering understanding of enduring attributes found in the more complex vertebrates. At present, we are undertaking an initiative to sequence the genomes of Oikopleura individuals in populations found among the seas surrounding the Ryukyu Islands in southern Japan. To facilitate and validate genome assemblies, karyotyping was employed to count individual animals chromosomes in situ using centromere-specific antibodies directed against H3S28P, a prophase-metaphase cell cycle-specific marker of histone H3. New imaging data of embryos and oocytes stained with two different antibodies were obtained; interpretation of these data lead us to conclude that the Okinawan Oikopleura dioica has three pairs of chromosomes, akin to previous results from genomic assemblies in Atlantic populations. The imaging data have been deposited to the open-access EBI BioImage Archive for reuse while additionally providing representative images of two commercially available anti-H3S28P antibodies staining properties for use in epifluorescent and confocal based fluorescent microscopy.

cell biology↗

bioRxiv2020

psiCLIP reveals dynamic RNA binding by DEAH-box helicases before and after exon ligation

Eight RNA helicases remodel the spliceosome to effect pre-mRNA splicing but their mechanism of action remains poorly understood. We have developed "purified spliceosome iCLIP" (psiCLIP) to define helicase-RNA contacts in specific spliceosomal states. psiCLIP reveals previously unappreciated dynamics of spliceosomal helicases. The binding profile of the helicase Prp16 is influenced by the distance between the branch-point and 3 splice site, while Prp22 binds diffusely on the intron before exon ligation but switches to more narrow binding downstream of the exon junction after exon ligation. Notably, depletion of the exon-ligation factor Prp18 destabilizes Prp22 binding to the pre-mRNA, demonstrating that psiCLIP can be used to study the relationships between helicases and auxiliary splicing factors. Thus, psiCLIP is sensitive to spliceosome dynamics and complements the insights from structural and imaging studies by providing crucial positional information on helicase-RNA contacts during spliceosomal remodeling.

molecular biology↗

bioRxiv2020

Intergenic RNA mainly derives from nascent transcripts of known genes

BackgroundEukaryotic genomes undergo pervasive transcription, leading to the production of many types of stable and unstable RNAs. Transcription is not restricted to regions with annotated gene features but includes almost any genomic context. Currently, the source and function of most RNAs originating from intergenic regions in the human genome remains unclear. ResultsWe hypothesised that many intergenic RNA can be ascribed to the presence of as-yet unannotated genes or the fuzzy transcription of known genes that extends beyond the annotated boundaries. To elucidate the contributions of these two sources, we assembled a dataset of >2.5 billion publicly available RNA-seq reads across 5 human cell lines and multiple cellular compartments to annotate transcriptional units in the human genome. About 80% of transcripts from unannotated intergenic regions can be attributed to the fuzzy transcription of existing genes; the remaining transcripts originate mainly from putative long non-coding RNA loci that are rarely spliced. We validated the transcriptional activity of these intergenic RNA using independent measurements, including transcriptional start sites, chromatin signatures, and genomic occupancies of RNA polymerase II in various phosphorylation states. We also analysed the nuclear localisation and sensitivities of intergenic transcripts to nucleases to illustrate that they tend to be rapidly degraded either on-chromatin by XRN2 or off-chromatin by the exosome. ConclusionsWe provide a curated atlas of intergenic RNAs that distinguishes between alternative processing of well annotated genes from independent transcriptional units based on the combined analysis of chromatin signatures, nuclear RNA localisation and degradation pathways.

genomics↗

bioRxiv2019

Widespread use of the "ascidian" mitochondrial genetic code in tunicates

BackgroundAscidians, a tunicate class, use a mitochondrial genetic code that is distinct from vertebrates and other invertebrates. Though it has been used to translate the coding sequences from other tunicate species on a case-by-case basis, it is has not been investigated whether this can be done systematically. This is an important because a) some tunicate mitochondrial sequences are currently translated with the invertebrate code by repositories such as NCBIs GenBank, and b) uncertainties about the genetic code to use can complicate or introduce errors in phylogenetic studies based on translated mitochondrial protein sequences. MethodsWe collected publicly available nucleotide sequences for non-ascidian tunicates including appendicularians such as Oikopleura dioica, translated them using the ascidian mitochondrial code, and built multiple sequence alignments covering all tunicate classes. ResultsAll tunicates studied here appear to translate AGR codons to glycine instead of serine (invertebrates) or as a stop codon (vertebrates), as initially described in ascidians. Among Oikopleuridae, we suggest further possible changes in the use of the ATA (Ile [->] Met) and TGA (Trp [->] Arg) codons. ConclusionsWe recommend using the ascidian mitochondrial code in automatic translation pipelines of mitochondrial sequences for all tunicates. Further investigation is required for additional species-specific differences.

genomics↗

bioRxiv2019

RNA modifications detection by comparative Nanopore direct RNA sequencing

RNA molecules undergo a vast array of chemical post-transcriptional modifications (PTMs) that can affect their structure and interaction properties. To date, over 150 naturally occurring PTMs have been identified, however the overwhelming majority of their functions remain elusive. In recent years, a small number of PTMs have been successfully mapped to the transcriptome using experimental approaches relying on high-throughput sequencing. Oxford Nanopore direct-RNA sequencing (DRS) technology has been shown to be sensitive to RNA modifications. We developed and validated Nanocompore, a robust analytical framework to evaluate the presence of modifications in DRS data. To do so, we compare an RNA sample of interest against a non-modified control sample. Our strategy does not require a training set and allows the use of replicates to model biological variability. Here, we demonstrate the ability of Nanocompore to detect RNA modifications at single-molecule resolution in human polyA+ RNAs, as well as in targeted non-coding RNAs. Our results correlate well with orthogonal methods, confirm previous observations on the distribution of N6-methyladenosine sites and provide novel insights into the distribution of RNA modifications in the coding and non-coding transcriptomes. The latest version of Nanocompore can be obtained at https://github.com/tleonardi/nanocompore.

genomics↗

bioRxiv2019

RADICL-seq identifies general and cell type-specific principles of genome-wide RNA-chromatin interactions

Mammalian genomes encode tens of thousands of noncoding RNAs. Most noncoding transcripts exhibit nuclear localization and several have been shown to play a role in the regulation of gene expression and chromatin remodelling. To investigate the function of such RNAs, methods to massively map the genomic interacting sites of multiple transcripts have been developed. However, they still present some limitations. Here, we introduce RNA And DNA Interacting Complexes Ligated and sequenced (RADICL-seq), a technology that maps genome-wide RNA-chromatin interactions in intact nuclei. RADICL-seq is a proximity ligation-based methodology that reduces the bias for nascent transcription, while increasing genomic coverage and unique mapping rate efficiency compared to existing methods. RADICL-seq identifies distinct patterns of genome occupancy for different classes of transcripts as well as cell type-specific RNA-chromatin interactions, and emphasizes the role of transcription in the establishment of chromatin structure.

genomics↗

bioRxiv2019

Widespread FUS mislocalization is a molecular hallmark of ALS

Amyotrophic lateral sclerosis (ALS)-causing mutations clearly implicate ubiquitously expressed and predominantly nuclear RNA binding proteins (RBPs), which form pathological cytoplasmic inclusions in this context. However, the possibility that wild-type RBPs mislocalize without necessarily becoming constituents of ALS cytoplasmic inclusions themselves remains unexplored. We hypothesized that nuclear-to-cytoplasmic mislocalization of the RBP Fused in Sarcoma (FUS), in an unaggregated state, may occur more widely in ALS that previously recognized. To address this hypothesis, we analysed motor neurons (MNs) from an human ALS induced-pluripotent stem cells (iPSC) model caused by the VCP mutation. Additionally, we examined mouse transgenic models and post-mortem tissue from human sporadic ALS cases. We report nuclear-to-cytoplasmic mislocalization of FUS in both VCP-mutation related ALS and, crucially, in sporadic ALS spinal cord tissue from multiple cases. Furthermore, we provide evidence that FUS protein binds to an aberrantly retained intron within the SFPQ transcript, which is exported from the nucleus into the cytoplasm. Collectively, these data support a model for ALS pathogenesis whereby aberrant intron-retention in SFPQ transcripts contributes to FUS mislocalization through their direct interaction and nuclear export. In summary, we report widespread mislocalization of the FUS protein in ALS and propose a putative underlying mechanism for this process.

neuroscience↗

bioRxiv2018

Nucleosome positioning stability is a significant modulator of germline mutation rate variation across the human genome

Understanding the patterns and genesis of germline de novo mutations is important for studying genome evolution and human diseases. Nucleosome organization is suggested to be a contributing factor to mutation rate variation across the genome. However, the small number of published de novo mutations and the low resolution of earlier nucleosome maps limited our understanding of how nucleosome organization affects germline mutation rates in the human genome. Here, we systematically investigated the relationship between nucleosome organization and fine-scale mutation rate variation by analyzing >300,000 de novo mutations from whole-genome trio sequencing and high-resolution nucleosome maps in human. We found that de novo mutation rates are elevated around strong, translationally stable nucleosomes, a previously under-appreciated aspect. We confirmed this observation having controlled for local sequence context and other potential confounding factors. Analysis of the underlying mutational processes suggests that the increased mutation rates around strong nucleosomes are shaped by a combination of low-fidelity replication, frequent DNA damage and insufficient/error-prone repair in these regions. Interestingly, strong nucleosomes are preferentially located in young SINE/LINE elements, implying frequent nucleosome re-positioning (i.e. shifting of dyad position) and their contribution to hypermutation at new retrotransposons during evolution. These findings provide novel insights into how chromatin organization affects germline mutation rates and have important implications in human genetics and genome evolution.

genomics↗

bioRxiv2018

Target-specific precision of CRISPR-mediated genome editing

The CRISPR-Cas9 system has successfully been adapted to edit the genome of various organisms. However, our ability to predict editing accuracy, efficacy and outcome at specific sites is limited by an incomplete understanding of how the bacterial system interacts with eukaryotic genomes and DNA repair machineries. Here, we performed the largest comparison of indel profiles to date, examining over one thousand sites in the genome of human cells, and uncovered general principles guiding CRISPR-mediated DNA editing. We find that precision of DNA editing varies considerably among sites, with some targets showing one highly-preferred indel and others displaying a wide range of infrequent indels. Editing precision correlates with editing efficiency, homology-associated end-joining for both insertions and deletions, and a preference for single-nucleotide insertions. Precise targets and the identity of their preferred indel can be predicted based on simple rules that mainly depend on the fourth nucleotide upstream of the PAM sequence. Regardless of precision, site-specific indel profiles are highly robust and depend on both DNA sequence and chromatin features. Our findings have important implications for clinical applications of CRISPR technology and reveal general patterns of broken end-joining that can inform us on DNA repair mechanisms in human cells.

molecular biology↗

bioRxiv2018

Heteromeric RNP assembly at LINEs controls lineage-specific RNA processing

It is challenging for RNA processing machineries to select exons within long intronic regions. We find that intronic LINE repeat sequences (LINEs) contribute to this selection by recruiting dozens of RNA-binding proteins (RBPs). This includes MATR3, which promotes binding of PTBP1 to multivalent binding sites in LINEs. Both RBPs repress splicing and 3 end processing within and around LINEs, as demonstrated in cultured human cells and mouse brain. Notably, repressive RBPs preferentially bind to evolutionarily young LINEs, which are confined to deep intronic regions. These RBPs insulate both LINEs and surrounding regions from RNA processing. Upon evolutionary divergence, gradual loss of insulation diversifies the roles of LINEs. Older LINEs are located closer to exons, are a common source of tissue-specific exons, and increasingly bind to RBPs that enhance RNA processing. Thus, LINEs are hubs for assembly of repressive RBPs, and contribute to evolution of new, lineage-specific transcripts in mammals.

genomics↗

bioRxiv2018

Identifying the genetic basis of variation in cell behaviour in human iPS cell lines from healthy donors

Large cohorts of human iPSCs from healthy donors are potentially a powerful tool for investigating the relationship between genetic variants and cellular phenotypes. Here we integrate high content imaging, gene expression and DNA sequence datasets for over 100 human iPSC lines to identify the genetic basis of inter-individual variability in cell behaviour. By applying a dimensionality reduction approach, Probabilistic Estimation of Expression Residuals (PEER), we identified genes that correlated in expression with intrinsic (genetic) and extrinsic (ECM) factors. However, variation in mRNA levels could not account for outlier cell behaviour. Instead, we identified rare, deleterious SNVs in the coding sequence of genes involved in ECM adhesion that occurred in cell lines that were outliers for one or more phenotypes such as cell spreading. These also correlated with altered germ layer differentiation on micropatterned surfaces. Our study thus establishes a strategy for integrating genetic and cell biological measurements for high-throughput analysis.

cell biology↗

bioRxiv2018

Generative adversarial networks uncover epidermal regulators and predict single cell perturbations

Recent advances have enabled gene expression profiling of single cells at lower cost. As more data is produced there is an increasing need to integrate diverse datasets and better analyse underutilised data to gain biological insights. However, analysis of single cell RNA-seq data is challenging due to biological and technical noise which not only varies between laboratories but also between batches. Here for the first time, we apply a new generative deep learning approach called Generative Adversarial Networks (GAN) to biological data. We apply GANs to epidermal, neural and hematopoietic scRNA-seq data spanning different labs and experimental protocols. We show that it is possible to integrate diverse scRNA-seq datasets and in doing so, our generative model is able to simulate realistic scRNA-seq data that covers the full diversity of cell types. In contrast to many machine-learning approaches, we are able to interpret internal parameters in a biologically meaningful manner. Using our generative model we are able to obtain a universal representation of epidermal differentiation and use this to predict the effect of cell state perturbations on gene expression at high time-resolution. We show that our trained neural networks identify biological state-determining genes and through analysis of these networks we can obtain inferred gene regulatory relationships. Finally, we use internal GAN learned features to perform dimensionality reduction. In combination these attributes provide a powerful framework to progress the analysis of scRNA-seq data beyond exploratory analysis of cell clusters and towards integration of multiple datasets regardless of origin.

genomics↗

bioRxiv2018

Machine learning models in electronic health records can outperform conventional survival models for predicting patient mortality in coronary artery disease

Prognostic modelling is important in clinical practice and epidemiology for patient management and research. Electronic health records (EHR) provide large quantities of data for such models, but conventional epidemiological approaches require significant researcher time to implement. Expert selection of variables, fine-tuning of variable transformations and interactions, and imputing missing values in datasets are time-consuming and could bias subsequent analysis, particularly given that missingness in EHR is both high, and may carry meaning.\n\nUsing a cohort of over 80,000 patients from the CALIBER programme, we performed a systematic comparison of several machine-learning approaches in EHR. We used Cox models and random survival forests with and without imputation on 27 expert-selected variables to predict all-cause mortality. We also used Cox models, random forests and elastic net regression on an extended dataset with 586 variables to build prognostic models and identify novel prognostic factors without prior expert input.\n\nWe observed that data-driven models used on an extended dataset can outperform conventional models for prognosis, without data preprocessing or imputing missing values, and with no need to scale or transform continuous data. An elastic net Cox regression based with 586 unimputed variables with continuous values discretised achieved a C-index of 0.801 (bootstrapped 95% CI 0.799 to 0.802), compared to 0.793 (0.791 to 0.794) for a traditional Cox model comprising 27 expert-selected variables with imputation for missing values.\n\nWe also found that data-driven models allow identification of novel prognostic variables; that the absence of values for particular variables carries meaning, and can have significant implications for prognosis; and that variables often have a nonlinear association with mortality, which discretised Cox models and random forests can elucidate.\n\nThis demonstrates that machine-learning approaches applied to raw EHR data can be used to build reliable models for use in research and clinical practice, and identify novel predictive variables and their effects to inform future research.

epidemiology↗

bioRxiv2017

Regionalization of the nervous system requires axial allocation prior to neural lineage commitment

SummaryNeural induction in vertebrates generates a central nervous system that extends the rostral-caudal length of the body. The prevailing view is that neural cells are initially induced with anterior (forebrain) identity, with caudalising signals then converting a proportion to posterior fates (spinal cord). To test this model, we used chromatin accessibility assays to define how cells adopt region-specific neural fates. Together with genetic and biochemical perturbations this identified a developmental time window in which genome-wide chromatin remodeling events preconfigure epiblast cells for neural induction. Contrary to the established model, this revealed that cells commit to a regional identity before acquiring neural identity. This \"primary regionalization\" allocates cells to anterior or posterior regions of the nervous system, explaining how cranial and spinal neurons are generated at appropriate axial positions. These findings prompt a revision to models of neural induction and support the proposed dual evolutionary origin of the vertebrate central nervous system.

developmental biology↗

bioRxiv2017

Data Science Issues in Understanding Protein-RNA Interactions

An interplay of experimental and computational methods is required to achieve a comprehensive understanding of protein-RNA interactions. Crosslinking and immunoprecipitation (CLIP) identifies endogenous interactions by sequencing RNA fragments that co-purify with a selected RBP under stringent conditions. Here we focus on approaches for the analysis of resulting data and appraise the methods for peak calling, visualisation, analysis and computational modelling of protein-RNA binding sites. We advocate a combined assessment of cDNA complexity and specificity for data quality control. Moreover, we demonstrate the value of analysing sequence motif enrichment in peaks assigned from CLIP data, and of visualising RNA maps, which examine the positional distribution of peaks around regulated landmarks in transcripts. We use these to assess how variations in CLIP data quality, and in different peak calling methods, affect the insights into regulatory mechanisms. We conclude by discussing future opportunities for the computational analysis of protein-RNA interaction experiments.

bioinformatics↗

bioRxiv2017

Identifying developmentally important genes with single-cell RNA-seq from an embryo

Single-cell RNA-seq has been established as a reliable and accessible technique enabling new types of analyses, such as identifying cell types and studying spatial and temporal gene expression variation and change at single-cell resolution. Recently, single-cell RNA-seq has been applied to developing embryos, which offers great potential for finding and characterising genes controlling the course of development along with their expression patterns. In this study, we applied single-cell RNA-seq to the 16-cell stage of the Ciona embryo, a marine chordate and performed a computational search for cell-specific gene expression patterns. We recovered many known expression patterns from our single-cell RNA-seq data and despite extensive previous screens, we succeeded in finding new cell-specific patterns, which we validated by in situ and single-cell qPCR.

developmental biology↗

bioRxiv2017

Integrated analysis sheds light on evolutionary trajectories of young transcription start sites in the human genome

Previous studies revealed widespread transcription initiation and fast turnover of transcription start sites (TSSs) in mammalian genomes. Yet how new TSSs originate and how they evolve over time remain poorly understood. To address these questions, we analyzed [~]200,000 human TSSs by integrating evolutionary and functional genomic data, particularly focusing on TSSs that emerged in the primate lineages. We found that intrinsic factors of repetitive sequences and their proximity to established regulatory modules (extrinsic factors) contribute significantly to origin of new TSSs. In early periods, young TSSs experience rapid sequence evolution driven by endogenous mutational mechanisms that reduce the instability of associated repetitive sequences. In later periods, the regulatory functions of young TSSs are gradually modified, and with evolutionary changes subject to temporal (fewer regulatory changes in younger TSSs) and spatial constraints (fewer regulatory changes in more isolated TSSs). These findings advance our understanding of how regulatory innovations arise in the genome throughout evolution and highlight the roles of repetitive sequences in these processes.

genomics↗