Biology subjects

Clark, S.

Publications and source records attributed to Clark, S..

At least 55 records · Page 3Linked to original sources

bioRxiv2021

Epigenetic therapy suppresses endocrine-resistant breast tumour growth by re-wiring ER-mediated 3D chromatin interactions

Three-dimensional (3D) epigenome remodelling is an important mechanism of gene deregulation in cancer. However, its potential as a target to overcome therapy resistance remains largely unaddressed. Here we show that FDA-approved epigenetic therapy Decitabine (5-Aza-mC) suppresses tumour growth in preclinical metastatic ER+ breast tumour xenograft models. Decitabine-induced genome-wide DNA hypomethylation results in large-scale 3D epigenome deregulation, including de-compaction of higher order chromatin structure and loss of topologically associated domain boundary insulation. Significant DNA hypomethylation at ER-enhancer elements was associated with gain in ER binding, creation of ectopic 3D enhancer-promoter interactions and concordant activation of ER-mediated transcription pathways. Importantly long-term withdrawal of epigenetic therapy partially restores methylation at ER-enhancer elements, resulting in loss of ectopic 3D enhancer-promoter interactions and associated gene repression. Our study illustrates how epigenetic therapy has potential to target ER+ endocrine-resistant breast cancer by DNA methylation-dependent rewiring of 3D chromatin interactions associated with suppression of tumour growth.

molecular biology↗

bioRxiv2021

Reconstruction of Contemporary Human Stem Cell Dynamics with Oscillatory Molecular Clocks

Molecular clocks record cellular ancestry. However, currently used clocks tick too slowly to measure the short-timescale dynamics of cellular renewal in adult tissues. Here we develop rapidly oscillating DNA methylation clocks where ongoing (de)methylation causes the clock to tick-tock back-and-forth between methylated and unmethylated states like a pendulum. We identify oscillators using standard methylation arrays and develop a mathematical modelling framework to quantitatively measure human adult stem cell dynamics from these data. Small intestinal crypts were inferred to contain slightly more stem cells than colon (6.5 {+/-} 1.0 vs 5.8 {+/-} 1.7 stem cells/crypt) with slower stem cell replacement in small intestine (0.79 {+/-} 0.5 vs 1.1 {+/-} 0.8 replacements/stem cell/year). Germline APC mutation increased the number of replacements per crypt (13.0 {+/-} 2.4 replacements/crypt/year vs 6.9 {+/-} 4.6 for healthy colon). In blood, we measure rapid expansion of acute leukaemia and slower growth of chronic disease. Rapidly oscillating molecular clocks are a new methodology to quantitatively measure human somatic cell dynamics.

bioinformatics↗

bioRxiv2021

Conserved and unique transcriptional features of pharyngeal arches in the skate (Leucoraja erinacea) and evolution of the jaw

The origin of the jaw is a long-standing problem in vertebrate evolutionary biology. Classical hypotheses of serial homology propose that the upper and lower jaw evolved through modifications of dorsal and ventral gill arch skeletal elements, respectively. If the jaw and gill arches are derived members of a primitive branchial series, we predict that they would share common developmental patterning mechanisms. Using candidate and RNAseq/differential gene expression analyses, we find broad conservation of dorsoventral patterning mechanisms within the developing mandibular, hyoid and gill arches of a cartilaginous fish, the skate (Leucoraja erinacea). Shared features include expression of genes encoding members of the ventralising BMP and endothelin signalling pathways and their effectors, the joint markers bapx1 and gdf5 and pro-chondrogenic transcription factors barx1 and gsc, and the dorsalising transcription factor pou3f3. Additionally, we find that mesenchymal expression of eya1/six1 is an ancestral feature of the mandibular arch of jawed vertebrates, while differences in notch signalling distinguish the mandibular and gill arches in skate. Comparative transcriptomic analyses of mandibular and gill arch tissues reveal additional genes differentially expressed along the dorsoventral axis of the pharyngeal arches, including scamp5 as a novel marker of the dorsal mandibular arch, as well as distinct transcriptional features of mandibular and gill arch muscle progenitors and developing gill buds. Taken together, our findings reveal conserved patterning mechanisms in the pharyngeal arches of jawed vertebrates, consistent with serial homology of their skeletal derivatives, as well as unique transcriptional features that may underpin distinct jaw and gill arch morphologies.

developmental biology↗

bioRxiv2021

Inference of emergent spatio-temporal processes from single-cell sequencing reveals feedback between de novo DNA methylation and chromatin condensates

Recent breakthroughs in single-cell genomics allow probing molecular states of cells with unprecedented detail along the sequence of the DNA. Biological function relies, however, on emergent processes in the three-dimensional space of the nucleus, such as droplet formation through phase separation. Here, we use single-cell multi-omics sequencing to develop a theoretical framework to rigorously map epigenome profiling along the DNA sequence onto a description of the emergent spatial dynamics in the nucleus. Drawing on scNMT-seq multi-omics sequencing in vitro and in vivo we exemplify our approach in the context of exit from pluripotency and global de novo methylation of the genome. We show how DNA methylation patterns of the embryonic genome are established through the interplay between spatially correlated DNA methylation and topological changes to the DNA. This feedback leads to the predicted formation of 30-40nm sized condensates of methylated DNA and determines genome-scale DNA methylation rates. We verify these findings with orthogonal single cell multi-omics data that combine the methylome with HiC measurements. Notably, this scale of chromatin organization has recently been described by super-resolution microscopy. Using this framework, we identify local methylation correlations in gene bodies that precede transcriptional changes at the exit from pluripotency. Our work provides a general framework of how mechanistic insights into emergent processes underlying cell fate decisions can be gained by the combination of single-cell multi-omics and methods from theoretical physics that have not been applied in the context of genomics before. HighlightsO_LIWe develop methodology to infer collective spatio-temporal processes in the physical space of the nucleus from single-cell methylome sequencing experiments. C_LIO_LIWe show that DNA methylation relies on a feedback between de novo methylation and nanoscale changes in DNA topology, leading to the formation of methylation condensates. C_LIO_LIChromatin condensates at this scale have recently been described by high-resolution microscopy but have remained without mechanistic explanation. C_LIO_LIUsing this framework, we identify changes in the distribution of DNA methylation marks in gene bodies that precede gene silencing at the exit from pluripotency. C_LI

genomics↗

bioRxiv2020

Evolution of a virus-like architecture and packaging mechanism in arepurposed bacterial protein

Viruses are ubiquitous pathogens of global impact. Prompted by the hypothesis that their earliest progenitors recruited host proteins for virion formation, we have used stringent laboratory evolution to convert a bacterial enzyme lacking affinity for nucleic acids into an artificial nucleocapsid that efficiently packages and protects multiple copies of its own encoding mRNA. Revealing remarkable convergence on the molecular hallmarks of natural viruses, the accompanying changes reorganized the protein building blocks into an interlaced 240-subunit icosahedral capsid impermeable to nucleases, while emergence of a robust RNA stem-loop packaging cassette ensured high encapsidation yields and specificity. In addition to evincing a plausible evolutionary pathway for primordial viruses, these findings highlight practical strategies for developing non-viral carriers for diverse vaccine and delivery applications.

biochemistry↗

bioRxiv2020

Rapid generation of potent antibodies by autonomous hypermutation in yeast

The predominant approach for antibody generation remains animal immunization, which can yield exceptionally selective and potent antibody clones owing to the powerful evolutionary process of somatic hypermutation. However, animal immunization is inherently slow, has poor compatibility with certain antigens (e.g., integral membrane proteins), and suffers from self-tolerance and immunodominance, which limit the functional spectrum of antibodies that can be obtained. Here, we describe Autonomous Hypermutation yEast surfAce Display (AHEAD), a synthetic recombinant antibody generation technology that imitates somatic hypermutation inside engineered yeast. In AHEAD, antibody fragments are encoded on an error-prone orthogonal DNA replication system, resulting in Saccharomyces cerevisiae populations that continuously mutate surface-displayed antibody repertoires. Simple cycles of yeast culturing and enrichment for antigen binding drive the evolution of high-affinity antibody clones in a readily parallelizable process that takes as little as 2 weeks. We applied AHEAD to generate nanobodies against the SARS-CoV-2 S glycoprotein, a GPCR, and other targets. The SARS-CoV-2 nanobodies, concurrently evolved from an open-source naive nanobody library in 8 independent experiments, reached subnanomolar affinities through the sequential fixation of multiple mutations over 3-8 AHEAD cycles that saw [~]580-fold and [~]925-fold improvements in binding affinities and pseudovirus neutralization potencies, respectively. These experiments highlight the defining speed, parallelizability, and effectiveness of AHEAD and provide a template for streamlined antibody generation at large with salient utility in rapid response to current and future viral outbreaks.

bioengineering↗

bioRxiv2020

Stabilising selection causes grossly altered but stable karyotypes in metastatic colorectal cancer

Aneuploidy, the loss and gain of whole and part chromosomes, is near-ubiquitous in cancer genomes and likely defines cancer cell biology. However, the temporal evolutionary dynamics that select for aneuploidy remain uncharacterised. Here we perform longitudinal genomic analysis of 755 samples from a total of 167 patients with colorectal-derived neoplastic lesions that represent distinct stages of tumour evolution through metastasis and treatment. Adenomas typically had few copy number alterations (CNAs) and most were subclonal, whereas cancers had many clonal CNAs, suggesting that progression goes through a CNA bottleneck. Individual CRC glands from the same tumour typically had very similar karyotypes, despite evidence of ongoing instability at the cell level in patient tumours, cell lines and organoids. CNAs in metastatic lesions sampled from liver and other organs, after chemotherapy or targeted therapies, and in late recurrences were typically similar to the primary tumour. Mathematical modelling and statistical inference indicated that these data are consistent with the action of negative selection on CNAs that traps cancer cell genomes on a fitness peak defined by the specific pattern of chromosomal aberrations. These data suggest that the initial progression of colorectal cancer requires the traversal of a rugged fitness landscape and subsequent CNA evolution, including metastatic dissemination and therapeutic resistance, is constrained by negative selection.

cancer biology↗

bioRxiv2020

POMC neurons functional heterogeneity relies on mTORC1 signaling

Hypothalamic Pro-opiomelanocortin (POMC) neurons are classically known to trigger satiety. However, they encompass heterogeneous subpopulations whose functions are unknown. Here we show that POMC neurons releasing GABA, glutamate or both neurotransmitters possess distinct spatial distribution, molecular signatures and functions. Functional specificity of these subpopulations relies on the energy sensor mechanistic Target of Rapamycin Complex 1 (mTORC1), since pharmacological blockade of mTORC1, by mimicking a cellular negative energy state, simultaneously inhibited POMC/glutamatergic and activated POMC/GABAergic neurons. Chemogenetics and conditional deletion of mTORC1 then demonstrated that mTORC1 blockade in POMC neurons causes hyperphagia. This is due to decreased POMC-derived anorexigenic -melanocyte-stimulating hormone and the recruitment of POMC/GABAergic neurotransmission, which is restrained by cannabinoid type 1 receptor signaling. Genetic inhibition of glutamate release from POMC neurons also produced hyperphagia, recapitulating the phenotype caused by mTORC1 blockade. Altogether, these findings pinpoint the molecular mechanisms engaged by POMC neurons to oppositely control feeding, thereby challenging conventional views about their functions.

neuroscience↗

bioRxiv2020

MethPanel: a parallel pipeline and interactive analysis tool for multiplex bisulphite PCR sequencing to assess DNA methylation biomarker panels for disease detection

BackgroundMultiplex bisulphite PCR sequencing is a convenient and scalable method to comprehensively profile DNA methylation at selected loci. The method is useful for validation of methylation biomarker panels on large clinical cohorts, as it can be applied to DNA isolated from fresh tissue, archival formalin fixed paraffin embedded tissue (FFPET) or circulating cell free DNA in plasma. However, successful clinical implementation of DNA methylation biomarkers for disease detection using multiplex bisulphite PCR sequencing, requires user-friendly sample analysis methods and a diversity of visualisation options, which are not met by current tools. ResultsWe have developed a computational pipeline with an interactive graphical interface, called MethPanel, in order to rapidly analyse multiplex bisulphite PCR sequencing data. MethPanel comprises a complete analysis workflow from genomic alignment to DNA methylation calling and supports an unlimited number of PCR amplicons and input samples. Moreover, MethPanel offers important and unique features, such as calculation of a polymorphism score and bisulphite PCR bias correction capabilities. MethPanel is designed so that the methylation data from all samples can be run in parallel on either a personal computer or a high performance computer. The outputs are also automatically forwarded to a shinyApp for convenient display, visualisation and sharing data with collaborators and clinicians. Importantly the data is centralised in one location, which aids storage management. Availability and ImplementationMethPanel is freely available at https://github.com/thinhong/MethPanel ConclusionMethPanel provides a novel parallel pipeline and interactive analysis tool for multiplex bisulphite PCR sequencing to assess DNA methylation marker panels for disease detection.

bioinformatics↗

bioRxiv2019

Dppa2/4 target chromatin bivalency enabling multi-lineage commitment

Bivalent chromatin marks developmental promoters in pluripotent cells, yet their targeting and precise impact on lineage commitment remains unclear. We uncover Developmental Pluripotency Associated 2 (Dppa2) and 4 (Dppa4) as epigenetic priming factors, establishing chromatin bivalency. Single-cell transcriptomics and differentiation assays reveal Dppa2/4 double knockout embryonic stem cells fail to exit pluripotency and differentiate efficiently. Dppa2/4 associate with COMPASS and Polycomb complexes and are required to recruit and maintain their binding at a subset of developmentally important bivalent promoters which are characterised by low expression and poised RNA polymerase. Consequently, upon Dppa2/4 knockout, these dependent promoters gain DNA methylation and are unable to be activated upon differentiation. Our findings uncover a novel targeting principle for bivalency to developmental promoters, poising them for future lineage specific activation.

molecular biology↗

bioRxiv2019

Iron-sulfur cluster repair contributes to Y. pseudotuberculosis survival within deep tissues

To successfully colonize host tissues, bacteria must respond to and detoxify many different host-derived antimicrobial compounds, such as nitric oxide (NO). NO has direct antimicrobial activity through attack on iron-sulfur (Fe-S) cluster-containing proteins. NO detoxification plays an important role in promoting bacterial survival, but it remains unclear if repair of Fe-S clusters is also important for bacterial survival within host tissues. Here we show that the Fe-S cluster repair protein, YtfE, contributes to the survival of Y. pseudotuberculosis within the spleen following nitrosative stress. Y. pseudotuberculosis forms clustered centers of replicating bacteria within deep tissues, where peripheral bacteria express the NO-detoxifying gene, hmp. ytfE expression also occurred specifically within peripheral cells at the edges of microcolonies. In the absence of ytfE, the area of microcolonies was significantly smaller than WT, consistent with ytfE contributing to the survival of peripheral cells. The loss of ytfE did not alter the ability of cells to detoxify NO, which occurred within peripheral cells in both WT and {Delta}ytfE microcolonies. In the absence of NO-detoxifying activity by hmp, NO diffused across {Delta}ytfE microcolonies, and there was a significant decrease in the area of microcolonies lacking ytfE, indicating that ytfE also contributes to bacterial survival in the absence of NO detoxification. These results indicate a role for Fe-S cluster repair in the survival of Y. pseudotuberculosis within the spleen, and suggest that extracellular bacteria may rely on this pathway for survival within host tissues.

microbiology↗

bioRxiv2019

Single cell multi-omics profiling reveals a hierarchical epigenetic landscape during mammalian germ layer specification

Formation of the three primary germ layers during gastrulation is an essential step in the establishment of the vertebrate body plan. Recent studies employing single cell RNA-sequencing have identified major transcriptional changes associated with germ layer specification. Global epigenetic reprogramming accompanies these changes, but the role of the epigenome in regulating early cell fate choice remains unresolved, and the coordination between different epigenetic layers is unclear. Here we describe the first single cell triple-omics map of chromatin accessibility, DNA methylation and RNA expression during the exit from pluripotency and the onset of gastrulation in mouse embryos. We find dynamic dependencies between the different molecular layers, with evidence for distinct modes of epigenetic regulation. The initial exit from pluripotency coincides with the establishment of a global repressive epigenetic landscape, followed by the emergence of local lineage-specific epigenetic patterns during gastrulation. Notably, cells committed to mesoderm and endoderm undergo widespread coordinated epigenetic rearrangements, driven by loss of methylation in enhancer marks and a concomitant increase of chromatin accessibility. In striking contrast, the epigenetic landscape of ectodermal cells is already established in the early epiblast. Hence, regulatory elements associated with each germ layer are either epigenetically primed or epigenetically remodelled prior to overt cell fate decisions during gastrulation, providing the molecular logic for a hierarchical emergence of the primary germ layers.\n\nHighlightsO_LIFirst map of mouse gastrulation using comprehensive single cell triple-omic analysis.\nC_LIO_LIExit from pluripotency is associated with a global repressive epigenetic landscape, driven by a sharp gain of DNA methylation and a gradual decrease of chromatin accessibility.\nC_LIO_LIDNA methylation and chromatin accessibility changes in enhancers, but not in promoters, are associated with germ layer formation.\nC_LIO_LIMesoderm and endoderm enhancers become open and demethylated upon lineage commitment.\nC_LIO_LIEctoderm enhancers are primed in the early epiblast and protected from the global repressive dynamics, supporting a default model of ectoderm commitment in vivo.\nC_LI

developmental biology↗

bioRxiv2018

Ageing affects DNA methylation drift and transcriptional cell-to-cell variability in muscle stem cells

Age-related tissue alterations have been associated with a decline in stem cell number and function1. Although increased cell-to-cell variability in transcription or epigenetic marks has been proposed to be a major hallmark of ageing2-5, little is known about the molecular diversity of stem cells during ageing. Here, by combined single-cell transcriptome and DNA methylome profiling in mouse muscle stem cells, we show a striking global increase of uncoordinated transcriptional heterogeneity together with context-dependent alterations of DNA methylation with age. Importantly, promoters with increased methylation heterogeneity are associated with increased transcriptional heterogeneity of the genes they drive. Notably, old cells that change the most with age reveal alterations in the transcription of genes regulating cell-niche interactions. These results indicate that epigenetic drift, by accumulation of stochastic DNA methylation changes in promoters, is a substantial driver of the degradation of coherent transcriptional networks with consequent stem cell functional decline during ageing.

genomics↗

bioRxiv2018

Predicting One-Year Outcome in First Episode Psychosis using Machine Learning

Lay SummaryO_ST_ABSEvidence before this studyC_ST_ABSOur knowledge of factors which predict outcome in first episode psychosis (FEP) is incomplete. Poor premorbid adjustment, history of developmental disorder, symptom severity at baseline and duration of untreated psychosis are the most replicated predictors of poor clinical, functional, cognitive, and biological outcomes. Yet, such group level differences are not always replicated in individuals, nor can observational results be clearly equated with causation. Advanced machine learning techniques have potential to revolutionise medicine by looking at causation and the prediction of individual patient outcome. Within psychiatry, Koutsouleris et al employed machine learning to predict 4- and 52-week functional outcome in FEP to a 75% and 73.8% test-fold balanced accuracy on repeated nested internal cross-validation. The authors suggest that before employing a machine learning model \"into real-world care, replication is needed in external first episode samples\".\n\nAdded value of this studyWe believe our study to be the first externally validated evidence, in a temporally and geographically independent cohort, for predictive modelling in FEP at an individual patient level. Our results demonstrate the ability to predict both symptom remission and functioning (in employment, education or training (EET)) at one-year. The performance of our EET model was particularly robust, with an ability to accurately predict the one-year EET outcome in more than 85% of patients. Regularised regression results in sparse models which are uniquely interpretable and identify meaningful predictors of recovery including specific individual PANSS items, and social support. This builds on existing studies of group-level differences and the elegant work of Koutsouleris et al.\n\nImplications of all the available evidenceWe have demonstrated the externally validated ability to accurately predict one-year symptomatic and functional status in individual patients with FEP. External validation in a plausibly related temporally and geographically distinct population assesses model transportability to an untested situation rather than simply reproducibility alone. We propose that our results represent important and exciting progress in unlocking the potential of predictive modelling in psychiatric illness. The next step prior to implementation into routine clinical practice would be to establish whether, by the accurate identification of individuals who will have poor outcomes, we can meaningful intervene to improve their prognosis.\n\nAbstractO_ST_ABSBackgroundC_ST_ABSEarly illness course correlates with long-term outcome in psychosis. Accurate prediction could allow more focused intervention. Earlier intervention corresponds to significantly better symptomatic and functional outcomes.\n\nWe use routinely collected baseline demographic and clinical characteristics to predict employment, education or training (EET) status, and symptom remission in patients with first episode psychosis (FEP) at 1 year.\n\nMethods83 FEP patients were recruited from National Health Service (NHS) Glasgow between 2011 and 2014 to a 24-month prospective cohort study with regular assessment of demographic and psychometric measures. An external independent cohort of 79 FEP patients were recruited from NHS Glasgow and Edinburgh during a 12-month study between 2006 and 2009.\n\nElastic net regularised logistic regression models were built to predict binary EET status, period and point remission outcomes at 1 year on 83 Glasgow patients (training dataset). Models were externally validated on an independent dataset of 79 patients from Glasgow and Edinburgh (validation dataset). Only baseline predictors shared across both cohorts were made available for model training and validation.\n\nOutcomesAfter excluding participants with missing outcomes, models were built on the training dataset for EET status, period and point remission outcomes and externally validated on the validation dataset. Models predicted EET status, period and point remission with ROC area under curve (AUC) performances of 0.876 (95%CI: 0.864, 0.887), 0.630 (95%CI: 0.612, 0.647) and 0.652 (95%CI: 0.635, 0.670) respectively. Positive predictors of EET included baseline EET and living with spouse/children. Negative predictors included higher PANSS suspiciousness, hostility and delusions scores. Positive predictors for symptom remission included living with spouse/children, and affective symptoms on the Positive and Negative Syndrome Scale (PANSS). Negative predictors of remission included passive social withdrawal symptoms on PANSS.\n\nInterpretationUsing advanced statistical machine learning techniques, we provide the first externally validated evidence for the ability to predict 1-year EET status and symptom remission in FEP patients.\n\nFundingThe authors acknowledge financial support from NHS Research Scotland, the Chief Scientist Office, the Wellcome Trust, and the Scottish Mental Health Research Network.

neuroscience↗

bioRxiv2018

3’ Branch Ligation: A Novel Method to Ligate Non-Complementary DNA to Recessed or Internal 3’OH Ends in DNA or RNA

Nucleic acid ligases are crucial enzymes that repair breaks in DNA or RNA during synthesis, repair and recombination. Various molecular tools have been developed using the diverse activities of DNA/RNA ligases. Herein, we demonstrate a non-conventional ability of T4 DNA ligase to join 5 phosphorylated blunt-end double-stranded DNA to DNA breaks at 3 recessive ends, gaps, or nicks to form a 3 branch structure. Therefore, this base pairing-independent ligation is termed 3 branch ligation (3BL). In an extensive study of optimal ligation conditions, similar to blunt-end ligation, the presence of 10% PEG-8000 in the ligation buffer significantly increased ligation efficiency. A low level of nucleotide preference was observed at the junction sites using different synthetic DNAs. Furthermore, we discovered that T4 DNA ligase efficiently ligated DNA to the 3 recessed end of RNA, not to that of DNA, in a DNA/RNA hybrid, whereas RNA ligases are less efficient in this reaction. These novel properties of T4 DNA ligase can be utilized as a broad molecular technique in many important applications. We performed a proof-of-concept study of a new directional tagmentation protocol for next generation sequencing (NGS) library construction that eliminates inverted adapters and allows sample barcode insertion adjacent to genomic DNA. 3BL after single transposon tagmentation can theoretically achieve 100% usable template, and our empirical data demonstrate that the new approach produced higher yield compared with traditional double transposon or Y transposon tagmentation. We further explore the potential use of 3BL for preparing targeted RNA NGS libraries with mitigated structure-based bias and adapter dimer problems.

molecular biology↗

bioRxiv2018

MicroRNA-196a is regulated by ER and is a prognostic biomarker in ER+ Breast Cancer

MicroRNAs are potent post-transcriptional regulators involved in all hallmarks of cancer. Mir-196a is transcribed from two loci and has been implicated in a wide range of developmental and pathogenic processes, with targets including Hox, Fox, Cdk inhibitors and annexins. Genetic variants and altered expression of miR196a are associated with risk and progression of multiple cancers including breast cancer, however little is known about the regulation of the genes encoding this miRNA, nor the impact of variants therein. Here we demonstrate that MIR196A displays complex and dynamic expression patterns, in part controlled by long range transcriptional regulation between promoter and enhancer elements bound by ER. Expression of this miRNA is significantly increased in models of hormone receptor positive disease resistance. The expression of MIR196A also proves to be a robust prognostic factor for patients with advanced and post-menopausal ER+ disease. This work sheds light on the normal and abnormal regulation of MIR196A and provides a novel stratification method for therapeutically resistant breast cancer.

cancer biology↗

bioRxiv2018

Combined single cell profiling of expression and DNA methylation reveals splicing regulation and heterogeneity

BackgroundAlternative splicing is a key regulatory mechanism in eukaryotic cells and increases the effective number of functionally distinct gene products. Using bulk RNA sequencing, splicing variation has been studied across human tissues and in genetically diverse populations. This has identified disease-relevant splicing events, as well as associations between splicing and genomic variations, including sequence composition and conservation. However, variability in splicing between single cells from the same tissue or cell type and its determinants remain poorly understood.\n\nResultsWe applied parallel DNA methylation and transcriptome sequencing to differentiating human induced pluripotent stem cells to characterize splicing variation (exon skipping) and its determinants. Our results shows that variation in single-cell splicing can be accurately predicted based on local sequence composition and genomic features. We observe moderate but consistent contributions from local DNA methylation profiles to splicing variation across cells. A combined model that is built based on sequence as well as DNA methylation information accurately predicts different splicing modes of individual cassette exons (AUC=0.85). These categories include the conventional inclusion and exclusion patterns, but also more subtle modes of cell-to-cell variation in splicing. Finally, we identified and characterized associations between DNA methylation and splicing changes during cell differentiation.\n\nConclusionsOur study yields new insights into alternative splicing at the single-cell level and reveals a previously underappreciated link between DNA methylation variation and splicing.

bioinformatics↗

bioRxiv2018

Effect of selection on bias and accuracy in genomic prediction of breeding values

Reference populations for genomic selection (GS) usually involve highly selected individuals, which may result in biased prediction of estimated genomic breeding values (GEBV). In the present study, bias and accuracy of GEBV were explored for various genetic models and prediction methods when using selected individuals for a reference. Data were simulated for an animal breeding program to compare Best Linear Unbiased Prediction of breeding values using pedigree based relationships (PBLUP), genomic relationships for genotyped animals only (GBLUP) and a Single Step approach (SSGBLUP), where information on genotyped individuals was used to infer a matrix H with relationships among all available genotyped and non-genotyped individuals that were linked through pedigree. In SSGBLUP, various weights (=0.95, 0.80, 0.50) for the genomic relationship matrix (G) relative to the numerator relationship matrix (A) were applied to construct H and in another version (SSGBLUP_F), inbreeding was accounted for while computing A-1. With GBLUP, accuracy of GEBV prediction increased linearly with an increase in the number of animals selected in reference. For the scenario with no-selection and random mating (RR) prediction was unbiased. For GBLUP, lower accuracy and bias observed in the scenarios with selection and random mating (SR) or selection and positive assortative mating (SA), in which prediction bias increased when a smaller and highly selected proportion genotyped. Bias disappeared when all individuals were genotyped. SSGBLUP_F showed higher accuracy compared to GBLUP and bias of prediction was negligible even with selective genotyping. However, PBLUP and SSGBLUP showed bias in SA owing to not fully accounting for allele frequency changes because of selection of quantitative trait loci (QTL) with larger effects and also due to high inbreeding rate. In genetic models with fewer QTL but each with larger effect, predictions were less accurate and more biased for selection scenarios. Results suggest that prediction accuracy and bias is affected by the genetic architecture of the trait. Selective genotyping lead to significant bias in GEBV prediction. SSGBLUP with appropriate scaling of A and G matrices can provide accurate and less biased prediction but scaling requires careful consideration in populations under selection and with high levels of inbreeding.

genomics↗