Biology subjects

Robinson, M.

Publications and source records attributed to Robinson, M..

At least 55 records · Page 3Linked to original sources

bioRxiv2019

Disruption of Nrxn1α within excitatory forebrain circuits drives value-based dysfunction

Goal-directed behaviors, complex action sequences that maximize reward, are essential for normal function and are significantly impaired across neuropsychiatric disorders. Despite extensive associations between genetic mutations and these brain disorders, the mechanisms by which candidate genes contribute to goal-directed dysfunction remains unclear, owing to challenges in (1) describing aspects of reward processing that drive goal-directed dysfunction, (2) localizing these deficits to specific brain circuits and (3) relating changes in physiology to behavioral alterations. Here we examined mice with mutations in Neurexin1, a presynaptically-localized adhesion molecule with widespread neuropsychiatric dis ease association, in value-based decision-making paradigms. We found that Neurexin1 knockout animals exhibited blunted choice bias towards outcomes associated with greater benefits. Mutant mice were similarly impaired in avoiding costlier, benefit-neutral actions. Analysis of trial-by-trial choice data via reinforcement learning models suggested these behavioral patterns were driven largely by deficits in the updating and representation of choice values. Employing conditional gene ablation and region-specific Cre-recombinase strains, we revealed that Neurexin1 disruption within forebrain excitatory projection neurons, but not thalamic population s, recapitulated most aspects of the whole-brain knockout phenotype. Finally, utilizing in vivo recordings of direct pathway spiny neuron population calcium activity, we demonstrated that selective knockout of Neurexin1 within forebrain excitatory neurons disrupts reward-associated neural signals within striatum, a major site of feedback-based learning. By relating deficits in value-based decision-making to region-specific Nrxn1 disruption and changes in reward-associated neural activity, we reveal potential neural substrates for the pathophysiology of neuropsychiatric disease-associated cognitive dysfunction.

neuroscience↗

bioRxiv2019

Functional characterization and lineage analysis of broadly neutralizing human antibodies against dengue virus identified by single B cell transcriptomics

Eliciting broadly neutralizing antibodies (bNAbs) against the four dengue virus serotypes (DENV1-4) that are spreading into new territories is an important goal of vaccine design. To delineate bNAb targets, we characterized 28 monoclonal antibodies belonging to expanded and hypermutated clonal families identified by transcriptomic analysis of single plasmablasts from DENV-infected individuals. Among these, we identified two somatically related bNAbs that potently neutralized DENV1-4. Mutagenesis studies revealed that the major recognition determinants of these bNAbs are in E protein domain I, distinct from the only known class of human bNAbs against flaviviruses with a well-defined epitope. B cell repertoire analysis from acute-phase peripheral blood suggested a memory origin and divergent somatic hypermutation pathways for these bNAbs, and a limited number of mutations was sufficient for neutralizing activity. Our study suggests multiple B cell evolutionary pathways leading to DENV bNAbs targeting a novel epitope that can be exploited for vaccine design.

microbiology↗

bioRxiv2019

BANDITS: Bayesian differential splicing accounting for sample-to-sample variability and mapping uncertainty

Alternative splicing is a biological process during gene expression that allows a single gene to code for multiple proteins. However, splicing patterns can be altered in some conditions or diseases. Here, we present BANDITS, a R/Bioconductor package to perform differential splicing, at both gene and transcript-level, based on RNA-seq data. BANDITS uses a Bayesian hierarchical structure to explicitly model the variability between samples, and treats the transcript allocation of reads as latent variables. We perform an extensive benchmark across both simulated and experimental RNA-seq datasets, where BANDITS has extremely favorable performance with respect to the competitors considered.

bioinformatics↗

bioRxiv2019

Neural crest cells bulldoze through the microenvironment using Aquaporin-1 to stabilize filopodia

Neural crest migration requires cells to move through an environment filled with dense extracellular matrix and mesoderm to reach targets throughout the vertebrate embryo. Here, we use high-resolution microscopy, computational modeling, and in vitro and in vivo cell invasion assays to investigate the function of Aquaporin-1 (AQP-1) signaling. We find that migrating lead cranial neural crest cells express AQP-1 mRNA and protein, implicating a biological role for water channel protein function during invasion. Differential AQP-1 levels affect neural crest cell speed, direction, and the length and stability of cell filopodia. Further, AQP-1 enhances matrix metalloprotease (MMP) activity and colocalizes with phosphorylated focal adhesion kinases (pFAK). Co-localization of AQP-1 expression with EphB guidance receptors in the same migrating neural crest cells raises novel implications for the concept of guided bulldozing by lead cells during migration.

developmental biology↗

bioRxiv2019

On the discovery of population-specific state transitions from multi-sample multi-condition single-cell RNA sequencing data

Single-cell RNA sequencing (scRNA-seq) has quickly become an empowering technology to profile the transcriptomes of individual cells on a large scale. Many early analyses of differential expression have aimed at identifying differences between subpopulations, and thus are focused on finding subpopulation markers either in a single sample or across multiple samples. More generally, such methods can compare expression levels in multiple sets of cells, thus leading to cross-condition analyses. However, given the emergence of replicated multi-condition scRNA-seq datasets, an area of increasing focus is making sample-level inferences, termed here as differential state analysis. For example, one could investigate the condition-specific responses of cell subpopulations measured from patients from each condition; however, it is not clear which statistical framework best handles this situation. In this work, we surveyed the methods available to perform cross-condition differential state analyses, including cell-level mixed models and methods based on aggregated "pseudobulk" data. We developed a flexible simulation platform that mimics both single and multi-sample scRNA-seq data and provide robust tools for multi-condition analysis within the muscat R package.

bioinformatics↗

bioRxiv2019

Maximum likelihood method quantifies the overall contribution of gene-environment interaction to complex traits: an application to obesity traits

As genome-wide association studies (GWAS) increased in size, numerous gene-environment interactions (GxE) have been discovered, many of which however explore only one environment at a time and may suffer from statistical artefacts leading to biased interaction estimates. Here we propose a maximum likelihood method to estimate the contribution of GxE to complex traits taking into account all interacting environmental variables at the same time, without the need to measure any. This is possible because GxE induces fluctuations in the conditional trait variance, the extent of which depends on the strength of GxE. The approach can be applied to continuous outcomes and for single SNPs or genetic risk scores (GRS). Extensive simulations demonstrated that our method yields unbiased interaction estimates and excellent confidence interval coverage. We also offer a strategy to distinguish specific GxE from general heteroscedasticity (scale effects). Applying our method to 32 complex traits in the UK Biobank reveals that for body mass index (BMI) the GRSxE explains an additional 1.9% variance on top of the 5.2% GRS contribution. However, this interaction is not specific to the GRS and holds for any variable similarly correlated with BMI. On the contrary, the GRSxE interaction effect for leg impedance [Formula] is significantly (P < 10-56) larger than it would be expected for a similarly correlated variable [Formula]. We showed that our method could robustly detect the global contribution of GxE to complex traits, which turned out to be substantial for certain obesity measures.

epidemiology↗

bioRxiv2019

Quality control of large genome datasets using genome fingerprints

The 1000 Genomes Project (TGP) is a foundational resource which serves the biomedical community as a standard reference cohort for human genetic variation. There are now seven public versions of these genomes. The TGP Consortium produced the first by mapping its final data release against human reference sequence GRCh37, then "lifted over these genomes to the improved reference sequence (GRCh38) when it was released, and remapped the original data to GRCh38 with two similar pipelines. As best practice quality validation, the pipelines that generated these versions were benchmarked against the Genome In A Bottle Consortiums platinum quality genome (NA12878). The New York Genome Center recently released the results of independently resequencing the cohort at greater depth (30X), a phased version informed by the inclusion of related individuals, and independently remapped the original variant calls to GRCh38. We evaluated all seven versions using genome fingerprinting, which supports ultrafast genome comparison even across reference versions. We noted multiple issues including discrepancies in cohort membership, disagreement on the overall level of variation, evidence of substandard pipeline performance on specific genomes and in specific regions of the genome, cryptic relationships between individuals, inconsistent phasing, and annotation distortions caused by the history of the reference genome itself. We therefore recommend global quality assessment by rapid genome comparisons, using genome fingerprints and other metrics, alongside benchmarking as part of best practice quality assessment of large genome datasets. Our observations also help inform the decision of which version to use, to support analyses by individual researchers.

genomics↗

bioRxiv2019

ARMOR: an Automated Reproducible MOdular workflow for preprocessing and differential analysis of RNA-seq data

The extensive generation of RNA sequencing (RNA-seq) data in the last decade has resulted in a myriad of specialized software for its analysis. Each software module typically targets a specific step within the analysis pipeline, making it necessary to join several of them to get a single cohesive workflow. Multiple software programs automating this procedure have been proposed, but often lack modularity, transparency or flexibility. We present ARMOR, which performs an end-to-end RNA-seq data analysis, from raw read files, via quality checks, alignment and quantification, to differential expression testing, geneset analysis and browser-based exploration of the data. ARMOR is implemented using the Snakemake workflow management system and leverages conda environments; Bioconductor objects are generated to facilitate downstream analysis, ensuring seamless integration with many R packages. The workflow is easily implemented by cloning the GitHub repository, replacing the supplied input and reference files and editing a configuration file. Although we have selected the tools currently included in ARMOR, the setup is modular and alternative tools can be easily integrated.

bioinformatics↗

bioRxiv2018

Active Receptor Tyrosine Kinases, but not Brachyury, are sufficient to trigger chordoma in zebrafish

The aberrant activation of developmental processes triggers diverse cancer types. Chordoma is a rare, aggressive tumor arising from transformed notochord remnants. Several potentially oncogenic factors, including several receptor tyrosine kinase (RTK) genes, have been found deregulated in chordoma, yet causation remains uncertain. In particular, sustained expression of the developmental notochord transcription factor Brachyury is hypothesized as key driver of chordoma; nonetheless, experimental evidence for an oncogenic role of Brachyury in chordoma and its prognostic impact remains missing. Here, we developed and applied a zebrafish chordoma model to identify the notochord-transforming potential of implicated genes in vivo. We find that Brachyury, including a form with augmented transcriptional activity, is insufficient to initiate notochord hyperplasia. In contrast, the chordoma-implicated RTKs EGFR and KDR/VEGFR2 are sufficient to transform notochord cells within two to five days of development. Transcriptome and structural analysis of transformed notochords revealed that the aberrant activation of RTK/Ras signaling attenuates processes required for notochord differentiation, including of the unfolded protein response. Our results provide first in vivo indication against a tumor-initiating potential of Brachyury in the notochord, and imply activated RTK signaling as possibly initiating event in chordoma. These results provide a mechanistic framework for the pursuit of chemotherapeutic compounds to combat this aggressive tumor type.

cancer biology↗

bioRxiv2018

Bayesian reassessment of the epigenetic architecture of complex traits

1Epigenetic DNA modification is partly under genetic control, and occurs in response to a wide range of environmental exposures. Linking epigenetic marks to clinical outcomes may provide greater insight into underlying molecular processes of disease, assist in the identification of therapeutic targets, and improve risk prediction. Here, we present a statistical approach, based on Bayesian inference, that estimates associations between disease risk and all measured epigenetic probes jointly, automatically controlling for both data structure (including cell-count effects, relatedness, and experimental batch effects) and correlations among probes. We benchmark our approach in simulation study, finding improved estimation of probe associations across a wide range of scenarios over existing approaches. Our method estimates the total proportion of disease risk captured by epigenetic probe variation, and when we applied it to measures of body mass index (BMI) and cigarette consumption behaviour in 5,101 individuals, we find that 66.7% (95% CI 60.0-72.8) of the variation in BMI and 67.7% (95% CI 58.4-76.9) of the variation in cigarette consumption can be captured by methylation array data from whole blood, independent of the variation explained by single nucleotide polymorphism markers. We find novel associations, with smoking behaviour associated with a methylation probe at the MNDA gene with >95% posterior inclusion probability, which is a myeloid cell nuclear differentiation antigen gene previously implicated as a biomarker for inflammation and non-Hodgkin lymphoma risk. We conduct unique genome-wide enrichment analyses, identifying blood cholesterol, lipid transport and sterol metabolism pathways for BMI, and response to xenobiotic stimulus and negative regulation of RNA polymerase II promoter transcription for smoking, all with >95% posterior inclusion probability of having methylation probes with associations >1.5 times larger than the average. Finally, we improve phenotypic prediction in two independent cohorts by 28.7% and 10.2% for BMI and smoking respectively over a LASSO model. These results imply that probe measures may capture large amounts of variance because they are likely a consequence of the phenotype rather than a cause. As a result, 'omics' data may enable accurate characterization of disease progression and identification of individuals who are on a path to disease. Our approach facilitates better understanding of the underlying epigenetic architecture of complex common disease and is applicable to any kind of genomics data.

genomics↗

bioRxiv2018

Scale-invariant geometric data analysis (SIGDA) provides robust, detailed visualizations of human ancestry specific to individuals and populations

Scale invariance is a common property of physical laws and a key concept in perspective drawing, which aims to provide a meaningful two-dimensional representation of a more complex, three-dimensional scene. Here we describe Scale Invariant Geometric Data Analysis (SIGDA), a new, general exploratory data analysis (EDA) method based on normalization of data to scale invariance. We discuss similarities and differences between SIGDA and two widely-used EDA methods, Correspondence Analysis (CA) and Principal Components Analysis (PCA). We then illustrate SIGDAs ability to analyze and visualize population structure relationships within the data that inspired its development: genetic marker data, in which context PCA is considered a standard method. We show that SIGDA provides significant advantages over PCA of the same data, including: (a) robust detection and separation of a larger number of population axes, leading to (b) better separation of annotated populations; (c) separation of an independent allele frequency axis interpretable as a proxy for allele age, (d) visualization of marker flow between populations (population history), and (d) robust detection and visualization of relationships between closely-related individuals and among family groups. Although this illustration focuses on a specific task, SIGDA is a general-purpose EDA method and derives its advantages from its novel approach to fundamental issues in data analysis, rather than clever sampling or other task-specific methodology.\n\nOne Sentence SummaryWe illustrate the advantages of Scale Invariant Geometric Data Analysis (SIGDA), a new exploratory data analysis method similar to PCA, by applying SIGDA to derive detailed, robust visualizations of the complex history of human population structure from a large sample of single nucleotide variants.

bioinformatics↗

bioRxiv2018

Virus-inclusive single cell RNA sequencing reveals molecular signature predictive of progression to severe dengue infection

Dengue virus (DENV) infection can result in severe complications. Yet, the understanding of the molecular correlates of severity is limited, partly due to difficulties in defining the peripheral blood mononuclear cells (PBMCs) that are associated with DENV in vivo. Additionally, there are currently no biomarkers predictive of progression to severe dengue (SD). Bulk transcriptomics data are difficult to interpret because blood consists of multiple cell types that may react differently to infection. Here we applied virus-inclusive single cell RNA-seq approach (viscRNA-Seq) to profile transcriptomes of thousands of single PBMCs derived early in the course of disease from six dengue patients and four healthy controls, and to characterize distinct DENV-associated leukocytes. Multiple genes, particularly interferon response genes, were upregulated in a cell-specific manner prior to progression to SD. Expression of MX2 in naive B cells and CD163 in CD14+ CD16+ monocytes was predictive of SD. The majority of DENV-associated cells in the blood of two patients who progressed to SD were naive IgM B cells expressing the CD69 and CXCR4 receptors and antiviral genes, followed by monocytes. Bystander uninfected B cells also demonstrated immune activation, and plasmablasts from two patients exhibited antibody lineages with convergently hypermutated heavy chain sequences. Lastly, assembly of the DENV genome revealed diversity at unexpected genomic sites. This study presents a multi-faceted molecular elucidation of natural dengue infection in humans and proposes biomarkers for prediction of SD, with implications for profiling any tissue and viral infection, and for the development of a dengue prognostic assay.\n\nSignificanceA fraction of the 400 million people infected with dengue annually progresses to severe dengue (SD). Yet, there are currently no biomarkers to effectively predict disease progression. We profiled the landscape of host transcripts and viral RNA in thousands of single blood cells from dengue patients prior to progressing to SD. We discovered cell-type specific immune activation and candidate predictive biomarkers. We also revealed preferential virus association with specific cell populations, particularly naive B cells and monocytes. We then explored immune activation of bystander cells, clonality and somatic evolution of adaptive immune repertoires, and viral genomics. This multi-faceted approach could advance understanding of pathogenesis of any viral infection, map an atlas of infected cells and promote the development of prognostics.

immunology↗

bioRxiv2018

Fast and simple comparison of semi-structured data, with emphasis on electronic health records

We present a locality-sensitive hashing strategy for summarizing semi-structured data (e.g., in JSON or XML formats) into data fingerprints: highly compressed representations which cannot recreate details in the data, yet simplify and greatly accelerate the comparison and clustering of semi-structured data by preserving similarity relationships. Computation on data fingerprints is fast: in one example involving complex simulated medical records, the average time to encode one record was 0.53 seconds, and the average pairwise comparison time was 3.75 microseconds. Both processes are trivially parallelizable.\n\nApplications include detection of duplicates, clustering and classification of semi-structured data, which support larger goals including summarizing large and complex data sets, quality assessment, and data mining. We illustrate use cases with three analyses of electronic health records (EHRs): (1) pairwise comparison of patient records, (2) analysis of cohort structure, and (3) evaluation of methods for generating simulated patient data.

bioinformatics↗

bioRxiv2018

BDQC: a general-purpose analytics tool for domain-blind validation of Big Data

Translational biomedical research is generating exponentially more data: thousands of whole-genome sequences (WGS) are now available; brain data are doubling every two years. Analyses of Big Data, including imaging, genomic, phenotypic, and clinical data, present qualitatively new challenges as well as opportunities. Among the challenges is a proliferation in ways analyses can fail, due largely to the increasing length and complexity of processing pipelines. Anomalies in input data, runtime resource exhaustion or node failure in a distributed computation can all cause pipeline hiccups that are not necessarily obvious in the output. Flaws that can taint results may persist undetected in complex pipelines, a danger amplified by the fact that research is often concurrent with the development of the software on which it depends. On the positive side, the huge sample sizes increase statistical power, which in turn can shed new insight and motivate innovative analytic approaches. We have developed a framework for Big Data Quality Control (BDQC) including an extensible set of heuristic and statistical analyses that identify deviations in data without regard to its meaning (domain-blind analyses). BDQC takes advantage of large sample sizes to classify the samples, estimate distributions and identify outliers. Such outliers may be symptoms of technology failure (e.g., truncated output of one step of a pipeline for a single genome) or may reveal unsuspected \" signal\" in the data (e.g., evidence of aneuploidy in a genome). We have applied the framework to validate real-world WGS analysis pipelines. BDQC successfully identified data outliers representing various failure classes, including genome analyses missing a whole chromosome or part thereof, hidden among thousands of intermediary output files. These failures could then be resolved by reanalyzing the affected samples. BDQC both identified hidden flaws as well as yielded new insights into the data. BDQC is designed to complement quality software development practices. There are multiple benefits from the application of BDQC at all pipeline stages. By verifying input correctness, it can help avoid expensive computations on flawed data. Analysis of intermediary and final results facilitates recovery from aberrant termination of processes. All these computationally inexpensive verifications reduce cryptic analytical artifacts that could seriously preclude clinical-grade genome interpretation. BDQC is available at https://github.com/ini-bdds/bdqc.

bioinformatics↗

bioRxiv2017

Genotype fingerprints enable fast and private comparison of genetic testing results for research and direct-to-consumer applications

As genetic testing expands out of the research laboratory into medical practice as well as the direct-to-consumer market, the efficiency with which the resulting genotype data can be compared between individuals is of increasing importance.\n\nWe present a method for summarizing personal genotypes, yielding genotype fingerprints that can be derived from any single nucleotide polymorphism (SNP)-based assay and readily compared to estimate relatedness. The resulting fingerprints remain comparable as chip designs evolve to higher marker densities. We demonstrate that they support applications including distinguishing genotypes of closely related individuals by relationship type, distinguishing closely related individuals from individuals from the same background population, identification of individuals in known background populations, and de novo identification of subpopulations within a large cohort in a high-throughput manner.\n\nAn important feature of genotype fingerprints is that, while fingerprints do not preserve anonymity, they summarize individual marker data in a way that prevents phenotype prediction. Genotype fingerprints are therefore well-suited to public sharing for ancestry determination purposes, without revealing personal health risk status.

bioinformatics↗

bioRxiv2017

Novel metrics for quantifying bacterial genome composition skews

BackgroundBacterial genomes have characteristic compositional skews, which are differences in nucleotide frequency between the leading and lagging DNA strands across a segment of a genome. It is thought that these strand asymmetries arise as a result of mutational biases and selective constraints, particularly for energy efficiency. Analysis of compositional skews in a diverse set of bacteria provides a comparative context in which mutational and selective environmental constraints can be studied. These analyses typically require finished and well-annotated genomic sequences.\n\nResultsWe present three novel metrics for examining genome composition skews; all three metrics can be computed for unfinished or partially-annotated genomes. The first two metrics, (dot-skew and cross-skew) depend on sequence and gene annotation of a single genome, while the third metric (residual skew) highlights unusual genomes by subtracting a GC content-based model of a library of genome sequences. We applied these metrics to all 7738 available bacterial genomes, including partial drafts, and identified outlier species. A number of these outliers (i.e., Borrelia, Ehrlichia, Kinetoplastibacterium, and Phytoplasma) display similar skew patterns despite only distant phylogenetic relationship. While unrelated, some of the outlier bacterial species share lifestyle characteristics, in particular intracellularity and biosynthetic dependence on their hosts.\n\nConclusionsOur novel metrics appear to reflect the effects of biosynthetic constraints and adaptations to life within one or more hosts on genome composition. We provide results for each analyzed genome, software and interactive visualizations at http://db.systemsbiology.net/gestalt/skew_metrics.

genomics↗

bioRxiv2017

Causal associations between risk factors and common diseases inferred from GWAS summary data

Health risk factors such as body mass index (BMI), serum cholesterol and blood pressure are associated with many common diseases. It often remains unclear whether the risk factors are cause or consequence of disease, or whether the associations are the result of confounding. Genetic methods are useful to infer causality because genetic variants are present from birth and therefore unlikely to be confounded with environmental factors. We develop and apply a method (GSMR) that performs a multi-SNP Mendelian Randomization analysis using summary-level data from large genome-wide association studies (sample sizes of up to 405,072) to test the causal associations of BMI, waist-to-hip ratio, serum cholesterols, blood pressures, height and years of schooling (EduYears) with a range of common diseases. We identify a number of causal associations including a protective effect of LDL-cholesterol against type-2 diabetes (T2D) that might explain the side effects of statins on T2D, a protective effect of EduYears against Alzheimers disease, and bidirectional associations with opposite effects (e.g. higher BMI increases the risk of T2D but the effect T2D of BMI is negative). HDL-cholesterol has a significant risk effect on age-related macular degeneration, and the effect size remains significant accounting for the other risk factors. Our study develops powerful tools to integrate summary data from large studies to infer causality, and provides important candidates to be prioritized for further studies in medical research and for drug discovery.

genetics↗

bioRxiv2017

Ultrafast comparison of personal genomes

We present an ultra-fast method for comparing personal genomes. We transform the standard genome representation (lists of variants relative to a reference) into genome fingerprints that can be readily compared across sequencing technologies and reference versions. Because of their reduced size, computation on the genome fingerprints is fast and requires little memory. This enables scaling up a variety of important genome analyses, including quantifying relatedness, recognizing duplicative sequenced genomes in a set, population reconstruction, and many others. The original genome representation cannot be reconstructed from its fingerprint; the method thus has significant implications for privacy-preserving genome analytics.

bioinformatics↗