Biology subjects

Wagner, J.

Publications and source records attributed to Wagner, J..

51 records · Page 3Linked to original sources

bioRxiv2021

Towards a Comprehensive Variation Benchmark for Challenging Medically-Relevant Autosomal Genes

The repetitive nature and complexity of multiple medically important genes make them intractable to accurate analysis, despite the maturity of short-read sequencing, resulting in a gap in clinical applications of genome sequencing. The Genome in a Bottle Consortium has provided benchmark variant sets, but these excluded some medically relevant genes due to their repetitiveness or polymorphic complexity. In this study, we characterize 273 of these 395 challenging autosomal genes that have multiple implications for medical sequencing. This extended, curated benchmark reports over 17,000 SNVs, 3,600 INDELs, and 200 SVs each for GRCh37 and GRCh38 across HG002. We show that false duplications in either GRCh37 or GRCh38 result in reference-specific, missed variants for short- and long-read technologies in medically important genes including CBS, CRYAA, and KCNE1. Our proposed solution improves variant recall in these genes from 8% to 100%. This benchmark will significantly improve the comprehensive characterization of these medically relevant genes and guide new method development.

genomics↗

bioRxiv2021

Mass Cytometric and Transcriptomic Profiling of Epithelial-Mesenchymal Transitions in Human Mammary Cell Lines

Epithelial-mesenchymal transition (EMT) equips breast cancer cells for metastasis and treatment resistance. Inhibition and elimination of EMT-undergoing cells are therefore promising therapy approaches. However, detecting EMT-undergoing cells is challenging due to the intrinsic heterogeneity of cancer cells and the phenotypic diversity of EMT programs. Here, we profiled EMT transition phenotypes in four non-cancerous human mammary epithelial cell lines using a FACS surface marker screen, RNA sequencing, and mass cytometry. EMT was induced in the HMLE and MCF10A cell lines and in the HMLE-Twist-ER and HMLE-Snail-ER cell lines by chronic exposure to TGF{beta}1 or 4-hydroxytamoxifen, respectively. We observed a spectrum of EMT transition phenotypes in each cell line and the spectrum varied across the time course. Our data provide multiparametric insights at single-cell level into the phenotypic diversity of EMT at different time points and in four human cellular models. These insights are valuable to better understand the complexity of EMT, to compare EMT transitions between the cellular models used herein, and for the design of EMT time course experiments. O_TBL View this table: org.highwire.dtl.DTLVardef@10d2b87org.highwire.dtl.DTLVardef@1a4cc67org.highwire.dtl.DTLVardef@67bc95org.highwire.dtl.DTLVardef@8df40dorg.highwire.dtl.DTLVardef@1919e7c_HPS_FORMAT_FIGEXP M_TBL C_TBL Mendeley Data: DOI: 10.17632/pt3gmyk5r2.1 ArrayExpress Data: Accession number E-MTAB-9365

cell biology↗

bioRxiv2020

precisionFDA Truth Challenge V2: Calling variants from short- and long-reads in difficult-to-map regions

The precisionFDA Truth Challenge V2 aimed to assess the state-of-the-art of variant calling in difficult-to-map regions and the Major Histocompatibility Complex (MHC). Starting with FASTQ files, 20 challenge participants applied their variant calling pipelines and submitted 64 variant callsets for one or more sequencing technologies (~35X Illumina, ~35X PacBio HiFi, and ~50X Oxford Nanopore Technologies). Submissions were evaluated following best practices for benchmarking small variants with the new GIAB benchmark sets and genome stratifications. Challenge submissions included a number of innovative methods for all three technologies, with graph-based and machine-learning methods scoring best for short-read and long-read datasets, respectively. New methods out-performed the 2016 Truth Challenge winners, and new machine-learning approaches combining multiple sequencing technologies performed particularly well. Recent developments in sequencing and variant calling have enabled benchmarking variants in challenging genomic regions, paving the way for the identification of previously unknown clinically relevant variants.

bioinformatics↗

bioRxiv2020

Benchmarking challenging small variants with linked and long reads

Genome in a Bottle (GIAB) benchmarks have been widely used to help validate clinical sequencing pipelines and develop new variant calling and sequencing methods. Here, we use accurate linked reads and long reads to expand the prior benchmarks in 7 samples to include difficult-to-map regions and segmental duplications that are not readily accessible to short reads. Our new benchmark adds more than 300,000 SNVs, 50,000 indels, and 16 % new exonic variants, many in challenging, clinically relevant genes not previously covered (e.g., PMS2). For HG002, we include 92% of the autosomal GRCh38 assembly, while excluding problematic regions for benchmarking small variants (e.g., copy number variants and reference errors) that should not have been in the previous version, which included 85% of GRCh38. By including difficult-to-map regions, this benchmark identifies eight times more false negatives in a short read variant call set relative to our previous benchmark.We have demonstrated the utility of this benchmark to reliably identify false positives and false negatives across technologies in more challenging regions, which enables continued technology and bioinformatics development.

genomics↗

bioRxiv2020

SARS-CoV-2 infects and induces cytotoxic effects in human cardiomyocytes

BackgroundThe coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and has emerged as global pandemic. SARS-CoV-2 infection can lead to elevated markers of cardiac injury associated with higher risk of mortality in COVID-19 patients. It is unclear whether cardiac injury may have been caused by direct infection of cardiomyocytes or is mainly secondary to lung injury and inflammation. Here we investigate whether human cardiomyocytes are permissive for SARS-CoV-2 infection. MethodsInfection was induced by two strains of SARS-CoV-2 (FFM1 and FFM2) in human induced pluripotent stem cells-derived cardiomyocytes (hiPS-CM) and in two models of human cardiac tissue. ResultsWe show that SARS-CoV-2 infects hiPS-CM as demonstrated by detection of intracellular double strand viral RNA and viral spike glycoprotein protein expression. Increasing concentrations of virus RNA are detected in supernatants of infected cardiomyocytes, which induced infections in CaCo-2 cell lines documenting productive infections. SARS-COV-2 infection induced cytotoxic and pro-apoptotic effects and abolished cardiomyocyte beating. RNA sequencing confirmed a transcriptional response to viral infection as demonstrated by the up-regulation of genes associated with pathways related to viral response and interferon signaling, apoptosis and reactive oxygen stress. SARS-CoV-2 infection and cardiotoxicity was confirmed in a iPS-derived human 3D cardiosphere tissue models. Importantly, viral spike protein and viral particles were detected in living human heart slices after infection with SARS-CoV-2. ConclusionsThe demonstration that cardiomyocytes are permissive for SARS-CoV-2 infection in vitro warrants the further in depth monitoring of cardiotoxic effects in COVID-19 patients. Clinical PerspectiveO_ST_ABSWhat is New?C_ST_ABSO_LIThis study demonstrates that human cardiac myocytes are permissive for SARS-CoV-2 infection. C_LIO_LIThe study documents that SARS-CoV-2 undergoes a full replicatory circle and induces a cytotoxic response in cardiomyocytes. C_LIO_LIInfection was confirmed in two cardiac tissue models, including living human heart slices. C_LI What are the Clinical Implications?O_LIThe study may provide a rational to explain part of the cardiotoxicity observed in COVID-19 patients C_LIO_LIThe demonstration of direct cardiotoxicity induced by SARS-CoV-2 warrants an in depth further analysis of cardiac tissue of COVID-19 patients and a close monitoring for putative direct cardiomyocyte injury. C_LIO_LIThe established models can be used to test novel therapeutic approaches targeting COVID-19. C_LI

cell biology↗

bioRxiv2019

A Diploid Assembly-based Benchmark for Variants in the Major Histocompatibility Complex

We develop the first human benchmark derived from a diploid assembly for the openly-consented Genome in a Bottle/Personal Genome Project Ashkenazi son (HG002). As a proof-of-principle, we focus on a medically important, highly variable, 5 million base-pair region - the Major Histocompatibility Complex (MHC). Most human genomes are characterized by aligning individual reads to the reference genome, but accurate long reads and linked reads now enable us to construct base-level accurate, phased de novo assemblies from the reads. We assemble a single haplotig (haplotype-specific contig) for each haplotype, and align reads back to each assembled haplotig to identify two regions of lower confidence. We align the haplotigs to the reference, call phased small and structural variants, and define the first small variant benchmark for the MHC, covering 21496 small variants in 4.58 million base-pairs (92 % of the MHC). The assembly-based benchmark is 99.95 % concordant with a draft mapping-based benchmark from the same long and linked reads within both benchmark regions, but covers 50 % more variants outside the mapping-based benchmark regions. The haplotigs and variant calls are completely concordant with phased clinical HLA types for HG002. This benchmark reliably identifies false positives and false negatives from mapping-based callsets, and enables performance assessment in regions with much denser, complex variation than regions covered by previous benchmarks. These methods demonstrate a path towards future diploid assembly-based benchmarks for other complex regions of the genome.

genomics↗

bioRxiv2018

Continuous Evaluation of Ligand Protein Predictions: A Weekly Community Challenge for Drug Docking

1Docking calculations can be used to accelerate drug discovery by providing predictions of the poses of candidate ligands bound to a targeted protein. However, studies in the literature use varied docking methods, and it is not clear which work best, either in general or for specific protein targets. In addition, a complete docking calculation requires components beyond the docking algorithm itself, such as preparation of the protein and ligand for calculations, and it is difficult to isolate which aspects of a method are most in need of improvement. To address such issues, we have developed the Continuous Evaluation of Ligand Protein Predictions (CELPP), a weekly blinded challenge for automated docking workflows. Participants in CELPP create a workflow to predict protein-ligand binding poses, which is then tasked with predicting 10-100 new (never before released) protein-ligand crystal structures each week. CELPP evaluates the accuracy of each workflows predictions and posts the scores online. CELPP is a new cyberinfrastructure resource to identify the strengths and weaknesses of current approaches, help map docking problems to the algorithms most likely to overcome them, and illuminate areas of unmet need in structure-guided drug design.

biophysics↗

bioRxiv2018

Memory trace superimposition impairs recall in a mouse model of AD

Learning and memory processes depend on the hippocampus and are impaired in Alzheimers disease (AD). Active neuronal ensembles form an engram by encoding information during learning. Their reactivation is required for memory recall. However, it remains unresolved whether the engram in CA1 principal neurons is impaired under AD-like conditions. We used two-photon in vivo imaging to visualize the expression of the immediate early gene c-fos within CA1 neurons during contextual fear conditioning and retrieval. Surprisingly, we identified engrams in wild-type mice and in the mouse model of AD indicating intact memory formation. However, under AD-like conditions engrams were superimposed by a high number of newly recruited fosGFP+ neurons during memory recall. This superimposition resembled the network configuration of wild-type mice exposed to a novel context. Artificial superimposition of the memory trace during recall in wild-type mice was sufficient to induce memory impairment. Thus, we propose superimposition of the CA1 memory trace as a mechanism for memory impairment in a mouse model of AD.\n\nHighlightsO_LIDecreased fosGFP expression in direct vicinity to amyloid-{beta} plaques\nC_LIO_LIIntact engram in CA1 of APP/PS1 mice\nC_LIO_LIImpurity of the retrieval network in CA1 is sufficient to impair memory recall\nC_LI\n\nPoll et al. present a novel mechanism for memory impairment in a mouse model of AD. The potential memory trace was found intact in the CA1 region of the hippocampus. However, excessive neuronal activity during retrieval, was superimposing the memory trace in a mouse model of AD.

neuroscience↗

bioRxiv2018

Combinatorial recognition of clustered RNA elementsby a multidomain RNA-binding protein, IMP3

How multidomain RNA-binding proteins recognize their specific target sequences, based on a combinatorial code, represents a fundamental unsolved question and has not been studied systematically so far. Here we focus on a prototypical multidomain RNA-binding protein, IMP3 (also called IGF2BP3), which contains six RNA-binding domains (RBDs): four KH and two RRM domains. We have established an integrative systematic strategy, combining single-domain-resolved SELEX-seq, motif-spacing analyses, in vivo iCLIP, functional validation assays, and structural biology. This approach identifies the RNA-binding specificity and RNP topology of IMP3, involving all six RBDs and a cluster of up to five distinct and appropriately spaced CA-rich and GGC-core RNA elements, covering a >100 nucleotide-long target RNA region. Our generally applicable approach explains both specificity and flexibility of IMP3-RNA recognition, providing a paradigm for the function of multivalent interactions with multidomain RNA-binding proteins in gene regulation.

molecular biology↗

bioRxiv2018

metagenomeFeatures: An R package for working with 16S rRNA reference databases and marker-gene survey feature data.

We developed the metagenomeFeatures R Bioconductor package along with annotation packages for the three primary 16S rRNA databases (Greengenes, RDP, and SILVA) to facilitate working with 16S rRNA sequence databases and marker-gene survey feature data. The metagenomeFeatures package defines two classes, MgDb for working with 16S rRNA sequence databases, and mgFeatures for working with marker-gene survey feature data. The associated annotation packages provide a consistent interface to the different 16S rRNA databases facilitating database comparison and exploration. The mgFeatures represents a crucial step in the development of a common data structure for working with 16S marker-gene survey data in R.\n\nAvailabilityhttps://bioconductor.org/packages/release/bioc/html/metagenomeFeatures.html\n\nContactnolson@nist.gov

bioinformatics↗

bioRxiv2018

NetrinG1/NGL-1 Axis promotes pancreatictumorigenesis through cancer associated fibroblastderived nutritional supply and immunosuppression

Pancreatic ductal adenocarcinoma (PDAC) has a poor 5-year survival rate and lacks effective therapeutics. Therefore, it is of paramount importance to identify new targets. Using multi-plex data from patient tissue, three-dimensional co-culturing in vitro assays, and orthotopic murine models, we identified Netrin G1 (NetG1) as a promoter of PDAC tumorigenesis. NetG1+ cancer-associated fibroblasts (CAFs) supported PDAC survival, through a NetG1 mediated effect on glutamate/glutamine metabolism. NetG1+ CAFs were intrinsically immunosuppressive and inhibited NK cell mediated killing of tumor cells. These pro-tumor functions were controlled by a signaling circuit downstream to NetG1, which was comprised of AKT/4E-BP1, p38/FRA1, vesicular glutamate transporter 1, and glutamine synthetase. Finally blocking NetG1 with a neutralizing antibody stunted in vivo tumorigenesis, suggesting NetG1 as potential target in PDAC. SignificancePDAC is a devastating disease lacking effective therapies. A major hallmark of PDAC is desmoplasia, characterized by the expansion of CAFs and their extracellular matrix, creating a unique microenvironment that limits blood-supplied nutrition and is highly immunosuppressive. A better understanding of the role of CAFs in PDAC may lead to the identification of new targets for therapeutic intervention. Here, we uncovered roles for NetG1 in CAFs to promote tumorigenesis. NetG1 was important for two major CAF functions: the metabolic support of PDAC cells and the intrinsic immunosuppressive capacity of CAFs. Our results helped clarify the role that CAFs play in PDAC, by defining CAF phenotypes through NetG1 expression. Moreover, we established a link between CAF driven metabolism and their intrinsic immunosuppressive capacity, and identified a signaling circuit that governs NetG1 functions. Finally, we demonstrated the therapeutic potential of inhibiting NetG1 in vivo by limiting tumorigenesis in mice with a neutralizing antibody, illustrating that targeting stromal NetG1 could be an attractive therapeutic approach.

cancer biology↗

bioRxiv2018

Mouse models of hereditary hemochromatosis do not develop early liver fibrosis in response to a high fat diet

Hepatic iron overload, a hallmark of hereditary hemochromatosis (HH), triggers progressive liver disease. There is also increasing evidence for a pathogenic role of iron in non-alcoholic fatty liver disease (NAFLD), which may progress to non-alcoholic steatohepatitis (NASH), fibrosis, cirrhosis and hepatocellular cancer. Mouse models of HH and NAFLD can be used to explore potential interactions between iron and lipid metabolic pathways. Hfe-/- mice, a model of moderate iron overload, were reported to develop early liver fibrosis in response to a high fat diet. However, this was not the case with Hjv-/- mice, a model of severe iron overload. These data raised the possibility that the Hfe gene may protect against liver injury independently of its iron regulatory function. Herein, we addressed this hypothesis in a comparative study utilizing wild type, Hfe-/-, Hjv-/- and double Hfe-/-Hjv-/- mice. The animals, all in C57/BL6 background, were fed with a high fat diet for 14 weeks and developed hepatic steatosis, associated with mild iron overload. Hfe co-ablation did not sensitize steatotic Hjv-deficient mice to liver injury. Moreover, we did not observe any signs of liver inflammation or fibrosis even in single steatotic Hfe-/- mice. Ultrastructural studies revealed a reduced lipid and glycogen content in Hjv-/- hepatocytes, indicative of a metabolic defect. Interestingly, glycogen levels were restored in double Hfe-/-Hjv-/- mice, which is consistent with a metabolic function of Hfe. We conclude that hepatocellular iron excess does not aggravate diet-induced steatosis to steatohepatitis or early liver fibrosis in mouse models of HH, irrespectively of the presence or lack of Hfe.

pathology↗

bioRxiv2018

Sidewall cell envelope synthesis and remodeling in pole-growing mycobacteria

O_SCPLOWDC_SCPLOW-amino acid probes label cell wall peptidoglycan at both the poles and sidewall of pole-growing mycobacteria. Since peptidoglycan assembly along the cell periphery could provide a rapid, growth-independent means by which to edit the cell wall, we sought to clarify the precise metabolic fates of these probes. O_SCPLOWDC_SCPLOW-amino acid monopeptides were incorporated into peptidoglycan by O_SCPLOWLC_SCPLOWO_SCPCAP,C_SCPCAPO_SCPLOWDC_SCPLOW-transpeptidase remodeling enzymes to varying extents. Dipeptides were incorporated into cytoplasmic precursors. While dipeptide-marked peptidoglycan synthesis at the poles was associated with cell elongation, synthesis along the periphery was highly responsive to cell wall damage. Our observations suggest a post-expansion role for peptidoglycan assembly along the mycobacterial sidewall and provide a conceptual framework for understanding cell wall robustness in the face of polar growth.

microbiology↗

bioRxiv2017

Forgot what you like? Evidence for hippocampal dependence of value-based decisions

Consistent decisions are intuitively desirable and theoretically important for utility maximization. Neuroeconomics has established the neurobiological substrate of value representation, but brain regions that provide input to the value-processing network is less explored. The constructed-preference tradition within behavioral decision research gives a critical role to cognitive processes that rely on associations, suggesting a role for the hippocampus in making decisions and to do so consistently. We compared the performance of 31 patients with mediotemporal lobe (MTL) epilepsy and hippocampal lesions, 30 patients with extratemporal lobe epilepsy, and 30 healthy controls on two tasks: binary choices between candy bars based on their preferences and a number-comparison control task where the larger number is chosen. MTL patients make more inconsistent choices than the other two groups for the value-based choice but not the number-comparison task. These inconsistencies increase with the volume of compromised hippocampal tissue. These results suggest a critical involvement of the MTL in preference construction and value-based choices.\n\nSignificanceOur days are full of choices that reflect our preferences. Economics lays out models of how to optimally make these decisions. Neuroeconomics has identified a cortical value-processing network whose activity correlates with constructs related to valuation and choice in economic models. However open questions remain: How are these value signals formed, and what regions might be necessary for retrieving and computing these value signals? Inspired by cognitive models calling on associative processes in value-based decisions, this paper uses unique neuropsychological data to establish the critical role of the medial temporal lobe in making consistent choices and further informs our understanding of the value-processing network.

animal behavior and cognition↗

bioRxiv2017

Metaviz: interactive statistical and visual analysis of metagenomic data

Along with the survey techniques of 16S rRNA amplicon and whole-metagenome shotgun sequencing, an array of tools exists for clustering, taxonomic annotation, normalization, and statistical analysis of microbiome sequencing results. Integrative and interactive visualization that enables researchers to perform exploratory analysis in this feature rich hierarchical data is an area of need. In this work, we present Metaviz, a web browser-based tool for interactive exploratory metagenomic data analysis. Metaviz can visualize abundance data served from an R session or a Python web service that queries a graph database. As metagenomic sequencing features have a hierarchy, we designed a novel navigation mechanism to explore this feature space. We visualize abundance counts with heatmaps and stacked bar plots that are dynamically updated as a user selects taxonomic features to inspect. Metaviz also supports common data exploration techniques, including PCA scatter plots to interpret variability in the dataset and alpha diversity boxplots for examining ecological community composition. The Metaviz application and documentation is hosted at http://www.metaviz.org.

bioinformatics↗