Biology subjects

Schork, N. J.

Publications and source records attributed to Schork, N. J..

12 recordsLinked to original sources

bioRxiv2025

Autophagy activators normalize aberrant Tau proteostasis and rescue synapses in human familial Alzheimer's disease iPSC-derived cortical organoids

Alzheimers disease (AD) is the most common form of dementia worldwide. Despite extensive progress, the cellular and molecular mechanisms of AD remain incompletely understood, partially due to inadequate disease models. To illuminate the earliest changes in hereditary (familial) Alzheimers disease, we developed an isogenic AD cerebrocortical organoid (CO) model. Our refined methodology produces COs containing excitatory and inhibitory neurons alongside glial cells, utilizing established isogenic wild-type and diseased human induced pluripotent stem cells (hiPSCs) carrying heterozygous familial AD mutations, namely PSEN1{Delta}E9/WT, PSEN1M146V/WT, or APPswe/WT. Our CO model reveals time-progressive accumulation of amyloid beta (A{beta}) species, loss of monomeric Tau, and accumulation of aggregated high-molecular-weight (HMW) phospho(p)-Tau species. This is accompanied by neuronal hyperexcitability, as observed in early human AD cases on electroencephalography (EEG), and synapse loss. Single-cell RNA-sequencing analyses reveal significant differences in molecular abnormalities in excitatory vs. inhibitory neurons, helping explain AD clinical phenotypes. Finally, we show that chronic dosing with autophagy activators, including a novel CNS-penetrant mTOR inhibitor-independent drug candidate, normalizes pathologic accumulation of A{beta} and HMW p-Tau, normalizes hyperexcitability, and rescues synaptic loss in COs. Collectively, our results demonstrate these COs are a useful human AD model suitable for assessing early features of familial AD etiology and for testing drug candidates that ameliorate or prevent molecular AD phenotypes. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/661453v2_ufig1.gif" ALT="Figure 1"> View larger version (58K): org.highwire.dtl.DTLVardef@12ac9c6org.highwire.dtl.DTLVardef@24eceaorg.highwire.dtl.DTLVardef@3e3238org.highwire.dtl.DTLVardef@1530d1e_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2024

A mRNA Vaccine Encoding for a 60-mer G Glycoprotein Nanoparticle Elicits a Robust Neutralizing Antibodies Response Against the Nipah Virus

The Nipah virus is a zoonotic pathogen causing encephalitis and acute respiratory illness in humans with very high fatality rates. Here we report a novel messenger RNA vaccine that directly encodes for a nanoparticle displaying 60 head domains of the Nipah virus G (NiV G) glycoprotein that acts as a highly effective antigen. A vaccine encoding for the NiV G nanoparticle elicits high antibody titers against NiV G and a robust neutralizing antibody response with a pseudotyped Nipah virus system. We ultimately find that the proposed mRNA NiV G nanoparticle (mRNA NiV G-NP) provides a flexible platform for the development of vaccines that will likely be of great value in combatting future Nipah virus outbreaks.

immunology↗

bioRxiv2023

Borrelia PeptideAtlas: A proteome resource of common Borrelia burgdorferi isolates for Lyme research

Lyme disease, caused by an infection with the spirochete Borrelia burgdorferi, is the most common vector-borne disease in North America. B. burgdorferi strains harbor extensive genomic and proteomic variability and further comparison is key to understanding the spirochetes infectivity and biological impacts of identified sequence variants. To achieve this goal, both transcript and mass spectrometry (MS)-based proteomics was applied to assemble peptide datasets of laboratory strains B31, MM1, B31-ML23, infective isolates B31-5A4, B31-A3, and 297, and other public datasets, to provide a publicly available Borrelia PeptideAtlas (http://www.peptideatlas.org/builds/borrelia/). Included is information on total proteome, secretome, and membrane proteome of these B. burgdorferi strains. Proteomic data collected from 35 different experiment datasets, with a total of 855 mass spectrometry runs, identified 76,936 distinct peptides at a 0.1% peptide false-discovery-rate, which map to 1,221 canonical proteins (924 core canonical and 297 noncore canonical) and covers 86% of the total base B31 proteome. The diverse proteomic information from multiple isolates with credible data presented by the Borrelia PeptideAtlas can be useful to pinpoint potential protein targets which are common to infective isolates and may be key in the infection process.

biochemistry↗

bioRxiv2022

Detecting boolean asymmetric relationships with a loop counting technique and its implications for analyzing heterogeneity within gene expression datasets

Many traditional methods for analyzing gene-gene relationships focus on positive and negative correlations, both of which are a kind of symmetric relationship. Biclustering is one such technique that typically searches for subsets of genes exhibiting correlated expression among a subset of samples. However, genes can also exhibit asymmetric relationships, such as if-then relationships used in boolean circuits. In this paper we develop a very general method that can be used to detect biclusters within gene-expression data that involve subsets of genes which are enriched for these boolean-asymmetric relationships (BARs). These BAR-biclusters can correspond to heterogeneity that is driven by asymmetric gene-gene interactions, e.g., reflecting regulatory effects of one gene on another, rather than more standard symmetric interactions. Unlike typical approaches that search for BARs across the entire population, BAR-biclusters can detect asymmetric interactions that only occur among a subset of samples. We apply our method to a single-cell RNA-sequencing data-set, demonstrating that the statistically-significant BAR-biclusters indeed contain additional information not present within the more traditional boolean-symmetric-biclusters. For example, the BAR-biclusters involve different subsets of cells, and highlight different gene-pathways within the data-set. Moreover, by combining the boolean-asymmetric- and boolean-symmetric-signals, one can build linear classifiers which outperform those built using only traditional boolean-symmetric signals.

bioinformatics↗

bioRxiv2022

A mRNA vaccine encoding for a RBD 60-mer nanoparticle elicits neutralizing antibodies and protective immunity against the SARS-CoV-2 delta variant in transgenic K18-hACE2 mice

Two years into the COVID-19 pandemic there is still a need for vaccines to effectively control the spread of novel SARS-CoV-2 variants and associated cases of severe disease. Here we report a messenger RNA vaccine directly encoding for a nanoparticle displaying 60 receptor binding domains (RBDs) of SARS-CoV-2 that acts as a highly effective antigen. A construct encoding the RBD of the delta variant elicits robust neutralizing antibody response with neutralizing titers an order of magnitude above currently approved mRNA vaccines. The construct also provides protective immunity against the delta variant in a widely used transgenic mouse model. We ultimately find that the proposed mRNA RBD nanoparticle-based vaccine provides a flexible platform for rapid development and will likely be of great value in combatting current and future SARS-CoV-2 variants of concern.

immunology↗

bioRxiv2020

Bisbee: A proteomics validated analysis package for detecting differential splicing, identifying splice outliers, and predicting splice event protein effects

Here we present a novel statistical approach to splicing outlier and differential splicing detection, implemented in a software package called Bisbee. We leverage Bisbees prediction of protein level effects to benchmark using matched RNAseq and mass spectrometry data from normal tissues. Bisbee exhibits improved sensitivity and specificity over existing approaches. We applied Bisbee to confirm a pathogenic splicing event in a rare disease and to identify tumor-specific splice isoforms associated with an oncogenic splice factor mutation. We also identified common tumor associated splice isoforms replicated in an independent dataset, demonstrating the utility of Bisbee in discovering disease relevant splice variants.

bioinformatics↗

bioRxiv2020

Conserved Genomic Terminals of SARS-CoV-2 as Co-evolving Functional Elements and Potential Therapeutic Targets

ABSTRACTTo identify features in the genome of the SARS-CoV-2 pathogen responsible for the COVID-19 pandemic that may contribute to its viral replication, host pathogenicity, and vulnerabilities, we investigated how and to what extent the SARS-CoV-2 genome sequence differs from other well-characterized human and animal coronavirus genomes. Our analyses suggest the presence of unique sequence signatures in the 3’-untranslated region (UTR) of betacoronavirus lineage B, which phylogenetically encompasses SARS-CoV-2, SARS-CoV, as well as multiple groups of bat and animal coronaviruses. In addition, we identified genome-wide patterns of variation across different SARS-CoV-2 strains that likely reflect the effects of selection. Finally, we provide evidence for a possible host microRNA-mediated interaction between the 3’-UTR and human microRNA hsa-miR-1307-3p based on predicted, yet extensive, complementary base-pairings and similar interactions involving the Influenza A H1N1 virus. This interaction also suggests a possible survival mechanism, whereby a mutation in the SARS-CoV-2 3’-UTR leads to a weakened host immune response. The potential roles of host microRNAs in SARS-CoV-2 replication and infection, and the exploitation of conserved features in the 3’-UTR as therapeutic targets warrant further investigation.Competing Interest StatementThe authors have declared no competing interest.View Full Text

genomics↗

bioRxiv2019

Transcriptomic evidence that von Economo neurons are regionally specialized extratelencephalic-projecting excitatory neurons

von Economo neurons (VENs) are bipolar, spindle-shaped neurons restricted to layer 5 of human frontoinsula and anterior cingulate cortex that appear to be selectively vulnerable to neuropsychiatric and neurodegenerative diseases, although little is known about other VEN cellular phenotypes. Single nucleus RNA-sequencing of frontoinsula layer 5 identified a transcriptomically-defined cell cluster that contained VENs, but also fork cells and a subset of pyramidal neurons. Cross-species alignment of this cell cluster with a well-annotated mouse classification shows strong homology to extratelencephalic (ET) excitatory neurons that project to subcerebral targets. This cluster also shows strong homology to a putative ET cluster in human temporal cortex, but with a strikingly specific regional signature. Together these results predict VENs are a regionally distinctive type of ET neuron, and we additionally describe the first patch clamp recordings of VENs from neurosurgically-resected tissue that show distinctive intrinsic membrane properties relative to neighboring pyramidal neurons.

neuroscience↗

bioRxiv2017

Equivalent high-resolution identification of neuronal cell types with single-nucleus and single-cell RNA-sequencing

Transcriptional profiling of complex tissues by RNA-sequencing of single nuclei presents some advantages over whole cell analysis. It enables unbiased cellular coverage, lack of cell isolation-based transcriptional effects, and application to archived frozen specimens. Using a well-matched pair of single-nucleus RNA-seq (snRNA-seq) and single-cell RNA-seq (scRNA-seq) SMART-Seq v4 datasets from mouse visual cortex, we demonstrate that similarly high-resolution clustering of closely related neuronal types can be achieved with both methods if intronic sequences are included in nuclear RNA-seq analysis. More transcripts are detected in individual whole cells (~11,000 genes) than nuclei (~7,000 genes), but the majority of genes have similar detection across cells and nuclei. We estimate that the nuclear proportion of total cellular mRNA varies from 20% to over 50% for large and small pyramidal neurons, respectively. Together, these results illustrate the high information content of nuclear RNA for characterization of cellular diversity in brain tissues.

neuroscience↗

bioRxiv2017

Transcriptomic and morphophysiological evidence for a specialized human cortical GABAergic cell type

We describe convergent evidence from transcriptomics, morphology and physiology for a specialized GABAergic neuron subtype in human cortex. Using unbiased single nucleus RNA sequencing, we identify ten GABAergic interneuron subtypes with combinatorial gene signatures in human cortical layer 1 and characterize a novel group of human interneurons with anatomical features never described in rodents having large, \"rosehip\"-like axonal boutons and compact arborization. These rosehip cells show an immunohistochemical profile (GAD1/CCK-positive, CNR1/SST/CALB2/PVALB-negative) matching a single transcriptomically-defined cell type whose molecular signature is not seen in mouse cortex. Rosehip cells make homotypic gap junctions, predominantly target apical dendritic shafts of layer 3 pyramidal neurons and inhibit backpropagating pyramidal action potentials in microdomains of the dendritic tuft. These cells are therefore positioned for potent local control of distal dendritic computation in cortical pyramidal neurons.

neuroscience↗

bioRxiv2017

Modeling prediction error improves power of transcriptome-wide association studies

1Transcriptome-wide association studies (TWAS) test for associations between imputed gene expression levels and phenotypes in GWAS cohorts using models of transcriptional regulation learned from reference transcriptomes. However, current methods for TWAS only use point estimates of imputed expression and ignore uncertainty in the prediction. We develop a novel two-stage Bayesian regression method which incorporates uncertainty in imputed gene expression and achieves higher power to detect TWAS genes than existing TWAS methods as well as standard methods based on missing value and measurement error theory. We apply our method to GTEx whole blood transcriptomes and GWAS cohorts for seven diseases from the Wellcome Trust Case Control Consortium and find 45 TWAS genes, of which 17 do not overlap previously reported case-control GWAS or differential expression associations. Surprisingly, we replicate only 2 of 40 previously reported TWAS genes after accounting for uncertainty in the prediction.

genetics↗

bioRxiv2016

Pan-Cancer Analysis Reveals Technical Artifacts in The Cancer Genome Atlas (TCGA) Germline Variant Calls

The degree to which germline variation drives cancer development and shapes tumor phenotypes remains largely unexplored, possibly due to a lack of large scale publicly available germline data for a cancer cohort. Here we called germline variants on 9,618 cases from The Cancer Genome Atlas (TCGA) database representing 31 cancer types. We identified batch effects affecting loss of function (LOF) variant calls that can be traced back to differences in the way the sequence data were generated both within and across cancer types. Overall, LOF indel calls were more sensitive to technical artifacts than LOF Single Nucleotide Variant (SNV) calls. In particular, whole genome amplification of DNA prior to sequencing led to an artificially increased burden of LOF indel calls, which confounded association analyses relating germline variants to tumor type despite stringent indel filtering strategies. Due to the inherent noise we chose to remove all 614 amplified DNA samples, including all acute myeloid leukemia and virtually all ovarian cancer samples, from the final dataset. This study demonstrates how insufficient quality control can lead to false positive germlinetumor type associations and draws attention to the need to be sensitive to problems associated with a lack of uniformity in data generation in TCGA data.\n\nAuthor SummaryCancer research to date has largely focused on genetic aberrations specific to tumor tissue. In contrast, the degree to which germline, or inherited, variation contributes to tumorigenesis remains unclear, possibly due to a lack of accessible germline variant data. In this study we identify germline variants in 9,618 samples using raw germline exome data from The Cancer Genome Atlas (TCGA). There are substantial differences in the way exome sequence data was generated both across and within cancer types in TCGA. We observe that differences in sequence data generation introduced batch effects, or variation that is due to technical factors not true biological variation, in our variant data. Most notably, we observe that amplification of DNA prior to sequencing resulted in an excess of predicted damaging indel variants. We show how these batch effects can confound germline association analyses if not properly addressed. Our study highlights the difficulties of working with large public genomic datasets like TCGA where samples are collected over time and across data centers, and particularly cautions the use of amplified DNA samples for genetic association analyses.

genomics↗