Biology subjects

Goldstein, D. B.

Publications and source records attributed to Goldstein, D. B..

16 recordsLinked to original sources

bioRxiv2022

Single Cell transcriptional analysis of ex vivo models of cortical and hippocampal development identifies unique longitudinal trends

Postnatal cortical and hippocampal mouse primary neuronal cultures are powerful and widely-used models of neuronal activity and neurological disease. While this model is frequently used to recapitulate what is seen in vivo, how the transcriptomic profiles of neuronal networks change over development is not fully understood. We use single-cell transcriptomics to provide a view of neuronal network establishment and maturation. Our data highlight region-specific differences and suggest how cell populations program the transcriptome in these brain regions. We demonstrate that patterns of expression markedly differ between and within neurological diseases, and explore why these differences are found and how well they compare to other models. In particular, we show significant expression differences between genes associated with epilepsy, autism spectrum disorder, and other neurological disorders. Collectively, our study provides novel insights on this popular model of development and disease that will better inform design for drug discovery and therapeutic intervention. O_FIG O_LINKSMALLFIG WIDTH=153 HEIGHT=200 SRC="FIGDIR/small/519959v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@1be59a3org.highwire.dtl.DTLVardef@1ef18b9org.highwire.dtl.DTLVardef@402c1dorg.highwire.dtl.DTLVardef@1103789_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical AbstractC_FLOATNO (A) Schematic representing select gene expression progression through neuronal network maturation from human cortical organoids (3- and 6-Month Organoid), newborn mice (P0 Mouse), immature ex vivo cortex derived cultures (DIV 3 ex vivo), functionally mature ex vivo cortex derived cultures (DIV15-31 ex vivo), and adult mice (P56 Mouse). Color represents proportion of excitatory neurons with detectable expression for selected representative genes Mapk10, Igfbp2, which increase and decrease through network maturation, respectively. (B) Schematic representing divergent expression patterns between genes associated with epilepsy and ASD through network maturation between the organoids and ex vivo cultures shown in (A). Color scales represent the change in the percentile, in respect to all genes, of the proportion of excitatory neurons with detectable expression. C_FIG

genetics↗

bioRxiv2020

Systematic evaluation of genome sequencing as a first-tier diagnostic test for prenatal and pediatric disorders

Current clinical guidelines recommend three genetic tests for the assessment of fetal structural anomalies: karyotype to detect microscopically-visible balanced and unbalanced chromosomal rearrangements, chromosomal microarray (CMA) to detect sub-microscopic copy number variants (CNVs), and exome sequencing (ES) to identify individual nucleotide changes in coding sequence. Advances in genome sequencing (GS) analysis suggest that it is poised to displace the sequential application of all three conventional tests to become a single diagnostic approach for the assessment of fetal structural anomalies. However, systematic benchmarking is required to assure that GS can capture the full mutational spectrum associated with fetal structural anomalies and to accurately quantify the added diagnostic yield of GS. We applied a novel GS analytic framework that included the discovery, filtration, and interpretation of nine classes of genomic variation to 7,195 individuals. We assessed the sensitivity of GS to detect diagnostic variants (pathogenic or likely pathogenic) from three standard-of-care tests using 1,612 autism spectrum disorder quartet families (ASD; n=6,448) with matched GS, ES, and CMA data, and validated these findings in 46 fetuses with a clinically reportable variant originally identified by karyotype, CMA, or ES. We then assessed the added diagnostic yield of GS in 249 trios (n=747) comprising a fetus with a structural anomaly detected by ultrasound and two unaffected parents that were pre-screened with a combination of all three standard-of-care tests. Across both cohorts, our GS analytic framework identified 98.2% of all diagnostic variants detected by standard-of-care tests, including 100% of those originally detected by CMA (n=88) and ES (n=61), as well as 78.6% (n=11/14) of the chromosomal rearrangements identified by karyotype. The diagnostic yield from GS was 7.8% across all 1,612 ASD probands, almost two-fold more than CMA (4.4%) and three-fold more than ES (3.0%). We also demonstrated that the yield of ES can approach that of GS when CNVs are captured with high sensitivity from exome data (7.4% vs. 7.8%, respectively). In 249 pre-screened fetuses with structural anomalies, GS provided an additional diagnostic yield of 0.4% beyond the combination of all three tests (karyotype, CMA, and ES). Applying our benchmarking results to existing data indicates that GS can achieve an overall diagnostic yield of 46.1% in unselected fetuses with fetal structural anomalies, providing an estimated 17.2% increase in diagnostic yield over karyotype, 14.1% over CMA, and 36.1% over ES when sequence variants are assessed, and 4.1% when CNVs are also identified from exome data. In this study we demonstrate that GS is sensitive to the detection of almost all pathogenic variation captured by karyotype, CMA, and ES, provides a superior diagnostic yield than any individual test by a wide margin, and contributes a modest increase in diagnostic yield beyond the combination of all three tests. We also outline several strategies to aid the interpretation of GS variants that are cryptic to conventional technologies, which we anticipate will be increasingly encountered as comprehensive variant identification from GS is performed. Taken together, these data suggest GS warrants consideration as a first-tier diagnostic approach for fetal structural anomalies.

genomics↗

bioRxiv2020

ATAV - a comprehensive platform for population-scale genomic analyses

BackgroundA common approach for sequencing studies is to do joint-calling and store variants of all samples in a single file. If new samples keep being added or controls are re-used for several studies, the cost and time required to perform joint-calling for each analysis can become prohibitive. ResultsWe present ATAV, an analysis platform for large-scale whole-exome and whole-genome sequencing projects. ATAV stores variant and per site coverage data for all samples in a centralized database, which is efficiently queried by ATAV to support diagnostic analyses for trios and singletons, as well as rare-variant collapsing analyses for finding disease associations in complex diseases. Runtime logs ensure full reproducibility and the modularized ATAV framework makes it extensible to continuous development. Besides helping with the identification of disease-causing variants for a range of diseases, ATAV has also enabled the discovery of disease-genes by rare-variant collapsing on datasets containing more than 20,000 samples. Analyses to date have been performed on data of more than 110,000 individuals demonstrating the scalability of the framework. The ATAV data browser (http://atavdb.org/) is a web-based interface that allows users to easily access variant-level data directly from the database. Summary-level data for more than 40,000 samples can be queried by the general public representing a mix of cases and controls of diverse ancestries. Users have access to phenotype categories of variant carriers, as well as predicted ancestry, gender, and quality metrics. In contrast to many other platforms, the data browser is able to show data of newly-added samples in real-time and is therefore evolving rapidly as more and more samples are sequenced. ConclusionsSince all code is freely available on GitHub, ATAV can easily be used by other groups to build up their own platform, database, and user interface. In addition to that users can query one of the largest variant databases for patients sequenced at a tertiary care center and look up their own genes or variants of interest.

bioinformatics↗

bioRxiv2020

Precision genetic cellular models identify therapies protective against endoplasmic reticulum stress

Congenital disorders of glycosylation (CDG) and deglycosylation (CDDG) are a collection of rare pediatric disorders with symptoms that range from mild to life threatening. They typically affect multiple organ systems and usually present with neurological abnormalities including hypotonia, cognitive impairment, and intractable seizures. Several genes have been implicated in the thirty-six types of CDG, but currently NGLY1 is the only known CDDG gene. A common biological mechanism among CDG types and in CDDG is endoplasmic reticulum (ER) stress. Here, we develop two isogenic human cellular models of CDG (PMM2, the most prevalent type of CDG, and DPAGT1) and of the only CDDG (NGLY1) in an effort to identify drugs that can alleviate ER stress. Systematic phenotyping identified elevated ER stress and autophagy levels among other cellular and morphological phenotypes in each of the cellular models. We screened a complex drug library for compounds able to correct aberrant morphological phenotypes in each of the models using an agnostic phenotypic cell painting assay based on >300 cellular features. The image-based screen identified multiple candidate compounds able to correct aberrant morphology, and we show a subset of these are able to correct cellular and molecular defects in each of the models. These results provide new directions for the treatment of rare diseases of glycosylation and deglycosylation and a framework for new drug screening paradigms for more common neurodegenerative diseases characterized by ER stress. Summary sentenceNovel drug screening modality identifies compounds that correct aberrant molecular phenotypes in precision cellular models of glycosylation defects.

neuroscience↗

bioRxiv2020

Establishing a transcriptome-based drug discovery paradigm for neurodevelopmental disorders

Advances in genetic discoveries have created substantial opportunities for precision medicine in neurodevelopmental disorders. Many of the genes implicated in these diseases encode proteins that regulate gene expression, such as chromatin associated proteins, transcription factors, and RNA-binding proteins. The identification of targeted therapeutics for individuals carrying mutations in these genes remains a challenge, as the encoded proteins can theoretically regulate thousands of downstream targets in a considerable number of cell types. Here, we propose the application of a drug discovery approach called "transcriptome reversal" for these disorders. This approach, originally developed for cancer, attempts to identify compounds that reverse gene-expression signatures associated with disease states.

genomics↗

bioRxiv2020

The paradoxical effects of K+ channel gain-of-function are mediated by GABAergic neuron hypoexcitability and hyperconnectivity.

Gain-of-function (GOF) variants in K+ channels cause severe childhood epilepsies, but there are no mechanisms to explain how increased K+ currents lead to network hyperexcitability. Here, we introduced a human Na+-activated K+ (KNa) channel variant (KCNT1-Y796H) into mice and, using a multiplatform approach, found motor cortex hyperexcitability and early-onset seizures, phenotypes strikingly similar to those of human patients. Although the variant increased KNa currents in cortical excitatory and inhibitory neurons, there was a selective increase in the KNa current across subthreshold voltages in inhibitory neurons, particularly in those with non-fast spiking properties, resulting in impaired excitability and AP generation. We further observed evidence of synaptic rewiring associated with hyperexcitable networks, including increases in homotypic synaptic connectivity and the ratio of excitatory-to-inhibitory synaptic input. These findings support inhibitory neuron-specific mechanisms in mediating the epileptogenic effects of K+ channel GOF, offering cell-type-specific currents and effects as promising targets for therapeutic intervention. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/978841v1_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@110d6e1org.highwire.dtl.DTLVardef@8b63ddorg.highwire.dtl.DTLVardef@14972a9org.highwire.dtl.DTLVardef@106be1c_HPS_FORMAT_FIGEXP M_FIG GRAPHICAL ABSTRACT C_FIG

neuroscience↗

bioRxiv2019

Predicting the genetic ancestry of 2.6 million New York City patients using clinical data

Ancestry is an essential covariate in clinical genomics research. When genetic data are available, dimensionality reduction techniques, such as principal components analysis, are used to determine ancestry in complex populations. Unfortunately, these data are not always available in the clinical and research settings. For example, electronic health records (EHRs), which are a rich source of temporal human disease data that could be used to enhance genetic studies, do not directly capture ancestry. Here, we present a novel algorithm for predicting genetic ancestry using only variables that are routinely captured in EHRs, such as self-reported race and ethnicity, and condition billing codes. Using patients that have both genetic and clinical information at Columbia University/ New York-Presbyterian Irving Medical Center, we developed a pipeline that uses only clinical data to predict the genetic ancestry of all patients of which more than 80% identify as other or unknown. Our ancestry estimates can be used in observational studies of disease inheritance, to guide genetic cohort studies, or to explore health disparities in clinical care and outcomes.

bioinformatics↗

bioRxiv2019

Modeling and treating GRIN2A developmental and epileptic encephalopathy in mice

NMDA receptors (NMDAR) play crucial roles in excitatory synaptic transmission. Rare variants of GRIN2A, which encodes the GluN2A NMDAR subunit, are associated with several intractable neurodevelopmental disorders, including developmental and epileptic encephalopathy (DEE). A de novo missense variant, p.Ser644Gly (c.1930A>G), was identified in a child with DEE, and Grin2a knockin mice were generated to model and extend understanding of this intractable childhood disease. Homozygous and heterozygous mutant mice exhibit altered hippocampal morphology at two weeks of age, and homozygotes exhibit lethal tonic-clonic seizures in the third week. Heterozygous adult mice display a variety of distinct features, including resistance to electrically induced partial seizures, as well as hyperactivity and repetitive and reduced anxiety behaviors. Multielectrode recordings of mutant neuronal networks reveal hyperexcitability and altered bursting and synchronicity. When expressed in heterologous cells, mutant receptors exhibit enhanced NMDAR agonist potency and slow deactivation following rapid removal of glutamate, as occurs at synapses. Consistent with these observations, NMDAR-mediated synaptic currents in hippocampal slices from mutant mice show a prolonged deactivation time course. Standard antiepileptic drug monotherapy was ineffective in the patient, but combined treatment of NMDAR antagonists with antiepileptic drugs substantially reduced the seizure burden albeit without appreciable developmental improvement. Chronic treatment of homozygous mutant mouse pups with NMDAR antagonists delayed the onset of lethal seizures but did not prevent them. These studies illustrate the power of modeling severe neurodevelopmental seizure disorders using multiple experimental modalities and suggest their extended utility in identifying and evaluating new therapies.

neuroscience↗

bioRxiv2019

G protein-coupled potassium channels implicated in mouse and cellular models of GNB1 Encephalopathy

De novo mutations in GNB1, encoding the G{beta}1 subunit of G proteins, cause a neurodevelopmental disorder with global developmental delay and epilepsy. Mice carrying a pathogenic mutation, K78R, recapitulate aspects of the disorder, including developmental delay and frequent spike-wave discharges (SWD). Cultured mutant cortical neurons display aberrant bursting activity on multi-electrode arrays. Strikingly, the antiepileptic drug ethosuximide (ETX) restores normal neuronal network behavior in vitro and suppresses SWD in vivo. In contrast, while valproic acid suppresses SWD, it does not restore normal network behavior, suggesting that ETX has mechanistic specificity for the effects of aberrant G{beta}1 signaling. Consistent with this, we show that K78R is a gain-of-function of G protein-coupled potassium channel (GIRK) activation that is potently inhibited by ETX. This work suggests that altered G{beta}1 signaling causes disease in part through effects on GIRK channels, illustrates the utility of cultured neuronal networks in pharmacological screening, and establishes effective pre-clinical models for GNB1 Encephalopathy.

neuroscience↗

bioRxiv2019

Natural selection shapes codon usage in the human genome

Synonymous codon usage has been identified as an important determinant of translational efficiency and mRNA stability in model organisms and human cell lines. However, to date, population genetics studies have failed to observe evolutionary constraint on human codon usage, and synonymous variation has been largely overlooked as a component of human genetic diversity. Using genetic sequencing data from nearly 200,000 individuals, we uncover clear evidence that natural selection optimizes codon content in the human genome. We derive intolerance metrics to quantify gene-level constraint on synonymous variation and demonstrate that dosage-sensitive, DNA damage response, and cell cycle regulated genes are more intolerant to synonymous variation than other genes in the genome. Notably, we illustrate that reductions in codon optimality can attenuate the function of BRCA1. Our results reveal that synonymous mutations likely play an important and underappreciated role in human variation.

genomics↗

bioRxiv2019

Incorporating prior information into signal-detection analyses across biologically informed gene-sets

Signal detection analyses are used to assess whether there is any evidence of signal within a large collection of hypotheses. For example, we may wish to assess whether there is any evidence of association with disease among a set of biologically related genes. Such an analysis typically treats all genes within the sets similarly, even though there is substantial information concerning the likely importance of each gene within each set. For example, deleterious variants within genes that show evidence of purifying selection are more likely to substantially affect the phenotype than genes that are not under purifying selection, at least for traits that are themselves subject to purifying selection. Here we improve such analyses by incorporating prior information into a higher-criticism-based signal detection analysis. We show that when this prior information is predictive of whether a gene is associated with disease, our approach can lead to a significant increase in power. We illustrate our approach with a gene-set analysis of amyotrophic lateral sclerosis (ALS), which implicates a number of gene-sets containing SOD1 and NEK1 as well as showing enrichment of small p-values for gene-sets containing known ALS genes.

genetics↗

bioRxiv2018

Enhancer redundancy predicts gene pathogenicity and informs complex disease gene discovery

Non-coding transcriptional regulatory elements are critical for controlling the spatiotemporal expression of genes. Here, we demonstrate that the number of bases in enhancers linked to a gene reflects its disease pathogenicity. Moreover, genes with redundant enhancer domains are depleted of cis-acting genetic variants that disrupt gene expression, and are buffered against the effects of disruptive non-coding mutations. Our results demonstrate that dosage-sensitive genes have evolved robustness to the disruptive effects of genetic variation by expanding their regulatory domains. This resolves a puzzle in the genetic literature about why disease genes are depleted of cis-eQTLs, suggesting that eQTL information may implicate the wrong genes at genome-wide association study loci, and establishes a framework for identifying non-coding regulatory variation with phenotypic consequences.

genomics↗

bioRxiv2018

Regional collapsing of rare variation implicates specific genic regions in ALS

Large-scale sequencing efforts in amyotrophic lateral sclerosis (ALS) have implicated novel genes using gene-based collapsing methods. However, pathogenic mutations may be concentrated in specific genic regions. To address this, we developed two collapsing strategies, one focuses rare variation collapsing on homology-based protein domains as the unit for collapsing and another gene-level approach that, unlike standard methods, leverages existing evidence of purifying selection against missense variation on said domains. The application of these two collapsing methods to 3,093 ALS cases and 8,186 controls of European ancestry, and also 3,239 cases and 11,808 controls of diversified populations, pinpoints risk regions of ALS genes including SOD1, NEK1, TARDBP and FUS. While not clearly implicating novel ALS genes, the new analyses not only pinpoint risk regions in known genes but also highlight candidate genes as well.

genetics↗

bioRxiv2018

Whole Exome Sequencing in 20,197 Persons for Rare Variants in Alzheimer Disease

ObjectiveThe genetic bases of Alzheimers disease remain uncertain. An international effort to fully articulate genetic risks and protective factors is underway with the hope of identifying potential therapeutic targets and preventive strategies. The goal here was to identify and characterize the frequency and impact of rare and ultra-rare variants in Alzheimers disease using whole exome sequencing in 20,197 individuals.\n\nMethodsWe used a gene-based collapsing analysis of loss-of-function ultra-rare variants in a case-control study design with data from the Washington Heights-Inwood Columbia Aging Project, the Alzheimers Disease Sequencing Project and unrelated individuals from the Institute of Genomic Medicine at Columbia University.\n\nResultsWe identified 19 cases carrying extremely rare SORL1 loss-of-function variants among a collection of 6,965 cases and a single loss-of-function variant among 13,252 controls (p = 2.17 x 10-8; OR 36.2 [95%CI 5.8 - 1493.0]). Age-at-onset was seven years earlier for patients with SORL1 qualifying variant compared with non-carriers. No other gene attained a study-wide level of statistical significance, but multiple top-ranked genes, including GRID2IP, WDR76 and GRN, were among candidates for follow-up studies.\n\nInterpretationThis study implicates ultra-rare, loss-of-function variants in SORL1 as a significant genetic risk factor for Alzheimers disease and provides a comprehensive dataset comparing the burden of rare variation in nearly all human genes in Alzheimers disease cases and controls. This is the first investigation to establish a genome-wide statistically significant association between multiple extremely rare loss-of-function variants in SORL1 and Alzheimers disease in a large whole-exome study of unrelated cases and controls.

genetics↗

bioRxiv2018

meaRtools: an R package for the Comprehensive Analysis of Neuronal Networks Recorded on Multi-Electrode Arrays

Here we present an open-source R package meaRtools that provides a platform for analyzing neuronal networks recorded on Microelectrode Arrays (MEAs). Cultured neuronal networks monitored with MEAs are now being widely used to characterize in vitro models of neurological disorders and to evaluate pharmaceutical compounds. meaRtools provides core algorithms for MEA spike train analysis, feature extraction, statistical analysis and plotting of multiple MEA recordings with multiple genotypes and treatments. meaRtools functionality covers novel solutions for spike train analysis, including algorithms to assess electrode cross-correlation using the spike train tiling coefficient (STTC), mutual information, synchronized bursts and entropy within cultured wells. Also integrated is a solution to account for bursts variability originating from mixed-cell neuronal cultures. The package provides a statistical platform built specifically for MEA data that can combine multiple MEA recordings and compare extracted features between different genetic models or treatments. We demonstrate the utilization of meaRtools to successfully identify epilepsy-like phenotypes in neuronal networks from Celf4 knockout mice. The package is freely available under the GPL license (GPL>=3) and is updated frequently on the CRAN web-server repository. The package, along with full documentation can be downloaded from: https://cran.r-project.org/web/packages/meaRtools/.\n\nAuthor summaryCultured neuronal networks are widely used to study and characterize neuronal network activity. Among the many uses of neuronal cultures are the capabilities to evaluate neurotoxicity and the effects of pharmacological compounds on cellular physiology. Multi-well microelectrode arrays (MEAs) can collect high-throughput data from multiple neuronal cultures simultaneously, and thereby make possible hypotheses-driven inquiries into neurobiology and neuropharmacology. The analysis of MEA-derived information presents many computational challenges. High frequency data recorded simultaneously from hundreds of electrodes can be difficult to handle. The need to compare network activity across various drug treatments or genotypes recorded on the same plate from experiments lasting several weeks presents another challenge. These challenges inspired us to develop meaRtools; an MEA data analysis package that contains new methods to characterize network activity patterns, which are illustrated here using examples from a genetic mouse model of epilepsy. Among the highlights of meaRtools are novel algorithms designed to characterize neuronal activity dynamics and network properties such as bursting and synchronization, options to combine multiple recordings and use a robust statistical framework to draw appropriate statistical inferences, and finally data visualizations and plots. In summary, meaRtools provides a platform for the analyses of singular and longitudinal MEA experiments.

neuroscience↗

bioRxiv2017

Whole Genome Sequencing in Psychiatric Disorders: the WGSPD Consortium

As technology advances, whole genome sequencing (WGS) is likely to supersede other genotyping technologies. The rate of this change depends on its relative cost and utility. Variants identified uniquely through WGS may reveal novel biological pathways underlying complex disorders and provide high-resolution insight into when, where, and in which cell type these pathways are affected. Alternatively, cheaper and less computationally intensive approaches may yield equivalent insights. Understanding the role of rare variants in the noncoding gene-regulating genome, through pilot WGS projects, will be critical to determine which of these two extremes best represents reality. With large cohorts, well-defined risk loci, and a compelling need to understand the underlying biology, psychiatric disorders have a role to play in this preliminary WGS assessment. The WGSPD consortium will integrate data for 18,000 individuals with psychiatric disorders, beginning with autism spectrum disorder, schizophrenia, bipolar disorder, and major depressive disorder, along with over 150,000 controls.

genomics↗