Biology subjects

Huang, A. Y.

Publications and source records attributed to Huang, A. Y..

24 records · Page 2Linked to original sources

bioRxiv2022

Screening of small molecule chemical library identifies leads that reduce cholesterol accumulation in human NPC1 fibroblasts

Although considerable progress has been made in finding a therapy for Niemann-Pick type C1 (NPC1) disease, there still is no effective approved treatment. Previously, we reported results from a small molecule screen that was conducted on Chinese Hamster Ovary (CHO) cells (CT60) that lacked NPC1 expression. The hit compounds that were effective in reducing cholesterol accumulation in CT60 cells were not effective on human cells bearing disease-causing mutations in NPC1. Hence, we conducted a new small molecule screen on patient-derived NPC1 skin fibroblasts using a combinatorial library of ~48,000 compounds. We have identified several compounds that corrected the phenotype in human heterozygous mutant NPC1 skin fibroblasts (GM03123) bearing the inactivating I1061T mutation in one allele and another set of inactivating mutations in the other. Among the hit compounds were several histone deacetylase inhibitors, which we have explored further and reported previously. With the determination of the structure of NPC1 protein and the development of computational docking algorithms, it seems worthwhile to show the structures of 128 compounds that reproducibly reduced cholesterol storage in mutant NPC1 fibroblasts but with no known mechanism of action. These compounds were not toxic and specifically effective on mutant cells but not on NPC1-/- cells, indicating on-target effect directly or indirectly.

neuroscience↗

bioRxiv2020

Parallel RNA and DNA analysis after Deep-sequencing (PRDD-seq) reveals cell type-specific lineage patterns in human brain

Elucidating the lineage relationships among different cell types is key to understanding human brain development. Here we developed Parallel RNA and DNA analysis after Deep-sequencing (PRDD-seq), which combines RNA analysis of neuronal cell types with analysis of nested spontaneous DNA somatic mutations as cell lineage markers, identified from joint analysis of single cell and bulk DNA sequencing by single-cell MosaicHunter (scMH). PRDD-seq enables the first-ever simultaneous reconstruction of neuronal cell type, cell lineage, and sequential neuronal formation ("birthdate") in postmortem human cerebral cortex. Analysis of two human brains showed remarkable quantitative details that relate mutation mosaic frequency to clonal patterns, confirming an early divergence of precursors for excitatory and inhibitory neurons, and an "inside-out" layer formation of excitatory neurons as seen in other species. In addition our analysis allows the first estimate of excitatory neuron-restricted precursors (about 10) that generate the excitatory neurons within a cortical column. Inhibitory neurons showed complex, subtype-specific patterns of neurogenesis, including some patterns of development conserved relative to mouse, but also some aspects of primate cortical interneuron development not seen in mouse. PRDD-seq can be broadly applied to characterize cell identity and lineage from diverse archival samples with single-cell resolution and in potentially any developmental or disease condition. Significance StatementStem cells and progenitors undergo a series of cell divisions to generate the neurons of the brain, and understanding this sequence is critical to studying the mechanisms that control cell division and migration in developing brain. Mutations that occur as cells divide are known as the basis of cancer, but have more recently been shown to occur with normal cell divisions, creating a permanent, forensic map of the clonal patterns that define the brain. Here we develop new technology to analyze both DNA mutations and RNA gene expression patterns in single cells from human postmortem brain, allowing us to define clonal patterns among different types of human brain neurons, gaining the first direct insight into how they form.

neuroscience↗

bioRxiv2020

MosaicBase: A Knowledgebase of Postzygotic Mosaic Variants in Noncancer Diseases and Asymptomatic Human Individuals

Mosaic variants resulting from postzygotic mutations are prevalent in the human genome and play important roles in human diseases. However, except for cancer-related variant collections, there are no collections of mosaic variants in noncancer diseases and asymptomatic individuals. Here, we present MosaicBase (http://mosaicbase.cbi.pku.edu.cn/ or http://49.4.21.8:8000/), a comprehensive database that includes 6,698 mosaic variants related to 269 noncancer diseases and 27,991 mosaic variants identified in 422 asymptomatic individuals. The genomic and phenotypic information for each variant was manually extracted and curated from 383 publications. MosaicBase supports the query of variants with Online Mendelian Inheritance in Man (OMIM) entries, genomic coordinates, gene symbols, or Entrez IDs. We also provide an integrated genome browser for users to easily access mosaic variants and their related annotations within any genomic region. By analyzing the variants collected in MosaicBase, we found that mosaic variants that directly contribute to disease phenotype showed features distinct from those of variants in individuals with a mild or no phenotype in terms of their genomic distribution, mutation signatures, and fraction of mutant cells. MosaicBase will not only assist clinicians in genetic counseling and diagnosis but also provide a useful resource to understand the genomic baseline of postzygotic mutations in the general human population.

bioinformatics↗

bioRxiv2019

Evidence that APP gene copy number changes reflect recombinant vector contamination

Mutations that occur in cells of the body, called somatic mutations, cause human diseases including cancer and some neurological disorders1. In a recent study published in Nature, Lee et al.2 (hereafter \"the Lee study\") reported somatic copy number gains of the APP gene, a known risk locus of Alzheimers disease (AD), in the neurons of AD-patients and controls (69% vs 25% of neurons with at least one APP copy gain on average). The authors argue that the mechanism of these copy number gains was somatic integration of APP mRNA into the genome, creating what they called genomic cDNA (gencDNA). We reanalyzed the data from the Lee study, revealing evidence that APP gencDNA originates mainly from contamination by exogenous APP recombinant vectors, rather from true somatic retrotransposition of endogenous APP. Our reanalysis of two recent whole exome sequencing (WES) datasets--one by the authors of the Lee study3 and the other by Park et al.4--revealed that reads claimed to support APP gencDNA in AD samples resulted from contamination by PCR products and mRNA, respectively. Lastly, we present our own single-cell whole genome sequencing (scWGS) data that show no evidence for somatic APP retrotransposition in AD neurons or in neurons from normal individuals of various ages.

genomics↗

bioRxiv2019

Identification of Pathogenic Structural Variants in Rare Disease Patients through Genome Sequencing

PurposeClinical whole genome sequencing is becoming more common for determining the molecular diagnosis of rare disease. However, standard clinical practice often focuses on small variants such as single nucleotide variants and small insertions/deletions. This leaves a wide range of larger \"structural variants\" that are not commonly analyzed in patients.\n\nMethodsWe developed a pipeline for processing structural variants for patients who received whole genome sequencing through the Undiagnosed Diseases Network (UDN). This pipeline called structural variants, stored them in an internal database, and filtered the variants based on internal frequencies and external annotations. The remaining variants were manually inspected and then interesting findings were reported as research variants to clinical sites in the UDN.\n\nResultsOf 477 analyzed UDN cases, 286 cases ({approx} 60%) received at least one structural variant as a research finding. The variants in 16 cases ({approx} 4%) are considered \"Certain\" or \"Highly likely\" molecularly diagnosed and another 4 cases are currently in review. Of those 20 cases, at least 13 were identified originally through our pipeline with one finding leading to identification of a new disease. As part of this paper, we have also released the collection of variant calls identified in our cohort along with heterozygous and homozygous call counts. This data is available at https://github.com/HudsonAlpha/UDN_SV_export.\n\nConclusionStructural variants are key genetic features that should be analyzed during routine clinical genomic analysis. For our UDN patients, structural variants helped solve {approx} 4% of the total number of cases ({approx} 13% of all genome sequencing solves), a success rate we expect to improve with better tools and greater understanding of the human genome.

genomics↗

bioRxiv2018

Contribution of common and rare variants to bipolar disorder susceptibility in extended pedigrees from population isolates

Current evidence from case/control studies indicates that genetic risk for psychiatric disorders derives primarily from numerous common variants, each with a small phenotypic impact. The literature describing apparent segregation of bipolar disorder (BP) in numerous multigenerational pedigrees suggests that, in such families, large-effect inherited variants might play a greater role. To evaluate this hypothesis, we conducted genetic analyses in 26 Colombian (CO) and Costa Rican (CR) pedigrees ascertained for BP1, the most severe and heritable form of BP. In these pedigrees, we performed microarray SNP genotyping of 856 individuals and high-coverage whole-genome sequencing of 454 individuals. Compared to their unaffected relatives, BP1 individuals had higher polygenic risk scores estimated from SNPs associated with BP discovered in independent genome-wide association studies, and also displayed a higher burden of rare deleterious single nucleotide variants (SNVs) and rare copy number variants (CNVs) in genes likely to be relevant to BP1. Parametric and non-parametric linkage analyses identified 15 BP1 linkage peaks, encompassing about 100 genes, although we observed no significant segregation pattern for any particular rare SNVs and CNVs. These results suggest that even in extended pedigrees, genetic risk for BP appears to derive mainly from small to moderate effect rare and common variants.

genetics↗