Biology subjects

Chang, D.

Publications and source records attributed to Chang, D..

51 records · Page 3Linked to original sources

bioRxiv2023

Nicotinic acetylcholine receptor signaling maintains epithelial barrier integrity

Disruption of epithelial barriers is a common disease manifestation in chronic degenerative diseases of the airways, lung and intestine. Extensive human genetic studies have identified risk loci in such diseases, including in chronic obstructive pulmonary disease (COPD) and inflammatory bowel diseases (IBD). The genes associated with these loci have not fully been determined, and functional characterization of such genes requires extensive studies in model organisms. Here, we report the results of a screen in Drosophila melanogaster that allowed for rapid identification, validation and prioritization of COPD risk genes that were selected based on risk loci identified in human genome-wide association studies (GWAS) studies. Using intestinal barrier dysfunction in flies as a readout, our results validate the impact of candidate gene perturbations on epithelial barrier function in 56% of the cases, resulting in a prioritized target gene list. We further report the functional characterization in flies of one family of these genes, encoding for nicotinic acetylcholine receptor subunits (nAchR). We find that nAchR signaling in enterocytes of the fly gut promotes epithelial barrier function and epithelial homeostasis by regulating the production of the peritrophic matrix. Our findings identify COPD associated genes critical for epithelial barrier maintenance, and provide insight into the role of epithelial nAchR signaling for homeostasis.

genetics↗

bioRxiv2022

Visualizing somatic alterations in spatial transcriptomics data of skin cancer

Tools to visualize genetic alterations within tissues remain underdeveloped despite the growth of spatial transcriptomic technologies, which measure gene expression in different regions of tissues. Since genetic alterations can be detected in RNA-sequencing data, we explored the feasibility of observing somatic alterations in spatial transcriptomics data. Extracting genetic information from spatial transcriptomic data would illuminate the spatial distribution of clones and allow for correlations with regional changes in gene expression to support genotype-phenotype studies. Recent work demonstrates that copy number alterations can be inferred from spatial transcriptomics data1. Here, we describe new software to further enhance the inference of copy number from spatial transcriptomics data. Moreover, we demonstrate that single nucleotide variants are also detectable in spatial transcriptomic data. We applied these approaches to map the location of point mutations, copy number alterations, and allelic imbalances in spatial transcriptomic data of two cutaneous squamous cell carcinomas. We show that both tumors are dominated by a single clone of cells, suggesting that their regional variations in gene expression2 are likely driven by non-genetic factors. Furthermore, we observe mutant cells in histologically normal tissue surrounding one tumor, which were not discernible upon histopathologic evaluation. Finally, we detected mono-allelic expression of immunoglobulin heavy chains in B-cells, revealing clonal populations of plasma cells surrounding one tumor. In summary, we put forward solutions to add the genetic dimension to spatial transcriptomic datasets, augmenting the potential of this new technology.

genomics↗

bioRxiv2022

GMHI-webtool: a user-friendly browser application for assessing health through metagenomic gut microbiome profiling

SummaryWe recently introduced the Gut Microbiome Health Index (GMHI), a stool-based indicator for monitoring health given the state of ones gut microbiome. GMHI depends on health-prevalent and health-scarce species determined and validated using a pooled dataset of 5,026 stool shotgun metagenomic samples from 43 independent studies. Encouragingly, GMHI has already been utilized in various studies focusing on identifying differences in the gut microbiome between cases and controls. However, current computational barriers and logistical issues prevent researchers from computing, interpreting, and contextualizing GMHI, thereby limiting its further widespread utilization. Herein, we introduce the GMHI-webtool, a user-friendly browser application that computes GMHI, health-prevalent/scarce species, -diversities, and taxonomic distributions of the gut microbiome from stool samples. Users of our interactive online tool can visualize their results and compare side-by-side with those from our pooled reference dataset, as well as export data in .csv format and high-resolution figures. Availability and implementationGMHI-webtool is freely available here: https://gmhi-webtool.github.io/. Source code: https://github.com/danielchang2002/GMHI-webtool.

bioinformatics↗

bioRxiv2021

Detection of Cell-Cell Interactions via Photocatalytic Cell Tagging

Cell-cell interactions drive essential biological processes critical to cell and tissue development, function, pathology, and disease outcome. The growing appreciation of immune cell interactions within disease environments has led to significant efforts to develop protein- and cell-based therapeutic strategies. A better understanding of these cell-cell interactions will enable the development of effective immunotherapies. However, characterizing these complex cellular interactions at molecular resolution in their native biological contexts remains challenging. To address this, we introduce photocatalytic cell tagging (PhoTag), a modality agnostic platform for profiling cell-cell interactions. Using photoactivatable flavin-based cofactors, we generate phenoxy radical tags for targeted labeling at the cell surface. Through various targeting modalities (e.g. MHC-Multimer, antibody, single domain antibody (VHH)) we deliver a flavin photocatalyst for cell tagging within monoculture, co-culture, and peripheral blood mononuclear cells. PhoTag enables highly selective tagging of the immune synapse between an immune cell and an antigen-presenting cell through targeted labeling at the cell-cell junction. This allowed for the ability to profile gene expression-level differences between interacting and bystander cell populations. Given the modality agnostic and spatio-temporal nature of PhoTag, we envision its broad utilization to detect and profile intercellular interactions within an immune synapse and other confined cellular regions for any biological system.

immunology↗

bioRxiv2020

The landscape of driver mutations in cutaneous squamous cell carcinoma

Cutaneous squamous cell carcinoma is a form of skin cancer originating from keratinocytes in the skin. It is the second most common type of cancer and is responsible for an estimated 8000 deaths per year in the United States. Compared to other cancer subtypes with similar incidences and death tolls, our understanding of the somatic mutations driving cutaneous squamous cell carcinoma is limited. The main challenge is that these tumors have high mutation burdens, primarily a consequence of UV-radiation-induced DNA damage from sunlight, making it difficult to distinguish driver mutations from passenger mutations. We overcame this challenge by performing a meta-analysis of publicly available sequencing data covering 105 tumors from 10 different studies. Moreover, we eliminated tumors with issues, such as low neoplastic cell content, and from the tumors that passed quality control, we utilized multiple strategies to reveal genes under selection. In total, we nominated 30 cancer genes. Among the more novel genes, mutations frequently affected EP300, PBRM1, USP28, and CHUK. Collectively, mutations in the NOTCH and p53 pathways were ubiquitous, and to a lesser extent, mutations affected genes in the Hippo pathway, genes in the Ras/MAPK/PI3K pathway, genes critical for cell-cycle checkpoint control, and genes encoding chromatin remodeling factors. Taken together, our study provides a catalogue of driver genes in cutaneous squamous cell carcinoma, offering points of therapeutic intervention and insights into the biology of cutaneous squamous cell carcinoma.

cancer biology↗

bioRxiv2020

Identification of potent and safe antiviral therapeutic candidates against SARS-CoV-2

COVID-19 pandemic has infected millions of people with mortality exceeding 300,000. There is an urgent need to find therapeutic agents that can help clear the virus to prevent the severe disease and death. Identifying effective and safer drugs can provide with more options to treat the COVID-19 infections either alone or in combination. Here we performed a high throughput screen of approximately 1700 US FDA approved compounds to identify novel therapeutic agents that can effectively inhibit replication of coronaviruses including SARS-CoV-2. Our two-step screen first used a human coronavirus strain OC43 to identify compounds with anti-coronaviral activities. The effective compounds were then screened for their effectiveness in inhibiting SARS-CoV-2. These screens have identified 24 anti-SARS-CoV-2 drugs including previously reported compounds such as hydroxychloroquine, amlodipine, arbidol hydrochloride, tilorone 2HCl, dronedarone hydrochloride, and merfloquine hydrochloride. Five of the newly identified drugs had a safety index (cytotoxic/effective concentration) of >600, indicating wide therapeutic window compared to hydroxychloroquine which had safety index of 22 in similar experiments. Mechanistically, five of the effective compounds were found to block SARS-CoV-2 S protein-mediated cell fusion. These FDA approved compounds can provide much needed therapeutic options that we urgently need in the midst of the pandemic.Competing Interest StatementThe authors have declared no competing interest.View Full Text

microbiology↗

bioRxiv2020

In silico tools for accurate HLA and KIR inference from clinical sequencing data empower immunogenetics on individual-patient and population scales

Immunogenetic variation in humans is important in research, clinical diagnosis and increasingly a target for therapeutic intervention. Two highly polymorphic loci play critical roles, namely the human leukocyte antigen (HLA) system, which is the human version of the major histocompatibility complex (MHC), and the Killer-cell immunoglobulin-like receptors (KIR) that are relevant for responses of Natural Killer (NK) cells and some subsets of T cells. Their accurate classification has typically required the use of dedicated biological specimens and a combination of in vitro and in silico efforts. Increased availability of next generation sequencing data has led to the development of ancillary computational solutions. Here, we report an evaluation of recently published algorithms to computationally infer complex immunogenetic variation in the form of HLA alleles and KIR haplotypes from whole-genome or whole-exome sequencing data. For both HLA allele and KIR gene typing, we identified tools that yielded >97% overall accuracy for 4-digit HLA types, and >99% overall accuracy for KIR gene presence, suggesting the readiness of in silico solutions for use in clinical and high-throughput research settings.

bioinformatics↗

bioRxiv2020

Measuring site-specific glycosylation similarity between influenza A virus variants with statistical certainty

Influenza A virus (IAV) mutates rapidly, resulting in antigenic drift and poor year-to-year vaccine effectiveness. One challenge in designing effective vaccines is that genetic mutations frequently cause amino acid variations in IAV envelope protein hemagglutinin (HA) that create new N-glycosylation sequons; resulting N-glycans cause antigenic shielding, allowing viral escape from adaptive immune responses. Vaccine candidate strain selection currently involves correlating antigenicity with HA protein sequence among circulating strains, but quantitative comparison of site-specific glycosylation information may likely improve the ability to design vaccines with broader effectiveness against evolving strains. However, there is poor understanding of the influence of glycosylation on immunodominance, antigenicity, and immunogenicity of HA, and there are no well-tested methods for comparing glycosylation similarity among virus samples. Here, we present a method for statistically rigorous quantification of similarity between two related virus strains that considers the presence and abundance of glycopeptide glycoforms. We demonstrate the strength of our approach by determining that there was a quantifiable difference in glycosylation at the protein level between wild-type IAV HA from A/Switzerland/9715293/2013 (SWZ13) and a mutant strain of SWZ13, even though no N-glycosylation sequons were changed. We determined site-specifically that WT and mutant HA have varying similarity at the glycosylation sites of the head domain, reflecting competing pressures to evade host immune response while retaining viral fitness. To our knowledge, our results are the first to quantify changes in glycosylation state that occur in related proteins of considerable glycan heterogeneity. Our results provide a method for understanding how changes in glycosylation state are correlated with variations in protein sequence, which is necessary for improving IAV vaccine strain selection. Understanding glycosylation will be especially important as we find new expression vectors for vaccine production, as glycosylation state depends greatly on the host species.

biochemistry↗

bioRxiv2020

The genomic landscapes of individual melanocytes from human skin

Every cell in the human body has a unique set of somatic mutations, yet it remains difficult to comprehensively genotype an individual cell. Here, we developed solutions to overcome this obstacle in the context of normal human skin, thus offering the first glimpse into the genomic landscapes of individual melanocytes from human skin. We comprehensively genotyped 133 melanocytes from 19 sites across 6 donors. As expected, sun-shielded melanocytes had fewer mutations than sun-exposed melanocytes. However, within sun-exposed sites, melanocytes on chronically sun-exposed skin (e.g. the face) displayed a lower mutation burden than melanocytes on intermittently sun-exposed skin (e.g. the back). Melanocytes located adjacent to a skin cancer had higher mutation burdens than melanocytes from donors without skin cancer, implying that the mutation burden of normal skin can be harnessed to measure cumulative sun damage and skin cancer risk. Moreover, melanocytes from healthy skin commonly harbor pathogenic mutations, likely explaining the origins of the melanomas that arise in the absence of a pre-existing nevus. Phylogenetic analyses identified groups of related melanocytes, suggesting that melanocytes spread throughout skin as fields of clonally related cells, invisible to the naked eye. Overall, our study offers an unprecedented view into the genomic landscapes of individual melanocytes, revealing key insights into the causes and origins of melanoma.

genomics↗

bioRxiv2019

Complexity and ultrastructure of infectious extracellular vesicles from cells infected by nonenveloped virus

Enteroviruses support cell-to-cell viral transmission prior to their canonical lytic spread of virus. Poliovirus (PV), a prototype for human pathogenic positive-sense RNA enteroviruses, and picornaviruses in general, transport multiple virions en bloc via infectious extracellular vesicles secreted from host cells. Using biochemical and biophysical methods we identify multiple components in these secreted vesicles, including PV virions; positive and negative-sense viral RNA; essential viral replication proteins; ribosomal and regulatory cellular RNAs; and numerous host cell proteins, such as regulators of cellular metabolism and structural remodeling. Using cryo-electron tomography, we visualize the near-native three-dimensional architecture of secreted infectious extracellular vesicles containing both virions and a unique mat-like structure. Based on our biochemical data (western blot, RNA-Seq, and mass spectrometry), these mat-like structures are expected to be comprised of unencapsidated RNA and proteins. Our data show that, prior to cell lysis, non-enveloped viruses are secreted within infectious vesicles that also transport viral and host RNAs and proteins.\n\nImportanceThe family of picornaviridae is comprised of small positive-sense RNA viruses, many of which are significant human pathogens. Picornaviruses exploit secreted extracellular vesicles for cell-to-cell viral transmission without cell lysis, and poliovirus serves as a model system for picornaviruses that are not protected by a surrounding membrane (non-enveloped viruses). The structure and contents of these vesicles secreted by virus-infected cells are described here. In addition to mature virions, these vesicles carry negative-sense, template viral RNA and essential replication proteins, as well as cellular resources from the host. Their complex contents may comprise an enhanced virulence factor for propagation of infection, and understanding their structure and function is helping elucidate the mechanism by which extracellular vesicles contribute to the spread of non-enveloped virus infection.

cell biology↗

bioRxiv2018

A Deep Learning Genome-Mining Strategy Improves Biosynthetic Gene Cluster Prediction

Natural products represent a rich reservoir of small molecule drug candidates utilized as antimicrobial drugs, anticancer therapies, and immunomodulatory agents. These molecules are microbial secondary metabolites synthesized by co-localized genes termed Biosynthetic Gene Clusters (BGCs). The increase in full microbial genomes and similar resources has led to development of BGC prediction algorithms, although their precision and ability to identify novel BGC classes could be improved. Here we present a deep learning strategy (DeepBGC) that offers more accurate BGC identification and an improved ability to extrapolate and identify novel BGC classes compared to existing tools. We supplemented this with downstream random forest classifiers that accurately predicted BGC product classes and potential chemical activity. Application of DeepBGC to bacterial genomes uncovered previously undetectable BGCs that may code for natural products with novel biologic activities. The improved accuracy and classification ability of DeepBGC represents a significant step forward for in-silico BGC identification.

bioinformatics↗

bioRxiv2018

Reporters of TCR signaling identify arthritogenic T cells in murine and human autoimmune arthritis

How pathogenic CD4 T cells in Rheumatoid Arthritis (RA) develop remains poorly understood. We used Nur77--a marker of T cell antigen receptor (TCR) signaling--to identify antigen-activated CD4 T cells in the SKG mouse model of autoimmune arthritis and in patients with RA. Using a fluorescent reporter of Nur77 expression in SKG mice, we found that higher levels of Nur77-eGFP in SKG CD4 T cells marked their autoreactivity, arthritogenic potential, and ability to more readily differentiate into IL-17 producing cells. Moreover, the enhanced autoreactivity was associated with upregulation of IL-6 cytokine signaling machinery. As a result, the more autoreactive GFPhi CD4 T cells from SKGNur mice were hyper-responsive to IL-6. This suggests that, despite impaired TCR signaling, autoreactive T cells exposed to chronic antigen stimulation exhibit heightened sensitivity to IL-6 which contributes to the arthritogenicity in SKG mice, and perhaps in patients with RA. Additionally, endogenous Nur77 protein expression was enriched in a subset of synovial CD4 T cells from arthritic joints in SKG mice, consistent with antigen-activated T cells. This was was extended to patients with seropositive RA, suggesting that pathogenic CD4 T cells are enriched in RA joints and are likely recognizing an intra-articular auto-antigen(s).

immunology↗

bioRxiv2018

Macrophage Released Pyrimidines Inhibit Gemcitabine Therapy in Pancreatic Cancer

Pancreatic Ductal Adenocarcinoma (PDA) is characterized by abundant infiltration of tumor associated macrophages (TAMs). TAMs have been reported to drive resistance to gemcitabine, the front-line chemotherapy in PDA, though the mechanism of this resistance remains unclear. Profiling metabolite exchange, we demonstrate macrophages programmed by PDA cells release a spectrum of pyrimidine species. These include deoxycytidine, which inhibits gemcitabine through molecular competition at the level of drug uptake and metabolism. Accordingly, genetic or pharmacological depletion of TAMs in murine models of PDA sensitizes these tumors to gemcitabine. Consistent with this, patients with low macrophage burden demonstrate superior response to gemcitabine treatment. Additionally, we report pyrimidine release is a general function of anti-inflammatory myeloid cells, suggesting an unknown physiological role of pyrimidine exchange by immune cells.

cancer biology↗

bioRxiv2018

Parkinson’s disease genetics: identifying novel risk loci, providing causal insights and improving estimates of heritable risk

We performed the largest genome-wide association study of PD to date, involving the analysis of 7.8M SNPs in 37.7K cases, 18.6K UK Biobank proxy-cases, and 1.4M controls. We identified 90 independent genome-wide significant signals across 78 loci, including 38 independent risk signals in 37 novel loci. These variants explained 26-36% of the heritable risk of PD. Tests of causality within a Mendelian randomization framework identified putatively causal genes for 70 risk signals. Tissue expression enrichment analysis suggested that signatures of PD loci were heavily brain-enriched, consistent with specific neuronal cell types being implicated from single cell expression data. We found significant genetic correlations with brain volumes, smoking status, and educational attainment. In sum, these data provide the most comprehensive understanding of the genetic architecture of PD to date by revealing many additional PD risk loci, providing a biological context for these risk factors, and demonstrating that a considerable genetic component of this disease remains unidentified.

genetics↗

bioRxiv2017

A powerful approach to estimating annotation-stratified genetic covariance using GWAS summary statistics

Despite the success of large-scale genome-wide association studies (GWASs) on complex traits, our understanding of their genetic architecture is far from complete. Jointly modeling multiple traits genetic profiles has provided insights into the shared genetic basis of many complex traits. However, large-scale inference sets a high bar for both statistical power and biological interpretability. Here we introduce a principled framework to estimate annotation-stratified genetic covariance between traits using GWAS summary statistics. Through theoretical and numerical analyses we demonstrate that our method provides accurate covariance estimates, thus enabling researchers to dissect both the shared and distinct genetic architecture across traits to better understand their etiologies. Among 50 complex traits with publicly accessible GWAS summary statistics (Ntotal {approx} 4.5 million), we identified more than 170 pairs with statistically significant genetic covariance. In particular, we found strong genetic covariance between late-onset Alzheimers disease (LOAD) and amyotrophic lateral sclerosis (ALS), two major neurodegenerative diseases, in single-nucleotide polymorphisms (SNPs) with high minor allele frequencies and in SNPs located in the predicted functional genome. Joint analysis of LOAD, ALS, and other traits highlights LOADs correlation with cognitive traits and hints at an autoimmune component for ALS.

genetics↗