Biology subjects

Chang, H.

Publications and source records attributed to Chang, H..

At least 91 records · Page 5Linked to original sources

bioRxiv2022

Inducible lncRNA transgenic mice reveal continual role of HOTAIR in promoting breast cancer metastasis

HOTAIR is a 2.2 kb long noncoding RNA (lncRNA) whose dysregulation has been linked to oncogenesis, defects in pattern formation during early development, and irregularities during the process of epithelial-to-mesenchymal transition (EMT). However, the oncogenic transformation determined by HOTAIR in vivo and its impact on chromatin dynamics are incompletely understood. Here we generate a transgenic mouse model with doxycycline-inducible expression of human HOTAIR in the context of the MMTV-PyMT breast cancer-prone background to systematically interrogate the cellular mechanisms by which human HOTAIR lncRNA acts to promote breast cancer progression. We show that sustained high levels of HOTAIR over time increased breast metastatic capacity and invasiveness in breast cancer cells, promoting migration and subsequent metastasis to the lung. Subsequent withdrawal of HOTAIR overexpression reverted the metastatic phenotype, indicating oncogenic lncRNA addiction. Furthermore, HOTAIR overexpression altered both the cellular transcriptome and chromatin accessibility landscape of multiple metastasis-associated genes and promoted epithelial to mesenchymal transition. These alterations are abrogated within several cell cycles after HOTAIR expression is reverted to basal levels, indicating an erasable lncRNA-associated epigenetic memory. These results suggest that a continual role for HOTAIR in programming a metastatic gene regulatory program. Targeting HOTAIR lncRNA may potentially serve as a therapeutic strategy to ameliorate breast cancer progression.

cancer biology↗

bioRxiv2022

Deep learning in human neurons predicts mechanistic subtypes of Parkinson's

Parkinsons disease (PD) is a common, devastating, and incurable neurodegenerative disorder. Several molecular mechanisms have been proposed to drive PD, with genetic and pathological evidence pointing towards aberrant protein homeostasis and mitochondrial dysfunction. PD is clinically highly heterogeneous, it is likely that different mechanisms underlie the pathology in different individuals, each requiring a specific targeted treatment. Recent advances in stem cell technology and fluorescent live-cell imaging have enabled the generation of patient-derived neurons with different mechanistic subtypes of PD. Here, we performed multi-dimensional fluorescent labelling of organelles in iPSC-derived neurons, in healthy control cells, and in four different disease subclasses. We generated a machine learning-based model that can simultaneously predict the presence of disease, and its primary mechanistic subtype. We independently trained a series of classifiers using both quantitative single-cell fluorescence variables and images to build deep neural networks. Quantitative cellular profile-based classifiers achieve an accuracy of 82%, whilst image based deep neural networks predict control, and four distinct disease subtypes with an accuracy of 95%. The classifiers achieve their accuracy across all subtypes primarily utilizing the organellar features of the mitochondria, with additional contribution of the lysosomes, confirming their biological importance in PD. Taken together, we show that machine learning approaches applied to patient-derived cells are able to predict disease subtypes, demonstrating that this approach may be used to guide personalized treatment approaches in the future.

cell biology↗

bioRxiv2022

Angiotensin converting enzyme 2 (ACE2): Virus accomplice or host defender

The current coronavirus disease-19 (COVID-19) caused by the acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has seriously disrupted the daily life of human, mainly attributed to the fact that we know too little about SARS-CoV-2. Increasing studies show that viral infection alters host cells glucose metabolism, which is crucial for viral nucleic acid replication. Here, we integrated RNA-sequencing results and found that SARS-CoV-2 infection alters the aerobic glycolysis, pentose phosphate pathway (oxiPPP), and DNA replication in lung tissues and cells. However, the direction of metabolic flux and DNA replication were dominated by angiotensin-converting enzyme 2 (ACE2), a host cell-expressed viral receptor protein. More interesting, although hosts with high expression of ACE2 are more likely to be infected with SARS-CoV-2, the invading virus cannot perform nucleic acid replication well due to the restriction of glucose metabolism, and eventually resulting prolonged infection-cycle or infection failure. Our findings, after a typical epidemiological investigation and modeling analysis, preliminarily explain the reasons for the emergence of asymptomatic infections or lower copy virus at early stage in host with higher ACE2 levels, which will provide important help for the development of more accurate and effective detection methods for diagnosing COVID-19. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=103 SRC="FIGDIR/small/483197v1_ufig1.gif" ALT="Figure 1"> View larger version (70K): org.highwire.dtl.DTLVardef@b51fcforg.highwire.dtl.DTLVardef@13b7f9corg.highwire.dtl.DTLVardef@136fe46org.highwire.dtl.DTLVardef@16fc92a_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2021

Divergent clonal differentiation trajectories of T cell exhaustion

T cells activated by chronic antigen exposure in the setting of viral infections or cancer can adopt an exhausted T cell (Tex) state, characterized by reduced effector function and proliferative capacity, and the upregulation of inhibitory receptors. However, whether all antigen-specific T cell clones follow the same molecular and cellular Tex differentiation trajectory remains unclear. Here, we generate a single-cell multi-omic atlas of T cell exhaustion that redefines the phenotypic diversity and molecular regulation of Tex phenotypes. Longitudinal analysis during chronic viral infection identifies an early effector phenotype that is epigenetically primed for Tex differentiation and two late-stage Tex cell states with either a terminal exhaustion or a killer cell lectin-like receptor (KLR)-expressing cytotoxic gene signature. We define clonal trajectories of antigen-specific T cells using paired single-cell RNA and T cell receptor sequencing and reveal distinct differentiation trajectories resulting in terminal Tex-biased, KLR Tex-biased, or divergent clones that differentiate into both phenotypes. Comparison of Tex phenotypes among shared T cell clones that traffic to multiple organs reveals that clonal differentiation trajectories are maintained across tissues. Finally, we show that differences in clonal differentiation trajectory are driven by TCR signal strength, whereby high-affinity T cell clones preferentially adopt a terminal Tex fate, while low-affinity clones adopt an effector-like KLR Tex fate that is detectable long-term but depleted in high antigen settings. These findings reveal clonal heterogeneity in the T cell response to chronic antigen and genomic programs that underlie Tex fates and persistence. HighlightsO_LIA single-cell atlas of T cell exhaustion identifies novel early effector and KLR Tex states. C_LIO_LIClonal T cell analysis defines divergent differentiation trajectories during chronic viral infection leading to terminal and KLR Tex fates. C_LIO_LIThe heterogeneity of the Tex pool arises from three primary differentiation patterns and are differentially persistent in the setting of high antigen. C_LIO_LIClonal Tex differentiation patterns are conserved across organ sites and driven by TCR signal strength. C_LI

immunology↗

bioRxiv2021

TCR-BERT: learning the grammar of T-cell receptors for flexible antigen-binding analyses

The T-cell receptor (TCR) allows T-cells to recognize and respond to antigens presented by infected and diseased cells. However, due to TCRs staggering diversity and the complex binding dynamics underlying TCR antigen recognition, it is challenging to predict which antigens a given TCR may bind to. Here, we present TCR-BERT, a deep learning model that applies self-supervised transfer learning to this problem. TCR-BERT leverages unlabeled TCR sequences to learn a general, versatile representation of TCR sequences, enabling numerous downstream applications. We demonstrate that TCR-BERT can be used to build state-of-the-art TCR-antigen binding predictors with improved generalizability compared to prior methods. TCR-BERT simultaneously facilitates clustering sequences likely to share antigen specificities. It also facilitates computational approaches to challenging, unsolved problems such as designing novel TCR sequences with engineered binding affinities. Importantly, TCR-BERT enables all these advances by focusing on residues with known biological significance. TCR-BERT can be a useful tool for T-cell scientists, enabling greater understanding and more diverse applications, and provides a conceptual framework for leveraging unlabeled data to improve machine learning on biological sequences.

bioinformatics↗

bioRxiv2021

Diverse pathogens activate the host RIDD pathway to subvert BLOS1-directed immune defense

The phagocytosis and destruction of pathogens in lysosomes constitute central elements of innate immune defense. Here, we show that Brucella, the causative agent of brucellosis, the most prevalent bacterial zoonosis globally, subverts this immune defense pathway by activating regulated IRE1-dependent decay (RIDD) of mRNAs encoding BLOS1, a protein that promotes endosome-lysosome fusion. RIDD-deficient cells and mice harboring a RIDD-incompetent variant of IRE1 were resistant to infection. Non-functional Blos1 struggled to assemble the BLOC-1-related complex (BORC), resulting in differential recruitment of BORC-related lysosome trafficking components, perinuclear trafficking of Brucella-containing vacuoles (BCVs), and enhanced susceptibility to infection. The RIDD-resistant Blos1 variant maintains the integrity of BORC and a higher-level association of BORC-related components that promote centrifugal lysosome trafficking, resulting in enhanced BCV peripheral trafficking and lysosomal-destruction, and resistance to infection. These findings demonstrate that host RIDD activity on BLOS1 regulates Brucella intracellular parasitism by disrupting BORC-directed lysosomal trafficking. Notably, coronavirus MHV also subverted the RIDD-BLOS1 axis to promote intracellular replication. Our work therefore establishes BLOS1 as a novel immune defense factor whose activity is hijacked by diverse pathogens.

microbiology↗

bioRxiv2021

The Mettl3 epitranscriptomic writer amplifies p53 stress responses

The p53 transcription factor, encoded by the most frequently mutated gene in human cancer, plays a critical role in tissue homeostasis in response to stress signals. The mechanisms through which p53 promotes downstream tumor suppressive gene expression programs remain, however, only superficially understood. Here, we used tandem affinity purification and mass spectrometry to reveal new components of the p53 response. This approach uncovered Mettl3, a component of the m6A RNA methyltransferase complex (MTC), as a p53-interacting protein. Analysis of Mettl3- deficient cells revealed that Mettl3 promotes p53 protein stabilization and target gene expression in response to DNA damage. Mettl3 acts in part by competing with the p53 negative regulator, Mdm2, for binding to the p53 transactivation domains to promote methyltransferase-independent stabilization of p53. In addition, Mettl3 relies on its catalytic activity to augment p53 responses, with p53 recruiting Mettl3 to p53 target genes to co-transcriptionally direct m6A modification of p53 pathway transcripts to enhance their expression. Mettl3 also promotes p53 activity downstream of oncogenic signals in vivo, in both allograft and autochthonous lung adenocarcinoma models, suggesting cooperative action of p53 and Mettl3 in tumor suppression. Accordingly, we found in diverse human cancers that mutations in MTC components perturb expression of p53 target genes and that MTC mutations are mutually exclusive with TP53 mutations, suggesting that the MTC enhances the p53 transcriptional program in human cancer. Together, these studies reveal a fundamental role for Mettl3 in amplifying p53 signaling through protein stabilization and epitranscriptome regulation.

cancer biology↗

bioRxiv2021

Higher-order olfactory neurons in the lateral horn supports odor valence and odor identity coding in Drosophila

Understanding neuronal representations of odor-evoked activities and their progressive transformation from the sensory level to higher brain centers features one of the major aims in olfactory neuroscience. Here, we investigated how odor information is transformed and represented in higher-order neurons of the lateral horn, one of the higher olfactory centers implicated in determining innate behavior, using Drosophila melanogaster. We focused on a subset of third-order glutamatergic lateral horn neurons (LHNs) and characterized their odor coding properties in relation to their presynaptic partner neurons, the projection neurons (PNs) by two-photon functional imaging. We found that odors evoke reproducible, stereotypic and odor-specific response patterns in LHNs. Notably, odor-evoked responses in these neurons are valence-specific in a way that their response amplitude is positively correlated with innate odor preferences. We postulate that this valence-specific activity is the result of integrating inputs from multiple olfactory channels through second-order neurons. GRASP and micro-lesioning experiments provide evidence that glutamatergic LHNs obtain their major excitatory input from uniglomerular PNs, while they receive an odor-specific inhibition through inhibitory multiglomerular PNs. In summary, our study indicates that odor representations in glutamatergic LHNs encode hedonic valence and odor identity and primarily retain the odor coding properties of second-order neurons.

neuroscience↗

bioRxiv2021

A genetic bottleneck of mitochondrial DNA during human lymphocyte development

Mitochondria are essential organelles in eukaryotic cells that provide critical support for energetic and metabolic homeostasis. Mutations that accumulate in mitochondrial DNA (mtDNA) in somatic cells have been implicated in cancer, degenerative diseases, and the aging process. However, the mechanisms used by somatic cells to maintain proper functions despite their mtDNA mutation load are poorly understood. Here, we analyzed somatic mtDNA mutations in more than 30,000 human single peripheral and bone marrow mononuclear cells and observed a significant overrepresentation of homoplastic mtDNA mutations in B, T and NK lymphocytes despite their lower mutational burden than other hematopoietic cells. The characteristic mutational landscape of mtDNA in lymphocytes were validated with data from multiple platforms and individuals. Single-cell RNA-seq and computational modeling demonstrated a stringent mitochondrial bottleneck during lymphocyte development likely caused by lagging mtDNA replication relative to cell proliferation. These results illuminate a potential mechanism used by highly metabolically active immune cells for quality control of their mitochondrial genomes.

genetics↗

bioRxiv2021

Large-scale miRNA-Target Data Analysis to Discover miRNA Co-regulation Network of Abiotic Stress Tolerance in Soybeans

Although growing evidence shows that microRNA (miRNA) regulates plant growth and development, miRNA regulatory networks in plants are not well understood. Current experimental studies cannot characterize miRNA regulatory networks on a large scale. This information gap provides a good opportunity to employ computational methods for global analysis and to generate useful models and hypotheses. To address this opportunity, we collected miRNA-target interactions (MTIs) and used MTIs from Arabidopsis thaliana and Medicago truncatula to predict homologous MTIs in soybeans, resulting in 80,235 soybean MTIs in total. A multi-level iterative bi-clustering method was developed to identify 483 soybean miRNA-target regulatory modules (MTRMs). Furthermore, we collected soybean miRNA expression data and corresponding gene expression data in response to abiotic stresses. By clustering these data, 37 MTRMs related to abiotic stresses were identified including stress-specific MTRMs and shared MTRMs. These MTRMs have gene ontology (GO) enrichment in resistance response, iron transport, positive growth regulation, etc. Our study predicts soybean miRNA-target regulatory modules with high confidence under different stresses, constructs miRNA-GO regulatory networks for MTRMs under different stresses and provides miRNA targeting hypotheses for experimental study. The method can be applied to other biological processes and other plants to elucidate miRNA co-regulation mechanisms.

plant biology↗

bioRxiv2021

Structure-Function in Helical Cardiac Musculature Using Additive Textile Manufacturing

For more than fifty years, it has been hypothesized that the helical alignment of the heart gives rise to its mechanical function. Testing this hypothesis in an engineered environment is difficult, as the fine spatial features and complex three-dimensional (3D) structures of the cardiac musculature are challenging to reproduce using current biofabrication techniques. Addressing this, here we report a new form of additive textile manufacturing, Focused Rotary Jet Spinning (FRJS). FRJS allows for the rapid manufacturing of micro/nanofibers with controlled alignments. Using this method, we manufacture 3D models of the left ventricle, showing that helically aligned scaffolds display increased strain uniformity, axial shortening, cardiac output, and ejection fractions as compared to circumferential models. We then demonstrate how FRJS can enable the assembly of a full-sized model of the human hearts musculature. This work experimentally confirms that ventricular alignment plays a critical role in ensuring healthy cardiac performance.

bioengineering↗

bioRxiv2021

Integrated spatial multi-omics reveals fibroblast fate during tissue repair

In the skin, tissue injury results in fibrosis in the form of scars composed of dense extracellular matrix deposited by fibroblasts. The therapeutic goal of regenerative wound healing has remained elusive in part because principles of fibroblast programming and adaptive response to injury remain incompletely understood. Here, we present a multimodal -omics platform for the comprehensive study of cell populations in complex tissue, which has allowed us to characterize the cells involved in wound healing across both time and space. We employ a stented wound model that recapitulates human tissue repair kinetics and multiple Rainbow transgenic lines to precisely track fibroblast fate during the physiologic response to injury. Through integrated analysis of single cell chromatin landscapes and gene expression states, coupled with spatial transcriptomic profiling, we are able to impute fibroblast epigenomes with temporospatial resolution. This has allowed us to define the mechanisms controlling cell fate during migration, proliferation, and differentiation following tissue injury and thereby reexamine the canonical phases of wound healing. These findings have broad implications for the study of tissue repair in complex organ systems.

bioinformatics↗

bioRxiv2021

Dynamic chromatin regulatory landscape of human CAR T cell exhaustion

Dysfunction in T cells limits the efficacy of cancer immunotherapy1-6. We profiled the epigenome, transcriptome, and enhancer connectome of exhaustion-prone GD2-targeting HA-28z chimeric antigen receptor (CAR) T cells and control CD19-targeting CAR T cells, which present less exhaustion-inducing tonic signaling, at multiple points during their ex vivo expansion7. We found widespread, dynamic changes in chromatin accessibility and 3D chromosome conformation preceding changes in gene expression, notably at loci proximal to exhaustion-associated genes such as PDCD1, CTLA4, and HAVCR2, and increased DNA motif access for AP-1 family transcription factors, which are known to promote exhaustion. Although T cell exhaustion has been studied in detail in mouse, we find that the regulatory networks of T cell exhaustion differ between the species and involve distinct loci of accessible chromatin and cis-regulated target genes in human CAR T cell exhaustion. Deletion of exhaustion-specific candidate enhancers of PDCD1 suppress the expression of PD-1 in an in vitro model of T cell dysfunction and in HA-28z CAR T cells, suggesting enhancer editing as a path forward in improving cancer immunotherapy.

genomics↗

bioRxiv2021

LKB1 inactivation modulates chromatin accessibility to drive metastatic progression

Metastasis is the leading cause of cancer-related deaths, enabling cancer cells to expand to secondary sites and compromise organ function1. Given that primary tumors and metastases often share the same constellation of driver mutations2-4, the mechanisms driving their distinct phenotypes are unclear. Here, we show that inactivation of the frequently mutated tumor suppressor gene, liver kinase B1 (LKB1), has evolving effects throughout lung cancer progression, leading to the differential epigenetic re-programming of early-stage primary tumors compared to late-stage metastases. By integrating genome-scale CRISPR/Cas9 screening with bulk and single-cell multi-omic analyses, we unexpectedly identify LKB1 as a master regulator of chromatin accessibility in lung adenocarcinoma primary tumors. Using an in vivo model of metastatic progression, we further reveal that loss of LKB1 activates the early endoderm transcription factor SOX17 in metastases and a metastatic-like sub-population of cancer cells within primary tumors. SOX17 expression is necessary and sufficient to drive a second wave of epigenetic changes in LKB1-deficient cells that enhances metastatic ability. Overall, our study demonstrates how the downstream effects of an individual driver mutation can appear to change throughout cancer development, with implications for stage-specific therapeutic resistance mechanisms and the gene regulatory underpinnings of metastatic evolution.

cancer biology↗

bioRxiv2020

A molecular clock controls periodically driven cell migration in confined spaces

Navigation through dense, physically confining extracellular matrix is common in invasive cell spread and tissue re-organization, but is still poorly understood. Here, we show that this migration is mediated by cyclic changes in the activity of a small GTP-ase RhoA, dependent on the oscillatory changes in the activity and abundance of the RhoA Guanine Exchange Factor, GEF-H1, triggered by a persistent increase in the intracellular Ca2+ levels. We show that the molecular clock driving these cyclic changes is mediated by two coupled negative feedback loops, dependent on the microtubule dynamics, with the frequency that can be experimentally modulated based on a predictive mathematical model. We further demonstrate that an increasing frequency of the clock translates into a faster cell migration within physically confining spaces. This work lays the foundation for a better understanding of the molecular mechanisms dynamically driving cell migration in complex environments.

systems biology↗

bioRxiv2020

BABEL enables cross-modality translation between multi-omic profiles at single-cell resolution

Simultaneous profiling of multi-omic modalities within a single cell is a grand challenge for single-cell biology. While there have been impressive technical innovations demonstrating feasibility - for example generating paired measurements of scRNA-seq and scATAC-seq - wide-spread application of joint profiling is challenging due to the experimental complexity, noise, and cost. Here we introduce BABEL, a deep learning method that translates between the transcriptome and chromatin profiles of a single cell. Leveraging a novel interoperable neural network model, BABEL can generate scRNA-seq directly from a cells scATAC-seq, and vice versa. This makes it possible to computationally synthesize paired multi-omic measurements when only one modality is experimentally available. Across several paired scRNA-seq and scATAC-seq datasets in human and mouse, we validate that BABEL accurately translates between these modalities for individual cells. BABEL also generalizes well to new biological contexts not seen during training. For example, starting from scATAC-seq of patient derived basal cell carcinoma (BCC), BABEL generated scRNA-seq that enabled fine-grained classification of complex cell states, despite having never seen BCC data. These predictions are comparable to analyses of the experimental BCC scRNA-seq data. We further show that BABEL can incorporate additional single-cell data modalities, such as CITE-seq, thus enabling translation across chromatin, RNA, and protein. BABEL offers a powerful approach for data exploration and hypothesis generation.

bioinformatics↗

bioRxiv2020

Three-dimensional residual channel attention networks denoise and sharpen fluorescence microscopy image volumes

We demonstrate residual channel attention networks (RCAN) for restoring and enhancing volumetric time-lapse (4D) fluorescence microscopy data. First, we modify RCAN to handle image volumes, showing that our network enables denoising competitive with three other state-of-the-art neural networks. We use RCAN to restore noisy 4D super-resolution data, enabling image capture over tens of thousands of images (thousands of volumes) without apparent photobleaching. Second, using simulations we show that RCAN enables class-leading resolution enhancement, superior to other networks. Third, we exploit RCAN for denoising and resolution improvement in confocal microscopy, enabling [~]2.5-fold lateral resolution enhancement using stimulated emission depletion (STED) microscopy ground truth. Fourth, we develop methods to improve spatial resolution in structured illumination microscopy using expansion microscopy ground truth, achieving improvements of [~]1.4-fold laterally and [~]3.4-fold axially. Finally, we characterize the limits of denoising and resolution enhancement, suggesting practical benchmarks for evaluating and further enhancing network performance.

bioengineering↗

bioRxiv2020

Cerebellar nuclei evolved by repeatedly duplicating a conserved cell type set

How have complex brains evolved from simple circuits? Here we investigated brain region evolution at cell type resolution in the cerebellar nuclei (CN), the output structures of the cerebellum. Using single-nucleus RNA sequencing in mice, chickens, and humans, as well as STARmap spatial transcriptomic analysis and whole-CNS projection tracing in mice, we identified a conserved cell type set containing two classes of region-specific excitatory neurons and three classes of region-invariant inhibitory neurons. This set constitutes an archetypal CN that was repeatedly duplicated to form new regions. Interestingly, the excitatory cell class that preferentially funnels information to lateral frontal cortices in mice becomes predominant in the massively expanded human Lateral CN. Our data provide the first characterization of CN transcriptomic cell types in three species and suggest a model of brain region evolution by duplication and divergence of entire cell type sets.

neuroscience↗