Biology subjects

Chen, M.

Publications and source records attributed to Chen, M..

At least 487 records · Page 27Linked to original sources

bioRxiv2020

Bitbow: a digital format of Brainbow enables highly efficient neuronal lineage tracing and morphology reconstruction in single brains

Identifying the cellular origins and mapping the dendritic and axonal arbors of neurons have been century old quests to understand the heterogeneity among these brain cells. Classical chemical and genetic methods take advantage of light microscopy and sparse labeling to unambiguously, albeit inefficiently, trace a few neuronal lineages or reconstruct their morphologies in each sampled brain. To improve the analysis throughput, we designed Bitbow, a digital format of Brainbow which exponentially expands the color palette to provide tens of thousands of spectrally resolved unique labels. We generated transgenic Bitbow Drosophila lines, established statistical tools, and streamlined sample preparation, image processing and data analysis pipelines to allow conveniently mapping neural lineages, studying neuronal morphology and revealing neural network patterns with an unprecedented speed, scale and resolution.

neuroscience↗

bioRxiv2020

Demystifying "drop-outs" in single cell UMI data

Analysis of scRNA-seq data has been challenging particularly because of excessive zeros observed in UMI counts. Prevalent opinions are that many of the detected zeros are "drop-outs" that occur during experiments and that those zeros should be accounted for through procedures such as normalization, variance stabilization, and imputation. Here, we extensively analyze publicly available UMI datasets and challenge the existing scRNA-seq workflows. Our results strongly suggest that resolving cell-type heterogeneity should be the foremost step of the scRNA-seq analysis pipeline because once cell-type heterogeneity is resolved, "drop-outs" disappear. Additionally, we show that the simplest parametric count model, Poisson, is sufficient to fully leverage the biological information contained in the UMI data, thus offering a more optimistic view of the data analysis. However, if the cell-type heterogeneity is not appropriately taken into account, pre-processing such as normalization or imputation becomes inappropriate and can introduce unwanted noise. Inspired by these analyses, we propose a zero inflation test that can select gene features contributing to cell-type heterogeneity. We integrate feature selection and clustering into iterative pre-processing in our novel, efficient, and straightforward framework for UMI analysis, HIPPO (Heterogeneity Inspired Pre-Processing tOol). HIPPO leads to downstream analysis with much better interpretability than alternatives in our comparative studies.

genomics↗

bioRxiv2020

Detecting Tumor Specific Antigen-Reactive T cells from Tumor Infiltrating Lymphocytes via Interaction Dependent Fucosyl-biotinylation

Re-activation and clonal expansion of tumor specific antigen (TSA)-reactive T cells are critical to the success of checkpoint blockade and adoptive transfer of tumor-infiltrating lymphocyte (TIL) based therapies. There are no reliable markers to specifically identify the repertoire of TSA-reactive T cells due to their heterogeneous composition. Here we introduce FucoID as a general platform to detect endogenous antigen-specific T cells and study their biology. Through this interaction dependent labeling approach, TSA-reactive T cells can be detected and separated from bystander T cells in primary tumor digests based on their cell-surface enzymatic fucosyl-biotinylation. Compared to bystander TILs, TSA-reactive TILs possess a distinct TCR repertoire and unique gene features. Though exhibiting a dysfunctional phenotype, this subset of TILs possesses substantial capabilities of proliferation and tumor specific killing. FucoID features genetic manipulation-free procedures and a quick turnover cycle, and therefore should have the potential of accelerating the pace of personalized cancer treatment. HighlightsInteraction dependent fucosylation enables the detection and isolation of bona fide intratumoral tumor specific antigen-reactive T cells Tumor specific antigen-reactive CD8+ T cells possess capabilities to be expanded and adoptively transferred for tumor control Tumor specific antigen-reactive CD8+ T cells feature oligoclonal expansion and upregulate genes for the steroid biosynthesis and metabolic process Intratumoral bystander CD8+ T cells can be separated into two groups based on PD-1 expression that feature distinct gene modules

immunology↗

bioRxiv2020

Disruption of oncogenic targeting by ISWI via phosphorylation of a prion-like domain

Chromosomal translocations generate oncogenic fusion proteins in approximately one-third of sarcomas, but how these proteins promote tumorigenesis and the effect of cancer therapies on their function are not well understood. Here, we reveal a molecular mechanism by which the fusion oncoprotein FUS-CHOP promotes tumor maintenance that also explains the remarkable radiation sensitivity of myxoid liposarcomas. We identified novel interactions between FUS-CHOP and chromatin remodeling complexes that regulate sarcoma cell proliferation. One of these chromatin remodelers, SNF2H, co-localizes with FUS-CHOP genome-wide at active enhancers. Following ionizing radiation, DNA damage response kinases phosphorylate the prion-like domain of FUS-CHOP to impede these protein-protein interactions, which are required for transformation. Therefore, the DNA damage response after irradiation disrupts oncogenic targeting of chromatin remodelers required for FUS-CHOP-driven sarcomagenesis. SignificancePrion-like domains translocated in cancer have been shown to drive global epigenetic changes that are oncogenic. However, some translocation-driven cancers exhibit dramatic clinical responses to therapy, though the mechanism for these responses are not well-understood. Here we show that ionizing radiation can disrupt oncogenic interactions between a fusion oncoprotein and a chromatin remodeling complex, ISWI. This mechanism of disruption links phosphorylation of the prion-like domain in an oncogenic fusion protein to DNA damage after ionizing radiation and reveals that a dependence on oncogenic chromatin remodeling underlies sensitivity to radiation therapy in myxoid liposarcoma.

cancer biology↗

bioRxiv2020

Somatic cell-derived BMPs induce male germ cell meiosis initiation during embryonic stage via regulating Dazl expression

Germ cell fate is believed to be determined by the signaling from sexually differentiated somatic cell. However, the molecular mechanism remains elusive. In this study, ectopic initiation of meiosis in male germ cells was observed during embryonic stage by over-activating CTNNB1 in Sertoli cells. Somatic cell transcriptome and single germ cell RNA-seq analysis indicated that TGF-{beta} signaling was activated after CTNNB1 over-activation. In vitro and in vivo experiments confirmed somatic cell-derived BMPs played crucial roles in germ cell meiosis initiation. Further studies revealed that Dazl was significantly increased in germ cells of CTNNB1 over-activated testes and induced by BMP signaling. DNMT3a and DNA methylation was also reduced in germ cells of CTNNB1 over-activated testes and increased by BMP signaling inhibitor treatment. Taken together, this study demonstrates that germ cell fate could be reprogrammed after sex determination. BMP signaling pathway is involved in germ cell meiosis initiation via up-regulating Dazl expression.

developmental biology↗

bioRxiv2020

A Novel Riboswitch Classification based on Imbalanced Sequences achieved by Machine Learning

Riboswitch, a part of mRNA (50-250nt in length), has two main classes: aptamer and expression platform. One of the main challenges raised during the classification of riboswitch is imbalanced data. That is a circumstance in which the records of a dataset of one group are very small compared to the others. Such circumstances lead classifier to ignore minority group and emphasize on majority class, that resulting with a skewed classification. We considered sixteen riboswitch families, to be in accord with recent riboswitch classification work, that contain imbalanced dataset ranging from 4,826 instances (RF00174) to 39 (RF01051) instances. The dataset was divided into training and test set using new developed pipeline. From 5460 k-mers, 156 features were produced calculated based on CfsSubsetEval and BestFirst. Statistically tested result was significantly difference between balanced and imbalanced dataset (p < 0.05). Besides, each algorithm also showed a significant difference in sensitivity, specificity, accuracy, and macro F-score when used in both groups (p < 0.05). Several k-mers clustered from heat map were discovered to have biological functions and motifs at the different positions like interior loops, terminal loops and helices. They were validated to have a biological function and some are riboswitch motifs. The analysis has discovered the importance of solving the challenges of majority bias analysis and overfitting. Presented results were generalized evaluation of both balanced and imbalanced models, which implies their ability of classifying novel riboswitches. The scientific community can use python source code at https://github.com/Seasonsling/riboswitch, which can contribute to the process of developing software packages. Author SummaryMachine learning application has been used in many ways in bioinformatics and computational biology. Its use in riboswitch classification is still limited and existing attempt showed challenges due to imbalanced dataset. Algorithms classify dataset with majority and minority group, but they tend to ignore minority group and emphasize on majority class, consequential return a skewed classification We used new pipeline including SMOTE for balancing datasets that showed better classified riboswitch as well as improved performance of algorithms selected. Statistically significant difference observed between balanced and imbalanced in sensitivity, specificity, accuracy and F-score, this proved balanced dataset better for classification of riboswitch. Biological functions and motif search of k-mers in riboswitch families revealed their presence in interior loops, terminal loops and helices, some of the k-mers were reported to be riboswitch motifs of aptamer domains and critical for metabolite binding. The pipeline can be used in machine learning and deep learning study in other domains of bioinformatics and computational biology suffering from imbalanced dataset. Finally, scientific community can use python source code, the work done and flow to develop packages.

bioinformatics↗

bioRxiv2020

Increases in ambient temperature progressively disassemble Arabidopsis phytochrome B from individual photobodies with distinct thermostabilities

Warm temperature is postulated to induce plant thermomorphogenesis through a signaling mechanism similar to shade, as both destabilize the active form of the photoreceptor and thermosensor phytochrome B (phyB). At the cellular level, shade antagonizes phyB signaling by triggering phyB disassembly from photobodies. Here we report temperature-dependent photobody localization of fluorescent protein-tagged phyB (phyB-FP) in the epidermal cells of Arabidopsis hypocotyl and cotyledon. Our results demonstrate that warm temperature operates through different photobody dynamics from shade. Increases in temperature from 12{degrees}C to 27{degrees}C incrementally reduce photobody number by stimulating phyB-FP disassembly from selective thermo-unstable photobodies. The thermostability of photobodies relies on phyBs photosensory module. Surprisingly, elevated temperatures inflict opposite effects on phyBs functions in the hypocotyl and cotyledon despite inducing similar photobody dynamics, indicative of tissue/organ-specific temperature signaling circuitry downstream of photobody dynamics. Our results thus provide direct cell biology evidence supporting an early temperature signaling mechanism via dynamic assembly/disassembly of individual photobodies possessing distinct thermostabilities.

plant biology↗

bioRxiv2020

JEDI: Circular RNA Prediction based on Junction Encoders and Deep Interaction among Splice Sites

Circular RNA is a novel class of endogenous non-coding RNAs that have been largely discovered in eukaryotic transcriptome. The circular structure arises from a non-canonical splicing process, where the donor site backsplices to an upstream acceptor site. These circular form of RNAs are conserved across species, and often show tissue or cell-specific expression. Emerging evidences have suggested its vital roles in gene regulation, which are further associated with various types of diseases. As the fundamental effort to elucidate its function and mechanism, numerous efforts have been devoted to predicting circular RNA from its primary sequence. However, statistical learning methods are constrained by the information presented with explicit features, and the existing deep learning approach falls short on fully exploring the positional information of the splice sites and their deep interaction. We present an effective and robust end-to-end framework, JEDI, for circular RNA prediction using only the nucleotide sequence. Our framework first leverages the attention mechanism to encode each junction site based on deep bidirectional recurrent neural networks and then presents the novel cross-attention layer to model deep interaction among these sites for backsplicing. Finally, JEDI is capable of not only addressing the task of circular RNA prediction but also interpreting the relationships among splice sites to discover the hotspots for backsplicing within a gene region. Experimental evaluations demonstrate that JEDI significantly outperforms several state-of-the-art approaches in circular RNA prediction on both isoform-level and gene-level. Moreover, JEDI also shows promising results on zero-shot backsplicing discovery, where none of the existing approaches can achieve. The implementation of our framework is available at https://github.com/hallogameboy/JEDI.

bioinformatics↗

bioRxiv2020

Tumor Neoantigenicity Assessment with CSiN Score Incorporates Clonality and Immunogenicity to Predict Immunotherapy Outcomes

Lack of responsiveness to checkpoint inhibitors is a central problem in the modern era of cancer immunotherapy. Tumor neoantigens are critical mediators of host immune response and immunotherapy treatment efficacy. Current studies of neoantigens almost entirely focus on total neoantigen load, which simplistically treats all neoantigens equally. Besides, neoantigen loads have been linked with treatment response and prognosis only in some studies, but not others. We developed a Cauchy-Schwarz index of Neoantigens (CSiN) score to characterize the degree of concentration of immunogenic neoantigens in truncal mutations. Unlike simple neoantigen loads, CSiN incorporates the effect of both clonality and MHC-binding affinity of neoantigens when characterizing patient neoantigen profiles. By exploiting the clinical responses in 501 treated patients (mostly by checkpoint inhibitors) and the overall survival of 1,978 baseline patients, we showed that CSiN scores predict treatment response to checkpoint inhibitors and prognosis in melanoma, lung cancer, and kidney cancer patients. CSiN substantially outperforms prior genetics-based prediction methods of responsiveness. Overall, our work fulfilled an important gap in current research involving neoantigens. One Sentence SummaryThe quality of tumor neoantigens predicts response to immunotherapy

bioinformatics↗

bioRxiv2020

PD-L1 upregulation by IFN-α/γ-mediated Stat1 suppresses anti-HBV T cell response

Programmed death ligand 1 (PD-L1) has been recently shown to be a major obstacle to antiviral immunity by binding to its receptor programmed death 1 (PD-1) on specific IFN-{gamma} producing T cells in chronic hepatitis B. Currently, IFN- is widely used to treat hepatitis B virus(HBV) infection, but its antiviral effect vary greatly and the mechanism is not totally clear. We found that IFN-/{gamma} induced a marked increase of PD-L1 expression in hepatocytes. Signal and activators of transcription (Stat1) was then identified as a major transcription factor involved in IFN-/{gamma}-mediated PD-L1 elevation both in vitro and in mice. Blockage of the PD-L1/PD-1 interaction by a specific mAb greatly enhanced HBV-specific T cell activity by the gp96 adjuvanted therapeutic vaccine, and promoted HBV clearance in HBV transgenic mice. Our results demonstrate the IFN-/{gamma}-Stat1-PD-L1 axis plays an important role in mediating T cell hyporesponsiveness and inactivating liver-infiltrating T cells in the hepatic microenvironment. These data raise further potential interest in enhancing the anti-HBV efficacy of IFN- and therapeutic vaccines.

molecular biology↗

bioRxiv2020

Alignment of single-cell RNA-seq samples without over-correction using kernel density matching

Single-cell RNA sequencing (scRNA-seq) technology is poised to replace bulk cell RNA sequencing for most biological and medical applications as it allows users to measure gene expression levels in a cell-type-specific manner. However, data produced by scRNA-seq often exhibit batch effects that can be specific to a cell-type, to a sample, or to an experiment, which prevent integration or comparisons across multiple experiments. Here, we present Dmatch, a method that leverages an external expression atlas of human primary cells and kernel density matching to align multiple scRNA-seq experiments for downstream biological analysis. Dmatch facilitates alignment of scRNA-seq datasets with cell-types that may overlap only partially, and thus allows integration of multiple distinct scRNA-seq experiments to extract biological insights. In simulation, Dmatch compares favorably to other alignment methods, both in terms of reducing sample-specific clustering, and in terms of avoiding over-correction. When applied to scRNA-seq data collected from clinical samples in a healthy individual and five autoimmune disease patients, Dmatch enabled cell-type-specific differential gene expression comparisons across biopsy sites and disease conditions, and uncovered a shared population of pro-inflammatory monocytes across biopsy sites in RA patients. We further show that Dmatch increases the number of eQTLs mapped from population scRNA-seq data. Dmatch is fast, scalable, and improves the utility of scRNA-seq for several important applications. Dmatch is freely available online (https://qzhan321.github.io/dmatch/).

bioinformatics↗

bioRxiv2019

SNAPSwitch: A Molecular Sensor to Quantify the Localization of Proteins, DNA and Nanoparticles in Cells

Intracellular trafficking governs receptor signalling, pathogenesis, immune responses and the cellular fate of nanomedicines. These processes are typically tracked by confocal microscopy, where colocalization of fluorescent markers implies an interaction or co-compartmentalization. However, this type of analysis is inherently low-throughput, is limited by the resolution of microscopy, and can miss fleeting interactions. To address this, we have developed a localization sensor composed of a quenched and attachable SNAP-tag substrate (SNAPSwitch). SNAPSwitch enables quantitative detection of protein, nucleic acid and nanoparticle trafficking to locations of interest within live cells using flow cytometry. Using this approach, we followed the trafficking of DNA complexes travelling from endosomes into the cytosol and to the nucleus. We also show that antibody targeted to the transferrin (CD71) or hyaluronan (CD44) receptor is initially sorted into different compartments following endocytosis. These results demonstrate SNAPSwitch is a high-throughput and broadly applicable tool to quantitatively track the localization of materials in cells.

cell biology↗

bioRxiv2019

Mutation effect estimation on protein-protein interactions using deep contextualized representation learning

The functional impact of protein mutations is reflected on the alteration of conformation and thermodynamics of protein-protein interactions (PPIs). Quantifying the changes of two interacting proteins upon mutations are commonly carried out by computational approaches. Hence, extensive research efforts have been put to the extraction of energetic or structural features on proteins, followed by statistical learning methods to estimate the effects of mutations to PPI properties. Nonetheless, such features require extensive human labors and expert knowledge to obtain, and have limited abilities to reflect point mutations. We present an end-to-end deep learning framework, MuPIPR, to estimate the effects of mutations on PPIs. MuPIPR incorporates a contextualized representation mechanism of amino acids to propagate the effects of a point mutation to surrounding amino acid representations, therefore amplifying the subtle change in a long protein sequence. On top of that, MuPIPR leverages a Siamese residual recurrent convolutional neural encoder to encode a wildtype protein pair and its mutation pair. Multiple-layer perceptron regressors are applied to the protein pair representations to predict the quantifiable changes of PPI properties upon mutations. Experimental evaluations show that MuPIPR outperforms various state-of-the-art systems on the change of binding affinity prediction and the buried surface area prediction. The software implementation is available at https://github.com/guangyu-zhou/MuPIPR

bioinformatics↗

bioRxiv2019

REPIC: A database for exploring N6-methyladenosine methylome

The REPIC (RNA Epitranscriptome Collection) database records about 10 million peaks called from publicly available m6A-seq and MeRIP-seq data using our unified pipeline. These data were collected from 672 samples of 49 studies, covering 61 cell lines or tissues in 11 organisms. REPIC allows users to query N6-methyladenosine (m6A) modification sites by specific cell lines or tissue types. In addition, it integrates m6A/MeRIP-seq data with 1,418 histone ChIP-seq and 118 DNase-seq data tracks from the ENCODE project in a modern genome browser to present a comprehensive atlas of m6A, histone modification sites and chromatin accessibility regions. REPIC is accessible at http://epicmod.uchicago.edu/repic.

bioinformatics↗

bioRxiv2019

Juvenile hormone regulation on the flight capability of Bactrocera dorsalis (Diptera: Tephritidae)

The oriental fruit fly, Bactrocera dorsalis (Hendel) (Diptera: Tephritidae), is considered a major economic threat in many regions worldwide. In order to better understand the flight capacity of B. dorsalis and its physiological basis, the functions and regulatory roles of juvenile hormone (JH) in the flight muscle of B. dorsalis were studied under a controlled environment. JH titer of B. dorsalis varied with age and sex. Females, irrespective of age, have higher JH than males for ovarian development and maturation in addition to better flight capabilities. The flight duration and distance of both males and females increased with the gradual increase of JH titer after adult emergences. JH titer peaked in 15-d-old adult and declined subsequently with age. Flight activity stimulated the production of JH as adults flown for 24 hours on the flight mills have the highest JH titers compare to adults tethered on shorter flight durations. Furthermore, JH III-treated adults were able to perform long-duration and long-distance flights. The mutual reinforcement of JH and flight activity provides fundamental understanding on the physiological aspects of the flight capability and dispersal, which facilitates strategies for the long-term control of this destructive pest.

animal behavior and cognition↗

bioRxiv2019

RADAR: Differential analysis of MeRIP-seq data with a random effect model

Epitranscriptome profiling using MeRIP-seq is a powerful technique for in vivo functional studies of reversible RNA modifications. We develop RADAR, a comprehensive analytical tool for detecting differentially methylated loci in MeRIP-seq data. RADAR enables accurate identification of altered methylation sites by accommodating variability of pre-immunoprecipitation expression level and post-immunoprecipitation count using different strategies. In addition, it is compatible with complex study design when covariates need to be incorporated in the analysis. Through simulation and real datasets analyses, we show that RADAR leads to more accurate and reproducible differential methylation analysis results than alternatives, which is available at https://github.com/scottzijiezhang/RADAR.

bioinformatics↗

bioRxiv2019

Allele-specific open chromatin in human iPSC neurons elucidates functional non-coding disease variants

Functional interpretation of noncoding disease variants, which likely regulate gene expression, has been challenging. Chromatin accessibility strongly influences gene expression during neurodevelopment; however, to what extent genetic variants can alter chromatin accessibility in the context of brain disorders/traits is unknown. Using human induced pluripotent stem cell (iPSC)-derived neurons as a neurodevelopmental model, we identified abundant open-chromatin regions absent in adult brain samples and thousands of genetic variants exhibiting allele-specific open-chromatin (ASoC). ASoC variants are overrepresented in brain enhancers, transcription-factor-binding sites, and quantitative-trait-loci associated with gene expression, histone modification, and DNA methylation. Notably, compared to open chromatin regions and other commonly used functional annotations, neuronal ASoC variants showed much stronger enrichments of risk variants for various brain disorders/traits. Our study provides the first snapshot of the neuronal ASoC landscape and a powerful framework for prioritizing functional disease variants.\n\nOne Sentence SummaryAllele-specific open chromatin informs functional disease variants

genetics↗

bioRxiv2019

AKT2 deficiency causes sarcopenia and metabolic disorder of skeletal muscle

Skeletal muscle is responsible for the majority of glucose disposal in the body. Insulin resistance in the skeletal muscle accounts for 85-90% of the impairment of total body glucose disposal in patients with tye 2 diabetes (T2D). However, the mechanism remains controversial. AKT2 is a protein kinase performing important functions in the regulation of glucose metabolism. We observed that mice deficient for AKT2 (AKT2 KO) exhibited decreased body weight and lean mass and showed impaired glucose tolerance, compared to their age- and gender-matched wild type mice (WT). Therefore, to test whether AKT2 deficiency causes deficits in skeletal muscle development and metabolism, we analyzed the expression of molecules related to skeletal muscle development, glucose uptake and metabolism in young (3 months) and old (8 months) mice. We found that AMPK phosphorylation and MEF2A expression were downregulated in young AKT2 KO mice, and this downregulation was inverted by AMPK activation. We also observed reduced mtDNA abundance and reduced expression of genes involved in mitochondrial biogenesis in the skeletal muscle of adult AKT2 KO mice, which was prevented by AMPK activation. However, GLUT4 expression was regulated by AKT2 in an AMPK-independent manner in skeletal muscle. During high-fat-diet (HFD)-induced obesity, AKT2 KO mice exhibited increased insulin resistance compared to WT mice. Our study establishes a new and important function of AKT2 in regulating glucose uptake and AMPK-dependent development and mitochondrial biogenesis in skeletal muscle.

molecular biology↗