Biology subjects

Peng, T.

Publications and source records attributed to Peng, T..

50 records · Page 3Linked to original sources

bioRxiv2021

Constructing a warhead of PROTAC with aptamer for tumor-selective degradation of nucleolin

PROteolysis TArgeting Chimeras (PROTACs) induce targeted protein degradation by hijacking the intracellular ubiquitin proteasome system, thus emerging as a new strategy for drug development. However, most PROTACs generated lack cell-type selectivity and have poorly soluble in water. To address this drawback, we developed a novel PROTAC ZL216 using aptamer AS1411 as a targeting ligand of nucleolin to conjugate with a small molecule ligand of E3 ligase VHL, which shows high aqueous solubility and serum stability. Based on the differential expression of nucleolin on the cell surface, ZL216 could bind to and internalize into breast cancer cells, but not normal breast cells. Furthermore, we revealed that ZL216 promoted the formation of a nucleolin-ZL216-VHL ternary complex in breast cancer cells and potently induced nucleolin degradation in vitro and in vivo. As a result, ZL216 inhibited the proliferation and migration of breast cancer cells. These studies demonstrate that in addition to peptides and small molecule compounds, nuclei acid aptamers can also be used to generate PROTACs, which broadens the toolbox constructing PROTACs and provides a promising strategy for development of tumor-selective PROTACs.

biochemistry↗

bioRxiv2021

GLUER: integrative analysis of single-cell omics and imaging data by deep neural network

Single-cell omics assays have become essential tools for identifying and characterizing cell types and states of complex tissues. While each single-modality assay reveals distinctive features about the sequenced cells, true multi-omics assays are still in early stage of development. This notion signifies the importance of computationally integrating single-cell omics data that are conducted on various samples across various modalities. In addition, the advent of multiplexed molecular imaging assays has given rise to a need for computational methods for integrative analysis of single-cell imaging and omics data. Here, we present GLUER (inteGrative anaLysis of mUlti-omics at single-cEll Resolution), a flexible tool for integration of single-cell multi-omics data and imaging data. Using multiple true multi-omics data sets as the ground truth, we demonstrate that GLUER achieved significant improvement over existing methods in terms of the accuracy of matching cells across different data modalities resulting in ameliorating downstream analyses such as clustering and trajectory inference. We further demonstrate the broad utility of GLUER for integrating single-cell transcriptomics data with imaging-based spatial proteomics and transcriptomics data. Finally, we extend GLUER to leverage true cell-pair labels when available in true multi-omics data, and show that this approach improves co-embedding and clustering results. With the rapid accumulation of single-cell multi-omics and imaging data, integrated data holds the promise of furthering our understanding of the role of heterogeneity in development and disease.

bioinformatics↗

bioRxiv2020

Use of waggle dance information in honey bees is linked to gene expression in the antennae, but not in the brain

Communication is essential for social animals, but deciding how to utilize information provided by conspecifics is a complex process that depends on environmental and intrinsic factors. Honey bees use a unique form of communication, the waggle dance, to inform nestmates about the location of food sources. However, as in many other animals, experienced individuals often ignore this social information and prefer to rely on prior experiences, i.e. private information. The neurosensory factors that drive the decision to use social information are not yet understood. Here we test whether the decision to use social dance information or private information is linked to gene expression differences in different parts of the nervous system. We trained bees to collect food from sugar water feeders and observed whether they utilize social or private information when exposed to dances for a new food source. We performed transcriptome analysis of four brain parts critical for cognition: the subesophageal ganglion, the central brain, the mushroom bodies, and the antennal lobes but, unexpectedly, detected no differences between social or private information users. In contrast, we found 413 differentially expressed genes in the antennae, suggesting that variation in sensory perception mediate the decision to use social information. Social information users were characterized by the upregulation of dopamine and serotonin genes while private information users upregualted several genes coding for odor perception. These results highlight that decision making in honey bees might also depend on peripheral processes of perception rather than higher-order brain centers of information integration.

animal behavior and cognition↗

bioRxiv2020

Site-specific lipidation enhances IFITM3 membrane interactions and antiviral activity

Interferon-induced transmembrane proteins (IFITMs) are S-palmitoylated proteins in vertebrates that restrict a diverse range of viruses. S-palmitoylated IFITM3 in particular directly engages incoming virus particles, prevents their cytoplasmic entry, and accelerates their lysosomal clearance by host cells. However, the precise molecular mechanisms of action for IFITM-mediated viral restriction are still unclear. To investigate how site-specific S-palmitoylation controls IFITM3 antiviral activity, here we employed computational, chemical, and biophysical approaches to demonstrate that site-specific lipidation of IFITM3 at highly conserved cysteine 72 modulates its conformation and interaction with lipid membranes leading to enhanced antiviral activity of IFITM3 in mammalian cells. Collectively, our results demonstrate that site-specific S-palmitoylation of IFITM3 directly alters its biophysical properties and activity in cells to prevent virus infection.

biochemistry↗

bioRxiv2020

Genome-wide association study of TP53 R249S mutation in hepatocellular carcinoma with aflatoxin B1 exposure and hepatitis B virus infection in Guangxi

Background/AimsDietary aflatoxin B1 (AFB1) exposure, which induces DNA damage and codon 249 mutation of the TP53 gene, is one of the major risk factors for hepatocellular carcinoma (HCC). Hepatitis B virus (HBV) infection and AFB1 exert synergistic effects to promote carcinogenesis and TP53 R249S mutation in HCC. MethodsA genome-wide association study (GWAS) was conducted on 485 cases of HCC with chronic HBV infection, followed by a two-stage replication study on 270 cases with chronic HBV infection. Susceptibility variants for the TP53 R249S mutation in HCC were identified based on both GWAS and replication analysis. The associations of identified variants with expression levels of their located genes were validated in 20 paired independent samples. ResultsOur results showed that TP53 R249S was significantly associated with ADAMTS18 rs9930984 (adjusted P = 4.84x10-6), WDR49 rs75218075 (adjusted P = 7.36 x 10-5) and SLC8A3 rs8022091 (adjusted P = 0.042). Additionally, ADAMTS18 mRNA expression was significantly higher in HCC tissue, compared with paired non-tumor tissue (P = 0.041) and patients carrying the TT genotype at rs9930984 showed lower ADAMTS18 expression in non-tumor tissue, compared with those carrying the GT genotype (P = 0.0028). ConclusionsTP53 expression is significantly associated with R249S mutation in HCC. Our collective results suggest that rs9930984, rs75218075 and rs8022091 are associated with susceptibility to the R249S mutation in cases of HCC exposed to AFB1 and HBV infection.

genetics↗

bioRxiv2020

Human alveolar Type 2 epithelium transdifferentiates into metaplastic KRT5+ basal cells during alveolar repair

Understanding differential lineage potential of orthologous stem cells across species can shed light on human disease. Here, utilizing 3D organoids, single cell RNAseq, and xenotransplants, we demonstrate that human alveolar type 2 cells (hAEC2s), unlike murine AEC2s, are multipotent and able to transdifferentiate into KRT5+ basal cells when co-cultured with primary fibroblasts in 3D organoids. Trajectory analyses and immunophenotyping of epithelial progenitors in idiopathic pulmonary fibrosis (IPF) indicate that hAEC2s transdifferentiate into metaplastic basal cells through alveolar-basal intermediate (ABI) cells that we also identify in hAEC2-derived organoids. Modulating hAEC2-intrinsic and niche factors dysregulated in IPF can attenuate metaplastic basal cell transdifferentiation and preserve hAEC2 identity. Finally, hAEC2s transplanted into fibrotic immune-deficient murine lungs engraft as either hAEC2s or differentiated KRT5+ basal cells. Our study indicates that hAEC2s-loss and expansion of alveolar metaplastic basal cells in IPF are causally connected, which would not have been revealed utilizing murine AEC2s as a model. HighlightsO_LIHuman AEC2s transdifferentiate into KRT5+ basal cells when accompanied by primary adult human lung mesenchyme in 3D organoid culture. C_LIO_LIAlterations of hAEC2-intrinsic and niche factors dysregulated in IPF can modify metaplastic hAEC2 transdifferentiation. C_LIO_LIhAEC2s engraft into fibrotic lungs of immune-deficient mice and transdifferentiate into metaplastic basal cells. C_LIO_LITranscriptional trajectory analysis suggests that hAEC2s in IPF gives rise to metaplastic basal cells via alveolar-basal intermediate cells. C_LI

cell biology↗

bioRxiv2020

CytoTalk: De novo construction of signal transduction networks using single-cell RNA-Seq data

Single-cell technology has opened the door for studying signal transduction in a complex tissue at unprecedented resolution. However, there is a lack of analytical methods for de novo construction of signal transduction pathways using single-cell omics data. Here we present CytoTalk, a computational method for de novo constructing cell type-specific signal transduction networks using single-cell RNA-Seq data. CytoTalk first constructs intracellular and intercellular gene-gene interaction networks using an information-theoretic measure between two cell types. Candidate signal transduction pathways in the integrated network are identified using the prize-collecting Steiner forest algorithm. We applied CytoTalk to a single-cell RNA-Seq data set on mouse visual cortex and evaluated predictions using high-throughput spatial transcriptomics data generated from the same tissue. Compared to published methods, genes in our inferred signaling pathways have significantly higher spatial expression correlation only in cells that are spatially closer to each other, suggesting improved accuracy of CytoTalk. Furthermore, using single-cell RNA-Seq data with receptor gene perturbation, we found that predicted pathways are enriched for differentially expressed genes between the receptor knockout and wild type cells, further validating the accuracy of CytoTalk. In summary, CytoTalk enables de novo construction of signal transduction pathways and facilitates comparative analysis of these pathways across tissues and conditions.

bioinformatics↗

bioRxiv2020

Three-dimensional genome structure and chromatin accessibility reorganization during in vivo induction of human T cell tolerance

Achieving T cell tolerance is a pivotal goal for the field of transplantation and autoimmune diseases. Here, we characterized the gene expression profiles, 3D genome architecture and chromatin accessibility in human steady-state and tolerant T cells, which had been induced in healthy donors by granulocyte-colony-stimulating factor in vivo. We provided the first high-resolution 3D genomic landscape of human tolerant T cells in vivo and identified highly expressed suppressor of cytokine signaling 1 (SOCS1), which is essential for maintaining T cell tolerance and was validated by ex vivo experiments. Mechanistically, SOCS1 is activated by STAT3, which mediates a new interaction between the SOCS1 locus and downstream super-enhancers and is accompanied by the disruption of the CTCF loop between the SOCS1 locus and upstream heterochromatin. This competitive regulation pattern between STAT3 and CTCF is present in the whole genome. Our study defines a regulatory model of transcription factors and provides insight into the induction of immune tolerance.

bioinformatics↗

bioRxiv2019

2'3'-cGAMP triggers a STING and NF-κB dependent broad antiviral response in Drosophila

We recently reported that an orthologue of STING regulates infection by picorna-like viruses in drosophila. In mammals, STING is activated by the cyclic dinucleotide 23-cGAMP produced by cGAS, which acts as a receptor for cytosolic DNA. Here, we show that injection of flies with 23-cGAMP can induce expression of dSTING-regulated genes. Co-injection of 23-cGAMP with a panel of RNA or DNA viruses results in significant reduction of viral replication. This 23-cGAMP-mediated protection is still observed in flies mutant for the genes Atg7 and AGO2, which encode key components of the autophagy and small interfering RNA pathways, respectively. By contrast, it is abrogated in flies mutant for the NF-{kappa}B transcription factor Relish. Analysis of the transcriptome of 23-cGAMP injected flies reveals a complex pattern of response, with early and late induced genes. Our results reveal that dSTING regulates an NF-{kappa}B-dependent antiviral program, which predates the emergence of Interferon Regulatory Factors and interferons in vertebrates.

immunology↗

bioRxiv2019

Gli1+ mesenchymal stromal cells modulate epithelial metaplasia in lung fibrosis

Organ fibrosis is often accompanied by aberrant epithelial reprogramming, culminating in a transformed barrier composed of scar and metaplastic epithelium. Understanding how the scar promotes an abnormal epithelial response could better inform strategies to reverse the fibrotic damage. Here we show that Gli1+ mesenchymal stromal cells (MSCs), previously shown to contribute to myofibroblasts in the scar, promote metaplastic differentiation of airway progenitors into KRT5+ basal cells in vitro and in vivo. During fibrotic repair, Gli1+ MSCs integrate hedgehog activation to promote metaplastic KRT5 differentiation by upregulating BMP antagonism in the progenitor niche. Restoring the balance towards BMP activation attenuated metaplastic KRT5+ differentiation while promoting adaptive alveolar differentiation. Finally, fibrotic human lungs demonstrate altered BMP activation in the metaplastic epithelium. These findings show that Gli1+ MSCs integrate hedgehog signaling as a rheostat to control BMP activation in the progenitor niche to determine regenerative outcome in fibrosis. HighlightsO_LIGli1+ MSCs are required for metaplastic airway progenitor differentiation into KRT5+ basal cells. C_LIO_LIHedgehog activation of MSCs promotes KRT5 differentiation of airway progenitors by suppressing BMP activation. C_LIO_LIRestoring BMP activation attenuates metaplastic KRT5 differentiation C_LIO_LIMetaplastic KRT5+ basal cells in human fibrotic lungs demonstrate altered BMP activation. C_LI

cell biology↗

bioRxiv2019

Critical role for P53 in regulating the cell cycle of ground state embryonic stem cells

Mouse Embryonic Stem Cells (ESCs) grown in serum-supplemented conditions are characterized by an extremely short G1-phase due to the lack of G1-phase control. Concordantly, the G1-phase-specific P53-P21 pathway is compromised in serum ESCs. Here we provide evidence that P53 is activated upon transition of serum ESCs to their pluripotent ground state using serum-free 2i conditions and modulates G1-phase progression. Our data shows that the elongated G1-phase characteristic of ground state ESCs is dependent on P53. RNA-seq and ChIP-seq analyses reveal that P53 directly regulates the expression of the Retinoblastoma (RB) protein and that the hypo-phosphorylated, active RB protein plays a key role in G1-phase control. Our findings suggest that the P53-P21 pathway is active in ground state 2i ESCs and that its role in the G1-checkpoint is abolished in serum ESCs. Taken together, the data reveals a mechanism by which inactivation of P53 can lead to loss of RB and uncontrolled cell proliferation.

molecular biology↗

bioRxiv2019

Multi-task learning of a deep k-nearest neighbour network for histopathological image classification and retrieval

Deep neural networks have achieved tremendous success in image recognition, classification and object detection. However, deep learning is often criticised for its lack of transparency and general inability to rationalize its predictions. The issue of poor model interpretability becomes critical in medical applications, as a model that is not understood and trusted by physicians is unlikely to be used in daily clinical practice. In this work, we develop a novel multi-task deep learning framework for simultaneous histopathology image classification and retrieval, leveraging on the classic concept of k-nearest neighbors to improve model interpretability. For a test image, we retrieve the most similar images from our training databases. These retrieved nearest neighbours can be used to classify the test image with a confidence score, and provide a human-interpretable explanation of our classification. Our original framework can be built on top of any existing classification network (and therefore benefit from pretrained models), by (i) adding a triplet loss function with a novel triplet sampling strategy to compare distances between samples and (ii) a Cauchy hashing loss function to accelerate neighbour searching. We evaluate our method on colorectal cancer histology slides, and show that the confidence estimates are strongly correlated with model performance. The explanations provided by nearest neighbors are intuitive and useful for expert evaluation by giving insights into understanding possible model failures, and can support clinical decision making by comparing archived images and patient records with the actual case.

pathology↗

bioRxiv2017

SOMSC: Self-Organization-Map for High-Dimensional Single-Cell Data of Cellular States and Their Transitions

Measurements of gene expression levels for multiple genes in single cells provide a powerful approach to study heterogeneity of cell populations and cellular plasticity. While the expression levels of multiple genes in each cell are available in such data, the potential connections among the cells (e.g. the lineage relationship) are not directly evident from the measurement. Classifying cellular states and identifying transitions among those states are challenging due to many factors, including the small number of cells versus the large number of genes collected in the data. In this paper we adapt a classical self-organizing-map approach to single-cell gene expression data, such as those based on qPCR and RNA-seq. In this method (SOMSC), a cellular state map (CSM) is derived and employed to identify cellular states inherited in a population of measured single cells. Cells located in the same basin of the CSM are considered as in one cellular state while barriers between the basins provide information on transitions among the cellular states. Consequently, paths of cellular state transitions (e.g. differentiation) and a temporal ordering of the measured single cells are obtained. Applied to a set of synthetic data, two single-cell qPCR data sets and two single-cell RNA-seq data sets for a simulated model of cell differentiation, and systems on the early embryo development, haematopoietic cell lineages, human preimplanation embryo development, and human skeletal muscle myoblasts differentiation, the SOMSC shows good capabilities in identifying cellular states and their transitions in the high-dimensional single-cell data. This approach will have broad applications in studying cell lineages and cellular fate specification.

bioinformatics↗

bioRxiv2017

SOMSC: Self-Organization-Map for High-Dimensional Single-Cell Data of Cellular States and Their Transitions

Measurement of gene expression levels for multiple genes in single cells provides a powerful approach to study heterogeneity of cell populations and cellular plasticity. While the expression levels of multiple genes in each cell are available in such data, the potential connections among the cells (e.g. the cellular state transition relationship) are not directly evident from the measurement. Classifying the cellular states, identifying their transitions among those states, and extracting the pseudotime ordering of cells are challenging due to the noise in the data and the high-dimensionality in the number of genes in the data. In this paper we adapt the classical self-organizing-map (SOM) approach for single-cell gene expression data (SOMSC), such as those based on single cell qPCR and single cell RNA-seq. In SOMSC, a cellular state map (CSM) is derived and employed to identify cellular states inherited in the population of the measured single cells. Cells located in the same basin of the CSM are considered as in one cellular state while barriers among the basins in CSM provide information on transitions among the cellular states. A cellular state transitions path (e.g. differentiation) and a temporal ordering of the measured single cells are consequently obtained. In addition, SOMSC could estimate the cellular state replication probability and transition probabilities. Applied to a set of synthetic data, one single-cell qPCR data set on mouse early embryonic development and two single-cell RNA-seq data sets, SOMSC shows effectiveness in capturing cellular states and their transitions presented in the high-dimensional single-cell data. This approach will have broader applications to analyzing cellular fate specification and cell lineages using single cell gene expression data

bioinformatics↗