Biology subjects

Zheng, S.

Publications and source records attributed to Zheng, S..

At least 163 records · Page 9Linked to original sources

bioRxiv2022

Rapid quantification of polyhydroxyalkanoates accumulated in living cells based on green fluorescence protein labeled phasin: The qPHA method

Polyhydroxyalkanoates (PHA), are microbial polyesters with possibility to replace non-biodegradable petro-plastics. No rapid in situ PHA quantitation method has been available for the past 40 years to replace the traditional method which is complicated, time and labor consuming. Quantification of PHA in living cells were finally developed from fluorescence intensities generated from green fluorescence protein (GFP) fused with the Halomonas bluephagenesis phasin proteins attached on the PHA granules. Phasins PhaP1 and PhaP2 were used to fuse with GFP which reflects PHA accumulation with an R-square over 0.9, respectively. Also, a standard correlation was established to calculate PHA contents based on the fluorescence and cell density recorded via a microplate reader with R-square over 0.95 when grown on various substrates, respectively. The PhaP2-GFP containing H. bluephagenesis was applied successfully to quantify PHA synthesis in a 7.5 L fermenter with high precision. The method is named qPHA.

bioengineering↗

bioRxiv2022

AreTomo: An integrated software package for automated marker-free, motion-corrected cryo-electron tomographic alignment and reconstruction

AreTomo, an abbreviation for Alignment and Reconstruction for Electron Tomography, is a GPU accelerated software package that fully automates motion-corrected marker-free tomographic alignment and reconstruction in a single package. By correcting in-plane rotation, translation, and importantly, the local motion resulting from beam-induced motion from tilt to tilt, AreTomo can produce tomograms with sufficient accuracy to be directly used for subtomogram averaging. Another major application is the on-the-fly reconstruction of tomograms in parallel with tilt series collection to provide users with real-time feedback of sample quality allowing users to make any necessary adjustments of collection parameters. Here, the multiple alignment algorithms implemented in AreTomo are described and the local motions measured on a typical tilt series are analyzed. The residual local motion after correction for global motion was found in the range of {+/-}80 [A], indicating that the accurate correction of local motion is critical for high-resolution cryo-electron tomography (cryoET).

cell biology↗

bioRxiv2022

Single-cell analysis reveals a pathogenic cellular module associated with early allograft dysfunction after liver transplantation

Liver transplantation (LT) is the standard therapy for patients with end-stage liver disease. Although LT technology has markedly progressed in recent decades, early allograft dysfunction (EAD) exacerbates the current organ shortage and impacts the prognosis of recipients. However, understanding of cellular characteristics and molecular events contributing to EAD is limited. Here, a large single-cell transcriptomic atlas of transplanted livers collected from four patients is constructed, including 58,243 cells, which are classified into 14 cell types and 29 corresponding subtypes with known markers, including liver parenchymal cells and non-parenchymal cells with different cell states. Compared to the pre-LT livers, graft remodeling is noted in the post-LT livers, with marked changes in several immune cells in either cell ratios or cell states. More importantly, an EAD-associated pathogenic cellular module is identified, consisting of mucosal-associated invariant T (MAIT) cells, granzyme B (GZMB)+ granzyme K (GZMK)+ natural killer (NK) cells, and S100A12+ neutrophils, all of which are elevated in EAD patient after LT. This cellular module is also verified in two independent datasets. Collectively, these results reveal the cellular characteristics of transplanted livers and the EAD-associated pathogenic cellular module at the single-cell level, offering new insights into the EAD occurrence after LT.

genomics↗

bioRxiv2022

Fate tracing reveals differences between Reelin+ HSCs and Desmin+ HSCs in activation, migration, and proliferation activities

The activation of hepatic stellate cells (HSCs) which comprise distinct clusters, is the main cause of liver fibrogenesis in response to different etiologies of chronic liver injuries. In this study, we constructed a novel ReelinCreERT2 transgenic mouse in which cells expressing Reelin were fully marked and demonstrated that about 50% HSCs were labeled. These ReelinCreERT2-labeled HSCs displayed distinct characteristics in migration, activation and proliferation compared to Desmin+ HSCs (total HSCs) in cholestatic (bile duct ligation; BDL) or hepatotoxic (carbon tetrachloride; CCl4) liver injuries. In BDL-induced fibrotic livers, Desmin+ HSCs were activated with increased proliferation and accumulation activities around the portal triad, but mGFP+ HSCs did not show proliferation or accumulation activity around the portal triad, and only a small part was activated. In CCl4-induced fibrotic livers, most of Desmin+ and mGFP+ HSCs were activated along with proliferation and accumulation potential around the central vein, however fewer mGFP+ HSCs were activated compared to Desmin+ HSCs. Moreover, in the regression of CCl4-induced fibrosis, mGFP+ HSCs were apoptosed whereas Desmin+ HSCs recovered to normal state. Besides, we didnt find evidence that mGFP+ HSCs transdifferentiated into hepatocytes or cholangiocytes through mesenchymal-epithelial transition (MET).

pathology↗

bioRxiv2022

Structural insights into the galanin receptors signaling

Galanin is a biologically active neuropeptide, and functions through three distinct G protein-coupled receptors (GPCRs), namely GALR1, GALR2 and GLAR3. GALR signaling plays important roles in regulating various physiological processes such as energy metabolism, neuropathic pain, epileptic activity, and sleep homeostasis. GALR1 and GALR3 signal through the Gi/o pathway, whereas GALR2 signals mainly through the Gq/11 pathway. However, the molecular basis for galanin recognition and G protein selectivity of GALRs remains poorly understood. Here, we report the cryoelectron microscopy structures of the GALR1-Go and the GALR2-Gq complexes bound to the endogenous ligand galanin or spexin. The galanin peptide mainly adopts an alpha helical structure, which binds at the extracellular vestibule of the receptors, nearly parallel to the membrane plane without penetrating deeply into the receptor core. Structural analysis combined with functional studies reveals important structural determinants for the G protein selectivity of GALRs as well as other class A GPCRs. In addition, we show that the zinc ion is a negative allosteric regulator of GALR1 but not GALR2. Our studies provide insight into the mechanisms of G protein selectivity of GPCRs and highlight potential novel function of the neuromodulator zinc ion as a modulator of GPCR signaling in the central nervous system. Significance StatementGalanin exerts various physiological functions through galanin receptors, including antinociceptive activity, depression and sleep. Here, we reveal a distinct binding site and binding pose of galanin peptide in galanin receptors from that of the published structures of peptide-bound GPCRs. Moreover, our work show that the neuromodulator zinc ion negatively modulates galanin signaling in the central nervous system, and further advances our understanding of mechanisms of G protein selectivity of GPCRs. These unique features of galanin receptors can be exploited for rational design of subtype selective ligands for treatments of neurological disorders.

neuroscience↗

bioRxiv2022

Structural insights into G protein activation by D1 dopamine receptor

G protein-coupled receptors (GPCRs) comprise the largest family of membrane receptors and are the most important drug targets. An agonist-bound GPCR engages heterotrimeric G proteins and triggers the exchange of GDP with GTP to promote G proteins activation. A complete understanding of the molecular mechanisms of G proteins activation has been hindered by a lack of structural information of GPCR-G protein complex in nucleotide-bound states. Here, we present the cryoelectron microscopy (cryo-EM) structures of D1 dopamine receptor (D1R)-Gs in the nucleotide-free state, the GDP-bound state and the GTP-bound state with endogenous ligand dopamine. These structures reveal important conformational changes accounting for the release of GDP and the GTP-dependent dissociation of G from G{beta}{gamma} subunits. Combining mutagenesis functional studies, we also identified an important sequence motif in D1R that determines its G protein selectivity. Taken together, these results shed light into the molecular basis of G protein selectivity and the entire molecular signaling events of GPCR-mediated G protein activation.

molecular biology↗

bioRxiv2021

Endothelial Rap1B mediates T-cell exclusion to promote tumor growth -a novel mechanism underlying vascular immunosuppression

Overcoming vascular immunosuppression: lack of endothelial cell (EC) responsiveness to inflammatory stimuli in the proangiogenic environment of tumors, is essential for successful cancer immunotherapy. The mechanisms through which Vascular Endothelial Growth Factor (VEGF) modulates tumor EC response to exclude T cells are not well understood. The goal was to determine the role of EC Rap1B, a small GTPase that positively regulates VEGF- angiogenesis during development, in tumor growth in vivo. Using mouse models of Rap1B deficiency, Rap1B+/- and EC-specific Rap1B KO (Rap1Bi{Delta}EC) we demonstrate that EC Rap1B restricts tumor growth and angiogenesis. More importantly, EC-specific Rap1B deletion leads to an altered tumor microenvironment with increased recruitment of leukocytes and increased activity of tumor CD8+ T cells. We find that tumor growth, albeit not angiogenesis, is restored in Rap1Bi{Delta}EC mice by depleting CD8+ T cells. Mechanistically, global transcriptome analysis indicated upregulation of the tumor cytokine, TNF-, -induced signaling and NF{kappa}B transcriptional activity in Rap1B-deficient ECs. Functionally, EC Rap1B deletion led to upregulation of NF{kappa}B activity and enhanced Cell Adhesion Molecules (CAMs) expression in TNF- stimulated ECs. Importantly, CAM expression was upregulated also in tumor ECs from Rap1Bi{Delta}EC mice, vs. controls. Significantly, deletion of Rap1B abrogated VEGF immunosuppressive downregulation of CAM expression, demonstrating that Rap1B is essential for VEGF-suppressive signaling. Thus, our studies identify a novel endothelial-endogenous mechanism underlying VEGF-dependent desensitization of EC to pro-inflammatory stimuli. Significantly, they identify EC Rap1 as a potential novel vascular target in cancer immunotherapy.

cancer biology↗

bioRxiv2021

Ptbp1 deletion does not induce glia-to-neuron conversion in adult mouse retina and brain.

Direct reprogramming of glia into neurons is a potentially promising approach for the replacement of neurons lost to injury or neurodegenerative disorders. Knockdown of the polypyrimidine tract-binding protein Ptbp1 has been recently reported to induce efficient conversion of retinal Muller glia and brain astrocytes into functional neurons. However, genetic analysis of Ptbp1 function in adult glia has not been conducted. Here, we use a combination of genetic lineage tracing, scRNA-Seq, and electrophysiological analysis to show that specific deletion of Ptbp1 in adult retinal Muller glia and brain astrocytes does not lead to any detectable level of glia-to-neuron conversion. Only a few changes in gene expression are observed in glia following Ptbp1 deletion, and glial identity is maintained. These findings highlight the importance of using genetic manipulation and lineage tracing methods in studying cell type conversion.

neuroscience↗

bioRxiv2021

AlphaFold-aware prediction of protein-DNA binding sites using graph transformer

Protein-DNA interactions play crucial roles in the biological systems, and identifying protein-DNA binding sites is the first step for mechanistic understanding of various biological activities (such as transcription and repair) and designing novel drugs. How to accurately identify DNA-binding residues from only protein sequence remains a challenging task. Currently, most existing sequence-based methods only consider contextual features of the sequential neighbors, which are limited to capture spatial information. Based on the recent breakthrough in protein structure prediction by AlphaFold2, we propose an accurate predictor, GraphSite, for identifying DNA-binding residues based on the structural models predicted by AlphaFold2. Here, we convert the binding site prediction problem into a graph node classification task and employ a transformer-based variant model to take the protein structural information into account. By leveraging predicted protein structures and graph transformer, GraphSite substantially improves over the latest sequence-based and structure-based methods. The algorithm is further confirmed on the independent test set of 181 proteins, where GraphSite surpasses the state-of-the-art structure-based method by 16.4% in AUPR and 11.2% in MCC, respectively. We provide the datasets, the predicted structures, and the source codes along with the pre-trained models of GraphSite at https://github.com/biomed-AI/GraphSite. The GraphSite web server is freely available at https://biomed.nscc-gz.cn/apps/GraphSite.

bioinformatics↗

bioRxiv2021

DeepANIS: Predicting antibody paratope from concatenated CDR sequences by integrating bidirectional long-short-term memory and transformer neural networks

MotivationAntibodies are a type of important biomolecules in the humoral immunity system, which can bind tightly to potential antigens with high affinity and specificity. An accurate identification of the paratope, the binding sites with antigens, is crucial for antibody mechanistic research and design. Although many methods have been developed for paratope prediction, further improvement of their accuracy is necessary. ResultsIn this study, we concatenated the sequences of Complementarity Determining Regions (CDRs) within a single antibody to better capture nonlocal interactions between different CDRs and loop type-specific features for improving paratope prediction. We further integrated BiLSTM and transformer networks to gain the dependencies among the residues within the concatenated CDR sequences and to increase the interpretability of the model. The new method called DeepANIS (Antibody Interacting Site prediction) outperforms other antibody paratope prediction methods compared. AvailabilityThe DeepANIS method is freely available as a webserver at https://biomed.nscc-gz.cn:9094/apps/DeepANIS and for download at https://github.com/HideInDust/DeepANIS Contactyangyd25@mail.sysu.edu.cn or zhouyq@szbl.ac.cn Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2021

The first crested duck genome reveals clues to genetic compensation and crest cushion formation

The Chinese crested (CC) duck is a unique indigenous waterfowl breed with a phenotypic crest trait that affects its high survival rate. Therefore, the CC duck is an ideal model to investigate the genetic compensation response to maintain genetic stability. In the present study, we first generated a chromosome-level genome of CC ducks. Comparative genomics revealed genes related to tissue repair, immune function, and tumors were under strong positive selection, which suggested that these adaptive changes might enhance cancer resistance and immune response to maintain the genetic stability of CC ducks. We sub-assembled a Chinese spot-billed duck genome and detected genome-assembled structure variants among three ducks. Functional analysis revealed that a large number of structural variants were related to the immune system, which strongly suggests the occurrence of genetic compensation in the anti-tumor and immune systems to further support the survival of CC ducks. Moreover, we confirmed that the CC duck originated from the mallard ducks. Finally, we revealed the physiological and genetic basis of crest traits and identified a causative mutation in TAS2R40 that leads to crest formation. Overall, the findings of this study provide new insights into the role of genetic compensation in adaptive evolution.

genomics↗

bioRxiv2021

Benchmarking supervised signature-scoring methods for single-cell RNA sequencing data in cancer

Quantifying the activity of gene expression signatures is common in analyses of single-cell RNA sequencing data. Methods originally developed for bulk samples are often used for this purpose without accounting for contextual differences between bulk and single-cell data. More broadly, these methods have not been benchmarked. Here we benchmark four such supervised methods, including single sample gene set enrichment analysis (ssGSEA), AUCell, Single Cell Signature Explorer (SCSE), and a new method we developed, Jointly Assessing Signature Mean and Inferring Enrichment (JASMINE). Using cancer as an example, we show cancer cells consistently express more genes than normal cells. This imbalance leads to bias in performance by bulk-sample-based ssGSEA in gold standard tests and down sampling experiments. In contrast, single-cell-based methods are less susceptible. Our results suggest caution should be exercised when using bulk-sample-based methods in single-cell data analyses, and cellular contexts should be taken into consideration when designing benchmarking strategies.

bioinformatics↗

bioRxiv2021

X-DPI: A structure-aware multi-modal deep learning model for drug-protein interactions prediction

MotivationIdentifying the drug-protein interactions (DPIs) is crucial in drug discovery, and a number of machine learning methods have been developed to predict DPIs. Existing methods usually use unrealistic datasets with hidden bias, which will limit the accuracy of virtual screening methods. Meanwhile, most DPIs prediction methods pay more attention to molecular representation but lack effective research on protein representation and high-level associations between different instances. To this end, we presented here a novel structure-aware multi-modal DPIs prediction model, X-DPI, performing on a curated industry-scale benchmark dataset. ResultsWe built a high-quality benchmark dataset named GalaxyDB for DPIs prediction. This industry-scale dataset along with an unbiased training procedure resulted in a more robust benchmark study. For informative protein representation, we constructed a structure-aware graph neural network method from the protein sequence by combining predicted contact maps and graph neural networks. Through further integration of structure-based representation and high-level pre-trained embeddings for molecules and proteins, our model captured more effectively the feature representation of the interactions between them. As a result, X-DPI outperformed state-of-the-art DPIs prediction methods and obtained 5.30% Mean Square Error (MSE) improved in the DAVIS dataset and 8.89% area under the curve (AUC) improved in GalaxyDB dataset. Moreover, our model is an interpretable model with the transformer-based interaction mechanism, which can accurately reveal the binding sites between molecule and protein.

bioinformatics↗

bioRxiv2021

High-speed multifocus phase imaging in thick tissue

Phase microscopy is widely used to image unstained biological samples. However, most phase imaging techniques require transmission geometries, making them unsuited for thick sample applications. Moreover, when applied to volumetric imaging, phase imaging generally requires large numbers of measurements, often making it too slow to capture live biological processes with fast 3D index-of-refraction variations. By combining oblique back-illumination microscopy and a z-splitter prism, we perform phase imaging that is both epi-mode and multifocus, enabling high-speed 3D phase imaging in thick, scattering tissues with a single camera. We demonstrate here 3D qualitative phase imaging of blood flow in chick embryos over a field of view of 546 x 546 x 137 m3 at speeds up to 47 Hz.

bioengineering↗

bioRxiv2021

A novel and accurate full-length HTT mouse model for Huntington's disease

Here we report the generation and characterization of a novel Huntingtons disease (HD) mouse model BAC226Q by using a bacterial artificial chromosome (BAC) system, expressing full-length human HTT with [~]226 CAG-CAA repeats and containing endogenous human HTT promoter and regulatory elements. BAC226Q recapitulated a full-spectrum of age-dependent and progressive HD-like phenotypes without unwanted and erroneous phenotypes. BAC226Q mice developed normally, and gradually exhibited HD-like mood and cognitive phenotypes at 2 months. From 3-4 months, BAC226Q mice showed robust progressive motor deficits. At 11 months, BAC226Q mice showed significant reduced life span, gradual weight loss and exhibit neuropathology including significant brain atrophy specific to striatum and cortex, striatal neuronal death, widespread huntingtin inclusions and reactive pathology. Therefore, the novel BAC226Q mouse accurately recapitulating robust, age-dependent, progressive HD-like phenotypes will be a valuable tool for studying disease mechanisms, identifying biomarkers and testing gene-targeting therapeutic approaches for HD.

neuroscience↗

bioRxiv2021

Extremely bright, near-IR emitting spontaneously blinking fluorophores enable ratiometric multicolor nanoscopy in live cells

New bright, photostable, emission-orthogonal fluorophores that blink without toxic additives are needed to enable multi-color, live-cell, single-molecule localization microscopy (SMLM), especially for experiments that demand ultra-high-resolution live imaging. Here we report the design, synthesis, and biological evaluation of Yale676sb, a photostable, near-IR emitting fluorophore that achieves these goals in the context of an exceptional quantum yield (0.59). When used alongside HMSiR, Yale676sb enables simultaneous, live-cell, two-color SMLM of two intracellular organelles (ER + mitochondria) with only a single laser and no chemical additives.

cell biology↗

bioRxiv2021

SynergyFinder Plus: towards a better interpretation and annotation of drug combination screening datasets

Combinatorial therapies have been recently proposed to improve the efficacy of anticancer treatment. The SynergyFinder R package is a software used to analyze pre-clinical drug combination datasets. Here, we report the major updates to the SynergyFinder R package for improved interpretation and annotation of drug combination screening results. Unlike the existing implementations, the updated SynergyFinder R package includes five main innovations. (1) We extend the mathematical models to higher-order drug combination data analysis and implement dimension reduction techniques for visualizing the synergy landscape. (2) We provide a statistical analysis of drug combination synergy and sensitivity with confidence intervals and P values. (3) We incorporate a synergy barometer to harmonize multiple synergy scoring methods to provide a consensus metric for synergy. (4) We evaluate drug combination synergy and sensitivity to provide an unbiased interpretation of the clinical potential. (5) We enable fast annotation of drugs and cell lines, including their chemical and target information. These annotations will improve the interpretation of the mechanisms of action of drug combinations. To facilitate the use of the R package within the drug discovery community, we also provide a web server at www.synergyfinderplus.org as a user-friendly interface to enable a more flexible and versatile analysis of drug combination data.

bioinformatics↗

bioRxiv2021

Integrated spatial genomics in tissues reveals invariant and cell type dependent nuclear architecture

Nuclear architecture in tissues can arise from cell-type specific organization of nuclear bodies, chromatin states and chromosome structures. However, the lack of genome-wide measurements to interrelate such modalities within single cells limits our overall understanding of nuclear architecture. Here, we demonstrate integrated spatial genomics in the mouse brain cortex, imaging thousands of genomic loci along with RNAs and subnuclear markers simultaneously in individual cells. We revealed chromatin fixed points, combined with cell-type specific organization of nuclear bodies, arrange the interchromosomal organization and radial positioning of chromosomes in diverse cell types. At the sub-megabase level, we uncovered a collection of single-cell chromosome domain structures, including those for the active and inactive X chromosomes. These results advance our understanding of single-cell nuclear architecture in complex tissues.

systems biology↗