Biology subjects

Yuan, Q.

Publications and source records attributed to Yuan, Q..

At least 55 records · Page 3Linked to original sources

bioRxiv2024

SHOOTMERISTEMLESS autoactivation: A prerequisite for fruit metamorphosis

In animals and plants, organ shape is primarily determined during primordium development by carefully coordinated growth and cell division1-3. Rare examples of post-primordial change in morphology (reshaping) exist that offer tractable systems to study mechanisms required for organ-shape determination and diversification. One such example is the heart-shape formation of Capsella fruits that occurs by reshaping the ovate spheroid gynoecium upon fertilization4. Here we use whole-organ live-imaging to show that dynamic changes in growth and cell division coupled with local maintenance of meristematic identity drives Capsella fruit-shape formation. At the molecular level, we reveal an auxin-induced mechanism ultimately descending on a single cis regulatory element to mediate morphological alteration. This element resides in the promoter of the Capsella rubella SHOOTMERISTEMLESS5 (CrSTM) gene. The CrSTM meristem identity factor positively regulates its own expression through binding to this element thereby providing a feed-forward loop at the position and time when protrusions emerge to form the heart. Independent evolution of the STM-binding element in STM promoters across Brassicaceae species correlates with those undergoing a gynoecium-to-fruit metamorphosis. Accordingly, genetic and phenotypic studies showed that the STM-binding element is required to facilitate the shape transition and reveals a conserved molecular mechanism for organ morphogenesis.

plant biology↗

bioRxiv2023

Structure basis for recognition of 26RFa by the pyroglutamylated RFamide peptide receptor

The neuropeptide 26RFa, a member of the RF-amide peptide family, activates the pyroglutamylated RF-amide peptide receptor (QRFPR), a class A GPCR. The 26RFa/QRFPR system plays critical roles in energy homeostasis, making QRFPR an attractive drug target for treating obesity, diabetes, and eating disorders. However, the lack of structural information has hindered our understanding of the peptide recognition and regulatory mechanism of QRFPR, impeding drug design efforts. In this study, we determined the cryo-EM structure of the Gq-coupled QRFPR bound to 26RFa. The structure reveals a unique assembly mode of the receptor extracellular regions and the peptide N-terminus and elucidates the recognition mechanism of the C-terminal heptapeptide of 26RFa within the transmembrane binding pocket of QRFPR. The study also clarifies the similarities and distinctions in the binding pattern of the RF-amide moiety in five RF-amide peptides and the RY-amide segment in neuropeptide Y. These findings deepen our understanding of the RF-amide peptides recognition, aiding in the rational design of drugs targeting QRFPR and other RF-amide receptors.

biophysics↗

bioRxiv2023

Genome-scale annotation of protein binding sites via language model and geometric deep learning

Revealing protein binding sites with other molecules, such as nucleic acids, peptides, or small ligands, sheds light on disease mechanism elucidation and novel drug design. With the explosive growth of proteins in sequence databases, how to accurately and efficiently identify these binding sites from sequences becomes essential. However, current methods mostly rely on expensive multiple sequence alignments or experimental protein structures, limiting their genome-scale applications. Besides, these methods havent fully explored the geometry of the protein structures. Here, we propose GPSite, a multi-task network for simultaneously predicting binding residues of DNA, RNA, peptide, protein, ATP, HEM, and metal ions on proteins. GPSite was trained on informative sequence embeddings and predicted structures from protein language models, while comprehensively extracting residual and relational geometric contexts in an end-to-end manner. Experiments demonstrate that GPSite substantially surpasses state-of-the-art sequence-based and structure-based approaches on various benchmark datasets, even when the structures are not well-predicted. The low computational cost of GPSite enables rapid genome-scale binding residue annotations for over 568,000 sequences, providing opportunities to unveil unexplored associations of binding sites with molecular functions, biological processes, and genetic variants. The GPSite webserver and annotation database can be freely accessed at https://bio-web1.nscc-gz.cn/app/GPSite.

bioinformatics↗

bioRxiv2023

Mechanisms of ligand recognition and activation of melanin-concentrating hormone receptors

Melanin-concentrating hormone (MCH) is a cyclic neuropeptide that regulates food intake, energy balance, and other physiological functions by stimulating MCHR1 and MCHR2 receptors, both of which are class A G protein-coupled receptors. MCHR1 can couple with multiple G-proteins, including Gi/o, Gq/11, and Gs, while MCHR2 only couple to Gq/11. Here we present cryo-electron microscopy structures of MCH-activated MCHR1 with Gi and MCH-activated MCHR2 with Gq complexes, at the global resolutions of 3.01 [A] and 2.40 [A], respectively. These structures reveal that MCH adopts a consistent cysteine-mediated hairpin loop configuration in both receptors. A central arginine from the LGRVY core motif between the two cysteines of MCH penetrates deeply into the transmembrane pocket, triggering receptor activation. Integrated with mutational and functional insights, our findings elucidate the molecular underpinnings of ligand recognition and MCH receptor activation, offering a structured foundation for targeted drug design.

molecular biology↗

bioRxiv2023

Polysaccharides from Balanophora harlandii Hook: Isolation, Characterization, and Anti-Inflammation Activities

Balanophora harlandii Hook (B. harlandii), a folk medicine, has been traditionally employed to treat traumatic bleeding, gastroenteritis, icteric hepatitis, hemorrhoids, and other conditions. In this work, polysaccharides with anti-inflammatory effects were extracted and purified from B. harlandii. The extraction conditions were optimized, and the properties of one purified neutral fraction, denoted as BHPs-W-S3, were analyzed. Gel permeation chromatography (GPC) was carried out to measure the molecular weight. The structure of BHPs-W-S3 was assessed based on monosaccharide composition analysis, Fourier transform infrared (FT-IR) spectroscopy, methylation analysis, and nuclear magnetic resonance (NMR) spectroscopy. BHPs-W-S3 has a molecular weight of 14.1 kDa, and its three main monosaccharides are glucose, galactose, and mannose with a molar ratio of 6.4:1.7:1.1. Its main chain consists of [->]6)--D-Glcp-(1[->], [->]4,6)--D-Glcp-(1[->], [->]6)-{beta}-D-Galp-(1[->], [->]3,6)-{beta}-D-Galp-(1[->], and it has branch chains at the O-4 and/or O-3 positions. In addition, in vitro experiments show that the polysaccharides from B. harlandi can decrease the phosphorylation level of p65 and IKB- in LPS-induced RAW264.7 cells to reduce the expression of the pro-inflammatory genes such as TNF-, IL-6, and IL-1{beta}. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=117 SRC="FIGDIR/small/559774v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@17ccfeforg.highwire.dtl.DTLVardef@137726borg.highwire.dtl.DTLVardef@139100dorg.highwire.dtl.DTLVardef@14cce50_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2023

PDBe CCDUtils: an RDKit-based toolkit for handling and analysing small molecules in the Protein Data Bank

While the Protein Data Bank (PDB) contains a wealth of structural information on ligands bound to macromolecules, their analysis can be challenging due to the large amount and diversity of data. Here, we present PDBe CCDUtils, a versatile toolkit for processing and analysing small molecules from the PDB in PDBx/mmCIF format. PDBe CCDUtils provides streamlined access to all the metadata for small molecules in the PDB and offers a set of convenient methods to compute various properties using RDKit, such as 2D depictions, 3D conformers, physicochemical properties, scaffolds, common fragments and cross-references to small molecule databases using UniChem. The toolkit also provides methods for identifying all the covalently attached chemical components in a macromolecular structure and calculating similarity among small molecules. By providing a broad range of functionality, PDBe CCDUtils caters to the needs of researchers in cheminformatics, structural biology, bioinformatics and computational chemistry.

bioinformatics↗

bioRxiv2023

Continuous lifelong learning for modeling of gene regulation from single cell multiome data by leveraging atlas-scale external data

Accurate context-specific Gene Regulatory Networks (GRNs) inference from genomics data is a crucial task in computational biology. However, existing methods face limitations, such as reliance on gene expression data alone, lower resolution from bulk data, and data scarcity for specific cellular systems. Despite recent technological advancements, including single-cell sequencing and the integration of ATAC-seq and RNA-seq data, learning such complex mechanisms from limited independent data points still presents a daunting challenge, impeding GRN inference accuracy. To overcome this challenge, we present LINGER (LIfelong neural Network for GEne Regulation), a novel deep learning-based method to infer GRNs from single-cell multiome data with paired gene expression and chromatin accessibility data from the same cell. LINGER incorporates both 1) atlas-scale external bulk data across diverse cellular contexts and 2) the knowledge of transcription factor (TF) motif matching to cis-regulatory elements as a manifold regularization to address the challenge of limited data and extensive parameter space in GRN inference. Our results demonstrate that LINGER achieves 2-3 fold higher accuracy over existing methods. LINGER reveals a complex regulatory landscape of genome-wide association studies, enabling enhanced interpretation of disease-associated variants and genes. Additionally, following the GRN inference from a reference sc-multiome data, LINGER allows for the estimation of TF activity solely from bulk or single-cell gene expression data, leveraging the abundance of available gene expression data to identify driver regulators from case-control studies. Overall, LINGER provides a comprehensive tool for robust gene regulation inference from genomics data, empowering deeper insights into cellular mechanisms.

bioinformatics↗

bioRxiv2023

Accurately identifying nucleic-acid-binding sites through geometric graph learning on language model predicted structures

The interactions between nucleic acids and proteins are important in diverse biological processes. The high-quality prediction of nucleic-acid-binding sites continues to pose a significant challenge. Presently, the predictive efficacy of sequence-based methods is constrained by their exclusive consideration of sequence context information, whereas structure-based methods are unsuitable for proteins lacKing Known tertiary structures. Though protein structures predicted by AlphaFold2 could be used, the extensive computing requirement of AlphaFold2 hinders its use for genome-wide applications. Based on the recent breaKthrough of ESMFold for fast prediction of protein structures, we have developed GLMSite, which accurately identifies DNA and RNA-binding sites using geometric graph learning on ESMFold predicted structures. Here, the predicted protein structures are employed to construct protein structural graph with residues as nodes and spatially neighboring residue pairs for edges. The node representations are further enhanced through the pre-trained language model ProtTrans. The networK was trained using a geometric vector perceptron, and the geometric embeddings were subsequently fed into a common networK to acquire common binding characteristics. Then two fully connected layers were employed to learn specific binding patterns for DNA and RNA, respectively. Through comprehensive tests on DNA/RNA benchmarK datasets, GLMSite was shown to surpass the latest sequence-based methods and be comparable with structure-based methods. Moreover, the prediction was shown useful for the inference of nucleic-acid-binding proteins, demonstrating its potential for protein function discovery. The datasets, codes, together with trained models are available at https://github.com/biomed-AI/nucleic-acid-binding.

bioinformatics↗

bioRxiv2023

A conserved and druggable pocket in class B G protein coupled receptors for orally active small molecule agonists

Class B G protein-coupled receptors (GPCRs), including glucagon-like receptor 1 (GLP-1R) and parathyroid hormone receptor 1 (PTH1R), are peptide hormone receptors and important drug targets. Injectable peptide drugs targeting class B GPCRs have been developed for the treatment of many diseases, including type 2 diabetes, obesity, and osteoporosis, but orally available small molecule drugs are hotly pursued in the field, especially small molecule agonists of GLP-1R and PTH1R. Here we report the first high-resolution structure of the human PTH1R in complex with the stimulatory G protein (Gs) and a small molecule agonist, PCO371, which reveals an unexpected binding mode of PCO371 at the interface of PTH1R and Gs. The binding site of PCO371 is totally different from all binding sites previously reported for small molecules or peptide ligands in GPCRs. Residues that make up the PCO371 binding pocket are mostly conserved in class B GPCRs and a single mutation in PTH type 2 receptor (PTH2R) and two residue mutations in GLP-1R convert these receptors to respond to PCO371 activation. Functional assays reveal that PCO371 is a G-protein biased agonist that is defective in promoting PTH1R-mediated arrestin signaling. Together, these results uncover a distinct binding site for designing small molecule agonists for PTH1R and possible other members of class B GPCRs and define a receptor conformation that is only specific for G protein activation but not arrestin signaling. These insights should facilitate the design of distinct types of class B GPCR small molecule agonists for various therapeutic indications.

biochemistry↗

bioRxiv2023

Design, evaluation and implementation of synthetic isopentyldiol pathways in Escherichia coli

Isopentyldiol (IPDO) is an important raw material in cosmetic industry. So far IPDO is exclusively produced through chemical synthesis. Growing interest in natural personal care products has inspired the quest to develop a bio-based process. We previously reported a biosynthetic route that produces IPDO via extending leucine catabolism (route A), the efficiency of which, however, is not satisfactory. To address this issue, we computational designed a novel non-natural IPDO synthesis pathway (Route B) using RetroPath RL, the state of art tool for bio-retrosynthesis based on Artificial Intelligence methods. We compared this new pathway with route A and another two intuitively designed routes for IPDO biosynthesis from various aspects. Route B, which exhibits the highest thermodynamic driving force, least non-native reaction steps and lowest energy requirements appeared to hold the greatest potential for IPDO production. All three newly designed routes were then implemented in E. coli BL21(DE3) strain. Results show that the computationally designed route B can produce 2.2 mg/L IPDO from glucose, whereas no IPDO production from routes C and D. These results highlight the importance and usefulness of in silico design and comprehensive evaluation of the potential efficiencies of candidate pathways in constructing novel non-natural pathways for the production of biochemicals.

bioengineering↗

bioRxiv2023

Lack of Fzd6 in Ciliated Cells Suppresses Ferroptotic Pulmonary Alveolar Cell Death Induced by LPS and Coronavirus

Pulmonary inflammation compromises lung barrier function and underlies many lung diseases including acute lung injury and acute respiratory distress syndrome (ARDS). However, mechanisms by which lung cells respond to the damage caused by the inflammatory insults are not completely understood. Here we show that Fzd6-deficiency in Foxj1+ ciliated cells reduces pulmonary permeability, lipid peroxidation, and alveolar cell death accompanied with an increase in alveolar number in lungs insulted by LPS or a mouse coronavirus. Single-cell RNA sequencing of lung cells indicates that the lack of Fzd6, which is expressed in Foxj1+ cells, increases expression of the aldo-keto reductase Akr1b8 in Foxj1+ cells. Intratracheal administration of the Akr1b8 protein phenocopies Fzd6-deficient lung phenotypes. In addition, ferroptosis inhibitors also phenocopy Fzd6-deficient lung phenotypes and exert no further effects in Fzd6-deficient lungs. These results reveal an important mechanism for protection of alveolar cells from ferroptotic death during pulmonary inflammation by Foxj1+ ciliated cells via paracrine action of Akr1b8.

pathology↗

bioRxiv2023

Structural basis of peptide recognition and activation of endothelin receptors

Endothelin system comprises three endogenous 21-amino-acid peptide ligands endothelin-1, -2, and -3 (ET-1/2/3), and two G protein-coupled receptor (GPCR) subtypes--endothelin receptor A (ETAR) and B (ETBR). Since ET-1, the first endothelin, was identified in 1988 as one of the most potent endothelial cell-derived vasoconstrictor peptides with long-lasting actions, the endothelin system has attracted extensive attention due to its critical role in vasoregulation and close relevance in cardiovascular-related diseases. Here we present three cryo-electron microscopy structures of ETAR and ETBR bound to ET-1 and ETBR bound to the selective peptide IRL1620. These structures reveal a highly conserved recognition mode of ET-1 and characterize the ligand selectivity by ETRs. They also present several conformation features of the active ETRs, thus revealing a specific activation mechanism. Together, these findings deepen our understanding of endothelin system regulation and offer an opportunity to design selective drugs targeting specific ETR subtypes.

molecular biology↗

bioRxiv2023

Illumination of the complement receptors C3aR and C5aR signaling by anaphylatoxins

The complement receptors C3aR and C5aR, whose signaling are selectively activated by anaphylatoxins C3a and C5a, are important regulators of both innate and adaptive immune responses. Dysregulations of C3aR and C5aR signaling lead to multiple inflammatory disorders, including sepsis, asthma, and acute respiratory distress syndrome (ARDS). The mechanism underlying endogenous anaphylatoxin recognition and activation of C3aR and C5aR remains elusive. Here we reported the structures of C3a-bound C3aR and C5a-bound C5aR1 as well as an apo C3aR structure. These structures, combined with mutagenesis analysis, reveal a conserved recognition pattern of anaphylatoxins to the complement receptors that is different from chemokine receptors, unique pocket topologies of C3aR and C5aR1 that mediate ligand selectivity, and a common mechanism of receptor activation. These results provide crucial insights into the molecular understandings of C3aR and C5aR1 signaling and structural templates for rational drug design for treating inflammation disorders.

biochemistry↗

bioRxiv2023

LDL Receptor-Related Protein 5 is a Selective Transporter for Unesterified Polyunsaturated Fatty Acids

Polyunsaturated fatty acids (PUFAs), which cannot be synthesized by animals and must be supplied from the diet, have been strongly associated with human health. However, the mechanisms for their accretion and actions remain poorly understood. Here, we show that LDL receptor-related protein 5 (LRP5), but not its homolog LRP6, selectively transports unesterified PUFAs into a number of cell types. The LDLa ligand-binding repeats of LRP5 directly bind to PUFAs and are required for PUFA transport. LRP5 transports PUFAs via internalization to intracellular compartments including lysosomes, and n-3 PUFAs depends on this transport mechanism to inhibit mTORC1. This LRP5-mediated PUFA transport mechanism suppresses neutrophil extracellular trap formation in neutrophils and protects mice from myocardial injury during ischemia-reperfusion. Thus, this study reveals a previously unknown and biologically important mechanism for PUFA transport and mTORC1 regulation.

cell biology↗

bioRxiv2022

Identifying B-cell epitopes using AlphaFold2 predicted structures and pretrained language model

MotivationIdentifying the B-cell epitopes is an essential step for guiding rational vaccine development and immunotherapies. Due to experimental approaches being expensive and time-consuming, many computational methods have been designed to assist B-cell epitope prediction. However, existing sequence-based methods have limited performance since they only use contextual features of the sequential neighbors while neglecting structural information. ResultsBased on the recent breakthrough of AlphaFold2 in protein structure prediction, we propose GraphBepi, a novel graph-based model for accurate B-cell epitope prediction. GraphBepi first generates the effective information sequence representations and protein structures from antigen sequences through the pretrained language model and AlphaFold2, respectively. GraphBepi then applies the edge-enhanced deep graph neural network (EGNN) to capture the spatial information from predicted protein structures and leverages the bidirectional long short-term memory neural networks (BiLSTM) to capture long-range dependencies from sequences. The low-dimensional representation learned by EGNN and BiLSTM is then combined to predict B-cell epitopes through a multilayer perceptron. Through comprehensive tests on the curated epitope dataset, GraphBepi was shown to outperform the state-of-the-art methods by more than 5.5% and 44.0% in terms of AUC and AUPR, respectively. We also provide the GraphBepi web server that is freely available at https://biomed.nscc-gz.cn/apps/GraphBepi. AvailabilityThe datasets, pre-computed features, source codes, and the pretrained model of GraphBepi are available at https://github.com/biomed-AI/GraphBepi. Contactyangyd25@mail.sysu.edu.cn or gaojz@nankai.edu.cn

bioinformatics↗

bioRxiv2022

Fast and accurate protein function prediction from sequence through pretrained language model and homology-based label diffusion

Protein function prediction is an essential task in bioinformatics which benefits disease mechanism elucidation and drug target discovery. Due to the explosive growth of proteins in sequence databases and the diversity of their functions, it remains challenging to fast and accurately predict protein functions from sequences alone. Although many methods have integrated protein structures, biological networks or literature information to improve performance, these extra features are often unavailable for most proteins. Here, we propose SPROF-GO, a Sequence-based alignment-free PROtein Function predictor which leverages a pretrained language model to efficiently extract informative sequence embeddings and employs self-attention pooling to focus on important residues. The prediction is further advanced by exploiting the homology information and accounting for the overlapping communities of proteins with related functions through the label diffusion algorithm. SPROF-GO was shown to surpass state-of-the-art sequence-based and even network-based approaches by more than 14.5%, 27.3% and 10.1% in AUPR on the three sub-ontology test sets, respectively. Our method was also demonstrated to generalize well on non-homologous proteins and unseen species. Finally, visualization based on the attention mechanism indicated that SPROF-GO is able to capture sequence domains useful for function prediction. Key pointsO_LISPROF-GO is a sequence-based protein function predictor which leverages a pretrained language model to efficiently extract informative sequence embeddings, thus bypassing expensive database searches. C_LIO_LISPROF-GO employs self-attention pooling to capture sequence domains useful for function prediction and provide interpretability. C_LIO_LISPROF-GO applies hierarchical learning strategy to produce consistent predictions and label diffusion to exploit the homology information. C_LIO_LISPROF-GO is accurate and robust, with better performance than state-of-the-art sequence-based and even network-based approaches, and great generalization ability on non-homologous proteins and unseen species C_LI

bioinformatics↗

bioRxiv2022

SAYSD1 senses UFMylated ribosome to safeguard co-translational protein translocation at the endoplasmic reticulum

Translocon clogging at the endoplasmic reticulum (ER) as a result of translation stalling triggers ribosome UFMylation, activating a Translocation-Associated Quality Control (TAQC) mechanism that degrades clogged substrates. How cells sense ribosome UFMylation to initiate TAQC is unclear. Here we use a genome-wide CRISPR/Cas9 screen to identify an uncharacterized membrane protein named SAYSD1 that facilitates TAQC. SAYSD1 associates with the Sec61 translocon, and also recognizes both ribosome and UFM1 directly, engaging a stalled nascent chain to ensure its transport via the TRAPP complex to lysosomes for degradation. Like UFM1 deficiency, SAYSD1 depletion causes the accumulation of translocation-stalled proteins at the ER and triggers ER stress. Importantly, disrupting UFM1- and SAYSD1-dependent TAQC in Drosophila leads to intracellular accumulation of translocation-stalled collagens, defective collagen deposition, abnormal basement membranes, and reduced stress tolerance. Together, our data support a model that SAYSD1 acts as a UFM1 sensor that collaborates with ribosome UFMylation at the site of clogged translocon, safeguarding ER homeostasis during animal development.

cell biology↗

bioRxiv2022

Disruption of fos causes craniofacial anomalies in developing zebrafish

Craniofacial development is a complex and tightly regulated process and disruptions can lead to structural birth defects, the most common being nonsyndromic cleft lip and palate (NSCLP). Previously, we identified FOS as a candidate regulator of NSCLP through family-based association studies, yet its specific contributions to oral and palatal formation are poorly understood. This study investigated the role of fos during zebrafish craniofacial development through genetic disruption and knockdown approaches. Fos was expressed in the periderm, olfactory epithelium and other cell populations in the head. Genetic perturbation of fos produced an abnormal craniofacial phenotype with a hypoplastic oral cavity that showed significant changes in midface dimensions by quantitative facial morphometric analysis. Loss and knockdown of fos caused increased cell apoptosis in the head, followed by a significant reduction in cranial neural crest cells (CNCCs) populating the upper and lower jaws. These changes resulted in abnormalities of cartilage, bone and pharyngeal teeth formation. Periderm cells surrounding the oral cavity showed altered morphology and a subset of cells in the upper and lower lip showed disrupted Wnt/{beta}-catenin activation, consistent with modified inductive interactions between mesenchymal and epithelial cells. Taken together, these findings demonstrate that perturbation of fos has detrimental effects on oral epithelial and CNCC-derived tissues suggesting that it plays a critical role in zebrafish craniofacial development and a potential role in NSCLP. Summary statementPerturbation of fos, a candidate gene associated with nonsyndromic cleft lip and palate in humans, causes a distinctive orofacial phenotype in zebrafish as a result of abnormal development of craniofacial tissues. Disruption of fos in the oral epithelial, cranial neural crest and Wnt responsive cell populations around the oral cavity causes anomalies that suggest a potential role in the etiology of NSCLP.

developmental biology↗