Biology subjects

Mu, Y.

Publications and source records attributed to Mu, Y..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

GABAAR-PPT1 palmitoylation homeostasis controls synaptic transmission and circuitry oscillation in CLN1 disease

CLN1 disease, also called infantile neuronal ceroid lipofuscinosis, is a fatal neurodegenerative disease caused by mutations in the CLN1 gene encoding palmitoyl protein thioesterase 1 (PPT1). To identify depalmitoylation substrate of PPT1 is crucial to understand CLN1 disease. In this study, we found that PPT1 depalmitoylates GABAAR 1 subunit at Cystein-260, while binding to Cystein-165 and -179. Mutations of PPT1 or its GABAAR 1 subunit binding site result in enhanced inhibitory synaptic transmission, strengthened and oscillation but disrupted phase coupling in CA1 region and impaired learning and memory in 1- to 2-months-old PPT1-deficient and Gabra1em1 mice. Our study highlights the critical role of PPT1 in maintaining GABAAR palmitoylation homeostasis and reveals a previously unknown molecular pathway in PPT1 induced diseases.

neuroscience↗

bioRxiv2024

ProAffinity-GNN: A Novel Approach to Structure-based Protein-Protein Binding Affinity Prediction via a Curated Dataset and Graph Neural Networks

Protein-protein interactions (PPIs) are crucial for understanding biological processes and disease mechanisms, contributing significantly to advances in protein engineering and drug discovery. The accurate determination of binding affinities, essential for decoding PPIs, faces challenges due to the substantial time and financial costs involved in experimental and theoretical methods. This situation underscores the urgent need for more effective and precise methodologies for predicting binding affinity. Despite the abundance of research on PPI modeling, the field of quantitative binding affinity prediction remains underexplored, mainly due to a lack of comprehensive data. This study seeks to address these needs by manually curating pairwise interaction labels on all available 3D structures of proteins complexes, with experimentally determined binding affinities, creating the largest dataset for structure-based pairwise protein interaction with binding affinity to date. Subsequently, we introduce "ProAffinity-GNN", a novel deep learning framework using protein language model and graph neural network (GNN) to improve the accuracy of prediction of structure-based protein-protein binding affinities. The evaluation results across several benchmark test sets demonstrate that ProAffinity-GNN not only outperforms existing models in terms of accuracy but also shows strong generalization capabilities.

bioinformatics↗

bioRxiv2024

Highly Dynamic and Sensitive NEMOer Calcium Indicators for Imaging ER Calcium Signals in Excitable Cells

Endoplasmic/sarcoplasmic reticulum (ER/SR) sits at the heart of the calcium (Ca2+) signaling machinery, yet current genetically encoded Ca2+ indicators (GECIs) lack the ability to detect elementary Ca2+ release events from ER/SR, particularly in muscle cells. Here we report a set of organellar GECIs, termed NEMOer, to efficiently capture ER Ca2+ dynamics with increased sensitivity and responsiveness. Compared to G-CEPIA1er, NEMOer indicators exhibit dynamic ranges that are an order of magnitude larger, which enables up to 5-fold more sensitive detection of Ca2+ oscillation in both non-excitable and excitable cells. The ratiometric version further allows super-resolution monitoring of local ER Ca2+ homeostasis and dynamics. Notably, the NEMOer-f variant enabled the inaugural detection of Ca2+ blinks, elementary Ca2+ releasing signals from the SR of cardiomyocytes, as well as in vivo spontaneous SR Ca2+ releases in zebrafish. In summary, the highly dynamic NEMOer sensors expand the repertoire of organellar Ca2+ sensors that allow real-time monitoring of intricate Ca2+ dynamics and homeostasis in live cells with high spatiotemporal resolution.

synthetic biology↗

bioRxiv2024

Unveiling Tryptophan Dynamics and Functions Across Model Organisms via Quantitative Imaging

Tryptophan is an essential amino acid involved in many cellular processes in vertebrates. Systemic and quantitative measurement of tryptophan is crucial for evaluating its essential role as a precursor of serotonin and kynurenine, key neuromodulators affecting neural and immune functions. We utilized a robust and highly responsive ratiometric indicator for tryptophan (GRIT) to quantitatively measure tryptophan dynamics in bacteria, mitochondria of mammalian cell cultures, and human serum. At the cellular scale, these analyses uncovered differences in tryptophan dynamics across cell types and organelles. At the whole-organism scale, we revealed that inflammation-induced tryptophan concentration increases in zebrafish brain led to elevated tryptophan metabolites serotonin and kynurenine levels, which is associated with the prolonged sleep duration. The reduction of zebrafish plasma tryptophan was mirrored in patients with inflammation symptoms and could serve as a biochemical marker of inflammation. In summary, this study introduces GRIT as a powerful method for studying tryptophan metabolism and functions across scales and species and suggests that tryptophan metabolic processes link the immune response and animal behavior.

biochemistry↗

bioRxiv2024

Systematic benchmarking of deep-learning methods for tertiary RNA structure prediction

The 3D structure of RNA critically influences its functionality, and understanding this structure is vital for deciphering RNA biology. Experimental methods for determining RNA structures are labour-intensive, expensive, and time-consuming. Computational approaches have emerged as valuable tools, leveraging physics-based-principles and machine learning to predict RNA structures rapidly. Despite advancements, the accuracy of computational methods remains modest, especially when compared to protein structure prediction. Deep learning methods, while successful in protein structure prediction, have shown some promise for RNA structure prediction as well but face unique challenges. This study systematically benchmarks state-of-the-art deep learning methods for RNA structure prediction across diverse datasets. Our aim is to identify factors influencing performance variation, such as RNA family diversity, sequence length, RNA type, multiple sequence alignment (MSA) quality, and deep learning model architecture. We show that generally ML-based methods perform much better than non-ML methods on most RNA targets, although the performance difference isnt substantial when working with unseen novel or synthetic RNAs. The quality of the MSA and secondary structure prediction both play an important role and most methods arent able to predict non-Watson-Crick pairs in the RNAs. Overall, DeepFoldRNA has the best prediction followed by DRFold as the second best method. Finally, we also suggest possible mitigations to improve the quality of the prediction for future method development.

bioinformatics↗

bioRxiv2023

Benchmarking Reverse Docking through AlphaFold2 Human Proteome

Predicting binding of a small molecule to the human proteome by reverse docking methods, we can predict the target interactions of drug compounds in the human body, as well as further evaluate their potential off-target effects or toxic side effects. In this study, we constructed 11 pipelines to evaluate and benchmark thoroughly the predictive capabilities of these reverse docking pipelines. The pipelines were built using site prediction tools (PointSite and SiteMap) based on the AF2 human proteome, docking programs (Glide and AutoDock Vina), and scoring functions (Glide, Autodock Vina, RTMScore, DeepRMSD, OnionNet-SFCT). The results show that pipeline glide_sfct (PS) exhibited the best target prediction ability and successfully predicted the similar proteins of native targets. This finding provides important clues for understanding the promiscuity between the drug ligand and the whole human proteome. In general, our study has the potential to increase the success rate and reduce the development timeline of drug discovery, thereby saving costs.

bioinformatics↗

bioRxiv2023

A New Paradigm for Applying Deep Learning to Protein-Ligand Interaction Prediction

Protein-ligand interaction prediction poses a significant challenge in the field of drug design. Numerous machine learning and deep learning models have been developed to identify the most accurate docking poses of ligands and active compounds against specific targets. However, the current models often suffer from inadequate accuracy and lack practical physical significance in their scoring systems. In this research paper, we introduce IGModel, a novel approach that leverages the geometric information of protein-ligand complexes as input for predicting the root mean square deviation (RMSD) of docking poses and the binding strength (the negative value of the logrithm of binding affinity, pKd) with the same prediction framework. By incorporating the geometric information, IGModel ensures that its scores carry intuitive meaning. The performance of IGModel has been extensively evaluated on various docking power test sets, including the CASF-2016 benchmark, PDBbind-CrossDocked-Core, and DISCO set, consistently achieving state-of-theart accuracies. Furthermore, we assess IGModels generalization ability and robustness by evaluating it on unbiased test sets and sets containing target structures generated by AlphaFold2. The exceptional performance of IGModel on these sets demonstrates its efficacy. Additionally, we visualize the latent space of protein-ligand interactions encoded by IGModel and conduct interpretability analysis, providing valuable insights. This study presents a novel framework for deep learning-based prediction of protein-ligand interactions, contributing to the advancement of this field. O_TEXTBOXKey MessagesO_LIWe introduce the first framework for simultaneously predicting the RMSD of the ligand docking pose and its binding strength to the target. C_LIO_LIIGModel can effectively improve the accuracy of identifying the near-native binding poses of the ligands, and can still outperform most baseline models in scoring power, ranking power and screening power tasks. C_LIO_LIIGModel is still ahead of other state-of-the-art models in the unbiased data set and the target structure predicted by AlphaFold2, proving its excellent generalization ability. C_LIO_LILatent space provided by IGModel learns the physical interactions, thus indicating the robustness of the model. C_LI C_TEXTBOX

bioinformatics↗

bioRxiv2023

A resource for probing influences of perinatal factors on neurodevelopment in 3-10-years-old Chinese children

Adverse perinatal factors can disrupt the normal development of the brain, with potential long-term impacts on childrens overall development. Currently, the neuropathological mechanisms by which these factors lead to various neurodevelopmental disorders (NDDs) remain largely unknown. An open resource that integrate perinatal factors with brain and mental health development is essential for investigating NDD-related aetiology. In this Data Descriptor, we introduce a multicentre database containing information on perinatal factors that influence childrens brain-mind development, namely, periCBD, that combines neuroimaging and behavioural phenotypes with perinatal factors associated with a high incidence of NDDs at county/region/central district hospitals. PeriCBD was designed to establish a platform for the investigation of individual differences in brain-mind development among children aged 3-10 years are associated with perinatal factors. Ultimately, our goal was to develop an early prediction and screening model for NDDs that leverages normative data to facilitate NDD aetiology research. Herein, we provide a systematic overview of the data acquisition/cleaning/quality control/sharing, processes of periCBD and present preliminary brain-mind associations.

neuroscience↗

bioRxiv2023

All-optical interrogation of brain-wide activity in freely swimming larval zebrafish

We introduce an all-optical technique that enables volumetric imaging of brain-wide calcium activity and targeted optogenetic stimulation of specific brain regions in freely swimming larval zebrafish. The system consists of three main components: a 3D tracking module, a dual color fluorescence imaging module, and a real-time activity manipulation module. Our approach uses a sensitive genetically encoded calcium indicator in combination with a long Stokes shift red fluorescence protein as a reference channel, allowing the extraction of Ca2+ activity from signals contaminated by motion artifacts. The method also incorporates rapid 3D image reconstruction and registration, facilitating real-time selective optogenetic stimulation of different regions of the brain. By demonstrating that selective light activation of the midbrain regions in larval zebrafish could reliably trigger biased turning behavior and changes of brain-wide neural activity, we present a valuable tool for investigating the causal relationship between distributed neural circuit dynamics and naturalistic behavior. HighlightsO_LIWe develop an all-optical technique that enables simultaneous whole brain imaging and optogenetic manipulation of selective brain regions in freely behaving larval zebrafish. C_LIO_LIA combination of a genetically encoded calcium indicator and a long Stokes-shift red fluorescence protein, together with the adaptive filter algorithm, enables us to reliably distinguish calcium activity from motion-induced signal contamination. C_LIO_LIRapid 3D image reconstruction and registration enables real-time targeted optogenetic stimulation of distinct brain regions in a freely swimming larval zebrafish. C_LI

neuroscience↗

bioRxiv2023

A Recombinant Antibody For Tracking Murine Gammaherpesvirus 68 Uracil DNA Glycosylase Expression.

Antibodies are powerful tools to detect expressed proteins. However off-target recognition can confound their use. Therefore, careful characterization is needed to validate specificity in distinct applications. Here we report the sequence and characterization of a mouse recombinant antibody that specifically detects ORF46 of murine gammaherpesvirus 68 (MHV68). This ORF encodes the viral uracil DNA glycosylase (vUNG). The antibody does not recognize murine uracil DNA glycosylase and is useful in detecting vUNG expressed in virally infected cells. It can detect expressed vUNG in cells via immunostaining and microscopy or flow cytometry analysis. The antibody can detect vUNG from lysates of expressing cells via immunoblot under native conditions but not denaturing conditions. This suggests it recognizes a confirmational based epitope. Altogether this manuscript describes the utility of the anti-vUNG antibody and suitability for use in studies of MHV68 infected cells.

molecular biology↗

bioRxiv2023

Characterization of a mouse model of ICF syndrome reveals enhanced CD19 activation in inducing hypogammaglobulinemia

Immunodeficiency, centromeric instability and facial anomalies (ICF) syndrome is a rare autosomal recessive disorder characterized by DNA hypomethylation and antibody deficiency. It is caused by mutations in DNMT3B, ZBTB24, CDCA7 or HELLS. While progress has been made in elucidating the roles of these genes in regulating DNA methylation, little is known about the pathogenesis of the life-threatening hypogammaglobulinemia phenotype. Here we show that mice deficient for Zbtb24 in the hematopoietic lineage recapitulate major clinical features of patients with ICF syndrome. Specifically, Vav-Cre-mediated ablation of Zbtb24 does not affect lymphocyte development but results in reduced plasma cells and low levels of IgM, IgG1 and IgA. Zbtb24-deficient mice are hyper- and hypo-responsive to T-dependent and Tindependent type 2 antigens, respectively, and marginal zone B cell activation is impaired. B cells from Zbtb24-deficient mice display elevated CD19 phosphorylation. Heterozygous disruption of Cd19 can revert the hypogammaglobulinemia phenotype in these mice. Mechanistically, Il5ra (interleukin-5 receptor subunit alpha) is derepressed in Zbtb24-deficient B cells, and elevated IL-5 signaling enhances CD19 phosphorylation. Our results reveal a novel link between IL-5 signaling and CD19 activation and suggest that abnormal CD19 activity contributes to immunodeficiency in ICF syndrome. SIGNIFICANCE STATEMENTICF syndrome is a rare immunodeficiency disorder first reported in the 1970s. The lack of appropriate animal models has hindered the investigation of the pathogenesis of antibody deficiency, the major cause of death in ICF syndrome. Here we show that, in mice, disruption of Zbtb24, one of the ICF-related genes, in the hematopoietic lineage results in low levels of immunoglobulins. Characterization of these mice reveals abnormal B cell activation due to elevated CD19 phosphorylation. Mechanistically, Il5ra (interleukin-5 receptor subunit alpha) is derepressed in Zbtb24-deficient B cells, and increased IL-5 signaling enhances CD19 phosphorylation.

immunology↗

bioRxiv2023

Variational graph encoders: a surprisingly effective generalist algorithm for holistic computer-aided drug design

1.While there has been significant progress in molecular property prediction in computer-aided drug design, there is a critical need to have fast and accurate models. Many of the currently available methods are mostly specialists in predicting specific properties, leading to the use of many models side-by-side that lead to impossibly high computational overheads for the common researcher. Henceforth, the authors propose a single, generalist unified model exploiting graph convolutional variational encoders that can simultaneously predict multiple properties such as absorption, distribution, metabolism, excretion and toxicity (ADMET), target-specific docking score prediction and drug-drug interactions. Considerably, the use of this method allows for state-of-the-art virtual screening with an acceleration advantage of up to two orders of magnitude. The minimisation of a graph variational encoders latent space also allows for accelerated development of specific drugs for targets with Pareto optimality principles considered, and has the added advantage of explainability.

bioinformatics↗

bioRxiv2022

Whole slide image representation in bone marrow cytology

One of the goals of AI-based computational pathology is to generate compact WSI representations, identifying the essential information required for diagnosis. While such approaches have been applied to histopathology, few applications have been reported in cytology. Bone marrow aspirate cytology is the basis for key clinical decisions in hematology. However, visual inspection of aspirate specimens is a tedious and complex process subject to variation in interpretation, and hematopathology expertise is scarce. The ability to generate a compact representation of an aspirate specimen may form the basis for clinical decision support tools in hematology. We have previously published an end-to-end AI-based system for counting and classifying cells from bone marrow aspirate WSI. Using deep embeddings from this model, we construct bags of individual cell features from each WSI, and apply multiple instance learning to extract vector representations for each WSI. Using these representations in vector search, we achieved 0.58 {+/-} 0.02 mAP@10 in WSI-level image retrieval, which outperforms the Random baseline (0.39 {+/-} 0.1). Using a weighted k-nearest-neighbours (k-NN) model on these slide vectors, we predict five broad diagnostic labels on individual aspirate WSI with a weighted-macro-average F1 score of 0.57 {+/-} 0.03 on the test set of 278 randomly sampled WSIs, which outperforms a classifier using empirical class prior probabilities (0.26 {+/-} 0.02). We present the first example of exploring trainable mechanisms to generate compact, slide-level representations in bone marrow cytology with deep learning. This method has the potential to summarize complex semantic information in WSIs toward improved diagnostics in hematology, and may eventually support AI-assisted computational pathology approaches.

bioinformatics↗

bioRxiv2022

Cell projection plots: a novel visualization of bone marrow aspirate cytology

Deep models for cell detection have demonstrated utility in bone marrow cytology, showing impressive results in terms of accuracy and computational efficiency. However, these models have yet to be implemented in the clinical diagnostic workflow. Additionally, the metrics used to evaluate cell detection models are not necessarily aligned with clinical goals and targets. In order to address these issues, we introduce cell projection plots (CPPs), which are novel, automatically generated visual summaries of bone marrow aspirate specimens. CPPs provide a compact summary of bone marrow aspirate cytology, and encompass relevant biological patterns such as neutrophil maturation. To gauge clinical relevance, CPPs were shown to three hematopathologists, who decided whether shown diagnostic synopses matched with generated CPPs. Pathologists were able to match CPPs to the correct synopsis with 85% accuracy. Our finding suggests CPPs can compactly represent clinically relevant information from bone marrow aspirate specimens, and may be used to efficiently summarize bone marrow cytology to pathologists. CPP could be a step toward human-centered implementation of artificial intelligence (AI) in hematopathology, and a basis for a diagnostic support tool for digital pathology workflows.

pathology↗

bioRxiv2022

Genome charaterization based on the Spike-614 and NS8-84 loci of SARS-CoV-2 reveals two major onsets of the COVID-19 pandemic

The global COVID-19 pandemic has lasted for three years since its outbreak, however its origin is still unknown. Here, we analyzed the genotypes of 3.14 million SARS-CoV-2 genomes based on the amino acid 614 of the Spke (S) and the amino acid 48 of NS8 (nonstructural protein 8), and identified 16 linkage haplotypes. The GL haplotype (S_614G and NS8_48L) was the major haplotype driving the global pandemic and accounted for 99.2% of the sequenced genomes, while the DL haplotype (S_614D and NS8_48L) caused the pandemic in China in the spring of 2020 and accounted for approximately 60% of the genomes in China and 0.45% of the global genomes. The GS (S_614G and NS8_48S), DS (S_614D and NS8_48S) and NS (S_614N and NS8_48S) haplotypes accounted for 0.26%, 0.06%, and 0.0067% of the genomes, respectively. The main evolutionary trajectory of SARS-CoV-2 is DS[->]DL[->]GL, whereas the other haplotypes are minor byproducts in the evolution. Surprisingly, the newest haplotype GL had the oldest time of most recent common ancestor (tMRCA), which was May 1 2019 by mean, while the oldest haplotype had the newest tMRCA with a mean of October 17, indicating that the ancestral strains that gave birth to GL had been extinct and replaced by the more adapted newcomer at the place of its origin, just like the sequential rise and fall of the delta and omicron variants. However, they arrived and evolved into toxic strains and ignited a pandemic in China where the GL strains did not exist at the end of 2019. The GL strains had spread all over the world before they were discovered, and ignited the global pandemic, which had not been noticed until the pandemic was declared in China. However, the GL haplotype had little influence in China during the early phase of the pandemic due to its late arrival as well as the strict transmission controls in China. Therefore, we propose two major onsets of the COVID-19 pandemic, one was mainly driven by the haplotype DL in China, the other was driven by the haplotype GL globally.

bioinformatics↗

bioRxiv2022

In silico repurposed drugs against monkeypox virus

Monkeypox is an emerging epidemic of concern. The disease is caused by the monkeypox virus and an increasing global incidence with a 2022 outbreak that has spread to Europe amid the COVID-19 pandemic. The new outbreak is associated with novel, previously undiscovered mutations a nd variants. Currently the US Food and Drug Administration (FDA) approved poxvirus treatment involves use of tecovirimat. However, there is limited pharmacopoeia otherwise, and limited research interest in monkeypox. In this study, virtual screening and molecular dynamics were employed to explore the potential repurposing of multiple drugs previously approved by the FDA or other jurisdictions for other applications. Several drugs are predicted to tightly bind to viral proteins which are crucial in viral replication, including molecules which show high potential for binding the monkeypox D13L capsid protein, whose inhibition has previously been demonstrated to suppress viral replication.

bioinformatics↗

bioRxiv2022

Genomic, transcriptomic, and metabolomic analysis of Traditional Chinese Medicine plant Oldenlandia corymbosa reveals the biosynthesis and mode of action of anti-cancer metabolites

Natural products from traditional medicinal plants are valuable candidates for clinical cancer therapy. Plants from the Oldenlandia-Hedyotis complex are popular ingredients of Traditional Chinese Medicine (TCM), however a major hurdle in the plant bioprospecting process of TCM plants is that the active metabolites, their biosynthetic pathways, and mode of action are often unknown. We show that Oldenlandia corymbosa extracts are active against breast cancer cell lines. To study the genes involved in the biosynthesis of active compounds in this medicinal plant, we assembled a high-quality genome. We show that the main active compound is ursolic acid and that abiotic stresses cause changes in anti-cancer activity, metabolite composition, and gene expression of plants. To reveal the mode of action of ursolic acid, we show that cancer cells undergo mitotic catastrophe, and we identify three high-confidence protein binding targets by Cellular Thermal Shift Assay (CETSA) and reverse docking.

plant biology↗

bioRxiv2022

Crystal structure of the Rubella virus protease reveals a unique papain-like protease fold

Rubella is well-controlled due to an effective vaccine, but outbreaks are still occurring without any available antiviral treatments. There is still much to learn about the rubella virus (RUBV) papain-like protease (RubPro) that could be a potential drug target. This protease is crucial to RUBV replication, cleaving the non-structural polyprotein p200 into 2 multi-functional proteins, p150 and p90. Here we report a novel crystal structure of RubPro at 1.64 [A] resolution. It has a similar catalytic core structure to that of SARS-CoV-2 and foot-mouth-disease virus (FMDV) proteases. RubPro has well-conserved sequence motifs that are also found in its newly discovered Rubivirus relatives. The RubPro construct was shown to have protease activity in trans against a construct of RUBV protease-helicase and fluorogenic peptide. A protease-helicase construct was also cleaved in E. coli expression. RubPro was demonstrated to possess deubiquitylation activity, suggesting a potential role of RubPro in modulating the hosts innate immune responses. The structural and functional insights of the RubPro will advance our current understanding of its function and point to more structure-based research into the RUBV replication machinery, in hopes of developing antiviral therapeutics in the future.

microbiology↗