Biology subjects

Chen, T.

Publications and source records attributed to Chen, T..

At least 127 records · Page 7Linked to original sources

bioRxiv2024

FTO (fat-mass and obesity-associated protein) deficiency aggravates age-dependent depression-like behaviors and cognitive impairment

AbstractO_ST_ABSBackgroundC_ST_ABSThe demethylase fat mass and obesity-related protein (FTO) is strongly associated with depression. Aging is a risk factor for synaptic plasticity damage in the brain and leads to neurocognitive dysfunctions. However, whether FTO is associated with susceptibility to depression in different age groups remains unknown. MethodsWe subjected 3-and 12-month-old C57BL/6J male mice to 6 weeks of chronic unpredictable mild stress (CUMS) and 3 weeks of hippocampal injection of FTO knockdown adeno-associated virus 9 shRNA (FTO-KD AAV9). Finally, 36 male mice in each 3-month-old and 12-month-old groups were divided into three groups (n=12): Sham, CUMS, and FTO-KD. After 6 weeks, we assessed behavioral deficits (depressive and anxiety-like behaviors and cognitive impairment) by behavioral tests and hippocampal neuronal damage (dendritic spine density, neuronal atrophy, and expression of proteins associated with synaptic plasticity) by molecular biochemical experiments. ResultsThe results showed that 12-month-old C57BL/6J mice were more likely to develop depression-like behavior and spatial learning and memory impairment induced by CUMS than 3-month-old mice. Chronic stress-induced depression-like behavior and cognitive impairment worsened after the FTO-KD intervention. In the hippocampus of 3-and 12-month-old mice, CUMS induced the downregulation of FTO, nerve growth factor (NGF), reelin, and synaptic plasticity-related proteins. It also caused abnormal BDNF-TrkB signaling, reduced density of dendritic spines, and an increased number of neuronal pyknotic nuclei, leading to neuronal disarray, which was more significant in 12-month-old animals. FTO deficiency accelerated neuronal damage in the hippocampus of 12-month-old CUMS mice. ConclusionsThis study provides rodent evidence that FTO deficiency may increase the susceptibility to depression in older adults by impairing hippocampal neuronal function and neuronal synaptic plasticity in an age-dependent manner. This suggests that the development of FTO activators may be an effective treatment for depression in older adults.

neuroscience↗

bioRxiv2024

scMoE: single-cell Multi-Modal Multi-Task Learning via Sparse Mixture-of-Experts

Recent advances in measuring high-dimensional modalities, including protein levels and DNA accessibility, at the single-cell level have prompted the need for frameworks capable of handling multi-modal data while simultaneously addressing multiple tasks. Despite these advancements, much of the work in the single-cell domain remains limited, often focusing on either a single-modal or single-task perspective. A few recent studies have ventured into multimodal, multi-task learning, but we identified a [circled1] Optimization Conflict issue, leading to suboptimal results when integrating additional modalities, which is undesirable. Furthermore, there is a [circled2] Costly Interpretability challenge, as current approaches predominantly rely on costly post-hoc methods like SHAP. Motivated by these challenges, we introduce scMoE1, a novel framework that, for the first time, applies Sparse Mixture-of-Experts (SMoE) within the single-cell domain. This is achieved by incorporating an SMoE layer into a transformer block with a cross-attention module. Thanks to its design, scMoE inherently possesses mechanistic interpretability, a critical aspect for understanding underlying mechanisms when handling biological data. Furthermore, from a post-hoc perspective, we enhance interpretability by extending the concept of activation vectors (CAVs). Extensive experiments on simulated datasets, such as Dyngen, and real-world multi-modal single-cell datasets, including {DBiT-seq, Patch-seq, ATAC-seq}, demonstrate the effectiveness of scMoE. Source code of scMoE is available at: https://github.com/UNITES-Lab/scMoE.

bioinformatics↗

bioRxiv2024

Machine learning engineered PoLixNano nanoparticles overcome delivery barriers for nebulized mRNA therapeutics

There continues to be a dearth of competent inhalable mRNA delivery although it holds great potential for addressing a wide variety of refractory diseases. The huge advances seen with parenteral-administered lipid nanoparticle (LNP) have not been translated into nebulized mRNA delivery due to the aggressive nebulization process and insurmountable barriers inherent to respiratory mucosa. Here, we show amphiphilic block copolymers revealed by machine learning (ML) can spontaneously form stabilized nanoparticles (PoLixNano) with the lipids components of LNP and simultaneously impart the PoLixNano with "shield" (shear force-resistant) and "spear" (pulmonary barriers-penetrating abilities) capabilities. We present a ML approach that leverages physicochemical properties and inhaled mRNA transfection profiles of a chemically diverse library of polymeric components to validate the integration of "shield" and "spear" properties as highly predictive indicators of transfection efficiency. This quantitative structure-mRNA transfection prediction (QSMTP) model identifies top-performing amphiphilic-copolymers from more than 10000 candidates and suggests their mucus-penetrating ability outweights the shear force-resistant property in contributing to efficient mRNA transfection. The optimized PoLixNano substantially outperforms the LNP counterpart and mediates up to 1114-times higher levels of mRNA transfection in animal models with negligible toxicities. The PoLixNano promotes overwhelming SARS-CoV-2 antigen-specific sIgA antibody secretion and expansion of TRM cells which collectively confers 100% protection in mice against lethal SARS-CoV-2 challenges and blocks the transmission of Omicron variant between hamsters. PoLixNano also displays versatile therapeutic potential in lung carcinoma and cystic fibrosis models. Our study provides new insights for designing delivery platforms of aerosol-inhaled mRNA therapeutics with clinical translation potential.

synthetic biology↗

bioRxiv2024

Deep generative model for protein subcellular localization prediction

Protein sequence determines not only its structure but also its subcellular localization. Although a series of artificial intelligence models have been reported to predict protein subcellular localization, most of them provide only textual outputs. Here, we present deepGPS, a deep generative model for protein subcellular localization prediction. After trained with both protein primary sequences and protein subcellular localization fluorescence images, deepGPS shows the ability to predict cytoplasmic and nuclear localizations by reporting both textual labels and generative images as outputs. In addition, deepGPS shows potential to be further extended for other types of subcellular localization prediction, even with limited input data volumes for training. Finally, an openGPS website (https://bits.fudan.edu.cn/opengps) is constructed to provide a public and convenient platform for protein subcellular localization prediction with the scientific community.

bioinformatics↗

bioRxiv2024

Screening of potential biomarkers using activity-based protein profiling in a rat model of chemotherapy-induced peripheral neuropathy

Chemotherapy-induced peripheral neuropathy (CIPN) is a debilitating condition affecting cancer patients, often resulting from the use of neurotoxic agents, such as paclitaxel. This study aimed to establish a rat model of CIPN through systemic administration of paclitaxel and to identify potential biomarkers using activity-based protein profiling (ABPP) with a desthiobiotin iodoacetamide (DBIA) probe. Male Sprague-Dawley rats received paclitaxel injections at a dosage of 2 mg/kg on Days 0, 2, 4, and 6. Behavioral assessments revealed significant mechanical and thermal pain hypersensitivity in paclitaxel-treated rats compared to control groups. ABPP analysis identified a total of 2,445 proteins and 6,765 peptides, resulting in the identification of 7 candidate proteins as potential biomarkers for CIPN. Notably, 5 proteins were significantly upregulated, while 2 protein including Q63425 (Prx) demonstrated downregulation in response to paclitaxel. These findings suggest that alterations in candidate protein levels may compromise alterations in the spinal cord in the context of CIPN. This study underscores the utility of ABPP in elucidating the proteomic changes associated with CIPN and highlights the potential of identified biomarkers like Prx for enhancing our understanding of chemotherapy-induced neuropathic pain mechanisms.

molecular biology↗

bioRxiv2024

Microbial succession, community assembly and adaptation over five years in a newly discovered deep-sea cold seep

Deep-sea cold seeps, characterized by the release of hydrocarbon-rich fluids such as methane from the seafloor, create unique ecosystems with evolving biological communities. Microorganisms serve as pioneers, initially colonizing early-stage cold seeps and providing essential matter and energy for subsequent colonizers. However, microbial succession, community assembly, and adaptation in these environments remain largely unexplored due to challenges associated with locating early-stage cold seeps. Here, we collected sediment samples from a newly discovered early-stage cold seep site, QDN-W07, in the South China Sea at varying depths over five years. We also sampled two previously reported cold seep sites, QDN-S18 and HM-ROV01, for comparative analysis. Amplicon sequencing revealed a high relative abundance of ANME-3 (up to 99%) and Methylomonadaceae (up to 83%) in surface sediments, and indicated changes in microbial community structures over time. Dispersal limitation dominated community assembly, while the contribution of homogeneous selection decreased over time, accompanied by a declined in the intensity of microbial association. Metagenomic analysis of core microbiome, comprising 172 metagenomic-assembled genomes at QDN-W07, revealed tolerance to low temperatures and oxygen. Core microbiome genomes were enriched with genes related to aerobic methane oxidation, urea metabolism, and the oxidation of sulfur, ammonia and carbon monoxide. These results indicate that core microorganisms obtain energy from the oxidation of inorganic compounds, allowing them to thrive in cold, methane-rich and unstable conditions. Overall, this study enhances our understanding of deep biosphere life, biogeochemical cycling, and the ecological management of cold seep environments.

microbiology↗

bioRxiv2024

Rag2Mol: Structure-based drug design based on Retrieval Augmented Generation

Artificial intelligence (AI) has brought tremendous progress to drug discovery, yet identifying hit and lead compounds with optimal physicochemical and pharmacological properties remains a significant challenge. Structure-based drug design (SBDD) has emerged as a promising paradigm, but the inherent data biases and ignorance of synthetic accessibility render SBDD models disconnected from practical drug discovery. In this work, we explore two methodologies, Rag2Mol-G and Rag2Mol-R, both based on retrieval-augmented generation (RAG) to design small molecules to fit a 3D pocket. These two methods involve searching for similar small molecules that are purchasable in the database based on the generated ones, or creating new molecules from those in the database that can fit into a 3D pocket. Experimental results demonstrate that Rag2Mol methods consistently produce drug candidates with superior binding affinities and drug-likeness. We find that Rag2Mol-R provides a broader coverage of the chemical landscapes and more precise targeting capability than advanced virtual screening models. Notably, both workflows identified promising inhibitors for the challenging target PTPN2, which was considered undruggable because of structural characteristics of phosphatases. Our highly extensible framework can integrate diverse SBDD methods, marking a significant advancement in AI-driven SBDD. The codes are available at: https://github.com/CQ-zhang-2016/Rag2Mol.

bioinformatics↗

bioRxiv2024

Monitoring lysosomal catabolism: a sensitive probe for assessing targeted lysosomal degradation of extracellular proteins

Extracellular targeted protein degradation (eTPD) is an emerging therapeutic field. The Lysosome targeting chimera (LYTAC) is a therapeutic modality that promotes degradation of extracellular drivers of disease in the lysosome. While widely available pH-sensitive probes may report on lysosome delivery, these probes do not necessarily report on the enzymatically active functional state of the lysosome. We report the development and application of a sensitive fluorescent probe, LysoLight Deep Red, to monitor catabolism of internalized proteins in the lysosome based on cleavage by cathepsin proteases. We demonstrate the application of Lysolight Deep Red to monitor the catabolic fate of therapeutic monoclonal antibodies, ASGPR-targeted LYTAC therapeutics and LYTAC targets in immortalized cell lines and in primary human hepatocytes.

cell biology↗

bioRxiv2024

SPLENDID incorporates continuous genetic ancestry in biobank-scale data to improve polygenic risk prediction across diverse populations

Polygenic risk scores are widely used in disease risk stratification, but their accuracy varies across diverse populations. Recent methods large-scale leverage multi-ancestry data to improve accuracy in under-represented populations but require labelling individuals by ancestry for prediction. This poses challenges for practical use, as clinical practices are typically not based on ancestry. We propose SPLENDID, a novel penalized regression framework for diverse biobank-scale data. Our method utilizes ancestry principal component interactions to model genetic ancestry as a continuum within a single prediction model for all ancestries, eliminating the need for discrete labels. In extensive simulations and analyses of 9 traits from the All of Us Research Program (N=224,364) and UK Biobank (N=340,140), SPLENDID significantly outperformed existing methods in prediction accuracy and model sparsity. By directly incorporating continuous genetic ancestry in model training, SPLENDID stands as a valuable tool for robust risk prediction across diverse populations and fairer clinical implementation.

genetics↗

bioRxiv2024

Impact of anionic lipids on the energy landscape of conformational transition in anion exchanger 1 (AE1)

Anion Exchanger 1 (AE1) is an elevator-type membrane transporter that plays a key role in erythrocytes by mediating the exchange of chloride and bicarbonate ions across the membrane, thus participating in acid-base homeostasis. While previous studies have provided structural insights into AE1 and its substrate binding, the conformational transitions and the role of lipid interactions remain elusive. In this study, we utilized cryo-electron microscopy (cryo-EM) to resolve three high resolution structures of distinct conformational states of AE1: two inward-facing (IF1 and IF2) and one outward-facing (OF). Furthermore, uptake assay revealed the modulatory effect of phosphatidylinositol 4,5-bisphosphate (PIP2) lipids on AE1. Molecular dynamics (MD) simulations were conducted on these structures to capture anion binding and determine the anion binding sites in AE1. We then used a combination of advanced enhanced sampling techniques together with system-specific collective variables to study the OF{lrdoublearrow}IF transition in AE1 and provided refined pathways for the process in three different systems: apo, HCO3- -bound, and an AE1 system in which cryo-EM-determined interfacial PIP2 lipids had been removed. The refined pathways were then used to calculate the free energy of the OF{lrdoublearrow}IF transition in AE1 under different conditions. The energies show how substrate binding reduces the transition barrier and, therefore, facilitates the transport. Furthermore, they clearly capture the inhibitory effect of PIP2 lipids at the dimer interface. Furthermore, the simulation results provide a molecular mechanism for this inhibitory effect. These results provide a molecular-level understanding of the mechanistic basis for ion transport in AE1 and the regulatory role of PIP2 on its function.

biophysics↗

bioRxiv2024

DEMINERS enables clinical metagenomics and comparative transcriptomic analysis by increasing throughput and accuracy of nanopore direct RNA sequencing

Nanopore direct RNA sequencing (DRS) advances RNA biology but is limited by relatively low basecalling accuracy, low throughput, yet high RNA input and costs. Here we introduce a novel DRS toolkit, DEMINERS, which integrates an RNA multiplexing experimental workflow, a machine-learning barcode classifier based on Random Forest and a novel basecaller built on an optimized convolutional neural network providing an additional species-specific training module. With the increased accuracy in barcode classification and basecalling, DEMINERS can demultiplex up to 24 samples and the required RNA input and running time are both substantially reduced. We demonstrated the applications of DEMINERS in clinical metagenomics, cancer transcriptomics and parallel comparison of transcriptomic features in different biological conditions, revealing altered airway microbial diversity in COVID-19 and a potential role of m6A in increasing transcriptomic diversity in glioma and the mature blood-stage of malaria parasites. Overall, DEMINERS is a simple, robust, high-throughput DRS method for accurately estimating transcript levels, poly(A) lengths, and mutation and RNA modification heterogeneity at single-read level, with minimal sequencing biases.

bioinformatics↗

bioRxiv2024

Super-Resolution Axial Imaging for Quantifying Piconewton Traction Forces in Live-cells

Cell mechanics play a pivotal role in regulating numerous biological processes. While super-resolution microscopy enables the imaging of cellular forces in the lateral dimension with sub-10-nanometer resolution, achieving comparable resolution along the axial dimension remains a significant challenge. In this study, we combine metal-induced energy transfer (MIET) imaging with novel DNA-hairpin-based molecular tension probes (MIET-MTP) to map integrin-mediated mechanical forces with nanometer precision in the axial direction. MIET-MTP allows for the simultaneous observation of both the plasma membrane and forceexerting molecules in the axial dimension. Using this approach, we mapped axial integrin tension in focal adhesions and podosomes, alongside their corresponding plasma membrane height profiles, offering detailed insights into the structures involved in force transmission.

biophysics↗

bioRxiv2024

Measuring 3-dimensional dynamics of membrane in living cell with metal-induced energy transfer imaging/spectroscopy

The dynamic behavior of cellular membranes underpins essential biological processes, including signal transduction, intracellular trafficking, and mechanotransduction. However, simultaneously quantifying lateral molecular diffusion and vertical membrane fluctuations in live cells remains challenging. Here, we present dynamic metal-induced energy transfer spectroscopy (dynaMIET), which integrates metal-induced energy transfer with fluorescence correlation spectroscopy to resolve three-dimensional membrane dynamics with nanometer axial sensitivity and microsecond temporal resolution. dynaMIET enables concurrent measurement of lateral diffusion and vertical undulations within a single acquisition. We validate the method using simulations and model membranes and demonstrate its robustness in living cells, applying it to the plasma membrane, endoplasmic reticulum, and nuclear envelope. By capturing both molecular mobility and membrane fluctuations, dynaMIET provides a powerful, non-invasive tool for probing membrane mechanics and organization. This advance opens new avenues for studying membrane-associated phenomena in health and disease, including cancer cell mechanics, protein-membrane interactions, and organelle dynamics.

biophysics↗

bioRxiv2024

Earlier endosomal escape improves the catalytic activity of delivered enzyme cargo

There is enormous interest in strategies to efficiently traffic biologics-proteins, nucleic acids, and complexes thereof-into the mammalian cell cytosol and internal organelles. Not only must these materials reach the appropriate cellular locale fully intact and in therapeutically relevant concentrations, they must also retain activity upon arrival. The question of residual activity is especially critical when delivery involves exposure to the late endocytic pathway, whose acidic lumenal environment can denature and/or degrade internalized material. ZF5.3 is a compact, stable, rationally designed mini-protein that efficiently escapes intact from late endocytic vesicles, with or without covalently linked protein cargo. Here, using insights from mechanistic studies on the pathway of endosomal escape and classic principles of zinc(II) bioinorganic chemistry, we re-designed the sequence of ZF5.3 to successfully alter the timing (but not the efficiency) of endosomal escape. The new mini-protein we describe, AV5.3, escapes earlier than ZF5.3 along the endocytic pathway with no loss in efficiency, with or without enzyme cargo. More importantly, earlier endosomal escape translates into higher enzymatic activity of a pH-sensitive enzyme upon arrival in the cytosol. Delivery of the pH-sensitive enzyme dihydrofolate reductase (DHFR) with AV5.3 results in substantial catalytic activity in the cytosol, whereas delivery with ZF5.3 does not. The activity of AV5.3-DHFR upon delivery is sufficient to rescue a genetic DHFR deletion in CHO cells. This work provides evidence that programmed trafficking through the endosomal pathway is a viable strategy for the efficient cytosolic delivery of therapeutic proteins. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=77 SRC="FIGDIR/small/615476v2_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@1de8a77org.highwire.dtl.DTLVardef@154c401org.highwire.dtl.DTLVardef@105ce8org.highwire.dtl.DTLVardef@43c1f3_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2024

Airway applied IVT mRNA vaccine needs specific sequence design and high standard purification that removes devastating dsRNA contaminant

The development of next-generation mucosal mRNA vaccines is promising but extremely challenging. Major efforts have been focused on optimizing delivery systems, whereas it is still unknown whether the intrinsic quality of IVT mRNA significantly impacts the potency of airway inoculated mRNA vaccines. Here, we systematically demonstrate the mucosal mRNA vaccine requires a higher standard of purification and tailor-designed sequence to fulfil its potency compared to the parenteral route inoculated counterpart. We found double strand RNA (dsRNA) contaminants are prone to trigger innate immunoreaction in the airway that activates the mRNA degradation mechanism, thereby diminishing the mRNA expression and subsequent antigen-specific immune responses. To address these challenges, we developed a strategy that combines optimized untranslated regions (UTRs) screened from endogenous genes of pulmonary cells with affinity chromatography-based purification which removes almost all the dsRNA contaminants. The optimized mRNA administered via the airway route not only demonstrates superior protein expression (30-fold increase) and reduces inflammation in the lung, but also promotes robust immunity comprising significantly elevated systemic, cellular, and mucosal immune responses, which is in stark contrast to intramuscular injected counterpart that displays less pronounced benefits. Our findings offer new insight into the development of mucosal mRNA therapeutics from an overlooked but crucial perspective of optimizing mRNA components.

synthetic biology↗

bioRxiv2024

Engineering Rubisco Condensation in Chloroplasts to Manipulate Plant Photosynthesis

Although Rubisco is the most abundant enzyme globally, it is inefficient for carbon fixation because of its low turnover rate and limited ability to distinguish CO2 and O2, especially under high O2 conditions. To address these limitations, phytoplankton, including cyanobacteria and algae, have evolved CO2-concentrating mechanisms (CCM) that involve compartmentalizing Rubisco within specific structures, such as carboxysomes in cyanobacteria or pyrenoids in algae. Engineering plant chloroplasts to establish similar structures for compartmentalizing Rubisco has attracted increasing interest for improving photosynthesis and carbon assimilation in crop plants. Here, we present a method to effectively induce the condensation of endogenous Rubisco within tobacco (Nicotiana tabacum) chloroplasts by genetically fusing superfolder green fluorescent protein (sfGFP) to the tobacco Rubisco large subunit (RbcL). By leveraging the intrinsic oligomerization feature of sfGFP, we successfully created pyrenoid-like Rubisco condensates that display dynamic, liquid-like properties within chloroplasts without affecting Rubisco assembly and catalytic function. The transgenic tobacco plants demonstrated comparable autotrophic growth rates and full life cycles in ambient air relative to the wild-type plants. Our study offers a promising strategy for modulating endogenous Rubisco assembly and spatial organization in plant chloroplasts via phase separation, which provides the foundation for generating synthetic organelle-like structures for carbon fixation, such as carboxysomes and pyrenoids, to optimize photosynthetic efficiency.

plant biology↗

bioRxiv2024

Accept-reject decision-making revealed via a quantitative and ethological study of C. elegans foraging

Decision-making is a ubiquitous component of animal behavior that is often studied in the context of foraging. Foragers make a series of decisions while locating food (food search), choosing between food types (diet or patch choice), and allocating time spent within patches of food (patch-leaving). Here, we introduce a framework for investigating foraging decisions using detailed analysis of individual behavior and quantitative modeling in the nematode Caenorhabditis elegans. We demonstrate that C. elegans make accept-reject patch choice decisions upon encounter with food. Specifically, we show that when foraging amongst small, dispersed, and dilute patches of bacteria, C. elegans initially reject several bacterial patches, opting to prioritize exploration of the environment, before switching to a more exploitatory foraging strategy during subsequent encounters. Observed across a range of bacterial patch densities, sizes, and distributions, we use a quantitative model to show that this decision to explore or exploit is guided by available sensory information, internal satiety signals, and learned environmental statistics related to the bacterial density of recently encountered and exploited patches. We behaviorally validated model predictions on animals that had been food-deprived, animals foraging in environments with multiple patch densities, and null mutants with defective sensory modalities. Broadly, we present a framework to study ecologically relevant foraging decisions that could guide future investigations into the cellular and molecular mechanisms underlying decision-making.

neuroscience↗

bioRxiv2024

Escaping the drug-bias trap: using debiasing design to improve interpretability and generalization of drug-target interaction prediction

Considering the high cost associated with determining reaction affinities through in-vitro experiments, virtual screening of potential drugs bound with specific protein pockets from vast compounds is critical in AI-assisted drug discovery. Deep-leaning approaches have been proposed for Drug-Target Interaction (DTI) prediction. However, they have shown overestimated accuracy because of the drug-bias trap, a challenge that results from excessive reliance on the drug branch in the traditional drug-protein dual-branch network approach. This casts doubt on the interpretability and generalizability of existing Drug-Target Interaction (DTI) models. Therefore, we introduce UdanDTI, an innovative deep-learning architecture designed specifically for predicting drug-protein interactions. UdanDTI applies an unbalanced dual-branch system and an attentive aggregation module to enhance interpretability from a biological perspective. Across various public datasets, UdanDTI demonstrates outstanding performance, outperforming state-of-the-art models under in-domain, cross-domain, and structural interpretability settings. Notably, it demonstrates exceptional accuracy in predicting drug responses of two crucial subgroups of Epidermal Growth Factor Receptor (EGFR) mutations associated with non-small cell lung cancer, consistent with experimental results. Meanwhile, UdanDTI could complement the advanced molecular docking software DiffDock. The codes and datasets of UdanDTI are available at https://github.com/CQ-zhang-2016/UdanDTI.

bioinformatics↗