Biology subjects

Song, C.

Publications and source records attributed to Song, C..

At least 73 records · Page 4Linked to original sources

bioRxiv2025

Painting Peptides with Antimicrobial Potency through Deep Reinforcement Learning

In the post-antibiotic era, antimicrobial peptides (AMPs) serve as ideal drug candidates for their lower likelihood of inducing resistance. Computational models offer an efficient way to design novel AMPs. However, current optimization and generation approaches are tailored for different application scenarios. To address this challenge, we propose a novel AMP design model named AMPainter. Based on deep reinforcement learning, AMPainter integrates both optimization and generation tasks in a unified framework. We apply AM-Painter to three types of peptides, including known AMPs, signal peptides (SPs), and random sequences. AMPainter outperforms ten related models in enhancing the activity of known AMPs, and evolves effective AMPs from membrane-active SPs with a success rate of 80%. Furthermore, several de novo designed AMPs from random sequences are validated along with their evolutionary paths. Therefore, AMPainter contributes to paint antimicrobial potency to diverse peptides, assisting in expanding the AMP sequence space and discovering novel antimicrobial agents.

bioinformatics↗

bioRxiv2024

Non-invasive diagnosis of early chronic colitis cancerization via amplified sensing of miRNA-21 in NIR-IIb window

Early diagnosis of the colitis-associated colorectal cancer (CRC) is of great significance for improving prognosis and survival rates. However, the clinical used colonoscopy and biopsy methods are invasive and lack of sensitivity at early stage of cancerization. Herein, we present an amplified sensing strategy in the second near-infrared b (NIR-IIb, 1500-1700 nm) window for non-invasive in situ visualization of early cancerization biomarker miRNA-21. A CRC nanosensor composed of Er3+ doped lanthanide nanoparticles, DNAzyme, and IR820 dye is designed as a NIR-IIb ratiometric luminescence reporter, providing rapid feedback and high sensitive miRNA-21 detection with limit of detection (LOD) of 1.26 pM. This strategy enables a non-invasive detection of colitis-associated cancerization up to [~]4 weeks ahead of the clinically used biopsy analysis, providing a promising alternative for early diagnosis of chronic colitis cancerization and guidance for therapeutic management.

bioengineering↗

bioRxiv2024

Multiple-Basin Go-Martini for Investigating Conformational Transitions and Environmental Interactions of Proteins

Proteins are inherently dynamic molecules, and their conformational transitions among various states are essential for numerous biological processes, which are often modulated by their interactions with surrounding environments. Although molecular dynamics (MD) simulations are widely used to investigate these transitions, all-atom (AA) methods are often limited by short timescales and high computational costs, and coarse-grained (CG) implicitsolvent G[o]-like models are usually incapable of studying the interactions between proteins and their environments. Here, we present an approach called Multiple-basin G[o]-Martini, which combines the recent G[o]-Martini model with an exponential mixing scheme to facilitate the simulation of spontaneous protein conformational transitions in explicit environments. We demonstrate the versatility of our method through five diverse case studies: GlnBP, Arc, Hinge, SemiSWEET, and TRAAK, representing ligand-binding proteins, fold-switching proteins, de novo designed proteins, transporters, and mechanosensitive ion channels, respectively. The Multiple-basin G[o]-Martini offers a new computational tool for investigating protein conformational transitions, identifying key intermediate states, and elucidating essential interactions between proteins and their environments, particularly protein-membrane interactions. In addition, this approach can efficiently generate thermodynamically meaningful datasets of protein conformational space, which may enhance deep learning-based models for predicting protein conformation distributions.

biophysics↗

bioRxiv2024

Deciphering Ca2+ Permeation and Valence Selectivity in Cav1: Molecular Dynamics Simulations Reveal the Three-Ion Knock-on Mechanism

Voltage-gated calcium (CaV) channels are pivotal in cellular signaling due to their selective calcium ion permeation upon membrane depolarization. While previous studies have established the highly selective permeability of CaV channels, the detailed molecular mechanism remains elusive. Here we use extensive atomistic molecular dynamics simulations to elucidate the mechanisms governing ion permeation and valence selectivity in CaV1 channels. Employing the electronic continuum correction method, we simulated a calcium conductance of approximately 9-11 pS, aligning closely with experimental measurement. Our simulations uncovered a three-ion knock-on mechanism critical for efficient calcium ion permeation, necessitating the binding of at least two calcium ions within the selectivity filter (SF) and the subsequent entry of a third ion. In silico mutation simulations further validated the importance of multi-ion coordination in the SF for efficient ion permeation, identifying two critical residues, D706 and E1101, that are essential for the binding of two calcium ions in the SF. Moreover, we explored the competitive permeation of calcium and sodium ions, and obtained a valence selectivity favoring calcium over sodium at a ratio of approximately 35:1 under the bi-cation condition. This selectivity arises from the strong electrostatic interactions of calcium ions in the confined SF and the three-ion knock-on mechanism. Our findings provide novel insights into the molecular underpinnings of CaV channel function, with implications for understanding calcium-dependent cellular processes.

biophysics↗

bioRxiv2024

UniSPAC: A Unified Segmentation Framework for Proofreading and Annotation in Connectomics

Reconstructing dense neuronal connections from volume electron microscopy (vEM) images is a critical challenge in neuroscience, driving the development of various automatic neuron segmentation methods1. Although current state-of-the-art automated segmentation methods can achieve high segmentation accuracy, they still require substantial manual proofreading and rely heavily on labeled datasets, which are often scarce, particularly for non-model organisms. Here, we introduce a Unified Segmentation framework for Proofreading and Annotation in Connectomics (UniSPAC) by providing the interactive segmentation model in 2D-level and the neuron tracing model in 3D-level. UniSPAC-2D allows users to correct its segmentation errors through point-based prompts, combining segmentation and proofreading in a single framework. UniSPAC-3D automatically traces neurons segmented by UniSPAC-2D across image slices, significantly reducing human involvement. Furthermore, UniSPAC-2D and UniSPAC-3D models can facilitate the semi-automatic generation of labeled data for new species, eliminating the need for external annotation tools. The fresh annotated data generated during proofreading in turn optimizes the interactive model through an online learning strategy, reducing the labeling effort for novel species over time. UniSPAC outperforms the start-of-the-art Segment Anything Model (SAM) in Drosophila segmentation, achieving 47x higher efficiency, and surpasses ACRLSD in cross-species segmentation on zebra finch data.

neuroscience↗

bioRxiv2024

Anatomical Region-Specific Transcriptomic Signatures and the Role of Epithelial Cells in Pterygium Inflammation: A Multi-Omics Analysis

BackgroundThe anatomy of pterygium consists of the head, neck, and body. Prior studies have examined the three parts collectively, yet the treatment methods and their success rates differ based on the anatomical characteristics. In this study, we divide the pterygium tissue into Main (head and neck) and Acc (body) to investigate its pathogenesis across regions. PurposesThis study aims to understand the pathogenesis and identify potential therapeutic targets of pterygium. Through a robust multi-omics analysis of bulk and single-cell RNA sequencing (RNA-seq) data from pterygium patients, we focus on the expression of inflammatory and mitochondrial energy pathways and identify potential genes responsible for the upregulated pathways in pterygium. MethodsWe collected bulk RNA-seq data from six pterygium patients and single-cell RNA-seq data from two pterygium patients. We then investigated the pathway enrichment, pathway correlation, differential gene expressions, protein-protein interactions, and cell-cell communications of pterygium. ResultsFrom the analysis of bulk RNA-seq data, the distribution of sample points from the principal component analysis plot and the pathways enriched from the Gene Ontology analysis showed distinct expression patterns in the Acc group compared to the Main and control groups. This suggested the need to separate the Main and Acc regions within pterygium samples and utilize single-cell RNA-seq data to understand the differences between the Main and control groups that the bulk data could not capture. The annotation of integrated single-cell data revealed a cluster of epithelial cells containing only pterygium samples. Cells from this cluster exhibited significant contributions to the ANGPTL, IL1, and KLK signaling networks in the cell-cell communication analysis. We also observed significant upregulation of the cells in the inflammatory pathways related to integrated stress response and the renin-angiotensin-aldosterone system, both of which showed high correlations with energy metabolism pathways. Significant changes in the expression of multiple pro-inflammatory, antioxidant, and immune-related genes were also identified. ConclusionThe different expression patterns between the Main and Acc groups suggest the need to consider different anatomical regions separately in future studies of pterygium. Additionally, the significant role epithelial cells from the Main group play in the inflammation of pterygium presents a potential clinical approach to the disease.

bioinformatics↗

bioRxiv2024

Dendritome Mapping Unveils Spatial Organization of Striatal D1/D2-Neuron Morphology

Morphology is a cardinal feature of a neuron and mediates its functions, but profiling neuronal morphologies at scale remains a formidable challenge. Here we describe a generalizable pipeline for large-scale brainwide profiling of dendritic morphology of genetically-defined single neurons in the mouse brain. We generated a dataset of 3,762 3D-reconstructed and reference-atlas mapped striatal D1- and D2-medium spiny neurons (MSNs). Integrative morphometric analyses reveal distinct impacts of D1/D2 and anatomical locations on MSN morphology. To analyze striatal regional features of MSN dendrites without prior anatomical constraints, we assigned MSNs to a lattice of cubic boxes in the reference brain atlas, and summarized morphometric representation ("eigen-morph") for each box and clustered boxes with shared morphometry. This analysis reveals 6 modules with characteristic dendritic features and spanning contiguous striatal territories, each receiving distinct corticostriatal inputs. Finally, we found aging confers robust and concordant dendritic length and branching defects in D1/D2-MSNs, while Huntingtons disease (HD) mice exhibit MSN-subtype and striatal regional specific pathology. Together, our study demonstrates a systems-biology approach to profile dendritic morphology of genetically-defined single-neurons; and defines novel striatal D1/D2-MSN morphological territories and aging- or HD-associated pathologies.

neuroscience↗

bioRxiv2024

Endogenous Huntingtin aggregates are a huge organized scaffold and mold for Golgi apparatus impaired by mutant Huntingtin protein

Abstract Huntingtin (HTT) is a naturally aggregating protein that causes Huntington's disease (HD) when its polyglutamine (polyQ) tract exceeds 38 repeats. Despite its importance, the biology of HTT aggregates remains poorly defined. Here, using high resolution imaging of cells derived from HD families, we redefine polyQ assemblies, traditionally regarded as pathogenic aggregates, as dynamic and structurally organized intracellular compartments resembling knitted-fabric patches. These assemblies closely associate with and encircle the Golgi apparatus, integrating Golgi ribbons and stacks to form a previously unrecognized polyQ assembly-Golgi complex. Mechanistically, fragmentation of polyQ assemblies is dynamically coupled with mitotic Golgi fragmentation, whereas inhibition of ADP-ribosylation factor (ARF) function by Brefeldin A disrupts and fragments the complex, revealing an intimate structural and functional coupling between polyQ assemblies and Golgi architecture. The presence of mutant HTT (mHTT) destabilizes these assemblies and complexes, altering their response to nutrient deprivation and autophagy enhancers but not to antisense oligonucleotide (ASO) therapy. Functionally, polyQ assemblies in HD cells compromise the scaffolding capacity of the Golgi apparatus, clathrin-coated vesicles, and ARF1, impair Golgi glycosylation, and promote aberrant neuronal firing activity. Collectively, these findings establish polyQ assemblies as dynamic structural regulators of Golgi organization and function and demonstrate that mHTT disrupts the homeostatic dynamics of the polyQ assembly-Golgi complex, leading to Golgipathy.

pathology↗

bioRxiv2024

Tumor Priming by Ultrasound Mechanogenetics for with SynNotch CAR T Therapy

Cell-based cancer immunotherapy holds potential as a therapeutic approach, yet its application for solid tumor treatment remains challenging. We created a system where focused ultrasound (FUS) is able to remotely stimulate gene expressions in a specific tissue area through mechanical induction, gated by a chemical inducer to minimize the background noise. This system, known as CaDox, integrates FUS-triggered mechanical and calcium stimulation with doxycycline-responsive genetic circuits, which allows the localized expression of the clinically validated and specific antigen CD19 within a subpopulation of cancer cells upon FUS stimulation. These CD19-expressing cells can then function as "training centers" that activate synNotch chimeric antigen receptor (CAR) T cells to generate CARs that recognize a less specific but widespread antigen in cancer cells, thereby attacking and suppress the whole cancer cell population nearby at the tumor site. We validated the functionality of this CaDox system in vitro, in organoids, and in vivo, demonstrating its potential for various cell types and as a versatile platform for precisely controllable immunotherapy. Our combinatorial approach thus offers a FUS-controlled remote and non-invasive priming of solid tumors for effective and safe CAR T immunotherapy via the induced production of clinically validated antigens.

synthetic biology↗

bioRxiv2024

Twist is the key to the gating of mechanosensitive ion channel NOMPC

NOMPC, a tethered mechanosensitive ion channel belonging to the transient receptor potential (TRP) family, converts mechanical stimuli into ionic electric signals that excite neuronal cells. Previous investigations have demonstrated that a pushing force applied to the linker helix domain or the compression of NOMPCs ankyrin repeat (AR) domain can trigger channel opening. In this study, we explored the direct mechanical causes of NOMPC channel opening as well as the torsional properties of the AR domain, using all-atom molecular dynamics simulations. Our results indicate that a torque directed toward the extracellular side, exerted on the amphipathic TRP domain, is the primary factor driving channel opening. The coupling between compression and twisting of the AR domain ensures that both types of deformation can induce channel opening. Therefore, we propose a twist-to-open model, facilitated by the compression-twist coupling property of the AR domain, to provide further insight into the gating mechanism of the NOMPC channel.

biophysics↗

bioRxiv2024

Rigorous (in)validation of ecological models

The complexity of ecosystems poses a formidable challenge for confidently invalidating ecological models, as current practices struggle to distinguish model inadequacies from the confounding effects of unobserved biotic or abiotic factors. The prevailing inability to falsify models has resulted in an accumulation of models but not an accumulation of confidence. Here, we introduce a new approach rooted in queueing theory, termed the covariance criteria, that establishes a rigorous test for model validity based on covariance relationships between observable quantities. These criteria set a high bar for models to pass by specifying necessary conditions that must hold regardless of unobserved factors. We demonstrate the broad applicability and discriminatory power of the covariance criteria by applying them to three long-standing challenges in ecological theory: resolving competing models of predator-prey functional responses, disentangling ecological and evolutionary dynamics in systems with rapid evolution, and detecting the often-elusive influence of higher-order species interactions. Across these diverse case studies, the covariance criteria consistently rule out inadequate models, while building strong confidence in those that provide strategically useful approximations. The covariance criteria approach is mathematically rigorous, computationally efficient, and often non-parametric, making it immediately applicable to existing data and models.

ecology↗

bioRxiv2024

The Precise Basecalling of Short-Read Nanopore Sequencing

The nanopore sequencing of short sequences, whose lengths are typically less than 0.3kb therefore comparable with Illumina sequencing techniques, has recently gained wide attention. Here, we design a scheme for training nanopore basecallers that are specialized for short biomolecules. With bioengineered RNA (BioRNA) molecules as examples, we demonstrate the superior accuracy of basecallers trained by our scheme.

bioinformatics↗

bioRxiv2024

Protocol for Simulating the Effect of THz Electromagnetic Field on Ion Channels

Terahertz (THz) electromagnetic fields are increasingly recognized for their crucial roles in various aspects of medical research and treatment. Recent computational studies have demon-strated that THz waves can modulate ion channel function by interacting with either the channel proteins or the bound ions through distinct mechanisms. Here we outline a universal simulation protocol to identify the THz frequencies that may affect ion channels, which consists of frequency spectrum analysis and ion conductance analysis. Following this protocol, we studied the effect of THz field on a CaV channel and found a broad frequency band in 1 to 20 THz range. We believe that this protocol, along with the identified characteristic frequencies, will provide a theoretical foundation for future terahertz experimental studies.

biophysics↗

bioRxiv2024

Nanoscale visualization of Drosophila E-cadherin ectodomain fragments and their interactions using DNA origami nanoblocks

The adhesive function of cell surface proteins can be visually assessed through direct observation; however, the underlying structures that mediate adhesion typically remain invisible at the nanoscale level. This hinders knowledge on the diversity of molecular architectures responsible for cell-cell adhesion. Drosophila E-cadherin (DE-cadherin), a classical cadherin with a unique domain structure, demonstrates adhesive function; however, it lacks a structural model that explains its adhesion mechanism. In this study, we present a novel application of DNA origami technology to create a cell-free, flat environment in which full DE-cadherin ectodomains are anchored using SNAP-tags and biotin-streptavidin interactions. DNA origami was assembled into a 120 nm long block, bearing 5 or 14 biotin:streptavidin sites that were evenly spaced on one lateral face. DE-cadherin ectodomain fragments were attached via biotinylated SNAP-tags. These decorated DNA origami nanoblocks were subjected to transmission electron and high-speed atomic force microscopy, which revealed a hinge-like site that separated the membrane-distal and -proximal portions of the DE-cadherin ectodomain, suggesting a role in mechanical flexibility. We also observed interactions between DE-cadherin ectodomains via their membrane-distal portions on single DNA origami nanoblocks. We reconstituted an adhesion-like process via pairing DNA origami nanoblocks using DE-cadherin ectodomain interactions. Homophilic associations of functional DE-cadherin ectodomains between the paired DNA origami nanoblocks were visualized at the nanoscale, displaying strand-like molecular configurations, likely representing the extracellular cadherin repeats without regular arrays of structural elements. This study introduces a DNA origami-based platform for reconstituting and visualizing cadherin ectodomain interactions, with potential applications for a broader range of adhesion molecules. HighlightsO_LIDNA origami technology was applied to perform a structure-function study of cadherin. C_LIO_LIDNA origami nanoblocks decorated with DE-cadherin ectodomains were observed by TEM/HS-AFM. C_LIO_LIA hinge-like site that separated the membrane-distal and -proximal portions of the DE-cadherin ectodomain was revealed. C_LIO_LIAn adhesion-like process was mimicked via pairing two nanoblocks using DE-cadherin ectodomain interactions. C_LIO_LIHomophilic associations of DE-cadherin ectodomains between the nanoblocks were visualized at the nanoscale level. C_LI

molecular biology↗

bioRxiv2024

The Effect of THz Electromagnetic Field on the Conductance of Potassium and Sodium Channels

Ion channels are essential to various physiological processes and their defects are associated with many diseases. Previous research has revealed that Terahertz electromagnetic field can alter the channel conductance by affecting the motion of chemical groups of ion channels, and hence regulate the electric signals of neurons. In this study, we conducted molecular dynamics simulations to systematically investigate the effects of terahertz electromagnetic fields on the conductance of voltage-gated potassium and sodium channels, particularly focusing on the bound ions in the selectivity filters that have not been studied previously. Our results identified multiple new characteristic frequencies and showed that 1.4, 2.2, or 2.9 THz field increases the conductance of Kv1.2, and 2.5 or 48.6 THz field increases the conductance of Nav1.5. The conductance-enhancing effects are specific to the frequencies and directions of the electric field, which are determined by the intrinsic oscillation motions of the permeating ions in the selectivity filter or certain chemical groups of the ion channels. The amplitude of the THz field positively correlates with the change in ion conductance. Therefore, this study demonstrates that THz fields can specifically regulate ion channel conductances, which may carry great potential in biomedical applications.

biophysics↗

bioRxiv2024

Quantifying irreversibility of ecological systems

Irreversibility--the asymmetry of population dynamics when played forward versus backward in time--is a fundamental property of ecological dynamics. Despite its early recognition in ecology, irreversibility has remained a high-level and unquantifiable concept. Here, we introduce a quantitative framework rooted in non-equilibrium statistical physics to measure irreversibility in general ecological systems. Through theoretical analyses, we demonstrate that irreversibility quantifies the degree to which a system is out of equilibrium, a property not captured by traditional ecological metrics. We validate this prediction empirically across diverse ecological systems structured by different forces, such as rapid evolution, nutrient availability, and temperature. In sum, our study provides a rigorous formalism for quantifying irreversibility in ecological systems, with the potential to integrate dynamical, energetic, and informational perspectives in ecology.

ecology↗

bioRxiv2024

Predicting the alternative conformation of a known protein structure based on the distance map of AlphaFold2

With AlphaFold2 (AF2) becoming the top structural prediction tool, multiple studies have found that AF2 often favors one conformation state over others in high-precision structure predictions. Meanwhile, it has also been demonstrated that the prediction of multi-state structures from a given protein sequence is possible by subsampling multiple sequence alignment (MSA). In this work, we reveal that AF2 predictions contain information on multi-state structures even with the deepest MSA: protein distance maps extracted from AF2 often exhibit multi-peak signals in the distance probability distributions for residue pairs. By fitting and separating these multi-peak distributions of residue pairs, one can extract distinct distance information of two states, which can be incorporated into Rosetta as restraint energy functions to model large and complex conformational changes. Twenty protein systems with different types of conformational changes were selected for validation in modeling their alternative conformations. With our protocol, we successfully predicted the alternative conformations of 19 systems and achieved a template-based modeling score (TM-score) above 0.90 for the best-sampled models in nine cases. This work further expands the usage of AlphaFold2 in studying multi-state proteins.

biophysics↗

bioRxiv2024

TRAPT: A multi-stage fused deep learning framework for transcriptional regulators prediction via integrating large-scale epigenomic data

It is a challenging task to identify functional transcriptional regulators, which control expression of gene sets via regulatory elements and epigenomic signals, involving context-specific studies such as development and diseases. Integrating large-scale multi-omics epigenomic data enables the elucidation of the complex epigenomic control patterns of regulatory elements and regulators. Here, we propose TRAPT, a multi-modality deep learning framework that predicts functional transcriptional regulators from a queried gene set by integrating large-scale multi-omics epigenomic data, including histone modifications, ATAC-seq and TR-ChIP-seq. We design two-stage self-knowledge distillation model to learn nonlinear embedded representation of upstream and downstream regulatory element activity, and merge multi-modality epigenomic features from TR and the queried gene sets for inferring regulator activity. Experimental results on 1072 TR-related datasets demonstrate that TRAPT outperforms current state-of-the-art methods in predicting transcriptional regulators, especially in the prediction of transcription co-factors and chromatin regulators. Additionally, we have successfully identified key transcriptional regulators associated with the disease, genetic variation, cell fate decisions, and tissues. Our method provides an innovative perspective for integrating epigenomic data and has the potential to significantly assist researchers in deepening their understanding of gene expression regulation mechanisms.

bioinformatics↗