Biology subjects

Yang, D.

Publications and source records attributed to Yang, D..

At least 73 records · Page 4Linked to original sources

bioRxiv2025

Multi-omic and phenotypic analysis of growth and resilience of open raceway pond production of Monoraphidium minutum 26B-AM

BackgroundGreen microalgae, such as Monoraphidium minutum 26B-AM, have garnered significant commercial interest due to their high biomass production and lipid yield, providing promising candidates for various bioprocessing applications. However, the economic viability of large-scale algal cultivation in open raceway ponds is limited by biocontamination and environmental stressors, necessitating deeper understanding of the molecular mechanisms that underpin resilience and productivity in these systems. We hypothesized that the molecular signature associated with the cellular responses of M. minutum to environmental stressors will reveal critical information for the timely prediction of resilience and productivity in algal cultures within open pond systems. ResultsTo test this hypothesis, we conducted a longitudinal multi-omic study, integrating transcriptomics, proteomics, metabolomics, and phenomics, to monitor the acclimation, growth dynamics, and pathogen responses of algal cultures in two 1000 L raceway ponds, before and after the introduction of a pathogen as a stressor. We identified a number of molecular patterns that correlate with changes in the algal environment, and we can track these changes within the ponds per time. Furthermore, we identify scale-up and infection-specific molecular pathways through integrated multi-omics, showing that most patterns are unique to each studied stressor/transition. ConclusionsUltimately, this study demonstrates the utility of multi-omics observations at scale, revealing unique signatures and laying the groundwork for developing molecular detection techniques and predictive models that can improve the sustainability and efficiency of large-scale algae biomass production.

molecular biology↗

bioRxiv2025

Representation Transfer via Invariant Input-driven Neural Manifolds for Brain-inspired Computations

Domain adaptation is a core challenge for embodied AI deployed in unpredictable, noisy environments. Conventional deep models degrade under domain shifts and require costly retraining. Inspired by biological brains, we propose a modular framework where each module is a recurrent neural network pretrained via a simple, task-agnostic protocol to learn robust, transferable features. This shapes stable yet flexible representations as invariant input-driven continuous attractor manifolds embedded in high-dimensional latent space, supporting robust transfer and resilience to noise. At deployment, only a lightweight adapter needs training, enabling rapid few-shot adaptation. Evaluated on the DVS Gesture benchmark and a custom RGB rehabilitation dataset, our framework matches or surpasses leading C3D and ViViT models while using ten times fewer parameters and only one training epoch. By unifying biologically inspired attractor dynamics with cortical-like modular composition, our approach offers a practical path toward robust, continual adaptation for real-world embodied AI. TeaserInvariant input-driven attractors enable representation transfer and domain adaptation in brain-inspired AI.

neuroscience↗

bioRxiv2025

Functional screening of TCR-like antibodies for targeted cancer immunotherapy

Although hybridomas and display technologies have long been used in antibody discovery, identifying TCR-like antibodies that recognize major histocompatibility (MHC)-presented neoantigens remains an inefficient process. Here, we present a functional screening approach for antibody discovery based on T cell-specific functionality. By constructing Synthetic T cell receptor and Antigen Receptor (STAR)-T cells expressing antibody libraries and co-culturing them with target cells, we observed distinct antigen-specific STAR endocytosis and T cell activation. These two indicators are combined into an E-A functional index, which we demonstrate as an effective screening strategy for identifying antibodies with strict antigen sensitivity and specificity. Using this E-A functional screening method, we successfully discovered antibodies or nanobodies targeting cell-surface tumor antigens and neoantigens presented by human leukocyte antigen (HLA). These antibodies exhibited potent anti-tumor efficacy both in vitro and in vivo. Moreover, converting these antibodies into chimeric antigen receptors (CARs) and bispecific antibodies also induced T cell activation and tumor killing. In summary, our study establishes a robust functional screening strategy for identifying antibodies or TCR-like nanobodies targeting tumor-specific neoantigens, offering significant potential for advancing targeted cancer therapies.

immunology↗

bioRxiv2025

mamp-ml: A deep learning approach to epitope immunogenicity in plants

Eukaryotes detect biomolecules through surface-localized receptors, key signaling components. A subset of receptors survey for pathogens, induce immunity, and restrict pathogen growth. Comparative genomics of both hosts and pathogens has unveiled vast sequence variation in receptors and potential ligands, creating an experimental bottleneck. We have developed mamp-ml, a machine learning framework for predicting plant receptor-ligand interactions. We leveraged existing functional data from over two decades of foundational research, together with the large protein language model ESM-2, to build a pipeline and model that predicts immunogenic outcomes using a combination of receptor-ligand features. Our model achieves 73% prediction accuracy on a held-out test set, even when an experimental structure is lacking. Our approach enables high-throughput screening of LRR receptor-ligand combinations and provides a computational framework for engineering plant immune systems.

immunology↗

bioRxiv2025

New Swiss-knife activities of GroEL/Hsp60 proteins

GroEL/Hsp60 chaperonins are key proteins that control cell metabolism, stress adaptation and survival. They usually form a tetradecameric structure that assists, coupled to ATP hydrolysis, 10% of all cellular protein folding. Using recombinant E. coli, human mitochondrial and M. tuberculosis chaperonins, we found that these proteins have thioesterase, esterase and even, for some of them, auto-acyltransferase activities. The smaller oligomers of Hsp60 and M. tuberculosis GroEL1 were more prone to use the long acyl carbon chain substrate palmitoyl-CoA compared to tetradecameric E. coli GroEL and Hsp60. Enzymatic competition and replacement of M. tuberculosis GroEL1 residues allow identifying Asp86 and Thr89 in the ATP-binding pocket and an additional Ser393 influencing the thioesterase activity. Additionally, M. tuberculosis GroEL1 might enhance palmitoylation of the PpsE protein, which plays a role in the phthiocerol dimycocerosate (PDIM) biosynthesis. This could explain at least partly the involvement of GroEL1 in PDIM biosynthesis and antibiotic resistance.

biochemistry↗

bioRxiv2025

Advancing Inter-brain Synchrony Measurement: A Comparative Hyperscanning Study of High-Density Diffuse Optical Tomography and Functional Near-infrared Spectroscopy

Inter-brain synchrony (IBS), measured by hyperscanning, refers to the synchronization of multiple individuals brain activities during social interactions. Traditional fNIRS-based hyperscanning suffers shortcomings like low spatial resolution and high susceptibility to superficial interference, causing imprecise estimation of IBS in complex social tasks. This study aims to fill the knowledge gap by comprehensively assessing how high-density diffuse optical tomography (HD-DOT), an enhanced alternative to fNIRS, can benefit hyperscanning studies of complex social interactions. Sixteen dyads were engaged in both collaborative and individual tangram puzzle tasks, and their brain activities were recorded simultaneously using HD-DOT and fNIRS. We found that HD-DOT demonstrated significantly stronger IBS and identified more brain regions with significant IBS compared to fNIRS during the collaborative task. Specifically, while fNIRS detected IBS only in the dorsolateral prefrontal cortex (DLPFC) and supramarginal gyrus (SMG), HD-DOT revealed additional IBS in the superior temporal gyrus (STG). Additionally, compared to the individual task, the collaborative task showed increased IBS in HD-DOT, not only in the DLPFC but also in the SMG, frontal eye fields (FEF), and inferior frontal gyrus (IFG). By highlighting the superior spatial resolution and sensitivity of HD-DOT in capturing detailed and extensive neural activity during complex social interactions, our findings for the first time clarified the potential strengths of HD-DOT in measuring IBS over traditional fNIRS. These advances provide a stronger empirical foundation for investigating the neural basis of social interaction, paving the way for future research on real-world, dynamic group behaviors. HighlightsO_LIFirst application of HD-DOT for inter-brain synchrony measurement. C_LIO_LIFirst demonstration of HD-DOTs superiority over fNIRS in IBS measurement. C_LIO_LIHD-DOT detects stronger IBS and more brain regions than fNIRS. C_LI

neuroscience↗

bioRxiv2025

Biosynthesis of lavandulol and lavandulyl acetate in Escherichia coli

Lavandulol and its ester derivative, lavandulyl acetate, are key fragrance constituents of lavender essential oil with widespread applications in cosmetics, perfumery, and food. However, traditional plant extraction suffers from low yield and unsustainable practices, and chemical synthesis relies on petrochemical feedstocks. Here, we report the first de novo microbial biosynthesis of lavandulol and lavandulyl acetate in Escherichia coli through modular pathway engineering. By screening 13 pyrophosphatases from E. coli, we identified RdgB as an efficient pyrophosphatase catalyzing the conversion of lavandulyl diphosphate into lavandulol, enabling its production at 24.9 mg/L. Building on this, we established a three-plasmid expression system and introduced a lavender-derived alcohol acyltransferase, LiAAT4, to achieve the biosynthesis of lavandulyl acetate at 42.4 mg/L. This study establishes the biosynthetic route to lavandulol in E. coli, demonstrating a viable and sustainable microbial platform for producing significant natural components of lavender oil. Our work provides the basis for future strain optimization and industrial-scale biomanufacturing of high-value monoterpene fragrance molecules.

synthetic biology↗

bioRxiv2025

Protein Folding accompanied by disulfide bond formation drives glutenin polymerization into multidimensional gluten networks

Wheat flour dough, which is routinely used to make bread, pasta and noodles, can be stretched several thousand-fold without rupture. The molecular basis of this extraordinary property arises due to the ability to crosslink glutenin subunits, the major constituent of dough, into linear and branched elastic networks. The mechanism by which the glutenin subunits self-assemble and polymerize is unknown. Here, we show, using mass spectrometry, confocal imaging and molecular dynamics simulations that structuring of two hydrophobic residues, phenylalanine and tyrosine, in the core region of the monomeric glutenin 1Dx5 N-terminal domain (1Dx5-NTD) initiates inter molecular interactions and network formation. Upon folding of monomeric 1Dx5-NTD, two cysteine residues (Cys10 and Cys40) form an intramolecular disulfide bond and poises the third cysteine (Cys25) to engage in inter molecular crosslink with other glutenin subunits. Propagation of such a disulfide linkage pattern results in the formation of a cohesive linear gluten network. In alternate pathways, crosslinking of Cys10 with Cys25 with a third glutenin subunit results in the creation of a junction, which drives the formation of a three-dimensional network. The disulfide patterns in the networks accord well with the measured chemical reactivity of each cysteine residue and the solvent accessibility of the associated side chains during 1Dx5-NTD interactions. Our study shows that the diversity in the folding of a single glutenin subunit that exposes the third cysteine to the solvent is the key event that initiates polymerization and directs the formation of networks with differing architecture. Significance statementGlutenin proteins polymerize to form intricate molecular networks that support the expansion of dough by several thousand-fold. Although network formation occurs spontaneously, the polymerization mechanism is unknown. There are multiple cysteine residues in glutenin that can form intra and intermolecular disulfide bonds. Multiple experimental techniques and molecular dynamics simulations are used to show that folding of glutenin, initiated by two hydrophobic residues, exposes cysteines that are poised to form intermolecular disulfide bonds. The disulfide crosslinking pattern, determined by the initial folding process, dictates the creation of a linear or branched network. The study demonstrates that the propensity to form linear or branched networks is encoded by the diversity of the disulfide patterns created at the level of monomeric folding.

biophysics↗

bioRxiv2025

High-resolution spatial mapping of cell state and lineage dynamics in vivo with PEtracer

Charting the spatiotemporal dynamics of cell fate determination in development and disease is a long-standing objective in biology. Here we present the design, development, and extensive validation of PEtracer, a prime editing-based, evolving lineage tracing technology compatible with both single-cell sequencing and multimodal imaging methodologies to jointly profile cell state and lineage in dissociated cells or while preserving cellular context in tissues with high spatial resolution. Using PEtracer coupled with MERFISH spatial transcriptomic profiling in a syngeneic mouse model of tumor metastasis, we reconstruct the growth of individually-seeded tumors in vivo and uncover distinct modules of cell-intrinsic and cell-extrinsic factors that coordinate tumor growth. More generally, PEtracer enables systematic characterization of cell state and lineage relationships in intact tissues over biologically-relevant temporal and spatial scales.

molecular biology↗

bioRxiv2025

Ultra-high field strength electroporation enables efficient DNA transformation and genome editing in nontuberculous mycobacteria

Efficient DNA delivery is essential for genetic manipulation of mycobacteria and for dissecting their physiology, pathogenesis, and drug resistance. Although electroporation enables transformation efficiencies exceeding 105 CFU per {micro}g DNA in Mycobacterium smegmatis and Mycobacterium tuberculosis, it remains highly inefficient in many non-tuberculous mycobacteria (NTM), including Mycobacterium abscessus. Here, we discovered that NTM such as M. abscessus exhibit exceptional tolerance to ultra-high electric field strengths, and that hypertonic preconditioning partially protects cells from electroporation-induced damage. Using ultra-high electric field strength (3kV/mm) electroporation, we achieved dramatic improvements in plasmid transformation efficiency--up to 106-fold in M. abscessus, 83-fold in Mycobacterium marinum, and 24-fold in Mycobacterium kansasii--compared to standard conditions (1.25 kV/ mm). Transformation efficiency was further influenced by the choice of selectable marker. Ultra-high field strength electroporation also markedly enhanced allelic exchange in M. abscessus expressing Che9c RecET recombinases, increasing recovery of gene deletion mutants by over 1,000-fold relative to conventional electroporation. In parallel, oligonucleotide-mediated recombineering for targeted point mutations produced nearly 10,000-fold more mutants under ultra-high field conditions. Together, these findings establish ultra-high field electroporation as a robust, broadly applicable platform for genetic engineering of NTMs. This method substantially enhances transformation efficiency and enables construction of advanced genetic tools--including expression libraries and CRISPRi knockdown libraries--in species that have historically resisted genetic manipulation. ImportanceInfections caused by non-tuberculous mycobacteria (NTM), including Mycobacterium abscessus, are increasing globally, yet genetic manipulation of these pathogens remains technically challenging due to inefficient DNA delivery and low gene editing success. The ultra-high electric field strength electroporation strategy described here overcomes these barriers, enabling dramatic improvements in both transformation and genome editing efficiency. This advance paves the way for high-throughput functional genomics in NTMs, including the construction of genome-wide knockout, CRISPRi knockdown, and expression libraries. Broad adoption of this approach will accelerate discovery of genetic determinants of virulence and drug resistance, facilitating the development of antimicrobials and vaccines.

microbiology↗

bioRxiv2025

VerteBrain reveals novel neural and non-neural protein assemblies conserved across vertebrate evolution

Protein-protein interactions underlie core brain functions, including neurotransmitter release, receptor activation, and intracellular signaling essential for learning, memory, and cognition. Here, we systematically map conserved brain protein interactions across five vertebrate species-rabbit, chicken, dolphin, pig, and mouse-using co-fractionation and immunoprecipitation mass spectrometry. From 2,197 biochemical fractions, we identify over 81,000 high-confidence interactions among 6,108 conserved proteins. This interaction map (VerteBrain) reveals both regulatory and structural complexes, including extensive synaptonemal protein associations likely involved in inter-neuronal coordination. Conservation across species underscores essential roles in neuronal and glial function, as well as in additional tissues for more widely expressed complexes. The VerteBrain dataset uncovers candidate disease mechanisms, including roles for ARHGEF1 in short stature syndromes, synaptic vesicle trafficking complexes in epilepsy, and RELCH in congenital deafness. VerteBrain provides a publicly accessible framework for investigating brain protein interactions and their relevance to human neurological disorders.

systems biology↗

bioRxiv2025

Bridging Scales: Spectral Theory Reveals How Local Connectivity Rules Sculpt Global Neural Dynamics in Spatially Extended Networks

The brains diverse spatiotemporal activity patterns are fundamental to cognition and consciousness, yet how these macroscopic dynamics emerge from microscopic neural circuitry remains a critical challenge. We address this by developing a spatially extended neural network model integrated with a spectral theory of its connectivity matrix. Our theory quantitatively demonstrates how local structural parameters, such as E/I neuron projection ranges, connection strengths, and density determine distinct features of the eigenvalue spectrum, specifically outlier eigenvalues and a bulk disk. These spectral signatures, in turn, precisely predict the networks emergent global dynamical regime, encompassing asynchronous states, synchronous states, oscillations, localized activity bumps, traveling waves, and chaos. Motivated by observations of shifting cortical dynamics in mice across arousal states, our framework not only explains this repertoire of behaviors but also offers a principled approach to inferring underlying effective connectivity changes from macroscopic brain activity. By mechanistically linking neural structure to dynamics, this work provides a powerful tool for understanding brain function and paves the way for identifying potential biomarkers for neurological disorders.

biophysics↗

bioRxiv2025

Hepatocyte-specific CLSTN3B ablation impairs lipid droplet maturation and alleviates diet-induced steatohepatitis in mice

Excessive lipid accumulation in hepatocytes, a hallmark of metabolic dysfunction-associated steatotic liver disease (MASLD), can lead to progressive liver damage. Understanding the molecular mechanisms governing lipid storage in hepatocytes is essential for identifying therapeutic targets to halt MASLD progression. Here, we show a pivotal role for the protein calsyntenin 3{beta} (CLSTN3B) in promoting lipid droplet (LD) maturation and lipid storage in hepatocytes. Previously characterized as an endoplasmic reticulum (ER)-LD contact protein that facilitates LD maturation in adipocytes, we now show that CLSTN3B expression is strongly induced in mouse hepatocytes by peroxisome proliferator-activated receptor gamma (PPAR{gamma}) in response to dietary caloric excess. Hepatocyte-specific deletion of CLSTN3B in mice significantly increases energy expenditure, reduces metabolic efficiency, and protects against diet-induced hepatic steatosis and fibrosis. Mechanistically, CLSTN3B deficiency causes reduced LD phospholipid coverage and increased lipase recruitment. This results in enhanced fatty acid oxidation driven by a futile cycle of lipolysis and re-esterification. Notably, human clinical data reveal a positive correlation between hepatic CLSTN3B expression and MASLD severity and progression, emphasizing its relevance to human disease. Together, our findings establish CLSTN3B as a key regulator of hepatocyte lipid storage and metabolic efficiency and highlight its potential as a therapeutic target in MASLD.

physiology↗

bioRxiv2025

A Universal 6iL/E4 Culture System for Deriving and Maintaining Embryonic Stem Cells Across Mammalian Species

The derivation of authentic embryonic stem cells (ESCs) from diverse mammalian species offers valuable opportunities for advancing regenerative medicine, studying developmental biology, and enabling species conservation. Here, we report the development of a robust, serum-free culture system, termed 6iL/E4 that enables the derivation and long-term self-renewal of ESCs from multiple mammalian species, including mouse, rat, bovine, rabbit, and human. Using systematic signaling pathway analysis, we identified key regulators--including GSK3, STAT3, PDGFR, BRAF, and LATS--critical for ESC maintenance across species. Additionally, inducible expression of KLF2 and NANOG enhances the naive pluripotency and chimeric potential of bovine ESCs. The E4 medium also supports stable ESC growth while minimizing lineage bias. These findings reveal conserved principles underlying ESC self-renewal across divergent mammalian species and provide a universal platform for cross-species stem cell research, disease modeling, and biotechnology applications. In BriefWang et al. developed 6iL/E4, a serum-free system sustaining ESCs from mouse, rat, bovine, rabbit, and human. These findings reveal conserved fundamental mechanisms governing ESC self-renewal across diverse mammalian species. HighlightsO_LIDeveloped 6iL/E4 system for ESC derivation across five mammalian species. C_LIO_LIPDGFR signaling inhibition as critical for ESC derivation across species. C_LIO_LIE4 medium improves ESC maintenance and avoids neural bias of traditional N2B27. C_LIO_LIInducible KLF2/NANOG enhances naive pluripotency and chimera formation in bovine. C_LI

cell biology↗

bioRxiv2025

Interpreting biochemical text with language models:a machine learning framework for reaction extraction and cheminformatic validation

Recent advancements in large language models (LLMs) offer new opportunities for automating the manual curation of biochemical reaction databases from scientific literature. In this study, we present an integrated pipeline that enhances LLM-based extraction of enzymatic reactions with machine learning and cheminformatics-informed validation. Using BRENDA-linked PubMed articles, we evaluate GPT-4s ability to extract reactions and infer missing chemical entities in textual descriptions of enzymatic reactions. Extracted reactions are converted to SMILES and InChI notations before being encoded into molecular fingerprint similarity scores and atom mapping metrics. These cheminformatics metrics are then used to train machine learning classifiers that validate GPT extractions. We employ a Positive-Unlabeled learning approach with synthetic invalid reactions to train various classifiers and assess model performances. The best classifier is then benchmarked on GPT extractions. Our findings show that GPT can accurately infer incomplete reactions and cheminformatics tools can serve as effective predictors of reaction validity. This work demonstrates a scalable framework for automated and reliable curation of enzymatic reaction databases, highlighting the potential of combining LLMs with cheminformatics and machine learning for reliable scientific knowledge extraction. Author SummaryCurating databases of biochemical reactions is a time-consuming and manual task, yet it plays a vital role in advancing research in biology and chemistry. Many scientific articles describe important enzymatic reactions, but often do so in incomplete ways--such as mentioning only the starting molecule or the enzyme, and leaving out the rest. In this work, we explore how recent advancements in artificial intelligence, specifically large language models like GPT, can help extract such information automatically from scientific literature. We show that these models can not only find reactions in text, but also infer missing parts of reactions based on the surrounding context. To make sure these inferred reactions are chemically plausible, we use computational chemistry tools that analyze the structure of the molecules involved. We then train a machine learning model to help us automatically detect which reactions are likely to be valid. This combination of tools offers a new way to speed up and improve how biochemical knowledge is extracted from the growing body of scientific literature. Our study suggests that this kind of automation could help scientists keep biological databases up to date and reduce the burden of manual data entry.

bioinformatics↗

bioRxiv2025

Triad-LMF: A Hierarchical Low-Rank Multimodal Fusion Framework for Robust Cancer Subtype Classification Using Multi-Omics Data

Cancer heterogeneity poses a major challenge for accurate molecular subtype classification. Conventional methods often fail to exploit complementary information across multiple omics modalities, leading to overfitting on high-dimensional data and limited representation of subtype heterogeneity. To address this, we propose Triad-LMF, a multiomics integration framework based on low-rank multimodal fusion to improve classification accuracy. Triad-LMF harmonizes heterogeneous omics data and integrates information through a two-stage hierarchical fusion strategy. Local Pairwise Fusion and Global Triadic Fusion are combined via the Two-Feature and Three-Way LMF modules, enabling a gradual transition from local modality interactions to global feature integration. Experimental results show that Triad-LMF consistently outperforms existing methods. UMAP visualization confirms enhanced subtype separability, and SHAP-based analysis highlights biologically meaningful features. Across independent datasets, Triad-LMF demonstrates strong generalization, offering a robust and interpretable framework for multiomics-driven cancer subtype classification.

bioinformatics↗

bioRxiv2025

Learning interpretable representation for context-specific transcription regulatory networks using a foundation model

Gene expression is shaped by transcription regulatory networks (TRNs), where transcription regulators interact within regulatory elements in a context-specific manner. Despite significant efforts, understanding the intricate interactions of transcription regulators across different genomic regions and cell types remains a major challenge, largely due to data sparsity. Here, we introduce ChromBERT, a foundation model pre-trained on large-scale human ChIP-seq datasets. ChromBERT effectively captures the interaction syntax of approximately one thousand transcription regulators across diverse genomic contexts, generating interpretable representations of context-specific TRNs and their constituent regulators. Fine-tuned for various transcription regulation tasks, ChromBERT demonstrates superior performance in imputing previously unseen cistromes and modeling regulatory effects and dynamics in specific cell types. Notably, ChromBERT adapts its TRN representations for cell-type-specific tasks, providing deep insights into the roles of transcription regulators underlying observed regulatory effects or dynamics without requiring cell-type-specific genomic data for each regulator. By overcoming the limitations of sparse transcription regulator data, ChromBERT significantly enhances our ability to model, interpret, and predict transcriptional regulation across diverse biological contexts.

bioinformatics↗

bioRxiv2025

The lysine side chain's swing motion in AapA1 toxin promotes water transport through transmembrane pores

The rising antibiotic resistance of Helicobacter pylori and the emergence of multi-drug-resistant strains pose significant challenges to its eradication. Understanding the molecular mechanisms driving this resistance is critical. This study investigates how AapA1, a toxin protein, may inhibit H. pylori growth and cause morphological changes by forming transmembrane pores in its inner membrane. Using experimental data and molecular dynamics simulations, we analyzed the pore structure and its formation mechanism. Results show that AapA1 forms pores on liposomes and exists stably in the membrane as -helices through different multimers, creating hydrophilic pores of varying sizes. Dynamic oscillation of K16/23 side chains in the membrane affects water transport through these pores, and restricting their movement significantly reduces transport. AapA1 monomers adopt three distinct conformations, with the K16/23 residues showing an up-down swing motion, potentially facilitating accelerated transport of substances. This study provides a new perspective on the dynamic structure-function relationship of AapA1-membrane interaction and highlights the role of positively charged residues in AapA1. Understanding the multimeric AapA1-membrane structure and the mechanism underlying AapA1-induced transmembrane pores is particularly crucial to develop new drugs and treatment options to reduce drug resistance emergence and combat any new emergence development.

biophysics↗