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Biology subjects

Chen, P.-Y.

Publications and source records attributed to Chen, P.-Y..

At least 19 recordsLinked to original sources

Structural and Functional Principles of Hcp-Mediated Antibacterial Toxin Delivery by the Type VI Secretion System

The type VI secretion system (T6SS) is a bacterial contractile nanomachine that translocates toxic effectors into neighboring cells, helping bacteria to survive and compete in polymicrobial environments. The core T6SS structural protein Hcp forms the inner tube and mediates the loading and delivery of diverse effectors. Yet, how Hcp selectively recruits structurally and functionally distinct effectors in the absence of recognizable secretion signals remains poorly understood. Here, using Pseudomonas aeruginosa H1-T6SS as a model, we combined competition-coupled deep mutational scanning with cryo-EM based structural analysis to determine how Hcp accommodates distinct effectors. The resulting mutational landscape distinguishes structurally constrained residues required for Hcp tube assembly from lumen-facing residues specialized for cargo engagement. Near-atomic-resolution structures of Hcp bound to Tse2 and Tse4, together with a high-confidence Hcp-Tse1 model, revealed that while effector-specific contacts exist, Hcp engages different cargos through largely shared, dispersed interfaces characterized by polar-biased and variation-tolerant interactions. Comparative structural analyses further showed that effector-interacting residues are not governed by conserved primary-sequence motifs, but by conserved lumen-facing geometry and permissive physicochemical properties across distantly related bacteria. Together, our findings support a model in which Hcp acts as a selective yet adaptable scaffold that accommodates diverse antibacterial effectors through conserved structural features and flexible interaction surfaces, providing a mechanistic framework for understanding cargo selection by the T6SS and explaining how a conserved secretion nanomachine can evolve to deliver diverse toxic payloads.

microbiology↗

Metabolic control of smooth muscle cell phenotype switching in atherosclerosis

The loss of smooth muscle cell (SMC) contractile phenotype contributes to various diseases including atherosclerosis. However, its metabolic basis is not entirely elucidated. Since the transforming growth factor beta (TGF{beta}) signaling is among principal regulators of SMC contractility, we studied metabolic regulation of TGF{beta} signaling in SMCs in vitro and atherosclerotic mouse models and human lesions. We found that TGF{beta} induced Ac-CoA synthetase 2 (ACSS2)-dependent Ac-CoA production, by suppressing pyruvate dehydrogenase kinase 4 (PDK4). This stabilized R-SMADs and TGF{beta} receptor 1, preserving SMC contractile phenotype. SMC-specific PDK4 knockout mimicked the effect of TGF{beta} signaling both metabolically and phenotypically, increasing glucose-derived synthesis of Ac-CoA and SMC contractile phenotype. SMC-specific Pdk4 knockout in ApoE knockout mice reduced atherosclerosis. Furthermore, human specimens demonstrated a strong correlation between PDK4 level and atherosclerosis severity. These findings indicate that continuous TGF{beta} signaling, critical to the maintenance of the normal SMC contractile state and is regulated by PDK4 and carbohydrate metabolism. TeaserReducing PDK4 metabolically restricts aortic plaque growth via TGF{beta}-dependent SMC contractility.

cell biology↗

spatiAlytica: Viewer-Grounded Multimodal Agentic System for Interactive Spatial Omics Analysis

Spatial transcriptomics and proteomics map tissue architecture and cellular interactions, but analysis remains limited by programming demands and text-centered AI agents that lack viewer grounding and cross-turn context. We present spatiAlytica, a viewer-centric multimodal interactive agentic system embedded in the Napari viewer that enables non-programmer biologists to perform iterative, hypothesis-driven spatial omics analysis via natural language. spatiAlytica couples viewer-state serialization, agentic memory, biological concept-to-data-field mapping, code generation and debugging, Spatial VQA, and grounded interpretation to support an exploratory analysis and interpretive reasoning workflow. We introduce spatiAlyticaBench, a comprehensive benchmark spanning 222 single-turn spatial analytical coding questions, 178 multi-turn sequential workflow questions, and 7,350 image-grounded reasoning questions. spatiAlytica outperformed strong agentic baselines, while using less time and tokens. Case studies across Kaposis sarcoma, colorectal cancer, and ovarian cancer recapitulated known spatial patterns and uncovered progressive CD8 T-cell dysfunction during KS progression.

bioinformatics↗

Arid2 promotes Follicular B-cell differentiation and antibody responses in vivo

SWI/SNF chromatin remodeling complexes regulate gene expression during development and differentiation. These complexes exist in distinct forms, including the polybromo-associated BAF (PBAF) complex, defined by the ARID2 subunit. ARID2 is frequently mutated in B cell leukemias, but its role in normal B cell development remains unknown. Using conditional knockout mice, we show that Arid2 deletion impairs B cell differentiation in vivo. Mb1-Cre-mediated deletion, active in early progenitors, caused a marked reduction of splenic and circulating Follicular B cells, whereas CD19-Cre deletion produced a milder phenotype. These defects were not due to altered proliferation or survival but reflected impaired differentiation potential. Transcriptomic profiling of isolated pro-B, pre-B, and Follicular B cells revealed that Arid2 loss disrupts stage-specific gene expression programs, with cumulative downregulation of B cell receptor signaling and altered lineage-specifying pathways. Functionally, Arid2-deficient mice exhibited impaired germinal center expansion after immunization and reduced IgG antibody production following transplantation. Together, these findings identify Arid2 as a critical regulator of B cell differentiation and maturation by coordinating transcriptional programs during early lymphopoiesis.

immunology↗

α2A-AR-Kv dysfunction drives LC hyperactivity and early sleep disturbance in amyloidogenic mice

Disrupted sleep-wake patterns are common in neurodegenerative disorders such as Alzheimers disease (AD), can emerge early, and are proposed as potent risk factors for disease onset and progression. However, the underlying mechanisms remain poorly understood. Here, we report that 5xFAD transgenic mice exhibit hyperarousal and reduced brain-state transitions, particularly during the dark phase, as early as two months of age. The Locus Coeruleus (LC), a key regulator of arousal and brain-state transitions and a region highly vulnerable in AD, shows time-specific hyperactivity during this phase. This increased tonic LC activity is mediated by heightened neuronal excitability due to impaired Kv4 and Kv7 potassium channel conductance. Pharmacological activation of 2A adrenergic receptors restored Kv4 and Kv7 function and normalized LC activity. Furthermore, local administration of the 2A agonist guanfacine or the Kv7 positive allosteric modulator retigabine substantially rescued the sleep-wake disturbances in young 5xFAD mice. These findings identify dark-phase-selective LC hyperexcitability as a key driver of early-onset sleep disruption in AD mice and implicate 2A adrenergic receptors and Kv7 channels as promising targets for early intervention.

neuroscience↗

In vivo validation of the palmitoylation cycle as a therapeutic target in NRAS-mutant cancer

Ras proteins are functionally dependent on one or more lipid modifications.1,2 The dynamic palmitoylation of N-Ras by DHHC palmitoyl acyltransferases and depalmitoylation by ABHD17 serine hydrolases is essential for the growth of NRAS-mutant acute myeloid leukemia (AML) cells.3-6 Here we show that ABD778, an in vivo-active ABHD17 inhibitor, selectively reduces the growth of NRAS-mutant AML and melanoma cell lines and is synergistic with the MEK inhibitor PD0325901 (PD901; mirdametinib). Mechanistically, ABD778 and PD901 induce deep and durable suppression of mitogen activated protein kinase (MAPK) pathway activation. Co-treatment extended the survival of mice transplanted with NrasG12D AMLs, which acquired by-pass mutations at relapse that conferred drug resistance and restored MAPK activation. ABD778 augmented the anti-leukemia activity of PI3 kinase, pan-Ras tri-complex, and FLT3 inhibitors, and restored gilteritinib sensitivity in a patient-derived xenograft model of FLT3 inhibitor resistance. These studies validate the palmitoylation cycle as a therapeutic target in NRAS-mutant cancers.

cancer biology↗

An Interbacterial Cysteine Protease Toxin Inhibits Cell Growth by Targeting Type II DNA Topoisomerases GyrB and ParE

Bacteria deploy a diverse arsenal of toxic effectors to antagonize competitors, profoundly influencing the composition of microbial communities. Previous studies have identified an interbacterial toxin predicted to exhibit proteolytic activity that is broadly distributed among Gram-negative bacteria. However, the precise mechanism of intoxication remains unresolved. Here, we demonstrate that one such protease toxin from Escherichia coli, Cpe1, disrupts DNA replication and chromosome segregation by cleaving conserved sequences within the ATPase domain of type II DNA topoisomerases GyrB and ParE. This cleavage effectively inhibits topoisomerase-mediated relaxation of supercoiled DNA, resulting in impaired bacterial growth. Cpe1 belongs to the papain-like cysteine protease family and is associated with toxin delivery pathways, including the type VI secretion system and contact-dependent growth inhibition. The structure of Cpe1 in complex with its immunity protein reveals a neutralization mechanism involving competitive substrate binding rather than active site occlusion, distinguishing it from previously characterized effector-immunity pairs. Our findings unveil a unique mode of interbacterial intoxication and provide insights into how bacteria protect themselves from self-poisoning by protease toxins.

microbiology↗

Lignocellulose Degradation in Bacteria and Fungi for Biomass Conversion

Lignocellulose biomass is one of the most abundant resources for sustainable biofuels. However, scaling up the biomass-to-biofuels conversion process for widespread usage is still pending. Bottlenecks during the process of enzymatic hydrolysis are the high cost of enzymes and the labor-intensive need for substrate-dependent enzyme mixtures. Current research efforts are therefore targeted at searching for or engineering lignocellulolytic enzymes of high efficiency. One way is to engineer multi-enzyme complexes that mimic the bacterial cellulosomal system, known to increase degradation efficiency up to 50-fold when compared to freely-secreted enzymes. However, these designer cellulosomes are instable and less efficient than wild type cellulosomes. Fungi cellulosomes discovered in recent years have significant differences from bacterial counterparts and hold great potential for industrial applications, both as designer cellulosomes and as additions to the enzymatic repertoire. Up to date, they are only found in a few anaerobic fungi. In this review, we extensively compared the degradation mechanisms in bacteria and fungi, and highlighted the essential gaps in applying these mechanisms in industrial applications. To better understand cellulosomes in microorganisms, we examined their sequences in 66,252 bacterial species and 823 fungal species and identified several bacterial species that are potentially cellulosome-producing. These findings act as a valuable resource in the biomass community for further proteomic and genetic sequence analysis. We also collated the current strategies of bioengineering lignocellulose degradation to suggest concepts that could be favorable for industrial usage.

microbiology↗

Surface-mediated Bacteriophage Defense Incurs Fitness Tradeoffs for Interbacterial Antagonism

Bacteria in polymicrobial habitats are constantly exposed to biotic threats from bacteriophages (or "phages"), antagonistic bacteria, and predatory eukaryotes. These antagonistic interactions play crucial roles in shaping the evolution and physiology of bacteria. To survive, bacteria have evolved mechanisms to protect themselves from such attacks, but the fitness costs of resisting one threat and rendering bacteria susceptible to others remain unappreciated. Here, we examined the fitness consequences of phage resistance in Salmonella enterica, revealing that phage-resistant variants exhibited significant fitness loss upon co-culture with competitor bacteria. These phage-resistant strains display varying degrees of lipopolysaccharide (LPS) deficiency and increased susceptibility to contact-dependent interbacterial antagonism, such as the type VI secretion system (T6SS). Utilizing mutational analyses and atomic force microscopy, we show that the long-modal length O-antigen of LPS serves as a protective barrier against T6SS-mediated intoxication. Notably, this competitive disadvantage can also be triggered independently by phages possessing LPS-targeting endoglycosidase in their tail spike proteins, which actively cleave the O- antigen upon infection. Our findings reveal two distinct mechanisms of phage-mediated LPS modifications that modulate interbacterial competition, shedding light on the dynamic microbial interplay within mixed populations.

microbiology↗

StomaVision: stomatal trait analysis through deep learning

StomaVision is an automated tool designed for high-throughput detection and measurement of stomatal traits, such as stomatal number, pore size, and closure rate. It provides insights into plant responses to environmental cues, streamlining the analysis of micrographs from field-grown plants across various species, including monocots and dicots. Enhanced by a novel collection method that utilizes video recording, StomaVision increases the number of captured images for robust statistical analysis. Accessible via an intuitive web interface at and available for local use in a containerized environment at , this tool ensures long-term usability by minimizing the impact of software updates and maintaining functionality with minimal setup requirements. The application of StomaVision has provided significant physiological insights, such as variations in stomatal density, opening rates, and total pore area under heat stress. These traits correlate with critical physiological processes, including gas exchange, carbon assimilation, and water use efficiency, demonstrating the tools utility in advancing our understanding of plant physiology. The ability of StomaVision to identify differences in responses to varying durations of heat treatment highlights its value in plant science research. Plain language summaryStomaVision is a tool that automatically counts and measures tiny openings on plant leaves, helping us learn how plants deal with their surroundings. It is easy to use and works well with various plant species. This tool helps scientists see how plants change under stress, making plant research easier and more accurate.

plant biology↗

A Whole-Cell Screening Platform to Discover Cell Adhesion Molecules that Enable Programmable Bacterial Cell-Cell Adhesion

Developing programmable bacterial cell-cell adhesion is of significant interest due to its versatile applications. Current methods that rely on presenting cell adhesion molecules (CAMs) on bacterial surfaces are limited by the lack of a generalizable strategy to identify such molecules targeting bacterial membrane proteins in their natural states. Here, we introduce a whole-cell screening platform designed to discover CAMs targeting bacterial membrane proteins within a synthetic bacteria-displayed nanobody library. Leveraging the potency of the bacterial type IV secretion system--a contact-dependent DNA delivery nanomachine--we have established a positive feedback mechanism to selectively enrich for bacteria displaying nanobodies that target antigen-expressing cells. Our platform successfully identified functional CAMs capable of recognizing three distinct outer membrane proteins (TraN, OmpA, OmpC), demonstrating its efficacy in CAM discovery. This approach holds promise for engineering bacterial cell-cell adhesion, such as targeted antimicrobial interventions in the microbiome by deploying programmed inhibitor cells.

microbiology↗

Unveiling the Robustness of Machine Learning Models in Classifying COVID-19 Spike Sequences

In the midst of the global COVID-19 pandemic, a wealth of data has become available to researchers, presenting a unique opportunity to investigate the behavior of the virus. This research aims to facilitate the design of efficient vaccinations and proactive measures to prevent future pandemics through the utilization of machine learning (ML) models for decision-making processes. Consequently, ensuring the reliability of ML predictions in these critical and rapidly evolving scenarios is of utmost importance. Notably, studies focusing on the genomic sequences of individuals infected with the coronavirus have revealed that the majority of variations occur within a specific region known as the spike (or S) protein. Previous research has explored the analysis of spike proteins using various ML techniques, including classification and clustering of variants. However, it is imperative to acknowledge the possibility of errors in spike proteins, which could lead to misleading outcomes and misguide decision-making authorities. Hence, a comprehensive examination of the robustness of ML and deep learning models in classifying spike sequences is essential. In this paper, we propose a framework for evaluating and benchmarking the robustness of diverse ML methods in spike sequence classification. Through extensive evaluation of a wide range of ML algorithms, ranging from classical methods like naive Bayes and logistic regression to advanced approaches such as deep neural networks, our research demonstrates that utilizing k-mers for creating the feature vector representation of spike proteins is more effective than traditional one-hot encoding-based embedding methods. Additionally, our findings indicate that deep neural networks exhibit superior accuracy and robustness compared to non-deep-learning baselines. To the best of our knowledge, this study is the first to benchmark the accuracy and robustness of machine-learning classification models against various types of random corruptions in COVID-19 spike protein sequences. The benchmarking framework established in this research holds the potential to assist future researchers in gaining a deeper understanding of the behavior of the coronavirus, enabling the implementation of proactive measures and the prevention of similar pandemics in the future.

bioinformatics↗

Stretchable, nano-crumpled MXene multilayers impart long-term antibacterial surface properties

Infections are a significant risk to patients who receive medical implants, and can often lead to implant failure, tissue necrosis, and even amputation. So far, although various surface modification approaches have been proposed for prevention and treatment of microbial biofilms on indwelling medical devices, most are too expensive/complicated to fabricate, unscalable, or limited in durability for clinical use. Here we present a new bottom-up design for fabricating scalable and durable nano-pattered coatings with dynamic topography for long-term antibacterial effects. We show that MXene layer-by-layer (LbL) self-assembled coatings -- with finely tunable crumple structures with nanometer resolution and excellent mechanical durability -- can be successfully fabricated on stretchable poly(dimethylsiloxane) (PDMS). The crumpled MXene coating with sharp-edged peaks shows potent antibacterial effects against Staphylococcus aureus and Escherichia coli. In addition, we find that on-demand dynamic deformation of the crumpled coating can remove [≥]99% of adhered bacterial cells for both species, resulting in a clean surface with restored functionality. This approach offers improved practicality, scalability, and antibacterial durability over previous methods, and its flexibility may lend itself to many types of biomaterials and implantable devices.

bioengineering↗

CherryML: Scalable Maximum Likelihood Estimation of Phylogenetic Models

Phylogenetic models of molecular evolution are central to diverse problems in biology, but maximum likelihood estimation of model parameters is a computationally expensive task, in some cases prohibitively so. To address this challenge, we here introduce CherryML, a broadly applicable method that achieves several orders of magnitude speedup. We demonstrate its utility by applying it to estimate a general 400 x 400 rate matrix for amino acid co-evolution at protein contact sites.

evolutionary biology↗

Effect of flavonoids hydroxygenkwanin on vascular smooth muscle cell proliferation, migration, and neointimal formation

BackgroundRestenosis and atherosclerosis are chronic inflammatory disease. Abnormal vascular smooth muscle cell (VSMC) proliferation and migration play crucial roles in neointimal hyperplasia and restenosis progression in response to stimulation with various inflammatory cytokines, such as platelet-derived growth factor-BB (PDGF-BB) and tumour necrosis factor- (TNF-). Hydroxygenkwanin (HGK) exerts remarkable anti-inflammatory, antitumour, antiproliferative and antimigratory effects. The aim of the study was to evaluate and elucidate the therapeutic effect and regulatory mechanism of HGK on neointimal hyperplasia. MethodsTo determine the therapeutic effects of HGK in PDGF-BB- or TNF--treated VSMCs, MTT assays, Western blotting analysis, cell cycle analysis, BrdU incorporation assay, wound healing assay and adhesion assay were performed in vitro. A docking assay was also used to elucidate the mechanism underlying the regulatory effect of HGK. Histological and immunohistochemical staining of denuded femoral arteries was conducted to elucidate the therapeutic effect of HGK in an in vivo assay. ResultsHGK inhibited the abnormal proliferation, migration, and inflammation of PDGF-BB- or TNF--treated VSMCs through regulation of the PDK1/AKT/mTOR pathway. In addition, HGK promoted circulating endothelial progenitor cell (EPC) chemotaxis. In an in vivo assay, HGK dramatically enhanced re-endothelization and reduced neointimal hyperplasia after femoral artery denudation with a guide wire in mice. ConclusionsIn the present study, HGK decreased the PDGF-BB- or TNF--induced abnormal proliferation, migration and inflammation in VSMCs and improved re-endothelialization and neointimal hyperplasia in denuded femoral arteries. These results provide a novel potential treatment for restenosis in the future. Graphic abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY HGK decreases VSMC abnormal proliferation, migration and inflammation through PDK1/AKT/mTOR/S6K inhibition and promotes EPC chemotaxis and reendothelialization. HGK is a potential therapeutic candidate for intimal hyperplasia and restenosis. C_FIG_DISPLAY

cell biology↗

NF-κB inhibitor alpha has a cross-variant role during SARS-CoV-2 infection in ACE2-overexpressing human airway organoids

As SARS-CoV-2 continues to spread worldwide, tractable primary airway cell models that accurately recapitulate the cell-intrinsic response to arising viral variants are needed. Here we describe an adult stem cell-derived human airway organoid model overexpressing the ACE2 receptor that supports robust viral replication while maintaining 3D architecture and cellular diversity of the airway epithelium. ACE2-OE organoids were infected with SARS-CoV-2 variants and subjected to single-cell RNA-sequencing. NF-{kappa}B inhibitor alpha was consistently upregulated in infected epithelial cells, and its mRNA expression positively correlated with infection levels. Confocal microscopy showed more I{kappa}B expression in infected than bystander cells, but found concurrent nuclear translocation of NF-{kappa}B that I{kappa}B usually prevents. Overexpressing a nondegradable I{kappa}B mutant reduced NF-{kappa}B translocation and increased viral infection. These data demonstrate the functionality of ACE2-OE organoids in SARS-CoV-2 research and identify an incomplete NF-{kappa}B feedback loop as a rheostat of viral infection that may promote inflammation and severe disease.

microbiology↗

The histone variant H2A.W cooperates with chromatin modifications and linker histone H1 to maintain transcriptional silencing of transposons in Arabidopsis

Transposable elements (TEs) are marked by a complex array of chromatin modifications, but a central unifying mechanism for how they are silenced remains elusive. Histone H3 Lysine 9 methylation (H3K9me) is an important component of heterochromatin in most eukaryotes, including plants. In flowering plants, the specialized histone variant H2A.W occupies nucleosomes found at TE sequences. This variant is deposited by the chromatin remodeler DDM1 and confers specific biophysical properties to the nucleosomes. Here we use genetic and genomic strategies to evaluate the role of H2A.W in transposon silencing in Arabidopsis. Compared with mutants lacking either H2A.W or H3K9me, the combined loss of both H2A.W and H3K9me causes a dramatic increase in both the number of expressed TEs and their expression levels. Synergistic effects are also observed when H2A.W is lost in combination with histone H1 or CH methylation. Collectively, these TEs are also upregulated in mutants lacking DDM1, which are impaired in H2A.W deposition and lose heterochromatic marks. We conclude that H2A.W acts in combination with different elements of heterochromatin to maintain silencing across a large spectrum of TEs present primarily in pericentric heterochromatin in Arabidopsis. In mammals, the DDM1 ortholog LSH deposits macroH2A to heterochromatin and silences TEs. We thus propose that specialized H2A variants localized to heterochromatin interact with a complex array of histone modifications to silence TEs in eukaryotes.

genomics↗

BSImp: imputing partially observed methylation patterns for evaluating methylation heterogeneity

DNA methylation is one of the most studied epigenetic modifications that has applications ranging from transcriptional regulation to aging, and can be assessed by bisulfite sequencing (BS-seq) at single base-pair resolution. The permutations of methylation statuses at bisulfite converted reads reflect the methylation patterns of individual cells. These patterns at specific genomic locations are sought to be indicative of cellular heterogeneity within a cellular population, which are predictive of developments and diseases; therefore, methylation heterogeneity has potentials in early detection of these changes. Computational methods have been developed to assess methylation heterogeneity using methylation patterns formed by four CpGs, but the nature of shotgun sequencing often give partially observed patterns, which makes very limited data available for downstream analysis. While many programs are developed to impute methylation levels genomewide, currently there is only one method developed for recovering partially observed methylation patterns; however, the program needs lots of data to train and cannot be used directly; therefore, we developed a probabilistic-based imputation method that uses information from neighbouring sites to recover partially observed methylation patterns speedily. It is demonstrated to allow for the evaluation of methylation heterogeneity at three times more regions genome-wide with high accuracy for data with moderate depth. To make it more user-friendly we also provide a computational pipeline for genome-screening, which can be used in both evaluating methylation levels and profiling methylation patterns genomewide for all cytosine contexts, which is the first of its kind. Our method allows for accurate estimation of methylation levels and makes evaluating methylation heterogeneity available for much more data with reasonable coverage, which has important implications in using methylation heterogeneity for monitoring changes within the cellular populations that were impossible to detect for the assessment of development and diseases.

bioinformatics↗