Cell Biology Research Articles

The macroscale routing mechanism of structural brain connectivity related to body mass index.

Understanding the brain's mechanisms in individuals with obesity is important for managing body weight. Prior neuroimaging studies extensively investigated alterations in brain structure and function related to body mass index (BMI). However, how the network communication among the large-scale brain networks differs across BMI is underinvestigated. This study used diffusion magnetic resonance imaging of 290 young adults to identify links between BMI and brain network mechanisms. Navigation efficiency, a measure of network routing, was calculated from the structural connectivity computed using diffusion tractography. The sensory and frontoparietal networks indicated positive associations between navigation efficiency and BMI. The neurotransmitter association analysis identified that serotonergic and dopaminergic receptors, as well as opioid and norepinephrine systems, were related to BMI-related alterations in navigation efficiency. The transcriptomic analysis found that genes associated with network routing across BMI overlapped with genes enriched in excitatory and inhibitory neurons, specifically, gene enrichments related to synaptic transmission and neuron projection. Our findings suggest a valuable insight into understanding BMI-related alterations in brain network routing mechanisms and the potential underlying cellular biology, which might be used as a foundation for BMI-based weight management.

Exploring the anticancer mechanism of cardiac glycosides using proteome integral solubility alteration approach.

Cardiac glycosides (CGs), traditionally used for heart failure, have shown potential as anti-cancer agents. This study aims to explore their multifaceted mechanisms in cancer cell biology using proteome integral solubility alteration (PISA), focusing on the interaction with key proteins implicated in cellular metabolism and mitochondrial function. We conducted lysate-based and intact-cell PISA assays on cancer cells treated with CGs (Digoxin, Digitoxin, Ouabain) to analyze protein solubility changes. This was followed by mass spectrometric analysis and bioinformatics to identify differentially soluble proteins (DSPs). Molecular docking simulations were performed to predict protein-CG interactions. Public data including gene expression changes upon CG treatment were re-analyzed for validation. The PISA assays revealed CGs' broad-spectrum interactions, particularly affecting proteins like PKM2, ANXA2, SLC16A1, GOT2 and GLUD1. Molecular docking confirmed stable interactions between CGs and these DSPs. Re-analysis of public data supported the impact of CGs on cancer metabolism and cell signaling pathways. Our findings suggest that CGs could be repurposed for cancer therapy by modulating cellular processes. The PISA data provide insights into the polypharmacological effects of CGs, warranting further exploration of their mechanisms and clinical potential.

Microtubules, Membranes, and Movement: New Roles for Stathmin-2 in Axon Integrity.

Neurons establish functional connections responsible for how we perceive and react to the world around us. Communication from a neuron to its target cell occurs through a long projection called an axon. Axon distances can exceed 1 m in length in humans and require a dynamic microtubule cytoskeleton for growth during development and maintenance in adulthood. Stathmins are microtubule-associated proteins that function as relays between kinase signaling and microtubule polymerization. In this review, we describe the prolific role of Stathmins in microtubule homeostasis with an emphasis on emerging roles for Stathmin-2 (Stmn2) in axon integrity and neurodegeneration. Stmn2 levels are altered in Amyotrophic Lateral Sclerosis and loss of Stmn2 provokes motor and sensory neuropathies. There is growing potential for employing Stmn2 as a disease biomarker or even a therapeutic target. Meeting this potential requires a mechanistic understanding of emerging complexity in Stmn2 function. In particular, Stmn2 palmitoylation has a surprising contribution to axon maintenance through undefined mechanisms linking membrane association, tubulin interaction, and axon transport. Exploring these connections will reveal new insight on neuronal cell biology and novel opportunities for disease intervention.

Thin microporous polydimethylsiloxane membrane prepared by phase separation and its applications for cell culture

Animal experiments are often required for biological studies. However, in vitro cell culture models, such as cell-culture inserts and microphysiological systems, can provide a suitable alternative, making them essential tools in cell biology research, including the simulation of an organ environments closely related to the human body. Cell-culture inserts with porous membranes assist in recreating in vivo cell culture environments to study and process cell-culture assays. However, conventional cell culture membranes typically made of polyethylene terephthalate or polycarbonate cannot accommodate cell types that require deformable substrates. As such, this paper introduced a novel approach using spin-casting-assisted polymer-blend phase separation to create thin, flexible, and highly porous membranes for cell culture applications. Polydimethylsiloxane (PDMS) was selected as the material for the porous membrane, and polystyrene (PS) was used as a counter pair to induce phase separation with PDMS. PDMS facilitated the necessary reversible deformations during cell culture owing to its low elastic modulus. The thickness of the membrane and connectivity of the phase-separated PS domains can be adjusted, facilitating the fine-tuning of the pore size and density to improve the membrane performance. Therefore, this study successfully fabricated thin microporous PDMS membranes with improved performance over standard membranes for cell-culture inserts, namely a higher porosity, flexibility, and softness. The results of this study can enhance cell culture methodologies and contribute to a deeper understanding of cellular processes.

Commercial perspectives: Genome editing as a breeding tool for health and well-being in dairy cattle

Genome editing is the latest breeding tool capable of accelerating the rate of genetic improvement for health and well-being traits in food animals. It enables the introduction of beneficial alleles within a single generation, including those of low frequency or absent in the population, while effectively bypassing linkage drag. For the dairy industry, genome editing can be used to make rapid genetic improvements that are precise, efficient, and transgene-free for functional traits that are not practically addressed without disrupting conventional breeding goals for overall economic merit based on genomic selection. Herein, various case studies for dairy cattle breeding are presented that demonstrate applications of genome editing for enhancing heat stress tolerance, reduced disease susceptibility, and/or other qualitative traits absent in some breeds. One case highlights the success of simultaneous editing of multiple loci through recent advancements in embryonic stem cell biology. Multiplexed editing is crucial for addressing the polygenic nature inherent to many economically important traits in livestock. However, maximizing the benefits of genome editing depends on the continued discovery of targets for editing that are commercially important. Commercialization also depends on rapidly evolving regulatory statutes for risk assessment, where some countries already permit the commercialization of cattle with non-GMO genome alterations through existing regulations. New breeding technologies like genome editing are now poised to have significant impact in equipping elite performance cattle to be more resilient to infectious disease and climate change without the loss of production gains obtained from decades of selection.

Positive-strand RNA virus replication organelles at a glance.

Membrane-bound replication organelles (ROs) are a unifying feature among diverse positive-strand RNA viruses. These compartments, formed as alterations of various host organelles, provide a protective niche for viral genome replication. Some ROs are characterised by a membrane-spanning pore formed by viral proteins. The RO membrane separates the interior from immune sensors in the cytoplasm. Recent advances in imaging techniques have revealed striking diversity in RO morphology and origin across virus families. Nevertheless, ROs share core features such as interactions with host proteins for their biogenesis and for lipid and energy transfer. The restructuring of host membranes for RO biogenesis and maintenance requires coordinated action of viral and host factors, including membrane-bending proteins, lipid-modifying enzymes and tethers for interorganellar contacts. In this Cell Science at a Glance article and the accompanying poster, we highlight ROs as a universal feature of positive-strand RNA viruses reliant on virus-host interplay, and we discuss ROs in the context of extensive research focusing on their potential as promising targets for antiviral therapies and their role as models for understanding fundamental principles of cell biology.

Deep learning permits imaging of multiple structures with the same fluorophores

Fluorescence microscopy, which employs fluorescent tags to label and observe cellular structures and their dynamics, is a powerful tool for life sciences. However, due to the spectral overlap between different dyes, a limited number of structures can be separately labeled and imaged for live-cell applications. In addition, the conventional sequential channel imaging procedure is quite time consuming, as it needs to switch either different lasers or filters. Here, we propose a novel double-structure network (DBSN) that consists of multiple connected models, which can extract six distinct subcellular structures from three raw images with only two separate fluorescent labels. DBSN combines the intensity-balance model to compensate for uneven fluorescent labels for different structures and the structure-separation model to extract multiple different structures with the same fluorescent labels. Therefore, DBSN breaks the bottleneck of the existing technologies and holds immense potential applications in the field of cell biology.

A cross-reactive imaging matrix of membrane protein profiling for single-cell analysis

Membrane proteins play a critical role in cellular processes such as chemical transport, signal transduction, and enzymatic catalysis. A comprehensive understanding of membrane protein types and accurately measuring their expression levels are essential for advancing cell biology. However, current methods often fail to characterize the types and amounts of multiple membrane proteins in specific cell types due to inherent measurement limitations. Here, we have developed a single-cell image analysis technique based on a cross-reactive imaging matrix using membrane protein tags. This technique allows quantitative and qualitative analysis of multiple membrane protein types and their expression levels within single cells. As a proof of concept, we used four specific DNA aptamers to tag four different membrane proteins in seven different cell types. The four proteins are MUC1, EGFR, HeR2, and TLR4. The seven cell lines are 3t3, 4T1, A549, CT26, HeLa, Hepa1–6, Raw, respectively. We captured a comprehensive image matrix using wide-field fluorescence imaging, allowing precise quantification of protein expression at the single-cell level. Our approach introduces a multi-labeling analysis method for membrane proteins and reveals the influence of membrane protein diversity on cell heterogeneity analysis. We found that the accuracy of cell heterogeneity recognition can be affected by adjusting the number of membrane proteins. As the diversity of membrane protein species increases, the accuracy of cell type recognition increases. Our approach facilitates the evaluation of heterogeneity in diverse cells, providing new insights into cellular diversity and a powerful tool for studying single-cell biology.

Instance segmentation of cells and nuclei from multi-organ cross-protocol microscopic images.

Light microscopy is a widely used technique in cell biology due to its satisfactory resolution for cellular structure analysis, prevalent availability of fluorescent probes for staining, and compatibility for the dynamic analysis of live cells. However, the segmentation of cells and nuclei from microscopic images is not a straightforward process because it has several challenges such as high variation in morphology and shape, the presence of noise and diverse contrast in backgrounds, clustering or overlapping nature of cells. Dealing with these challenges and facilitating more reliable analysis necessitates the implementation of computer-aided methods that leverage image processing techniques and deep learning algorithms. The major goal of this study is to propose a model, for instance segmentation of cells and nuclei, applying the most cutting-edge deep learning techniques. A fine-tuned You Only Look at Once version 9 extended (YOLOv9-E) model is initially applied as a prompt generator to generate bounding box prompts. Using the generated prompts, a pre-trained segment anything model (SAM) is subsequently applied through zero-short inferencing to produce raw segmentation masks. These segmentation masks are then refined using non-max suppression and simple image processing methods such as image addition and morphological processing. The proposed method is developed and evaluated using an open-sourced dataset called Expert Visual Cell Annotation (EVICAN), which is relatively large and contains 4,738 microscopy images extracted from cross organs using different protocols. Based on the evaluation results on three different levels of EVICAN test sets, the proposed method demonstrates noticeable performances showing average mAP50 [mean average precision at intersection over union (IoU) =0.50] scores of 96.25, 95.05, and 94.18 for cell segmentation, and 68.04, 54.66, and 38.29 for nucleus segmentation on easy, medium, and difficult test sets, respectively. Our proposed method for instance segmentation of cells and nuclei provided favorable performance compared to the existing methods in literature, indicating its potential utility as an assistive tool for cell culture experts, facilitating prompt and reliable analysis.

VEMcomp: a Virtual Elements MATLAB package for bulk-surface PDEs in 2D and 3D

AbstractWe present a Virtual Element MATLAB solver for elliptic and parabolic, linear and semilinear Partial Differential Equations (PDEs) in two and three space dimensions, which is coined VEMcomp. Such PDEs are widely applicable to describing problems in material sciences, engineering, cellular and developmental biology, among many other applications. The library covers linear and nonlinear models posed on different simple and complex geometries, involving time-dependent bulk, surface, and bulk-surface PDEs. The solver employs the Virtual Element Method (VEM) of lowest polynomial order $${k=1}$$ k = 1 on general polygonal and polyhedral meshes, including the Finite Element Method (FEM) of order $${k=1}$$ k = 1 as a special case when the considered mesh is simplicial. VEMcomp has three main purposes. First, VEMcomp generates polygonal and polyhedral meshes optimized for fast matrix assembly. Triangular and tetrahedral meshes are encompassed as special cases. For surface PDEs, VEMcomp is compatible with the well-known Matlab package DistMesh for mesh generation. Second, given a mesh for the considered geometry, possibly generated with an external package, VEMcomp computes all the matrices (mass and stiffness) required by the VEM or FEM method. Third, for multiple classes of stationary and time-dependent bulk, surface and bulk-surface PDEs, VEMcomp solves the considered PDE problem with the VEM or FEM in space and IMEX Euler in time, through a user-friendly interface. As an optional post-processing, VEMcomp comes with its own functions for plotting the numerical solutions and evaluating the error when possible. An extensive set of examples illustrates the usage of the library.

Loss of prohibitin 2 in Schwann cells dysregulates key transcription factors controlling developmental myelination.

Schwann cells are critical for the proper development and function of the peripheral nervous system (PNS), where they form a collaborative relationship with axons. Past studies highlighted that a pair of proteins called the prohibitins play major roles in Schwann cell biology. Prohibitins are ubiquitously expressed and versatile proteins. We have previously shown that while prohibitins play a crucial role in Schwann cell mitochondria for long-term myelin maintenance and axon health, they may also be present at the Schwann cell-axon interface during development. Here, we expand on this, showing that drug-mediated modulation of prohibitins in vitro disrupts myelination and confirming that Schwann cell-specific ablation of prohibitin 2 (Phb2) in vivo results in severe defects in radial sorting and myelination. We show in vivo that Phb2-null Schwann cells cannot effectively proliferate and the transcription factors EGR2 (KROX20), POU3F1 (OCT6), and POU3F2 (BRN2), necessary for proper Schwann cell maturation, are dysregulated. Schwann cell-specific deletion of Jun, a transcription factor associated with negative regulation of myelination, confers partial rescue of the developmental defect seen in mice lacking Schwann cell Phb2. Finally, we identify a pool of candidate PHB2 interactors that change their interaction with PHB2 depending on neuronal signals, and thus are potential mediators of PHB2-associated developmental defects. This work develops our understanding of Schwann cell biology, revealing that Phb2 may modulate the timely expression of transcription factors necessary for proper PNS development, and proposing candidates that may play a role in PHB2-mediated integration of axon signals in the Schwann cell.

Evaluation of a Deep Learning Based Approach to Computational Label Free Cell Viability Quantification.

One of the most common techniques found in a cell biology or tissue engineering lab is the cytotoxicity assay. This can be performed using a variety of different dyes and stains and various protocols to result in a clear indication of dead and live cells within a culture to quantify the viability of a culture and monitor for sudden drops or increases in viability by a drug, material, viral vector, etc introduced into the culture. This assay helps cell biologists determine the health of their culture and what toxicity added substances may add to the culture and whether they are appropriate and safe to use with human cells. However, many of the dyes and stains used for this process are eventually toxic to cells, rendering the cells useless after testing and preventing real time monitoring of the same culture over a period of hours or days. Computation biology is moving cell biology towards novel and innovative techniques such as in silico labeling and dye free labeling using deep learning algorithms. In this work, we investigate whether it is feasible to train a Resnet CNN model to detect morphological changes in human cells that indicate cell death in order to classify cells as live or dead without utilizing a stain or dye. This work also aims to train one CNN model to count all cells regardless of viability status to get a total cell count, and then one CNN model that specifically identifies and counts all of the dead cells for an accurate dead and live cell total by utilizing both pieces of data to determine a general viability percentage for the culture. Additionally, this work explores the use of various image enhancements to understand if this process helps or impedes the deep learning models in their detection of total cells and dead cells.

AMPK, a hub for the microenvironmental regulation of bone homeostasis and diseases.

AMP-activated protein kinase (AMPK), a crucial regulatory kinase, monitors energy levels, conserving ATP and boosting synthesis in low-nutrition, low-energy states. Its sensitivity links microenvironmental changes to cellular responses. As the primary support structure and endocrine organ, the maintenance, and repair of bones are closely associated with the microenvironment. While a series of studies have explored the effects of specific microenvironments on bone, there is lack of angles to comprehensively evaluate the interactions between microenvironment and bone cells, especially for bone marrow mesenchymal stem cells (BMMSCs) which mediate the differentiation of osteogenic lineage. It is noteworthy that accumulating evidence has indicated that AMPK may serve as a hub between BMMSCs and microenvironment factors, thus providing a new perspective for us to understand the biology and pathophysiology of stem cells and bone. In this review, we emphasize AMPK's pivotal role in bone microenvironment modulation via ATP, inflammation, reactive oxygen species (ROS), calcium, and glucose, particularly in BMMSCs. We further explore the use of AMPK-activating drugs in the context of osteoarthritis and osteoporosis. Moreover, building upon the foundation of AMPK, we elucidate a viewpoint that facilitates a comprehensive understanding of the dynamic relationship between the microenvironment and bone homeostasis, offering valuable insights for prospective investigations into stem cell biology and the treatment of bone diseases.

Cytidine deaminase-dependent mitochondrial biogenesis as a potential vulnerability in pancreatic cancer cells

Cytidine deaminase (CDA) converts cytidine and deoxycytidine into uridine and deoxyuridine as part of the pyrimidine salvage pathway. Elevated levels of CDA are found in pancreatic tumors and associated with chemoresistance. Recent evidence suggests that CDA has additional functions in cancer cell biology. In this work, we uncover a novel role of CDA in pancreatic cancer cell metabolism. CDA silencing impairs mitochondrial metabolite production, respiration, and ATP production in pancreatic cancer cells, leading to a so-called Pasteur effect metabolic shift towards glycolysis. Conversely, we find that CDA expression promotes mitochondrial biogenesis and oxidative phosphorylation, independently of CDA deaminase activity. Furthermore, we observe that patient primary cells overexpressing CDA are more sensitive to mitochondria-targeting drugs. Collectively, this work shows that CDA plays a non-canonical role in pancreatic cancer biology by promoting mitochondrial function, which could be translated into novel therapeutic vulnerabilities.

3D-Aligner: An advanced computational tool designed to correct image distortion in expansion microscopy for precise 3D reconstitution and quantitative analysis.

Expansion Microscopy (ExM) is an innovative and cost-effective super-resolution microscopy technique that has become popular in cell biology research. It achieves super-resolution by physically expanding specimens. Since its introduction, ExM has undergone continuous methodological developments to enhance its resolution and labeling capabilities. However, ExM imaging often encounters sample drift during image acquisition due to the physical movement of the expanded hydrogel, posing a significant challenge for accurate image reconstruction. Despite many proposed experimental solutions to mitigate sample drift, a universal solution has yet to be established. In response to this challenge, we developed 3D-Aligner, an advanced and user-friendly image analysis tool designed to computationally correct drift in ExM images for precise three-dimensional image reconstruction and downstream quantification. We demonstrate that 3D-Aligner effectively determines and corrects drift in ExM images with different expansion rates and various fluorescently labeled biological targets, showcasing its capabilities and robustness in drift correction. Additionally, we validate the precision of 3D-Aligner by comparing drift values across different labeled targets and highlight the importance of drift correction in quantification of biological structures.

Establishing Three-Dimensional Explant Culture of Human Dental Pulp Tissue.

Mesenchymal stem cells in the dental tissue indicate a disposition for differentiation into diverse dental lineages and contain enormous potential as the important means for regenerative medicine in dentistry. Among various dental tissues, the dental pulp contains stem cells, progenitor cells and odontoblasts for maintaining dentin homeostasis. The conventional culture of stem cells holds a limit as the living tissue constitutes the three-dimensional (3D) structure. Recent development in the organoid cultures have successfully recapitulated 3D structure and advanced to the assembling of different types. In the current study, the protocol for 3D explant culture of the human dental pulp tissue has been established by adopting the organoid culture. After isolating dental pulp from human tooth, the intact tissue was placed between two layers for Matrigel with addition of the culture medium. The reticular outgrowth of pre-odontoblast layer continued for a month and the random accumulation of dentin was observed near the end. Electron microscopy showed the cellular organization and in situ development of dentin, and immunohistochemistry exhibited the expression of odontoblast and stem cell markers in the outgrowth area. Three-dimensional explant culture of human dental pulp will provide a novel platform for understanding stem cell biology inside the tooth and developing the regenerative medicine.

RNA Methylation and Tumor Occurrence

RNA methylation is a post-transcriptional modification involving the addition of methyl groups to RNA molecules, playing a crucial role in gene expression regulation. Recent studies have highlighted the importance of RNA methylation, particularly N6-methyladenosine (m6A) modification, in cancer development and progression. RNA methylation affects mRNA stability, splicing, transport, and translation, thereby regulating various aspects of tumor cell biology, including proliferation, migration, and apoptosis. Abnormal RNA methylation patterns are closely linked to tumor aggressiveness and drug resistance, with dysregulation of methylation writers, erasers, and readers contributing to cancer onset and progression. Moreover, RNA methylation plays a significant role in cancer immunotherapy by influencing tumor cell immune evasion, offering new therapeutic targets. Future research should focus on elucidating the specific mechanisms of RNA methylation in different cancer types and its interactions with the tumor microenvironment. As technologies advance, RNA methylation modulators hold promising potential for cancer treatment, providing new strategies and therapeutic targets.

Peds1 deficiency in zebrafish results in myeloid cell apoptosis and exacerbated inflammation

Plasmalogens are glycerophospholipids with a vinyl ether bond that confers unique properties. Recent identification of the gene encoding PEDS1, the desaturase generating the vinyl ether bond, enables evaluation of the role of plasmalogens in health and disease. Here, we report that Peds1-deficient zebrafish larvae display delayed development, increased basal inflammation, normal hematopoietic stem and progenitor cell emergence, and cell-autonomous myeloid cell apoptosis. In a sterile acute inflammation model, Peds1-deficient larvae exhibited impaired inflammation resolution and tissue regeneration, increased interleukin-1β and NF-κB expression, and elevated ROS levels at the wound site. Abnormal immune cell recruitment, neutrophil persistence, and fewer but predominantly pro-inflammatory macrophages were observed. Chronic skin inflammation worsened in Peds1-deficient larvae but was mitigated by exogenous plasmalogen, which also alleviated hyper-susceptibility to bacterial infection, as did pharmacological inhibition of caspase-3 and colony-stimulating factor 3-induced myelopoiesis. Overall, our results highlight an important role for plasmalogens in myeloid cell biology and inflammation.

Constraint-based reasoning in cell biology: on the explanatory role of context.

Cell biologists, including those seeking molecular mechanistic explanations of cellular phenomena, frequently rely on experimental strategies focused on identifying the cellular context relevant to their investigations. We suggest that such practices can be understood as a guided decomposition strategy, where molecular explanations of phenomena are defined in relation to natural contextual (cell) boundaries. This "top-down" strategy contrasts with "bottom-up" reductionist approaches where well-defined molecular structures and activities are orphaned by their displacement from actual biological functions. We focus on the central role of microscopic imaging in cell biology to uncover possible constraints on the system. We show how identified constraints are used heuristically to limit possible mechanistic explanations to those that are biologically meaningful. Historical examples of this process described here include discovery of the mechanism of oxidative phosphorylation in mitochondria, molecular explanation of the first steps in protein secretion, and identification of molecular motors. We suggest that these instances are examples of a form of downward causation or, more specifically, constraining relations, where higher-level structures and variables delimit and enable lower-level system states. The guided decomposition strategy in our historical cases illustrates the irreducibility of experimentally identified constraints in explaining biological activities of cells. Rather than viewing decomposition and recomposition as separate epistemic activities, we contend that they need to be iteratively integrated to account for the ontological complexity of multi-level systems.

Ropivacaine Administration Suppressed A549 Lung Adenocarcinoma Cell Proliferation and Migration via ACE2 Upregulation and Inhibition of the Wnt1 Pathway.

Previous studies have suggested that perioperative anesthesia could have direct impacts on cancer cell biology. The present study investigated the effects of ropivacaine administration on lung adenocarcinoma cells. Ropivacaine was administered to A549 cells at concentrations of 0.1, 1, and 6 mM for 2 h. Angiotensin-converting enzyme 2 (ACE2) small interfering RNA (siRNA) transfection was performed 6 h prior to ropivacaine administration. Cell proliferation and migration were assessed with cell counting kit 8 (CCK-8) and a wound healing assay at 0 and 24 h after anesthesia exposure. PCR arrays were performed, followed by PCR validation. Ropivacaine administration inhibited A549 cell proliferation and migration in a concentration-dependent manner, with ACE2 upregulation and HIF1α (hypoxia-inducible factor 1α) downregulation. The anticancer effect of ropivacaine was canceled out via ACE2 siRNA transfection. PCR arrays showed specific gene change patterns in the ropivacaine and respective ACE2-knockdown groups. EGFR (epidermal growth factor receptor), BAX (Bcl-2-associated X protein) and BCL2 (B-cell/CLL lymphoma 2) were suppressed with ropivacaine administration; these effects were reversed via ACE2 siRNA induction. Ropivacaine administration inhibited A549 cell biology in conjunction with ACE2 upregulation via the inhibition of the Wnt1 (wingless/Integrated 1) pathway.