Biology subjects

Ouyang, W.

Publications and source records attributed to Ouyang, W..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Wireless, Multimodal Monitoring of Organ Health Using 3D-Printed, Barbed, Bioresorbable Stretchable Microneedle Sensor Arrays

Comprehensive and continuous assessment of organ physiology and biochemistry, beyond the capabilities of conventional monitoring tools, can enable timely interventions for intraoperative complications like organ ischemia and nerve injuries and postoperative conditions such as organ dysfunction and transplant rejection. Here, we report a wireless implant with a 3D-printed, barbed, bioresorbable stretchable microneedle sensor array that offers multimodal monitoring of organ metabolism, oxygenation, and electrophysiology as well as spatiotemporal mapping of biomarkers across different organ regions. The development of a deformation-coupled 3D-printing technique enables 3D-programmable manufacturing of microneedles with monolithic backward-facing barbs, offering conformal yet robust 3D probing of organs with dynamic mechanics. Electrochemical functionalization of microneedle tips serves as a universal approach for localized sensing of physiological and biochemical parameters, enabling concurrent monitoring of up to 36 parameters (glucose, uric acid, oxygen, etc.) and spatiotemporal mapping of up to 32 sites. An electrically programmable self-destruction mechanism based on crevice corrosion and bioresorption eliminates the need for traumatic retrieval of microneedles. Demonstrations in clinically relevant complications like kidney ischemia and gut disorders in animal models highlight the broad applications of this device in intra- and postoperative monitoring.

bioengineering↗

bioRxiv2024

BEACON: Benchmark for Comprehensive RNA Tasks and Language Models

RNA plays a pivotal role in translating genetic instructions into functional outcomes, underscoring its importance in biological processes and disease mechanisms. Despite the emergence of numerous deep learning approaches for RNA, particularly universal RNA language models, there remains a significant lack of standardized benchmarks to assess the effectiveness of these methods. In this study, we introduce the first comprehensive RNA benchmark BEACON (BEnchmArk for COmprehensive RNA Task and Language Models). First, BEACON comprises 13 distinct tasks derived from extensive previous work covering structural analysis, functional studies, and engineering applications, enabling a comprehensive assessment of the performance of methods on various RNA understanding tasks. Second, we examine a range of models, including traditional approaches like CNNs, as well as advanced RNA foundation models based on language models, offering valuable insights into the task-specific performances of these models. Third, we investigate the vital RNA language model components from the tokenizer and positional encoding aspects. Notably, our findings emphasize the superiority of single nucleotide tokenization and the effectiveness of Attention with Linear Biases (ALiBi) over traditional positional encoding methods. Based on these insights, a simple yet strong baseline called BEACON-B is proposed, which can achieve outstanding performance with limited data and computational resources. The datasets and source code of our benchmark are available at https://github.com/terry-r123/RNABenchmark.

bioinformatics↗

bioRxiv2024

Pi-PrimeNovo: An Accurate and Efficient Non-Autoregressive Deep Learning Model for De Novo Peptide Sequencing

Peptide sequencing via tandem mass spectrometry (MS/MS) is fundamental in proteomics data analysis, playing a pivotal role in unraveling the complex world of proteins within biological systems. In contrast to conventional database searching methods, deep learning models excel in de novo sequencing peptides absent from existing databases, thereby facilitating the identification and analysis of novel peptide sequences. Current deep learning models for peptide sequencing predominantly use an autoregressive generation approach, where early errors can cascade, largely affecting overall sequence accuracy. And the usage of sequential decoding algorithms such as beam search suffers from the low inference speed. To address this, we introduce{pi} -PrimeNovo, a non-autoregressive Transformer-based deep learning model designed to perform accurate and efficient de novo peptide sequencing. With the proposed novel architecture,{pi} -PrimeNovo achieves significantly higher accuracy and up to 69x faster sequencing compared to the state-of-the-art methods. This remarkable speed makes it highly suitable for computation-extensive peptide sequencing tasks such as metaproteomic research, where{pi} -PrimeNovo efficiently identifies the microbial species-specific peptides. Moreover,{pi} -PrimeNovo has been demonstrated to have a powerful capability in accurately mining phosphopeptides in a non-enriched phosphoproteomic dataset, showing an alternative solution to detect low-abundance post-translational modifications (PTMs). We suggest that this work not only advances the development of peptide sequencing techniques but also introduces a transformative computational model with wide-range implications for biological research.

bioinformatics↗

bioRxiv2024

DeProt: A protein language model with quantizied structure and disentangled attention

Protein language models (PLMs) have shown remarkable capabilities in various protein function prediction tasks. However, while protein function is intricately tied to structure, most existing PLMs do not incorporate protein structure information. To address this issue, we introduce ProSST, a Transformer-based protein language model that seamlessly integrates both protein sequences and structures. ProSST incorporates a structure quantization module and a Transformer architecture with disentangled attention. The structure quantization module translates a 3D protein structure into a sequence of discrete tokens by first serializing the protein structure into residue-level local structures and then embeds them into dense vector space. These vectors are then quantized into discrete structure tokens by a pre-trained clustering model. These tokens serve as an effective protein structure representation. Furthermore, ProSST explicitly learns the relationship between protein residue token sequences and structure token sequences through the sequence-structure disentangled attention. We pre-train ProSST on millions of protein structures using a masked language model objective, enabling it to learn comprehensive contextual representations of proteins. To evaluate the proposed ProSST, we conduct extensive experiments on the zero-shot mutation effect prediction and several supervised downstream tasks, where ProSST achieves the state-of-the-art performance among all baselines. Our code and pretrained models are publicly available 2.

bioinformatics↗

bioRxiv2024

U-FISH: a universal deep learning approach for accurate FISH spot detection across diverse datasets

In the rapidly advancing landscape of fluorescence in situ hybridization (FISH) technologies, there is a critical need for sophisticated yet adaptable methods for spot detection. This study introduces U-FISH, a deep learning approach that significantly improves accuracy and generalization capabilities. Our method utilizes a U-Net model to transform noisy and ambiguous FISH images into a standardized representation with consistent signal characteristics, facilitating efficient spot detection. For the training and evaluation of the U-FISH model, we have constructed a comprehensive dataset comprising over 4,000 images and more than 1.6 million manually annotated spots, sourced from both experimental and simulated environments. Our benchmarks demonstrate that U-FISH outperforms existing methods for FISH spot detection, offering improved versatility by eliminating the need for laborious manual parameter adjustments. This allows for its application across a broad spectrum of datasets and formats. Furthermore, U-FISH is designed for high scalability and is capable of processing 3D data, supporting the latest generation of file formats for large and complex datasets. To promote community adoption and ensure accessibility, we provide a user-friendly interfaces: Napari plugin, web application and command-line interface. The complete training dataset is made publicly available, laying a solid foundation for future research in this field.

bioinformatics↗

bioRxiv2024

ESPressoscope: a small and powerful platform for in situ microscopy

Microscopy is essential for detecting, identifying, analyzing, and measuring small objects. Access to modern microscopy equipment is crucial for scientific research, especially in the biomedical and analytical sciences. However, the high cost of equipment, limited availability of parts, and challenges associated with transporting equipment often limit the accessibility and operational capabilities of these tools, particularly in field sites and other remote or resource-limited settings. Thus, there is a need for affordable and accessible alternatives to traditional microscopy systems. We address this challenge by investigating the feasibility of using a simple microcontroller board not only as a portable and field-ready digital microscope, but furthermore as a versatile platform which can easily be adapted to a variety of imaging applications. By adding a few external components, we demonstrate that a low-cost ESP32 camera board can be used to build an autonomous in situ platform for digital time-lapse imaging of cells. This platform, which we call the ESPressoscope, can be adapted to applications ranging from monitoring incubator cell cultures in the lab to observing ecological phenomena in the sea, and it can be adapted for other techniques such as microfluidics or spectrophotometry. The ESPressoscope achieves a low power consumption and small size, which makes it ideal for field research in environments and applications where microscopy was previously infeasible. Its Wi-Fi connectivity enables integration with external image processing and storage systems, including on cloud platforms when internet access is available. Finally, we present several web browser-based tools to help users operate and manage the ESPressoscopes software. Our findings demonstrate the potential for low-cost, portable microscopy solutions to enable new and more accessible experiments for biological and analytical applications.

microbiology↗

bioRxiv2024

Artificial Sweeteners in US-Marketed Oral Nicotine Pouch Products: Correlation with Nicotine Contents and Effects on Product Preference

IntroductionArtificial sweeteners are listed as ingredients of oral nicotine pouches (ONPs), a new product category with rapidly growing market share. The exact sweetener contents of ONPs remain unknown. Artificial sweeteners in ONPs may facilitate initiation and encourage consumption behavior. Aims and MethodsArtificial sweetener contents in major US-marketed ONP brands (Zyn, on!, Velo) were determined by Liquid Chromatography-Mass Spectrometry (LC-MS). Sweetener effects during the initiation of ONP consumption were modeled in single- and two-bottle tests, offering mice ONP extracts calibrated to contain nicotine levels similar to saliva of people who use smokeless tobacco. To examine the contribution of sweet taste perception, consumption behavior was compared between wild-type mice and mice deficient in the sweet taste receptor (Tas1r2-/-). ResultsAcesulfame-K was detected in on!, Zyn and Velo ONPs ([~]0.3-0.9 mg/pouch), including products marketed as "Unflavored" or "Flavor ban approved". In Velo ONPs, sweetened with sucralose (0.6-1.2 mg/pouch), higher nicotine strength products contained higher sucralose levels. Tas1r2-/- mice consumed less ONP extracts than wild-type mice in both sexes. ONP extracts with both higher nicotine and sweetener strengths were tolerated by wild-type mice, but produced stronger aversion in Tas1r2-/- mice. ConclusionsONPs contain significant amounts of artificial sweeteners, with some brands adding more sweetener to ONPs with higher nicotine strengths. Artificial sweeteners, at levels present in ONPs, increase nicotine consumption. Increasing sweetener contents facilitates consumption of ONPs with higher nicotine strengths. Sweetness is a key determinant of ONP use initiation, likely reducing the aversive sensory effects of nicotine and other ONP constituents. ImplicationsArtificial sweeteners such as acesulfame-K or sucralose reduce aversion and facilitate initiation and continued consumption of ONPs. The marketing of some artificially sweetened ONPs as "Unflavored" of "Flavor ban-approved" suggests that the tobacco industry rejects sweet taste as a determinant for the presence of a characterizing flavor. Sweetness as imparted by artificial sweeteners in tobacco products needs to be addressed by regulators as a component of a characterizing flavor, with the aim to reduce product appeal and initiation by never users, and especially youth attracted to sweet flavors.

animal behavior and cognition↗

bioRxiv2024

Bridging the Gap: Integrating Cutting-edge Techniques into Biological Imaging with deepImageJ

This manuscript showcases the latest advancements in deepImageJ, a pivotal Fiji/ImageJ plugin for bioimage analysis in the life sciences. The plugin, known for its user-friendly interface, facilitates the application of diverse pre-trained neural networks to custom data. The manuscript demonstrates a number of deepImageJ capabilities, particularly in executing complex pipelines, 3D analysis, and processing large images. A key development is the integration of the Java Deep Learning Library (JDLL), expanding deepImageJs compatibility with various deep learning frameworks, including TensorFlow, PyTorch, and ONNX. This allows for running multiple engines within a single Fiji/ImageJ instance, streamlining complex bioimage analysis tasks. The manuscript details three case studies to demonstrate these capabilities. The first explores integrated image-to image translation and nuclei segmentation. The second focuses on 3D nuclei segmentation. The third case study deals with large image segmentation. These studies underscore deepImageJs versatility and power in bioimage analysis, emphasizing its role as a critical tool for life scientists and researchers. The advancements in deepImageJ bridge the gap between deep learning model developers and end-users, enabling a more accessible and efficient approach to biological image analysis. The advancements in deepImageJ, detailed in this paper, represent a significant leap in bioimage analysis, crucial for life sciences. By enhancing this Fiji/ImageJ plugin, the research bridges the gap between complex deep learning models and practical applications, making advanced bioimage analysis accessible to a broader audience. This integration of the Java Deep Learning Library (JDLL) within deepImageJ is particularly noteworthy, as it expands compatibility with leading deep learning frameworks. This allows for the seamless execution of multiple models in a single instance, simplifying the construction of complex image analysis pipelines. The implications of this research are far-reaching, extending beyond academic circles to potentially impact various sectors, including healthcare, pharmaceuticals, and biotechnology. The enhanced capabilities of deepImageJ in handling intricate pipelines, 3D analysis, and large images facilitate detailed and efficient analysis of biological data. Such advancements are vital for accelerating research and development in medical imaging, drug discovery, and understanding complex biological processes. This manuscript contribution to the field of bioimage analysis is significant, offering a tool that empowers researchers, irrespective of their computational expertise, to leverage advanced technologies in their work. The wide applicability and ease of use of deepImageJ have the potential to foster interdisciplinary collaborations, drive innovation, and facilitate discoveries across various scientific and industrial sectors.

bioinformatics↗

bioRxiv2023

A multiplexed time-resolved fluorescence resonance energy transfer ultrahigh-throughput screening assay for targeting SMAD4-SMAD3-DNA complex

The signaling pathway of transforming growth factor-beta (TGF{beta}) plays crucial roles in the establishment of an immunosuppressive tumor microenvironment, making anti-TGF{beta} agents a significant area of interest in cancer immunotherapy. However, the clinical translation of current anti-TGF{beta} agents that target upstream cytokines and receptors remains challenging. Therefore, the development of small molecule inhibitors specifically targeting SMAD4, the downstream master regulator of TGF{beta} pathway, would offer an alternative approach with significant therapeutic potential for anti-TGF-{beta} signaling. In this study, we present the development of a cell lysate-based multiplexed time-resolved fluorescence resonance energy transfer (TR-FRET) assay in an ultrahigh-throughput screening (uHTS) 1536-well plate format. This assay enables simultaneous monitoring of the protein-protein interaction (PPI) between SMAD4 and SMAD3, as well as the protein-DNA interaction (PDI) between SMADs and their consensus DNA binding motif. The multiplexed TR-FRET assay exhibits high sensitivity, allowing the dynamic analysis of the SMAD4-SMAD3-DNA complex at single amino acid resolution. Moreover, the multiplexed uHTS assay demonstrates robustness for screening small molecule inhibitors. Through a pilot screening of an FDA-approved and bioactive compound library, we identified gambogic acid and gambogenic acid as potential hit compounds. These proof-of-concept findings underscore the utility of our optimized multiplexed TR-FRET platform for large-scale screening to discover small molecule inhibitors that target the SMAD4-SMAD3-DNA complex as novel anti-TGF{beta} signaling agents.

cancer biology↗

bioRxiv2022

ISG15-modification of the Arp2/3 complex restricts pathogen spread

The ubiquitin-like protein, ISG15, can act as a cytokine or can covalently modify host and pathogen-derived proteins. The consequences of ISG15 modification on substrate fate remain unknown. Here we reveal that ISGylation of the Arp2/3 complex slows actin filament formation and stabilizes Arp2/3 dependent structures including cortical actin and lamella. When properly controlled, this serves as an antibacterial and antiviral host defense strategy to directly restrict actin-mediated pathogen spread. However, Listeria monocytogenes takes advantage in models of dysregulated ISGylation, leading to increased mortality due to augmented spread. The underlying molecular mechanism responsible for the ISG15-dependent impact on actin-based motility is due to failed bacterial separation after division. This promotes spread by enabling the formation of multi-headed bacterial "bazookas" with stabilized comet tails that can disseminate deeper into tissues. A bacterial mutant that cannot recruit Arp2/3 or a non-ISGylatable mutant of Arp3 is sufficient to rescue slowed comet tail speed and restrict spread. Importantly, ISG15-deficient neonatal mice have aberrant epidermal epithelia characterized by keratinocytes with diffuse cortical actin, which could underlie observed defects in wound healing in human patients who lack ISG15. Ultimately, our discovery links host innate immune responses to cytoskeletal dynamics with therapeutic implications for viral infection and metastasis.

cell biology↗

bioRxiv2022

Uncovering molecular grammars of intrinsically disordered regions that organize nucleolar fibrillar centers

The nucleolus is a multilayered structure. Each layer is thought to be a compositionally distinct phase, although how these phases form and interface with one another remains unclear. Using computational, proteomics, in vitro, and in vivo studies, we uncover distinct molecular grammars within intrinsically disordered regions (IDRs) of nucleolar proteins that localize to fibrillar centers (FCs) and dense fibrillar components (DFCs). FC and DFC proteins feature two distinct types of IDRs namely those with long tracts of acidic residues and those with blocks of lysines interspersed by acid-rich-regions. We find that phase separation driven by complex coacervation in mixtures of nucleolar proteins, with their distinctive IDRs, and ribosomal DNA and RNA molecules is sufficient to drive the formation of structural facsimiles of FCs and DFCs. One-Sentence SummaryFacsimiles of core nucleolar substructures were reconstituted via phase separation of key protein and nucleic acid mixtures.

biophysics↗

bioRxiv2022

BioImage Model Zoo: A Community-Driven Resource for Accessible Deep Learning in BioImage Analysis

Deep learning-based approaches are revolutionizing imaging-driven scientific research. However, the accessibility and reproducibility of deep learning-based workflows for imaging scientists remain far from sufficient. Several tools have recently risen to the challenge of democratizing deep learning by providing user-friendly interfaces to analyze new data with pre-trained or fine-tuned models. Still, few of the existing pre-trained models are interoperable between these tools, critically restricting a models overall utility and the possibility of validating and reproducing scientific analyses. Here, we present the BioImage Model Zoo (https://bioimage.io): a community-driven, fully open resource where standardized pre-trained models can be shared, explored, tested, and downloaded for further adaptation or direct deployment in multiple end user-facing tools (e.g., ilastik, deepImageJ, QuPath, StarDist, ImJoy, ZeroCostDL4Mic, CSBDeep). To enable everyone to contribute and consume the Zoo resources, we provide a model standard to enable cross-compatibility, a rich list of example models and practical use-cases, developer tools, documentation, and the accompanying infrastructure for model upload, download and testing. Our contribution aims to lay the groundwork to make deep learning methods for microscopy imaging findable, accessible, interoperable, and reusable (FAIR) across software tools and platforms.

bioinformatics↗

bioRxiv2022

Subcellular mapping of the protein landscape of SARS-CoV-2 infected cells for target-centric drug repurposing

The COVID-19 pandemic has resulted in millions of deaths and affected socioeconomic structure worldwide and the search for new antivirals and treatments are still ongoing. In the search for new drug target and to increase our understanding of the disease, we used large scale immunofluorescence to explore the host cell response to SARS-CoV-2 infection. Among the 602 host proteins studied in this host response screen, changes in abundance and subcellular localization were observed for 97 proteins, with 45 proteins showing increased abundance and 10 reduced abundances. 20 proteins displayed changed localization upon infection and an additional 22 proteins displayed altered abundance and localization, together contributing to diverse reshuffling of the host cell protein landscape. We then selected existing and approved small-molecule drugs (n =123) against our identified host response proteins and identified 3 compounds - elesclomol, crizotinib and rimcazole, that significantly reduced antiviral activity. Our study introduces a novel, targeted and systematic approach based on host protein profiling, to identify new targets for drug repurposing. The dataset of [~]75,000 immunofluorescence images from this study are published as a resource available for further studies.

cell biology↗

bioRxiv2022

Functional stemness-related genes revealed by single-cell profiling of naïve and stimulated human CD34+ cells from CB and mPB

Hematopoietic stem cells (HSCs) from different sources show varied repopulating capacity, and HSCs lose their stemness after long-time ex vivo culture. However, the underlying mechanisms of the stemness differences because of the cell sources and the culture stimulation are not fully understood. Here, we applied single-cell RNA-seq (scRNA-seq) to analyze the naive and stimulated human CD34+ cells from cord blood (CB) and mobilized peripheral blood (mPB). We collected over 16,000 single-cell data to construct a comprehensive trajectory inference map and characterized the HSCs population on the hierarchy top, which is under quiescent state. Then we compared HSCs in CB to those in mPB and HSCs of naive samples to those of cultured samples, and identified stemness-related genes (SRGs) associated with culture time (CT-SRGs) and cell source (CS-SRGs), respectively. Interestingly, CT-SRGs and CS-SRGs share genes enriched in the signaling pathways such as mRNA catabolic process, Translational initiation, Ribonucleoprotein complex biogenesis and Cotranslational protein targeting to membrane, suggesting dynamic protein translation and processing may be a common requirement for stemness maintenance. Meanwhile, CT-SRGs are enriched in pathways involved in glucocorticoid and corticosteroid response that affect HSCs homing and engraftment. In contrast, CS-SRGs specifically contain genes related purine and ATP metabolic process which is important to initiate hematopoiesis. Finally, we presented an application through a small-scale drug screening using Connectivity Map (CMap) against CT-SRGs and found a small molecule cucurbitacin I, targeting STAT3/JAK2, can efficiently expand HSCs ex vivo while maintaining its stemness. These results indicate SRGs revealed by scRNA-seq can provide helpful insights to understand the stemness differences under diverse circumstances, and CT-SRGs can be a valuable database to identify candidates enhancing functional HSCs expansion during ex vivo culture.

cell biology↗

bioRxiv2021

ShareLoc - an open platform for sharing localization microscopy data

Novel insights and more powerful analytical tools can emerge from the reanalysis of existing data sets, especially via machine learning methods. Despite the widespread use of single molecule localization microscopy (SMLM) for super-resolution bioimaging, the underlying data are often not publicly accessible. We developed ShareLoc (https://shareloc.xyz), an open platform designed to enable sharing, easy visualization and reanalysis of SMLM data. We discuss its features and show how data sharing can improve the performance and robustness of SMLM image reconstruction by deep learning.

bioinformatics↗

bioRxiv2021

FISH-quant v2: a scalable and modular analysis tool for smFISH image analysis

Regulation of RNA abundance and localization is a key step in gene expression control. Single-molecule RNA fluorescence in-situ hybridization (smFISH) is a widely used single-cell-single-molecule imaging technique enabling a quantitative understanding of gene expression and its regulatory mechanisms. Recent progress in experimental techniques provides larger data-sets, requiring adequate tools for data analysis and exploration. Here, we present FISH-quant v2, a highly modular analysis tool accessible both for non-experts and experts, which we validated and applied on large-scale smFISH image datasets. Our package allows the user to detect isolated and clustered mRNA spots, segment nuclei and cells, quantify RNA localization patterns and visualize these results at the single-cell level.

bioinformatics↗

bioRxiv2021

An Open-Source Modular Framework for Automated Pipetting and Imaging Applications

The number of samples in biological experiments are continuously increasing, but complex protocols and human experimentation in many cases lead to suboptimal data quality and hence difficulties in reproducing scientific findings. Laboratory automation can alleviate many of these problems by precisely reproducing machine-readable protocols. These instruments generally require high up-front investments and due to lack of open APIs they are notoriously difficult for scientists to customize and control outside of the vendor-supplied software. Here, we demonstrate automated, high-throughput experiments for interdisciplinary research in life science that can be replicated on a modest budget, using open tools to ensure reproducibility by combining the tools Openflexure, Opentrons, ImJoy and UC2. Our automated sample preparation and imaging pipeline can easily be replicated and established in many laboratories as well as in educational contexts through easy-to-understand algorithms and easy-to-build microscopes. Additionally, the creation of feedback loops, with later pipetting or imaging steps depending on analysis of previously acquired images, enables the realization of smart microscopy experiments, featuring completely autonomously performed experiments. All documents and source-files are publicly available (https://beniroquai.github.io/Hi2) to prove the concept of smart lab automation using inexpensive, open tools. We believe this democratizes access to the power and repeatability of automated experiments.

cell biology↗

bioRxiv2021

Variable Induction of Pro-inflammatory Cytokines by Commercial SARS CoV-2 Spike Protein Reagents: Potential Impacts of LPS on In Vitro Modeling and Pathogenic Mechanisms In Vivo

Proinflammatory cytokine production following infection with severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) is associated with poor clinical outcomes. Like SARS CoV-1, SARS CoV-2 enters host cells via its spike protein, which attaches to angiotensin-converting enzyme 2 (ACE2). As SARS CoV-1 spike protein is reported to induce cytokine production, we hypothesized that this pathway could be a shared mechanism underlying pathogenic immune responses. We herein compared the capabilities of Middle East Respiratory Syndrome (MERS), SARS CoV-1 and SARS CoV-2 spike proteins to induce cytokine expression in human peripheral blood mononuclear cells (PBMC). We observed that only specific commercial lots of SARS CoV-2 induce cytokine production. Surprisingly, recombinant SARS CoV-2 spike proteins from different vendors and batches exhibited different patterns of cytokine induction, and these activities were not inhibited by blockade of spike protein-ACE2 binding using either soluble ACE2 or neutralizing anti-S1 antibody. Moreover, commercial spike protein reagents contained varying levels of endotoxin, which correlated directly with their abilities to induce cytokine production. The lipopolysaccharide (LPS) inhibitor, polymyxin B, blocked this cytokine induction activity. In addition, SARS CoV-2 spike protein avidly bound soluble LPS in vitro, rendering it a cytokine inducer. These results not only suggest caution in monitoring the purity of SARS CoV-2 spike protein reagents, but they indicate the possibility that interactions of SARS CoV-2 spike protein with LPS from commensal bacteria in virally infected mucosal tissues could promote pathogenic inflammatory cytokine production.

microbiology↗