Biology subjects

Devos, S.

Publications and source records attributed to Devos, S..

At least 19 recordsLinked to original sources

bioRxiv2026

Proteomic profiling of whole tissue sections in cardiac ATTR amyloidosis reveals increased extracellular matrix remodeling

Cardiac transthyretin amyloidosis (ATTR-CA) is caused by myocardial deposition of misfolded transthyretin, leading to progressive heart failure. Disease pathology, however, extends beyond passive amyloid deposition and also involves active processes such as extracellular matrix (ECM) remodeling and immune activation. Mass spectrometry (MS) is the gold standard for amyloid typing in diagnostics. Here, we applied quantitative MS-driven proteomics on formalin-fixed paraffin-embedded whole cardiac tissue sections from six ATTR-CA cases, ten unaffected controls and four AL-CA controls to investigate protein expression changes. In addition to transthyretin, over 500 proteins were upregulated in ATTR-CA biopsies, including complement and coagulation factors as well as extracellular matrix (ECM) remodeling proteins. Among these, members of the A Disintegrin and Metalloproteinase with Thrombospondin Motifs (ADAMTS) family, metalloproteinases (MMPs), and Tissue Inhibitor of Metalloproteinases (TIMP3) showed significant upregulation. These proteins are key regulators of ECM turnover and structural integrity. Immunohistochemistry confirmed ADAMTS4 enrichment in amyloid deposits, while TIMP3 showed strong expression in cardiomyocytes and weaker staining within amyloid deposits. Together, these findings indicate that ECM remodeling, alongside complement and coagulation activation, represents a reproducible feature of cardiac ATTR amyloidosis. Whole-tissue proteomics provides biological insights that extend beyond amyloid typing, with potential implications for biomarker discovery and therapeutic targeting in ATTR-CA.

pathology↗

bioRxiv2026

A platform for high-throughput and ultrasensitive immunopeptidomics

Mass spectrometry (MS)-based immunopeptidomics is a powerful approach for untargeted discovery of peptides presented on major histocompatibility complex (MHC) molecules, which can guide the selection of vaccine antigens and immunotherapy targets. First-generation immunopeptidomics workflows require processing of hundreds of millions of cells using lengthy, manual procedures. More recent approaches focus on increasing either sensitivity or throughput, but rarely combine both aspects. Here, we describe a semi-automated immunopeptidomics platform that combines high sensitivity with high throughput by implementing highly optimized conditions for immunoprecipitation, elution and purification of MHC class I and II peptides on a 96-well positive-pressure device. Upon analysis of 25% of the eluate from 16 million cells, our workflow identified over 13,500 MHC I and 6,000 MHC II peptides on a timsTOF SCP mass spectrometer, operating in DDA-PASEF mode. Exploring the sensitivity limits of our platform, we identified over 1,000 MHC I peptides from as few as 20,000 JY cells. Validating the platforms performance for quantitative biological discovery, we report the identification of known and novel bacterial immunopeptides from U937 macrophages infected with Listeria monocytogenes or Bacillus Calmette-Guerin (BCG). Together, our optimized immunopeptidomics platform enables robust immunopeptide detection from lower-input samples in a high-throughput fashion, enabling its use for biological applications where sample amounts are limiting.

immunology↗

bioRxiv2025

Identification of DksA as a novel pro-inflammatory mediator of Pseudomonas aeruginosa under conditions mimicking chronic cystic fibrosis lung infection

Chronic infection with Pseudomonas aeruginosa is a major driver of airway inflammation, which plays a central role in the progression of cystic fibrosis (CF) lung disease. During long-term colonization, P. aeruginosa adapts to the CF lung by downregulating virulence factors and adopting a biofilm-associated, mucoid lifestyle. Despite the expected reduction in immune activation due to these adaptations, excessive inflammation persists, a paradox that remains poorly understood. Our objective was to identify novel bacterial mediators sustaining persistent inflammation by P. aeruginosa in the CF lung. To this end, we analyzed clinical P. aeruginosa CF isolates, cultured them in synthetic CF sputum medium, and exposed 3D lung epithelial cell cultures to the resulting cell-free supernatants. There was considerable variability in pro-inflammatory activity among the isolates, with a subset of the isolates inducing strong IL-8 secretion by the 3D cells despite low production of known virulence factors. Comparative proteomics analysis of the cell-free supernatants of pro- and anti-inflammatory isolates revealed several mediators not previously linked to inflammation. Thirteen of these candidate pro-inflammatory mediators were selected for further analysis. Using P. aeruginosa transposon mutants lacking the respective mediators, DksA (a transcription factor) was confirmed as an immunomodulatory mediator in the 3D lung model. Finally, analysis of existing transcriptomes of P. aeruginosa in CF sputum, revealed that dksA was found to be one of the most strongly expressed genes in this patient population, highlighting the relevance of our findings. In conclusion, we identified a novel P. aeruginosa mediator that may contribute to CF airway inflammation.

microbiology↗

bioRxiv2025

ProteoBench: the community-curated platform for comparing proteomics data analysis workflows

Mass spectrometry (MS)-based proteomics is a well-established strategy for analyzing complex biological mixtures. Many MS instruments and data acquisition strategies are available, and the data they acquire differ substantially, thus requiring tailored analysis algorithms. Hence, many dedicated bioinformatics workflows are developed. These are in constant evolution, and the community lacks a centralized platform for comparing their performance. Here, we propose ProteoBench, a single platform that brings together software developers and software users to provide an ever-evolving comparison of state-of-the-art proteomics data processing tools. ProteoBench is an open-source resource that enables the community to evaluate data analysis workflows, develop benchmarking modules dedicated to specific comparisons, and discuss the best methods to compare software tools. The platform ensures that the benchmark evolves alongside advances in proteomics data analysis workflows. ProteoBench guides researchers towards the best-suited tool and parameters for their specific project and data according to their needs, and developers can test their newly developed tools or workflows privately, before adding them as public references. This community-driven effort will increase transparency and reproducibility between MS data analysis workflows, as well as facilitate the development and publication of software workflows in the field.

bioinformatics↗

bioRxiv2025

Optimizing efficacy to safety ratio of glucocorticoids in rheumatoid arthritis models by leveraging PPARα agonism

ObjectivesGlucocorticoids remain essential therapies for several immune- and inflammatory diseases such as rheumatoid arthritis (RA) but are notorious for their (metabolic) side effects. Given the anti-inflammatory and metabolically favorable actions of peroxisome-proliferator activated nuclear receptor (PPAR) agonists, we investigated whether PPAR agonism could enhance the therapeutic efficacy and/or mitigate the (metabolic) side effects of glucocorticoids. MethodsWe evaluated the effects of the synthetic glucocorticoid dexamethasone and the PPAR agonist GW7647 (GW) across three RA model systems: L929sA fibroblasts, primary human fibroblast-like synoviocytes (FLS) and collagen-induced arthritis (CIA) mice. ResultsDexamethasone reduced the inflammatory TNF response in L929sA cells, which was further potentiated by GW. In vivo however, GW reduced the dexamethasone-induced adiposity and hypertriglyceridemia, but not arthritis severity. Curiously, GW alone induced several proinflammatory genes within arthritic synovium which were counteracted by glucocorticoids. Proteomic profiling of TNF-stimulated human FLS revealed that combined use of dexamethasone and GW selectively suppressed interferon-stimulated proteins. In line herewith, co-stimulation with TNF and IFN{beta} amplified the suppressive effect of combined dexamethasone and GW treatment on pro-inflammatory gene expression in L929sA versus TNF alone. ConclusionGW enhances the anti-inflammatory effects of glucocorticoids in human FLS and L929sA, and mitigates metabolic side effects of dexamethasone in vivo, without compromising their efficacy. In addition, PPAR agonism permits to broaden its anti-inflammatory profile to interferon driven pathways. Given that both synthetic glucocorticoids and PPAR agonists are already widely used in (general) clinical practice, these findings offer a promising strategy to optimize glucocorticoid-based therapies. KEY MESSAGESO_ST_ABSWhat is already known on this topicC_ST_ABSO_LISynthetic glucocorticoids such as dexamethasone are widely used as immunosuppressive drugs, but cause many (metabolic) side effects. C_LIO_LIPeroxisome-proliferator-activated nuclear receptor (PPAR) agonists are clinically primarily used to treat symptoms that resemble glucocorticoid-induced side effects, but they also exert (modest) immunosuppressive effects. C_LI What this study addsO_LIThis study explores the therapeutic potential of a combination treatment with dexamethasone and PPAR agonist GW7647 (GW) in cellular and murine models of rheumatoid arthritis C_LIO_LIWe reveal that GW attenuates dexamethasone-induced adiposity and hypertriglyceridemia in vivo, hereby improving glucocorticoid-related side effects. C_LIO_LIThe therapeutic efficacy of dexamethasone is maintained or even enhanced by GW in murine and cellular models of rheumatic arthritis, respectively. C_LIO_LIWe offer novel mechanistic insights in the proposed combination treatment by revealing the selective suppression of interferon signaling pathways in human FLS. C_LI How this study might affect research, practice or policyO_LIGiven that both synthetic glucocorticoids and PPAR agonists are already used in clinical practice, this study offers a promising, translatable strategy to optimize glucocorticoid-based therapies with an improved efficacy/safety ratio. C_LI

immunology↗

bioRxiv2025

Deconvoluting fiber type proportions from human skeletal muscle transcriptomics and proteomics data using FibeRtypeR

Muscle fibers are the dominant, multinucleated cell type in skeletal muscle. In humans, they can be classified as slow (type 1) and fast (type 2) fibers, traditionally based on distinct properties of their contractile machinery. Slow and fast fibers are characterized by shared and specific cellular complexities that are pivotal for their adaptive capacity to exercise or role in metabolic disease progression. In this era of omics research, there is a critical need to accurately infer muscle fiber type proportions from bulk tissue omics datasets, as single-fiber approaches are not feasible in large-scale or retrospective studies. Here we present FibeRtypeR, an easy-to-use web application to accurately estimate fiber type proportions from bulk transcriptomics and proteomics datasets: https://muscleapps.ugent.be. FibeRtypeR exploits transcriptomics and proteomics profiles of 1000 fiber-typed individual human skeletal muscle fibers as a reference dataset and is validated against paired immunohistochemical fiber type determinations in 160 muscle biopsies. We show that FibeRtypeR can be applied to public datasets, illustrating the application potential across a wide range of biological contexts such as aging, disease and exercise training. This new freely accessible computational tool will prove valuable to the skeletal muscle research community. Key pointsO_LIBulk muscle omics datasets lack fiber type specific information C_LIO_LIOur new tool, FibeRtypeR, leverages in-house collected single-fiber profiles allowing for accurate fiber type inference C_LIO_LIFibeRtypeR is methodologically robust across omics technologies and workflows C_LIO_LIWe host FibeRtypeR as an intuitive open-access Shiny app, applicable to new and publicly available transcriptomics and proteomics datasets C_LI

physiology↗

bioRxiv2025

TurboID proximity interactome mapping reveals NR2E3 association with AP-1 and retinal developmental complexes

The photoreceptor-specific nuclear receptor NR2E3 is a key transcription factor in the retinal transcriptional network, essential in photoreceptor cell fate and maintenance. Various biallelic loss-of-function variants lead to autosomal recessive inherited retinal disease (IRD), while a single missense variant G56R has been identified to cause autosomal dominant retinitis pigmentosa (adRP). However, a comprehensive understanding of the interactome of NR2E3 remains elusive. Here, we aimed to map the interactome of this nuclear receptor and the G56R pathogenic variant through proximity labelling followed by mass spectrometry. We used the TurboID "T2A split/link design" to identify the proximity interactome of wild-type NR2E3 and mutant G56R-NR2E3 in ARPE-19 cells. Several protein complexes involved in retinal development, including the AP-1 complex, were enriched. The results suggest similar interactomes for NR2E3 and G56R-NR2E3 in ARPE-19. However, BCOR was exclusively enriched in the G56R-NR2E3 network, strengthening the connection with the IRD phenotype. In conclusion, our approach has mapped the proximity interactome of NR2E3 and G56R-NR2E3, revealing interaction with protein complexes involved in (retinal) development.

systems biology↗

bioRxiv2025

Data-independent immunopeptidomics discovery of low-abundant bacterial epitopes

Mass spectrometry-based immunopeptidomics is a powerful approach to uncover peptides presented by human leukocyte antigen (HLA) molecules that can guide vaccine design and immunotherapies. While data-dependent acquisition (DDA) has been the standard for navigating through the complexity associated with non-enzymatic immunopeptide database searches, data-independent acquisition (DIA) is increasingly adopted in immunopeptidomics research. In this work, we compare diaPASEF to conventional ddaPASEF in terms of global immunopeptidome profiling and bacterial epitope discovery of the model intracellular pathogen Listeria monocytogenes. We show that DIA spectrum-centric workflows that search pseudo-MS/MS spectra complement DDA analysis by uncovering additional human and bacterial immunopeptides. Furthermore, we leveraged DIA-NN for generating and searching proteome-wide predicted HLA class I peptide spectral libraries, scoring approximately 150 million immunopeptide peptide precursors. This approach outperformed other spectrum-based methods in identification of MHC class I peptides and recovered low-abundant peptide precursors missed by other methods. Taken together, our results demonstrate how both DIA spectrum- and peptide-centric immunopeptidomics analysis are promising strategies to identify low-abundant immunopeptides.

molecular biology↗

bioRxiv2025

Critical Assessment of MetaProteome Investigation 2 (CAMPI-2): Multi-laboratory assessment of sample processing methods to stabilize fecal microbiome for functional analysis

BackgroundFecal samples are widely used as a proxy for studying gut microbiome composition in both human and animal research. Fecal metaproteomics provides valuable insights by tracking changes in the relative abundance of microbial taxa and their protein functions. To ensure reliable results, it is crucial to minimize alterations in the metaproteome occurring from sample collection to protein extraction. Therefore, employing effective stabilization methods is essential to preserve the integrity of the fecal metaproteome from sample collection to laboratory analysis, particularly over long distances or when rapid freezing options are not readily available. In line with these needs, the second edition of the Critical Assessment of MetaProteome Investigation (CAMPI-2) was specifically focused on testing sample stabilization protocols to be applied before metaproteomic analysis. ResultsThis collaborative multicenter study assessed the ability of five different stabilization methods, based on two commercial devices and three specific reagents (acetone, lithium dodecyl sulfate, and an RNAlater-like buffer), respectively, to stabilize the fecal metaproteome during room-temperature storage (14 days) and shipment to mass spectrometry facilities. The five methods were tested simultaneously by eight different laboratories across Europe, using aliquots from the same fecal sample. After protein extraction and digestion, duplicate aliquots of the resulting peptides were analyzed independently by two mass spectrometry facilities at distinct international locations. Analysis of the mass spectrometric data using two different search engines revealed that the fecal metaproteome profile differed considerably depending on the stabilization method used in terms of richness, alpha and beta diversity, reproducibility, and quantitative distribution of main taxa and functions. Although each method showed unique strengths and weaknesses, a commercial swab-based device stood out for its remarkable reproducibility and ranked highest for most of the metrics measured. ConclusionsCAMPI-2 allowed a robust evaluation of five different methods for preserving fecal metaproteome samples. The present investigation provides useful data for the design of metaproteomics and multi-omics studies where fecal sampling cannot be immediately followed by long-term storage at -80{degrees}C. Further optimization of the tested protocols is necessary to improve stabilization efficiency and control bias in the taxonomic and functional profile of the gut microbiome.

microbiology↗

bioRxiv2024

A multi-year longitudinal harmonization of quality controls in mass spectrometry proteomics core facilities

Quality control procedures play a pivotal role in ensuring the reliability and consistency of data generated in mass spectrometry-based proteomics laboratories. However, the lack of standardized quality control practices across laboratories poses challenges for data comparability and reproducibility. In response, we conducted a harmonization study within proteomics laboratories of the Core for Life alliance with the aim to establish a common quality control framework, which facilitates comprehensive quality assessment and identification of potential sources of performance drift. Through collaborative efforts, we developed a consensus quality control standard for longitudinal assessment and we adopted a common processing software. We generated a 4-year longitudinal dataset from multiple instruments and laboratories, which enabled us to assess intra- and inter-laboratory variability, to identify causes of performance drift, and to establish community reference values for several quality control parameters. Our study enhances data comparability and reliability and fosters a culture of collaboration and continuous improvement within the proteomics community to ensure the integrity of proteomics data.

biochemistry↗

bioRxiv2024

Yeast-based production platform for potent and stable heavy chain-only antibodies

Monoclonal antibodies are the leading drug of the biopharmaceutical market because of their high specificity and tolerability, but the current CHO-based manufacturing platform remains expensive and time-consuming leading to limited accessibility, especially in the case of diseases with high incidence and pandemics. Therefore, there is an urgent need for an alternative production system. In this study, we present a rapid and cost-effective microbial platform for heavy chain-only antibodies (VHH-Fc) in the methylotrophic yeast Komagataella phaffii (aka Pichia pastoris). We demonstrate the potential of this platform using a simplified single-gene VHH-Fc fusion construct instead of the conventional monoclonal antibody format, as this is more easily expressed in Pichia pastoris. We demonstrate that the Pichia-produced VHH-Fc fusion construct is stable and that a Pichia-produced VHH-Fc directed against the SARS-CoV-2 spike has potent SARS-CoV-2 neutralizing activity in vitro and in vivo. We expect that this platform will pave the way towards faster and cheaper development and production of broadly neutralizing single-chain antibodies in yeast.

microbiology↗

bioRxiv2024

High-throughput nano-flow proteomics using a dual-column electrospray source

With current trends in proteomics, especially regarding clinical and low input (to single cell) samples, it is increasingly important to both maximize the throughput of the analysis and maintain as much sensitivity as possible. The new generation of mass spectrometers (MS) are taking a huge leap in sensitivity, allowing to analyze samples with shorter liquid chromatography (LC) methods while digging as deep in the proteome. However, the throughput can be doubled by implementing a dual column nano-LC-MS configuration. For this purpose, we used a dual-column setup with a two-outlet electrospray source, and compared it to a classic dual-column setup with a single-outlet source.

biochemistry↗

bioRxiv2023

Integrating multiple omics levels using the human protein complexome as a framework, a multi-omics study of inborn errors of metabolism

Proteins organize into functional assemblies that drive diverse biological activities. Leveraging a comprehensive dataset of manually curated annotations for the human protein complexome, we investigated biological perturbations at the protein complex level. Using proteomics and transcriptomics data from fibroblasts of patients with inborn errors of metabolism (IEM) and control samples, we globally mapped information onto complex subunits to discern affected processes. Across the patient cohort (consisting of organic acidaemias, fatty acid oxidation defects and mitochondrial respiratory chain defect IEMs), mitochondrial oxidative phosphorylation emerged as the most perturbed pathway, identified through proteomics datasets. Simultaneously, metabolomics highlighted significant regulation of phospholipids in patients with Fatty Acid and Mitochondrial IEM. Moreover, proteomics analysis also revealed the dysregulation of protein complexes involved in histone (de)acetylation, a finding validated through Western Blot analysis measuring histone acetylation levels. This introduces a novel epigenetic dimension to IEM and metabolic research, suggesting avenues for further exploration. Our study demonstrates a multiomics data integration concept that maps proteomics and transcriptomics data onto model organism complexomes. This integrative approach can be extended to metabolomics and lipidomics, associating information with complexes having metabolic functions, such as enzymatic complexes. This global strategy for identifying disease-relevant perturbations offers a systems-wide perspective on molecular-level physiological and pathological changes. Such insights are crucial for devising clinical intervention strategies and prioritizing druggable pathways and complexes. The presented methodology provides a foundation for future investigations, emphasizing the importance of integrating multiomics data to comprehensively understand cellular machinery alterations and facilitate targeted therapeutic approaches.

biochemistry↗

bioRxiv2023

Benchmarking DIA data analysis workflows

Data independent acquisition (DIA) has become a well-established method in LC-MS driven proteomics. Nonetheless, there are still a lot of possibilities at the data analysis level. By benchmarking different DIA analysis workflows using a ground-truth sample, consisting of a differential spike-in of UPS2 in a constant yeast background, we provide a roadmap for DIA data analysis of shotgun samples based on whether sensitivity, precision or accuracy is of the essence. Three different commonly used DIA software tools (DIA-NN, EncyclopeDIA and SpectronautTM) were tested in both spectral library mode and spectral library-free mode. In spectral library mode we used the independent spectral library prediction tools PROSIT and MS2PIP together with DeepLC, next to the classical DDA-based spectral libraries. In total we benchmarked 12 DIA workflows. DIA-NN in library-free mode or using in silico predicted libraries, together with Spectronaut in library-free mode, shows the highest sensitivity maintaining a high reproducibility and accuracy. In general, DIA-NN shows the best reproducibility, while the accuracy is comparable for all DIA workflows.

bioinformatics↗

bioRxiv2023

Ultrapotent SARS coronavirus-neutralizing single-domain antibodies that bind a conserved membrane proximal epitope of the spike

Currently circulating SARS-CoV-2 variants have gained complete or significant resistance to all SARS-CoV-2-neutralizing antibodies that have been used in the clinic. Such antibodies can prevent severe disease in SARS-CoV-2 exposed patients for whom vaccines may not provide optimal protection. Here, we describe single-domain antibodies (VHHs), also known as nanobodies, that can broadly neutralize SARS-CoV-2 with unusually high potency. Structural analysis revealed their binding to a unique, highly conserved, membrane proximal, quaternary epitope in the S2 subunit of the spike. Furthermore, a VHH-human IgG1 Fc fusion, efficiently expressed in Chinese hamster ovary cells as a stable antibody construct, protected hamsters against SARS-CoV-2 replication in a therapeutic setting when administered systemically at low dose. This VHH-based antibody represents a new candidate anti-COVID-19 biologic that targets the Achilles heel of the viral spike.

microbiology↗

bioRxiv2022

GlycoVHH: Introducing N-glycans on the camelid VHH antibody scaffold - Optimal sites and use for macrophage delivery

As small and stable high-affinity antigen binders, VHHs boast attractive characteristics both for therapeutic use in various disease indications, and as versatile reagents in research and diagnostics. To further increase the versatility of VHHs, we explored the VHH scaffold in a structure-guided approach to select regions where the introduction of an N-glycosylation N-X-T sequon and its associated glycan should not interfere with protein folding or epitope recognition. We expressed variants of such glycoengineered VHHs in the Pichia pastoris GlycoSwitchM5 strain, allowing us to pinpoint preferred sites at which Man5GlcNAc2-glycans can be introduced at high site occupancy without affecting antigen binding. A VHH carrying predominantly a Man5GlcNAc2 N-glycan at one of these preferred sites showed highly efficient, glycan-dependent uptake by Mf4/4 macrophages in vitro and by alveolar lung macrophages in vivo, illustrating one potential application of glyco-engineered VHHs: a glycan-based targeting approach for lung macrophage endolysosomal system delivery. The set of optimal artificial VHH N-glycosylation sites identified in this study can serve as a blueprint for targeted glyco-engineering of other VHHs, enabling site-specific functionalization through the rapidly expanding toolbox of synthetic glycobiology.

molecular biology↗

bioRxiv2021

GlyConnect: a glycan-based conjugation extension of the GlycoDelete technology

Recently, our lab developed GlycoDelete, a technology suite that allows a radical simplification of eukaryotic N-glycosylation. The technology allows to produce glycoproteins that carry single GlcNAc, LacNAc, or LacNAc-Sia type glycans on their N-linked glycosylation sequons. GlycoDelete-type N-glycans are uniquely suited for glycan-based conjugation purposes, as these provide a short, homogeneous and hydrophilic link to the protein backbone. Targeting GlycoDelete-glycans allows for highly site-specific conjugation at sites in the protein which are normally occupied by bulky glycans, thus ensuring minimal interference with protein structure and function. The current manuscript describes the evaluation and optimization of both chemical and chemo-enzymatic conjugation of molecules onto the GlycoDelete-type glycans of a limited set of benchmark proteins.

bioengineering↗

bioRxiv2021

Drug development of an affinity enhanced, broadly neutralizing heavy chain only antibody that restricts SARS-CoV-2 in hamsters

We have identified camelid single-domain antibodies (VHHs) that cross-neutralize SARS-CoV-1 and -2, such as VHH72, which binds to a unique highly conserved epitope in the viral receptor-binding domain (RBD) that is difficult to access for human antibodies. Here, we establish a protein engineering path for how a stable, long-acting drug candidate can be generated out of such a VHH building block. When fused to human IgG1-Fc, the prototype VHH72 molecule prophylactically protects hamsters from SARS-CoV-2. In addition, we demonstrate that both systemic and intranasal application protects hACE-2-transgenic mice from SARS-CoV-2 induced lethal disease progression. To boost potency of the lead, we used structure-guided molecular modeling combined with rapid yeast-based Fc-fusion prototyping, resulting in the affinity-matured VHH72_S56A-Fc, with subnanomolar SARS-CoV-1 and -2 neutralizing potency. Upon humanization, VHH72_S56A was fused to a human IgG1 Fc with optimized manufacturing homogeneity and silenced effector functions for enhanced safety, and its stability as well as lack of off-target binding was extensively characterized. Therapeutic systemic administration of a low dose of VHH72_S56A-Fc antibodies strongly restricted replication of both original and D614G mutant variants of SARS-CoV-2 virus in hamsters, and minimized the development of lung damage. This work led to the selection of XVR011 for clinical development, a highly stable anti-COVID-19 biologic with excellent manufacturability. Additionally, we show that XVR011 is unaffected in its neutralizing capacity of currently rapidly spreading SARS-CoV-2 variants, and demonstrate its unique, wide scope of binding across the Sarbecovirus clades.

molecular biology↗