Biology subjects

Bai, M.

Publications and source records attributed to Bai, M..

At least 19 recordsLinked to original sources

bioRxiv2026

onsite: An Integrated Framework for Phosphosite Localization and False Localization Rate Estimation

With the rapid development of mass spectrometry-based proteomics, the volume of phosphoproteomic data has increased substantially. However, accurate localization of phosphorylation sites and standardized statistical validation remain critical analytical bottlenecks. To address the lack of standardized cross-algorithm evaluation, we introduce onsite, a unified and open-source Python framework. onsite integrates an alanine-decoy strategy to estimate the false localization rate (FLR) across three algorithms: AScore, PhosphoRS, and pyLucXor. This modular architecture efficiently processes large-scale datasets and enables global FLR calculation. Benchmarking on the standard synthetic phosphopeptide dataset PXD000138 highlighted distinct inter-algorithmic variations. Using the same 5% global FLR threshold, pyLucXor localized the most target sites (28,353). It also reached a high accuracy (91.22%) against the known ground truth, resulting in the largest number of correctly localized sites (25,865). Reanalysis of the highly fractionated, large-scale PXD012255 dataset further demonstrated that native integration of onsite into the quantms pipeline enables scalable processing and provides a standardized framework for FLR control in large-scale phosphoproteomics. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=64 SRC="FIGDIR/small/737157v1_ufig1.gif" ALT="Figure 1"> View larger version (14K): org.highwire.dtl.DTLVardef@e4c85dorg.highwire.dtl.DTLVardef@1e8464org.highwire.dtl.DTLVardef@185cea1org.highwire.dtl.DTLVardef@1c0d1bc_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2026

π-MSNet: A billion-scale, AI-ready living proteomics data portal

Artificial intelligence (AI) is reshaping proteomics workflows, delivering remarkable gains in both peptide identification sensitivity and quantitative performance. However, the potential of deep learning models in proteomics has not been fully exploited due to the scarcity of large-scale, high-quality and consistently labeled datasets. Here, we present {pi}-MSNet, a billion-scale, AI-ready living mass spectrometry (MS) data portal. Using a uniform identification and quality control workflow, it comprises over 1.66 billion MS/MS spectra, 501 million peptide-spectrum matches (PSMs), and 9 million precursors from 36,356 LC-MS/MS runs across ten instrument types and 55 diverse species. Through community collaboration, the data are shared via international, interactive, and living web resources. Enabled by the built-in MSNetLoader Python API for seamless and scalable data access--with native support for PyTorch and TensorFlow--{pi}-MSNet provides an AI-ready data framework for efficient training and systematic benchmarking of multiple models across three representative tasks (e.g., MS/MS spectrum prediction, retention time prediction, and de novo peptide sequencing). In particular, by retraining multiple models on {pi}-MSNet, we achieved consistent performance improvements over their original versions. These improved models were subsequently integrated into the {pi}-MSNet agent to enable interactive, deployment-free use. Through SDRF (Sample and Data Relationship Format) metadata, an open-source cloud analysis workflow, and a community-driven interactive data portal that supports continuous data submission, {pi}-MSNet serves as a living, AI-ready resource for reproducible benchmarking, robust model training, and accelerated AI innovation in proteomics.

bioinformatics↗

bioRxiv2026

quantms-rescoring enables deep proteome coverage across protein quantification, immunopeptidomics, and post-translational modifications experiments.

The growing volume of public proteomics datasets and the advent of novel machine learning (ML)-based methods create unprecedented opportunities for discovery through large-scale reanalysis. However, traditional desktop tools are increasingly insufficient for processing and integrating data at this scale. To address this challenge, we present a novel package, quantms-rescoring, that extends the cloud-native quantms workflow with a machine learning-based rescoring module. Unlike prior tools that rescore single-engine outputs, quantms-rescoring seamlessly integrates multiple search engines (SAGE, COMET, and MSGF+), performs automatic model selection, model fine-tuning, and scales reproducibly on cloud infrastructures. In quantms-rescoring, we rely on multiple fragment-ion intensity (AlphaPeptDeep and MS2PIP) and retention-time prediction (DeepLC) methods to improve results from multiple peptide database search engines. It features automatic model selection, fine-tuning, and retraining for MS/MS intensity and retention time prediction to select the best model for a given dataset. We applied the novel workflow to five representative datasets spanning DDA label-free quantification, TMT 10-plex isobaric labelling of tumor proteomics data, immunopeptidomics, phospho-proteomics, and unseen lysine malonylation experiments. We achieved a 16-22.8% increase in identified spectra, along with the quantification of 2191 additional phosphorylated peptides and 1337 phosphosites. In the tandem mass tag (TMT)-labeled clear cell renal cell carcinoma dataset, 76 novel differentially expressed multiple search engines identified proteins with quantms-rescoring. Additionally, novel 11,688 HLA-II potential binders were detected in the immunopeptidomics dataset by multiple search engines with quantms-rescoring. For unseen malonylation data, we reported more than 58.8% malonylation PSMs and 30.5% modification sites than COMET alone. Together, these results show that integrating multi-engine searches with machine learning-derived features can be combined in a scalable workflow that enhances identification, PTM localization, and quantification performance.

bioinformatics↗

bioRxiv2026

Interorganelle competition for linoleic acid underlies steatotic liver pathology

Lipid droplets (LDs) are traditionally viewed as protective organelles that sequester potentially cytotoxic lipids. However, whether and how LD biogenesis in pathological contexts actively rewires interorganelle lipid homeostasis to drive organelle dysfunction and disease progression remains unexplored. Here, we identify the adipocyte-enriched protein calsyntenin 3{beta} (CLSTN3B) as a critical promoter of metabolic dysfunction-associated steatotic liver disease (MASLD). CLSTN3B, an ER-LD contact protein previously shown to support LD maturation in adipocytes, is robustly induced in mouse hepatocytes by peroxisome proliferator-activated receptor {gamma} (PPAR{gamma}) in response to dietary caloric overload. CLSTN3B drives LD biogenesis and neutral lipid storage by stabilizing hemifusion-like ER-LD membrane bridges via its arginine-rich segment. These bridges preferentially recruit cone-shaped linoleoylated phosphatidic acid (PA), diverting linoleic acid (LA) into triacylglycerides (TAGs) rather than mitochondrial cardiolipin (CL), leading to disrupted cristae architecture, deficient ETC supercomplex assembly, elevated electron leak, and oxidative stress. Hepatocyte-specific CLSTN3B deletion impairs LD formation, reduces TAG accumulation, enhances fatty acid oxidation, restores CL maturation, and mitigates oxidative stress, collectively attenuating MASLD progression. Consistently, hepatic CLSTN3B expression correlates with fibrosis severity and progression in human MASLD. These findings position LDs as active regulators of interorganelle lipid partitioning and establish CLSTN3B as a key determinant of mitochondrial vulnerability, providing a general framework for how dysregulated organelle interfaces shape disease.

cell biology↗

bioRxiv2025

Mitochondrial membrane junction-mediated ATP channeling drives activity-dependent glucose metabolism

Neurons and brown adipocytes rely on rapid ATP production from accelerated glucose metabolism to sustain bursts of activity upon stimulation, a process known as activity-dependent glucose metabolism. The first committed step in this pathway, the hexokinase I (HK1)-catalyzed phosphorylation of glucose, consumes ATP, raising the question of how this reaction can be accelerated when cytosolic ATP becomes limiting during stimulation. We identify Cell Cycle Exit and Neuronal Differentiation protein 1 (CEND1), expressed in both cell types, as a critical regulator of this process. Loss of CEND1 impairs activity-dependent glucose utilization, ATP generation, and stimulation-evoked activity both in vitro and in vivo. Mechanistically, CEND1 assembles a complex with HK1, voltage-dependent anion channel 1 (VDAC1), and adenine nucleotide translocase 1 (ANT1) at hemifusion-like membrane junction between the outer/inner mitochondrial membrane, channeling mitochondrially derived ATP directly to HK1. These findings uncover a previously unrecognized mechanism that sustains activity-dependent glucose metabolism, with broad implications for energy homeostasis in specialized cell types.

physiology↗

bioRxiv2025

pmultiqc: An open-source, lightweight, and metadata-oriented QC reporting library for MS proteomics

The increasing scale and complexity of proteomics data demand robust, scalable, and interpretable quality control (QC) frameworks to ensure data reliability and reproducibility. Here, we present pmultiqc, an open-source Python package that standardizes and generates web-based QC reports across multiple proteomics data analysis platforms. Built on top of the widely adopted MultiQC framework, pmultiqc offers specialized modules tailored to mass spectrometry workflows, with full initial support for quantms, DIA-NN, MaxQuant/MaxDIA, and mzIdentML/mzML-based pipelines. The package computes a wide range of QC metrics, including raw intensity distributions, identification rates, retention time consistency, and missing value patterns, and presents them in interactive, publication-ready reports. By leveraging sample metadata in the SDRF format, pmultiqc enables metadata-aware QC and introduces, for the first time in proteomics, QC reports and metrics guided by standardized sample metadata. Its modular architecture allows easy extension to new workflows and formats. Alongside comprehensive documentation and examples for running pmultiqc locally or integrated into existing workflows, we offer a cloud-based service that enables users to generate QC reports from their own data or public PRIDE datasets.

bioinformatics↗

bioRxiv2025

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

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

physiology↗

bioRxiv2025

China Autism Brain Imaging Consortium: Charting Brain Growth in Chinese Children with Autism

Autism Spectrum Disorder (ASD) is a lifelong neurodevelopmental condition characterized by atypical brain growth. While advances in neuroimaging and openly sharing large-sample datasets such as the Autism Brain Imaging Data Exchange (ABIDE) have improved understanding of ASD, most studies focus on adolescents and adults, with early brain development-critical for diagnosis and intervention-remaining underexplored. Existing research predominantly involves Western samples, offering limited insight and generalizability into non-Caucasian populations. We introduce the China Autism Brain Imaging Consortium (CABIC) (https://php.bdnilab.com/resources/), a grassroots effort by researchers across the country to aggregate previously collected multi-site structural MRI datasets and phenotypic information from 1,451 autistic children and 1,119 typically developing children, covering an age range from early childhood to school age (1.0 - 12.92 years). Here, we present this resource and depict brain growth charts to push forward a more comprehensive understanding of the brain development in Chinese autism children. We constructed brain growth charts that reveal a developmental shift in autistic children, transitioning from early overgrowth to delayed maturation. Regional analyses identified distinct atypical trajectories across specific brain regions. Individual deviation scores quantified inter-subject variability, characterizing the heterogeneity of brain development in ASD. Comparative analyses between CABIC and ABIDE highlighted differences potentially attributable to ethnicity and culture, advancing our understanding of cross-population neurodevelopmental diversity. CABIC MRI datasets will be shared publicly to foster investigation of the potential neural mechanisms underlying ASD in non-Western populations and support efforts toward precision medicine for autistic individuals across diverse backgrounds.

neuroscience↗

bioRxiv2025

ibaqpy: A scalable Python package for baseline quantification in proteomics leveraging SDRF metadata

Intensity-based absolute quantification (iBAQ) is essential in proteomics as it allows for the assessment of a proteins absolute abundance in various samples or conditions. However, the computation of these values for increasingly large-scale and high-throughput experiments, such as those using DIA, TMT, or LFQ workflows, poses significant challenges in scalability and reproducibility. Here, we present ibaqpy (https://github.com/bigbio/ibaqpy), a Python package designed to compute iBAQ values efficiently for experiments of any scale. ibaqpy leverages the Sample and Data Relationship Format (SDRF) metadata standard to incorporate experimental metadata into the quantification workflow. This allows for automatic normalization and batch correction while accounting for key aspects of the experimental design, such as technical and biological replicates, fractionation strategies, and sample conditions. Designed for large-scale proteomics datasets, ibaqpy can also recompute iBAQ values for existing experiments when an SDRF is available. We showcased ibaqpys capabilities by reanalyzing 17 public proteomics datasets from ProteomeXchange, covering HeLa cell lines with 4,921 samples and 5,766 MS runs, quantifying a total of 11,014 proteins. In our reanalysis, ibaqpy is a key component in automating reproducible quantification, reducing manual effort and making quantitative proteomics more accessible while supporting FAIR principles for data reuse.

bioinformatics↗

bioRxiv2024

Proteogenomics analysis of human tissues using pangenomes

The genomics landscape is evolving with the emergence of pangenomes, challenging the conventional single-reference genome model. The new human pangenome reference provides an extra dimension by incorporating variations observed in different human populations. However, the increasing use of pangenomes in human reference databases poses challenges for proteomics, which currently relies on UniProt canonical/isoform-based reference proteomics. Including more variant information in human proteomes, such as small and long open reading frames and pseudogenes, prompts the development of complex proteogenomics pipelines for analysis and validation. This study explores the advantages of pangenomes, particularly the human reference pangenome, on proteomics, and large-scale proteogenomics studies. We reanalyze two large human tissue datasets using the quantms workflow to identify novel peptides and variant proteins from the pangenome samples. Using three search engines SAGE, COMET, and MSGF+ followed by Percolator we analyzed 91,833,481 MS/MS spectra from more than 30 normal human tissues. We developed a robust deep-learning framework to validate the novel peptides based on DeepLC, MS2PIP and pyspectrumAI. The results yielded 170142 novel peptide spectrum matches, 4991 novel peptide sequences, and 3921 single amino acid variants, corresponding to 2367 genes across five population groups, demonstrating the effectiveness of our proteogenomics approach using the recent pangenome references.

bioinformatics↗

bioRxiv2024

Relaxed selection and horizontal gene transfer fuel underwater adaptation in a water spider

Spiders have primarily adapted to terrestrial life, yet a number of species have made evolutionary transitions to marine and freshwater environments. While its physiological and behavioral adaptations have been characterized, the genetic basis of semi-aquatic and aquatic adaptation in spiders remains poorly understood. Here, we provide a high-quality, chromosome-level genome assembly for the aquatic spider Argyroneta aquatica, alongside a reference transcriptome for the semi-aquatic spider Desis martensi. We performed comparative genomes analyses of 22 spider species, including a unique aquatic spider, two semi-aquatic spiders and 19 terrestrial spider species, with a focus on those in the marronoid clade. By integrating morphological, phylogenomic, comparative genomic, transcriptomic, and metabolomic analyses, we explored the genomic adaptations of aquatic and semi-aquatic spiders. Phylogenomic analysis suggests that aquatic and semi-aquatic spiders have independently evolved from their terrestrial ancestors and represent divergent evolutionary routes We found hundreds of genes tend to experience relaxed selection, positive selection, and evidence of horizontal gene transfer (HGT) associated with the transition to aquatic and semi-aquatic life in spiders. These genes are associated with respiratory, osmoregulatory, fat metabolism and digestion, hypoxia, and thermal functions, putatively facilitate the adaptations to diverse underwater life. Altogether, our findings highlight the divergent evolutionary mechanisms enabling spiders to thrive in diverse aquatic environments, providing insights into the genomic basis of adaptations to semi-aquatic and aquatic habitats.

genomics↗

bioRxiv2023

Whole genome sequencing reveals autooctoploidy in the Chinese sturgeon and its evolutionary trajectories

The Order Acipenseriformes, which include sturgeons and paddlefishes, represent "living fossils" with complex genomes that are good models for understanding whole genome duplication (WGD) and ploidy evolution in fishes. Here we sequenced and assembled the first high-quality chromosome-level genome for the complex octoploid Acipenser sinensis (Chinese sturgeon), a critically endangered species that also represents a poorly understood ploidy group in Acipenseriformes. Our results show that A. sinensis is a complex autooctoploid species containing four kinds of octovalents (8 n), a hexavalent (6 n), two tetravalents (4 n), and a divalent (2 n). We propose based on an analysis taking into account delayed rediploidization that its octoploid genome composition results from two rounds of homologous whole genome duplications (WGDs), and further provide insight into the timing of its ploidy evolution. This study provides the first octoploid genome resource of Acipenseriformes for understanding ploidy composition and evolutionary trajectories of polyploidy fishes.

genomics↗

bioRxiv2023

PENet: A phenotype encoding network for automatic extraction and representation of morphological discriminative features

Discriminative traits are important in biodiversity and macroevolution, but extracting and representing these features from huge natural history collections using traditional methods can be challenging and time-consuming. To fully utilize the collections and their associated metadata, it is urgent now to increase the efficiency of automatic feature extraction and sample retrieval. We developed a Phenotype Encoding Network (PENet), a deep learning-based model that combines hashing methods to automatically extract and encode discriminative features into hash codes. We tested the performance of PENet on six datasets, including a newly constructed beetle dataset with six subfamilies and 6566 images, which covers more than 60% of the genera in the family Scarabaeidae. PENet showed excellent performance in feature extraction and image retrieval. Two visualization methods, t-SNE, and Grad-CAM, were used to evaluate the representation ability of the hash codes. Further, by using the hash codes generated from PENet, a phenetic distance tree was constructed based on the beetle dataset. The result indicated the hash codes could reveal the phenetic distances and relationships among categories to a certain extent. PENet provides an automatic way to extract and represent morphological discriminative features with higher efficiency, and the generated hash codes serve as a low-dimensional carrier of discriminative features and phenotypic distance information, allowing for broader applications in systematics and ecology.

evolutionary biology↗

bioRxiv2023

Perturbomics of tumor-infiltrating NK cells

Natural killer (NK) cells are an innate immune cell type that serves at the first level of defense against pathogens and cancer. NK cells have clinical potential, however, multiple current limitations exist that naturally hinder the successful implementation of NK cell therapy against cancer, including their effector function, persistence, and tumor infiltration. To unbiasedly reveal the functional genetic landscape underlying critical NK cell characteristics against cancer, we perform perturbomics mapping of tumor infiltrating NK cells by joint in vivo AAV-CRISPR screens and single cell sequencing. We establish a strategy with AAV-SleepingBeauty(SB)- CRISPR screening leveraging a custom high-density sgRNA library targeting cell surface genes, and perform four independent in vivo tumor infiltration screens in mouse models of melanoma, breast cancer, pancreatic cancer, and glioblastoma. In parallel, we characterize single-cell transcriptomic landscapes of tumor-infiltrating NK cells, which identifies previously unexplored sub-populations of NK cells with distinct expression profiles, a shift from immature to mature NK (mNK) cells in the tumor microenvironment (TME), and decreased expression of mature marker genes in mNK cells. CALHM2, a calcium homeostasis modulator that emerges from both screen and single cell analyses, shows both in vitro and in vivo efficacy enhancement when perturbed in chimeric antigen receptor (CAR)-NK cells. Differential gene expression analysis reveals that CALHM2 knockout reshapes cytokine production, cell adhesion, and signaling pathways in CAR- NKs. These data directly and systematically map out endogenous factors that naturally limit NK cell function in the TME to offer a broad range of cellular genetic checkpoints as candidates for future engineering to enhance NK cell-based immunotherapies.

immunology↗

bioRxiv2023

CTLA-4 tail fusion enhances CAR-T anti-tumor immunity

Chimeric antigen receptor (CAR) T cells are powerful therapeutics; however, their efficacy is often hindered by critical hurdles. Here, utilizing the endocytic feature of the cytotoxic T-lymphocyte-associated antigen-4 (CTLA-4) cytoplasmic tail (CT), we reprogram CAR function and substantially enhance CAR-T efficacy in vivo. CAR-T cells with monomeric, duplex, or triplex CTLA-4 CTs (CCTs) fused to the C-terminus of CAR exhibit a progressive increase in cytotoxicity under repeated stimulation, accompanied by reduced activation and production of pro-inflammatory cytokines. Further characterization reveals that CARs with increasing CCT fusion show a progressively lower surface expression, regulated by their constant endocytosis, recycling and degradation under steady state. The molecular dynamics of reengineered CAR with CCT fusion results in reduced CAR-mediated trogocytosis, loss of tumor antigen, and improved CAR-T survival. CARs with either monomeric (CAR-1CCT) or duplex CCTs (CAR-2CCT) have superior anti-tumor efficacy in a relapsed leukemia model. Single-cell RNA sequencing and flow cytometry analysis reveal that CAR-2CCT cells retain a stronger central memory phenotype and exhibit increased persistence. These findings illuminate a unique strategy for engineering therapeutic T cells and improving CAR-T function through synthetic CCT fusion, which is orthogonal to other cell engineering techniques.

immunology↗

bioRxiv2023

Immunogenetic metabolomics revealed key enzymes that modulate CAR-T metabolism and function

Immune evasion is a critical step of cancer progression that remains a major obstacle for current T cell-based immunotherapies. Hence, we seek to genetically reprogram T cells to exploit a common tumor-intrinsic evasion mechanism, whereby cancer cells suppress T cell function by generating a metabolically unfavorable tumor microenvironment (TME). Specifically, we use an in silico screen to identify ADA and PDK1 as metabolic regulators, in which gene overexpression (OE) enhances the cytolysis of CD19-specific CD8 CAR-T cells against cognate leukemia cells, and conversely, ADA or PDK1 deficiency dampens such effect. ADA-OE in CAR-T cells improves cancer cytolysis under high concentrations of adenosine, the ADA substrate and an immunosuppressive metabolite in the TME. High-throughput transcriptomics and metabolomics in these CAR-Ts reveal alterations of global gene expression and metabolic signatures in both ADA- and PDK1- engineered CAR-T cells. Functional and immunological analyses demonstrate that ADA-OE increases proliferation and decreases exhaustion in -CD19 and -HER2 CAR-T cells. ADA-OE improves tumor infiltration and clearance by -HER2 CAR-T cells in an in vivo colorectal cancer model. Collectively, these data unveil systematic knowledge of metabolic reprogramming directly in CAR-T cells, and reveal potential targets for improving CAR-T based cell therapy. SynopsisThe authors identify the adenosine deaminase gene (ADA) as a regulatory gene that reprograms T cell metabolism. ADA-overexpression (OE) in -CD19 and -HER2 CAR-T cells increases proliferation, cytotoxicity, memory, and decreases exhaustion, and ADA-OE -HER2 CAR-T cells have enhanced clearance of HT29 human colorectal cancer tumors in vivo.

cancer biology↗

bioRxiv2022

Function and Cryo-EM structures of broadly potent bispecific antibodies against multiple SARS-CoV-2 Omicron sublineages

The SARS-CoV-2 variant, Omicron (B.1.1.529), rapidly swept the world since its emergence. Compared with previous variants, Omicron has a high number of mutations, especially those in its spike glycoprotein that drastically dampen or abolish the efficacy of currently available vaccines and therapeutic antibodies. Several major sublineages of Omicron evolved, including BA.1, BA.1.1, BA.2, BA.2.12.1, BA.3, BA.4/5, and BA.2.75, which rapidly changing the global and regional landscape of the pandemic. Although vaccines are available, therapeutic antibodies remain critical for infected and especially hospitalized patients. To address this, we have designed and generated a panel of human/humanized therapeutic bispecific antibodies against Omicron and its sub-lineage variants, with activity spectrum against other lineages. Among these, the top clone CoV2-0213 has broadly potent activities against multiple SARS-CoV-2 ancestral and Omicron lineages, including BA.1, BA.1.1, BA.2, BA.2.12.1, BA.3, BA.4/5, and BA.2.75. We have solved the cryo-EM structure of the lead bi-specific antibody CoV-0213 and its major Fab arm MB.02. Three-dimensional structural analysis shows distinct epitope of antibody - spike receptor binding domain (RBD) interactions and reveals that both Fab fragments of CoV2-0213 can simultaneously target one single spike RBD or two adjacent ones in the same spike trimer, further corroborating its mechanism of action. CoV2-0213 represents a unique and potent broad-spectrum SARS-CoV-2 neutralizing bispecific antibody (nbsAb) against the currently circulating major Omicron variants (BA.1, BA.1.1, BA.2, BA.2.12.1, BA.2.75, BA.3, and BA.4/5). CoV2-0213 is primarily human and ready for translational testing as a countermeasure against the ever-evolving pathogen.

immunology↗

bioRxiv2022

Potent and specific human monoclonal antibodies against SARS-CoV-2 Omicron variant by rapid mRNA immunization of humanized mice

The Omicron variant (B.1.1.529) of SARS-CoV-2 rapidly becomes dominant globally. Its extensive mutations confer severe efficacy reduction to most of existing antibodies or vaccines. Here, we developed RAMIHM, a highly efficient strategy to generate fully human monoclonal antibodies (mAbs), directly applied it with Omicron-mRNA immunization, and isolated three potent and specific clones against Omicron. Rapid mRNA immunization elicited strong anti-Omicron antibody response in humanized mice, along with broader anti-coronavirus activity. Customized single cell BCR sequencing mapped the clonal repertoires. Top-ranked clones collectively from peripheral blood, plasma B and memory B cell populations showed high rate of Omicron-specificity (93.3%) from RAMIHM-scBCRseq. Clone-screening identified three highly potent neutralizing antibodies that have low nanomolar affinity for Omicron RBD, and low ng/mL level IC50 in neutralization, more potent than majority of currently approved or authorized clinical RBD-targeting mAbs. These lead mAbs are fully human and ready for downstream IND-enabling and/or translational studies.

bioengineering↗