Biology subjects

Andersen, J. T.

Publications and source records attributed to Andersen, J. T..

7 recordsLinked to original sources

bioRxiv2026

Putative target antigens of the stereotyped intrathecal B cell response in multiple sclerosis

Intrathecal antibody production is a hallmark of multiple sclerosis (MS), yet the antigenic targets of these antibodies remain elusive. Intrathecal antibody-secreting cells in MS patients characteristically produce IgG1 antibodies carrying the G1m1 allotype and preferentially use IGHV4 heavy-chain genes paired with IGKV1 or IGKV3 light chains. This stereotyped pattern points to a common antigen-driven selection process. To test this hypothesis, we generated monoclonal antibodies from intrathecal B-lineage cells bearing this stereotyped B-cell receptor configuration and screened them against a comprehensive library of human and Epstein-Barr virus proteins. In parallel, we developed and validated an immunoprecipitation-tandem mass-spectrometry (IPMS/MS) workflow for fresh-frozen brain tissue that preserves conformational epitopes. Several proteins, like CLDN11 and PLD1, were enriched by antibodies from multiple patients. Other targets, like MED21 and GIPC2, were unique to individual patients. Our study delivers a scalable pipeline for antigen discovery in MS and nominates candidate antigens for future validation. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=167 SRC="FIGDIR/small/697481v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@b522eaorg.highwire.dtl.DTLVardef@76240org.highwire.dtl.DTLVardef@c36205org.highwire.dtl.DTLVardef@1587019_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2024

Systematic benchmarking of mass spectrometry-based antibody sequencing reveals methodological biases

The circulating antibody repertoire is crucial for immune protection, holding significant immunological and biotechnological value. While bottom-up mass spectrometry (MS) is the most widely used proteomics technique for profiling the sequence diversity of circulating antibodies (Ab-seq), it has not been thoroughly benchmarked. We quantified the replicability and robustness of Ab-seq using six monoclonal antibodies with known protein sequences in 70 different combinations of concentration and oligoclonality, both with and without polyclonal serum IgG background. Each combination underwent four protease treatments and was analyzed across four experimental and three technical replicates, totaling 3,360 LC-MS/MS runs. We quantified the dependence of MS-based Ab-seq identification on antibody sequence, concentration, protease, background signal diversity, and bioinformatics setups. Integrating the data from experimental replicates, proteases, and bioinformatics tools enhanced antibody identification. De novo peptide sequencing showed similar performance to database-dependent methods for higher antibody concentrations, but de novo antibody reconstruction remains challenging. Our work provides a foundational resource for the field of MS-based antibody profiling.

immunology↗

bioRxiv2023

Cartography of the developability landscapes of native and human-engineered antibodies

Designing effective monoclonal antibody (mAb) therapeutics faces a multi-parameter optimization challenge known as "developability", which reflects an antibodys ability to progress through development stages based on its physicochemical properties. While natural antibodies may provide valuable guidance for mAb selection, we lack a comprehensive understanding of natural developability parameter (DP) plasticity (redundancy, predictability, sensitivity) and how the DP landscapes of human-engineered and natural antibodies relate to one another. These gaps hinder fundamental developability profile cartography. To chart natural and engineered DP landscapes, we computed 40 sequence- and 46 structure-based DPs of over two million native and human-engineered single-chain antibody sequences. We found lower redundancy among structure-based compared to sequence-based DPs. Sequence DP sensitivity to single amino acid substitutions varied by antibody region and DP, and structure DP values varied across the conformational ensemble of antibody structures. Sequence DPs were more predictable than structure-based ones across different machine-learning tasks and embeddings, indicating a constrained sequence-based design space. Human-engineered antibodies were localized within the developability and sequence landscapes of natural antibodies, suggesting that human-engineered antibodies explore mere subspaces of the natural one. Our work quantifies the plasticity of antibody developability, providing a fundamental resource for multi-parameter therapeutic mAb design.

bioinformatics↗

bioRxiv2023

NNV024, a novel humanized anti-CD37 antibody with enhanced ADCC and prolonged plasma half-life in human FcRn transgenic mice for treatment of NHL

There is an unmet medical need for new therapeutic approaches and targets for patients with non- Hodgkin lymphoma (NHL) who relapse or are refractory to anti-CD20 immunotherapy. Therefore, we developed a humanized IgG1 antibody targeting CD37, which was tailored to be afucosylated for enhanced antibody-dependent cellular cytotoxicity (ADCC) (NNV024). In line with this, NNV024 induced three-fold more potent ADCC activity against patient-derived chronic lymphocytic leukemia (CLL) cells compared with anti-CD20 obinutuzumab. Moreover, NNV024 showed 2-fold higher ADCC activity than anti-CD20 rituximab and a recombinant version of DuoHexaBody-CD37 against both NHL and CLL cells. Survival was significantly longer after NNV024 treatment than with obinutuzumab in a mouse model. In addition, NNV024 showed a favourable plasma half-life in human FcRn transgenic mice of about 9-days, which was 2-fold longer than that of obinutuzumab and DuoHexaBody-CD37. These results warrant the further development of NNV024 as a treatment for NHL.

cancer biology↗

bioRxiv2023

Phage display assisted discovery of a pH-dependent anti-alpha-cobratoxin antibody from a natural variable domain library

Recycling antibodies can bind to their target antigen at neutral pH in the blood stream and release them upon endocytosis when pH levels drop, allowing the antibodies to be recycled into circulation via FcRn-mediated pathway, while the antigens undergo lysosomal degradation. This enables recycling antibodies to achieve the same therapeutic effect at lower doses than their non-recyclable counterparts. The development of such antibodies is typically achieved by histidine doping of the variable regions of specific antibodies or by performing in vitro antibody selection campaigns utilizing histidine doped libraries. While often successful, these strategies may introduce sequence liabilities, as they often involve mutations that may render the resultant antibodies to be non-natural. Here, we present a methodology that employs a naive antibody phage display library, consisting of natural variable domains, to discover antibodies that bind -cobratoxin from the venom of Naja kaouthia in a pH-dependent manner. Upon screening of the discovered antibodies with immunoassays and bio-layer interferometry, a pH-dependent antibody was discovered that exhibits an 8-fold higher dissociation rate at pH 5.5 than 7.4. Interestingly, the variable domains of the pH-dependent antibody were found to be entirely devoid of histidines, demonstrating that pH-dependency may not always be driven by this amino acid. Further, given the high diversity available in a naive antibody library, the methodology presented here can likely be applied to discover pH-dependent antibodies against different targets ab initio without the need of histidine doping. For broader audienceHere, we present the discovery of an -cobratoxin targeting pH-dependent antibody, with a variable region devoid of histidines, from a naive antibody library with natural variable domains. Our findings suggest that the commonly taken approach of histidine doping to find pH-dependent antibodies may not always be required, and thus offer an alternative strategy for the discovery of pH-dependent antibodies.

bioengineering↗

bioRxiv2022

Multiplexed measurement of binding- and neutralizing antibodies to SARS-CoV-2 variants in 12.000 post-vaccine sera

Diagnostic assays currently used to monitor the efficacy of COVID-19 vaccines measure levels of antibodies to the receptor-binding domain of ancestral SARS-CoV-2 (RBDwt). However, the predictive value for protection against new variants of concern (VOCs) has not been firmly established. Here, we used bead-based arrays and flow cytometry to measure binding of antibodies to spike proteins and receptor-binding domains (RBDs) from VOCs in 12,000 sera. Effects of sera on RBD-ACE2 interactions were measured as a proxy for neutralizing antibodies. The samples were obtained from healthy individuals or patients on immunosuppressive therapy who had received two to four doses of COVID-19 vaccines and from COVID-19 convalescents. The results show that anti-RBDwt titers correlate with the levels of binding- and neutralizing antibodies against the Alpha, Beta, Gamma, Delta, Epsilon and Omicron variants. The benefit of multiplexed analysis lies in the ability to measure a wide range of anti-RBD titers using a single dilution of serum for each assay. The reactivity patterns also yield an internal reference for neutralizing activity and binding antibody units per milliliter (BAU/ml). Results obtained with sera from vaccinated healthy individuals and patients confirmed and extended results from previous studies on time-dependent waning of antibody levels and effects of immunosuppressive agents. We conclude that anti-RBDwt titers correlate with levels of neutralizing antibodies against VOCs and propose that our method may be implemented to enhance the precision and throughput of immunomonitoring.

immunology↗

bioRxiv2021

In silico proof of principle of machine learning-based antibody design at unconstrained scale

Generative machine learning (ML) has been postulated to be a major driver in the computational design of antigen-specific monoclonal antibodies (mAb). However, efforts to confirm this hypothesis have been hindered by the infeasibility of testing arbitrarily large numbers of antibody sequences for their most critical design parameters: paratope, epitope, affinity, and developability. To address this challenge, we leveraged a lattice-based antibody-antigen binding simulation framework, which incorporates a wide range of physiological antibody binding parameters. The simulation framework enables both the computation of antibody-antigen 3D-structures as well as functions as an oracle for unrestricted prospective evaluation of the antigen specificity of ML-generated antibody sequences. We found that a deep generative model, trained exclusively on antibody sequence (1D) data can be used to design native-like conformational (3D) epitope-specific antibodies, matching or exceeding the training dataset in affinity and developability variety. Furthermore, we show that transfer learning enables the generation of high-affinity antibody sequences from low-N training data. Finally, we validated that the antibody design insight gained from simulated antibody-antigen binding data is applicable to experimental real-world data. Our work establishes a priori feasibility and the theoretical foundation of high-throughput ML-based mAb design. HighlightsO_LIA large-scale dataset of 70M [3 orders of magnitude larger than the current state of the art] synthetic antibody-antigen complexes, that reflect biological complexity, allows the prospective evaluation of antibody generative deep learning C_LIO_LICombination of generative learning, synthetic antibody-antigen binding data, and prospective evaluation shows that deep learning driven antibody design and discovery at an unconstrained level is feasible C_LIO_LITransfer learning (low-N learning) coupled to generative learning shows that antibody-binding rules may be transferred across unrelated antibody-antigen complexes C_LIO_LIExperimental validation of antibody-design conclusions drawn from deep learning on synthetic antibody-antigen binding data C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/451480v1_ufig1.gif" ALT="Figure 1"> View larger version (64K): org.highwire.dtl.DTLVardef@1d947bcorg.highwire.dtl.DTLVardef@1f4c51corg.highwire.dtl.DTLVardef@b8df67org.highwire.dtl.DTLVardef@1530ea5_HPS_FORMAT_FIGEXP M_FIG We leverage large synthetic ground-truth data to demonstrate the (A,B) unconstrained deep generative learning-based generation of native-like antibody sequences, (C) the prospective evaluation of conformational (3D) affinity, paratope-epitope pairs, and developability. (D) Finally, we show increased generation quality of low-N-based machine learning models via transfer learning. C_FIG

immunology↗