Institut für Medizintechnik und Medizininformatik
Dauerhafte URI für die Sammlunghttps://irf.fhnw.ch/handle/11654/23
Listen
16 Ergebnisse
Ergebnisse nach Hochschule und Institut
Publikation Computational deconvolution of the dengue immune response complexity with identification of novel broadly neutralizing antibodies(21.09.2022) Natali, Eriberto Noel; Horst, Alexander; Meier, Patrick; Greiff, Victor; Nuvolone, Mario; Babrak, Lmar Marie; Djordjevic, Kristina; Fink, Katja; Traggiai, Elisabetta; Miho, EnkelejdaDengue virus poses a serious threat to global health as the causative agent of the dengue fever. Currently, there is no approved therapeutic, and broadly neutralizing antibodies recognizing all four serotypes may be an effective treatment. High-throughput immune repertoire sequencing and bioinformatic analysis enable in-depth understanding of the immune response in dengue infection. Here, we use these technologies and apply machine learning to identify rare and underrepresented broadly neutralizing antibody sequences through investigation of antibody response in dengue. We observed challenging the immune system with dengue elicits the following signatures on the antibody repertoire: (i) an increase of the diversity in the CDR3 regions and the germline genes; (ii) a change in the architecture by eliciting power-law network distributions and enrichment in polar amino acids of the CDR3; (iii) an increase in the expression of transcription factors of the JNK/Fos pathways and ribosomal proteins. Moreover, our work demonstrates the applicability of computational methods and machine learning to high-throughput antibody repertoire sequencing datasets for neutralizing antibody candidate identification. Further investigation with antibody expression and functional assays is planned to validate the obtained results.06 - PräsentationPublikation Author Correction. The dengue-specific immune response and antibody identification with machine learning(Nature, 20.01.2024) Natali, Eriberto Noel; Horst, Alexander; Meier, Patrick; Greiff, Victor; Nuvolone, Mario; Babrak, Lmar Marie; Fink, Katja; Miho, EnkelejdaDengue virus poses a serious threat to global health and there is no specific therapeutic for it. Broadly neutralizing antibodies recognizing all serotypes may be an effective treatment. High-throughput adaptive immune receptor repertoire sequencing (AIRR-seq) and bioinformatic analysis enable in-depth understanding of the B-cell immune response. Here, we investigate the dengue antibody response with these technologies and apply machine learning to identify rare and underrepresented broadly neutralizing antibody sequences. Dengue immunization elicited the following signatures on the antibody repertoire: (i) an increase of CDR3 and germline gene diversity; (ii) a change in the antibody repertoire architecture by eliciting power-law network distributions and CDR3 enrichment in polar amino acids; (iii) an increase in the expression of JNK/Fos transcription factors and ribosomal proteins. Furthermore, we demonstrate the applicability of computational methods and machine learning to AIRR-seq datasets for neutralizing antibody candidate sequence identification. Antibody expression and functional assays have validated the obtained results.01A - Beitrag in wissenschaftlicher ZeitschriftPublikation The dengue-specific immune response and antibody identification with machine learning(Nature, 20.01.2024) Natali, Eriberto Noel; Horst, Alexander; Meier, Patrick; Greiff, Victor; Nuvolone, Mario; Babrak, Lmar Marie; Fink, Katja; Miho, EnkelejdaDengue virus poses a serious threat to global health and there is no specific therapeutic for it. Broadly neutralizing antibodies recognizing all serotypes may be an effective treatment. High-throughput adaptive immune receptor repertoire sequencing (AIRR-seq) and bioinformatic analysis enable in-depth understanding of the B-cell immune response. Here, we investigate the dengue antibody response with these technologies and apply machine learning to identify rare and underrepresented broadly neutralizing antibody sequences. Dengue immunization elicited the following signatures on the antibody repertoire: (i) an increase of CDR3 and germline gene diversity; (ii) a change in the antibody repertoire architecture by eliciting power-law network distributions and CDR3 enrichment in polar amino acids; (iii) an increase in the expression of JNK/Fos transcription factors and ribosomal proteins. Furthermore, we demonstrate the applicability of computational methods and machine learning to AIRR-seq datasets for neutralizing antibody candidate sequence identification. Antibody expression and functional assays have validated the obtained results.01A - Beitrag in wissenschaftlicher ZeitschriftPublikation Single-molecule real-time sequencing of the M protein.Toward personalized medicine in monoclonal gammopathies(Wiley, 05.08.2022) Cascino, Pasquale; Nevone, Alice; Piscitelli, Maggie; Scopelliti, Claudia; Girelli, Maria; Mazzini, Giulia; Caminito, Serena; Russo, Giancarlo; Milani, Paolo; Basset, Marco; Foli, Andrea; Fazio, Francesca; Casarini, Simona; Massa, Margherita; Bozzola, Margherita; Ripepi, Jessica; Sesta, Melania Antonietta; Acquafredda, Gloria; De Cicco, Marica; Moretta, Antonia; Rognoni, Paola; Milan, Enrico; Ricagno, Stefano; Lavatelli, Francesca; Petrucci, Maria Teresa; Miho, Enkelejda; Klersy, Catherine; Merlini, Giampaolo; Palladini, Giovanni; Nuvolone, Mario01A - Beitrag in wissenschaftlicher ZeitschriftPublikation In silico proof of principle of machine learning-based antibody design at unconstrained scale(Taylor & Francis, 04.04.2022) Akbar, Rahmad; Robert, Philippe A.; Weber, Cédric R.; Widrich, Michael; Frank, Robert; Pavlović, Milena; Scheffer, Lonneke; Chernigovskaya, Maria; Snapkov, Igor; Slabodkin, Andrei; Mehta, Brij Bhushan; Miho, Enkelejda; Lund-Johansen, Fridtjof; Andersen, Jan Terje; Hochreiter, Sepp; Hobæk Haff, Ingrid; Klambauer, Günter; Sandve, Geir Kjetil; Greiff, VictorGenerative machine learning (ML) has been postulated to become 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 the computation of synthetic antibody-antigen 3D-structures, and it functions as an oracle for unrestricted prospective evaluation and benchmarking of antibody design parameters of ML-generated antibody sequences. We found that a deep generative model, trained exclusively on antibody sequence (one dimensional: 1D) data can be used to design conformational (three dimensional: 3D) epitope-specific antibodies, matching, or exceeding the training dataset in affinity and developability parameter value variety. Furthermore, we established a lower threshold of sequence diversity necessary for high-accuracy generative antibody ML and demonstrated that this lower threshold also holds on experimental real-world data. Finally, we show that transfer learning enables the generation of high-affinity antibody sequences from low-N training data. Our work establishes a priori feasibility and the theoretical foundation of high-throughput ML-based mAb design.01A - Beitrag in wissenschaftlicher ZeitschriftPublikation Kidins220 regulates the development of B cells bearing the λ light chain(eLife Sciences Publications, 25.01.2024) Schaffer, Anna-Maria; Fiala, Gina Jasmin; Hils, Miriam; Natali, Eriberto; Babrak, Lmar; Herr, Laurenz Alexander; Romero-Mulero, Mari Carmen; Cabezas-Wallscheid, Nina; Rizzi, Marta; Miho, Enkelejda; Schamel, Wolfgang W.A.; Minguet, SusanaThe ratio between κ and λ light chain (LC)-expressing B cells varies considerably between species. We recently identified Kinase D-interacting substrate of 220 kDa (Kidins220) as an interaction partner of the BCR. In vivo ablation of Kidins220 in B cells resulted in a marked reduction of λLC-expressing B cells. Kidins220 knockout B cells fail to open and recombine the genes of the Igl locus, even in genetic scenarios where the Igk genes cannot be rearranged or where the κLC confers autoreactivity. Igk gene recombination and expression in Kidins220-deficient B cells is normal. Kidins220 regulates the development of λLC B cells by enhancing the survival of developing B cells and thereby extending the time-window in which the Igl locus opens and the genes are rearranged and transcribed. Further, our data suggest that Kidins220 guarantees optimal pre-BCR and BCR signaling to induce Igl locus opening and gene recombination during B cell development and receptor editing.01A - Beitrag in wissenschaftlicher ZeitschriftPublikation Unconstrained generation of synthetic antibody–antigen structures to guide machine learning methodology for antibody specificity prediction(Nature, 19.12.2022) Robert, Philippe A.; Akbar, Rahmad; Frank, Robert; Pavlović, Milena; Widrich, Michael; Snapkov, Igor; Slabodkin, Andrei; Chernigovskaya, Maria; Scheffer, Lonneke; Smorodina, Eva; Rawat, Puneet; Mehta, Brij Bhushan; Vu, Mai Ha; Mathisen, Ingvild Frøberg; Prósz, Aurél; Abram, Krzysztof; Olar, Alex; Miho, Enkelejda; Haug, Dag Trygve Tryslew; Lund-Johansen, Fridtjof; Hochreiter, Sepp; Haff, Ingrid Hobæk; Klambauer, Günter; Sandve, Geir Kjetil; Greiff, VictorMachine learning (ML) is a key technology for accurate prediction of antibody–antigen binding. Two orthogonal problems hinder the application of ML to antibody-specificity prediction and the benchmarking thereof: the lack of a unified ML formalization of immunological antibody-specificity prediction problems and the unavailability of large-scale synthetic datasets to benchmark real-world relevant ML methods and dataset design. Here we developed the Absolut! software suite that enables parameter-based unconstrained generation of synthetic lattice-based three-dimensional antibody–antigen-binding structures with ground-truth access to conformational paratope, epitope and affinity. We formalized common immunological antibody-specificity prediction problems as ML tasks and confirmed that for both sequence- and structure-based tasks, accuracy-based rankings of ML methods trained on experimental data hold for ML methods trained on Absolut!-generated data. The Absolut! framework has the potential to enable real-world relevant development and benchmarking of ML strategies for biotherapeutics design.01A - Beitrag in wissenschaftlicher ZeitschriftPublikation Maturation of the human immunoglobulin heavy chain repertoire with age(Frontiers Research Foundation, 06.08.2020) Ghraichy, Marie; Galson, Jacob D.; Kovaltsuk, Aleksandr; von Niederhäusern, Valentin; Pachlopnik Schmid, Jana; Recher, Mike; Jauch, Annaïse J.; Miho, Enkelejda; Kelly, Dominic F.; Deane, Charlotte M.; Trück, JohannesB cells play a central role in adaptive immune processes, mainly through the production of antibodies. The maturation of the B cell system with age is poorly studied. We extensively investigated age-related alterations of naïve and antigen-experienced immunoglobulin heavy chain (IgH) repertoires. The most significant changes were observed in the first 10 years of life, and were characterized by altered immunoglobulin gene usage and an increased frequency of mutated antibodies structurally diverging from their germline precursors. Older age was associated with an increased usage of downstream IgH constant region genes and fewer antibodies with self-reactive properties. As mutations accumulated with age, the frequency of germline-encoded self-reactive antibodies decreased, indicating a possible beneficial role of self-reactive B cells in the developing immune system. Our results suggest a continuous process of change through childhood across a broad range of parameters characterizing IgH repertoires and stress the importance of using well-selected, age-appropriate controls in IgH studies.01A - Beitrag in wissenschaftlicher ZeitschriftPublikation Adaptive immune receptor repertoire (AIRR) community guide to TR and IG gene annotation(Springer, 28.05.2022) Babrak, Lmar; Marquez, Susanna; Busse, Christian; Lees, William; Miho, Enkelejda; Ohlin, Mats; Rosenfeld, Aaron; Stervbo, Ulrik; Watson, Corey; Schramm, Chaim; Langerak, Anton W.High-throughput sequencing of adaptive immune receptor repertoires (AIRR, i.e., IG and TR) has revolutionized the ability to carry out large-scale experiments to study the adaptive immune response. Since the method was first introduced in 2009, AIRR sequencing (AIRR-Seq) has been applied to survey the immune state of individuals, identify antigen-specific or immune-state-associated signatures of immune responses, study the development of the antibody immune response, and guide the development of vaccines and antibody therapies. Recent advancements in the technology include sequencing at the single-cell level and in parallel with gene expression, which allows the introduction of multi-omics approaches to understand in detail the adaptive immune response. Analyzing AIRR-seq data can prove challenging even with high-quality sequencing, in part due to the many steps involved and the need to parameterize each step. In this chapter, we outline key factors to consider when preprocessing raw AIRR-Seq data and annotating the genetic origins of the rearranged receptors. We also highlight a number of common difficulties with common AIRR-seq data processing and provide strategies to address them.04A - Beitrag SammelbandPublikation Unconstrained generation of synthetic antibody–antigen structures to guide machine learning methodology for antibody specificity prediction(Nature, 19.12.2022) Robert, Philippe A.; Akbar, Rahmad; Pavlović, Milena; Widrich, Michael; Snapkov, Igor; Slabodkin, Andrei; Chernigovskaya, Maria; Scheffer, Lonneke; Smorodina, Eva; Rawat, Puneet; Mehta, Brij Bhushan; Vu, Mai Ha; Mathisen, Ingvild Frøberg; Prósz, Aurél; Abram, Krzysztof; Olar, Axel; Miho, Enkelejda; Haug, Dag Trygve Tryslew; Lund-Johansen, Fridtjof; Hochreiter, Sepp; Hobæk Haff, Ingrid; Klambauer, Günter; Sandve, Geir Kjetil; Greiff, VictorMachine learning (ML) is a key technology for accurate prediction of antibody–antigen binding. Two orthogonal problems hinder the application of ML to antibody-specificity prediction and the benchmarking thereof: the lack of a unified ML formalization of immunological antibody-specificity prediction problems and the unavailability of large-scale synthetic datasets to benchmark real-world relevant ML methods and dataset design. Here we developed the Absolut! software suite that enables parameter-based unconstrained generation of synthetic lattice-based three-dimensional antibody–antigen-binding structures with ground-truth access to conformational paratope, epitope and affinity. We formalized common immunological antibody-specificity prediction problems as ML tasks and confirmed that for both sequence- and structure-based tasks, accuracy-based rankings of ML methods trained on experimental data hold for ML methods trained on Absolut!-generated data. The Absolut! framework has the potential to enable real-world relevant development and benchmarking of ML strategies for biotherapeutics design.01A - Beitrag in wissenschaftlicher Zeitschrift