Miho, Enkelejda

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Miho
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Enkelejda
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Miho, Enkelejda

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2014 - 2019112020 - 20249
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Thema: 600 - Technik, Medizin, angewandte Wissenschaften ×

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Gerade angezeigt 1 - 10 von 20
  • Vorschaubild
    Publikation
    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, Susana [in: eLife]
    The 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 Zeitschrift
  • Vorschaubild
    Publikation
    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, Enkelejda [in: npj Vaccines]
    Dengue 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 Zeitschrift
  • Vorschaubild
    Publikation
    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, Enkelejda [in: npj Vaccines]
    Dengue 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 Zeitschrift
  • Vorschaubild nicht verfügbar
    Publikation
    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, Victor [in: Nature Computational Science]
    Machine 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
  • Vorschaubild nicht verfügbar
    Publikation
    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, Victor [in: Nature Computational Science]
    Machine 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
  • Vorschaubild
    Publikation
    RWD-Cockpit. Application for quality assessment of real-world data
    (JMIR Publications, 18.10.2022) Degen, Markus; Babrak, Lmar; Smakaj, Erand; Agac, Teyfik; Asprion, Petra; Grimberg, Frank; Van der Werf, Daan; Van Ginkel, Erwin Willem; Tosoni, Deniz David; Clay, Ieuan; Brodbeck, Dominique; Natali, Eriberto; Schkommodau, Erik; Miho, Enkelejda [in: JMIR Formative Research]
    Digital technologies are transforming the health care system. A large part of information is generated as real-world data (RWD). Data from electronic health records and digital biomarkers have the potential to reveal associations between the benefits and adverse events of medicines, establish new patient-stratification principles, expose unknown disease correlations, and inform on preventive measures. The impact for health care payers and providers, the biopharmaceutical industry, and governments is massive in terms of health outcomes, quality of care, and cost. However, a framework to assess the preliminary quality of RWD is missing, thus hindering the conduct of population-based observational studies to support regulatory decision-making and real-world evidence.
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild nicht verfügbar
    Publikation
    Sequencing of the M protein. Toward personalized medicine in monoclonal gammopathies
    (Wiley, 23.08.2022) 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; Klersy, Catherine; Merlini, Giampaolo; Palladini, Giovanni; Nuvolone, Mario; Miho, Enkelejda [in: American Journal of Hematology]
    Each patient with a monoclonal gammopathy has a unique monoclonal (M) protein, whose sequence can be used as a tumoral fingerprint to track the presence of the B cell or plasma cell (PC) clone itself. Moreover, the M protein can directly cause potentially life-threatening organ damage, which is dictated by the specific, patient's unique clonal light and/or heavy chain amino acid sequence, as in patients affected by immunoglobulin light chain (AL) amyloidosis.1 However, patients' specific M protein sequences remain mostly undefined and molecular mechanisms underlying M protein-related clinical manifestations are largely obscure.
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild nicht verfügbar
    Publikation
    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, Mario [in: American Journal of Hematology]
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild
    Publikation
    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, Victor [in: mAbs]
    Generative 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 Zeitschrift
  • Vorschaubild
    Publikation
    Maturation of the human B-cell receptor repertoire with age
    (Cold Spring Harbor Laboratory, 20.12.2019) Ghraichy, Marie; Galson, Jacob D.; Kovaltsuk, Aleksandr; Niederhäusern, Valentin von; Schmid, Jana Pachlopnik; Recher, Mike; Jauch, Annaïse J; Miho, Enkelejda; Kelly, Dominic F.; Deane, Charlotte M.; Trück, Johannes [in: bioRxiv]
    B 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 B-cell receptor (BCR) 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 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 BCR repertoires and stress the importance of using well-selected, age-appropriate controls in BCR studies
    01A - Beitrag in wissenschaftlicher Zeitschrift