Proteomic meta-study harmonization, mechanotyping and drug repurposing candidate prediction with ProHarMeD
Abstract Proteomics technologies, which include a diverse range of approaches such as mass spectrometry-based, array-based, and others, are key technologies for the identification of biomarkers and disease mechanisms, referred to as mechanotyping. Despite over 15,000 published studies in 2022 alone,...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-10-01
|
| Series: | npj Systems Biology and Applications |
| Online Access: | https://doi.org/10.1038/s41540-023-00311-7 |
| _version_ | 1797558795608522752 |
|---|---|
| author | Klaudia Adamowicz Lis Arend Andreas Maier Johannes R. Schmidt Bernhard Kuster Olga Tsoy Olga Zolotareva Jan Baumbach Tanja Laske |
| author_facet | Klaudia Adamowicz Lis Arend Andreas Maier Johannes R. Schmidt Bernhard Kuster Olga Tsoy Olga Zolotareva Jan Baumbach Tanja Laske |
| author_sort | Klaudia Adamowicz |
| collection | DOAJ |
| description | Abstract Proteomics technologies, which include a diverse range of approaches such as mass spectrometry-based, array-based, and others, are key technologies for the identification of biomarkers and disease mechanisms, referred to as mechanotyping. Despite over 15,000 published studies in 2022 alone, leveraging publicly available proteomics data for biomarker identification, mechanotyping and drug target identification is not readily possible. Proteomic data addressing similar biological/biomedical questions are made available by multiple research groups in different locations using different model organisms. Furthermore, not only various organisms are employed but different assay systems, such as in vitro and in vivo systems, are used. Finally, even though proteomics data are deposited in public databases, such as ProteomeXchange, they are provided at different levels of detail. Thus, data integration is hampered by non-harmonized usage of identifiers when reviewing the literature or performing meta-analyses to consolidate existing publications into a joint picture. To address this problem, we present ProHarMeD, a tool for harmonizing and comparing proteomics data gathered in multiple studies and for the extraction of disease mechanisms and putative drug repurposing candidates. It is available as a website, Python library and R package. ProHarMeD facilitates ID and name conversions between protein and gene levels, or organisms via ortholog mapping, and provides detailed logs on the loss and gain of IDs after each step. The web tool further determines IDs shared by different studies, proposes potential disease mechanisms as well as drug repurposing candidates automatically, and visualizes these results interactively. We apply ProHarMeD to a set of four studies on bone regeneration. First, we demonstrate the benefit of ID harmonization which increases the number of shared genes between studies by 50%. Second, we identify a potential disease mechanism, with five corresponding drug targets, and the top 20 putative drug repurposing candidates, of which Fondaparinux, the candidate with the highest score, and multiple others are known to have an impact on bone regeneration. Hence, ProHarMeD allows users to harmonize multi-centric proteomics research data in meta-analyses, evaluates the success of the ID conversions and remappings, and finally, it closes the gaps between proteomics, disease mechanism mining and drug repurposing. It is publicly available at https://apps.cosy.bio/proharmed/ . |
| first_indexed | 2024-03-10T17:36:27Z |
| format | Article |
| id | doaj.art-4b2ab75f8e404e708760f72bb3c8d516 |
| institution | Directory Open Access Journal |
| issn | 2056-7189 |
| language | English |
| last_indexed | 2024-03-10T17:36:27Z |
| publishDate | 2023-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Systems Biology and Applications |
| spelling | doaj.art-4b2ab75f8e404e708760f72bb3c8d5162023-11-20T09:49:55ZengNature Portfolionpj Systems Biology and Applications2056-71892023-10-019111010.1038/s41540-023-00311-7Proteomic meta-study harmonization, mechanotyping and drug repurposing candidate prediction with ProHarMeDKlaudia Adamowicz0Lis Arend1Andreas Maier2Johannes R. Schmidt3Bernhard Kuster4Olga Tsoy5Olga Zolotareva6Jan Baumbach7Tanja Laske8Institute for Computational Systems Biology, University of HamburgInstitute for Computational Systems Biology, University of HamburgInstitute for Computational Systems Biology, University of HamburgDepartment of Preclinical Development and Validation, Fraunhofer Institute for Cell Therapy and Immunology IZIChair of Proteomics and Bioanalytics, Technical University of MunichInstitute for Computational Systems Biology, University of HamburgInstitute for Computational Systems Biology, University of HamburgInstitute for Computational Systems Biology, University of HamburgInstitute for Computational Systems Biology, University of HamburgAbstract Proteomics technologies, which include a diverse range of approaches such as mass spectrometry-based, array-based, and others, are key technologies for the identification of biomarkers and disease mechanisms, referred to as mechanotyping. Despite over 15,000 published studies in 2022 alone, leveraging publicly available proteomics data for biomarker identification, mechanotyping and drug target identification is not readily possible. Proteomic data addressing similar biological/biomedical questions are made available by multiple research groups in different locations using different model organisms. Furthermore, not only various organisms are employed but different assay systems, such as in vitro and in vivo systems, are used. Finally, even though proteomics data are deposited in public databases, such as ProteomeXchange, they are provided at different levels of detail. Thus, data integration is hampered by non-harmonized usage of identifiers when reviewing the literature or performing meta-analyses to consolidate existing publications into a joint picture. To address this problem, we present ProHarMeD, a tool for harmonizing and comparing proteomics data gathered in multiple studies and for the extraction of disease mechanisms and putative drug repurposing candidates. It is available as a website, Python library and R package. ProHarMeD facilitates ID and name conversions between protein and gene levels, or organisms via ortholog mapping, and provides detailed logs on the loss and gain of IDs after each step. The web tool further determines IDs shared by different studies, proposes potential disease mechanisms as well as drug repurposing candidates automatically, and visualizes these results interactively. We apply ProHarMeD to a set of four studies on bone regeneration. First, we demonstrate the benefit of ID harmonization which increases the number of shared genes between studies by 50%. Second, we identify a potential disease mechanism, with five corresponding drug targets, and the top 20 putative drug repurposing candidates, of which Fondaparinux, the candidate with the highest score, and multiple others are known to have an impact on bone regeneration. Hence, ProHarMeD allows users to harmonize multi-centric proteomics research data in meta-analyses, evaluates the success of the ID conversions and remappings, and finally, it closes the gaps between proteomics, disease mechanism mining and drug repurposing. It is publicly available at https://apps.cosy.bio/proharmed/ .https://doi.org/10.1038/s41540-023-00311-7 |
| spellingShingle | Klaudia Adamowicz Lis Arend Andreas Maier Johannes R. Schmidt Bernhard Kuster Olga Tsoy Olga Zolotareva Jan Baumbach Tanja Laske Proteomic meta-study harmonization, mechanotyping and drug repurposing candidate prediction with ProHarMeD npj Systems Biology and Applications |
| title | Proteomic meta-study harmonization, mechanotyping and drug repurposing candidate prediction with ProHarMeD |
| title_full | Proteomic meta-study harmonization, mechanotyping and drug repurposing candidate prediction with ProHarMeD |
| title_fullStr | Proteomic meta-study harmonization, mechanotyping and drug repurposing candidate prediction with ProHarMeD |
| title_full_unstemmed | Proteomic meta-study harmonization, mechanotyping and drug repurposing candidate prediction with ProHarMeD |
| title_short | Proteomic meta-study harmonization, mechanotyping and drug repurposing candidate prediction with ProHarMeD |
| title_sort | proteomic meta study harmonization mechanotyping and drug repurposing candidate prediction with proharmed |
| url | https://doi.org/10.1038/s41540-023-00311-7 |
| work_keys_str_mv | AT klaudiaadamowicz proteomicmetastudyharmonizationmechanotypinganddrugrepurposingcandidatepredictionwithproharmed AT lisarend proteomicmetastudyharmonizationmechanotypinganddrugrepurposingcandidatepredictionwithproharmed AT andreasmaier proteomicmetastudyharmonizationmechanotypinganddrugrepurposingcandidatepredictionwithproharmed AT johannesrschmidt proteomicmetastudyharmonizationmechanotypinganddrugrepurposingcandidatepredictionwithproharmed AT bernhardkuster proteomicmetastudyharmonizationmechanotypinganddrugrepurposingcandidatepredictionwithproharmed AT olgatsoy proteomicmetastudyharmonizationmechanotypinganddrugrepurposingcandidatepredictionwithproharmed AT olgazolotareva proteomicmetastudyharmonizationmechanotypinganddrugrepurposingcandidatepredictionwithproharmed AT janbaumbach proteomicmetastudyharmonizationmechanotypinganddrugrepurposingcandidatepredictionwithproharmed AT tanjalaske proteomicmetastudyharmonizationmechanotypinganddrugrepurposingcandidatepredictionwithproharmed |