The ubiquitin-like modifier FAT10 inhibits retinal PDE6 activity and mediates its proteasomal degradation

The retina-specific chaperone aryl hydrocarbon interacting protein-like 1 (AIPL1) is essential for the correct assembly of phosphodiesterase 6 (PDE6), which is a pivotal effector enzyme for phototransduction and vision because it hydrolyzes cGMP. AIPL1 interacts with the cytokine-inducible ubiquitin-like modifier FAT10, which gets covalently conjugated to hundreds of proteins and targets its conjugation substrates for proteasomal degradation, but whether FAT10 affects PDE6 function or turnover is unknown. Here, we show that FAT10 mRNA is expressed in human retina and identify rod PDE6 as a retina-specific substrate of FAT10 conjugation. We found that AIPL1 stabilizes the FAT10 monomer and the PDE6-FAT10 conjugate. Additionally, we elucidated the functional consequences of PDE6 FAT10ylation. On the one hand, we demonstrate that FAT10 targets PDE6 for proteasomal degradation by formation of a covalent isopeptide linkage. On the other hand, FAT10 inhibits PDE6 cGMP hydrolyzing activity by noncovalently interacting with the PDE6 GAFa and catalytic domains. Therefore, FAT10 may contribute to loss of PDE6 and, as a consequence, degeneration of retinal cells in eye diseases linked to inflammation and inherited blindness-causing mutations in AIPL1

The integrity and organization of the human AIPL1 functional domains is critical for its role as a HSP90-dependent co-chaperone for rod PDE6

Biallelic mutations in the photoreceptor-expressed aryl hydrocarbon receptor interacting protein-like 1 (AIPL1) are associated with autosomal recessive Leber congenital amaurosis (LCA), the most severe form of inherited retinopathy in early childhood. AIPL1 functions as a photoreceptor-specific co-chaperone that interacts with the molecular chaperone HSP90 to facilitate the stable assembly of the retinal cyclic GMP (cGMP) phosphodiesterase (PDE6) holoenzyme. In this study, we characterized the functional deficits of AIPL1 variations, some of which induce aberrant pre-mRNA AIPL1 splicing leading to the production of alternative AIPL1 isoforms. We investigated the ability of the AIPL1 variants to mediate an interaction with HSP90 and modulate the rod cGMP PDE6 stability and activity. Our data revealed that both the FK506 binding protein (FKBP)-like domain and the tetratricopeptide repeat (TPR) domain of AIPL1 are required for interaction with HSP90. We further demonstrate that AIPL1 significantly modulates the catalytic activity of heterologously expressed rod PDE6. Although the N-terminal FKBP-like domain of AIPL1 binds the farnesylated PDE6α subunit through direct interaction with the farnesyl moiety, mutations compromising the integrity of the C-terminal TPR domain of AIPL1 also failed to modulate PDE6 activity efficiently. These AIPL1 variants moreover failed to promote the HSP90-dependent stabilization of the PDE6α subunit in the cytosol. In summary, we have successfully validated the disease-causing status of the AIPL1 variations in vitro. Our findings provide insight into the mechanism underlying the co-chaperone role of AIPL1 and will be critical for ensuring an early and effective diagnosis of AIPL1 LCA patients

The FAT10- and ubiquitin-dependent degradation machineries exhibit common and distinct requirements for MHC class I antigen presentation

Like ubiquitin (Ub), the ubiquitin-like protein FAT10 can serve as a signal for proteasome-dependent protein degradation. Here, we investigated the contribution of FAT10 substrate modification to MHC class I antigen presentation. We show that N-terminal modification of the human cytomegalovirus-derived pp65 antigen to FAT10 facilitates direct presentation and dendritic cell-mediated cross-presentation of the HLA-A2 restricted pp65495–503 epitope. Interestingly, our data indicate that the pp65 presentation initiated by either FAT10 or Ub partially relied on the 19S proteasome subunit Rpn10 (S5a). However, FAT10 distinguished itself from Ub in that it promoted a pp65 response which was not influenced by immunoproteasomes or PA28. Further divergence occurred at the level of Ub-binding proteins with NUB1 supporting the pp65 presentation arising from FAT10, while it exerted no effect on that initiated by Ub. Collectively, our data establish FAT10 modification as a distinct and alternative signal for facilitated MHC class I antigen presentation

The inherited blindness protein AIPL1 regulates the ubiquitin-like FAT10 pathway

Mutations in AIPL1 cause the inherited blindness Leber congenital amaurosis (LCA). AIPL1 has previously been shown to interact with NUB1, which facilitates the proteasomal degradation of proteins modified with the ubiquitin-like protein FAT10. Here we report that AIPL1 binds non-covalently to free FAT10 and FAT10ylated proteins and can form a ternary complex with FAT10 and NUB1. In addition, AIPL1 antagonised the NUB1-mediated degradation of the model FAT10 conjugate, FAT10-DHFR, and pathogenic mutations of AIPL1 were defective in inhibiting this degradation. While all AIPL1 mutants tested still bound FAT10-DHFR, there was a close correlation between the ability of the mutants to interact with NUB1 and their ability to prevent NUB1-mediated degradation. Interestingly, AIPL1 also co-immunoprecipitated the E1 activating enzyme for FAT10, UBA6, suggesting AIPL1 may have a role in directly regulating the FAT10 conjugation machinery. These studies are the first to implicate FAT10 in retinal cell biology and LCA pathogenesis, and reveal a new role of AIPL1 in regulating the FAT10 pathway

Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches

Introduction: The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. Methods: Extensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors. Results: Results revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. Discussion: The key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.Peer Reviewe

Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches

© 2024 Niarakis, Ostaszewski, Mazein, Kuperstein, Kutmon, Gillespie, Funahashi, Acencio, Hemedan, Aichem, Klein, Czauderna, Burtscher, Yamada, Hiki, Hiroi, Hu, Pham, Ehrhart, Willighagen, Valdeolivas, Dugourd, Messina, Esteban-Medina, Peña-Chilet, Rian, Soliman, Aghamiri, Puniya, Naldi, Helikar, Singh, Fernández, Bermudez, Tsirvouli, Montagud, Noël, Ponce-de-Leon, Maier, Bauch, Gyori, Bachman, Luna, Piñero, Furlong, Balaur, Rougny, Jarosz, Overall, Phair, Perfetto, Matthews, Rex, Orlic-Milacic, Gomez, De Meulder, Ravel, Jassal, Satagopam, Wu, Golebiewski, Gawron, Calzone, Beckmann, Evelo, D’Eustachio, Schreiber, Saez-Rodriguez, Dopazo, Kuiper, Valencia, Wolkenhauer, Kitano, Barillot, Auffray, Balling, Schneider and the COVID-19 Disease Map Community. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.Introduction: The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing.Methods: Extensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors.Results: Results revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19.Discussion: The key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.The author(s) declare financial support was received for the research, authorship, and/or publication of this article. AN acknowledges support from SANOFI-AVENTIS R&D via the CIFRE contract, n° 2020/0766. MK, FH, NP, FE, and CE acknowledge the support of the ZonMw COVID-19 programme (Grant No. 10430012010015). JD Spanish Ministry of Science and Innovation (Grant no. PID2020-117979RB-I00) and Instituto de Salud Carlos III (Grant no. IMP/00019). MAi, KK, FS: Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - Project-ID 251654672 - TRR 161 and under Germany’s Excellence Strategy - EXC 2117 - 422037984. FM: “5 per 1000–2021” grant of the Italian Ministry of Health (Grant No. 5M-2021-23683787) and European Commission with HORIZON programme, BY-COVID project (Grant No. 101046203—BY-COVID). National Institute for Infectious Diseases Lazzaro Spallanzani–IRCCS received financial support from the Italian Ministry of Health grant “Ricerca Corrente”. JP, LF: IMI2-JU grants, resources which are composed of financial contributions from the European Union’s Horizon 2020 Research and Innovation Programme and EFPIA [GA: 777365 eTRANSAFE], and the EU H2020 Programme [GA:964537 RISKHUNT3R]; Project 001-P-001647—Valorisation of EGA for Industry and Society funded by the European Regional Development Fund (ERDF) and Generalitat de Catalunya; Institute of Health Carlos III (project IMPaCT-Data, exp. IMP/00019), co-funded by the European Union, European Regional Development Fund (ERDF, “A way to make Europe”). AMo, MP and AV acknowledge the support of the European Commission under the INFORE project (H2020-ICT-825070) and the PerMedCoE (H2020-ICT-951773). Contributions by TH and BLP were supported by NIH grant #R35GM119770 to TH. MaGo acknowledges funding from Deutsche Forschungsgemeinschaft (DFG) through grants no. 442326535 (NFDI4Health) and 451265285 (NFDI4Health Task Force COVID-19), from the European Commission through the Horizon 2020 framework program under grant no. 825843 (EU-STANDS4PM) and through the Digital Europe program under grant no. 101083771 (EDITH), as well as from the Klaus Tschira Foundation. AL acknowledges support from the Intramural Research Program of the National Library of Medicine (NLM), National Institutes of Health (NIH).Peer reviewe

Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches

IntroductionThe COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. MethodsExtensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors.ResultsResults revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. DiscussionThe key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies

Tiled Stereoscopic 3D Display Wall - Concept, Applications and Evaluation

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