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Pérez-Stuardo D, Frazão M, Ibaceta V, Brianson B, Sánchez E, Rivas-Pardo JA, Vallejos-Vidal E, Reyes-López FE, Toro-Ascuy D, Vidal EA, Reyes-Cerpa S. KLF17 is an important regulatory component of the transcriptomic response of Atlantic salmon macrophages to Piscirickettsia salmonis infection. Front Immunol 2023; 14:1264599. [PMID: 38162669 PMCID: PMC10755876 DOI: 10.3389/fimmu.2023.1264599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 11/07/2023] [Indexed: 01/03/2024] Open
Abstract
Piscirickettsia salmonis is the most important health problem facing Chilean Aquaculture. Previous reports suggest that P. salmonis can survive in salmonid macrophages by interfering with the host immune response. However, the relevant aspects of the molecular pathogenesis of P. salmonis have been poorly characterized. In this work, we evaluated the transcriptomic changes in macrophage-like cell line SHK-1 infected with P. salmonis at 24- and 48-hours post-infection (hpi) and generated network models of the macrophage response to the infection using co-expression analysis and regulatory transcription factor-target gene information. Transcriptomic analysis showed that 635 genes were differentially expressed after 24- and/or 48-hpi. The pattern of expression of these genes was analyzed by weighted co-expression network analysis (WGCNA), which classified genes into 4 modules of expression, comprising early responses to the bacterium. Induced genes included genes involved in metabolism and cell differentiation, intracellular transportation, and cytoskeleton reorganization, while repressed genes included genes involved in extracellular matrix organization and RNA metabolism. To understand how these expression changes are orchestrated and to pinpoint relevant transcription factors (TFs) controlling the response, we established a curated database of TF-target gene regulatory interactions in Salmo salar, SalSaDB. Using this resource, together with co-expression module data, we generated infection context-specific networks that were analyzed to determine highly connected TF nodes. We found that the most connected TF of the 24- and 48-hpi response networks is KLF17, an ortholog of the KLF4 TF involved in the polarization of macrophages to an M2-phenotype in mammals. Interestingly, while KLF17 is induced by P. salmonis infection, other TFs, such as NOTCH3 and NFATC1, whose orthologs in mammals are related to M1-like macrophages, are repressed. In sum, our results suggest the induction of early regulatory events associated with an M2-like phenotype of macrophages that drives effectors related to the lysosome, RNA metabolism, cytoskeleton organization, and extracellular matrix remodeling. Moreover, the M1-like response seems delayed in generating an effective response, suggesting a polarization towards M2-like macrophages that allows the survival of P. salmonis. This work also contributes to SalSaDB, a curated database of TF-target gene interactions that is freely available for the Atlantic salmon community.
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Affiliation(s)
- Diego Pérez-Stuardo
- Centro de Genómica y Bioinformática, Facultad de Ciencias, Ingeniería y Tecnología, Universidad Mayor, Santiago, Chile
- Programa de Doctorado en Genómica Integrativa, Vicerrectoría de Investigación, Universidad Mayor, Santiago, Chile
| | - Mateus Frazão
- Centro de Genómica y Bioinformática, Facultad de Ciencias, Ingeniería y Tecnología, Universidad Mayor, Santiago, Chile
- Escuela de Biotecnología, Facultad de Ciencias, Ingeniería y Tecnología, Universidad Mayor, Santiago, Chile
| | - Valentina Ibaceta
- Centro de Genómica y Bioinformática, Facultad de Ciencias, Ingeniería y Tecnología, Universidad Mayor, Santiago, Chile
- Escuela de Biotecnología, Facultad de Ciencias, Ingeniería y Tecnología, Universidad Mayor, Santiago, Chile
| | - Bernardo Brianson
- Centro de Genómica y Bioinformática, Facultad de Ciencias, Ingeniería y Tecnología, Universidad Mayor, Santiago, Chile
- Escuela de Biotecnología, Facultad de Ciencias, Ingeniería y Tecnología, Universidad Mayor, Santiago, Chile
| | - Evelyn Sánchez
- Centro de Genómica y Bioinformática, Facultad de Ciencias, Ingeniería y Tecnología, Universidad Mayor, Santiago, Chile
- Programa de Doctorado en Genómica Integrativa, Vicerrectoría de Investigación, Universidad Mayor, Santiago, Chile
- Agencia Nacional de Investigación y Desarrollo (ANID) Millennium Science Initiative Program-Millennium Institute for Integrative Biology (iBio), Santiago, Chile
| | - J. Andrés Rivas-Pardo
- Centro de Genómica y Bioinformática, Facultad de Ciencias, Ingeniería y Tecnología, Universidad Mayor, Santiago, Chile
- Escuela de Biotecnología, Facultad de Ciencias, Ingeniería y Tecnología, Universidad Mayor, Santiago, Chile
| | - Eva Vallejos-Vidal
- Núcleo de Investigaciones Aplicadas en Ciencias Veterinarias y Agronómicas, Facultad de Medicina Veterinaria y Agronomía, Universidad De Las Américas, La Florida, Santiago, Chile
- Centro de Biotecnología Acuícola, Facultad de Química y Biología, Universidad de Santiago de Chile, Santiago, Chile
- Centro de Nanociencia y Nanotecnología (CEDENNA), Universidad de Santiago de Chile, Santiago, Chile
| | - Felipe E. Reyes-López
- Centro de Biotecnología Acuícola, Facultad de Química y Biología, Universidad de Santiago de Chile, Santiago, Chile
| | - Daniela Toro-Ascuy
- Laboratorio de Virología, Departamento de Biología, Facultad de Ciencias, Universidad de Chile, Santiago, Chile
| | - Elena A. Vidal
- Centro de Genómica y Bioinformática, Facultad de Ciencias, Ingeniería y Tecnología, Universidad Mayor, Santiago, Chile
- Escuela de Biotecnología, Facultad de Ciencias, Ingeniería y Tecnología, Universidad Mayor, Santiago, Chile
- Agencia Nacional de Investigación y Desarrollo (ANID) Millennium Science Initiative Program-Millennium Institute for Integrative Biology (iBio), Santiago, Chile
| | - Sebastián Reyes-Cerpa
- Centro de Genómica y Bioinformática, Facultad de Ciencias, Ingeniería y Tecnología, Universidad Mayor, Santiago, Chile
- Escuela de Biotecnología, Facultad de Ciencias, Ingeniería y Tecnología, Universidad Mayor, Santiago, Chile
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Danishuddin, Jamal MS, Song KS, Lee KW, Kim JJ, Park YM. Revolutionizing Drug Targeting Strategies: Integrating Artificial Intelligence and Structure-Based Methods in PROTAC Development. Pharmaceuticals (Basel) 2023; 16:1649. [PMID: 38139776 PMCID: PMC10747325 DOI: 10.3390/ph16121649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 11/20/2023] [Accepted: 11/22/2023] [Indexed: 12/24/2023] Open
Abstract
PROteolysis TArgeting Chimera (PROTAC) is an emerging technology in chemical biology and drug discovery. This technique facilitates the complete removal of the target proteins that are "undruggable" or challenging to target through chemical molecules via the Ubiquitin-Proteasome System (UPS). PROTACs have been widely explored and outperformed not only in cancer but also in other diseases. During the past few decades, several academic institutes and pharma companies have poured more efforts into PROTAC-related technologies, setting the stage for several major degrader trial readouts in clinical phases. Despite their promising results, the formation of robust ternary orientation, off-target activity, poor permeability, and binding affinity are some of the limitations that hinder their development. Recent advancements in computational technologies have facilitated progress in the development of PROTACs. Researchers have been able to utilize these technologies to explore a wider range of E3 ligases and optimize linkers, thereby gaining a better understanding of the effectiveness and safety of PROTACs in clinical settings. In this review, we briefly explore the computational strategies reported to date for the formation of PROTAC components and discuss the key challenges and opportunities for further research in this area.
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Affiliation(s)
- Danishuddin
- Department of Biotechnology, Yeungnam University, Gyeongsan 38541, Republic of Korea;
| | | | - Kyoung-Seob Song
- Department of Medical Science, Kosin University College of Medicine, 194 Wachi-ro, Yeongdo-gu, Busan 49104, Republic of Korea;
| | - Keun-Woo Lee
- Division of Life Science, Department of Bio & Medical Big-Data (BK4 Program), Research Institute of Natural Science (RINS), Gyeongsang National University (GNU), 501 Jinju-daero, Jinju 52828, Republic of Korea
- Angel i-Drug Design (AiDD), 33-3 Jinyangho-ro 44, Jinju 52650, Republic of Korea
| | - Jong-Joo Kim
- Department of Biotechnology, Yeungnam University, Gyeongsan 38541, Republic of Korea;
| | - Yeong-Min Park
- Department of Integrative Biological Sciences and Industry, Sejong University, 209, Neugdong-ro, Gwangjin-gu, Seoul 05006, Republic of Korea
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