Pub Date : 2024-06-14DOI: 10.1038/s12276-024-01266-3
Xin Le Yap, Jun-An Chen
MicroRNAs (miRNAs) are pivotal regulators of gene expression and are involved in biological processes spanning from early developmental stages to the intricate process of aging. Extensive research has underscored the fundamental role of miRNAs in orchestrating eukaryotic development, with disruptions in miRNA biogenesis resulting in early lethality. Moreover, perturbations in miRNA function have been implicated in the aging process, particularly in model organisms such as nematodes and flies. miRNAs tend to be clustered in vertebrate genomes, finely modulating an array of biological pathways through clustering within a single transcript. Although extensive research of their developmental roles has been conducted, the potential implications of miRNA clusters in regulating aging remain largely unclear. In this review, we use the Mir-23-27-24 cluster as a paradigm, shedding light on the nuanced physiological functions of miRNA clusters during embryonic development and exploring their potential involvement in the aging process. Moreover, we advocate further research into the intricate interplay among miRNA clusters, particularly the Mir-23-27-24 cluster, in shaping the regulatory landscape of aging. MicroRNAs significantly influence everything from growth to aging. However, the specific roles of certain miRNA clusters, like Mir-23-27-24, in aging are less understood. This review investigates the Mir-23-27-24 cluster’s function in embryonic growth and possible aging regulation. It’s a comprehensive review of how the Mir-23-27-24 cluster affects vital biological processes and aging, combining existing research to fill knowledge gaps. The Mir-23-27-24 cluster includes miRNAs vital in development stages and linked to various diseases, including cancer. Researchers underline the cluster’s role in embryonic development and its overlooked role in aging. They explore how these miRNAs control gene expression and contribute to the complexity of biological functions. This study implies that miRNA clusters like Mir-23-27-24 could be crucial in aging, opening new paths for aging research and potential treatments for age-related diseases. This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author.
{"title":"Elucidation of how the Mir-23-27-24 cluster regulates development and aging","authors":"Xin Le Yap, Jun-An Chen","doi":"10.1038/s12276-024-01266-3","DOIUrl":"10.1038/s12276-024-01266-3","url":null,"abstract":"MicroRNAs (miRNAs) are pivotal regulators of gene expression and are involved in biological processes spanning from early developmental stages to the intricate process of aging. Extensive research has underscored the fundamental role of miRNAs in orchestrating eukaryotic development, with disruptions in miRNA biogenesis resulting in early lethality. Moreover, perturbations in miRNA function have been implicated in the aging process, particularly in model organisms such as nematodes and flies. miRNAs tend to be clustered in vertebrate genomes, finely modulating an array of biological pathways through clustering within a single transcript. Although extensive research of their developmental roles has been conducted, the potential implications of miRNA clusters in regulating aging remain largely unclear. In this review, we use the Mir-23-27-24 cluster as a paradigm, shedding light on the nuanced physiological functions of miRNA clusters during embryonic development and exploring their potential involvement in the aging process. Moreover, we advocate further research into the intricate interplay among miRNA clusters, particularly the Mir-23-27-24 cluster, in shaping the regulatory landscape of aging. MicroRNAs significantly influence everything from growth to aging. However, the specific roles of certain miRNA clusters, like Mir-23-27-24, in aging are less understood. This review investigates the Mir-23-27-24 cluster’s function in embryonic growth and possible aging regulation. It’s a comprehensive review of how the Mir-23-27-24 cluster affects vital biological processes and aging, combining existing research to fill knowledge gaps. The Mir-23-27-24 cluster includes miRNAs vital in development stages and linked to various diseases, including cancer. Researchers underline the cluster’s role in embryonic development and its overlooked role in aging. They explore how these miRNAs control gene expression and contribute to the complexity of biological functions. This study implies that miRNA clusters like Mir-23-27-24 could be crucial in aging, opening new paths for aging research and potential treatments for age-related diseases. This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author.","PeriodicalId":50466,"journal":{"name":"Experimental and Molecular Medicine","volume":"56 6","pages":"1263-1271"},"PeriodicalIF":9.5,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11263685/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141318881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-14DOI: 10.1038/s12276-024-01177-3
Keonyong Lee, Jayoung Ku, Doyeong Ku, Yoosik Kim
Alu elements are highly abundant primate-specific short interspersed nuclear elements that account for ~10% of the human genome. Due to their preferential location in gene-rich regions, especially in introns and 3′ UTRs, Alu elements can exert regulatory effects on the expression of both host and neighboring genes. When two Alu elements with inverse orientations are positioned in close proximity, their transcription results in the generation of distinct double-stranded RNAs (dsRNAs), known as inverted Alu repeats (IRAlus). IRAlus are key immunogenic self-dsRNAs and post-transcriptional cis-regulatory elements that play a role in circular RNA biogenesis, as well as RNA transport and stability. Recently, IRAlus dsRNAs have emerged as regulators of transcription and activators of Z-DNA-binding proteins. The formation and activity of IRAlus can be modulated through RNA editing and interactions with RNA-binding proteins, and misregulation of IRAlus has been implicated in several immune-associated disorders. In this review, we summarize the emerging functions of IRAlus dsRNAs, the regulatory mechanisms governing IRAlus activity, and their relevance in the pathogenesis of human diseases. Understanding the role of Alu elements—short DNA sequences scattered throughout our genome—is crucial for grasping human genetics. These elements can affect how our genes function, and sometimes, their misregulation causes diseases. However, there’s still much we don’t know about their impact on gene regulation and cell signaling, including innate immune responses. Lee et al. summarized how Alu elements interact with our immune system by generating specific structures similar to ones from viruses. The authors suggest that further understanding of biological processes regulated by Alu elements could lead to advancements in the development of potential treatment for diseases linked to immune system dysfunction. Future research could explore how to manipulate these elements to benefit human health. This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author.
{"title":"Inverted Alu repeats: friends or foes in the human transcriptome","authors":"Keonyong Lee, Jayoung Ku, Doyeong Ku, Yoosik Kim","doi":"10.1038/s12276-024-01177-3","DOIUrl":"10.1038/s12276-024-01177-3","url":null,"abstract":"Alu elements are highly abundant primate-specific short interspersed nuclear elements that account for ~10% of the human genome. Due to their preferential location in gene-rich regions, especially in introns and 3′ UTRs, Alu elements can exert regulatory effects on the expression of both host and neighboring genes. When two Alu elements with inverse orientations are positioned in close proximity, their transcription results in the generation of distinct double-stranded RNAs (dsRNAs), known as inverted Alu repeats (IRAlus). IRAlus are key immunogenic self-dsRNAs and post-transcriptional cis-regulatory elements that play a role in circular RNA biogenesis, as well as RNA transport and stability. Recently, IRAlus dsRNAs have emerged as regulators of transcription and activators of Z-DNA-binding proteins. The formation and activity of IRAlus can be modulated through RNA editing and interactions with RNA-binding proteins, and misregulation of IRAlus has been implicated in several immune-associated disorders. In this review, we summarize the emerging functions of IRAlus dsRNAs, the regulatory mechanisms governing IRAlus activity, and their relevance in the pathogenesis of human diseases. Understanding the role of Alu elements—short DNA sequences scattered throughout our genome—is crucial for grasping human genetics. These elements can affect how our genes function, and sometimes, their misregulation causes diseases. However, there’s still much we don’t know about their impact on gene regulation and cell signaling, including innate immune responses. Lee et al. summarized how Alu elements interact with our immune system by generating specific structures similar to ones from viruses. The authors suggest that further understanding of biological processes regulated by Alu elements could lead to advancements in the development of potential treatment for diseases linked to immune system dysfunction. Future research could explore how to manipulate these elements to benefit human health. This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author.","PeriodicalId":50466,"journal":{"name":"Experimental and Molecular Medicine","volume":"56 6","pages":"1250-1262"},"PeriodicalIF":9.5,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11263572/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141318882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-14DOI: 10.1038/s12276-024-01239-6
Sumin Yang, Sung-Hyun Kim, Eunjeong Yang, Mingon Kang, Jae-Yeol Joo
It is apparent that various functional units within the cellular machinery are derived from RNAs. The evolution of sequencing techniques has resulted in significant insights into approaches for transcriptome studies. Organisms utilize RNA to govern cellular systems, and a heterogeneous class of RNAs is involved in regulatory functions. In particular, regulatory RNAs are increasingly recognized to participate in intricately functioning machinery across almost all levels of biological systems. These systems include those mediating chromatin arrangement, transcription, suborganelle stabilization, and posttranscriptional modifications. Any class of RNA exhibiting regulatory activity can be termed a class of regulatory RNA and is typically represented by noncoding RNAs, which constitute a substantial portion of the genome. These RNAs function based on the principle of structural changes through cis and/or trans regulation to facilitate mutual RNA‒RNA, RNA‒DNA, and RNA‒protein interactions. It has not been clearly elucidated whether regulatory RNAs identified through deep sequencing actually function in the anticipated mechanisms. This review addresses the dominant properties of regulatory RNAs at various layers of the cellular machinery and covers regulatory activities, structural dynamics, modifications, associated molecules, and further challenges related to therapeutics and deep learning. Regulatory RNAs, such as long noncoding RNAs (lncRNAs, RNAs that do not code for proteins), microRNAs (miRNAs, small RNAs that regulate gene expression), and circular RNAs (circRNAs, RNAs that form a covalently closed continuous loop), are important in controlling gene expression. The exact ways and roles of these RNAs are not completely known. This study by Joo et al. reviews current knowledge on regulatory RNAs, focusing on their structure and function in cell parts. The authors talk about the different methods used to study these RNAs, including RNA-Chromatin, RNA-Protein, and RNA structure sequencing. They also emphasize the role of RNA modifications in controlling gene expression and the potential of deep learning (a type of machine learning) in predicting RNA functions. The study concludes that understanding regulatory RNAs better could lead to new treatment strategies for various diseases. This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author.
{"title":"Molecular insights into regulatory RNAs in the cellular machinery","authors":"Sumin Yang, Sung-Hyun Kim, Eunjeong Yang, Mingon Kang, Jae-Yeol Joo","doi":"10.1038/s12276-024-01239-6","DOIUrl":"10.1038/s12276-024-01239-6","url":null,"abstract":"It is apparent that various functional units within the cellular machinery are derived from RNAs. The evolution of sequencing techniques has resulted in significant insights into approaches for transcriptome studies. Organisms utilize RNA to govern cellular systems, and a heterogeneous class of RNAs is involved in regulatory functions. In particular, regulatory RNAs are increasingly recognized to participate in intricately functioning machinery across almost all levels of biological systems. These systems include those mediating chromatin arrangement, transcription, suborganelle stabilization, and posttranscriptional modifications. Any class of RNA exhibiting regulatory activity can be termed a class of regulatory RNA and is typically represented by noncoding RNAs, which constitute a substantial portion of the genome. These RNAs function based on the principle of structural changes through cis and/or trans regulation to facilitate mutual RNA‒RNA, RNA‒DNA, and RNA‒protein interactions. It has not been clearly elucidated whether regulatory RNAs identified through deep sequencing actually function in the anticipated mechanisms. This review addresses the dominant properties of regulatory RNAs at various layers of the cellular machinery and covers regulatory activities, structural dynamics, modifications, associated molecules, and further challenges related to therapeutics and deep learning. Regulatory RNAs, such as long noncoding RNAs (lncRNAs, RNAs that do not code for proteins), microRNAs (miRNAs, small RNAs that regulate gene expression), and circular RNAs (circRNAs, RNAs that form a covalently closed continuous loop), are important in controlling gene expression. The exact ways and roles of these RNAs are not completely known. This study by Joo et al. reviews current knowledge on regulatory RNAs, focusing on their structure and function in cell parts. The authors talk about the different methods used to study these RNAs, including RNA-Chromatin, RNA-Protein, and RNA structure sequencing. They also emphasize the role of RNA modifications in controlling gene expression and the potential of deep learning (a type of machine learning) in predicting RNA functions. The study concludes that understanding regulatory RNAs better could lead to new treatment strategies for various diseases. This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author.","PeriodicalId":50466,"journal":{"name":"Experimental and Molecular Medicine","volume":"56 6","pages":"1235-1249"},"PeriodicalIF":9.5,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11263585/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141318883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-14DOI: 10.1038/s12276-024-01251-w
Seo-Won Choi, Jin-Wu Nam
Circular RNAs are an unusual class of single-stranded RNAs whose ends are covalently linked via back-splicing. Due to their versatility, the need to express circular RNAs in vivo and in vitro has increased. Efforts have been made to efficiently and precisely synthesize circular RNAs. However, a review on the optimization of the processes of circular RNA design, synthesis, and delivery is lacking. Our review highlights the multifaceted aspects considered when producing optimal circular RNAs and summarizes the available options for each step of exogenous circular RNA design and synthesis, including circularization strategies. Additionally, this review describes several potential applications of circular RNAs. Circular RNAs were once considered errors in splicing. However, we now understand that circRNAs are common and play roles in health and disease, including potential use in creating vaccines. This review examines ways to create and use circRNAs, with a focus on improving their creation and delivery for medical purposes. They discuss various methods, including using natural and synthetic elements to enhance circRNA formation and stability. The main findings suggest that improving the creation and delivery of circRNAs could greatly enhance their medical potential. Researchers conclude that with more development, circRNAs could become powerful tools in gene therapy and vaccine creation, offering new ways to treat diseases. The future implications of this research could transform how we approach disease treatment and vaccine creation, making therapies more effective and long-lasting. This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author.
{"title":"Optimal design of synthetic circular RNAs","authors":"Seo-Won Choi, Jin-Wu Nam","doi":"10.1038/s12276-024-01251-w","DOIUrl":"10.1038/s12276-024-01251-w","url":null,"abstract":"Circular RNAs are an unusual class of single-stranded RNAs whose ends are covalently linked via back-splicing. Due to their versatility, the need to express circular RNAs in vivo and in vitro has increased. Efforts have been made to efficiently and precisely synthesize circular RNAs. However, a review on the optimization of the processes of circular RNA design, synthesis, and delivery is lacking. Our review highlights the multifaceted aspects considered when producing optimal circular RNAs and summarizes the available options for each step of exogenous circular RNA design and synthesis, including circularization strategies. Additionally, this review describes several potential applications of circular RNAs. Circular RNAs were once considered errors in splicing. However, we now understand that circRNAs are common and play roles in health and disease, including potential use in creating vaccines. This review examines ways to create and use circRNAs, with a focus on improving their creation and delivery for medical purposes. They discuss various methods, including using natural and synthetic elements to enhance circRNA formation and stability. The main findings suggest that improving the creation and delivery of circRNAs could greatly enhance their medical potential. Researchers conclude that with more development, circRNAs could become powerful tools in gene therapy and vaccine creation, offering new ways to treat diseases. The future implications of this research could transform how we approach disease treatment and vaccine creation, making therapies more effective and long-lasting. This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author.","PeriodicalId":50466,"journal":{"name":"Experimental and Molecular Medicine","volume":"56 6","pages":"1281-1292"},"PeriodicalIF":9.5,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11263348/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141318885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The exponential growth of big data in RNA biology (RB) has led to the development of deep learning (DL) models that have driven crucial discoveries. As constantly evidenced by DL studies in other fields, the successful implementation of DL in RB depends heavily on the effective utilization of large-scale datasets from public databases. In achieving this goal, data encoding methods, learning algorithms, and techniques that align well with biological domain knowledge have played pivotal roles. In this review, we provide guiding principles for applying these DL concepts to various problems in RB by demonstrating successful examples and associated methodologies. We also discuss the remaining challenges in developing DL models for RB and suggest strategies to overcome these challenges. Overall, this review aims to illuminate the compelling potential of DL for RB and ways to apply this powerful technology to investigate the intriguing biology of RNA more effectively. This review spotlights the revolutionary role of deep learning (DL) in expanding the understanding of RNA. RNA is a fundamental biomolecule that shapes and regulates diverse phenotypes including human diseases. Understanding the principles governing the functions of RNA is a key objective of current biology. Recently, big data produced via high-throughput experiments have been utilized to develop DL models aimed at analyzing and predicting RNA-related biological processes. This review emphasizes the role of public databases in providing these big data for training DL models. The authors introduce core DL concepts necessary for training models from the biological data. By extensively examining DL studies in various fields of RNA biology, the authors suggest how to better leverage DL for revealing novel biological knowledge and demonstrate the potential of DL in deciphering the complex biology of RNA. This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author.
{"title":"Big data and deep learning for RNA biology","authors":"Hyeonseo Hwang, Hyeonseong Jeon, Nagyeong Yeo, Daehyun Baek","doi":"10.1038/s12276-024-01243-w","DOIUrl":"10.1038/s12276-024-01243-w","url":null,"abstract":"The exponential growth of big data in RNA biology (RB) has led to the development of deep learning (DL) models that have driven crucial discoveries. As constantly evidenced by DL studies in other fields, the successful implementation of DL in RB depends heavily on the effective utilization of large-scale datasets from public databases. In achieving this goal, data encoding methods, learning algorithms, and techniques that align well with biological domain knowledge have played pivotal roles. In this review, we provide guiding principles for applying these DL concepts to various problems in RB by demonstrating successful examples and associated methodologies. We also discuss the remaining challenges in developing DL models for RB and suggest strategies to overcome these challenges. Overall, this review aims to illuminate the compelling potential of DL for RB and ways to apply this powerful technology to investigate the intriguing biology of RNA more effectively. This review spotlights the revolutionary role of deep learning (DL) in expanding the understanding of RNA. RNA is a fundamental biomolecule that shapes and regulates diverse phenotypes including human diseases. Understanding the principles governing the functions of RNA is a key objective of current biology. Recently, big data produced via high-throughput experiments have been utilized to develop DL models aimed at analyzing and predicting RNA-related biological processes. This review emphasizes the role of public databases in providing these big data for training DL models. The authors introduce core DL concepts necessary for training models from the biological data. By extensively examining DL studies in various fields of RNA biology, the authors suggest how to better leverage DL for revealing novel biological knowledge and demonstrate the potential of DL in deciphering the complex biology of RNA. This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author.","PeriodicalId":50466,"journal":{"name":"Experimental and Molecular Medicine","volume":"56 6","pages":"1293-1321"},"PeriodicalIF":9.5,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11263376/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141318827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-12DOI: 10.1038/s12276-024-01259-2
Sang Hoon Kim, Bo Ryeong Lee, Sung-Min Kim, Sungsik Kim, Min-seok Kim, Jaehyun Kim, Inkyu Lee, Hee-Soo Kim, Gi-Hoon Nam, In-San Kim, Kyuyoung Song, Yoonjoo Choi, Dong-Sup Lee, Woong-Yang Park
Neoantigens are ideal targets for cancer immunotherapy because they are expressed de novo in tumor tissue but not in healthy tissue and are therefore recognized as foreign by the immune system. Advances in next-generation sequencing and bioinformatics technologies have enabled the quick identification and prediction of tumor-specific neoantigens; however, only a small fraction of predicted neoantigens are immunogenic. To improve the predictability of immunogenic neoantigens, we developed the in silico neoantigen prediction workflows VACINUSpMHC and VACINUSTCR: VACINUSpMHC incorporates physical binding between peptides and MHCs (pMHCs), and VACINUSTCR integrates T cell reactivity to the pMHC complex through deep learning-based pairing with T cell receptors (TCRs) of putative tumor-reactive CD8 tumor-infiltrating lymphocytes (TILs). We then validated our neoantigen prediction workflows both in vitro and in vivo in patients with hepatocellular carcinoma (HCC) and in a B16F10 mouse melanoma model. The predictive abilities of VACINUSpMHC and VACINUSTCR were confirmed in a validation cohort of 8 patients with HCC. Of a total of 118 neoantigen candidates predicted by VACINUSpMHC, 48 peptides were ultimately selected using VACINUSTCR. In vitro validation revealed that among the 48 predicted neoantigen candidates, 13 peptides were immunogenic. Assessment of the antitumor efficacy of the candidate neoepitopes using a VACINUSTCR in vivo mouse model suggested that vaccination with the predicted neoepitopes induced neoantigen-specific T cell responses and enabled the trafficking of neoantigen-specific CD8 + T cell clones into the tumor tissue, leading to tumor suppression. This study showed that the prediction of immunogenic neoantigens can be improved by integrating a tumor-reactive TIL TCR-pMHC ternary complex. Cancer scientists have created a new way to identify and choose neoantigens (new proteins on cancer cells) for use in cancer vaccines. The research, led by Woong-Yang Park, included 33 patients with different cancer types. The scientists used a mix of computer-based prediction techniques and lab-based tests to find the most effective neoantigens. The results showed that the new technique, named VACINUS, was better at predicting effective neoantigens than older methods. The scientists concluded that the VACINUS technique could improve the immune response of predicted neoantigens, making them better at activating an immune reaction against cancer cells. This could result in the creation of more effective cancer vaccines in the future. This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author.
{"title":"The identification of effective tumor-suppressing neoantigens using a tumor-reactive TIL TCR-pMHC ternary complex","authors":"Sang Hoon Kim, Bo Ryeong Lee, Sung-Min Kim, Sungsik Kim, Min-seok Kim, Jaehyun Kim, Inkyu Lee, Hee-Soo Kim, Gi-Hoon Nam, In-San Kim, Kyuyoung Song, Yoonjoo Choi, Dong-Sup Lee, Woong-Yang Park","doi":"10.1038/s12276-024-01259-2","DOIUrl":"10.1038/s12276-024-01259-2","url":null,"abstract":"Neoantigens are ideal targets for cancer immunotherapy because they are expressed de novo in tumor tissue but not in healthy tissue and are therefore recognized as foreign by the immune system. Advances in next-generation sequencing and bioinformatics technologies have enabled the quick identification and prediction of tumor-specific neoantigens; however, only a small fraction of predicted neoantigens are immunogenic. To improve the predictability of immunogenic neoantigens, we developed the in silico neoantigen prediction workflows VACINUSpMHC and VACINUSTCR: VACINUSpMHC incorporates physical binding between peptides and MHCs (pMHCs), and VACINUSTCR integrates T cell reactivity to the pMHC complex through deep learning-based pairing with T cell receptors (TCRs) of putative tumor-reactive CD8 tumor-infiltrating lymphocytes (TILs). We then validated our neoantigen prediction workflows both in vitro and in vivo in patients with hepatocellular carcinoma (HCC) and in a B16F10 mouse melanoma model. The predictive abilities of VACINUSpMHC and VACINUSTCR were confirmed in a validation cohort of 8 patients with HCC. Of a total of 118 neoantigen candidates predicted by VACINUSpMHC, 48 peptides were ultimately selected using VACINUSTCR. In vitro validation revealed that among the 48 predicted neoantigen candidates, 13 peptides were immunogenic. Assessment of the antitumor efficacy of the candidate neoepitopes using a VACINUSTCR in vivo mouse model suggested that vaccination with the predicted neoepitopes induced neoantigen-specific T cell responses and enabled the trafficking of neoantigen-specific CD8 + T cell clones into the tumor tissue, leading to tumor suppression. This study showed that the prediction of immunogenic neoantigens can be improved by integrating a tumor-reactive TIL TCR-pMHC ternary complex. Cancer scientists have created a new way to identify and choose neoantigens (new proteins on cancer cells) for use in cancer vaccines. The research, led by Woong-Yang Park, included 33 patients with different cancer types. The scientists used a mix of computer-based prediction techniques and lab-based tests to find the most effective neoantigens. The results showed that the new technique, named VACINUS, was better at predicting effective neoantigens than older methods. The scientists concluded that the VACINUS technique could improve the immune response of predicted neoantigens, making them better at activating an immune reaction against cancer cells. This could result in the creation of more effective cancer vaccines in the future. This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author.","PeriodicalId":50466,"journal":{"name":"Experimental and Molecular Medicine","volume":"56 6","pages":"1461-1471"},"PeriodicalIF":9.5,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11263684/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141312197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-11DOI: 10.1038/s12276-024-01265-4
Ha Thu Nguyen, Andreas Wiederkehr, Claes B. Wollheim, Kyu-Sang Park
{"title":"Author Correction: Regulation of autophagy by perilysosomal calcium: a new player in β-cell lipotoxicity","authors":"Ha Thu Nguyen, Andreas Wiederkehr, Claes B. Wollheim, Kyu-Sang Park","doi":"10.1038/s12276-024-01265-4","DOIUrl":"10.1038/s12276-024-01265-4","url":null,"abstract":"","PeriodicalId":50466,"journal":{"name":"Experimental and Molecular Medicine","volume":"56 6","pages":"1474-1474"},"PeriodicalIF":9.5,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11263552/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141302049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-03DOI: 10.1038/s12276-024-01240-z
Jared D. Rhodes, James R. Goldenring, Su-Hyung Lee
Research on the microenvironment associated with gastric carcinogenesis has focused on cancers of the stomach and often underestimates premalignant stages such as metaplasia and dysplasia. Since epithelial interactions with T cells, macrophages, and type 2 innate lymphoid cells (ILC2s) are indispensable for the formation of precancerous lesions in the stomach, understanding the cellular interactions that promote gastric precancer warrants further investigation. Although various types of immune cells have been shown to play important roles in gastric carcinogenesis, it remains unclear how stromal cells such as fibroblasts influence epithelial transformation in the stomach, especially during precancerous stages. Fibroblasts exist as distinct populations across tissues and perform different functions depending on the expression patterns of cell surface markers and secreted factors. In this review, we provide an overview of known microenvironmental components in the stroma with an emphasis on fibroblast subpopulations and their roles during carcinogenesis in tissues including breast, pancreas, and stomach. Additionally, we offer insights into potential targets of tumor-promoting fibroblasts and identify open areas of research related to fibroblast plasticity and the modulation of gastric carcinogenesis. This review summarizes how metaplasia (normal cells changing into different types) turns into dysplasia (abnormal cell growth) in the stomach due to injury and the role of stroma during the process. Led by Dr. James R. Goldenring, the team has discovered that the stomach develops metaplasia to heal damaged tissue and suggested several biomarkers to define the change. They recently found that certain types of metaplasia repopulate the stomach lining after damage and can progress into the next stage under the influence of the microenvironment. The current study provides insights into how the stromal components in the stomach contribute to carcinogenesis, focusing on fibroblast subpopulations. This could be important for future gastric cancer treatments. This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author.
对与胃癌发生相关的微环境的研究主要集中在胃癌上，但往往低估了癌前病变阶段，如移行期和发育不良期。由于上皮细胞与 T 细胞、巨噬细胞和 2 型先天性淋巴细胞（ILC2）的相互作用是胃癌前病变形成不可或缺的因素，因此了解促进胃癌前病变的细胞相互作用值得进一步研究。虽然各种类型的免疫细胞已被证明在胃癌发生中发挥重要作用，但成纤维细胞等基质细胞如何影响胃上皮转化，尤其是在癌前病变阶段，目前仍不清楚。成纤维细胞作为不同的群体存在于不同的组织中，并根据细胞表面标志物和分泌因子的表达模式发挥不同的功能。在这篇综述中，我们概述了基质中已知的微环境成分，重点是成纤维细胞亚群及其在乳腺、胰腺和胃等组织癌变过程中的作用。此外，我们还深入探讨了肿瘤促进成纤维细胞的潜在靶点，并确定了与成纤维细胞可塑性和胃癌发生调控相关的开放研究领域。
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Pub Date : 2024-06-03DOI: 10.1038/s12276-024-01235-w
Su Hwan Park, Jin-Sung Ju, Hyunmin Woo, Hye Jin Yun, Su Bin Lee, Seok-Ho Kim, Balázs Győrffy, Eun-jeong Kim, Ho Kim, Hee Dong Han, Seong-il Eyun, Jong-Ho Lee, Yun-Yong Park
N6-adenosine methylation (m6A) is critical for controlling cancer cell growth and tumorigenesis. However, the function and detailed mechanism of how m6A methyltransferases modulate m6A levels on specific targets remain unknown. In the current study, we identified significantly elevated levels of RBM15, an m6A writer, in basal-like breast cancer (BC) patients compared to nonbasal-like BC patients and linked this increase to worse clinical outcomes. Gene expression profiling revealed correlations between RBM15 and serine and glycine metabolic genes, including PHGDH, PSAT1, PSPH, and SHMT2. RBM15 influences m6A levels and, specifically, the m6A levels of serine and glycine metabolic genes via direct binding to target RNA. The effects of RBM15 on cell growth were largely dependent on serine and glycine metabolism. Thus, RBM15 coordinates cancer cell growth through altered serine and glycine metabolism, suggesting that RBM15 is a new therapeutic target in BC. RNA methylation, a key process controlling gene activity, involves proteins like RNA-binding motif protein 15 (RBM15). Its role in breast cancer is unclear. Studies show RBM15 is highly active in a type of breast cancer called triple-negative and is linked to patient outcomes. It controls genes related to serine and glycine metabolism; substances that help cancer cells grow. The research demonstrates that RBM15 controls genes involved in the cancer metabolism of serine and glycine, two types of amino acids, which contributes to cancer cell proliferation. This suggests that targeting RBM15 can be new therapeutic approaches for triple-negative breast cancer patients. This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author.
{"title":"The m6A writer RBM15 drives the growth of triple-negative breast cancer cells through the stimulation of serine and glycine metabolism","authors":"Su Hwan Park, Jin-Sung Ju, Hyunmin Woo, Hye Jin Yun, Su Bin Lee, Seok-Ho Kim, Balázs Győrffy, Eun-jeong Kim, Ho Kim, Hee Dong Han, Seong-il Eyun, Jong-Ho Lee, Yun-Yong Park","doi":"10.1038/s12276-024-01235-w","DOIUrl":"10.1038/s12276-024-01235-w","url":null,"abstract":"N6-adenosine methylation (m6A) is critical for controlling cancer cell growth and tumorigenesis. However, the function and detailed mechanism of how m6A methyltransferases modulate m6A levels on specific targets remain unknown. In the current study, we identified significantly elevated levels of RBM15, an m6A writer, in basal-like breast cancer (BC) patients compared to nonbasal-like BC patients and linked this increase to worse clinical outcomes. Gene expression profiling revealed correlations between RBM15 and serine and glycine metabolic genes, including PHGDH, PSAT1, PSPH, and SHMT2. RBM15 influences m6A levels and, specifically, the m6A levels of serine and glycine metabolic genes via direct binding to target RNA. The effects of RBM15 on cell growth were largely dependent on serine and glycine metabolism. Thus, RBM15 coordinates cancer cell growth through altered serine and glycine metabolism, suggesting that RBM15 is a new therapeutic target in BC. RNA methylation, a key process controlling gene activity, involves proteins like RNA-binding motif protein 15 (RBM15). Its role in breast cancer is unclear. Studies show RBM15 is highly active in a type of breast cancer called triple-negative and is linked to patient outcomes. It controls genes related to serine and glycine metabolism; substances that help cancer cells grow. The research demonstrates that RBM15 controls genes involved in the cancer metabolism of serine and glycine, two types of amino acids, which contributes to cancer cell proliferation. This suggests that targeting RBM15 can be new therapeutic approaches for triple-negative breast cancer patients. This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author.","PeriodicalId":50466,"journal":{"name":"Experimental and Molecular Medicine","volume":"56 6","pages":"1373-1387"},"PeriodicalIF":9.5,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11263342/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141201005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-03DOI: 10.1038/s12276-024-01255-6
Sanjib Chaudhary, Jawed Akhtar Siddiqui, Muthamil Iniyan Appadurai, Shailendra Kumar Maurya, Swathi P. Murakonda, Elizabeth Blowers, Ben J. Swanson, Mohd Wasim Nasser, Surinder K. Batra, Imayavaramban Lakshmanan, Apar Kishor Ganti
Non-small cell lung carcinoma (NSCLC) exhibits a heightened propensity for brain metastasis, posing a significant clinical challenge. Mucin 5ac (MUC5AC) plays a pivotal role in the development of lung adenocarcinoma (LUAD); however, its role in causing brain metastases remains unknown. In this study, we aimed to investigate the contribution of MUC5AC to brain metastasis in patients with LUAD utilizing various brain metastasis models. Our findings revealed a substantial increase in the MUC5AC level in LUAD brain metastases (LUAD-BrM) samples and brain-tropic cell lines compared to primary samples or parental control cell lines. Intriguingly, depletion of MUC5AC in brain-tropic cells led to significant reductions in intracranial metastasis and tumor growth, and improved survival following intracardiac injection, in contrast to the observations in the control groups. Proteomic analysis revealed that mechanistically, MUC5AC depletion resulted in decreased expression of metastasis-associated molecules. There were increases in epithelial-to-mesenchymal transition, tumor invasiveness, and metastasis phenotypes in tumors with high MUC5AC expression. Furthermore, immunoprecipitation and proteomic analysis revealed a novel interaction of MUC5AC with Annexin A2 (ANXA2), which activated downstream matrix metalloproteases and facilitated extracellular matrix degradation to promote metastasis. Disrupting MUC5AC-ANXA2 signaling with a peptide inhibitor effectively abrogated the metastatic process. Additionally, treatment of tumor cells with an astrocyte-conditioned medium or the chemokine CCL2 resulted in upregulation of MUC5AC expression and enhanced brain colonization. In summary, our study demonstrates that the MUC5AC/ANXA2 signaling axis promotes brain metastasis, suggesting a potential therapeutic paradigm for LUAD patients with high MUC5AC expression. Lung cancer frequently moves to the brain, but why is unclear. Scientists have found that a protein, MUC5AC, is crucial in this. The research, led by Sanjib Chaudhary and team, discovered that MUC5AC works with another protein, ANXA2, to help lung cancer cells move to the brain. They also found that astrocytes (a type of brain cell), release a substance that boosts the presence of MUC5AC in lung cancer cells. This research was a lab experiment using lung cancer cells and mice. They found that lowering MUC5AC in lung cancer cells greatly reduced their movement to the brain in mice. This suggests that focusing on MUC5AC could help stop lung cancer from moving to the brain. Future studies will need to confirm these results and look into possible treatments. This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author.
{"title":"Dissecting the MUC5AC/ANXA2 signaling axis: implications for brain metastasis in lung adenocarcinoma","authors":"Sanjib Chaudhary, Jawed Akhtar Siddiqui, Muthamil Iniyan Appadurai, Shailendra Kumar Maurya, Swathi P. Murakonda, Elizabeth Blowers, Ben J. Swanson, Mohd Wasim Nasser, Surinder K. Batra, Imayavaramban Lakshmanan, Apar Kishor Ganti","doi":"10.1038/s12276-024-01255-6","DOIUrl":"10.1038/s12276-024-01255-6","url":null,"abstract":"Non-small cell lung carcinoma (NSCLC) exhibits a heightened propensity for brain metastasis, posing a significant clinical challenge. Mucin 5ac (MUC5AC) plays a pivotal role in the development of lung adenocarcinoma (LUAD); however, its role in causing brain metastases remains unknown. In this study, we aimed to investigate the contribution of MUC5AC to brain metastasis in patients with LUAD utilizing various brain metastasis models. Our findings revealed a substantial increase in the MUC5AC level in LUAD brain metastases (LUAD-BrM) samples and brain-tropic cell lines compared to primary samples or parental control cell lines. Intriguingly, depletion of MUC5AC in brain-tropic cells led to significant reductions in intracranial metastasis and tumor growth, and improved survival following intracardiac injection, in contrast to the observations in the control groups. Proteomic analysis revealed that mechanistically, MUC5AC depletion resulted in decreased expression of metastasis-associated molecules. There were increases in epithelial-to-mesenchymal transition, tumor invasiveness, and metastasis phenotypes in tumors with high MUC5AC expression. Furthermore, immunoprecipitation and proteomic analysis revealed a novel interaction of MUC5AC with Annexin A2 (ANXA2), which activated downstream matrix metalloproteases and facilitated extracellular matrix degradation to promote metastasis. Disrupting MUC5AC-ANXA2 signaling with a peptide inhibitor effectively abrogated the metastatic process. Additionally, treatment of tumor cells with an astrocyte-conditioned medium or the chemokine CCL2 resulted in upregulation of MUC5AC expression and enhanced brain colonization. In summary, our study demonstrates that the MUC5AC/ANXA2 signaling axis promotes brain metastasis, suggesting a potential therapeutic paradigm for LUAD patients with high MUC5AC expression. Lung cancer frequently moves to the brain, but why is unclear. Scientists have found that a protein, MUC5AC, is crucial in this. The research, led by Sanjib Chaudhary and team, discovered that MUC5AC works with another protein, ANXA2, to help lung cancer cells move to the brain. They also found that astrocytes (a type of brain cell), release a substance that boosts the presence of MUC5AC in lung cancer cells. This research was a lab experiment using lung cancer cells and mice. They found that lowering MUC5AC in lung cancer cells greatly reduced their movement to the brain in mice. This suggests that focusing on MUC5AC could help stop lung cancer from moving to the brain. Future studies will need to confirm these results and look into possible treatments. This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author.","PeriodicalId":50466,"journal":{"name":"Experimental and Molecular Medicine","volume":"56 6","pages":"1450-1460"},"PeriodicalIF":9.5,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11263355/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141200981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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