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Article ; Online: Variation of DNA methylation on the IRX1/2 genes is responsible for the neural differentiation propensity in human induced pluripotent stem cells

Asato Sekiya / Ken Takasawa / Yoshikazu Arai / Shin-ichi Horike / Hidenori Akutsu / Akihiro Umezawa / Koichiro Nishino

Regenerative Therapy, Vol 21, Iss , Pp 620-

2022 Volume 630

Abstract: Introduction: Human induced pluripotent stem cells (hiPSCs) are useful tools for reproducing neural development in vitro. However, each hiPSC line has a different ability to differentiate into specific lineages, known as differentiation propensity, ... ...

Abstract	Introduction: Human induced pluripotent stem cells (hiPSCs) are useful tools for reproducing neural development in vitro. However, each hiPSC line has a different ability to differentiate into specific lineages, known as differentiation propensity, resulting in reduced reproducibility and increased time and funding requirements for research. To overcome this issue, we searched for predictive signatures of neural differentiation propensity of hiPSCs focusing on DNA methylation, which is the main modulator of cellular properties. Methods: We obtained 32 hiPSC lines and their comprehensive DNA methylation data using the Infinium MethylationEPIC BeadChip. To assess the neural differentiation efficiency of these hiPSCs, we measured the percentage of neural stem cells on day 7 of induction. Using the DNA methylation data of undifferentiated hiPSCs and their measured differentiation efficiency into neural stem cells as the set of data, and HSIC Lasso, a machine learning-based nonlinear feature selection method, we attempted to identify neural differentiation-associated differentially methylated sites. Results: Epigenome-wide unsupervised clustering cannot distinguish hiPSCs with varying differentiation efficiencies. In contrast, HSIC Lasso identified 62 CpG sites that could explain the neural differentiation efficiency of hiPSCs. Features selected by HSIC Lasso were particularly enriched within 3 Mbp of chromosome 5, harboring IRX1, IRX2, and C5orf38 genes. Within this region, DNA methylation rates were correlated with neural differentiation efficiency and were negatively correlated with gene expression of the IRX1/2 genes, particularly in female hiPSCs. In addition, forced expression of the IRX1/2 impaired the neural differentiation ability of hiPSCs in both sexes. Conclusion: We for the first time showed that the DNA methylation state of the IRX1/2 genes of hiPSCs is a predictive biomarker of their potential for neural differentiation. The predictive markers for neural differentiation efficiency identified in this ...
Keywords	Human iPSCs ; Neural stem cells ; DNA methylation ; Differentiation propensity ; Machine learning ; Medicine (General) ; R5-920 ; Cytology ; QH573-671
Subject code	612
Language	English
Publishing date	2022-12-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Epigenetics Analysis and Integrated Analysis of Multiomics Data, Including Epigenetic Data, Using Artificial Intelligence in the Era of Precision Medicine

Ryuji Hamamoto / Masaaki Komatsu / Ken Takasawa / Ken Asada / Syuzo Kaneko

Biomolecules, Vol 10, Iss 1, p

2019 Volume 62

Abstract: To clarify the mechanisms of diseases, such as cancer, studies analyzing genetic mutations have been actively conducted for a long time, and a large number of achievements have already been reported. Indeed, genomic medicine is considered the core ... ...

Abstract	To clarify the mechanisms of diseases, such as cancer, studies analyzing genetic mutations have been actively conducted for a long time, and a large number of achievements have already been reported. Indeed, genomic medicine is considered the core discipline of precision medicine, and currently, the clinical application of cutting-edge genomic medicine aimed at improving the prevention, diagnosis and treatment of a wide range of diseases is promoted. However, although the Human Genome Project was completed in 2003 and large-scale genetic analyses have since been accomplished worldwide with the development of next-generation sequencing (NGS), explaining the mechanism of disease onset only using genetic variation has been recognized as difficult. Meanwhile, the importance of epigenetics, which describes inheritance by mechanisms other than the genomic DNA sequence, has recently attracted attention, and, in particular, many studies have reported the involvement of epigenetic deregulation in human cancer. So far, given that genetic and epigenetic studies tend to be accomplished independently, physiological relationships between genetics and epigenetics in diseases remain almost unknown. Since this situation may be a disadvantage to developing precision medicine, the integrated understanding of genetic variation and epigenetic deregulation appears to be now critical. Importantly, the current progress of artificial intelligence (AI) technologies, such as machine learning and deep learning, is remarkable and enables multimodal analyses of big omics data. In this regard, it is important to develop a platform that can conduct multimodal analysis of medical big data using AI as this may accelerate the realization of precision medicine. In this review, we discuss the importance of genome-wide epigenetic and multiomics analyses using AI in the era of precision medicine.
Keywords	epigenetics ; precision medicine ; dna methylation ; histone modifications ; machine learning ; deep learning ; Microbiology ; QR1-502
Subject code	006
Language	English
Publishing date	2019-12-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Epigenetic Mechanisms Underlying COVID-19 Pathogenesis

Syuzo Kaneko / Ken Takasawa / Ken Asada / Norio Shinkai / Amina Bolatkan / Masayoshi Yamada / Satoshi Takahashi / Hidenori Machino / Kazuma Kobayashi / Masaaki Komatsu / Ryuji Hamamoto

Biomedicines, Vol 9, Iss 1142, p

2021 Volume 1142

Abstract: In 2019, a novel severe acute respiratory syndrome called coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was reported and was declared a pandemic by the World Health Organization (WHO) in ... ...

Abstract	In 2019, a novel severe acute respiratory syndrome called coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was reported and was declared a pandemic by the World Health Organization (WHO) in March 2020. With the advancing development of COVID-19 vaccines and their administration globally, it is expected that COVID-19 will converge in the future; however, the situation remains unpredictable because of a series of reports regarding SARS-CoV-2 variants. Currently, there are still few specific effective treatments for COVID-19, as many unanswered questions remain regarding the pathogenic mechanism of COVID-19. Continued elucidation of COVID-19 pathogenic mechanisms is a matter of global importance. In this regard, recent reports have suggested that epigenetics plays an important role; for instance, the expression of angiotensin I converting enzyme 2 (ACE2) receptor, an important factor in human infection with SARS-CoV-2, is epigenetically regulated; further, DNA methylation status is reported to be unique to patients with COVID-19. In this review, we focus on epigenetic mechanisms to provide a new molecular framework for elucidating the pathogenesis of SARS-CoV-2 infection in humans and of COVID-19, along with the possibility of new diagnostic and therapeutic strategies.
Keywords	COVID-19 ; SARS-CoV-2 ; epigenetics ; ACE2 ; DNA methylation ; histone modifications ; Biology (General) ; QH301-705.5
Subject code	610
Language	English
Publishing date	2021-09-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Single-Cell Analysis Using Machine Learning Techniques and Its Application to Medical Research

Ken Asada / Ken Takasawa / Hidenori Machino / Satoshi Takahashi / Norio Shinkai / Amina Bolatkan / Kazuma Kobayashi / Masaaki Komatsu / Syuzo Kaneko / Koji Okamoto / Ryuji Hamamoto

Biomedicines, Vol 9, Iss 1513, p

2021 Volume 1513

Abstract: In recent years, the diversity of cancer cells in tumor tissues as a result of intratumor heterogeneity has attracted attention. In particular, the development of single-cell analysis technology has made a significant contribution to the field; ... ...

Abstract	In recent years, the diversity of cancer cells in tumor tissues as a result of intratumor heterogeneity has attracted attention. In particular, the development of single-cell analysis technology has made a significant contribution to the field; technologies that are centered on single-cell RNA sequencing (scRNA-seq) have been reported to analyze cancer constituent cells, identify cell groups responsible for therapeutic resistance, and analyze gene signatures of resistant cell groups. However, although single-cell analysis is a powerful tool, various issues have been reported, including batch effects and transcriptional noise due to gene expression variation and mRNA degradation. To overcome these issues, machine learning techniques are currently being introduced for single-cell analysis, and promising results are being reported. In addition, machine learning has also been used in various ways for single-cell analysis, such as single-cell assay of transposase accessible chromatin sequencing (ATAC-seq), chromatin immunoprecipitation sequencing (ChIP-seq) analysis, and multi-omics analysis; thus, it contributes to a deeper understanding of the characteristics of human diseases, especially cancer, and supports clinical applications. In this review, we present a comprehensive introduction to the implementation of machine learning techniques in medical research for single-cell analysis, and discuss their usefulness and future potential.
Keywords	single-cell analysis ; next-generation sequencing ; machine learning ; multi-omics analysis ; Biology (General) ; QH301-705.5
Subject code	004
Language	English
Publishing date	2021-10-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Application of Artificial Intelligence in COVID-19 Diagnosis and Therapeutics

Ken Asada / Masaaki Komatsu / Ryo Shimoyama / Ken Takasawa / Norio Shinkai / Akira Sakai / Amina Bolatkan / Masayoshi Yamada / Satoshi Takahashi / Hidenori Machino / Kazuma Kobayashi / Syuzo Kaneko / Ryuji Hamamoto

Journal of Personalized Medicine, Vol 11, Iss 886, p

2021 Volume 886

Abstract: The coronavirus disease 2019 (COVID-19) pandemic began at the end of December 2019, giving rise to a high rate of infections and causing COVID-19-associated deaths worldwide. It was first reported in Wuhan, China, and since then, not only global leaders, ...

Abstract	The coronavirus disease 2019 (COVID-19) pandemic began at the end of December 2019, giving rise to a high rate of infections and causing COVID-19-associated deaths worldwide. It was first reported in Wuhan, China, and since then, not only global leaders, organizations, and pharmaceutical/biotech companies, but also researchers, have directed their efforts toward overcoming this threat. The use of artificial intelligence (AI) has recently surged internationally and has been applied to diverse aspects of many problems. The benefits of using AI are now widely accepted, and many studies have shown great success in medical research on tasks, such as the classification, detection, and prediction of disease, or even patient outcome. In fact, AI technology has been actively employed in various ways in COVID-19 research, and several clinical applications of AI-equipped medical devices for the diagnosis of COVID-19 have already been reported. Hence, in this review, we summarize the latest studies that focus on medical imaging analysis, drug discovery, and therapeutics such as vaccine development and public health decision-making using AI. This survey clarifies the advantages of using AI in the fight against COVID-19 and provides future directions for tackling the COVID-19 pandemic using AI techniques.
Keywords	COVID-19 ; artificial intelligence ; diagnosis ; therapeutics ; public health ; Medicine ; R
Subject code	006
Language	English
Publishing date	2021-09-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Introducing AI to the molecular tumor board

Ryuji Hamamoto / Takafumi Koyama / Nobuji Kouno / Tomohiro Yasuda / Shuntaro Yui / Kazuki Sudo / Makoto Hirata / Kuniko Sunami / Takashi Kubo / Ken Takasawa / Satoshi Takahashi / Hidenori Machino / Kazuma Kobayashi / Ken Asada / Masaaki Komatsu / Syuzo Kaneko / Yasushi Yatabe / Noboru Yamamoto

Experimental Hematology & Oncology, Vol 11, Iss 1, Pp 1-

one direction toward the establishment of precision medicine using large-scale cancer clinical and biological information

2022 Volume 23

Abstract: Abstract Since U.S. President Barack Obama announced the Precision Medicine Initiative in his New Year’s State of the Union address in 2015, the establishment of a precision medicine system has been emphasized worldwide, particularly in the field of ... ...

Abstract	Abstract Since U.S. President Barack Obama announced the Precision Medicine Initiative in his New Year’s State of the Union address in 2015, the establishment of a precision medicine system has been emphasized worldwide, particularly in the field of oncology. With the advent of next-generation sequencers specifically, genome analysis technology has made remarkable progress, and there are active efforts to apply genome information to diagnosis and treatment. Generally, in the process of feeding back the results of next-generation sequencing analysis to patients, a molecular tumor board (MTB), consisting of experts in clinical oncology, genetic medicine, etc., is established to discuss the results. On the other hand, an MTB currently involves a large amount of work, with humans searching through vast databases and literature, selecting the best drug candidates, and manually confirming the status of available clinical trials. In addition, as personalized medicine advances, the burden on MTB members is expected to increase in the future. Under these circumstances, introducing cutting-edge artificial intelligence (AI) technology and information and communication technology to MTBs while reducing the burden on MTB members and building a platform that enables more accurate and personalized medical care would be of great benefit to patients. In this review, we introduced the latest status of elemental technologies that have potential for AI utilization in MTB, and discussed issues that may arise in the future as we progress with AI implementation.
Keywords	Molecular tumor board ; Precision medicine ; Artificial intelligence ; Next-generation sequencing ; Natural language processing ; Diseases of the blood and blood-forming organs ; RC633-647.5 ; Neoplasms. Tumors. Oncology. Including cancer and carcinogens ; RC254-282
Subject code	610
Language	English
Publishing date	2022-10-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Application of Artificial Intelligence Technology in Oncology

Cancers, Vol 12, Iss 3532, p

Towards the Establishment of Precision Medicine

2020 Volume 3532

Abstract: In recent years, advances in artificial intelligence (AI) technology have led to the rapid clinical implementation of devices with AI technology in the medical field. More than 60 AI-equipped medical devices have already been approved by the Food and ... ...

Abstract	In recent years, advances in artificial intelligence (AI) technology have led to the rapid clinical implementation of devices with AI technology in the medical field. More than 60 AI-equipped medical devices have already been approved by the Food and Drug Administration (FDA) in the United States, and the active introduction of AI technology is considered to be an inevitable trend in the future of medicine. In the field of oncology, clinical applications of medical devices using AI technology are already underway, mainly in radiology, and AI technology is expected to be positioned as an important core technology. In particular, “precision medicine,” a medical treatment that selects the most appropriate treatment for each patient based on a vast amount of medical data such as genome information, has become a worldwide trend; AI technology is expected to be utilized in the process of extracting truly useful information from a large amount of medical data and applying it to diagnosis and treatment. In this review, we would like to introduce the history of AI technology and the current state of medical AI, especially in the oncology field, as well as discuss the possibilities and challenges of AI technology in the medical field.
Keywords	machine learning ; deep learning ; artificial intelligence ; precision medicine ; radiology ; pathology ; Neoplasms. Tumors. Oncology. Including cancer and carcinogens ; RC254-282
Language	English
Publishing date	2020-11-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Epigenetic-scale comparison of human iPSCs generated by retrovirus, Sendai virus or episomal vectors

Koichiro Nishino / Yoshikazu Arai / Ken Takasawa / Masashi Toyoda / Mayu Yamazaki-Inoue / Tohru Sugawara / Hidenori Akutsu / Ken Nishimura / Manami Ohtaka / Mahito Nakanishi / Akihiro Umezawa

Regenerative Therapy, Vol 9, Iss , Pp 71-

2018 Volume 78

Abstract: Human induced pluripotent stem cells (iPSCs) are established by introducing several reprogramming factors, such as OCT3/4, SOX2, KLF4, c-MYC. Because of their pluripotency and immortality, iPSCs are considered to be a powerful tool for regenerative ... ...

Abstract	Human induced pluripotent stem cells (iPSCs) are established by introducing several reprogramming factors, such as OCT3/4, SOX2, KLF4, c-MYC. Because of their pluripotency and immortality, iPSCs are considered to be a powerful tool for regenerative medicine. To date, iPSCs have been established all over the world by various gene delivery methods. All methods induced high-quality iPSCs, but epigenetic analysis of abnormalities derived from differences in the gene delivery methods has not yet been performed. Here, we generated genetically matched human iPSCs from menstrual blood cells by using three kinds of vectors, i.e., retrovirus, Sendai virus, and episomal vectors, and compared genome-wide DNA methylation profiles among them. Although comparison of aberrant methylation revealed that iPSCs generated by Sendai virus vector have lowest number of aberrant methylation sites among the three vectors, the iPSCs generated by non-integrating methods did not show vector-specific aberrant methylation. However, the differences between the iPSC lines were determined to be the number of random aberrant hypermethylated regions compared with embryonic stem cells. These random aberrant hypermethylations might be a cause of the differences in the properties of each of the iPSC lines. Keywords: Human iPSCs, Human ESCs, DNA methylation, Aberrant methylation
Keywords	Medicine (General) ; R5-920 ; Cytology ; QH573-671
Subject code	310
Language	English
Publishing date	2018-12-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Tetraploid Embryonic Stem Cells Maintain Pluripotency and Differentiation Potency into Three Germ Layers.

Hiroyuki Imai / Kiyoshi Kano / Wataru Fujii / Ken Takasawa / Shoichi Wakitani / Masato Hiyama / Koichiro Nishino / Ken Takeshi Kusakabe / Yasuo Kiso

PLoS ONE, Vol 10, Iss 6, p e

2015 Volume 0130585

Abstract: Polyploid amphibians and fishes occur naturally in nature, while polyploid mammals do not. For example, tetraploid mouse embryos normally develop into blastocysts, but exhibit abnormalities and die soon after implantation. Thus, polyploidization is ... ...

Abstract	Polyploid amphibians and fishes occur naturally in nature, while polyploid mammals do not. For example, tetraploid mouse embryos normally develop into blastocysts, but exhibit abnormalities and die soon after implantation. Thus, polyploidization is thought to be harmful during early mammalian development. However, the mechanisms through which polyploidization disrupts development are still poorly understood. In this study, we aimed to elucidate how genome duplication affects early mammalian development. To this end, we established tetraploid embryonic stem cells (TESCs) produced from the inner cell masses of tetraploid blastocysts using electrofusion of two-cell embryos in mice and studied the developmental potential of TESCs. We demonstrated that TESCs possessed essential pluripotency and differentiation potency to form teratomas, which differentiated into the three germ layers, including diploid embryonic stem cells. TESCs also contributed to the inner cell masses in aggregated chimeric blastocysts, despite the observation that tetraploid embryos fail in normal development soon after implantation in mice. In TESCs, stability after several passages, colony morphology, and alkaline phosphatase activity were similar to those of diploid ESCs. TESCs also exhibited sufficient expression and localization of pluripotent markers and retained the normal epigenetic status of relevant reprogramming factors. TESCs proliferated at a slower rate than ESCs, indicating that the difference in genomic dosage was responsible for the different growth rates. Thus, our findings suggested that mouse ESCs maintained intrinsic pluripotency and differentiation potential despite tetraploidization, providing insights into our understanding of developmental elimination in polyploid mammals.
Keywords	Medicine ; R ; Science ; Q
Subject code	571
Language	English
Publishing date	2015-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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