CDiP technology for reverse engineering of sporadic Alzheimer’s disease

Alzheimer’s disease (AD) is a neurodegenerative disease that causes cognitive impairment for which neither treatable nor preventable approaches have been confirmed. Although genetic factors are considered to contribute to sporadic AD, for the majority of AD patients, the exact causes of AD aren’t fully understood. For AD genetics, we developed cellular dissection of polygenicity (CDiP) technology to identify the smallest unit of AD, i.e., genetic factors at a cellular level. By CDiP, we found potential therapeutic targets, a rare variant for disease stratification, and polygenes to predict real-world AD by using the real-world data of AD cohort studies (Alzheimer’s Disease Neuroimaging Initiative: ADNI and Japanese Alzheimer’s Disease Neuroimaging Initiative: J-ADNI). In this review, we describe the components and results of CDiP in AD, induced pluripotent stem cell (iPSC) cohort, a cell genome-wide association study (cell GWAS), and machine learning. And finally, we discuss the future perspectives of CDiP technology for reverse engineering of sporadic AD toward AD eradication

Simple derivation of skeletal muscle from human pluripotent stem cells using temperature‐sensitive Sendai virus vector

温度感受性センダイウイルスベクターを用いて ヒトES細胞／iPS細胞から骨格筋細胞を簡便に作製する技術開発 --神経筋疾患病態モデル構築と創薬研究への利用--. 京都大学プレスリリース. 2021-09-13.Human pluripotent stem cells have the potential to differentiate into various cell types including skeletal muscles (SkM), and they are applied to regenerative medicine or in vitro modelling for intractable diseases. A simple differentiation method is required for SkM cells to accelerate neuromuscular disease studies. Here, we established a simple method to convert human pluripotent stem cells into SkM cells by using temperature-sensitive Sendai virus (SeV) vector encoding myoblast determination protein 1 (SeV-Myod1), a myogenic master transcription factor. SeV-Myod1 treatment converted human embryonic stem cells (ESCs) into SkM cells, which expressed SkM markers including myosin heavy chain (MHC). We then removed the SeV vector by temporal treatment at a high temperature of 38℃, which also accelerated mesodermal differentiation, and found that SkM cells exhibited fibre-like morphology. Finally, after removal of the residual human ESCs by pluripotent stem cell-targeting delivery of cytotoxic compound, we generated SkM cells with 80% MHC positivity and responsiveness to electrical stimulation. This simple method for myogenic differentiation was applicable to human-induced pluripotent stem cells and will be beneficial for investigations of disease mechanisms and drug discovery in the future

Induction of inverted morphology in brain organoids by vertical-mixing bioreactors

上下動撹拌培養装置を用いた流体制御により誘導した反転型脳オルガノイド 脳オルガノイド誘導の流体シミュレーションと流体力学的理解. 京都大学プレスリリース. 2021-10-27.How you mix cells changes the brain. 京都大学プレスリリース. 2021-10-28.Organoid technology provides an opportunity to generate brain-like structures by recapitulating developmental steps in the manner of self-organization. Here we examined the vertical-mixing effect on brain organoid structures using bioreactors and established inverted brain organoids. The organoids generated by vertical mixing showed neurons that migrated from the outer periphery to the inner core of organoids, in contrast to orbital mixing. Computational analysis of flow dynamics clarified that, by comparison with orbital mixing, vertical mixing maintained the high turbulent energy around organoids, and continuously kept inter-organoid distances by dispersing and adding uniform rheological force on organoids. To uncover the mechanisms of the inverted structure, we investigated the direction of primary cilia, a cellular mechanosensor. Primary cilia of neural progenitors by vertical mixing were aligned in a multidirectional manner, and those by orbital mixing in a bidirectional manner. Single-cell RNA sequencing revealed that neurons of inverted brain organoids presented a GABAergic character of the ventral forebrain. These results suggest that controlling fluid dynamics by biomechanical engineering can direct stem cell differentiation of brain organoids, and that inverted brain organoids will be applicable for studying human brain development and disorders in the future

High molecular weight amyloid β1-42 oligomers induce neurotoxicity via plasma membrane damage.

Amyloid β-protein (Aβ) molecules tend to aggregate and subsequently form low MW (LMW) oligomers, high MW (HMW) aggregates such as protofibrils, and ultimately fibrils. These Aβ species can generally form amyloid plaques implicated in the neurodegeneration of Alzheimer disease (AD), but therapies designed to reduce plaque load have not demonstrated clinical efficacy. Recent evidence implicates amyloid oligomers in AD neuropathology, but the precise mechanisms are uncertain. We examined the mechanisms of neuronal dysfunction from HMW-Aβ1-42 exposure by measuring membrane integrity, reactive oxygen species (ROS) generation, membrane lipid peroxidation, membrane fluidity, intracellular calcium regulation, passive membrane electrophysiological properties, and long-term potentiation (LTP). HMW-Aβ1-42 disturbed membrane integrity by inducing ROS generation and lipid peroxidation, resulting in decreased membrane fluidity, intracellular calcium dysregulation, depolarization, and impaired LTP. The damaging effects of HMW-Aβ1-42 were significantly greater than those of LMW-Aβ1-42. Therapeutic reduction of HMW-Aβ1-42 may prevent AD progression by ameliorating direct neuronal membrane damage.-Yasumoto, T., Takamura, Y., Tsuji, M., Watanabe-Nakayama, T., Imamura, K., Inoue, H., Nakamura, S., Inoue, T., Kimura, A., Yano, S., Nishijo, H., Kiuchi, Y., Teplow, D. B., Ono, K. High molecular weight amyloid β1-42 oligomers induce neurotoxicity via plasma membrane damage

Human induced pluripotent stem cells generated from a patient with idiopathic basal ganglia calcification

Idiopathic basal ganglia calcification (IBGC) is a rare neurodegenerative disease, characterized by abnormal calcium deposits in basal ganglia of the brain. The affected individuals exhibit movement disorders, and progressive deterioration of cognitive and psychiatric ability. The genetic cause of the disease is mutation in one of several different genes, SLC20A2, PDGFB, PDGFRB, XPR1 or MYORG, which inheritably or sporadically occurs. Here we generated an induced pluripotent stem cell (iPSC) line from an IBGC patient, which is likely be a powerful tool for revealing the pathomechanisms and exploring potential therapeutic candidates of IBGC

Establishment of induced pluripotent stem cells from schizophrenia discordant fraternal twins

Schizophrenia (SCZ) is one of the major psychiatric disorders. The genetic factor is certainly influential in the onset of the disease but is not decisive. There is no identified molecular/cellular marker of the disease, and the pathomechanism is still unknown. In this study, we generated human induced pluripotent stem cells (iPSCs) derived from SCZ-discordant fraternal twins, and they could contribute to elucidation of the pathomechanism of SCZ

iPSC screening for drug repurposing identifies anti‐RNA virus agents modulating host cell susceptibility

RNAウイルスの感染を阻害する既存薬の同定 --複数の異なるRNAウイルスに対して宿主細胞の感受性を下げることにより感染を抑制する薬剤--. 京都大学プレスリリース. 2021-04-07.iPS cells in drug screenings for COVID-19. 京都大学プレスリリース. 2021-04-07.Human pathogenic RNA viruses are threats to public health because they are prone to escaping the human immune system through mutations of genomic RNA, thereby causing local outbreaks and global pandemics of emerging or re‐emerging viral diseases. While specific therapeutics and vaccines are being developed, a broad‐spectrum therapeutic agent for RNA viruses would be beneficial for targeting newly emerging and mutated RNA viruses. In this study, we conducted a screen of repurposed drugs using Sendai virus (an RNA virus of the family Paramyxoviridae), with human‐induced pluripotent stem cells (iPSCs) to explore existing drugs that may present anti‐RNA viral activity. Selected hit compounds were evaluated for their efficacy against two important human pathogens: Ebola virus (EBOV) using Huh7 cells and severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) using Vero E6 cells. Selective estrogen receptor modulators (SERMs), including raloxifene, exhibited antiviral activities against EBOV and SARS‐CoV‐2. Pioglitazone, a PPARγ agonist, also exhibited antiviral activities against SARS‐CoV‐2, and both raloxifene and pioglitazone presented a synergistic antiviral effect. Finally, we demonstrated that SERMs blocked entry steps of SARS‐CoV‐2 into host cells. These findings suggest that the identified FDA‐approved drugs can modulate host cell susceptibility against RNA viruses

Prediction Model of Amyotrophic Lateral Sclerosis by Deep Learning with Patient Induced Pluripotent Stem Cells

Deep LearningとALS iPS細胞を用いた疾患予測テクノロジー --人工知能のALS検知・診断への応用--. 京都大学プレスリリース. 2021-02-24.Deep learning amyotrophic lateral sclerosis by taking pictures. 京都大学プレスリリース. 2021-02-24.In amyotrophic lateral sclerosis (ALS), early diagnosis is essential for both current and potential treatments. To find a supportive approach for the diagnosis, we constructed an artificial intelligence‐based prediction model of ALS using induced pluripotent stem cells (iPSCs). Images of spinal motor neurons derived from healthy control subject and ALS patient iPSCs were analyzed by a convolutional neural network, and the algorithm achieved an area under the curve of 0.97 for classifying healthy control and ALS. This prediction model by deep learning algorithm with iPSC technology could support the diagnosis and may provide proactive treatment of ALS through future prospective research. ANN NEUROL 202

Deep Learning and ALS

In amyotrophic lateral sclerosis (ALS), early diagnosis is essential for both current and potential treatments. To find a supportive approach for the diagnosis, we constructed an artificial intelligence-based prediction model of ALS using induced pluripotent stem cells (iPSCs). Images of spinal motor neurons derived from healthy control subject and ALS patient iPSCs were analyzed by a convolutional neural network, and the algorithm achieved an area under the curve of 0.97 for classifying healthy control and ALS. This prediction model by deep learning algorithm with iPSC technology could support the diagnosis and may provide proactive treatment of ALS through future prospective research