Recent Advances in iPSC Disease Modeling

Recent Advances in iPSC Disease Modeling, Volume One addresses how induced pluripotent stem cells can be used to model various diseases. This new volume teaches readers about current advances in the field, describing the use of induced pluripotent stem cells to model several diseases in vitro, and thus enabling us to study the cellular and molecular mechanisms involved in different pathologies. Further insights into these mechanisms will have important implications for our understanding of disease appearance, development and progression. The volume is written for researchers and scientists in stem cell therapy, cell biology, regenerative medicine and organ transplantation specialists.

In recent years, remarkable progress has been made in the obtention of induced pluripotent stem cells and their differentiation into several cell types, tissues and organs using state-of-art techniques. Hence, these advantages have facilitated the identification of key targets and further defining on the molecular basis of several disorders.

• Provides an overview on the fast-moving field of induced pluripotent stem cell technology, regenerative medicine and therapeutics
• Covers the following diseases: severe congenital neutropenia, sickle cell and Diamond-Blackfan anemias, muscular dystrophies, Bernard-Soulier syndrome, familial hypercholesterolemia type II A, Werner syndrome, lysosomal storage diseases, and more
• Contains descriptions of cutting-edge research on the development of disease-specific human pluripotent stem cells

• Language: English
• Publisher: Academic Press
• Release date: Jul 16, 2020
• ISBN: 9780128232699

Book preview

Recent Advances in iPSC Disease Modeling, Volume 1

Editor

Alexander Birbrair

Federal University of Minas Gerais, Department of Pathology, Belo Horizonte, Minas Gerais, Brazil

Columbia University Medical Center, Department of Radiology, New York, NY, United States

Table of Contents

Cover image

Title page

Copyright

Dedication

About the Editor

Preface

Chapter 1. iPSCs for modeling lysosomal storage diseases

Introduction

Conclusion and future perspectives

Chapter 2. Sickle cell anemia: HBB haplotypes; clinical heterogeneity; iPSC modeling

Introduction

Haplotypes of the HbS gene

Regulation of gene expression in the HBB gene cluster: hemoglobin switching

Clinical heterogeneity

The Arab-Indian haplotype: cis- and trans-acting HbF regulation and clinical phenotypes

Saudi Benin and Cameroon haplotypes

Induced pluripotent stem cells and sickle cell anemia

Conclusions

Chapter 3. iPSCs for modeling mtDNA diseases

Introduction

mtDNA diseases

Clinical pathologies of mtDNA diseases

Heteroplasmy

Model systems for mtDNA disease

Advantages and limitations of iPSCs in evaluating mtDNA diseases

iPSC models for mtDNA diseases

Future directions

Chapter 4. iPSCs for modeling Diamond–Blackfan anemia

The DBA puzzle

Development of induced pluripotent stem cells for disease modeling

Use of DBA iPSC models

Identify therapeutic for DBA

Limitations and future trends of DBA iPSC

Chapter 5. Modeling severe congenital neutropenia in induced pluripotent stem cells

Severe congenital neutropenia

Severe congenital neutropenia mouse models

Human cell line models

Induced pluripotent stem cell models for severe congenital neutropenia

Hematopoietic induction of induced pluripotent stem cells

HAX1-mutant induced pluripotent stem cells

ELANE-mutant induced pluripotent stem cells

Unfolded protein response activation in ELANE-mutant cells

Use of SCN-iPSCs to develop new therapeutic strategies

Induced pluripotent stem cells to study leukemic progression

Outlook

Chapter 6. iPSCs for modeling Duchenne muscular dystrophy

Introduction

Currently available treatments of Duchenne muscular dystrophy

New hope for Duchenne muscular dystrophy treatment: stem cell–based therapy and gene-editing techniques

Differentiation of induced pluripotent stem cells into myogenic progenitors/myoblasts

Duchenne muscular dystrophy modeling by induced pluripotent stem cells

Future perspectives and concluding remarks

Chapter 7. Induced pluripotent stem cell modeling of genetic small vessel disease

Abbreviation list

Introduction

Cerebral small vessel disease

The neurovascular unit

Noninduced pluripotent stem cell research models and methods for small vessel disease

Induced pluripotent stem cell modeling of genetic small vessel disease

Conclusion

Chapter 8. The contribution of human pluripotent stem cells to the study of myotonic dystrophy type 1

Introduction

Pluripotent stem cells

RNA toxicity

Repeat instability

Chromatin reorganization

Therapeutic gene correction for myotonic dystrophy type 1

Mutant human embryonic stem cells versus patient-derived induced pluripotent stem cells

Conclusion

Chapter 9. Induced pluripotent stem cells for the modeling of Bernard-Soulier syndrome

Introduction

Bernard-Soulier syndrome

Glycoprotein Ib-IX-V complex

Platelet production from megakaryocytes

Induced pluripotent stem cells as a model for platelet production

Induced pluripotent stem cells as a model for Bernard-Soulier syndrome

Future perspectives

Chapter 10. iPSCs for modeling familial hypercholesterolemia type II A

Introduction

Familial hypercholesterolemia

Animal and cell models

Getting hepatocytes from human pluripotent stem cells

Familial hypercholesterolemia modeling using patients' specific human-induced pluripotent stem cells

FH-HLCs and drug screening

Genetic correction in FH-iPSC models (Table 10.2)

Future trends (Fig. 10.3)

Chapter 11. Induced pluripotent stem cells for modeling elastin-associated vasculopathy

Elastin biology

Elastin-associated vasculopathies

Modeling elastin-associated vasculopathy using human induced pluripotent stem cells

Potential challenges and future directions

Summary

Chapter 12. iPSCs for modeling of sarcomeric cardiomyopathies

Introduction

Sarcomeric cardiomyopathies

Human induced pluripotent stem cells for modeling hypertrophic and dilated cardiomyopathy

Outlook/future trends

Chapter 13. Werner syndrome induced pluripotent stem cells, a study of pathologic aging

Introduction

Clinical features of Werner syndrome

Genetics and molecular functions of the Werner syndrome gene

Molecular pathogenesis and animal models of Werner syndrome

Disease modeling by induced pluripotent stem cells

Modeling premature aging in Werner syndrome by induced pluripotent stem cells and embryonic stem cells

Conclusions and perspectives

Index

Copyright

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Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary.

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Dedication

This book is dedicated to my mother, Marina Sobolevsky, of blessed memory, who passed away during the creation of this volume. Professor of Mathematics at the State University of Ceará (UECE), she was loved by her colleagues and students, whom she inspired by her unique manner of teaching. I owe all success in my career and personal life to her.

My beloved mom Marina Sobolevsky of blessed memory (July 28, 1959–June 3, 2020)

Contributors

Cinzia Allegrucci,     University of Nottingham, Nottingham, United Kingdom

Amerikos Argyriou,     Division of Evolution and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom

Jérôme Caron

UMR_S1193 INSERM, Paris-Saclay University, Villejuif, France

FHU Hépatinov, Hôpital Paul Brousse, Villejuif, France

Hoi-Hung Cheung,     School of Biomedical Sciences, Medicine, The Chinese University of Hong Kong, Hong Kong, People's Republic of China

Daria S. Chulpanova,     Kazan Federal University, Kazan, Russia

M. Csobonyeiová,     Institute of Histology and Embryology, Faculty of Medicine, Comenius University Bratislava, Slovakia

Anne Dubart-Kupperschmitt

UMR_S1193 INSERM, Paris-Saclay University, Villejuif, France

FHU Hépatinov, Hôpital Paul Brousse, Villejuif, France

Rachel Eiges

Stem Cell Research Laboratory, Medical Genetics Institute Shaare Zedek Medical Center, Jerusalem, Israel

The Hebrew University School of Medicine, Jerusalem, Israel

Jingping Ge,     Eutropics Pharmaceutical Inc., Cambridge, MA, United States

Kaomei Guan,     Institute of Pharmacology and Toxicology, Technische Universität Dresden, Dresden, Germany

Riikka H. Hämäläinen,     A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland

Tayma Handal

Stem Cell Research Laboratory, Medical Genetics Institute Shaare Zedek Medical Center, Jerusalem, Israel

The Hebrew University School of Medicine, Jerusalem, Israel

Kristina V. Kitaeva,     Kazan Federal University, Kazan, Russia

Jiesi Luo

Yale Cardiovascular Research Center, Section of Cardiovascular Medicine, Department of Internal Medicine Yale School of Medicine, New Haven, CT, United States

Yale Stem Cell Center, New Haven, CT, United States

Shalem R. Modi,     A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland

Aysilu I. Mullagulova,     Kazan Federal University, Kazan, Russia

George P. Murphy,     Department of Medicine, Division of Hematology/Oncology, Center of Excellence for Sickle Cell Disease and Center for Regenerative Medicine, Boston University School of Medicine and Boston Medical Center, Boston, MA, United States

Katie Newman,     Division of Evolution and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom

Patricia A. Olofsen,     Department of Hematology, Erasmus MC Cancer Institute, Erasmus University Medical Center, Rotterdam, The Netherlands

Mareike S. Poetsch,     Institute of Pharmacology and Toxicology, Technische Universität Dresden, Dresden, Germany

Owen M. Rennert,     Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, United States

Albert A. Rizvanov,     Kazan Federal University, Kazan, Russia

Ponlapat Rojnuckarin,     Division of Hematology, Department of Medicine, Chulalongkorn University, King Chulalongkorn Memorial Hospital, Bangkok, Thailand

Alisa A. Shaimardanova,     Kazan Federal University, Kazan, Russia

Valeriya V. Solovyeva,     Kazan Federal University, Kazan, Russia

Martin H. Steinberg,     Department of Medicine, Division of Hematology/Oncology, Center of Excellence for Sickle Cell Disease and Center for Regenerative Medicine, Boston University School of Medicine and Boston Medical Center, Boston, MA, United States

Ivo P. Touw,     Department of Hematology, Erasmus MC Cancer Institute, Erasmus University Medical Center, Rotterdam, The Netherlands

Kim Vanuytsel,     Department of Medicine, Division of Hematology/Oncology, Center of Excellence for Sickle Cell Disease and Center for Regenerative Medicine, Boston University School of Medicine and Boston Medical Center, Boston, MA, United States

Tao Wang

Division of Evolution and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom

Manchester Centre for Genomic Medicine, The University of Manchester and Manchester University NHS Foundation Trust, Manchester, United Kingdom

Anne Weber

UMR_S1193 INSERM, Paris-Saclay University, Villejuif, France

FHU Hépatinov, Hôpital Paul Brousse, Villejuif, France

About the Editor

Dr. Alexander Birbrair received his bachelor's biomedical degree from Santa Cruz State University in Brazil. He completed his PhD in Neuroscience, in the field of stem cell biology, at the Wake Forest School of Medicine under the mentorship of Osvaldo Delbono. Then, he joined as a postdoc in stem cell biology at Paul Frenette's laboratory at Albert Einstein School of Medicine in New York. In 2016, he was appointed faculty at Federal University of Minas Gerais in Brazil, where he started his own lab. His laboratory is interested in understanding how the cellular components of different tissues function and control disease progression. His group explores the roles of specific cell populations in the tissue microenvironment by using state-of-the-art techniques. His research is funded by the Serrapilheira Institute, CNPq, CAPES, and FAPEMIG. In 2018, Alexander was elected affiliate member of the Brazilian Academy of Sciences (ABC), and in 2019, he was elected member of the Global Young Academy (GYA). He is the Founding Editor and Editor-in-Chief of Current Tissue Microenvironment Reports and Associate Editor of Molecular Biotechnology. Alexander also serves in the editorial board of several other international journals: Stem Cell Reviews and Reports, Stem Cell Research, Stem Cells and Development, and Histology and Histopathology.

Preface

This book's initial title was iPSCs: Recent Advances. Nevertheless, because of the ongoing strong interest in this theme, we were capable to collect more chapters than would fit in one single volume, covering induced pluripotent stem cells (iPSCs) biology from different perspectives. Therefore, the book was subdivided into several volumes.

This volume Recent Advances in iPSC Disease Modeling offers contributions by known scientists and clinicians in the multidisciplinary areas of biological and medical research. The chapters bring up-to-date comprehensive overviews of current advances in the field. This book describes the use of iPSCs to model several diseases in vitro, enabling us to study the cellular and molecular mechanisms involved in different pathologies. Further insights into these mechanisms will have important implications for our understanding of disease appearance, development, and progression. The authors focus on the modern state-of-art methodologies and the leading-edge concepts in the field of stem cell biology. In recent years, remarkable progress has been made in the obtention of iPSCs and their differentiation into several cell types, tissues, and organs using state-of-art techniques. These advantages facilitated identification of key targets and definition of the molecular basis of several disorders. Thus, this book is an attempt to describe the most recent developments in the area of iPSCs biology, which is one of the rising hot topics in the field of molecular and cellular biology today. Here, we present a selected collection of detailed chapters on what we know so far about the use of iPSCs for modeling multiple diseases. Thirteen chapters written by experts in the field summarize the present knowledge about iPSC disease modeling.

Albert A. Rizvanov and colleagues from Kazan Federal University discuss iPSCs for modeling lysosomal storage diseases. George P. Murphy and colleagues from Boston University School of Medicine describe iPSC modeling of sickle cell anemia. Shalem R Modi and Riikka H Hämäläinen from the University of Eastern Finland compile our understanding of iPSCs for modeling mtDNA diseases. Jingping Ge from Harvard University updates us with what we know about iPSCs for modeling Diamond Blackfan anemia. Patricia A. Olofsen and Ivo P. Touw from Erasmus MC summarize current knowledge on modeling severe congenital neutropenia with iPSCs. Maria Csobonyeiová from Comenius University addresses the importance of iPSCs for modeling Duchenne muscular dystrophy. Tao Wang and colleagues from The University of Manchester talk about the iPSC modeling of genetic small vessel disease. Tayma Handal and Rachel Eiges from The Hebrew University School of Medicine focus on the contribution of human iPSCs to the study of myotonic dystrophy type 1. Ponlapat Rojnuckarin from Chulalongkorn University and King Chulalongkorn Memorial Hospital gives an overview of the iPSCs for modeling of Bernard–Soulier syndrome. Anne Weber and colleagues from Université Paris-Sud present the iPSCs for modeling familial hypercholesterolemia type II A. Jiesi Luo from Yale School of Medicine introduce what we know so far about iPSCs for modeling elastin-associated vasculopathy. Mareike S. Poetsch and Kaomei Guan from Dresden University of Technology discuss iPSCs for modeling of sarcomeric cardiomyopathies. Finally, Hoi-Hung Cheung and Owen M. Rennert from the National Institutes of Health update us the use of iPSCs to model pathologic aging.

It is hoped that the articles published in this book will become a source of reference and inspiration for future research ideas. I would like to express my deep gratitude to my wife Veranika Ushakova, and Ms. Billie Jean Fernandez and Ms. Elisabeth Brown from Elsevier, who helped at every step of the execution of this project.

Alexander Birbrair

Editor

Chapter 1: iPSCs for modeling lysosomal storage diseases

Daria S. Chulpanova ¹ , Alisa A. Shaimardanova ¹ , Valeriya V. Solovyeva ¹ , Aysilu I. Mullagulova ¹ , Kristina V. Kitaeva ¹ , Cinzia Allegrucci ² , and Albert A. Rizvanov ¹       ¹ Kazan Federal University, Kazan, Russia      ² University of Nottingham, Nottingham, United Kingdom

Abstract

Lysosomal storage diseases (LSDs) represent a heterogeneous group of inherited diseases caused by mutations in the genes coding for proteins involved in the degradation and transfer of lipids and other macromolecules. Current LSD animal models and fibroblasts isolated from LSD patients have made it possible to evaluate the course of the disease, including before onset of symptoms. However, animal models cannot fully reflect the molecular mechanisms of pathogenesis of human LSDs. The generation of LSD models from iPSCs can allow the investigation of disease pathogenesis in different types of LSD affected cells, as well as the development of new therapeutic strategies for these disorders. This chapter discusses current results of iPSC-based modeling of various LSDs, methods for the generation of iPSCs, and the application of iPSC-differentiated cells and organoids for the investigation of the disease pathogenesis and testing of therapeutic compounds.

Keywords

Cystinosis; Danon disease; Fabry disease; Gaucher disease; GM1 gangliosidosis; Induced pluripotent stem cells; Lysosomal acid lipase deficiency; Lysosomal storage diseases; Metachromatic leukodystrophy; Mucopolysaccharidosis; Neuronal ceroid lipofuscinoses; Niemann–Pick disease; Pompe disease; Sandhoff disease; Tay–Sachs disease

Acknowledgment

Introduction

Lipid storage disorders

GM2 gangliosides

Other gangliosidoses

Sphingolipidosis

Neuronal ceroid lipofuscinoses

Other lipid storage disorders

Mucopolysaccharidosis

Lysosomal transport diseases

Cystinosis

Glycogenosis type II

Pompe disease

Danon disease

Conclusion and future perspectives

References

Introduction

Lysosomal storage diseases (LSD) are a group of approximately 50 genetic disorders caused by mutations in the enzyme genes that are involved in cell degradation and transfer of lipids and other macromolecules (Parenti et al., 2015) (Table 1.1). The aberrant accumulation of lipids and other macromolecules in lysosomes leads to the destruction of affected cells. Although the clinical manifestation of different LSDs vary widely, more than half of the LSDs exhibit symptoms of central nervous system (CNS) degeneration (Schultz et al., 2011).

Early onset and small number of patients make clinical trials difficult to evaluate the effectiveness of various LSD treatments. The use of LSD animal models can help to better understand the natural course of the disease, as well as to determine the pharmacokinetics and pharmacodynamics of the tested drugs. However, often animal models have low clinical relevance, ultimately leading to the evidence that drugs that have been shown to be effective in animal models fail clinical trials (Xu et al., 2016). Therefore, there is need for novel clinically relevant models to advance translational research for LSDs. The use of induced pluripotent stem cells (iPSCs) as in vitro LSD model can allow a detailed investigation of the mechanisms involved in LSD pathogenesis, finding new biomarkers of the diseases and creating a new and more effective platform for primary drug screening (Huang et al., 2012). Lentiviral vectors (LVs) encoding OCT4, SOX2, KLF4, and c-MYC genes are most often used to create iPSCs (Panicker et al., 2012; Sun et al., 2015). The production of iPSCs using episomal plasmids encoding human OCT4, KLF4, L-MYC, SOX2, and LIN28 genes are also described (Nagel et al., 2019; Duarte et al., 2019). The most recent technique using Sendai virus vectors encoding four human transcription factors OCT3/4, SOX2, KLF4, and с-MYC is currently being adopted (Tofoli et al., 2019).

This chapter discusses current evidence of iPSC-based modeling of various LSDs, sources and methods for iPSC differentiation from patient cells, and their application in the investigation of the disease pathogenesis, as well as in drug screening for LSD therapy.

Lipid storage disorders

GM2 gangliosides

Tay–Sachs disease

Tay–Sachs disease (TSD) is an autosomal recessive inherited disease caused by the mutations in HEXA gene, which encodes the α subunit of β-hexosaminidase A (HexA). HexA deficiency leads to the accumulation of GM2 ganglioside predominantly in the cells of the nervous system, which results in severe neurodegeneration in patients (Solovyeva et al., 2018). Since the accumulation of GM2 ganglioside occurs mainly in nerve cells, one recurrent problem remains to obtain model lines of nerve cells to investigate the disease mechanisms, as well as to develop new approaches to TSD therapy.

Table 1.1

For this, fibroblasts from TSD patients have been reprogrammed into iPSCs in various ways, for example, using transduction with LV STEMCCA encoding OCT4, SOX2, KLF4, and c-MYC (Liu and Zhao, 2016) or with nonintegrating Sendai virus, which encodes KLF4, OCT3/4, and SOX2. The obtained iPSCs have been differentiated into neural stem cells (NSCs) or neural precursor cells (NPCs). iPSC-derived NSCs and iPSC-derived NPCs have exhibited a characteristic TSD phenotype, namely they have low HexA activity, accumulate lipids (mainly GM2 gangliosides), have increased lysosomes, and overexpress lysosomal marker LAMP-1. The resulting iPSC-derived NSC model has been used to evaluate the effect of enzyme replacement therapy (ERT). It has been shown that after combined treatment with recombinant HexA, hydroxypropyl-β-cyclodextrin, and δ-tocopherol, the lysosomal lipid accumulation in the model of iPSC-derived NSCs was reduced to the healthy control level (Matsushita et al., 2019; Vu et al., 2018).

iPSC-derived NPCs have been differentiated into mature neurons, and their ability to form synapses in vitro has been analyzed. It has been shown that cells obtained from patients and healthy donors showed the same ability to form synapses; however, a decrease in exocytotic activity in TSD-iPSC-derived neurons has been revealed. Synaptic exocytosis is an important link in signal transmission; its disruption affects the overall activity of the neural network. It has also been shown that oxidative stress enhances the death of TSD-NPCs (Matsushita et al., 2019).

Skin fibroblasts were previously used as a cellular model of TSD; however, they do not show disease phenotype, as well as HEXA-deficient mouse models that have normal lifetime and lack of TDS clinical manifestations. Large animal models of TSD, for example, Jacob's sheep, are difficult to manage and conduct studies (Solovyeva et al., 2018). Thus, the creation of a model reflecting both biochemical and morphological properties of TSD is relevant. The resulting models based on iPSC-derived NSCs and NPCs provide a valuable platform for studying pathogenesis and developing therapeutic approaches for TSD.

Sandhoff disease

Sandhoff disease (SD) is caused by a deficiency of HexA enzyme due to a mutation in the β subunit of this enzyme gene (HEXB gene), resulting in aberrant lysosomal accumulation of GM2 ganglioside mainly in neurons (Bley et al., 2011). The first iPSC-based SD models have been obtained from SD model mice. The resulting iPSCs have been differentiated into NSCs that had reduced HexA activity and significant accumulation of GM2 ganglioside. It has been shown that SD-NSCs were able to differentiate into neurons, but did it significantly worse than wild-type NSCs. HEXB gene recovery in SD-iPSCs has improved neuronal differentiation (Ogawa et al., 2013). At the same time, the differentiation of SD-NSCs into astrocytes has been significantly increased (Ogawa et al., 2017).

iPSCs derived from human fibroblasts with SD have been used to create cerebral organoids that were used to model the development of the human brain in SD. The organoids have been shown GM2 ganglioside accumulation beginning at 4 weeks. The proliferation of SD organoids has been abnormally elevated, and neuronal differentiation has been impaired (Allende et al., 2018). The use of 3D model of cerebral organoids with SD provides new tools for studying the early effects of the GM2 ganglioside accumulation during human brain development.

Other gangliosidoses

GM1 gangliosidosis

GM1 gangliosidosis is caused by impaired activity of the β-galactosidase (β-gal) lysosomal enzyme due to mutations in the GLB1 gene (Brunetti-Pierri and Scaglia, 2008). The main substrate hydrolyzed by β-gal is the lysosomal sphingolipid GM1 ganglioside, which is mainly found in the brain (Yu et al., 2012).

To study the molecular mechanisms of the pathogenesis of this disease, iPSCs have been obtained from fibroblasts of patients with GM1 gangliosidosis, and then differentiated into NPCs. The resulting NPCs had many morphological features associated with GM1 gangliosidosis, including insufficient β-gal activity and abnormal GM1 ganglioside accumulation. In addition, an unfolded protein response activation, which was previously proposed as a pathological hallmark of GM1 gangliosidosis, was detected in GM1-NPCs. This model revealed that inflammation inhibitors reduce molecular defects in GM1-NPCs in vitro and the level of neuroinflammation stimulated by GM1-NPCs in vivo (Son et al., 2015).

The molecular mechanisms underlying the pathogenesis of human gangliosidosis GM1 remain unclear. Although the mouse model closely imitates many characteristics of human GM1 gangliosidosis, the pathogenesis of GM1 in mice is different from that in human. Therefore, the production of NPCs from iPSCs differentiated from GM1 gangliosidosis patients can allow the investigation of the molecular mechanisms of pathogenesis and serve as a reliable model for the analysis of potential therapeutic compounds.

Sphingolipidosis

Niemann–Pick disease

Niemann–Pick type C1 (NPC1) disease is caused by a mutation in the NPC1 gene encoding the intracellular membrane glycoprotein, which leads to the accumulation of cholesterol in late endosomes and lysosomes (Ordonez and Steele, 2017). The disruption of glycoprotein transport ultimately leads to the massive degeneration and loss of CNS neurons (Sturley et al., 2004). Existing animal models cannot accurately reflect the pathogenesis of the disease; therefore, the generation of iPSCs from patient cells can effectively expand the understanding of the pathological mechanism leading to massive degeneration of neurons.

Most often, NPC1 models have been iPSCs obtained from patient fibroblasts, which then have been differentiated into NPCs (Trilck et al., 2016; Peter et al., 2017) or into hepatocytes, which are also affected in NPC1 (Maetzel et al., 2014). The resulting cells accumulate an abnormal amount of cholesterol, the main sign of NPC1, and reflect the pathological mechanisms of the disease manifested in humans (Trilck et al., 2013).

The generation of such models made it possible to clarify the mechanisms leading to the abnormal cholesterol accumulation. The sequestration of cholesterol in late endosomal/lysosomal compartments led to a disruption in the synthesis of cholesterol ester in hepatocyte-like cells with NPC1 deficiency. Also this model of NPC1 disease has shown that autophagosome accumulation occurs due to a block in autophagic flux. This was associated with an increased level of autophagic flux regulating proteins LC3-II and p62, the level of which have been increased in NPC1 hepatocyte-like cells (Maetzel et al., 2014). Neurons derived from NPC1-derived fibroblasts had abnormalities in WNT signaling as well as increased expression of genes that regulate calcium signaling (Efthymiou et al., 2015). An increase in the expression of the GluA2 gene and protein, which inhibit the entry of calcium ions into the cell, leads to a decrease in the entry of Ca2+ into neurons differentiated from NPC1-derived iPSCs (Rabenstein et al., 2017).

Along with studying the mechanism of the NPC1 pathogenesis, iPSC-derived neurons have also been used to evaluate the effectiveness of compounds that reduce cholesterol accumulation (Yu et al., 2014). Genetic correction of NPC1I1061T mutation in renal and neuronal cells from NPC1-iPSCs resulted in the normalization of cholesterol distribution in the modified cells (Maetzel et al., 2014). The effectiveness of a number of drugs has also been shown in a model of hepatocyte-like cells, where treatment can reduce cholesterol accumulation and restore functional and molecular abnormalities in the cells obtained from NPC1 patients (Soga et al., 2015).

A limited number of iPSC models have been developed for other types of Niemann–Pick disease, although this area has been actively developing in the last few years. The production of iPSCs from the fibroblasts of patient with NPC2, which caused by the defects in the NPC2 gene, has been reported (Volkner et al., 2019). Niemann–Pick type A (NPA) disease is caused by mutations in the SMPD1 gene encoding acid sphingomyelinase, which leads to the accumulation of sphingomyelin (SM) in the lysosomes of affected cells (Ledesma et al., 2011). Fibroblasts isolated from NPA patients have been differentiated into iPSCs and then differentiated into NPCs, which have showed a typical phenotype of NPA disease, accumulation of SM, and increased lysosomes. This model has shown the effectiveness of using δ-tocopherol and α-tocopherol to reduce SM accumulation (Long et al., 2016). The production of iPSCs from fibroblasts of patients with NPA or Niemann–Pick disease type B, also caused by the mutations in SMPD1 gene, using integration-free CytoTune-Sendai viral vector kit containing OCT3/4, KLF4, SOX2, and c-MYC pluripotency transcription factors has also been reported (Baskfield et al., 2019a, 2019b).

Another approach to create NPCs by direct conversion, with NPC1 patient fibroblasts directly differentiated into induced NPCs (iNPCs) using only SOX2 and HMGA2, is being developed. Such iNPCs have been able to differentiate into different types of neurons, but the ability to self-renew and to form neurospheres was significantly reduced compared to wild-type iNPCs probably due to the pathogenesis of the NPC1 disease (Sung et al., 2017).

Thus, iPSC-differentiated NPCs can more accurately reflect the pathogenesis of the NP disease at the cellular level than animal models, and they are also a better model than patient fibroblasts, since they demonstrate greater reliability in drug screening, showing efficacy in lower doses (Xu et al., 2016). However, iPSC differentiation from patient fibroblasts may be impaired due to abnormal cholesterol accumulation, as treatment with drugs that reduce cholesterol accumulation can increase the efficiency of iPSC production (Yu et al., 2014). Also, the development of new protocols of direct differentiation, avoiding iPSC generation, can simplify the production of NPCs and the development of models for drug testing.

Metachromatic leukodystrophy

Metachromatic leukodystrophy (MLD) is an autosomal recessive inherited disorder resulting from mutations in the ARSA gene, which lead to arylsulfatase A deficiency (ARSA). ARSA is a specific enzyme that catalyzes 3-O-sulfogalactosylceramide (sulfatide). The lack of ARSA leads to the accumulation of sulfatide, which deranges functioning of nervous system cells and leads to progressive demyelination (Frati et al., 2018).

Creating an appropriate cell model for MLD is a rather difficult task, since the main problem that arises in MLD is demyelination. Since the process of axonal myelination requires the interaction with Schwann cells (in the case of peripheral nervous system) and oligodendrocytes (in the case of CNS) (Salzer, 2015). Therefore, to obtain a complete picture of MLD pathogenesis, it is necessary to create a coculture consisting of neurons and glia cells (Schwann cells and oligodendrocytes).

To obtain an MLD cell model, patient skin fibroblasts have been reprogrammed by lentiviral transduction using the vector encoding OCT4, SOX2, and KLF4 genes. The resulting iPSCs (MLD-iPSCs) have been differentiated into NPCs. After that, the efficiency of transplantation of iPSC-derived NPCs genetically modified to overexpress ARSA in MLD model mice has been evaluated. The obtained cells have high therapeutic potential, since NPCs overexpressing ARSA can provide both enzymatic restoration and replacement of damaged cells (Meneghini et al., 2017).

Later, the same iPSC-based MLD model has been described in detail. A marked lysosome expansion, an increased LAMP1 expression, abnormal Golgi complex (GC) structure, and increased susceptibility to oxidative stress and apoptosis of MLD-iPSCs have been shown. The effect of ARSA deficiency on the differentiation of MLD-iPSC–derived NPCs has also been investigated. It has been shown that the level of sulfatide accumulation and its composition changed during the differentiation of NPCs into mature neurons, astrocytes and ,oligodendrocytes. The ability to neuronal and glial differentiation of MLD-NPCs was significantly lower compared to wild-type NPCs, a decrease in the level of oligodendroglial and astroglial markers, as well as a decrease in the number of neurons have been observed. However, ARSA gene delivery has significantly improved the biochemical and morphological characteristics of MLD-NPCs, which come near those of control NPCs (Frati et al., 2018). The obtained iPSC-based model allows a full investigation of the pathological processes in the MLD-affected cells of CNS. However, there have not been large-scale studies aimed at elucidating the molecular mechanisms of MLD, so further studies are required.

Gaucher disease

Gaucher disease (GD) is the most common LSD and caused by defects in the acid β-glucocerebrosidase (GCase) gene, which lead to disruption of sphingolipid cleavage and the accumulation of glucosylceramide (GlcCer) in lysosomes of various cells throughout the body (Jmoudiak and Futerman, 2005). Neurons and macrophages are most affected, with macrophages forming typical lipid cells (Gaucher cells), which