Childhood-Onset Genetic White Matter Disorders of the Brain in Northern Finland

D 1615 OULU 2021 D 1615

UNIVERSITY OF OULU P.O. Box 8000 FI-90014 UNIVERSITY OF OULU FINLAND ACTA UNIVERSITATIS OULUENSIS ACTA UNIVERSITATIS OULUENSIS ACTA

DMEDICA Oula Knuutinen Oula Knuutinen University Lecturer Tuomo Glumoff CHILDHOOD-ONSET University Lecturer Santeri Palviainen GENETIC WHITE MATTER

Postdoctoral researcher Jani Peräntie DISORDERS OF THE BRAIN IN NORTHERN FINLAND University Lecturer Anne Tuomisto

University Lecturer Veli-Matti Ulvinen

Planning Director Pertti Tikkanen

Professor Jari Juga

Associate Professor (tenure) Anu Soikkeli

University Lecturer Santeri Palviainen UNIVERSITY OF OULU GRADUATE SCHOOL; UNIVERSITY OF OULU, FACULTY OF MEDICINE; Publications Editor Kirsti Nurkkala MEDICAL RESEARCH CENTER OULU; OULU UNIVERSITY HOSPITAL ISBN 978-952-62-2933-1 (Paperback) ISBN 978-952-62-2934-8 (PDF) ISSN 0355-3221 (Print) ISSN 1796-2234 (Online)

ACTA UNIVERSITATIS OULUENSIS D Medica 1615

OULA KNUUTINEN

CHILDHOOD-ONSET GENETIC WHITE MATTER DISORDERS OF THE BRAIN IN NORTHERN FINLAND

Academic dissertation to be presented with the assent of the Doctoral Training Committee of Health and Biosciences of the University of Oulu for public defence in Auditorium 12 of Oulu University Hospital (Kajaanintie 50), on 21 May 2021, at 12 noon

Supervised by Professor Johanna Uusimaa Docent Päivi Vieira Docent Maria Suo-Palosaari

Reviewed by Docent Maija Castrén Doctor Hannele Koillinen

Opponent Docent Tarja Linnankivi

ISBN 978-952-62-2933-1 (Paperback) ISBN 978-952-62-2934-8 (PDF)

ISSN 0355-3221 (Printed) ISSN 1796-2234 (Online)

Cover Design Raimo Ahonen

PUNAMUSTA TAMPERE 2021 Knuutinen, Oula, Childhood-onset genetic white matter disorders of the brain in Northern Finland. University of Oulu Graduate School; University of Oulu, Faculty of Medicine; Medical Research Center Oulu; Oulu University Hospital Acta Univ. Oul. D 1615, 2021 University of Oulu, P.O. Box 8000, FI-90014 University of Oulu, Finland

Abstract Genetic white matter disorders (GWMD), leukodystrophies and genetic leukoencephalopathies, are a major cause of neurodevelopmental disorders. Over a hundred related genes are recognised. The recent implementation of next-generation sequencing has facilitated the frequent characterisation of novel disorders seen today. This study was intended to evaluate the current epidemiology, genetic aetiologies, clinical phenotypes, and natural history of childhood-onset GWMDs. Additional aims were to characterise novel and rare phenotypes and to examine the effect of Finnish disease heritage on the distribution of specific disorders. This population-based cohort study consisted of 80 children diagnosed with a genetically defined white matter disorder in Northern Finland between 1990 and 2019. The cumulative childhood incidence was 30/100,000 live births, and it was higher in the later study period. In total, 49 distinct disorders were identified, of which 20% were leukodystrophies and 80% were genetic leukoencephalopathies. Mitochondrial aetiology was noted in 21% of the cases. Disorders belonging to the Finnish disease heritage constituted 10% of the cases. Motor developmental delay (79%), intellectual disability (56%), hypotonia (60%), and spasticity (49%) were the most frequent clinical findings. The study identified 20 disorders that were either recently characterised or not previously associated with white matter abnormalities. These included a novel TAF1C related phenotype. Additionally, a rare neonatal Alexander disease with a novel GFAP variant was described, consolidating the recently proposed neonatal phenotype. These findings provide comprehensive data on the current epidemiology and clinical features of GWMDs, benefitting future clinical trials. In addition to enabling genetic counselling, genetic phenotype data guide clinicians and researchers studying the pathomechanisms of GWMDs.

Keywords: brain MRI, children, genetics, intellectual disability, leukodystrophy, white matter

Knuutinen, Oula, Lapsuusiän geneettiset valkean aivoaineen sairaudet Pohjois- Suomessa. Oulun yliopiston tutkijakoulu; Oulun yliopisto, Lääketieteellinen tiedekunta; Medical Research Center Oulu; Oulun yliopistollinen sairaala Acta Univ. Oul. D 1615, 2021 Oulun yliopisto, PL 8000, 90014 Oulun yliopisto

Tiivistelmä Geneettiset valkean aivoaineen sairaudet, leukodystrofiat ja geneettiset leukoenkefalopatiat, ovat merkittäviä neurologisten kehityshäiriöiden aiheuttajia. Taudinaiheuttajageenejä tunnetaan yli sata, ja eksomi- ja genomisekvensoinnin yleistyminen on mahdollistanut uusien tautien löytämi- sen aiempaa tehokkaammin. Tämän tutkimuksen tavoitteena oli selvittää lapsuusiän geneettisten valkean aivoaineen sairauksien epidemiologiaa, geneettisiä etiologioita, kliinisiä ilmiasuja ja tautien luonnollista kulkua. Lisäksi tavoitteena oli löytää uusia ja harvinaisia fenotyyppejä sekä tarkastella suomalaisen tau- tiperinnön vaikutusta tautikirjoon. Tämän väestöpohjaisen kohortti-tutkimuksen aineistona oli 80 Pohjois-Suomessa asunutta lasta, joilla diagnosoitiin geneettinen valkean aivoaineen sairaus vuosina 1990–2019. Tautien kumulatiivinen ilmaantuvuus lapsuudessa oli 30/100 000 elävänä syntynyttä lasta, ja insidenssi oli suurempi myöhempinä vuosikymmeninä. Löydetyistä 49 taudista 20 % oli leukodystrofioita ja 80 % geneettisiä enkefalopatioita. Mitokondriaalinen tausta liittyi 21 %:iin tapauksista. Kym- menellä prosentilla oli suomalaiseen tautiperintöön kuuluva sairaus. Yleisimpiä kliinisiä löydök- siä olivat motoriikan kehityshäiriöt (49 %), älyllinen kehitysvammaisuus (56 %), heikko lihas- jäntevyys (60 %) ja spastisuus (49 %). Aineistoon kuului 20 viime vuosina löydettyä sairautta tai sairautta, joita ei ole aiemmin pidetty valkean aivoaineen sairauksina. Yksi näistä oli aiemmin kuvaamaton TAF1C-geeniin liittyvä neurologinen sairaus. Toisessa osatyössä kuvattiin uusi GFAP-geenin muutos ja harvinainen Alexanderin taudin vastasyntyneisyyskauden tautimuoto, mikä tukee tämän olemassaoloa muista ikäkausista erillisenä tautimuotona. Tutkimustulokset muodostavat kokonaisvaltaisen kuvauksen geneettisten valkean aivoaineen sairauksien epidemiologiasta ja kliinisistä piirteistä. Löydökset auttavat tulevaisuudessa kliinisten tutkimusten suunnittelua ja perinnöllisyysneuvontaa sekä ohjaavat tautien parissa työs- kenteleviä kliinikkoja ja tutkijoita.

Asiasanat: aivojen magneettikuvaus, lapset, leukodystrofia, perinnölliset sairaudet, valkea aivoaine, älyllinen kehitysvammaisuus

Ostinato rigore

RIGORE

8 Acknowledgements

This work was carried out in the Clinic for Children and Adolescents, Oulu University Hospital, and in the PEDEGO Research Unit at the University of Oulu during 2015–2021. The work was funded by the Arvo and Lea Ylppö Foundation; Stiftelsen Alma och K. A. Snellman Säätiö; Oulun Lääketieteellinen Tutkimussäätiö; Academy of Finland; the Finnish Medical Foundation; the Pediatric Research Foundation; Special State Grants for Health Research in the Clinic of Paediatrics and Adolescents, Oulu University Hospital, Finland; the University of Oulu Graduate School; Folkhälsan Research Foundation; the Newton Fund; the Medical Research Council; the Wellcome Investigator Award; the Lily Foundation UK; and the Evelyn Trust. First, I would like to thank my supervisors, Professor Johanna Uusimaa, MD, PhD; Adjunct Professor Päivi Vieira, MD, PhD; and Adjunct Professor Maria Suo- Palosaari, MD, PhD. Johanna welcomed me into her research group as a buoyant medical student interested in the brain. Her unrelenting vigour, enthusiasm, and optimism broke through several barriers and smoothed the path during the project. This project would truly never have come into being without her. Päivi exemplified a constant thoroughness in science and medicine that I wish to imitate, and she taught me much of scientific writing. Maria’s clinical work in the radiology of genetic white matter disorders truly embodies da Vinci’s obstinate rigor, turning every stone, searching every nook. She supported me in adversity, and without her kind words I might have cut myself off this project and moved to other work. I wish to thank Reetta Hinttala, MSc, PhD, who warmly welcomed me to the research group and made arrangements for me to learn the basics of laboratory work (something I never really employed, being drawn into clinical work). For her patience in this endeavour, I want to thank our lovely laboratory technician, Pirjo Keränen. I am truly grateful to Salla Kangas, MSc, PhD, for patiently teaching me about genetic databases, among other things. I thank all the rest of the paediatric neurology research group members. I also owe special thanks to Dr. Jaakko Oikarainen, MD, who not only reviewed countless brain MRI scans but also discussed research problems as a fellow PhD student. I am grateful for the effortless collaboration between numerous co-authors in Oulu, Helsinki, the United Kingdom, and Jordan. Professor Anna-Elina Lehesjoki, MD, PhD, Professor Rita Horvath, MD, PhD, and Dr. Angela Pyle, PhD, deserve special appreciation for making the collaboration across several organisations possible. I thank Associate Professor Tawfiq Froukh, PhD, and Dr. Latifa Marawa, MD, for their collaboration and

9 contribution in characterising the TAF1C related disorder. Adjunct Professor Jukka Moilanen introduced me to the in silico prediction studies that were later applied in all studies in this thesis. Dr. Elisa Rahikkala, MD, PhD, greatly helped in the collection of cohort genetic data. I thank Professor Vesa Kiviniemi, MD, PhD, and Dr. Heli Helander, MD, PhD, for our follow-up group meetings and interesting conversations that sparked enthusiasm for future research work. I am grateful for my colleagues in the Clinic of Neurosurgery and the tight-knit group that has taught me many things about life, death, and neurosurgery. Special thanks go to Tommi Korhonen, Jenni Määttä, Master Cheng Qian, and paediatric neurosurgeons, Susanna Piironen, Maija Lahtinen, Niina Salokorpi, and Willy Serlo, for their continuous support and inspiration. I also want to thank my colleagues at the Kiiminki health centre and all my patients for entrusting their medical care to my hands. I warmly thank Adjunct Professor Maija Castrén, MD, PhD, and Dr. Hannele Koillinen, MD, PhD, for their constructive criticism, which substantially improved this thesis. I am truly honoured to have Adjunct Professor Tarja Linnankivi, MD, PhD, as my opponent. After meeting her in the Nordic White Matter Diseases Network meeting in Copenhagen, Denmark, I have wished to defend my thesis under the scrutiny of her expertise and experience. My sincerest gratitude falls to all my friends scattered across Finland, too many and important to name here. A special mention, however, goes to my research- oriented fellows, Esa Kari, MD, PhD, and Emily Pan, MD, PhD, for showing what true motivation, effort, and innate skill can accomplish. Lastly, the humble gratitude I feel for my family is beyond words. I thank my late mother for the fire burning within me. I miss you dearly.

Oulu, March 2021 Oula Antti Juhani Knuutinen

10 Abbreviations

ALD adrenoleukodystrophy AxD Alexander disease CADASIL autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy CNS central nervous system COXPD7 combined oxidative phosphorylation deficiency 7 CPS I carbamoyl phosphate synthetase I CRIPSR clustered regularly interspaced short palindromic repeats CRMCC1 cerebroretinal microangiopathy with calcifications and cysts 1 EEG electroencephalography EIEE early infantile epileptic encephalopathy FINCA fibrosis, neurodegeneration, and cerebral angiomatosis FLAIR fluid-attenuated inversion recovery GFAP glial fibrillary acidic protein gLE genetic leukoencephalopathy GWMD genetic white matter disorder HDLS hereditary diffuse leukoencephalopathy with spheroids HMGCLD 3-hydroxy-3-methylglutaryl-CoA lyase deficiency HSCT haematopoietic stem cell transplantation ICD International Statistical Classification of Diseases IOSCA infantile-onset spinocerebellar ataxia LBSL leukoencephalopathy with brain stem and spinal cord involvement and lactate elevation LKENP progressive leukoencephalopathy with ovarian failure LTBL leukoencephalopathy with thalamus and brainstem abnormalities and lactate elevation MBP myelin basic protein MDDGA3 congenital muscular dystrophy-dystroglycanopathy type A3 MELAS mitochondrial myopathy, encephalopathy, lactic acidosis, and stroke-like episodes MLD metachromatic leukodystrophy MICPCH mental retardation and microcephaly with pontine and cerebellar hypoplasia MIM Mendelian Inheritance in Man

11 MIRAGE myelodysplasia, infection, restriction of growth, adrenal hypoplasia, genital phenotypes, and enteropathy MIRAS mitochondrial recessive ataxia syndrome MRD13 mental retardation, autosomal dominant 13 MRI magnetic resonance imaging mtDNA mitochondrial deoxyribonucleic acid MTDPS mitochondrial DNA depletion syndrome NBIA5 neurodegeneration with brain iron accumulation 5 NCL neuronal ceroid lipofuscinosis NECFM neurodevelopmental disorder with epilepsy, cataracts, feeding difficulties, and delayed brain myelination NGS next-generation sequencing PEHO progressive encephalopathy with oedema, hypsarrhythmia, and optic atrophy PLP proteolipid protein PMD Pelizaeus-Merzbacher disease Pol I RNA polymerase I qPCR quantitative real-time PCR SND Standardized Normal Deviate SPG4 autosomal dominant spastic paraplegia 4 SWI susceptibility-weighted imaging RUSP Recommended Uniform Screening Panel TAF1C TATA-box binding protein associated factor, RNA polymerase I subunit C T1W T1-weighted T2W T2-weighted UBF Upstream binding factor UK United Kingdom USA United States of America VAIHS vasculitis, autoinflammation, immunodeficiency, and hematologic defects syndrome WHO World Health Organization WMD white matter disease

12 Original publications

This thesis is based on the following publications, which are referred to throughout the text by their Roman numerals:

I Knuutinen, O., Oikarainen, J., Suo-Palosaari, M., Kangas, S., Rahikkala, E., Pokka, T., Moilanen, J., Hinttala, R., Vieira, P., Uusimaa, J. (2021). Epidemiology, clinical, and genetic characteristics of paediatric genetic white matter disorders in Northern Finland. Developmental Medicine & Child Neurology. In press. II Knuutinen, O.*, Pyle, A.*, Suo-Palosaari, M.**, Duff, J.**, Froukh, T., Lehesjoki, A.- E., Kangas, S., Cassidy, J., Maraqa, L., Keski-Filppula, R., Kokkonen, H., Uusimaa, J., Horvath, R.***, Vieira, P.*** (2020). Homozygous TAF1C variants are associated with a novel childhood-onset neurological phenotype. Clinical Genetics, 98, 493–498. III Knuutinen, O.*, Kousi, M.*, Suo-Palosaari, M., Moilanen, J. S., Tuominen, H., Vainionpää, L., Joensuu, T., Anttonen, A.-K., Uusimaa, J., Lehesjoki, A..-E.*, Vieira, P*. (2018). Neonatal Alexander Disease: Novel GFAP Mutation and Comparison to Previously Published Cases. Neuropediatrics, 49, 256-261.

*Shared first authorship

**Shared second authorship

***Shared senior authorship

14 Contents

Abstract Tiivistelmä Acknowledgements 9 Abbreviations 11 Original publications 13 Contents 15 1 Introduction 17 2 Review of literature 19 2.1 Definition of genetic white matter disorders ...... 19 2.1.1 Classification of genetic white matter disorders ...... 19 2.2 White matter of the brain, myelin, and glial cells ...... 20 2.2.1 Pathophysiology of Alexander disease ...... 24 2.3 Epidemiology ...... 24 2.3.1 Occurrence ...... 24 2.3.2 Previous epidemiological studies ...... 25 2.4 Clinical features ...... 26 2.4.1 Natural history ...... 27 2.5 Overview of diagnostics ...... 27 2.6 Neuroradiology and MRI pattern recognition ...... 29 2.7 Genetics ...... 32 2.8 Treatment and disease screening ...... 33 2.8.1 Future aspects ...... 34 3 Aims of the study 37 4 Materials and methods 39 4.1 Patients ...... 39 4.2 Collection of clinical data ...... 40 4.3 Neuroradiological assessment ...... 41 4.4 Molecular genetic studies ...... 42 4.5 Statistical analysis ...... 43 4.6 Ethical considerations ...... 43 5 Results 45 5.1 Epidemiology (I) ...... 45 5.2 Clinical characteristics and natural history (I–III) ...... 45 5.2.1 Comparison between classic leukodystrophies and genetic leukoencephalopathies ...... 45

15 5.3 Genotypes and phenotypes of genetic white matter disorders (I– III) ...... 49 5.3.1 Recently discovered disorders and disorders not previously associated with white matter abnormalities (I and II) ...... 51 5.3.2 Novel TAF1C related disorder (II) ...... 51 5.3.3 Neonatal Alexander disease (III) ...... 56 6 Discussion 59 6.1 Epidemiology ...... 59 6.2 Clinical characteristics and natural history (I–III) ...... 59 6.3 Genotypes and phenotypes of genetic white matter disorders ...... 60 6.3.1 Novel and uncommon genetic white matter disorders ...... 61 6.4 Strengths and limitations of the study ...... 63 6.5 Prospects for future research ...... 64 7 Conclusions 67 References 69 Original publications 85

16 1 Introduction

The white matter of the brain is a lipid-rich tissue containing nerve fibres surrounded by the myelin sheath. Genetic white matter disorders (GWMDs) are a diverse disease group affecting the central nervous system (CSN) white matter. Over one hundred related genes have been recognised as affecting various cellular processes like amino nucleic acid metabolism, mitochondrial function, the mitochondrial electron transport chain, and organelles including lysosomes and peroxisomes (van der Knaap, Schiffmann, Mochel, & Wolf, 2019). Similar to genetic background, the clinical picture of GWMDs is highly variable with frequent progressive course, developmental abnormalities, and motor findings (Parikh et al., 2015). Childhood onset is common, but phenotypes presenting later in adulthood are also known (Köhler, Curiel, & Vanderver, 2018). Brain magnetic resonance imaging (MRI) and genetic studies are cornerstones of the modern diagnostics. Estimates of childhood onset GWMD occurrence vary from 1.1 to 13 per 100,000 live births (Bonkowsky et al., 2010; Vanderver, Hussey, Schmidt, Pastor, & Hoffman, 2012). The economic burden of GWMDs is substantial as the average hospital and clinic costs per patient in the United States of America (USA) have been estimated to be $22,000 per year (Bonkowsky et al., 2010). The implementation of exome and genome sequencing at the beginning of the 21st century has accelerated GWMD research (van der Knaap et al., 2019). Novel disorders are frequently characterised, and the understanding of disease pathomechanisms has translated to potential therapeutic targets (Schiller, Henneke, & Gärtner, 2019). While trials of promising therapies are being established, the current epidemiology of GWMDs is not well understood. This study examined the epidemiology, genetic phenotypes, clinical features, and natural history of childhood-onset GWMDs in Northern Finland.

18 2 Review of literature

2.1 Definition of genetic white matter disorders

GWMDs are heritable diseases with prominent involvement of CNS white matter. The archetype of GWMDs is leukodystrophy. The word leukodystrophy is derived from ancient Greek λευκός (leukós, “white”), δυσ- (dus-, “hard, difficult, bad”), and τροφή (trophḗ, “food, nourishment”). Its definition has evolved throughout the history of modern medicine, first with the clinical implementation of brain MRI and later by the rapid development of genetic sequencing techniques (Kevelam et al., 2016). Today leukodystrophies are considered as genetic disorders primarily affecting the CNS white matter. The full definition as proposed by the Global Leukodystrophy Initiative in 2015 is as follows: Leukodystrophies are heritable disorders affecting the white matter of the central nervous system with or without peripheral nervous system involvement. These disorders have in common glial cell or myelin sheath abnormalities. Where known, neuropathology is primarily characterized by the involvement of oligodendrocytes, astrocytes and other non-neuronal cell types, although in many disorders the mechanism of disease remains unknown, and in other cases is suspected to include significant axonal pathology. (Vanderver et al., 2015, p. 496) Furthermore, the definition excludes acquired white matter diseases (e.g., multiple sclerosis, demyelinating disease caused by infectious processes, toxic injury, and stroke). Genetic disorders where white matter abnormalities are significant but not necessarily the primary disease manifestation are considered genetic leukoencephalopathies (gLEs). In addition to leukodystrophies, these include disorders primarily affecting grey matter and some inborn errors of metabolism where white matter abnormalities are secondary to systemic illness (Vanderver et al., 2015).

2.1.1 Classification of genetic white matter disorders

Along with the definition, a classification of leukodystrophies and gLEs has been published (Vanderver et al., 2015). It defined the disorders categorised as leukodystrophies and gLEs at that time, noting that the classification should be adapted as evidence of disorder pathomechanisms increases. The classification to

19 leukodystrophies and gLEs requires significant knowledge of pathophysiology and disease manifestations. Yet, this data is often insufficient, and studies on human brain tissue histopathology are rare (Ashrafi et al., 2019). Of note, in this thesis leukodystrophies categorised by the Global Leukodystrophy Initiative are referred to as classic leukodystrophies. In distinction, other GWMDs are termed genetic leukoencephalopathies. GWMDs can be classified based on myelin abnormality (Naidu, 1999), the specificity of white matter involvement (Vanderver et al., 2015), affected white matter components (van der Knaap & Bugiani, 2017), or molecular pathomechanism (van der Knaap et al., 2019). In the earlier classification systems, the division between hypomyelinating (i.e., deficient myelin deposition) and demyelinating (i.e., loss of deposited myelin) disorders is prominent. This distinction, however, fails to acknowledge defects not directly related to myelin (van der Knaap & Bugiani, 2018). As reviewed in the following chapter, GWMDs can arise from several components of white matter, not just myelin, intricately interacting to construct and maintain myelin and the white matter.

2.2 White matter of the brain, myelin, and glial cells

The human brain is mainly composed of grey matter and white matter in a proportion of 2:1 (Lüders, Steinmetz, & Jäncke, 2002). The grey matter contains a high density of neuron cell bodies forming the brain cortices and deep grey matter structures, for example, thalami, basal ganglia, spinal cord grey matter (Gilmore, Knickmeyer, & Gao, 2018; Walhovd et al., 2005). The white matter harbours few neuron bodies but is replete with myelinated axons, thin nerve fibres branching from the neuron cell body (Bells et al., 2019; Suminaite, Lyons, & Livesey, 2019). The structure of the white matter is illustrated in Figure 1. The essential components of white matter in the CNS are myelinated axons and glial cells including oligodendrocytes, astrocytes, and microglia. The primary function of white matter is to house axons and ensheath them in myelin, a multilayer lipid-rich membrane encapsulating axons (Nave & Werner, 2014; Saab & Nave, 2017). Axons transmit action potentials across the neurons that form the brain’s neuronal network, which is known for its complexity (Karpova & Kiani, 2016). Myelin enables rapid and efficient nerve conduction and the complex cognitive processes seen in vertebrates such as humans. The myelin is mostly deposited during the first and second years of life (Welker & Patton, 2012) and requires constant maintenance and remodelling throughout life (Saab & Nave, 2017). Its

20 highly regulated metabolism predisposes the brain to myelin-related disorders (Domingues, Portugal, Socodato, & Relvas, 2016). In addition to genetic GWMDs, impairment of the myelin sheath has been associated with multiple sclerosis (Pusic, Mitchell, Kunkler, Klauer, & Kraig, 2015), inflammatory demyelinating polyneuropathies (Lewis, 2005), Alzheimer’s disease (Nasrabady, Rizvi, Goldman, & Brickman, 2018), and traumatic brain injury (Armstrong, Mierzwa, Sullivan, & Sanchez, 2016; Mierzwa, Marion, Sullivan, McDaniel, & Armstrong, 2015). In the CNS, the myelin sheath is formed by oligodendrocytes spirally wrapping a cell membrane extension around axons (Duncan & Radcliff, 2016). In addition to myelination, oligodendrocytes support axon metabolism, regulate the ionic environment, and maintain the long-term integrity of axons (Allen & Lyons, 2018; Saab & Nave, 2017). The major structural components of myelin are the lipid bilayer (i.e., oligodendrocyte cell membrane), myelin basic protein (MBP), and proteolipid protein (PLP) (Barkovich, 2000; Stadelmann, Timmler, Barrantes-Freer, & Simons, 2019). The lipid bilayer is composed of cholesterol, phospholipids, galactolipids, and plasmalogens (Nave & Werner, 2014). MBP and PLP attach to and interact with the two opposing lipid bilayers. This contributes to compaction and stabilisation of the myelin membrane layers (Nave & Werner, 2014). Pathogenic variants of PLP1 cause Pelizaeus-Merzbacher disease (PMD, MIM #312080), whereas no disorders directly related to MBP are known (Stadelmann et al., 2019). However, a causal role of the MBP gene deletion in chromosome 18q deletion syndrome (MIM #601808) has been speculated (Anzai et al., 2016). Myelin formation is primarily regulated by oligodendrocytes, but a reciprocal interaction between myelin and axon is also observed (Gibson et al., 2014). Neuronal activity promotes myelination, and conversely, axonal dysfunction in leuko-axonopathies (e.g., neuronal ceroid lipofuscinoses) causes defective myelination (van der Knaap & Bugiani, 2017). Glial cells execute a plethora of tasks including regulation and maintenance of white matter by astrocytes and microglia (Kwon & Koh, 2020). Astrocytes sustain white matter homeostasis and extracellular ion balance, regulate cerebral blood flow, and support neuronal metabolism and the blood-brain barrier (Greenhalgh, David, & Bennett, 2020; van der Knaap & Bugiani, 2017). Impairment of astrocytes is essential in astrocytopathies such as Alexander disease (MIM #203450) and vanishing white matter disease (MIM #603896).

21 Fig. 1. Structure of CNS white matter. The white matter is primarily composed of axons (yellow) projecting from neuron cell bodies, glial cells, and blood vessels (red). Dysfunction of several white matter components is related to genetic GWMDs. Examples of disorders with primary involvement of specific white matter components are given in parenthesis. Oligodendrocytes (blue) plasma membrane extensions enwrap axons and form the myelin sheath. Star-shaped astrocytes (green) maintain tissue homeostasis and support neurons, blood vessels, the blood-brain barrier, and other glial cells. Microglia (purple) establish the primary immune system in the CNS. Abbreviations: AxD, Alexander disease; CRMCC1, cerebroretinal microangiopathy with calcifications and cysts 1; HDLS, hereditary diffuse leukoencephalopathy with spheroids; NCL, neuronal ceroid lipofuscinosis; PMD, Pelizaeus-Merzbacher disease.

22 Microglia, sharing features of both glial and immune cells, derive from haematopoietic stem cells and primarily establish the immune response in the CNS (Greenhalgh et al., 2020). They regulate immune and glial cells and, notably, myelin maintenance. Microgliopathies result from dysfunction of microglia in, for example, hereditary diffuse leukoencephalopathy with spheroids (HDLS, MIM #221820) and Nasu-Hakola disease (MIM #221770). The latter belongs to the Finnish disease heritage (van der Knaap & Bugiani, 2017). Lastly, leuko-vasculopathies affect the small cerebral blood vessels, causing consequent lacunar infarcts and haemorrhages (van der Knaap & Bugiani, 2017). Examples include Fabry disease (MIM #301500) and cerebroretinal microangiopathy with calcifications and cysts (CRMCC1, also called Coats plus, MIM #612199) (Polvi et al., 2012; Søndergaard, Nielsen, Hansen, & Christensen, 2017; van der Knaap et al., 2019). The classification system proposed by van der Knaap and Bugiani (2017) categorises WMDs based on the primary white matter component involved. The development and homeostasis of white matter, however, requires intricate interplay between glial cells and axons, and the pathology of one component can have extensive effects on white matter (Gaesser & Fyffe-Maricich, 2016). The interconnected nature of white matter cells is illustrated by the panglial syncytium and neurovascular coupling. These refer to a network of glial cells and neurons that promote the transport of ions and water across white matter (Beiersdorfer, Scheller, Kirchhoff, & Lohr, 2019; Min & van der Knaap, 2018). Saltatory conduction increases the extracellular concentration of potassium ions in the periaxonal space (Stadelmann et al., 2019). To counterbalance this, potassium is transported to astrocytes through gap junctions in the myelin and oligodendrocytes. Astrocytes further distribute ions to each other and the perivascular space by astrocyte endfeet (Min & van der Knaap, 2018). Gap junction protein connexin 47 is affected in Pelizaeus-Merzbacher-like disease 1 (MIM #608804), and mouse models with defective connexin 47 have demonstrated damage to oligodendrocytes, myelin, neurons, and perivascular astroglia (Tress et al., 2011). Furthermore, WMDs WMDs – regardless of primary cellular pathology – ultimately affect saltatory conduction and neuronal activity manifesting as neurological sequalae (Stadelmann et al., 2019).

23 2.2.1 Pathophysiology of Alexander disease

Impairment of one or more white matter structures contributes to the pathophysiology of GWMDs. Alexander disease is an example of GWMD pathophysiology and the subject of study II. It is a classic leukodystrophy and an astrocytopathy caused by autosomal dominant variants encoding glial fibrillary acidic protein (GFAP). Three traditionally acknowledged forms categorised by age at onset are infantile, juvenile, and adult. Common childhood-onset features are developmental delay, spasticity, epilepsy, and macrocephaly (R. Li et al., 2005). MRI criteria as proposed by van der Knaap et al. (2001) are white matter signal abnormalities with frontal predominance, a periventricular rim with T2- hypointensity and T1-hyperintensity, abnormalities of basal ganglia, thalami, and brainstem, and contrast enhancement of specific brain structures. Microscopically, loss of myelin, and GFAP and αB-crystallin rich Rosenthal fibres in astrocytes are seen. Gain-of-function variants of GFAP contribute to defects in the intermediate filament network and proteasomes, and aggregation of GFAP and αB-crystallin (Sawaishi, 2009; van der Knaap & Bugiani, 2017). Treatment is symptomatic, but antisense oligonucleotide therapy is under investigation (Hagemann et al., 2018).

2.3 Epidemiology

Few studies examining the epidemiology of GWMDs have been published since the 1990s (Ashrafi et al., 2018; Bonkowsky et al., 2010; Heim et al., 1997; Stellitano, Winstone, van der Knaap, & Verity, 2016; Stromme et al., 2007; Vanderver et al., 2012). However, interpreting previous findings is impaired by the heterogeneous use of epidemiological measures and classification of GWMDs.

2.3.1 Occurrence

Various epidemiological measures have been used to describe the occurrence of GWMDs including prevalence, incidence, lifetime risk, and birth prevalence. However, some of these measures poorly depict the epidemiology of childhood- onset genetic disorders (Foss, Duffner, & Carter, 2013). The mortality and age at onset of specific GWMDs are highly variable. However, measures like point prevalence (i.e., the proportion of observed population affected with the disease at a specific time point) do not acknowledge deceased cases, resulting in a lower estimate of prevalence for disorders with high mortality rate.

24 To examine the use of epidemiological measures of leukodystrophies, Foss, Duffner, and Carter (2013) reviewed the literature of Krabbe disease epidemiology. They demonstrated that most published measures of disease occurrence are best interpreted as lifetime risk at birth. This describes the cumulative incidence during the period of remaining lifetime, that is, the proportion of newborns who would develop the disease during their expected lifetime. When childhood-onset disorders are considered, this translates to development of the disease during childhood. Based on a Monte Carlo simulation, Foss et al. recommend reporting the lifetime risk using the life-table method or the Dx method. The simpler and more frequently used Dx method is calculated by dividing the number of diagnoses by the number of live births within the diagnosis period. 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑑𝑖𝑎𝑔𝑛𝑜𝑠𝑒𝑑 𝑐𝑎𝑠𝑒𝑠 𝐿𝑖𝑓𝑒𝑡𝑖𝑚𝑒 𝑟𝑖𝑠𝑘 𝑎𝑡 𝑏𝑖𝑟𝑡ℎ 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑙𝑖𝑣𝑒 𝑏𝑖𝑟𝑡ℎ𝑠

2.3.2 Previous epidemiological studies

Previous studies have reported the occurrence of GWMDs interpretable as cumulative incidence or lifetime risk of 1.1–13 per 100,000 live births. A summary of previous studies is shown in Table 1. Except for an Iranian study by Ashrafi et al. (2018), studies examined Caucasian populations. Overall, the most common leukodystrophies were metachromatic leukodystrophy (MLD, MIM # 250100), cerebral adrenoleukodystrophy (ALD, MIM #300100), Krabbe disease (MIM #245200), and PMD. Previously, up to 50% of leukodystrophy cases remained without a specific diagnosis (Bonkowsky et al., 2010). However, after the advent of advanced genetic sequencing, a diagnosis can currently be established in up to 80% of cases (Helman et al., 2020; Vanderver et al., 2016). A study by Soderholm et al. (2020) calculated the expected cumulative incidence of GWMDs based on allele frequencies of disease-causing variants in the Genome Aggregation Database (gnomAD) (Karczewski et al., 2020). This estimate, 1 in 4,733 live births (21 per 100,000 live births), is considerably higher than that observed in epidemiological studies.

25 Table 1. Previous estimates of genetic white matter disorder occurrence.

Reference Country Patient recruitment Estimate per 100,000 units Leukodystrophies All GWMDs Ashrafi et al., 2018 Iran Registry 1.61 3.01 Stellitano, Winstone, van der Knaap, UK Registry 2.67 6.14 & Verity, 2016 Vanderver, Hussey, Schmidt, Pastor, USA ICD-9 based search 0.76 1.12 & Hoffman, 2012 Bonkowsky et al., 2010 USA ICD-9 based search 13.04 NA Heim et al., 1997 Germany Questionnaire 2.0 NA

2.4 Clinical features

The clinical presentation of GWMDs, particularly leukodystrophies, is mainly neurological, manifesting as developmental delay, motor symptoms, and learning difficulties (Stellitano et al., 2016). Motor symptoms include motor developmental delay and regression, gait abnormality, hypotonia, spasticity, and extrapyramidal signs (Kohlschütter & Eichler, 2011; Parikh et al., 2015). In addition to brain pathology, spinal cord involvement can be noted, for example, in leukoencephalopathy with brainstem and spinal cord involvement and lactate elevation (LBSL, MIM #611105) (Scheper et al., 2007). Peripheral nerve involvement is prominent in some disorders like Krabbe disease (Graziano & Cardile, 2015) and Fabry disease (Fellgiebel, Müller, & Ginsberg, 2006). Epileptic seizures occur in 50% of cases, and the average age at seizure onset is four years (Bonkowsky et al., 2010). Certain disorders have distinguishing extra-neurologic features aiding diagnostic workup and selection of genetic testing (Parikh et al., 2015). For example, adrenal insufficiency is a hallmark of ALD (Kemp, Huffnagel, Linthorst, Wanders, & Engelen, 2016). 4H leukodystrophy (hypomyelination, hypodontia, hypogonadotropic hypogonadism, MIM #607694) and progressive leukoencephalopathy with ovarian failure (LKENP, MIM #615889) were named after dental abnormalities (Wolf et al., 2014) and ovarian failure (Dallabona et al., 2014), respectively. Dysmorphic features are associated with numerous GWMDs including Salla disease (MIM #604369) (Barmherzig et al., 2017) and 18q deletion syndrome (Anzai et al., 2016). Visual and hearing impairments are frequent, and various ophthalmological abnormalities have been described (Ashrafi et al., 2019). Congenital and childhood cataracts are common in cerebrotendinous xanthomatosis (MIM #213700) (Duell

26 et al., 2018) and neurodevelopmental disorder with epilepsy and cataracts, feeding difficulties, and delayed brain myelination (NECFM, MIM #617393) (Schoch et al., 2017). On retinal exam, cherry-red spots are seen in lysosomal storage diseases, especially in GM1 and GM2 gangliosidoses (Chen, Chan, Stone, & Mandal, 2014). Retinitis pigmentosa is reported in, for example, Kearns-Sayre syndrome (MIM #530000) and peroxisome biogenesis disorders (Ashrafi et al., 2019; Khambatta, Nguyen, Beckman, & Wittich, 2014). CRMCC1 (alternatively Coats plus syndrome) is characterised by retinal telangiectasia and exudates (Coats disease) (Briggs et al., 2008).

2.4.1 Natural history

Although the onset of GWMDs can occur at any age, onset is most common in childhood. In general, the clinical course of GWMDs is highly variable, and the age at onset and disease severity can vary even among siblings. A majority of disorders, however, have a characteristic age at onset (Kohlschütter & Eichler, 2011). For example, Krabbe disease presents in the first year of life and leads to early death in 95% of cases (Graziano & Cardile, 2015). LBSL commonly (53%) has an onset in late-infancy (Van Berge et al., 2014). The onset of cerebral ALD is mostly juvenile and has not been reported before the age of three years (Kemp et al., 2016). Generally, earlier onset predicts more severe disease and a faster rate of progression (Hamilton et al., 2018; Van Berge et al., 2014). Leukodystrophies were previously considered “relentlessly progressive and fatal” (van der Knaap et al., 2019, p. 966). This was changed, however, after the identification of phenotypes with spontaneous improvement such as leukoencephalopathy with thalamus and brainstem abnormalities and lactate elevation (LTBL, MIM #614924) (Steenweg et al., 2012; van der Knaap, Wolf, & Heine, 2016). It is now recognised that although most GWMDs are progressive, certain GWMDs may include long episodes of stability (Van Berge et al., 2014) or even improvement (Dubey et al., 2014; Güngör et al., 2016).

2.5 Overview of diagnostics

Two methodological advancements define the modern diagnostics of GWMDs: MRI pattern recognition and next-generation sequencing (NGS). In the 1990s, locating candidate genes was laborious and time-consuming, and the majority of molecular aetiologies of GWMDs known today were yet to be discovered.

27 (Kevelam et al., 2016). Then, van der Knaap, Valk, Neeling, and Nauta (1991) published a system of MRI pattern recognition, expediting the characterisation of novel disorders. This led to the definition of several originally MRI-based diagnoses including vanishing white matter disease and LBSL (Kevelam et al., 2016). At the beginning of the 21st century, the advent of NGS again revolutionised the field and initiated a shift of focus from MRI to genetics. NGS refers to massive parallel sequencing in distinction to older methods including Sanger sequencing (Metzker, 2010). NGS enables relatively fast and cost-effective sequencing of a whole exome or genome (Schwarze, Buchanan, Taylor, & Wordsworth, 2018). In 2011, NGS was deployed to identify the gene CSF1R as the cause of HDLS (Rademakers et al., 2012). Subsequently, reports of new variants and novel GWMDs have been published with increasing frequency. Recently, van der Knaap et al. (2019) proposed a diagnostic algorithm for leukodystrophies. As a framework for GWMD diagnostics, this algorithm is summarised below and in Figure 2. Developmental delay commonly prompts a referral to a paediatric neurologist and brain MRI (Ji et al., 2018). Subsequently, the diagnostic algorithm proceeds first with brain MRI followed by molecular testing. Despite advancements made by NGS, brain MRI retains a crucial role in the recognition of a GWMD and distinguishing between genetic and acquired aetiologies. MRI pattern recognition or special clinical characteristics can suggest a limited selection of differential diagnoses and direct the choice of subsequent molecular testing (e.g., targeted sequencing of suspected causal genes). If no eminent MRI pattern emerges, expert opinion, a gene panel, or nonselective screening with NGS is recommended. MRI and NGS are appropriately complemented by other molecular studies such as metabolic screening (e.g., plasma and urine organic acids, urine oligosaccharides, lysosomal enzymes), measurement of the activity of mitochondrial and cytoplasmic tRNA synthases (Boczonadi, Jennings, & Horvath, 2018) and mitochondrial respiratory chain enzymes, and mitochondrial DNA sequencing (Morató et al., 2014). Diagnostic screening tests with biomarkers for leukodystrophies include measurement of very long chain fatty acids in ALD, cholestanol in cerebrotendinous xanthomatosis, and activity of lysosomal enzymes galactocerebrosidase in Krabbe disease and arylsulfatase A in MLD (Parikh et al., 2015).

28 WM abnormalities are noted on brain MRI

Are WM abnormalities No Consider other diagnoses (acquired WMD, e.g. multiple suggestive of a GWMD? sclerosis, stroke, infectious or toxic encephalopathy)

Yes

MRI pattern Expert opinion suggestive or clinical Or features of one or few Non-targeted genetic testing (NGS targeted gene panel specific disorders? or open exome/genome sequencing)

No If NGS not available or slow turnaround time, perform Yes metabolic screening - Plasma amino acids, homocysteine, lactate, pyruvate, VLCFA, cholestanol - Urine organic acids, sulfatides, sialic acid Targeted testing with - Activity of lysosomal enzymes, mitochondrial and metabolic or genetic and cytoplasmic tRNA synthases and mitochondrial confirmation respiratory chain enzymes

Yes

Diagnosis? Diagnosis? No Yes Yes No

- Counselling Refer to GWMD - Family testing expert centre - Management options - Therapy options

Fig. 2. Diagnostic algorithm of genetic white matter disorders. Adapted from van der Knaap et al. (2019). Abbreviations: GWMD, genetic white matter disorder; NGS, next- generation sequencing; VLCFA, very long chain fatty acid; WM, white matter; WMD, white matter disease.

2.6 Neuroradiology and MRI pattern recognition

Although brain computed tomography (CT) has some diagnostic utility, structural MRI is the unrivalled imaging modality of choice. Brain MRI has two special roles. Firstly, MRI reveals the involvement of CNS white matter. Secondly, distinct MRI patterns aid in the selection of subsequent diagnostic studies (van der Knaap et al., 2019). In addition to T1- and T2-weighted (T1W and T2W) and fluid-attenuated inversion recovery (FLAIR) sequences, other pulse sequences (e.g., diffusion-

29 weighted sequences) and contrast-enhanced imaging may be needed for the characterisation of GWMD lesions (Welker & Patton, 2012). CT is useful for detecting calcifications, but gradient-echo MRI, such as the susceptibility-weighted imaging (SWI) sequence, is also sensitive to calcium deposits without radiation (Schweser, Deistung, Lehr, & Reichenbach, 2010; Tong et al., 2008). Myelination in the CNS occurs mainly during the first two postnatal years. The temporal development of neuroanatomic structures and the pattern of MRI signal development during myelination is well known (van der Knaap & Valk, 2005). In neonates, unmyelinated brain white matter is hypointense in T1W and conversely hyperintense in T2W images compared to grey matter. Following myelination, the white matter becomes progressively more T1-hyperintense, and only minimal change is noted after the 1st year of life in normal myelination (Welker & Patton, 2012). On T2W images, however, the white matter signal changes later than on T1W images and initially remains nearly unaffected (i.e., hyperintense). Normally myelinated white matter is hyperintense on T1W and hypointense on T2W images compared to the cortex after the age of 1.5 years (Schiffmann & van der Knaap, 2009), as shown in Figure 3. Most white matter abnormalities are prominently T2-hyperintense and T1- hypointense, similar to unmyelinated white matter (Schiffmann & van der Knaap, 2009). In contrast, hypomyelination (i.e., permanent deficient myelination) often resembles partially myelinated white matter with T1-isointensity or T1- hyperintensity. The white matter is still T2-hyperintense. However, depending on the amount of deposited myelin and the patient’s age, T1-hypointensity can also be seen (Pouwels et al., 2014; Schiffmann & van der Knaap, 2009). This notion emphasizes the importance of considering the patient’s age at the time of imaging in relation to the white matter findings. Hypomyelination is defined as “an unchanged pattern of deficient myelination on two MRIs at least 6 months apart in a child older than 1 year” (Schiffmann & van der Knaap, 2009, p. 753). Additionally, in children older than 2 years, severely deficient myelination likely signifies hypomyelination. Importantly, hypomyelination means permanent deficient deposition of myelin in contrast to delayed myelination (van der Knaap & Wolf, 2010). This distinction holds important diagnostic value. Firstly, many aetiologies other than GWMDs (e.g., hydrocephalus and idiopathic developmental delay) can cause delayed myelination that improves in follow-up imaging (Maricich et al., 2007; van der Knaap, Valk, Bakker, et al., 1991). Secondly, hypomyelination has limited differential diagnoses (Barkovich & Deon, 2016; Steenweg et al., 2010) and

30 is an important variable in MRI pattern recognition (Schiffmann & van der Knaap, 2009).

Fig. 3. Representative T2-weighted (T2W) axial and T1-weighted (T1W) coronal images of normally myelinated brain (Panel 1), Salla disease (Panel 2) and adrenoleukodystrophy (Panel 3). Panel 1 shows T2-hypointense and T1-hyperintense fully myelinated white matter of a 6-year-old patient without a white matter disorder. Panel 2 shows hypomyelination with mild subcortical T2-hyperintensity (T2W axial, arrows) and T1-isointensity (T1W coronal, arrows) in a 13-year-old patient with Salla disease. The deep white matter is more myelinated and appears T1-hyperintense (arrowheads). Panel 3 shows confluent T2-hyperintensity (T2W axial, arrows) and T1- hypointensity (T1W coronal, arrows) predominantly in the parieto-occipital deep white matter crossing the splenium of the corpus callosum in a 8-year-old patient with adrenoleukodystrophy.

Computer-assisted applications of MRI pattern recognition may be a valuable tool in clinical and research practice in the future. MRI pattern recognition builds on the

31 notion that certain disorders have specific MRI findings distinct from other disorders. The main features of MRI pattern recognition are 1) hypomyelination versus other white matter abnormality, 2) confluent versus isolated or multifocal white matter involvement, and 3) symmetry and predominance of the location of white matter abnormalities (Schiffmann & van der Knaap, 2009). These are supplemented by special MRI characteristics typical for specific disorders including white matter swelling or atrophy, white matter cysts, myelin microvacuolisation, contrast enhancement, calcifications, micro-bleeds, grey matter lesions, involvement of the peripheral nervous system, and evolution over time (i.e., progressive, relapsing-remitting, stable, or improving) (van der Knaap et al., 2019; van der Knaap & Valk, 2005). The complexity of MRI findings and the rarity of specific disorders impose challenges in diagnostics even for experienced neuroradiologists. Computerised pattern recognition algorithms combined with artificial intelligence and machine learning may prove to be revolutionary tools in the future (Noor, Zenia, Kaiser, Mamun, & Mahmud, 2020; Valliani, Ranti, & Oermann, 2019; Zhou, Yu, & Duong, 2014).

2.7 Genetics

Most currently known GWMDs are monogenic disorders, but copy number variants have also recently been associated with GWMDs (e.g., 18q deletion syndrome) (Vigdorovich et al., 2019). Most GWMDs display an autosomal recessive pattern of inheritance (Kohlschütter & Eichler, 2011). Other patterns include autosomal dominant, X-linked, and mitochondrial inheritance (Ashrafi et al., 2019; van der Knaap et al., 2019). As recently as 2009, 50% of leukodystrophies remained without a specific diagnosis (Bonkowsky et al., 2010). This proportion of unsolved cases has diminished, however, after the implementation of NGS. In 2016, the Myelin Disorders Bioregistry Project investigated unsolved leukodystrophies using exome sequencing (Vanderver et al., 2016). Pathogenic variants were identified in 30 of 71 patients, which yielded a diagnostic rate of 77% for leukodystrophies. In a subsequent study (Helman et al., 2020), the remaining 41 patients with unsolved disorders underwent genome sequencing which identified causal genetic defects in 14 patients and reanalysis of exome data in 8 additional patients. In total, the diagnostic rate further increased from 77% to 85%, demonstrating the utility of NGS in GWMD diagnostics. Considering the high cost-effectiveness of NGS, it

32 should be prioritised over targeted gene panels. Furthermore, genome sequencing may be considered over exome sequencing (Helman et al., 2020).

2.8 Treatment and disease screening

Current therapeutic options for GWMDs are, with few exceptions, notably limited. For leukodystrophies, in addition to symptomatic care (Adang et al., 2017; van der Knaap et al., 2019), disease-modifying therapies in clinical practice are haematopoietic stem cell transplantation (HSCT) in a few leukodystrophies and chenodeoxycholic acid replacement in cerebrotendinous xanthomatosis (van der Knaap et al., 2019; Yahalom et al., 2013). Additionally, several therapies for inborn errors of metabolism are suggested to have beneficial effects on cerebral and white matter pathology (van Karnebeek & Stockler, 2012). Examples include enzyme replacement therapy in Fabry disease (El Dib et al., 2016), dietary treatment in glutaric aciduria I (MIM #231670) (Kölker et al., 2011), vitamin supplementation in biotinidase deficiency (MIM #253260) (Grünewald, Champion, Leonard, Schaper, & Morris, 2004), Miglustat in Niemann-Pick disease type C (MIM #257220) (Bowman, Velakoulis, Desmond, & Walterfang, 2018), and copper chelating agents in Wilson disease (MIM #277900) (Larnaout, Ammar, Mourad, Naji, & Hentati, 2008). The efficacy of allogenic HSCT for survival and the prevention of cognitive deterioration was first shown in ALD (Aubourg et al., 1990). Currently, evidence supports HSCT in selected patients with ALD (Miller et al., 2011), MLD (Boucher et al., 2015; Wolf et al., 2020), and Krabbe disease (Allewelt et al., 2018; Schiller et al., 2019). Transplantation of unrelated umbilical-cord blood is an alternative to HSCT with better availability (Escolar et al., 2005). Response to transplantation is dependent on administration at an early disease stage, preferably in a presymptomatic stage. In a later, symptomatic disease course, the risks (e.g., toxicity and graft-versus-host disease) can outweigh the potential benefits (Mallack, Turk, Yan, & Eichler, 2019; Mikulka & Sands, 2016; Miller et al., 2011). To enable early disease identification and treatment, screening strategies have been developed. Disease screening is directed towards treatable childhood-onset disorders (Watson et al., 2006). In the USA, ALD is screened in newborns in several states as recommended by the Recommended Uniform Screening Panel (RUSP) (Secretary of the Department of Health and Human Services, 2018). After promising results with stem cell transplantation for asymptomatic patients with Krabbe disease (Escolar et al., 2005), the state of New York, USA, adopted

33 newborn screening using galactocerebrosidase activity measurement in 2005 (Wasserstein et al., 2016). Several states have followed the example. The newborn screening of MLD was recently investigated in the state of Washington, USA, and is likely to be adapted in several states in the near future (Hong et al., 2020). In addition to leukodystrophies, more established screening for inborn errors of metabolism categorised as gLEs is available. For example, the American RUSP recommends screening for phenylketonuria (MIM #261600), glutaric acidemia type I (MIM #231670), aspartylglucosaminuria (MIM #208400), and maple syrup urine disease (MIM #248600). In Finland, newborn screening of inborn errors has a participation rate of 97% (Salo, 2018), and includes phenylketonuria and glutaric aciduria type I (Lapatto, Niinikoski, Näntö-Salonen, & Mononen, 2018). In Europe, patient advocacy groups and health-care experts have advocated for wider newborn screening (Luxner, 2018). In addition to newborn screening, Israel has implemented a national genetic carrier–screening program for reproductive purposes. This includes parental genetic screening before or during pregnancy. The protocol attempts to identify couples at risk of having a child with a severe genetic disorder including MLD, Canavan disease, and GM2-gangliosidosis (Singer & Sagi-Dain, 2020).

2.8.1 Future aspects

Increasing interest in rare genetic disorders, in addition to advancements of genetic sequencing and engineering, has been translated into a myriad of potential therapies (Gordon-Lipkin & Fatemi, 2018; Helman et al., 2015; van der Knaap et al., 2019). Naturally, gene therapy is believed to have substantial potential in the treatment of genetic neurological disease. This was first realised in human patients by Cartier et al. (Cartier et al., 2009). With gene-modified autologous HSCT using ex-vivo lentiviral vectors, they were able to establish ABCD1 protein expression and halt disease progression in two patients with ALD. In 2017, a larger trial with 17 patients corroborated these findings (Eichler et al., 2017). Currently, the two most promising gene therapies are HSCT combined with lentiviral vector and direct surgical injection of adeno-associated viral vectors to the brain (Gordon-Lipkin & Fatemi, 2018; Schiller et al., 2019). In 2020, the European Medicines Agency recommended authorising LibmeldyTM, a lentiviral gene therapy that modifies harvested CD34+ haematopoietic stem and progenitor cells to produce the arylsulfatase A enzyme defective in MLD (European Medicines Agency, 2020b). Similar to HSCT in ALD, LibmeldyTM is more effective in nonsymptomatic or

34 early-stage MLD (European Medicines Agency, 2020a). Most recently, clustered regularly interspaced short palindromic repeats (CRISPR) gene editing is expected to introduce major advancements in genetic engineering (Nelson et al., 2019). In addition to cell and gene therapies, other emerging techniques include antisense nucleotide (Hagemann et al., 2018; Hull et al., 2020) and RNA-based therapies (Martier et al., 2019), reverse transcriptase inhibitors (Tonduti, Fazzi, Badolato, & Orcesi, 2020), substrate reduction therapies (Y. Li et al., 2019), pharmacological chaperones (Graziano, Pannuzzo, Avola, & Cardile, 2016), and enzyme replacement (M. Li, 2018). Several theorised mitochondria-targeted approaches are under investigation, for example, mitochondrially-targeted nucleases and viral vector-mediated gene therapy (Nightingale, Pfeffer, Bargiela, Horvath, & Chinnery, 2016). Moreover, animal studies have demonstrated the benefit of multimodal treatments combining, for example, HSCT, enzyme replacement, and substrate reduction therapies (van der Knaap et al., 2019). Increased focus on clinical trials underscores the need for biomarkers and clinical outcome measures to evaluate therapy efficacy (European Commission, 2014; van der Knaap et al., 2019). For example, the measurement of galactocerebrosidase proved to have low specificity in screening newborns for Krabbe disease (Wasserstein et al., 2016). Currently, a substrate of galactocerebrosidase, psychosine, has yielded promising results as a more accurate biomarker of Krabbe disease (Corado et al., 2020; Guenzel et al., 2020). Endorsed by GWMD experts (van der Knaap et al., 2016) and patient advocacy groups (Luxner, 2018), screening protocols for GWMDs are likely to become more frequent as screening protocols (Guenzel et al., 2020) and therapeutic options improve (Schiller et al., 2019). Additionally, the rarity of specific disorders accentuates the role of clinical research networks facilitating multidisciplinary international collaboration in the future (Gordon-Lipkin & Fatemi, 2018).

36 3 Aims of the study

Advanced genetic techniques and the pursuit of curative therapies have galvanised the research field of GWMDs. Novel disorders and genetic aetiologies have been described at an increasing pace, and clinical trials are being set up for many disorders. Still, the current epidemiology of GWMDs is insufficiently known. Effective trials of novel therapies and screening protocols depend on comprehensive epidemiological data to design trials and evaluate efficacy. The aim of this research was to evaluate childhood-onset GWMDs in a defined population area of Northern Finland. The specific aims were the following: 1. To examine the epidemiology and the distribution of childhood-onset GWMDs (I) 2. To evaluate the clinical features and natural history of GWMDs (I) 3. To characterise novel or rare genetic phenotypes and aetiologies of GWMDs (I–III)

Roman numerals refer to original publications.

38 4 Materials and methods

4.1 Patients

The study included children with genetically defined childhood-onset GWMDs evaluated at the Clinic for Children and Adolescents of the Oulu University Hospital between 1990 and 2019. The hospital serves as the only tertiary care centre for child neurology in Northern Finland. Four central hospitals in the tertiary catchment area refer GWMD patients to Oulu University Hospital (Figure 4).

Fig. 4. Map of Finland showing the tertiary catchment area of Oulu University Hospital. Adapted from Knuutinen et al. (2020a).

Patients were identified retrospectively from the hospital patient registry using the International Statistical Classification of Diseases (ICD) 9th (Finnish National Board of Health, 1986; World Health Organization (WHO), 1978) and 10th revision codes (WHO, 1993) and prospectively by the attending senior physician. The selected ICD codes are shown in Table 2. The inclusion criteria were as follows:

39 1. Age under 18 years at diagnosis of GWMD 2. Living in the tertiary catchment area of Oulu University Hospital 3. Defined genetic disease and molecular confirmation of diagnosis 4. Available brain MRI for detailed evaluation or original radiology report for leukodystrophies 5. GWMD previously associated with white matter abnormalities or a genetic disorder with WM abnormalities noted in the current study The flow chart of patient selection is shown in Figure 5. The ICD-based search identified 195 patients for review, and an additional 69 patients were prospectively identified by the attending senior physicians between January 1990 and December 2019. Patient charts and radiology reports were reviewed of 264 patients, followed by a review of brain MRIs of 124 patients. In total, 184 subjects were excluded for not fulfilling inclusion criteria. Study II also included a Jordanian patient harbouring a TAF1C variant, which was identified using GeneMatcher, an online database connecting researchers investigating matching genes of interest (Sobreira, Schiettecatte, Valle, & Hamosh, 2015).

Table 2. ICD codes used to identify GWMD patients from hospital patient registry.

ICD-9 revision, Finnish Modification ICD-10 revision, Finnish Modification 330 (including 3300A, 3301A, 3301B, 3301X, E70, E71, E72, E74, E75, E76, E77, E79.8, 3308A, 3308X, 3309X), 2701A, 2727A, 2705B, E83.0, E88.8#, F01.1, G71.3, I67.3, Q87.11, 271, 2751A, 2775A, 2775B Q87.19, Q87.82, Q87.83, Q87.86, Q99.2

4.2 Collection of clinical data

In study I, clinical variables were retrospectively collected from patient records according to a preformatted form. Collected clinical data included gender, ethnicity, date of birth, date of death, diagnosed disorder and related genetic defect, parental consanguinity, family history of GWMD, history of motor developmental delay, hypotonia, spasticity, extrapyramidal signs, ataxia, and feeding tube placement. ICD-10 codes for intellectual disability and epilepsy in addition to age at disease onset, diagnosis, onset of epilepsy, and feeding tube placement were extracted. Disease onset was defined as the first record of disease-related symptoms or findings in patient records. More detailed clinical patient data were gathered in studies II and III, including data on a Jordanian patient in study II. Autopsy findings were included in study III.

195 retrospectively identified using 69 prospectively identified ICD-9 and ICD-10 codes by attending senior physician

264 selected for patient chart review

140 excluded: 59 had no genetical neurological disorder 38 had undefined aetiology 2 aged over 18 years 1 lived outside the study area 40 had no WM abnormalities in radiology report or related literature

124 selected for detailed brain MRI evaluation

44 excluded: 25 had no available brain MRI 18 had no WM abnormalities in evaluation or related literature 1 had WM abnormalities related to an acquired disorder

80 cases with a GWMD

16 classic leukodystrophies 64 genetic leukoencephalopathies

15 mitochondial 49 other genetic leukoencephalopathies leukoencephalopathies

Fig. 5. Flow chart of patient selection. Adapted from Knuutinen et al. (2020a).

4.3 Neuroradiological assessment

Digital brain MRIs were accessed on a research PACS/DICOM viewing application for diagnostic radiology (neaView, Neagen, Helsinki, Finland). If digital MRIs were not available in the database in study I, digital or film MRIs were requested from hospital archives of Oulu University Hospital and respective central hospitals. Available digital or film MRIs were evaluated by a radiologist in training (5 years of experience in radiology) and a paediatric radiologist (17 years of experience in

41 neuroradiology). Neuroradiological findings were collected according to van der Knaap et al. (van der Knaap & Valk, 2005; van der Knaap, Valk, de Neeling, et al., 1991) on a preformatted form. Collected variables included delayed myelination, hypomyelination, distribution of white matter abnormalities, white matter atrophy, and corpus callosum abnormalities. In study II, a serial brain ultrasound was also evaluated.

4.4 Molecular genetic studies

In study I, the results of metabolic, enzymatic, and genetic (i.e., single gene, whole exome, or whole genome sequencing, karyotyping, and microarray analysis) studies conducted as a part of clinical diagnostics were collected from patient records and the electronic laboratory database. In studies II and III, DNA was extracted from peripheral blood of patients and parents. In study II, the Finnish patient’s and her parent’s trio exome sequencing and analysis was performed at Newcastle University, United Kingdom (UK), as described in Knuutinen, Pyle, et al. (2020). Exomes were captured using Nextera Rapid Exome Capture (Illumina, UK) and sequenced on an Illumina NextSeq500. Variants with a minor allele frequency of ≥ 0.01 (1%) in genomic databases were filtered. Homozygous or compound heterozygous exonic or splice site variants were prioritised. Candidate variants were validated by Sanger sequencing (TAF1C reference sequence NM_005679.3). Additionally, the genes associated with PEHO syndrome (progressive encephalopathy with oedema, hypsarrhythmia, optic atrophy, MIM #260565) and PEHO-like phenotypes were analysed with Sanger sequencing (University of Helsinki, Finland). The exome sequencing of the Jordanian patient was performed in an accredited genetic laboratory (Macrogen Inc., Seoul, South Korea). In study III, the coding region of GFAP was analysed with Sanger sequencing (reference sequence NM_001242376.1) in an accredited clinical laboratory. Variant pathogenicity was assessed using prediction tools Sorting Intolerant from Tolerant (SIFT) (Sim et al., 2012), PolyPhen-2 (Adzhubei et al., 2010), and Combined Annotation Dependent Depletion (CADD) (Rentzsch, Witten, Cooper, Shendure, & Kircher, 2019) in study I and II, and Condel (González-Pérez & López-Bigas, 2011) and PON-P2 (Niroula, Urolagin, & Vihinen, 2015) in study III. In study I, the minor allele frequencies of observed variants were retrieved from the gnomAD database (Karczewski et al., 2020). The classification of variant pathogenicity was retrieved from the ClinVar database (Landrum et al., 2018).

42 When this was unavailable, the variant was classified according to the American College of Medical Genetics and Genomics guidelines (Kelly et al., 2018; Richards et al., 2015). In study II, Western blotting and quantitative real-time PCR (qPCR) was performed from primary skin fibroblasts at Newcastle University. For Western blot, protein lysates were run on NuPAGE™ 4–12% Bis-Tris Protein Gels and transferred to a PVDF membrane using the iBlot®2 Dry Blotting System (Thermo Fisher Scientific, UK). The membrane was probed with antibodies against TAF1C (Abcam, ab210750) and GAPDH (Santa Cruz, sc-25778). For qPCR, RNA was isolated from fibroblasts using an RNeasy Mini Kit (Qiagen, Manchester, UK). Complementary DNA was generated using a High-Capacity cDNA Reverse Transcription Kit (Life Technologies Ltd, Paisley, UK). qPCR was performed on a CFX96 Touch™ PCR system with iTaq™ Universal SYBR® Green Supermix (BioRad, Hertfordshire, UK). TAF1C expression levels were measured relative to GAPDH and β-Actin. In study III, the location of GFAP protein variants related to the neonatal phenotype found in the literature were compared to other phenotypes of Alexander disease. Variants in infantile, juvenile, and adult phenotypes reported in R. Li et al. (2005) were used as a reference.

4.5 Statistical analysis

SPSS version 25.0 (IBM Corp, Armonk, NY, USA) and StatsDirect version 3 (StatsDirect Ltd., Cheshire, England) were used for statistical analyses. Cumulative childhood incidence was calculated using the Dx method (Foss et al., 2013). The number of live births between 1990 and 2019 was obtained from the Official Statistics of Finland (Official Statistics of Finland [OSF], 2020). Disorders were classified as classic leukodystrophies or gLEs according to Vanderver et al. (2015). Proportions and continuous variables between two groups were compared using a standardized normal deviate (SND) test and a Mann–Whitney U test. Statistical significance was defined as p ≤ 0.05.

4.6 Ethical considerations

This study was carried out in accordance with the Declaration of Helsinki and its later amendments (World Medical Association, 2013). The study was approved by the Ethics Committee of the Northern Ostrobothnia Hospital District (68/2018).

43 Additionally, study II was approved by the Ethics Committee of the Newcastle and North Tyneside 1 National Research Ethics Committee, and studies II and III were approved by the Regional Ethics Committee of the Helsinki University Hospital.

44 5 Results

5.1 Epidemiology (I)

Eighty children with confirmed genetic GWMDs were included. The cumulative childhood incidence was 30 during the whole study period, 5.1. during 1990–1999, 17.9 during 2000–2009, and 71.8 per 100,000 live births during 2010–2019. The distribution of the 49 observed disorders is shown in Table 3. Sixteen cases (20%) were leukodystrophies and 64 (80%) gLEs. Of the gLEs, 15 (23%) were mitochondrial and 49 (77%) other gLEs. The proportion of GWMDs categorised by primary white matter defects as proposed by van der Knaap et al. (2019) are shown in Figure 6.

5.2 Clinical characteristics and natural history (I–III)

Clinical features of GWMD patients are shown in Table 4. In total, 56% of patients were males and 86% ethnic Finnish. Patient history revealed parental consanguinity in 7% and family history of GWMD in 33%. The most common clinical findings were motor developmental delay (79%), intellectual disability (56%), hypotonia (60%), and spasticity (49%). Ataxia and extrapyramidal signs were noted in 29% and 23%, respectively. Epileptic seizures occurred in 36% of patients and had a median onset at 12 months of age. The median ages at disease onset and diagnosis were 5 months and 46 months (3 years and 10 months), respectively. At the end of the study, 26% were deceased, and the median age at death was 31 months (3 years and 7 months).

5.2.1 Comparison between classic leukodystrophies and genetic leukoencephalopathies

When variables were compared across groups of leukodystrophies and gLEs, statistically significant differences were noted between sex and age at diagnosis (p ≤ 0.05). Patients with leukodystrophies were more frequently male (81% and 50%, p = 0.02). The diagnosis was made later in the gLEs group compared to the leukodystrophy group, and the median age at diagnosis was 57 months and 20 months, respectively (p = 0.01). A nonsignificant trend was noted regarding mortality and the onset of epilepsy. The mortality rate at the end of the study was

45 44% for leukodystrophies and 22% for gLEs (p = 0.07). The median age at seizure onset was 110 months for leukodystrophies and 12 months for gLEs.

Table 3. Diagnosed 49 genetic white matter disorders (N = 80).

Disorder, MIM number Related gene n (%) Leukodystrophies Total 16 (20%) Cerebral adrenoleukodystrophy, #300100 ABCD1 6 (7.5%) Krabbe disease, #245200 GALC 3 (3.8%) Salla disease, #604369 SLC17A5 3 (3.8%) Alexander disease, #203450 GFAP 1 (1.3%) Chromosome 18q deletion syndrome, #601808 18q 1 (1.3%) LBSL, #611105 DARS2 1 (1.3%) Metachromatic leukodystrophy, #250100 ARSA 1 (1.3%) Mitochondrial gLEs Total 15 (19%) MELAS, #540000 mtDNA 3 (3.8%) MTDPS7 (IOSCA), #271245 TWNK 2 (2.5%) Kearns-Sayre and Pearson syndrome, #530000 and mtDNA 2 (2.5%) #557000 LKENP, #615889 AARS2 2 (2.5%) COXPD7, #613559 C12orf65 1 (1.3%) Glutaric acidemia IIC, #231680 ETFDH 1 (1.3%) Leigh syndrome due to mitochondrial complex I SURF1 1 (1.3%) deficiency, #256000 MIRAS, #607459 POLG 1 (1.3%) MTDPS5, #612073 SUCLA2 1 (1.3%) Neuronal ceroid lipofuscinosis 5, #256731 CLN5 1 (1.3%) Other gLEs Total 49 (61%) CRMCC1 (Coats plus), #612199 CTC1 5 (6.0%) Ataxia-pancytopenia syndrome, #159550 SAMD9L 3 (3.8%) FINCA disease, #618278 NHLRC2 3 (3.8%) Allan-Herndon-Dudley syndrome, #300523 SLC16A2 2 (2.5%) Cockayne syndrome type B, #133540 ERCC6 2 (2.5%) Hereditary homocystinurias, #236250 and #250940 MTHFR and MTR 2 (2.5%) Lujan-Fryns syndrome, #309520 MED12 2 (2.5%) MDDGA3 (muscle-eye-brain disease), #253280 POMGNT1 2 (2.5%) MICPCH, #300749 CASK 2 (2.5%) Mucolipidosis II, #252500 GNPTAB 2 (2.5%) NBIA5, #300894 WDR45 2 (2.5%) SPG4, #182601 SPAST 2 (2.5%)

46 Disorder, MIM number Related gene n (%) Cardiofaciocutaneous syndrome 1, #115150 BRAF 1 (1.2%) Chromosome 6p25 deletion syndrome, #612582 6p25 1 (1.2%) CPS I deficiency, #237300 CPS1 1 (1.3%) EIEE34, #616645 SLC12A5 1 (1.3%) EIEE42, #617106 CACNA1A 1 (1.3%) EIEE44, #617132 UBA5 1 (1.3%) Fabry disease, #301500 GLA 1 (1.3%) HMGCLD, #246450 HMGCL 1 (1.3%) Lissencephaly 3, #611603 TUBA1A 1 (1.3%) MDR13, #614563 DYNC1H1 1 (1.3%) MIRAGE syndrome, #617053 SAMD9 1 (1.3%) X-linked mental retardation 1, #309530 IQSEC2 1 (1.3%) X-linked mental retardation 102, #300958 DDX3X 1 (1.3%) NECFM, #617393 NACC1 1 (1.3%) Rett syndrome, congenital variant, #613454 FOXG1 1 (1.3%) Ring chromosome 18 syndrome Chromosome 18 1 (1.3%) Spinocerebellar ataxia 29, #117360 ITPR1 1 (1.3%) TAF1C related disorder TAF1C 1 (1.3%) VAIHS, #615688 ADA2 1 (1.3%) Wilson disease, #277900 ATP7B 1 (1.3%) Abbreviations: COXPD7, combined oxidative phosphorylation deficiency 7; CPS I, carbamoyl phosphate synthetase I; CRMCC1, cerebroretinal microangiopathy with calcifications and cysts 1; EIEE, early infantile epileptic encephalopathy; FINCA, fibrosis, neurodegeneration, and cerebral angiomatosis; gLE, genetic leukoencephalopathy; HMGCLD, 3-hydroxy-3-methylglutaryl-CoA lyase deficiency; IOSCA, infantile-onset spinocerebellar ataxia; LBSL, leukoencephalopathy with brain stem and spinal cord involvement and lactate elevation; LKENP, progressive leukoencephalopathy with ovarian failure; MDDGA3, congenital muscular dystrophy-dystroglycanopathy type A3; MELAS, mitochondrial myopathy, encephalopathy, lactic acidosis, and stroke-like episodes; MDR13, autosomal dominant mental retardation 13; MICPCH, mental retardation and microcephaly with pontine and cerebellar hypoplasia; MIM, Mendelian Inheritance in Man; MIRAGE, myelodysplasia, infection, restriction of growth, adrenal hypoplasia, genital phenotypes, and enteropathy; MIRAS, mitochondrial recessive ataxia syndrome; mtDNA, mitochondrial deoxyribonucleic acid; MTDPS, mitochondrial DNA depletion syndrome; NBIA5, neurodegeneration with brain iron accumulation 5; NECFM, neurodevelopmental disorder with epilepsy, cataracts, feeding difficulties, and delayed brain myelination; SPG4, autosomal dominant spastic paraplegia 4; VAIHS, vasculitis, autoinflammation, immunodeficiency, and hematologic defects syndrome

47 Lysosomal 44 %

Classic leukodystrophies

Peroxisomal Miscellaneous 38 % 12 %

mRNA translation 6 % Genetic vasculopathies Lysosomal 11 % 5 %

Ion and water homeostasis 9 % Mitochondrial or respiratory chain 20 %

Amino acid and organic acid metabolism 5 % Genetic leuko-

DNA repair encephalopathies 3 %

mRNA translation 2 %

Miscellaneous 45 %

Fig. 6. Categories of leukodystrophy and gLE molecular pathology. Adapted from Knuutinen et al. (2020a).

48 Table 4. Clinical features of 80 patients with genetic white matter disorders.

Variable All GWMDs Leukodystrophies gLEs p value1 (N = 86) (n = 16) (n = 70) Cumulative childhood 30 6 24 NA incidence per 100,000 live births Male 45/80 (56%) 13/16 (81%) 32/64 (50%) 0.016 Finnish ethnicity 69/78 (86%) 14/16 (88%) 55/62 (86%) 0.999 Parental consanguinity 6/69 (7.5%) 0/14 (0%) 6/55 (9.4%) 0.174 Family history of GWMD 26/78 (33%) 7/16 (47%) 19/63 (30%) 0.256 Intellectual disability 45/71 (56%) 5/11 (45%) 40/60 (63%) 0.123 Motor developmental delay 63/80 (79%) 13/16 (81%) 50/61 (78%) 0.999 Hypotonia 48/79 (60%) 9/16 (56%) 39/63 (61%) 0.585 Spasticity 39/80 (49%) 8/16 (50%) 31/64 (48%) 0.999 Extrapyramidal signs 18/80 (23%) 2/16 (13%) 16/64 (25%) 0.340 Ataxia 23/80 (29%) 6/16 (38%) 17/64 (27%) 0.376 Epilepsy 29/80 (36%) 5/16 (31%) 24/64 (38%) 0.582 Age at onset, median 12 (0–180) 110 (0–130) 12 (0–180) 0.524 (range), mo Age at disease onset, 5 (0–180) 6 (0–100) 5 (0–180) 0.312 median (range), mo Age at diagnosis, median 46 (0–270) 20 (1–120) 57 (0–270) 0.013 (range), mo Age at death, median 31 (1–260) 41 (1–120) 31 (5–260) 0.659 (range), mo Deceased 21/80 (26%) 7/16 (44%) 14/64 (22%) 0.068 Required feeding tube 31/79 (39%) 7/16 (44%) 24/63 (38%) 0.585 Age at placement, 18 (5–190) 18 (6–120) 18 (5–190) 0.773 median (range), mo 1p value is compared between leukodystrophies and gLEs. gLE: genetic leukoencephalopathy; mo: months; NA: not applicable; GWMD: genetic white matter disorder.

5.3 Genotypes and phenotypes of genetic white matter disorders (I–III)

The genetic aetiologies comprised of 72 (90%) nuclear single gene defects, 5 (6%) mitochondrial DNA defects, and 3 (4%) chromosomal abnormalities. The causal nuclear variants were homozygous in 37%, compound heterozygous in 24%, heterozygous in 28%, and hemizygous in 12%. The proportion of genetic findings are shown in Figure 7. The three observed chromosomal abnormalities were 6p25

49 deletion, 18q deletion, and ring chromosome 18. Mitochondrial aetiology was found in 16 cases, 15 gLEs and 1 leukodystrophy. Of those, the genetic defect was located in the nuclear genome in 11 cases (69%) and the mitochondrial genome in 5 cases (31%). The most common mitochondrial disorder was mitochondrial myopathy, encephalopathy, lactic acidosis, and stroke-like episodes (MELAS, MIM #540000, n = 3) caused by mitochondrial DNA variants.

Heterozygous 28%

Nuclear gene defect 90% Hemizygous 12%

Genetic Zygosity Chromosomal abnormality abnormalities 4% Compound Mitochondrial heterozygous 24% DNA defect 6%

Homozygous 37%

Fig. 7. Donut charts presenting the proportion of genetic abnormalities and the zygosity of nuclear gene defects.

Nine patients (10%) were diagnosed with a Finnish disease heritage disorder displayed in Table 5. Additionally, rare or previously undescribed phenotypes included neonatal Alexander disease and 20 GWMDs that were either recently characterised or not previously associated with white matter abnormalities. These included the TAF1C related disorder.

Table 5. Patients with Finnish disease heritage disorders (n = 8).

Disorder MIM number Gene n Salla disease 604369 SLC17A5 3 Congenital muscular dystrophy-dystroglycanopathy type A3 253280 POMGNT1 2 (muscle-eye-brain disease) Mitochondrial DNA depletion syndrome 7 (IOSCA) 271245 TWNK 2 Neuronal ceroid lipofuscinosis 5 256731 CLN5 1 Abbreviations: IOSCA, infantile-onset spinocerebellar ataxia; MIM, Mendelian Inheritance in Man.

50 5.3.1 Recently discovered disorders and disorders not previously associated with white matter abnormalities (I and II)

Table 6 presents 29 patients (36%) with 20 recently characterised disorders and disorders, where white matter abnormalities have not been associated with the genetic aetiology. Recently characterised disorders included ataxia-pancytopenia syndrome (MIM #159550, SAMD9L, n = 3); fibrosis, neurodegeneration, and cerebral angiomatosis (FINCA disease, MIM #618278, NHLRC2, n = 3); myelodysplasia, infection, restriction of growth, adrenal hypoplasia, genital phenotypes, and enteropathy (MIRAGE syndrome, MIM #617053, SAMD9, n = 1); X-linked mental retardation 102 (MIM #300958, DDX3X, n = 1); and the TAF1C related disorder defined in study II (n = 1). Disorders not previously associated with white matter abnormalities included Lujan-Fryns syndrome (MIM #309520, MED12, n = 2), mucolipidosis II (MIM #252500, GNPTAB, n = 2), cardiofaciocutaneous syndrome (MIM #115150, BRAF, n = 1), autosomal dominant spastic paraplegia 4 (SPG4, MIM #182601, SPAST, n = 2), and three epileptic encephalopathies, early infantile epileptic encephalopathy 34 (EIEE34, MIM #616645, SLC12A5, n = ), EIEE42 (MIM #617106, CACNA1A, n = 1), and EIEE44 (MIM #617132, UBA5, n = 1).

5.3.2 Novel TAF1C related disorder (II)

Study II included two patients with clinical phenotypes not compatible with any previously known disorder. The variants related to PEHO syndrome and PEHO- like phenotype were excluded in the Finnish index patient at the research laboratory of Professor Anna-Elina Lehesjoki (University of Helsinki, Finland). Then, the exome was studied in collaboration with the research group of Professor Rita Horvath (Newcastle University and University of Cambridge, UK). A candidate variant of TAF1C was identified. A Jordanian patient harbouring another TAF1C variant was identified through GeneMatcher (Sobreira et al., 2015) and recruited for the study. Clinical data were gathered in collaboration with Dr. Tawfiq Froukh (Philadelphia University, Amman, Jordan) and Dr. Latifa Maraqa (PKU & Genetic Consultation Clinic, Amman, Jordan).

52 52

Table 6. Disorders that were either recently characterised or not previously associated with white matter abnormalities (n = 29).

Disorder, MIM number n Sex Affected Zygosity Clinical features gene Ataxia-pancytopenia syndrome, 3 M (2) and SAMD9L Het. Migraine, nystagmus, ADHD, cytopenias, myelodysplastic # 159550 F (1) syndrome Cardiofaciocutaneous syndrome 1 F BRAF Het. Hypotonia, macrocephaly, ID, cardiomyopathy, growth failure 1, # 115150 COXPD7, # 613559 1 M C12orf65 Hom. Spastic paraparesis, ataxia, ID EIEE34, # 616645 1 F SLC12A5 Hom. Hypotonia, epilepsy, ID EIEE42, # 617106 1 F CACNA1A Het. Ataxia, choreoathetosis, epilepsy, ID EIEE44, # 617132 1 M UBA5 Compound het. Spastic tetraparesis, ataxia, epilepsy, ID, microcephaly FINCA disease, # 618278 3 M NHLRC2 Compound het. Dystonic tetraparesis, epilepsy, progressive respiratory insufficiency, anaemia Lissencephaly 3, # 611603 1 F TUBA1A Het. Spastic tetraparesis, epilepsy, ID, microcephaly Lujan-Fryns syndrome, # 309520 2 M MED12 Hemi. Motor developmental delay, ID, optic nerve hypoplasia MICPCH, # 300749 2 F CASK Het. Hypotonia, ID, epilepsy, microcephaly MIRAGE, # 617053 1 M SAMD9 Het. Spasticity, ataxia, myelodysplastic syndrome MRD13, # 614563 1 M DYNC1H1 Het. Hypotonia, epilepsy, ID, microcephaly X-linked mental retardation 102, 1 F DDX3X Het. Motor developmental delay, ID, microcephaly # 300958 Mucolipidosis II, # 252500 2 F GNPTAB Compound het. Spasticity, ID, congestive heart failure, skeletal abnormalities NBIA5, # 300894 2 F WDR45 Het. Spastic tetraparesis, epilepsy, ID Rett syndrome, congenital 1 F FOXG1 Het. Hypotonia, athetosis, epilepsy, ID, microcephaly variant, # 613454 SPG4, # 182601 2 M and F SPAST Het. Spastic paraparesis, mild ID Spinocerebellar ataxia 29, # 1 M ITPR1 Het. Motor and speech developmental delay, ataxia, atrial septal 117360 defect

Disorder, MIM number n Sex Affected Zygosity Clinical features gene TAF1C related disorder 1 F TAF1C Hom. Hypotonia, tetraparesis, epilepsy, ID, microcephaly, precocious puberty VAIHS, # 615688 1 F ADA2 Hom. Motor developmental delay, epilepsy, ID, skin vasculitis F: female; hemi.: hemizygous; het.: heterozygous; hom.: homozygous; ID: intellectual disability; M: male. For disorder abbreviations, see Table 3. 53

The Finnish index patient was the fourth child of healthy consanguineous Finnish parents. At three months of age, she was referred to a child neurologist due to poor eye contact, opisthotonus, and delayed motor development. Later, she developed refractory epilepsy, spasticity, and precocious puberty. At six years of age, she had microcephaly (-2.3 SD) and intellectual impairment. She was nonambulatory, and her speech was limited to vocalisations. Brain MRI findings at 6 months, 17 months, and 3 years of age are shown in Figure 8. Initially at 6 months, brain MRI was normal. Later, at ages 17 months and 3 years, progressive brain atrophy and white matter T2-hyperintensities developed.

Fig. 8. Neuroradiological findings of the Finnish patient with TAF1C related disorders. T1-weighted (T1W, A–B), T2-weighed (T2W, C), and T2 fluid-attenuated inversion recovery (FLAIR, D) images at 6 months of age show normal myelination relative to age and no atrophy or white matter abnormalities. At 17 months, mild cerebral, cerebellar, and brain stem atrophy is seen on T1W (E) and T2W (F and G) with hypoplasia of corpus callosum (E, arrow). Cerebellar peduncles are atrophic (G, arrows). The FLAIR coronal image shows T2-hyperintensities in periventricular white matter (H, arrows). At three years, T1W (I) and T2W (J and K) images demonstrate progression of atrophy in the cerebellum, cerebellar peduncles, and brain stem. T2-hyperintensities are unchanged on the FLAIR image (L, arrows). Adapted from Knuutinen et al. (2020b).

The Jordanian patient was born to consanguineous parents of Jordanian and Syrian ancestry. At the age of 4 months, she started displaying global developmental delay. Later, she developed spastic paraplegia, hypotonia, and speech impairment. Her brain MRI at the age of 6 months was normal, and no follow-up imaging was done. Exome sequencing identified two previously unreported homozygous missense variants in the TAF1C gene, c.1165C>T p.Arg389Cys in the Finnish patient and c.1213C>T p.Arg405Cys in the Jordanian patient. The variants were located in the exon 10 of TAF1C, as shown in Figure 9. The Finnish variant was heterozygous in the Finnish parents. Figure 10 shows the molecular study findings in the TAF1C related disorder. The variants were highly conserved across species and rare in gnomAD and SISu databases (frequency of < 0.002 and 0.004, respectively) and there were no reported homozygous variants. In silico prediction tools SIFT, PolyPhen-2, and CADD suggested pathogenicity. In fibroblasts derived from the Finnish patient, substantial reduction of TAF1C protein expression in Western blot and mRNA expression in qPCR was seen.

Fig. 9. Location of TAF1C on chromosome 16 (A) and the structure of the TAF1C gene with identified variants (B). Adapted from Knuutinen et al. (2020b).

Fig. 10. Conservation of TAF1C variant amino acid positions (A). In patient-derived fibroblasts of the Finnish patient, Western blot (B), protein concentration in immunoblotting (C), and mRNA expression in real-time qPCR (D) show decreased TAF1C expression compared to controls. Adapted from Knuutinen et al. (2020b).

5.3.3 Neonatal Alexander disease (III)

Study III described a male patient with a rare phenotype of neonatal-onset Alexander disease. The locations of other GFAP variants associated with neonatal Alexander disease in the literature were studied. Alexander disease is a classic leukodystrophy disorder. At birth, the Finnish patient had the head circumference of 37.5 cm (2.1 SD), indicating macrocephaly. Drug-resistant epileptic seizures began on the 4th day of life. The patient demonstrated truncal hypotonia and lethargy at the age of 2.5 weeks and died at the age of 6 weeks after prolonged seizures.

Neuroradiological findings are shown in Figure 11. Brain ultrasonography demonstrated progressive hydrocephalus after two weeks of age. Brain MRI at the age of three weeks showed a swollen tectum compressing the cerebral aqueduct, contributing to hydrocephalus. Characteristic findings of Alexander disease included (1) white matter abnormalities with frontal predominance, (2) a periventricular rim with T1-hyperintensity and T2-hypointensity, and abnormalities of (3) basal ganglia, optic chiasm, (4) pons, and medulla.

Fig. 11. Neuroradiological findings in the patient with neonatal Alexander disease. Ultrasonography (US) shows progressive hydrocephalus at ages 1 (A), 2 (B), 3 (C), and 4.5 (D) weeks of age. T1-weighted (T1W, E–H) and T2-weighted (T2W, I–L) brain MRI at three weeks of age demonstrates characteristic findings of Alexander disease. The basal ganglia (E, arrows) are swollen and T1-hyperintense. A periventricular rim (F) and deep white matter hyperintensities (G) are visible in T1W images. The tectum (H and I, arrows) is swollen, compressing the cerebral aqueduct. The infratentorial abnormalities are T2-hyperintensity in the pons (J, arrows) and medulla (K and L, arrow). Adapted from Knuutinen et al. (2018).

The diagnosis of Alexander disease was made based on genetic testing and brain histopathology findings. DNA sequencing revealed a previously unreported heterozygous de novo c.1106T>C p.Leu369Pro variant of GFAP. The variant was likely pathogenic, according to in silico prediction programs, Condel and PON-P2. The calculated Condel score was 0.729 (0 = neutral, 1 = deleterious), and PolyPhen- 2 probability for pathogenicity was 0.959. Post-mortem cortical histopathology supported the diagnosis of Alexander disease with eosinophilic cytoplasmic inclusions staining positive for GFAP and αB-crystallin. All reported cases of neonatal Alexander disease (R. Li et al., 2005; Meins et al., 2002; Rodriguez et al., 2001; Springer et al., 2000; van der Knaap et al., 2005; Vázquez et al., 2008) were reviewed in study III. The protein structure of GFAP with variants in neonatal Alexander disease is shown in Figure 12. The variant of the Finnish patient was located in the coil 2B domain of GFAP, where variants related to earlier disease onset and death are concentrated.

neonatal form

73 76 79 88 97115 239 253349 352 364 366 369

N 1A 1B 2A 2B C

6373 76 77 79 88115 207 210 235 239 244 253 279 359 373 374 416 other forms

Fig. 12. Protein structure of GFAP with reported variants in neonatal Alexander disease shown above (blue). In comparison, all other forms of Alexander disease as reported by Li et. al (2005) are shown below (green). The four α-helical domains (grey) are connected by nonhelical linker regions (white).

6 Discussion

6.1 Epidemiology

In this study, the cumulative incidence of childhood-onset GWMDs was 30 per 100,000 live births and the observed incidence increased during the study period. Previous studies have reported lower estimates ranging from 1.2–13. Several factors can account for this difference. First, population-based studies can be confounded by heterogeneous study population areas, for example, in single-centre studies of large metropolitan areas (Bonkowsky et al., 2010; Vanderver et al., 2012). Secondly, national databases (Stellitano et al., 2016) and questionnaire-based studies (Heim et al., 1997) can be affected by underreporting of cases. Thirdly, with the implementation of next-generation sequencing, the diagnostics of GWMDs has developed rapidly, translating into a higher diagnostic rate. This was recently demonstrated by Helman et al. (2020), who achieved a diagnostic yield of 85% with genome sequencing. The finding of higher cumulative incidence is also supported by the expected incidence based on allele frequencies in genomics databases as estimated by Soderholm et al. (Soderholm et al., 2020).

6.2 Clinical characteristics and natural history (I–III)

The clinical phenotypes of GWMDs are highly variable even among siblings (Hamilton et al., 2018). Nonetheless, developmental delay, intellectual disability, hypotonia, and spasticity are key clinical findings of GWMDs (Parikh et al., 2015), as confirmed by this study. Ataxia and extrapyramidal signs were also notable (29% and 23%, respectively). Epilepsy was common (36%), confirming the findings of Bonkowsky et al. (2010), who reported 49% of leukodystrophy patients having epilepsy. The onset of epilepsy occurred later in leukodystrophies (median 110 months) than in gLEs (median 12 months), but the difference was not statistically significant (p = 0.52). This could relate to the selective involvement of the CNS white matter defining leukodystrophies. Epilepsy is a neuronal disease (Fisher et al., 2014), and primary grey matter or neuronal disorders are by definition classified as gLEs (Vanderver et al., 2015). Additionally, gLEs include disorders where early- onset epilepsy is a prominent feature, including epileptic encephalopathies EIEE34, EIEE42, and EIEE 44.

Although the natural history of some disorders has been recorded (Hamilton et al., 2018), data on the natural history of GWMDs in general is scarce. Age at disease onset (median 5 months) and diagnosis (median 46 months) was similar to an Iranian study (Ashrafi et al., 2018). Moreover, the age at diagnosis was significantly older in gLEs than in leukodystrophies (median 57 and 20 months, respectively, p = 0.01), while age at onset was similar between groups (5 and 6 months, respectively, p = 0.31). This could suggest that making the diagnosis of gLEs could be more challenging or time-consuming than in leukodystrophies. The number of known gLE disorders and related variants is excessive, and mitochondrial aetiologies not identifiable in nuclear NGS appear more common in this disease group. Also, many gLEs were only recently defined, and diagnosis was not possible before this.

6.3 Genotypes and phenotypes of genetic white matter disorders

In total, 49 distinct childhood-onset GWMDs were observed. Nuclear single gene variants were the most common (90%), and mitochondrial genome defects (6%) and chromosomal abnormalities (4%) were a minority. Not surprisingly, homozygous and compound heterozygous variants accounted for 60% of nuclear gene variants, indicating a frequent autosomal recessive inheritance. Male sex was more common (56%), relating to X-linked disorders like ALD. Indeed, ALD was the most frequent disorder among all GWMDs. Mitochondrial aetiology was noted in 21% of the cases, and disease-causing genetic defects were located in the mitochondrial genome in five cases. Traditional gene panels or nuclear genome sequencing cannot identify these variants. Therefore, mitochondrial studies should be considered in unsolved cases of GWMDs. Eight patients (10%) with Finnish disease heritage disorders were observed. Salla disease and congenital muscular dystrophy-dystroglycanopathy A3 (MIM #253280) were the most frequent. Finnish disease heritage refers to rare hereditary diseases overrepresented in Finland due to Finnish population history. Phenomena like the founder effect, genetic drift, isolation, and population bottlenecks have enriched certain variants in the Finnish population, while some variants are less frequent than in other populations (Norio, 2003; Polvi et al., 2013). For example, cystic fibrosis is substantially rarer in Finland than elsewhere in Europe. In the current Finnish cohort, 10% had disorders belonging to the Finnish disease heritage, but the figure was 4% in a study from the UK (Stellitano et al., 2016). Moreover, certain disorders common elsewhere were not present in the Finnish population.

These included Aicardi-Goutières syndrome, Pelizaeus-Merzbacher, and vanishing white matter disease. The distribution of the most common leukodystrophies in the current study and previous studies (Ashrafi et al., 2018; Bonkowsky et al., 2010; Stellitano et al., 2016) is compared in Table 7.

Table 7. Relative frequency of the most common leukodystrophies in the current and previous studies. Note that percentages refer to proportions of leukodystrophies, not all genetic white matter disorders.

Relative frequency Disorder Finland UK USA Iran ALD 38% 21% 4% 8% Krabbe 19% 16% 2% 0% Salla disease 19% 0.3% 0% 0% MLD 6% 22% 8% 35% AGS 0% 7% 0% 0% Canavan 0% 5% 1% 10% VWM 0% 5% 2% 0% PMD 0% 5% 7% 2% AGS: Aicardi-Goutières syndrome; ALD: cerebral adrenoleukodystrophy; MLD: metachromatic leukodystrophy; PMD: Pelizaeus-Merzbacher disease; UK: United Kingdom; USA: United States of America; VWM: vanishing white matter disease.

6.3.1 Novel and uncommon genetic white matter disorders

Nowadays, novel disorders affecting CNS white matter are frequently characterised. This study identified 20 disorders that were either recently characterised or not previously associated with white matter abnormalities. These included a novel disorder related to TAF1C in study II. In addition, a rare neonatal phenotype of Alexander disease was described in study III. Recently discovered disorders and genetic aetiologies (Table 6) included ataxia-pancytopenia syndrome (Gorcenco et al., 2017; Tesi et al., 2017), FINCA disease (Uusimaa et al., 2018), MIRAGE syndrome (Narumi et al., 2016), X-linked mental retardation 102 (Snijders Blok et al., 2015), and the TAF1C related disorder defined in study II. The characterisation of these disorders was substantially facilitated by the implementation of NGS. Earlier defined disorders not previously associated with white matter abnormalities included Lujan-Fryns syndrome, mucolipidosis II, cardiofaciocutaneous syndrome 1, SPG4, and epileptic encephalopathies EIEE34, EIEE42, and EIEE44. Although not previously

considered as GWMDs, white matter abnormalities have been reported relating to some disorders, which indicates white matter involvement. For example, white matter atrophy has been described with cardiofaciocutaneous syndrome 1 (Yoon, Rosenberg, Blaser, & Rauen, 2007) and SPG4 (Lindig et al., 2015). Agenesis or hypoplasia of corpus callosum is rarely seen in Lujan-Fryns syndrome (Lerma- Carrillo et al., 2006) and cardiofaciocutaneous syndrome 1 (Yoon et al., 2007). In study II, a novel TAF1C related GWMD was described. This was the first description of a neurological genetic disease related to TAF1C. Laboratory studies supported the pathogenicity of the identified variant c.1165C>T p.Arg389Cys of TAF1C. An unrelated Jordanian patient with a different TAF1C variant and comparable phenotype was found, strengthening the association of the genotype and phenotype. This was consolidated by a comparison with a UBTF related disorder with a strikingly similar phenotype (Bastos et al., 2020; Edvardson et al., 2017; Sedláčková et al., 2019; Toro et al., 2018). TAF1C and upstream binding factor (UBF) encoded by UBTF are both transcription factors of the RNA polymerase I (Pol I) Transcription Initiation Complex. Its function is shown in Figure 13. Brain MRI findings of the Finnish patient with a TAF1C variant and the patients with a UBTF variant shared similarity as well, including diffuse brain atrophy, white matter T2-hyperintensities, and thinning of the corpus callosum. The findings suggest that the TAF1C variant is likely pathogenic with an autosomal recessive inheritance pattern. The exact pathomechanism of neurologic and white matter impairment is currently unknown, however. A possible hypothesis is that impairment of Pol I and related ribosomal biogenesis leads to cell cycle arrest and induction of apoptosis (Hetman & Slomnicki, 2019; Pelava, Schneider, & Watkins, 2016).

Fig. 13. Components of RNA polymerase I transcription initiation complex. Adapted from Knuutinen et al. (2020b).

In study III, neonatal-onset Alexander disease was studied. A classification based on age at onset divides Alexander disease into infantile, juvenile, and adult forms (Johnson & Brenner, 2003). In distinction to the infantile form, a fourth form, neonatal, has been proposed, with onset within the first month of life (Singh, Bixby, Etienne, Tubbs, & Loukas, 2012; Springer et al., 2000). The study described a patient with a neonatal phenotype and a novel GFAP variant. All other reported cases of neonatal Alexander disease were reviewed (R. Li et al., 2005; Meins et al., 2002; Rodriguez et al., 2001; Springer et al., 2000; van der Knaap et al., 2005; Vázquez et al., 2008). The findings solidify the phenotype of neonatal Alexander disease with a considerably poorer prognosis than later-onset forms. The brain MRI findings of the patient fulfilled all five diagnostic criteria of Alexander disease excluding contrast enhancement, which was not studied because of the patient’s young age (van der Knaap et al., 2001). However, compared to findings common in the infantile form, the patient had only mild white matter signal abnormality. This was likely due to the very early stage of myelination in the neonatal period. At a very young age, the distinction between unmyelinated white matter and normal myelinating white matter is subtle (van der Knaap et al., 2001, 2005). When assessing age at onset and genotype, a higher frequency of coil 2B–affecting variants was noted in the neonatal-onset form. Also, cases with coil 2B variants died earlier than other cases with other GFAP variant locations. This could imply that variants of coil 2B are related to higher pathogenicity and more severe phenotype than variants affecting other domains of GFAP.

6.4 Strengths and limitations of the study

Study I was a population-based, retrospective, longitudinal cohort study of all GWMD patients diagnosed at the Clinic for Children and Adolescents of Oulu University Hospital between 1990 and 2019. Studies II and III examined isolated cohort cases of a novel TAF1C related disorder and a rare neonatal phenotype of Alexander disease, respectively. The strengths of this study were the population-based study design, the method of patient identification, the high coverage of the study population area, and the high availability of patient data in the Finnish health care system. All GWMD cases were presumably evaluated at some point at the Oulu University Hospital, which is the only tertiary care centre within the well-defined study population area. Retrospective search of patient records was complemented by prospective patient identification. Good availability of patient records dating back three decades is

uncommon and enabled the long span of the study. In the Finnish comprehensive public healthcare system, child health clinics monitor the welfare and development of over 90% of the paediatric population (National Institute for Health Welfare, 2019). Routine developmental surveillance and a low threshold of referral to a child neurologist enable early identification of GWMD-related findings (e.g., motor developmental delay, hypotonia, spasticity, and speech delay). Additional strengths included the systematic review of brain MRI findings, as advocated by Schiffmann and van der Knaap (2009). A substantial strength of the study was its long study period. It covered three decades, during which the availability of genomic sequencing has increased historically, as described in section 2.5. First, metabolic testing receded as targeted genetic testing became mainstream. Then, in the 2010s, the breakthrough of NGS (exome and genome sequencing) in clinical practice enabled the exhaustive genetic diagnostics seen today. Combined with the increased knowledge of genetic aetiologies, this could explain the higher cumulative incidence of GWMDs observed in later decades of study I. However, the long study period also introduces possible limitations in light of the data availability in the earlier study decades. The limitations of the study included the availability of MRI data and the ICD- based patient identification. Firstly, if no brain MRI data was available, cases were excluded from the study. The data were more readily preserved in the digital hospital database than in the manual archive. This disproportionate availability of MRI data might introduce selection bias that censors earlier diagnosed patients and favours later presenting patients. Secondly, patients were identified retrospectively through ICD-9 and ICD-10 code searches in addition to prospective identification. ICD-10 does not include a general category of GWMD or findings of white matter abnormalities (WHO, 1993). Interestingly, WHO has added a category for leukodystrophies to its 11th revision (2019). These factors and the Finnish disease heritage should be noted when interpreting and generalising the study findings.

6.5 Prospects for future research

Research on treatments and screening of GWMDs is speeding up, highlighting the need for comprehensive data on epidemiology and natural history. As a continuation of study I, a multicentre research project is being established by the clinical researchers in four Scandinavian countries, Finland, Sweden, Norway, and Denmark. Patient selection is crucial for potentially lethal treatments like HSCT. Furthermore, early administration of therapy, preferably in the presymptomatic

stage of disease, is essential to maximum efficacy. Considering this, precise and reliable screening algorithms are necessary. Additionally, there is a demand for research on factors modifying treatment efficacy and natural disease course. As the specific disorders are rare, clinical research networks like the LEUKOTREAT project (European Commission, 2014), the European Reference Networks (European Commission, 2017), and the Nordic White Matter Diseases Network are essential for patient recruitment and the design of future clinical trials and studies. The advancements brought on by NGS continue to drive the characterisation of new GWMDs (van der Knaap et al., 2019). Implementing artificial intelligence in the analysis of MRI data (Valliani et al., 2019) and novel imaging techniques (Schmidbauer et al., 2019) might further add to the momentum of GWMD research. Application of new health-care data repositories and data lake technologies currently investigated in Finland hopefully promote epidemiological and medical research in general (Mehmood et al., 2019; Parikka, 2019; Rajala et al., 2020). Findings of novel phenotypes and genetic aetiologies may translate into improved understanding of GWMD pathomechanisms on a molecular and cellular level. Knowledge of genetic phenotypes benefits clinicians well as individual patients and their families. Data on genetic aetiologies and inheritance patterns enable, for example, prenatal diagnostics and family counselling. On a larger scale, epidemiological data guide health policymakers and direct the use of health care resources. For example, when the Finnish Institute for Health and Welfare re- evaluates the need for newborn screening, the relatively high incidence of treatable disorder ALD (2.2 per 100,000 live births) could support its addition to the selection of screened disorders.

7 Conclusions

This work was intended to describe the epidemiology, clinical features, and specific genetic phenotypes of childhood-onset GWMDs in Northern Finland during three decades from 1990 to 2019. The findings facilitate the planning of future clinical trials of GWMD treatments and screening protocols in addition to aiding clinical diagnostics and patient counselling. The following conclusions can be drawn from this work: 1. The cumulative childhood incidence of childhood-onset GWMDs is 30 per 100,000 live births in Northern Finland. Of all GWMDs, 20% are leukodystrophies and 80% gLEs. Mitochondrial aetiology was noted in 21% of the cases. The effect of the Finnish disease heritage manifests as the higher frequency of certain disorders and the rarity of others. 2. Developmental delay, intellectual disability, hypotonia, and spasticity are the most frequent findings of GWMDs. Natural history is characterised by early onset, considerable childhood mortality, and variable development of epilepsy. The diagnosis of gLEs is made later than that of leukodystrophies. 3. Twenty recently characterised disorders or disorders not previously associated with white matter abnormalities were identified, highlighting the fast pace of current GWMD research. These included ataxia-pancytopenia syndrome and FINCA disease. 4. A novel genetic disorder was characterised associating a neurologic phenotype with the gene TAF1C for the first time. 5. A rare patient with neonatal-onset Alexander disease was described along with a review of the literature consolidating the recently proposed phenotype of neonatal Alexander disease.

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Original publications

I Knuutinen, O., Oikarainen, J., Suo-Palosaari, M., Kangas, S., Rahikkala, E., Pokka, T., Moilanen, J., Hinttala, R., Vieira, P., Uusimaa, J. (2021). Epidemiology, clinical, and genetic characteristics of paediatric genetic white matter disorders in Northern Finland. Developmental Medicine & Child Neurology. In press. II Knuutinen, O., Pyle, A., Suo-Palosaari, M., Duff, J., Froukh, T., Lehesjoki, A.-E., Kangas, S., Cassidy, J., Maraqa, L., Keski-Filppula, R., Kokkonen, H., Uusimaa, J., Horvath, R, Vieira, P. (2020). Homozygous TAF1C variants are associated with a novel childhood-onset neurological phenotype. Clinical Genetics, 98, 493–498. III Knuutinen, O., Kousi, M., Suo-Palosaari, M., Moilanen, J. S., Tuominen, H., Vainionpää, L., Joensuu, T., Anttonen, A.-K., Uusimaa, J., Lehesjoki, A..-E., Vieira, P. (2018). Neonatal Alexander Disease: Novel GFAP Mutation and Comparison to Previously Published Cases. Neuropediatrics, 49, 256-261. Reprinted with permission from Developmental Medicine & Child Neurology (I), Clinical genetics (II), and Neuropediatrics (III).

Original publications are not included in the electronic version of the dissertation.

86 ACTA UNIVERSITATIS OULUENSIS SERIES D MEDICA

1600. Haukilahti, Anette (2021) Sudden cardiac death, cardiac mortality, and morbidity in women : electrocardiographic risk markers 1601. Lukkari, Sari (2021) Association between perinatal and childhood risk factors with mental disorders in adolescence 1602. Karjula, Salla (2021) Long-term consequences of polycystic ovary syndrome on mental health and health-related quality of life 1603. Kinnunen, Lotta (2021) The association of parental somatic illness with offspring’s mental health 1604. Jaurila, Henna (2021) Tissue repair and metabolic dysfunction in sepsis 1605. Kiviahde, Heikki (2021) Occlusal characteristics in Northern Finland Birth Cohort (NFBC) 1966 subjects : a longitudinal study 1606. Fouda, Shaimaa (2021) Studies on tooth loss, mandibular morphology and dental prostheses : clinical, radiological and in vitro examinations 1607. Leinonen, Anna-Maiju (2021) Technology for promoting physical activity in young men 1608. Lingaiah, Shilpa (2021) Markers assessing bone and metabolic health in polycystic ovary syndrome 1609. Ukkola, Leila (2021) Potilaan informointi ionisoivalle säteilylle altistavien kuvantamistutkimusten yhteydessä 1610. Mattila, Orvokki (2021) Proteolytic processing of G protein-coupled receptor 37 by ADAM10 and furin 1611. Gebre, Robel Kebede (2021) Computed tomography assessment of low-energy acetabular fractures in the elderly 1612. Siira, Heidi (2021) Ikääntyneiden näkövammaisten henkilöiden näönkuntoutus, terveyteen liittyvä elämänlaatu ja siihen yhteydessä olevat tekijät : kahden vuoden monimenetelmäinen seurantatutkimus 1613. Choudhary, Priyanka (2021) Early origins of cardiometabolic risk factors : lifecourse epidemiology and pathways 1614. Raatikainen, Ville (2021) Dynamic lag analysis of human brain activity propagation : a fast fMRI study

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