The Cellular and Molecular Consequences of Maternal Immune Activation on the Cytoarchitecture of the Developing Spinal Cord

Home , ANK2, DPF2, Forkhead box b1, MAP11, Myelin regulatory factor, NPAS3

The cellular and molecular consequences of maternal immune activation on the cytoarchitecture of the developing spinal cord

Submitted in fulfilment of the requirements for the degree of Doctor of Philosophy

Rebecca C Anderson

Supervisor: Professor Kieran McDermott Co-Supervisor: Dr Gerard O’Keeffe

February 2020 Table of Contents: Declaration: ...... i Acknowledgements: ...... ii Abstract: ...... v Abbreviations: ...... viii Table of Figures: ...... xv Table of Tables:...... xviii

Chapter 1. Introduction ...... 1 1.1 Nervous system development ...... 2 1.1.1 Neurulation ...... 2 1.1.2 Cell signalling and nervous system development ...... 4 1.1.2.1 Stem cell renewal and pluripotency ...... 4 1.1.2.2 Migration ...... 5 1.1.2.4 Cytokines, signalling, and nervous system development ...... 7 1.1.3 Spinal cord development & anatomy ...... 8 1.2. Cellular development ...... 11 1.2.1 Neurogenesis ...... 11 1.2.2 Oligodendrogliogenesis ...... 13 1.2.3 Astrogliogenesis ...... 17 1.2.4 Microgliogenesis ...... 19 1.3 Maternal immune activation ...... 22 1.3.1 Epidemiological studies ...... 22 1.3.2 Animal models ...... 24 1.3.2.1 Polyinosinic:polycytidylic acid ...... 24 1.3.2.2 Lipopolysaccharide ...... 24 1.3.2.3 Other models of maternal infection...... 25 1.3.3 The rodent spinal cord as a model of MIA ...... 25 1.3.4 MIA and Neurons ...... 26 1.3.5 MIA and oligodendrocytes...... 31 1.3.5.1 Cytokines, inflammation and oligodendrocyte development ...... 31 1.3.5.2 The consequences of MIA on the developing oligodendrocyte ...... 32 1.3.6 MIA and Astrocytes ...... 37 1.3.6.1 The role of astrocytes in mediating the consequences of MIA ...... 37 1.3.6.2 The consequences of MIA on the developing astrocyte ...... 37

1.3.7 MIA and Microglia ...... 40 1.3.7.1 Cytokines, inflammation and microglial development ...... 40 1.3.7.2 The consequences of MIA on the developing microglia...... 41 1.4 MicroRNAs ...... 44 1.4.1 Structure and function of microRNAs ...... 45 1.4.2 Nomenclature ...... 46 1.4.3 MicroRNAs and cellular development ...... 47 1.4.3.1 MicroRNAs in neurogenesis ...... 47 1.4.3.2 MicroRNAs in astrogliogenesis ...... 49 1.4.3.3 MicroRNAs in microgliogenesis...... 50 1.4.3.4 MicroRNAs in Oligodendrogliogenesis ...... 50 1.5 Aims and objectives: ...... 51

Chapter 2. Methodology ...... 52 2.1 Time mating and maternal immune activation...... 53 2.2 Tissue collection and processing ...... 54 2.2.1 Tissue collection for embryonic study ...... 54 2.2.2 Tissue collection for postnatal study ...... 54 2.2.3 Gelation coating of slides ...... 55 2.2.4 Cryosectioning ...... 55 2.3 Immunohistochemical staining ...... 57 2.3.1 Immunofluorescent staining ...... 57 2.3.2 Quantification of immunofluorescent staining...... 60 2.3.2.1 Olig2 analysis ...... 61 2.3.2.2 MBP ...... 63 2.3.2.3 Iba-1 ...... 64 2.3.2.4 GFAP ...... 64 2.3.2.5 Reelin ...... 65 2.4 RNA extraction and yield determination ...... 66 2.4.1 Total RNA extraction ...... 66 2.4.2 Quality and yield determination ...... 66 2.5 RNA expression studies ...... 67 2.5.1 Microarray expression profiling...... 67 2.5.2 RNASeq ...... 68 2.5.3 RT qPCR ASSAY ...... 68 2.6 Bioinformatic analysis ...... 71 2.6.1 Literature review ...... 71

2.6.2 miRECORDS ...... 71 2.6.3 Webgestalt...... 72 2.6.4 String analysis ...... 72

Chapter 3. The effect of MIA on the oligodendrocytes of the spinal cord ...... 74 3.1 Chapter Abstract...... 75 3.2 Introduction ...... 76 3.3 Chapter aims...... 79 3.4 Results: ...... 80 3.4.1 Characterisation of Olig2+ cell distribution in the embryonic spinal cord during normal development at E12 and E16; and following MIA at E12 and E16 ...... 80 3.4.1.1 Olig2+ cell distribution in the E12 spinal cord in normal development, and post MIA with 100µg/kg LPS ...... 80 3.4.1.2 Olig2+ cell distribution in the E16 spinal cord in normal development, and post MIA with 100µg/kg LPS ...... 82 3.4.2 Characterisation of Olig2+ cell distribution in the postnatal spinal cord during normal development; and following MIA at E12 and E16 ...... 87 3.4.2.1 Olig2+ cell distribution in the P14 spinal cord during normal development ...... 87 3.4.2.2 Olig2+ cell number in the P14 spinal cord following MIA with 100µg/kg LPS on E12 or E16 ...... 88 3.4.3 Characterisation of MBP expression in the postnatal spinal cord during normal development; and following MIA at E12 and E16 ...... 90 3.4.3.1 MBP expression in the P14 spinal cord during normal development ...... 90 3.4.3.2 MBP expression in the P14 spinal cord following MIA with 100µg/kg LPS on E12 or E16 ...... 92 3.5 Discussion ...... 95

Chapter 4. The effect of MIA on the microglia and astrocytes of the spinal cord 103 4.1 Chapter Abstract...... 104 4.2 Introduction ...... 105 4.3 Chapter aims...... 109 4.4 Results ...... 110 4.4.1 Characterisation of Iba-1+ cell distribution in the embryonic spinal cord during normal development at E12, E14 and E16; and following MIA at E12 and E16 ...... 110 4.4.1.1 Iba-1+ cell distribution in the E12 spinal cord in normal development and post MIA with 100µg/kg LPS ...... 110 4.4.1.2 Iba-1+ cell distribution in the E14 spinal cord in normal development and post MIA with 50µg/kg LPS ...... 112 4.4.1.3 Iba-1+ cell distribution in the E16 spinal cord in normal development and post MIA with 100µg/kg LPS ...... 115

4.4.2 Characterisation of Iba-1+ cell distribution in the rostral P14 spinal during normal postnatal development; and following MIA at E12 and E16 ...... 119 4.4.2.1 Iba-1+ cell distribution in the P14 spinal cord during normal postnatal development ...... 119 4.4.2.2 Iba-1+cell number in the P14 spinal cord following MIA with 100µg/kg LPS on E12 or E16 ...... 120 4.4.3 Characterisation of GFAP expression in the rostral P14 spinal cord during normal postnatal development; and following MIA at E12 and E16 ...... 121 4.4.3.1 GFAP expression in the P14 spinal cord during normal postnatal development .. 121 4.4.3.2 GFAP expression in the P14 spinal cord following MIA with 100µg/kg LPS on E12 or E16 ...... 123 4.5 Discussion ...... 126

Chapter 5. The effect of Maternal Immune Activation on Reelin expression in the spinal cord ...... 132 5.1 Chapter Abstract...... 133 5.2 Introduction: ...... 134 5.3 Chapter Aims ...... 142 5.4 Results ...... 143 5.4.1 Characterisation of reelin expression in the embryonic spinal cord at E12 and E16 during normal development; and following MIA at E12 and E16 ...... 143 5.4.1.1 Reelin expression in the E12 spinal cord in normal development and post MIA with 100µg/kg LPS ...... 143 5.4.1.2 Reelin expression in the E16 spinal cord in normal development and post MIA with 50µg/kg or 100µg/kg LPS ...... 145 5.4.2 Characterisation of reelin expression in the P14 spinal cord during normal development; and following MIA at E12 and E16 ...... 147 5.4.2.1 Reelin expression in the rostral P14 spinal cord during normal postnatal development ...... 147 5.4.2.2 Reelin expression in the P14 spinal cord following MIA with 100µg/kg LPS on E12 or E16 ...... 149 5.4.3 Co-localisation of reelin with markers of neurons, oligodendrocytes and microglia in the E16 and P14 spinal cords during normal development ...... 151 5.5 Discussion ...... 153

Chapter 6. Gene expression changes in the developing spinal cord following MIA ...... 158 6.1 Chapter Abstract...... 159 6.2 Introduction: ...... 160 6.3 Chapter Aims ...... 162 6.4 Results ...... 163

6.4.1 Microarray analysis ...... 163 6.4.1.1 Principal component analysis ...... 163 6.4.1.2 Gene expression 5hr post MIA with 100µg/kg LPS at E12 or E16 ...... 164 6.4.1.3 Bioinformatic interrogation of genes differentially expressed in the microarray analysis 5hr post MIA with 100µg/kg LPS at E16 ...... 168 6.4.2 RNASeq analysis ...... 170 6.4.2.1 Principal component analysis ...... 170 6.4.2.2 Gene expression at P14 following MIA with 100µg/kg LPS at E12 or E16 ...... 172 6.4.2.3 Bioinformatic interrogation of genes differentially expressed in RNASeq analysis at P14 post MIA with 100µg/kg LPS at E16 ...... 175 6.4.2.4 PCR confirmation of gene expression changes at P14 following MIA with 100µg/kg LPS at E16 ...... 178 6.5 Discussion ...... 180

Chapter 7. The role of miRNAs in Oligodendrogliogenesis: an in-silico study ..... 186 7.1 Chapter Abstract...... 187 7.2 Introduction: ...... 188 7.3 Chapter Aims ...... 192 7.4 Results ...... 193 7.4.1 Identification of predicted targets ...... 193 7.4.2 Gene ontology analysis...... 193 7.4.3 STRING analysis of protein-protein interactions ...... 194 7.5 Discussion ...... 202

Chapter 8. General Discussion ...... 210

Bibliography ...... 218

Appendix A ...... 265 Appendix B ...... 276 Appendix C ...... 305

Declaration:

I hereby declare that I am the sole author of this thesis. I declare that it has not been submitted to any other University or higher education institution, or for any other academic award in this University. References and acknowledgements have been made, where necessary, to the work of others.

Signature: Date:

27/02/2020

------

Rebecca Anderson Graduate Entry Medical School University of Limerick

Acknowledgements:

There are so many people to thank for their role in the completion of this PhD. First and foremost I would like to thank my supervisor Professor Kieran McDermott for his knowledge, guidance and support over the years, as well as the laughs. They were plentiful. Thank you for being just down the hall whenever I needed help, I appreciate it. I would also like to thank my co-supervisor Dr Gerard O’Keeffe for his constant presence on the end of an email as well as his feedback and suggestions. Thanks also to the staff of the Graduate Entry Medical School for making UL feel like home, and to

Tara Enright and Jason Radford for their help with the animal work and making UCC a home away from home for the short time I was there.

A PhD would be impossible without a building full of friends. A massive thanks goes out to the ladies of the graduate entry medical school lab for their help, suggestions, troubleshooting, laughter and at times of trouble (or dark room days!) sympathy. A special thanks is due to Joanne for coaxing me through PCR protocols for months on end! Thank you to Johnny for the year one company, and the summer students that came and went each year without fail for making the long days of summer in the lab more fun. Lex, Diane, Jane and Swiri Limerick would have been nothing without you

(and this thesis still wouldn’t be done!). Thanks for the chats and the support. The cawfii dates, the nights out and the loops around the pitches when everything was just a bit too overwhelming.

Thank you to my non-UL friends, you all know who you are. To the non-scientists among you for listening to my constant talk of embryos and spinal cords with only the

ii barest grimace, and to my fellow science nerds for your infectious interest and commiserations.

Stuart, Ruth and Ben, it’s finally happening! Your big sister is leaving school! Thanks for all the support, and for making home a great place to hide away from the books.

Ken, I could never have done this without you. Thank you for your unending love and support, and for your unwavering belief that I could do it even at the hardest of times.

Finally I’d like to thank my parents for fostering a love of learning that lead me to this point. That said, I know you’re looking forward to telling people I’m something other than a student! Thank you for your endless love, support and patience, and for

‘swinging by Limerick’ to pick me up on the way from Cork so I might come home for a weekend.

Onwards and upwards.

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For Anya

(1950-2017)

You never doubted it for a second

Abstract:

Epidemiological studies have suggested that maternal immune activation (MIA) during gestation is a risk factor for the development of neuropsychiatric and neurological disorders. These epidemiological studies have indicated that specific trimesters during human gestation may be more susceptible to the effects of MIA. Studies in animal models have attempted to delineate further the temporal specificity of this vulnerability.

These animal studies have corroborated the claims made in epidemiological studies, but have not yet precisely identified individual windows of vulnerability.

The first study in this thesis investigated the effect of MIA on the oligodendrocytes of the spinal cord, using immunofluorescence microscopy to investigate the expression of the markers Olig2 and MBP following MIA with 50µg/kg or 100µg/kg at E12 or E16.

Olig2 and MBP expression were examined 5h post MIA (Olig2 only) and at P14 (Olig2 and MBP). The number of Olig2+ cell nuclei in the grey and white matter of the rostral spinal cord of offspring decreased 5h post MIA with 100µg/kg LPS. The number of

Olig2+ cell nuclei was unchanged at P14 following MIA at E16. Conversely, Olig2+ cell number was unchanged in the E12 spinal cord 5h post MIA. However, Olig2+ cell number was decreased in the ventral grey matter of the rostral spinal cord at P14 following MIA at E12. MBP expression was unchanged at P14 following MIA with

100µg/kg LPS at the E12 and E16 time points.

The second study investigated the effect of MIA on microglia and astrocytes of the spinal cord, using immunofluorescence microscopy to investigate expression of the markers Iba-1 and GFAP following MIA with 50µg/kg or 100µg/kg LPS at E12, E14 or

E16. Iba-1 and GFAP expression were examined 5h post MIA (Iba-1 only) and at P14

(Iba-1 and GFAP). MIA at E16 with 100µg/kg LPS decreased Iba-1+ cell number in the grey and white matter of the rostral and middle spinal cord 5h post MIA. Iba-1+ cell number was unchanged 5h post MIA at E12, and at P14 following MIA at E12 or E16.

GFAP expression was unchanged at P14 following MIA at E12 and E16.

The third study investigated the effect of MIA on reelin expression in the spinal cord. The study used immunofluorescence microscopy to investigate Reelin expression after MIA with 50µg/kg or 100µg/kg at E12 or E16. Expression was examined 5h post MIA and at

P14. Reelin expression decreased 5h post MIA with 100µg/kg LPS at E16 in the rostral, middle and caudal cord. Semi-quantitative analysis of reelin expression at E12 5h post

MIA suggested decreased expression. No change was observed at P14 following MIA at

E12 or E16.

The fourth study examined gene expression following MIA at E12 and E16 with

100µg/kg LPS using microarray and RNA Seq. Examination points were again 5h and

P14. Microarray analysis showed no change in gene expression 5h post MIA at E12. RNA

Seq showed that gene expression was also unchanged at P14 after MIA at E12. In contrast, microarray analysis identified 42 differentially regulated genes 5h post MIA at E16. RNA

Seq identified 19 differentially regulated genes at P14.

The final study used an in-silico investigation to interrogate pathways and processes predicted to be targeted by microRNAs which function in oligodendrogliogenesis and may be vulnerable to episodes of MIA. The study identified inflammatory pathways, developmental pathways and apoptotic pathways which may be modulated by microRNAs during development, and potential targets which may function in the cell’s response to MIA.

In conclusion, MIA effects on some aspects of CNS development significantly. E16 may be a particularly vulnerable period, at least in the spinal cord. The acute effects of MIA

vi at E16 have prolonged and far-reaching consequences on the cytoarchitecture and normal functioning of the spinal cord and, indeed, the entire CNS, in later life.

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Abbreviations:

µg/Kg - micrograms per kilogram C/EBP-α - CCAAT/enhancer-binding

protein-α ACC - Anterior cingulate cortex

CAM - Cell adhesion molecule adjP - adjusted P-value

CASC3 - cancer susceptibility candidate AIF - Allograft inflammatory factor 3 AKT - Protein kinase B Casp - Caspase AMPA - α-amino-3-hydroxy-5-methyl- Cat - Catalase 4-isoxazolepropionic acid

CC - Central canal ApoER2 - Apolipoprotein E receptor 2

CC - Corpus callosum APT1 - Acyl protein thioesterase 1

CD45 - leukocyte common antigen Aqp4 - Aquaporin 4

CFU - Colony forming units ARHGAP32 - Rho GTPase activating protein 32 cKit - Tyrosine-protein kinase KIT, or

CD117 ASD - Autism spectrum disorder

Clip3 - CAP-Gly domain-containing AU - Arbitary units linker protein 3-like;CAP-GLY domain B2M - Beta-2 microglobulin containing linker protein 3 BAF - Barrier to autointegration factor CNPase - 2',3'-Cyclic-nucleotide 3'-

BDNF - Brain derived neurotrophic phosphodiesterase factor CNS - Central nervous system BH - Benjamini-Hochberg CNTF - Ciliary neurotrophic factor BMP- Bone morphogenic protein CP - Cerebral palsy viii

CREB - CAMP responsive element Erk - extracellular signal–regulated binding protein kinases

CSF - cerebral spinal fluid ESCs - Embryonic stem cells

CT-1 - Cardiotrophin 1 EtOH - Ethanol

CX3CL1 - Fractalkine Ezh2 - Enhancer of zeste homolog 2

D - Dorsal FAM - Fluorescein amidite

Dab-1 - Disabled -1 FGF - Fibroblast growth factor

Dbx - Developing Brain Homeobox FGF-R - Fibroblast growth factor protein receptor

DG - Dentate gyrus FMRP - Fragile X mental retardation

protein DGCR8 - DiGeorge syndrome critical region 8 FoxO1 - Forkhead Box O1

E - Embryonic day FP - Floor plate

EAE - experimental autoimmune Fscn1 - Fascin actin-bundling protein 1 encephalomyelitis Fzd9 - Frizzled class receptor 9

E-coli - Escherichia coli GABA - Gamma aminobutyric acid

Emcn - Endomucin GAD - Glutamate decarboxylase

Emd - Emerin GADPH - Glyceraldehyde 3-

EMR1 - EGF module-containing phosophate dehydrogenase mucin-like receptor GBS - Group B streptococcus

Emx1 - Empty Spiracles Homeobox 1 GD - Grey dorsal

EPO - Erythropoietin ix

GFAP - Glial fibrillary acidic protein IFN-ɣ - Interferon gamma

GO - gene ontology IGF-R - Insulin growth factor receptor

GP - Glycoprotein IL - Interleukin

GTP - Guanosine triphosphate Irx - Iroquois homeobox protein

GV - Grey ventral Isl1 - Insulin gene enhancer protein

H1N1 - Influenza A virus subtype JAK - Janus kinase

H1N1, swine flu K.O - knockout

HBSS - HANKS balanced salt solution KCC2 - Potassium chloride transporter

Her - Hairy related kDA - Kilodalton

Hes - hairy/ enhancer of split Kif19 - Kinesin Family Member 19

HGF - Hepatocyte growth factor LF - Longitudinal fibres

HLA-DR - Human Leukocyte Antigen Lhx - LIM homeobox – DR isotype LIF - Leukemia inhibitory factor Hr - Hour(s) Limk1 - LIM domain kinase 1 i.p -intraperitoneal Lis1 - Lissencephaly gene 1 i.v - intravenous LPS - Lipopolysaccharide Iba-1 - Ionised calcium-binding adapter LRP - Low density lipoprotein receptor- molecule 1 related protein Id - Inhibitor of differentiation LSD1 - lysine-specific histone Idi1 - Isopentenyl-Diphosphate Delta demethylase 1 Isomerase 1 LTF - Long-term facilitation

MAG -Myelin associated glycoprotein MOBP - Myelin-associated

oligodendrocyte basic protein MAL - Myelin and lymphocyte protein

MOG - Myelin oligodendrocyte MAPK - Mitogen activated protein glycoprotein kinase

mPFC - Medial prefrontal cortex MBP - Myelin basic protein

mRNA - messanger ribonucleic acid Mcam - Melanoma cell adhesion molecule MS - Multiple sclerosis

MD2 - Lymphocyte antigen 96 MTHFR - methylenetetrahydrofolate

reductase MeCP2 - methyl CpG binding protein 2

Myrf -Myelin regulatory factor Med25 - Mediator complex subunit 25

Nac - Nucleus accumbens Mef2d - Myocyte enhancer factor 2D

NeuroD1 - neurogenic differentiation 1 MHCII - major histocompatibility complex class II NF - Neurofillament

MIA - Maternal immune activation NFAT - nuclear factor for activated T

cells Mib-1 - Mindbomb homolog 1

NFkB - nuclear factor kappa-light- Midn - Midnolin chain-enhancer of activated B cells miR - MicroRNA NG2 - Neural/glial antigen 2 miRNA - MicroRNA NGF - Nerve growth factor MMP - Matrix metalloproteinase NICD - notch intracellular domain MN - motor neuron NKCC1 - Na-K-Cl cotransporter

NMD - Nonsense mediated decay xi

NMDA - N-Methyl-d-aspartic acid PFA - Paraformaldehyde

NPAS3 - Neuronal PAS domain protein PFC - prefrontal cortex

3 PI3K - phosphoinositide-3-kinase

Nrbp2 - Nuclear receptor binding Pla2g3 - Phosphlipase A2 group III protein 2 PLP - Phospholipid protein NSC- Neuronal stem cell pMN - Motor neuron progenitor O.N - Overnight Pmvk - Phosphomevalonate Kinase OL - Oligodendrocyte Poly(I:C) - polyinosinic:polycytidylic OPC - oligodendrocyte progenitor cell acid

P - Postnatal Pomt1 - Protein-O-mannosyltransferase

PAM - Proliferation associated 1 microglia PPI - Pre-pulse inhibition

PAR-3 - Partitioning defective protein-3 PPNs - Parasympathetic preganglionic

Pax - Paired box protein neurons

PBS - Phosophate buffered saline Ppp1r10 - Protein Phosphatase 1

Regulatory Subunit 10 Pcgf2 - Polycomb group ring finger 2

Pri-miRNA -Primary microRNA Pcsk1n - proSAAS;proprotein convertase subtilisin/kexin type 1 PTBP1 - Polypyrimidine tract binding inhibitor protein 1

PDGF - Platelet derived growth factor PTEN - Phosphatase and tensin

homolog Pex11a - Peroxisomal Biogenesis Factor

11 Alpha PVL - Periventricular leukomalacia

xii

Pxmp4 - Peroxisomal Membrane SCP1 - Small CTD Phosphatases 1

Protein 4 SD - Sprague Dawley

RANTES - Regulated on Activation, SFKs - Serine family kinases Normal T Cell Expressed and Secreted, Sh3bgrl - SH3 Domain Binding CCL5 Glutamate Rich Protein Like RELN - Reelin SHH - sonic hedgehog REST - Repressor element 1-Silencing SIRT1 - Sirtuin 1 Transcription factor Slco1c1 - Solute Carrier Organic Anion RGC - radial glial cell Transporter Family Member 1C1 Rin - RNA integrity number SMNs - Somatic motor neurons RISC - RNA induced silencing complex Snca - Alpha synuclein ROI - region of interest SNP - Single nucleotide polymorphism ROS - Reactive oxygen species SOX10 - SRY-box 10 RT - Room temperature Sp1 - Specificity protein 1 RTK - receptor tyrosine kinases Spag7 - Sperm Associated Antigen 7 RT-PCR - Reverse transcription SPNs - Sympathetic preganglionic polymerase chain reaction neurons S1pr1 - Sphingosine-1-Phosphate STAT - Signal transducers and Receptor 1 activators of transcription SC - Spinal cord Sulf1 - Sulfatase 1 Scara3 - Scavenger Receptor Class A SVZ - subventricular zone Member 3

xiii

TCF - T-cell Factor UHRF1 - Ubiquitin-like, containing

PHD and RING finger domains, 1 TGF-β - Transforming growth factor beta UPF-1 - regulator of nonsense

transcripts 1 TLR - Toll-like receptor

V - Ventral TLX (NR2E1) – Homologue of the

Drosophila tailless gene / Nuclear VEGF - Vascular endothelial growth receptor subfamily 2 group E member 1 factor

TNF - Tumour necrosis factor VGAT - vesicular GABA transporter

Tnfrsf11a - TNF Receptor Superfamily VIP - Vasoactive intestinal peptide

Member 11a VLDLR - Very low density lipoprotein

TREM - Triggering receptor expressed receptor on myeloid cells VZ - Ventricular zone

Txnip - Thioredoxin Interacting Protein WD - White dorsal

Ubc - Ubiquitin C WI - White intermediate

WV - White ventral

xiv

Table of Figures:

Chapter 1:

Figure 1.1 Neurulation ...... 3 Figure 1.2. Progenitor domains of the ventral spinal cord develop along the gradient of SHH expression...... 10 Figure 1.3. Neurogenesis...... 12 Figure 1.4: Oligodendrocyte differentiation ...... 15 Figure 1.5. Molecular control of oligodendrogliogenesis...... 16 Figure 1.6. miRNA synthesis ...... 46

Chapter 2

Figure 2.1 Schema of the time mating protocol ...... 53 Figure 2.2 Sectioning protocol for E14 and E16 embryonic tissue ...... 56 Figure 2.3 Sectioning protocol for postnatal studies ...... 57 Figure 2.4. Immunofluorescent staining protocol...... 58 Figure. 2.5 Schematic representation of the sampling strategy in the E14 and E16 spinal cord...... 62 Figure. 2.6 Schematic representation of the sampling strategy in the P14 spinal cord... 63 Figure 2.7 Schematic representation of study methodology...... 73

Chapter 3

Figure 3.1 Characterisation of Olig2+ cell distribution in the E12 spinal cord...... 81 Figure 3.2 Olig2+ cell number is not decreased in the E12 spinal cord 5h post MIA with 100μg/kg LPS ...... 81 Figure 3.3 Characterisation of Olig2+ cell distribution in the E16 spinal cord...... 84 Figure 3.4 Olig2+ cell number is decreased in the dorsal grey matter (A, P<0.001), ventral grey matter (B, P<0.01), ventral white matter (C, P<0.01) and the intermediate white matter (D, P<0.001) of the rostral region of the E16 spinal cord 5h post MIA with 100μg/kg LPS...... 86 Figure 3.5 Characterisation of Olig2+ cell distribution in the P14 spinal cord...... 87 Figure 3.6 Olig2+ cell number is decreased in the ventral grey matter of the rostral spinal cord of P14 offspring following MIA with 100μg/kg LPS at E12 ...... 89 Figure 3.7 Olig2+ cell number was unchanged in the dorsal and ventral grey matter (A) and in the intermediate and ventral white matter (B) of the rostral P14 spinal cord of offspring following MIA with 100μg/kg LPS at E16 ...... 89 Figure 3.8 Characterisation of MBP expression in the P14 spinal cord ...... 91 Figure 3.9 MBP mean fluorescence intensity was unchanged in grey matter (A) and white matter (B) of the rostral P14 spinal cord of offspring following MIA with 100μg/kg LPS at E12 ...... 92 Figure 3.10 MBP mean fluorescence intensity was unchanged in grey matter (A) and white matter (B) of the rostral P14 spinal cord of offspring following MIA with 100μg/kg LPS at E16 ...... 93 xv

Chapter 4

Figure 4.1 Characterisation of Iba-1+ cell distribution in the E12 Spinal cord ...... 111 Figure 4.2 Iba-1+ cell number per unit volume was not altered in the rostral, middle or caudal spinal cord 5h after 100µg/kg maternal LPS injection at E12 ...... 112 Figure 4.3 Characterisation of Iba-1+ cell distribution in the E14 spinal cord ...... 114 Figure 4.4 Iba-1 expression is decreased at E14 5h post maternal injection with 50ug/kg LPS...... 115 Figure 4.5 Characterisation of Iba-1+ cell distribution in the E16 spinal cord...... 117 Figure 4.6 Iba-1+ cell number per unit volume is significantly decreased in the E16 spinal cord 5h post MIA with 100ug/kg LPS ...... 118 Figure 4.7 Characterisation of Iba-1+ cell distribution in the P14 spinal cord...... 119 Figure 4.8 Iba-1+ cell number is unaltered in the dorsal and ventral grey matter (A), and the intermediate and ventral white matter (B), of the rostral P14 spinal cord following maternal 100µg/kg LPS injection at E12 ...... 120 Figure 4.9 Iba-1+ cell number is unaltered in the dorsal and ventral grey matter (A), and the intermediate and ventral white matter (B), of the rostral P14 spinal cord following maternal 100µg/kg LPS injection at E16...... 121 Figure 4.10 Characterisation of GFAP expression in the P14 spinal cord...... 122 Figure 4.11 GFAP fluorescence intensity is unaltered in the dorsal and ventral grey matter (A) and the dorsal, intermediate and ventral white matter (B) of the rostral P14 spinal cord following MIA with 100µg/kg LPS at E12 ...... 123 Figure 4.12 GFAP fluorescence intensity is unaltered in the dorsal and ventral grey matter (A) and the dorsal, intermediate and ventral white matter (B) of the rostral P14 spinal cord following MIA with 100µg/kg LPS at E16 ...... 124

Chapter 5

Figure 5.1. Reelin signalling ...... 135 Figure 5.2 Reelin expression in the E12 spinal cord...... 144 Figure 5.3. Semi-quantitative analysis of reelin expression in the E12 spinal cord 5h after MIA with 100μg/kg LPS...... 144 Figure 5.4 Reelin expression in the E16 spinal cord...... 146 Figure 5.5. Reelin expression in the E16 spinal cord 5h post MIA...... 147 Figure 5.6. Reelin expression in the P14 spinal cord...... 148 Figure 5.7. Reelin is expressed by specific cell populations of the P14 spinal cord. ... 149 Figure 5.8 Reelin expression in the P14 spinal cord following MIA with 100μg/kg LPS at E12...... 150 Figure 5.9. Reelin expression in the P14 spinal cord following MIA with 100μg/kg LPS at E16...... 150 Figure 5.10. Reelin co-localisation in the E16 and P14 spinal cord during normal development...... 152

Chapter 6 Figure 6.1. Principle component analysis (PCA) plot of the 12 pooled samples used in the microarray study...... 164 xvi

Figure 6.2 Heat map illustrating the 42 differentially expressed genes 5hr post MIA with 100µg/kg LPS at E16...... 165 Figure 6.3 STRING analysis of the protein protein interactions between the 42 differentially regulated genes in the spinal cords of offspring following MIA at E16 with 100µg/kg LPS...... 169 Figure 6.4. PCA plot of the 12 samples used in the RNASeq study ...... 171 Figure 6.5: MA plot of the gene expression changes in offspring spinal cords at P14 following MIA at E16 with 100µg/kg LPS ...... 173 Figure 6.6 16 genes were differentially expressed at P14 following MIA with 100µg/kg LPS at E16...... 175 Figure 6.8 PCR expression of peroxisome-related genes identified as differentially expressed at P14 by RNASeq following MIA at E16 with 100µg/kg LPS...... 178 Figure 6.9 PCR expression of remaining genes identified as differentially expressed at P14 by RNASeq following MIA at E16 with 100µg/kg LPS...... 179

Chapter 7 Figure 7.1. STRING v11 output for the molecular interaction of predicted targets of miR-9 ...... 196 Figure 7.2. STRING v11 output for the molecular interaction of predicted targets of miR-124 ...... 197 Figure 7.3. STRING v11 output for the molecular interaction of predicted targets of miR-138 ...... 198 Figure 7.4. STRING v11 output for the molecular interaction of predicted targets of miR-146a ...... 199 Figure 7.5. STRING v11 output for the molecular interaction of predicted targets of miR-219 (2-3p) ...... 200 Figure 7.6. STRING v11 output for the molecular interaction of predicted targets of miR-338 ...... 201

xvii

Table of Tables:

Chapter 2 Table 2.1. Primary and secondary antibody details. Concentrations and catalogue numbers of all antibodies used in immunofluoresence protocols. Conc – Concentration, TF – Thermofisher. Antibody solutions can be found in table 2.2 . 59 Table 2.2: Solutions used in immunofluoresence protocols...... 60 Table 2.3: TaqMan®Gene expression assays ...... 71

Chapter 3 Table 3.1 Summary table of the results of embryonic and postnatal Olig2 investigation...... 93 Table 3.2 Summary table of the results of the postnatal MBP investigation...... 94

Chapter 4 Table 4.1 Summary table of the results of the embryonic and postnatal Iba-1 investigation...... 124 Table 4.2 Summary table of the results of postnatal GFAP investigation...... 125

Chapter 6 Table 6.1 42 genes were differentially regulated in the spinal cords of E16 offspring 5hr after MIA with 100µg/kg LPS ...... 168 Table 6.2. 16 genes were differentially regulated in the spinal cords of offspring at P14 following MIA with 100µg/kg LPS on E16...... 174 Table 6.3 Gene Ontology analysis ...... 177 Table 6.4 KEGG pathways ...... 177 Table 6.5 Uniprot keywords ...... 177

Chapter 7 Table 7.1 Gene ontology analysis ...... 194 Table 7.2 Legend for the interaction type of predicted targets using ‘molecular action’ as the meaning of the network edges...... 195 Table 7.3 – Definition of symbols present in the network analysis using ‘molecular target’ as the meaning of the network edges...... 195

xviii

Chapter 1. Introduction

1.1 Nervous system development

1.1.1 Neurulation

The development of neural tissue begins early in embryonic development, in the third week of human gestation (embryonic day (E)9-E12 in rats). Gastrulation results in the release of diffusible factors such as chordin, noggin, follistatin, and Cerberus, as well as

FGF, IGF and Wnt signals, by a layer of mesodermal cells along the dorsal midline, the notochord, initiating neural induction and specifying the ectoderm to a neural fate. The thickening of the midline neuro-ectoderm leads to the formation of the neural plate. This occurs around day 17 in human development. The neural plate, around day 18 in human development, begins to involute and forms the neural groove. The neural folds are formed at the lateral margins of the neural plate, ultimately coming together in a process known as convergence to form the neural tube through neurulation (Figure 1). Neural tube formation is usually complete around day 21 in human development, however the anterior and posterior neuropores do not close until day 24 and day 26 respectively. Cells dorsolateral of the neural tube, which have proliferated and differentiated at the edges of the neural groove during convergence, become the neural crest (Bassuk and Kibar 2009).

The cells of the neural crest give rise to the peripheral and enteric nervous systems, glial cells, melanocytes, craniofacial cartilage and bone, and smoot muscle(Trainor et al.

2004). Over time, through both cell intrinsic programs and cell extrinsic factors, the neural tube gives rise to neurons, astrocytes and oligodendrocytes, which themselves can be categorised into the vast array of cellular subtypes, which make up the complex connectome that we know as the central nervous system (CNS).

(A)

(B)

(C)

(D)

Figure 1.1 Neurulation. Diffusible factors such as chordin, noggin, FGF and WNT are released by the notochord, initiating neural induction and specifying the ectoderm to a neural fate. Thickening of the midline neuro-ectoderm leads to the formation of the neural plate (A). The neural folds are formed at the lateral margins of the involuting neural plate

(B), coming together through convergence to form the neural tube (C). The neural crest is formed from proliferating cells at the edge of the neural folds (B), and separates from the neural tube on convergence (C). These neural crest cells give rise to the spinal

ganglions, as well as the peripheral and enteric nervous systems, peripheral glia like the myelinating Schwann cells, smooth muscle, melanocytes, cartilage and bone (D).

1.1.2 Cell signalling and nervous system development

CNS development is dependent on the complex interaction of a large number of transcription factors which control cell proliferation, migration and differentiation. These transcription factors can be inducible or constitutive and are expressed in a regionally and temporally precise manner.

1.1.2.1 Stem cell renewal and pluripotency

Stem cell renewal is maintained by the combination of a number of complex cell signalling cascades. Activation of the insulin growth factor receptor (IGF-R) and the fibroblast growth factor receptor (FGF-R) leads to the activation of the phosphoinositide-

3-kinase (PI3K), protein kinase B (AKT) signalling and self-renewal via the activation of the pluripotency genes Oct-4, Sox-2 and Nanog among others (Maric et al. 2007; L. Wang et al. 2007). FGFR activation can also lead to the activation of extracellular signal– regulated kinase (Erk) signalling, which is necessary for telomere maintenance, genomic stability, and self-renewal of mouse ESCs (Maric et al. 2007; Chen et al. 2015). WNT signalling, via the frizzled and Low-density lipoprotein receptor-related protein (LRP) receptors, also promotes self-renewal in cells. The activation of WNT signalling is transduced to ß-catenin via a number of cytoplasmic intermediaries, namely Axin, APC,

GSK3β, which form the destruction complex. ß-catenin then translocates to the nucleus and forms a complex with T-cell factor (TCF) to activate WNT target genes (Xu et al.

2016). Smad signalling, specifically Smad2/3 signalling via the transforming growth factor (TGF)-β receptor also promotes self-renewal. Conversely, Smad1, 5 and 9 signalling, via bone morphogenic protein (BMP) receptors and notch signalling, 4

negatively regulate the process of self-renewal by repressing the expression of pluripotency genes or triggering the activation of genes associated with differentiation

(Hegarty et al. 2013; Mullen and Wrana 2017).

1.1.2.2 Migration

Migration of CNS cells is dependent on a wide range of factors such as physical contact, chemotaxis (for example in response to cytokines or extracellular matrix proteins like reelin), and neurotrophins like nerve growth factor (NGF) and brain derived neurotrophic factor (BDNF) (Maisonpierre et al. 1990; Zhang et al. 2010; Courtès et al. 2011). All of these factors act upon signalling cascades involved in neurite outgrowth and growth cone formation, affecting cytoskeletal dynamics and inducing movement. The most important of these signalling cascades is the mitogen activated protein kinase (MAPK) Erk pathway, downstream targets of which include cytoskeletal genes like Neurofillament (NF)-H and paxillin (Veeranna et al. 1998; Teranishi et al. 2009). Neuronal migration may be radial, following the radial path, for example the pyramidal and excitatory neurons of the cerebral cortex, or tangential, across the radial plane, for example the inhibitory neurons of the cortex (Reiner and Gerlitz 2013).

Cells which migrate radially do so along the radial glial cells (RGCs). RGCs are a type of glial cell present predominantly during development, which may divide to produce more radial glia, further progenitor cells, neurons or glia. First identified in the spinal cord by Camillo Golgi in 1885, they possess a long radial process (Rakic 1972; Barry et al.

2014). They act as cables to direct the migration of newborn neurons through the cerebral cortex into the correct cortical lamina, as well as providing a source of newborn neurons and neuronal progenitors (Rakic 2003). It is important to note that in the spinal cord RGCs appear to play a different role. Present from E14 in rats, RGCs appear to play a role in axon guidance rather than neuronal migration. Their appearance precedes

oligodendrogliogenesis and astrogliogenesis in the spinal cord suggesting that they may not contribute to neurogenesis here (McDermott et al. 2005).

1.1.2.3 Differentiation

As differentiation of cells is ultimately the result of withdrawal from the cell cycle, it is perhaps unsurprising that a number of the signalling cascades involved in the differentiation of progenitor cells are negative regulators of proliferation and self- renewal. BMP signalling, particularly BMP signalling through smad 1/5/8 effectors, is known to play a pivotal role in neurogenesis. BMP signalling is involved in the specification of dopaminergic neurons in the ventral midbrain, Gamma aminobutyric acid

(GABA)ergic neurons in the cortex, cholinergic neurons of the basal forebrain and granular cells of the dentate gyrus (DG) during development (Yung et al. 2002; Lopez-

Coviella et al. 2005; Clayton and Sullivan 2007; Caronia et al. 2010). Notch signalling is also a key regulator of differentiation. Activation of notch signalling leads to proteolytic cleavage of the notch intracellular domain (NICD), which translocates to the nucleus and alters gene expression. A key target of NICD is the hairy/ enhancer of split (Hes) gene family, known transcription factors in the process of self-renewal (Kageyama and

Ohtsuka 1999). Modulation of Notch signalling, for example by notch antagonists Delta or Numb, leads to differentiation. Notch mediated lateral inhibition is necessary for binary cell fate decisions and notch has been shown to play a role in asymmetric cell division

(Wakamatsu et al. 2000). Notch signalling has been known to play a role in the differentiation of neurons, oligodendrocytes and astrocytes (Wakamatsu et al. 2000;

Scheer et al. 2001; Grandbarbe et al. 2003). Other known modulators of differentiation include the hedgehog signalling pathways and receptor tyrosine kinases (RTKs) ligands, for example EGF, FGF, Platelet derived growth factor (PDGF) and vascular endothelial growth factor (VEGF) (For review see (Fuccillo et al. 2006) and (Lemmon and

Schlessinger 2010)). 6

1.1.2.4 Cytokines, signalling, and nervous system development

Cytokines, small pleiotropic molecules largely associated with the immune system, play a significant role in nervous system development at all levels. Cytokines of the glycoprotein (gp)130 family such as leukaemia inhibitory factor (LIF) and ciliary neurotrophic factor (CNTF), working in conjunction with growth factors such as FGF and notch signalling, are instrumental in the self-renewal of radial glial cells (RGCs), structural scaffolds and precursors of glia (Li and Grumet 2007). Gp130 family cytokines are involved in the subsequent differentiation of these cells into glia alongside BMPs (also cytokines) and notch modulation (Hatta et al. 2002; Gregg and Weiss 2005), (For review see (Pinto and Gotz 2007)). Chemokines, or chemoattractant cytokines, play roles in axon pathfinding and migration of both neurons and glia. CXCL12 and it’s receptor CXCR4 are responsible for normal migration and proliferation in a number of brain regions including the spinal cord, the cerebellum, the dentate gyrus of the hippocampus and the cortex, where CXCL12 controls the migration of the Cajal-Retzius cells, a transient population critical for the formation of the inside-out laminar development of the cortex

(Zou et al. 1998; M. Lu et al. 2002; Dziembowska et al. 2005; Borrell and Marin 2006).

During development microglia express interleukin (IL)-1, a known astrocyte mitogen, and may play a role in their migration during normal development (Giulian et al. 1988).

During development more cells than are necessary are produced in the CNS. The excess cells must be pruned via apoptosis and in this way cytokines also play a role in cell survival. Members of the TGF-β superfamily in particular are involved in apoptotic processes in neurons (Barker et al. 2001). The gp130 family member cardiotrophin-1

(CT-1) negatively regulates the apoptosis of motor neurons during development while il-

9 and its receptor are important for neuronal survival in the cortex (Oppenheim et al.

2001; Fontaine et al. 2008). In oligodendrocyte progenitor cells (OPCs) gp130 family members LIF and CNTF, along with il-6 and il-11, promote OPC survival while IFN-ɣ 7

promotes OPC death (Louis et al. 1993; LaFerla et al. 2000; Pizzi et al. 2004; Zhang et al. 2006). For a full review of the multi-faceted role of cytokine signalling during CNS development see (Deverman and Patterson 2009).

1.1.3 Spinal cord development & anatomy

During development, the spinal cord arises from the caudal region of the neural tube, with the central cavity of the neural tube forming the central canal of the spinal cord and the neural crests giving rise to the ganglia of the spinal nerves. The neuroepithelial cells which line the cavity of the neural tube proliferate, giving rise to the mantel layer. The mantel layer can be further divided into cells which have proliferated in the dorsal neural tube, which form the alar plate, and cells which have proliferated in the ventral neural tube, which form the basal plate. These plates are divided geographically by the sulcus limitans, located in the lateral walls of the developing spinal cord. In the thoracic and upper lumbar regions of the cord a third plate forms. This is known as the intermediate plate and is found between the alar and basal plates. These plates give rise to distinct neuronal populations in the adult spinal cord. The alar plate gives rise to the sensory tracts of the dorsal horn, which transmit information to the brain. The basal plate gives rise to the motor neurons of the ventral horn, which transmit information to the periphery, and finally the intermediate plate gives rise to the preganglonic sympathetic neurons of the dorsolateral spinal cord. The marginal zone, containing the roofplate and floorplate, will form the white matter in the adult spinal cord while the neuroepithelial cells will become the ependymal cells lining the central canal (Bakkum and Bachop 2014).

The spinal cord exhibits a rostro-caudal gradient of development, with cells of the rostral spinal cord developmentally more mature than cells of the caudal spinal cord. Similar to the rostro-caudal gradient of development, the spinal cord also exhibits a dorso-ventral

gradient (Ericson et al. 1997). Graded signalling of sonic hedgehog (SHH), produced in the floorplate, leads to the production of five molecularly distinct regions of the ventral spinal cord from which specific neuronal cell types arise (Ericson et al. 1995; Ericson et al. 1997; Fuccillo et al. 2006; Richardson et al. 2006). The more dorsal the region the less SHH is required for differentiation of the particular neuronal subgroup. This SHH gradient modulates the expression of the homeodomain proteins Pax7, Dbx1, Dbx2, Irx3 and Pax6 (class I) and Nkx6.1 and Nkx2.2 (class II) to define discrete progenitor domains from which distinct cell types arise (Yamada et al. 1991; Ribes and Briscoe 2009). Some progenitor domains, like the motor neuron progenitor (pMN) domain, give rise to multiple distinct cell types depending on the developmental timing, indicating that although SHH signalling and its effect on homeodomain proteins can tell us much about cell specification in the ventral spinal cord other factors contribute. This can be illustrated by the diversity of the motor neurons of the cord, all of which arise from the pMN domain

(Stifani 2014). The birth of specific cell types in the spinal cord will be further discussed in section two of this chapter.

In the adult, the function of the spinal cord is to send and receive information between the periphery and the brain. As previously alluded to the dorsal horn gives rise the the second order sensory neurons, which relay information to the brain. The first order sensory neurons are part of the peripheral nervous system, formed during development from neural crest cells, and enter the spinal cord via the spinal nerve and dorsal root ganglion. The ventral horn on the other hand houses the motor neurons, the function of which is to transmit information from the brain to the periphery. These neurons arise from the basal plate during development and exit the cord via the ventral root, joining the spinal nerve. The preganglionic sympathetic neurons derived from the intermediate plate also exit the spinal cord via the ventral root , and synapse in the ventral root ganglion with

postganglionic sympathetic neurons, also derived from the neural crest cells, which rejoin the spinal nerve (Bakkum and Bachop 2014).

Figure 1.2. Progenitor domains of the ventral spinal cord develop along the gradient of SHH expression. Each domain gives rise to distinct progenitors.V0 is a source of commissural neurons which project rostrally. V1 gives rise to inhibitory interneurons which project rostrally and ipsilaterally. Both excitatory glutamatergic interneurons (V2a) and inhibitory interneurons (V2b) arise from V2 and project ipsilaterally and caudally, while inhibitory commissural interneurons arise from V3 and project caudally. The MN gives rise to motor neurons (Goulding 2009). FP –floor plate,

V – ventral, MN – motor neuron, SHH – sonic hedgehog

1.2. Cellular development

1.2.1 Neurogenesis

Neurogenesis is the first event in a stepwise process to produce the cells of the central nervous system. Cellular division within an embryo is usually mitotic in nature, producing two daughter cells. Symmetric cell division produces two identical daughter cells while asymmetric cell division involves the production of two cells that are not identical. Cells may also divide to produce daughter cells that are identical at a physical level, but differ at a molecular level. These divisions are “molecularly asymmetric” and add to the complexity of the CNS (Hartenstein and Stollewerk 2015).

During neurogenesis cells of the neuroectoderm can give rise to neuronal precursors.

These precursors may directly differentiate into neurons, through asymmetric neurogenic division, or differentiate into proliferating progenitor cells, through asymmetric proliferative division. In neurogenesis the asymmetric proliferative form of division is the more common, allowing a stepwise progression of cell fate specification. RGCs, a type of apical progenitor cell, arise from the neuroectoderm at the onset of neurogenesis, and give rise to many of these asymmetric divisions. Asymmetric division of an RGC typically produces an identical daughter RGC, and a daughter cell that has progressed further through the fate specification process (Paridaen and Huttner 2014). This cell terminally differentiates into a neuron, or, in the case of more complex regions such as the human neocortex, into an intermediate or basal progenitor, which carries further cell fate restrictions (Figure 1.3). Asymmetric differentiation has been attributed at least in part to asymmetric Delta-Notch expression in the daughter cells, with the cell possessing the lower level of notch signalling undergoing differentiation. This apical daughter cell is the source of notch ligand for the basal, self-renewing, daughter cell due to the polarity regulator Partitioning defective protein-3 (Par-3), which segregates the fate determinant

mind bomb to the apical daughter cell restricting self-renewal (Dong et al. 2012). Both

RGCs and basal progenitors are capable of symmetric neurogenic division to terminally differentiate (Gotz and Huttner 2005).

This manner of neurogenesis has interesting spatial and temporal consequences, allowing for terminal differentiation at differing times, and via differing and oftentimes increasingly complex routes of differentiation through multiple classes of basal progenitors. The cell cycle length of cells undergoing terminal differentiation is longer than that of cells undergoing proliferative division, adding to the temporal complexity of neurogenesis (Gotz and Huttner 2005). These temporal and spatial consequences are particularly obvious in the neurogenesis of the human neocortex. Here neurogenesis occurs in an inside-out pattern, resulting in structurally distinct layers, with neurons of the inner layer born first, while the neurons of subsequent outer layers are born later

(Florio and Huttner 2014). These layers have individual structural and functional characteristics, with differing gene expression (Franco et al. 2011).

Figure 1.3. Neurogenesis. During neurogenesis a neuroectodermal cell may divide symmetrically to produce an identical daughter cell (red arrow), asymmetrically, to produce a radial glial cell (blue arrow) or differentiate into a neuron (black arrow).

Radial glial cells arising from asymmetrical division may differentiate, divide

symmetrically or divide asymmetrically to produce an intermediate or basal progenitor, which itself may differentiate, divide symmetrically or divide asymmetrically to produce a progenitor with further cell fate specifications. This process accounts for the spatial and temporal complexities of the process of neurogenesis.

1.2.2 Oligodendrogliogenesis

Oligodendrocytes (OLs) are one of three types of glial cells of the CNS, the others being microglia and astrocytes (Stern 2010). OLs produce myelin, a lipid based substance that produces an insulating sheath for axons and is essential for proper conduction of action potential between neurons. OLs can be found ubiquitously throughout the CNS, and are present in both white and grey matter (Miller 1996). Data obtained from rodent studies on OL development show that in the spinal cord, the majority of oligodendrocyte progenitor cells (OPCs) originate in the ventral ventricular zone (VZ), from motor neuron progenitors following a neurogenic-gliogenic switch (Rabadan et al. 2012). These OPCs migrate throughout the cord, eventually undergoing differentiation into mature myelinating OLs (Q.R. Lu et al. 2002; Takebayashi et al. 2002). As development progresses a second wave of OPCs are produced in the dorsal spinal cord. These cells contribute 10-15% of the total oligodendrocyte number (Cai et al. 2005; Fogarty et al.

2005). In mice and rats, generation of ventral and dorsal OL progenitors begins at E12.5 and E14.5 respectively (Cai et al. 2005). In the rodent brain OPCs arise in three distinct waves, first in the medial ganglionic eminence and anterior entopeduncular area of the ventral forebrain, then in the lateral or caudal ganglionic eminence, and finally in the postnatal cortex (Kessaris et al. 2006).

Tight molecular control of oligodendrogliogenesis is important to ensure timely differentiation of immature OPCs to mature myelinating OLs through a stepwise process

(Figure 1.4). Newly specified OPCs migrate to their final destination in response to a variety of signalling molecules, including BMPs, Shh and WNT morphogens, as well as under the control other factors, such as growth factors (e.g PDGF, VEGF, FGF and hepatocyte growth factor (HGF)) (Yan and Rivkees 2002; Kakinuma et al. 2004; Bribian et al. 2006; Merchan et al. 2007; Hayakawa et al. 2012; Choe et al. 2014; Tripathi et al.

2017). Extracellular matrix proteins such as laminin and fibronectin, cell adhesion molecules (CAMs), sempaphorins and chemokines have all been described as influencing oligodendrocyte migration through either attraction or repulsion (Barral-Moran et al.

2003; Bernard et al. 2012; Vora et al. 2012; Linneberg et al. 2015; Tripathi et al. 2017).

On arrival at their destination OPCs proliferate under the control of pathways such as

PDGF, Notch and WNT, which simultaneously inhibit differentiation (Calver et al. 1998;

Wang et al. 1998; Fancy et al. 2011). Transcription factors such as inhibitor of differentiation (Id)2, Id4 and Hes5 act downstream of these signalling molecules to supress premature maturation. Suppression of these inhibitory factors is necessary to allow differentiation to pre-oligodendrocytes and finally to mature oligodendrocytes to occur (Liu et al. 2006; Zhao et al. 2010; Emery and Lu 2015). Differentiation of OPCs involves the co-expression of NKX2.2 and Olig2, factors which actively repress each other in proliferative stages. To induce differentiation Olig2 induces the expression of

SOX10, a transcription factor and determinant of OL maturation. Sox10 maintains Olig2 expression while inducing expression of NKX2.2 following the inhibition of Olig2 mediated NKX2.2 repression by NFAT family member NFATC2 (Figure 1.5). SOX10 also induces the expression of transcription factors such as myelin regulatory factor

(Myrf), resulting in the expression of myelin genes such as myelin associated glycoprotein (MAG), myelin oligodendrocyte glycoprotein (MOG), phospholipid protein

(PLP) and myelin basic protein (MBP) to produce mature, myelinating oligodendrocytes

(Bujalka et al. 2013; Emery and Lu 2015; Simons and Nave 2015).

Figure 1.4: Oligodendrocyte differentiation is a multistep process. Neural stem cells

(NSCs) undergo differentiation to oligodendrocyte progenitor cells (OPCs). This differentiation can be inhibited by factors which promote NSC proliferation (eg BMPs), or promote motor neuron proliferation (eg Insulin gene enhancer protein (Isl1)). OPCs undergo further proliferation to pre-myelinating oligodendrocytes, a process which can be inhibited by downstream targets of PDGF, notch and WNT signalling (PDGFRα, Id2,

Id4, Hes5). Pre-myelinating oligodendrocytes finally become mature myelinating oligodendrocytes. This process can be inhibited by Ubiquitin-like, containing PHD and

RING finger domains, 1 (UHRF1, soluble neuregulin) (Grinspan et al. 2000; Dugas et al.

2006; Dugas et al. 2010).

Figure 1.5. Molecular control of oligodendrogliogenesis. Differentiation of OPCs involves the co-expression of NKX2.2 and Olig2, which actively repress each other during proliferation. To induce differentiation Olig2 induces the expression of SRY-box

10 (Sox10). Sox10 maintains Olig2 expression while inducing expression of NKX2.2 following the inhibition of Olig2 mediated NKX2.2 repression by Nuclear factor of activated T cells (NFAT) family member NFATC2 (adapted from (Elbaz and Popko

2019)).

Data on human OL development is largely circumstantial although the same core principles seem to apply to both rodents and humans. Understanding of human OL development has been garnered from studies of the human foetal forebrain during midgestation, which suggests the existence the three differing OPC populations: cortical, dorsal, and OPCs migrating between ganglionic eminences and subventricular zone

(SVZ) (Rakic and Zecevic 2003). The first myelination of axons occurs at midgestation in humans, within the spinal cord, and myelination continues for at least two decades of

life (Baumann and Pham-Dinh 2001). In order to fully elucidate the myelination process in the human studies need to continue into adolescence.

OLs are notorious for being a highly vulnerable cell population, particularly during development. It has been estimated that OLs can support membrane up to 100x the weight of its cell body (McTigue and Tripathi 2008), which results in a vulnerable cell phenotype, largely due to the metabolic demands placed upon the cell. Briefly, OLs have many differing ways of malfunctioning. For example, OLs have high metabolic rates and iron levels and require large amounts of oxygen and ATP. Waste metabolites can produce reactive oxygen species (ROS), toxic by-products, free radicals and can result in lipid peroxidation (Bradl and Lassmann 2010). However, OLs contain low levels of the antioxidative enzyme glutathione (Juurlink 1997) and are therefore susceptible to oxidative stress.

1.2.3 Astrogliogenesis

Astrocytes are the most abundant macroglia in the CNS, accounting for approximately

50% of all CNS cells (Azevedo et al. 2009). Originally believed to be a homogeneous cell population with a structural support function, more recent evidence has implicated astrocytes in a diverse array of processes including structural support, maintenance of water balance, blood-brain barrier formation as well as the secretion of factors influencing neuronal function (Sofroniew and Vinters 2010). Astrocytes can be divided morphologically into two types, fibrous astrocytes, which populate the white matter, and protoplasmic astrocytes which populate the grey matter (Goursaud et al. 2009).

Astrogliogenesis, generally occurs after neurogenesis although some overlap does exist.

Astrocytes are derived from a number of sources, the most common of which, like in neurogenesis, is asymmetrical cell division of a radial glial cell in the SVZ, which can give rise to a number of intermediate progenitors and eventually immature astrocytes

(Barry and McDermott 2005). These immature astrocytes migrate before undergoing further proliferation and eventually differentiating into mature astrocytes (Ge et al. 2012).

Astrogliogenesis from specific intermediate progenitors, for example Neural/glial antigen

2 – positive (NG2+) progenitors in the forebrain and Empty Spiracles Homeobox 1 – positive (emx1+) progenitors in cortex, appears to be responsible for the generation of an array of astrocyte subtypes, and the regional restriction of astrocytes of a specific lineage

(Gorski et al. 2002; Zhu et al. 2008). Originally termed astrocytes for their star shaped morphology it is now clear that astrocytes are a family of structurally and functionally distinct cells. Muller glia of the retina provide an inarguable example of the adaptability of astrocytes to their cellular and molecular environment, with processes of these radially polarised astrocytes adapting to the anatomical and molecular features of the individual retinal layers to which they project (for review see (Derouiche et al. 2012)). Bergmann glia of the cerebellar cortex are a molecularly distinct group of astrocytes. These cells are polarised, with the soma of these cells being closely associated with neighbouring purkinje cells while their processes extend throughout the molecular layer to envelope synapses (Miyazaki et al. 2017). Bergmann cells are uniquely equipped to survive the dense glutamatergic innervation of this layer, expressing high levels of glutamatergic transporters as well as α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors, the presence of which is necessary for glutamate detection, glutamine synthase production, and fine motor coordination (Saab et al. 2012; Miyazaki et al. 2017; Tiburcio-

Felix et al. 2018). Astroglia of the arcuate nucleus of the hypothalamus are hormone sensitive and influence neuronal activity in response to hormones like leptin while suprachiasmatic astrocytes of the dorsal suprachiasmatic nucleus are critical to the regulation of the circadian rhythm (J.G. Kim et al. 2014; Brancaccio et al. 2017).

Hochstim and colleagues have identified three distinct subgroups of white matter astrocytes in the spinal cord, identified by their differing expression of Reelin and Slit1. 18

These subgroups are positionally distinct. Reelin+ Slit1- astrocytes lie dorsolaterally and ventrolaterally in the white matter, Reelin- Slit1+ astrocytes are found close to the ventral midline, and Reelin+ Slit1+ astrocytes are found between them. This positional distinction was found to result from the distinct progenitor domain expression of homeodomain proteins Pax6 and Nkx6.1 (Hochstim et al. 2008). Far from the homogeneous population astrocytes were once believed to be, these cells are now known to be morphologically and molecularly diverse.

In response to this new understanding of the functional significance of astrocytes the mechanisms controlling astrogliogenesis have become an area of interest. Evidence has implicated modulation of the JAK-STAT3 signalling pathway as a core regulator of the onset of astrocyte development (Bonni et al. 1997). Interestingly, the JAK/STAT3 pathway is activated by the il-6 family of cytokines, resulting in receptor dimerization, either with other il-6 receptors, or with other family members. This dimerization occurs with the aid of gp130, a signal transducer, to ultimately induce the transcription of astrocytic genes (Heinrich et al. 1998). Importantly the promotors of these genes are insensitive to il-6 family cytokines and JAK-STAT3 activation in early and mid- gestational periods, an important clue in the temporal control of astrocyte development as well as the step-wise development of the CNS as a whole (Takizawa et al. 2001). This insensitivity appears to be mediated by epigenetic changes, specifically methylation of astrocytic promoters and chromatin modification (Song and Ghosh 2004; Shaked et al.

2008). For full review see (Namihira and Nakashima 2013).

1.2.4 Microgliogenesis

Microglia, unlike the macroglia previously discussed in this introduction, originate outside the CNS (Rezaie and Male 1999; Ginhoux et al. 2010). Microglia are known as the immune cells of the CNS, comprising 10-15% of the glial cell melieu, but play

complex roles in cell survival, cell death and dendritic and synaptic pruning during development (Lawson et al. 1990; Lawson et al. 1992; Nayak et al. 2014; Fernandez de

Cossio et al. 2017). Microglial development begins at roughly E8.5 in the mouse with the emergence of CD45+ cKit- erythromyeloid progenitors in the yolk sac. A subset of these progenitors mature to express CX3CR1. These microglial progenitors undergo

Matrix metalloproteinase (MMP)-dependent, circulation-dependent ,migration into the brain between E9.5 and E10.5. They comprise the sole source of microglia in the brain after blood-brain-barrier formation and self-renew within the CNS (Ajami et al. 2007;

Ginhoux et al. 2010; Schulz et al. 2012; Kierdorf et al. 2013; Gomez Perdiguero et al.

2015).

Investigations into the heterogeneity of microglial cells have been fewer than their astrocytic and oligodendrogliogenic counterparts, this is partially due to the problematic nature of cryopreservation of microglia, known alterations to microglial phenotype in cultures, and the auto-fluorescence of human post-mortem tissue (Böttcher et al. 2019).

The first study to identify heterogeneity in microglial expression was performed in 1980, identifying a higher density of microglial cells in the hippocampus and thalamus than in the cortex and cerebellum of mice. Interestingly this study noted a lower level of expression of the marker F4/80 in microglia of the cerebellum, which was the first suggestion of regional heterogeneity in microglial cells (Lawson et al. 1990). Microglia have subsequently been divided into two subclasses, ameboid and ramified. These subclasses are both present within all brain regions, but appear to have distinct molecular expression. In the corpus callosum for example ameboid microglia express similar levels of cytokines and chemokines to ramified microglia, but also cell cycle molecules

CyclinA2 and B2 as well as caspase molecules (Casp2 and Casp3) (Parakalan et al. 2012).

A 2008 study by De Haas et al. was one of the first to show a regional phenotypic heterogeneity in microglia. It identified microglia by differential expression of CD11b,

CD40, CD45, CD80, CD86, EGF module-containing mucin-like receptor (EMR)1 ,

Triggering receptor expressed on myeloid cells (TREM)‐2b, CXCR3, and CCR9 in the hippocampus, cerebellum, cerebellar cortex, striatum and spinal cord (de Haas et al.

2008). A microarray study in adult murine populations found a distinct spatial and temporal heterogeneity in microglial phenotype, with distinct bioenergetic and immunomodulatory pathways in the microglia of the hippocampus and cerebellum of young adults suggesting that microglia may be in a more immune vigilant state. The augmentation of these pathways in cerebellar microglia and contrasting loss of phenotype in the hippocampus appear to be distinct occurrences during aging (Grabert et al. 2016).

In contrast another single cell transcriptomic study, investigating microglial development as a stepwise process, found microglial populations to be surprisingly homogeneous across their established phases of development (Matcovitch-Natan et al. 2016). A human study using multiplexed single-cell mass cytometry found that the subventricular zone and thalamus displayed a similar microglial phenotype that was distinct from that displayed in the frontal and temporal lobes and the cerebellum. Higher levels of expression of CD11c, CCR5, CD45, CD64, CD68, CX3CR1, EMR1 and Human

Leukocyte Antigen – DR isotype (HLA-DR) were observed in the SVZ and thalamus.

Higher levels of expression of CD 206 were observed in the frontal and temporal lobes.

The microglial phenotype found in the cerebellum was distinct from the other regions, displaying low levels of all of these markers (Böttcher et al. 2019). Another recent study utilising single cell RNA-seq investigated the spatiotemporal heterogeneity of microglia at E14.5, Postnatal (P)7 and P60 in the cortex, cerebellum, hippocampus, striatum, olfactory bulb and choroid plexus of mice. This study found little heterogeneity in the different regions of the adult brain but did identify a proliferation associated microglia

(PAM) phenotype in the white matter of the corpus callosum and cerebellum at P7 (Li et

al. 2019). Much work remains in order to fully elucidate the heterogeneity of microglia in the CNS and the importance of this heterogeneity to development.

1.3 Maternal immune activation

Maternal immune activation (MIA) refers to the activation of the immune system of a pregnant female during the gestational process, frequently by accidental infection or injury in humans, but also deliberately in animals, by infection, injury or the use of specified immunoreactive agents in a laboratory setting. In recent decades, there has been an intensive focus on investigating the impact of MIA on the proliferation and differentiation of cells in the developing CNS.

1.3.1 Epidemiological studies

Epidemiological studies were the first to suggest that MIA during gestation may be a risk factor in the development of numerous neurological disorders in offspring (Brown et al.

2004b; Buka et al. 2008; Sun et al. 2008; Brown 2012; Wu et al. 2013). Since the original retrospective studies linked maternal influenza infection during pregnancy to an increased incidence of schizophrenia in offspring numerous studies, both retrospective and prospective in nature, have linked a wide variety of maternal infections to an increased incidence of schizophrenia in offspring. These infections include influenza, toxoplasma gondii, rubella and herpes simplex virus type 2 (Brown et al. 2000; Brown et al. 2004a;

Babulas et al. 2006; Alan S. Brown and Elena J. Derkits 2010; Brown 2012). Autism spectrum disorder (ASD), like schizophrenia, has also been linked to MIA through epidemiological cohort studies and meta-analyses (Atladottir et al. 2010; Brown 2012;

Hornig et al. 2018). More recently cohort studies have implicated MIA in both cerebral palsy and epilepsy (Sun et al. 2008; Brown 2012; Miller et al. 2012; Miller et al. 2013;

Wu et al. 2013). 22

Intriguingly, some epidemiological studies have highlighted the gestational timing at which the insult occurs as a key determinant of the neurological outcome of MIA, however there is no clear consensus. A large nested cohort study in a Californian population between 1959 and 1967 suggested that the incidence of schizophrenia in offspring was 3-fold higher in children of mothers who suffered an influenza infection during the first half of gestation and 7-fold higher in offspring of those suffering infection in the first trimester. No such increase was seen in the second half of gestation (Brown et al. 2004a; Brown 2012). In contrast a retrospective study in a Danish population over a

40 year period between 1911-1950 found that maternal influenza infection during the sixth month of gestation was associated with an increased incidence of schizophrenia in offspring (Barr et al. 1990). This is in line with a number of other previous studies which have largely linked influenza exposure in the second trimester (for review see (Alan S.

Brown and Elena J. Derkits 2010)). Epidemiological studies examining the association between MIA and ASDs have also reported similar findings. A Danish study following the children of women hospitalised for infections during pregnancy over a 25 year period showed no association between infection and ASDs in offspring when investigating the total gestational period, however maternal viral infection during the first trimester, and maternal bacterial infection during the second trimester, were both associated with increased risk of ASDs (Atladottir et al. 2010). Contrary to this a Swedish study encompassing births between 1984 and 2007 found that maternal infection requiring hospitalisation increased the incidence of ASDs regardless of insult type or gestational period. A third, smaller study using a Californian population associated maternal infection requiring hospitalisation with an increased incidence of ASD that was particularly associated with bacterial infections diagnosed in the second and third trimester (Zerbo et al. 2013).

1.3.2 Animal models

Epidemiological studies, while intriguing and highly informative in many ways, have raised numerous questions with their lack of general consensus. This has resulted in the development of a number of animal models in which to better study the effects of MIA on offspring outcomes. Animal models allow for the control of the numerous variables encountered in human studies. Tight control over insult timing allows researchers to more accurately investigate windows of vulnerability associated with MIA. Other factors easily controlled in animal models include insult type, bacterial or viral, and critically, insult severity. Animal models also eliminate other confounding factors such as maternal stress or deprivation. Today animal models mimicking specific infections, and indeed general immune activation using specific inflammatory cytokines, exist. However, the most commonly utilised models mimic viral infection using polyinosinic:polycytidylic acid

(Poly(I:C)), and bacterial infection using Lipopolysaccharide (LPS).

1.3.2.1 Polyinosinic:polycytidylic acid

Poly(I:C) is a viral mimetic and an immunostimulant. It consists of a mis-matched double- stranded RNA, one a polymer of inosinic acid and the other a polymer of cytidlylic acid.

Double-stranded RNA is a feature of many viruses and Poly(I:C) is known to interact with toll-like receptor 3 (TLR3) present on the surface of many immune cells leading to the activation of the cytokine cascade, the common downstream pathway of immune system activation (Homan et al. 1972; Fortier et al. 2004).

1.3.2.2 Lipopolysaccharide

Lipopolysaccharide (LPS) is a bacterial mimetic and an immunostimulant. It is a prototypical endotoxin consisting of a lipid and an O-polysaccharide and is found in the outer membrane of gram-negative bacteria. LPS binds the CD14/TLR4/MD2 receptor complex on the surface of multiple immune cells and triggers the cytokine cascade. It is 24

responsible for the natural immune response to gram-negative bacteria (Rietschel et al.

1994; Palsson-McDermott and O'Neill 2004).

1.3.2.3 Other models of maternal infection

Other models of MIA are less common, namely due to inability to control the severity of the infection, but commonly include inoculation with a sublethal dose of the Influenza A virus subtype H1N1 (H1N1) or other influenza strains, exposure to Escherichia coli (E- coli) or exposure to inactivated Group B streptococcus (GBS). MIA studies using individual cytokines such as IL-6 have also been completed (Fatemi et al. 1998; Fatemi et al. 2005a; Pang et al. 2005; Smith et al. 2007; Garbett et al. 2012; Bergeron et al. 2013).

1.3.3 The rodent spinal cord as a model of MIA

Although it is likely that any major cognitive deficits resulting from MIA will arise in the cortical structures, the complexity of cortical development makes it challenging to unravel the small cellular and structural deficits that may arise. The spinal cord provides a number of key advantages for the study of MIA at a cellular level. Spinal cord development is particularly well characterised, owing largely to the fact that a number of the seminal works in early neuroscience were carried out in the region. Papers by Bidder and Kuffner (1857), His (1886, 1889), Retzius (1898) and Ramon y Cajal (1909) all investigated and characterised the anatomy of the spinal cord (Altman and Bayer 1984;

Altman and Bayer 2001). The spinal cord provides an abundant tissue source for investigation, particularly in embryological studies where traditionally tissue from specific CNS regions may be scarce. One unique aspect of the spinal cord is the ability to study multiple points of development within the cord of the same animal due to the rostro- caudal gradient of development (Hajihosseini et al. 1997; Nakayama et al. 1999). This allows precise identification not only of the windows of vulnerability of cells in the spinal cord as a whole to MIA but also allows comment on the developmental state of the cells

affected. When investigating MIA from a cellular perspective the spinal cord proves a straightforward model as a result of this detailed characterisation and relative simplicity when compared to other regions of the CNS which may not be as well characterised.

(Lumsden and Kiecker 2013; Horiuchi et al. 2017; Spitzer et al. 2019) .

1.3.4 MIA and Neurons

A large volume of literature has suggested that MIA can alter neurogenesis and circuit formation in offspring. This is particularly true in the case of GABAergic neurogenesis and the establishment of inhibitory circuitry. Incorrect GABAergic signalling and neuronal circuitry development has been linked to the development of a number of neuropsychiatric disorders including schizophrenia and ASD (Fatemi et al. 2002a;

Fazzari et al. 2010; Curley et al. 2011; Penagarikano et al. 2011).

MIA as early as E9 has been shown to affect GABAergic neurotransmission. In a study by Corradini et al offspring of mice who received an intraperitoneal (i.p) injection of

2mg/kg poly(I:C) displayed a delayed excitatory to inhibitory GABAegric switch and a higher susceptibility to seizures. This susceptibility endured at P90, due to dysregulation of potassium chloride cotransporter (KCC2) activity and higher intracellular chloride levels (Corradini et al. 2018). KCC2 activity maintains the Cl- gradient necessary for the inhibitory actions of the GABAA receptor in neurons (Price et al. 2005). Similarly another study, also in mice at E9 but this time utilising an intravenous (i.v) injection of 1mg/kg poly(I:C), showed reduced functional GABAergic neurotransmission in the medial PFC.

This reduced functionality was shown to be due to a decrease in release probability and was specific to paravalbumin expressing interneurons (Canetta et al. 2016). A third study at E9 by Harvey and Boska utilised a stereological approach to investigate the effect of

MIA with either poly(I:C) or LPS on Glutamate decarboxylase (GAD)67 and Reelin expression in the hippocampus stratum oriens of mice offspring at P14 and P28.

Interestingly this study saw age, sex and immunogen specific changes. At P14 neither

GAD67 or reelin expression were changed in response to MIA with 20mg/kg i.v poly(I:C) or 200µg/kg i.v LPS, although offspring of dams treated with LPS showed an increased neuronal density as measured by NeuN+ cell number. At P28 offspring of dams exposed to poly(I:C) had a decreased number of reelin+ cells in the dorsal hippocampal stratum oriens. GAD67 expression was found to increase in the ventral striatum oriens of male offspring of LPS treated animals and female offspring of poly(I:C) treated animals

(Harvey and Boksa 2012).

Two studies at E12/E12.5 have further investigated changes in GABAergic neurotransmission postnatally in response to MIA. A study by Fernandez et al showed that an i.p injection of 20mg/kg poly(I:C) at E12 was enough to abolish the oxytocin- mediated shift of GABAergic signalling from depolarising to hyperpolarising in CA3 pyramidal neurons of the hippocampus at birth in offspring. These mice showed an increased apical arborisation and apical dendritic spine length of these neurons as well as a higher frequency of spontaneous post-synaptic currents, which persisted at P14

(Fernandez et al. 2018). Another study at E12.5 looked at medial PFC – basal amgdayla

GABAergic neurotransmission and showed that maternal i.p injection with 20mg/kg poly(I:C) increased the synaptic strength of glutamatergic projections from the mPFC to the basolateral amgdayla in mice. Changes in the excitatory/inhibitory balance were differentially translated into the modified spike output in neurons of the basal amgdayla.

Interestingly when male offspring subsequently received a subcutaneous 10mg/kg poly(I:C) injection at P9 (roughly 39-40 weeks human gestation) they displayed decreases in feedforward GABAergic inhibitory postsynaptic responses resulting from activation of local circuit interneurons in the basal amgdayla by medial PFC-originating fibres (Li et al. 2018).

A large number of studies between E15 and E16 have investigated the effect of MIA on

GABAergic transmission. Many of these have focused specifically on the effect of MIA on parvalbumin expressing neurons. Boska et al identified an increased density of parvalbumin-immunoreactive neurons in the medial PFC, dorsolateral PFC, and ventral subiculum of offspring at P14, as well as increased cholecystokinin neuron density in the medial PFC at P28 following MIA at E15 and E16 with i.p injections of 50µg/kg LPS.

The study illustrated the differing trajectories of parvalbumin and somatostatin neurons in the normally developing PFC, with an increase in parvalbumin neuron density from

P14 to P28 in the medial PFC and subiculum that was not observed in other brain regions, and significant decreases in somatostatin neuron density from P14 to P28 in all brain regions. This differential trajectory indicates that MIA may affect different subpopulations of neurons in different ways at the same time point during development

(Boksa et al. 2016). In another study GAD67 levels were assayed in the dorsal and ventral hippocampus and medial PFC of male adult offspring of dams which received an i.v injection of 4mg/kg poly(I:C) at E15. GAD67 expression in parvalbumin+ cells was reduced in the dorsal hippocampus relative to ventral hippocampus at P96. This decrease was observed in the absence of parvalbumin+ neuronal loss (Dickerson et al. 2014). In contrast, in an LPS study parvalbumin+ and somatostatin+ interneurons were decreased in layer II/III of the PFC of offspring of mice who received consecutive i.p injections of

150µg/kg LPS on E15.5 and E16.5 at P30, but only in conjunction with chronic hypoxic conditions post-birth. GAD65+ and GAD67+ neuron number were also decreased at P30 under these conditions. MIA alone was capable of altering the distribution and density of

Vasoactive intestinal peptide (VIP)+, Calreticulin+ and NeuropeptideY+ interneurons in the ACC layer V and the prelimbic layer II/III. Reelin+ interneurons were the only subgroup examined which showed no change in expression following MIA (Lacaille et al. 2019). A study by Wischhof et al also identified a decrease in the number of

parvalbumin+ interneurons, this time following MIA on E15 and E16 with i.p injection of 100µg/kg LPS alone. This decrease in the number of parvalbumin+ cells was observed in the medial PFC, the entorhinal cortex and the hippocampus at adulthood in male offspring and was less severe in female offspring. This again suggests a sex-dependent effect of MIA on GABAergic neurotransmission, which may be an important factor in the prevalence of disorders such as ASD and schizophrenia in males over females. Indeed, in a study by Zhang and Van Praag male offspring showed a decrease in pavalbumin+ interneurons in the dentate gyrus of the hippocampus and a decreased dentate gyrus volume at P84 following i.p injection of 5mg/kg Poly(I:C) at E15. Both mature and new hippocampal neurons showed modifications in intrinsic properties such as increased input resistance and lower current threshold, and decreased action potential number. Reduced

GABAergic inhibitory transmission was observed in mature dentate gyrus neurons only.

The differential impairment of mature versus newly born dentate gyrus neurons may have implications in MIA attributed behavioural deficits (Zhang and van Praag 2015). These changes in interneuron distribution and density, and GABAergic transmission may be underpinned by alterations in specific gene expression profiles. MIA with an i.p injection of 250µg/kg of LPS at E15 resulted in decreased expression of both GAD1 and GAD2, as well as distal-less (Dlx)1,2 and 5, homeobox domain proteins in a whole-brain microarray study 4h post-injection. These proteins have roles in interneuron migration

(Dlx1 and Dlx2) and preferential expression of parvalbumin+ cortical neurons (Dlx5 expressing). Other changes in gene expression, related to neuronal migration and axon guidance more broadly, for example in semaphorins, their receptors the plexins, and in

POU genes were also recorded (Oskvig et al. 2012).

Three studies from the same laboratory have investigated the effect of MIA on

GABAergic interneurons at E17 in the PFC. In all instances mice received a 5mg/kg i.v injection of poly(I:C) on E17. Analysis at P35 and P100 revealed decreased messenger

ribonucleic acid (mRNA) and protein expression of GAD65 and GAD67 and the vesicular

GABA transporter (VGAT). In P100 offspring, the Na-K-Cl cotransporter (NKCC1), involved in excitatory GABAergic signalling, was significantly increased while KCC2, involved in inhibitory GABAergic transmission, was decreased. Age-related changes in

GABA(A) α-receptor subunits were also observed. mRNA levels of the α2 and α4 subunits were significantly increased in offspring at P35 but significantly decreased at

P100. At P100 mRNA levels of α3 were upregulated and α5 were downregulated but no change was observed at P35 further illustrating the temporal nature of the effect of MIA on development. Methylation of the GAD1 and GAD2 promoters was higher in MIA offspring through increased binding of methyl CpG binding protein 2 (MeCP2). A number of the genes methylated at P114 in response to MIA at E17 were related to differentiation of GABAergic neurons. These included members of the LIM homeobox

(Lhx) and Dlx transcription factor families. Interestingly, offspring of dams treated at E9 rather than E17 in the same study showed differential methylation of genes involved in neuronal differentiation. In these animals many of the methylated genes were members of the WNT family (Richetto et al. 2014; Giovanoli et al. 2016; Richetto et al. 2017). A study looking at the effect of 3mg/kg i.p poly(I:C) injection at E12.5 and subsequent

1.5mg/kg Poly(I:C) injection at E17.5 found further evidence of the effect of MIA on inhibitory neurotransmission. Patch clap recordings found that layer II/III pyramidal cells were hyperactive in the Anterior cingulate cortex (ACC) of offspring of MIA treated dams, and that this hyperexcitation was associated with a reduction in inhibitory synapse activity. Local injection of Clonazepam into the ACC restored deficient social behaviours observed in these offspring (Okamoto et al. 2018). A single study has looked later in gestation. Male offspring of dams which received an i.p injection of 500µg/kg LPS on

E19 showed a premature loss of long-term depression in hippocampal neurons, occurring between 12 and 25 days postnatally instead of after the first postnatal month as is normal.

The deficit in GABAergic interneurons in the hippocampus following MIA, associated with presynaptic GABAergic transmission, was reversed by the GABA uptake inhibitor tiagabine (Rideau Batista Novais et al. 2014).

1.3.5 MIA and oligodendrocytes

1.3.5.1 Cytokines, inflammation and oligodendrocyte development

The consequences of maternal inflammation on developing OLs are complex, and can result in devastating disorders such as periventricular leukomalacia (PVL), which involves the death or small areas of white matter around the ventricles, and cerebral palsy, a movement disorder resulting from white matter damage predominantly in the motor cortex (K. Kim et al. 2014). OLs are known to be particularly vulnerable to cytokines but, like in all cells in the CNS, cytokines also play a role in the normal physiological development of OLs. For example, in postnatal day 6 mouse pups, TGFβ signalling has a role in the regulation of normal myelination processes through modulation of c-myc and p21 gene expression by cooperation of SMAD3/4 with the transcription factors Forkhead

Box O1 (FoxO1) and Specificity protein 1 (Sp1) resulting in cell cycle exit, OL differentiation and myelination (Palazuelos et al. 2014). In vitro studies on OL progenitors and OLs treated with IL-1β, found that IL-1β is capable of blocking proliferation of late-stage OL progenitors (O4+) but not earlier progenitors (A2B5+) while simultaneously promoting the maturation of cells to a mature MBP+ phenotype.

Moreover, IL-1β decreases PDGFR-α gene expression, a marker of immature oligodendrocytes, in a time dependent manner, cumulatively suggesting a role for IL-1β in enhancing survival of differentiating OLs and playing a part in mitotic arrest and OL progenitor differentiation (Vela et al. 2002). On the other hand, transgenic mice that express IFN-γ under the transcriptional control of MBP exhibit hypomyelination

indicative of a potential interference with normal CNS development (Corbin et al. 1996).

LIF receptor signalling has been shown to enhance of the myelination process in some mouse models. In mice exposed to cuprizone, a copper chelator which creates a toxic oligodendrocytopathy, exogenous LIF limited demyelination, while knockout of LIF potentiated the demyelination process, indicating a role for LIF in the myelination process at normal physiological levels (Marriott et al. 2008). Chemokines, in particular astrocytic chemokines such as CXCL1 and CXCL12, play roles in multiple facets of OL development including migration, proliferation and maturation (Maysami et al. 2006;

Filipovic and Zecevic 2008). In vitro studies using an OL precursor-like cell line showed

CXCL12, CXCL8 and CCL5 all increased proliferation while both CXCL8 and CXCL12 also increased MBP production and maturation. CXCL12 induced proliferation of mixed cortical primary cultures through the receptor CXCR4 (Kadi et al. 2006). Another group have shown that activation of CXCR7 by CXCL12 induced maturation of OPCs both in vivo and in vitro (Gottle et al. 2010).

1.3.5.2 The consequences of MIA on the developing oligodendrocyte

It is perhaps unsurprising, therefore, that due to their complex developmental process OLs are particularly vulnerable to the effects of MIA. Maternal inflammation has been shown to alter cytokine levels in the fetal brain and to damage white matter (Cammer and Zhang

1999; Cai et al. 2000). TNFα is present in white matter lesions associated with PVL and plays a role in the potentiation of IFNɣ mediated oligodendroglial death during development. (Volpe et al. 2011). This alteration in cytokine levels can have varied effects on OLs, dependent on the severity of the insult, the specific brain region and the time at which the insult occurred. In the human, like in the rodent, myelination begins in late gestation and continues during postnatal life. Despite this however, the pathological effects of MIA are not limited to this vulnerable period of myelination. Indeed white matter damage is characteristic of a number of neuropsychiatric and neurological 32

disorders including schizophrenia, autism and PVL, suggesting that MIA during gestation may have serious consequences on the developing white matter (Gilstrap and Ramin

2000; Volpe et al. 2011).

White matter injury and abnormality can result following immune insult during multiple gestational windows. A number of studies by Fatemi and colleagues have investigated gene expression after maternal exposure to a sub-lethal dose of the H1N1 human influenza virus in mice at multiple time points; E9 (early gestation), E16 (mid-second trimester) and E18 (late second trimester) (Fatemi et al. 2005a; Fatemi et al. 2008b;

Fatemi et al. 2009a; Fatemi et al. 2009b). The E16 time point revealed abnormal expression of a number of myelination genes in the cerebellum of offspring post maternal inflammation including MAG, MOG, myelin-associated oligodendrocytic basic protein

(MOBP), MBP and PLP1. A number of these alterations were confirmed at a protein level and were long lasting, including a decrease in MBP isoforms at P14 and P56 time points, a decrease in MAG expression at P14 and P35, and, interestingly, an increase in DM20

(a PLP1 splice variant) at P35 and P56, perhaps indicating a change to the molecular make up of myelin produced. Decreases in MBP were also observed in other studies by the group, including in the hippocampus, also at E16, and in whole brain homogenates of offspring of mice exposed to the human influenza virus at E9. Changes in MBP expression were not reported after E18 treatment in the hippocampus, frontal cortex or cerebellum. Despite it being difficult to make direct comparison between studies due to differences in time point of insult, the brain region examined, the type and severity of the insult, and in many cases the time point examined postnatally, Fatemi and colleagues have provided evidence strongly suggestive of a change in expression of proteins involved in myelination in response to influenza virus exposure that is temporally and spatially specific. This regional specificity is echoed in a study in mice in which dams received a 60mg/kg dose of Poly (I:C) at E9.5. Upon examination of offspring at P14, all

MBP isoforms were found to be decreased in the hippocampus, but not in the cortex.

Interestingly this effect was transient in the hippocampus and was not seen at P63, suggesting that myelination was delayed but not prevented in the hippocampus and unaffected in the cortex (M. Makinodan et al. 2008).

Multiple further studies have investigated the effects of an array of different immune insults on developing oligodendrocytes at differing time points and levels of insult severity. These studies report a range of changes in myelin protein expression, oligodendrocyte number and function, and white matter connectivity while suggesting a number of causal mechanisms and potential treatments (Fatemi et al. 2008b; Li et al.

2010). Sequential maternal injections of 100µg/kg LPS at E15 and E16 revealed a decrease in myelination of both the cortical and limbic regions in rat offspring, an effect that was more pronounced in male rodents than in female. Such sex difference is an important observation in schizophrenia, and as the authors noted in this study, is also potentially relevant to the clinical presentation of ASD, which is more common in males and also associated with white matter injury (Bergeron et al. 2013; L. Wischhof et al.

2015). Recently, also at E15, a study by Makinson et al, in which dams received an intrauterine injection of 50µg/kg LPS. echoed these sex-specific differences in response to MIA. This study identified sex-specific differences in MBP expression in the P2 whole brain and the corpus callosum of mouse offspring at 28 weeks (Makinson et al. 2017).

Finally at E15, a study using a 4mg/kg poly(I:C) injection showed a reduction in MBP and Rhombex 29 expression in the PFC of rat offspring after treatment which could be corrected by treatment with the atypical antipsychotic risperidone. Rhombex 29 is homologous to the PLP/DM20-M6 family (Shimokawa and Miura 2000; Farrelly et al.

2015).

Additional studies have furthered the scope of traditional MIA models to examine potential exacerbating factors of the effects of MIA on oligodendrocytes, as well as 34

reparative or even preventative treatments for the effects of MIA. A study by Graf et al at E16 modelled PVL, a white matter disorder commonly associated with cerebral palsy in mice. They found that hyperoxia, like that experienced by a neonate in a ventilation chamber, exacerbated the response to an i.p 80µg/kg maternal LPS injection leading to decreased oligodendrocytes in the cortex and hippocampus as well as decreased MBP staining at P14 (Graf et al. 2014). Another study, also investigating PVL from an MIA perspective but in rats, found white matter injury in the periventricular region at P7 after a 500µg/kg i.p maternal LPS injection at E18 and E19, characterised by TUNEL+ cell death and a decrease in MBP staining. Interestingly this LPS induced injury was attenuated with Erythropoietin (EPO) treatment, which stimulates red blood cell production in response to cellular hypoxia. EPO treatment was associated with a decrease in TNF alpha, IL-6 and IL-1 in this study, all of which were elevated in offspring in response to maternal LPS treatment (Kumral et al. 2007). Maternal exposure to E.coli (1 x 107 colony forming units (CFU)) via intrauterine inoculation at E17 induced white matter injury in rat offspring characterised by a decrease in 2',3'-Cyclic-nucleotide 3'- phosphodiesterase (CNPase) staining in the corpus callosum (CC), the cingulate region of the subcortex and the external capsule at P8. O4 immunostaining, marking an intermediate stage of oligodendrocyte development, was also decreased in white matter areas. Oligodendrocyte numbers were reduced in periventricular areas in offspring of

E.coli exposed dams. At P15, decreased MBP staining indicated hypomyelination in line with these previous findings. IL-10 treatment partially repaired the damage caused by

E.coli exposure, again potentially implicating pro-inflammatory cytokines in the pathogenesis of MIA. A previous study by Meyers et al showed that 2mg/kg intravenous

Poly (I:C) induced changes in whole-brain levels of the proinflammatory cytokines IL-6 and TNF-α, and that IL-10 could reverse the behavioural phenotypes induced by treatment at E9 in mice. (Pang et al. 2005; Meyer et al. 2008).

At the transcriptome level, a recent microarray study by Richetto et al identified a decrease in multiple genes involved in myelination in the mPFC and the nucleus accumbens (NAc) in mice following MIA induced by an i.v injection of 5mg/kg Poly(I:C) at E17. These included MAL, MAG, MOBP, MOG, and claudin 11. MOBP was confirmed to be decreased at the level of protein expression in the mPFC following MIA

(Richetto et al. 2017). Maternal i.p 700µg/kg LPS injection at E18 induced peroxisome depletion in oligodendrocytes resulting in hypomyelination of the cingulum and corpus callosum in adolescent rat offspring. This peroxisome depletion was corrected by N- acetylcysteine, an antioxidant and suggests a cellular mechanism by which oligodendrocytes are damaged during MIA (Paintlia et al. 2008). Maternal exposure to inactivated GBS by injection every 12 hours from E19 to E22 resulted in dysmyelination of the external capsule in rat offspring. Myelin density was reduced and the cellular architecture disorganised, characterised by the misalignment of nuclei and a decrease in the number of mature oligodendrocytes. These cellular deficits translated into behavioural manifestations such as a decrease in nest seeking behaviour (P9), reduced pre-pulse inhibition (PPI) (P30-35) and reduced social interaction (P40) all of which were more common in males than in females, again illustrating the applicability of MIA models to

ASD (Bergeron et al. 2013). Interestingly just one study identified increases of white matter associated proteins in response to MIA. A recent study by Mouihate et al described increases in NG2+ cell density at P2 following an i.p 100µg/kg LPS injection at E12, indicative of an increase in the number of OPCs present in the corpus callosum. They found no change in the density of CC1+ cells indicating that OLs were not increased.

Contrary to other studies the group also identified an increase in the 21.5kDa MBP isoform in rat offspring examined between P63 and P91(Mouihate et al. 2017).

1.3.6 MIA and Astrocytes

1.3.6.1 The role of astrocytes in mediating the consequences of MIA

The effect of MIA on developing astrocytes is an underexplored topic in the examination of the cellular consequences of MIA. Astrocytes are the most abundant of the macroglia in the CNS (Azevedo et al. 2009). Originally believed to be a homogeneous cell type with a structural support function, more recent evidence has implicated astrocytes in a diverse array of processes including structural support, maintenance of water balance, blood- brain barrier formation and the secretion of factors influencing neuronal function

(Sofroniew and Vinters 2010) . With such complex and varied roles in the everyday function of the CNS, there is a need to investigate the effects of MIA on the development of these cells. Astrocytes are capable of releasing a host of cytokines which can affect the normal function and development of surrounding cells (S.C. Lee et al. 1993). MIA is widely accepted to result in astrogliosis, and this effect is often the extent to which the effects of MIA on astrocytes is studied (Cai et al. 2000; Rousset et al. 2006; K. Kim et al. 2014; Zager et al. 2015). Astrogliosis is a normal response to a host of CNS insults and, like all inflammatory processes, can have both positive and negative results which are largely dependent on which intracellular signalling cascades are activated (Sofroniew

2009; Sofroniew and Vinters 2010). Although it is critical to understand the astrogliosis induced by MIA and the effect of this astrogliosis on other cells of the CNS it is also necessary to understand the more intricate effects of MIA on astrocyte development and functionality.

1.3.6.2 The consequences of MIA on the developing astrocyte

Astrogliosis is strongly associated with disorders of the white matter such as PVL, cerebral palsy (CP) and multiple sclerosis (MS) (Deng et al. 2008; Volpe et al. 2011; 37

Hartung et al. 2014). Reactive astrocytes often characterise the white matter lesions found in these disorders. Understandably then, most MIA studies which have investigated the effect of MIA on astrocytes have done so in the context of the effect of astrogliosis on oligodendrocytes. In line with the developmental timeline of the CNS, most studies investigating white matter damage (and often the resulting astrocytic response) have been carried out in later gestation. A PVL study, involving repeated intracervical injection of

1mg/kg LPS at E9, E13 and E17 saw an increase in Glial fibrillary acidic protein (GFAP) expression in the striatum in rat offspring at P84. This study found that regular endurance exercise was capable of reversing this increase in GFAP (K. Kim et al. 2014). Another study, investigating a potential developmental origin of MS susceptibility, found that a

120ug/kg i.p maternal LPS injection at E17 in mice exacerbated astrogliosis. This was characterised by an increase in GFAP protein levels and immunoreactivity in the cortex and cerebellum of an experimental autoimmune encephalomyelitis (EAE) model of MS offspring at P60 when compared to normal EAE model control offspring (Zager et al.

2015). A further study at E18 using an i.p injection of a higher dose of LPS (500ug/kg) confirmed this upregulation of GFAP in cortical regions and also in hippocampal regions of rat offspring at P8 (Cai et al. 2000). A 300ug/kg i.p maternal LPS injection at E19 and

E20 caused astrogliosis (again measured through increase in GFAP expression) in both the internal and external capsule of rat offspring at P7. These astrocytes had abnormal morphologies, exhibiting hypertrophy, and were spread diffusely through white matter regions (Rousset et al. 2006). These features are consistent with white matter damage observed clinically in human neonates (Back and Rosenberg 2014). Taken together these studies strongly support the association of a number of white matter disorders with astrogliosis but did not investigate the effect of MIA on the astrocyte outside of this inflammatory response. Further studies are necessary to establish the effect of MIA on

other astrocytic functions, for example osmoregulation, neuronal communication and blood brain barrier formation.

A group of studies by Fatemi et al., utilising microarray technology to investigate the effect of exposure to human influenza virus induced MIA in mice is appears to be the only investigation in the literature into the effects of MIA on expression of astrocytic genes outside of the realm of astrogliosis-induced white matter damage to date. The study identified alternations in Aquaporin 4 (Aqp4) expression, the most prevalent member of the aquaporin family of water channel proteins in the CNS (Saadoun and Papadopoulos

2010). Expression of Aqp4 is restricted to astrocyte end feet, the glia limitans, and ependymal cells (Yoneda et al. 2001; Saadoun and Papadopoulos 2010). It plays a role in the regulation of cerebral spinal fluid (CSF) in the ventricles and deletion of Aqp4 reduces downstream activation of intracellular signalling cascades in response to oedema

(Thrane et al. 2011; Desai et al. 2016). Aqp4 has been implicated in the processes of memory consolidation and cognition as well as in the pathogenesis of Alzheimer’s disease and ASD (Fatemi et al. 2008a; Lan et al. 2017). In whole brain samples of offspring of mothers treated with Influenza virus at E9, Aquaporin 4 (Aqp4) was found to be downregulated (Fatemi et al. 2005a). Aqp4 was also decreased in the hippocampus after

E16 influenza exposure and remained so at P56 (Fatemi et al. 2009b). When analysed by

Reverse transcription polymerase chain reaction (RT-PCR), GFAP was increased by influenza virus exposure at E9 suggesting astrogliosis (Fatemi et al. 2005a). GFAP levels, which also increased after influenza treatment at E16, remained altered at P56 (Fatemi et al. 2009b). Interestingly when treatment was at E18, neither GFAP nor Aqp4 were altered significantly in the PFC, cerebellum or hippocampus suggesting a temporal dependence for these effects (Fatemi et al. 2008b).

1.3.7 MIA and Microglia

1.3.7.1 Cytokines, inflammation and microglial development

Microglia, unlike macroglia, originate outside the CNS (Rezaie and Male 1999; Ginhoux et al. 2010). Microglia are known as the immune cells of the CNS, comprising 10-15% of the glial cell melieu, but also play complex roles in cell survival, cell death and synaptic pruning during development(Lawson et al. 1992; Nayak et al. 2014; Fernandez de Cossio et al. 2017).

A recent study by Matcovitch-Natan et al. in mice has identified dynamic changes in gene expression which divide microglia into early microglia (E10.5-E14), pre-microglia

(E14 – P9), and adult microglia (P28 onwards). Progression of the genetic programme from a proliferative focus (early microglia) to a focus on synaptic pruning and neuronal maturation (pre-microglia) and finally to a focus on homeostasis and surveillance (adult microglia) appears to be controlled by wide-spread changes in the chromatin landscape of the cell (Matcovitch-Natan et al. 2016). This study found that MIA with an i.p injection of 5mg/kg poly(I:C) at E14.5 resulted in a substantial change in gene expression and a premature maturation of the developing microglial cells of offspring (Matcovitch-Natan et al. 2016). Although not occurring during gestation, a second study has supported these findings by showing that an i.p injection of 330µg/kg LPS at P60 can also accelerate microglial maturation at the level of transcription in mice, but only in males, potentially the result of differing developmental trajectories of male and female microglia during normal development (Hanamsagar et al. 2017). This study hints once more at a potential explanation for the sex differences seen in certain neurological disorders like ASD and schizophrenia. Another study, also postnatal, has shown that subcutaneous E.Coli injection (0.1 × 106 CFU) in rats at P4 can cause a long-lasting alteration in microglia.

This resulted in exaggerated IL-1β response to an i.p injection of 25 µg/kg LPS on second

exposure in adulthood, supporting the concept of a ‘two-hit’ hypothesis, whereby early exposure to immune insult exasperates the effects of secondary exposure. Minocycline, which prevents the activation of microglia, was sufficent to prevent this exaggerated IL-

1β response in the hippocampus (Williamson et al. 2011).

1.3.7.2 The consequences of MIA on the developing microglia

In a study at E9 a single i.p 20mg/kg poly(I:C) injection increased numbers of microglia in both the hippocampus and striatum at P30 as indicated by Iba1 (Ionised calcium- binding adapter molecule 1) immunostaining of microglia in mice. Furthermore, reduced arborisation (branching) of microglia indicated a higher level of microglial activation in these regions. This result was not seen in the prefrontal cortex indicative of a regional effect of MIA on microglial activation (Juckel et al. 2011). Contrary to this however, another study, also at E9 and in mice, found that i.p injection of 8µg/kg LPS induced a significant increase in Iba1+ microglia in the forebrain at E18 and P0. It may be the case that this upregulation in the forebrain is transient and had already decreased by P30.

Indeed, Iba1 levels were found to be higher in the forebrain of E18 embryos than P0 pups in this study (Le Belle et al. 2014). A further study at E9 found that i.p injection of

20mg/kg poly(I:C) increased microglial activity in the hippocampus, thalamus and cortex of offspring at P56, also analysed by Iba-1 immunohistochemistry. Interestingly minocycline, a broad spectrum tetracycline antibiotic known to inhibit microglial activation not only dampened this microglial activation in response to MIA but also decreased deficits in social behaviour and prepulse inhibition observed in these mice leading these authors to believe that microglial activation may play a key role in behavioural deficits associated with schizophrenia (Zhu et al. 2014). A study by

Mouihate et al saw no change in the number of microglia in the CC of offspring at P63-

P91 following a 100µg/kg i.p maternal LPS injection in rats at E12 (Mouihate et al. 2017).

However, a different study, also using rats, identified more subtle changes in microglial 41

morphology, with microglia displaying an ameboid, more activated morphology in the amygdala of offspring at both P7 and P40 following an i.p 50µg/kg maternal LPS injection at E12 (O'Loughlin et al. 2017).

Two studies have investigated the cytokine profile of microglia in response to MIA. In a study of microglial activity in the context of ASD Pratt et al. isolated CD11b+ microglial cells from the whole brain samples of E16 mouse embryos of mothers treated with i.p

20mg/kg poly(I:C) injection at E12. The cytokine profiles of these cells were investigated by both RT-PCR and ‘Luminex’ cytokine assay. Il-6 mRNA level was found to be increased in response to MIA by RT-PCR, however, this result was not significant in the cytokine assay. The cytokine assay revealed a significant increase in expression of a number of cytokines (Il-1α, granulocyte-macrophage/monocyte colony-stimulating factor, macrophage/monocyte colony stimulating factor, IL-4 and IL-9), as well as a number of chemokines (eotaxin, lipopolysaccharide-induced CXC chemokine, Regulated on Activation, Normal T Cell Expressed and Secreted (RANTES, CCL5) and macrophage inflammatory protein 1β), indicating that MIA at E12 drastically alters the cytokine profile of microglia (Pratt et al. 2013). A microarray study after i.p 120µg/kg

LPS treatment at E15.5 also investigated the cytokine profile of microglia in response to

MIA. Two days later at E17.5, Il-1β, Ccl4, Ccl5, nuclear factor kappa-light-chain- enhancer of activated B cells (Nfkb) and Stat5b were all upregulated in response to maternal LPS infection (Pont-Lezica et al. 2014). Importantly a study by McQuillan et al. has suggested that microglia obtained by cell isolation protocols may possess an activated phenotype, characterised by increased CD11b and CD40 expression (McQuillan et al. 2010). The artefactual effect of this potentially activated phenotype on the cytokine profile of microglia after MIA is unknown so further investigation may be necessary.

Offspring of rat dams which received a 4mg/kg subcutaneous poly(I:C) injection at E15 showed no increase in CD11b+ and Iba1+ immunohistochemical staining in the 42

frontotemporal cortex, CC, hippocampus, thalamus, striatum or pons between P90 and

P104 (Missault et al. 2014). This is in contrast to another study by the same laboratory, which saw increased CD11b+ microglia in the hippocampus, CC and thalamus at P180 at the same dosage and insult timing but with tail vein injection of Poly(I:C). These microglia did not show high reactivity to an antibody against ED-1, a marker of activated microglia indicating that they may have been in a transitory phase, possibly post- activation. These immunohistochemical results were region-specific, with no increase in intensity of staining or number of microglia in the pons, cortex or striatum (Van den

Eynde et al. 2014). Another study by Makinson et al. investigated the microglial number per gram of brain tissue using flow cytometry following 50µg/kg intrauterine LPS- induced MIA at E15, and showed a significant increase in microglial number, and TNF-

α+ microglia cell number (Makinson et al. 2017). Delving deeper into the effects of MIA on microglial behaviour, a study by Fernández de Cossío et al. investigated the effect of

MIA on microglia-mediated synaptic pruning in the hippocampus. This study found that male offspring of dams treated with i.p 100µg/kg LPS at E15 had an increased spine density in the dentate gyrus. This corresponded to decreased CX3CR1 expression by microglia which may affect microglial migration and synaptic pruning again pointing to an altered microglial phenotype (Fernandez de Cossio et al. 2017).

Two studies have investigated the effect of MIA on microglia in late gestation.

Intrauterine E-coli treatment at E17 resulted in an increase in ED-1 staining in the cingulum at P8 and P15. This increased ED-1 staining was prevented by treatment with anti-inflammatory cytokine IL-10 (Pang et al. 2005). In another study investigating the effects of MIA on an EAE model of MS in mice, primary mixed cultures were isolated from the whole brains of offspring of dams treated with 120µg/kg i.p injection on E17.

Cultures isolated from the EAE model offspring showed higher Iba1 staining in response to LPS treatment in vitro than non-MIA offspring in the EAE models, indicative of a

potential prenatal susceptibility to MS, or a ‘two-hit’ vulnerability. Flow cytometry analysis at P1-P3 showed an increased major histocompatibility complex class II

(MHCII) expression in microglial cells of MIA offspring in an EAE model than control

EAE models indicative of exacerbated gliosis (Zager et al. 2015).

The activation of microglia in response to immune challenge is oftentimes seen as a contributing factor to CNS disorders. Indeed, Monji et al. have proposed a microglial hypothesis of schizophrenia whereby the release of cytokines and reactive species from activated microglia has been proposed as the main culprits in the neurodegeneration, white matter damage and decreased neurogenesis observed in schizophrenia (Monji et al.

2009). This hypothesis, combined with the complex gene expression changes observed during microglial development, and the postnatal studies demonstrating the continued effects of immune activation on microglial gene expression and cytokine response, illustrate that changes in cytokine levels can affect the development and function of microglial cells in the long term. They also demonstrate that the effect of MIA on microglial cells requires further, more complex, investigation beyond the immediate detrimental effects of the activation of these cells on other cells of the CNS.

1.4 MicroRNAs

MicroRNAs (miRNAs, miRs) are small, non-coding RNAs consisting of 21-23 nucleotides first identified in 1993 (R.C. Lee et al. 1993). MicroRNAs are negative regulators of gene expression, binding to their target mRNA and downregulating it post- transcriptionally. MicroRNAs have a wide array of biological functions, including a role in the regulation of immune system and response to hypoxic-ischemic injury in oligodendrocytes (Nahid et al. 2011; Birch et al. 2014; Liu et al. 2017). The convergence of this capacity of microRNAs to regulate immune response and their well-documented roles in normal developmental processes make them an area of particular interest in this

thesis, where the processes of immune response and development intersect in the MIA model.

1.4.1 Structure and function of microRNAs microRNAs are usually transcribed from the introns of protein coding and non protein coding sequences, although transcription from longer, exonic non-coding transcripts, is also possible (Rodriguez et al. 2004). Primary miRNA (pri-miRNA) is transcribed from these sequences and forms an imperfect hairpin loop, which may contain several miRNA transcripts. pri-miRNA is processed by a microprocessing unit consisting of the ribonuclease II enzyme Drosha, and DiGeorge syndrome critical region 8 (DGCR8) to produce pre-miRNA (Fitzpatrick et al. 2015). Exportin 5, a member of the Karyopherin family, recognises the 3’ tail of pre-miRNA and exports the pre-miRNA to the cytoplasm, where it is further processed by dicer, another ribonuclease enzyme to produce a mature miRNA (Figure 1.6) (He and Hannon 2004; Y. Wang et al. 2007).

Mature miRNAs, as a part of the RNA induced silencing complex (RISC) can bind to target mRNA at the 3’-UTR resulting in the downregulation of that target mRNA by mRNA repression, mRNA degradation or mRNA deadenylation (He and Hannon 2004;

Eulalio et al. 2009). The type of downregulation which occurs is commonly believed to be dependent on sequence complimentarity between the target mRNA and the miRNA.

It is widely accepted that in order for canonical binding to occur there must be exact complementarity between the ‘seed region’, nucleotides 2-7 of the miRNA, and the target mRNA sequence. However certain non-canonical instances of binding have been suggested (Lewis et al. 2005; Grimson et al. 2007; Helwak et al. 2013; Khorshid et al.

2013).

Figure 1.6. miRNA synthesis. In the nucleus, miRNA is transcribed by RNA polymerase

II/III to produce pri-miRNA. Pri-miRNA is cleaved by enzymes Drosha and DGCR8 before export into the cytoplasm by exportin 5. In the cytoplasm further processing by dicer produces a mature miRNA which may bind a target sequence as a part of RISC.

1.4.2 Nomenclature

MicroRNAs are often referred to by the suffix ‘miR’ followed by a number. MicroRNAs discovered earlier generally have lower numbers (for example miR-124 was discovered before miR-338). A capitalised ‘R’ references a mature miRNA while use of ‘mir’ relates to the immature pre- or pri-miRNAs. Identical mature miRNAs derived from different areas of the genome are identified by a number eg -1, -2, -3, while miRNAs derived from different sides of the hairpin loop are denoted as -3p or -5p. However this is only the case if the miRNAs are present in similar amounts. If multiple forms of the same miRNA exist but one is far more abundant than the other the less abundant form may be defined by an

‘*’, for example miR-124* (Ambros et al. 2003; Bartel 2004).

1.4.3 MicroRNAs and cellular development

Since their discovery in 1993 microRNAs have been repeatedly identified as modulators of developmental processes. In the CNS these small, non-coding RNAs are implicated in the regulation of neurogenesis, oligodendrogliogenesis, astrogliogenesis and microgliogenesis.

1.4.3.1 MicroRNAs in neurogenesis

Neurogenesis is a stepwise process of NSC proliferation, migration, differentiation and maturation in which microRNAs are known to play a role in every step. The expression of miR-9, a well characterised microRNA enriched in the CNS, plays an important role in proliferation and differentiation. miR-9 induces differentiation of NSCs through the modulation of a number of downstream pro-proliferative targets including the nuclear receptor TLX, Sirtuin 1 (SIRT1) and Foxg1 (Shibata et al. 2008; Zhao et al. 2009;

Saunders et al. 2010). In zebrafish miR-9 has been shown to induce neurogenesis by repressing hairy-related (her)-5 and her-9, fgf8, fgfr1 and canopyl while in xenopus miR-

9 downregulates Hes-1, resulting the the modulation of WNT signalling (Leucht et al.

2008; Bonev et al. 2011). Despite this well-characterised role for miR-9 in differentiation during neurogenesis, a study by Delaloy et al has shown that miR-9 is upregulated following neural induction in human embryonic stem cells and encourages proliferation at this early stage (Delaloy et al. 2010). In the Xenopus study identifying Hes-1 as a target of miR-9, miR-9 prevented progenitor apoptosis through modulation of the p53 pathway through repression of Hes-1 (Bonev et al. 2011). miR-137 has also been identified as having a role in differentiation during neurogenesis, through modulation of TLX and it’s co-repressor lysine-specific histone demethylase 1

(LSD1). Like miR-9 miR-137 also has roles in maintaining proliferation, modulating mindbomb homolog 1 (mib-1) to decrease neuronal maturation (Smrt et al. 2010).

Epigenetic modification of miR-137 by MeCP2 and SOX2 modulated hippocampal neurogenesis by activating enhancer of zeste homolog 2 (Ezh2) (Szulwach et al. 2010). miR-124 is a nervous system specific miRNA known to have pro-neuronal properties. It induces neuronal differentiation through modulation of the expression of Small CTD

Phosphatases 1 (SCP1), present in non-neuronal tissues and recruited by the transcriptional repressor element 1-Silencing Transcription factor (REST) to suppress neuronal gene expression (Visvanathan et al. 2007). miR-124 also modulates barrier-to- autointegration factor (BAF) subunit BAF53a, allowing its replacement with BAF53b, a neuronal specific subunit during differentiation (Yoo et al. 2009). Sox9, necessary for gliogenesis is also a target of miR-124, as is Polypyrimidine tract binding protein 1

(PTBP1), a repressor of neuronal specific splicing which is replaced by PTBP2 during differentiation, and ephrin-B1 (Makeyev et al. 2007; Cheng et al. 2009; Arvanitis et al.

2010). Like miR-9 and miR-137 miR-124 can also induce proliferation in early neurogenic stages through targeting of basic helix-loop-helix transcription factor, neurogenic differentiation 1 (NeuroD1) (Liu et al. 2011). Unlike previously discussed miRs however it also plays a role in neuronal maturation, targeting CREB expression to inhibit the formation of long-term facilitation (LTF), and LIM/homeobox protein 2 to facilitate maturation and survival of hippocampal DG neurons (Rajasethupathy et al.

2009; Sanuki et al. 2011).

Other microRNAs involved in the maturation of neurons include miR-125b, which targets

N-Methyl-d-aspartic acid (NMDA) receptor subunit NR2A in association with Fragile X mental retardation protein (FMRP) to enhance dendritic spine maturation. miR-128, targets regulator of nonsense transcripts 1 (UPF1) and cancer susceptibility candidate 3

(CASC3), both a part of the nonsense-mediated decay (NMD), to increase dendritic length (Chan et al. 2007; Edbauer et al. 2010). miR-132, miR-134 and miR-138 are all involved in the maturation and morphology of dendritic spines through modulation of 48

their targets Rho GTPase activating protein 32 (ARHGAP32), LIM domain kinase 1

(Limk1) and acyl protein thioesterase 1 (APT1) respectively (Wayman et al. 2008; Siegel et al. 2009; Gaughwin et al. 2011). For a complete review of the microRNAs involved in neurogenesis see the excellent review by (Lang and Shi 2012).

1.4.3.2 MicroRNAs in astrogliogenesis

In contrast to the role of microRNAs in neurogenesis far less is known about the role of these molecules in astrogliogenesis, with much of the research on microRNAs in astrocytes focused on the endosomal release of astrocytic miRNAs and the effect of those miRNAs on other cell types. That said a small number of studies have looked at the necessity of microRNA expression in astrogliogenesis. In a study utilising adult hippocampal stem cells in vitro knockout of dicer, an endoribonuclease necessary for microRNA biosynthesis, failed to prevent astrogliogenesis from NSCs (as identified by s100b), suggesting miRNAs may not be necessary for adoption of an astrocytic phenotype

(Pons-Espinal et al. 2017). In contrast a study using primary neuronal stem cells derived from embryonic mouse Dicer-null cerebral cortex showed that miRNAs are necessary for the generation of both neurons and glia (Andersson et al. 2010). This result was supported in an in-vitro conditional knockout model in embryonic stem cells (ESCs), which showed that conditional knock out of dicer was sufficient to prevent gliogenesis when differentiation was induced using standard protocols. Furthermore this study found that reintroduction of Let-7 and miR-125 was sufficient to rescue the glial phenotype in these cells (Shenoy et al. 2015). A conditional knockout study using Olig1Cre/+/Dicerflox/flox mice to delete dicer expression in the spinal cord saw complete ablation of GFAP, S100b and

ID3 staining in the triangular region of the spinal cord closest to the floorplate. This indicated that miRNAs are necessary for the development of the slit-1+ astrocytes found in this region, and that the role of microRNAs in astrogliogenesis may simply be more subtle than in neurogenesis (Zheng et al. 2010). A recent study by Meares et al has 49

identified miR-31 as necessary for the terminal differentiation of astrocytes. During proliferation miR-31 expression is suppressed by Oct4, Sox2, Lin28 and cMyc. During differentiation it is activated by STAT3 and Smad1/5/8. The study confirmed that Lin28 is a target of miR-31, and also noted that the expression of Sox2, Oct4 and cMyc was inversely correlated to miR-31 expression (Meares et al. 2018).

1.4.3.3 MicroRNAs in microgliogenesis

Similar to the situation in astrocytes, much of the research on the role of miRNAs in microglia has been focused outside of their role in microgliogenesis, instead focusing on maintenance of quiescence and microglial activation in response to immune activation.

Two studies by Ponomarev et al have identified miR-124 as important in the maintenance of quiescence in microglia through targeting of CCAAT/enhancer-binding protein-α

(C/EBP-α) and it’s downstream effector PU.1 (Ponomarev et al. 2011; Ponomarev et al.

2013). Another study, also focusing on miR-124, confirmed that overexpression of miR-

124 reduced microglial motility and phagocytosis and suppression induced motility and phagocytic phenotypes in zebrafish (Svahn et al. 2016). Although maintenance of quiescence and microglial activation is of huge importance in development, and the adoption of a quiescent state is a marker of mature microglia in a healthy CNS, it is clear that much work remains to be completed in order to establish the role of miRNAs in microgliogenesis.

1.4.3.4 MicroRNAs in Oligodendrogliogenesis

MicroRNAs have been implicated in oligodendrogliogenesis in similar ways to neurogenesis, effecting the proliferation, migration, differentiation and maturation of oligodendroglial cells. Key microRNAs involved in oligodendrogliogenesis include miR-

9, miR-124, miR-138, miR-338 and miR-146a. The individual roles of these microRNAs and their known and predicted targets will be described in detail in chapter 7.

1.5 Aims and objectives:

The overall aim of this thesis is to investigate the consequences of MIA on the developing spinal cords of offspring. This thesis aims to delineate further the temporal and spatial specificity of vulnerability to MIA, investigate the effect of MIA on the different cell populations of the spinal cord, and identify the molecular fingerprint of MIA on spinal cord development.

The overall objectives of this thesis are:

1. To investigate the consequences of MIA at E12, E14 and E16 at a cellular level,

using immunofluorescence studies to determine outcomes in the spinal cords of

offspring both during development (5hr post MIA) and in early postnatal life

(P14). Specifically, this thesis examines the effects of MIA on the

oligodendrocytes, astrocytes, microglia and neurons of the cord to give a wide-

angle view of the effects of MIA on the cytoarchitecture of the developing spinal

cord.

2. To investigate the consequences of MIA at E12 and E16 using microarray and

RNA Seq studies to pinpoint differential gene expression in the spinal cords of

offspring in response to MIA, both during development and in early postnatal life.

3. To use an in-silico study to further clarify the role of microRNAs in

oligodendrogliogenesis, and identify miRNA targets in inflammatory pathways

which may play a role in oligodendroglial response to MIA.

The aims and objectives of individual studies in this thesis are indicated at the start of each chapter.

Chapter 2. Methodology

2.1 Time mating and maternal immune activation

Time mated, pregnant, Sprague Dawley rats were obtained from the Biological Services

Unit at University College Cork, Ireland under full ethical approval (DoH license

B100/4485 with AEEC approval 2012/037). Presence of a vaginal plug confirmed pregnancy and was designated as E0. Dams were housed throughout the experiment on a

12h light/dark cycle (light on 0800h) under a constant temperature of 21± 2̊C and had ad libitum access to food and water. To induce maternal immune activation, pregnant dams received a single i.p injection of either 50µg/kg or 100µg/kg E.Coli 026.B6 LPS (Sigma-

Aldrich, Ireland) on E12, E14 or E16. Control animals received a single i.p injection of

100µl of a 0.9% saline injection on E12, E14 or E16. (Figure. 2.1)

Figure 2.1 Schema of the time mating protocol. Dams received an injection of either

50µg/kg or 100µg/kg LPS on E12, E14 or E16. For embryonic experiments, sacrifice took place 5h post injection. In postnatal experiments, offspring were sacrificed at P14.

2.2 Tissue collection and processing

2.2.1 Tissue collection for embryonic study

Dams assigned to the embryonic study were anesthetised with pentobarbital and decapitated 5h post injection. Embryos were harvested by laparotomy and placed in ice- cold HANKS’ Balanced salt solution (HBSS; Sigma-Aldrich, Ireland). Embryos assigned to immunohistochemical studies were submerged whole (E12 embryos) or decapitated

(E14 and E16 embryos) in 4% paraformaldehyde (PFA) solution overnight (O.N) at 4̊C.

Fixed embryos were then immersed in 30% sucrose for cryoprotection before snap freezing and storage at -80̊C. Embryos assigned to molecular studies were micro- dissected in ice-cold HBSS to isolate the spinal cord. Isolated cords were stored at -80̊C until use.

2.2.2 Tissue collection for postnatal study

Dams assigned to the postnatal study continued through pregnancy without further intervention after injection. P14 pups were sexed, and assigned at random to either immunohistochemical or molecular studies. Pups assigned to the immunohistochemical studies were anesthetised by i.p injection of pentobarbital and transcardally perfused with

10mM phosphate buffered saline (PBS) followed by 4% PFA solution. Spinal cords were post-fixed in 4% PFA O.N and were then immersed in 30% sucrose for cryoprotection.

Cords were divided evenly into roughly rostral, middle and caudal tissues for storage at -

80̊C. The spinal cords of pups assigned to molecular studies were dissected and stored at

-80̊C.

2.2.3 Gelation coating of slides

1.5g gelatin was added to 500ml dH2O which was then heated to 60̊C and stirred until gelatin was dissolved. 0.25g of chromium (III) potassium sulfate dodecahydrate was added to the gelatin solution. When this was dissolved the gelatin solution was cooled to

~45̊C and racked microscope slides were submerged for 1 min. Excess solution was tapped off and slides were placed in slide boxes to dry, and stored at ambient temperature prior to use.

2.2.4 Cryosectioning

Both whole embryos and P14 spinal cords were transversely sectioned at 15µm using a cryostat (Lecia Microsystem GmbH, Wetlzar, Germany). Sections of E12, E14 and E16 embryos were collected on gelatin-coated slides in a non-serial manner and were stored at -80̊C until required. The length of the E14 and E16 embryonic spinal cords were estimated through complete sectioning of one representative embryo. These data were used to establish sectioning boundaries for rostral, middle and caudal tissue (Figure 2.2).

In P14 samples the total length of the spinal cord was measured in each animal and used to establish sectioning boundaries for rostral, middle and caudal tissue. Rostral tissue was further divided into ‘rostral’, ‘middle’ and ‘caudal’ rostral tissue (Figure 2.3).

Figure 2.2 Sectioning protocol for E14 and E16 embryonic tissue. Waste tissue was disposed of between each to establish sectioning boundaries. Pictured: E16 embryo.

Figure 2.3 Sectioning protocol for postnatal studies. Spinal cords from P14 animals were divided into rostral middle and caudal regions as in embryonic studies. The rostral cord was then further subdivided into ‘rostral’, ‘middle’, and ‘caudal’ sections with waste discarded in between sections to establish sectioning boundaries.

2.3 Immunohistochemical staining

2.3.1 Immunofluorescent staining

Slides were removed from the -80̊C freezer and dried for 30min at 37̊C. Slides were then rehydrated in 10mM PBS (3 x 5 min washes) before incubation in blocking solution (5% donkey serum, 0.4% Triton X in 10mM PBS) for 1 h at room temperature (RT). After 1 h excess blocking solution was removed and slides were incubated in primary antibody solution (2.5% donkey serum, 0.1% triton X, primary antibody as required (Table 2.1), in 10mM PBS) O.N at 4ᵒC. Following incubation slides were rinsed with PBS (4 x 6 min)

before incubation in secondary antibody solution (2.5% donkey serum, 0.1% triton X,

Alexafluor secondary antibody as necessary (Table 2.1), in 10mM PBS) for 2 h at RT for all protocols except Reelin staining where incubation was 2.5h at RT. Slides were rinsed with 10mM PBS (4 x 6 min) and incubated with bisbenzimide solution (1:5000) for 3 min. Slides were rinsed again with 10mM PBS (3 x 10 min) and mounted with fluoromount G (Affymetrix/eBiosciences, Cheshire, United Kingdom). See table 2.2 for a full list of solutions and Figure 2.4 for a graphical protocol.

During co-localisation investigations the above methodology was adapted to allow for double staining. Following incubation with the first primary antibody and appropriate secondary antibody, slides were washed (4x6min) and returned to blocking solution for

30 min at RT. The staining procedure was then repeated to completion for the desired second target.

Figure 2.4. Immunofluorescent staining protocol.

Primary Conc antibody Catalogue Secondary Catalogue Number Antibody Conc Number Olig2 1:400 Alexa- anti- AB9610 fluor 488 A32790 Rabbit (Merek/Millapore) Donkey (Invitrogen/TF) anti-rabbit MBP 1:1000 Alexa- anti- GT3412 fluor 488 A32766 mouse (Invitrogen/TF) Donkey (Invitrogen/TF) anti- 1:200 mouse Iba-1 1:800 Alexa- anti- AB5076 fluor 594 A32758 goat (Abcam) Donkey (Invitrogen/TF) anti-Goat Reelin 1:800 Alexa- anti- AB78540 fluor 594 mouse (Abcam) Donkey anti- mouse A21203 NeuN 1:500 Alexa- (Invitrogen/TF) anti- AB_2802653 fluor 594 mouse (Invitrogen/TF) Donkey anti- mouse GFAP 1:800 Alexa- anti- AB4648 fluor 594 mouse (Abcam) Donkey anti- mouse

Table 2.1. Primary and secondary antibody details. Concentrations and catalogue numbers of all antibodies used in immunofluoresence protocols. Conc – Concentration,

TF – Thermofisher. Antibody solutions can be found in table 2.2

Solution Ingredients PBS (10X) 80g of NaCl 2.0g of KCl 14.4g of Na2HPO4 2.4g of KH2PO4 1L Distilled H2O Wash Solution 1X (10mM) PBS Blocking Solution 5% Donkey Serum 0.4% Triton X 10mM PBS as required Primary Antibody Solution 2.5% Donkey Serum 0.1% Triton X Primary antibody at desired concentration 10mM PBS as required Secondary Antibody Solution 2.5% Donkey Serum 0.1% Triton X Secondary antibody at 1:200 concentration 10mM PBS as required Counterstain Bisbenzimide 1:5000 10mM PBS as required

Table 2.2: Solutions used in immunofluoresence protocols.

2.3.2 Quantification of immunofluorescent staining

All samples were imaged on an Olympus IX83 research inverted microscope using a

DP80 digital microscope camera (Olympus, Japan). All analysed images were in TIFF format, and taken at magnification 20x using the monochrome sensor. Images were post- coloured where necessary using the Cell Sens Dimensions software (Olympus, Japan).

All image post-processing and analysis was carried out using the Fiji imagej distribution package (Schindelin et al. 2012). Statistical analysis of the numeric output of image analysis was competed using Graphpad Prism version 5.0 for Windows (GraphPad

Software, La Jolla California USA). Representative controls for all staining may be found in appendix C. 60

2.3.2.1 Olig2 analysis

In E12 samples, images spanning the entirety of the spinal cord were randomised and the number of olig2+ cell nuclei in whole transverse sections of the spinal cord were counted using the ‘cell counter’ add-on in Fiji. Rostral, middle and caudal cord were identified anatomically as described in section 2.2.4 The average number of olig2+ nuclei for 4, non-serial, rostral, middle and caudal slices of the same cord was recorded. In both E14 and E16 samples representative images of the dorsal, intermediate and ventral white matter and dorsal and ventral grey matter were obtained (Fig 2.5). The images were randomised and the number of olig2+ nuclei in a known volume of tissue for each region was recorded (Fig 2.5), again using the cell counter add-on for Fiji. The average value across 4 non-serial sections was recorded for each region in individual animals.

In postnatal samples representative images of dorsal, intermediate and ventral white matter and dorsal and ventral grey matter were obtained (Figure 2.6). The images were randomised and the number of Olig2+ nuclei for a known volume of tissue was again recorded for each region of each animal (Figure 2.6). The number of Olig2+ nuclei for each region was averaged across the 4 non-consecutive sections of each animal.

Figure. 2.5 Schematic representation of the sampling strategy in the E14 and E16 spinal cord. WD – white dorsal, WI – white intermediate, WV – white ventral, GD – grey dorsal, GV – grey ventral. Pictured: E16 spinal cord.

Figure. 2.6 Schematic representation of the sampling strategy in the P14 spinal cord.

WD – white dorsal, WI – white intermediate, WV – white ventral, GD – grey dorsal, GV

– grey ventral.

2.3.2.2 MBP

The presence of MBP in postnatal samples was analysed by fluorescence intensity analysis again using Fiji. The background was subtracted from each image using the sliding paraboloid function on Fiji. The image was then duplicated. On one image the region of interest (ROI) was selected. These consisted of a 200x200µm region in the dorsal white matter (fasciculus gracilis), a 250x250µm region in the intermediate white matter (dorsolateral funiculus), and a 150x150µm region in the ventral white matter

(spanning the anterior cortical spinal and reticulospinal tracts). In the grey matter the regions of interest included a 300x150µm region in the substantia gelatinosa, a

250x250µm region in the dorsal grey matter (~ nucleus intermediolateralis) and a

250x250µm region in the ventral horn grey matter. The selected ROI was measured and the image outside the ROI cleared. The ROI was then thresholded using the selected

autothreshold ‘Triangle’ in the case of the substantia gelatinosa and dorsal horn or ‘Otsu’ in the case of all other regions measured. The areas of the ROI reaching threshold were selected and the mean grey matter fluorescence intensity measured by redirecting the measurement of the selection to the duplicated image, which remained unaltered beyond background subtraction.

2.3.2.3 Iba-1

In E12 samples representative images of whole tranverse sections of the spinal cord were obtained. These images were randomised and the number of Iba-1+ cells possessing a clear nucleus was counted by eye. The average number of Iba-1+ cells present was recorded for four, non-serial sections of each spinal cord in rostral, middle and caudal regions. In E16 samples representative images of the dorsal, intermediate and ventral white matter and dorsal and ventral grey matter were obtained from rostral, middle and caudal regions (see Figure 2.5). These images were randomised and, using the ‘cell- counter’ add-on in Fiji, the number of iba-1+ cells with a visible nucleus was counted in each region for four non-serial sections of each cord. The average for each region was recorded. Similarly, in P14 cords, images of the same representative regions were obtained (see Figure 2.6). Cells were counted in the same manner and the average recorded.

2.3.2.4 GFAP

GFAP expression was analysed in randomised postnatal samples by fluorescence intensity analysis. Randomised images were input into Fiji, the background was subtracted from the image using the sliding paraboloid function, and the image was duplicated. The ROI was selected on the original image and measured. The area outside the ROI was then cleared and a threshold was applied to ROI using the auto-threshold 64

function. ‘Triangle’ was identified as a suitable threshold for the grey matter regions, while ‘moments’ was identified as suitable for white matter analysis. Once the suitable threshold was applied to the ROI the areas of the ROI which reached the threshold were selected. This selection was applied to the duplicate image, which remained unaltered beyond background subtraction, and the average grey matter value in arbitrary units for the areas meeting threshold was recorded. These values were recorded for each region of interest in 4, non-consecutive, slices of spinal cord for each animal and averaged. Once analysis was complete the samples were identified and saline and test compared using two way anova analysis followed by bonfferoni post-hoc.

2.3.2.5 Reelin

In E12 samples reelin staining was scored in randomised samples in a semi-quantitative manner. In E16 studies reelin fluorescence intensity was analysed in the total grey matter of cord slices using Fiji. Randomised greyscale images were input into Fiji and duplicated. In this instance the ROI was identified as the whole spinal cord and selected and the area was measured. The area outside the ROI was then excluded, and the ROI was filtered using a Guassian blur to remove tiny areas of inappropriately high fluorescence. The ROI was thresholded using the auto-threshold ‘max entropy’. The area of the ROI reaching threshold was then selected, and measured in arbitrary units by redirection to the unaltered, duplicate image. The resulting figures underwent two way

ANOVA in Graphpad prism followed by a bonferonni post hoc analysis. In P14 samples representative images of the dorsal horn and the dorsolateral grey matter were randomised. The number of cells in the superficial dorsal horn and in the dorsolateral grey matter was analysed across four non-consecutive slices of each cord using the ‘cell-

counter’ add-on in Fiji. The average across the slices was recorded. Reelin co-localisation was analysed by observation in the P14 spinal cord.

2.4 RNA extraction and yield determination

2.4.1 Total RNA extraction

Embryonic spinal cords for RNA extraction were pooled, with six individuals from a single litter forming one sample due to small tissue quantities. For postnatal samples, total

RNA was extracted from the rostral ends of individual postnatal spinal cords. Total RNA was extracted using the miRvana miRNA isolation kit (Life Technologies/ Thermofisher,

Massachusetts, United States) as per manufacturer’s guidelines. Briefly, samples were submerged, frozen, in RNAlater-ICE (Thermofisher, Massachusetts, United states) for

48h at -20ᵒC. On removal, samples were submerged in lysis/binding buffer and homogenised before miRNA homogenate additive was added. Acid-Phenol:chloroform was used for RNA extraction and the aqueous phase was recovered. 1.25 volumes of

100% EtOH at RT was added to the retrieved aqueous phase. The lysate/EtOH solution was then passed through a glass-fibre spin column and washed. Total RNA was eluted in

100µl of 95ᵒC nuclease-free H2O. The total RNA was stored at -80ᵒC.

2.4.2 Quality and yield determination

The concentration of each sample of total RNA was measured using a SpectroStar Nano plate reader (BMG Labtech, USA). 2µl of total RNA from each sample was pipetted onto a clean evaluation (EV) plate. The exact concentration of RNA was determined with the following formula:

A260 x dilution factor x 40 = ng/µl

where A260 is the maximum wavelength at which DNA and RNA absorb light. The purity of the RNA samples was measured using the A260/A280 value where A280 is the maximum wavelength at which proteins absorb light. Values ~2.0 (at minimum >1.8) indicated pure

RNA. The overall quality of the RNA was confirmed through non-denaturing agarose gel electrophoresis. 1µg of total RNA sample was separated on the gel and distinct 22S and

18S bands without smearing indicated good quality RNA.

2.5 RNA expression studies

2.5.1 Microarray expression profiling

RNA extracted from the pooled spinal cords of offspring 5h post treatment of dams with either saline or 100µg/kg LPS at either E12 or E16 were sent for microarray analysis by

Source Bioscience (Nottingham, UK). Each experimental group (E12 saline, E12 LPS,

E16 saline and E16 LPS) consisted of 3 independent litters (n=3). Briefly, total RNA was quality controlled using both Nanodrop and Aglient bioanalyser technologies before fragmentation and hybridisation to an affymetrix Gene Chip Rat Gene 2.0 S.T array.

Samples were washed and stained, an image was acquired and image analysis completed.

Analysis of the raw data was completed by Source Bioscience. Raw data underwent quantile normalisation to eliminate low-level signals and ensure reproducible analysis. A design matrix was set up and a linear model used to summarise the data from each condition replicate into a single value. Using moderated t-statistics with empirical Bayes shrinkage, array quality weights were produced to check the relative reliability of each array. The variance measured how well the expression values from each array followed the linear model. The variances in each case were converted to relative weights and use down-weight observations from less reliable arrays within the linear model. This provided 67

the power to detect differential expression (adjusted P-value (adjP) <0.07). Differentially expressed genes were annotated with their gene symbol, Entrez ID and gene name. Gene symbols were entered into the STRING database of protein-protein interactions to identify any potential interactions and pathways of interest (Szklarczyk et al. 2015). A review of the literature was completed to identify each gene and the potential function of each gene during development.

2.5.2 RNASeq

RNA samples extracted from the spinal cords of P14 offspring of dams treated with either saline or 100µg/kg LPS at either E12 or E16 were sent for RNASeq by Source Bioscience

(Nottingham, UK). Each experimental group (E12 P14 saline, E12 P14 LPS, E16 P14 saline and E16 P14 LPS) consisted of 3 animals, 1female and 2 males, from three different litters (n=3). Briefly, samples underwent QC upon arrival before library preparation using Illumina TruSeq stranded mRNA prep technology. The samples were quality controlled using the Aligent bio-analyser to confirm a RNA integrity number (rin)

>8.0 before pooling and sequencing on the Illumina HiSeq4000 on one lane at 50bp single-end read.

2.5.3 RT qPCR ASSAY

Real-time quantitative PCR (RT qPCR) was carried out to confirm differential expression of genes in the E16 P14 100μg/kg LPS treatment group.

Using a SuperScript™ VILO™ cDNA Synthesis Kit (Thermofisher, Massachusetts,

United states) cDNA was synthesised from total RNA according to the manufacturer’s guidelines. 1µg of total RNA was reverse transcribed in a 20µl reaction volume.

Generated cDNA was diluted 1/10 with molecular grade water before qPCR. qPCR was 68

performed in technical triplicate on a 384 well plate with a 5µl reaction volume per well.

Each plate contained a positive control and a non-template control to monitor for plate contamination and ensure plate continuity. qPCR was carried out on the QuantStudio 7

Flex Machine (Applied Biosystems) using Taqman Universal Master Mix II, no UNG

(biosciences, Ireland) and appropriate Taqman gene assays (Biosciences, Ireland; Table

2.2). The experiment was designed and the results recorded on the Quantstudio RT-PCR software (Applied Biosystems). Briefly, after incubation at 95̊C for 10 minutes 40 cycles of PCR were completed. This involved heating the reactions to 95° 15 seconds to enable melting before 1 minute at 60̊C for annealing and extension. Threshold values were recorded for each reaction well. All probes used were fluorescein amidite (FAM) – labelled and results were normalised to the geometric mean of three appropriate, pre- defined, housekeepers, Ubc, B2M and GAPDH, as identified using the Normfinder add- in for excel (http://moma.dk/normfinder-software). Data was analysed using the

∆∆c comparative cycle threshold (CT) method (2- T method) where:

-∆∆C 2 T =[(CT gene of interest – CT internal control)sample A minus

(CT gene of interest – CT internal control)sample B)]

Gene symbol Name Assay ID

Cat Catalase Rn00560930_m1

Txnip Thioredoxin Interacting Protein Rn01533891_g1

Pmvk Phosphomevalonate Kinase Rn01462515_m1

Pex11a Peroxisomal Biogenesis Factor 11 Alpha Rn00585152_m1

Spag7 Sperm Associated Antigen 7 Rn01460477_m1

Idi1 Isopentenyl-Diphosphate Delta Isomerase 1 Rn00585526_m1

Pxmp4 Peroxisomal Membrane Protein 4 Rn00597183_m1

Scara3 Scavenger Receptor Class A Member 3 Rn01762953_m1

Kif19 Kinesin Family Member 19 Rn01470721_m1

Fzd9 Frizzled Class Receptor 9 Rn00596271_s1

Snca Alpha Synuclein Rn00569821_m1

S1pr1 Sphingosine-1-Phosphate Receptor 1 Rn02758712_s1

Mcam Melanoma Cell Adhesion Molecule Rn00576900_m1

Pla2g3 Phospholipase A2 Group III Rn01442987_m1

Ppp1r10 Protein Phosphatase 1 Regulatory Subunit 10 Rn00576196_m1

Emcn Endomucin Rn01521919_m1

Slco1c1 Solute Carrier Organic Anion Transporter Rn00584891_m1

Family Member 1C1

Tnfrsf11a TNF Receptor Superfamily Member 11a Rn04340164_m1

Sh3bgrl SH3 Domain Binding Glutamate Rich Protein Rn01753977_m1

Ubc Ubiquitin C Rn01789812_g1

B2m Beta-2 microglobulin Rn00560865_m1

Gapdh Glyceraldehyde-3-phosphate dehydrogenase Rn99999916_s1

Table 2.3: TaqMan®Gene expression assays (Biosciences, Ireland) and ID numbers for all genes identified as differentially regulated in RNASeq analysis of P14 spinal cords post 100μg/kg LPS treatment at E16

2.6 Bioinformatic analysis

2.6.1 Literature review

A literature review investigating the current understanding of the role of miRNAs in oligodendrogliogenesis was completed. Pubmed

(http://www.ncbi.nlm.nih.gov/pubmed/) was searched using the advanced search function with ‘fields’ set to ‘title/abstract’. Search terms included:

‘oligodendrogliogenesis’, ‘oligodendrocyte’, ‘development’, “oligodendrocyte development”, “white matter”, ‘miRNA’, ‘microRNA’, ‘miR’, ‘miRs’ and “micro

RNA”. Using the results the miRNAs most commonly associated with oligodendrogliogenesis were identified. Of these the six most frequently referenced were selected for further analysis.

2.6.2 miRECORDS

miRecords, an open source resource, amalgamates eleven miRNA-target interaction prediction programs. These programs are used to computationally predict interactions between miRNA and the genome, most often through seed region complementarity, but also using evolutionary conservation, thermostability of the miRNA-mRNA duplex, and the presence of an unmatched region among other strategies for prediction. The program, available at http://c1.accurascience.com/miRecords/prediction_query.php, was searched for the predicted targets of previously selected miRNAs in rats. A list of

miRNA targets predicted by one or more of the miRNA-target interaction prediction programs was generated. This was downloaded for further analysis.

2.6.3 Webgestalt

Enrichment analysis of the miRNA targets identified using miRecords was completed using Webgestalt (http://bioinfo.vanderbilt.edu/webgestalt/), a free-to-use, web-based gene set analysis toolkit. Gene symbols of previously predicted miRNA targets were imported and compared to the entrez protein coding database for rattus norvegicus.

Gene ontology (GO) analysis was carried out using hypergeometric testing utilising the

Benjamini- Hochberg (BH) procedure for multiple hypothesis testing to control for false discovery rate. P<0.01 was deemed significant enrichment in all instances.

2.6.4 String analysis

The miR targets contained within GO categories most relevant to the study were imported into STRING (http://string-db.org/), an open source database for the analysis of protein-protein interactions, to identify pathways potentially targeted by miRNAs during oligodendrogliogenesis. Analysis was completed at the highest confidence score of interaction. The analysis was first completed with disconnected nodes, (predicted targets of the miR that did not interact with another predicted target) removed to identify targeted pathways of interest. ‘White nodes’ (computer-generated nodes representing proteins that were not present in the predicted target list but serve as connection points for multiple predicted targets) were then introduced to counteract the removal of relevant pathways due to lack of immediate connection between identified nodes in that pathway. Pathway-related filtering of predicted targets served to reduce the number of targets of interest to a manageable level for further literature review (Fig

2.7).

Figure 2.7 Schematic representation of study methodology. Arrows indicate the volume of information carried forward at each step.

Chapter 3. The effect of MIA on the oligodendrocytes of the spinal cord

3.1 Chapter Abstract

CNS white matter has been recognised as being particularly vulnerable to the effects of inflammation for a number of years, resulting in devastating conditions such as cerebral palsy and periventricular leukomalacia.

This chapter investigates the immediate effects of MIA on the white matter of the developing spinal cord at E12 and E16 as well as the effects of MIA at these time points at P14 using immunofluorescence studies of the pan-oligodendrocyte marker Olig2 and the myelin marker MBP.

MIA with 100µg/kg LPS at E12 was found to have no measurable effect on Olig2+ cell number in the spinal cord 5h post injection. In contrast MIA at E16 was found to decrease the number of Olig2+ cell nuclei in the spinal cord 5h post injection. At P14 the number of Olig2+ cell nuclei was decreased in the ventral grey matter of the cord following MIA at E12 suggesting a delayed effect of MIA at this time point. Conversely no change in the number of Olig2+ cell nuclei was seen in the P14 spinal cord after

MIA at E16 suggesting recovery from the immediate effect of MIA at E16. There was no change in MBP expression in the P14 spinal cord following MIA at E12 or E16.

These results indicate that E16 may be a specific period of early vulnerability of white matter to MIA, and that the spinal cord may be able to compensate in some way for these gross early changes. The results also suggest that MIA at E12 may result in changes in white matter development which develop more gradually.

3.2 Introduction

It is understood that the white matter of the CNS is particularly vulnerable to inflammation, and that the developing oligodendrocyte specifically is extremely susceptible to damage (Volpe et al. 2011). Inflammation during gestation has been well documented as resulting in devastating disorders such as PVL and CP. Retrospective studies in humans, as well as work in animal models has suggested that gestational inflammation may also be linked to changes in white matter related to neurodevelopmental disorders such as autism and neuropsychiatric disorders such as schizophrenia. Such disorders are believed to have, at least to some extent, a developmental origin (Brown et al. 2004b; Buka et al. 2008; Brown 2012; Fatemi et al.

2012; Makinson et al. 2017). This chapter investigates the effect of MIA at E12and E16 on white matter development in the spinal cord, focusing specifically on the cellular consequences of MIA through immunofluorescence studies of Olig2 and MBP.

Olig2 is a basic helix-loop-helix transcription factor expressed by different cells of the

CNS in a time dependent manner. Olig2 is a master regulator, and is expressed by all oligodendrocyte progenitor cells. It is therefore considered a key marker of these cells during development. Use of Olig2 as a marker is particularly advantageous to the study of oligodendrocyte proliferation and migration during development due to its distinct nuclear expression.

As Olig2 is a key regulator of the oligodendrocyte lineage, developmental expression of the transcription factor is synonymous with oligodendrocyte emergence and migration, which is well characterised in the spinal cord. However, it is important to note that before

Olig2 expression in OL progenitors begins, Olig2 is expressed by developing MNs. Both

MNs and the vast majority of OLs in the spinal cord arise from a specific region of the

VZ known as the pMN. Before OLs can be produced in the pMN the region must undergo

an MN to OL switch, which is mediated by the temporal expression of sulfatase 1 (Sulf1) which activates SHH expression and subsequent OL proliferation around E12.5 in mice or E14 in rats (Timsit et al. 1995; Touahri et al. 2012).

Ventrally derived Olig2 positive oligodendrocytes arise in the pMN and migrate laterally and dorsally to populate the spinal cord during development. These OLs account for roughly 85% of the OL population in the spinal cord. The remaining 15% of OLs are dorsally derived and also express Olig2. Unlike their ventrally derived counterparts, specification of dorsal OLs is Shh-independent, with dorsally derived OL specification relying on the expression FGF and other local factors. Dorsally derived OLs emerge in mice from E15 (~E16.5 in rats) (Fogarty et al. 2005; Richardson et al. 2006).

In contrast to Olig2 MBP expression is more diffuse and begins much later in development. Studies utilising MBP in this chapter allow investigation of the effect of

MIA on myelination in the developing spinal cord.

MBP is the second most abundant component of CNS myelin, comprising 30% of all myelin proteins (Doyle 1978; Moscarello 1997). It is the only myelin protein essential to

CNS myelin formation (Moscarello 1997). Shiver mutants, a naturally occurring MBP mutant in which most of the gene is deleted, show an almost complete lack of compact myelin (Readhead et al. 1990). Similarly the Long Evans shaker rat, which possesses a mutation causing aberrant MBP transcription, also lacks compact myelin (Carre et al.

2002). MBP is a multifunctional protein with roles in cell signalling, cytoskeletal dynamics, and regulation of other myelin gene expression (Readhead et al. 1990; Dyer et al. 1995; Staugaitis et al. 1996). MBP has four major isoforms in humans, 21.5, 20.2,

18.5, and 17.2 kilodalton (kDa), and 6 known isoforms in mice 21.5, 20.2, 18.5, 17.24,

17.22, and 14 kDa. The most common isoform in humans is 18.5 kDa while the most

common isoform in mice and rats is 14 kDa (Boggs 2006). Some isoforms of MBP can enter the nucleus and can therefore alter transcription.

MBP expression in the rostral spinal cord of the immature rat begins at P1, in the ventral funiculus. MBP expression spreads in a mosaic fashion thereafter, appearing in the fasciculus cuneatus and ventro-lateral funiculus at P2, and the fasciculus gracilis and dorso-lateral funiculus between P3 and P4. MBP first appears in the cortical spinal tract at P11 and the Lissauer tract as late as P14. MBP expression in the grey matter begins between P11 and P14 (Schwab and Schnell 1989). Temporal and spatial differences in

MBP isoform expression have been recorded in the immature rat. The 18.5 kDa and 14 kDa isoforms are reportedly restricted to the plasma membrane while the 21.5 kDa and

17kDa isoforms may enter the cytosol and nucleus (Allinquant et al. 1991). Furthermore the 14 kDa isoform is highly expressed during active myelination becoming the most abundant isoform in the adult. Expression of the 14.0-kDa and 18.5-kDa MBP isoforms occurs approximately one week earlier in the cerebellum and spinal cord than in the cerebrum (Akiyama et al. 2002).

This chapter aims to investigate the effect of MIA on the white matter of the developing spinal cord in a comprehensive manner. Firstly, the study examines the effect of MIA on the development of oligodendrocytes themselves, by way of olig2 immunofluorescence.

Careful regional investigation allowed for investigation of the effect of MIA on more subtle parameters such as oligodendrocyte positioning in the developing spinal cord, as well as more robust measures such as oligodendrocyte cell number. Secondly the study investigates the potential effects of MIA on a function of these cells, namely myelination of the spinal cord, by analysing MBP expression in postnatal animals. It should be noted that some of the microscopic images contained within this thesis have been brightened for printing purposes.

3.3 Chapter aims

The objective of this study was:

1. To investigate the immediate early effects of maternal LPS injection at either E12

or E16 on oligodendrocyte emergence and migration in the embryonic rat spinal

cord

2. To investigate the effects of maternal LPS injection at either E12 or E16 on

oligodendrocytes of the P14 rat spinal cord

3. To examine the postnatal effects of maternal LPS injection at E12 or E16 on MBP

expression in the P14 rat spinal cord

3.4 Results:

3.4.1 Characterisation of Olig2+ cell distribution in the embryonic spinal cord during normal development at E12 and E16; and following MIA at E12 and E16

3.4.1.1 Olig2+ cell distribution in the E12 spinal cord in normal development, and post MIA with 100µg/kg LPS

During normal development Olig2+ cell nuclei are expressed in the rostral, middle, and caudal regions of the spinal cord at E12. In the rostral E12 cord olig2+ nuclei are largely restricted to the pMN of the VZ from which they arise (Figure 3.1 (A)). Olig2+ nuclei are expressed in higher numbers in the intermediate E12 cord, and are not restricted to the pMN, occupying much of the ventral spinal cord although higher numbers are still visible in the VZ (Figure 3.1 (B)). Olig2+ cell nuclei numbers were highest in the caudal spinal cord, occupying the entirety of the ventral spinal cord (Figure 3.1 (C)).

Following MIA at E12 Olig2+ cell nuclei number per unit volume was examined in whole cord slices. No change in the number of Olig2+ cell nuclei was identified in rostral, middle or caudal cord sections 5h after MIA with 100µg/kg LPS at E12 (Figure 3.2,

P>0.05 in all instances).

Figure 3.1 Characterisation of Olig2+ cell distribution in the E12 spinal cord. At E12 small numbers of Olig2+ cells are visible in the VZ of the ventral spinal cord at rostral levels (A, arrow). Olig2+ cell number increases in the VZ and surrounding ventral cord at intermediate level (B, arrow). At the caudal level of the E12 spinal cord large numbers of Olig2+ cells are present throughout the ventral spinal cord (C, arrows).

Figure 3.2 Olig2+ cell number is not decreased in the E12 spinal cord 5h post MIA with 100μg/kg LPS (P>0.05 in all instances). A statistical analyses was performed using

Graphpad prism 5. The differences between groups were determined by one-way analysis

of variance (ANOVA) and groups were compared using Bonferroni multiple comparison post hoc analysis. (N= 6 Saline, N=5 LPS)

3.4.1.2 Olig2+ cell distribution in the E16 spinal cord in normal development, and post MIA with 100µg/kg LPS

During normal development Olig2+ cell nuclei are present in the rostral, middle, and caudal regions of the spinal cord at E16. In the rostral E16 spinal cord olig2+ cells have migrated out of the pMN and the VZ to occupy the whole spinal cord. Olig2+ cell nuclei are visible in large numbers in the intermediate grey and white matter as well as the ventral grey and white matter (Figure 3.3 (B)). Smaller numbers of Olig2+ nuclei are visible in the dorsal grey and white matter (Figure 3.3 (A)). In the caudal spinal cord

Olig2+ cell nuclei are still largely restricted to the VZ although migration is underway and a number of olig2+ nuclei are visible in the dorsal grey matter (Figure 3.3 (C, D)).

Following MIA at E16 with 100µg/kg LPS Olig2+ cell number per unit volume was examined in five distinct regions of the spinal cord and compared to control animals.

These regions included the dorsal and ventral grey matter and the dorsal, ventral and intermediate white matter. Significantly fewer Olig2+ nuclei were observed in the dorsal grey matter (P<0.001), ventral grey matter (P<0.01), ventral white matter (P<0.01) and intermediate white matter (P<0.001) of the rostral spinal cord of offspring at E16 following MIA with 100µg/kg LPS 5h previously (Figure 3.4 (A-D)). With the exception of the ventral grey matter, where the number of Olig2+ cell nuclei decreased in middle regions of the spinal cord (P<0.05) (Figure 3.4 (B)), these decreases were not observed in middle and caudal regions of the spinal cord. No decrease in the number of Olig2+ cell nuclei was observed in the dorsal white matter of the rostral or middle spinal cord under

the same conditions (Figure 3.4(E)). The dorsal white matter of the caudal cord was not examinable at this age.

In addition to the 100µg/kg LPS study at E16, a preliminary study using a single litter, investigating the effect of MIA at E16 with 50µg/kg LPS on the spinal cords of offspring after 5h was also completed. This study saw no significant difference in the number of

Olig2+ cell nuclei in any of the 5 regions of examination (Figure 3.4 (A-E)), P>0.05 in all instances.

Figure 3.3 Characterisation of Olig2+ cell distribution in the E16 spinal cord. At E16

Olig2+ cells have migrated out of the pMN domain and the VZ to populate the rostral spinal cord (A, B). Olig2+ cells can be found in the ventral grey and white matter and intermediate grey and white matter (B). Smaller numbers can be observed in the dorsal grey and white matter as migration is ongoing (A). In the caudal spinal cord Olig2+ cells remain largely restricted within the ventricular zone although migration is underway

(C,D). Very few cells are present in the white matter of the caudal cord at this time. GM

– Grey matter, WM – White matter, D – Dorsal, V – Ventral

Figure 3.4 Olig2+ cell number is decreased in the dorsal grey matter (A, P<0.001), ventral grey matter (B, P<0.01), ventral white matter (C, P<0.01) and the intermediate white matter (D, P<0.001) of the rostral region of the E16 spinal cord

5h post MIA with 100μg/kg LPS. With the exception of the ventral grey matter where olig2+ cell number decreased in middle regions of spinal cord (B, P<0.05) these effects were not observed in the middle and caudal regions of the spinal cord. No decrease in the number of Olig2+ cell nuclei was observed in the dorsal white matter ((E), P>0.05).

Olig2+ cell number was not decreased in the E16 spinal cord 5h post MIA with 50µg/kg

LPS (P>0.05). Statistical analyses was performed using Graphpad prism 5. The differences between groups were determined by one-way analysis of variance (ANOVA) and groups were compared using Bonferroni multiple comparison post hoc analysis. (N=

6 Saline, N=5 LPS, * =P<0.05, ** = P<0.01, ***= P<0.001)

3.4.2 Characterisation of Olig2+ cell distribution in the postnatal spinal cord during normal development; and following MIA at E12 and E16

3.4.2.1 Olig2+ cell distribution in the P14 spinal cord during normal development

Olig2+ cell distribution was examined in the rostral region of the spinal cord at P14. By

P14 Olig2+ cell nuclei have migrated throughout the rostral cord occupying the grey and white matter in their entirety (Figure 3.5).

Figure 3.5 Characterisation of Olig2+ cell distribution in the P14 spinal cord. By

P14 Olig2+ cell nuclei have migrated to populate both the grey and white matter of the spinal cord.

3.4.2.2 Olig2+ cell number in the P14 spinal cord following MIA with 100µg/kg LPS on E12 or E16

Olig2+ cell number per unit volume was examined at P14 in the rostral spinal cord of offspring of dams exposed to MIA (100µg/kg LPS) at E12. Olig2+ cell number was examined in four regions, the dorsal and ventral grey matter and the intermediate and ventral white matter. The number of olig2+ cell nuclei was found to be decreased in the ventral grey matter of P14 offspring spinal cord after MIA at E12 (P<0.01) (Figure 3.6

(A)). No decreases were observed in the dorsal grey matter or the intermediate and ventral white matter (P>0.05 in all instances) (Figure 3.6 (A,B)). The number of Olig2+ nuclei in the dorsal white matter was not examinable in a statistically robust way in these samples.

Olig2+ cell nuclei number per unit volume was also examined at P14 in the rostral spinal cord of offspring of dams exposed to the same dose of LPS at E16. Olig2+ cell number was examined in the same 4 regions, the dorsal and ventral grey matter and the intermediate and ventral white matter. Olig2+ cell number was unchanged in the dorsal and ventral grey matter (Figure 3.7 (A)) and in the intermediate and ventral white matter

(Figure 3.7(B)) of the rostral P14 spinal cord of offspring following MIA with 100μg/kg

LPS at E16 (P>0.05 in all instances).

Figure 3.6 Olig2+ cell number is decreased in the ventral grey matter of the rostral spinal cord of P14 offspring following MIA with 100μg/kg LPS at E12 (A, P<0.01) .

Olig2+ cell number was unchanged in the dorsal grey matter and in the intermediate and ventral white matter (A,B, P>0.05 in all instances) The differences between groups were determined by one-way analysis of variance (ANOVA) and groups were compared using

Bonferroni multiple comparison post hoc analysis. GM – Grey matter, WM – White matter. (N =6 Saline, N=5 LPS, * =P<0.05, ** = P<0.01, ***= P<0.001)

Figure 3.7 Olig2+ cell number was unchanged in the dorsal and ventral grey matter

(A) and in the intermediate and ventral white matter (B) of the rostral P14 spinal cord of offspring following MIA with 100μg/kg LPS at E16 (P>0.05 in all instances).

The differences between groups were determined by one-way analysis of variance

(ANOVA) and groups were compared using Bonferroni multiple comparison post hoc analysis. GM – Grey matter, WM – White matter. (N=6 Saline, N=6 LPS)

3.4.3 Characterisation of MBP expression in the postnatal spinal cord during normal development; and following MIA at E12 and E16

3.4.3.1 MBP expression in the P14 spinal cord during normal development

MBP expression was examined in the rostral region of the spinal cord at P14 by mean fluorescence intensity analysis. At P14 MBP is expressed throughout the spinal cord

(Figure 3.8 (A)). MBP+ longitudinal fibres are visible in the dorsal horn, traversing the characteristically myelin-devoid substantia gelatinosa (Figure 3.8 (B)). Bundles of

MBP+ axons are evident in the dorsal grey matter (Figure 3.8 (C)). MBP is expressed throughout the white matter (although still at lower levels in the cortical spinal tract)

(Figure 3.8 (A,D)) and grey matter (Figure 3.8 (E)).

Figure 3.8 Characterisation of MBP expression in the P14 spinal cord. MBP is expressed throughout the rostral P14 spinal cord (A). MBP+ longitudinal (B) and transverse (C) fibres are present in the dorsal horn (arrows). MBP is expressed throughout

the white matter of the spinal cord (D) as well as in the ventral grey matter (E). MBP – myelin basic protein. Scale bar 200µm (A), 50µm (B-E)

3.4.3.2 MBP expression in the P14 spinal cord following MIA with 100µg/kg LPS on E12 or E16

At E12, mean fluorescence intensity of MBP was unchanged in the dorsal white matter, intermediate white matter and ventral white matter of the P14 spinal cord of offspring following MIA. Mean fluorescence intensity of MBP was unchanged in the longitudinal and transverse fibres of the grey matter, as well as the ventral grey matter of the P14 spinal cord (Figure 3.9, P>0.05 in all instances)

Mean fluorescence intensity of MBP was also unchanged in the same regions of the P14 spinal cord of offspring following MIA with 100µg/kg LPS on E16 (Figure 3.10, P>0.05 in all instances)

Figure 3.9 MBP mean fluorescence intensity was unchanged in grey matter (A) and white matter (B) of the rostral P14 spinal cord of offspring following MIA with

100μg/kg LPS at E12 (P>0.05 in all instances). The differences between groups were determined by one-way analysis of variance (ANOVA) and groups were compared using

Bonferroni multiple comparison post hoc analysis. GM – Grey matter, WM – white

matter, LF – longitudinal fibres, TF – transverse fibres, AU – arbitrary units. (N= 6 Saline,

N=6 LPS except dorsal WM where N=5)

Figure 3.10 MBP mean fluorescence intensity was unchanged in grey matter (A) and white matter (B) of the rostral P14 spinal cord of offspring following MIA with

100μg/kg LPS at E16 (P>0.05 in all instances). The differences between groups were determined by one-way analysis of variance (ANOVA) and groups were compared using

Bonferroni multiple comparison post hoc analysis. GM – Grey matter, WM – white matter, LF – longitudinal fibres, TF – transverse fibres, AU – arbitrary units. (N= 6 Saline,

N=6 LPS)

Injection Point

E12 E16

5H - ↓

point P14 ↓ -

Examination

Table 3.1 Summary table of the results of embryonic and postnatal Olig2 investigation. Olig2+ cell number was not decreased in the E12 offspring spinal cord 5h after maternal injection with 100µg/kg LPS. Olig2+ cell number was decreased in the ventral grey matter of the P14 offspring rostral spinal cord under the same injection

conditions. Olig2+ cell number was decreased in the dorsal and ventral grey matter and intermediate and ventral white matter of the rostral spinal cord of offspring 5h after

100µg/kg maternal LPS injection at E16. Olig2+ cell number was not decreased in the rostral spinal cord under the same conditions at P14. E – embryonic day, P – postnatal day

Injection Point

E12 E16

point P14 - -

Examination

Table 3.2 Summary table of the results of the postnatal MBP investigation. MBP mean fluorescence intensity was unchanged in the rostral P14 spinal cords of offspring following 100µg/kg maternal LPS injection on either E12 or E16. E – embryonic day, P

– postnatal day.

3.5 Discussion

It has been established that oligodendrocytes are particularly vulnerable to the effects of inflammation during development, resulting in devastating disorders like PVL and CP

(Volpe et al. 2011). In investigating the effect of MIA on oligodendrocyte progenitors during key gestational windows this study has demonstrated once again the susceptibility of these cells to inflammation in the form of MIA. This study has also demonstrated that the effects of MIA on oligodendrocytes are temporally and spatially dependent.

The first study investigated the early effect of MIA induced by a single i.p 100µg/kg LPS injection on Olig2+ cell number and migration in the E16 spinal cord of offspring.

Previous studies in our own laboratory and others have indicated that changes in gene expression are evident and measurable as early as 4h post MIA (Garbett et al. 2012;

Oskvig et al. 2012; Park et al. 2018), O’Loughlin EK unpublished data. This study identified a decrease in Olig2+ cell number per unit volume in four of the five regions of interest, specifically the dorsal and ventral grey matter and the intermediate and ventral white matter, 5h post MIA by LPS injection (Figure 3.4). Interestingly, these decreases were only evident in the rostral spinal cord of offspring, and were not observed in the middle and caudal regions with the notable exception of the ventral grey matter of middle spinal cord in which the decrease in Olig2+ cells were also noted. Decreases in Olig2+ cell number in the ventral grey matter of the rostral spinal cord were among the most statistically striking (P<0.0001). This is perhaps to be expected as the region of interest included the pMN, the region of the VZ from which olig2+ cells arise, and surrounding

VZ. Newly born Olig2+ cells populate the VZ before migrating further dorsolaterally.

The noted decrease in Olig2+ cell number in the ventral grey matter of the middle spinal cord region suggests that MIA can also affect the number of olig2+ cell nuclei in the middle cord. The restriction of this decrease to the ventral grey matter is likely due to the

inclusion of the pMN and surrounding VZ in that area, as olig2+ cell nuclei are still largely restricted to these regions in middle samples at E16. This effect of MIA is lost in the less developmentally mature caudal cord.

The development of the spinal cord has been well characterised previously and displays a distinct rostro-caudal gradient, with the rostral spinal cord being the most developmentally mature. The spinal cord becomes progressively less mature as one investigates middle and caudal spinal cord (Schwab and Schnell 1989; Hajihosseini et al.

1997; Nakayama et al. 1999). The loss of the effect of MIA on Olig2 expressing cells identified in the E16 spinal cord as we move down the rostro-caudal axis demonstrates how precise the window of developmental vulnerability to MIA truly is, with MIA effecting only the more mature rostral tissue at E16.

Previous studies have also investigated the effect of MIA around the E16 time point on oligodendrocyte development. A large study by Fatemi and colleagues identified decreases in white matter markers such as MBP, MOBP, MOG, MAL and PLP1 in the hippocampus of P0 offspring following MIA with a sublethal dose of H1N1 at E16 in mice (Fatemi et al. 2009a). An E15 study in rats meanwhile saw dams receive a 4mg/kg injection of Poly(I:C) and identified decreases in MBP and Rhombex29 that were measurable in the prefrontal cortex at P120 (Farrelly et al. 2015). A further study in E15 mice saw decreases in MBP levels in the corpus callosum of offspring 28 weeks postnatally following a 50µg/kg LPS injection to induce MIA. The same study found that this was not true of the anterior commissure where no change in MBP levels was observed at the 28 week time point, another indication of the spatial dependence of the effects of

MIA on white matter development (Makinson et al. 2017).

Although variables such as time, type and dosage of immune insult as well as location of observation vary significantly between studies it is clear from the literature that MIA

around the E16 time point has an effect on white matter development. This study is the first to investigate the early effects of MIA on white matter development, utilising the 5h time point, and is the only study in the literature to have investigated these effects using the marker Olig2 at this gestational point. This study suggests that E16 presents a specific window of vulnerability during which the emergence and subsequent migration of OL progenitors may be hindered by MIA, and that this effect is spatially dependent in the spinal cord.

The embryonic study also investigated the early effects of MIA at E12 at the 5h time point. The study again utilised a single i.p injection of 100µg/kg LPS to induce MIA. This study found no significant difference in the number of olig2+ cell nuclei per unit volume in whole spinal cord sections 5 h after MIA at E12 (Figure 3.2).

Just one study in the literature has investigated the effect of MIA on white matter at E12 previously. In that study the authors used the same dose (100µg/kg LPS) to induce MIA as in this study, but only observed effects in the corpus callosum. They found that NG2 and MBP were increased at P2 and P63—91 respectively, indicative of the very different, yet seemingly still susceptible cellular and molecular environment at E12 (Mouihate et al. 2017). It may be the case that Olig2 expression is unchanged at this time point in the

E12 spinal cord. Indeed, a study at E9.5 in mice identified a transient decrease in MBP expression in the hippocampus of offspring following a 60mg/kg poly(I:C) injection, but no change in the level of Olig2 expression (Manabu Makinodan et al. 2008).

The study of Olig2 expression in the E12 spinal cord is complicated by the rostro-caudal gradient of cord development. This study identified larger numbers of olig2+ cell nuclei in the middle and caudal sections of the E12 cord than in the developmentally more mature rostral sections, this was potentially due to observation of the MN to OL switch in action (Figure 3.1). Before producing OL progenitors, the pMN, the specific region of

the VZ responsible for producing 85% of spinal cord oligodendrocytes, contains olig2+ motor neuron precursors. Before oligodendrocyte production can begin the region must undergo an MN to OL switch. This appears to be regulated by the temporal expression of sulf1 which activates the SHH expression necessary for ventral OL proliferation at around

E12.5 in mice (Touahri et al. 2012). This timing is consistent with the observed higher numbers of caudal oligodendrocytes in this study, although this cannot be verified without co-staining with a marker of early motor neurons.

The co-expression of Olig2 by two different cell types identified in this study in different regions of the same tissue at the E12 time point indicates that Olig2 may not be an effective marker for the investigation of the effect of MIA on white matter development at this time point. Future work could utilise markers such as NG2 or PDGFRα to mark progenitor cells, or a combination of olig2 and SOX10 to mark all oligodendrocytes in order to mitigate this issue. Despite this it is clear from the literature that further investigation of the effects of MIA at this early time point in development are needed to clarify how vulnerable the white matter is to MIA at this time.

To further the investigation into the effects of 100µg/kg maternal LPS injection at E12 or

E16 on the development of the white matter of the spinal cord of offspring, the prolonged effect of MIA on the P14 offspring spinal cord was investigated. In these experiments dams received the same single i.p injection of LPS or saline that dams in embryonic experiments did but were allowed to give birth naturally, with pups subsequently sacrificed at P14.

In the E16 study at the 5h time point there was a significant decrease in the number of

Olig2+ cell number per unit volume in the dorsal and ventral grey matter and the intermediate and ventral white matter of the rostral spinal cord following 100µg/kg LPS injection. By P14 these effects appear to be lost. No change was observed in the number

of Olig2+ nuclei in the rostral P14 spinal cord in any of the four regions examined (P>0.05 in all instances) (Figure 3.7).

As previously outlined, the existing literature suggests that during the gestational period around E16 white matter is particularly vulnerable to the effects of MIA. Multiple studies have shown decreases in a number of white matter markers, most notably MBP (Fatemi et al. 2009a; Farrelly et al. 2015; Makinson et al. 2017), but also MAG, MAL, MOBP,

MOG, (all (Fatemi et al. 2009a)) CNP (Makinson et al. 2017) and Rhombex29 (Farrelly et al. 2015). Many of these decreases are persistent, with one mouse study at E15 showing a decrease in MBP that was still present in the corpus callosum at P196, twenty-eight weeks after birth, following a 50µg/kg LPS injection (Makinson et al. 2017). Notably, a human influenza study at E16 in mice showed decreases in the white matter markers

MBP, MAG, MAL, MOBP MOG and PLP1 in the hippocampus and cerebellum that were still present at P14 (Fatemi et al. 2009a). No studies in the literature have investigated the effects of MIA at E16 using Olig2. It may be the case that changes are simply more subtle than a change to gross Olig2+ cell number.

The unchanged Olig2+ cell number in this postnatal study following MIA at E16 may be in line with existing literature, which suggests that programmed cell death, or apoptosis of oligodendrocyte cells, is a normal part of the developmental process (Caprariello et al.

2015). It may be the case that this programmed apoptosis is responsible for realigning the number of olig2+ cells in controls and MIA exposed spinal cords. If so, it may mask the early decrease observed in oligodendrocyte cell number and result in the unchanged number of Olig2+ cell nuclei observed at P14. An unchanged cell number at P14 does not preclude more subtle change such as those recorded in other studies of individual white matter markers, indeed the early decrease in Olig2+ cell number at 5h, and subsequent recovery at P14, may provide an interesting clue as to the mechanisms underlying some of these more subtle changes. 99

Despite unchanged Olig2+ cell number at the 5h time point following 100µg/kg LPS injection at E12, a decrease in the Olig2+ cell number per unit volume was observed in the ventral grey matter of the rostral spinal cord at P14 following E12 maternal LPS injection (P<0.01, Figure 3.6). This seemingly delayed effect of MIA on oligodendroglial cell number suggests a different mechanism by which the LPS is effecting the Olig2+ cells, perhaps indirectly or further upstream of the oligodendrocyte itself, perhaps at the level of progenitor.

This study also investigated the mean fluorescence intensity of MBP following MIA at

E12 and E16 to further elucidate the effect of MIA on the white matter of the postnatal spinal cord. No significant change in MBP expression in the spinal cords of offspring was observed following MIA at E12 or E16 (Figure 3.9, 3.10). This result suggests the decreased number of Olig2+ cell nuclei in the ventral grey matter of the P14 rostral spinal cord following MIA at E12 is likely not linked to changes in MBP expression. This result is also contrary to the only study which has investigated the effect of MIA at E12 in which

MBP was shown to increase at P63-90 following MIA with 100µg/kg LPS although this study was performed in the corpus callosum and spatial differences may apply (Mouihate et al. 2017). Interestingly another study, consisting of three consecutive injections of

1.0mg/kg at E9, E13 and E17 in rats showed decreased MBP expression in the corpus callosum at P56 (K. Kim et al. 2014).

This study has identified E16 as a gestational period of vulnerability in white matter development. The decrease in the number of Olig2+ cells as early as 5h after LPS injection suggests a rapid effect on the emergence and subsequent migration of Olig2+ cells in the spinal cord. The apparent restriction of this change to the rostral spinal cord illustrates the spatial dependence of the cellular effects of MIA even in a single region like the spinal cord, and the complexity of the cellular consequences of MIA. However, this effect does not appear to be sustained at P14 following maternal LPS injection at E16, 100

where no decrease in the number of olig2+ nuclei was observed in the rostral spinal cord of offspring. As previously mentioned this may be due to developmental apoptosis masking the effect, or simply a recovery. Olig2+ cell number may not be the best measurement of the effect of MIA on oligodendrocytes postnatally when migration is complete, apoptosis has occurred, and oligodendrocytes are mature and myelinating. This is addressed in the subsequent immunofluorescent study of the effect of MIA on MBP expression at P14, arguably a much more sensitive reading of the more subtle effects of

MIA on the postnatal oligodendrocyte. However, no changes in MBP expression were observed by fluorescence intensity analysis at P14 following MIA at either E12 or

E16.This again, may be due to recovery of the oligodendrocytes or compensation for early changes in oligodendrocyte number by other oligodendrocytes during the myelination process. Regardless, the identification of an immediate effect of MIA on developing oligodendrocyte number provides an important clue as to the mechanisms underlying the effect of MIA on white matter. Future research may address this with careful regional analysis of further white matter markers at embryonic stages, and other markers of myelination at P14.

The apparent failure of maternal LPS injection to produce an immediate effect on the number of olig2+ cells in the E12 cord is a clear example of the importance of the gestational timing of an inflammatory insult to the effect of that insult. While E16 appears to be a gestational window of immediate vulnerability for oligodendrocytes, at least in the more rostral cord, the E12 spinal cord was seemingly unaffected 5h after LPS injection. This contrast between the E12 and E16 gestational windows continued postnatally where a decrease in the number of Olig2+ cell nuclei in the ventral grey matter of the rostral P14 cord was visible following E12 maternal LPS injection but not E16, indicative of a delayed response to injection at E12 which is distinctly different.

Unchanged MBP expression at P14 following LPS injection at E12 adds to the complexity

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of this delayed response. Future work will further delineate the cellular and molecular mechanisms underlying this delay. It may also be prudent to look at the effect of MIA on olig2+ cell number at the 5h time point following MIA at E17/E18, specifically in the middle and caudal spinal cord regions which were mostly unaffected by MIA at E16. This thesis however aims to investigate the effect of MIA across multiple cell populations, and this deeper work is beyond the current scope.

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Chapter 4. The effect of MIA on the microglia and astrocytes of the spinal cord

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4.1 Chapter Abstract

Microglia and astrocytes are key cells in the structure and function of the CNS, with roles in immune response, synaptic pruning and maintenance and osmoregulation.

This chapter investigates the effect of MIA at E12, E14 and E16 on the microglia and astrocytes of the developing spinal cord through immunofluorescence studies of Iba-1 and GFAP respectively. It focuses on the consequences of MIA on these cells themselves, rather than the downstream consequences of the activation of these cells on other cells of the CNS.

MIA with 100µg/kg LPS at E16 was found to decrease the number of Iba-1+ cells in the spinal cord 5h post injection. This decrease was not observed at P14. MIA at E12 did not change the number of Iba-1+ cells in the spinal cord 5h post injection or at P14. A preliminary study using a single litter suggested the MIA at E14 may reduce the number of Iba-1+ cells 5hr post injection. GFAP expression was found to be unchanged at P14 following MIA at either E12 or E16.

These results suggest that E16 is a specific period of early vulnerability in the microglia of the spinal cord to the effects of MIA.

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4.2 Introduction

Astrocytes and microglia are the key players in the normal function of the CNS.

Responsible for processes such as water and ion balance, synapse formation and phagocytosis, they are often the cells at the forefront of CNS response to inflammatory insult. Astrogliosis and microglial activation are necessary cellular behaviours for immune system function. However, these inflammatory processes can result in the release of cytokines, chemokines and other intercellular signalling factors which may damage or otherwise change the developing CNS.

Iba-1 is a 17kDa EF hand calcium binding protein. In the CNS Iba-1 is expressed specifically by microglial cells, the resident immune cells of the central nervous system

(Imai et al. 1996; Ito et al. 1998). The gene encoding the Iba-1 protein, Allograft inflammatory factor 1 (AIF-1) is located in the MHCIII region of human chromosome 6

(Imai et al. 1996). Iba-1 is also expressed by circulating macrophages (which may be detected in the CNS during development), meningeal macrophages, circumventricular organ macrophages, and macrophages of the choroid plexus. Despite this, microglia remain the majority of the Iba-1 positive cells in the CNS (Perry and Teeling 2013).

Research by our laboratory and others has shown that Iba-1 levels rise in the CNS in response to nerve injury, IL-1b induced inflammation and LPS administration (S.C. Lee et al. 1993; Ito et al. 1998; Cai et al. 2000; O'Loughlin et al. 2017). This increase in Iba-

1 in response to inflammatory factors identifies Iba-1 as a marker not only of CNS resident immune cells, but also as an effective measure of the activation of these immune cells in response to immune activation.

Microglia, unlike macroglia, originate outside the CNS, and arise from CD45+ cKit- erythromyeloid progenitors in the yolk sac. The CX3CR1+ sub-population of these progenitor cells migrate into the CNS between E9.5 and E10.5 in mice and comprise the 105

sole source of all microglia following blood brain barrier formation (Ajami et al. 2007;

Ginhoux et al. 2010; Schulz et al. 2012; Kierdorf et al. 2013; Gomez Perdiguero et al.

2015). Microglia are believed to have several routes of entry into the CNS, including via the peripheral vasculature, the meninges, the choroid plexus, the ventricles and central canal, and, in later development, via the CNS blood vessels (Boya et al. 1991; Navascues et al. 2000; Monier et al. 2007; Rigato et al. 2011; Cunningham et al. 2013). Once present in the CNS, microglia migrate to their final destination where they mature and self-renew into adult life (Ajami et al. 2007).

In the human spinal cord, the presence of microglia can be identified by week 9 of gestation (Rezaie et al. 1999). These cells are present largely in the ependymal layer and are associated with developing GFAP+ RGCs in the region. Microglial influx into the spinal cord via the developing white matter is highest in the dorsal and ventral white matter (Rezaie et al. 1999). In a study by Rigato et al. microglia have been shown to begin to invade the parenchyma of the mouse spinal cord at E11.5, reaching the spinal cord through the peripheral vasculature. Over the course of development microglia were shown to aggregate close to the central terminals of dorsal root ganglia neurons undergoing developmental apoptosis (E12.5) and within the lateral motor columns where motor neurons were undergoing developmental apoptosis (E13.5). Microglia were shown to interact with GFAP+ cells and growing capillaries of the parenchyma at E13.5, and were randomly distributed within the parenchyma by E15.5 (Rigato et al. 2011).

Glial fibrillary acid protein (GFAP) is a type III intermediate filament protein found in astrocytes and ependymal cells of the CNS (Jacque et al. 1978; Roessmann et al. 1980;

Reeves et al. 1989). GFAP has been mapped to human chromosome 17q2 (Bongcam-

Rudloff et al. 1991). The function of GFAP in astrocytes appears to be diverse. GFAP has been demonstrated to add structural support to astrocytes themselves as well as to broader CNS structures such as the blood brain barrier, which is structurally and 106

functionally impaired in GFAP knockout studies (Liedtke et al. 1996; Gomes et al. 1999;

Gimenez et al. 2000). GFAP knock out is also known to have a detrimental effect on oligodendrocytes resulting in white matter destruction and impaired myelination (Liedtke et al. 1996; Gimenez et al. 2000). GFAP is also known to play a role in mitosis, with phosphorylated GFAP present in the cleavage furrow of cells (Sekimata et al. 1996;

Kawajiri et al. 2003). GFAP is intimately involved in cell-cell communication processes, specifically astrocyte-neuron communication (Weinstein et al. 1991). GFAP, although preferentially expressed in white matter astrocytes, is an effective marker of both fibrous and reactive astrocytes allowing for a broad study of the effects of MIA on the astrocytic populations of the postnatal spinal cord (Bignami et al. 1972; Cahoy et al. 2008).

In the spinal cord, the neurogenic to astrocytogenic switch is reported to occur at E12.5 in mice (Deneen et al. 2006). Upon specification astrocyte progenitor cells undergo migration from the VZ to the intermediate zone where they undergo proliferation (Ge et al. 2012). On reaching their final location, astrocytes mature and become functional during the postnatal stages of development. Astrocytes, including those in the spinal cord, appear to be a diverse cell type with a number of subtypes (Hochstim et al. 2008; Tsai et al. 2012). In the spinal cord, Hochstim et al. identified three positionally distinct types of white matter astrocyte which can also be defined by their relative expression of Reelin and Slit1 (Reelin+ Slit1-, Reelin- Slit1+ or Reelin+ Slit1+). These subtypes arise from homeobox domains expressing Pax6 and Nkx6.1 respectively, which work in a combinatorial manner to specify subtype localisation in the spinal cord white matter

(Hochstim et al. 2008).

This chapter investigates the effects of MIA on microglia and astrocyte expression in the embryonic and postnatal spinal cords of offspring using a mixture of cell counting techniques and fluorescence intensity analysis. Comment is made on the microglial colonisation of the rat spinal cord as observed in the outlined experiments and the 107

potential consequences of MIA directly on these cell types is addressed. . It should be noted that some of the microscopic images contained within this chapter have been brightened for printing purposes.

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4.3 Chapter aims

The objective of this study was:

1. To investigate the early effects of maternal LPS injection at either E12, E14 or

E16 on microglial colonisation of the embryonic rat spinal cord and to investigate

the effect of LPS injection on subsequent microglial distribution in the embryonic

rat spinal cord.

2. To investigate the effects of LPS injection at either E12 or E16 on the microglia

of the P14 rat spinal cord

3. To investigate the effects of maternal LPS injection at either E12 or E16 on the

astrocytes of the P14 rat spinal cord

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4.4 Results

4.4.1 Characterisation of Iba-1+ cell distribution in the embryonic spinal cord during normal development at E12, E14 and E16; and following MIA at E12 and E16

4.4.1.1 Iba-1+ cell distribution in the E12 spinal cord in normal development and post MIA with 100µg/kg LPS

At E12 Iba-1+ cells are sparsely present in the rostral, middle and caudal spinal cord

[Figure 4.1, A and Figure 4.2 (<10 Iba-1+ cells per whole cord slice in all instances)].

Higher numbers of Iba-1+ cells are present per unit volume in the more developmentally mature rostral spinal cord, with progressively lower numbers present in the middle and caudal spinal cord (Figure 4.2). Iba-1+ cells can be observed in the tissues surrounding the E12 spinal cord and on the pial surface (Figure 4.1, B). These cells, and those found within the parenchyma of the spinal cord, are largely amoeboid in shape (Figure 4.1, C).

Following MIA with 100µg/kg LPS Iba-1+ cell number per unit volume was examined in whole cord slices of the E12 rostral, middle and caudal spinal cord. No change in the number of Iba-1+ cells was observed in the spinal cords of offspring 5hr after MIA when compared to controls. (Figure 4.2, P>0.05 in all instances).

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Figure 4.1 Characterisation of Iba-1+ cell distribution in the E12 Spinal cord. Iba-

1+ cells are expressed sparsely in the E12 middle spinal cord (A). Iba-1+ cells can be observed in the pial tissues surrounding the spinal cord (B, arrows). Iba-1+ cells present in the E12 spinal cord are largely ameboid in shape (C, arrows). D = dorsal, V = ventral,

SC = spinal cord, CC = central canal, magnification = 20x, Scale bar = 50µm

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Figure 4.2 Iba-1+ cell number per unit volume was not altered in the rostral, middle or caudal spinal cord 5h after 100µg/kg maternal LPS injection at E12. The differences between groups were determined by one-way analysis of variance (ANOVA) and groups were compared using Bonferroni multiple comparison post hoc analysis.

P>0.05 in all instances. (N= 6 Saline, N=6 LPS)

4.4.1.2 Iba-1+ cell distribution in the E14 spinal cord in normal development and post MIA with 50µg/kg LPS

At E14 Iba-1+ cells are present throughout the rostral, middle and caudal regions of the spinal cord. As in the E12 spinal cord, higher numbers of Iba-1+ cells are present per unit volume in the more developmentally mature rostral spinal cord with successively lower numbers in the middle and caudal spinal cord (Figure 4.4 A-D). Iba-1+ cells are most dense in the presumptive white mater of the rostral E14 spinal cord and along the pial layer (Figure 4.3, A, C, Figure 4.4 A-D). Iba-1+ cells are present in the central canal (CC) of the E14 rat spinal cord (Figure 4.3, B). The morphology of Iba-1+ cells in the E14 cord is slightly more ramified than those in the E12 cord, with visible processes on a number

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of cells (Figure 4.3, A, arrow), however amoeboid cells remain present (Figure 4.3, A, asterisks).

Following MIA with 50µg/kg LPS Iba-1+ cell number per unit volume was examined in the dorsal and ventral grey and white matter of the rostral, middle and caudal regions of the E14 spinal cord. The number of Iba-1+ cells per unit volume was decreased 5h after

MIA in the dorsal grey matter of the rostral spinal cord (Figure 4.4, A, P<0.05), the dorsal white matter of the rostral and caudal spinal cord (Figure 4.4, B, P<0.01 and P<0.05 respectively), and the ventral white matter of the rostral spinal cord (Figure 4.4, D,

P<0.05). The number of Iba-1+ cells remained unchanged in all other instances when compared to controls (P>0.05) (Figure 4.4 A-D).

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Figure 4.3 Characterisation of Iba-1+ cell distribution in the E14 spinal cord. Iba1+ cells have colonised the parenchyma of the spinal cord by E14 and possess a more ramified phenotype although amoeboid Iba-1+ cells remain present (A, arrows). Iba-1+ cells can be identified in the presumptive white matter (C) and in the central canal (B). D

= dorsal, V= ventral, SC = spinal cord, CC = central canal, magnification = 20x (A, C), magnification = 40x (B) scale bar = 50µm (A, C), scale bar = 20µm (B)

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Figure 4.4 Iba-1 expression is decreased at E14 5h post maternal injection with

50ug/kg LPS in the dorsal grey matter of the rostral spinal cord (P<0.05, A), the dorsal white matter of the rostral and caudal spinal cord (P<0.01 and P<0.05 respectively, C), and the ventral white matter of the rostral spinal cord (P<0.05, D). There were no changes in Iba-1 expression in the ventral grey matter of the spinal cord (B). The differences between groups were determined by one-way analysis of variance (ANOVA) and groups were compared using Bonferroni multiple comparison post hoc analysis. (N=5 saline,

N=6 LPS, single litter experiment.)

4.4.1.3 Iba-1+ cell distribution in the E16 spinal cord in normal development and post MIA with 100µg/kg LPS

At E16 Iba-1+ cells are present throughout the rostral, middle and caudal regions of the spinal cord. Once again, the number of Iba-1+ cells per unit volume follows the rostro- caudal gradient of spinal cord development, with more cells present in the rostral spinal cord and successively fewer in the middle and caudal spinal cord (Figure 4.6). Iba-1+ cells in the E16 spinal cord display a more ramified phenotype with multiple visible

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processes (Figure 4.5, A). Localised regions of microglial activation can be observed in some individuals in the E16 spinal cord as illustrated by an amoeboid phenotype (Figure

4.5, C). In the caudal region Iba-1+ cells can still be observed in the CC of some E16 spinal cord sections (Figure 4.5, B). These cells have largely migrated out of the CC in more rostral regions (Figure 4.5 A).

5h post MIA at E16 with 100µg/kg LPS Iba-1+ cell number per unit volume was examined in the dorsal and ventral grey and white matter of the rostral, middle and caudal spinal cord. The number of Iba-1+ cells per unit volume decreased in the dorsal grey matter of rostral and middle spinal cord (Figure 4.6, A, P<0.001 and P<0.05 respectively), the dorsal white matter of rostral and middle spinal cord (Figure 4.6, B, P<0.01), the ventral grey matter of rostral, middle and caudal regions of the spinal cord (Figure 4.6,

C, P<0.001), and the ventral white matter of rostral, middle and caudal regions of the spinal cord (Figure 4.6, D, P<0.001 rostral and middle regions, P<0.05 in caudal regions).

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Figure 4.5 Characterisation of Iba-1+ cell distribution in the E16 spinal cord. At E16 the majority of Iba-1+ cells in the rostral spinal cord display a ramified morphology

(A,arrows). In the caudal cord Iba-1+ cells continue to be observed in the central canal

(B, arrow). Evidence of localised microglial activation as identified by amoeboid-like morphology of motile, phagocytic microglia, can be seen in some samples (C, arrow). D

= dorsal, V = ventral, SC = spinal cord, CC = central canal, magnification = 20x, scale bar = 50µm

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Figure 4.6 Iba-1+ cell number per unit volume is significantly decreased in the E16 spinal cord 5h post MIA with 100ug/kg LPS in the dorsal grey matter of the rostral and middle spinal cord (P<0.001 and P<0.05 respectively, A), the dorsal white matter of the rostral and middle spinal cord (P<0.01, B), the ventral grey matter of the rostral middle and caudal spinal cord (P<0.001, C), and the ventral white matter of the rostral and middle and caudal spinal cord (P<0.001 rostral and middle cord, P<0.05 caudal cord, D). The differences between groups were determined by one-way analysis of variance (ANOVA) and groups were compared using Bonferroni multiple comparison post hoc analysis.

(N=6 LPS and Saline, * = P<0.05, ** = P<0.01, *** = P< 0.001)

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4.4.2 Characterisation of Iba-1+ cell distribution in the rostral P14 spinal during normal postnatal development; and following MIA at E12 and E16

4.4.2.1 Iba-1+ cell distribution in the P14 spinal cord during normal postnatal development

At P14 Iba-1+ cells have populated the grey and white matter of the rostral spinal cord in its entirety (Figure 4.7, A). These cells display the ramified morphology of mature microglial cells (Figure 4.7, B) and display an activated, amoeboid-like phenotype in response to localised inflammation (Figure 4.7, C).

Figure 4.7 Characterisation of Iba-1+ cell distribution in the P14 spinal cord. By P14

Iba-1+ cells have populated both the grey and white matter (A). Microglia display the ramified phenotype of mature microglia (B, arrows). Areas of localised microglial

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activation are visible in some samples (C, arrow). Magnification = 20x, scale bar = 200µm

(A), scale bar = 20µm (B), scale bar = 50 µm (C)

4.4.2.2 Iba-1+cell number in the P14 spinal cord following MIA with 100µg/kg LPS on E12 or E16

At P14 the number of Iba-1+ cells per unit volume was unchanged in the dorsal and ventral grey matter and the intermediate and ventral white matter of the rostral spinal cord following MIA with 100µg/kg LPS at E12. Iba-1+ cell number was not counted in the dorsal white matter of these sections due to tissue damage during processing (Figure 4.8

A, B, P>0.05 in all instances).

The number of Iba-1+ cells per unit volume was also unchanged in these regions at P14 following MIA with 100µg/kg LPS at E16. Again, Iba-1+ cell number was not counted in the dorsal white matter of these sections due to tissue damage during processing (Figure

4.9 A, B, P>0.05 in all instances).

Figure 4.8 Iba-1+ cell number is unaltered in the dorsal and ventral grey matter (A), and the intermediate and ventral white matter (B), of the rostral P14 spinal cord following maternal 100µg/kg LPS injection at E12. The differences between groups were determined by one-way analysis of variance (ANOVA) and groups were compared

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using Bonferroni multiple comparison post hoc analysis. P>0.05 in all instances. (N=6

Saline, N=5 LPS)

Figure 4.9 Iba-1+ cell number is unaltered in the dorsal and ventral grey matter (A), and the intermediate and ventral white matter (B), of the rostral P14 spinal cord following maternal 100µg/kg LPS injection at E16. The differences between groups were determined by one-way analysis of variance (ANOVA) and groups were compared using Bonferroni multiple comparison post hoc analysis. P>0.05 in all instances. (N=6

Saline and LPS)

4.4.3 Characterisation of GFAP expression in the rostral P14 spinal cord during normal postnatal development; and following MIA at E12 and E16

4.4.3.1 GFAP expression in the P14 spinal cord during normal postnatal development

At P14 GFAP+ cells have populated the entirety of the grey and white matter of the spinal cord (Figure 4.10, A). In the grey matter GFAP+ cells displayed the stellate shape of protoplasmic astrocytes (Figure 4.10, B, arrow). Astrocytes of the white matter were fibrous in morphology projecting unbranched apical processes. (Figure 4.10, C and D, arrows).

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Figure 4.10 Characterisation of GFAP expression in the P14 spinal cord. By P14

GFAP is expressed in both the protoplasmic astrocytes of the grey matter (B, arrow) and the fibrous astrocytes of the white matter, the processes of which extend into the cord (C and D, arrows). GM = grey matter WM = white matter, magnification = 20x, scale bar =

200µm (A), scale bar = 50µm (B-D)

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4.4.3.2 GFAP expression in the P14 spinal cord following MIA with 100µg/kg LPS on E12 or E16

At P14 GFAP expression was examined using fluorescence intensity analysis in the dorsal, intermediate and ventral white matter, and the dorsal and ventral grey matter, of the rostral spinal cord. There was no significant difference in GFAP fluorescence intensity following MIA with 100µg/kg LPS at E12 (Figure 4.11, A and B, P>0.05 in all instances).

GFAP expression was also examined in these regions of the P14 spinal cord following

MIA with 100µg/kg LPS at E16. Once again there was no significant difference in GFAP fluorescence intensity following MIA with 100µg/kg LPS (Figure 4.12, A and B, P>0.05 in all instances).

Figure 4.11 GFAP fluorescence intensity is unaltered in the dorsal and ventral grey matter (A) and the dorsal, intermediate and ventral white matter (B) of the rostral

P14 spinal cord following MIA with 100µg/kg LPS at E12. The differences between groups were determined by one-way analysis of variance (ANOVA) and groups were compared using Bonferroni multiple comparison post hoc analysis. P>0.05 in all instances. AU = arbitrary units (N=5 Saline and LPS).

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Figure 4.12 GFAP fluorescence intensity is unaltered in the dorsal and ventral grey matter (A) and the dorsal, intermediate and ventral white matter (B) of the rostral

P14 spinal cord following MIA with 100µg/kg LPS at E16. The differences between groups were determined by one-way analysis of variance (ANOVA) and groups were compared using Bonferroni multiple comparison post hoc analysis. P>0.05 in all instances. AU = arbitrary units (N=6 Saline and LPS).

IBA-1

Injection Point

E12 E14 E16

5H - ↓* ↓

point P14 - N/A -

Examination

Table 4.1 Summary table of the results of the embryonic and postnatal Iba-1 investigation. Iba-1+ cell number per unit volume was unchanged in the spinal cord of

E12 embryos 5h post maternal 100µg/kg LPS injection. Iba-1+ cell number was also unchanged in the spinal cord of offspring at P14 following MIA at E12 with the same

LPS dosage. The number of Iba-1+ cells per unit volume was decreased in the dorsal grey

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matter of the rostral spinal cord, the dorsal white matter of the rostral and caudal spinal cord and the ventral white matter of the rostral spinal cord of E14 embryos 5h post MIA with 50µg/kg LPS. The P14 time point was not examined. The number of Iba-1+ cells per unit volume decreased in the dorsal grey matter of rostral and middle regions, the dorsal white matter of rostral and middle regions, the ventral grey matter of rostral, middle and caudal regions and the ventral white matter of rostral, middle and caudal regions of the E16 embryonic spinal cord 5h post MIA with 100µg/kg LPS. Iba-1+ cell number was unchanged in the spinal cord of offspring at P14 following MIA at E16 with the same

LPS dosage.

GFAP

Injection point

E12 E16

5H N/A N/A

point P14 - -

Examination

Table 4.2 Summary table of the results of postnatal GFAP investigation. GFAP expression as measured through fluorescence intensity analysis, was unchanged in the rostral P14 spinal cord of offspring following MIA with 100µg/kg LPS at either E12 or

E16. The 5h examination point was not investigated in this study.

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4.5 Discussion

Microglia and astrocytes are oftentimes the glial cells at the centre of the CNS response to inflammation. Microglia are the immune cells of the CNS, responding to threat through antigen presentation, cytokine activation and phagocytosis. Once believed to be simple structural support cells, astrocytes are now known to play vital roles in homeostasis of extracellular fluid and ion balance, regulation of blood flow, and synapse formation and maintenance. Astrogliosis in response to inflammation or injury results in the accumulation of astrocytes in the affected area and the formation of a glial scar. This study has characterised the distribution of microglia and astrocytes during embryonic and postnatal development through examination of the expression of microglial and astrocyte- associated markers. It has also investigated the effect of MIA on the distribution of these cells and the expression of these markers.

Characterisation of the E16 spinal cord revealed that larger numbers of Iba-1+ cells were present in the dorsal and ventral white matter of animals than in the grey matter (Figure

4.6). This echoes a study by Rezaie et al. in the developing human spinal cord which suggested that the influx of microglia into the spinal cord is largely via the developing dorsal and ventral white matter (Rezaie et al. 1999). The number of Iba-1+ cells decreased in both grey and white matter in line with the rostro-caudal gradient of development, with more Iba-1+ cells present in the more developmentally mature rostral spinal cord and progressively fewer in the more immature middle and caudal spinal cord.

Utilising this understanding of normal development, an investigation was carried out into the potential early effects of MIA on the microglia of the developing rat spinal cord.

Previous research in our own laboratory and in others has suggested that changes in gene expression following MIA are observable as early as 4h after the insult (Garbett et al.

2012; Oskvig et al. 2012; Park et al. 2018), O’Loughlin EK unpublished data. This study

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found that MIA with 100µg/kg LPS at E16 resulted in a decrease in Iba-1+ cell number in the spinal cords of embryos 5 hours later. These decreases were most severe in the ventral grey and white matter where they were observable in rostral, middle and caudal spinal cord regions. In the dorsal grey and white matter decreases were observed in the rostral and middle parts of the spinal cord but not in the caudal spinal cord. These findings suggest that there may be a regional specificity to the effects of MIA on Iba-1+ cell number in the developing spinal cord, with larger decreases in the dorsal spinal cord than in the ventral, and that such effects are preferentially observed in the more mature spinal cord (Figure 4.6).

Decreases in the number of microglia in response to immune activation appear counterintuitive at first glance. However, it may be the case that MIA at E16 causes a delay in microglial colonisation of the spinal cord resulting in the decreased cell numbers observed. A study characterising the colonisation of the mouse spinal cord by microglia has suggested that colonisation begins at E11.5 (~E13 in the rat) and that microglia are distributed throughout the parenchyma by E15.5 (~E17 in the rat) (Rigato et al. 2011).

Further research confirming that this is also the case in rats, as well as Iba-1+ cell counts in the tissues surrounding the spinal cord to investigate whether Iba-1+ cells simply have not infiltrated the cord, may shed further light on these findings.

At P14 no alteration in the number of Iba-1+ cells was observed in the rostral spinal cord following MIA at E16 suggesting that the effects observed at the 5h time point may have been transitory in nature (Figure 4.9). Other studies investigating the effects of MIA on microglia in the CNS have produced mixed results. A study at E15 in rats using 4mg/kg poly(I:C) as the immune insult identified no changes in Iba-1 expression or CD11b expression in the frontotemporal cortex, corpus callosum, dorsal hippocampus, thalamus, striatum or pons between P90 and P104 (Missault et al. 2014). In contrast however, another study by the same group using the same immune challenge observed an increase 127

in CD11b in the hippocampus, thalamus and corpus callosum of offspring at P180 (Van den Eynde et al. 2014). A study at E15 in mice using 50µg/kg LPS saw an increase in the number of CD45intCD11b+ cells in whole brain samples at 28 weeks (P196) (Makinson et al. 2017). Another mouse study, again at E15 but this time using 100µg/kg LPS saw a decrease in CXCR3 expression in the dorsal hippocampus at P15 (Fernandez de Cossio et al. 2016). A study utilising e-coli as the immune challenge at E17 in rats saw an increase in ED-1 in the cingulum of offspring at P8 which persisted to P15 (Pang et al.

2005).

The mixed results observed in the literature as well as the findings in this study suggest that further investigation into the effects of MIA on microglial cells in the CNS is necessary. From the results, it appears that the effects of MIA are dependent on the insult time, the time at which the effect of the insult is investigated, and the region of the CNS examined. Some of the studies, including this one, have suggested transience in the effect of MIA on microglia. A study at E14.5 in mice, using 5 mg/kg poly(I:C) identified global changes in gene expression in microglia consistent with the adoption of a more developmentally mature microglial phenotype following MIA (Matcovitch-Natan et al.

2016). The authors have suggested that it is the adoption of a mature phenotype that results in the transience in effect observed in some studies, as this early maturation resulting from the insult is masked following the timely maturation of unaffected microglial cells.

This study also examined the effect of MIA on microglia at E12. The E12 spinal cord, like the E16 spinal cord had higher numbers of Iba-1+ cells in the rostral spinal cord, with progressively fewer cells in the middle and caudal spinal cord samples (Figure 4.2). The number of Iba-1+ cells in the cord is sparse at E12, with <10 cells per cord in the examined rostral regions. This low level of expression may be because microglial colonisation of the cord has only just begun. A study in mice has suggested that colonisation only begins 128

at E11.5, or E13 in rats, predating the intervention in this rat study (Rigato et al. 2011).

Iba-1 expression is not limited to microglial cells in the CNS, although they compose the majority of Iba-1+ cells, and has been documented in circulating macrophages, meningeal macrophages, circumventricular organ macrophages, and macrophages of the choroid plexus (Perry and Teeling 2013). If, indeed, microglial colonisation has not yet occurred it is likely that the Iba-1+ cells in the E12 study belong to one of these populations. It is also possible that the Iba-1+ cells comprise both these macrophages and the newly present microglia as the precision of time-mating is sometimes uncertain in these studies.

In this study, MIA with 100µg/kg LPS at E12 appeared to have no effect on Iba-1+ cell number in the spinal cord of offspring 5h later, or in fact at P14 (Figure 4.2, 4.8) . Just one study has investigated the effect of MIA at E12 on the CNS microglia, finding no change in Iba-1 expression in the corpus callosum between P63 and P91 following

100µg/kg LPS administration at E12 in rats (Mouihate et al. 2017). It may be the case that E12 is simply too early a time point to study the effect of MIA on microglia in the spinal cord.

A preliminary study in a single litter group at E14 showed some intriguing results. At E14 microglia have colonised most of the spinal cord, however large numbers of Iba-1+ cells are still observed in the presumptive white matter and the central canal, both proposed routes of entry into the spinal cord during microglial colonisation (Rezaie and Male 1999;

Rezaie et al. 1999). Particularly high numbers of Iba-1+ cells are observed in the region of the dorsal horn (Figure 4.3). This is in line with a study by Rigato et al. characterising the microglial colonisation of the mouse spinal cord which showed that at E12.5 in the mouse (E14 in the rat) microglia aggregated close to the central terminals of dorsal root ganglia neurons undergoing developmental apoptosis. In the preliminary MIA study using the lower dose of 50µg/kg LPS Iba-1+ cell number was shown to be decreased in the dorsal white matter rostrally and caudally, the dorsal grey matter of the rostral spinal 129

cord, and the ventral white matter of the rostral spinal cord 5h after injection (Figure 4.4).

These preliminary data suggest that E14 in the rat, and at equivalent times in other rodents, may be a developmental window during which microglia are particularly vulnerable to MIA, perhaps due to the levels of phagocytic activity occurring at this time of development. Inter-litter variability is a known confounding factor in rodent studies and what is true of a single litter may not be true across multiple litters with larger genetic variability. Further study using multiple litters is necessary, however, this preliminary data does raise an interesting starting point for further research.

The second part of this chapter investigated the effect of MIA on astrocytes. As this study used GFAP as a marker of astrocytes, embryonic study was not completed. In the embryo

GFAP is a marker of radial glial cells and is therefore unsuitable for the study of astrocytes alone.

In this study MIA at E12 or E16 appeared to have no effect on the astrocytes of the postnatal spinal cord at P14, as measured by GFAP fluorescence intensity analysis

(Figure 4.11, 4.12). A number of studies have investigated the effect of MIA on astrocytes, although none have looked at the effects of MIA at E12 or E16 in rats to date.

An E9 study in mice, utilising a sublethal dose of the influenza virus saw a whole brain increase in the level of GFAP expression at P0 (Fatemi et al. 2005a). It is important to note however, that during early development GFAP is a marker of radial glial cells, and this result could be indicative of a change in these cells rather than in astrocytes. Another study by the same group using the same insult conditions at E16 saw an increase in GFAP in the hippocampus at P56 (Fatemi et al. 2009b). Interestingly, a third study at E18, again in mice, and the only existing study to date to investigate the effects of MIA on astrocytes at the survival time point P14, saw no change in the expression of GFAP in the cortex, cerebellum or hippocampus following influenza-induced MIA (Fatemi et al. 2008b). The latter study is the most similar examination of GFAP expression following MIA to this 130

study, with regards the gestational timing of insult, and might suggest that the E16 insult may be too late to alter GFAP expression in the rat. However this is contradicted by two other studies completed in rats. The first used repeated injection of 500µg/kg LPS at E18 and E19 and showed increased GFAP in the cortex and hippocampus at P8 (Cai et al.

2000). While the second study used repeated injections of 300µg/kg LPS at E19 and E20, which showed increased GFAP expression in the internal and external capsule at P7

(Rousset et al. 2006). It may be the case that E16 in the rat is simply not a period of astroglial vulnerability.

There is very limited existing research which has investigated the effect of MIA on GFAP expression in the spinal cord, and it may be that alterations in astrocytic expression and function in the cord are more subtle than analysis by GFAP fluorescence intensity allow.

Hochstim et al have identified a number of positionally distinct astrocytic subtypes in the spinal cord and future investigations could investigate the effect of MIA on these groups

(Hochstim et al. 2008).

The data contained within this chapter have once again suggested that E16 is an intrinsically vulnerable period during spinal cord development, with MIA at E16 altering microglial presence in the cords of offspring 5h post treatment. Like in the case of oligodendrocytes, this loss is undetectable at P14 following E16 treatment. No change, immediate or postnatal, was detected in offspring of animals treated at E12. Interestingly

E14 appeared to be a timepoint of microglial vulnerability at E14 in the spinal cord in a preliminary study and this requires further study, however this is beyond the scope of the current research. No change in GFAP expression was detected at P14 following MIA with

100µg/kg LPS at E12 or E16, and other methods of astroglial analysis may serve to better investigate the effect of MIA on this cell type.

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Chapter 5. The effect of Maternal Immune Activation on Reelin expression in the spinal cord

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5.1 Chapter Abstract

Reelin is an extracellular matrix glycoprotein with an established role in cell migration and positioning during development, and cell-cell communication and synaptic structural integrity in the adult.

This chapter investigates the effect of MIA at E12 and E16 on reelin expression embryonically, 5hr after insult, as well as at P14 using immunofluorescence studies to analyse reelin fluorescence intensity and count reelin+ cells. This chapter also investigates the co-localisation of reelin with specific cell populations.

Reelin expression was decreased 5h post MIA with 100µg/kg LPS at E16 in the rostral, middle and caudal cord of offspring. Semi-quantitative analysis of reelin expression at

E12 5h post MIA also suggested decreased expression. No change was observed in the number of reelin+ cells in the dorsal horn or medio-lateral grey matter at P14 following

MIA at E12 or E16. In the co-localisation experiment reelin expression was found to co- localise with NeuN expression in neurons but not with markers of oligodendrocytes or microglia.

These results suggest E16 and potentially E12 are periods of early vulnerability of

Reelin+ cells to MIA, but that the spinal cord may be able to compensate for these early effects in later development.

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5.2 Introduction:

First identified in the 1950’s reelin is an extracellular matrix glycoprotein coded for by the RELN gene. Mutation of this gene is responsible for the characteristic reeling gait, poor balance, and mobility issuses of the ‘reeler’ mouse, a naturally occurring mutant in which the gene was first identified (Falconer 1951; D'Arcangelo et al. 1995). These mice display striking cytoarchitectural anomalies including widespread disruption of lamination in the CNS, heterotopia (ectopic tissue presence), lissencephaly (lack of development of gyri and sulci of the brain) and polymicrogyria (the development of an unusually high number of small gyri) (Hamburgh 1963).

Synthesised from a large 3460 amino acid precursor protein, reelin has a molecular weight of 388kDa. The protein consists of a 27 amino acid signalling peptide at the N-terminus, the reeler domain, 8 ‘reelin’ repeats, each one made up of an EGF-like domain and a

BNR/Asp-box repeat, and a conserved N-terminus of 32 amino acids (Royaux et al.

1997). Reelin is cleaved in two locations, the N-t and C-t sites between the second and third and sixth and seventh reelin repeats producing three fragments (Lambert de Rouvroit et al. 1999).

In the canonical pathway reelin has two known receptors, the very low density lipoprotein receptor (VLDLR) and the apolipoprotein E receptor two (ApoER2) (Trommsdorff et al.

1998). ApoE is a known antagonist of reelin signalling. Receptor binding in the presence of calcium results in oligomerisation of the lipoprotein receptors. The NPxY motif of the cytoplasmic tail serves as the docking site for disabled-1 (dab-1) via its phosphotyrosine kinase domain (Hoe et al. 2006). Dab-1 is phosphorylated by Fyn kinase, and to a lesser extent Src and Yes(Arnaud et al. 2003). Phosphorylated dab further activates the PI3K,

Crk, Crk-like and Lis1 pathways in the embryonic neuron to modify cell migration and positioning within the developing CNS (Bock and May 2016). In the postnatal neuron, 134

phosophorylated Dab-1 interacts with the serine family kinases (SFKs) to phosphorylate

NMDARs resulting in CAMP responsive element binding protein-1 (CREB-1) activation and downstream processes such as dendritic spine formation and modification of synaptic plasticity (Figure 5.1) (Sinagra et al. 2005).

Figure 5.1. Reelin signalling. Reelin, released from cells such as the Cajal-retzius neurons of the cortex, activates its receptors VLDL and ApoER2 resulting in the binding of, and phosphorylation of, Dab-1. Phosphorylated Dab-1 activates downstream signalling pathways such as the PI3K, Crk, Crk-like and Lis1 pathways, modifying cell migration, and the organisation of structural lamination in the cortex. In the postnatal neuron, shown on the right hand side of the image, phosphorylated dab-1 phosphorylates the NMDAR via SFKs resulting in the activation of CREB-1, dendritic spine formation and synaptic plasticity.

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Reelin is probably best known for its role in the correct lamination of the cerebral cortex during mammalian development. It is secreted by the cajal-retzius neurons of the marginal, or outermost layer, of the cortex where neuronal migration halts, and plays a role in the formation of the well-characterised inside-out pattern of cellular organisation whereby the innermost neurons of the cortex are born first with younger neurons sequentially occupying layers II-VI (Franco et al. 2011). This cortical lamination is disrupted in the reeler mouse as well as in animals in which reelin signalling has been impaired (Hamburgh 1963; Franco et al. 2011). Conditional knockout of reelin in two month old animals resulted in normal cortical formation indicating that this role in gross lamination is specific to development (Lane-Donovan et al. 2015).

Reelin is also known to play a role in cerebellar development, where it is expressed in cells of the rhombic lip migratory stream, the nuclear transitory zone and in the external granular layer (Fink et al. 2006). It is also expressed by some cells in the internal granular layer and in the deep cerebellar nuclei (Miyata et al. 2010). In the cerebellum reelin appears to function in the detachment of purkinje cells from the radial glial scaffolds along which they migrate, indicative of a role in cell positioning similar to that in the cortex (Larouche et al. 2008; Miyata et al. 2010). Reelin is also known to be expressed in the brainstem and optic tectum, among other regions, and is likely to participate in cell migration and positioning throughout the CNS (Di Donato et al. 2018).

Reelin expression in the spinal cord has been particularly well-characterised. Beginning around E9.5 in the mouse reelin expression is restricted to a subset of neurons in the ventral and intermediate spinal cord (Ikeda and Terashima 1997). By E11.5 reelin is expressed medially and dorsally (Ikeda and Terashima 1997). Between E11.5 and E12.5 reelin is still expressed in the ventral and intermediate cord but is excluded from Somatic motor neurons (SMNs) and the cholinergic preganglionic neurons which run from the

CNS to the ganglion (Yip et al. 2000; Yip et al. 2004a). Most reelin expression is 136

associated with the V1 and V2 interneurons at this time in development and it has been suggested that these cells may be the source of reelin (Yip et al. 2004b). Between E13.5 and E14.5 reelin expression appears in a band of cells in the superficial dorsal horn and in the dorsolateral VZ (Kubasak et al. 2004). Reelin+ cells of the dorsal horn are largely

Lmx1b+ suggesting they are glutamatergic in nature (Yvone et al. 2020). Prenatally reelin expression is maintained in the intermediate grey matter, in the superficial dorsal horn and in a subset of the white matter astrocytes located in the ventral cord (Ikeda and

Terashima 1997; Kubasak et al. 2004; Hochstim et al. 2008).

Reelin is known to facilitate the migration of sympathetic preganglionic neurons (SPNs), parasympathetic preganglionic neurons (PPNs) and SMNs in the spinal cord(Yip et al.

2000; Yip et al. 2003; Kubasak et al. 2004; Palmesino et al. 2010). Reeler mice, as well as VLDLR/ApoE2R K.O’s and dab-1 K.O’s display defects in the tangential migration of these cells as well as aberrant migration of cells outside the spinal cord in the case of

SPNs, illustrating the importance of reelin for both normal cell migration and as a stop signal (Yip et al. 2000; Yip et al. 2003; Chai et al. 2009).

Reelin is frequently considered a stop-signal in neuronal migration during development, expressed by cells of the marginal layer of the cortex and the outer layers of the cerebellum to prevent migration beyond designated stop points. However, receptor knockout studies have indicated that this may not be entirely true (Chai et al. 2009). While activation of the VLDL receptor does appear to negatively regulate migration, studies indicate that activation of the ApoE2 receptor is necessary for granular cell migration in the cortex suggesting that reelin modulates cell positioning through two different mechanisms (Hack et al. 2007). Indeed, recent evidence in the visual system of the zebrafish has suggested that reelin may be expressed as a gradient, in order to attract retinal ganglion cell axons to their appropriate final destinations and counterbalance the repulsive gradient posed by slit1 (Di Donato et al. 2018). Suggestions as to how these 137

two competing behaviours can occur in the same system include differential affinities of reelin to the receptors and receptor sorting at a surface level (Andersen et al. 2003; Duit et al. 2010).

Expression of reelin in the CNS peaks just after birth and then begins to decline, however some reelin expression is still detectable in the adult brain (Alcántara et al. 1998). Reelin expression is present in the intermediate grey matter, the ventricular zone, the superficial dorsal horn, the intermedio-lateral nucleus and the Clarke nucleus of the adult rat spinal cord. Reelin+ motor neurons co-localised with choline acetyltransferase. Krzyzanowska and co-authors suggested that this may indicate a role for reelin in fore and hind limb locomotion in the adult (Krzyzanowska et al. 2020). As in development however, much research has focused on reelin expression in the cortex where, in the adult, it is expressed by layer I and layer II GABAergic interneurons which project to other cortical layers and a few extant cajal-retzius neurons which have persisted into postnatal life (Alcántara et al. 1998; Y. Chen et al. 2005). Reelin expression is also maintained in GABAergic cells of the hippocampus and spinal cord and glutamatergic cells of the cerebellum (Pesold et al. 1998; Kubasak et al. 2004; Sinagra et al. 2005; Qiu and Weeber 2007; Hochstim et al. 2008; Pujadas et al. 2010). The role of reelin in these regions appears to be in dendritic spine growth and modulation, synaptic function, neurotransmitter release and neuronal maturation. In the cortex, release of reelin from layer I and layer II GABAergic interneurons results in the surrounding of dendritic shafts and spines of pyramidal cells suggestive of a role for reelin in regulating dendritic spine morphology (Rodriguez et al.

2000). In the hippocampus appropriate reelin expression is necessary for the expression of NMDAR subunits NR2A and NR2B, postsynaptic density-95 (a scaffolding protein) and the phosphatase PTEN, all necessary for efficient glutamatergic signalling through

CREB-1 (Figure 5.1) (Ventruti et al. 2011). Addition of reelin to hippocampal slices appears to significantly enhance long term potentiation and reelin signalling has been

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implicated in membrane trafficking via small guanosine triphosphate (GTP)ases Rho and

Rap(Y. Chen et al. 2005; Pujadas et al. 2010). It is clear that although present in lesser amounts in the adult CNS, reelin still plays a significant role synaptic functioning.

Aberrations in reelin expression have been implicated in a number of structural anomalies of the brain, including lissencephally, polymicrogyria and heterotopia (Hamburgh 1963).

However more recent research has identified an emerging role for reelin in the pathology of neurological and neuropsychiatric disorders such as schizophrenia, autism and epilepsy

(Fatemi et al. 2000; Persico et al. 2001; Haas et al. 2002).

A large body of research using post-mortem tissue from schizophrenic patients has identified decreases in reelin mRNA expression in the cortex, hippocampus, cerebellum and basal ganglia (Impagnatiello et al. 1998; Fatemi et al. 2000; Guidotti et al. 2000;

Fatemi et al. 2005c; Veldic et al. 2007). Reelin has established roles in cell migration and positioning, synapse formation and plasticity, and regulating dendritic spine morphology, defects and deficiencies in which have been identified as features of schizophrenia (For review see (Lewis 2012)). Epigenetic modulation of reelin mRNA expression has been proposed as a cause of this decrease (Grayson et al. 2005). Hypermethylation of the reelin promotor is one such modulation, with a post-mortem study indicating that DNA methyltransferase expression is three times higher in the reelin expressing GABAergic interneurons of layer I of the cortex of schizophrenic patients than healthy controls, which is associated with a two-fold decrease in reelin mRNA expression (Ruzicka et al. 2007).

Another study has indicated that histone deacetylase is upregulated in the hippocampus of schizophrenic patients, hypoacetylating histones at the reelin gene locus, thereby reducing reelin transcription (Benes et al. 2007). Aside from epigenetic modifications, other studies have indicated that VLDLR expression is decreased in drug-naïve schizophrenic patients and may be a potential biomarker for schizophrenia (Suzuki et al.

2008). Mutations in certain genes have been associated with schizophrenia through large 139

gene sequencing studies. Knockout of neuronal PAS domain protein 3 (NPAS3), a risk gene for schizophrenia, is associated with decreased reelin mRNA expression in adult mouse brain tissue. Similarly knockout of methylenetetrahydrofolate reductase (MTHFR) resulted in decreased reelin mRNA expression in the cerebellum of mice (Erbel-Sieler et al. 2004; Z. Chen et al. 2005).

Investigations into the role of reelin in autism spectrum disorders have produced conflicting results. Autism is associated with grey and white matter structural anomalies and brain volume anomalies like macrocephaly with defects in cognition, communication and social abilities (For review see (Courchesne et al. 2005)). Given the role of reelin in cell migration, positioning and communication during development it has been suggested that alterations in reelin signalling may play a role in the aetiology of ASD. A number of studies have shown that reelin levels are reduced in the sera of autistic patients. A series of studies by Fatemi et al have shown decreases in reelin protein levels in the cerebella, superior frontal cortex and parietal cortex of patients with autism when compared with healthy controls (Fatemi et al. 2001; Fatemi et al. 2002b; Fatemi et al. 2005b). In the frontal cortex and cerebellum these decreases were associated with an increase in

VLDLRs, which may serve as a compensatory mechanism (Fatemi et al. 2005b). Despite these studies gene association studies investigating the role of reelin in ASDs have provided conflicting findings. In 2001, a study by Persico et al in an Italian population, demonstrated an association between ASD and the length of a GGC repeat 5’ of the RELN gene initiation codon (Persico et al. 2001). This finding was confirmed by studies in North

American populations and in a Slovakian population (Zhang et al. 2002; Skaar et al. 2005;

Kelemenova et al. 2010). However, multiple other studies have demonstrated no association between this GCC repeat and ASD (Bonora et al. 2003; Devlin et al. 2004;

Dutta et al. 2008). Similar issues have been identified in the association of reelin-related single nucleotide polymorphisms (SNPs) with ASD, with a number of studies identifying

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SNPs associated with ASDs and a number of others finding no association (Ashley-Koch et al. 2007; Dutta et al. 2008; Li et al. 2008; He et al. 2011). Given the heterogeneous nature of ASD such contradictory findings are perhaps unsurprising and further studies in smaller but more homogeneous populations may shed some light on the disagreement.

Reelin has been linked to temporal lobe epilepsy in a number of studies, and is suggested to play a role in dentate gyrus granule cell dispersion, a key feature of temporal lobe epilepsy (Haas et al. 2002). These studies suggest that a decrease in reelin producing interneurons due to prolonged seizures results in ectopic granule cell migration and abnormalities in integration of newborn cells (Gong et al. 2007). Another study suggests that this effect may be due to hypermethylation of the reelin promotor, with another showing that this effect on granule cell dispersion can be alleviated by the addition of exogenous reelin (Kobow et al. 2009; Muller et al. 2009).

To date, no studies on the effect of MIA on reelin expression in the developing CNS have been completed. Despite this, MIA has also been associated with the development of many of the neurological disorders in which alterations in reelin expression are believed to play a part (Fatemi et al. 2008b; Meyer et al. 2009). This chapter aims to investigate the effect of MIA with 100µg/kg LPS at E12 and E16 on reelin expression in the embryonic and postnatal spinal cord of offspring using immunohistochemical methods.

Understanding of the cellular changes underpinning the effect of MIA on reelin expression is crucial to the investigation of the effect of MIA on reelin expression, and may provide another piece of the complex puzzle that is the aetiology of complex neurological disorders like schizophrenia, autism and epilepsy. It should be noted that some of the images within this chapter have been brightened for printing purposes.

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5.3 Chapter Aims

The objective of this study was:

1. To investigate the expression of reelin in the E12, E16 and P14 spinal cord

2. To investigate the co-localisation of reelin with neuronal, oligodendroglial,

microglial and astroglial markers in the P14 spinal cord

3. To investigate the early effects of maternal LPS injection at either E12 or E16 on

reelin expression in the embryonic rat spinal cord

4. To investigate the effects of maternal LPS injection at either E12 or E16 on reelin

expression in the P14 rat spinal cord

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5.4 Results

5.4.1 Characterisation of reelin expression in the embryonic spinal cord at E12 and E16 during normal development; and following MIA at E12 and E16

5.4.1.1 Reelin expression in the E12 spinal cord in normal development and post MIA with 100µg/kg LPS

Immunofluorescent staining of reelin in the E12 spinal cord is very weak (Figure 5.2, A).

Despite this, reelin expression in the E12 spinal cord can be detected, if unreliably, in the rostral, middle and caudal spinal cord sections. Reelin expression appears to follow the rostrocaudal axis of development, with higher levels of reelin expression in the more mature rostral spinal cord than in the intermediate and caudal spinal cord (Figure 5.3).

Reelin expression can be detected within individual cells of the dorsolateral spinal cord

(Figure 5.2, B) as well as diffusely in the same region (Figure 5.2, B).

Reelin expression in the E12 spinal cord was not quantifiable using image analysis software due to low levels of reelin expression at this time point and high levels of background staining. Reelin expression 5h post MIA at E12 with 100µg/kg LPS was instead analysed in the spinal cords of these animals using semi-quantitative observational analysis (Figure 5.3). This analysis confirmed that reelin expression is highest in rostral regions of the spinal cord with progressively lower levels of expression in the middle and caudal sections. The analysis suggests that levels of diffuse reelin, as well as reelin+ processes, were lower in the rostral spinal cord of offspring of LPS treated animals, while more reelin was present in the roof plate of these animals than in saline controls. The expression of reelin in the roof plate and diffusely appears to be lower in the middle regions of spinal cords of offspring of mothers treated with LPS, but these animals appeared to have a higher number of reelin positive cells. In the caudal spinal cord offspring of LPS treated mothers appeared to have higher levels of reelin positive processes. These results remain unconfirmed. (N=3 saline, N=5 LPS) 143

Figure 5.2 Reelin expression in the E12 spinal cord. At E12 reelin staining in the E12 spinal cord is very weak. Diffuse reelin staining can be observed in the presumptive ventro-lateral grey matter of the rostral spinal cord (A,C). Reelin+ cells can also be observed in the same region (B).

Figure 5.3. Semi-quantitative analysis of reelin expression in the E12 spinal cord 5h after MIA with 100μg/kg LPS. At E12 highest levels of reelin expression are observable in the rostral spinal cord, with no observable expression of diffuse reelin or reelin+ cells in the caudal spinal cord. Little difference is observed in the expression of Reelin+ cells, 144

Reelin+ processes and diffuse reelin following MIA at E12. Reelin expression appears to increase in the rostral roof plate following MIA. ( - = no expression; + = weak expression;

++ = moderate expression; +++ = high expression. N=3 saline, N=5 LPS)

5.4.1.2 Reelin expression in the E16 spinal cord in normal development and post MIA with 50µg/kg or 100µg/kg LPS

Reelin was expressed in the rostral, middle and caudal spinal cord at E16 during normal development (Figure 5.5). At E16 reelin staining was largely diffuse, with some expression observed in individual cells (Figure 5.4 A, B). Reelin expression can be observed in the lateral marginal zone and ventricular zone in rostral, middle and caudal spinal cord sections (Figure 5.4 A,C,D). Reelin expression was present along the superficial edge of the dorsal horn in rostral samples only (Figure 5.4, B).

Following MIA reelin expression was measured in whole sections of rostral, middle and caudal spinal cord using fluorescence intensity analysis. Reelin fluorescence intensity was decreased in the rostral, middle and caudal spinal cord 5h post MIA with 100µg/kg

LPS at E16 (P<0.001 in all instances, N=5 saline N=6 LPS, Figure 5.5). No significant change in the fluorescence intensity of reelin in whole cord sections of rostral, middle and caudal cord was observed in a preliminary study using 50µg/kg LPS at the same time point (P>0.05 in all instances, N=6 saline N=6 LPS, single litter study, Figure 5.5).

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Figure 5.4 Reelin expression in the E16 spinal cord. In the E16 spinal cord reelin expression is largely diffuse (A-D), with some evidence of cellular expression (arrow, C).

Areas of high expression include the presumptive dorsal horn (B), the dorsolateral grey matter (C) and the area around the central canal and ventricular zone (D). Scale bar:

100µm A, 20µm B-C, 10µm D.

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Figure 5.5. Reelin expression in the E16 spinal cord 5h post MIA. The mean fluorescence intensity of reelin is decreased 5h post MIA with 100µg/kg LPS in the rostral, middle and caudal spinal cord of offspring (P<0.001 in all instances). The mean fluorescence intensity of reelin is not significantly decreased in the spinal cords of offspring following 50µg/kg injection of LPS (P>0.05 in all instances). The differences between groups were determined by one-way analysis of variance (ANOVA) and groups were compared using Bonferroni multiple comparison hoc analysis. (N=6 saline, N=6

50µg/kg LPS, N=6 100µg/kg LPS. * = P<0.05, ** = P<0.01, *** = P<0.001). AU = arbitrary units.

5.4.2 Characterisation of reelin expression in the P14 spinal cord during normal development; and following MIA at E12 and E16

5.4.2.1 Reelin expression in the rostral P14 spinal cord during normal postnatal development

Reelin is expressed both diffusely and by distinct cell populations of the rostral P14 spinal cord (Figure 5.6). Reelin is expressed by a band of cells on the superficial edge of the dorsal horn (Figure 5.7, A,D), by cells of the medio-lateral grey matter (Figure 5.7 B,D) and by apical astrocytes of the ventral white matter (figure 5.7, C,D). 147

Figure 5.6. Reelin expression in the P14 spinal cord. At P14 reelin expression is far lower in the spinal cord than at E16. Expression is largely limited to individual cell types of the dorsal horn, medio-lateral grey matter and ventral white matter. Some diffuse staining can be observed.

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Figure 5.7. Reelin is expressed by specific cell populations of the P14 spinal cord.

Reelin expression in the P14 spinal cord is restricted to specific locations, namely a band of cells in the superficial dorsal horn (A), the medio-lateral grey matter (B), and the apical astrocytes of the white matter (C), represented schematically in (D). Scale Bars: A -

50µm, B - 50µm, C - 20µm.

5.4.2.2 Reelin expression in the P14 spinal cord following MIA with 100µg/kg LPS on E12 or E16

Reelin expression in the medio-lateral grey matter and on the superficial edge of the dorsal horn were measured by cell counting at P14. Reelin+ cell number per unit volume was unchanged in the medio-lateral grey matter of the rostral P14 spinal cord following

MIA at E12 (P>0.05 in all instances, N=6, Figure 5.8, A). Reelin+ cell number per unit volume was also unchanged in the superficial edge of the dorsal horn of the rostral P14 spinal cord following MIA at E12 (P>0.05 in all instances, N=6, Figure 5.8, B).

Reelin expression by cell populations in the medio-lateral grey matter and on the superficial edge of the dorsal horn was also measured at P14 following MIA at E16.

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Reelin+ cell number per unit volume was again unchanged in the examined regions.

(P>0.05 in all instances, N=6, Figure 5.9, A, B).

Figure 5.8 Reelin expression in the P14 spinal cord following MIA with 100μg/kg

LPS at E12. The number of reelin+ cells per unit volume is unchanged in the medio- lateral grey matter and on the superficial dorsal horn edge in the rostral spinal cord of P14 offspring following MIA at E12 with 100μg/kg LPS (P>0.05 in all instances). The differences between groups were determined by student’s t-test. (N=6 saline, N=6 LPS).

Figure 5.9. Reelin expression in the P14 spinal cord following MIA with 100μg/kg

LPS at E16. The number of reelin+ cells per unit volume is unchanged in the medio- lateral grey matter and the on the superficial edge of the dorsal horn in the rostral spinal cord of P14 offspring following MIA at E16 with 100μg/kg LPS (P>0.05 in all instances).

The differences between groups were determined by Student’s t-test. (N=4 saline, N=5

LPS).

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5.4.3 Co-localisation of reelin with markers of neurons, oligodendrocytes and microglia in the E16 and P14 spinal cords during normal development

Reelin co-localisation with markers of neurons (NeuN), Oligodendrocytes (Olig2), and microglia (Iba-1) was examined at E16 and P14 during normal development. Reelin was expressed diffusely in the ventro-lateral grey matter of the developing spinal cord at E16

(Figure 5.10, A-C). Microglia (A), oligodendrocytes (B) and Neurons (C) are also found in the ventro-lateral grey matter of the spinal cord. However, no discernible co- localisation of reelin with Iba-1+ microglia (A) or Olig2+ oligodendrocytes (B) is observed at E16. Reelin does appear to co-localise with some NeuN+ neurons at E16

(arrows, C). At P14 reelin expression is observed in a reelin+ cell population in the ventrolateral grey matter (D-F). These cells do not co-localise with markers of microglia (D) or oligodendrocytes (E) but, like at E16, reelin expression is observed in a number of NeuN positive neurons (F).

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s expressed diffusely s in expressed diffusely ventro the

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a (A, arrow) or Olig2+ oligodendrocytes (B, arrow) is observed at E16. Reelin does appear appear does Reelin E16. at observed is arrow) (B, oligodendrocytes Olig2+ or arrow) (A, a

1+ microgli 1+

C). C). Microglia (A, red), (B, oligodendrocytes green) and Neurons (C, green) are also expressed in this region.

localise with markers of microglia (D, arrows) or oligodendrocytes (E, arrows) but reelin expression is observed in in isobserved expression reelin but (E,arrows) or oligodendrocytes arrows) (D, microglia of with markers localise

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localisation of reelin with Iba with reelin of localisation

These cells do not co do Thesecells

F).

localise with some NeuN+ neurons at E16 (arrows, C). At P14, reelin expression is observed in a reelin+ cell population in th in population cell reelin+ a in observed is expression reelin P14, At C). (arrows, E16 at neurons NeuN+ some with localise

Figure Reelin 5.10. co grey matter of the spinal cord at E16 (A co discernible No co to (D matter bar=20µm Scale D,E, B, Bar50µm = Scale arrows) (F, neurons positive of NeuN number a

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5.5 Discussion

Reelin is an extracellular matrix glycoprotein with a well-established role in cell migration and positioning during development and cell-cell communication and synaptic structural integrity in the adult. To date the effect of MIA during development on reelin expression has not been investigated despite numerous studies linking abberations in reelin expression to neurological disorders such as schizophrenia, autism and epilepsy, disorders in which MIA is proposed to play a role. This study investigated the immediate effect of MIA at E12 and E16 on reelin expression, as well as the effect postnatally at

P14.

Reelin was found to be expressed at low levels in the E12 embryonic spinal cord (Figure

5.2). This low level of expression hindered quantitative analysis. Semi-quantitative analysis through a blinded observational study allowed for some characterisation of reelin expression in the E12 cord, and indicated that reelin is expressed both diffusely and by specific cells. This expression follows the rostro-caudal gradient of development. These observations echo those of Ikeda et al who found that reelin expression is restricted to a subset of neurons in the ventral and intermediate spinal cord of mice at E9.5 and is visible ventrally and medially at E11.5 (Ikeda and Terashima 1997). The current E12 study in rats falls immediately between these investigative time points at around E10.5 in mice.

The observational analysis of the effect of MIA with 100µg/kg LPS on reelin expression in the E12 spinal cord 5h after injection suggested a decrease in diffuse reelin in the rostral and middle spinal cord and in reelin positive processes in the rostral spinal cord. A small increase in the number of reelin+ cells in the middle spinal cord and in the reelin+ fluorescence in the roof plate following MIA was also observed. However these data should be viewed with caution due to the nature of the study and the low numbers of offspring observed (N=3 saline, N=5 LPS). Further investigation, perhaps using western

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blotting or ELISA, may shed some light on the effect of MIA on reelin expression at E12.

If reelin expression is changed at E12 this is indicative of an early effect on cellular migration within the spinal cord which may have prolonged consequences on offspring into postnatal life.

Unlike at E12 reelin expression in the E16 spinal cord was robust, and quantifiable using fluorescence intensity analysis (Figure 5.5). At E16, reelin was expressed in the ventrolateral spinal cord, around the ventricular zone and in a band of cells in the superficial region of the dorsal horn. The expression pattern observed in this study was again comparable with previous research. Other studies have suggested that reelin is expressed in the ventrolateral spinal cord by E12.5 in mice (E14 in rats) and that this expression is maintained prenatally (Yip et al. 2000; Kubasak et al. 2004; Yip et al.

2004a; Yip et al. 2004b). Similarly, another study has confirmed that reelin expression begins in the band of cells located in the superficial part of the dorsal horn, and in the ventricular zone between E13.5 and E14.5 (E15-16 in rats) (Kubasak et al. 2004).

This study investigated the effect of MIA with 100µg/kg LPS on reelin expression in the

E16 spinal cord 5h after LPS administration. MIA caused a significant decrease in reelin fluorescence intensity in whole cord slices of rostral, middle and caudal spinal cord. This decrease in reelin fluorescence intensity was consistent, and did not differ between rostral middle and caudal samples (P<0.001 in all instances, Figure 5.6) as observed in other studies in this thesis, for example the Iba-1 and Olig2 studies. Further investigations to determine whether these decreases are universal or specific to a certain type of expression

(cellular or diffuse), or region of expression (Dorsal horn, ventrolateral cord, ventricular zone) would be interesting. A preliminary study using a single litter was also undertaken but did not show a significant decrease in reelin fluorescence intensity following MIA with 50µg/kg LPS.

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To my knowledge this is the first study to investigate the effect of MIA on reelin expression in the developing CNS. As reelin is a key signalling molecule known to be involved in cell-cell communication as well as a known stop signal and modulator of cell migration the consequences of aberrant reelin expression during development could be far-reaching. Decreased reelin expression during development could result in subtle alterations in neuronal migration and subsequently mis-wiring of developing neuronal networks. Such alterations, if they exist, could persist postnatally, although further research remains to be completed in order to confirm such changes.

At P14 reelin expression in the spinal cord appeared to be restricted to specific populations of cells in the rostral spinal cord (Figure 5.6, 5.7). Reelin expression was observed in a band of cells in the superficial region of the dorsal horn, in a number of cells in the medio-lateral grey matter, and in a subset of astrocytes in the ventral white matter. This pattern of expression matched that previously observed in other studies in the postnatal spinal cord. Reelin is known to be expressed in cells of the medio-lateral and ventrolateral grey matter. These are thought to be V1 and V2 interneurons due to the large amount of reelin expression observed around these cells during development (Yip et al. 2004b). The band of cells in the superficial dorsal horn, first observed during development between E13.5 and E14.5 in mice, are known to persist postnatally

(Kubasak et al. 2004). According to existing literature, Reelin expression in the ventral white matter is known to be restricted to specific subsets of white matter astrocytes in the spinal cord. These may be Reelin+ Slit-1- or Reelin+ Slit-1+ astrocytes, and play a role in cell positioning during development (Hochstim et al. 2008). Their function in the postnatal spinal cord is still unknown.

Analysis of the effects of MIA at E12 or E16 with 100µg/kg LPS on Reelin expression in the P14 spinal cord suggested that MIA did not effect reelin expression in the P14 spinal cord (Figure 5.8, 5.9). No differences in the number of cells in either the medio- 155

lateral grey matter or in the dorsal horn were observed. This suggests that the acute decrease in reelin fluorescence intensity observed 5h after injection at the E16 time point was transient, and that MIA at either E12 or E16 had no delayed effect observable at P14.

The transient effect observed following the E16 injection could indicate that the decrease in reelin expression can be corrected by P14, without a lasting observable effect. Further studies into the cellular origin of the acute change in the level of reelin fluorescence intensity at E16 after MIA could provide valuable information as to why this transient effect occurs. As reelin expression is restricted to specific cell populations in the P14 cord, a decrease in the diffusible reelin at E16 would not be visible in the P14 cord for example. Despite the transience of the E16 decrease it is important to note that a decrease in reelin expression at that age may have altered the positioning of a number of neuronal and glial cells in the spinal cord, and that this change in connectivity may persist postnatally.

Changes in Reelin expression in the spinal cord can result in heterotopia. A number of studies have shown that loss of reelin expression can result in mis-migration of neurons, specifically SMNs in the spinal cord which may have postnatal effects on locomotion

(Yip et al. 2000; Yip et al. 2003). Alterations in reelin expression have previously been associated with neurological disorders such as schizophrenia, autism and epilepsy, likely due to the role of reelin in the establishment of connectivity in the developing CNS

(Fatemi et al. 2000; Persico et al. 2001; Haas et al. 2002). MIA during gestation is also an established risk factor for these disorders (Fatemi et al. 2005a; Fatemi et al. 2008b;

Meyer et al. 2009). This project has suggested that MIA during gestation can alter the expression of reelin in the spinal cord during development. Further research should elaborate on the effects of this alteration of reelin expression due to MIA on neuron- neuron interaction. A co-localisation experiment would have allowed for the identification of the reelin+ cells of the medio-lateral grey matter and superficial edge of

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the dorsal horn and would be an important next step. A combination of further imaging studies and behavioural models would seek to establish whether MIA could be a factor in the abnormal neuronal connectivity widely associated with these neurological disorders.

This work is, however, beyond the scope of this thesis.

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Chapter 6. Gene expression changes in the developing spinal cord following MIA

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6.1 Chapter Abstract

High throughput studies such as microarray and RNA seq have changed how we formulate research questions, handle data and design experiments, allowing a broad, unbiased approach to the investigation of the effect of a condition or treatment across the whole genome at a moment in time.

In this chapter, the effect of MIA on the spinal cord at E12 or E16 was investigated 5hr post insult and at P14 using microarray and RNASeq studies respectively.

Microarray analysis showed no differential gene expression 5h post MIA at E12 with

100µg/kg LPS. RNA Seq showed that gene expression was also unchanged at P14 after

MIA at E12. In contrast, microarray analysis identified 42 differentially regulated genes

5h post MIA at E16. RNA Seq identified 16 differentially regulated genes at P14. The 16 differentially regulated genes were identified, and PCR analysis was carried out.

The results of this study again identify E16 as a period of specific early vulnerability to

MIA, and also suggests that MIA at E16 continues to effect gene expression in the spinal cord postnatally. A subset of 7 genes differentially regulated in RNASeq at P14 following

MIA at E16 identified peroxisomal function as a potential underlying factor in the consequences of MIA on the early postnatal spinal cord. However these results were not confirmed by PCR and more research is required.

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6.2 Introduction:

RNA expression profiling studies such as microarray and RNASeq have become common place in the laboratory environment over the past twenty years. These powerful, high throughput techniques can provide researchers with a snap shot of the gene expression landscape in a sample, opening numerous new pathways of investigation in the biomedical sciences.

DNA microarray technology allows for the high throughput analysis of gene expression by quantification of relative abundance. The methodology involves an array of microscopic DNA spots called probes or oligos housed on a chip. The chip is washed with the sample of interest, which allows for hybridisation of complimentary DNA

(cDNA) and the measurement of relative gene expression levels, usually through measurement of luminescence. In this chapter microarray methodology is used to investigate the effect of MIA on gene expression in the spinal cords of offspring following

MIA with 100µg/kg LPS at either E12 or E16.

RNA sequencing (RNASeq), first described by Wang et al. in 2009, has been rapidly replacing microarray technologies over the past decade. Like microarrays, RNASeq allows for the interrogation of differential gene expression. However, unlike microarrays it also allows for interrogation of non-coding RNA, splicing, and allele specific expression, as well as overall differential expression analysis. Like microarray RNAseq utilises cDNA, but does so without need for probes. Instead a library is prepared; total

RNA undergoes fragmentation, is poly A selected, and randomly primed before cDNA synthesis. cDNA undergoes end repair, phosphorylation and A-tailing. Finally, the samples undergo adapter ligation, PCR amplification and sequencing. One major advantage of RNA sequencing over microarray is that it removes the bias of probe selection. It also allows for repeated interrogation of digitalised data. As the technology

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has become more readily available RNAseq has become more cost effective, with the ability to run 6-30 samples multiplexed in a single sequencing well. In this chapter

RNASeq analysis is used to investigate the effect of MIA on gene expression and microRNA expression in the P14 spinal cord after MIA with 100µg/kg LPS on either E12 or E16.

The advantages to high throughput screening such as gene expression analyses are multi- fold, changing completely how we approach experimental design. Where traditional experimentation requires the establishment of a narrow research question expression profiling studies allow for broader questions, such as the effect of a condition or treatment across the whole genome at a moment in time. These studies also allow for a more unbiased view. Traditional experiments may favour the interrogation of genes or gene families which are already documented to play a role in the condition or treatment response of interest. Expression profiling studies, therefore, are an excellent way of identifying novel genes and pathways which may further elucidate the underlying genetic architecture of a condition or response to treatment, or provide us with new biomarkers and potentially druggable targets. The data produced from expression profiling studies may be probed repeatedly using bioinformatic techniques, ensuring these studies are cost effective way to produce the starting point for more traditional experimentation for many years after their completion.

This chapter approaches the broad, unbiased question of the effects of MIA on gene expression in the spinal cord in an attempt to identify novel genes of interest for future studies.

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6.3 Chapter Aims

The objective of this study was:

 To investigate using a microarray study the gene expression changes in the spinal

cord of offspring 5hr after MIA with i.p injection of 100µg/kg LPS at E12 and

E16

 To investigate using RNASeq the gene expression changes in the spinal cords of

offspring at P14 following MIA with i.p injection of 100µg/kg LPS at E12 and

E16

 To identify novel pathways and targets of interest from the genes identified to be

differentially expressed following MIA with an i.p injection of 100µg/kg LPS at

E12 and E16

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6.4 Results

6.4.1 Microarray analysis

6.4.1.1 Principal component analysis

Principal component analysis uses orthogonal transformation to covert observations of possibly correlated variables (for example gene expression levels) into a smaller number of values of linear uncorrelated variables known as principal components. The first component accounts for as much of the variation within the data set as possible and the second has the highest variance possible under the constraint of being orthogonal

(perpendicular, unrelated) to the first component (Jolliffe and Cadima 2016). This technique is used to reduce dimensionality of the data and simplify large volumes of data for analysis. In this instance, PCA is used to visualise the relatedness and genetic similarities between samples. Sample similarity correlates to distance on PCA plots, with like samples (relative to the defined principal components) plotted close to each other. To this end a PCA plot also allows for the identification of outliers, with samples differing from each other plotted at greater distances.

The samples for microarray analysis from E12 and E16 LPS treated and control offspring were graphed on a principle component analysis (PCA) plot (Figure 6.1). E12 samples and E16 samples formed separate clusters along the principle component (PC)1 axis (X axis). Saline and LPS samples within the E12 and E16 groups did not form identifiable separate clusters along the PC2 axis (y-axis). At E12 Saline samples 2 and 3 clustered closely with LPS sample 3, while LPS samples 1 and 2 clustered closely. E12 saline sample 1 did not cluster with either group. At E16 LPS samples 1 and 2 clustered closely but were separate from E16 LPS sample 3 while saline samples 2 and 3 clustered relatively closely but saline sample 1 was more closely aligned with LPS samples 1 and

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Figure 6.1. Principle component analysis (PCA) plot of the 12 pooled samples used in the microarray study. This plot allows the visualisation of the relatedness and genetic similarities between the 12 samples. (N=3 saline, N=3 LPS for both E12 and E16 treatment groups). Samples clustered along the PC1 axis (x axis), indicating that E12 and

E16 samples were genetically different, however no clear clustering of samples was visible on the PC2 axis (y axis), indicating genetic similarity between the saline treated and LPS treated samples.

6.4.1.2 Gene expression 5hr post MIA with 100µg/kg LPS at E12 or E16

The microarray study identified no differential gene expression in the spinal cords of offspring 5hr after MIA at E12 with 100µg/kg LPS (N= 3 Saline N=3 LPS where each sample is one pooled litter. Adjusted P-value (adjP) > 0.07 cut-off in all instances, data

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not shown). An adjusted P-value (or q-value) is used in this instance to minimise the error associated with multiple comparison analysis of thousands of variables using a small number of individuals.

At E16 however, the microarray study identified differential expression of 42 genes in the spinal cords of offspring 5hr after MIA with 100µg/kg LPS (N= 3 Saline N=3 LPS where each sample is one pooled litter, Figure 6.2). A list of differentially regulated genes and their corresponding adjP values can be viewed in table 6.1. For a short synopsis of the function of each differentially regulated gene see appendix A, Table A.1.2.1.

Figure 6.2 Heat map illustrating the 42 differentially expressed genes 5hr post MIA with 100µg/kg LPS at E16. Three genes were upregulated following MIA (Red, Spetex-

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2F, LOC690507 and Olr115). 39 genes were downregulated (Blue, LOC683674, Med25,

Ccdc5, Wars, Arhgap1, Tp53, Celf3, LOC10036317, Srm, Midn, Fscn1, S100a11, Clip3,

Pcsk1n, Ubl7, Ftl1, Rab11b, LOC10035966, LOC10036008, RGD1305455, Pcgf2, Ctc1,

Nrbp2, Fam189b, Cherp, Tox, E2f4, Ppif, Dpf2, Cnot3, Bcas3, Atf7, Mef2d, Lmf2,

Pomt1, Emd, Sf3a2, Tbc1d25 and LOC10036238). For a full list of genes and brief synopsis of function see Appendix A.1.2

Gene symbol Gene Name adjP value Up/Down

Tp53 tumour protein p53 0.042210405 ↓

Olr115 olfactory receptor 115 0.042210405 ↑

E2f4 E2F transcription factor 4 0.042210405 ↓

LOC100363177 ferritin light chain 1-like 0.042210405 ↓

Sf3a2 splicing factor 3a, subunit 2 0.042210405 ↓

S100a11 S100 calcium binding protein A11 0.042210405 ↓

LOC100359668 ferritin light chain 2 0.042210405 ↓

Cherp calcium homeostasis endoplasmic 0.042210405 ↓

reticulum protein

Pomt1 protein-O-mannosyltransferase 1 0.042210405 ↓

LOC690507 similar to Vomeromodulin 0.042210405 ↑

Fam189b family with sequence similarity 0.042210405 ↓

189, member B

Ftl1 ferritin light chain 1 0.042210405 ↓

Fscn1 fascin actin-bundling protein 1 0.042210405 ↓

Clip3 CAP-GLY domain containing 0.042210405 ↓

linker protein 3

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Tbc1d25 TBC1 domain family, member 25 0.042210405 ↓

Srm spermidine synthase 0.043194796 ↓

LOC100360087 ferritin light chain 1-like 0.043194796 ↓

Arhgap1 Rho GTPase activating protein 1 0.048417516 ↓

Spetex-2F Spetex-2F protein 0.049418636 ↑

RGD1305455 similar to hypothetical protein 0.049430646 ↓

FLJ10925

Ppif peptidylprolyl isomerase F 0.049430646 ↓

Emd emerin 0.049430646 ↓

Rab11b RAB11B, member RAS oncogene 0.049430646 ↓

family

Pcsk1n proprotein convertase 0.049430646 ↓

subtilisin/kexin type 1 inhibitor

Lmf2 lipase maturation factor 2 0.04944345 ↓

Mef2d myocyte enhancer factor 2D 0.050840562 ↓

Wars tryptophanyl-tRNA synthetase 0.053145487 ↓

Ubl7 ubiquitin-like 7 0.055027412 ↓

Ctc1 CST telomere replication complex 0.055027412 ↓

component 1

Tox thymocyte selection-associated high 0.057299302 ↓

mobility group box

Med25 mediator complex subunit 25 0.059658075 ↓

Dpf2 double PHD fingers 2 0.059658075 ↓

LOC100362384 ferritin light chain 1-like 0.062040982 ↓

LOC683674 similar to Protein C7orf26 homolog 0.062040982 ↓

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Bcas3 BCAS3, microtubule associated cell 0.062040982 ↓

migration factor

Ccdc6 coiled-coil domain containing 6 0.062040982 ↓

Nrbp2 nuclear receptor binding protein 2 0.065076744 ↓

Celf3 CUGBP, Elav-like family member 0.065076744 ↓

Midn midnolin 0.065076744 ↓

Pcgf2 polycomb group ring finger 2 0.065076744 ↓

Atf7 activating transcription factor 7 0.065076744 ↓

Cnot3 CCR4-NOT transcription complex, 0.07067053 ↓

subunit 3

Table 6.1 42 genes were differentially regulated in the spinal cords of E16 offspring

5hr after MIA with 100µg/kg LPS. N=3 Saline, N=3 LPS where each sample is one pooled litter and the adjP <0.07.

6.4.1.3 Bioinformatic interrogation of genes differentially expressed in the microarray analysis 5hr post MIA with 100µg/kg LPS at E16

Gene ontology (GO) analysis of the 42 differentially expressed genes was attempted using webgestalt. No Biological process, molecular function or cellular component was found to be enriched within the 42 genes. Kyoto encyclopaedia of genes and genome (KEGG) pathway analysis did not identify any enriched pathways and uniprot keyword analysis did not identify any enriched keywords (data not shown). Protein-protein interaction (PPI) was investigated using STRING v11, an open source database of protein interactions curated from existing literature, experimental data, and computational predictions.

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STRING can be used to visualise genomic data at a systems level and identify pathways and groups of proteins of interest. 41 of the 42 differentially regulated genes mapped to a known protein in STRING, displayed in Figure 6.3. The PPI network did not display significantly more interactions than those expected by chance when interrogated at the medium confidence level (0.400). The confidence level is the approximate probability that a predicted link exists between two enzymes in the same metabolic map in the KEGG database (Szklarczyk et al. 2015). A medium confidence level uses a threshold limit of

0.400, excluding predicted links below this with a view to reducing false positives. The

PPI enrichment value was 0.195. A smaller PPI enrichment value would indicate that the nodes are not random and that the observed number of edges in the data is significant.

This data indicates that the proteins differentially regulated 5hr after MIA at E16 were not functionally related.

Figure 6.3 STRING analysis of the protein protein interactions between the 42 differentially regulated genes in the spinal cords of offspring following MIA at E16

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with 100µg/kg LPS. The network did not display significantly more interactions than expected when interrogated at the medium confidence level (0.400) with a PPI enrichment value of p=0.124.

6.4.2 RNASeq analysis

6.4.2.1 Principal component analysis

The P14 samples for RNASeq analysis from E12 and E16 LPS treated and control offspring were graphed on a PCA plot (Figure 6.4). E12 treated animals and E16 treated animals did not display differential clustering at P14. LPS treated animals and saline treated control animals did not cluster differently following intervention at either E12 or at E16. Two animals, one LPS treated and one saline treated, from the E12 group did show differential expression of the primary principle component (PC1) compared to other animals (Figure 6.4, samples E12 P14 S1 and E12 P14 L3). This differential expression can also be observed on the sample to sample heat map of all 12 samples (Appendix A,

Figure A.2.1)

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Figure 6.4. PCA plot of the 12 samples used in the RNASeq study (N=3 saline, N=3

LPS for both E12 and E16 treatment groups). Samples were not clearly clustered according to treatment group or time of intervention, indicative of an overall genetic similarity between the samples.

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6.4.2.2 Gene expression at P14 following MIA with 100µg/kg LPS at E12 or E16

RNASeq identified no differential gene expression in the spinal cords of offspring at P14 following MIA at E12 with 100µg/kg LPS (N= 3 Saline N=3 LPS adjP > 0.1 in all instances, with adjP<0.07 deemed significant. Appendix A, Figure A.2.2).

RNASeq identified differential expression of 16 genes in the spinal cords of offspring at

P14 after MIA at E16 with 100µg/kg LPS (N= 3 Saline N=3 LPS). These differentially expressed genes are displayed in red on the MA plot (Figure 6.5). An MA plot is an application of the Bland-Altman plot. Data is transformed to the M (log ratio, y-axis) and

A (mean of normalised counts, x-axis) scales, in order to visualise the genetic differences between samples. A list of differentially regulated genes and their corresponding adjP values can be viewed in table 6.2. Genes with an adjP value <0.07 were deemed significant. These genes were both up and downregulated (Figure 6.6). For a short synopsis of the function of each differentially regulated gene see Appendix A, Table

A.2.1.

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Figure 6.5: MA plot of the gene expression changes in offspring spinal cords at P14 following MIA at E16 with 100µg/kg LPS. The MA plot is an application of a Bland-

Altman plot for visualising the genetic differences between two samples, in this case saline vs LPS samples. The data is transformed to the M (log ratio, y-axis) and A (mean of normalised counts, x-axis) scales. Differentially expressed genes are shown in red

(adjP<0.07).

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Gene Gene name padj Up/down symbol Cat Catalase 1.32335338901198e-07 ↓

Txnip Thioredoxin interacting 3.29264818451849e-06 ↑ protein Pmvk Phosphomevalonate Kinase 0.000268356859590681 ↓

Pex11a Peroxin 11A 0.000512221797613451 ↑

Spag7 Sperm Associated Antigen 7 0.00465501261640134 ↓

Idi1 Isopentenyl-diphosphate delta 0.00922816789691003 ↓ isomerase 1 Pxmp4 Peroxisomal membrane 0.011402842421314 ↓ protein 4 Scara3 Scavenger receptor class A 0.0198081109893107 ↑ member 3 Kif19 Kinesin Family Member 19 0.0251877569130536 ↓

Fzd9 Frizzled class receptor 9 0.0251877569130536 ↑

Snca Alpha synuclein 0.0251877569130536 ↓

S1pr1 Sphingosine-1-phosphate 0.035168648168927 ↓ receptor 1 Mcam Melanoma cell adhesion 0.035168648168927 ↓ molecule Pla2g3 Phospholipase A2 Group III 0.0370378271847036 ↑

Ppp1r10 Protein Phosphatase 1 0.0555721333954697 ↑ Regulatory Subunit 10 Emcn Endomucin 0.0592220084010742 ↓

Table 6.2. 16 genes were differentially regulated in the spinal cords of offspring at

P14 following MIA with 100µg/kg LPS on E16. N=3 Saline, N=3 LPS where each sample is an individual and the adjP <0.07.

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Figure 6.6 16 genes were differentially expressed at P14 following MIA with

100µg/kg LPS at E16. These genes were both upregulated and down regulated and are plotted as log2 of the fold change. N = 3 saline, N = 3 LPS.

6.4.2.3 Bioinformatic interrogation of genes differentially expressed in RNASeq analysis at P14 post MIA with 100µg/kg LPS at E16

Protein-protein interaction was investigated using STRING v11. The network displayed significantly more interactions than expected by chance when interrogated at the medium confidence level (0.400) with a PPI enrichment value of p = 0.00092. A small PPI enrichment value suggests the nodes are not random and that the observed number of edges in the data is significant (Figure 6.7). The network displayed five edges. The expected number of edges was one. The average node degree was 0.526, indicating the

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number of interactions each member of the network has on average. The average clustering coefficient was 0.105, indicating that the nodes of the network are connected.

GO analysis using webgestalt identified a number of significantly enriched cellular components, the most significant of which were the ‘peroxisome’ and the ‘peroxisomal membrane’ (Tables 6.3). KEGG pathway analysis identified the pathways ‘peroxisome’ and ‘terpenoid backbone biosynthesis’ as the most significantly enriched (Table 6.4), while Uniprot keywords identified included ‘peroxisome’ and ‘cholesterol biosynthesis’

(Table 6.5).

Figure 6.7 STRING analysis of the protein protein interactions between the 19 differentially regulated genes in the spinal cords of offspring at P14 following MIA at E16 with 100µg/kg LPS. The network displayed significantly more interactions than expected when interrogated at the medium confidence level (0.400), with a PPI enrichment P-value of 0.00092. Edges = 5, average node degree = 0.526, average local clustering co-efficient = 0.105.

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Cellular Component GO-term Description Count in False discovery network rate GO:0005777 Peroxisome 4 of 92 0.00016 GO:0005778 Peroxisomal membrane 3 of 34 0.00018 GO:0005758 Mitochondrial intermembrane space 3 of 46 0.00025

Table 6.3 Gene Ontology analysis showing enriched GO categories ‘peroxisome’,

‘peroxisomal membrane’ and ‘mitochondrial intermembrane space’.

KEGG Pathways Pathway Description Count in False discovery network rate Rno04146 Peroxisome 4 of 82 0.0000295 Rno00900 Terpenoid backbone biosynthesis 2 of 22 0.0038

Table 6.4 KEGG pathways ‘peroxisome’ and ‘terpenoid backbone synthesis’ were enriched within the 19 differentially regulated genes.

Uniprot Keywords Keyword Description Count in network False discovery rate KW-0576 Peroxisome 4 of 72 0.0000294 KW-0152 Cholesterol biosynthesis 2 of 16 0.0035

Table 6.5 Uniprot keywords ‘peroxisome’ and ‘cholesterol biosynthesis’ were enriched within the 19 differentially regulated genes.

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6.4.2.4 PCR confirmation of gene expression changes at P14 following MIA with 100µg/kg LPS at E16

PCR assay for the 16 genes which were differentially expressed at P14 after MIA at E16 with 100µg/kg LPS was completed at n=7 (Table 6.2). Of the 16 differentially expressed genes 7 recapitulated the changes seen in the RNASeq data with some variation in log2FC values (Cat, Pmvk, Idi1, Pxmp4, Spag7, Kif19, and Snca). 3 genes differentially regulated in RNASeq showed little change in PCR analysis (S1pr1, Pla2g3 and Emcn). 4 genes which were upregulated in RNASeq were downregulated in PCR analysis (Txnip,

Pex11a, Scara3 and Ppp1r10 ) while one gene which was originally downregulated showed upregulation on PCR analysis (Mcam) Figures 6.8 and 6.9.

0.0

-0.5

Log2 FC Log2 -1.0

-1.5

Cat Idi1 Txnip Pmvk Pex11a Pxmp4 Scara3

Gene Figure 6.8 PCR expression of peroxisome-related genes identified as differentially expressed at P14 by RNASeq following MIA at E16 with 100µg/kg LPS. Log2FC = log2 of the fold change. N = 7 Saline N = 7 LPS.

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E16 P14 Remaining genes PCR 0.5

0.0

-0.5

Log2 FC Log2 -1.0

-1.5

Fzd9

Snca

Kif19

Emcn

S1pr1

Mcam

Spag7

Pla2g3

Sh3bgrl

Slco1c1

Ppp1r10 Tnfrsf11a

Figure 6.9 PCR expression of remaining genes identified as differentially expressed at P14 by RNASeq following MIA at E16 with 100µg/kg LPS. Log2FC = log2 of the fold change. N = 7 Saline N = 7 LPS.

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6.5 Discussion

High throughput studies such as microarray and RNA seq have changed how we formulate research questions, handle data and design experiments. This chapter has investigated the genome wide effects of MIA 5 hours and 14 days post maternal treatment with LPS at E12 or E16, with a view to forming an unbiased picture of the gene expression changes associated with MIA under the determined experimental conditions.

Interestingly, at both 5hr and 14 days post treatment gene expression was unchanged in the spinal cords of offspring of dams treated with 100µg/kg LPS at E12 (Appendix A

Figure A.2.4). This recapitulates the lack of change identified in many of the immunohistochemical studies in this project at E12, and further suggests that E12 may not be a particularly vulnerable period during rat spinal cord development for the investigated parameters.

In contrast, MIA at E16 resulted in differential gene expression at both the 5hr and P14 time points, further establishing E16 as a potential period of innate vulnerability to the effect of inflammation during rat spinal cord development. These gene expression changes differed between the embryonic and postnatal time points (Figure 6.2, 6.6)

In the microarray study 42 genes were identified as differentially regulated 5hr after MIA at E16 (Table 6.1). These genes were largely unrelated to each other, showing very little interaction at a protein level when interrogated using STRING. GO analysis did not identify any enriched categories, KEGG analysis did not identify any pathways of interest and uniprot did not identify any enriched keywords within the 42 differentially regulated genes. However, these genes may be split into broader categories manually.

Many of the differentially regulated genes play a role in the transcription machinery of the cell, for example, Mediator complex subunit 25 (Med25) encodes a part of the mediator complex, a transcriptional co-activator (Lee et al. 2007; Rana et al. 2011). 180

Polycomb group ring finger 2 (Pcgf2), is a negative regulator of transcription involved in the repression of genes involved in embryogenesis, cell cycle progression and tumourigenesis, while CST Telomere Replication Complex Component 1 (Ctc1), is a component of the CST complex, which may act to promote DNA replication under stress

(Zakrzewska et al. 2011; Stewart et al. 2018).

A number of the differentially regulated genes were involved in cell mobility and migration, including the microtubule associated cell migration factor Bcas3, which is involved in cytoskeletal rearrangement at the leading edge of the cell (Jain et al. 2012).

Emerin (Emd), is an actin-binding and beta-tubulin-binding protein involved in the anchoring of the nuclear membrane to the cytoskeleton, the absence of which is associated with X-linked Emery–Dreifuss muscular dystrophy (Muntoni et al. 2006; Chang et al.

2013). CAP-Gly Domain Containing Linker Protein 3 (Clip3), is a member of the cytoplasmic linker protein 170 family. Finally, fascin Actin-Bundling Protein 1 (Fscn1), plays critical roles in migration, motility, cell adhesion and cell-cell interactions (De

Arcangelis et al. 2004; Kraft et al. 2006; Deng et al. 2012). The differential regulation of genes involved in migration during CNS development, a period of massive cellular migration, could be a factor which underpins a variety of potential outcomes of MIA during development.

Many of the differentially regulated genes at the E16 5hr time point have known roles in the CNS, and more specifically in CNS development and maturation. Fscn1 is known to interact with the low-affinity nerve growth factor receptor (P75), and plays a role in neurite shape and trajectory (Kraft et al. 2006). Mutations in Med25 are associated with

Charcot Marie Tooth disease, while Pcgf2 expression is limited to neural tissues where it is believed to modulate cytokine, chemokine and chemokine receptor function, which is interesting given the role of these molecules in neurodevelopment (Leal et al. 2009;

Zakrzewska et al. 2011). Mindolin (Midn) is expressed in the midbrain of mice at E12.5 181

and is assumed a regulator of genes related to neurogenesis due to nuclear/nucleolar- restricted expression, meanwhile Proprotein Convertase Subtilisin/Kexin Type 1

Inhibitor (Pcskn1) inhibits prohormone convertase 1, which regulates the proteolytic cleavage of neuroendocrine peptide precursors (Tsukahara et al. 2000; Liu et al. 2012;

Obara et al. 2019). Nuclear receptor binding protein 2 (Nrbp2) may regulate apoptosis of neural progenitor cells during their differentiation (Larsson et al. 2008). Myocyte

Enhancer Factor 2D (Mef2d) is a member of the myocyte-specific enhancer factor 2

(MEF2) family of transcription factors and is thought to play a role in neuronal development and differentiation (Assali et al. 2019). Mutations of Mef2d have been associated with the development of Parkinson’s disease (Yao et al. 2012). Other broad categories of genes differentially regulated 5hr after MIA at E16 included the Ras family of proteins, a number of tumor suppressor proteins and proteins involved in iron homeostasis. For a full list of the 42 genes differentially expressed in response to MIA and a short synopsis of their function see Appendix A, Table A.1.1.

Contrary to the E16 5hr time point a number of the 16 genes differentially expressed at

P14 following MIA at E16 were related, displaying protein-protein interaction on

STRING analysis, as well as enrichment in gene ontology analysis, KEGG pathway analysis and uniprot keyword analysis (Figure 6.7, Table 6.2- 6.5). These 7 genes:

(catalase (Cat), thioredoxin interacting protein (Txnip), phosphomevalonate kinase

(Pmvk), peroxisomal biogenesis factor 11 alpha (Pex11a), Isopentenyl-Diphosphate

Delta Isomerase 1 (Idi1), peroxisomal membrane protein 4 (PXMP4) and scavenger receptor class A member 3 (Scara3), were related to peroxisome structure and function.

Peroxisomes are ubiquitous single-membrane organelles responsible for the catabolism of very long chain fatty acids, branched chain acids, D-amino acids and polyamines, as well as the synthesis of plasmalogens, cholesterol and bile acids (Smith and Aitchison

2013). Peroxisomes reduce ROS in the cell through the action of catalase. Peroxisomes 182

are enriched in neural tissues and are expressed at higher levels during CNS development

(Faust et al. 2005; Berger et al. 2016). A number of peroxisomal disorders effect the CNS including Zellweger syndrome and X-linked adrenoleukodystrophy. Peroxisomal disorders are known to result in abnormal neuronal migration, abnormalities or defects in the white matter and lipid inclusions (Faust et al. 2005). Oligodendrocytes, which are normally peroxisome-rich, are particularly vulnerable to peroxisomal deficits. Selective loss of peroxisomes in oligodendrocytes has been shown to result in demyelination, lipid inclusions, axonal death and inflammation (Paintlia et al. 2008). This may be due to the role of peroxisomes in plasmalogen and cholesterol biosysthesis, both of which are necessary building blocks of myelin. The seven peroxisomal genes differentially regulated at E16 play a variety of roles. Perhaps the best known, Cat is an anti-oxidant enzyme present in the peroxisome of all aerobic cells. It converts hydrogen peroxide to water and oxygen to prevent oxidative stress (Abramov and Wells 2011). Txnip is a negative regulator of thioredoxin, which protects against oxidative stress (Alhawiti et al.

2017) . Pmvk and Idi1 meanwhile are peroxisomal enzymes involved in isoprenoid biosynthesis. Isoprenoids include cholesterol, haem, the steroid hormones and vitamin K

(Lange et al. 2000). Pex11a and Pxmp4 both play roles in the peroxisomal membrane, interacting with Pex19 (Sacksteder et al. 2000). Pex11a is a membrane elongation protein, which has a role in increasing peroxisomal abundance in the cell (Koch et al. 2010).

Pxmp4 is a component of the peroxisomal membrane the exact function of which is unknown. Differential expression of these genes suggest that MIA at E16 may result in unexpected changes in peroxisome function, which may underpin some of the effects of

MIA on CNS development at this time point and later. Interestingly Scara3, which is a macrophage scavenger-like receptor that depletes ROS in the cell, is upregulated at P14 following MIA at E16, potentially an attempt to correct the anticipated increase in ROS

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in the cell in response to decreases in catalase and thioredoxin functioning (Han et al.

1998).

Similarly to the 5hr time point a number of genes differentially regulated at P14 following

MIA at E16 included genes which may be involved in neuronal migration, cell motility and cell-environment interactions. These included the cell adhesion related genes

Sphingosine-1-phosphate receptor 1 (S1pr1), activation of which causes cell-cell adhesion and the melanoma cell adhesion molecule (Mcam), which may be activated during the development of the neural crest (Motohashi et al. 2016; Jiang et al. 2017).

Endomucin (Emcn) is known to inhibit interaction between the cells and the extracellular matrix (Zahr et al. 2016). Kinesin family member 19 (Kif19), a regulator of microtubule polymerisation in motile cilia, and phospholipase A2 group III (Pla2g3), which negatively regulates ciliogenesis, are also differentially expressed (Niwa et al. 2012; Gijs et al.

2015). A number of genes related to the progression of the cell cycle were also altered at

P14 following MIA at E16, including Protein Phosphatase 1 Regulatory Subunit 10

(Ppp1r10) and frizzled class receptor 9 (Fzd9).

The changes in gene expression identified in the spinal cords of offspring using sequencing studies at E16 and P14 following MIA at E16 with 100µg/kg LPS have provided evidence that MIA at E16 can affect the CNS at the level of gene expression.

These changes differed at the 5hr and P14 time point further emphasising the complexity of the effect of MIA. The studies also confirmed that MIA with the relatively low dose of

100µg/kg LPS was sufficient to provoke gene expression changes in offspring. RNA sequencing of spinal cords of P14 offspring identified the peroxisome as a cell organelle which is potentially vulnerable to MIA, and alterations in these single membrane structures may play a role in the effect of MIA at cellular level, particularly in the case of oligodendrocytes, a cell type already identified as particularly vulnerable to MIA.

Unfortunately PCR confirmation of the changes observed at P14 was unsuccessful in a 184

number of genes (Figure 6.8, 6.9). This could be due to differences in comparative methods between the studies, variations in gene expression between offspring sequenced in the relatively small n=3 study, or the increase in offspring number in the PCR study from n=3 to n=7. PCR analysis of the three animals used in the RNASeq experiment did not recapitulate the gene expression changes seen in the RNASeq experiment (data not shown), indicating that differences in methodologies is a factor in this instance. It is likely that a combination of all three possibilities is at play. The relatively small gene expression changes observed in the original RNASeq may also have contributed to this failure to confirm. Further investigation in both RNASeq and RT-PCR experiments would be necessary before drawing conclusions. These future studies would ideally use a greater number of litters and a single sex group. Analysis of specific regions of the spinal cord, such as the dorsal or ventral horn, or specific cell types such as oligodendrocytes following flow cytometry, may also prove useful for pinpointing the source of gene expression changes, as those observed in whole spinal cord samples were relatively small.

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Chapter 7. The role of miRNAs in Oligodendrogliogenesis: an in-silico study

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7.1 Chapter Abstract

This study used in-silico analysis to investigate the role of miRNAs in oligodendrogliogenesis, with a view to identify potential proteins and pathways targeted by miRs which may be vulnerable to the effects of MIA.

The study identified thousands of genes potentially targeted by the miRNAs miR-9, miR-

124, miR-138, miR-146a, miR-219(2-3p) and miR-338 . GO analysis, KEGG pathway analysis and STRING analysis of protein-protein interaction were used to further focus the results and identified inflammatory pathways, developmental pathways and apoptotic pathways which may be modulated by microRNAs during development, and potential targets which may function in oligodendroglial response to MIA.

This study suggests that miRNAs may be an interesting pathway for further investigation in the attempt delineate the factors underlying the oligodendroglial response to MIA.

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7.2 Introduction:

MicroRNAs (miRNAs, miRs) are small ~21-25 nucleotide (nt) long non-coding RNAs. miRNAs modulate translation of mRNA to protein by binding to an mRNA target sequence, usually at the 3’- untranslated region (UTR), as a part of the RNA-induced silencing complex (RISC)(R.C. Lee et al. 1993; He and Hannon 2004; Rodriguez et al.

2004). Inhibition of translation occurs through mRNA cleavage, if the miRNA is an exact match for its target mRNA sequence, or more commonly in animals, through repression of translation or deadenylation, where the miRNA is an imperfect match(Brennecke et al.

2005; Eulalio et al. 2009). This appears to be largely dependent on the perfect compatibility of nucleotides 2-7nt known as the ‘seed region’ with target mRNA, though non-canonical binding has been suggested (Lewis et al. 2005; Grimson et al. 2007;

Helwak et al. 2013; Khorshid et al. 2013). The ability of an miRNA to imperfectly match and repress an mRNA target ensures that each miRNA may have multiple mRNA targets on which they act. Indeed it is predicted that around 30% of the human genome is targeted by these miRNAs(Lewis et al. 2005; Lim et al. 2005). Since their discovery in 1993 miRNAs have been implicated in a vast array of processes, including cancers, heart disease, inflammatory disorders, neurodevelopmental diseases and liver diseases, as well as normal physiological processes such as development (R.C. Lee et al. 1993; Ardekani and Naeini 2010; Sayed and Abdellatif 2011). For a full review of miRNA structure and function as well as the roles of miRNA in development see Chapter 1, 4.1-4.2.

A number of microRNAs have been implicated in the process of oligodendrogliogenesis.

Indeed, a study by Lau et al. has identified as many as 98 miRNAs associated with the

OL lineage, 43 of which are differentially regulated during differentiation (Lau et al.

2008). This chapter looks at six of the miRNAs most commonly associated with oligodendrogliogenesis in the existing literature (miR-9, miR-124, miR-138, miR-146a,

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miR-219 and miR-338), and attempts to delineate the mechanisms and pathways these miRs target in order to alter oligodendrogliogenesis. miR-124 is a CNS specific miR reportedly neuronal in origin(Cheng et al. 2009). In HeLa cells, miR-124 is known to induce the expression of a neuronal phenotype(Lim et al.

2005). miR-124 is downregulated during oligodendrogliogenesis, however selected knockdown of miR-124 in a zebrafish model has been shown to result in a decrease in oligodendrocyte number and myelination of axonal projections in the ventral hindbrain

(Morris et al. 2015). miR-9 is a key regulator of differentiation, and is thought to repress NSC proliferation through binding of TLX (Zhao et al. 2009). In the 2008 study by Lau et al. a decrease in miR-9 was also found to promote oligodendrocyte proliferation (Lau et al. 2008). Levels of peripheral myelin protein 22 (PMP22) are known to inversely correlate to miR-9, with increasing levels of PMP22 associated with decreases in miR-9 (Verrier et al. 2009). miR-219 on the other hand is known to repress factors known to promote OPC proliferation (PDGFRα, Sox6, FoxJ3, Hes5 and ZFP238) thereby allowing differentiation to occur (Dugas et al. 2010). A study by Zhao et al. has also shown miR-219 to target the neurogenic transcription factors Isl1, NeuroD1 and Otx2 (Zhao et al. 2010). miR-219 expression is induced 100-fold during oligodendrocyte differentiation, and is increased in both early and later stages. Expression of miR-219 and miR-138 is sufficient to induce differentiation (Dugas et al. 2010). ELOVL7 is a known target of miR-219, and is the main biochemical abnormality in x-linked Adrenoleukodystrophy, which results in the build-up of long chain fatty acids in the brain and ultimately myelin sheath destruction.

This indicates that miR-219 may also have a role in the production and maintenance of lipids in mature myelinating OLs (Shin et al. 2009).

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In the same study that identified miR-219 and miR-138 as sufficient to induce OPC differentiation Dugas et al. also showed that miR-138 is increased during OPC differentiation. Use of a miR-138 mimetic showed that miR-138 promotes the expression of markers associated with early differentiation, not late, suggesting that it may serve to increase the time spent in early stages of differentiation, possibly to allow for correct axon selection and myelination (Dugas et al. 2010). A study by Birch et al. showed a delayed but significant increase in miR-138 and miR-338 following perinatal hypoxia ischemia

(Birch et al. 2014).

Aside from a role in hypoxia-ischemia miR-338 is known to promote OL differentiation, inhibiting proliferation (Lau et al. 2008; Zhao et al. 2010). Overexpression of miR-338 through lentiviral infection is enough to induce the differentiation of human endometrial derived stromal cells to OLs (Ebrahimi-Barough et al. 2013).

Similar to the role in hypoxia-ischemia and immune response identified in miR-138 and miR-338, miR-146a also appears to have a role in immune function. miR-146a is known to downregulate the TLR pathway and NFκB activation (Nahid et al. 2011; Santra et al.

2014). IRAK1 and TRAF6 are both targets of miR-146a. Increase in miR-146a is associated with the increased maturation of OLs, while inhibition leads to a decrease in

MBP expression (Santra et al. 2014; Liu et al. 2017). Interestingly miR-146a knockout has been examined in a model of MIA, where chronic LPS exposure accompanied by miR-146a knockout lead to a decrease in haemopoetic stem cell numbers, differentiation to dysfunctional lymphocytes and myeloid cells, and an increase in IRAK1, TRAF6 and

NFkB, activating the IL-6 Pathway (Zhao et al. 2013).

This chapter investigates the underlying mechanisms and pathways by which the above mentioned miRs effect oligodendrogliogenesis, through use of free-to-access software and publicly available datasets. In-silico investigation of pre-exsiting data is extremely

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cost effective, allowing researchers to streamline projects and identify potential molecules of interest before ever entering the laboratory environment.

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7.3 Chapter Aims

The objective of this study was:

1. To use the literature to identify miRNAs which appear to play a role in

oligodendrogliogenesis

2. To use open source software to identify predicted targets of these miRNAs in

order to further elucidate the mechanisms by which these miRNAs affect

oligodendrogliogenesis

3. To identify signalling networks targeted repeatedly by these miRNAs and

examine the roles of these pathways in oligodendrogliogenesis.

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7.4 Results

7.4.1 Identification of predicted targets

Predicted targets of miR-9, miR-124, miR-138, miR-338, miR-219(2-3p) and miR-146a were identified using miRecords’ open source software

(http://c1.accurascience.com/miRecords/prediction_query.php). Targets identified by one or more of the target identification softwares used by miRecords numbered in the thousands for each miRNA (miR-9 – 3766 predicted targets, miR-124 – 2080 predicted targets miR-138 – 2931 predicted targets, miR-338 – 2928 predicted targets, miR-219 (2-

3p) – 1845 predicted targets and miR-146a – 2682 predicted targets). These predicted targets were then further filtered using gene ontology (GO) analysis.

7.4.2 Gene ontology analysis.

Predicted targets identified using the miRecords software underwent GO analysis using webgestalt (http://bioinfo.vanderbilt.edu/webgestalt/), an open source, web-based gene set analysis toolkit. Enriched ontologies of interest included neurogenesis and nervous system development, with the most specific nervous system-related gene ontology category brought forward for further analysis. The miRNAs, enriched gene ontologies of interest, the number of potential targets in that category, and the adjusted P-Values of this enrichment can be viewed in table 7.1. A complete list of all predicted targets contained in the enriched GO categories of interest, either ‘neurogenesis’ or ‘nervous system development’ can be found in appendix B (tables B.1-B.5).

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Predicted targets miR GO category AdjP in category

miR-9 Neurogenesis 189 2.6 x 10-5

miR-124 Neurogenesis 119 4.54 x 10-5

miR-138 Nervous system development 224 1.26 x 10-7

miR-146a Nervous system development 174 0.0052

miR-219(2-3p) Nervous system development 138 0.0046

miR-338 Nervous system development 204 8.89 x 10-5

Table 7.1 Gene ontology analysis identified enriched ontologies of interest. The potential targets contained within these categories were brought forward for STRING analysis

7.4.3 STRING analysis of protein-protein interactions

Predicted targets contained within each enriched GO category of interest were imported into STRING (v.11) and their protein-protein interactions interrogated at the highest confidence level (0.900). To achieve a more manageable number of predicted targets for further investigation, and to allow investigation of key pathways which may be effected by miRNAs during oligodendrogliogenesis, nodes or proteins which were not connected to another potential target at the highest confidence level were excluded from further investigation. The remaining predicted targets were displayed in a STRING network using ‘molecular action’ as the meaning of the network edge to allow visualisation of the methods of interaction between predicted targets.

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Line Colour Interaction Type Green Activation Red Inhibition Blue (royal) Binding Blue (turquoise) Phenotype Purple Catalysis Pink Post-translational modification Black Reaction Gold/Yellow Expression

Table 7.2 Legend for the interaction type of predicted targets using ‘molecular action’ as the meaning of the network edges.

Symbol Action effect → Positive ―I Negative ―• Unspecified

Table 7.3 – Definition of symbols present in the network analysis using ‘molecular target’ as the meaning of the network edges

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miR-9 The 189 predicted targets of miR-9 enriched in the gene ontology category ‘neurogenesis’ created a network with 60 edges, or interactions (Figure 7.1). The expected number of interactions or edges for a random group of proteins of similar size is 30, resulting in a

PPI enrichment p-value of 1.32 x 10-6.

Figure 7.1. STRING v11 output for the molecular interaction of predicted targets of miR-9 contained within the GO category ‘neurogenesis’. Number of nodes – 188,

Number of edges – 60, Average node degree – 0.638, Average local clustering co- efficient – 0.276, Expected number of edges – 30, PPI enrichment P-value – 1.32 x 10-6.

Unconnected nodes not shown. Gene symbol abbreviations appendix B, Table B.1)

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miR-124 The 119 predicted targets of miR-124 enriched in the GO category neurogenesis created a network with 32 edges (Figure 7.2). The expected number of edges for a random group of proteins of a similar size is 15 edges, resulting in a PPI enrichment p-value of 7.61 x

10-5.

Figure 7.2. STRING v11 output for the molecular interaction of predicted targets of miR-124 contained within the GO category ‘neurogenesis’. Number of nodes – 119,

Number of edges – 32, Average node degree – 0.538, Average local clustering co- efficient – 0.215, Expected number of edges – 15, PPI enrichment P-value – 7.61 x 10-5.

Unconnected nodes not shown. Gene symbol abbreviations appendix B, Table B.2).

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miR-138 The 224 predicted targets of miR-138 enriched in the GO category ‘nervous system development’ created a network with 89 edges (Figure 7.3). The expected number of edges for a random group of proteins of a similar size is 36 edges, resulting in a PPI enrichement p-value of 6.14 x 10-14.

Figure 7.3. STRING v11 output for the molecular interaction of predicted targets of miR-138 contained within the GO category ‘nervous system development’. Number of nodes – 224, Number of edges – 89, Average node degree – 0.795, Average local clustering co-efficient – 0.272, Expected number of edges – 36, PPI enrichment P-value

– 6.14 x 10-14. Unconnected nodes not shown. Gene symbol abbreviations appendix B,

Table B.3

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miR-146a

The 174 predicted targets of miR-146a enriched in the GO category ‘nervous system development’ created a network with 68 edges (Figure 7.4). The expected number of edges for a random group of proteins of a similar size is 28 edges, resulting in a PPI enrichement p-value of 1.75 x 10-10.

Figure 7.4. STRING v11 output for the molecular interaction of predicted targets of miR-146a contained within the GO category ‘nervous system development’. Number of nodes – 174, Number of edges – 68, Average node degree – 0.786, Average local clustering co-efficient – 0.328, Expected number of edges – 28, PPI enrichment P-value

– 1.75 x 10-10. Unconnected nodes not shown. Gene symbol abbreviations appendix B,

Table B.4)

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miR-219 (2-3p)

The 138 predicted targets of miR-219 (2-3p) enriched in the GO category ‘nervous system development’ created a network with 50 edges (Figure 7.5). The expected number of edges for a random group of proteins of a similar size is 23 edges, resulting in a PPI enrichement p-value of 1.09 x 10-6.

Figure 7.5. STRING v11 output for the molecular interaction of predicted targets of miR-219 (2-3p) contained within the GO category ‘nervous system development’.

Number of nodes – 138, Number of edges – 50, Average node degree – 0.735, Average local clustering co-efficient – 0.243, Expected number of edges – 23, PPI enrichment P- value – 1.09 x 10-6. Unconnected nodes not shown. Gene symbol abbreviations appendix

B, Table B.5)

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miR-338

The 204 predicted targets of miR-338 enriched in the GO category ‘nervous system development’ created a network with 60 edges (Figure 7.6). The expected number of edges for a random group of proteins of a similar size is 33 edges, resulting in a PPI enrichement p-value of 1.89 x 10-5.

Figure 7.6. STRING v11 output for the molecular interaction of predicted targets of miR-338 contained within the GO category ‘nervous system development’. Number of nodes – 204, Number of edges – 60, Average node degree – 0.594, Average local clustering co-efficient – 0.229, Expected number of edges – 33, PPI enrichment P-value

– 1.89 x 10-5. Unconnected nodes not shown. Gene symbol abbreviations appendix B,

Table B.6)

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7.5 Discussion

STRING analysis of the enriched pathways associated with the predicted targets of the selected miRNAs yielded some unexpected results. Possibly counter intuitively just two miRNAs, miR-9 and miR-146a, yielded predicted targets traditionally associated with oligodendrocyte function (Appendix B, Table B.1 and Table B.4). miR-9 was predicted to target both Olig2 and Sox10. The bHLH transcription factor Olig2 is expressed by newly arisen oligodendrocyte lineage cells and is necessary for the activation of HMG- domain transcription factor Sox10, a key component in oligodendrocyte maturation

(Küspert et al. 2011). Previous research by Lau et al. has indicated that miR-9 is downregulated in Oligodendrocytes during differentiation. In OPCs mir-9 is expressed at high levels while miR-9 targets are expressed at low levels. In OLs the opposite occurs

(Lau et al. 2008). Decreases in miR-9 levels at differentiation are suggestive of decreased regulation of the Olig2 mediated activation of Sox10 allowing for the maturation of OPCs to OLs. miR-146a meanwhile, was predicted to target MBP and PLP (proteolipid protein).

PLP and MBP are located within the myelin sheath and comprise 80% of all myelin proteins (Baumann and Pham-Dinh 2001). MBP and PLP bind each other, and appear to be responsible for the compaction of myelin during the myelination process. They are markers of mature oligodendrocytes. Interestingly previous work has indicated that miR-

146a expression is upregulated during oligodendrocyte differentiation (Lau et al. 2008).

This suggests that, should its predicted targets MBP and PLP1 be validated, fine tuning of the myelination process may be necessary. This is in line with research which suggests that increased expression of miR-146a promotes the differentiation of OPC’s to MBP expressing OLs and that the inhibition of miR-146a may lead to the inhibition of MBP expression (Santra et al. 2014; Liu et al. 2017). Other work has suggested that increases in PLP beyond the norm are associated with decreases in MBP levels and dysmyelination,

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indicative of the fine balance necessary for the induction of correct myelination (Karim et al. 2007).

Although the number of traditionally oligodendrocyte-associated pathways identified was low, a number of pathways predicted to be targeted by the selected miRNAs were common to multiple miRNAs. Predicted targets associated with pathways involving

MAPK8, or c-Jun N-terminal kinase 1 (JNK1) signalling were common to five of the six investigated miRs; miR-9, miR-138, miR146a, miR-219 and miR-338. JNK1 itself was a predicted target of all five miRs. miR-124, which is downregulated during differentiation, did not have potential targets related to JNK signalling. JNK signalling is traditionally associated with inflammatory processes. Indeed, IRAK1 and Traf6 are both validated targets of miR-146a. miR-146a knock out in a chronic LPS model was enough to decrease haemopoetic stem cell numbers and induce differentiation of dysfunctional lymphocytes, increasing IRAK1, TRAF6 and NFkB expression. During oligodendrogliogenesis modulation of inflammatory pathways is of incredible importance as oligodendrocytes are particularly prone to inflammatory injury (for review see (Volpe et al. 2011)). JNK signalling is also associated with a number of developmental processes including differentiation, proliferation, cell death, cell survival and protein expression. It is therefore unsurprising that predicted targets associated with this pathway are numerous, and span miRNAs both upregulated and downregulated during oligodendrocyte differentiation (Lau et al. 2008). Previous research has shown oligodendrocyte development to rely on p38-MAPK mediated Sox-10 regulation and cross talk with the parallel JNK pathway to repress c-Jun activity. Inhibition of c-jun accumulation and p38-

MAPK inhibition promotes the expression of myelin genes and developmental progression to OL (Chew et al. 2010). In this study just one miRNA potentially targeted

Jun directly, miR-338. miR-338 has been previously validated as playing a role in oligodendrogliogenesis. It is preferentially upregulated during differentiation and myelin

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mutants show decreased expression(Lau et al. 2008; Zhao et al. 2010). Contrary to this a study by Dugas et al has suggested that miR-338 alone is not necessary for myelination to occur (Dugas et al. 2010). This was confirmed in a study by Wang et al, who found that miR-338 -/- mice showed normal myelination. miR-338 knock out did however exasperate a dysmyelination phenotype shown by miR-219 -/- mice, indicating that these miRNAs may cooperate with one another (Wang et al. 2017). Nuclear factor kB (NFkB) activation is a potential outcome of JNK signalling. Traditionally a pathway associated with inflammation and cell death the role of NFkB in normal myelination is uncertain.

One study has shown that patients with additional copies of the IKBKG gene, which encodes the NFkB essential modulator (NEMO), have myelination defects, suggesting

NFkB is needed for myelination, however a number of transgenic animal studies have suggested that NFkB signalling is dispensable (Raasch et al. 2011; Philippe et al. 2013;

Kretz et al. 2014). Regardless of the uncertainty to NFkB’s developmental role in oligodendrocytes, five of the six miRNAs investigated in the study had potential targets such as Tumour necrosis factor receptor associated factor 6 (TRAF6; miR-138, miR-

146a, miR-219), Protein kinase C iota (PRKCI; miR-9, miR-146a, miR-219) and Nerve growth factor receptor (NGFR; miR-9, miR-219, miR-338). Traf6 and Prkci bind and form part of a complex with Ngf (nerve growth factor), Ngfr and interleukin 1 receptor associated kinase (Irak1) which favours NfkB activation (Mamidipudi et al. 2004). miR-

138, which only had one potential target in the complex, is the only miRNA to potentially target REL associated protein (Rela). Also known as p65 Rela is a member of the NFkB family and is involved in NFkB heterodimer formation, nuclear translocation and activation (Chen and Greene 2004). Negative regulation of NFkB signalling by miR-138, miR-146a, miR-219 and miR-338 is in line with studies showing that NFkB signalling is harmful to oligodendrocytes, however targeting of NFkB by miR-9, which is

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downregulated during differentiation, and Jun itself by miR-338 are indicative of the complex signalling pathways at play (Raasch et al. 2011; Kretz et al. 2014).

In line with the activation of NFkB and potentially apoptotic pathways miR-138, miR-

146a, miR-219 and to a lesser extent miR-338, all had targets in or related to the caspase family, a family of protease enzymes with roles in programmed cell death or apoptosis.

Capsases are responsible for programmed cell death in the oligodendrocyte and apoptosis of excess OPCs is a normal part of oligodendrocyte development. Unsurprisingly spatial and temporal control of this process is crucial and apoptosis of more mature, MBP+ oligodendrocytes impacted myelination. This impact was transitory, however did effect remyelination in the animals as adults (Gu et al. 1999; Caprariello et al. 2015). Neither miR-9 or miR-124, both of which are downregulated at differentiation, displayed targets within the Caspase family, perhaps indicative of the role of apoptosis in early development, when both miRNAs are strongly upregulated. miR-338, the only miR which targeted NFkB signalling through the Ngfr, but not through Traf6 or Prkci, targeted just a single member of the Caspase family, Casp 2, suggesting that miR-338 may be more involved in the potential regulation of JNK targets other than NFkB signalling and cell death. miR-138 and miR-338 have previously been characterised in a study investigating oligodendroglial response to Hypoxia-ischemia (HI), which damages the vulnerable white matter and causes cerebral palsy and leukomalacia when occurring perinatally

(Volpe et al. 2011). The study showed that both miR-138 and miR-338 underwent delayed upregulation in response to perinatal HI at P7 (Birch et al. 2014). This delay in upregulation is counterintuitive as miR-138 and miR-338 are both upregulated during differentiation, a process HI is known to negatively effect (Dugas et al. 2010). It is possible that HI transiently inhibits dicer and that this delayed upregulation is a rebound response although this remains to be proven. The current investigation found that both 205

miR-138 and miR-338 have potential targets involved in HI. miR-138 displayed potential targets Hypoxia inducible factor 1a (Hif1a), Aryl hydrocarbon receptor nuclear translocator (Arnt) and Heparin-binding EGF-like growth factor (Hbegf). Hif1a and Arnt are both components of Hif1. Hifs, or hypoxia inducible factors are bHLH transcription factors upregulated in response to low cellular oxygen or hypoxia. During embryonic development Hifs are important for angiogenesis. This role for Hifs in angiogenesis is particularly important in OPCs, which display large metabolic requirements as they produce myelin and therefore require adequate blood supply. A study by Yeun et al has shown that postnatal myelination is dependent on oxygen levels in the cell, as mediated by OPC encoded Hif (Yuen et al. 2014). Under normoxic conditions Hifs are hydroxylated at conserved proline residues which marks them for ubiquitination. During hypoxia the Hif prolyl-hydroxylase is inhibited and Hifs are stabilised. Constitutive stabilisation of Hif1/2α (hypoxic conditions) resulted in the arrest of OPC maturation through autocrine activation of canonical Wnt7a/7b, while OPC-specific Hif1/2α loss of function leads to insufficient angiogenesis in corpus callosum and catastrophic axon loss.

As well as Hif1a and Arnt, components of Hif1, this study has also suggested that Hbegf is also a potential target of miR-138. Hbegf is upregulated in response to hypoxia- ischemia and has been shown to stimulate neurogenesis in response to HI (Jin et al. 2002).

Hbegf is expressed in oligodendrocytes and is a physiological ligand of the EGF receptor, recent evidence suggests that EGF is an important mitogen in oligodendrocyte development suggesting that HI induced Hbegf expression may cause oligodendrogliogenesis (Hayase et al. 1998; Yang et al. 2017). Negative regulation of

Hbegf by miR-138 may help to prevent the spurious development of oligodendrocytes, a process that is under physiological conditions, strongly temporally and spatially controlled, in response to HI. miR-338 similarly to miR-138 potentially targets both Hif1a and Arnt indicating that both miRNAs may play an important role in both the

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development of oligodendrocytes and angiogenesis, and in oligodendrocyte response to

HI.

As suggested in the study by Yuen et al WNT signalling plays a key role in both oligodendrocyte development and development as a whole. WNT signalling is involved in axon patterning, cell fate specification, proliferation and migration and is tightly regulated. Of the investigated miRNAs miR-138, miR-146a, miR-219 and miR-338 potentially negatively regulate wnt signalling during oligodendrocyte differentiation.

These miRs display potential targets such as the WNT proteins (miR-138, miR-146a, miR-219 and miR338) and Frizzled (Fzd1; miR-146a) themselves as well as co-receptor lipoprotein receptor-related protein 6 (LRP6; miR-138 and miR-219) and negative regulators Axin1, Dickkopf (DKK; miR-219) and Secreted frizzled related protein 1

(Sfrp1; miR-146a). WNT related functions like axis patterning, cell fate determination and cell proliferation are unnecessary in later development, as such negative regulation is sensible. miR-9 and miR-124, the miRNAs upregulated during development, do not display potential targets in the Wnt signalling pathway.

Across the miRNAs in this study proteins involved in axon guidance have been common potential targets. miR-9, miR-124, miR138, miR-146a and miR-338 all potentially target members of the Ephrin family. Ephrins and their receptors, Eph, are membrane bound.

Contact between Eph and Ephrin ligands and the bidirectional signalling that results is necessary for the control of cytoskeletal dynamics in both axons and dendrites and the development of functional circuits and synapses (Xu and Henkemeyer 2012). In the oligodendrocyte, Ephrins have been shown to be regulators of the intial axo-glio interaction that precedes myelination. The distinct bidirectional signalling exhibited by

Eph and Ephrin has opposite and opposing effects on myelination. Both Epha and Ephb forward signalling have a negative effect on myelin sheath formation whereas ephrin-B reverse signalling induced by EphA4 or EphB1 enhances myelin sheet formation. This 207

suggests that Ephrins may play a role in the selection of which axons are myelinated

(Linneberg et al. 2015). The potential negative modulation of Ephrin and Eph family members by miRNAs during this process adds another layer of complexity to the bidirectional process. Two of the miRNAs, miR-9 and miR-124 are downregulated during oligodendrocyte differentiation but upregulated during early development. Both potentially target Ephrins suggesting a role for Ephrins in early development. Indeed

Eph/ephrin bidirectional signalling appears to regulate the migration of OPCs in the optic axonal tracts to their target locations (Prestoz et al. 2004).

The semaphorin family, and their receptors, the plexins, are another group of proteins involved in axonal guidance that are potential targets of a number of the miRs investigated in this study. miR-138, miR-146a, and miR-338 all have potential targets within the family. In the oligodendrocyte semaphorins are known as inhibitory molecules.

Semaphorin 4D is expressed during myelination and has been shown to have a strongly inhibitory effect on axonal growth (Moreau-Fauvarque et al. 2003). Likewise Semaphorin

6A is highly expressed during myelination. Interestingly Sema6A deficient mice show delayed and disturbed myelination, suggesting that the expression of Sema6A is a necessary part of the myelination process (Bernard et al. 2012). The same group found in an in-vitro study that differentiation of purified oligodendrocytes lacking Sema6A was perturbed and this correlated with a reduction of the expression level of Myelin Basic

Protein. Negative modulation of the semaphorin family during differentiation by miRs like miR-138, miR-146a and miR-338 may play an important role in oligodendrocyte differentiation and later myelination.

This in-silico investigation into the role of microRNAs in oligodendrogliogenesis has yielded a number of potential pathways of particular interest. The roles of miRNAs in oligodendrogliogenesis seem to be diverse and overlapping. Fine tuning of complex cell signalling pathways such as the JNK and NFkB signalling pathways identified here as 208

potential targets, is important for normal oligodendroglial development. As illustrated regulation of spatial and temporal expression can be the difference between migration and myelination in the case of the Ephrin family, and well-timed and spurious neurogenesis in the case of the hypoxia protein Hbegf. miR-9 and miR-124 displayed markedly different enriched pathways to miR-138, miR146a, miR-219, and miR-338, in line with their differential expression during oligodendrocyte development. Pathways common to both, such as the NFkB pathway in the case of miR-9, and the Ephrins, have significant roles to play in both pre and post differentiation. The results were in line with the existing literature on validated targets of the miR’s investigated, in particular in the case of miR-138 and miR-338 and their demonstrated role in HI, suggesting that this study provides an excellent starting point for an investigation of the downstream targets of the miRNAs associated with oligodendrogliogenesis.

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Chapter 8. General Discussion

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Since epidemiological studies first suggested a link between MIA and the occurrence of neurodevelopmental and neuropsychiatric disorders researchers have been attempting to further delineate the critical periods during neurodevelopment at which the adverse effects of MIA are felt. These studies have largely utilised animal models of MIA, which allow researchers to more directly observe the effects of MIA in offspring; with tighter control over variables such as insult type, dose and timing during gestation. This project aimed to investigate both the immediate and longer-term cellular and molecular consequences of MIA on the cytoarchitecture of the developing spinal cord of rats, focusing on a number of cell types and insult time points.

Over the course of this research E16 has emerged as a developmental stage of partiular vulnerability to MIA in the developing rodent spinal cord. More specifically, inducing

MIA at E16 with an i.p injection of 100µg/kg LPS appeared to have an acute effect on oligodendrocytes and microglia of offspring, as well as on the expression of reelin. In sequencing studies MIA at E16 had an acute effect on gene expression in offspring, as well as an effect on gene expression at P14, which differed from that at the 5h time point.

E16 in the rat spinal cord is a period of significant developmental change. Neurogenesis, microglial invasion, astrogliogenesis and oligodendrogliogenesis have all begun (Barry and McDermott 2005; Cai et al. 2005; Gotz and Huttner 2005; Rigato et al. 2011). There is a considerable amount of cell and axonal migration occurring in order to establish the correct neuro-circuitry as well as laying the framework for later processes such as myelination and synaptic refinement. In humans, this time period correlates to roughly midway through the second trimester of pregnancy, a period already identified as potentially vulnerable to MIA in a number of epidemiological studies (Atladottir et al.

2010; A. S. Brown and E. J. Derkits 2010; Patten et al. 2014).

The findings of this project suggests that the acute changes observed in oligodendrocytes, microglia and indeed reelin at E16 are not observed at P14. This could be due to a number 211

of factors, and transience in the effect of MIA has been regularly reported in previous studies (Rousset et al. 2006; M. Makinodan et al. 2008; Rousset et al. 2013). It may simply be the case that the spinal cord has recovered from the insult due to the high plasticity of the developing CNS (Kaas 2001). In the case of microglia it may be that colonisation of the cord was simply delayed by MIA, as these cells arise outside the CNS.

It is also possible that changes in cell number and marker expression observed 5h after

MIA at E16 are masked by synaptic pruning and apoptosis, which occur as part of normal development in the CNS (Riccomagno and Kolodkin 2015). The fact that the acute changes did not persist at P14 could also be the result of decisions made at an experimental level. The use of broad markers such as Olig2 and Iba-1 for example, which allow for cell counting but may not necessarily illustrate the increasingly complex environment of the spinal cord. Cell counts, while extremely robust, may not reflect more delicate changes in the examined cell populations. It is possible that changes in the cord postnatally are simply too subtle for these observation protocols.

Despite the observed transience in the effect of MIA on the spinal cord changes at a cellular level at E16 may have long lasting consequences for neuronal positioning and connectivity, as well as myelination of the mature spinal cord. Although the methods chosen in the immunohistochemical section of this project may not capture it, changes at a cellular level at E16 could facilitate more subtle molecular changes postnatally. Indeed

RNA Seq changes did indicate postnatal molecular changes. These subtle changes in neuronal connectivity, myelination and gene expression could serve to ‘prime’ the CNS to further insult.

The studies contained within this thesis used a spinal cord model to investigate the cellular and molecular changes resulting from MIA to good effect. Using the spinal cord model, it was able to illustrate gross cellular changes as well as molecular changes in response to MIA. The presence of the rostro-caudal gradient of development in the spinal cord at 212

any given embryonic and early postnatal age made it possible to demonstrate the importance and constraints of timing and anatomical location on the effect of MIA on offspring while simultaneously demonstrating the complexity of the CNS response to

MIA in a presumed simple model. Unfortunately, relatively little work has looked at the effect of maternal infection on the human spinal cord in the past. Congenital varicella syndrome, an extremely rare disorder usually resulting from maternal varicella infection at 8-20 weeks gestation, is known to effect the spinal cord, leading to nerve damage and denervation. Following the primary infection the virus is believed to remain dormant in the sensory root ganglion of the spinal cord. Viral inflammation of this ganglion is believed to be the cause of the skin lesions, muscular skeletal deficits, and problems encountered in autonomic nervous system in the periphery as a result of infection(Adams

Waldorf and McAdams 2013). Both cerebral palsy and PVL have known links to maternal infection in the human also. Both effect communication between the motor cortex and the spinal cord leading to spasticity, problems with coordination and sensory processing, and in the case of cerebral palsy, potential upper motor neuron lesion of the corticospinal/pyramidal tracts, however much of the neurological research in these diseases is focused in the brain (Bar-On et al. 2015). Despite this, the rat spinal cord has proven to be a robust model for the investigation of the effect of MIA on the developing

CNS. Many of the observations of normal cellular development in control animals recapitulated previous studies on spinal cord development which is encouraging in relation to the validity of the model.

Acknowledgement of the limitations of experimentation is important, and careful interpretation of results is key to effective scientific enquiry. Accordingly a number of limitations of the studies presented in this thesis must acknowledged. A number of the studies in this thesis were affected by a small sample size, in particular the sequencing studies which used a sample size of three at postnatal time points and a sample size of

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three in embryonic studies where each sample was a pooled litter. This is also true of the

E12 5h reelin immunofluorescence study, in which the saline group number was also three. Ideally, in all studies a higher number of litters would have better served to eliminate the effects of inter-litter variability. The small sample sizes in these studies were multi-factorial in nature but included problems with the availability of tissue, issues with the quantities of embryonic spinal cord tissue recovered during dissection, and time constraints in relation to the preparation and processing of whole spinal cords for immunohistochemical study. Nonetheless, I believe the data produced from the available animals to be robust and informative.

Some may consider the breadth of this study a limitation of its effectiveness. Indeed many studies have investigated the effect of MIA on individual cell types at deeper levels, using more markers and multiple molecular studies. Future studies could perhaps investigate migration of cells in the spinal cord more closely, using BrdU studies to track migrating oligodendrocytes, astrocytes or microglia, or markers of cells at different developmental time points to further elucidate the developmental status of the cells. A transgenic approach to this effect is also a possibility. Few studies, however, have investigated the effect of MIA induced by the same dose of immunostimulant at a single time point during gestation over two survival periods and across multiple cell types. This is the strength of this study, and provides significant insights into what might underpin the potentially deleterious effect of MIA on a whole CNS region such as the spinal cord. Due to commonalities of cell type and molecular control mechanisms during development, such regional effects may well be replicated in other CNS regions or in the CNS as a whole.

Finally as with all experiments utilising animal models one must acknowledge that rodents and humans are not the same. Animal models have, for many years, provided an excellent avenue of investigation for scientific studies, including this one. It is important to note that studies like the time-mating protocol contained within this thesis, and indeed 214

many developmental studies in general, are not possible in humans. The animal models used in these studies provide insight into events which are unobservable in humans, despite this it is necessary to caution against over-interpretation of results observed in animal models in a human context, and to acknowledge that what is true in animals may not necessarily be true in humans.

Much of the research reported here would benefit from future work. In all studies expansion of the litter number to target inter-litter variability in a more meaningful way would be useful for example. It would also be interesting to reintroduce the E14 insult time point to all studies. This time point of investigation was excluded from all but the earliest studies due to constraints of timing and animal availability, however it did display vulnerability to MIA in the early microglial studies. Finally, with higher sample sizes available, future studies would benefit from the use of single-sex groups. This would make this research more comparable to other studies in the field, most of which use single-sex populations. This is important as many studies have illustrated marked sex differences, including a number of MIA studies (Zager et al. 2012; Bergeron et al. 2013;

Lena Wischhof et al. 2015). In the context of neurodevelopmental and neuropsychiatric disorders use of a single sex population is also crucial as many disorders, including autism and schizophrenia occur at higher rates in males.

In relation to each individual study there are a number of further investigations which could be carried out. In the oligodendrocyte study (Chapter 3) I would like to look more closely at the olig2 cell count in whole spinal cord slices, and to investigate oligodendrocyte migration more closely, potentially using timelapse microscopy in slice cultures. I would also like to look at Olig2 and MBP expression using western blot or possibly ELISA protocols. In the postnatal age group I would like to look at other markers of myelin immunofluorescently.

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In chapter 4, the effect of MIA on microglia and astrocytes was investigated. In relation to microglia, it would be useful to investigate the number of Iba-1+ cells in the immediate vicinity of the spinal cord in order to answer the question as to whether a delay in colonisation is responsible for the decreased numbers of Iba-1+ cells in the E16 cord 5h post MIA. In the astrocyte study it would be interesting to introduce other markers to better identify the astrocytic subtypes, for example slit-1 and reelin, which mark specific populations of white matter astrocytes in the spinal cord (Hochstim et al. 2008). In both studies I would like to quantify the activation status of the cells to determine the inflammatory environment of the cord. Both studies would benefit from use of lightsheet or confocal microscopy to do this as it would give a clearer view of the cellular processes,

In chapter 5, the reelin study, it would be interesting to investigate the presence of reelin at E12 using other protocols such as western blot or ELISA, as this time point did not prove amenable to immunofluorescent investigation. It would also be important to identify a method to quantify the reelin expression in the white matter astrocytes of the

P14 cord.

In the sequencing studies of chapter 6 an important factor in future studies would be increasing the sample size, and potentially restricting the study to a single sex, in order to better elucidate the gene expression changes in response to MIA. In relation to the in- silico studies of chapter 7 it would be interesting to look at the effects of miRs on oligodendrogliogenesis in an in-vitro study.

Future studies aside, this thesis has demonstrated that the effects of MIA on the developing spinal cord are robust, measurable, and diverse in nature. It has illustrated the importance of the timing of immune insult, identifying a period of critical vulnerability to MIA in the developing spinal cord, and demonstrated the propensity of the developing

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CNS for recovery, while cautioning to the long lasting and potentially profound consequences of MIA during gestation.

217

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Appendix A

A.1 Embryonic sequencing

A.1.1 Quality control

Figure A.1.1 Microarray samples pre quantile normalisation (A) and post quantile normalisation (B) to reduce non-biological variability.

265

A.1.2 Sequencing Gene Function Reference(s) symbol Tp53 Tumour suppressor which plays a role in (Jain and Barton 2018) apoptosis, genomic stability, and inhibition of angiogenesis. p53 is maintained at low levels in human (h)ESCs to maintain stemness. Activation of p53 leads to rapid differentiation. p53 knockout delays differentiation while exogenous p53 treatment causes spontaneous differentiation. Olr115 Interacts with odourant molecules in the nose to (Gibbs et al. 2004) trigger smell perception. G-protein coupled receptor. E2f4 The E2F family is involved in the control of cell (Hsu and Sage 2016) cycle and action of tumour suppressor proteins. E2f4 binds the tumour supressors pRB, p107 and p130. Mutation of E2f4 and increased expression may be associated with human cancer. LOC100363 Ferritin light chain 1-like. Stores iron in a soluble, (Carmona et al. 2014) 177 non-toxic, readily available form. Important for iron homeostasis. Sf3a2 Encodes subunit 2 of the splicing factor 3a protein (Pellacani et al. 2018) complex which is necessary for the in vitro conversion of 15S U2 snRNP into an active 17S particle that performs pre-mRNA splicing. Subunit 2 may also function as a microtubule- binding protein. S100a11 A member of the S100 family of proteins. S100 (Sakaguchi et al. 2008) proteins are involved in the regulation of a number of cellular processes such as cell cycle progression and differentiation. S100a11 inhibits cell growth when phosphorylated by activating p21.

LOC100359 Ferritin light chain 2. Encodes a protein that 668 exhibits ferric iron binding. Involved in iron ion transport Cherp Responsible for intracellular Ca2+ mobilization (Lin-Moshier et al. and cell growth. Controls Ca2+ release from the 2013) endoplasmic reticulum. Depletion of CHERP results in endoplasmic reticulum stress induced apoptosis. CHERP has been associated with the maintenance of neuroblastoma cell proliferation and tumorigenicity Pomt1 O-mannosylation (glycosylation) of proteins. (Willer et al. 2004; van Found in the membrane of the endoplasmic Reeuwijk et al. 2006) reticulum. Defects in this gene are a cause of Walker-Warburg syndrome (WWS) and limb- girdle muscular dystrophy type 2K (LGMD2K)

266

LOC690507 Similar to Vomeromodulin. Involved in the (Khew-Goodall et al. sensory perception of smell. Also known as 1991) Bpifb5. Fam189b Also known as COTE1. Associated with Gaucher (Kallin et al. 2007; disease. Expression of COTE1 was correlated Zhang et al. 2014) with endogenous SREBP-1 activation in-vitro and may play a role in lipid metabolism . Potential binding partner of a WW domain-containing protein which is involved in tumour suppression. Ftl1 Ferritin light chain 1. Stores iron, maintains iron (Carmona et al. 2014; homeostasis. Delivery of iron to cells. It infers Zarjou et al. 2019) protection against sepsis induced organ injury and inflammation. Fscn1 Member of the fascin family of actin-binding (De Arcangelis et al. proteins. Fascin proteins organize F-actin into 2004; Kraft et al. 2006; parallel bundles and are involved in cell migration, Sonego et al. 2013) motility, adhesion and cellular interactions. Overexpression may play a role in the metastasis of multiple types of cancer by increasing cell motility Clip3 Encodes a member of the cytoplasmic linker (Deng et al. 2012) protein 170 family. Members of this family mediate the interaction of microtubules and cellular organelles. Plays a role in T cell apoptosis by facilitating the association of tubulin and the lipid raft ganglioside GD3. Tbc1d25 Involved in vesicle-mediated transport. Functions (Itoh et al. 2011) as a Rab GTPase activating protein. The encoded protein is involved in the fusion of autophagosomes with endosomes and lysosomes. Srm Spermidine Synthase. Spermidine is a ubiquitous (Lee et al. 2005) polyamine and mediates cell growth and differentiation. Spermidine synthase is one of four enzymes in the polyamine-biosynthetic pathway and carries out the final step of spermidine biosynthesis. This enzyme catalyses the conversion of putrescine to spermidine. LOC100360 Ferritin light chain 1-like. Encodes a protein that (Carmona et al. 2014) 087 exhibits iron ion binding. Involved in iron ion transport and found in the intracellular ferritin complex. Arhgap1 Member of a large family of proteins that activate (Clay and Halloran Rho-type guanosine triphosphate (GTP) 2013) metabolizing enzymes Spetex-2F Spetex2 mRNA is highly expressed in testis and (Chang et al. 2010) spleen. Very little is known about it. It was upregulated in the dorsal root ganglion of rats subject to a complete Freund's adjuvant induced intraplantar inflammation.

267

RGD130545 Also known as Microtubule associated protein (Perez et al. 2019) 5 (map) 11. Has alpha-tubulin binding activity as well as a role in proliferation. Predicted to localise to several cellular components including the microtubule cytoskeleton, midbody, and plasma membrane Ppif Peptidylprolyl Isomerases catalyse the cis-trans (Baines et al. 2005) isomerization of proline imidic peptide bonds in oligopeptides and accelerate protein folding. Activates oxidative stress-induced necrosis in cooperation with mitochondrial Tp53. Emd Member of the nuclear lamina-associated protein (Muntoni et al. 2006; family. Mediates membrane anchorage to the Chang et al. 2013) cytoskeleton. Stabilises and promotes the formation of a nuclear actin cortical network, linking centrosomes to the nuclear envelope via microtubule association. Dreifuss-Emery muscular dystrophy is the result of a mutation of Emd. Rab11b Member RAS Oncogene Family. Rab family (Vernoud et al. 2003) proteins play a critical role in regulating exocytotic and endocytotic pathways, as well as intracellular membrane trafficking, from the formation of transport vesicles to their fusion with membranes Pcsk1n Functions as an inhibitor of prohormone (Liu et al. 2012) convertase 1, which regulates the proteolytic cleavage of neuroendocrine peptide precursors. Lmf2 Involved in the maturation of specific proteins in (Xu et al. 2017) the endoplasmic reticulum. Possibly required for maturation and transport of active lipoprotein lipase (LPL) through the secretory pathway. Mef2d Member of myocyte-specific enhancer factor 2 (Yao et al. 2012) (MEF2) family of transcription factors. Involved in control of muscle and neuronal cell differentiation and development. Regulated by class II histone deacetylases it plays a critical role in neuronal apoptosis and has been linked to Parkinson's disease. Wars Tryptophanyl-TRNA Synthetase. Aminoacyl- (Jin 2019) tRNA synthetases catalyze the aminoacylation of tRNA by their cognate amino acid. It appears to play a role in immune control. Ubl7 Ubiquitination. Affects proteins in many ways. (Kirkin and Dikic 2007) Can mark them for degradation via the proteasome, alter their cellular location, affect their activity, and promote or prevent protein interactions.

268

Ctc1 A compnent of the telomere replication complex. (Stewart et al. 2018) Promotes DNA replication under conditions of replication stress or natural barriers to replication such as teleomere duplex. Inhibits telomerase maintaining teleomere length. Tox Thymocyte Selection Associated High Mobility (Aliahmad et al. 2011) Group Box. The protein encoded by this gene contains a HMG box DNA binding domain involved in chromatin assembly, transcription and replication. May function to regulate T-cell development. Med25 Mediator complex subunit 25. Encodes a (Lee et al. 2007; Rana component of the transcriptional co-activator et al. 2011) known as the mediator complex. Required for the transcription of most of the RNA Pol II-dependent genes. Mutations are associated with Charcot Marie Tooth disease Dpf2 Transcription factor necessary for apoptotic (Gabig et al. 1994) response following deprivation of survival factors. Regulates rapid hematopoietic cell growth and turnover. It binds RNA polymerase II core promoter proximal region. LOC100362 Ferritin. Stores iron in a soluble, non-toxic, readily (Carmona et al. 2014) 384 available form. Important for iron homeostasis. LOC683674 Similar to Protein C7orf26 homolog. Well (Boeing et al. 2016) conserved. It interacts with RNA polymerase II and the Integrator complex, which regulates a number of aspects of gene transcription. Bcas3 Microtubule Associated Cell Migration Factor. (Jain et al. 2012) Participates in the regulation of cell polarity and directional endothelial cell migration. Promotes filipodia formation. Functions synergistically with PELP1 as a transcriptional coactivator of estrogen receptor-responsive genes. Stimulates histone acetyltransferase activity. Binds chromatin.

Ccdc6 May function as a tumour suppressor. (Morra et al. 2015) Chromosomal rearrangement resulting in the expression of a fusion gene containing a portion of this gene and the intracellular kinase-encoding domain of the ret proto-oncogene is the causes thyroid papillary carcinoma. Nrbp2 May regulate apoptosis of neural progenitor cells (Larsson et al. 2008) during differentiation. Downregulation did not affect cell fate but did affect the amount of apoptosis occurring so Nrbp2 may be involved in cell survival. Expression is temporally and spatially regulated during development.

269

Celf3 CUGBP Elav-Like Family Member 3. Regulates (Ladd et al. 2001) pre-mRNA alternative splicing and may also be involved in mRNA editing, and translation. Midn Facilitates ubiquitin-independent proteasomal (Tsukahara et al. 2000; degradation of polycomb protein CBX4. Has been Obara et al. 2019) associated with familial Parkinson's disease in both a Japanese and British cohort. MIDN regulates neurite outgrowth and Parkin expression in neuronal cells. Pcgf2 Polycomb group gene products form complexes (Zakrzewska et al. via protein-protein interaction and maintain the 2011) transcriptional repression of genes involved in embryogenesis, cell cycles, and tumorigenesis. The expression of this gene is limited in neural organs in normal tissues. Knockout studies in mice have suggested that this protein may negatively regulate the expression of different cytokines, chemokines, and chemokine receptors.

Atf7 Plays a role in early cell signalling by binding (Vinson et al. 2002; cAMP response element (CRE) – present in many Maekawa et al. 2018) cellular promotors. Has no intrinsic transcriptional activity, but activates transcription on formation of JUN or FOS heterodimers. Also binds TRE promoter sequences when heterodimerized with JUN family members. Involved in SMAD Signaling Network and TNFR1 Pathway. Cnot3 Component of the CCR4-NOT complex which is (Zheng et al. 2012) one of the major cellular mRNA deadenylases. Involved in bulk mRNA degradation, miRNA- mediated repression, translational repression and general transcription regulation. Involed in the maintenance of ESC identity. Downregulation causes ESC differentiation.

Table A.1.1 Synopsis of function - genes differentially regulated 5hr post MIA with

100µg/kg LPS at E16.

270

A.2 Postnatal Sequencing

A.2.1 Quality control

Figure A.2.1 – Q scores for RNA Seq. All samples were >99.9% accurate where Q > 30

= 99.9% accurate or ~1 in 1000 bases miscalled.

Figure A.2.2 – (A) N content across all bases. Where N = undetermined base. The graph shows the relative number of undetermined bases when compared to the overall number of bases called at each position in the reads derived from the forward primer. (B) Quality scores across all bases. Per base sequence quality plot generated for the forward read.

271

Figure A.2.3 Sample to sample heat map indicative of similarity between samples in the RNA Seq study.

272

A.2.2 Sequencing

Figure A.2.4. MA plot of the mean of the gene expression changes in offspring spinal cords at P14 following MIA at E12 with 100µg/kg LPS. M = log ratio, A = mean of the normalised count. No genes were differentially expressed at this time point.

273

Gene Function Reference(s) symbol Cat A key antioxidant enzyme. Protects against (Faust et al. 2005; oxidative stress. Found in the peroxisome of Berger et al. 2016) nearly all aerobic cells. Catalase converts hydrogen peroxide to water and oxygen. Txnip Major regulator of cellular redox signalling (Alhawiti et al. 2017) which protects cells from oxidative stress. Inhibits the antioxidative function of thioredoxin resulting in the accumulation ROS and cellular stress. Pmvk Peroxisomal enzyme involved in isoprenoid (Lange et al. 2000) biosynthesis. The encoded protein catalyzes the conversion of mevalonate 5-phosphate to mevalonate 5-diphosphate, which is the fifth step in the mevalonate pathway of isoprenoid biosynthesis. Pex11a A membrane elongation factor involved in (Sacksteder et al. regulation of peroxisome maintenance and 2000) proliferation. This gene product interacts with PEX19 and may respond to outside stimuli to increase peroxisome abundance. Spag7 Function relatively unknown.

Idi1 Peroxisomal enzyme involved in isoprenoid (Lange et al. 2000) biosynthesis.The encoded protein catalyses the interconversion of isopentenyl diphosphate (IPP) to its isomer, dimethylallyl diphosphate (DMAPP). Pxmp4 Component of the peroxisome membrane. (Sacksteder et al. Exact function unknown. Known to bind 2000) PEX19. Scara3 Macrophage scavenger receptor-like protein. Shown to deplete reactive oxygen species.. The expression of this gene is induced by oxidative stress Kif19 A microtubule-dependent motor protein. (Niwa et al. 2012) Regulates the length of motile cilia by mediating depolymerisation of microtubules at ciliary tips. Fzd9 Receptor for Wnt signaling proteins. Negative (Ranheim et al. 2005) regulator of neuromuscular junction (NMJ) assembly through the beta-catenin canonical signaling pathway. Role in NPC viability through the beta-catenin canonical signaling pathway by negatively regulating cell cycle arrest leading to inhibition of neuron apoptotic process. Snca Member of the synuclein family. Abundantly (Burré et al. 2018) expressed in the brain. Defects in SNCA have been implicated in the pathogenesis of Parkinson disease. SNCA peptides are a major 274

component of amyloid plaques in AD. Induces fibrillisation of microtubule-associated protein tau. S1pr1 Binds the ligand sphingosine-1-phosphate. (Jiang et al. 2017) Activation of this receptor induces cell-cell adhesion. Plays an important role in cell migration, likely due to a role in the reorganisation of the actin cytoskeleton and the formation of lamellipodia in response to stimuli that increase the activity of the sphingosine kinase (SPHK1). Mcam Role in cell adhesion. May function as a cell (Motohashi et al. adhesion molecule active in neural crest cells 2016) during embryonic development. Pla2g3 The encoded enzyme functions in lipid (Gijs et al. 2015) metabolism. A negative regulator of ciliogenesis, Associated with oxidative stress. Polymorphisms in this gene are linked to risk for Alzheimer's disease Ppp1r10 Roles in cell cycle progression, DNA repair (Fisher et al. 2014) and apoptosis by regulating the activity of protein phosphatase 1. Emcn Interferes with the assembly of focal adhesion (Zahr et al. 2016) complexes. Inhibits cell-ECM interaction. Table A.2.1 Synopsis of function - genes differentially regulated at P14 following

MIA with 100µg/kg LPS at E16.

275

Appendix B

miR-9 Gene symbol Gene Ssh2 slingshot homolog 2 (Drosophila) Foxa2 forkhead box A2 Golga2 golgin A2 Scrib scribbled homolog (Drosophila) Rtn1 reticulon 1 Cops2 COP9 constitutive photomorphogenic homolog subunit 2 (Arabidopsis) Cd24 CD24 molecule Arl3 ADP-ribosylation factor-like 3 Tnn tenascin N Smarca4 SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily a, member 4 Oprm1 opioid receptor, mu 1 Exoc4 exocyst complex component 4 Ctf1 cardiotrophin 1 Uhmk1 U2AF homology motif (UHM) kinase 1 Olig2 oligodendrocyte lineage transcription factor 2 Ube2v2 ubiquitin-conjugating enzyme E2 variant 2 Celsr3 cadherin, EGF LAG seven-pass G-type receptor 3 (flamingo homolog, Drosophila) Drd2 dopamine receptor D2 Ank3 ankyrin 3, node of Ranvier Fas Fas (TNF receptor superfamily, member 6) Shc1 SHC (Src homology 2 domain containing) transforming protein 1 Snph syntaphilin Cdh1 cadherin 1 Ext1 exostosin 1 Gnat1 guanine nucleotide binding protein (G protein), alpha transducing activity polypeptide 1 Calu calumenin Ndrg1 N-myc downstream regulated 1 Lamc1 laminin, gamma 1 Hhip Hedgehog-interacting protein Ube4b ubiquitination factor E4B Gas7 growth arrest specific 7 Dynlt1 dynein light chain Tctex-type 1 Gsx2 GS homeobox 2 Stk24 serine/threonine kinase 24 Socs2 suppressor of cytokine signaling 2 Pax2 paired box 2 Tal1 T-cell acute lymphocytic leukemia 1 Synj1 synaptojanin 1 Sod2 superoxide dismutase 2, mitochondrial 276

Cln5 ceroid-lipofuscinosis, neuronal 5 Ppp2r3a protein phosphatase 2, regulatory subunit B'', alpha Epha4 Eph receptor A4 Cdc42 cell division cycle 42 (GTP binding protein) Adm adrenomedullin Cntn2 contactin 2 (axonal) Gdf11 growth differentiation factor 11 Dpysl5 dihydropyrimidinase-like 5 Lzts1 leucine zipper, putative tumor suppressor 1 Uqcrq ubiquinol-cytochrome c reductase, complex III subunit VII Sema5a sema domain, seven thrombospondin repeats (type 1 and type 1-like), transmembrane domain (TM) and short cytoplasmic domain, (semaphorin) 5A Efna1 ephrin A1 Prickle2 prickle homolog 2 (Drosophila) Pvrl1 poliovirus receptor-related 1 Tcf7 transcription factor 7 (T-cell specific, HMG-box) Foxg1 forkhead box G1 Dmd dystrophin Kcnma1 potassium large conductance calcium-activated channel, subfamily M, alpha member 1 Lyn v-yes-1 Yamaguchi sarcoma viral related oncogene homolog Foxa1 forkhead box A1 LOC689826 hypothetical protein LOC689826 Nlgn1 neuroligin 1 Ctdsp1 CTD (carboxy-terminal domain, RNA polymerase II, polypeptide A) small phosphatase 1 Psd2 pleckstrin and Sec7 domain containing 2 Klf7 Kruppel-like factor 7 (ubiquitous) Lmo4 LIM domain only 4 Cnp 2',3'-cyclic nucleotide 3' phosphodiesterase Pmp22 peripheral myelin protein 22 Hdac11 histone deacetylase 11 Nrcam neuronal cell adhesion molecule Prdm6 PR domain containing 6 Eomes eomesodermin Nr4a3 nuclear receptor subfamily 4, group A, member 3 Ulk2 Unc-51 like kinase 2 (C. elegans) Ptprm protein tyrosine phosphatase, receptor type, M Dgkg diacylglycerol kinase, gamma Tcf12 transcription factor 12 Grm4 glutamate receptor, metabotropic 4 Sall3 sal-like 3 (Drosophila) Aspa aspartoacylase Ppp1r9b protein phosphatase 1, regulatory subunit 9B Bbs4 Bardet-Biedl syndrome 4 Mmp2 matrix metallopeptidase 2 277

Gpc1 glypican 1 Twist1 twist homolog 1 (Drosophila) Nkx2-1 NK2 homeobox 1 L1cam L1 cell adhesion molecule Fgf13 fibroblast growth factor 13 Nbn nibrin Ep300 E1A binding protein p300 Six4 SIX homeobox 4 Cxcl12 chemokine (C-X-C motif) ligand 12 Mapk8 mitogen-activated protein kinase 8 Nab1 Ngfi-A binding protein 1 Itm2c integral membrane protein 2C Cspg4 chondroitin sulfate proteoglycan 4 RGD1305560 similar to RIKEN cDNA 9430031J16 Robo2 roundabout homolog 2 (Drosophila) Lhx6 LIM homeobox 6 Nphp1 nephronophthisis 1 (juvenile) homolog (human) Prkci protein kinase C, iota Tnr tenascin R (restrictin, janusin) Myo16 myosin XVI Hmg20a high mobility group 20A Grn granulin Ngfr nerve growth factor receptor Vcan versican Lhx5 LIM homeobox 5 Slc1a3 solute carrier family 1 (glial high affinity glutamate transporter), member 3 Hipk1 homeodomain interacting protein kinase 1 Ctnna1 catenin (cadherin-associated protein), alpha 1, 102kDa Mtpn myotrophin Esr2 estrogen receptor 2 (ER beta) Epo erythropoietin Ppp1cc protein phosphatase 1, catalytic subunit, gamma isozyme Mdga1 MAM domain containing glycosylphosphatidylinositol anchor 1 Actr3 ARP3 actin-related protein 3 homolog (yeast) Cln8 ceroid-lipofuscinosis, neuronal 8 (epilepsy, progressive with mental retardation) Plxnb1 plexin B1 Foxb1 forkhead box B1 Dcx doublecortin Ret ret proto-oncogene Snap25 synaptosomal-associated protein 25 Mycn v-myc myelocytomatosis viral related oncogene, neuroblastoma derived (avian) Nrl neural retina leucine zipper Cdkn1c cyclin-dependent kinase inhibitor 1C Slc6a4 solute carrier family 6 (neurotransmitter transporter, serotonin), member 4

278

Bbs1 Bardet-Biedl syndrome 1 Cthrc1 collagen triple helix repeat containing 1 Mdga2 MAM domain containing glycosylphosphatidylinositol anchor 2 Cdh11 cadherin 11 Ephb2 Eph receptor B2 Galr2 galanin receptor 2 Cntn4 contactin 4 Pou3f1 POU class 3 homeobox 1 Nptx1 neuronal pentraxin I Pafah1b1 platelet-activating factor acetylhydrolase, isoform 1b, subunit 1 Cntnap1 contactin associated protein 1 Prrxl1 paired related homeobox protein-like 1 P2ry2 purinergic receptor P2Y, G-protein coupled, 2 Caprin1 cell cycle associated protein 1 Lmx1a LIM homeobox transcription factor 1 alpha Tgfbr1 transforming growth factor, beta receptor 1 Ttc3 tetratricopeptide repeat domain 3 Bok BCL2-related ovarian killer Cdon Cdon homolog (mouse) Hes5 hairy and enhancer of split 5 (Drosophila) Cxcr4 chemokine (C-X-C motif) receptor 4 Ncam1 neural cell adhesion molecule 1 Hmbs hydroxymethylbilane synthase En1 engrailed homeobox 1 Bcl2 B-cell CLL/lymphoma 2 Rarb retinoic acid receptor, beta Dst dystonin Gad1 glutamate decarboxylase 1 Dag1 dystroglycan 1 (dystrophin-associated glycoprotein 1) Wdr62 WD repeat domain 62 Rnf6 ring finger protein (C3H2C3 type) 6 Celsr1 cadherin, EGF LAG seven-pass G-type receptor 1 (flamingo homolog, Drosophila) Sema6a sema domain, transmembrane domain (TM), and cytoplasmic domain, (semaphorin) 6A Kirrel3 kin of IRRE like 3 (Drosophila) Bdnf brain-derived neurotrophic factor Jak2 Janus kinase 2 Slitrk6 SLIT and NTRK-like family, member 6 Efna3 ephrin A3 Pld2 phospholipase D2 Ak1 adenylate kinase 1 Tacc1 transforming, acidic coiled-coil containing protein 1 Apbb2 amyloid beta (A4) precursor protein-binding, family B, member 2 Ptpru protein tyrosine phosphatase, receptor type, U Gata2 GATA binding protein 2

279

Trpv2 transient receptor potential cation channel, subfamily V, member 2 Btg2 BTG family, member 2 Dclk1 doublecortin-like kinase 1 Syne1 spectrin repeat containing, nuclear envelope 1 Rpgrip1 retinitis pigmentosa GTPase regulator interacting protein 1 Etv4 ets variant 4 Slitrk5 SLIT and NTRK-like family, member 5 Sclt1 sodium channel and clathrin linker 1 Sfrp4 secreted frizzled-related protein 4 P2ry1 purinergic receptor P2Y, G-protein coupled, 1 Dner delta/notch-like EGF repeat containing Ccdc88a coiled coil domain containing 88A Dnmt3b DNA (cytosine-5-)-methyltransferase 3 beta Acsl4 acyl-CoA synthetase long-chain family member 4 Itgb1 integrin, beta 1 Dixdc1 DIX domain containing 1 Nlgn3 neuroligin 3 Top2a topoisomerase (DNA) II alpha Sox10 SRY-related HMG-box 10

Table B.1: Predicted targets of miR-9 GO category ‘Neurogenesis’ C=1033; O=189;

E=133.20; R=1.42; rawP=1.69e-07; adjP=2.61e-05. ( C = the number of reference genes in the category, O= the number of genes in the gene set and also in the category, E= the expected number in the category, R = ratio of enrichment, rawP = raw P-value from hypergeometric test, and adjP = P-value adjusted by the multiple test adjustment).

280

miR-124 Gene Gene symbol Palm paralemmin Prkd1 protein kinase D1 Foxa2 forkhead box A2 Snapap SNAP-associated protein Golga2 golgin A2 Efnb1 ephrin B1 Sphk1 sphingosine kinase 1 Plp1 proteolipid protein 1 Lhx6 LIM homeobox 6 Foxa3 forkhead box A3 Cd24 CD24 molecule Gja1 gap junction protein, alpha 1 Hmg20a high mobility group 20A Jag1 jagged 1 Kcnip2 Kv channel-interacting protein 2 Adnp2 ADNP homeobox 2 Ssh3 slingshot homolog 3 (Drosophila) Ube2v2 ubiquitin-conjugating enzyme E2 variant 2 Ptprr protein tyrosine phosphatase, receptor type, R Shc1 SHC (Src homology 2 domain containing) transforming protein 1 Wee1 wee 1 homolog (S. pombe) Vcan versican Calu calumenin Lamc1 laminin, gamma 1 Nrg1 neuregulin 1 Myh10 myosin, heavy chain 10, non-muscle Hipk1 homeodomain interacting protein kinase 1 Hoxa2 homeo box A2 Clrn1 clarin 1 Hipk2 homeodomain interacting protein kinase 2 Nkx2-5 NK2 homeobox 5 Cdh2 cadherin 2 Ptk6 PTK6 protein tyrosine kinase 6 Pldn pallidin homolog (mouse) Cln8 ceroid-lipofuscinosis, neuronal 8 (epilepsy, progressive with mental retardation) Ighmbp2 immunoglobulin mu binding protein 2 Fzd1 frizzled family receptor 1 Lpar1 lysophosphatidic acid receptor 1 Stk24 serine/threonine kinase 24 Tal1 T-cell acute lymphocytic leukemia 1 Stat3 signal transducer and activator of transcription 3 (acute-phase response factor)

281

Smarca1 SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily a, member 1 Dact1 dapper, antagonist of beta-catenin, homolog 1 (Xenopus laevis) Dlx5 distal-less homeobox 5 Myo6 myosin VI Cln5 ceroid-lipofuscinosis, neuronal 5 Rora RAR-related orphan receptor A Ptprz1 protein tyrosine phosphatase, receptor-type, Z polypeptide 1 Vim vimentin Ache acetylcholinesterase Trpv4 transient receptor potential cation channel, subfamily V, member 4 Dpysl5 dihydropyrimidinase-like 5 Napa N-ethylmaleimide-sensitive factor attachment protein, alpha Lzts1 leucine zipper, putative tumor suppressor 1 Cntf ciliary neurotrophic factor Erbb2 v-erb-b2 erythroblastic leukemia viral oncogene homolog 2, neuro/glioblastoma derived oncogene homolog (avian) Xrcc6 X-ray repair complementing defective repair in Chinese hamster cells 6 Sema5a sema domain, seven thrombospondin repeats (type 1 and type 1-like), transmembrane domain (TM) and short cytoplasmic domain, (semaphorin) 5A Agt angiotensinogen (serpin peptidase inhibitor, clade A, member 8) Klf15 Kruppel-like factor 15 Nf1 neurofibromin 1 Gpm6a glycoprotein m6a Pafah1b1 platelet-activating factor acetylhydrolase, isoform 1b, subunit 1 Ezh2 enhancer of zeste homolog 2 (Drosophila) Pax3 paired box 3 Tgfbr1 transforming growth factor, beta receptor 1 Jag2 jagged 2 Gsn gelsolin Cdon Cdon homolog (mouse) Sgk1 serum/glucocorticoid regulated kinase 1 Egr2 early growth response 2 Ctdsp1 CTD (carboxy-terminal domain, RNA polymerase II, polypeptide A) small phosphatase 1 Psd2 pleckstrin and Sec7 domain containing 2 Lmo4 LIM domain only 4 Pex5 peroxisomal biogenesis factor 5 Bcl11b B-cell CLL/lymphoma 11B (zinc finger protein) Dag1 dystroglycan 1 (dystrophin-associated glycoprotein 1) Rnf6 ring finger protein (C3H2C3 type) 6 Celsr1 cadherin, EGF LAG seven-pass G-type receptor 1 (flamingo homolog, Drosophila) Nrcam neuronal cell adhesion molecule Slitrk6 SLIT and NTRK-like family, member 6 Flot1 flotillin 1 Otx2 orthodenticle homeobox 2

282

Sema4f sema domain, immunoglobulin domain (Ig), transmembrane domain (TM) and short cytoplasmic domain, (semaphorin) 4F Efna3 ephrin A3 Gnao1 guanine nucleotide binding protein (G protein), alpha activating activity polypeptide O Stmn2 stathmin-like 2 Foxe1 forkhead box E1 (thyroid transcription factor 2) Tsku tsukushi small leucine rich proteoglycan homolog (Xenopus laevis) Lamc3 laminin gamma 3 Apbb2 amyloid beta (A4) precursor protein-binding, family B, member 2 Btg2 BTG family, member 2 Nr4a3 nuclear receptor subfamily 4, group A, member 3 Pcsk1 proprotein convertase subtilisin/kexin type 1 Bmpr1a bone morphogenetic protein receptor, type IA Fgfr3 fibroblast growth factor receptor 3 Epha2 Eph receptor A2 Clcf1 cardiotrophin-like cytokine factor 1 Ppp1r9b protein phosphatase 1, regulatory subunit 9B Arhgap4 Rho GTPase activating protein 4 Nde1 nudE nuclear distribution gene E homolog 1 (A. nidulans) Rela v-rel reticuloendotheliosis viral oncogene homolog A (avian) Slitrk5 SLIT and NTRK-like family, member 5 Sox8 SRY (sex determining region Y)-box 8 Axin1 axin 1 Gpc1 glypican 1 Sfrp4 secreted frizzled-related protein 4 Slit1 slit homolog 1 (Drosophila) Kdr kinase insert domain receptor Gfap glial fibrillary acidic protein Itgb1 integrin, beta 1 Ccdc88a coiled coil domain containing 88A Cdkn2c cyclin-dependent kinase inhibitor 2C (p18, inhibits CDK4) Ndn necdin homolog (mouse) Six4 SIX homeobox 4 Cxcl12 chemokine (C-X-C motif) ligand 12 Sema3f sema domain, immunoglobulin domain (Ig), short basic domain, secreted, (semaphorin) 3 F Dll4 delta-like 4 (Drosophila) Itm2c integral membrane protein 2C

Table B.2: Predicted targets of miR-124 GO category ‘Neurogenesis’. C=1033;

O=119; E=75.44; R=1.58; rawP=2.63e-07; adjP=4.54e-05. (C = the number of reference genes in the category, O= the number of genes in the gene set and also in the category,

283

E= the expected number in the category, R = ratio of enrichment, rawP = raw P-value from hypergeometric test, and adjP = P-value adjusted by the multiple test adjustment).

284

miR-138 Gene Symbol Gene Bak1 BCL2-antagonist/killer 1 Cdh22 cadherin 22 Ssh2 slingshot homolog 2 (Drosophila) Cfl1 cofilin 1, non-muscle Nnat neuronatin Ngef neuronal guanine nucleotide exchange factor Sox9 SRY-box containing gene 9 Men1 multiple endocrine neoplasia I Gfra1 GDNF family receptor alpha 1 Scn5a sodium channel, voltage-gated, type V, alpha subunit Fmr1 fragile X mental retardation 1 Tnn tenascin N Smarca4 SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily a, member 4 Gja1 gap junction protein, alpha 1 Exoc4 exocyst complex component 4 Cdk5 cyclin-dependent kinase 5 Alx1 ALX homeobox 1 Prkch protein kinase C, eta Celsr3 cadherin, EGF LAG seven-pass G-type receptor 3 (flamingo homolog, Drosophila) Fas Fas (TNF receptor superfamily, member 6) Ext1 exostosin 1 Gnat1 guanine nucleotide binding protein (G protein), alpha transducing activity polypeptide 1 Nkx6-1 NK6 homeobox 1 Ndrg1 N-myc downstream regulated 1 Lamc1 laminin, gamma 1 Eif2b2 eukaryotic translation initiation factor 2B, subunit 2 beta Hbegf heparin-binding EGF-like growth factor Myh10 myosin, heavy chain 10, non-muscle Camk2b calcium/calmodulin-dependent protein kinase II beta Sema4d sema domain, immunoglobulin domain (Ig), transmembrane domain (TM) and short cytoplasmic domain, (semaphorin) 4D Nrxn2 neurexin 2 Bcan brevican Gas7 growth arrest specific 7 Mapk8ip3 mitogen-activated protein kinase 8 interacting protein 3 Dynlt1 dynein light chain Tctex-type 1 Id4 inhibitor of DNA binding 4 Stk24 serine/threonine kinase 24 Shox2 short stature homeobox 2 Dicer1 dicer 1, ribonuclease type III Map3k7 mitogen activated protein kinase kinase kinase 7 Mbp myelin basic protein 285

Rab23 RAB23, member RAS oncogene family Ppard peroxisome proliferator-activated receptor delta Cln5 ceroid-lipofuscinosis, neuronal 5 Slc7a11 solute carrier family 7 (anionic amino acid transporter light chain, xc- system), member 11 Epha4 Eph receptor A4 Ank2 ankyrin 2, neuronal Pllp plasmolipin Gfra2 GDNF family receptor alpha 2 Cbfa2t2 core-binding factor, runt domain, alpha subunit 2; translocated to, 2 Cntn2 contactin 2 (axonal) Aplp2 amyloid beta (A4) precursor-like protein 2 Prom1 prominin 1 Dpysl5 dihydropyrimidinase-like 5 Sncg synuclein, gamma (breast cancer-specific protein 1) Chrdl1 chordin-like 1 Sema4a sema domain, immunoglobulin domain (Ig), transmembrane domain (TM) and short cytoplasmic domain, (semaphorin) 4A Uqcrq ubiquinol-cytochrome c reductase, complex III subunit VII Afg3l2 AFG3 ATPase family gene 3-like 2 (S. cerevisiae) Uchl1 ubiquitin carboxyl-terminal esterase L1 (ubiquitin thiolesterase) Nid67 putative small membrane protein NID67 Sema5a sema domain, seven thrombospondin repeats (type 1 and type 1-like), transmembrane domain (TM) and short cytoplasmic domain, (semaphorin) 5A Pou4f3 POU class 4 homeobox 3 Pgap1 post-GPI attachment to proteins 1 Cdk5rap3 CDK5 regulatory subunit associated protein 3 Pgrmc1 progesterone receptor membrane component 1 Prickle2 prickle homolog 2 (Drosophila) Nlgn2 neuroligin 2 Hif1a hypoxia-inducible factor 1, alpha subunit (basic helix-loop-helix transcription factor) Tcf7 transcription factor 7 (T-cell specific, HMG-box) Pax3 paired box 3 Stk4 serine/threonine kinase 4 Wnt10b wingless-type MMTV integration site family, member 10B Npap60 nuclear pore associated protein Kcnma1 potassium large conductance calcium-activated channel, subfamily M, alpha member 1 Foxa1 forkhead box A1 Limk1 LIM domain kinase 1 Ntrk2 neurotrophic tyrosine kinase, receptor, type 2 Pitx1 paired-like homeodomain 1 Pura purine rich element binding protein A Lrp6 low density lipoprotein receptor-related protein 6 Psd2 pleckstrin and Sec7 domain containing 2

286

Klf7 Kruppel-like factor 7 (ubiquitous) Celsr2 cadherin, EGF LAG seven-pass G-type receptor 2 (flamingo homolog, Drosophila) Mobp myelin-associated oligodendrocyte basic protein Tbx19 T-box 19 Ptk2 PTK2 protein tyrosine kinase 2 Pip5k1c phosphatidylinositol-4-phosphate 5-kinase, type I, gamma Lphn1 latrophilin 1 Arhgef2 rho/rac guanine nucleotide exchange factor (GEF) 2 Wnt3a wingless-type MMTV integration site family, member 3A Rrm1 ribonucleotide reductase M1 Tsku tsukushi small leucine rich proteoglycan homolog (Xenopus laevis) Lamc3 laminin gamma 3 Nova1 neuro-oncological ventral antigen 1 Tcf3 transcription factor 3 Nrxn3 neurexin 3 Nr4a3 nuclear receptor subfamily 4, group A, member 3 Ak7 adenylate kinase 7 Dgkg diacylglycerol kinase, gamma Rara retinoic acid receptor, alpha Grm4 glutamate receptor, metabotropic 4 Ppp1r9b protein phosphatase 1, regulatory subunit 9B Ina internexin neuronal intermediate filament protein, alpha Arhgap4 Rho GTPase activating protein 4 Nde1 nudE nuclear distribution gene E homolog 1 (A. nidulans) Rela v-rel reticuloendotheliosis viral oncogene homolog A (avian) Sox8 SRY (sex determining region Y)-box 8 Pde5a phosphodiesterase 5A, cGMP-specific Foxp3 forkhead box P3 Sstr3 somatostatin receptor 3 Nr3c1 nuclear receptor subfamily 3, group C, member 1 Axin1 axin 1 Gpc1 glypican 1 Ipmk inositol polyphosphate multikinase Nkx2-1 NK2 homeobox 1 Ahsg alpha-2-HS-glycoprotein Gfap glial fibrillary acidic protein Kit v-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog Cxcl12 chemokine (C-X-C motif) ligand 12 Mapk8 mitogen-activated protein kinase 8 Atg7 autophagy related 7 Palm paralemmin RGD1305560 similar to RIKEN cDNA 9430031J16 Sox4 SRY (sex determining region Y)-box 4 Atxn2 ataxin 2 Lhx6 LIM homeobox 6

287

Inppl1 inositol polyphosphate phosphatase-like 1 Ncoa6 nuclear receptor coactivator 6 Epha8 Eph receptor A8 Tnr tenascin R (restrictin, janusin) Hmg20a high mobility group 20A Dbn1 drebrin 1 Gbx2 gastrulation brain homeobox 2 Timp4 tissue inhibitor of metalloproteinase 4 Dlc1 deleted in liver cancer 1 Gabrb1 gamma-aminobutyric acid (GABA) A receptor, beta 1 Tmem126a transmembrane protein 126A Eif2b5 eukaryotic translation initiation factor 2B, subunit 5 epsilon Luzp1 leucine zipper protein 1 Kremen1 kringle containing transmembrane protein 1 Sox12 SRY (sex determining region Y)-box 12 Vcan versican Lhx5 LIM homeobox 5 Slc1a3 solute carrier family 1 (glial high affinity glutamate transporter), member 3 Srd5a1 steroid-5-alpha-reductase, alpha polypeptide 1 (3-oxo-5 alpha-steroid delta 4- dehydrogenase alpha 1) Adnp activity-dependent neuroprotector homeobox Prkdc protein kinase, DNA activated, catalytic polypeptide Tpp1 tripeptidyl peptidase I Akr1c14 aldo-keto reductase family 1, member C14 Cdh2 cadherin 2 Scn3b sodium channel, voltage-gated, type III, beta Sh2b2 SH2B adaptor protein 2 Synj2 synaptojanin 2 Mal mal, T-cell differentiation protein Hrh3 histamine receptor H3 Ncdn neurochondrin Rb1 retinoblastoma 1 Plxnb1 plexin B1 Prox1 prospero homeobox 1 Dcx doublecortin Snap25 synaptosomal-associated protein 25 Kidins220 kinase D-interacting substrate 220 Pcsk2 proprotein convertase subtilisin/kexin type 2 Cdkn1c cyclin-dependent kinase inhibitor 1C Bbs1 Bardet-Biedl syndrome 1 Casp3 caspase 3 Chn1 chimerin (chimaerin) 1 Vim vimentin Napa N-ethylmaleimide-sensitive factor attachment protein, alpha Plag1 pleiomorphic adenoma gene 1 Nptx1 neuronal pentraxin I

288

Slc11a2 solute carrier family 11 (proton-coupled divalent metal ion transporters), member 2 Rgma RGM domain family, member A Cttn cortactin Pafah1b1 platelet-activating factor acetylhydrolase, isoform 1b, subunit 1 Olig1 oligodendrocyte transcription factor 1 Unc5c unc-5 homolog C (C. elegans) Zeb1 zinc finger E-box binding homeobox 1 P2ry2 purinergic receptor P2Y, G-protein coupled, 2 Ezh2 enhancer of zeste homolog 2 (Drosophila) Zhx2 zinc fingers and homeoboxes 2 Caprin1 cell cycle associated protein 1 Tgfbr1 transforming growth factor, beta receptor 1 Ptprf protein tyrosine phosphatase, receptor type, F Rbp3 retinol binding protein 3, interstitial Htr5a 5-hydroxytryptamine (serotonin) receptor 5A, G protein-coupled Foxp2 forkhead box P2 Clic5 chloride intracellular channel 5 Smad1 SMAD family member 1 Robo3 roundabout homolog 3 (Drosophila) Heyl hairy/enhancer-of-split related with YRPW motif-like Rarb retinoic acid receptor, beta Ywhah tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, eta polypeptide Celsr1 cadherin, EGF LAG seven-pass G-type receptor 1 (flamingo homolog, Drosophila) Traf6 Tnf receptor-associated factor 6 Arsa arylsulfatase A Lppr4 lipid phosphate phosphatase-related protein type 4 Pld2 phospholipase D2 Ak1 adenylate kinase 1 Sdc2 syndecan 2 Apbb2 amyloid beta (A4) precursor protein-binding, family B, member 2 Cd44 Cd44 molecule Notch3 notch 3 Lhx4 LIM homeobox 4 H3f3b H3 histone, family 3B Pcsk1 proprotein convertase subtilisin/kexin type 1 Dclk1 doublecortin-like kinase 1 Nkx6-2 NK6 homeobox 2 Syne1 spectrin repeat containing, nuclear envelope 1 Lrp4 low density lipoprotein receptor-related protein 4 Rpgrip1 retinitis pigmentosa GTPase regulator interacting protein 1 Scn2a1 sodium channel, voltage-gated, type II, alpha 1 Msx1 msh homeobox 1 Abr active BCR-related Casp2 caspase 2 289

Twsg1 twisted gastrulation homolog 1 (Drosophila) Atp2b2 ATPase, Ca++ transporting, plasma membrane 2 Chrd chordin Dmbx1 diencephalon/mesencephalon homeobox 1 Ccdc88a coiled coil domain containing 88A Itgb1 integrin, beta 1 Scn3a sodium channel, voltage-gated, type III, alpha Arnt2 aryl hydrocarbon receptor nuclear translocator 2

Table B.3 Predicted targets of miR-138 GO category ‘Nervous System

Development’. C=1543; O=224; E=150.92; R=1.48; rawP=1.86e-10; adjP=1.26e-07. (C

= the number of reference genes in the category, O= the number of genes in the gene set and also in the category, E= the expected number in the category, R = ratio of enrichment, rawP = raw P-value from hypergeometric test, and adjP = P-value adjusted by the multiple test adjustment).

290

miR-146a Gene symbol Gene Sstr1 somatostatin receptor 1 Arf4 ADP-ribosylation factor 4 Plp1 proteolipid protein 1 Gfra1 GDNF family receptor alpha 1 Cd24 CD24 molecule Tnn tenascin N Slc7a5 solute carrier family 7 (amino acid transporter light chain, L system), member 5 Spast spastin Sema7a semaphorin 7A, GPI membrane anchor Ube2v2 ubiquitin-conjugating enzyme E2 variant 2 Gli2 GLI family zinc finger 2 Snph syntaphilin Ext1 exostosin 1 Pafah1b2 platelet-activating factor acetylhydrolase 1b, catalytic subunit 2 Dlg4 discs, large homolog 4 (Drosophila) Mapt microtubule-associated protein tau Bcan brevican Gas7 growth arrest specific 7 Snx27 sorting nexin family member 27 Stxbp3 syntaxin binding protein 3 Pbx1 pre-B-cell leukemia homeobox 1 Stk24 serine/threonine kinase 24 Tal1 T-cell acute lymphocytic leukemia 1 Mbp myelin basic protein Synj1 synaptojanin 1 Rab23 RAB23, member RAS oncogene family Ppard peroxisome proliferator-activated receptor delta Sod2 superoxide dismutase 2, mitochondrial Cln5 ceroid-lipofuscinosis, neuronal 5 Ppp2r3a protein phosphatase 2, regulatory subunit B'', alpha Slc7a11 solute carrier family 7 (anionic amino acid transporter light chain, xc- system), member 11 Epha4 Eph receptor A4 Ank2 ankyrin 2, neuronal Mafb v-maf musculoaponeurotic fibrosarcoma oncogene homolog B (avian) Cntn2 contactin 2 (axonal) Gdf11 growth differentiation factor 11 Aplp2 amyloid beta (A4) precursor-like protein 2 Chrdl1 chordin-like 1 Lzts1 leucine zipper, putative tumor suppressor 1 Uqcrq ubiquinol-cytochrome c reductase, complex III subunit VII Aldh1a2 aldehyde dehydrogenase 1 family, member A2

291

Nid67 putative small membrane protein NID67 Sema5a sema domain, seven thrombospondin repeats (type 1 and type 1-like), transmembrane domain (TM) and short cytoplasmic domain, (semaphorin) 5A Pgrmc1 progesterone receptor membrane component 1 Pvrl1 poliovirus receptor-related 1 Prickle2 prickle homolog 2 (Drosophila) Kcnma1 potassium large conductance calcium-activated channel, subfamily M, alpha member 1 Ptpra protein tyrosine phosphatase, receptor type, A Foxa1 forkhead box A1 Olfm3 olfactomedin 3 Pura purine rich element binding protein A Klf7 Kruppel-like factor 7 (ubiquitous) Fos FBJ osteosarcoma oncogene RGD1561431 similar to homeobox-containing transcription factor Hdac11 histone deacetylase 11 Gas6 growth arrest specific 6 Stxbp1 syntaxin binding protein 1 Prdm6 PR domain containing 6 Wnt3a wingless-type MMTV integration site family, member 3A Tgif1 TGFB-induced factor homeobox 1 Nova1 neuro-oncological ventral antigen 1 Nr4a3 nuclear receptor subfamily 4, group A, member 3 Thbs2 thrombospondin 2 Chl1 cell adhesion molecule with homology to L1CAM Csrp1 cysteine and glycine-rich protein 1 Klk6 kallikrein related-peptidase 6 Hook3 hook homolog 3 (Drosophila) Sox8 SRY (sex determining region Y)-box 8 Mapk9 mitogen-activated protein kinase 9 Sstr3 somatostatin receptor 3 Vangl2 vang-like 2 (van gogh, Drosophila) Gpc1 glypican 1 Ipmk inositol polyphosphate multikinase Fgf13 fibroblast growth factor 13 Ywhag tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, gamma polypeptide Cxcl12 chemokine (C-X-C motif) ligand 12 Mapk8 mitogen-activated protein kinase 8 Lama2 laminin, alpha 2 Snapap SNAP-associated protein Star steroidogenic acute regulatory protein Gdf5 growth differentiation factor 5 Cables1 Cdk5 and Abl enzyme substrate 1 Grin2b glutamate receptor, ionotropic, N-methyl D-aspartate 2B Pex13 peroxisomal biogenesis factor 13 292

Ccng1 cyclin G1 Cbln2 cerebellin 2 precursor Rgs6 regulator of G-protein signaling 6 Cyp26a1 cytochrome P450, family 26, subfamily a, polypeptide 1 Sfrp5 secreted frizzled-related protein 5 Prkci protein kinase C, iota Zfhx3 zinc finger homeobox 3 Timp4 tissue inhibitor of metalloproteinase 4 Sh3tc2 SH3 domain and tetratricopeptide repeats 2 Atxn10 ataxin 10 Pls3 plastin 3 Pak1 p21 protein (Cdc42/Rac)-activated kinase 1 Luzp1 leucine zipper protein 1 Lrp2 low density lipoprotein receptor-related protein 2 Vcan versican Nrg1 neuregulin 1 Adnp activity-dependent neuroprotector homeobox Prkdc protein kinase, DNA activated, catalytic polypeptide Hipk1 homeodomain interacting protein kinase 1 Tpp1 tripeptidyl peptidase I Akr1c14 aldo-keto reductase family 1, member C14 Cdh2 cadherin 2 Sall2 sal-like 2 (Drosophila) Epo erythropoietin Scn3b sodium channel, voltage-gated, type III, beta Synj2 synaptojanin 2 Actr3 ARP3 actin-related protein 3 homolog (yeast) Ncdn neurochondrin C1s complement component 1, s subcomponent Fzd1 frizzled family receptor 1 Ctnnd1 catenin (cadherin associated protein), delta 1 Cited2 Cbp/p300-interacting transactivator, with Glu/Asp-rich carboxy- terminal domain, 2 Ppp2r2b protein phosphatase 2, regulatory subunit B, beta T2 brachyury 2 Dcx doublecortin Casp3 caspase 3 Vim vimentin S1pr1 sphingosine-1-phosphate receptor 1 Cdh11 cadherin 11 Hhex hematopoietically expressed homeobox Cntf ciliary neurotrophic factor Slc11a2 solute carrier family 11 (proton-coupled divalent metal ion transporters), member 2 Hap1 huntingtin-associated protein 1 Drd1a dopamine receptor D1A

293

Acsl3 acyl-CoA synthetase long-chain family member 3 Unc5c unc-5 homolog C (C. elegans) Prrxl1 paired related homeobox protein-like 1 Zeb1 zinc finger E-box binding homeobox 1 Tgfbr1 transforming growth factor, beta receptor 1 Uchl5 ubiquitin carboxyl-terminal hydrolase L5 Htr5a 5-hydroxytryptamine (serotonin) receptor 5A, G protein-coupled Cxcr4 chemokine (C-X-C motif) receptor 4 Gdf10 growth differentiation factor 10 Hmbs hydroxymethylbilane synthase Pcsk9 proprotein convertase subtilisin/kexin type 9 Rfx4 regulatory factor X, 4 (influences HLA class II expression) Oxct1 3-oxoacid CoA transferase 1 Smad1 SMAD family member 1 Dlx1 distal-less homeobox 1 Rarb retinoic acid receptor, beta Gad1 glutamate decarboxylase 1 Celsr1 cadherin, EGF LAG seven-pass G-type receptor 1 (flamingo homolog, Drosophila) Traf6 Tnf receptor-associated factor 6 Lppr4 lipid phosphate phosphatase-related protein type 4 Hmgcs1 3-hydroxy-3-methylglutaryl-CoA synthase 1 (soluble) Efna3 ephrin A3 Pld2 phospholipase D2 Apbb2 amyloid beta (A4) precursor protein-binding, family B, member 2 Cd44 Cd44 molecule Trim32 tripartite motif-containing 32 Btg2 BTG family, member 2 Dclk1 doublecortin-like kinase 1 Nkx6-2 NK6 homeobox 2 Rpgrip1 retinitis pigmentosa GTPase regulator interacting protein 1 Scn2a1 sodium channel, voltage-gated, type II, alpha 1 Hmg20b high mobility group 20 B Areg amphiregulin Pebp1 phosphatidylethanolamine binding protein 1 Slitrk5 SLIT and NTRK-like family, member 5 Qk quaking Hey2 hairy/enhancer-of-split related with YRPW motif 2 Sfrp4 secreted frizzled-related protein 4 P2ry1 purinergic receptor P2Y, G-protein coupled, 1 Dmbx1 diencephalon/mesencephalon homeobox 1 Plxna2 plexin A2 Dnmt3b DNA (cytosine-5-)-methyltransferase 3 beta RGD1310964 similar to RIKEN cDNA 9430031J16 Gfi1 growth factor independent 1 transcription repressor Dixdc1 DIX domain containing 1

294

Sfrp1 secreted frizzled-related protein 1

Table B.4: Predicted targets of miR-146a GO category ‘Nervous System

Development’. C=1543; O=174; E=130.30; R=1.34; rawP=2.62e-05; adjP=0.0052. (C = the number of reference genes in the category, O= the number of genes in the gene set and also in the category, E= the expected number in the category, R = ratio of enrichment, rawP = raw P-value from hypergeometric test, and adjP = P-value adjusted by the multiple test adjustment).

295

miR-219 (2-3p) Gene symbol Gene Sepp1 selenoprotein P, plasma, 1 Oprm1 opioid receptor, mu 1 Exoc4 exocyst complex component 4 Slc7a5 solute carrier family 7 (amino acid transporter light chain, L system), member 5 Uhmk1 U2AF homology motif (UHM) kinase 1 Ube2v2 ubiquitin-conjugating enzyme E2 variant 2 Drd2 dopamine receptor D2 Fas Fas (TNF receptor superfamily, member 6) Pou3f4 POU class 3 homeobox 4 Camk2b calcium/calmodulin-dependent protein kinase II beta Ube4b ubiquitination factor E4B Mapk8ip3 mitogen-activated protein kinase 8 interacting protein 3 Snx27 sorting nexin family member 27 RGD1560691 similar to calcium/calmodulin-dependent protein kinase 1D Pldn pallidin homolog (mouse) Srf serum response factor (c-fos serum response element-binding transcription factor) Dscam Down syndrome cell adhesion molecule Pbx1 pre-B-cell leukemia homeobox 1 Lpar1 lysophosphatidic acid receptor 1 Shox2 short stature homeobox 2 Mbp myelin basic protein Synj1 synaptojanin 1 Grip1 glutamate receptor interacting protein 1 Shank3 SH3 and multiple ankyrin repeat domains 3 Ppp2r3a protein phosphatase 2, regulatory subunit B'', alpha Slc7a11 solute carrier family 7 (anionic amino acid transporter light chain, xc- system), member 11 Dynll2 dynein light chain LC8-type 2 Itga3 integrin, alpha 3 Dpysl5 dihydropyrimidinase-like 5 Kif2a kinesin family member 2A Pou4f3 POU class 4 homeobox 3 Ncam2 neural cell adhesion molecule 2 Pou4f2 POU class 4 homeobox 2 Rapgef4 Rap guanine nucleotide exchange factor (GEF) 4 Hif1a hypoxia-inducible factor 1, alpha subunit (basic helix-loop-helix transcription factor) Pvrl1 poliovirus receptor-related 1 Olfm3 olfactomedin 3 S100b S100 calcium binding protein B Lrp6 low density lipoprotein receptor-related protein 6 Psd2 pleckstrin and Sec7 domain containing 2

296

Sstr2 somatostatin receptor 2 Ss18l1 synovial sarcoma translocation gene on chromosome 18-like 1 Ncor1 nuclear receptor co-repressor 1 Lphn1 latrophilin 1 Wnt3a wingless-type MMTV integration site family, member 3A Gabra5 gamma-aminobutyric acid (GABA) A receptor, alpha 5 Tgif1 TGFB-induced factor homeobox 1 Ghsr growth hormone secretagogue receptor Ak7 adenylate kinase 7 Thbs2 thrombospondin 2 Tmed2 transmembrane emp24 domain trafficking protein 2 Ina internexin neuronal intermediate filament protein, alpha Foxp3 forkhead box P3 Axin1 axin 1 Ipmk inositol polyphosphate multikinase Alk anaplastic lymphoma receptor tyrosine kinase Dkk1 dickkopf 1 homolog (Xenopus laevis) Cxcl12 chemokine (C-X-C motif) ligand 12 Six4 SIX homeobox 4 Mapk8 mitogen-activated protein kinase 8 Itm2c integral membrane protein 2C Snapap SNAP-associated protein RGD1305560 similar to RIKEN cDNA 9430031J16 Sox4 SRY (sex determining region Y)-box 4 Spp1 secreted phosphoprotein 1 Robo2 roundabout homolog 2 (Drosophila) Ccng1 cyclin G1 Ptpro protein tyrosine phosphatase, receptor type, O Pex13 peroxisomal biogenesis factor 13 Tmem57 transmembrane protein 57 Cbln2 cerebellin 2 precursor Sfrp5 secreted frizzled-related protein 5 Prkci protein kinase C, iota Prpf19 PRP19/PSO4 pre-mRNA processing factor 19 homolog (S. cerevisiae) Zfhx3 zinc finger homeobox 3 Eng endoglin Tnr tenascin R (restrictin, janusin) Ngfr nerve growth factor receptor Tmem126a transmembrane protein 126A Luzp1 leucine zipper protein 1 Six1 SIX homeobox 1 Hipk1 homeodomain interacting protein kinase 1 Nefm neurofilament, medium polypeptide Scn3b sodium channel, voltage-gated, type III, beta Sall2 sal-like 2 (Drosophila) Actr3 ARP3 actin-related protein 3 homolog (yeast)

297

Mal mal, T-cell differentiation protein Scn1b sodium channel, voltage-gated, type I, beta Pax8 paired box 8 Dcx doublecortin Ednrb endothelin receptor type B Casp3 caspase 3 Cyb5d2 cytochrome b5 domain containing 2 RGD1311558 similar to 4930506M07Rik protein Lamb2 laminin, beta 2 Cntn4 contactin 4 Acsl3 acyl-CoA synthetase long-chain family member 3 Pafah1b1 platelet-activating factor acetylhydrolase, isoform 1b, subunit 1 Olig1 oligodendrocyte transcription factor 1 Skil SKI-like oncogene Unc5c unc-5 homolog C (C. elegans) Ptprf protein tyrosine phosphatase, receptor type, F Lmx1a LIM homeobox transcription factor 1 alpha Tgfbr1 transforming growth factor, beta receptor 1 Htr5a 5-hydroxytryptamine (serotonin) receptor 5A, G protein-coupled Gdf10 growth differentiation factor 10 Wnt7a wingless-type MMTV integration site family, member 7A Robo3 roundabout homolog 3 (Drosophila) Nkx2-6 NK2 homeobox 6 Stk3 serine/threonine kinase 3 Heyl hairy/enhancer-of-split related with YRPW motif-like Rarb retinoic acid receptor, beta Dst dystonin Gad1 glutamate decarboxylase 1 Celsr1 cadherin, EGF LAG seven-pass G-type receptor 1 (flamingo homolog, Drosophila) Traf6 Tnf receptor-associated factor 6 Casp8 caspase 8 Slitrk6 SLIT and NTRK-like family, member 6 Hmgcs1 3-hydroxy-3-methylglutaryl-CoA synthase 1 (soluble) Ccl3 chemokine (C-C motif) ligand 3 Sdc2 syndecan 2 Rock2 Rho-associated coiled-coil containing protein kinase 2 Gata2 GATA binding protein 2 Lbx1 ladybird homeobox 1 Pcsk1 proprotein convertase subtilisin/kexin type 1 Pcdh9 protocadherin 9 Scn2a1 sodium channel, voltage-gated, type II, alpha 1 Slitrk5 SLIT and NTRK-like family, member 5 Twsg1 twisted gastrulation homolog 1 (Drosophila) Qk quaking Chrd chordin

298

Dmbx1 diencephalon/mesencephalon homeobox 1 Shank2 SH3 and multiple ankyrin repeat domains 2 Dixdc1 DIX domain containing 1 Sema3c sema domain, immunoglobulin domain (Ig), short basic domain, secreted, (semaphorin) 3C Slit2 slit homolog 2 (Drosophila) Mafk v-maf musculoaponeurotic fibrosarcoma oncogene homolog K (avian) Nptn neuroplastin

Table B.5: Predicted targets of miR-219(2-3p) GO category ‘Nervous System

Development’. C=1543; O=138; E=97.64; R=1.41; rawP=1.20e-05; adjP=0.0046. (C = the number of reference genes in the category, O= the number of genes in the gene set and also in the category, E= the expected number in the category, R = ratio of enrichment, rawP = raw P-value from hypergeometric test, and adjP = P-value adjusted by the multiple test adjustment).

299

miR-338 Gene Symbol Gene Neurog1 neurogenin 1 Ssh2 slingshot homolog 2 (Drosophila) Arhgef15 Rho guanine nucleotide exchange factor (GEF) 15 Irs2 insulin receptor substrate 2 Cops2 COP9 constitutive photomorphogenic homolog subunit 2 (Arabidopsis) Tnn tenascin N Gja1 gap junction protein, alpha 1 Pdx1 pancreatic and duodenal homeobox 1 Uhmk1 U2AF homology motif (UHM) kinase 1 Coq7 coenzyme Q7 homolog, ubiquinone (yeast) Bbc3 Bcl-2 binding component 3 Mpdz multiple PDZ domain protein Spast spastin Sema7a semaphorin 7A, GPI membrane anchor Prkch protein kinase C, eta Drd2 dopamine receptor D2 Ndst1 N-deacetylase/N-sulfotransferase (heparan glucosaminyl) 1 Gli2 GLI family zinc finger 2 Cdh1 cadherin 1 Cyp11a1 cytochrome P450, family 11, subfamily a, polypeptide 1 Gnat1 guanine nucleotide binding protein (G protein), alpha transducing activity polypeptide 1 Syngap1 synaptic Ras GTPase activating protein 1 Calu calumenin Ndrg1 N-myc downstream regulated 1 Lamc1 laminin, gamma 1 Nfib nuclear factor I/B Pfkfb3 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 Gas7 growth arrest specific 7 Snx27 sorting nexin family member 27 RGD1560691 similar to calcium/calmodulin-dependent protein kinase 1D Ets1 v-ets erythroblastosis virus E26 oncogene homolog 1 (avian) Stxbp3 syntaxin binding protein 3 Pbx1 pre-B-cell leukemia homeobox 1 Cast calpastatin Ihh Indian hedgehog Dicer1 dicer 1, ribonuclease type III Tal1 T-cell acute lymphocytic leukemia 1 Dact1 dapper, antagonist of beta-catenin, homolog 1 (Xenopus laevis) Synj1 synaptojanin 1 Tmem30a transmembrane protein 30A Bcl11a B-cell CLL/lymphoma 11A (zinc finger protein) Slc7a11 solute carrier family 7 (anionic amino acid transporter light chain, xc- system), member 11

300

Epha4 Eph receptor A4 Ank2 ankyrin 2, neuronal Dynll2 dynein light chain LC8-type 2 Mafb v-maf musculoaponeurotic fibrosarcoma oncogene homolog B (avian) Adm adrenomedullin Psd3 pleckstrin and Sec7 domain containing 3 RGD1306772 similar to RIKEN cDNA 1110008J03 Prom1 prominin 1 Dpysl5 dihydropyrimidinase-like 5 Chrdl1 chordin-like 1 Lzts1 leucine zipper, putative tumor suppressor 1 Wnt8b wingless-type MMTV integration site family, member 8B Nid67 putative small membrane protein NID67 Sema5a sema domain, seven thrombospondin repeats (type 1 and type 1-like), transmembrane domain (TM) and short cytoplasmic domain, (semaphorin) 5A Egf epidermal growth factor Nrp1 neuropilin 1 Hif1a hypoxia-inducible factor 1, alpha subunit (basic helix-loop-helix transcription factor) Prickle2 prickle homolog 2 (Drosophila) Stk4 serine/threonine kinase 4 Gfra3 GDNF family receptor alpha 3 Pitx1 paired-like homeodomain 1 Jun jun proto-oncogene Nlgn1 neuroligin 1 LOC689826 hypothetical protein LOC689826 Lig4 ligase IV, DNA, ATP-dependent Klf7 Kruppel-like factor 7 (ubiquitous) Aqp1 aquaporin 1 Celsr2 cadherin, EGF LAG seven-pass G-type receptor 2 (flamingo homolog, Drosophila) Hdac1 histone deacetylase 1 Nrcam neuronal cell adhesion molecule Cux1 cut-like homeobox 1 Pip5k1c phosphatidylinositol-4-phosphate 5-kinase, type I, gamma Mkks McKusick-Kaufman syndrome Stxbp1 syntaxin binding protein 1 Prdm6 PR domain containing 6 Foxe1 forkhead box E1 (thyroid transcription factor 2) Id3 inhibitor of DNA binding 3 Lamc3 laminin gamma 3 Nova1 neuro-oncological ventral antigen 1 Eomes eomesodermin Ak7 adenylate kinase 7 Hexa hexosaminidase A Tmed2 transmembrane emp24 domain trafficking protein 2 301

RGD1307443 similar to mKIAA0319 protein Ppp1r9b protein phosphatase 1, regulatory subunit 9B Bbs4 Bardet-Biedl syndrome 4 Ina internexin neuronal intermediate filament protein, alpha Stmn1 stathmin 1 Mapk8ip2 mitogen-activated protein kinase 8 interacting protein 2 Rab11a RAB11a, member RAS oncogene family Slc1a2 solute carrier family 1 (glial high affinity glutamate transporter), member 2 Gpc1 glypican 1 Kdr kinase insert domain receptor Gfap glial fibrillary acidic protein Fgf13 fibroblast growth factor 13 Ywhag tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, gamma polypeptide Nr2f1 nuclear receptor subfamily 2, group F, member 1 Cxcl12 chemokine (C-X-C motif) ligand 12 Six4 SIX homeobox 4 Mapk8 mitogen-activated protein kinase 8 Anapc2 anaphase promoting complex subunit 2 Snapap SNAP-associated protein Lgi4 leucine-rich repeat LGI family, member 4 RGD1305560 similar to RIKEN cDNA 9430031J16 Sox4 SRY (sex determining region Y)-box 4 Atxn2 ataxin 2 Robo2 roundabout homolog 2 (Drosophila) Lhx6 LIM homeobox 6 Grin2b glutamate receptor, ionotropic, N-methyl D-aspartate 2B Acvr1b activin A receptor, type IB Eif2b1 eukaryotic translation initiation factor 2B, subunit 1 alpha Ccng1 cyclin G1 Cbln2 cerebellin 2 precursor Rgs6 regulator of G-protein signaling 6 Cckar cholecystokinin A receptor Epha8 Eph receptor A8 Tnr tenascin R (restrictin, janusin) Cldn11 claudin 11 Hmg20a high mobility group 20A Ngfr nerve growth factor receptor Gabrb1 gamma-aminobutyric acid (GABA) A receptor, beta 1 Luzp1 leucine zipper protein 1 Lrp2 low density lipoprotein receptor-related protein 2 Lhx5 LIM homeobox 5 Sulf2 sulfatase 2 Hipk1 homeodomain interacting protein kinase 1 Duox2 dual oxidase 2

302

Mtpn myotrophin Mtmr2 myotubularin related protein 2 Ppp1cc protein phosphatase 1, catalytic subunit, gamma isozyme Foxd1 forkhead box D1 Pkd2 polycystic kidney disease 2 homolog (human) Mal mal, T-cell differentiation protein Il1b interleukin 1 beta Plxnb1 plexin B1 Etv1 ets variant 1 Prox1 prospero homeobox 1 Dcx doublecortin Kidins220 kinase D-interacting substrate 220 Mycn v-myc myelocytomatosis viral related oncogene, neuroblastoma derived (avian) Nrl neural retina leucine zipper Lzic leucine zipper and CTNNBIP1 domain containing Bbs1 Bardet-Biedl syndrome 1 Atp2b1 ATPase, Ca++ transporting, plasma membrane 1 RGD1311558 similar to 4930506M07Rik protein Fkbp4 FK506 binding protein 4 Cdh11 cadherin 11 Pou3f1 POU class 3 homeobox 1 Rgma RGM domain family, member A Drd1a dopamine receptor D1A Pafah1b1 platelet-activating factor acetylhydrolase, isoform 1b, subunit 1 Tgfbr1 transforming growth factor, beta receptor 1 Jag2 jagged 2 Foxp2 forkhead box P2 Cxcr4 chemokine (C-X-C motif) receptor 4 Gdf10 growth differentiation factor 10 Reln reelin En1 engrailed homeobox 1 Wnt7a wingless-type MMTV integration site family, member 7A Smad1 SMAD family member 1 Kctd11 potassium channel tetramerisation domain containing 11 Htt huntingtin Rarb retinoic acid receptor, beta Dag1 dystroglycan 1 (dystrophin-associated glycoprotein 1) Acan aggrecan Ywhae tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, epsilon polypeptide Cspg5 chondroitin sulfate proteoglycan 5 (neuroglycan C) Mef2c myocyte enhancer factor 2C Lppr4 lipid phosphate phosphatase-related protein type 4 Jak2 Janus kinase 2 Otx2 orthodenticle homeobox 2

303

Hmgcs1 3-hydroxy-3-methylglutaryl-CoA synthase 1 (soluble) C5ar1 complement component 5a receptor 1 Ak1 adenylate kinase 1 Sdc2 syndecan 2 Tacc1 transforming, acidic coiled-coil containing protein 1 Tspan2 tetraspanin 2 Cd44 Cd44 molecule Ptpru protein tyrosine phosphatase, receptor type, U Lhx4 LIM homeobox 4 Pcsk1 proprotein convertase subtilisin/kexin type 1 Adra2b adrenoceptor alpha 2B Dclk1 doublecortin-like kinase 1 Syne1 spectrin repeat containing, nuclear envelope 1 Pcdh9 protocadherin 9 Rpgrip1 retinitis pigmentosa GTPase regulator interacting protein 1 Scn2a1 sodium channel, voltage-gated, type II, alpha 1 Casp2 caspase 2 Twsg1 twisted gastrulation homolog 1 (Drosophila) Qk quaking Dner delta/notch-like EGF repeat containing Dmbx1 diencephalon/mesencephalon homeobox 1 Eif2b3 eukaryotic translation initiation factor 2B, subunit 3 Hoxc10 homeo box C10 Plxna2 plexin A2 Ccdc88a coiled coil domain containing 88A Thra thyroid hormone receptor alpha Dixdc1 DIX domain containing 1 Sec16a SEC16 homolog A (S. cerevisiae) Cadm1 cell adhesion molecule 1 Sfrp1 secreted frizzled-related protein 1 Arnt2 aryl hydrocarbon receptor nuclear translocator 2

Table B.6: Predicted targets of miR-338 GO category ‘Nervous System

Development’. C=1543; O=204; E=146.91; R=1.39; rawP=2.99e-07; adjP=8.89e-05. (C

304

Appendix C

Figure C.1 Representative images of controls for Olig2 (A,C) and MBP (B,D) immunofluorescence experiments. Olig2 controls, negative (A) and positive (C) are in

P40 rat brain tissue. The negative control for MBP experiments (B) is in P40 rat brain tissue. The positive control for the MBP antibody (D) is a diffuse neurosphere culture.

Scale bar = 50µm

305

Figure C.2 Representative images of controls for Iba-1 (A,C) and GFAP (B,D) experiments. Negative (A) and positive (C) control images for Iba-1 experiments were taken in P40 rat brain samples. Negative (B) control images for GFAP experiment were taken in P140 rat brain (B). Positive control image is a disassociated neurosphere culture. Scale = 50µm (A,C), 100µm (B) and 200µm (D).

306

Figure C.3 Representative images of controls for Reelin (A, C) and NeuN (B, D) immunofluorescence staining. Reelin negative (A) and positive (C) controls are in P40 rat brain tissue. NeuN negative (B) and positive control (D) are also in P40 rat brain tissue. Scale = 50 µm (A-C) and 20µm (D)

307