Interactions of the enteric nervous system with
the gut microbiome in the neuroligin-3R451C
mouse model of autism
M.H.M. Madusani Herath
Submitted in total fulfilment of the requirements of the degree of
Doctor of Philosophy
September 2020
Department of Physiology
The University of Melbourne
ABSTRACT
Autism patients are four times more likely to be hospitalized due to gastrointestinal (GI) dysfunction compared to the general public. However, the exact cause of GI dysfunction in individuals with autism is currently unknown. Genetic predisposition to autism spectrum disorder (ASD) has been highlighted in various studies and mutations in genes that affect nervous system function can drive both behavioural abnormalities and GI dysfunction in autism. Neuroligin-3 (NLGN3) is a postsynaptic membrane protein and the R451C missense mutation in the NLGN3 gene is associated with ASD. Recent studies revealed that the NLGN3
R451C mutation induces GI dysfunction in autism patients as well as in mice but, the cellular localization and the effects of this mutation on NLGN3 production in the enteric nervous system (ENS) have not been reported to date.
The intestinal mucosal barrier is the interface separating the external environment from the interior of the body. Mucosal barrier functions are directly regulated by the enteric nervous system. Therefore, ENS dysfunction can induce mucosal barrier impairments. An impaired intestinal barrier has been reported in autism patients, but neurally-mediated barrier dysfunctions have not been assessed in transgenic autism mouse models with an altered nervous system.
The intestinal mucus layer is the outermost layer of the mucosa which separates the intestinal microbiota from the intestinal epithelium. The mucus layer also serves as an energy source for mucus-residing microbes in the intestine. Although the composition of mucus-residing microbiota is altered in a subset of autism patients, the underlying physiological interactions between the host and these microbes are unclear. Identifying the precise spatial location of microbial populations in the gut is essential in order to understand host-microbial interactions but this has not been investigated in Nlgn3R451C mice.
In Chapter 3, I developed a method combining RNAScope in situ hybridization and immunohistochemistry technique to localize Nlgn3 mRNA in enteric neuronal subpopulations and glia. Further, a 3-dimensional quantitative image analysis method was developed to measure the Nlgn3 mRNA expression in the ENS. The same protocol was used to determine the effects of the Nlgn3 R451C mutation on Nlgn3 mRNA synthesis in the enteric nervous system. Findings from this study showed that, Nlgn3 mRNA is expressed in most submucosal and myenteric neurons in the ENS. Interestingly, this study revealed for the first time that Nlgn3 mRNA is expressed in enteric glia. In addition, analysis from this study demonstrated that the
R451C mutation reduces Nlgn3 mRNA expression in most enteric neurons in mutant mice compared to WT.
In Chapter 4, I investigated the effects of the Nlgn3 R451C mutation on intestinal mucosal barrier functions including the paracellular pathway and mucosal secretion in the small intestine. The Ussing chamber technique was used to measure the paracellular permeability and mucosal secretion ex vivo. Since the paracellular pathway is regulated by tight junctions, effects of this mutation on tight junction protein gene expression were measured using real- time (RT) PCR array and droplet digital (dd) PCR approaches. The impact of the Nlgn3 R451C mutation on the neurochemistry of the submucosal plexus was examined using immunocytochemistry. Results from these experiments indicated that the R451C mutation increases paracellular permeability and decreases transepithelial resistance (TER) in the distal ileum. However, ileal tight junction protein gene expression is unchanged in mutant mice compared to WT. Pharmacological stimulation of submucosal ganglia decreased the neurally- evoked mucosal secretion in mutant mice compared to WT. In addition, immunohistochemistry data revealed increased numbers of non-cholinergic and decreased cholinergic neuronal populations in the submucosal plexus in the distal ileum but not in the jejunum in Nlgn3R451C mutant mice.
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Given that, I identified altered barrier functions in the distal ileum of Nlgn3R451C mutant mice, in chapter 5, I also analysed the spatial distribution of the mucus-residing microbial populations in this region. To determine the spatial distribution of total bacteria, phylum Bacteroidetes, phylum Firmicutes, Akkermansia muciniphila (A. muciniphila) and Bacteroides thetaiotamicron (B. thetaiotamicron) were labelled using fluorescent in situ hybridization.
Immunofluorescence for the mucin-2 protein was incorporated to co-stain the mucus and determine the thickness of the mucus layer. Both the spatial pattern of microbial populations and mucus layer thickness were analysed using MATLAB-based BacSpace software.
Immunofluorescence experiments revealed that the R451C mutation increases mucus density adjacent to the epithelium. Along with increased mucus density, the total bacterial density was higher in the mucosa in mutant mice. Further, a decreased ratio of Bacteroidetes/Firmicutes, a decreased A. muciniphila density and increased density of B. thetaiotamicron were observed in mutant mice.
Overall, findings from this thesis revealed that NLGN3 is expressed in the enteric nervous system and that the R451C mutation reduces Nlgn3 mRNA expression levels in enteric neurons. Furthermore, the Nlgn3 R451C mutation impairs intestinal mucosal barrier integrity.
Findings from this study also revealed that this mutation alters mucus density as well as the spatial distribution and composition of the microbial community in the distal ileum in mice.
Therefore, these findings highlight that an autism-associated gene mutation that affects nervous system function impairs the mucosal barrier and may contribute to the pathophysiology of GI dysfunction in ASD.
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DECLARATION
This is to certify that:
• this thesis comprises only my original work except where indicated in the preface,
• due acknowledgement has been made in the text to all other material used,
• this thesis is less than 100,000 in length, inclusive of footnotes, but exclusive of tables,
maps, appendices and bibliography
Madusani Herath
September 2020
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PREFACE
Under my direction, the following people have made the stated contributions to my work:
Chapter 3: All the experiments in this chapter were conducted by me. Technical assistance and advice for RNAScope data analysis was provided by Dr. Ellie Cho, Biological Optical
Microscopy Platform, The University of Melbourne.
Chapter 4: All the experiments in this chapter were conducted by me. Technical assistance and advice for Ussing chamber, immunohistochemistry was provided by Ms. Pavitha Parathan.
Some technical assistance for RT-PCR array was provided by Dr. Andrew Jobling and Dr.
Gayathri Balasuriya. Technical assistance for droplet digital PCR was provided by Ms. Franca
Casagranda.
Chapter 5: All the experiments in this chapter were conducted by me. Technical assistance and advice for fluorescent in situ hybridization was provided by Dr. Lucie Semenec.
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Publications arising from this thesis include:
Herath, M., Hosie, S., Bornstein, J. C., Franks, A. E., & Hill-Yardin, E. L. (2020). The Role of the Gastrointestinal Mucus System in Intestinal Homeostasis: Implications for Neurological
Disorders. Frontiers in Cellular and Infection Microbiology, 10, 248.
Abstracts (refereed*)
Herath, M., Franks, A.E., Bornstein, J.C., Hill-Yardin, E.L. (2019) Neuronal regulation of mucosal barrier function in the neuroligin-3R451C mouse model of autism. Australasian
Neuroscience Society, Adelaide, Australia.
Herath, M., Franks, A.E., Bornstein, J.C., Hill-Yardin, E.L. (2018) Investigating the mucosal barrier function in the Neuroligin-3R451C mouse model of autism. Australasian Neuroscience
Society, Brisbane, Australia.
Herath, M., Leembruggen. A., Seger.G., Franks, A.E., Bornstein, J.C., Hill-Yardin, E.L.
(2017) Investigating the interactions between the gut microbiome and the enteric nervous system in autism, Australasian Society for Autism Research, Melbourne, Australia.
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ACKNOWLEDGMENTS
My PhD journey has been a challenging and truly life-changing experience for me, and this thesis would not have been possible without the support and guidance that I received from many knowledgeable, generous and kind people.
Firstly, I would like to express my sincere gratitude to my supervisors Prof. Joel Bornstein,
A/Prof. Elisa Hill-Yardin and Prof. Ashley Franks. Joel, thank you so much for having me in your lab and thank you for your guidance, support and advice. Your expertise and insightful feedback changed the way I think and helped me to bring this work to a higher level. Elisa, thank you so much for your guidance and support during this journey. I would like to thank you for sharing invaluable non-academic opportunities with me which will help immensely with my next career path. Ashley, thank you so much for your guidance and support and thank you so much for taking time out of your busy schedule to support my work.
I would like to thank my advisory committee, A/Prof. Rene Koopman, A/Prof. Vicky
Lawson and Prof. Andrew Allen for reviewing my progress and giving me feedback, suggestions and advice. I also would like to thank the Department of Physiology. It has been a great pleasure to work with such amazing people.
I would also like to acknowledge Dr. Ellie Cho, Dr. Lucie Semenec, Dr. Andrew Jobling and Ms. Franka Casagandra and Dr. Lucie Semenec for their technical support.
I would like to thank Ms. Pavitha Parathan for her time in teaching me all the techniques that I needed to complete this thesis. Pavitha was more than a friend to me and thank you so much for your kindness, love and always being there for me.
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I am also grateful to Mathusi Swaminathan, Katerina Koussoulas, Lin Hung and Gayathri
Balasuriya for sharing their PhD experience which encouraged me to finish my PhD on time.
I am also thankful for former and current lab members of the Bornstein lab for their support, suggestions and advice.
I would like to thank again Dr. Gayathri Balasuriya for taking care of me when I first came to Melbourne in 2017. I would also like to thank Gaya’s family for their kindness and support during this time.
I would like to thank my truly amazing friends Anderson, Moubarak, Andrew, Anita, Yi,
Sarah, Ryan, Mitchell, Sabrina, Lawrence, Stephanie and Julana for making this journey colourful. Thank you so much for your support and being there for me all the time. I am blessed to have friends like you in my life and I wish our friendship lasts forever.
I finally would like to thank my family for their support. I would like to thank Amma and
Sameera for their massive support, encouragement and sacrifices they made to make life perfect. I have no words to express how grateful I am to both of you and this journey would not be possible without you. I would like to thank Thaththa, nangi and in-laws for their love and support.
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TABLE OF CONTENT
TABLE OF CONTENTS
LIST OF TABLES ...... XVI
LIST OF FIGURES ...... XVII
LIST OF ABBREVIATIONS ...... XIX
CHAPTER 1 ...... 1
INTRODUCTION ...... 1
1. AUTISM SPECTRUM DISORDER ...... 1
2. ASD AND GASTROINTESTINAL ABNORMALITIES ...... 2
3. THE GASTROINTESTINAL TRACT ...... 7
4. THE ENTERIC NERVOUS SYSTEM ...... 9
4.1 The enteric neuronal plexuses ...... 12
4.2 Classification of enteric neurons ...... 16
4.3 Enteric glia ...... 26
5. INTESTINAL MUCOSAL BARRIER ...... 29
5.1 Intestinal epithelium ...... 31
5.2 Intestinal mucus layer ...... 42
6. MUCOSAL MICROBIOME ...... 54
6.1 Distinct microbial populations inhabit the mucus layer ...... 55
6.2 Microbial dysbiosis in ASD ...... 56
6.3 Dysbiosis of mucus associated microbial populations in ASD ...... 57
6.4 Effect of the microbiome on the ENS and intestinal functions ...... 58
7. GENETIC MUTATIONS AND ASD ...... 59
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7.1 Synaptic cell adhesion molecules and ASD ...... 60
7.2 Neuroligins ...... 60
7.3 Function of NLGNs ...... 64
7.4 Neuroligin-3 ...... 65
8. THE NLGN3 R451C MUTATION ...... 66
8.1 Expression of neuroligin-3 in the intestine ...... 68
8.2 Effects of the NLGN3 R451C mutation on intestinal structure and function ...... 69
9. THESIS HYPOTHESIS AND AIMS ...... 71
CHAPTER 2 ...... 72
MATERIALS AND METHODS ...... 72
1. ANIMALS ...... 72
2. DUAL RNASCOPE IN SITU HYBRIDIZATION AND IMMUNOCYTOCHEMISTRY ...... 73
2.1 Tissue preparation ...... 73
2.2 RNAScope in situ hybridization ...... 73
2.3 Pre-treatment ...... 74
2.4 RNAscope in situ hybridization ...... 74
2.5 Immunofluorescence ...... 74
2.6 Image acquisition and quantification ...... 75
3. USSING CHAMBER TECHNIQUE ...... 75
3.1 Assembling the Ussing chamber and elimination of electrical bias ...... 75
3.2 Tissue preparation ...... 76
3.3 Electrical measurement ...... 76
3.4 Data collection and analysis ...... 77
4. REVERSE TRANSCRIPTION PCR AND DROPLET DIGITAL PCR ...... 77
4.1 Tissue preparation ...... 77
ix
4.2 RNA extraction ...... 78
4.3 Complimentary DNA (cDNA) synthesis ...... 79
4.4 Real-time quantitative PCR array ...... 79
4.5 Droplet digital PCR ...... 79
5. FLUORESCENT IN SITU HYBRIDIZATION ...... 80
5.1 Tissue collection and fixation ...... 80
5.2 Fluorescent in situ hybridization (FISH) ...... 81
5.3 Immunofluorescence ...... 81
6. IMMUNOHISTOCHEMISTRY ...... 82
6.1 Tissue collection ...... 82
6.2 Tissue preparation and immunofluorescence ...... 82
6.3 Microscopy ...... 83
7. STATISTICAL ANALYSIS ...... 83
CHAPTER 3 ...... 84
QUANTITATIVE ANALYSIS OF NEUROLIGIN-3 EXPRESSION IN THE
ENTERIC NERVOUS SYSTEM ...... 84
1. ABSTRACT ...... 84
2. INTRODUCTION ...... 85
3. METHODS ...... 87
3.1 Animals ...... 87
3.2 Tissue preparation ...... 87
3.3 Dual RNAScope in situ hybridization and immunofluorescence ...... 87
3.4 Image acquisition and analysis ...... 89
3.5 Statistical analysis ...... 89
4. RESULTS ...... 91
x
4.1 A novel method to simultaneously detect cellular Nlgn3 mRNA and protein
expression in the enteric nervous system ...... 91
4.2 A quantitative image analysis method to assess Nlgn3 mRNA expression in the
enteric nervous system ...... 93
4.3 Nlgn3 mRNA is expressed in the majority of distal ileal submucosal neurons ...... 95
4.4 Myenteric neurons express Nlgn3 mRNA ...... 99
4.5 Enteric glia express Nlgn3 ...... 103
4.6 Nlgn3 mRNA expression in submucosal neurons is affected by the R451C mutation
...... 105
4.7 The R451C mutation alters Nlgn3 mRNA expression in myenteric neurons ...... 109
4.8 Expression of Nlgn3 mRNA is altered in ileal submucosal and myenteric glia in
Nlgn3R451C mice ...... 113
5. DISCUSSION ...... 115
5.1 Dual RNAScope and immunocytochemistry is a reliable and highly sensitive
technique to detect Nlgn3 mRNA in the enteric nervous system ...... 115
5.2 Imaris-based quantification is an automated and unbiased technique to measure
Nlgn3 mRNA expression ...... 117
5.3 Nlgn3 mRNA is expressed in most enteric neurons ...... 117
5.4 Nlgn3 mRNA is expressed in enteric glia ...... 120
5.5 The R451C mutation alters Nlgn3 mRNA expression in the ENS ...... 121
6. CONCLUSION ...... 122
CHAPTER 4 ...... 123
NEURONAL REGULATION OF INTESTINAL MUCOSAL BARRIER FUNCTION
IN THE NEUROLIGIN-3R451C MOUSE MODEL OF AUTISM...... 123
1. ABSTRACT ...... 123
xi
2. INTRODUCTION ...... 124
3. METHODS ...... 126
3.1 Animals ...... 126
3.2 Paracellular permeability and transepithelial resistance ...... 126
3.3 Tight junction protein gene expression ...... 128
3.4 Immunohistochemistry ...... 129
3.5 Statistical analysis ...... 130
4. RESULTS ...... 132
4.1 The Nlgn3 R451C mutation alters the proportions of ileal submucosal VIP and
ChAT neurons...... 132
4.2 The Nlgn3 R451C mutation decreases neurally-evoked mucosal secretion ...... 134
4.3 The Nlgn3 R451C mutation increases ileal paracellular permeability ...... 136
4.4 The Nlgn3 R451C mutation does not affect the expression of tight junction protein
genes in the ileum...... 138
4.5 Jejunal VIP and ChAT submucosal neuron proportions are unchanged by the
neuroligin 3 R451C mutation...... 140
4.6 The Nlgn3 R451C mutation does not affect jejunal paracellular permeability ...... 142
4.7 The Nlgn3 R451C mutation does not affect jejunal tight junction protein gene
expression...... 144
5. DISCUSSION ...... 146
5.1 Nlgn3 R451C mutation induces neurochemical changes to the submucosal plexus146
5.2 Nlgn3 plays a role in submucosal neuronal regulation of mucosal secretion through
a nACh receptor-mediated pathway ...... 149
5.3 The Nlgn3 R451C mutation induces region-specific paracellular pathway
dysfunction ...... 151
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5.4 Increased ileal paracellular permeability in Nlgn3R451C mutant mice is not due to
impaired tight junction protein complexes ...... 153
6. CONCLUSION ...... 155
CHAPTER 5 ...... 156
THE SPATIAL DISTRIBUTION OF MICROBIAL POPULATIONS IN THE
NLGN3R451C MOUSE MODEL OF AUTISM...... 156
1. ABSTRACT ...... 156
2. INTRODUCTION ...... 157
3. METHODS ...... 160
3.1 NLGN3R451C mice ...... 160
3.2 Tissue collection and fixation ...... 160
3.3 Fluorescent in situ hybridization ...... 161
3.4 Immunofluorescence ...... 163
3.5 Image acquisition and analysis ...... 163
3.6 Image analysis ...... 163
4. RESULTS ...... 168
4.1 The R451C mutation increases mucus density adjacent to the mucosa ...... 168
4.2 Altered spatial distribution and an increased bacterial abundance adjacent to the
mucosa in Nlgn3R451C mutant mice...... 171
4.3 Decreased Bacteroidetes abundance in Nlgn3R451C mice ...... 174
4.4 The Nlgn3 R451C reduces the abundance of phylum Firmicutes ...... 177
4.5 The spatial patterning of A. muciniphila is shifted in Nlgn3R451C mice...... 180
4.6 The distribution of Bacteroides thetaiotamicron is altered in Nlgn3R451C mice...... 183
5. DISCUSSION ...... 185
5.1 Nlgn3R451C mice have increased mucus density in the distal ileum ...... 186
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5.2 Increased bacterial density adjacent to the mucosa in Nlgn3R451C mice ...... 187
5.3 Shifts in the spatial distribution of Bacteroidetes and Firmicutes phyla in Nlgn3R451C
mice ...... 188
5.4 Shifted spatial distribution of B. thetaiotamicron in Nlgn3R451C mutant mice ...... 189
5.5 The spatial patterning of A. muciniphila is altered in the distal ileum in Nlgn3R451C
mice ...... 190
6. CONCLUSION ...... 191
CHAPTER 6 ...... 193
DISCUSSION ...... 193
6.1 NLGN3 IS EXPRESSED IN ENTERIC NEURONS AND ENTERIC GLIA ...... 195
6.2 THE R451C MUTATION ALTERS THE CELLULAR EXPRESSION OF NLGN3 MRNA ...... 197
6.3 MUCOSAL BARRIER DYSFUNCTION IN NLGN3R451C MICE ...... 197
6.3.1 Altered neurochemistry in the submucosal plexus of Nlgn3R451C mutant mice .... 198
6.3.2 Decreased nAChR evoked mucosal secretion in Nlgn3R451C mutant mice...... 198
6.3.3 Increased paracellular permeability in Nlgn3R451C mutant mice...... 200
6.3.4 The Nlgn3R451C mutation causes mucus accumulation in the distal ileum ...... 202
6.4 SMALL INTESTINAL BACTERIAL OVERGROWTH IN THE DISTAL ILEUM OF NLGN3R451C
MODEL? ...... 203
6.5 DOES IMPAIRED MUCUS EXPANSION RESTRICT THE ENTRY OF MUCOLYTIC BACTERIA TO
ACCESS MUCUS GLYCANS IN NLGN3R451C ASD MOUSE MODEL? ...... 203
6.6 THE NLGN3R451C MUTATION ALTERS THE MUCOSAL BARRIER ENVIRONMENT WHICH
COULD LEAD TO GI DYSFUNCTION IN ASD...... 204
6.7 CONCLUSION ...... 205
CHAPTER 7 ...... 206
xiv
REFERENCES ...... 206
APPENDIX ...... 269
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LIST OF TABLES
Table 1.1 Effects of autism-associated gene mutations on the gut functions and the ENS...... 4
Table 1.2 Neurochemical coding and relevant neuronal functional types in the submucosal plexus ...... 14
Table 3.1 Primary antibodies used for immunocytochemistry ...... 90
Table 3.2 Secondary antibodies used for immunocytochemistry ...... 90
Table 4.1 Primary antibodies used for immunocytochemistry ...... 131
Table 4.2 Secondary antibodies used for immunocytochemistry ...... 131
Table 5.1 Previously validated FISH probes used to identify total bacteria and subsets of bacteria in the WT and Nlgn3R451C mouse distal ileum...... 162
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LIST OF FIGURES
Figure 1.1 The organisation of the enteric nervous system ...... 11
Figure 1.2 Morphology of myenteric neurons in the guinea-pig small intestine...... 18
Figure 1.3 Electrophysiological properties of an S neuron and AH neurons ...... 20
Figure 1.4 Different enteric neuronal subtypes interact to form the neuronal circuitry of ENS.
...... 22
Figure 1.5 The morphological types of enteric glia ...... 28
Figure 1.6 An illustration of the cross-section of the gut ...... 30
Figure 1.7 Apical junctional complex ...... 32
Figure 1.8 Electrical circuit model of the intestinal epithelium ...... 36
Figure 1.9 The structure of the mucus layer varies with regional locations within the GI tract.
...... 48
Figure 1.10 Dimerization of neuroligins at the synaptic membrane...... 63
Figure 3.1 RNAScope combined with immunofluorescence to determine the distribution of
Nlgn3 mRNA in enteric neurons...... 92
Figure 3.2 3D rendering of neuronal surfaces and Nlgn3 mRNA labeling in distal ileal submucosal neurons...... 94
Figure 3.3 The distribution of Nlgn3 mRNA in ileal submucosal neurons...... 97
Figure 3.4 Expression and the distribution of Nlgn3 mRNA in distal ileal myenteric neurons
...... 101
Figure 3.5 The expression and distribution of Nlgn3 mRNA in enteric glia...... 104
Figure 3.6 Expression of Nlgn3 mRNA in the ileal submucosal plexus of Nlgn3R451C mutant mice...... 107
Figure 3.7 Effects of the Nlgn3 R451C mutation on Nlgn3 mRNA expression in myenteric neurons...... 111
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Figure 3.8 Effects of Nlgn3 R451C mutation on Nlgn3 mRNA expression in enteric glia. . 114
Figure 4.1 Proportions of VIP and ChaT neurons in the ileum ...... 133
Figure 4.2 The effect of Nlgn3 R451C mutation on short circuit current in the distal ileum 135
Figure 4.3 Effect of Nlgn3 R451C mutation on transepithelial resistance and paracellular permeability in the ileum...... 137
Figure 4.4 Expression of genes encoding tight junction proteins in the distal ileum ...... 139
Figure 4.5 Proportions of VIP and ChAT immunoreactive neurons in the jejunum ...... 141
Figure 4.6 Effect of the Nlgn3 R451C mutation on TER and paracellular permeability in the jejunum...... 143
Figure 4.7 Expression of genes encoding tight junction proteins in the jejunum ...... 145
Figure 5.1 Detection of epithelial boundary and quantification of fluorescence intensity along the length of the epithelium and from the epithelial boundary...... 166
Figure 5.2 Ileal mucus layer density variation in the presence of the Nlgn3R451C mutation.
...... 169
Figure 5.3 The Nlgn3R451C mutation mediates shifts in the bacterial spatial pattern in the ileal mucosa...... 172
Figure 5.4 The Nlgn3 R451C mutation drives changes in Bacteroidetes localization in the distal ileum...... 175
Figure 5.5 The Nlgn3 R451C mutation affects the spatial organization of Firmicutes in the distal ileum ...... 178
Figure 5.6 A. muciniphila spatial distribution is altered by the Nlgn3 R451C mutation in the ileum ...... 181
Figure 5.7 The Nlgn3 R451C mutation alters the Bacteroides thetaiotamicron spatial pattern in the ileum...... 184
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LIST OF ABBREVIATIONS
5-HT 5-hydroxytryptamine, serotonin
Ach Acetylcholine
ADP Adenosine diphosphate
AGR Anterior gradient
AHP After-hyperpolarizing potential
AMP Adenosine monophosphate
ASD Autism spectrum disorder
ATP Adenosine triphosphate
BSA Bovine serum albumin
Ca Calcium cAMP Cyclic adenosine monophosphate cDNA Complimentary deoxyribonucleic acid
CFTR Cystic fibrosis transmembrane conductance regulator
ChAT Choline acetyltransferase
Cl Chloride
CLDN Claudin
CMMC Colonic migrating motor complex
CNS Central nervous system
DMPP 1,1-dimethyl-4-phenylpiperazinium
DNA Deoxyribonucleic acid
EEC Enteroendocrine cells
ENS Enteric nervous system
ER Endoplasmic reticulum
FCGB IgG Fc-binding protein
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GABA Gamma aminobutyric acid
GDNF Glial cell line-derived neurotrophic factor
GFAP Glial fibrillary acidic protein
GI Gastrointestinal
IBD Inflammatory bowel disease
IBS Irritable bowel syndrome
ICC Interstitial cells of cajal
IPAN Intrinsic primary afferent neuron
ISC Short circuit current
ISN Intrinsic sensory neuron
KCl Potassium chloride
LMMP Longitudinal muscle-myenteric plexus
LPS Lipopolysaccharide mRNA Messenger ribonucleic acid
MUC Mucin nAChR Nicotinic acetylcholine receptor
NKCCl Sodium-potassium-chloride cotransporter
NLGN Neuroligin
NO Nitric oxide
NOS Nitric oxide synthase
OCLN Occluden
PBS Phosphate buffered saline
PCR Polymerase chain reaction
PSD Postsynaptic density protein
PTS proline, threonine, serine
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PYY Peptide YY qPCR Quantitative polymerase chain reaction
REG Regenerating islet-derived
RNA Ribonucleic acid rRNA Ribosomal ribonucleic acid
RT Room temperature
RT-PCR Reverse-transcriptase polymerase chain reaction
SEM Standard error of the mean
SMP Submucosal plexus
SNAP Synaptosomal nerve- associated proteins
SOM Somatostatin
Spdef SAM pointed domain-containing ETS transcription factor
TJP Tight junction protein
TLR Toll-like receptor
UTP Uridine triphosphate
VACT Vesicular acetylcholine transporter
VAMP Vesicle-associated membrane proteins
VIP Vasoactive intestinal peptide
VPAC Vasoactive intestinal polypeptide receptor
WT Wild type
ZG Zymogen granule membrane protein
ZO Zonula occludens
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CHAPTER 1
INTRODUCTION
1. Autism spectrum disorder
Autism spectrum disorder (ASD) is a neurodevelopmental disorder which was first described by the psychiatrist Leo Kanner in 1943 (Kanner, 1968). ASD is characterised by impaired social interactions and communication alongside restrictive and repetitive behaviour
(American Psychiatric Association, 2013). ASD neuropathology includes altered synaptic transmission, altered neurochemical signalling, excitatory and inhibitory imbalance and disrupted neurocircuit connectivity. These abnormalities were specifically observed in brain regions which contribute to social cognition including the medial prefrontal cortex, the posterior temporal sulcus, adjacent temporo-parietal junction, the amygdala, the temporal poles and adjacent anterior cingulate cortex.
In 2020, 1 in 54 children are diagnosed with ASD by age of 8 in the United States (Maenner et al., 2020). Increased ASD prevalence is thought to be due to the availability of a better monitoring system and new diagnostic criteria of the disorder which identifies the under- identified populations previously (Maenner et al., 2020). Males are four times more likely to develop ASD than females (Maenner et al., 2020) and potential explanations for the male predisposition to autism include abnormalities of sex chromosomes, exposure to androgens during brain development and a female autism phenotype which does not fit with existing diagnostic criteria (Werling and Geschwind, 2013, Baron-Cohen et al., 2011, Zhang et al.,
2020).
The exact cause of ASD is currently unknown and both genetics and environmental risk factors are believed to play a role in ASD etiology. Perinatal infections, maternal pharmacological
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exposure, early life stress, advanced parental age are the most common environmental risk factors associated with autism (Lyall et al., 2014). But rare mutations in more than 1000 genes emphasise that genetics play a major role in ASD etiology (Brainstorm et al., 2018).
2. ASD and gastrointestinal abnormalities
Although autism research has been mostly confined to the central nervous system, a range of autism-associated comorbid conditions indicate that the peripheral nervous system is also affected in these patients.
GI issues in autism patients have gained attention recently due to their higher prevalence and correlation with the severity of core autism traits. Individuals diagnosed with autism are four times more likely to be hospitalised due to GI problems compared to members of the general public (McElhanon et al., 2014). GI issues can contribute to the severity of ASD behaviours, particularly in autistic individuals who are incapable of communicating their discomfort
(Chaidez et al., 2014).
GI abnormalities associated with autism can range from chronic constipation to inflammatory bowel conditions such as ulcerative colitis. The most frequently reported GI symptoms of ASD patients are constipation, diarrhea, and abdominal pain (Buie et al., 2010a, Buie et al., 2010b,
Coury et al., 2012). Individuals with ASD also exhibit GI disorders including bloating, belching, vomiting, flatulence, bloody stool, disaccharidase deficiencies, gastroesophageal reflux, gastritis, esophagitis, celiac disease, Crohn’s disease, and colitis; most of which are symptoms of inflammatory bowel disease (Coury et al., 2012, Buie et al., 2010a). This may be because GI dysfunction in autistic individuals is highly subjective or because patients may have either one or a combination of these symptoms in differing levels of severity (Masi et al., 2017).
GI dysfunction in different autism populations have been reported across multiple studies. Buie and colleagues compared the evaluation, diagnosis and treatment methods of those studies to
2
generate a consensus report to generate evidence-based recommendations for the management of GI problems in ASD (Buie et al., 2010a). In addition, emerging evidence for the potential pathophysiological mechanisms which could drive GI dysfunction in autism are summarized in Table 1.1.
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Table 1.1 Effects of autism-associated gene mutations on the gut functions and the ENS.
Mutated Species GI dysfunction ENS phenotype Reference
gene
CHD8/chd8 Human Constipation and 50% reduction of (Bernier et
diarrhea, slow GI transit enteric neurons in al., 2014)
hindgut of chd8
morphant
NOS1 Human Eating and drinking Not analyzed (Shteyer et
problems, achalasia al., 2015)
Nos1 Mice Eating and drinking Interstitial cells of (Sivarao et
problems, hypertensive Cajal impairment al., 2001)
and poorly relaxing
lower esophageal
sphincter
Tcf4 Mice Constipation, slow GI Not analyzed (Grubisic et
transit al., 2015)
Slc6a4 Mice Constipation, slow GI Reduced number of (Margolis
(Sert) transit, decreased villus neurons in plexus of et al.,
height and crypt depth small and large 2016)
intestines and TH,
CGRP, or GABA
positive neurons
Foxp1 Mice Eating and drinking Not analysed (Frohlich et
problems, constipation, al., 2019)
4
achalasia, impaired
relaxation of the lower
esophageal sphincter,
slow GI transit
Nlgn3 Mice Diarrhea, increased GI Hyperplasia in small (Hosie et
transit, GABA-mediated intestine, al., 2019)
alterations of colon dysregulation of
motility GABAergic neurons
NLGN3 Human Diarrhea poor bowel Not analyzed (Hosie et
control, esophagitis, al., 2019)
esophageal regurgitation,
abdominal pain
Nlgn3 Mice Faster colonic migrating No changes in the (Leembrug
motor complexes, ENS gen et al.,
increased colonic 2019)
diameter shank3a/sh Zebrafish Slow GI transit, reduced No changes (James et ank3b peristalsis and motility, al., 2019)
constipation and/or
diarrhea, reflux, cyclical
vomiting
BTBR Mice Delayed intestinal transit, 50% reduction in the (Golubeva
increased intestinal numbers of et al., 2017)
permeability, decreased inhibitory motor
electrolyte transport neurons (nNOS) and
5
total myenteric
neurons
6
3. The gastrointestinal tract
The gastrointestinal (GI) tract is a tubular organ system that extends from the mouth to the anus. It consists of the mouth, oesophagus, stomach, small intestine and the large intestine.
There are various other accessory glands such as liver, gallbladder and pancreas associated with the GI tract, which release secretions to aid food digestion. The small intestine extends from the pylorus to ileocecal valve and contains three regions: duodenum, jejunum and ileum.
The primary function of the small intestine is nutrient absorption. The caecum is the most proximal part of the large intestine and the precise role of caecum is unclear in human. The large intestine or the colon extends from the ileocecal valve to rectum and the major function of the large intestine is water and electrolyte reabsorption. Every region of the gut is functionally distinct and therefore, shows characteristic anatomical features.
Although the gross anatomical structure of the mammalian GI tract is well conserved, there are prominent differences in the anatomy and physiology among different species. This may be due to different factors including diet, metabolic requirements, feeding patterns and body size
(Nguyen et al., 2015). For example, despite a number of similarities, the mouse GI tract differs to the human in terms of anatomy, physiology, cellular structure and genetics. Therefore, although mouse models are extensively utilized in gastrointestinal research, they cannot fully replicate the human system.
The GI tract contains structurally and functionally distinct layers. The mucosa is the innermost layer of the GI tract and is composed of a single layer of epithelial cells. The intestinal epithelium is the interface which separates the gut lumen from the interior of the body.
Epithelial cells are adapted to absorb nutrients, water and electrolytes and produce various secretions that are essential for digestion and they also form a physical barrier against potentially toxic luminal content. Enteroendocrine cells and other modified epithelial cell subtypes secrete regulatory proteins which act locally in a paracrine or a neurocrine manner.
7
For example, Paneth cells secrete antibacterial mediators to maintain a separation between the intestinal microbiome and the epithelium (Allaire et al., 2018, Elphick and Mahida, 2005).
Goblet cells secrete mucus to lubricate the epithelial lining and trap and repel gut microbes.
The distribution of secretory cell types varies according to the region of the gut and is closely associated with the function of each gut region.
The submucosa is located immediately beneath the mucosa and it mainly contains the immune system, blood vessels, neurons and glia. The GI tract has the largest immune system of the body and protects the host against pathogenic agents that breach the intestinal epithelium. The mucosa is mainly innervated by neurons from the submucosal plexus (Neunlist et al., 2013,
Song et al., 1998, Song et al., 1992). Immune cells and neurons are in close contact to coordinate inflammatory responses and elicit neural signalling pathways to eliminate pathogens from the body.
The GI tract contains three layers of smooth muscle cells. The cells in the inner smooth muscle layer are orientated in the circular direction (the circular muscle layer) and the outer muscle layer is orientated in the longitudinal direction of the gut (the longitudinal muscle layer). The muscularis mucosa is a thin layer of muscle located outside the lamina propria and serve to separate it from the submucosa. Contractions of the circular muscle layer control the diameter of the gut tube and those of the longitudinal muscle layer shorten the gut. Coordinated contractions and relaxation of circular and longitudinal muscle layers give rise to motility patterns that vary from non-propulsive mixing movements to highly propulsive peristaltic contractions (Said et al., 2018).
The digestive system is innervated by the enteric nervous system (ENS), an intrinsic neuronal network that can regulate intestinal functions independently from the central nervous system
(Furness et al., 2014). The neurons of the ENS are arranged into two ganglionated plexus; the submucosal plexus and the myenteric plexus. The ENS extends from the oesophagus to the
8
anus and regulates virtually all gut functions such as mucosal barrier function, secretion, motility and blood flow for efficient food digestion and absorption. Although the ENS can function independently of the central nervous system, it receives extrinsic innervation from the brain and the spinal cord to coordinate gut functions critical for maintaining homeostasis.
4. The enteric nervous system
The ENS is the third and the largest division of the autonomic nervous system and contains more than 100 million neurons plus 400 million neuron-supporting glial cells extending from the upper oesophagus to the anal sphincter. This complex nervous system plays an important role in maintaining optimal digestive function (reviewed by Furness, 2012). These cells are derived from neural crest cells, migrate to the gut and colonise the entire length of the GI tract.
In the ENS, enteric nerve cells and glia are clustered into small groups known as ganglia.
Ganglia are interconnected via nerve fibre bundles to form a neural plexus. The ENS contains two ganglionated plexuses: the submucosal plexus and the myenteric plexus (Figure 1.1). The myenteric plexus lies between the longitudinal and the circular muscle layers of the external musculature and the submucosal plexus can be found between the circular muscle layer and the mucosa. The myenteric plexus is largely responsible for regulating gut motility whereas the submucosal plexus regulates mucosal barrier function. Both plexuses, however, interact in order to maintain optimum GI functions.
Extrinsic innervation of the GI tract
Even though the ENS can function independently of the CNS, it is not autonomous. Thus, neuronal regulation of the GI system is mediated by the integrated network of the ENS and
CNS to govern local enteric reflexes. Neural communication between the CNS and the ENS is
9
mediated by vagal nerve pathways, the spinal thoracolumbar spinal cord and the pelvic pathway (reviewed in Furness et al., 2013).
Vagal and pelvic neurons supply parasympathetic innervation to the GI tract (Olsson et al.,
2004). Prevertebral ganglia and paravertebral ganglia provide sympathetic innervation (Chen et al., 2016, Tan et al., 2010, Trudrung et al., 1994).
Bidirectional communication between the ENS and the CNS is essential to maintain optimal
GI conditions (Yuyama et al., 2002, Furness et al., 2014). Disturbances to the ENS-CNS axis often result in impaired GI functions, and this has been proposed as one of the underlying pathophysiological mechanisms for the high prevalence of GI dysfunction in ASD.
10
Figure 1.1 The organisation of the enteric nervous system
The diagram represents the different layers of the wall of the intestine and the organization of the ENS in humans and large animals. The mucosa is the outermost layer of the intestine and demarcates the lumen and the internal tissues. The two ganglionic plexuses of the ENS are the submucosal and the myenteric plexus. The submucosal plexus is located between the mucosa and the circular muscle layer and the ganglia of this plexus are arranged into one or three layers
(divided into the inner and outer submucosal plexus in this illustration). The mucosa is innervated by the submucosal plexus which regulates mucosal barrier functions such as mucosal secretion and permeability. The myenteric plexus is located between the longitudinal and circular muscle layers and primarily regulates intestinal motility (Image from Furness,
2012).
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4.1 The enteric neuronal plexuses
4.1.1 Submucosal plexus
The submucosal plexus is a ganglionated plexus located between the mucosa and the circular muscle layer. It is more prominent in the small intestine and the colon than the oesophagus and stomach. Submucosal neurons are sparsely distributed in the oesophagus and stomach but do not form a ganglionated plexus. Submucosal neurons continue around the circumference and along the length of the small and large intestine (Furness, 2006). The arrangement of ganglia and functional and neurochemical types of neurons in these ganglia vary between species. In small animals such as the mouse, rat and guinea-pig, submucosal ganglia are arranged into a single layer (Furness, 2006). In large animals such as pigs and humans, the submucosal plexus contains more than one layer (Scheuermann et al., 1987a, Scheuermann et al., 1987b, Gunn,
1968, Hoyle and Burnstock, 1989, Timmermans et al., 2001). In humans, the submucosal plexus has three anatomically distinct layers and each layer has a different neural population and neurochemistry (Hoyle and Burnstock, 1989, Dhatt and Buchan, 1994, Timmermans et al.,
1994).
The primary role of submucosal neurons is to regulate mucosal functions. The submucosal plexus maintains fluid homeostasis and contains neurons that regulate water and electrolyte secretion as well as local blood flow. In mammals, blocking submucosal neural activity inhibits net fluid secretion in vivo (Lundgren, 2002).
In mice and guinea-pigs, two pharmacologically and neurochemically distinct populations of neurons exist in the submucosal plexus (Bornstein and Foong, 2018). Cholinergic neurons express choline acetyltransferase (ChAT), the enzyme which synthesises acetylcholine. There are also non-cholinergic neurons which contain vasoactive intestinal peptide (VIP) but do not express ChAT. In humans and rats, however, many VIP neurons also express ChAT. Other than these two major neurotransmitters, submucosal neurons also express unique molecules
12
which provide them with a distinct neurochemical code. The neurochemical coding and function of different types of submucosal neurons in mouse and guinea-pig are summarised in
Table 1.2.
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Table 1.2 Neurochemical coding and relevant neuronal functional types in the submucosal plexus
Neurochemical coding Functional type Species Gut
region
ChAT/CGRP/SOM/calretinin Cholinergic secretomotor Mouse Ileum
neuron
ChAT Cholinergic secretomotor
neuron
VIP/NPY/calretinin Non-cholinergic secretomotor
neuron
VIP/NPY/calretinin/TH Non-cholinergic vasodilator
neuron
Neither VIP or ChAT Unknown
ChAT/SP ISN Guinea- Ileum
ChAT/calretinin Cholinergic secretomotor/ pig
vasodilator neuron
ChAT/NPY/CCK/SOM/CGRP/ Cholinergic secretomotor/
GAL vasodilator neuron/interneuron
VIP/DYN/GAL Non-cholinergic secretomotor
neuron
ChAT/TK/calbindin ISN Guinea- Distal
ChAT/calretinin Cholinergic secretomotor/ pig colon
vasodilator neuron
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ChAT/NPY/SOM/CGRP Cholinergic secretomotor
neuron
VIP/NOS Non-cholinergic secretomotor
neuron
Abbreviations: ACh acetylcholine; CCK cholecystokinin, ChAT choline acetyltransferase,
CGRP calcitonin gene-related peptide; CRF corticotrophin- releasing factor; GAL galanin;
NeuN neuronal nuclear protein; NYP neuropeptide Y; PACAP pituitary adenylyl cyclase activating peptide; SOM somatostatin; TH tyrosine hydroxylase; TK tachykinin; VIP vasoactive intestinal peptide. The table is adapted from (Bornstein and Foong, 2018).
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4.1.2 Myenteric plexus
The myenteric plexus is located between the longitudinal and circular muscle layers and around the circumference and along the GI tract. It contains about two-thirds of neurons of the ENS.
The size, shape and orientation of the ganglia vary between gut regions and between species but the neuron meshwork is characteristic in every gut region (Furness, 2006).
4.2 Classification of enteric neurons
Enteric neurons are classified based on their morphology, electrophysiology, function and neurochemistry.
4.2.1 Morphological classification of enteric neurons
The morphological characterization of enteric neurons was first described by Dogiel who defined and illustrated three types of neurons in the ENS: Dogiel type I, Dogiel type II and
Dogiel type III neurons (Dogiel, 1899). Methods developed later such as dye injections and immunohistochemistry identified other neuronal morphologies and extended the Dogiel’s classification to type IV, V, VI and VII and mini neurons (Brehmer et al., 1999) (Figure 1.2).
Dogiel type I neurons consist of relatively small cell bodies with short dendrites and a single axon. Neurons of this group include ascending and descending interneurons and inhibitory and excitatory motor neurons (Furness, 2006). Dogiel type II neurons comprise 10-20% of myenteric neurons in mouse but may be completely absent from the submucosal plexus
(Mongardi Fantaguzzi et al., 2009, Foong et al., 2014). They have oval or round, large smooth cell bodies and multiple axons that run circumferentially (Bornstein et al., 1991, Foong et al.,
2014, Nurgali et al., 2004). Dogiel type III describes neurons with long branched processes
(also known as filamentous neurons) (Furness, 2006). Type IV neurons are uniaxonal and project vertically with short, branched tapering dendrites. Type V neurons are uniaxonal with
16
long branched dendrites and these neurons form clusters. Type VI Dogiel neurons have a single axon and fine dendrites (Furness, 2006).
17
Figure 1.2 Morphology of myenteric neurons in the guinea-pig small intestine.
A. Dogiel type II neurons have smooth cell bodies and multiple axons that project circumferentially. B. Dogiel type II neurons are uniaxonal with numerous short lamellar dendrites C. An example of a filamentous interneuron (Figure from Clerc et al., 1998).
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4.2.2 Electrophysiological classification of enteric neurons
Enteric neurons are categorised into two groups based on their electrophysiological properties:
S neurons and AH neurons. S neurons are characterised by short action potentials that lack slow after-hyperpolarizing potentials and exhibit fast excitatory postsynaptic potentials. AH neurons are classified by their large action potentials which have an inflection on the falling phase and are followed by long after-hyperpolarizing potentials (Hirst et al., 1974) (Figure
1.3). This classification system also corresponds with Dogiel’s morphological classification where S neurons are uni axonal and include Dogiel type I and filamentous morphologies. AH neurons have Dogiel type II morphology (Bornstein et al., 1994, Bornstein et al., 1984).
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Figure 1.3 Electrophysiological properties of an S neuron and AH neurons
A. S neurons display a fast after-hyperpolarization potential after an action potential.
B-D. In AH neurons, an action potential is followed by a slow after hyperpolarizing potential
(Figure from Furness, 2006).
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4.2.3 Functional classification of enteric neurons
There are approximately 20 different types of neurons in the ENS (Brookes and Costa, 2003,
Brookes and Costa, 2002). These neurons can be categorized into three broad functionally defined classes including intrinsic sensory neurons (ISNs), interneurons and motor neurons.
ISNs detect signals relating to the physical state of organs and the chemistry of the luminal content to initiate appropriate reflexes to control gut motility, mucosal secretion, absorption and blood flow etc. ISNs connect with each other, with interneurons and directly with motor neurons. Interneurons connect with each other and with motor neurons. There are six different types of motor neurons including muscle motor neurons, secretomotor neurons, secretomotor/vasodilator neurons, motor neurons synapsing onto enteroendocrine cells and motor neurons innervating lymphoid follicles (Figure 1.4).
21
Figure 1.4 Different enteric neuronal subtypes interact to form the neuronal circuitry of
ENS.
There are three major classes of neuronal classes; Intrinsic Sensory Neurons, interneurons and motor neurons. ISNs synapse with each other, with interneurons and with motor neurons and interneurons to contribute to GI function. Interneurons synapse with each other and with motor neurons (Figure from Furness, 2012).
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Intrinsic sensory neurons
The state of the GI tract is detected by intrinsic sensory neurons. ISNs may also be referred to as intrinsic primary afferent neurons (IPANs). These neurons encode information about the chemical environment of the gut lumen and the state of the tissue they innervate and send information to the integrated enteric neuronal circuitry to modulate gut function. ISNs detect a range of stimuli including luminal chemistry, the stretch of the intestinal wall and distortion of the mucosa and they also activate intrinsic reflex pathways to regulate physiological functions such as gut motility, secretion and blood flow (Furness et al., 2014). The ISNs have Dogiel type II morphology with a large smooth cell body and multiple axons. These neurons have AH electrophysiological properties such as prolonged afterhyperpolarization potentials (Kunze et al., 1995, Bertrand et al., 1997). ISNs form synaptic connections with all other types of myenteric neurons including interneurons and motor neurons and also with other ISNs (Furness et al., 2004). They also send axons to submucosal ganglia. Submucosal Dogiel type II neurons connect with other neurons in the submucosal plexus as well as with neurons in the myenteric plexus. Electrophysiological and neurochemical studies, however, have shown that ISNs are absent in the submucosal plexus of mice (Foong et al., 2014, Mongardi Fantaguzzi et al., 2009).
In the guinea-pig small intestine as well as the mouse duodenum and colon, axons of myenteric
ISNs project circumferentially and send synaptic outputs to neighbouring myenteric ganglia
(Bornstein et al., 1991, Foong et al., 2012, Nurgali et al., 2004). Neurons of this class send at least one axon to the mucosa (Song et al., 1991). In guinea-pig ileum, each villus receives axons from about 65 different myenteric ISNs and each neuron innervates between 10-50 villi
(Bertrand et al., 1998).
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Interneurons
Interneurons have been identified in all gut regions. Descending and ascending interneurons form chains in the myenteric plexus (Pompolo and Furness, 1993, Portbury et al., 1995a,
Young and Furness, 1995). In the guinea-pig small intestine, four different classes of interneurons have been identified immunohistochemically (Costa et al., 1996). Three classes of descending interneurons have a long descending projection which make synapses with both myenteric and submucosal neurons. Typically, all three classes of descending interneurons make synaptic contacts with neurons of the same class, forming long neuronal chains. Guinea pig ileum contains only one class of ascending interneuron. These neurons express acetylcholine and tachykinins as their major neurotransmitters. These neurons also form cellular chains and have a single orally-directed axon. Ascending interneurons form synaptic contacts with other ascending interneurons and with excitatory muscle motor neurons and appear to be involved in local motility reflexes.
Some interneurons are Dogiel type III neurons with numerous branching, tapering and filamentous dendrites (Portbury et al., 1995b). These neurons receive a few synaptic inputs from ISNs (Stebbing and Bornstein, 1996, Pompolo and Furness, 1998). All three classes of descending interneurons innervate submucosal ganglia (Brookes et al., 1991a).
In the guinea-pig ileum, there is a small proportion of submucosal interneurons that have a single axon which projects to the myenteric plexus but does not innervate the mucosa (Song et al., 1998). These neurons are immunoreactive for VIP and a small percentage of submucosal interneurons are immunoreactive for NOS (Furness et al., 1994). Virtually nothing is known about the properties of any interplexus submucosal interneurons in the mouse.
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Motor neurons
Muscle motor neurons
Motor neurons innervate the longitudinal muscles, circular muscles and muscularis mucosa throughout the GI tract. Based on their effects on the muscle they innervate, these neurons can be divided into two groups as excitatory and inhibitory motor neurons (Furness, 2006). The primary neurotransmitters of excitatory motor neurons are acetylcholine and tachykinins. The major neurotransmitter of inhibitory motor neurons is nitric oxide (NO) and, these neurons also express VIP and ATP (Grundy and Brookes, 2012). Muscle-innervating motor neurons are uni- axonal and have S-type electrophysiology (Furness, 2006).
Most circular muscle motor neurons have cell bodies in the myenteric plexus. In the guinea- pig, all circular muscle motor neurons are located in the myenteric plexus (Wilson et al., 1987).
But in other species including rat, dog, pig and human, a proportion of circular muscle motor neurons are in the submucosal plexus (Ekblad et al., 1987, Timmermans et al., 2001, Furness,
2006). Inhibitory circular muscle motor neurons are immunoreactive for NOS or VIP and excitatory circular muscle motor neurons express tachykinin or the vesicular acetylcholine transporter (VAChT) (Furness, 2006). Expression patterns of both inhibitory and excitatory neurotransmitters are well conserved between mouse and guinea pig small intestine (Qu et al.,
2008).
Longitudinal muscle motor neurons have small cell bodies with a single axon and short projections. In small animals, longitudinal muscle motor neurons have cell bodies that are located in the myenteric plexus (Furness, 2006). In large animals, the cell bodies of most of these neurons are located in the myenteric plexus, although some neuronal cell bodies are found in the outer submucosal layer (Furness, 2006).
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4.3 Enteric glia
Enteric glia are a large population of supporting cells derived from neural crest precursors.
Mature enteric glial cells exhibit strong morphological similarities to brain astrocytes and express similar astrocyte-associated markers such as GFAP and S100b (Jessen et al., 1983).
They also express transcription factors such as SOX 10.
The ratio of neurons to glia in enteric ganglia varies substantially along the length of the gut and also between species (Gabella and Trigg, 1984). Myenteric glia are larger than submucosal glia. Four major subtypes of enteric glia are classified based on morphology, the cellular environment and function. Type I “protoplasmic” enteric glia are associated with neurons in enteric ganglia. Type II “fibrous” enteric glia are found within nerve fiber bundles that connect ganglia. Type III “mucosal” enteric glia are associated with nerve fibers and epithelial cells in the mucosa and Type IV “intramuscular” enteric glia are associated with nerve fibers interspersed between smooth muscle cells (Gulbransen, 2014) (Figure 1.5).
Enteric glial cells express a large array of receptors for almost every neurotransmitter and neuromodulator that is present in the gut. Enteric glia express receptors for neurotransmitters including ADP (Gomes et al., 2009, Gulbransen and Sharkey, 2012, McClain et al., 2014),
ATP, UTP, adenosine, norepinephrine (Nasser et al., 2006), glutamate (Nasser et al., 2007), acetylcholine and VIP. Enteric glia also express cell surface ion channels such as voltage-gated sodium channel and potassium channels (Broussard et al., 1993, Hanani et al., 2000,
Costagliola et al., 2009). Glial cells, in particular intra-ganglionic glia, are involved in maintaining homeostasis of enteric neurotransmission. During this process, intra-ganglionic glia provide essential precursors for the synthesis of neurotransmitters including nitric oxide
(NO), glutamate and g-amino butyric acid (GABA) (Aoki et al., 1991, Nagahama et al., 2001,
Jessen and Mirsky, 1983). Enteric glia also provide enteric neurons with antioxidants and growth factors (Abdo et al., 2012, von Boyen et al., 2006) required for survival and growth, as
26
well as ensuring the bioavailability of neuroactive compounds in the extracellular matrix
(Gulbransen, 2014).
Apart from supporting neurons, emerging data suggest that enteric glia are important for many gut functions such as intestinal motility (Nasser et al., 2006, Broadhead et al., 2012, Gulbransen and Sharkey, 2012) and mucosal barrier function (Aube et al., 2006, Savidge et al., 2007,
Neunlist et al., 2013, MacEachern et al., 2015). Glial cells are also implicated in intestinal pathophysiology and they are modulated in inflammation, involved in antigen presentation and release cytokines and neurotrophins (Nasser et al., 2007, MacEachern et al., 2015, Brown et al., 2016).
27
Figure 1.5 The morphological types of enteric glia
A.Type I “protoplasmic” enteric glia are star-shaped with short irregular branched processes.
B. Type II “fibrous” enteric glia are associated with interganglionic fiber tracts C. Type II
“mucosal” enteric glia possess several long, branched processes. D. Type IV “intramuscular” enteric glia are elongated glia running alongside the nerve fibres in the musculature (Figure from Gulbransen and Sharkey, 2012).
28
5. Intestinal mucosal barrier
Remarkably, the intestinal mucosa functions as a barrier by separating the hostile intestinal lumen from the interior of the body. The stratified architecture of the intestinal mucosa ensures efficient sieving of metabolites through its multiple layers including the mucus layer, intestinal epithelium and underlying lamina propria (Figure 1.6). The mucus layer, which is the outermost layer of the mucosa and located adjacent to the epithelium, traps and maintains intestinal microbiota at a distance from the intestinal epithelium. The intestinal epithelium is a semipermeable barrier, mainly responsible for nutrient uptake and waste disposal, and continuously monitors the luminal content and sends signals to the underlying lamina propria to initiate inflammatory responses. The lamina propria mainly contains the intestinal immune system.
In this thesis, the main focus is on how enteric neuronal changes might influence the intestinal epithelium and the mucus layer which directly contact the intestinal microbiome. Therefore, the importance of the intestinal epithelium and the mucus layer in maintaining barrier integrity will be discussed further.
29
Figure 1.6 An illustration of the cross-section of the gut
The intestinal mucosa is made up of the intestinal mucus layer, intestinal epithelium, lamina propria and the submucosal plexus. The intestinal epithelium is the central component of the barrier and is adapted to simultaneously absorb nutrients and eliminate pathogens from the body. The mucosa is innervated mainly by submucosal neurons and also by some myenteric neurons. Enteric glia and some sensory neurons form synaptic connections with “neuron-like” enteroendocrine cells (EEC, green) (Figure from Sharkey, 2015).
30
5.1 Intestinal epithelium
The intestinal epithelium consists of absorptive and secretory cell lineages; enterocytes accomplish the absorptive function and three secretory cell types are responsible for the secretion, including i) mucus-producing goblet cells, ii) antimicrobial peptide-producing
Paneth cells and iii) hormone-releasing enteroendocrine cells. Chemosensory tuft cells and immune reactive M cells are less frequently found in the epithelium.
The intestinal mucosal barrier consists of various extrinsic and intrinsic elements which control the homeostasis of the mucosa. An intrinsic element of the mucosal barrier includes the intestinal epithelial cell layer and the tight junctions which link these cells to form this continuous single layer throughout the GI tract.
5.1.1 Apical junction complex
Epithelial cells are held together via apical junction protein complexes located in the lateral membranes of adjacent cells. As illustrated in Figure 1.7, there are three types of intercellular junctions that give rise to the apical junctional complex; i) tight junctions ii) adherens junctions and iii) desmosomes (Farquhar and Palade, 1963).
31
Figure 1.7 Apical junctional complex
Apical junctional complexes are composed of tight junctions, adherens junctions and desmosomes. The apical lateral plasma membranes of two adjacent cells are fused at the tight junction which is composed of claudin, occludin, Zonular occluden (ZO), junctional adhesion molecule (JAM) and F-actin interact. E-cadherin, a-catenin, b-catenin and F-actin interact to form adherens junctions. Desmosomes are formed by interacting desmoglein, desmocollin, desmoplakin and keratin. (Image from Neunlist et al., 2013).
32
5.1.2 Tight junctions
The tight junction of an apical junction complex is the principal determinant of mucosal permeability. Tight junctions limit the solute flux across the intestinal epithelium and are therefore the rate-limiting factor for paracellular permeability. Different types of proteins in the tight junctions grant the paracellular pathway its selectively permeable nature which distinguishes the molecules passing through intercellular spaces by their size and charge. Tight junctional proteins can be broadly categorised into three groups including transmembrane proteins, scaffolding proteins (Zonular occludens etc.) and regulatory proteins (Odenwald and
Turner, 2017).
Transmembrane proteins are the pore-forming elements of tight junctions. Four integral transmembrane proteins have been investigated to date including occludin, claudins, junctional adhesion molecules and tricelluin (Furuse et al., 1993, Furuse et al., 1998, Ikenouchi et al.,
2005). Of these transmembrane proteins, claudins play the most important functional role in mucosal permeability by forming a pore between adjacent cells to regulate tight junctional ion selectivity (Van Itallie and Anderson, 2006). The role of occludin in barrier regulation is not fully understood, but both animal model and cell culture studies report a crucial nature of occludin in the tight junction structure and barrier function (Findley and Koval, 2009).
The scaffolding proteins are the bridge between transmembrane proteins and actin cytoskeleton of the cell permitting the cell to interact with transmembrane proteins to regulate pore size accordingly. The scaffolding proteins (Zonular occludens (ZO) family of proteins) include
ZO1, ZO2, ZO3 (also known as TJP1, TJP2 and TJP3) (Furuse et al., 1993, Raleigh et al.,
2010, Cording et al., 2013).
33
5.1.3 Paracellular permeability
In the intestine, there are two major pathways that enable the transportation of substances through the epithelium; the transcellular pathway; where material can pass through cellular membranes, and the paracellular pathway where substances travel through tight junction complexes. The plasma membrane of the epithelial cell lining is impermeable to most hydrophilic solutes in the absence of specific transporters and therefore transportation of material across the epithelial barrier is primarily mediated by intercellular junctions (Turner,
2009). Paracellular permeation occurs through two different pathways; i) the pore pathway which allows the flux of small molecules and ii) the leaky pathway, which involves in flux of large macromolecules. The paracellular pathway discriminates between solutes on the basis of their size and charge (Ma et al., 2018). Charge selectivity is essential for creating an ion gradient to support the passive paracellular transport of solutes.
5.1.4 Properties of the paracellular pathway
Transepithelial resistance
The magnitude of material transport across an epithelium is often measured by ionic conductance or its reciprocal; transepithelial electrical resistance, and the level of permeability to paracellular and transcellular markers. Based on ionic conductance, epithelia are categorised as “leaky” or “tight” epithelium (Powell, 1981). The small intestine and colon contain leaky epithelium and the gastric fundus is a typical example of a tight epithelium (Powell, 1981). The transepithelial resistance is the sum of the resistance generated by an asymmetric distribution of proteins on the apical, basolateral and lateral (tight junction proteins) membranes of cells.
Apical and basolateral proteins determine the transcellular resistance. Since the membrane resistance is very high in leaky epithelium such as the small intestine, the contribution of the transcellular resistance is often neglected or considered minimal. In this context, the
34
transepithelial resistance of the paracellular pathway accounts for more than 90% of the total transepithelial resistance and the magnitude of the total transepithelial resistance is mainly conferred by tight junction proteins.
The transepithelial resistance of a tissue is often measured using a standard Ussing chamber apparatus (Clarke, 2009). The typical Ussing chamber set up contains two fluid-filled, electrically isolated chambers which resemble the mucosal and serosal side of the gut when the intestinal tissue is mounted. Each chamber contains two electrodes to measure the voltage and the current across the membrane.
35
Figure 1.8 Electrical circuit model of the intestinal epithelium
The resistance across the transcellular pathway is the sum of the resistance generated by the proteins on the apical (Rap) and basolateral (Rbl) membrane. Resistance across the paracellular pathway is generated by the resistance of tight junction proteins (RTJ) and lateral intercellular spaces (RLIS) (Image from Ma et al., 2018).
36
Charge selectivity
Charge selectivity is important for leaky epithelia where a large proportion of ions flow through the paracellular pathway. It is an essential feature of the paracellular pathway which is crucial for generating electrochemical gradients across an epithelium to support passive paracellular transport. Almost all leaky tight junctions show a preference for Na+ over Cl- ions and the magnitude of charge discrimination varies among epithelia (Artursson et al., 1993, Knipp et al., 1997). Certain protein components of tight junctions confer charge selectivity to the paracellular pathway. Some claudin family members such as claudin 15 are able to distinguish cations and anions (Van Itallie et al., 2003). The first extracellular loop of claudin proteins creates an electrostatic selective filter that controls the charge selectivity of tight junctions. In addition, the charged amino acid side chains on the claudin protein filter the permeation of charged ions (Hou et al., 2005, Van Itallie et al., 2003).
Ion selectivity
Tight junctions discriminate between solutes based on their size. Paracellular permeability monitored with a mixture of polyethylene glycol oligomers with the radii ranging from ~3 to
~7 Å revealed that paracellular pores have high permeability to molecules smaller than ~4 Å radii and relatively low permeability to larger molecules (Watson et al., 2001). Claudin-2 expression increases the flux of polyethylene glycol oligomers with radii less than ~4 Å, demonstrating that the claudin-2 protein confers size selectivity of the paracellular pathway
(Van Itallie et al., 2008). Larger polyethylene glycol oligomers traverse the paracellular pathway at a much lower rate through a size non-restricted route (Shen et al., 2011).
37
5.3.5 Neuronal regulation of epithelial barrier function
Innervation of the intestinal mucosa
The intestinal mucosa is extensively innervated by submucosal and myenteric neurons. Each villus of the guinea-pig small intestine receives axons from 70-92 submucosal neurons (Song et al., 1995). Similarly, the human colon receives same level of innovation from the submucosal plexus (Neunlist et al., 2003b, Porter et al., 1999).
In the small intestine in multiple species, both the crypts and the villi receive axons from secretomotor neurons and similarly colonic crypts and the surface epithelium are innervated by the axons of equivalent neurons. Retrograde tracing from the mucosa in guinea pigs, pigs and humans showed that secretomotor neurons project to the mucosa (Song et al., 1992, Hens et al., 2000, Domoto et al., 1990). Other than submucosal innervation, myenteric secretomotor neurons send axons to the mucosa in guinea pigs and humans (Song et al., 1992, Furness et al.,
1985). In addition to secretomotor innervation, axons from ISNs innervate the mucosa in guinea-pigs and humans. In the guinea-pig ileum, each villus receives axons from about 65 different myenteric ISNs (Bertrand et al., 1998).
Neurons innervating the intestinal mucosa contain various neurotransmitters including VIP, acetylcholine, substance P and neuropeptide Y (Cooke, 1986). Axons from most submucosal
ChAT and VIP expressing neurons whose cell bodies are located in the submucosal plexus also project to the mucosa. VIPergic innervation of the mucosa is prominent both in guinea-pig small intestine and colon and in the mouse (Song et al., 1992, Neunlist et al., 1998, Seillet et al., 2020, Schwerdtfeger and Tobet, 2020).
Neuronal control of paracellular permeability
Growing evidence supports the neuronal regulation of paracellular permeability specifically by the submucosal neurons of the ENS and certain neurotransmitters. Both VIP and ChAT
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containing submucosal neurons are known to increase paracellular permeability in the gut. VIP regulates the expression of key tight junction proteins and other elements connecting enterocyte which determine paracellular permeability (Wu et al., 2015b). In rats, increased macromolecular permeability is mediated largely by cholinergic pathways via muscarinic and nicotinic receptors (Gareau et al., 2007). Direct stimulation of muscarinic receptors located on the jejunal epithelium causes increased paracellular permeability in rabbits (Greenwood and
Mantle, 1992). Furthermore, submucosal neurons regulate intestinal permeability and expression of the tight junction-associated protein ZO-1 via VIPergic pathways in humans
(Neunlist et al., 2003b).
Neuronal regulation of mucosal secretion and absorption.
The epithelial lining of the mucosa is the primary site where absorption and secretion occur.
Absorption of water from the intestinal lumen is coupled with sodium absorption. Sodium absorption occurs via three main mechanisms, predominantly by electrically silent Na+/H+ exchange in both small intestine and in the colon (Said and Ghishan, 2018). Electroneutral
NaCl absorption is attributed to three main Na+/H+ exchangers expressed on the apical membrane of enterocytes: NHE2, NHE3 and NHE8. Amongst those three, NHE3 predominantly contributes to intestinal Na+ and water absorption (Johnson et al., 2012). In the small intestine, Na+ absorption is also coupled to nutrient absorption. It involves the absorption of Na+ via electrogenic Na+/solute symporters for glucose and amino acids (Kato and Romero,
2011). Although NHE3 is the main transporter in the proximal colon, electrogenic Na+ absorption occurs via the epithelial Na+ channel (ENaC) in the distal colon.
Mucosal secretion involves secretion of fluids from absorptive cells, mucus secretion by goblet cells and the secretion of acids, hormones and antibacterial compounds by various other cell
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types. In the intestine, fluid flux occurs through an osmotic gradient generated by the active transportation of ions to increase the electrolyte concentration in the lumen. The osmotic gradient is mainly established by chloride and sodium ions, as well as by bicarbonate ions in certain cases. Overall, however, active chloride transportation largely maintains the osmotic gradient and hence this is the major determinant of net fluid secretion in the intestine.
In terms of active chloride transportation, enterocytes first take up mucosal chloride ions via
Na+ /K+ /2Cl- (NKCCl) cotransporters on the basolateral membrane. The accumulated chloride ions are then secreted to the lumen mainly via cystic fibrosis transmembrane conductance regulator (CFTR) channels on the apical membrane. CTFR channels are activated by increased levels of cAMP. Activation of adenylyl cyclase by several secretagogues leads to elevation of cyclic AMP (cAMP). This leads to increased levels of cAMP in crypt cells which activates the
CFTR channel, resulting in secretion of chloride into the lumen (Bornstein and Foong, 2018).
Activation of adenylyl cyclase is mediated by various factors such as VIP, extracellular PGE2 and other mediators (Bornstein and Foong, 2018).
Increases in intracellular calcium levels also induce mucosal chloride secretion. Acetylcholine, another major secretomotor neurotransmitter acting on M3 muscarinic receptors, induces the release of Ca2+ ions from intracellular calcium stores. This process opens basolateral Ca2+- gated potassium channels creating an electrochemical gradient for chloride ions, which are then secreted into the lumen via CFTR channel (Dharmsathaphorn and Pandol, 1986, Bornstein and
Foong, 2018).
Glial control of intestinal barrier function
Enteric glia are key regulators of mucosal barrier function. A dense network of S100β positive enteric glia was reported along the crypt-villus axis with a high concentration at the base of the crypts (Mestres et al., 1992, Neunlist et al., 2007). Mucosal glia regulate barrier function by releasing factors such as S-nitrosoglutathione, transforming growth factor-b and pro-epidermal
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growth factor which influence the differentiation, adhesion, migration, proliferation of epithelial cells (Savidge et al., 2007, Neunlist et al., 2007, Van Landeghem et al., 2011).
Evidence for glial regulation of intestinal barrier function comes from models of conditional ablation of enteric glial cells in vivo, which induces a fulminant jejuno-ileitis causing increased paracellular permeability together with intestinal inflammation (Bush et al., 1998). In addition, increased paracellular permeability in the absence of intestinal inflammation has been reported in a transgenic mouse model of enteric glia disruption (Aube et al., 2006). Experiments utilizing a non-contact co-culture model of enteric glia and an intestinal epithelial monolayer show that enteric glia increase transepithelial resistance and decrease paracellular permeability due to upregulation of tight junction proteins (Savidge et al., 2007).
5.3.5 Epithelial barrier, GI distress and ASD
Increased intestinal permeability has been reported in autism patients as well as in various animal models of autism. Most clinical data to date originate from sugar permeability tests where the urine recovery of two sugars (mannitol and lactulose) with different molecular sizes and absorption routes is measured (D'Eufemia et al., 1996). Mannitol is excreted through the transcellular pathway where the sugar passes through aqueous pores in the cell membrane. In contrast, lactulose passes through intercellular tight junctions and extrusion zones at the small intestinal villus tips. Increased urinary recovery of lactulose was reported in autistic patients, suggesting altered gut permeability is due to altered levels of intestinal tight junction proteins
(de Magistris et al., 2010). Children with autism reportedly show relatively high intestinal permeability (compared to age-matched controls) even in the absence of GI abnormalities
(D'Eufemia et al., 1996). Alterations in intestinal permeability as identified by sugar permeability assays in first-degree relatives of individuals with autism suggest the presence of hereditary factors in these families that potentially alter levels of tight junction proteins (de
Magistris et al., 2010).
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Altered expression levels of genes encoding tight junction proteins are reported in autism patients as well as in animal models of autism confirming disrupted mucosal barrier function at the molecular level. Altered expression of tight junction proteins in the intestinal barrier as well as in the blood-brain barrier has been reported in ASD individuals. In those patients,
CLDN5 and CLDN12 levels were increased in postmortem cortex and cerebellum tissue samples and CLDN3, tricellulin and MMP9 were increased in the cerebral cortex tissue. In the intestine, reduced expression of CLDN1, OCLN and TRIC along with increased levels of expression of CLDN2, CLDN10 and CLDN15 were observed (Fiorentino et al., 2016).
Showing similarities to findings in patient tissue samples, a decrease in gene expression of ZO-
1, ZO-2, OCLN and CLDN8, and increased expression of CLDN15 were observed in the rodent offspring of maternal inflammation activation model of autism (Hsiao et al., 2013).
Despite changes to the paracellular permeability, no focused studies have assessed the impact of ASD on neurally mediated barrier functions in detail. Therefore, studying the mucosal barrier in transgenic models of autism shed light on understanding biological processes contributing to GI dysfunction in ASD.
5.2 Intestinal mucus layer
Certain material presented in this section has been published in the Frontiers in Cellular and
Infection Microbiology (2020); 10:248. The review article is attached in the appendix.
The mucus layer is the first line of defence against infiltration of microorganisms, digestive enzymes and acids, digested food particles, microbial by-products, and food-associated toxins.
This layer coats the interior surface of the GI tract, lubricates luminal contents and acts as a physical barrier to bacteria and other antigenic substances present in the lumen. The moist, nutrient-rich mucus layer adjacent to the epithelial barrier of the GI tract is also essential in the
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maintenance of intestinal homeostasis and contains a thriving biofilm including beneficial and pathogenic microbial populations.
5.2.1 Goblet cells
The intestinal epithelium consists of absorptive and secretory cell lineages including enterocytes, enteroendocrine cells (EECs), Paneth cells, and goblet cells. Goblet cells are specialized cells equipped with specific biological machinery for the secretion of mucus and are present throughout the entire length of the intestine. These cells, as their name suggests, are easily identifiable in histologically stained cross-sections of the intestine due to their characteristic “goblet-like” shape. Intestinal epithelial cells, including goblet cells, arise from multipotential stem cells residing at the base of the intestinal crypts and subsequently migrate from the crypts to the top of the villus prior to eventually being shed into the lumen (Cheng and Leblond, 1974). In mice, this migratory process occurs over 2–3 days (Specian and Oliver,
1991). Differentiation of goblet cells is directly controlled by the transcription factor SAM pointed domain-containing ETS transcription factor (Spdef) (Noah et al., 2010) and also via a network of transcriptional factors regulated by the Notch and Wnt signaling pathways known to influence developmental and inflammation pathways (van Es et al., 2005, Clarke, 2006, Fre et al., 2009, Gersemann et al., 2009, Gregorieff et al., 2009, Kwon et al., 2011, Heuberger et al., 2014, Tian et al., 2015). Furthermore, enteric neural activity has been shown to influence the maturation and production of stem cells in the GI tract (Lundgren et al., 2011) which, in turn, suggests a role for the ENS in goblet cell proliferation and differentiation.
Goblet cell morphology changes dramatically during their cellular lifespan (Specian and
Oliver, 1991). Immature goblet cells are larger and pyramidal in shape with cellular organelles dispersed throughout the cell and interspersed with mucus granules in the apical cellular region.
As these goblet cells migrate toward the colonic epithelial surface, they reduce in volume,
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shedding cytoplasmic content and organelles that are trapped between mucus upon secretion of mucus granules. During this phase of volume reduction, goblet cells reduce contact with the basal laminar surface adjacent to the epithelium and simultaneously increase contact with the luminal surface of the GI tract. The goblet cells then rapidly produce and store mucus granules, resulting in the distention of the apical cellular region to produce the typical “cup” shape. The nucleus and other cellular organelles of the goblet cells are concentrated in narrowed stem-like subcellular regions located at the base of the cells (Specian and Oliver, 1991).
5.2.2 Regional variations of the mucus layer
Although mucus located throughout the gut contains the same biological components, mucus properties vary with regional differences in function along the gastrointestinal tract (Ermund et al., 2013, Figure 1.9).
The majority of nutrient uptake from digested food occurs in the small intestine and therefore there is a single, discontinuous and more penetrable mucus layer in this region (Johansson et al., 2011). The discontinuity of the small intestinal mucus layer is important not only for the absorptive function of this region but also for the release of digestive enzymes localized in the brush border membrane of epithelial cells. Experiments assessing passage of fluorescent beads across small intestinal mucosal samples showed that small intestinal mucus in mice is penetrable by beads equivalent to the size of bacteria (i.e., 0.5–2 μ3) and hence contains pores as large as 2 μ2 (Ermund et al., 2013b). These large mucus pores ensure efficient nutrient absorption by the host epithelium.
The bacterial content of the mucosal barrier in the small intestine is also regulated by a cocktail of antibacterial mediators such as defensins, lysozymes, and other peptides released by Paneth
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cells (Peterson et al., 2007). Together, these mediators repel bacteria by generating an antibacterial gradient toward the lumen (Vaishnava et al., 2011, Johansson and Hansson, 2011).
Specific mediators include the abundant Regenerating islet-derived 3 (REG3) peptides, IgA,
Toll-like receptor 5 (TLR5 regulates levels of anti-flagellin antibody in the gut) (Cullender et al., 2013) and phospholipase A2-IIA (Meyer-Hoffert et al., 2008, Bevins and Salzman, 2011).
Overall, antibacterial peptides kill bacteria via a range of mechanisms including by the formation of aggregates, recognition, and binding to bacterial cell wall peptidoglycans, and permeabilization of bacterial cell membranes (Chairatana and Nolan, 2017). This serves to neutralize invasion by foreign particles and maintain epithelial crypts. This antimicrobial defence mechanism is critical in the small intestine due to the discontinuous and penetrable nature of the mucus in this region and is reflected by a higher density of Paneth cells and corresponding peptides (Ouellette, 2010).
The organization of the mucus layer varies along the length of the colon. In the distal colon, there are two layers of mucus, but whether these layers adhere to the epithelium or the colonic content is under debate. In the proximal colon, the presence of two mucus layers has been queried based on histological studies in animal models (Kamphuis et al., 2017, Ermund et al.,
2013a). This discrepancy is mainly due to the shrinkage of the mucus layer during fixation.
Such shrinkage could leave gaps in between the epithelium and the mucus layer compared to observing the mucus layer in live tissues (Furter et al., 2019).
Johansson and colleagues reported that the mouse distal colon contains two continuous mucus layers; an inner mucus layer that is ∼50μm thick and anchored to the mucus-producing goblet cells of the epithelial membrane, and an outer mucus layer that is loosely adherent and harbors bacteria (Johansson et al., 2008). These researchers also reported that the thickness of the outer mucus layer is determined by the composition of the mucus-inhabiting bacteria. Interestingly,
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this group reported that the inner mucus layer of the proximal colon is also penetrable to bacteria (Ermund et al., 2013b). In contrast, Kamphuis and colleagues reported that the two distal colonic mucus layers adhere to the fecal pellet rather than the intestinal epithelium in rodents and that the organization of the colonic mucus layers is dependent on the presence of fecal content (Kamphuis et al., 2017). Specifically, this study utilized fluorescence in situ hybridization and histological techniques in longitudinal sections to demonstrate that the fecal pellet is covered by a sterile mucus layer of variable thickness that is not attached to the epithelium. They also showed that within the proximal part of the proximal colon, which contains colon content prior to the formation of a fecal pellet, the mucus layer is loosely organized and the bacteria in this region are in contact with the epithelial surface (Kamphuis et al., 2017).
The dissimilarities in the mucus layers of the colon reported may be due to methodological variations including the orientation of tissue sectioning and mucus staining techniques. Overall, multiple studies examining mucus properties carried out in both mice (Mark Welch et al., 2017,
Motta et al., 2015, Macfarlane et al., 2011) and humans (Swidsinski et al., 2007a) describe two mucus layers in the colon that include a firm mucus layer adjacent to the epithelium that is devoid of bacteria.
Some commensal bacteria secrete mucinases and proteinases that continuously degrade the outer mucus layer contributing to its highly disorganized nature (Donaldson et al., 2016).
Similarly, a role for bacteria in mucus thickness has been demonstrated in germ-free mice which have a more penetrable colonic mucus layer (Johansson et al., 2015). Simply adding components of the bacterial cell wall (e.g., lipopolysaccharide; LPS) is sufficient to increase mucus thickness in this model, highlighting a role for bacteria in regulating the structure of the outer mucus layer (Petersson et al., 2011). The continual release of mucus contributes to a
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dynamic process whereby the inner mucus layer is gradually converted to the irregular and less adherent outer mucus layer. In the small intestine, this process involves Meprin β, an endogenous protease which aids mucus detachment (Wichert et al., 2017) whereas in the colon the inner mucus layer is expanded by endogenous protease activity (Johansson et al., 2008).
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Figure 1.9 The structure of the mucus layer varies with regional locations within the GI tract.
(A) The small intestine contains a single layer of mucus, which is loosely attached to the epithelium and easily penetrable. Bacteria within the small intestine are primarily repelled from the epithelium by antibacterial modulators. Paneth cells and antimicrobial peptides are produced when physiological conditions are altered. (B) The distal colon contains two mucus layers; a stratified adherent inner mucus layer and a loosely adhesive outer mucus layer. The inner mucus layer of the colon is essentially sterile and the outer mucus layer harbors the intestinal microbiota (Image from Herath et al., 2020).
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5.2.3 Mucus composition
Mucus is primarily composed of branched glycoproteins (including mucins) that interact with the external environment and via their hydrophilic nature, influence mucus viscosity
(Bergstrom and Xia, 2013). There are more than 20 subtypes of mucin identified in humans and their distribution varies throughout the GI tract. For example, the salivary glands produce
MUC5B and MUC7 to lubricate food (Khan et al., 1998, Bobek et al., 1993, Thornton et al.,
1999) and the mucus layer in the stomach contains MUC5AC (Ho et al., 1995, Atuma et al.,
2001, Nordman et al., 2002). Although MUC5AC is not typically expressed in the large intestine, it has been detected in the distal colon along with MUC-2 during inflammation associated with ulcerative colitis and adenocarcinoma in patients (Forgue-Lafitte et al., 2007).
It is well-established that the major glycoprotein within the intestinal mucus layer is mucin-2
(MUC- 2 protein).
There are three major structural domains within the MUC2 protein; the N-terminal domain, a central large PTS (proline, threonine, and serine) domain and the C- terminal domain. Although the main component of mucus in the small intestine and the colon is mucin-2, a rich variety of other proteins including IgG Fc-binding protein (FCGBP), Calcium-activated chloride channel
1 (ClCA1), Zymogen granule membrane protein 16 (ZG16), Anterior gradient 2 (AGR2), and immunoglobulins are produced and actively secreted by goblet cells (Johansson et al., 2008).
5.2.4 Mucus production and packaging into secretory granules
Following translation, full-length MUC2 protein cores form dimers via disulfide bridges near their C-terminus within the endoplasmic reticulum (ER) of goblet cells. Within the Golgi apparatus, the MUC2 proteins undergo O-linked glycosylation. In this process glycans such as xylose, mannose, N-acetylglucosamine and N-acetylgalactosamine (O-GalNAc) (Godl et al.,
2002) are covalently attached to the hydroxyl group (-OH) of threonine and serine residues of
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the PTS domain. Glycans account for 80% of the total mass of the MUC2 protein and extend perpendicularly from the protein core giving the molecule a ‘bottle brush-like’ appearance. The branched structure of glycan allows interaction with the external environment and the hydrophilic nature of these glycans influences mucus viscosity (Bergstrom and Xia, 2013). O-
Glycans can be modified via formation of linkages with sulphate, sialic acid and fucose to shape a range of biologically relevant functional mucus types that play important roles in host- microbial interactions (Arike and Hansson, 2016). A complex polymerization process occurs within the trans-Golgi network by which MUC2 protein dimers interact firstly as trimers and then are tightly bundled into MUC2 secretory granules (Ambort et al., 2012, Godl et al., 2002).
5.2.5 Mucus secretion
Mucus secretion is influenced by nervous system activity and occurs via two processes; (i) vesicle secretion and (ii) compound exocytosis. During vesicle secretion, mucus-secreting goblet cells release their mucus content by fusion of the mucus granule membrane with the overlying plasma membrane (Lang et al., 2004). This process is regulated by vesicle exocytotic components like syntaxin, Munc 18, vesicle-associated membrane proteins (VAMPs) and synaptosomal nerve- associated protein (SNAP) proteins (Cosen-Binker et al., 2008). During compound exocytosis, all mucus granules are fused together and empty the mucus as a single unit. The molecular pathways regulating compound exocytosis have not yet been defined.
5.2.6 Neuronal regulation of mucus secretion
VIP and ACh are the two main secretagogues responsible for neurally-evoked mucosal secretion (Specian and Neutra, 1980), (Neutra et al., 1984), (Lelievre et al., 2007), (Gustafsson et al., 2012a, Ermund et al., 2013b). ACh induces mucus secretion by activating M3 muscarinic receptors located on goblet cells within the epithelium in both the small intestine and the colon
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(Specian and Neutra, 1980, Neutra et al., 1984, Gustafsson et al., 2012b, Ermund et al., 2013b).
Exocytosis of mucus-containing granules is regulated by intracellular Ca2+ and Ca2+- mobilizing agents including acetylcholine (Birchenough et al., 2015). The activation of M3 muscarinic receptors mobilizes Ca2+ from intracellular stores to induce mucus secretion
(Ambort et al., 2012).
Mucus release is differentially regulated in a region-specific manner in the GI tract. ACh specifically targets both crypt and villus-associated goblet cells in the small intestine
(Birchenough et al., 2015). In contrast, in the colon, goblet cells located in crypts are responsive to ACh, but equivalent cells at the epithelial surface do not respond to ACh or the cholinergic agonist, carbachol (Gustafsson et al., 2012a). Release of the neuropeptide VIP enhances mucus secretion (Lelievre et al., 2007) via modulating CFTR-dependent secretions (Alcolado et al.,
2014). Furthermore, VIP deficiency in mice results in reduced goblet cell number and reduced muc-2 gene expression levels (Wu et al., 2015b). A recent study showed that mucosal VIP- containing neurons are in close proximity with ileal goblet cells and VPAC receptor antagonist alter the goblet cell numbers in the ileum (Schwerdtfeger and Tobet, 2020).
5.2.7 Mucus impairments and gut dysfunction
Preclinical models have demonstrated that abnormalities in GI structure and function are associated with altered mucus production. For example, the colonic mucus layer thickness is decreased alongside progressive inflammation in a mouse model of colitis (Petersson et al.,
2011). In the absence of an inner mucus layer, bacteria can penetrate deep into the epithelial crypts and interact with the colonic epithelium (Johansson et al., 2010) which can exacerbate the disease. Furthermore, multiple studies report that alterations in mucus secretory processes
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result in an underdeveloped colonic inner mucus layer, often associated with sparsely filled goblet cells and increased susceptibility to colitis (Park et al., 2009, Stone et al., 2009, Fu et al., 2011, Tsuru et al., 2013, Bergstrom et al., 2014).
Mice lacking the mucus protein MUC2 (MUC2−/− mice) lack an inner colonic mucus layer despite the presence of goblet cells and other mucus layer components. Interestingly, Rahman and colleagues showed changes in colonic innervation in mice expressing a point mutation in
Muc-2 (Rahman et al., 2015) highlighting interactions between mucus production and innervation of the GI tract. Knockout mice also exhibit altered intestinal cell maturation, migration, and abnormal intestinal crypt morphology (Velcich et al., 2002). These mice develop adenomas and rectal tumours as well as increased infiltration of neutrophils and lymphocytes, loose stools, diarrhea with blood, rectal prolapses, and fail to thrive (Petersson et al., 2011, Velcich et al., 2002). In the longer term, these mice also show increased susceptibility to developing colon cancer (Velcich et al., 2002, Van der Sluis et al., 2006).
Patients with cystic fibrosis are commonly diagnosed with concomitant GI abnormalities including meconium ileus and distal intestinal obstruction syndrome (Colombo et al., 2011) due to an increase in secreted mucus volume, mucus dehydration, and increased viscosity that contributes to blockage of the small intestine. Both mucus build-up and reduced mucus movement occur in these patients due to dysregulated mucus secretion. Cystic fibrosis is caused by mutations in the gene encoding the Cystic Fibrosis Transmembrane Conductance Regulator
(CFTR) channel important for mucus hydration. These mutations cause defective chloride ion transport out of epithelial cells and dehydration of mucus overlying the epithelium. In patients, mucus remains tightly attached to the small intestinal epithelium and peristaltic movements fail to propel the mucus forward within the GI tract. In keeping with these changes, an increased bacterial load has been observed in cystic fibrosis patients (O'Brien et al., 1993), likely due to
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the elevated volume and viscosity of mucus that provides an ideal environment for commensal microbes. Mouse models expressing CFTR mutations also display severe intestinal dysfunction and a mucus layer that is firmly attached to the mucosal epithelium (Grubb and Gabriel, 1997,
Seidler et al., 2009, Frizzell and Hanrahan, 2012). Since a prominent role of mucus is to trap and transport bacteria to the distal regions of the gastrointestinal tract via peristalsis, animal models provide an excellent experimental tool to investigate the effects of mucus perturbation on microbial dysbiosis.
Extreme effects of neuronal loss on goblet cell function and on mucus layer properties have been observed in Hirschsprung disease, a life-threatening developmental disorder where the distal colon lacks enteric neurons due to the failure of neural crest cells to completely migrate during gastrointestinal development. Patients with Hirschsprung disease have a reduced mucin turnover rate, a decreased goblet cell population and reduced expression of Spdef and Krueppel like factor 4 which drive goblet cell differentiation and maturation (Aslam et al., 1997,
Nakamura et al., 2018). These findings highlight the importance of the ENS in the development and function of mucus-producing goblet cells in the clinical setting.
Mouse models of Hirschsprung Disease additionally provide evidence for neural-mucus interactions. For example, endothelin receptor B knockout mice (Ednrb−/− mice) along with mice expressing a mutation in the RET gene that encodes the receptor for the glial cell line- derived neurotrophic factor (GDNF) are well-characterized models which have been examined for alterations in mucus and goblet cell structure. Mice lacking endothelin receptor B, known for its role in angiogenesis and neurogenesis, show colonic aganglionosis resembling the clinical presentation. Ednrb−/− mice showed an increase in both goblet cell numbers and size as well as increased expression of Spdef and Math 1 transcription factors in the distal colon
(Thiagarajah et al., 2014). In addition, the absence of Ednrb in mice alters mucus structure as
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evidenced by reduced permeability to 200 nm nanoparticles in vitro (Thiagarajah et al., 2014,
Yildiz et al., 2015). Furthermore, significant differences in the commensal microbiome were also present in this model (Ward et al., 2012).
The absence of GDNF signalling in mice similarly results in a severely underdeveloped ENS.
Furthermore, these mice have altered mucus composition and mucus retention (Porokuokka et al., 2019). Overall, these clinical and animal model data illustrate the involvement of the nervous system in the regulation of goblet cell differentiation and maturation as well as influencing mucus properties.
Although the importance of mucus layer integrity has been well addressed in various GI disorders, how the mucus layer is affected by nervous system associated disorders has not been investigated.
6. Mucosal microbiome
Microbial populations are spatially organized along the length of the intestine as well as from the luminal to mucosal axis (Palestrant et al., 2004). Mucus viscosity increases toward the distal region of the GI tract. This viscosity gradient along the length of the GI tract reportedly determines the spatial distribution of intestinal microbiota (Swidsinski et al., 2007b). The composition of bacteria adjacent to the mucosa is different to the bacterial populations that reside within the luminal content (Swidsinski et al., 2005). This mucosal to luminal bacterial distribution is likely driven by various factors including pH, oxygen levels and nutrient availability (Yasuda et al., 2015).
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6.1 Distinct microbial populations inhabit the mucus layer
Mucus residing bacterial populations are distinct compared to faecal bacterial populations primarily in that mucosal bacteria have the ability to secrete specific enzymes to digest mucus glycoproteins. Certain bacterial species known as adherent bacteria use O-glycan as ligands to adhere to mucus layer (Juge, 2012). For example, pathogenic bacteria are known to bind to mucins by expressing specific proteins, pili, fimbriae and flagella (Sicard et al., 2017).
The mucus layer serves as a carbon and energy source, predominantly in the form of glycans, for mucus residing bacteria. As an adaptation to residing in a glycan-rich environment, these bacteria produce mucus-degrading enzymes such as glycosidase, sulphatase, and sialidases that cleave the mucus network to enhance the utilization of mucus as an energy source. A range of mucus-degrading bacteria present within the mucus, includes Akkermansia muciniphila
(Derrien et al., 2004), Bacteroides thetaiotaomicron (Xu et al., 2003), Bifidobacterium bifidium
(He et al., 2001), Bacteroides fragilis (Macfarlane and Gibson, 1991), and Ruminoccous gnavus (Png et al., 2010). These bacterial species cleave mucus O-glycans to produce monosaccharides (Berry et al., 2013) which can be further utilized by other mucus-residing bacteria including Lachnospiraceae (Nava et al., 2011), Clostridium cluster XIV (Van den
Abbeele et al., 2013), Enterobacteriaceae (Ashida et al., 2008), and Clostridium difficile (Ng et al., 2013). Further adaptation of bacteria has been identified in Lactobacillus (Etzold and
Juge, 2014) and Bacteroides (Sicard et al., 2017) where the presence of multi-repeat cell- surface adhesins enable retention of the bacteria within the mucus layer. The syntrophic, symbiotic, and mutualistic interactions of the microbes in the mucus layer create the environment which drives microbial community selection and defines physical properties of the mucus layer.
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Some mucus residing bacteria form mucosal biofilms, complex microbial communities embedded in a polymeric matrix. Techniques including fluorescent in situ hybridization and electron microscopic studies reported the presence of bacterial biofilms in the healthy colon of mice, humans and rats (Swidsinski et al., 2005, Palestrant et al., 2004, Motta et al., 2015,
Macfarlane et al., 2011, Bollinger, 2007). Altered levels of biofilm-associated bacteria such as
Bacteroides fragilis, and the Enterobacteriaceae family were reported in inflammatory bowel disease (Masseret et al., 2001, Macfarlane and Dillon, 2007, DuPont and DuPont, 2011,
Srivastava et al., 2017). Therefore, the mucus associated bacterial biofilm also could play a role in these disorders. Alterations in these complex community structures could result in abnormal mucus invasion, epithelial adherence, and spatial distribution of bacterial species.
6.2 Microbial dysbiosis in ASD
Research into the role of the microbiota-gut-brain axis is an emerging field and addresses the interactions between the gut microbiome and the nervous system with the potential to shed light on neuropathologies of ASD.
Microbiome sequencing data and in situ hybridization techniques show microbial shifts in ASD patients (Xu et al., 2019). Furthermore, manipulation of the microbiome in animal models of autism exhibited potential microbiome mediated behavioural changes. Faecal bacterial profiling data strongly support the notion of microbial dysbiosis in autistic patients. ASD patients exhibited higher abundance of the genus Clostridium (Parracho et al., 2005, Finegold et al., 2002, Song et al., 2004) which synthesizes metabolic products potentially toxic to humans including phenols, p-cresol and indole derivatives. Overall, ASD patients show decreased Bacteroidetes/Firmicutes ratios, increased Desulfovibrio species and different composition of lactic acid bacteria including Lactobacillus, Enterococcus and Streptococcus
(Buie et al., 2010a), although there is significant variation in the reported microbial changes between such clinical studies (Mayer et al., 2014a). Moreover, bacterial genera including
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Prevotella, Coprococcus and Veilonellaceae, which aid carbohydrate degradation and fermentation, are decreased in some ASD patients (Kang et al., 2013). On the other hand, some bacterial genera including Bacteroides, Alistipes were found to be present in higher levels in
ASD patient faecal samples (Kang et al., 2013). Bacteroides species produce metabolic products that can affect GI pathology lipopolysaccharides and propionic acid produced by
Bacteroides fragilis and act as bacterial virulent factors (Jotwani and Gupta, 1991). For example, propionic acid influences behavioural, electrophysiological, neuropathological and biochemical effects on adult rat brain and is suggested to play a role in ASD (MacFabe et al.,
2007). Other studies also reported that the abundance of Bifidobacterium species is decreased in faecal samples in ASD individuals (Wang et al., 2011, De Angelis et al., 2015). Other studies identified higher levels of bacterial species in the family Enterobacteriaceae in fecal samples from children with autism (Salazar et al., 2008, De Angelis et al., 2013). In addition to these findings, increased levels of Porphyromonas, Pseudomonas, Aeromonas, Lachnospiraceae and Rumiococcacaceae in faecal samples from ASD patients were reported (De Angelis et al.,
2013). Fecal metagenomic profiles of children with ASD show higher levels of Actinobacteria,
Proteobacteria, Bacilli, Erysipelotrichi and Gammaproteobacteria compared to the healthy group (Plaza-Diaz et al., 2019).
6.3 Dysbiosis of mucus associated microbial populations in ASD
In neurological disease, changes in mucus properties could additionally alter commensal microbial populations. Dysbiosis has been reported for the mucus-residing microbiome in patients with various neurological disorders including autism, Parkinson’s disease,
Alzheimer’s disease, and multiple sclerosis (reviewed in Herath et al., 2020).
Shifts in mucus-residing bacteria have been observed in ASD patients (De Angelis et al., 2013,
Luna et al., 2017). Finegold and colleagues reported the presence of Akkermansia muciniphila
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at higher levels in ASD children compared to controls (De Angelis et al., 2013). Discrete microbial signatures have also been found in mucosal samples from children with ASD compared to controls who both had GI pain: a significant increase in several mucus associated
Clostridiales and a marked decrease in Dorea, Blautia and Sutterella was observed (Luna et al., 2017). Faecal bacterial profiling in ASD patients further revealed elevated levels of
Sutterella species which are known to modulate mucosal barrier integrity and mucosal metabolism, however, little is known of their potential interactions with the intestinal epithelium (Williams et al., 2012).
Nonetheless, although the ASD evoked changes to the mucosal microbiome has been reported, how nervous system implicated modifications alter the spatial distribution of mucosal microbial populations is yet to be elucidated.
6.4 Effect of the microbiome on the ENS and intestinal functions
The intestinal microbiota influences the ENS in terms of enteric neural development, neuronal activity and plasticity. Evidence for the effects of the microbiome on ENS development predominantly comes from germ free animal studies (Luczynski et al., 2016). Effects of germ- free conditions on the myenteric plexus architecture have been reported in rats and mice. For example, germ free environment significantly alters myenteric nerve fibre density, total neuronal number, number of NOS and calbindin positive neurons in the small intestine
(Husebye et al., 2001, Collins et al., 2014, McVey Neufeld et al., 2015). Further, certain microbes, particularly Saccharomyces boulardii and Pediococcus acidilactici can influence the neurochemistry of the ENS (Kamm et al., 2004, di Giancamillo et al., 2010).
There is ample evidence to suggest that the microbiome can alter enteric neuronal activity. In germ free mice, electrophysiological characteristics of ISNs are different compared to observations in ISNs from conventional mice (McVey Neufeld et al., 2015). In Lactobacillus
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rhamnosus fed animals, AH neurons showed increased excitability while no change to the S type neurons (Kunze et al., 2009). Certain bacterial species can influence the secretomotor neuronal functions in the gut. Lactobacillus salivarius influences secretomotor functions ex vivo and similar results have been observed in germ free colon (Lomasney et al., 2014).
Bacterial toxins can affect multiple mechanisms which alter neuronal function. Enterotoxins such as cholera toxin can directly influence the ENS to stimulate secretomotor reflexes (Popoff and Poulain, 2010).
The intestinal microbiome also influences mucosal functions such as intestinal permeability.
Enteric pathogens such as E. coli and Salmonella typhimurium alter tight junction protein expression leading to intestinal inflammation (Berkes et al., 2003). An outer cell wall component of gram-negative bacteria known as lipopolysaccharide (LPS) is responsible for alterations in tight junction assembly. Further, LPS can also impact the expression of tight junction proteins such as ZO-1 and occludens (Gu et al., 2011). Moreover, endotoxins from
Clostridium perfringens, Salmonella typhimurium, Shigella flexneri, Vibrio cholerae induce functional alterations to tight junctions (Berkes et al., 2003).
7. Genetic mutations and ASD
Genetic factors are a major cause of ASD. Findings from family and twin studies strongly emphasized the genetic contribution to ASD and suggesting it is one of the most common genetic neuropsychiatric disorders. Recurrence risk of autism in siblings of autistic children is
~ 45 times greater than in the general population and a higher concordance rate is seen in monozygotic (60-91%) than in dizygotic twins (0-6%) (Constantino and Todd, 2003, Ronald et al., 2006). Although ~65-90% of autistic traits can be explained by genetic factors, identification and modelling of genetic risk factors has been challenging due to unknown contributions of a highly heterogeneous genetic architecture, polygenic or oligogenic modes of
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inheritance and the presence of significant gene-gene and gene-environment interactions
(Chaste and Leboyer, 2012, de la Torre-Ubieta et al., 2016). Multiple gene interactions or rare mutations are implicated in autism, which further complicates such studies. At least 1031 genes are associated with ASD and comorbid conditions, where 262 genes are involved only in autism and 779 involved in ASD and at least one comorbid condition (David et al., 2016).
Interestingly many of these genes are involved in synaptic cell adhesion molecule pathways.
7.1 Synaptic cell adhesion molecules and ASD
Many gene mutations associated with ASD in patients encode proteins involved in neuronal communication including synaptic adhesion molecules (Bourgeron, 2015). Synaptic adhesion molecules connect pre and postsynaptic membranes in either a heterophilic or homophilic manner. These molecules play a key role in synapse development, function and plasticity
(Dalva et al., 2007). The synaptic adhesion molecules also interact with cytoplasmic elements such as scaffolding proteins (postsynaptic density protein 95, SHANK), cytoskeleton and cell signalling molecules and therefore aid in downstream signalling cascades (Iida et al., 2004,
Harris and Lim, 2001). A well-characterised synaptic cell adhesion molecule pathway implicated in autism is the neuroligin-neurexin pathway.
7.2 Neuroligins
NLGNs are type 1 postsynaptic membrane proteins which were first identified as postsynaptic binding partners for neurexins located at the presynapse (Ichtchenko et al., 1995). NLGN is composed of a large extracellular domain with an esterase-like sequence, a single transmembrane domain and a short cytoplasmic tail (Ichtchenko et al., 1996, Ichtchenko et al.,
1995). NLGNs form constitutive oligomers via the extracellular domain (Sudhof, 2008). The
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cytoplasmic tail of NLGNs contains a type I PDZ-domain binding motif that recruits synaptic proteins to aid intracellular protein-protein interactions and signalling process (Kim and Sheng,
2004, Bolliger et al., 2001).
Four NLGN isoforms have been identified in mammals and an additional NLGN homologue is present on the Y chromosome in humans. These NLGNs include NLGN1, NLGN2, NLGN3 and NLGN4X and NLGN5Y. The sequences of NLGN1, NLGN3, NLGN4 are more similar to each other than NLGN2. Despite high sequence similarity between each NLGN isoforms, the cellular distribution and the function of every NLGNs are distinct (Bolliger et al., 2001,
Ichtchenko et al., 1996). NLGNs are specifically localized to particular synapses. NLGN1 is exclusively expressed at excitatory synapses (Song et al., 1999), NLGN2 and NLGN4 are localized at inhibitory synapses (Varoqueaux et al., 2004, Hoon et al., 2011) and NLGN3 is present at both excitatory and inhibitory synapses (Budreck and Scheiffele, 2007).
NLGNs are arranged into homodimers (Comoletti et al., 2003, Arac et al., 2007, Fabrichny et al., 2007, Poulopoulos et al., 2012). Biochemical analysis showed that NLGNs function at the postsynaptic membrane are in dimeric state. Dimerization occurs in the early secretory pathway and assembled into dimers is a prerequisite for NLGNs to traffic to the postsynaptic membrane (Poulopoulos et al., 2012). NLGNs are also assembled into heterodimers.
NLGN1/NLGN3 and NLGN1/NLGN2 heterodimers have been observed in vitro (Poulopoulos et al., 2012, Budreck and Scheiffele, 2007).
The PDZ binding motif of NLGNs interacts with other synaptic proteins including PDZ domain scaffolding protein PSD-95, Shank, collybistin and gephyrin (Irie et al., 1997, Meyer et al.,
2004, Iida et al., 2004). These proteins link NLGNs to downstream signalling cascades, particularly PSD-95 which is associated with ion channels and neurotransmitter receptors
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including GABAergic, glutamatergic, and NMDA receptors (Kim et al., 1995, Levinson et al.,
2005, Hu et al., 2015).
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Figure 1.10 Dimerization of neuroligins at the synaptic membrane.
Structure of the neuroligin/neurexin complex at the synaptic membrane (left). Calcium ions located at the neuroligin-neurexin surface are shown in grey spheres. Schematic of the neuroligin (blue)/ neurexin (purple) complex (right). Two neuroligin molecules indicated by dark blue and light blue form a dimer and bound to neurexin shown in purple. Locations of gene mutations which inhibit dimerization are indicated in orange (Figure from Shipman and
Nicoll, 2012).
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7.3 Function of NLGNs
Cell-based assays of synaptic assembly showed that NLGN expressing non-neuronal cells induce the recruitment of presynaptic markers in co-cultured neurons (Scheiffele et al., 2000).
Overexpression of NLGNs in neurons leads to a dramatic increase in neuronal synapse density with specific effects of NLGN1 on excitatory and NLGN2 on inhibitory synapses (Chubykin et al., 2007, Chih et al., 2005, Boucard et al., 2005). Moreover, microRNA-mediated knockdown of NLGNs results in decreased synapse and spine numbers in the hippocampus
(Shipman et al., 2011, de Wit et al., 2009). Taken together, these studies suggest that NLGNs may be involved in synapse formation.
Evidence from NLGN knockout mice, however, revealed that NLGNs are essential for synaptic function but not synapse formation. Triple knockout of NLGN1, NLGN2 and NLGN3 causes perinatal death due to severely impaired synaptic transmission (Varoqueaux et al., 2006).
However, triple NLGN knockout does not change overall synapse numbers and synaptic ultrastructure supporting the fact that NLGNs aid in organizing synapses rather than synapse formation (Varoqueaux et al., 2006). Although single knockouts of NLGN1, NLGN2 and
NLGN3 survive at birth, electrophysiological analysis revealed significant synaptic impairments in mice. Knockout of NLGN1 decreases the NMDA receptor and AMPA receptor mediated synaptic responses (Chubykin et al., 2007, Jiang et al., 2017, Jedlicka et al., 2015,
Soler-Llavina et al., 2011). NLGN2 deletion on the other hand alters synaptic transmission in subsets of GABAergic synapses (Chubykin et al., 2007). NLGN3 knockout mice also show altered inhibitory activity in both the striatum and hippocampus (Rothwell et al., 2014, Foldy et al., 2013). Altogether, these findings suggest that NLGNs are required for synaptic maturation and function but not for the synapse formation.
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7.4 Neuroligin-3
The NLGN3 gene is located on the X chromosome at locus Xq13.1. NLGN3 was initially reported to be primarily expressed in glia (Gilbert et al., 2001) but several subsequent studies reported the expression of NLGN3 in neurons (Chih et al., 2005, Varoqueaux et al., 2006).
NLGN3 was originally identified in the brain (Ichtchenko et al., 1996), but the presence of
NLGN3 mRNA has been detected in other tissues (Philibert et al., 2000). The NLGN3 protein is highly enriched in all brain regions. In rats, the expression of NLGN3 increases through the development, peaks around P14 and it is maintained during adulthood (Budreck and Scheiffele,
2007). NLGN3 is also enriched in synaptic fractions from the developing and adult rat brain
(Budreck and Scheiffele, 2007). Expression of NLGN3 in brain tissues has also been detected using in situ hybridization in mice (Varoqueaux et al., 2006, Lein et al., 2007).
NLGN3 is expressed in a majority of GABAergic and glutamatergic synapses in cultured rat hippocampal neurons with NLGN3 being associated with NLGN2 in GABAergic synapses of the CNS that also expressed gephyrin, a marker of inhibitory synapses (Budreck and Scheiffele,
2007). In addition, NLGN3 interacts with NLGN1 in excitatory synapses (Budreck and
Scheiffele, 2007, Poulopoulos et al., 2012) suggesting that selectivity of interactions between neuroligin isoforms could align with synapse function in the rodent brain.
As expected, based on earlier reports (Gilbert et al., 2001), NLGN3 is also expressed in non- neuronal cells such as glia. Specifically, expression of NLGN3 has been detected in glial subtypes such as astrocytes and oligodendrocytes (Sakers and Eroglu, 2019, Stogsdill et al.,
2017, Gilbert et al., 2001). Interestingly, a secreted version of NLGN3 promotes the proliferation of high-grade glioma in the CNS (Venkatesh et al., 2015). Venkatesh and colleagues showed that NLGN3 is secreted into the tumour microenvironment resulting in the promotion of robust and high-grade glioma cell proliferation. More frequent observations of
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genetic mutations and amplifications of the Nlgn3 gene are predominantly found in other types of cancers including gastric, thyroid, prostate, and pancreatic cancers (Venkatesh et al., 2015).
In terms of biological actions, overexpression of NLGN3 increases the vesicular glutamate transporter (VGLUT) and vesicular GABA transporter (VGAT) expression and conversely,
NLGN3 knockdown reduces VGLUT expression and spine numbers in rat hippocampal neurons (Chih et al., 2005). In addition, electrophysiological studies show that knockdown of
NLGN3 alters the synaptic transmission in the hippocampus, striatum and cerebellum in mice
(Etherton et al., 2011, Baudouin et al., 2012, Rothwell et al., 2014, Foldy et al., 2013).
8. The NLGN3 R451C mutation
Genetic mutations that lead to deletion and point mutations of NLGN3 are associated with
ASD. The R451C mutation in the NLGN3 gene is by far the most extensively studied NLGN3 mutation in autism.
The NLGN3 R451C mutation denotes the substitution of a cysteine residue for an arginine residue at position 451 within the esterase domain of the NLGN3 gene. The mutation was first found in two brothers: where one was diagnosed with Asperger’s syndrome and the other with severe autism (Jamain et al., 2003a). This mutation is located in the highly conserved esterase domain which is known to confer structural integrity and Ca2+ dependent functions (reviewed by Sudhof and Malenka, 2008). Therefore, it is proposed that the R451C mutation modifies the binding of NLGN3 to neurexin at the presynaptic membrane, since this interaction occurs in the presence of Ca2+ ions (Jamain et al., 2003a).
In the brain, the R451C mutation has been characterised at the biochemical level and this mutation causes folding and trafficking defects in the NLGN3 protein. The R451C substitution does not affect Nlgn3 mRNA levels but significantly decreases the export of mutant NLGN3 protein from the endoplasmic reticulum. In fact, only <10% of NLGN3 protein is exported
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from the endoplasmic reticulum and incorporated into the synapse (Comoletti et al., 2004,
Tabuchi et al., 2007). Although the R451C mutation depressed NLGN3 protein levels in synapses by about 90%, interestingly, the remaining 10% of the mutant protein increases inhibitory synaptic transmission as demonstrated by whole cell patch clamp recording in brain slices from Nlgn3R451C mice (Tabuchi et al., 2007). Based on this evidence, it has been suggested that the R451C mutation is a ‘gain-of-function’ mutation.
Along with increased GABAergic transmission, the R415C mutation significantly increases the expression of VGAT and the postsynaptic protein gephyrin, which are markers for inhibitory synapses. However, changes in the expression of VGLUT or other proteins that are characteristically expressed at excitatory synapses have not been observed (Tabuchi et al.,
2007).
When this mutation is introduced to mice, these animals exhibit a phenotype that shares some features with ASD patients. Nlgn3R451C mutant mice show elevated aggressive behaviour, changes in social interactions and repetitive behaviour relevant to ASD in patients (Burrows et al., 2015, Etherton et al., 2011, Tabuchi et al., 2007). Electrophysiological studies demonstrated altered synaptic activity in the basolateral amygdala, dorsal striatum, hippocampus and cortex in mice expressing the Nlgn3 R451C mutation (Martella et al., 2018,
Etherton et al., 2011, Gogolla et al., 2009, Tabuchi et al., 2007, Hosie et al., 2019, Foldy et al.,
2013, Rothwell et al., 2014).
In addition to the Nlgn3R451C mutation, the deletion of the Nlgn3 gene was observed in a patient with autism and also in another patient with pervasive developmental disorder (Levy et al.,
2011, Sanders et al., 2011). Nlgn3 knock-out mice show reduced vocalization and social interactions (Jaramillo et al., 2014, Radyushkin et al., 2009, Tabuchi et al., 2007, Etherton et al., 2011, Rothwell et al., 2014). These mice also have decreased frequency of miniature excitatory postsynaptic currents and increased frequency of miniature inhibitory postsynaptic
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currents in the hippocampus (Etherton et al., 2011). Nlgn3 knock-out mice display increased
GABAergic neurotransmission, impaired tonic cannabinoid signalling confirming altered synaptic function (Foldy et al., 2013). The Nlgn3 knock-out mutation does not induce any gross anatomical, morphological or neurochemistry changes to the intestine or ENS. However, these mice have faster CMMCs and an increased colonic diameter compared to WT animals
(Leembruggen et al., 2019).
8.1 Expression of neuroligin-3 in the intestine
Although the expression of NLGN3 protein has been well characterised in the brain, few studies have shown that NLGN3 is present in the gut. In human tissue, NLGN3 expression was reported in myenteric interstitial cells of Cajal, which are myogenic pacemaker cells, and altered expression of NLGN3 has also been reported in Hirschsprung’s disease (Zhang et al.,
2013, Wang et al., 2013). The presence of Nlgn3 mRNA was also reported in gene expression analysis of peptide YY (PYY)-containing enteroendocrine cells (Bohorquez et al., 2015).
NLGN3 is expressed in neuronal-rich tissue preparations containing longitudinal muscle and myenteric plexus (LMMP) of both duodenum and colon, but whether the NLGN3 is present in specific neuron classes or other cell types within the GI tract is unclear (Hosie et al., 2019).
Although these studies confirm the presence of NLGN3 in the GI tract, a detailed analysis of cellular expression of NLGN3 in the ENS has not been undertaken, nor have the specific effects of the R451C mutation on enteric cellular expression been examined in mice.
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8.2 Effects of the NLGN3 R451C mutation on intestinal structure and function
Patients expressing the R451C mutation present with oesophageal regurgitation, diarrhea, and chronic gut pain (Hosie et al., 2019) and this suggest that the R451C mutation could contribute to GI dysfucntion.
The R451C mutation impacts myenteric neuronal density within the jejunum and colon in
NlgnR451C mice. Mice expressing the R451C mutation showed 30% more myenteric neurons per ganglion in the proximal jejunum compared to wildtype mice (Hosie et al., 2019). Jejunal samples from mutant mice also show an increased number of NOS immunoreactive neurons per ganglion (Hosie et al., 2019). Similarly, Sharna et al., (2020) showed an increase in neuronal numbers in both the myenteric and submucosal enteric ganglia in the Nlgn3R451C mouse caecum (Sharna et al., 2020). These mice also show increased numbers of NOS- expressing neurons in the caecal myenteric and submucosal plexus.
Immunocytochemical studies showed no increase in neuronal numbers in the colon in mutant mice. Further, there was no difference in baseline colonic motility in mutant mice compared to littermate controls (Hosie et al., 2019). Interestingly, Nlgn3R451C mice show reduced colonic motility in the presence of GABAA antagonists suggesting that the R451C mutation increases sensitivity to GABA via the GABAA receptor in the ENS (Hosie et al., 2019). In line with these observations, multiple studies have shown that the R415C mutation alters the GABAergic neurotransmission in different regions of the brain (Etherton et al., 2011, Tabuchi et al., 2007,
Foldy et al., 2013, Rothwell et al., 2014). In the GI tract of mice, GABA is localized in a subset of interneurons and motor neurons in the colon (Sang and Young, 1996, Li et al., 2011) and is implicated in excitatory synaptic transmission in the ENS (Krantis, 2000). Taken together, these findings suggest that the R451C mutation might alter GABAergic transmission in the mouse colon.
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Of particular interest to the current project, preliminary data from the current laboratory has demonstrated that the R415C mutation alters the microbial composition in the gut (Hill-Yardin et al., in preparation). Furthermore, microbial sequencing analysis of faecal samples from mutant mice revealed that the R451C mutation causes dysbiosis and increases the abundance of Firmicutes and reduces the proportion of Bacteroidetes. In addition, altered levels of
Clostridium and Candida were observed in Nlgn3R451C mutant mice (Hosie et al., 2019) suggesting that, in addition to causing aberrant neuronal activity in the brain as well as gut, the
R451C mutation impacts microbial populations of the gut.
Given these observations, it is likely that Nlgn3R451C mice show changes in mucosal barrier functions, however these aspects have not been assessed in theses mice. Further, how the
R451C mutation in mice alter the distribution of gut microbial populations or the mucus environment has not been examined. To address this question, a novel approach including the detection of microbial subpopulations in combination with immunofluorescence for mucus components is required to understand how the neural changes in the Nlgn3R451C mouse GI tract might impact the mucus and microbial environment.
These findings have significant relevance for understanding fundamental mechanisms of how the enteric nervous system interacts with the microbiome and can assist in unravelling pathogenic mechanisms in autism spectrum disorder.
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9. Thesis hypothesis and aims
As detailed above, effects on the intestinal mucosal barrier have not been fully elucidated in
ASD, leaving a gap in our understating of pathophysiological mechanism in GI dysfunction in autism. This led me to the main focus of this thesis is to investigate the impact of the ASD- associated NLGN3 R451C mutation on the intestinal mucosal barrier function and the microbiome. Therefore, I hypothesise that the R451C mutation in the Nlgn3 alters the mucosal barrier integrity and the composition of intestinal microbiome in mice.
There are three aims in this study:
1. To develop a method for quantitative analysis of NLGN3 expression in the enteric
nervous system.
Chapter 3 assesses the expression of Nlgn3 mRNA in the enteric nervous system. This
study extends further to assess the effects of the Nlgn3 R451C mutation on Nlgn3 mRNA
expression in the ENS.
2. To investigate the effects of the NLGN3 R415C mutation on intestinal mucosal barrier
function.
Chapter 4 examines whether the Nlgn3 mRNA R451C mutation affects the submucosal
plexus and mucosal barrier functions; in particular, paracellular permeability and mucosal
secretion.
3. To identity the mucus layer variations and spatial distribution of microbial
populations in Nlgn3 R415C mice.
Chapter 5 outlines the Nlgn3 R415C mutation evoked bacterial spatial distribution changes
and impact on the mucus layer.
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CHAPTER 2
MATERIALS AND METHODS
This chapter details the core animal information and all experimental procedures used in this thesis for data acquisition. Some techniques described here were applied to multiple studies in the thesis. Any modifications to the original protocols described in this chapter are detailed in the methods section of the relevant result chapters. Specifically, this chapter describes dual
RNAScope in situ hybridization and immunofluorescence (Chapter 3), the use of the Ussing chamber technique (Chapter 4), qPCR studies (Chapter 4), fluorescent in situ hybridization
(Chapter 5), paraffin processing of tissue (Chapter 5) and immunofluorescence labelling in the
GI tract (Chapters 3-5).
1. Animals
B6;129‐NLGN3tm1Sud/J mice were obtained from Jackson Laboratories (Bar Harbor, ME,
USA). Mice were maintained to generation F9 on a hybrid Sv129/C57Bl6 background.
Nlgn3R451C and WT animals were obtained by mating heterozygous females with
Nlgn3R451C males, which produced 50:50 WT and Nlgn3R451C male offspring (Y/+ and Y/R451C).
Genotyping was performed as described in Tabuchi et al., 2007 (Tabuchi et al., 2007). These mice were then bred for over 10 generations on a pure C57BL/6 background at the Howard
Florey Institute, Melbourne, Australia. Mice were subsequently housed in the Biomedical
Science Animal Facility at The University of Melbourne and used for the experiments described in this thesis. Mice were killed by cervical dislocation in accordance with The
University of Melbourne Animal Experimentation Ethics Committee (ethics number 1914843).
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2. Dual RNAScope in situ hybridization and immunocytochemistry
2.1 Tissue preparation
Nlgn3R451C male mice were killed and the abdomen of the animal was opened using coarse dissection scissors. A 2 cm segment of the distal ileum (1 cm proximal to the caecum) was isolated and immediately placed in oxygenated ice-cold physiological saline solution
(composition in mM: 118 NaCl, 4.6 KCl, 2.5 CaCl2, 1.2 MgSO4, 1 NaH2PO4, 25 NaHCO3, 11
D-glucose). The intestinal content was gently flushed clean using a Pasteur pipette. Using small dissecting scissors, the distal ileal tissue sample was cut along the mesenteric border, stretched and pinned flat on a chilled silicon elastomer-lined dish (Sylgard 184, Dow Corning, Australia).
The tissue was fixed in 4% formaldehyde for 24 h at 4oC. After fixation, the tissues were washed three times in 1M phosphate buffer saline (PBS) (composition in mM: 137 NaCl, 2.7
KCl, 8 Na2HPO4, 2 KH2PO4). The wholemount submucosal plexus preparation was isolated by peeling the mucosa-submucosal layer and then removing the epithelium using fine forceps
(Fine Science Tools, Canada). Myenteric plexus preparations with the longitudinal muscle layer (LMMP) were obtained by microdissection to remove the circular muscle layer.
2.2 RNAScope in situ hybridization
RNAScope in situ hybridization was performed using the ACDBio multiplex RNAScope assay kit (ACD, 320850, USA) according to manufacturer’s instructions. Briefly, the wholemount preparations were pre-treated and hybridized with the RNAScope probe of interest. Then the hybridization signal was amplified via a sequence of amplifiers and fluorescently labelled probes.
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2.3 Pre-treatment
Wholemount preparations were permeabilized via pre-treatment with protease IV prior to
RNAScope in situ hybridisation. The tissue samples were incubated in 30 µl of protease IV for 30 min at room temperature (RT) in a sealed humid chamber and washed twice (1min in each) in 1M PBS.
2.4 RNAscope in situ hybridization
Following pre-treatment, the tissue preparations were incubated in 20 µl of Nlgn3 RNAScope
probe and incubated for 2 h at 40 oC in a humid chamber. The tissues were washed twice in
1x wash buffer for 2 min at RT with agitation. Amplification and detection steps were
performed using the RNAScope detection kit reagents. For the signal amplification, tissues
were incubated in 50 µl of amplifier 1 solution (AMP 1) for 30 min at 40 oC and washed twice
in 1x wash buffer for 2 min at RT with occasional agitation. Subsequently, the tissue was
incubated in 50 µl of AMP 2 solution for 15 min at 40oC and then washed twice in 1x wash
buffer for 2 min at RT with occasional agitation. After AMP 2 amplification, 50 µl of AMP
3 solution was added to the tissue preparations and incubated for 30 min at 40oC. The tissue
was washed twice in 1x wash buffer for 2 min at RT with occasional agitation. Lastly, the
tissue was incubated in 50 µl of AMP 4 solution and incubated for 15 min at 40oC prior to
washing the tissue twice in 1x wash buffer for 2 min at RT with occasional agitation.
2.5 Immunofluorescence
After washing three times in PBS the wholemount preparations were permeabilised with 1%
Triton (ProSciTech, Australia) for 30 min at RT and washed three times in PBS. The tissues
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were then double labelled with various combinations of primary antibodies at different
incubation conditions (detailed in Chapter 3). Excess primary antibodies were removed by
washing in PBS (3 x 10 min) and the tissue samples were subsequently incubated in secondary
antibodies for 2 h at 4oC After removing excess antisera by washing with 0.1M PBS (3 x 10
min), tissue preparations were mounted using Dakocytomation fluorescence mounting
medium (DAKO; Carpenteria, USA). Details of specific antisera used in this study are
described in Chapter 3.
2.6 Image acquisition and quantification
Confocal images of submucosal and myenteric wholemount preparations were acquired using
a laser scanning microscope (LSM 800; Zeiss, Germany) using a 40x objective. The
expression of Nlgn3 mRNA expression was analysed using Imaris 9.0 image analysis software
(Bitplane, UK).
3. Ussing chamber technique
The Ussing chamber method was developed by the Danish biologist Hans H. Ussing to measure the active NaCl transport across an epithelium using frog skin as a model system (Ussing and
Zerahn, 1951).
3.1 Assembling the Ussing chamber and elimination of electrical bias
The Ussing Chamber (CHM8; World Precision Instruments, Inc. (WPI), Sarasota, FL, USA) was connected to water-jacketed reservoirs (custom designed at The Department of
Physiology, University of Melbourne, VIC, Australia). The mucosal and serosal compartments were independently superfused in equal volumes of physiological saline solution and
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constantly oxygenated (95% O2, 5%CO2) throughout the experiment. Each half of the chamber was water-jacketed to maintain the temperature of the superfusate at 370C.
3.2 Tissue preparation
A 2 cm segment of mouse jejunum (8 cm distal to the stomach) or distal ileum (1 cm proximal to the caecum) was collected and immediately placed in ice-cold oxygenated physiological saline solution. The tissue was opened longitudinally along the mesenteric border using small dissection scissors and pinned flat on a silicon elastomer-lined dish. For mucosa-submucosa preparations, serosa, longitudinal muscle and circular smooth muscle layer were removed by microdissection under the stereomicroscope (Olympus, SZ61, Japan). The preparation was then mounted across the opening (5 mm diameter in size) of an Ussing half-chamber under the stereomicroscope.
3.3 Electrical measurement
Tissue preparations were equilibrated for 20 min prior to collecting data. The transepithelial voltage (Vt) was measured using a potentiometer (VCC600 Single Channel Voltage-Current
Clamp: Physiological Instruments, Inc., World trade Drive, San Diego, CA, USA) and voltage electrodes immersed in 3 M KCl solution (SR4 Reference Electrode Calomel Separable:
ThermoFisher Scientific, Station Road, Auchtermuchty Fife, Scotland, UK) that are connected to each half of the chamber by salt bridges (3% agar melted in 3 M KCl-filled polyethylene tubing (OD 1.52 mm x ID 0.86 mm: Tyco Electronics, Huntingdale, VIC, Australia). Two separate current-passing electrodes (Ag-AgCl electrodes; WPI) were also connected to each half of the Ussing chamber by salt bridges (ID 5 mm; WPI) containing 3% agar melted in 3M
KCl. The electrical bias inherent to the system was eliminated by “zeroing’ the system in the
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absence of tissue. An offset voltage was applied to nullify the voltage difference between two voltage sensing electrodes and the resistance of the superfusate was compensated.
Experimental protocols were restricted to a maximum duration of 3 h after mounting the preparation to maintain the optimum tissue integrity during the experiments. Drugs were added either directly to the serosal or mucosal half-chamber (depending on the experimental procedure) which was then filled with superfusate to achieve the final concentration.
3.4 Data collection and analysis
Data collection was performed using AcqKnowledge 3.9.0 software (BIOPAC Systems, NSW,
Australia) and data were analysed using Graph pad Prism software 8.4.2 (GraphPad software,
USA).
4. Reverse transcription PCR and droplet digital PCR
4.1 Tissue preparation
Tissue preparation was conducted under sterile conditions to avoid contamination. The abdominal cavity of the mice was opened using coarse dissecting scissors and the proximal jejunum (8 cm distal to the stomach) and the distal ileum (1 cm proximal to the caecum) was excised and immediately placed in ice-cold physiological saline solution. The intestinal content was gently flushed out with sterile ice-cold physiological saline solution and the tissue was cut along the mesenteric border using small dissection scissors. The tissue was stretched and pinned flat on a chilled silicon elastomer-lined dish. The mucosal layer was separated by microdissection using fine dissection forceps and spring scissors under a dissecting microscope
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as mentioned previously. Mucosal preparations were frozen in liquid nitrogen immediately after dissection and stored at -80 oC until further use.
4.2 RNA extraction
RNA extraction was performed using the ReliaPrepTM RNA tissue Miniprep System
(Promega, USA). The frozen tissue samples were homogenised in a TissueLyser LT adapter
(Qiagen, Germany). To disrupt the tissue, 500µl lysis buffer (LBA+TG buffer) was added before the homogenisation. The homogenised samples were pipetted 7-10 times to shear the
DNA. The samples were centrifuged at 14,000 x g for 3 min to clear the homogenate and the eluate was transferred to a clean tube. Subsequently, 170 µl of 100% isopropanol was added and mixed by vortexing for 5 s.
The lysate was transferred to a minicolumn (Promega, USA) in a collection tube and centrifuged at 12,000 g at 25 oC for 1 min. Following centrifugation, 500 µl RNA wash solution was added to the minicolumn and centrifuged at 12,000 g for 30 s. The tubes were then incubated with 30 µl DNase1 incubation mix (composition; 24 µl core buffer, 3 µl 0.09
o M MnCl2, 3µl DNase1) for 15 min at 25 C and 200 µl of column wash solution was added.
Following centrifugation at 12,000 g for 15 s, 500 µl of RNA wash solution was added and then the preparation was centrifuged at 12,000 g for 30 s. Another 300 µl of RNA wash solution was added to the tubes which were then centrifuged at 14,000 g for 2 min. The minicolumn was placed on an elution tube and 30 µl of nuclease-free water was added. The minicolumn in an elution tube was centrifuged at 12,000 g for 1 min. The quality and quantity of purified RNA samples were measured on a Qubit 4.0 fluorometer (ThermoFisher, USA).
The RNA samples were then stored at -80oC until required.
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4.3 Complimentary DNA (cDNA) synthesis cDNA was synthesised using an RT2 First Strand Kit (Promega, USA). For each RNA reaction mix, 10 µl of genomic DNA elimination mix was prepared (composition for 400 ng RNA: 2
µl buffer GE, RNase free water) and incubated for 5 min at 42oC then immediately placed on ice for 1 min. Subsequently, 10 µl of reverse transcription mix (4 µl 5x buffer BC3, 1 µl control
P2, 2 µl RE3 reverse transcriptase mix, 3 µl RNase-free water) was added to the 10 µl of genomic DNA elimination mix and pipetted up and down to mix the sample. The samples were incubated at 42oC for 15 min. Following incubation at 95oC for 5 min, 91 µl of RNase- free water was added and mixed by pipetting up and down several times.
4.4 Real-time quantitative PCR array
RT PCR array was performed using the RT2 Profiler PCR Array (Qiagen, Germany). The PCR reaction mix was prepared by adding 650 µl of 2x RT2 SYBR Green Mastermix, 102 µl cDNA synthesis reaction and 548 µl RNase-free water. The reaction mix (10 µl) was pipetted to each well of the RT PCR array. The plate was tightly sealed and centrifuged for 1 min at 1000 g at
RT. RT-PCR was performed in a QuantStudio 6 flex real-time PCR cycler (Applied
Biosystems, USA) which was set to perform 1 cycle of initial denaturation at 95 oC for 10 min and 40 cycles of denaturation for 15 s at 95oC and annealing at 60 oC for 1 min.
4.5 Droplet digital PCR
Preparation of 25 µl of the dd PCR reaction mixture included 12.5 µl 2x dd PCR supermix
(Bio-Rad, USA) 1.25 µl primers (Table 1) (IDT, Australia), 10.25 µl Nuclease-free water
(Invitrogen, Australia) and 1 µl cDNA. Droplets were then generated in the QX200 droplet
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generator (Bio-Rad, Australia) by loading 20µl of the reaction mixture and 70 µl of droplet generation oil (Bio-Rad, Australia) to the appropriate wells of the cartridge. The gasket was attached over the cartridge and placed into the droplet generator. Approximately 40 µl of the droplet mixture was transferred into a PCR plate. The plate was heat sealed and placed in a thermal cycler (Bio-Rad, Australia) for PCR amplification. The following protocol was used for PCR amplification; 95 oC 10 min, 94 oC for 30 sec, 60 oC for 1 min, 98 oC for 10 min).
Upon completion of PCR amplification, the PCR plate was read using the QX200 droplet reader (Bio-Rad, USA). Droplet counts were exported and analysed using the QuantaSoft software (Bio-Rad, USA).
5. Fluorescent in situ hybridization
5.1 Tissue collection and fixation
A segment of the distal ileum (1 cm proximal to the caecum) containing faecal material was dissected and immediately placed in methanol Carnoy’s fixative (Composition; 60% V/V absolute methanol, 30% V/V chloroform, 10% V/V acetic acid) for 48 hours at RT. After fixation, the tissue was washed twice in absolute methanol for 30 min, followed by two times in absolute ethanol for 20 min each. Following washing steps, the tissue was incubated twice in xylene for 15 minutes each. The tissue was paraffin-embedded and sectioned at 4 µm thickness. The paraffin embedding and sectioning were done by the Biomedical Sciences
Histology Facility of the University of Melbourne (Parkville, Australia).
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5.2 Fluorescent in situ hybridization (FISH)
Paraffin sections were dewaxed prior to fluorescent in situ hybridization. The sections were dewaxed in xylene 2 x 10 minutes. The dewaxed sections were then incubated in 99.5% ethanol for 5 min and the slides were air-dried. Then 30 µl of FISH probes diluted to 10 ng/µl in preheated (50 oC) hybridization buffer (composition; 0.9 M NaCl, 20mM Tris-HCl (pH 7.4),
0.01% sodium dodecyl sulfate, 10% formamide) was added onto the tissue and incubated at
50 oC for 18 h in a sealed, humid chamber. After the incubation period sections were washed in FISH washing buffer (composition; 0.9 NaCl, 20mM Tris HCl (pH 7.4) that was preheated to 50 oC.
5.3 Immunofluorescence
The sections were washed three times in PBS before performing immunofluorescence. After washing in PBS, the sections were incubated in 1% bovine serum albumin in 0.1 M Triton solution for 30 min in a dark, humid chamber at 40C to permeabilise the tissue and block the nonspecific binding of antibodies. The slides containing the tissue sections were washed three times in PBS solution prior to adding the primary antibody solution. After the incubation period, the slides were washed in PBS three times for 10 min each. Secondary antibody diluted in PBS and was added on to the tissues. The slides were incubated at 4 0C in a dark, humid chamber for 2 h then washed in PBS three times for 10 minutes each. The tissue sections were subsequently counterstained with 10µg/ml DAPI (Sigma, USA) and washed in PBS. The sections were allowed to dry completely and mounted with DAKO fluorescent mounting medium (DAKO, Carpenteria, CA).
5.4 Measuring the spatial distribution of bacteria
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The MATLAB based BacSpace software is a computational quantitative analytical tool which has been developed for automated quantification of microbial localization in in vivo gut tissues.
BacSpace is a user friendly and flexible platform which enables both automated and manual detection of landmarks such as the epithelial boundary, differentiation of microbial fluorescence signals from autofluorescence generated by diet-derived materials and shed epithelial cells, and the measurement of cell-to-landmark distance distribution using a local coordinate system integrated into the platform (Earle et al., 2015).
6. Immunohistochemistry
6.1 Tissue collection
The mouse abdominal cavity was opened and a 2 cm segment of the jejunum (8 cm distal from the stomach) and distal ileum (1 cm proximal to the caecum) was excised using fine dissecting tools. The tissue was placed in PBS and the intestinal content was flushed clean. Using small dissecting scissors, the tissue was opened along the mesenteric border and stretched and pinned flat with the mucosal side up in a silicone elastomer-lined dish.
6.2 Tissue preparation and immunofluorescence
Tissue samples were fixed in 4% formaldehyde for 24 h at 4 oC and washed in PBS (3 x 10 min washes) at RT. The submucosal plexus was obtained by microdissection to remove seromuscular layers (serosa, longitudinal muscle and the circular smooth muscle layer) under a stereomicroscope. Preparations were permeabilised in 1% Triton X-100 in PBS for 30 min at
RT and washed in PBS (3 x 10 min washes). Preparations were incubated in solutions containing primary antibody combinations and incubated for 36 h at 4 oC. After being washed in PBS (3 x 10 min washes), preparations were incubated in secondary antibody solution for 2
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h at 4 oC. Tissue preparations were then washed in PBS three times (10 min washes) and mounted onto slides using DAKO mounting medium (Carpentaria, USA).
6.3 Microscopy
The images of the tissue samples were captured using an Axio Imager2 fluorescence microscope (Zeiss, Germany) and cell counts were performed using ImageJ software (NIH).
7. Statistical analysis
The multiple statistical methods used to analyse data in this study were described in the methods sections specific to each chapter. Power analysis was not conducted because there were no prior estimates available for the any of the parameters tested for the WT control condition.
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CHAPTER 3
Quantitative analysis of neuroligin-3 expression in the enteric
nervous system
1. ABSTRACT
NLGN3 is a postsynaptic cell adhesion molecule which aids in neuronal communication.
Mutations in the NLGN3 gene, including the R451C missense mutation, are implicated in autism spectrum disorder. Recent studies revealed that NLGN3 is important for gut function, but the cellular expression of NLGN3 has not been characterised within the GI tract. NLGN3 localisation in intestinal tissue is challenging due to the lack of target-specific antibodies. In this study, a dual RNAScope in situ hybridization and immunohistochemistry technique was developed to overcome this challenge. RNAScope in situ hybridization was used to label Nlgn3 mRNA and immunofluorescence to label enteric neuronal subpopulations and glia. A quantitative 3-dimensional image analysis method was developed to measure Nlgn3 mRNA expression levels in enteric neurons. Quantitative analysis revealed that Nlgn3 mRNA is expressed in most submucosal and myenteric neurons in the distal ileum. Interestingly, this study also revealed that Nlgn3 mRNA is expressed in enteric glia. Using the same approach, the impact of the autism associated Nlgn3 R451C mutation on Nlgn3 expression was measured.
It was revealed that the R451C mutation reduces Nlgn3 mRNA expression in most enteric neurons in mutant mice compared to WT. In contrast, the expression of Nlgn3 mRNA in VIP submucosal neurons is increased in Nlgn3R451C mutant mice. In conclusion, these findings show for the first time that Nlgn3 mRNA is expressed in enteric neurons and enteric glia in both the submucosal and myenteric plexus in mice. Furthermore, this research demonstrates that the
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autism associated R451C mutation in the Nlgn3 gene affects Nlgn3 mRNA expression in the
ENS in mice.
2. INTRODUCTION
Neurons communicate via specialised intercellular junctions known as synapses. During synapse formation, synaptic cell adhesion molecules play an important role in aligning the membrane components of the synapse and assist accurate signal transduction processes
(Sudhof and Malenka, 2008, Sudhof, 2008). NLGN3 is a synaptic cell adhesion molecule found in the postsynaptic membrane of some synapses. During synapse formation, NLGN3 binds to its presynaptic counterpart, neurexin, to shape synaptic function (Craig and Kang,
2007). Genetic mutations resulting in both the complete deletion of NLGN3 and the R451C point mutation in the NLGN3 gene are implicated in autism (Jamain et al., 2003b). Modification of NLGN3 expression induces GI dysfunction in mice as shown in both Nlgn3 knockout (KO) and Nlgn3R451C mice. Patients expressing the NLGN3 R451C mutation show GI dysfunction including diarrhea, faecal incontinence, post-meal regurgitation, oesophageal inflammation, chronic intestinal pain as well as delayed bladder and bowel control (Hosie et al., 2019). Mice expressing the Nlgn3 R451C mutation display faster small intestinal transit and increased numbers of myenteric neurons alongside GABAA receptor-mediated colon dysmotility (Hosie et al., 2019). In addition, Nlgn3 KO mice exhibit distended colons and faster colonic muscle contractions (Leembruggen et al., 2019), suggesting that NLGN3 is important for proper gut function.
In the CNS, NLGN3 is expressed in the postsynaptic membranes of both excitatory and inhibitory synapses (Budreck and Scheiffele, 2007). It has been demonstrated that that NLGN3 is additionally expressed in non-neuronal cell types. For example, NLGN3 is secreted into the
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tumour microenvironment and promotes tumour growth in mice with high-grade glioma, an aggressive brain cancer arising in glial cells (Venkatesh et al., 2015). Expression of NLGN3 in the GI tract was previously reported in rodents and humans (Zhang et al., 2013, Hosie et al.,
2019, Bohorquez et al., 2015), but a detailed study of NLGN3 expression in different cell types of the mouse enteric nervous system has not been conducted. Functional effects of the R451C mutation on Nlgn3 mRNA and protein expression at the cellular level has been reported in the brain. In both cell systems and rodents, Nlgn3 mRNA expression is unaffected by the Nlgn3
R451C mutation but the protein expression at the synaptic membrane is drastically reduced
(Tabuchi et al., 2007, Comoletti et al., 2004). The potential effects of the R451C mutation on
NLGN3 expression in the enteric nervous system, however, have not been assessed.
Localizing the NLGN3 protein in the GI tract is challenging due to non-specific labelling of commercially available antibodies which commonly yield false-positive results (Leembruggen et al., unpublished). Instead of protein labelling, it is envisaged that pursuing novel in situ hybridization techniques to label RNA will provide more accurate and reliable data.
RNAScope is a novel and advanced proprietary in situ hybridization technique which was designed to achieve visualisation of individual RNA molecules using a novel probe design and amplification system to magnify the signal and suppress the background simultaneously (Wang et al., 2012). Due to its high sensitivity and specificity, RNAScope is a promising platform to specifically localise RNA including in instances where there is low abundance gene expression.
Combining RNAScope with immunofluorescence generates a powerful tool to detect RNA and protein expression at the cellular level (Grabinski et al., 2015), which will assist in unravelling underlying cellular mechanisms fundamental to homeostasis in the GI tract. Therefore, this study aimed to develop a novel dual RNAScope and immunofluorescence technique combined with high-resolution microscopy and 3D image analysis software to generate a highly sensitive
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and unbiased quantification method to analyse Nlgn3 mRNA expression in the enteric nervous system of wild type and Nlgn3R451C mice.
3. METHODS
3.1 Animals
NLGN3R451C mice were obtained from The Jackson Laboratory (Tabuchi et al., 2007) and bred for over 10 generations on a pure C57Bl/6 background at the Howard Florey Institute,
Melbourne Australia. Mice were subsequently housed in the Biomedical Science Animal
Facility at The University of Melbourne. NLGN3R451C male mice (aged 12-13 weeks) were killed by cervical dislocation in accordance with The University of Melbourne Animal
Experimentation Ethics Committee (ethics number 1914843).
3.2 Tissue preparation
A segment of the distal ileum (1 cm proximal to the caecum) was excised and immediately placed in ice-cold physiological saline solution. The tissue was opened longitudinally along the mesenteric border and pinned flat and fixed in 4% formaldehyde for 24 h at 4oC. After fixation, the tissue was washed in PBS (3 x 10 min). Wholemount submucosal and myenteric plexus preparations were prepared as described in Chapter 2 section 2.1.
3.3 Dual RNAScope in situ hybridization and immunofluorescence
A multiplex RNAScope assay kit was obtained from Advanced Cell Diagnostics (ACD,
320850, USA) and the procedure was performed as per manufacturer’s instructions (detailed in Chapter 2 section 2.2). Briefly, tissue samples were permeabilized via pre-treatment with
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protease IV for 30 min at RT in a sealed, humid chamber. Tissue samples were then washed twice in 1x PBS (2 x 10 min). Following pre-treatment, 20 µl of Nlgn3 probe solution was added to the tissues and incubated for 2 h at 40 oC in a humid chamber. Following that, the tissue samples were washed twice in 1x wash buffer for 2 min at RT while being agitated on a shaker (Ratek, Australia). After hybridization, tissues were incubated in 50 µl of amplifier 1
(AMP 1) for 30 min at 40 oC. Tissues were then washed twice in 1x wash buffer for 2 min at
RT with occasional agitation. Subsequently, the tissues were incubated in 50 µl of AMP 2 solution for 15 min at 40 oC and then washed twice in 1x wash buffer for 2 min at RT with occasional agitation. After AMP 2 amplification, 50 µl of AMP 3 was added to the tissue preparations and incubated for 30 min at 40 oC. The tissues were washed twice in 1x wash buffer for 2 min at RT with occasional agitation. Lastly, the tissues were incubated in 50 µl of
AMP 4 and incubated for 15 min at 40oC and preparations were subsequently washed twice in
1x wash buffer for 2 min at RT with occasional agitation. Control experiments were conducted similarly using Mm-Ppib and DapB RNAScope probes for positive and negative controls respectively.
Following the RNAScope in situ hybridization procedure, the tissues were washed three times in PBS. Tissue preparations were then permeabilised in 1% Triton in PBS for 30 min at RT and washed in PBS (3 x 10 min). The tissue preparations were then incubated in primary antibodies (Table 3.1) for 24-36 h at 4oC and washed in PBS (3 x 10 min). Then the tissue preparations were incubated in secondary antibodies (Table 3.2) for 2h at 4oC, washed in PBS
(3 x 10 min). The preparations were mounted using DAKO mounting medium (Carpenteria,
USA).
The same protocol was followed for control experiments (results shown in Appendix, Figure
A1).
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3.4 Image acquisition and analysis
Multi-channel image acquisition was performed with a laser scanning confocal microscope 800
(Carl Zeiss Microscopy, North Ryde, NSW, Australia) using a 40x oil immersion objective lens. The samples were labelled with Alexa fluoro 488, Alexa fluoro 594 and Alexa fluoro 647 for Nlgn3, Hu and either ChAT, VIP or S100b in the submucosal plexus and NOS, Calretinin or S100b in the myenteric plexus. The samples were excited with Diode lasers at 561, 488 and
647 wavelengths. The pinhole diameter, detector gain and laser powers were optimised to obtain the highest pixel intensity at the same time eliminating pixel saturation. Consecutive Z stacks on the horizontal plane with a frame size of 1024x1024 pixels and a file-depth of 16 bits were captured at 1 µm intervals. Z-stacks together with tile scans were performed where necessary. The quantification was performed using Imaris 9.0 image analysis software
(Bitplane, UK).
3.5 Statistical analysis
Data were analysed using GraphPad Prism 8.4.1 (GraphPad software, USA). Frequency distribution analysis was performed to analyse the distribution of Nlgn3 mRNA in enteric neurons and glia. A Kolmogorov-Smirnov test was conducted to determine statistical significance. It is a nonbiased, nonparametric test which does not depend on the underlying cumulative distribution function being tested. Unlike chi-square which depends on adequate predicted numbers in each cell (>5) compared for the calculations to be valid, Kolmogorov-
Smirnov can be used even if the sample size less than 5. P<0.05 was considered as statistically significant.
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Table 3.1 Primary antibodies used for immunocytochemistry
Primary antisera Raised in Dilution Source
Hu Human 1:5000 Gift from Dr. V.
Lennon
VIP Rabbit 1:1000 Merck Millipore
ChAT Goat 1:100 Chemicon
NOS Sheep 1:1000 Gift from Dr P.
Emson
Calretinin Goat 1:1000 SWANT
Table 3.2 Secondary antibodies used for immunocytochemistry
Secondary antisera Raised in Dilution Source
Anti-human AF 594 Donkey 1:500 Jackson Immuno
Labs
Anti-sheep AF 647 Donkey 1:500 Molecular Probes
Anti-rabbit AF 647 Donkey 1:400 Molecular Probes
Anti-sheep AF 594 Donkey 1:100 Molecular Probes
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4. RESULTS
4.1 A novel method to simultaneously detect cellular Nlgn3 mRNA and protein expression in the enteric nervous system
The main goal of this study was to develop a method for qualitative and quantitative analysis of Nlgn3 mRNA expression in the mouse enteric nervous system in order to identify the distribution of Nlgn3 mRNA in neuronal and glial subpopulations in the submucosal and the myenteric plexus.
The pan-neuronal marker Hu was used to label both submucosal (Figure 3.1 A) and myenteric neurons (Figure 3.1 D). Nlgn3 mRNA in situ hybridization signal appears as puncta which are sparsely distributed in both submucosal and myenteric plexus (Figure 3.1 C and F respectively).
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Figure 3.1 RNAScope combined with immunofluorescence to determine the distribution of Nlgn3 mRNA in enteric neurons.
A. Immunofluorescent labelling of submucosal neurons using Hu. B. Nlgn3 mRNA expression in the mouse ileal submucosal plexus. C. Dual RNAScope and immunohistochemistry labelling of submucosal neurons expressing Nlgn3 mRNA. The confocal images of D. myenteric neurons E. Nlgn3 mRNA expression F. Dual RNAScope and immunofluorescent labelling of Nlgn3 mRNA in myenteric neurons. Hu staining is indicated by the open arrowhead and Nlgn3 mRNA is indicated by the arrow. Scale bar=10 µm.
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4.2 A quantitative image analysis method to assess Nlgn3 mRNA expression in the enteric nervous system
In the current study, a quantitative image analysis method was developed for Nlgn3 mRNA quantification in enteric neurons and glia using Imaris 9.0 image analysis software. Imaris software includes 3D reconstruction tools and statistical annotation for quantification purposes.
Three-dimensional rendering of the neuron was produced using high-resolution Z-stack fluorescent images generated using confocal microscopy. To create a detailed rendered surface, the “Create surface” tool in the Imaris software package was used. The minimum diameter of the surface was determined using the “Slice view” option. The interactive software histogram within the “Create surface” window was used to set a threshold (the minimum diameter of the neuronal surface) to exclusively include neuronal surfaces and to exclude background noise.
Aggregated neurons within images were separated using the ‘Split surfaces” tool in Imaris. The
Imaris software is additionally equipped with “Filter options” to label specific populations of surfaces either manually or automatically and these were utilized in the current study to identify specific cell populations in any given image (Figure 3.2 D).
The RNAScope signal appears as puncta, so the “spot” analysis tool in Imaris was used to create the 3D structure of individual mRNA molecules. The average diameter of spots detected was determined using “slice view” in Imaris (Figure 3.2 E). Once spots were generated, they were assigned as being located within the neurons using “split spots on to surface” tool (Figure
3.2 F). The Imaris 9.0 software additionally generates statistical data yielding the number of spots per surface. This option was used to identify the number of mRNA signals within the neuronal surface for the images analysed in the current study.
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Figure 3.2 3D rendering of neuronal surfaces and Nlgn3 mRNA labeling in distal ileal submucosal neurons.
A. Immunolabelling of submucosal neurons using the pan-neuronal marker Hu. B. RNAScope detects the expression of Nlgn-3 mRNA. C. The expression of Nlgn3 mRNA in submucosal neurons. D. 3D-rendered neuronal surfaces created using the ‘surface’ tool of Imaris 9.0 software. E. The ‘spot detection’ tool recreates the Nlgn3 mRNA RNAScope signal as 3D spots. F. The distribution of spots within the 3D rendered neuronal surfaces. Hu staining is indicated by the open arrowhead and Nlgn3 mRNA is indicated by the arrow. Scale bar=10 µm
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4.3 Nlgn3 mRNA is expressed in the majority of distal ileal submucosal neurons
To determine the distribution and expression levels of Nlgn3 mRNA in the distal ileal submucosal plexus, the dual-RNAScope/immunofluorescence and Imaris-based quantitative image analysis method was utilised. All submucosal neurons were labelled with the pan- neuronal marker Hu and Nlgn3 mRNA expression was detected using RNAScope (Figure 3.3
A1-A3). 3D reconstruction of neuronal surfaces and Nlgn3 mRNA were generated using Imaris software (Figure 3.3 A4-A6).
To analyse the distribution of Nlgn3 mRNA expression in individual submucosal neurons, a total of 1,985 submucosal neurons in 14 WT mice were assessed. Frequency distribution analysis revealed that 87% of submucosal neurons expressed Nlgn3 mRNA within the cell soma whereas about 13% of submucosal neurons do not express Nlgn3 mRNA (Figure 3.3
A7).
To understand whether differential distribution patterns of Nlgn3 mRNA exist within the submucosal plexus, Nlgn3 mRNA expression levels in different subpopulations of ileal submucosal neurons were evaluated. The expression of Nlgn3 mRNA in cholinergic neurons was assessed as described previously (Figure 3.3 B1-B8). Of a total of 1,014 submucosal neurons in 7 WT mice, 298 neurons were ChAT positive. Using a frequency distribution analysis approach, it was identified that the vast majority (92%) of cholinergic neurons express
Nlgn3 mRNA. The distribution of Nlgn3 mRNA in cholinergic neurons is similar to the Nlgn3 mRNA distribution in Hu positive submucosal neurons (p=0.09, Hu positive neurons; n=1,014,
ChAT positive neurons; n=298) (Figure 3.3 B9). On average, Nlgn3 mRNA-expressing cholinergic neurons contain 30.1 ± 3.8 (n=298 neurons) copies of Nlgn3 mRNA and it is similar to the average number of copies in Hu-positive cells (29.0 ± 1.8, n=1014 neurons; p=0.7)
(Figure 3.3 B10).
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The Nlgn3 mRNA expression in non-cholinergic (VIP-containing) submucosal neurons was measured (Figure 3.3 C1-C8). A total of 411 VIP submucosal neurons from 7 WT mice were analysed. As shown by the frequency distribution analysis, the majority (91%) of VIP-positive neurons express Nlgn3 mRNA and the distribution of Nlgn3 mRNA in VIP neurons is significantly different to the distribution of Nlgn3 mRNA in Hu positive neurons (p<0.0001)
(Figure 3.3 C9). Despite the difference in distribution of Nlgn3 mRNA, neurons that co- express Nlgn3 mRNA and VIP contain an average of 28.5 ± 1.1 Nlgn3 mRNA copies per neuron (n=411 neurons), similar to the number of Nlgn3 mRNA copies expressed in total submucosal neurons overall (25.4 ± 0.7 copies, n=971 neurons; p=0.8) (Figure 3.4 C10).
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Figure 3.3 The distribution of Nlgn3 mRNA in ileal submucosal neurons.
Confocal images of (A1) submucosal neurons labelled with Hu pan neuronal marker (A2)
Nlgn3 mRNA expression (A3) Nlgn3 mRNA expression in a submucosal ganglion. 3D reconstruction of (A4) Submucosal neurons (A5) Nlgn3 mRNA (A6) Nlgn3 mRNA-expressing submucosal neurons. (A7) Frequency distribution of Nlgn3 mRNA expression in submucosal
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neurons. Confocal images of (B1) Submucosal ganglion (B2) Nlgn3 mRNA expression (B3)
ChAT neuron (B4) Nlgn3 mRNA expression in an individual ChAT neuron. 3D structure of
(B5) submucosal neurons (B6) Nlgn3 mRNA (B7) a cholinergic submucosal neuron (B8)
Nlgn3 mRNA expression in an individual ChAT neuron. B9. The distribution of Nlgn3 mRNA expression in cholinergic neurons is similar to that in total neurons in the submucosal plexus.
B10. Average number of Nlgn3 mRNA copies in cell soma in cholinergic neurons is similar to that in total neurons in the submucosal plexus. Confocal micrographs of (C1) submucosal neurons (C2) Nlgn3 mRNA expression (C3) VIP expression in submucosal neurons (C4)
Nlgn3 mRNA expression in VIP neurons. 3D reconstruction of (C5) submucosal neurons (C6)
Nlgn3 mRNA expression (C7) VIP-expressing neurons (C8) Nlgn3 mRNA expression in VIP- positive neurons. C9. Frequency distribution of Nlgn3 mRNA expression in VIP submucosal neurons is significantly different to total submucosal neurons. C10. On average, VIP submucosal neurons express similar numbers of Nlgn3 mRNA copies as observed in the total number of neurons in the submucosal plexus. Hu staining is indicated by open arrowheads and
Nlgn3 mRNA is indicated by arrows. Scale bar=10 µm
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4.4 Myenteric neurons express Nlgn3 mRNA
Nlgn3 mRNA expression was also assessed in neuronal subpopulations within the distal ileal myenteric plexus using the dual RNAScope/immunofluorescence approach described above
(Figure 3.4 A1-A3). Nlgn3 mRNA expression was additionally quantified using Imaris software (Figure 3.4. A4-A6).
The distribution of Nlgn3 mRNA in a total of 3,788 myenteric neurons from 14 WT mice was analysed. The majority (87%) of myenteric neurons expressed Nlgn3 mRNA within the cell soma. The remaining 13% of myenteric neurons did not express detectable levels of Nlgn3 mRNA. Overall, myenteric neurons that express Nlgn3 mRNA in the cell soma contain an average of 16.8 ± 0.2 copies of Nlgn3 mRNA (Figure 3.4 A7).
To understand whether Nlgn3 mRNA is differentially expressed between neuronal subtypes within the myenteric plexus, subpopulations of myenteric neurons were labelled using immunofluorescence for cell type-specific markers. Myenteric neurons expressing calretinin were labelled using immunofluorescence (Figure 3.4 B1-B8). Expression data from 550 calretinin-immunoreactive neurons from 5 WT mice showed that 86% of these neurons express
Nlgn3 mRNA. Frequency distribution analysis showed that the distribution of Nlgn3 mRNA in calretinin neurons is similar to Nlgn3 mRNA expression in the total neuronal population
(Hu-positive neurons; n=1484, calretinin-positive neurons; n=550; p=0.2, Figure 3.4 B9).
There is no significant difference in the levels of Nlgn3 mRNA expression in calretinin neurons compared to Hu positive myenteric neuron populations (calretinin positive neurons; 17.0 ± 0.6 mRNA copies, Hu-positive neurons;15.6 ± 0.3 mRNA copies; p=0.2) (Figure 3.4 B10).
The expression of Nlgn3 mRNA in myenteric NOS-containing neurons was assessed using the same method (Figure 3.4 C1-C8). Co-labelling of NOS-expressing neurons with RNAScope revealed that 87% of NOS neurons express Nlgn3 mRNA in the cell soma and 13% of NOS
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neurons do not express Nlgn3 mRNA. The frequency distribution of Nlgn3 mRNA in NOS- containing neurons is similar to that of Hu-positive neurons in the myenteric plexus (NOS positive neurons; n=593, Hu positive neurons; n=2304, p=0.09; Figure 3.4 C9). An individual
NOS-containing neuron contains an average of 17.6 ± 0.9 Nlgn3 mRNA copies, n=593 neurons), a similar number to Hu-positive myenteric neurons (17.5 ± 0.4 Nlgn3 mRNA copies, n=2304 neurons, p=0.9) (Figure 3.4 C10).
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Figure 3.4 Expression and the distribution of Nlgn3 mRNA in distal ileal myenteric neurons
A1. Myenteric neurons labelled using Hu by immunofluorescence. A2. Nlgn3 mRNA expression was detected using RNAScope. A3. Nlgn3 mRNA expression in myenteric neurons.
The 3D structure of (A4) myenteric neurons (A5) Nlgn3 mRNA (A6) Nlgn3 mRNA containing myenteric neurons. A7. Frequency distribution of Nlgn3 expression in myenteric neurons.
Confocal micrographs of (B1) myenteric neurons labelled with the pan-neuronal marker, Hu
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(B2) Nlgn3 mRNA (B3) Calretinin positive myenteric neurons. B4. Expression of Nlgn3 mRNA in calretinin neurons. The 3D rendering of (B5) myenteric neurons (B6) Nlgn3 mRNA distribution in the myenteric plexus, (B7) calretinin expressing neurons and (B8) Nlgn3 mRNA expression in calretinin-expressing myenteric neurons. B9. The frequency distribution of
Nlgn3 mRNA expression in calretinin-positive myenteric neurons is similar to total myenteric neurons. B10. Calretinin-expressing neurons contain similar copy numbers of Nlgn3 mRNA to that of myenteric neurons overall. Triple labelling of (C1) myenteric neurons, (C2) Nlgn3 mRNA and (C3) NOS expressing myenteric neurons. C4. The expression of Nlgn3 mRNA in
NOS-containing myenteric neurons. The 3D structure of (C5) myenteric neurons, (C6) Nlgn3 mRNA, (C7) NOS-expressing myenteric neurons and (C8) Nlgn3 mRNA in NOS-containing neurons. C9. The frequency distribution of Nlgn3 mRNA in NOS-containing neurons is similar to that in the total population (i.e. Hu-positive neurons). C10. The average number of Nlgn3 mRNA copies in myenteric NOS-containing neurons is similar to the total number of myenteric neurons. Hu staining is indicated by open arrowheads and Nlgn3 mRNA labelling is indicated by arrows. Scale bar=10 µm.
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4.5 Enteric glia express Nlgn3
Although Nlgn3 is thought to be expressed specifically in neurons, recent studies revealed that
Nlgn3 is also expressed in non-neuronal cells in the intestine. Whether Nlgn3 is expressed in enteric glia has not been assessed, largely due to issues with antisera specificity using immunofluorescence (Leembruggen, unpublished).
To investigate whether Nlgn3 is expressed by enteric glia in the submucosal plexus in the distal ileum, the dual RNAScope and immunofluorescence approach was utilised. Submucosal glia were labelled using an antiserum against the calcium-binding protein, S100b (Figure 3.5 A1-
A4). Imaris-based image analysis methods were performed to analyse Nlgn3 mRNA expression in glia (Figure 3.5 A5-A8). A total of 226 glial cells in 6 individual WT mice labelled for
S100b were quantified to measure the Nlgn3 mRNA expression in submucosal glia. These findings show that the most (64%) submucosal glial cells express Nlgn3 mRNA (Figure 3.5
A9).
A similar methodological approach was used to measure the Nlgn3 mRNA expression in myenteric glia (Figure 3.5 B1-B8). A total of 1203 glial cells in 4 WT mice immunoreactive for S100b were assessed. Only 37% of glial cells expressed Nlgn3 mRNA (Figure 3.5 B9).
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Figure 3.5 The expression and distribution of Nlgn3 mRNA in enteric glia.
Confocal micrographs of (A1) submucosal neurons labelled with the pan-neuronal marker Hu
(A2) Nlgn3 mRNAexpression in the submucosal plexus (A3) submucosal glia labelled with
S100b (A4) Nlgn3 mRNA in submucosal glia. Imaris-based 3D rendering of (A5) submucosal neurons (A6) Nlgn3 mRNA (A7) submucosal glia (A8) Nlgn3 mRNA expression in submucosal glia. A9. The frequency distribution analysis demonstrates that Nlgn3 mRNA is expressed in the majority of submucosal glial cells. Triple labelling of (B1) myenteric neurons using Hu pan-neuronal marker (B2) Nlgn3 mRNA (B3) myenteric glia using S100b. B4. The expression of Nlgn3 mRNA in myenteric glia. 3D rendering of (B5) myenteric neurons (B6)
Nlgn3 mRNA (B7) myenteric glia (B8) Nlgn3 mRNA expression in myenteric glia. B9.
Frequency distribution analysis displayed that the majority of myenteric glia do not express
Nlgn3 mRNA. Hu staining is indicated by the open arrowhead, Nlgn3 mRNA is indicated by the filled arrow and submucosal glia are labelled with open arrows. Scale bar=10 µm
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4.6 Nlgn3 mRNA expression in submucosal neurons is affected by the R451C mutation
Effects of the R451C mutation on Nlgn3 mRNA and protein expression has been thoroughly investigated in the brain (Tabuchi et al., 2007, Etherton et al., 2011, Sudhof, 2008, Jamain et al., 2003b) and in cell lines (Comoletti et al., 2004), but whether this mutation affects mRNA expression profiles in the ENS has not been reported. The current study therefore investigated the effects of the R451C mutation on Nlgn3 mRNA expression in the ENS. Dual RNAScope and immunocytochemistry method was performed to assess Nlgn3 mRNA expression levels in the ileal submucosal plexus of Nlgn3R451C mutant mice (Figure 3.6 A1-A6).
A total of 1985 ileal submucosal neurons in 14 WT and 1300 in 10 Nlgn3R451C mice were assessed. Frequency distribution analysis showed that 78% of submucosal neurons express
Nlgn3 mRNA and 22% of submucosal neurons do not contain Nlgn3 mRNA in Nlgn3R451C mutant mice. The frequency distribution of Nlgn3 mRNA in submucosal neurons in mutant mice is significantly different from the distribution of Nlgn3 mRNA in WT (WT; n=1985,
Nlgn3R451C mutant mice; n=1300, p<0.0001, Figure 3.6 A7). In Nlgn3R451C mutant mice, submucosal neurons express 21.6 ± 0.8 (n=1300) copies of Nlgn3 mRNA in their cell bodies, significantly less Nlgn3 mRNA than WT (24.8 ± 1.0, n=1985), p<0.0001 (Figure 3.6 A8).
In the current study, the expression of Nlgn3 mRNA in different submucosal neuronal populations was characterised (Figure 3.6 B1-B8). In WT, 92% ChAT neurons express Nlgn3 mRNA (section 4.3). Of a total of 241 ChAT neurons in 7 Nlgn3R451C mice examined only 69% of neurons express Nlgn3 mRNA while about 31% ChAT-expressing neurons in the submucosal plexus do not express Nlgn3 mRNA. The frequency distribution analysis revealed that the distribution of Nlgn3 mRNA in ChAT neurons in the mutant is significantly different from WT (n=298 in 5 WT mice, n=241 in Nlgn3R451C mice, p<0.0001 Figure 3.6 B9). In
Nlgn3R451C mutant mice there is a significant reduction in Nlgn3 mRNA expression in ChAT
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neurons compared to WT (WT; 30.1 ± 3.8, n=298., Nlgn3R451C mutant mice ;16.2 ± 2.9, n=24, p=0.006) (Figure 3.6 B10).
The impact of the R451C mutation on Nlgn3 mRNA production in non-cholinergic neurons was evaluated (Figure 3.6 C1-C8). The frequency distribution of Nlgn3 mRNA copy number distribution in VIP neurons in ileal tissue from Nlgn3R451C mice differs significantly from WT
(n=623 in 7 WT mice, n=449 in 5 Nlgn3R451C mice, p<0.0001 Figure 3.6 C9). In WT, about
91% of VIP neurons express Nlgn3 mRNA (section 4.3). In mutant mice 82% of VIP containing submucosal neurons express Nlgn3 mRNA and 18% of VIP positive neurons do not contain Nlgn3 mRNA in their cell bodies. In VIP neurons, Nlgn3 mRNA expression is trending to an increase in mutant mice compared to WT (WT; 27.1 ± 1.1, n=623, mutant mice; 30.4 ±
1.2 Nlgn3 mRNA copies per cell, n=449, p=0.05 (Figure 3.6 C10).
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Figure 3.6 Expression of Nlgn3 mRNA in the ileal submucosal plexus of Nlgn3R451C mutant mice.
Confocal micrographs of (A1) Submucosal neurons (A2) Nlgn3 mRNA expression (A3) Nlgn3 mRNA expression in submucosal neurons. The 3D structure of (A4) submucosal neurons (A5)
Nlgn3 mRNA (A6) Nlgn3 mRNA expression in submucosal neurons. A7. The distribution of
Nlgn3 mRNA in Nlgn3R451C mutant mice is significantly different compared to WT. A8.
Nlgn3R451C mutant mice express less Nlgn3 mRNA in submucosal neurons compared to WT.
Triple labelling of (B1) Submucosal neurons with Hu pan-neuronal marker (B2) Nlgn3 mRNA
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(B3) cholinergic neurons with ChAT antibody. B4. The expression of Nlgn3 mRNA in cholinergic neurons. 3D rendering of (B5) Hu neurons (B6) Nlgn3 mRNA (B7) ChAT neuron
(B8) Nlgn3 mRNA expression in ChaT neurons. B9. The frequency distribution of Nlgn3 mRNA in cholinergic submucosal neurons in Nlgn3R451C mutant mice compared to WT B10.
Cholinergic neurons express significantly less Nlgn3 mRNA in Nlgn3R451C mutants compared to WT. Confocal micrographs of (C1) Submucosal neurons labelled with Hu pan-neuronal marker (C2) Nlgn3 mRNA labelled with RNAScope (C3) Non-cholinergic neurons labelled with VIP (C4) Nlgn3 expression in non-cholinergic submucosal neurons. 3D reconstruction of
(C5) submucosal neurons (C6) Nlgn3 mRNA (C7) VIP neurons (C8) Nlgn3 mRNA in VIP neurons. C9. The frequency distribution of Nlgn3 mRNA in non-cholinergic submucosal neurons in Nlgn3R451C mutant mice compared to WT. C10. Nlgn3R451C mice express similar numbers of Nlgn3 mRNA copies in non-cholinergic neurons compared to WT in the submucosal plexus. Nlgn3 mRNA is indicated by the filled arrow and submucosal glia are labelled with open arrows **p<0.01, ****p<0.0001, Scale bar=10 µm
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4.7 The R451C mutation alters Nlgn3 mRNA expression in myenteric neurons
Using the same method, the impact of the Nlgn3 R451C mutation on Nlgn3 mRNA expression in the myenteric plexus was assessed (Figure 3.7 A1-A6).
Of a total of 3,788 myenteric neurons in Nlgn3R451C mutant mice, about 75% myenteric neurons express Nlgn3 mRNA. The frequency distribution analysis illustrated that Nlgn3 mRNA expression in mutant mice is significantly different between WT and Nlgn3R451C mice (2,825 neurons in 10 WT mice versus 3,788 in 13 Nlgn3R451C mice, p<0.0001) (Figure 3.7 A7). As expected, Nlgn3 mRNA expression in myenteric neurons was significantly reduced in
Nlgn3R451C mutant mice compared to WT (WT; 16.8 ± 0.3 mRNA copies per cell, n=3,788,
Nlgn3R451C mice; 11.3 ± 0.2 mRNA copies per cell, n=2,825, p<0.0001) (Figure 3.7 A8).
To gain insight into how the R451C mutation impacts Nlgn3 mRNA expression in calretinin- immunoreactive myenteric neurons, dual RNAScope and immunohistochemistry was performed. Calretinin neurons were labelled using immunofluorescence and RNAScope was utilised to label Nlgn3 mRNA (Figure 3.7 B1-B8). The frequency distribution of Nlgn3 mRNA in calretinin neurons in Nlgn3R451C mice is significantly different from the WT (n=550 in 5 WT mice, n=685 in 5 Nlgn3R451C mice, p<0.0001, Figure 3.7 B9) In WT, 86% of calretinin neurons express Nlgn3 mRNA (section 4.4). Of the total calretinin positive cellular population,
78% of neurons co-express Nlgn3 mRNA in Nlgn3R451C mutant mice. Significantly fewer
Nlgn3 mRNA copies were detected in calretinin-immunoreactive neurons in Nlgn3R451C mutant mice compared to WT (WT;17.0 ± 0.6 mRNA copies, n=550 neurons, Nlgn3R451C mice ;12.3
± 0.42 mRNA copies, n=685 neurons, p<0.0001) (Figure 3.7 B10).
The Impact of the R451C mutation on Nlgn3 mRNA expression in NOS-immunoreactive myenteric neurons was analysed (Figure 3.7 C1-C8). The frequency distribution analysis
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revealed that the Nlgn3 mRNA distribution in NOS immunoreactive neurons in Nlgn3R451C mice differs significantly from WT (WT; n=593 neurons in 8 mice, Nlgn3R451C mice; n=331, p<0.0001, Figure 3.7 C9). In WT, about 87% neurons express Nlgn3 mRNA (section 4.4). Of the total 331 of NOS-immunoreactive neurons assessed in Nlgn3R451C mutant mice, the about
73% of NOS-containing neurons co-express Nlgn3 mRNA. Nlgn3 mRNA expression levels in NOS-immunoreactive neurons in the ileal myenteric plexus were significantly reduced in mutant mice compared to WT (WT; 17.6 ± 0.9 mRNA copies per cell, n=593, (Nlgn3R451C mutant mice 9.0 ± 0.4 mRNA copies per cell, n=331), p<0.0001 (Figure 3.7 C10).
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Figure 3.7 Effects of the Nlgn3 R451C mutation on Nlgn3 mRNA expression in myenteric neurons.
Confocal images of (A1) myenteric neurons (A2) Nlgn3 mRNA expression (A3) Nlgn3 mRNA expression in myenteric neurons. The 3D structure of (A4) myenteric neurons (A5) Nlgn3 mRNA (A6) Nlgn3 mRNA expression in myenteric neurons. A7. The frequency distribution of Nlgn3 mRNA in Nlgn3R451C mutant mice compared WT A8. In the Nlgn3R451C mouse ileum,
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myenteric neurons express fewer copies of Nlgn3 mRNA compared to WT. Triple labelling of
(B1) myenteric neurons using Hu pan-neuronal marker (B2) Nlgn3 mRNA (B3) calretinin neurons. B4. The expression of Nlgn3 mRNA in calretinin expressing neurons in Nlgn3R451C mutant mice. 3D reconstruction of (B5) myenteric neurons (B6) Nlgn3 mRNA (B7) calretinin expressing neurons and (B8) Nlgn3 mRNA expression calretinin neurons in Nlgn3R451C mutant mice. B9. The frequency distribution of Nlgn3 mRNA in calretinin positive myenteric neurons in Nlgn3R451C mutant mice and WT. B10. The Nlgn3 R451C mutation reduces the Nlgn3 mRNA expression in mutant mice compared to WT. Triple labelling of (C1) myenteric neurons
(C2) Nlgn3 mRNA (C3) NOS neurons (C4) Nlgn3 mRNA in NOS expressing myenteric neurons. 3D rendering of (C5) myenteric neurons (C6) NOS positive neurons (C7) Nlgn3 mRNA and (C8) Nlgn3 mRNA expressing NOS neurons. C9. The frequency distribution of
Nlgn3 mRNA expression in NOS containing neurons in Nlgn3R451C mutant mice is significantly different compared to WT C10. In Nlgn3R451C mutant mice, NOS neurons contain less Nlgn3 mRNA compared to WT. Nlgn3 mRNA is indicated by the filled arrow and submucosal glia are labelled with open arrows, ****p<0.0001, Scale bar=10 µm.
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4.8 Expression of Nlgn3 mRNA is altered in ileal submucosal and myenteric glia in Nlgn3R451C mice
As described earlier, Nlgn3 mRNA is expressed in both submucosal and myenteric glia in the
WT mouse ileum. To determine if the R451C mutation alters glial expression patterns of Nlgn3 mRNA, expression levels of Nlgn3 mRNA were assessed in glia of the ileal submucosal plexus and the myenteric plexus in Nlgn3R451C mutant mice.
The same method described in control experiments (section 4.5) was used to label and analyse the Nlgn3 mRNA expression in submucosal glia (Figure 3.8 A1-A8). A total of 226 glial cells in the WT and 173 glial cells in Nlgn3R451C mutant mice of submucosa were analysed. The frequency distribution data show that WT and Nlgn3R451C mice have similar levels of Nlgn3 mRNA expression in submucosal glia (n=226 in 6 WT mice, n=173 in Nlgn3R451C mice, p=0.09, Figure 3.8 A9). In submucosal glia, Nlgn3 mRNA expression is trending to an increase in Nlgn3R451C mutants (5.0 ± 1.2 mRNA copies, n=173 glial cells) compared to WT mice (2.8 ± 0.3 mRNA copies, n=226 glial cells) p=0.05 (Figure 3.8 A10).
Nlgn3 mRNA expression in myenteric glia in Nlgn3R451C mice were measured using the same methodological approach (Figure 3.8 B1-B8). In Nlgn3R451C mutant mice, of a total of 1318 glial cells only 758 myenteric glia express NLGN-3 mRNA and the frequency distribution of
Nlgn3 mRNA in myenteric glia in mutant mice is significantly different to WT (n=1203 in 5
WT mice, n=1318 in 5 Nlgn3R451C mice, p=0.001, Figure 3.8 B9). Nlgn3 mRNA expression is reduced in myenteric glia in Nlgn3R451C mutant mice compared to WT (WT; 14.2 ± 1.2 mRNA copies/cell, n=1203, Nlgn3R451C mutant mice; 4.4 ± 0.1 mRNA copies/cell, n=1,318, p<0.0001, Figure 3.8 B10).
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Figure 3.8 Effects of Nlgn3 R451C mutation on Nlgn3 mRNA expression in enteric glia.
Confocal images of (A1) submucosal neurons label with Hu (A2) Nlgn3 mRNA expression in the submucosal plexus (A3) submucosal glia labelled with S100b (A4) Nlgn3 mRNA expression in submucosal glia. The 3D structure of (A5) submucosal neurons (A6) Nlgn3 mRNA (A7) submucosal glia (A8) Nlgn3 mRNA expression in submucosal glia. A9.
Frequency distribution of Nlgn3 mRNA in submucosal glia in Nlgn3R451C mutant mice compared to WT. A10. The copy number expression of Nlgn3 mRNA per cell in WT compared to Nlgn3R451C mutant mice. Immunofluorescent labelling of (B1) myenteric neurons (B2) Nlgn3 mRNA (B3) myenteric glia (B4) Nlgn3 mRNA localization in myenteric glia in Nlgn3R451C mutant mice. 3D reconstruction of (B5) myenteric neurons (B6) Nlgn3 mRNA (B7) myenteric glia (B8) Nlgn3 mRNA expression in myenteric glia in Nlgn3R451C mice. B9. Frequency distribution of Nlgn3 mRNA expression in Nlgn3R451C mutant mice is significantly different to
WT mice. B10. Mutant mice express significantly fewer Nlgn3 mRNA copies in myenteric glia compared to WT. ****p<0.0001, Scale bar=10 µm
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5. DISCUSSION
In this chapter, the expression of Nlgn3 mRNA in the ENS was assessed. The localization of
NLGN3 protein in the ENS is challenging due to lack of suitable antibodies. Therefore, an
RNAScope in situ hybridization based method was introduced to localise Nlgn3 mRNA in enteric neurons in mice. For the quantification of Nlgn3 mRNA expression in the ENS, an automated method was developed using Imaris image analysis software. Findings from this study revealed that most submucosal and myenteric neurons express Nlgn3 mRNA in the cell soma. For the first time, this study revealed that Nlgn3 mRNA is expressed in submucosal and myenteric glia. The effects of the Nlgn3 R451C mutation on Nlgn3 mRNA expression levels were also assessed in this chapter. Overall, the R451C mutation reduces the Nlgn3 mRNA expression in both submucosal and myenteric neurons. Further, this mutation decreases the
Nlgn3 mRNA expression in myenteric glia.
5.1 Dual RNAScope and immunocytochemistry is a reliable and highly sensitive technique to detect Nlgn3 mRNA in the enteric nervous system
RNA biomarkers have emerged as major tools to detect the mRNA expression of a gene of interest. Although real-time PCR is the most widely utilised RNA-based method to measure gene expression levels in various tissue samples, there are major limitations associated with such PCR-based techniques. For example, PCR-based approaches have a significant risk of contamination from undesired cell types and surrounding tissue elements. In addition, the
RNA extraction process destroys the tissue integrity which could substantially reduce the gene expression levels or lead to false-positive outcomes. To overcome these issues and also to increase the sensitivity and specificity of gene expression measurement, various other gene expression analysis techniques have been introduced.
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In situ hybridization is a powerful tool which can be used to measure the gene expression level directly in specific cell populations. In-situ hybridization is a commonly used technique to visualise the distribution and expression of RNA or DNA within a tissue sample. Using in situ hybridization techniques, the desired segment of nucleic acid can be detected using a modified complementary nucleic acid strand. The sensitivity of traditional in situ hybridization platforms remained a concern, specifically in detecting low abundance RNA transcripts. To enhance the sensitivity of the technique, signal amplification methods such as in situ PCR and biotinylated tyramine-mediated amplification methods were conjugated prior to the in situ hybridization setting (Andras et al., 2001, Speel et al., 1999, Qian and Lloyd, 2003). However, the target amplification prior to the in situ hybridization is highly likely to amplify noise, limiting the differentiation of signal and noise which results in incorrect quantification (Wang et al., 2012).
RNAScope is a novel and advanced in situ hybridization technique which has a single transcript sensitivity (Wang et al., 2017a). In contrast to the traditional in situ hybridization techniques,
RNAScope has been designed to improve the signal to noise ratio by amplifying the target specific signal but not the background. This technique enables a fluorescent signal to be enhanced multiple times using a series of amplification steps, therefore it is an invaluable technique for detecting RNA with low abundance levels. Unlike routine PCR based methods,
RNAScope can be incorporated with protein immunohistochemistry which creates a powerful tool to detect the expression of single transcripts of RNA at the cellular level (Grabinski et al.,
2015).
The current study outlined a method combining RNAScope in situ hybridization with immunofluorescence to detect the expression of Nlgn3 transcripts in individual enteric neuronal subtypes in both submucosal and myenteric plexus of mouse ileum.
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5.2 Imaris-based quantification is an automated and unbiased technique to measure Nlgn3 mRNA expression
The RNAScope signal appears as punctate staining and an individual punctum corresponds to a single mRNA molecule (Wang et al., 2012, Itzkovitz and van Oudenaarden, 2011). When the gene expression is high these puncta usually appear as overlapped and fused clusters which may lead to inaccurate quantification. To overcome this, the current study developed automated quantification method which can detect and count individual mRNA molecules precisely in 3- dimensional space using the Imaris image analysis software package.
Imaris image analysis software contains a range of functionalities which are ideal for the automated detection and quantification of cells and cellular components. In this study, an
Imaris based automated quantification method was developed to detect and quantify
RNAScope signal in individual neurons. The ‘surface tool’ was applied to create a 3D structure of enteric neurons using high-resolution stacks of confocal images. The software is also equipped with ‘filter options’ to manually or automatically detect different neurone populations.
The ‘spot’ detection tool of the Imaris software was used to create an automated quantification method to calculate Nlgn3 mRNA copies per neuron. Therefore, this method generates a direct measurement of Nlgn3 mRNA expression in enteric neurons.
5.3 Nlgn3 mRNA is expressed in most enteric neurons
Although recent studies show that mutations in NLGN3 gene affect gut functions, a detailed characterisation of NLGN3 expression in the enteric nervous system has not been reported previously. Interestingly, the current study discovered that most enteric neurons express Nlgn3 mRNA in their cell soma. However, the 3D rendering method revealed that some of the Nlgn3 mRNA puncta are located on the surface of the neurons. The major limitation here is that the
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Hu antibody labels the nucleus and the cytoplasm, but not the membrane. Therefore, a membrane labelling antibody is required to determine the localization of puncta within or on the surface of the neurons.
In the submucosal plexus, 87% of neurons express Nlgn3 mRNA. Of particular interest, however, Nlgn3 mRNA is differentially expressed in different submucosal neuronal subpopulations. Non-cholinergic (VIP-containing) neurons contain more copies of Nlgn3 mRNA compared to other submucosal neuronal subtypes. Similar to the submucosal plexus, approximately 87% of myenteric neurons express Nlgn3 mRNA. Data from this study also showed that submucosal neurons express more copies of Nlgn3 mRNA compared to myenteric neurons. The area of submucosal neurons is generally larger compared to myenteric neurons and this could be why the cell soma of submucosal neurons contain more Nlgn3 mRNA compared to myenteric neurons. Alternatively, submucosal neurons may form more synaptic contacts compared to myenteric neurons and hence express a greater number of Nlgn3 mRNA copies compared with neurons within the myenteric plexus.
Qualitative and quantitative analysis of Nlgn3 mRNA expression has been widely studied in the CNS. Nlgn3 mRNA is expressed in all brain regions and enriched in synaptic plasma membranes and postsynaptic densities. For example, Nlgn3 mRNA is expressed in the adult human CNS (Philibert et al., 2000). In mice, Nlgn3 mRNA is expressed in the cortex, hippocampus, hypothalamus, thalamus and cerebellum (Kumar and Thakur, 2015).
In this study, the expression of Nlgn3 mRNA in different subpopulations of enteric neurons was investigated. Analysis from Chapter 3 revealed that most acetylcholine expressing submucosal neurons contain Nlgn3 mRNA. Acetylcholine is the primary enteric excitatory neurotransmitter which has multiple roles in the ENS. Within the enteric neural circuity,
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acetylcholine is reported to be expressed in non-cholinergic secretomotor neurons, excitatory muscle motor neurons, ascending interneurons, descending interneurons and intrinsic sensory neurons (Brookes, 2001, Grider, 2003, Gwynne and Bornstein, 2007, Brookes et al., 1991b).
Further, findings from this study revealed that, Nlgn3 mRNA is expressed in VIP expressing submucosal neurons. In the submucosal plexus, VIP is a primary neurotransmitter of secretomotor neurons as it has been demonstrated that VIP stimulates intestinal secretion
(Foong et al., 2014, Mongardi Fantaguzzi et al., 2009). With respect to neuromuscular transmission, VIP is predominantly inhibitory (Costa et al., 1992). In contrast, VIP is expressed in excitatory neurons in the myenteric plexus. In addition, VIP is a co-transmitter in a subset of descending interneurons.
Nitric oxide (NO) is the dominant inhibitory neurotransmitters in the ENS (Qu et al., 2008,
Sang and Young, 1996). One of the major findings of this study is that most NO containing enteric neurons express Nlgn3 mRNA in the cell soma, which confirms that the NLGN3 is expressed in inhibitory neurons in the ENS. In addition, NOS is expressed in descending interneurons with other neurotransmitters (Young et al., 1995).
Data from this study also revealed that Nlgn3 mRNA is expressed in calretinin expressing myenteric neurons. In the ENS, calretinin is expressed in intrinsic sensory neurons.
Additionally, calretinin is also expressed in interneurons and excitatory motor neurons to smooth muscle layer (Sang and Young, 1996, Qu et al., 2008).
Collectively, data from this study suggest that Nlgn3 is expressed in almost all types of neurons within the enteric neural circuitry. Therefore, it can be postulated that Nlgn3 might involve in synapse formation between different classes of enteric neurons during synaptic transmission.
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5.4 Nlgn3 mRNA is expressed in enteric glia
For the first time, the current study revealed that Nlgn3 mRNA is expressed in enteric glia. In the submucosal plexus, 64% of glial cell bodies express Nlgn3 mRNA. Interestingly, only 37% of myenteric glia express Nlgn3 mRNA. Based on these findings, this study suggests that
NLGN3 expressed in enteric glia might involve in glial-neuron and glia-glia synaptic formation.
Together with enteric neurons, enteric glia play a major role in ENS-mediated GI functions including mucosal secretion, intestinal permeability, mucosal sensation, GI motility and immune responses (Gulbransen and Sharkey, 2012, Grubisic et al., 2018). Enteric glia synapse onto enteric neurons to regulate ENS coordinated gut functions. The association between glia and neurons in the ENS has been revealed by immunocytochemistry where it has been shown that enteric glia are in close contact with nerve fibers and varicose release sites (Boesmans et al., 2013). Evidence for neuro-glia communication also comes from live imaging experiments and these studies report that the presence of several signalling pathways between neurons and glia. A number of Ca2+ imaging studies displayed that neuron-glia communication is regulated by neurally released purines which activates purinergic receptors on enteric glia (Boesmans et al., 2013, Gomes et al., 2009, Gulbransen and Sharkey, 2009). Another form of enteric neuron- glia communication involves VIP acting on vasoactive intestinal peptide receptor-1 expressed by cholinergic neurons (Fung et al., 2017). In addition, neuronal ATP release through pannexin-1 channel also play a role in neuro-glia interactions (Wang and Dahl, 2018, Hanstein et al., 2016).
Cell adhesion molecules play an important role in glia-neuronal interactions and glia-glia interactions. Accordingly, RNA sequencing of the transcriptome revealed that Nlgn3 transcripts are enriched in glial cell types including astrocytes and oligodendrocytes in the
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mouse brain (Zhang et al., 2014). Interestingly, NLGNs expressed in astrocytes aid in neuron- astrocyte communication via bi- and tripartite synapses in mice and in C. elegans (Hillen et al.,
2018). NLGN3 expression in enteric glia, therefore, suggest that bi-partite and tripartite synapses can also be established in the ENS. Given this evidence, this study strongly suggests that NLGN3 might play a role in synapse formation during neuron-glia communication in the
ENS.
5.5 The R451C mutation alters Nlgn3 mRNA expression in the ENS
How the Nlgn3 R451C mutation affects Nlgn3 mRNA expression in the enteric nervous system is not understood. In the mouse ileal submucosal plexus, the R451C mutation significantly decreases Nlgn3 mRNA expression in both neurons and glia. For example, cholinergic submucosal neurons contain reduced Nlgn3 mRNA copy numbers in mutant mice compared to
WT.
In the myenteric plexus of the distal ileum, the R451C mutation reduces Nlgn3 mRNA expression levels in neurons. Specifically, the expression of Nlgn3 mRNA is substantially reduced in both calretinin and NOS-immunoreactive neuronal populations. The expression of
Nlgn3 mRNA in myenteric glia is also dramatically reduced by the Nlgn3 R451C mutation.
The impact of the Nlgn3 R451C mutation on Nlgn3 expression in glia has not previously been reported. In the current study, Nlgn3 mRNA expression was reduced in myenteric glia but was unchanged in glia located within the submucosal plexus in Nlgn3R451C mice. These data suggest that Nlgn3 mRNA expression in submucosal glia is not affected by the R451C mutation.
The effects of the R451C mutation on the Nlgn3 mRNA and protein expression have been extensively analysed in the mouse brain. The R451C mutation does not alter the Nlgn3 mRNA level but decreases the export of Nlgn3 from the endoplasmic reticulum causing a 90% reduction in NLGN3 protein level at the synaptic membrane (Tabuchi et al., 2007).
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Electrophysiological studies reported that mutant mice displayed an increase in inhibitory synaptic transmission resulting in the excitatory/inhibitory imbalance. Despite reducing the
NLGN3 protein level, the R451C mutation creates a gain-of-function effect in the CNS.
Signalling pathways and many neurotransmitters are similar between the CNS and the ENS.
Therefore, it can be proposed that the molecules that are involved in the formation and function of CNS synapses also participate in the ENS synapse formation and function. Hence, disrupted synaptic transmission could contribute both to the pathogenesis of ASD-related behavioural abnormalities and GI dysfunction.
6. CONCLUSION
The main focus of this chapter was to develop a qualitative and quantitative method to localise
Nlgn3 in the enteric nervous system to correlate the effects of the Nlgn3 R451C mutation and
GI dysfunction in ASD. The dual RNAScope and immunofluorescence approach is a promising platform for detecting and localising the expression of Nlgn3 mRNA in the GI tract. When combined with quantitative 3D image analysis software, Nlgn3 mRNA expression levels can be measured in specific ENS cellular subpopulations. Data from the current study demonstrates that Nlgn3 mRNA is expressed in most enteric neuronal and glial populations. In contrast with previous findings in the CNS where there is no change in Nlgn3 mRNA levels, the autism- associated R451C missense mutation decreases Nlgn3 mRNA expression in ileal enteric neurons in mice. In contrast to the findings in submucosal neuronal subtypes, Nlgn3 mRNA expression in submucosal glia is unaffected by the R451C mutation. In the myenteric plexus, mRNA expression is reduced in glia in Nlgn3R451C mice, suggesting that the R451C mutation in Nlgn3 results in differential modulation of gene expression in the ENS. These findings have implications for understanding the role of postsynaptic proteins in the ENS and GI dysfunction in autism.
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CHAPTER 4
Neuronal regulation of intestinal mucosal barrier function in the
neuroligin-3R451C mouse model of autism.
1. ABSTRACT
The intestinal mucosal barrier is the interface of the gut which demarcates the external environment from the interior of the body. The importance of an intact mucosal barrier has been highlighted with emerging data in the context of autism illustrating microbial dysbiosis alongside functional GI dysfunction in ASD patients. Several studies have reported structural impairments in the mucosal barrier in autism, but neurally-mediated barrier dysfunctions have not been reported to date. This study investigated the role of the NLGN3 synaptic adhesion protein in mucosal barrier functions including the paracellular pathway and mucosal secretion in the small intestine in the Nlgn3R451C mouse model of autism. The Ussing chamber technique was used to measure paracellular permeability and mucosal secretion ex vivo. Tight junction protein gene expression levels were measured using real-time (RT) PCR array and droplet digital (dd) PCR approaches. The impact of the Nlgn3 R451C mutation on the submucosal plexus was investigated using immunocytochemistry. Results from these assays indicated that
Nlgn3R451C mutant mice have increased paracellular permeability and decreased transepithelial resistance (TER) in the distal ileum. However, tight junction protein gene expression is unchanged. Immunohistochemistry data revealed increased numbers of non-cholinergic and decreased cholinergic neuronal populations in the distal ileal submucosal plexus in Nlgn3R451C mice. Pharmacological stimulation of submucosal ganglia using DMPP, a nicotinic receptor agonist increases mucosal secretion in mutant mice compared to WT, suggesting that NLGN3 plays a role in nicotinic acetylcholine receptor (nAChR) meditated neuronal activity. Overall,
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these data indicate that the Nlgn3 R451C mutation affects the mucosal barrier function through a nicotinic acetylcholine receptor-mediated neuronal pathway.
2. INTRODUCTION
ASD is diagnosed based on behavioural criteria including impaired communication, social interactions and restrictive and repetitive behaviour (Association, 2013). In addition to core behavioural symptoms, there are several comorbid conditions associated with ASD. Of these conditions, gastrointestinal (GI) dysfunction has gained attention recently due to its high prevalence in ASD patients (Buie et al., 2010a). GI issues can contribute to the severity of
ASD- relevant behaviour, particularly for individuals who are incapable of communicating their discomfort. Although the exact cause for GI problems is not clear, clinical data suggest that mucosal barrier dysfunction may play a role (D'Eufemia et al., 1996, Horvath and Perman,
2002).
The intestinal mucosa separates the potentially toxic luminal environment from the interior of the body. When the barrier is disrupted, luminal metabolites breach the underlying tissues and these metabolites can travel the brain via the vagus and spinal sensory nerves or through the circulatory system (Cryan and Dinan, 2012) exacerbating autism-related behavioural symptoms. The intestinal epithelium is the central component of the mucosal barrier which is adapted to absorb nutrients and protect the host from invading pathogens simultaneously.
Epithelial cells are tightly sealed to each other by tight junction protein complexes which strictly control the movement of materials via the paracellular pathway. Impaired paracellular pathway has been reported in autism patients as well as in animal models of autism (D'Eufemia et al., 1996, de Magistris et al., 2010, Hsiao et al., 2013, Golubeva et al., 2017, Margolis et al.,
2016, Wu, 2017). Importantly, these changes are reportedly due to altered expression of tight
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junction proteins (de Magistris et al., 2010, Hsiao et al., 2013, Fiorentino et al., 2016, Margolis et al., 2016).
Intestinal mucosal barrier functions are regulated by the submucosal plexus of the ENS. The intestinal mucosa is extensively innervated by submucosal neurons (Song et al., 1992, Porter et al., 1999, Jabari et al., 2012). There are two populations of secretomotor neurons in the submucosal plexus that regulate barrier function; i) cholinergic neurons, which contain choline acyltransferase (ChAT) and ii) non-cholinergic neurons that lack ChAT (Mongardi Fantaguzzi et al., 2009, Bornstein and Furness, 1988). Neurochemical changes are often accompanied with functional abnormalities in autism and an altered submucosal neurochemistry has been reported in the presence of ASD-associated nervous system mutations (Margolis et al., 2016).
The submucosal plexus regulates mucosal secretion and intestinal permeability (Neunlist et al.,
2013). When a pathogen invades the intestine, the intestinal secretory system releases secretions including water and mucus to flush the pathogen from the body (Bornstein and
Foong, 2018). To date, it is unknown whether the mucosal secretory system is affected in ASD.
Acetylcholine is the primary excitatory neurotransmitter of the ENS and nicotinic acetylcholine receptors contribute to fast synaptic transmission. Submucosal neurons express several nAChRs including a3, a4, a5, a7 (Glushakov et al., 2004). Nicotinic acetylcholine receptor agonist 3,4-Dimethylpyrazole phosphate (DMPP) is selective for ganglionic subtypes and
Ussing chamber studies have shown that DMPP evokes neurogenic secretory responses.
The effects of both the Nlgn3 R451C mutation and Nlgn3 ablation in ex vivo on GI motility and the structure of the myenteric plexus have been characterized in mice (Hosie et al., 2019,
Leembruggen et al., 2019), but the impact of this mutation on the submucosal plexus and the mucosal barrier function has not been investigated. Therefore, to shed light on potential enteric neural mechanisms in GI dysfunction in ASD, the work described in this chapter characterized
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the intestinal mucosal barrier function in the Nlgn3R451C mouse model of autism. Experimental data revealed that the Nlgn3 R451C mutation affects ileal mucosal barrier function via a submucosal neuron-regulated pathway.
3. METHODS
3.1 Animals
NLGN3R451C mice were obtained from The Jackson Laboratory (Tabuchi et al., 2007) and were bred for over 10 generations on a pure C57Bl/6 background. Mice were housed in the
Biomedical Science Animal Facility at The University of Melbourne. NLGN3 R451C male mice
(aged 12-13 weeks) were killed by cervical dislocation in accordance with The University of
Melbourne Animal Experimentation Ethics Committee.
3.2 Paracellular permeability and transepithelial resistance
3.2.1 Tissue preparation
Tissue samples were prepared from WT and Nlgn3R451C mice as described in Chapter 2 section
3.1. Briefly, a 2cm segment of mouse jejunum (8 cm distal to the stomach) or distal ileum (1 cm proximal to the caecum) was excised and immediately placed in ice-cold oxygenated physiological saline solution. The tissue was opened longitudinally along the mesenteric border. For mucosa-submucosa preparations, tissue was pinned mucosal side down and seromuscular layers (serosa, longitudinal muscle and circular smooth muscle layer) were removed by microdissection. The mucosal preparation was then mounted on the pins of an
Ussing half-chamber.
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3.2.2 Assembling the Ussing chamber
Ussing chambers were set up as described in Chapter 2 section 3.2. In brief, the mucosal and serosal reservoirs were filled with physiological saline solution (10 ml) and constantly oxygenated throughout the experiment. The temperature of the superfusate was maintained at
370C. The electrical bias of the system was eliminated by “zeroing’ the system before conducting the experiment.
3.2.3 Transepithelial resistance
The transepithelial resistance of fresh mouse intestinal samples was measured using the Ussing chamber technique. After assembling the Ussing chamber, each side of the chamber was independently superfused with an equal volume (10 ml) of constantly oxygenated physiological saline solution. The preparations were incubated for 20 min in order to reach an equilibrium prior to obtaining data. After 20 min of the equilibration period, the tissue was short-circuited at 1 mV and the resulting short circuit current (Isc) was monitored. The procedure was repeated 5 times at 50s intervals. The TER was calculated using Ohm’s law.
3.2.4 Paracellular permeability
After measuring the TER, 2 ml of superfusate from the mucosal compartment was removed and 2.2 mg/mL of 4 kDa fluorescein isothiocyanate-dextran (FITC-dextran, Sigma Aldrich,
Australia) dissolved in 2 ml of superfusate was added. At t0, 200 µl of superfusate from the serosal compartment was collected and replaced with fresh superfusate to maintain equal volumes in both compartments. The procedure was repeated every 15 min for 3 h. The absorbance of the solution was measured, and FITC-dextran concentration in the serosal superfusate was calculated using a FITC-dextran standard curve.
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3.2.5 In vitro measurement of short circuit current
For measurement of short-circuit current (Isc), an index of electrogenic secretion, the mucosa- submucosal preparation was mounted in an Ussing chamber as described above. The transepithelial voltage potential of the preparation was clamped at zero, and the short circuit current was monitored to measure the basal Isc. DMPP was added to the serosal side of the chamber. DMPP stock was prepared at 100-fold concentration and diluted in physiological saline to a final concentration of 10 µM in the serosal chamber.
3.3 Tight junction protein gene expression
3.3.1 Tissue preparation
Under sterile conditions, the proximal jejunum (8 cm distal to the stomach) or distal ileum (1 cm proximal to the caecum) was excised and immediately placed in ice-cold physiological saline solution. The intestinal content was flushed out with sterile ice-cold physiological saline solution and the tissue was opened along the mesenteric border using small dissection scissors.
The mucosal layer was separated by microdissection as mentioned previously (Chapter 2 section 4.1). Mucosal preparations were frozen in liquid nitrogen immediately after they were dissected and stored at -80 oC until further use.
3.3.2 RNA extraction and cDNA synthesis
RNA extraction was performed using the ReliaPrepTM RNA tissue Miniprep System.
(Promega, USA) as mentioned in Chapter 2 section 4.2. cDNA was synthesised using an RT2
First Strand Kit (Promega, USA). The procedure is described in Chapter 2 section.
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3.3.3 Real-time quantitative PCR array
RT PCR array was performed using RT2 Profiler PCR Array (Qiagen, Germany). The PCR component mix was prepared by adding 650 µl 2x RT2 SYBR Green Mastermix, 102 µl cDNA synthesis reaction, 548 µl RNase-free water and 10 µl of the mix was pipetted to each well of the RT PCR array. The plate was tightly sealed with optical adhesive film and centrifuged for
1 min at 1000 g at room temperature. RT-PCR was performed in a QuantStudio 6 flex real- time PCR cycler (Applied Biosystems, USA) which was set to perform 1 cycle of initial denaturation at 95oC for 10 min and 40 cycles of denaturation for 15s at 95oC and annealing at
60oC for 1 min.
3.3.4 Droplet digital PCR (dd PCR)
The dd PCR reaction mixture was prepared by adding 12.5 µl 2x dd PCR super mix (Bio-Rad,
USA) 1.25 µl primers (IDT, Australia), 10.25 µl nuclease-free water and 1 µl cDNA. For droplet generation, 20 ul from the reaction mixture was transferred to the middle row of the cartridge. After loading the PCR reaction mixture, 70 ul of droplet generation oil was loaded into the bottom wells of the cartridge. The gasket was attached over the cartridge and it was placed into the droplet generator (Bio-Rad, USA). The droplets were transferred into a PCR plate. The plate was heat sealed and placed in a thermal cycler for PCR amplification (95 oC
10 min, 94 oC for 30 sec, 60 oC for 1 min, 98 oC for 10 min). Following the PCR amplification, the PCR place was placed in a droplet reader. The QuntaSoft software (Bio-Rad, USA) was utilised to determine the concentration of DNA in each sample.
3.4 Immunohistochemistry
3.4.1 Tissue collection
A segment of the jejunum (approximately 8 cm distal from the stomach) or distal ileum (1 cm proximal to the caecum) was excised. The tissue was placed in PBS and the intestinal content
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was flushed clean. The tissues were opened longitudinally along the mesenteric border and pinned in a silicone elastomer-lined dish.
3.4.2 Tissue preparation and immunocytochemistry
The detailed protocol for immunocytochemistry is described in Chapter 2 section 6.1. Briefly, the tissues were fixed in 4% formaldehyde for 24 h at 4oC and subsequently washed in PBS (3 x 10 min). The submucosal plexus was obtained by microdissection. Preparations were permeabilised in 1% Triton X-100/PBS for 30 min at room temperature and washed in PBS (3 x10 min). Preparations were incubated in primary antibodies (Table 4.1) for 36 h at 4oC and washed in PBS (3 x 10 min). Preparations were incubated in secondary antibodies (Table 4.2) for 2h at 4oC, washed in PBS (3 x 10 min) and mounted using DAKO mounting medium
(Carpentaria, USA).
3.4.3 Image acquisition
The images were captured using an Axio Imager2 fluorescence microscope and cell counts were performed using ImageJ software with the cell-counter plug-in (NIH, Bethesda,USA). To calculate the proportion of each neuronal subtype, co-expression with at least 100 Hu+ submucosal neurons from 20 randomly selected ganglia was examined.
3.5 Statistical analysis
Statistical analysis was performed using GraphPad Prism software (GraphPad, San Diego,
USA). One-way ANOVA was used to analyse the paracellular permeability and TER during the paracellular permeability experiments. The two-tailed unpaired t-test was performed to compare the basal TER values. Immunohistochemistry data were analysed using two-tailed unpaired t-test. Results are presented as mean ± SEM (standard error of the mean).
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Table 4.1 Primary antibodies used for immunocytochemistry
Primary antisera Raised in Dilution Source
Hu Human 1:5000 Gift from Dr. V. Lennon
VIP Rabbit 1:1000 Merck Millipore
ChAT Goat 1:100 Chemicon
Table 4.2 Secondary antibodies used for immunocytochemistry
Secondary antisera Raised in Dilution Source
Anti-human AF 594 Donkey 1:500 Jackson ImmunoResearch
Laboratories
Anti-Rabbit AF 488 Donkey 1:400 Molecular Probes
Anti-sheep AF 647 Donkey 1:500 Molecular Probes
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4. RESULTS
4.1 The Nlgn3 R451C mutation alters the proportions of ileal submucosal VIP and ChAT neurons.
The submucosal plexus of the ENS is directly involved in regulating mucosal barrier function via innervation of the intestinal mucosa. To investigate the effects of the Nlgn3 R451C mutation on the submucosal plexus circuitry, the proportions of two major neuronal populations (collectively accounting for >90% of submucosal neurons in the mouse (Mongardi
Fantaguzzi et al., 2009)) were studied.
The total number of submucosal neurons per ganglion was determined using immunofluorescence labelling for Hu, a pan-neuronal marker. An antiserum targeting ChAT, the synthesising enzyme for acetylcholine, was used as a marker of cholinergic neurons. An antiserum targeting VIP was used to stain non-cholinergic neurons (Figure 4.1 A-H).
In the ileum, the total number of neurons per ganglia was similar in Nlgn3R451C mutant mice and WT (WT; 6.3 ± 0.3, n=3, Nlgn3R451C mutant mice; 6.7 ± 0.4, n=6; p=0.54) (Figure 4.1 I).
Interestingly, mutant mice showed an increase in the proportion of VIP neurons compared to
WT. Specifically, in WT (n=3), 54.3 ± 1.5% of Hu+ submucosal neurons were immunoreactive for VIP compared to 61.9 ± 0.9% of Hu+ neurons in Nlgn3R451C mutants (n=6; p=0.0029)
(Figure 4.1 J). Further, there was a decreased proportion of ChAT neurons in Nlgn3R451C mutant mice compared to WT (Figure 4.1). In WT (n=3), 45.2 ± 1.5% of Hu+ neurons were colocalized with ChAT compared to 38.3 ± 1.2% neurons in Nlgn3R451C mutants (n=6; p=0.012;
Figure 4.1 K).
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Figure 4.1 Proportions of VIP and ChaT neurons in the ileum
Triple immunofluorescence labelling for (A) Hu (B) VIP and (C) ChAT and (D) merged image of Hu, VIP and ChAT in WT mice. Fluorescent microscopic images of (E) Hu (F) VIP (G)
ChAT and (H) triple labelling of Hu, VIP and ChAT in Nlgn3R451C mutant mice. I. There is no significant difference in the average number of Hu+ neurons in mutant mice and in WT in the distal ileum. J. The R451C mutation significantly increases the proportion of VIP neurons in mutant mice. K. A decreased proportion of ChAT neurons were observed in Nlgn3R451C mutants compared to WT. *P< 0.05, **P<0.01, scale bar=10µm.
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4.2 The Nlgn3 R451C mutation decreases neurally-evoked mucosal secretion
The short circuit current (Isc) is the sum of all ionic currents across the epithelium and a measure
R451C of mucosal secretion. The Isc of the ileal tissues from WT and Nlgn3 mice were measured using an Ussing chamber. There was no significant difference of the basal Isc between WT and
Nlgn3R451C mutant mice (WT; 53.7 ± 7.1 µA/cm2, (n=3), Nlgn3R451C mice; 48.3 ± 4.1 µA/cm2,
(n=4), p=0.5 (Figure 4.2 A).
To investigate whether Nlgn3 R451C mutation affects neurally-evoked mucosal secretion, the submucosal ganglia were chemically stimulated using DMPP and resulting Isc was measured.
DMPP is a nicotinic acetylcholine receptor agonist selective for the ganglionic subtype (Prado and Segalla, 2004). DMPP addition to the serosal side caused an increase in the Isc in both
R451C R451C Nlgn3 mutant mice and WT, but the magnitude of the Isc increment is lower in Nlgn3 mutant mice than WT (WT; 10.7 ± 1.5 rISc (µA/cm2), (n=3), Nlgn3R451C mutant mice 4.6 ±
0.9 rISc (µA/cm2), (n=4), p= 0.01) (Figure 4.2 B,C).
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Figure 4.2 The effect of Nlgn3 R451C mutation on short circuit current in the distal ileum
A. There is no significant difference in basal short circuit current between the Nlgn3R451C
R451C mutant mice and WT. B. The DMPP-mediated increment of Isc is lower in Nlgn3 mice compared to WT. C. Corresponding traces of DMPP induced Isc in submucosal neurons.
*P<0.05.
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4.3 The Nlgn3 R451C mutation increases ileal paracellular permeability
To evaluate the effects of the R451C mutation on the ileal paracellular pathway, TER and paracellular permeability were compared in mucosal preparations from WT and Nlgn3R451C mutant mice. The TER of ex vivo ileal samples was measured using the Ussing chamber technique. The TER is a well-established measure of tissue integrity and changes in TER indicate altered barrier functions including changes in tight junction protein complexes, intestinal permeability or secretion (Srinivasan et al., 2015).
Analysis from this study revealed that there were no significant differences in basal TER in
Nlgn3R451C mutants compared to the WT (WT; 0.52 ± 0.02 Ω/cm2, n=5, Nlgn3R451C mutant mice; 0.50 ± 0.06 Ω/cm3, n=6, P=0.3) (Figure 4.3B). Paracellular permeability was measured ex vivo in ileal mucosal tissue mounted in an Ussing chamber using the paracellular marker,
FITC-Dextran. Increased paracellular permeability was observed in the ileal mucosa of
Nlgn3R451C mutants compared to WT (WT; n=5, Nlgn3R451C mice; n=6, p=0.001; Figure 4.3A).
The TER of ileal tissue was also measured throughout the experiments and following the addition of FITC-dextran. In parallel with increased intestinal permeability, data from these experiments showed that, the TER in the presence of FITC-dextran was decreased in
Nlgn3R451C mutant mice compared to WT (p<0.0001) (Figure 4.3C).
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Figure 4.3 Effect of Nlgn3 R451C mutation on transepithelial resistance and paracellular permeability in the ileum.
A. Nlgn3R451C mutant mice showed higher paracellular permeability compared to WT. B.
Transepithelial resistance (TER) was measured ex vivo in ileal mucosal tissue mounted in an
Ussing chamber. There was no significant difference in TER was observed in Nlgn3R451C mutants compared to WT. C. Addition of FITC-Dextran to the mucosal side of the ileum preparation significantly decreased the TER in Nlgn3R451C mutants compared to WT.
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4.4 The Nlgn3 R451C mutation does not affect the expression of tight junction protein genes in the ileum.
Paracellular permeability data showed that Nlgn3R451C mutants have increased intestinal permeability in the distal ileum. This finding suggests that Nlgn3R451C mutant mice might have damaged tight junctions, which directly influence paracellular permeability. To determine if these mice show deficient expression of tight junction proteins, expression levels of 84 genes encoding tight junction-associated proteins were measured by RT PCR array. These proteins include cell surface receptors (claudins, Occludin, cell adhesion molecules and others), alpha actinins and catenins, junction-associated proteins, cytoskeleton regulators, G-protein signalling proteins and protein kinase signalling proteins (Figure 4.4 C). Amongst these genes, the expression of claudin-8 gene was significantly downregulated in the ileal mucosa in
Nlgn3R451C mutant mice compared to WT (Fold change= -2.13; p=0.02) (Figure 4.4 C). A major limitation associated with RT PCR array, however, is sample pooling which reduces the accuracy of analytical process. Therefore, individual PCR experiments should be performed to reconfirm statistically significant data.
In order to confirm RT PCR array data, individual dd PCR experiments were performed for claudin-8 and other tight junction protein genes which are reportedly altered in ASD (claudin-
15, TJP-1, occluden) (Fiorentino et al., 2016, Hsiao et al., 2013). In contrast with the RT-PCR array findings, the dd PCR data showed similar levels of claudin-8 expression in Nlgn3R451C mutant mice compared to WT (Figure 4.4 D). Data from the dd PCR experiments also confirmed that claudin-15, TJP-1 and occluden expression was not affected by Nlgn3 R451C mutation (Figure 4.4 E, F and G respectively).
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Figure 4.4 Expression of genes encoding tight junction proteins in the distal ileum
A. The volcano plot displays statistically significant gene expression change. Genes with unchanged expression levels are indicated in black, downregulated genes in blue and upregulated genes are marked in yellow. The horizontal line shows the 0.05 p value threshold.
B. The table shows the total of 84 genes and their corresponding fold change data and p values between samples from WT and Nlgn3R451C mutant mice. In the distal ileum, expression of claudin-8 was downregulated in Nlgn3R451C mutants compared to WT. C. Single dd PCR
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experiments, however, confirmed that the expression of claudin-8, claudin-15, Tjp-1 and occludin are not affected by Nlgn3 R451C mutation.
4.5 Jejunal VIP and ChAT submucosal neuron proportions are unchanged by the neuroligin 3 R451C mutation.
Effects of the Nlgn3 R451C mutation on the neurochemistry of the submucosal plexus in the jejunum were assessed. Submucosal neurons were labelled using immunofluorescence (using the pan-neuronal marker, Hu) to determine the total number of neurons per ganglia, and the proportion of VIP and ChAT neurons as above (Figure 4.5 A-H).
In the jejunum, there was no significant difference in the total number of neurons per ganglion in Nlgn3R451C mutant mice compared to WT (WT; 6.5 ± 0.3 neurons, Nlgn3R451C mutant mice;
7.3 ± 0.2 neurons, n=6 in both group, p=0.1) (Figure 4.5 I). In WT jejunal samples, 48.3 ±
1.6% of Hu+ submucosal neurons were immunoreactive for VIP. This percentage was similar to that observed in Nlgn3R451C mutants (48.5 ± 3.0% of Hu+ neurons were immunoreactive for
VIP in jejunal samples from Nlgn3R451C mice) (p=0.9) (Figure 4.5 J).
The proportion of ChAT neurons in the Nlgn3R451C mutant mouse jejunum was also unchanged compared to WT mice. In WT, 51.8±1.6% of Hu+ neurons were colocalized with ChAT and in Nlgn3R451C mutant mice 51.4 ± 3.1% of Hu+ submucosal neurons were immunoreactive for
ChAT (n=6 in each group) (p=0.9) (Figure 4.5 K).
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Figure 4.5 Proportions of VIP and ChAT immunoreactive neurons in the jejunum
Images of submucosal neurons label for (A) Hu (B)VIP (C) ChAT. D. Merged image of Hu,
VIP and ChAT in WT. Triple immunofluorescence labelling for (E) Hu (F) VIP (G) ChAT.
H. Merged image of Hu, VIP and ChAT in Nlgn3R451C mutants. There was no significant difference in the average number of (I) Hu (J)VIP and (K) ChAT neurons in mutant mice compared to WT in the submucosal plexus of jejunum. Scale bar=10 µm
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4.6 The Nlgn3 R451C mutation does not affect jejunal paracellular permeability
In order to understand whether the R451C missense mutation in the Nlgn3 gene affects the paracellular pathway, the paracellular permeability and the TER of jejunal mucosal preparations were measured. To assess the effects of the R451C mutation on the paracellular pathway, ex vivo paracellular permeability of jejunum was measured for 3h using the paracellular marker FITC-dextran in an Ussing chamber. In addition, basal TER and TER after adding FITC dextran were also measured in this study.
There was no significant difference in basal TER in Nlgn3R451C mutants compared to the WT mice (WT; 1.09 ± 0.24 ng/ml, n=6, Nlgn3R451C mutant mice; 1.48 ± 0.35 ng/ml, n=4, p = 0.3,
Figure 4.6A). In addition, there was no significant difference in paracellular permeability observed in the jejunum in Nlgn3R451C mutants compared to WT mice (WT; n=6, Nlgn3R451C mutants; n=4, p=0.5) (Figure 4.6C). The TER of jejunal preparations was also monitored throughout the experiment. Interestingly, application of FITC-dextran increased the TER in
Nlgn3R451C mutants compared to WT (WT; n=6, Nlgn3R451C mutant mice; n=4, p<0.0001)
(Figure 4.6B).
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Figure 4.6 Effect of the Nlgn3 R451C mutation on TER and paracellular permeability in the jejunum.
A. There is no significant difference in paracellular permeability was observed in jejunal mucosal preparations from Nlgn3R451C mutants compared to WT. B. TER was measured ex vivo in jejunal mucosal tissue and there was no significant difference in TER observed in Nlgn3R451C mutants compared to WT. C. Addition of FITC-Dextran to the mucosal side of the jejunum preparation significantly increased the TER in Nlgn3R451C mutant mice.
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4.7 The Nlgn3 R451C mutation does not affect jejunal tight junction protein gene expression.
The expression of 84 genes encoding tight junction-associated proteins was detected by quantitative RT-PCR in jejunal mucosa samples from WT and Nlgn3R451C mice. These experiments probed for the expression levels of the main pore-forming proteins and cell surface receptors (including Claudins, Occludin, cell adhesion molecules, alpha actinins and catenins, junction-associated proteins, cytoskeleton regulators, G-protein signalling proteins and protein kinase signalling proteins (Figure 4.7 C). Consistent with the findings of similar paracellular permeability data in the jejunum of WT and Nlgn3R451C mice, no significant difference in expression levels of these 84 genes was observed in the jejunum (Figure 4.7 C).
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Figure 4.7 Expression of genes encoding tight junction proteins in the jejunum
A. The volcano plot displays statistically significant gene expression change. The unchanged genes are indicated in black, downregulated gene were in dark blue and upregulated genes were marked in yellow. The horizontal line shows the 0.05 p value threshold. B. The table shows the genes on the array and corresponding fold change data and p values.
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5. DISCUSSION
This study demonstrated that the Nlgn3R451C mouse model of autism exhibits a defective mucosal barrier, in particular altered submucosal neurochemistry, compromised paracellular pathway and impaired mucosal secretion, in the distal ileum. Altered paracellular permeability has previously been reported in subsets of autism patients as well as in other mouse models of autism (de Magistris et al., 2010, Fiorentino et al., 2016, Dalton et al., 2014).
In patients, however, mucosal barrier function was tested by measuring the presence of orally administered paracellular markers (mannitol and lactulose) in the urine. Therefore, although, theses studies demonstrated dysregulated barrier function in ASD, these findings do not address region-specific modifications to the intestinal mucosal barrier. Since the anatomy and physiology vary along the length of the gut, this information is invaluable for identifying potential drug targets and treatment strategies. Therefore, the focus of this study is to identify potential mechanisms which cause GI dysfunction in ASD in a region-specific manner.
Previous studies which investigated the effects of the Nlgn3 R451C mutation on gut function have predominantly been conducted in the colon. But an understanding of how this mutation affects the physiology of the small intestine is lacking. Therefore, two areas of the small intestine including the jejunum and the distal ileum were selected for analysis within this study.
5.1 Nlgn3 R451C mutation induces neurochemical changes to the submucosal plexus
Since mucosal barrier functions are directly regulated by the submucosal plexus, the impact of the R451C mutation in the Nlgn3 gene on the submucosal neurochemistry was determined.
Altered CNS neurochemistry is often associated with autism and a range of neurochemical
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abnormalities has been reported for ASD including within cholinergic, VIPergic, serotonergic,
GABAergic and glutamatergic systems in the CNS (reviewed in Marotta et al., 2020).
Interestingly, findings from the current study showed that the Nlgn3 R451C mutation alters the neurochemistry of the submucosal plexus in the distal ileum although the total number of neurons in the submucosal plexus was unchanged.
In the distal ileum, the Nlgn3 R451C mutation increases the proportion of VIPergic neurons in the submucosal plexus. VIP is a 28 amino acid neuropeptide, originally characterised in the gut and it is widespread in the central and peripheral nervous system. In the CNS, VIP is known to regulate developmentally important events including mitogenesis, differentiation, neuron outgrowth, neuron survival and protection (White et al., 2010). In the intestine, VIP plays a major role in a diverse range of activities including vasodilation, smooth muscle relaxation, intestinal secretion, permeability and modulation of the immune system (Neunlist et al., 2003a,
Lelievre et al., 2007, Wu et al., 2015b, Marpegan et al., 2009, Seillet et al., 2020, Talbot et al.,
2020). VIP released from enteric neurons stimulates anion secretion from enterocytes. VIP activates VAPC1 receptors which subsequently activate adenylyl cyclase followed by increased intracellular cAMP (Banks et al., 2005). Increased cAMP levels activate protein kinase A which then activates the CFTR channel (Seidler et al., 1997, Schwartz et al., 1974).
- - In the ileum, VIP increases electrogenic Cl and HCO3 secretion (Kuwahara et al., 2019,
Kuwahara et al., 1993, Schwartz et al., 1974). VIP also regulates paracellular permeability by modulating the expression of tight junction proteins. Specifically, the VIP pathway reduces the paracellular permeability by increasing the expression of ZO-1 in human polarized colonic epithelial monolayers co-cultured with human submucosa with the submucosal plexus,
(Neunlist et al., 2003b, Conlin et al., 2009).
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An altered VIPergic system is reportedly associated with ASD. For example, in line with findings from the current study, higher concentrations of VIP have been observed in blood samples from newborn babies who were later diagnosed with autism (Nelson et al., 2001).
The expression of NLGN3 or any other NLGN isoforms in VIP-containing synapses in the
CNS or peripheral nervous system has not been reported to date. The Nlgn3 mRNA localization experiments conducted in Chapter 3 revealed that Nlgn3 mRNA is expressed VIPergic neurons in the submucosal plexus. This suggest that Nlgn3 might play a role in regulating the function of VIPergic synapses in the ENS. In addition, findings from Chapter 3 also showed that the
Nlgn3 mRNA expression in VIP neurons is altered by the R451C mutation. Based on these findings, it is possible that altered Nlgn3 expression in VIPergic synapses might drive the altered VIP neurochemistry in the distal ileal submucosal plexus.
The R451C mutation in the Nlgn3 gene decreases the proportion of cholinergic submucosal neuronal proportion in the submucosal plexus of the distal ileum. Cholinergic neurons are associated with the pathophysiology of autism (Perry et al., 2001, Deutsch et al., 2010).
Specifically, the basal forebrain tissues from ASD patients exhibited altered cholinergic neuronal numbers, size and structure (Kemper and Bauman, 1998). In addition, a decreased concentration of choline, a precursor for acetylcholine has been reported in ASD patients
(Sokol et al., 2002, Wenk and Hauss-Wegrzyniak, 1999). Further, reduced levels of cytosolic choline have been correlated with autism severity (Sokol et al., 2002). In addition, reduced levels of several nAChRs and M1 muscarinic receptors has been observed in several brain regions including the cerebellum, thalamus and striatum of autism patients (Deutsch et al.,
2010, Mikhail et al., 2011).
There is no evidence available about the expression of NLGN3 in the cholinergic system of the central or peripheral nervous systems. But, the expression of other NLGN subtypes in
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cholinergic synapses has been reported. For example, NLGN2 is expressed at the postsynaptic membrane of cholinergic synapses in the mouse brain (Takacs et al., 2013). Expression of
NLGN1 has been found in the cholinergic neurons in the chick ciliary ganglion and it is enriched particularly at synapses containing the a3 nAChR subunit (Rosenberg et al., 2010).
It is also established that NLGN3 and other NLGN isoforms can be present in the same synapse.
In cultured primary hippocampal neurons from rodents, a subset of GABAergic synapses express both NLGN2 and NLGN3 and several glutamatergic synapses express both NLGN1 and NLGN3 (Budreck and Scheiffele, 2007). Therefore, it is possible that NLGN3 could also be co-expressed with NLGN1 and NLGN2 in cholinergic synapses and mutations in NLGN3 gene might cause alterations in the cholinergic system.
Findings from Chapter 3 showed that Nlgn3 mRNA is expressed in cholinergic submucosal neurons in the distal ileum. As virtually all submucosal neurons receive synaptic inputs via cholinergic synapses containing nAChR (Foong et al., 2014), this suggests that Nlgn3 is present in cholinergic synapses in the submucosal plexus. Moreover, findings from Chapter 3 revealed that the expression of Nlgn3 mRNA in cholinergic neurons is reduced by the R451C mutation.
Therefore, reduced levels of Nlgn3 in cholinergic synapses could contribute to alterations in the cholinergic neurochemistry in the submucosal plexus of the distal ileum.
5.2 Nlgn3 plays a role in submucosal neuronal regulation of mucosal secretion through a nACh receptor-mediated pathway
It is clear that the R451C mutation in the Nlgn3 gene alters the cholinergic and VIPergic systems in the submucosal plexus of the distal ileum. To further investigate potential neurally mediated mechanisms, submucosal ganglia were stimulated using DMPP and short circuit current, as a measure of mucosal secretion, was monitored.
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The basal short circuit current was unchanged in mutant mice, indicating that the mucosal secretion is not affected by Nlgn3 R451C mutation under resting physiological conditions.
However, when the submucosal neurons are stimulated using DMPP, the R451C mutation triggers a decrease in the increment of the short circuit current compared to the WT.
DMPP acts exclusively on the ganglionic subtype of nAChRs, so these data suggest that the
R451C mutation may alter the expression of these receptors. Nevertheless, whether these receptors are on the cholinergic or noncholinergic (VIP) secretomotor neurons, or both, remains an open question. These findings revealed a potential mechanism through which
NLGN3 might involve in an nAChR-mediated signal transduction pathway in the submucosal plexus.
Abnormal nAChR expression and function is implicated in cholinergic systems and associated with ASD pathophysiology. Significant reduction of a4b2 nAChRs in the cortex was detected in post-mortem brain samples from autism patients (Perry et al., 2001, Martin-Ruiz et al.,
2004). In addition, reduced cerebellar a4nAChRs expression has been reported to link with autism.
Furthermore, involvement of nAChRs in modulating social and repetitive behaviour has been shown in animal models of ASD (Deutsch et al., 2010, Ray et al., 2005).
In the mouse brain, NLGN3 and α4 subunit of the nAChR are colocalised in the piriform cortex
(Lee et al., 2002). In mice, nAChR α4β2 receptor modulates autism-like behaviour through increased expression of NLGN3 (Takechi et al., 2016). It has also been reported that NLGNs enhance nicotinic innervation of neurons in the brain (Conroy et al., 2007). Several studies have identified the expression of nAChR subunits in enteric neurons. Expression of a3, a5, b2 and b4 subunits of nAChRs have been identified in cultured myenteric neurons (Zhou et al.,
2002). In addition, the presence of a4, a7, b2 and b4 nAChRs have been observed in the mouse intestine (Foong et al., 2015). Further, the involvement of a3b2 and a3b4 receptors in synaptic
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transmission has been observed in the myenteric plexus in mice (Foong et al., 2015). Based on this evidence, it is possible that NLGN3 might be co-expressed with nAChRs and involves in nAChR-mediated synaptic functions in the submucosal plexus.
5.3 The Nlgn3 R451C mutation induces region-specific paracellular pathway dysfunction
For the first time, this study revealed that the Nlgn3 R451C mutation induces region-specific paracellular permeability dysfunction. Overall, Nlgn3R451C mutant mice display an impaired paracellular pathway in the ileum, but the paracellular pathway in the jejunum is unaffected.
With respect to transepithelial permeability, epithelia vary greatly according to their ability to maintain ionic and osmotic gradient between physiological solutions with different compositions. Based on the ionic conductance (for which the reciprocal measure is TER) epithelia are categorised into two groups: leaky and tight epithelia. The functional relevance of
TER is that leaky epithelia allows a large fraction of passive flux of isosmotic fluids to pass through the epithelium via paracellular pathway. In tight epithelia, the resistance of the paracellular pathway to passive movement is substantially increased to maintain a high electrochemical gradient across the membrane. This variability in tight and leaky epithelia are contributed by differential distribution of tight junction proteins.
When the TER of an epithelium is lower than 1500 Ω/cm2, it is considered as a leaky epithelium
(Powell, 1981). In the small intestine, fluid absorption and secretion occur isosmotically, therefore it is generally considered to contain a leaky epithelium. The Ussing chamber experiments showed that the TER of both WT ileum and jejunum is lower than 1500 Ω/cm2.
This level of resistance of the tissue samples indicates that both ileum and the jejunum contain
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a leaky epithelium. Similar results have been reported previously in the rat and rabbit ileum and in rat jejunum (Powell, 1981).
Although, both jejunum and ileum contain leaky epithelia, Ussing chamber experiments showed that the jejunum contains a tighter epithelium than the distal ileum. This suggests that paracellular flux is limited in the jejunum and the TER is dominated by the active transcellular pathway. Since the primary function of the jejunum is nutrient absorption, which is coupled to an active transcellular pathway, it is possible that transepithelial transport is prominent in the jejunum. On the other hand, the nutrient absorption in the ileum is minimal and mainly involves active transportation of bile acids and vitamin B12. Therefore, the transcellular pathway coupled active transportation is less prominent in the ileum. In addition, the distal ileum contains the highest microbial density in the small intestine. As a defence mechanism, anion secretion is coupled to passive water secretion as well as mucus and Paneth cell secretion.
Ussing chamber experiments also revealed that the jejunal basal TER is not affected by the
R451C mutation in the Nlgn3 gene. However, the TER of the tissue samples from mutant mice was increased after addition of FITC-dextran. These data suggest that the R451C mutation increases the sensitivity of the epithelium to increased solute concentration in the lumen. The effects of luminal solutes on TER has been studied in hamster small intestine and addition of glucose or amino acids to the luminal side causes a decrease in the TER by a two to three-fold
(Wilson and Landau, 1960). Altogether, these findings suggest that the R451C mutation alters the paracellular responses to the luminal environment in a region-specific manner, in particular in the jejunum in mutant mice.
Increased paracellular permeability has been reported in autism patients as well as in animal models of autism. Similar to the current study, the BTBR mouse model of autism shows a dramatic increase in paracellular permeability despite similar expression levels of tight junction proteins in the ileum (Golubeva et al., 2017). Hsiao and colleges demonstrated impaired barrier
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integrity in a mouse model of autism reflected by an increased translocation of FITC-dextran through the epithelium into the circulatory system (Hsiao et al., 2013). In addition, lactulose/mannitol tests revealed increased paracellular permeability in autism patients as well as their first-degree relatives (de Magistris et al., 2010). In another cohort of autism patients, increased levels of gut permeability modulating the protein zonulin have been reported and increased levels of zonulin have been correlated to autism behavioural symptom severity
(Fasano and Hill, 2017). In contrast, increased intestinal permeability has not been observed in autistic individuals without GI dysfunction suggesting that mucosal barrier disruption may be a marker of ASD-associated GI dysfunction.
5.4 Increased ileal paracellular permeability in Nlgn3R451C mutant mice is not due to impaired tight junction protein complexes
Nlgn3R451C mutant mice displayed increased paracellular permeability in the ileum. Altered paracellular permeability was previously observed in a subset of ASD patients as well as in animal models of autism due to altered expression of tight junction protein genes (Powell,
1981, D'Eufemia et al., 1996, de Magistris et al., 2010, Hsiao et al., 2013, Fiorentino et al.,
2016, Wei et al., 2017). The current study monitored the expression of 84 different tight junction protein genes and found no significant difference in gene expression levels between
Nlgn3R451C mutants and WT. This suggests that the Nlgn3 R451C mutation does not cause any structural damage to the tight junction protein complex. Tight junction gene expression data also correspond with basal TER where there is no significant difference between the WT and mutant mice. However, the addition of FITC-dextran causes a reduction in TER which correlates with increased paracellular permeability in the distal ileum in mutant mice. These data suggest that the increased paracellular permeability observed in the ileum is more likely
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due to short term remodelling or localization of tight junction proteins rather than long term modifications to tight junction complexes.
Decreased TER is associated with an increase in paracellular permeation of water-soluble solutes including the paracellular markers inulin and mannitol (Pappenheimer, 1987). Luminal solutes can also modulate morphological changes in the tight junction complex. Addition of glucose and amino acids to the luminal surface results in a distention of the intercellular space
(Pappenheimer, 1990) and formation of focal tight junction dilations as a result of dilations within the tight junction strand meshwork (Madara, 1987, Madara and Pappenheimer, 1987).
In addition, Na+ coupled nutrient transport evokes the contraction of the peri-junctional actin- myosin ring, resulting in functional opening of the tight junction (Pappenheimer, 1990,
Pappenheimer, 1987, Madara and Pappenheimer, 1987). Therefore, although it is beyond the scope of the current project it would be of interest to investigate whether similar changes occur in Nlgn3R451C mice to results in reduced TER (and the corresponding increased permeability) in the distal ileum.
Long term stimulation of the human ENS generates modifications to the paracellular permeability through differential expression of tight junction proteins (Neunlist et al., 2003b).
However, the enteric neuronal control of tight junction protein relocation or remodelling to regulate the paracellular space has not been reported to date.
In the intestine, nAChRs are also involved in controlling paracellular permeability. Burn- induced mucosal barrier injury causes altered paracellular permeability through changes in ZO-
1 localization in the absence of ZO-1 expression variations and these changes are modulated by treatment with an nAChR α7 agonist (Costantini et al., 2012). This finding indicates that nAChRs are involved in regulating paracellular permeability by localizing tight junction proteins such as ZO-1. The paracellular permeability data in the current study also suggest that a similar mechanism may occur in the distal ileum of Nlgn3R451C mutant mice. Therefore, it
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could be concluded that the observed increase in paracellular permeability and decreased TER observed in the distal ileum of Nlgn3R451C mutant mice is likely mediated through several factors including increased luminal concentration and altered nicotinic acetylcholine receptor- facilitated submucosal neural activity.
6. CONCLUSION
This study showed that NLGN3 is required to regulate neurally-driven mucosal barrier functions such as paracellular permeability and mucosal secretion. Data suggest that the Nlgn3
R451C mutation does not cause structural differences to the mucosal barrier specific to the paracellular pathway. Mucosal secretion data suggest that NLGN3 is involved in nAChR- mediated synaptic transmission. Overall, from these findings it can be concluded that NLGN3 plays an important role in nAChR mediated synaptic transmission in the submucosal plexus of the distal ileum in mice.
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CHAPTER 5
The spatial distribution of microbial populations in the Nlgn3R451C mouse
model of autism.
1. ABSTRACT
Emerging evidence suggests that the composition of mucus-residing microbiota is altered in people diagnosed with ASD and therefore may contribute to GI dysfunction in the autism population. Identifying the precise spatial location of microbial populations is challenging but essential to improve understanding of how changes in microbial abundance and diversity influence host intestinal function in ASD as well as in health. Several transgenic mouse models of ASD also show dysbiosis but spatial distribution profiling of microbes in the mucosa to identify bacterial subtypes in close contact with host tissues has not been conducted. In this study, the spatial distribution of the mucosal microbiome in the distal ileum of Nlgn3R451C mice was analysed. The spatial distribution of total bacteria, Bacteroidetes and Firmicutes phyla and the distributions of Akkermansia muciniphila (A. muciniphila) and Bacteroides thetaiotamicron (B. thetaiotamicron) were investigated using fluorescent in situ hybridization.
Immunofluorescence was incorporated to co-stain the mucus and to determine the thickness of the mucus layer. Changes in the spatial pattern of microbial populations and mucus layer thickness were analysed using the MATLAB-based BacSpace platform. Immunofluorescence revealed that the R451C mutation in the Nlgn3 gene increases mucus thickness adjacent to the epithelium. The spatial distributions of total bacteria, Bacteroidetes, Firmicutes, A. muciniphila and B. thetaiotamicron were altered by the Nlgn3 R451C mutation. These findings show that the Nlgn3 R451C mutation alters mucus density as well as the spatial distribution and composition of the microbial community in the distal ileum in mice.
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2. INTRODUCTION
The intestinal microbiota is a heterogeneous community residing along the length of the GI tract and confers many health benefits. An unbalanced gut microbial community has been reported in patients and rodent models of ASD (De Angelis et al., 2015, Finegold et al., 2002,
Song et al., 2004, Critchfield et al., 2011, Wang et al., 2011, De Angelis et al., 2013, Hsiao et al., 2013, Hosie et al., 2019). A role for the gut microbiome in neurological disorders is now well supported based on associations between microbial dysbiosis and altered gut physiology, metabolism, immunity and overall body health (Mayer et al., 2014b, Shreiner et al., 2015,
Sampson et al., 2016, Sharon et al., 2019). The distribution and function of members within a microbial community is impacted by the physiological features of their environment (Earle et al., 2015, Donaldson et al., 2016, Proctor and Relman, 2017).
Different regions of the GI tract display distinct features due to anatomical and functional variations that create diverse habitats selective for specific microbial populations. In the intestine, microbial localization varies both across the mucosal-lumen axis as well as along the length of the gut (Yasuda et al., 2015, Nava et al., 2011, Malmuthuge et al., 2014). The spatial organization of microbes along these two axes is determined by factors such as oxygen levels, chemical gradient, nutrient availability and antimicrobial mediators (O'May et al., 2005,
Albenberg et al., 2014, Berry et al., 2013, Vaishnava et al., 2011). In addition, the regional functional differentiation of the GI tract also influences the spatial distribution of gut microbes; the small intestine favours facultative anaerobic, proteolytic bacteria whereas the majority of the colonic microbial population comprises anaerobic saccharolytic bacteria (Gu et al., 2013,
Turnbaugh et al., 2009, Kamphuis et al., 2017).
The intestinal mucosa demarcates the enteric microbiota content from the interior of the body.
The mucus layer is the outermost layer of the intestinal mucosa which traps and maintains microbes at a certain distance from the epithelium (Ermund et al., 2013b). Goblet cells, a
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specialised epithelial secretory cell type, secrete mucus and maintain the mucus layer. The
MUC2 is the major component of the intestinal mucus layer (Johansson et al., 2008). The properties of the intestinal mucus layer also vary along the length of the gut. In both the small intestine and proximal parts of the colon there is a single mucus layer. In the distal colon, the mucus forms two layers; the inner mucus layer being devoid of bacteria and a loosely adherent outer mucus layer which houses the intestinal microbiome (Johansson et al., 2013). Animal models with an impaired mucus layer are predisposed to colitis suggesting that mucus layer integrity is important for maintaining intestinal homeostasis (Van der Sluis et al., 2006, Lennon et al., 2014, Johansson et al., 2013).
As a further indicator of the specialization of this region, the microbial populations located in the mucus adjacent to the intestinal mucosa is distinct from the faecal microbial content (Li et al., 2015, Stearns et al., 2011, Yasuda et al., 2015, Sommer and Backhed, 2013). The mucus serves as a source of carbon and energy for mucus-residing bacteria (Marcobal et al., 2013) and as an adaptation to residing in the mucus environment, some bacteria produce mucus- degrading enzymes such as glycosidase, sulphatase, and sialidases (reviewed in Herath et al.,
2020). An altered abundance of mucolytic bacteria such as A. muciniphila, the mucus- associated Clostridiales, Dorea, Blautia and Sutterella has been reported in ASD (Wang et al.,
2011, Luna et al., 2017) suggesting that the composition of the mucus layer is affected in these patients. Although these studies investigated relative abundance changes in these bacteria using microbial sequencing, no studies have assessed the microbial spatial distribution pattern alterations in ASD patients or animal models of autism. Furthermore, potential correlations between alterations in the abundance of mucolytic bacteria with mucus layer variation in ASD have not been explored.
Tools for studying microbial spatial distribution patterns in understanding host-microbial interactions have recently been highlighted (Mark Welch et al., 2017, Donaldson et al., 2016,
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Earle et al., 2015, Albenberg et al., 2014, Nava et al., 2011, Swidsinski et al., 2005, Chan et al., 2005). Improved mucus preservation methods (Johansson et al., 2008) incorporated with fluorescent in situ hybridization and, more recently, spectral imaging have enabled significant advances in the field and enhanced our understanding of microbial spatial patterning in the gut
(Mark Welch et al., 2017, Johansson et al., 2008, Swidsinski et al., 2005, Geva-Zatorsky et al.,
2015, Chan et al., 2005).
Mice expressing the autism-associated R451C mutation in the Nlgn3 gene show a shift in the faecal microbial composition (Hosie et al., 2019). But the impact of this mutation on the intestinal mucus layer and distribution patterns of the microbial communities within the mucus layer has not been investigated. Therefore, the main focus of this chapter was to investigate the impact of the Nlgn3 R451C mutation on the mucus layer thickness and the mucosa-associated microbial community. Findings from Chapter 4 showed that mucosal secretion is affected by the R451C mutation in the distal ileum which suggests that mutant mice might have altered mucus secretion. Therefore, the mucus layer thickness and the distribution patterns of bacteria in the distal ileum was analysed in this study. To accomplish this task, a mucus preservation and staining method together with fluorescent in situ hybridization were used to localize bacteria within the mucus layer of the distal ileum. Dominant components of the microbiome in human and mice including the Bacteroidetes and Firmicutes phyla for which the ratio has been shown to be altered in ASD were selected to characterise the spatial distribution adjacent to the mucosal surface in WT and Nlgn3R451C mice. In addition, the spatial distribution of mucus degrading species including A. muciniphila and B. thetaiotamicron were analysed in this study.
The MATLAB based BacSpace image analysis platform (Earle et al., 2015) was used to evaluate the effects of the R451C mutation on mucus layer density variation and microbial localization patterns in the distal ileum. Findings from this study showed that the R451C mutation increases mucus density and alters the spatial distribution of mucosa-associated
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bacterial populations in the distal ileum, suggesting that autism-associated gene mutations in the nervous system may influence mucus layer properties and the distribution of microbial populations in the intestinal mucosa.
3. METHODS
3.1 NLGN3R451C mice
NLGN3R451C male mice (aged 12-13 weeks) were used in this study. Mice were housed in the
Biomedical Science Animal Facility at The University of Melbourne. WT and NLGN3R451C mice were killed by cervical dislocation in accordance with The University of Melbourne
Animal Experimentation Ethics Committee.
3.2 Tissue collection and fixation
The intestine was dissected and a segment of distal ileum (1 cm proximal to the caecum) containing fecal material was immediately placed in methanol Carnoy’s fixative (60% V/V absolute methanol, 30% V/V chloroform, 10% V/V acetic acid) for 48 hours at RT. Fixed tissues were washed twice in absolute methanol for 30 min, followed by two washes in absolute ethanol for 20 min each. Following that, the tissues were incubated in two baths of xylene for
15 min before paraffin embedding and sectioning at 4 µm thickness. Paraffin embedding and sectioning were performed by the Biomedical Sciences Histology Facility of the University of
Melbourne.
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3.3 Fluorescent in situ hybridization
Tissue sections were dewaxed prior to conducting fluorescent in situ hybridization (FISH). For dewaxing, sections were incubated in xylene solution twice for 10 min. The tissue sections were then incubated in 99.5% ethanol for 5 min and the slides were air-dried. Tissue sections were incubated with FISH probes diluted to 10 ng/µl in preheated (50 oC) hybridization buffer
(0.9 M NaCl, 20mM Tris-HCl (pH 7.4), 0.01% sodium dodecyl sulfate, 10% formamide) at 50 oC for 18 h. After the incubation period, the sections were incubated in FISH washing buffer
(0.9 NaCl, 20mM Tris HCl (pH 7.4) preheated to 50 oC. Tissue sections were then washed three times in PBS (3 x 10 min). The fluorescently-labelled DAN probes that were used to label bacteria in this study are listed in Table 5.1.
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Table 5.1 Previously validated FISH probes used to identify total bacteria and subsets of bacteria in the WT and Nlgn3R451C mouse distal ileum.
Specificity FISH probe Sequence (5’-3’) Reference
All bacteria EUB338 GCTGCCTCCCGTAGGAGT (Daims et al., 1999)
Akkermansia MUC1437 CCTTGCGGTTGGCTTCAGAT (Derrien et al.,
muciniphila 2004)
Bacteroides Bthe CATTTGCCTTGCGGCTA (Momose et al.,
thetaiotamicron 2011)
Bacteroidetes BAC303 CCAATGTGGGGGACCTT (Manz et al., 1996)
Firmicutes LGC354A TGGAAGATTCCCTACTGC (Meier et al., 1999)
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3.4 Immunofluorescence
Following completion of the fluorescent in situ hybridization protocol, slides containing the ileal sections were washed in PBS (3 x 10 min). Tissue sections were then incubated in 1% bovine serum albumin in 0.1 M Triton solution for 30 min in a dark, humid chamber at 40C for
30 min. Slides were washed three times in PBS (3 x 10 min) solution. The tissue sections were then incubated in 1:500 MUC2C3 antibody (kindly provided by Prof. Gunnar Hansson,
University of Gothenburg, Sweden) in a dark, humid chamber at 40C for 24 h. The slides were subsequently washed in PBS (3 x 10 min). The secondary antibody was diluted at 1:400 in PBS and 30 µl added to the tissue section. Slides were incubated at 4 0C in a dark, humid chamber for 2 h prior to being washed in PBS three times for 10 min each. To detect host nuclei, tissue samples were counterstained with 10 µg/ml DAPI (Sigma). The sections were allowed to dry completely and mounted with DAKO fluorescent mounting medium (DAKO, Carpenteria,
CA).
The same method was used to conduct control experiments (images included in the appendix,
Figure A2).
3.5 Image acquisition and analysis
Images were acquired on a Zeiss laser scanning microscope (Zeiss, Germany) at 20x magnification. To obtain the image of an entire ileum transverse section, a tile scan was performed. The tiles were stitched using Zen software prior to image analysis. The images were acquired at a frame size of 1024x1024 with 16-bit depth.
3.6 Image analysis
For image analysis, MATLAB based BacSpace software (MATLAB 9.8, MathWorks, USA) was used (Earle et al., 2015).
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3.6.1 Identification of mouse ileal tissue to determine the epithelial boundary
To detect the epithelial boundary, the BacSpace contains an automated, iterative, multiscale algorithm (Earle et al., 2015). The epithelial boundary was detected based on the DAPI fluorescent signal using the ‘Draw contour’ tool within the BacSpace software package.
BacSpace re-mapped the image to enhance the contrast of host nuclei by saturating the DAPI signal. The thresholding tool associated with BacSpace was used to increase or decrease the saturation of the DAPI signal. For the initial estimation of the epithelial boundary, the user must identify the epithelium and the luminal content by simply clicking on the host tissue and the lumen. Following that, BacSpace created an initial estimate for the epithelial boundary.
Using the ‘threshold for initial guess’ tool, the boundary was refined. Using this initial contour,
BacSpace then generated a refined boundary. Finally, inaccuracies of the contour were corrected manually.
3.6.2 Subtraction of the autofluorescence generated by luminal material
The autofluorescence generated by diet-derived material within the luminal content interferes with the microbial signal. Therefore, before detecting the bacterial signal, the autofluorescence from the debris was subtracted using the ‘subtract debris’ tool. Subtraction of the autofluorescence signal included two steps. First, large objects were subtracted using a thresholding operation to creates an initial binary ‘debris mask’. Secondly, a secondary thresholding operation was used to detect debris that were touching the previously identified debris and were initially missed.
3.6.3 Measurement of bacterial density and mucus layer thickness
After subtracting debris present in the luminal content, the BacSpace platform detects the fluorescent signal generated by bacteria and the mucus layer. The microbial density and mucus
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layer thickness was determined by computing the fluorescence signal intensity and averaged it along the length of the epithelium and normalised as a function of distance from the epithelium
(1000 pixel from mucosal to luminal direction).
BacSpace contains automated functions to determine the fluorescence intensity of an image
(Earle et al., 2015). Briefly, the epithelial boundary of the image was computationally identified first (Figure 5.1 A). Then BacSpace computationally straighten the image along the boundary of the entire intestinal segment (Figure 5.1 B). The straightening algorithm within the BacSpace software calculate the distance along the epithelial boundary and distance from the boundary towards mucosal to luminal axis. Subsequently, BacSpace creates the Voronoi diagram to estimate the pixel intensity along above mentioned directions. To determine the pixel intensity in the straightened image, BacSpace collects all pixel intensities in a Voronoi cell. To determine the intensity of the entire image, pixel intensities from each points are then averaged using Gaussian weight exp(-(d-j)2/2σ2), where d is the actual distance from the boundary, σ is the kernel width (typically 5 pixels) j is the distance from the boundary in the straightened image (Earle et al., 2015).
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Figure 5.1 Detection of epithelial boundary and quantification of fluorescence intensity along the length of the epithelium and from the epithelial boundary.
A. Stitched image of the colon with the computationally determined apical boundary. B. Full straightened image along entire length of the boundary (Figure adapted from Earle et al., 2015).
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3.6.4 Statistical test
Statistical analysis was performed using GraphPad Prism software (GraphPad, San Diego,
USA). A one-way ANOVA was used to statistically test the relative distribution density of bacteria. A two-tailed unpaired t-test was performed to compare the mucus layer thickness between WT and mutant mice. Results are presented as mean ± SEM (Standard error of the mean).
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4. RESULTS
4.1 The R451C mutation increases mucus density adjacent to the mucosa
The impact of the Nlgn3R451C mutation on the mucus layer in the distal ileum was assessed using immunofluorescence (Figure 5.2 A and B) and the mucus density along the mucosa- luminal axis was measured using BacSpace (Figure 5.2 C). In Nlgn3R451C mice, the relative mucus layer density is significantly altered compared to WT (p<0.0001, n=5 in both groups).
Interestingly, the mucus density adjacent to the mucosa was significantly increased in
Nlgn3R451C mutant mice compared to WT (WT: -0.04 ± 0.5, Nlgn3R451C mutant mice: 1.89 ±
0.2, p=0,009, n=5 in each group), (Figure 5.2 D). In both WT and Nlgn3R451C mice, the mucus density gradually increases towards the lumen and the maximum mucus density is reached approximately 11 µm from the edge of the epithelium. The maximum mucus density at this location is significantly higher in Nlgn3R451C mutant mice compared to WT (WT: 0.84 ± 0.3,
Nlgn3R451C mutant mice: 2.55 ± 0.2, p=0.003, n=5) (Figure 5.2 E). These findings suggest that the Nlgn3 R451C mutation increases the mucus density along the mucosa-lumen axis in the distal ileum.
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Figure 5.2 Ileal mucus layer density variation in the presence of the Nlgn3R451C mutation.
Confocal images of transverse sections of the distal ileum in (A) WT and (B) Nlgn3R451C mutant mice. C. Mucus density variation from the mucosal to luminal direction averaged over the length of the epithelium in WT (blue) and Nlgn3R451C mutant mice (green). Dashed lines indicate the location of the maximum mucus density. D. Comparison of the mucus density on
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the edge of the epithelium in WT and Nlgn3R451C mice. A significant increase in mucus density was observed in Nlgn3R451C mice compared to WT. E. Maximum mucus density for WT and
Nlgn3R451C mutant mice. The maximum mucus density was observed approximately 11µm from the epithelium in both WT and Nlgn3R451C mice and the R451C mutation increased the maximum mucus density in the distal colon. Graphs are mean-subtracted and divided by the standard deviation for normalization. **P<0.01, scale bar=100 µm.
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4.2 Altered spatial distribution and an increased bacterial abundance adjacent to the mucosa in Nlgn3R451C mutant mice.
To determine whether the spatial distribution of total bacterial population within the mucosa is altered in the presence of the R451C mutation, fluorescent in situ hybridization was performed.
Bacteria were labelled using the universal bacterial probe EUB338 and host cell nuclei were labelled using DAPI (Figure 5.3 A and B).
To determine the spatial distribution of bacteria in the distal ileum the BacSpace platform was used. Overall, The R451C mutation did not alter the overall spatial distribution of bacteria along the mucosa-luminal axis in the distal ileum (p=0.8, n=5 in each group) (Figure 5.3 C).
Analysis of the mucus density in the distal ileum shows that the mucus density variation is high within 0-20 µm from the epithelial edge. Therefore, the total bacterial density within 20 µm of the edge of the epithelium was measured to investigate whether the bacterial abundance is correlated with mucus density. Interestingly, the bacterial distribution pattern within 20 µm range was shifted towards the mucosa in Nlgn3R451C mutant mice compared to WT (p<0.0001, n=5 in both groups) and the relative total bacterial density immediately adjacent to the mucosa was increased in Nlgn3R415C mutant mice compared to WT (Figure 5.3 D). These results indicate that the bacterial spatial pattern adjacent to the mucosa is affected by the Nlgn3 R451C mutation in the distal ileum.
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Figure 5.3 The Nlgn3R451C mutation mediates shifts in the bacterial spatial pattern in the ileal mucosa.
High-resolution confocal images of the distal ileum labelled with the universal bacterial marker
EUB338, and DAPI, DNA marker in (A) WT and (B) Nlgn3R451C mutant mice. White inset indicates bacterial density in close proximity to the epithelium in each case. C. Spatial distribution profile of bacteria in WT and Nlgn3R451C mutant mice from the epithelium to 300 microns into the lumen. There is no significant difference in overall bacterial distribution in the distal ileum in WT and Nlgn3R451C mutant mice. D. The local pattern of bacteria within 20
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µm from the epithelium. In Nlgn3R451C mice, the relative bacterial density was increased compared to WT. Graphs are mean-subtracted and divided by the standard deviation for normalization (n=5 in both groups, scale bar=100 µm).
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4.3 Decreased Bacteroidetes abundance in Nlgn3R451C mice
The impact of the Nlgn3 R451C mutation on the spatial pattern of the phylum Bacteroidetes was examined using fluorescent in situ hybridization. The BAC303 probe was used to label bacteria in this phylum (Figure 5.4 A and B). The density of Bacteroidetes phylum along the mucosal to luminal axis was determined using the BacSpace software platform. Based on the relative density data, the spatial distribution of Bacteroidetes in Nlgn3R451C mutant mice was significantly shifted towards the lumen compared to WT (p=0.008, n=5 in both groups) (Figure
5.4 C).
In order to identify the spatial organization of mucus-residing Bacteroidetes in close proximity to the mucosa, the Bacteroidetes density within a distance of 20 µm from the epithelium was measured using the BacSpace platform. In Nlgn3R451C mice, there was a lower density of
Bacteroidetes within the 0-20 µm range from the epithelium compared to WT. These data show that the spatial pattern of Bacteroidetes is significantly shifted by the R451C mutation
(p<0.0001, n=5 in both groups) (Figure 5.4 D).
These findings suggest that the R451C mutation in the Nlgn3 gene significantly reduces the density of Bacteroidetes density adjacent to the mucosal epithelium and alters the spatial organization of Bacteroidetes in the mouse distal ileum.
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Figure 5.4 The Nlgn3 R451C mutation drives changes in Bacteroidetes localization in the distal ileum.
Confocal images of distal ileum labelled with the BAC303 probe for the phylum Bacteroidetes and DAPI, a DNA marker for host nuclei in (A) WT and (B) Nlgn3R451C mutant mice. White inset shows bacterial density closer to the epithelium. C. In the distal ileum, the spatial distribution of Bacteroidetes is significantly shifted by Nlgn3R451C mutation compared to WT.
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D. Bacteroidetes density is plotted within 20 µm from the epithelium. Graphs are mean- subtracted and divided by the standard deviation for normalization. The Bacteroidetes density is significantly reduced near the epithelium in mutant mice. scale bar=100
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4.4 The Nlgn3 R451C reduces the abundance of phylum Firmicutes
To detect whether the Nlgn3R451C mutation drives shifts in the localisation of phylum
Firmicutes, these bacteria were labelled using LGC354 probe and host cell nuclei were labelled with DAPI (Figure 5.5 A and B).
The spatial patterning of Firmicutes within the mucus adjacent to the mucosal epithelium is significantly shifted toward the mucosa in mice expressing the Nlgn3R451C mutation compared to WT (WT mice; (n=3), Nlgn3R451C mutant mice; (n=3), p<0.0001) (Figure 5.5 C).
The distribution of Firmicutes within 20 µm of the epithelial boundary was significantly shifted in Nlgn3R451C mutant mice compared to WT (n=3), Nlgn3R451C mutant mice; (n=3), p<0.0001)
(Figure 5.5 D). Specifically, the density of Firmicutes was higher in Nlgn3R451C mice than in
WT in ileal sections.
These findings suggest that the Nlgn3 R451C mutation modifies the spatial distribution of mucus-residing Firmicutes bacteria in the distal ileum in mice, resulting in an increase in the abundance of Firmicutes bacteria adjacent to the mucosa.
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Figure 5.5 The Nlgn3 R451C mutation affects the spatial organization of Firmicutes in the distal ileum
Confocal micrographs of distal ileum samples labelled with LGC354 FISH probe for the phylum Firmicutes and DAPI in (A) WT and (B) Nlgn3R415C mutant mice. The white insets display the bacterial density near the epithelial boundary. C. The spatial organization of
Firmicutes distribution in WT and Nlgn3R415C mutant mice. The Firmicutes spatial distribution is significantly altered by the Nlgn3 R451C mutation. D. The Firmicutes organization withing
20µm from the epithelial boundary. The Firmicutes density is higher closer to the edge of the epithelium in Nlgn3R415C mutant mice and the spatial distribution adjacent to the mucosa is
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significantly altered by this mutation in the mucosa. Graphs are mean-subtracted and divided by the standard deviation for normalization. Scale bar=100 µm
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4.5 The spatial patterning of A. muciniphila is shifted in Nlgn3R451C mice.
In the current study, localization patterns of A. muciniphila were examined using the fluorescent in situ hybridization technique in combination with histological staining for host nuclei with DAPI. The MUC1437 probe was used to label A. muciniphila (Figure 5.6 A and
B). A. muciniphila exclusively feeds on mucus O-glycans, therefore any changes to the mucus layer properties could drive shifts in A. muciniphila abundance and distribution.
Overall, the spatial distribution of A. muciniphila in Nlgn3R451C mutant mice was similar to WT
(WT; n=4, Nlgn3R451C mutants; n=5, p=0.6) (Figure 5.6 C).
The A. muciniphila density proximal to the epithelium (within 20 um) was also measured using
BacSpace platform. In WT mice, the A. muciniphila density was high immediately adjacent to the mucosa and decreased towards the lumen. In contrast to WT, the A. muciniphila density in
Nlgn3R451C mutant mice remained unchanged within 0-20 µm of the mucosa. The analysis revealed that the A. muciniphila spatial distribution was significantly reduced close to the mucosa (< 10 μm), but increased to above WT levels at a greater distance (> 10 μm) from the epithelium in Nlgn3R451C mutant mice (WT; n=4, Nlgn3R451C mutant mice; n=5, p<0.000 )
(Figure 5.6 D).
These findings suggest that the R451C mutation in the Nlgn3 gene does not alter the overall density of A. muciniphila but changes its the spatial distribution in the distal ileal mucosa.
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Figure 5.6 A. muciniphila spatial distribution is altered by the Nlgn3 R451C mutation in the ileum
High-resolution confocal micrographs of the distal ileum labelled with MUC1437 probe for A. muciniphila and DAPI, DNA marker in (A) WT and (B) Nlgn3R451C mutant mice. A. muciniphila density near the epithelium is displayed in white insets. C. The spatial localization of A. muciniphila is similar in both Nlgn3R451C mutant mice and WT. D. The A. muciniphila density was plotted withing the 20 µm from the epithelial edge. The A. muciniphila distribution
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is significantly affected by the R451C mutation. Graphs are mean-subtracted and divided by the standard deviation for normalization. Scale bar=100µm
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4.6 The distribution of Bacteroides thetaiotamicron is altered in Nlgn3R451C mice.
To quantitatively evaluate the spatial distribution of B. thetaiotamicron, a mucus-degrading bacterium, fluorescent in situ hybridization was performed and host nuclei were labelled with
DAPI. B. thetaiotamicron was labelled using Bthe probe. (Figure 5.7 A and B).
In both WT and Nlgn3R451C mutant mice, B. thetaiotamicron density was low adjacent to the mucosa (i.e from 0-30 µm) and gradually increased towards the lumen reaching a peak ~50 μm from the epithelial margin. Therefore, the B. thetaiotamicron density within 50 μm from the epithelium was measured. The spatial distribution of B. thetaiotamicron along the mucosa- luminal axis was significantly shifted towards the mucosa in Nlgn3R451C mutant mice compared to WT (WT; n=5, Nlgn3R451C mutant mice; n=4, p<0.0001) (Figure 5.7 C) and the R451C mutation of the Nlgn3 gene increases the density of B. thetaiotamicron adjacent to the epithelium.
The relative density of B. thetaiotamicron within 50 µm of the mucosa was high in Nlgn3R451C mutant mice compared to WT (WT; n=5, Nlgn3R451C mutant mice; n=4, p=0.007) (Figure 5.7
D). Results from this analysis showed that the density and the spatial organization of B. thetaiotamicron in the distal ileal mucosa is affected by the Nlgn3 R451C mutation.
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Figure 5.7 The Nlgn3 R451C mutation alters the Bacteroides thetaiotamicron spatial pattern in the ileum.
Confocal images of distal ileum sections labelled with the Bthe probe and DAPI, DNA marker in (A) WT and (B) Nlgn3R451C mutant mice, B. thetaiotamicron density near the epithelium is displayed in white insets. C. The spatial distribution of B. thetaiotamicron. D. The density of
B. thetaiotamicron between 0-50 µm from the mucosal epithelium in WT and Nlgn3R451C mice.
Graphs are mean-subtracted and divided by the standard deviation for normalization. Scale bar=100 µm.
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5. DISCUSSION
The role of the mucosa-associated microbiome in ASD has gained significant attention because dysbiosis could contribute to the coexistence of GI dysfunction. The intestinal mucus layer is an integral component of the mucosal barrier and serves to protect the epithelial lining of the gut. Since mucus production and secretion are regulated by neuronal activity, impaired neuronal function might also affect the mucus structure. Microbial communities associated with the epithelial mucosa resides within the GI mucus layer and has adapted to live in this glycan rich environment. Because many of these opportunistic microbes derive energy from mucus, alterations in the mucus layer could therefore alter the composition of mucosal bacteria.
However, the impact of altered neuronal function on mucus layer properties has not previously been investigated.
In this study, I combined a mucus preservation method, fluorescent in situ hybridization and a computational tool to determine the localization of bacteria in the distal ileum. Although these methods are widely accepted to measure the mucus layer properties and the distribution of bacteria, it is important to note that the fixation and sectioning of the intestinal tissues could alter the mucus layer properties and the distribution of bacteria. Since the mucus has high water content, tissue fixation results in the shrinkage of the mucus layer generating changes to the native state of the mucus layer. Shrinkage of the mucus also leaves empty spaces adjacent to the epithelium (Ermund et al., 2013b, Johansson et al., 2008, Furter et al., 2019). Although
Carnoy’s fixation preserves the mucus layer better than conventional fixatives such as 4% formaldehyde, it causes the shrinkage of the mucus layer by more than 50%. In addition, fixation of the mucus layer makes the tissue sectioning difficult and is therefore prone to generating artefacts. Similarly, tissue sectioning could make alterations to the spatial distribution of the intestinal microbiome. Therefore, proper controls are necessary to ensure that the sample handling has been conducted in the same manner for WT and mutant tissues.
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5.1 Nlgn3R451C mice have increased mucus density in the distal ileum
In this chapter, I investigated the impact of a nervous system mutation on the intestinal mucus layer in a mouse model of autism and found that the Nlgn3R451C mutation increases the mucus density adjacent to the mucosa in the distal ileum and influences mucus density along the mucosa-lumen axis.
The ENS plays an important role in control of mucus secretion and maintaining the mucus layer to protect the intestine from pathogenic invasion. The submucosal plexus of the ENS directly regulates mucus secretion and maintenance of the mucus layer blanket in the intestine.
Both cholinergic and non-cholinergic submucosal neurons can modulate the properties of the mucus layer (Specian and Neutra, 1980, Neutra, 1982, Lelievre et al., 2007, Wu et al., 2015a,
Plaisancie et al., 1998). Findings from Chapter 4 show that neurally evoked mucosal secretion in the distal ileum is affected by the R451C mutation in the Nlgn3 gene. Specifically,
- DMPP induced anion secretion (including HCO3 ) is significantly reduced in mice expressing the R451C mutation. Bicarbonate plays an important role in mucus release and expansion upon secretion.
In the intestine, goblet cells produce and secrete mucus. Mucus packaging, release and expansion are pH and calcium-dependent. MUC2, the major intestinal mucin, is arranged into a ring-like structure and subsequently packaged into secretory granules. Packaging of MUC2 occurs at low pH and in the presence of Ca2+ in secretory granules. Ca2+ is incorporated to stabilize the MUC2 ring structure. Upon secretion, Ca2+ is removed which serve to disrupt the mucus ring structure and this process aids unfolding and stretching of the MUC2 net-like sheet
2+ - (Ambort et al., 2012). To chelate Ca , HCO3 is released; a process mediated by CFTR channel-mediated bicarbonate secretion (Gustafsson et al., 2012a). The presence of lower
- concentrations of HCO3 during mucus unfolding can slow down or stop mucus expansion and this may cause mucus accumulation and a viscous mucus phenotype. This phenomenon is
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- observed in cystic fibrosis patients with absent HCO3 transport due to defective CFTR channels (Grubb and Gabriel, 1997, Choi et al., 2001). Thus, the increased mucus density observed in the ileum of Nlgn3R451C mice could be due to mucus accumulation caused by
- decreased HCO3 secretion.
Taken together, increased mucus density adjacent to the epithelial lining in Nlgn3R451C mice could be due to improper mucus unfolding process provoked by defective anion secretion in the distal ileum.
5.2 Increased bacterial density adjacent to the mucosa in Nlgn3R451C mice
Although dysbiosis has been identified in people diagnosed with ASD, the spatial localization patterns of bacterial communities within the intestine have not been reported in this population.
Dysbiosis in ASD patients has predominantly been identified using 16s rRNA sequencing to measure the microbial abundance in faecal and mucosal samples (Finegold et al., 2002,
MacFabe et al., 2007, Buie et al., 2010b, Finegold et al., 2010, Wang et al., 2011, Williams et al., 2011, Coury et al., 2012, De Angelis et al., 2013, Luna et al., 2017) which does not yield spatial information. The current study showed that the bacterial spatial distribution is similar in WT and Nlgn3R451C mutant mice when the profile of bacteria located at the mucosal barrier extending to 300 microns inside the lumen was examined. Remarkably, however, bacterial density and localization patterns in the region adjacent to the mucosa (i.e. 0-20 microns from the mucosal barrier) were disturbed by the Nlgn3R451C mutation.
Recently, microbial sequencing studies have generated invaluable information to shed light on understanding how individual bacterial group abundance is altered in the GI tract specifically in the disease state. But, to elucidate dynamic host-microbial interactions, it is essential to know the location of these bacteria, how their function is connected to their local habitat and how global spatial pattern of these bacteria is altered in diseases.
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In the intestine, alterations in the mucosal microbiome are highly likely to occur in tandem with changes in the mucus layer properties. The mucus layer analysis indicated that mice expressing the Nlgn3 R451C mutation have an increase in the mucus layer density adjacent to the epithelium. Therefore, it can be assumed that mucus accumulation causes bacterial retention which increased bacterial density adjacent to the mucosa in the ileum of Nlgn3R451C mice. This phenomenon has been previously reported in small intestinal bacterial overgrowth syndrome, a condition commonly observed in cystic fibrosis patients due to increased mucus retention because of the dysfunctional Cystic Fibrosis Transmembrane conductance Regulator
(Dukowicz et al., 2007, Norkina et al., 2004, Wu et al., 2019, Sakamaki et al., 2020, Belza et al., 2020, Tremellen and Pearce, 2020, Mouillot et al., 2020). Therefore, in line with these studies, findings from this chapter suggest that increased mucus accumulation in the mucosa in the distal ileum causes bacterial retention in Nlgn3R451C mutant mice.
5.3 Shifts in the spatial distribution of Bacteroidetes and Firmicutes phyla in
Nlgn3R451C mice
To identify whether the R451C mutation modifies mucus-residing microbial populations, the spatial distribution and the density of the two major phyla Bacteroidetes and Firmicutes were analysed in transverse sections of the mouse distal ileum. Shifts in both the global distribution of Bacteroidetes and Firmicutes as well as the local distributions (i.e. adjacent to the mucosa) were observed in Nlgn3R451C compared to WT mice.
An imbalance in Bacteroidetes and Firmicutes phyla has been previously reported in ASD.
Similar to the current study, several studies reported a lower Bacteroidetes/Firmicutes ratio in
ASD populations; although some studies showed solely a significant increase in Firmicutes and/or a reduction in Bacteroidetes (Williams et al., 2011, Tomova et al., 2015, Zhang et al.,
2018). Bacteroidetes and Firmicutes are the most prevalent phyla in mucosal samples from
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both children diagnosed with ASD and controls, but in children with ASD, Bacteroidetes abundance in ileal mucosal samples was lower than in the control group (Williams et al., 2011,
Williams et al., 2012).
Altered Bacteroidetes/Firmicutes ratios have also been reported in individuals with Cystic
Fibrosis (Flass et al., 2015). This suggests that mucus retention might be a general cause of dysbiosis involving Firmicutes and Bacteroidetes in the small intestine.
5.4 Shifted spatial distribution of B. thetaiotamicron in Nlgn3R451C mutant mice
B. thetaiotamicron is a mucus-degrading bacterium found in the GI tract. To date, however, there have been no studies on B. thetaiotamicron abundance in the context of neurological disorders. The current study revealed abnormalities in the global spatial pattern of B. thetaiotamicron across the mucosal-lumen axis in Nlgn3R451C mice.
In the mouse ileum, B. thetaiotamicron induces mucus fucosylation to utilize mucus as energy and nutrient source (Bry et al., 1996). B. thetaiotamicron also promotes goblet cell differentiation by stimulating secretory cell lineage in rat colonic epithelium (Wrzosek et al.,
2013). In addition, the presence of B. thetaiotamicron increases the expression of mucus glycans (Wrzosek et al., 2013). Overall it is clear that B. thetaiotamicron interacts intimately with the mucus glycan structure to utilize mucus as an energy resource. In line with these studies, it is possible that increased mucus density observed adjacent to the mucosa drives the B. thetaiotamicron density variation in the distal ileum.
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5.5 The spatial patterning of A. muciniphila is altered in the distal ileum in
Nlgn3R451C mice
A. muciniphila is an abundant mucin-degrading bacterium present in the healthy intestine
(Derrien et al., 2017, Derrien et al., 2004). Altered levels of A. muciniphila have been reported in various diseases including IBD and metabolic syndromes (Dao et al., 2016, Png et al., 2010,
Derrien et al., 2017). Impacts of A. muciniphila on intestinal barrier function and immune responses have been highlighted in various studies (Derrien et al., 2017, de Vos, 2017). In addition, A. muciniphila degrades mucus and synthesises short chain fatty acids which cross feed other beneficial microbes in the gut (Caesar et al., 2015, Belzer et al., 2017).
Interestingly, the abundance of A. muciniphila is altered in neurological disorders including
ASD (Herath et al., 2020). In Nlgn3R451C mutant mice, although the mucus density is increased adjacent to the mucosa, a decrease in the density of A. muciniphila was observed.
In the context of autism, a few studies have reported elevated A. muciniphila abundance in stool samples from patients diagnosed with ASD (Kang et al., 2013, De Angelis et al., 2013,
De Angelis et al., 2015) whereas another study reported lower levels of A. muciniphila in ASD patients compared to controls (Wang et al., 2011). However, the effects of the mucus layer on altered A. muciniphila or the impact of A. muciniphila abundance on mucus layer thickness have not been addressed in either of these studies.
Findings detailed in this chapter indicate that increased mucus density is correlated with A. muciniphila distribution in Nlgn3R451C mice. A. muciniphila in this context is particularly relevant as this bacterium utilizes mucin as its sole source of carbon, nitrogen, and energy for growth by secreting mucolytic enzymes encoded in its genome (Png et al., 2010, van Passel et al., 2011). Therefore, it is possible that the Nlgn3 R451C mutation mediated decrease in the A. muciniphila density in the mucosa increases the mucus density in the distal ileum.
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In contrast, mucolytic bacteria play an important role in degrading the mucus layer in the intestine. The presence of mucolytic bacteria is important for mucus degradation as emphasized by studies in germ-free animals which demonstrated that these mice exhibit a thickened and tightly attached mucus layer (Johansson et al., 2015, Johansson et al., 2008, Schutte et al.,
2014).
The spatial distribution data from this study showed that the density of A. muciniphila is inversely correlated with the mucus density in Nlgn3R451C mutant mice. Within the mucus layer, pores in the mucus mesh allow bacteria to penetrate the mucus layer and access various carbohydrates including O-glycan to use as a nutrient and energy source (Johansson et al.,
2011). Some mucolytic species such as A. muciniphila exclusively feed on mucus O-glycans
(Derrien et al., 2004). When the mucus is not fully expanded, these bacteria cannot penetrate the mucus mesh, thus limiting their access to O-glycans and other carbohydrates associated with mucus which are required as energy sources. Therefore, a lack of mucus expansion might cause a reduction in the density of mucolytic bacteria associated with the mucosa. Taken together, improper mucus expansion may drive the mucus accumulation observed in the distal ileum of Nlgn3R451C mice and the lower level of mucolytic mucus-residing bacteria in these mice
6. CONCLUSION
This study revealed that the Nlgn3 R451C mutation increases mucus density adjacent to the mucosal epithelium in the mouse distal ileum. The mucus accumulation observed in mutant mice is potentially due to decreased bicarbonate secretion resulting in defective mucus unfolding and expansion. Along with an increased mucus density, bacterial dysbiosis was observed adjacent to the ileal mucosa in mutant mice. In parallel with previous literature in
ASD patients, a decrease in the Bacteroidetes/Firmicutes ratio including both decreased
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Bacteroidetes and increased Firmicutes density was observed in the ileal mucosa of Nlgn3R451C mutant mice. Interestingly, the R451C mutation alters both the density and spatial distribution of mucolytic bacteria in the ileal mucosa. In summary, this study revealed an increase in the microbial density of the mucus layer adjacent to the intestinal epithelium in mice expressing the Nlgn3 R451C mutation. These mice also show dysbiosis of the mucosal microbiome and these findings suggest that mucus accumulation in the mucosa could contribute to microbial dysbiosis in Nlgn3R451C mutant mice.
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CHAPTER 6
DISCUSSION
GI dysfunction is one of several highly prevalent comorbid conditions associated with ASD, but the exact cause remains unknown. The ENS coordinates gut functions to maintain intestinal homeostasis and therefore, GI disorders are often associated with ENS dysfunction. Because there are common cellular properties, neurochemistry and signalling pathways between the
ENS and the CNS, the pathophysiology underlying CNS disorders might also lead to ENS dysfunction. NLGN3, a postsynaptic membrane protein, aids in synapse formation in the CNS.
The R451C mutation in the NLGN3 gene has been reported in ASD patients who also showed
GI dysfunction (Hosie et al., 2019). This finding suggests that the R451C mutation in Nlgn3 might also be expressed in the ENS and lead to GI dysfunction in these patients. Despite this, the precise expression patterns of Nlgn3 in the enteric nervous system are unclear.
There is now significant evidence for links between the gut microbiome and the CNS and this has been highlighted recently with implications for understanding the pathophysiology of GI dysfunction in ASD (Sharon et al., 2019, Hsiao et al., 2013). Neuronal processes from both the central and the enteric nervous system connect with the gut microbiome at the gut interface, the intestinal mucosa. The intestinal mucosa demarcates the intestinal milieu and the hostile external environment, maximizing nutrient absorption and simultaneously barring undesirable luminal matter from the body. Permeation and/or absorption of nutrients and water through the intestinal epithelium is regulated by the intestinal permeability pathways including paracellular and transcellular pathways. The intestinal secretory machinery protects the intestine by keeping noxious luminal material segregated from the epithelium and flushing out invading pathogens from the body. To coordinate the complex permeation and secretion circuitry, the mucosa is
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innervated by neurons projecting from the submucosal plexus. Although intestinal barrier dysfunctions have been reported in ASD patients (Fiorentino et al., 2016, de Magistris et al.,
2010, Kushak et al., 2016, Buie et al., 2010a), the impact of the autism implicated NLGN3
R451C mutation on mucosal barrier function remains to be elucidated.
Dysbiosis of the intestinal microbiome has been suggested as a potential etiology for GI dysfunction in ASD. In the intestine, the mucosa-associated microbiome is distinct from the luminal residing microbiome. Due to specialised properties of the intestinal mucus layer, bacteria with mucus degrading abilities reside in the mucosa. Dysbiosis of the mucosal microbiome has been reported in GI disorders and ASD. However, a major limitation in the current literature is the lack of a well-defined correlation between mucus changes and the mucosal microbiome dysbiosis in ASD.
Therefore, the main focus of this study was to investigate the effects of the NLGN3 R451C mutation on the enteric nervous system, mucosal barrier function and the mucosa-associated microbiome. First, I evaluated the expression of Nlgn3 and the impact of the Nlgn3 R451C mutation on Nlgn3 mRNA expression in the ENS. To do this, I developed a quantitative method by combining RNAScope in situ hybridization and immunofluorescence with an image analysis platform to simultaneously detect and enumerate Nlgn3 mRNA expression in subsets of enteric neurons and glia. Secondly, I assessed the impact of the Nlgn3 R451C mutation on cellular proportions within the submucosal plexus and on mucosal barrier function. Thirdly, I investigated the effects of the R451C mutation on the intestinal mucus layer and mucosa- associated bacterial populations using fluorescent in situ hybridization and immunofluorescence techniques. The novel findings from this study enhance current understanding of the pathophysiology of mucosal barrier dysregulation and mucosal microbiome dysbiosis in ASD. Furthermore, these findings support the need to target further research questions to investigate mucosal barrier dysfunction and mucosal microbiome
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dysbiosis in this mouse model of ASD. Hence, this chapter will discuss implications of these novel findings and highlight gaps to be addressed in future studies.
6.1 NLGN3 is expressed in enteric neurons and enteric glia
A key finding of this study is that Nlgn3 mRNA is expressed in most neurons in both the submucosal and the myenteric plexus of the ENS in the mouse distal ileum. Although it is well established that the NLGN3 is broadly expressed within the CNS, this study is the first to report cellular expression of Nlgn3 in both ganglionated plexuses of the enteric nervous system. Data from Chapter 3 showed that Nlgn3 mRNA is expressed in secretomotor neurons, excitatory muscle motor neurons, inhibitory muscle motor neurons, interneurons and intrinsic sensory neurons. In the CNS, NLGN3 protein expression has been found at both excitatory and inhibitory synapses (Budreck and Scheiffele, 2007). Although, excitatory neurotransmission is well characterised in the ENS, a little is known about inhibitory neurotransmission within enteric neural circuitry.
In the CNS, NLGNs on the postsynaptic membrane interact with presynaptic b-neurexins during synapse formation. In support of this, the expression of b-neurexins has been reported in the GI tract of mice (Zhang et al., 2013, Wang et al., 2017a). Splice variants of b-neurexins that can interact with NLGNs have been studied in the colon of fetal rat (Gershon and Ratcliffe,
2004). In addition, NLGN3 interacts with postsynaptic density proteins, linking NLGN3 to downstream signalling cascades that are important for signal transduction in the nervous system. Co-localizing NLGN3 protein collectively with presynaptic neurexins and other postsynaptic proteins will generate invaluable information for tackling the exact function of
NLGN3 in the GI tract. The major obstacle in the localization of post-synaptic density makers in the ENS is the current lack of suitable antibodies. Therefore, novel protein tagging technologies such as SNAP, Halo tags, FIAsH and ReAsH can be used to characterise the association of NLGN3 with other synaptic proteins.
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Findings from this study also suggest that NLGN3 might play a role in neuron-glia communication in the ENS. Here, for the first time, this work revealed that Nlgn3 mRNA is expressed in enteric glia in both submucosal and myenteric plexus. Similar to these results, the expression of NLGN3 protein in glial cells in the CNS has been reported. In the brain, NLGN3 is expressed in various glial types including astrocytes and oligodendrocytes (Stogsdill et al.,
2017, Zhang et al., 2014, Sakers and Eroglu, 2019). The binding partners of NLGNs, the neurexins, are also expressed in glial cells in the CNS (Hillen et al., 2018). In the visual cortex, astrocytes express NLGNs and contact neurons by binding to neurexins. Deletion of NLGNs in astrocytes reduces the morphological complexity of this cellular population and it alters the balance of inhibitory and excitatory synapses in the cortex (Stogsdill et al., 2017). More recently, Matta and colleagues reported increased microglial density and altered astrocytic morphology in the dentate gyrus in Nlgn3R451C mice (Matta et al., 2020). This evidence for the
R451C mutation impacting neuronal-glial interactions in the brain supports a role for NLGN3 in glial-neuron synapse formation in the ENS. Enteric glia receive neuronal input from both the submucosal and myenteric plexus (Okamoto et al., 2014, Gautron et al., 2013) and glial cells form specialised direct contacts with neurons known as ‘neuroglial junctions’. At these junctions, enteric glia form synapse-like or synaptoid contacts with enteric neurons (Gabella,
1981). These findings hint that the presence of NLGN3 in enteric glial cells might influence synapse formation during neuron-glia communication and therefore future studies are required to dissect the synaptic connections between enteric glia and neurons to investigate the role of
NLGN3 in enteric glial function.
Further, preliminary data from this study (not shown) revealed that Nlgn3 mRNA are expressed in neuronal processes and epithelial cell types. It has been confirmed that mRNA can be transported to where it is expressed before being translated to the relevant functional protein.
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Therefore, these data suggest that Nlgn3 mRNA could be initially transported to the synapse and then translated to the NLGN3 protein at the synaptic membrane.
6.2 The R451C mutation alters the cellular expression of Nlgn3 mRNA
For the first time, the current study revealed that Nlgn3 mRNA expression is reduced by the
Nlgn3 R451C mutation in mice. In contrast to mRNA expression data reported in the mouse brain, Chapter 3 shows that the R451C mutation reduces Nlgn3 mRNA expression in most enteric neurons and glia. Studies of the impact of the R451C mutation on both Nlgn3 mRNA and protein expression in the mouse brain reported that Nlgn3 mRNA expression was unchanged but NLGN3 protein expression at the synapse is drastically reduced by the R451C mutation (Comoletti et al., 2004, Tabuchi et al., 2007). Biochemical analysis revealed that the mutation causes retention of the NLGN3 protein in the endoplasmic reticulum such that only
10% of the NLGN3 protein is present on the synaptic membrane (Tabuchi et al., 2007, Sudhof,
2008). Altogether, these findings suggest that the regulation of Nlgn3 mRNA expression differs significantly between the CNS and the ENS. Decreased Nlgn3 mRNA levels observed in the
Nlgn3R451C mutant mice could contribute to a low level of NLGN3 protein production in the
ENS. However, biochemical analysis is required to understand the cellar machinery of NLGN3 translation, packaging and exportation in the ENS. In addition, effects of this mutation on ENS synaptic function are unclear. Therefore, electrophysiological analysis is essential to determine the functional consequences for synaptic activity in the ENS.
6.3 Mucosal barrier dysfunction in Nlgn3R451C mice
In Chapter 3, I observed that Nlgn3 mRNA is expressed in submucosal neurons and Nlgn3 mRNA expression in the submucosal plexus is reduced by the Nlgn3 R451C mutation.
Although this has not been confirmed in the ENS, reduced Nlgn3 expression alters the synaptic activity in the CNS (Tabuchi et al., 2007). Therefore, altered expression of Nlgn3 in the
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submucosal plexus could also affect neuronal activity and this can lead to changes in neurally- mediated mucosal barrier functions in Nlgn3R415C mutant mice. This hypothesis led to the aim of chapter 4, the investigation of the impact of the Nlgn3 R451C mutation on the submucosal plexus and mucosal barrier function.
6.3.1 Altered neurochemistry in the submucosal plexus of Nlgn3R451C mutant mice
The submucosal plexus in Nlgn3R451C mutant mice contains fewer cholinergic and more VIP neurons than the wild type. Alterations in many neurochemical pathways are associated with
ASD etiology. Similar to the ENS, altered VIPergic and cholinergic systems have been reported in ASD (Nelson et al., 2001, Takacs et al., 2013, Costantini et al., 2012, Deutsch et al., 2010, Ray et al., 2005, Lee et al., 2002, Perry et al., 2001, Wenk and Hauss-Wegrzyniak,
1999). Elevated levels of VIP in blood samples have been reported in newborn babies who were later diagnosed with autism (Nelson et al., 2001). In ASD, neurochemical changes begin relatively early during the development of the CNS. Therefore, it is possible that these changes could also appear during ENS development. Since Nlgn3 is expressed in both cholinergic and non-cholinergic neurons in the ENS, altered synaptic function during development could contribute to altered proportions of these neurons in the submucosal plexus. Analysing the correlation between the NLGN3 expression and neurochemical changes during different developmental stages therefore shed light on the role of NLGN3 in ENS development.
6.3.2 Decreased nAChR evoked mucosal secretion in Nlgn3R451C mutant mice.
The effects of altered secretomotor submucosal neuronal populations in the submucosal plexus on mucosal secretion were also investigated in Chapter 4. The submucosal ganglia were
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stimulated using DMPP, a nicotinic acetylcholine receptor agonist which exclusively acts on neurons. The Nlgn3 R451C mutation decreases the magnitude of the neurally-evoked short circuit current, a measure of mucosal secretion in the distal ileum.
Several mechanisms could account for reduced sensitivity to DMPP in mutant mice. The expression of nAChRs in the ENS and their significance in modulating GI functions has been highlighted in various studies studied in the ENS (Foong et al., 2015, Galligan and North, 2004,
Gwynne and Bornstein, 2007, Albuquerque et al., 2009). Thus, altered regulation of nAChR expression, changes in neurotransmitter release from neurons excited by DMPP, changes in the excitability of neurons that are responsive to DMPP as well as altered nAChR subunit clustering may alter the nAChR mediated functions in the GI tract. In addition, preliminary data showed that a subset of epithelial cell types express Nlgn3 mRNA in the mouse ileum (not shown). This suggests that altered Nlgn3 expression in these epithelial cells could lead to reduced epithelial sensitivity in response to secretomotor neurotransmitters.
Submucosal neurons can make synaptic connections with other submucosal neurons.
Transmission at such synapses being excitatory with modelling data indicating that at least two interacting recurrent positive feedback networks are present (Chambers et al., 2005). If the properties of these submucosal positive feedback loops are affected or altered it can modify the electrogenic mucosal secretion. This suggests that reduced positive feedback could exist within the submucosal neuronal network in Nlgn3R451C mutant mice and it could contribute to reduced neurally-mediated mucosal secretion in the distal ileum.
Since most submucosal neurons express NLGN3, it can be postulated that nicotinic transmission is altered by the R451C mutation. In the brain, neuronal nAChRs have been implicated in ASD (Deutsch et al., 2010) and a significant reduction of several nAChR subunits have been reported (Ray et al., 2005, Martin-Ruiz et al., 2004, Lee et al., 2002). Furthermore,
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a7 nAChRs are implicated in ASD and stimulation of a7 nAChRs has procognitive and neuroprotective effects in in vivo and in vitro ASD models (Deutsch et al., 2015).
The exact role of NLGN3 in nicotinic synaptic transmission in the ENS is currently unknown.
Therefore, nAChR expression analysis and functional studies to measure the cholinergic transmission via nAChR are required to understand the role of NLGN3 in nicotinic transmission in the ENS.
6.3.3 Increased paracellular permeability in Nlgn3R451C mutant mice.
In Chapter 4, I demonstrated that the Nlgn3 R451C mutation causes increased paracellular permeability in the distal ileum alongside decreased transepithelial resistance was observed in parallel. The basal transepithelial resistance, however, was not changed by the Nlgn3 R415C mutation. In contrast with the observations in the distal ileum, findings from this study showed that this mutation does not affect the paracellular pathway in jejunum.
Tight junction protein complexes are the key mediators of the paracellular pathway (Turner,
2009). Therefore, in order to investigate whether increased paracellular permeability is linked to tight junction dysfunction, a tight junction protein gene expression analysis was performed.
Data from the gene expression analysis showed that the increased paracellular permeability observed in Nlgn3R451C mutant mice is not due to altered tight junction protein expression.
However, findings from this study indicate that the ileal tissues from the mutant mice are more sensitive to the luminal content. The addition of FITC-dextran to the luminal side causes a reduction in the transepithelial resistance suggesting that the tight junction proteins are temporarily relocated due to the increased luminal concentration of solutes. This is an interesting finding because it suggests that the Nlgn3 R451C mutation increases the sensitivity
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of submucosal neurons to environmental insults such as diet, luminal antigens and the dysbiosis of intestinal microbiome.
Paracellular permeability in the intestine is regulated by VIP and ChAT secretomotor submucosal neurons (Tremellen and Pearce, 2020, Wu et al., 2015b, Neunlist et al., 2013,
Costantini et al., 2012, Neunlist et al., 2003b). VIPergic innervation is prominent in the mucosa in mice (Song et al., 1992, Neunlist et al., 1998), so VIP may have a substantial impact on mucosal barrier functions. In Nlgn3R451C mutant mice, an increased proportion of VIPergic neurons was observed in the submucosal plexus which could potentially results in increased the VIP innervation in the mucosa. It is well established that VIP promotes intestinal barrier homeostasis and protection against intestinal inflammation (Seillet et al., 2020, Wu et al.,
2015b, Talbot et al., 2020). Elevated VIP plasma concentration is a biomarker for intestinal inflammation in GI disorders (Duffy et al., 1989) and higher VIP levels in ileal and colonic tissues have been observed in patients with ulcerative colitis (Casado-Bedmar et al., 2019).
Increased plasma levels of VIP induced increased paracellular permeability in patients with irritable bowel syndrome (Bednarska et al., 2017). VIP is also able to prevent the Citrobacter rodentium mediated translocation of tight junction proteins such as claudins, occludens and zonula occludens from the lateral membrane to the cytoplasm (Conlin et al., 2009). Other than direct neuronal regulation of tight junctions, VIP indirectly increases the barrier function by activating immune cells. Food intake stimulates the release of VIP, which activates the group
3 innate lymphoid cell mediated interleukin-22 release (Seillet et al., 2020, Talbot et al., 2020) and interleukin-22 increases the paracellular permeability by upregulating claudin-2 expression (Seillet et al., 2020, Wang et al., 2017b).
Other than that, nAChR mediated tight junction protein relocation can also induce paracellular permeability. In mice, burn injury causes increased paracellular permeability and stimulation of a7 nAChR in enteric neurons prevents the barrier dysfunction by altering the tight junction
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protein localization (Costantini et al., 2012, Huang et al., 2018). The secretion experiments I conducted revealed that nAChR mediated submucosal neuronal activity is modified by the
Nlgn3 R415C mutation. Therefore, tight junction proteins distribution may be the temporally altered by nAChR mediated neuronal activation in Nlgn3R451C mice, but further evidence is required to prove this assumption.
6.3.4 The Nlgn3R451C mutation causes mucus accumulation in the distal ileum
A key finding of Chapter 5 is that the Nlgn3 R451C mutation causes increased mucus density adjacent to the mucosa. Intestinal mucus secretion is directly regulated by the secretory machinery of the mucosa. Ussing chamber studies from this thesis indicated that the amplitude of the epithelial anion current (which is generated by chloride and bicarbonate ions) was reduced by the Nlgn3 R451C mutation. Bicarbonate is the key determinant of the solubility and expansion of the small intestinal mucus layer. An adequate amount of bicarbonate secretion is mediated through the CFTR channel on the apical membrane of epithelial cells. A decrease in bicarbonate secretion leads to increased mucus viscosity and impaired mucus propagation in the small intestine. This phenomenon has been observed in CF patients who have dysfunctional CFTR channels causing mucus retention in the mucosa (Choi et al., 2001, Garcia et al., 2009, Gustafsson et al., 2012a). Therefore, it is possible that the R451C mutation might cause CFTR channel dysfunction in mutant mice. Thus, a consideration for future study is characterising the role of NLGN3 in regulating CFTR channel function in the GI tract. In addition, altered water and ion balance can change the mucus viscosity. Therefore, impaired mucosal secretion in mutant mice may affect the water and ion balance in mucus causing increased mucus viscosity in the distal ileum.
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6.4 Small intestinal bacterial overgrowth in the distal ileum of Nlgn3R451C model?
In Chapter 5, I examined the bacterial spatial distribution pattern in order to understand the effects of the Nlgn3 R451C mutation on the mucosal microbiome in the distal ileum. Since the
Nlgn3 R415C mutation causes mucus accumulation adjacent to the mucosa, the impact of this increased mucus density on bacterial distribution was examined.
One interesting finding revealed in this chapter was that, alongside increased mucus density, increased bacterial density was observed in the mucus of the distal ileum of Nlgn3R415C mice.
Mucus accumulation and increased bacterial load are the major symptoms of small intestinal bacterial overgrowth syndrome (Vantrappen et al., 1977, Pimentel et al., 2002, Dukowicz et al., 2007). Thickened and increased mucus serves as an ideal environment for the colonization of mucus-associated bacteria.
Moreover, increased bacterial abundance could also lead to an alteration in the normal microbial flora in the intestine. Analysis from this chapter showed that the R415C mutation deceases the level of Bacteroidetes and increases the Firmicutes abundance adjacent to the mucosa. Similarly changes in Bacteroidetes and Firmicutes levels are often reported in patients with small intestinal bacterial overgrowth (Vantrappen et al., 1977, Dukowicz et al., 2007, Wu et al., 2019, Ghoshal et al., 2020). Altogether, it is likely that the Nlgn3 R451C mutation induces small intestinal bacterial overgrowth-like symptoms due to impaired secretory circuitry in the distal ileum. However, further investigations are required to understand the underlying causes for the retention of bacteria in the distal ileum of Nlgn3R415C mutant mice.
6.5 Does impaired mucus expansion restrict the entry of mucolytic bacteria to access mucus glycans in Nlgn3R451C ASD mouse model?
Another significant finding of Chapter 5 is decreased mucolytic bacterial levels in the distal ileal mucosa. Findings from this study suggest that inadequate bicarbonate secretion causes
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mucus retention and impaired mucus expansion in the distal ileal mucosa. Proper mucus expansion allows mucus foraging bacteria to penetrate mucus and access O-glycan and other mucus associated carbohydrates to be utilized as an energy and nutrient source (Johansson et al., 2014). Therefore, when mucus is not fully expanded it limits the access of mucolytic bacteria to the mucus layer, which could lead to a reduction in bacterial types that are exclusively dependent on mucus as a carbon source. Further biochemical analysis is required to investigate the mucus network structure, pore size, mucus penetrability and mucus solubility in Nlgn3R415C mutant mice to establish whether this mechanism operates in these animals.
6.6 The Nlgn3R451C mutation alters the mucosal barrier environment which could lead to GI dysfunction in ASD.
Findings from this study show that Nlgn3 mRNA is expressed in the ENS and this suggests that the NLGN3 protein could be expressed at the postsynaptic membrane of synapses.
Therefore, similar to the CNS, the R451C mutation in the Nlgn3 gene could alter synaptic transmission in the ENS. Altered synaptic transmission often leads to developmental impairments of the nervous system and altered neurochemistry. Data from Chapter 4 showed that the neurochemistry of the submucosal plexus is altered by this mutation. Altered cholinergic and non-cholinergic proportions can alter neurally-mediated mucosal secretion and this was observed in mutant mice. Altered water and ion secretion as well as changes in mucus secretion produce changes in mucus viscosity. Altered mucus viscosity leads to shifts in bacterial populations reside in the mucus layer. Findings from Chapter 4 indicated that the
R451C mutation causes increased paracellular permeability which can increase translocation of antigenic materials such as bacterial biproducts to underlying tissues to cause GI dysfunction. Further, these molecules can then reach the brain either via the vagus nerve or the circulatory system and potentially exacerbate autism-related behavioural abnormalities.
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6.7 Conclusion
This thesis for the first time revealed that the Nlgn3 is expressed in the majority of enteric neurons as well as enteric glia in both the submucosal and myenteric plexus of the distal ileum.
Further, findings from this thesis revealed that the Nlgn3 R451C mutation alters Nlgn3 mRNA expression in enteric neurons and enteric glia in both plexuses of the ENS. However, it is of interest to clarify the expression pattern of NLGN3 protein in the ENS and how the Nlgn3
R451C mutation affects synaptic transmission in order to further understand the underlying causes for GI dysfunction. Therefore, this thesis opens a future avenue for understanding the role of NLGN3 in the ENS. One of my main foci was to investigate mucosal barrier function in the Nlgn3R451C autism model and I was able to depict that the Nlgn3 R451C mutation induces mucosal barrier dysfunction such as increased paracellular permeability and decreased anion secretion. Since mucus expansion is mediated by adequate amount of bicarbonate levels, decreased anion secretion in the distal ileum of Nlgn3R451C mice could reduce the bicarbonate concentration in the mucus layer causing mucus accumulation in the distal ileum. Alongside increased mucus retention increased bacterial density was detected in the distal ileal mucosa.
Further, dysbiosis of mucosal bacteria was observed in Nlgn3R451C mice. Taken together, this study showed that that the nervous system associated Nlgn3 R451C mutation induces intestinal mucosal barrier dysfunction in mice, and in parallel with other ASD studies, the current findings also support the proposal that an impaired barrier function could contribute to pathophysiology of GI dysfunction in ASD.
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CHAPTER 7
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APPENDIX
Figure A1. RNAScope negative control experiments for the myenteric plexus and the submucosal plexus.
Myenteric neurons labelled with (A) Hu pan neuronal marker (B) negative control probe; dapB.
C: Double labelling of myenteric neurons with Hu and daPB. Submucosal neurons labelled with (D) Hu (E) dapB and (F): co-labelling of myenteric neurons with Hu and dapB probe.
Scale bar=50µm.
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Figure A2. Fluorescent in situ hybridization negative control experiments
Cross section of the ileum labelled with (A) the DNA marker DAPI, (B) negative control (NC) probe. (C) Merged image. Scale bar=100 µm.
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Edited by: Frederic Antonio Carvalho, INSERM U1107 Douleur et Biophysique Neurosensorielle (Neuro-Dol), France Reviewed by: Kristina Endres, Johannes Gutenberg University Mainz, Germany Jean-Paul Motta, INSERM U1220 Institut de Recherche en Santé Digestive, France Yosuke Kurashima, Chiba University, Japan *Correspondence: Elisa L. Hill-Yardin [email protected] †These authors have contributed equally to this work
Specialty section: This article was submitted to Microbiome in Health and Disease, a section of the journal
REVIEW published:28 May 2020 doi:10.3389/fcimb.2020.0024 8
Frontiers in Cellular and Infection Microbiology Received: 19 December 2019 Accepted: 29 April 2020 Published: 28 May 2020 Citation: Herath M, Hosie S, Bornstein JC, Franks AE and Hill-Yardin EL (2020) The Role of the Gastrointestinal Mucus System in Intestinal Homeostasis: Implications for Neurological Disorders. Front. Cell. Infect. Microbiol. 10:248. doi: 10.3389/fcimb.2020.00248 The Role of the Gastrointestinal Mucus System in Intestinal Homeostasis: Implications for Neurological Disorders Madushani Herath 1, Suzanne Hosie 2, Joel C. Bornstein 1†, Ashley E. Franks 3† and Elisa L. Hill-Yardin 1,2*† 1 Department of Physiology, University of Melbourne, Parkville, VIC, Australia, 2 School of Health and Biomedical Sciences, RMIT University, Bundoora, VIC, Australia, 3 School of Life Sciences, La Trobe University, Bundoora, VIC, Australia
Mucus is integral to gut health and its properties may be affected in neurological disease. Mucus comprises a hydrated network of polymers including glycosylated mucin proteins. We propose that factors that influence the nervous system may also affect the volume, viscosity, porosity of mucus composition and subsequently, gastrointestinal (GI) microbial populations. The gut has its own intrinsic
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neuronal network, the enteric nervous system, which extends the length of the GI tract and innervates the mucosal epithelium. The ENS regulates gut function including mucus secretion and renewal. Both dysbiosis and gut dysfunction are commonly reported in several neurological disorders such as Parkinson’s and Alzheimer’s disease as well in patients with neurodevelopmental disorders including autism. Since some microbes use mucus as a prominent energy source, changes in mucus properties could alter, and even exacerbate, dysbiosis-related gut symptoms in neurological disorders. This review summarizes existing knowledge of the structure and function of the mucus of the GI tract and highlights areas to be addressed in future research to better understand how intestinal homeostasis is impacted in neurological disorders. Keywords: mucus, MUC-2, goblet cells, intestine, microbes, neurological disorders
PROPERTIES OF THE GASTROINTESTINAL MUCUS LAYER The mucus layer is the first line of defense against infiltration of microorganisms, digestive enzymes and acids, digested food particles, microbial by-products, and food-associated toxins. This layer coats the interior surface of the GI tract, lubricates luminal contents and acts as a physical barrier to bacteria and other antigenic substances present in the lumen. The moist, nutrient-rich mucus layer adjacent to the epithelial barrier of the GI tract is also essential in the maintenance of intestinal homeostasis and contains a thriving biofilm including beneficial and pathogenic microbial populations. Emerging evidence demonstrates changes in the gut-brain axis in neurological disease involving the enteric nervous system located within the wall of the GI tract. Interestingly, mucus production is regulated by molecular pathways involved in developmental processes and nervous system activity. Multiple neurological disorders present with gastrointestinal dysfunction and microbial dysbiosis but whether alterations in mucus structure and function are driving these changes is unknown. Therefore, we propose that alterations in enteric nervous system function and mucus production may occur in neurological disease and contribute to GI symptoms and dysbiosis.
Regional Mucus Variations Although mucus located throughout the gut contains the same biological components, mucus properties vary with regional differences in function along the gastrointestinal tract (Ermund et al., 2013, Figure 1). Small Intestine The majority of nutrient uptake from digested food occurs in the small intestine and therefore there is a single, discontinuous and more penetrable mucus layer in this region (Johansson et al., 2011). The discontinuity of the small intestinal mucus layer is important not only for the absorptive function of this region but also for the release of digestive enzymes localized in the brush border membrane of epithelial cells. Experiments assessing passage of fluorescent beads across small intestinal mucosal samples showed that small intestinal mucus in mice is penetrable by beads equivalent to the size of bacteria (i.e., 0.5–2 µ3) and hence contains pores as large as 2 µ2 (Ermund et al., 2013). These large mucus pores ensure efficient nutrient absorption by the host epithelium.
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The bacterial content of the mucosal barrier in the small intestine is also regulated by a cocktail of antibacterial mediators such as defensins, lysozymes, and other peptides released by Paneth cells (Peterson et al., 2007). Together, these mediators repel bacteria by generating an antibacterial gradient toward the lumen (Johansson and Hansson, 2011; Vaishnava et al., 2011). Specific mediators include the abundant Regenerating islet-derived 3 (REG3) peptides, IgA, Toll-like receptor 5 (TLR5 regulates levels of anti-flagellin antibody in the gut) (Cullender et al., 2013) and phospholipase A2-IIA (Meyer-Hoffert et al., 2008; Bevins and Salzman, 2011). Overall, antibacterial peptides kill
FIGURE 1 The| structure of the mucus layer varies with regional locations within( A the) The GI smalltract. intestine contains a single layer of mucus, which is loosely attached to the epithelium and easily penetrable.within Bacteria the small intestine are primarily repelled from the epithelium by antibacterial(B ) The modulators. distal colon contains two mucus layers; a stratified adherent inner mucus layer and loosely adhesive outer mucusmucus layer. layer The of theinner colon is essentially sterile and the outer mucus layer harbors the intestinal microbiota. bacteria via a range of mechanisms including by the formation of aggregates, recognition, and binding to bacterial cell wall peptidoglycans, and permeabilization of bacterial cell membranes (Chairatana and Nolan, 2017). This serves to neutralize invasion by foreign particles and maintain epithelial crypts. This antimicrobial defense mechanism is critical in the small intestine due to the discontinuous and penetrable nature of the mucus in this region and is reflected by a higher density of Paneth cells and corresponding peptides (Ouellette, 2010). Colon The organization of the mucus layer varies along the length of the colon. In the distal colon, there are two layers of mucus, however, whether these layers adhere to the epithelium or the colonic content is under debate. In the proximal colon, the presence of two mucus layers has been queried based on histological studies in animal models. Johansson and colleagues reported that the mouse distal colon contains two continuous mucus layers; an inner mucus layer that is ∼50 µm thick and anchored to the mucus-producing goblet cells of the epithelial membrane, and an outer mucus layer that is loosely adherent and harbors bacteria (Johansson et al., 2008). These researchers also reported that the thickness of the outer mucus layer is determined by the composition of the mucus-inhabiting bacteria. Interestingly, this group reported that the inner mucus layer of the proximal colon is also penetrable to bacteria (Ermund et al., 2013). In contrast, Kamphuis and colleagues reported that the two distal colonic mucus layers adhere to the fecal pellet rather than the intestinal epithelium in rodents and that the organization of the colonic mucus layers is dependent on the presence of fecal content (Kamphuis et al., 2017). Specifically, this study utilized fluorescence in situ hybridization and histological techniques in longitudinal sections to demonstrate that the fecal pellet is covered by a sterile mucus layer of variable thickness that is not attached to the epithelium. They also showed that within the proximal part of the proximal colon, which contains colon content prior to formation of a fecal pellet, the mucus layer is loosely organized and the bacteria in this region are in contact with the epithelial surface (Kamphuis et al., 2017). The dissimilarities in the mucus layers of the colon reported may be due to methodological variations including the orientation of tissue sectioning and mucus staining techniques. Overall, multiple studies examining mucus properties carried out in both mice (Macfarlane et al., 2011; Motta et al., 2015; Welch et al., 2017) and humans (Swidsinski et al., 2007a) describe two mucus layers in the colon that include a firm mucus layer adjacent to the epithelium that is devoid of bacteria.
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Commensal bacteria secrete mucinases and proteinases that continuously degrade the outer mucus layer contributing to its highly disorganized nature (Donaldson et al., 2016). Similarly, a role for bacteria in mucus thickness has been demonstrated in germ free mice which have a thinner inner colonic mucus layer. Simply adding components of the bacterial cell wall (e.g., lipopolysaccharide; LPS) is sufficient to increase mucus thickness in this model, highlighting a role for bacteria in regulating the structure of the outer mucus layer (Petersson et al., 2011). The continual release of mucus contributes to a dynamic process whereby the inner mucus layer is gradually converted to the irregular and less adherent outer mucus layer. This process involves Meprin β, an endogenous protease which aids mucus detachment (Wichert et al., 2017) and also bacteria penetration by increasing pore size in the outer mucus layer (Schutte et al., 2014).
Intestinal Mucus Composition Mucus is primarily composed of branched glycoproteins (including mucins) that interact with the external environment and via their hydrophilic nature, influence mucus viscosity (Bergstrom and Xia, 2013). There are more than 20 subtypes of mucin identified in humans and their distribution varies throughout the GI tract. For example, the salivary glands produce MUC5B and MUC7 to lubricate food (Bobek et al., 1993; Nielsen et al., 1996; Khan et al., 1998; Thornton et al., 1999) and the mucus layer in the stomach contains MUC5AC (Ho et al., 1995; Atuma et al., 2001; Nordman et al., 2002). Although MUC5AC is not typically expressed in the large intestine, it has been detected in the distal colon along with MUC-2 during inflammation associated with ulcerative colitis and adenocarcinoma in patients (Forgue-Lafitte et al., 2007). It is well-established that the major glycoprotein within the intestinal mucus layer is mucin-2 (MUC2 protein). There are three major structural domains within the MUC2 protein; the N-terminal domain, a central large PTS (proline, threonine, and serine) domain and the Cterminal domain. Following translation, full-length MUC2 protein cores form dimers via disulfide bridges near their Cterminus within the endoplasmic reticulum (ER) of goblet cells. Within the Golgi apparatus, MUC2 proteins undergo O-linked glycosylation. In this process glycans such as xylose, mannose, Nacetylglucosamine, and N-acetylgalactosamine (O-GalNAc) are covalently attached to the hydroxyl group (-OH) of threonine and serine residues of the PTS domain (Godl et al., 2002). Glycans account for 80% of the total mass of the MUC2 protein and extend perpendicularly from the protein core giving the molecule a “bottle brush-like” appearance (Figure 2). O-Glycans can be modified via formation of linkages with sulfate, sialic acid, and fucose. These modifications play an important role in influencing interactions between the host microbial populations with mucus (Arike and Hansson, 2016). A complex polymerization process occurs within the transGolgi network by which MUC2 protein dimers interact firstly as trimers and then are tightly bundled into MUC2 secretory granules (Godl et al., 2002; Ambort et al., 2012). High Ca2+ ion concentration alongside low pH enables mucus packing by masking negatively charged glycans on the MUC2 protein. During this process, concatenated ring structures are formed (Grubb and Gabriel, 1997; Choi et al., 2001; Ambort et al., 2012; Gustafsson et al., 2012b; Schutte et al., 2014). Although the main component of mucus in the small intestine and the colon is mucin-2, a rich variety of other proteins largely originating from shredded epithelial cell debris that becomes trapped in the mucus are also present within the mucus biofilm, including IgG Fc-binding protein (FCGBP), Calcium activated chloride channel 1 (ClCA1), Zymogen granule membrane protein 16 (ZG16), Anterior gradient 2 (AGR2), and immunoglobulins (Johansson et al., 2008).
Mucus Expansion After mucus secretion, the MUC2 protein complex expands dramatically to form a net-like structure (Ambort et al., 2012). Mucin expansion occurs due to increased pH and decreased Ca2+ levels driven by cystic fibrosis transmembrane regulator (CFTR) channels. CFTR-mediated secretion of HCO3− reduces Ca2+ levels which weakens the ring structure of the mucin complex and allows the densely packed MUC2 mucin to expand into large flat sheets (Ambort et al., 2012). The newly secreted mucus sheets are laid down on the epithelium by interacting with previously secreted mucus and subsequently attaching to the epithelium (Johansson and Hansson, 2016) (Figure 2). In the colon, expansion of the outer mucus layer is also triggered by bacteria that release glycosidases that sequentially cleave individual monosaccharides from mucin glycans (Johansson and Hansson, 2016) to further relax the tight-knit structure of mucin glycans (Johansson et al., 2008).
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FIGURE 2 Neuronal| innervation of goblet cells in the intestinal mucosa. Neurons of the submucosal plexus (SMP)cells innervate by release goblet of neurotransmitters such as acetylcholine (ACh) and vasoactive internal peptide (VIP). Maturation of gobletSAM cells pointed is influenced domain by-containing Ets transcription factor(Spdef ), Wnt/Notch signaling and neuronal activity. Maturecells goblet have a characteristic goblet shape. The apicalis distended region by the presence of mucin granules, giving the cell the characteristic cup shape with other cellular organelles condensed in the basal “stem-like” region. Muc-2 protein comprises multiple O-glycans arranged in a “bottle brush” like formation. SMP, submucosal plexus; CM, circular muscle; MP, myenteric plexus; LM, longitudinal muscle; EC cell, enteroendocrine cells. Mucus Secreting Goblet Cells The intestinal epithelium consists of absorptive and secretory cell lineages including enterocytes, enteroendocrine cells (EECs), Paneth cells, and goblet cells. Goblet cells are specialized cells equipped with specific biological machinery for the secretion of mucus and are present throughout the entire length of the intestine (Figure 2). These cells, as their name suggests, are easily identifiable in histologically stained cross sections of the intestine due to their characteristic “goblet-like” shape. Intestinal epithelial cells, including goblet cells, arise from multipotential stem cells residing at the base of the intestinal crypts and subsequently migrate from the crypts to the top of the villus prior to eventually being shed into the lumen (Cheng and Leblond, 1974). In mice, this migratory process occurs over 2–3 days (Specian and Oliver, 1991). Differentiation of goblet cells is directly controlled by the transcription factor SAM pointed domain- containing ETS transcription factor (Spdef ) (Noah et al., 2010) and also via a network of transcriptional factors regulated by the Notch and Wnt signaling pathways known to influence developmental and inflammation pathways (van Es et al., 2005; Clarke, 2006; Fre et al., 2009; Gersemann et al., 2009; Gregorieff et al., 2009; Kwon et al., 2011; Heuberger et al., 2014; Tian et al., 2015). Furthermore, enteric neural activity has been shown to influence the maturation and production of stem cells in the GI tract (Lundgren et al., 2011) which, in turn, suggests a role for the ENS in goblet cell proliferation and differentiation. Goblet cell morphology changes dramatically during the cellular lifespan (Specian and Oliver, 1991). Immature goblet cells are larger and pyramidal in shape with cellular organelles dispersed throughout the cell and interspersed with mucus granules in the apical cellular region. As these goblet cells migrate toward the colonic epithelial surface, they reduce in volume as a result of shedding cytoplasmic content and organelles. During this phase of volume reduction, goblet cells reduce contact with the basal laminar surface adjacent to the epithelium and simultaneously increase contact with the luminal surface of the GI tract. The goblet cells then rapidly produce and store mucus granules, resulting in the distention of the apical cellular region to produce the typical “cup” shape. The nucleus and other cellular organelles of the goblet cells are concentrated in narrowed stem-like subcellular regions located at the base of the cells (Specian and Oliver, 1991). These processes could be altered in neurological disorders. For example in Alzheimer’s disease, the metalloprotease Meprin β, which cleaves amyloid precursor protein (Schönherr et al., 2016;
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Becker-Pauly and Pietrzik, 2017) also regulates mucus detachment from goblet cells in the small intestine (Wichert et al., 2017).
Mucus Interactions With Microbes Microbial populations are spatially organized along the length of the intestine as well as from the luminal to mucosal axis (Palestrant et al., 2004). Mucus viscosity increases toward the distal region of the GI tract. This viscosity gradient along the length of the GI tract reportedly determines the spatial distribution of intestinal microbiota (Swidsinski et al., 2007b). The composition of bacteria adjacent to the mucosa is different to the bacterial populations that reside within the luminal content (Swidsinski et al., 2005). This mucosal to luminal bacterial distribution is likely driven by variations in oxygen levels and nutrient availability (Yasuda et al., 2015). The mucus layer serves as a carbon and energy source, predominantly in the form of glycans, for mucus residing bacteria. As an adaptation to residing in a glycan-rich environment, these bacteria produce mucus-degrading enzymes such as glycosidase, sulphatase, and sialidases (Table 1) that cleave the mucus network to enhance the utilization of mucus as an energy source. A range of mucus-degrading bacteria present within the mucus, includes Akkermansia muciniphila (Derrien et al., 2004), Bacteroides thetaiotaomicron (Xu et al., 2003), Bifidobacterium bifidium (He et al., 2001), Bacteroides fragilis (Macfarlane and Gibson, 1991), and Ruminoccous gnavus (Png et al., 2010). These bacterial species cleave mucus O-glycans to produce monosaccharides (Berry et al., 2013) which can be further utilized by other mucus-residing bacteria including Lachnospiraceae (Nava et al., 2011), Clostridium cluster XIV (van den Abbeele et al., 2013), Enterobacteriaceae (Ashida et al., 2008), and Clostridium difficile (Ng et al., 2013). Further adaptation of bacteria has been identified in Lactobacillus (Etzold et al., 2014) and Bacteroides (Sicard et al., 2017) where the presence of multi-repeat cell-surface adhesins enable retention of the bacteria within the mucus layer. The syntrophic, symbiotic, and mutualistic interactions of the microbes in the mucus layer create the environment which drives microbial community selection and defines physical properties of the mucus layer. Some mucus residing bacteria form mucosal biofilms, complex microbial communities embedded in a polymeric TABLE 1 | Predominant mucus-degrading bacteria and secreted digestive enzymes. Bacteria Mucus degrading enzyme References Glycosidase Png et al., 2010; van Akkermansia muciniphila Passel et al., 2011
Bacteroides Sulfatase, neuraminidase, α- Xu et al., 2003 thetaiotaomicron fucosidase, β-galactosidase α- N-acetylgalactosaminidase β- N-acetylglucosaminidase
Rumminococcus gnavus α-galactosidases Png et al., 2010 Rumminococcus torques α-N-acetylgalactosaminidase Png et al., 2010 Bacteroides fragilis Neuraminidase, sulfatase, Macfarlane and protease, α- Gibson, 1991 N-acetylgalactosaminidase, β- galactosidase, β -N-acetylglucosaminidase, α- fucosidases Bacteroides vulgatus Neuraminidase, α and β- Onderdonk et al., galactosidases, α-fucosidase 1983; McCarthy et al., 1988 β-N-acetylglucosaminidase, α and β -N-acetylgalactosaminidase
Adherent invasive Vat protease Gibold et al., 2016 Escherichia coli Giardia duodenalis Cysteine protease Amat et al., 2017 Entamoeba histolytica Cysteine protease Lidell et al., 2006 matrix. Techniques including fluorescent in situ hybridization and electron microscopic studies reported the presence of bacterial biofilms in the healthy colon of mice, humans and rats (Palestrant et al., 2004; Swidsinski et al., 2005; Bollinger et al., 2007; Macfarlane et al., 2011; Motta et al., 2015). Altered levels of biofilm associated bacteria such as Bacteroides fragilis, Enterobacteriaceae family were reported in Crohn’s disease and inflammatory bowel disease (Masseret et al., 2001; Macfarlane and Dillon, 2007; DuPont and DuPont, 2011; Srivastava et al., 2017). Therefore, the mucus associated bacterial biofilm also could play a role in these disorders. Alterations in these complex community structures could result in abnormal mucus invasion, epithelial adherence, and spatial distribution of bacterial species.
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THE ENTERIC NERVOUS SYSTEM (ENS) The digestive tract is innervated by the enteric nervous system (ENS), an intrinsic neuronal network that regulates GI functions (Furness et al., 2013) in addition to extrinsic innervation from the parasympathetic and sympathetic components of the autonomic nervous system (reviewed in Uesaka et al., 2016). Neuronal control of intestinal function is largely regulated by two ganglionated plexuses; the myenteric and submucosal plexus. The myenteric plexus predominantly regulates GI motility while the submucosal plexus regulates the secretion of water and electrolytes primarily via the neurotransmitters acetylcholine (ACh) and vasoactive intestinal peptide (VIP). The ENS Influences Mucus Secretion Mucus secretion is influenced by nervous system activity and occurs via two processes; (i) vesicle secretion and (ii) compound exocytosis. During vesicle secretion, mucussecreting goblet cells release mucus content by fusion of the mucus granule membrane with the overlying plasma membrane (Lang et al., 2004). This process is regulated by vesicle exocytotic components like syntaxin, Munc 18, vesicleassociated membrane proteins (VAMP) and synaptosomal nerveassociated proteins (SNAP) proteins (Cosen-Binker et al., 2008). During compound exocytosis, all mucus granules are fused together and empty the mucus as a single unit. As yet, the molecular pathways regulating compound exocytosis have not been defined. VIP and ACh are the two main secretagogues responsible for neurally-evoked mucosal secretion (Specian and Neutra, 1980; Neutra et al., 1984; Lelievre et al., 2007; Gustafsson et al., 2012a; Ermund et al., 2013). ACh induces mucus secretion by activating M3 muscarinic receptors located on goblet cells within the epithelium in both the small intestine and in the colon (Specian and Neutra, 1980; Neutra et al., 1984; Gustafsson et al., 2012b; Ermund et al., 2013). Exocytosis of mucuscontaining granules is regulated by intracellular Ca2+ and Ca2+−mobilizing agents (including acetylcholine; Birchenough et al., 2015). The activation of M3 muscarinic receptors mobilizes Ca2+ from intracellular stores to induce mucus secretion (Ambort et al., 2012). Mucus release is differentially regulated in a region-specific manner in the GI tract. ACh specifically targets both crypt and villus-associated goblet cells in the small intestine (Birchenough et al., 2015). In contrast, in the colon, goblet cells located in crypts are responsive to ACh, but equivalent cells at the epithelial surface do not respond to ACh or the cholinergic agonist, carbachol (Gustafsson et al., 2012b). Release of the neuropeptide VIP enhances mucus secretion (Lelievre et al., 2007) via modulating CFTR-dependent secretions (Alcolado et al., 2014). Furthermore, VIP deficiency in mice results in reduced goblet cell number and reduced muc-2 gene expression levels (Wu et al., 2015). A recent study displayed that mucosal VIP-containing neurons are in close proximity with ileal goblet cells and VPAC receptor antagonist alter the goblet cell numbers in the ileum (Schwerdtfeger and Tobet, 2020).
Gut Motility and Mucus Movement In addition to its prominent action in regulating GI motility and peristalsis, the myenteric plexus plays a key role in mucus renewal. GI motility regulates mucus levels by propelling mucus to the distal GI tract. Myenteric neurons coordinate cyclic motility patterns known as migrating motor complexes (MMCs) that contribute to the “housekeeping” functions of the intestine by flushing undigested materials, mucus, and bacteria along the small intestine. Altered ENS regulation of motility can therefore also perturb mucus renewal. Interestingly, patients with irritable bowel syndrome (IBS) report lower MMC frequencies and show bacterial overgrowth in the small intestine (Pimentel et al., 2002) implicating alterations in the mucus environment. ANIMAL MODELS OF MUCUS IMPAIRMENT Preclinical models have demonstrated that abnormalities in GI structure and function are associated with altered mucus production. For example, colonic mucus layer thickness is decreased alongside progressive inflammation in a mouse model of colitis (Petersson et al., 2011). In the absence of an inner mucus layer, bacteria can penetrate deep into the epithelial crypts and interact with the colonic epithelium (Johansson et al., 2008) which can exacerbate disease. Furthermore, multiple studies report that alterations in mucus secretory processes result in an underdeveloped colonic inner mucus layer, often associated with sparsely filled goblet cells and an increased susceptibility to colitis (An et al., 2007; Park et al., 2009; Stone et al., 2009; Fu et al., 2011; Tsuru et al., 2013; Bergstrom et al., 2014).
Muc-2 Knockout Mice Mice lacking the mucus protein MUC2 (MUC2−/− mice) lack an inner colonic mucus layer despite the presence of goblet cells and other mucus layer components. Interestingly, Rahman and colleagues showed changes in colonic innervation in mice expressing a point mutation in Muc-2 (Rahman et al., 2015) highlighting interactions between mucus production and innervation of the GI tract. Knockout mice also exhibit altered intestinal cell maturation, migration, and abnormal intestinal crypt morphology (Velcich et al., 2002). These mice develop adenomas and rectal tumors as well as increased infiltration of neutrophils and lymphocytes, loose stools, diarrhea with blood, rectal prolapses, and
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fail to thrive (Velcich et al., 2002). In the longer term, these mice also show increased susceptibility to developing colon cancer (Velcich et al., 2002; van der Sluis et al., 2006).
Cystic Fibrosis Patients with cystic fibrosis are commonly diagnosed with concomitant GI abnormalities including meconium ileus and distal intestinal obstruction syndrome (Colombo et al., 2011) due to an increase in secreted mucus volume, mucus dehydration, and increased viscosity that contributes to blockage of the small intestine. Both mucus buildup and reduced mucus movement occur in these patients due to dysregulated mucus secretion. Cystic fibrosis is caused by mutations in the gene encoding the Cystic Fibrosis Transmembrane conductance Regulator (CFTR) channel important for mucus hydration. These mutations cause defective chloride ion transport out of epithelial cells and dehydration of mucus overlying the epithelium. In patients, mucus remains tightly attached to the small intestinal epithelium and peristaltic movements fail to propel the mucus forward within the GI tract. In keeping with these changes, an increased bacterial load has been observed in cystic fibrosis patients (O’Brien et al., 1993), likely due to the elevated volume and viscosity of mucus that provides an ideal environment for commensal microbes. Mouse models expressing CFTR mutations also display severe intestinal dysfunction and a mucus layer that is firmly attached to the mucosal epithelium (Grubb and Gabriel, 1997; Seidler et al., 2009; Frizzell and Hanrahan, 2012). Since a prominent role of mucus is to trap and transport bacteria to the distal regions of the gastrointestinal tract via peristalsis, animal models provide an excellent experimental tool to investigate the effects of mucus perturbation on microbial dysbiosis.
Hirschsprungs Disease Extreme effects of neuronal loss on goblet cell function and on mucus layer properties have been observed in Hirschsprung disease, a life-threatening developmental disorder where the distal colon lacks enteric neurons due to the failure of neural crest cells to completely migrate during gastrointestinal development. Patients with Hirschsprung disease have a reduced mucin turnover rate, a decreased goblet cell population and reduced expression of Spdef and Krueppel like factor 4 which drive goblet cell differentiation and maturation (Aslam et al., 1997a,b; Nakamura et al., 2018). These findings highlight the importance of the ENS in the development and function of mucus-producing goblet cells in the clinical setting. Mouse models of Hirschprung Disease additionally provide evidence for neural-mucus interactions. For example, endothelin receptor B knockout mice (Ednrb−/− mice) along with mice expressing a mutation in the RET gene that encodes the receptor for the glial cell line-derived neurotrophic factor (GDNF) are well-characterized models which have been examined for alterations in mucus and goblet cell structure. Mice lacking endothelin receptor B, known for its role in angiogenesis and neurogenesis, show colonic aganglionosis resembling the clinical presentation. Ednrb−/− mice showed an increase in both goblet cell numbers and size as well as increased expression of Spdef and Math 1 transcription factors in the distal colon (Thiagarajah et al., 2014). In addition, the absence of Ednrb in mice alters mucus structure as evidenced by reduced permeability to 200 nm nanoparticles in vitro (Thiagarajah et al., 2014; Yildiz et al., 2015). Furthermore, significant differences in the commensal microbiome were also present in this model (Ward et al., 2012). The absence of GDNF signaling in mice similarly results in a severely underdeveloped ENS. Furthermore, these mice have altered mucus composition and mucus retention (Porokuokka et al., 2019). Overall, these clinical and animal model data illustrate involvement of the nervous system in the regulation of goblet cell differentiation and maturation as well as influencing mucus properties.
NEUROLOGICAL DISORDERS AND MUCUS DYSFUNCTION Patients with neurological disorders frequently present with coexistent bowel diseases but whether this is due to nervous system changes per se or additional downstream effects such as dysbiosis, immune dysregulation and/or altered mucus production is uncertain. Gut disorders are often associated with, and precede, the core diagnostic symptoms of autism, Parkinson’s disease, Alzheimer’s disease, and Multiple Sclerosis (Pfeiffer, 2003; Buie et al., 2010; Preziosi et al., 2013; Coggrave et al., 2014). Severe gastrointestinal dysfunction can be debilitating, exacerbate core symptoms of neurological disease, and decrease quality of life. Thus, clarifying the role of the nervous system in mucus production and maintenance could improve understanding of the pathophysiology of neurological disease. Furthermore, modulating mucus properties to optimize probiotics and microbial engineering could provide additional “psychobiotic” therapeutic options for these disorders. A major function of the intestinal mucus layer is to form a barrier between the intestinal epithelium and the luminal content to protect the intestine from pathogenic invasion. A number of biological pathways influence mucus production and volume: (i) stem cell proliferation and subsequent maturation of goblet cells is influenced by the SPDEF
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transcription factor and the Wnt/notch signaling pathways, as well as neural activity; (ii) multiple neurotransmission pathways directly activate mucus release from goblet cells, including via muscarinic receptors; (iii) motility driven by the enteric nervous system can also affect mucus renewal; (iv) vesicular signaling molecules govern mucus release; and (v) microbes are integral in maintaining mucus homeostasis (Figure 3).
Developmental Pathways Key developmental pathways implicated in neurological disease are involved in goblet cell maturation, mucus production and release. For example, the Spdef and Wnt/Notch signaling pathways, known to be crucial for neuronal development in the brain, also influence stem cell maturation in the GI tract. As Spdef regulates the terminal differentiation of goblet cells and Paneth cells (Noah et al., 2010) alterations in these pathways would influence goblet cell turnover and numbers (Lo et al., 2017), therefore modulating mucus properties. The Wnt-beta catenin pathway is also associated with neurological disease (Sani et al., 2012; Zhang et al., 2012, 2014; Ferrari et al., 2014; Huang et al., 2015; Hoseth et al., 2018). This pathway stimulates the synaptic expression and localization of neuroligin-3, a synaptic adhesion protein associated with autism spectrum disorder (Medina et al., 2018). Wnt signaling pathways are also implicated in Parkinson’s Disease via interactions with PARK genes (Berwick and Harvey, 2012). Although potential changes in goblet cell number and morphology or mucus properties have not been studied in animal models of autism or several other models of neurological disorders, we predict that Wnt-mediated pathways are altered in the gastrointestinal tract and affect mucus properties, thereby contributing to patient GI symptoms.
Protein Misfolding Due to the high levels of protein produced, mucus production processes within goblet cells are susceptible to protein misfolding, retention in the endoplasmic reticulum (ER), and ER stress. Protein misfolding is known to trigger the unfolded protein response (UPR), which is associated with chronic inflammation and autoimmune changes in neurodegenerative diseases such as PD, Alzheimer’s disease, and multiple sclerosis (Mhaille et al., 2008; Matus et al., 2011). Accordingly, protein misfolding could result in altered production and apoptosis of goblet cells, therefore affecting mucus properties.
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FIGURE 3 How| neurological disease may impact mucus production. Schematic representation of potential changes in mucus production and microbial communities in neurological disorders. SMP, submucosal plexus; CM, circular muscle; MP, myenteric plexus; LM, longitudinal muscle. Vesicle-Associated Proteins Biological pathways required for neurotransmission and mucus release share molecular components. Multiple neurological disorders are associated with gene mutations that impair neuronal communication via synapses, therefore mutations in the brain potentially affect mucus properties in the gastrointestinal tract. Examples of mucus release components that overlap with synaptic neurotransmitter systems include syntaxin, Munc 18, VAMP, and SNAP proteins. These vesicleassociated proteins are commonly expressed at neuronal synaptic membranes and have been identified as being mutated in neurological disorders (syntaxin; ASD, SNAP; ADHD, Munc 18; epilepsy/ASD (Guerini et al., 2011; Durdiaková et al., 2014; Hamada et al., 2017). Changes in the function of these proteins will not only contribute to brain disorders but may also disrupt vesicular secretion of mucus. Further investigation of mucus TABLE 2 | Altered mucosal microbiome in patients with neurological disease. Altered mucosal microbiome (↓↑ abundance) References Neurological Gastrointestinal disorder dysfunction
Autism spectrum Constipation, diarrhea, ↓ Akkermansia muciniphila Wang et al., 2011 disorder functional abdominal ↓ Bifidobacteria species pain, food allergies, Mucosa-associated Clostridiales (Lachnospiraceae and Luna et al., 2017 bloating ↑ Ruminococcaceae) Dorea, Blautia, Sutterella ↓ ↑ Burkholderia Kushak et al., 2017 ↓ Neisseria
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↑ Sutterella Williams et al., 2012 ↓ Bacteroidetes Williams et al., 2011 ↑ Firmicutes (↑ Ruminococcaceae ↑ Lachnospiraceae) Parkinson’s disease Constipation ↓ Faecalibacterium (Blautia, Coprococcus, Keshavarzian et al., R and oseburia) 2015 ↑ Pro-inflammatory Proteobacteria of the genus Ralstonia ↓ Dorea, ↑ Christensenella, Petrov et al., 2017 ↓ Bacteroides, ↑ Catabacter, ↓ Prevotella, ↑ Lactobacillus, ↓ Faecalibacterium, ↑ Oscillospira, ↓ Bacteroides massiliensis, ↓ ↑ Bifidobacterium, ↑ Christensenella minuta, Bacteroides coprocola, ↓ ↑ Catabacter hongkongensis, Stoquefichus ↓ ↑ Lactobacillus mucosae, Blautia glucerasea, ↓ ↑ Ruminococcus bromii, Dorea longicatena, ↓ ↑ Papillibacter cinnamivorans ↓ Bacteroides dorei, ↓ Bacteroides plebeus, ↓ Prevotella copri, ↓ Coprococcus eutactus, Ruminococcus callidus ↓ Prevotellaceae Scheperjans et al., 2015 ↑ Akkermansia muciniphila Heintz-Buschart et al., 2018 Alzheimer’s disease Constipation, incontinence ↑ Akkermansia muciniphila Zhuang et al., 2018 ↑ Prevotella denticola ↓ Firmicutes and Vogt et al., 2017 ↑ Bifidobacterium Bacteroidetes ↑ Escherichia/Shigella (pro-inflammatory) Cattaneo et al., 2017 ↓ E. rectale (anti-inflammatory) Multiple sclerosis Constipation, diarrhea ↑ Methanobrevibacter Jangi et al., 2016 ↑ Akkermansia ↓ muciniphila Butyricimonas ↑ Akkermansia muciniphila, Berer et al., 2017 ↓ Faecalibacterium Cantarel et al., 2015 ↑ Akkermansia muciniphila, Cekanaviciute et al., ↑ Acinetobacter calcoaceticus 2017 ↓ Parabacteroides distasonis
Arrows indicate an increase or decrease in abundance of bacteria.
properties is therefore warranted in these models and in patients with neurological disorders that potentially express mutations in these and related synaptic genes.
Mucosa-Associated Microbial Dysbiosis In neurological disease, changes in mucus properties could additionally alter commensal microbial populations. Dysbiosis has been reported for the mucus-residing microbiome in patients with various neurological disorders including autism, Parkinson’s disease, Alzheimer’s disease, and multiple sclerosis (Table 2). Because dysbiosis can alter gut barrier function (i.e., via altering mucus thickness), this could contribute to disease progression. Microbial populations influence mucus hydration by releasing enzymes that modify mucus structural networks. Microbes release enzymes that degrade mucus, and this enzymatic cleavage of mucin complexes expands and hydrates the mucus 3dimensional structure. For example, increased release of mucindegrading enzymes due to an overgrowth of mucus- residing bacteria (such as Akkermansia muciniphila) increases mucus thickness and strengthens the protective mucosal barrier (Ottman et al., 2017). An additional effect of increasing mucus thickness may be reduced nutrient absorption. Such an increase could be beneficial (i.e., in the case of obesity) but detrimental in neurodegenerative diseases such as multiple sclerosis and Parkinson’s Disease (Cani, 2018).
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Autism Autism spectrum disorder is a neurodevelopmental disorder characterized by impaired social interactions and restrictive and repetitive behavior. In 2018, 1 in 59 children are diagnosed with autism in the United Status. GI dysfunction is a major comorbidity for autism patients (Kohane et al., 2012; Chaidez et al., 2014; McElhanon et al., 2014) and includes symptoms such as abdominal pain, diarrhea, constipation, and bloating. Altered levels of mucosa- associated bacterial species are reported in autism patients with GI dysfunction with Akkermansia muciniphila Dorea, Blautia, Sutterella Neisseria having decreased abundance, while mucosaassociated Clostridiales (Lachnospiraceae and Ruminococcaceae), Burkholderia, Ruminococcaceae, Lachnospiraceae, and Sutterella have increased abundance (Wang et al., 2011; Williams et al., 2011, 2012; Kushak et al., 2017; Luna et al., 2017). Parkinson’s Disease Parkinson’s disease (PD) is the second most common neurodegenerative disease observed in people over 60 years of age (de Lau and Breteler, 2006). In addition, PD is increasingly correlated with GI disorders prior to the onset of characteristic motor symptoms such as tremor and coordination of complex movement. Although the pathophysiology of PD remains unclear, the accumulation of α-synuclein appears to cause neuronal death (Kirik et al., 2002; Braak et al., 2003). Parkinson’s patients with colonic inflammation also showed α-synuclein deposits in their colon (Holmqvist et al., 2014). The mucosal biopsy samples of PD patients showed increased abundance of Akkermansia muciniphila, and Ralstonia, and a decrease in abundance of Faecalibacterium (Blautia, Coprococcus, Roseburia) and Prevotella (Keshavarzian et al., 2015; Scheperjans et al., 2015; Petrov et al., 2017; Heintz-Buschart et al., 2018). Alzheimer’s Disease Alzheimer’s disease is an increasingly prevalent neurodegenerative disease characterized by progressive cognitive decline and also reported to have comorbid GI dysfunction. Patients with Alzheimer’s disease who also had symptoms indicative of IBS showed dysbiosis involving increased abundance of mucolytic bacteria including Akkermansia muciniphila and Prevotella denticola (Zhuang et al., 2018). Similarly stool samples of Alzheimer patients examined for targeted bacteria showed an increase in the abundance of Escherichia/Shigella (pro-inflammatory taxa) and a decrease in abundance of E. rectale (anti-inflammatory taxa) (Cattaneo et al., 2017). Microbial dysbiosis in Alzheimer’s disease has been implicated in increasing gut permeability, which may influence systemic inflammation and impairment of the blood brain barrier (Vogt et al., 2017; Kowalski and Mulak, 2019). Multiple Sclerosis Multiple sclerosis involves an aberrant immune system that causes inflammation and results in demyelination in the central nervous system. Multiple studies in patients with multiple sclerosis have found increased abundance of mucosal bacteria including Akkermansia muciniphila, Methanobrevibacter, and Acinetobacter calcoaceticus and decreased abundance of Butyricimonas, Faecalibacterium, and Parabacteroides distasonis (Cantarel et al., 2015; Jangi et al., 2016; Berer et al., 2017; Cekanaviciute et al., 2017). Such alterations in the mucosal microbiome potentially favor the growth of pathogenic bacteria that alter the composition of the mucus layer and therefore may exacerbate core symptoms of these disorders (Camara-Lemarroy et al., 2018; Buscarinu et al., 2019)
CONCLUSION In summary, multiple pathways relevant to mucus homeostasis may be impacted by nervous system impairments in neurological disease. Furthermore, altered mucus properties could contribute to the widespread observations of microbial dysbiosis in autism, Parkinson’s Disease, Alzheimer’s Disease, and multiple sclerosis, and potentially exacerbate core symptoms. Overall, this review highlights that mucus properties could be impaired in neurological disease and provides new avenues for clinically relevant research into GI dysfunction in these disorders.
AUTHOR CONTRIBUTIONS All authors contributed to the design and drafting of the final manuscript.
FUNDING MH received a Melbourne University PhD Stipend. This work was supported by an Australian Research Council Future Fellowship (FT160100126) and an RMIT Vice Chancellor’s Senior Research Fellowship to EH-Y, which supported SH. JB received an NHMRC project grant (APP1158952).
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Minerva Access is the Institutional Repository of The University of Melbourne
Author/s: Maduwelthanne Herath Mudiyanselage, Madusani Herath
Title: Interactions of the enteric nervous system with the gut microbiome in the neuroligin-3 R451C mouse model of autism
Date: 2020
Persistent Link: http://hdl.handle.net/11343/258607
File Description: Final thesis file
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