Exclusive enteral nutrition therapy reduces Clostridia flagellins within the Crohn’s disease gut microbiome
Aleece Andrews
A thesis submitted for the degree of Master of Science
Department of Microbiology and Immunology
Dunedin
New Zealand Abstract
Crohn’s disease is a highly prevalent form of inflammatory bowel disease, with rapidly increasing incidence both within New Zealand, and around the world. Although Crohn’s disease aetiology is poorly understood, it has been linked to the host gut microbiome, alongside a range of host genetic and environmental factors. Temporary Crohn’s disease remission can be induced by exclusive enteral nutrition, which has previously been hypothesised to be effective through direct alteration of the gut microbiome. This treatment method avoids many of the substantial side effects associated with traditional Crohn’s disease treatments, such as corticosteroids. In this study, 16S rRNA and whole genome sequencing of stool samples, alongside measurement of stool short-chain fatty acid content, were used to measure the impact of exclusive enteral nutrition and partial enteral nutrition on the gut microbiomes of both adult Crohn’s disease patients and healthy controls. Exclusive enteral nutrition altered beta diversity and taxonomic composition, as well as functional capacity and short-chain fatty acid production, within both Crohn’s disease and healthy gut microbiomes. Perturbation of species belonging to the Clostridia class dominated the taxonomic changes observed both with Crohn’s disease, and during exclusive enteral nutrition. These changes were predominantly transient, with the gut microbiomes of both Crohn’s disease patients and healthy people largely rebounding after treatment. There were no changes to the gut microbiome observed during partial enteral nutrition, and no changes in alpha diversity observed during any form of enteral nutrition. Exclusive enteral nutrition reduced the capacity of the gut microbiome to produce flagella, through perturbation of species belonging to Clostridium cluster XIVa. Through analysis of the role played by commensal Clostridia class species in the guts of Crohn’s disease patients, a novel model of Crohn’s disease aetiology was developed, implicating Clostridium cluster XIVa flagellin proteins CBir1 and Fla-X. This model describes an inflammatory immune response to CBir1 and Fla-X present in the gut, where normal regulation by regulatory T cells is lost. This model may allow for the investigation of novel Crohn’s disease treatments.
I Acknowledgements
I would like to extend my most sincere thanks to my supervisor Dr Xochitl Morgan, both for her seemingly endless support and patience, and for the vast amount of knowledge she has imparted on me over the course of this process. I will always be thankful for this.
I would also like to thank the many other people who have contributed to this study, including Gerald Tannock and Cecilia Wang in the Department of Microbiology and Immunology, as well as our collaborators in the Gearry and Day research groups at the University of Otago, Christchurch.
I would further like to thank everyone in the Department of Microbiology and Immunology who has supported me through this study, particularly within the 5th floor, and the Morgan/Morales labs, as these people have made this process substantially more enjoyable.
II Table of contents
Abstract ...... I
Acknowledgements ...... II
Table of contents ...... III
List of figures ...... VI
List of tables ...... VII
List of abbreviations ...... VIII
1 Introduction ...... 1
1.1 Epidemiology of inflammatory bowel disease ...... 1
1.2 Clinical presentation of inflammatory bowel disease...... 1
1.3 Crohn’s disease associated risk factors ...... 2
1.3.1 Host genetic factors in Crohn’s disease ...... 2 1.3.2 Environmental factors in Crohn’s disease ...... 4
1.4 The gut microbiome in health and disease ...... 6
1.4.1 Gut microbiome function ...... 6 1.4.2 Crohn’s disease and the gut microbiome ...... 7
1.5 Regulation of immune responses ...... 9
1.5.1 Regulatory T cells in immunological tolerance ...... 9 1.5.2 Short-chain fatty acids modulate regulatory T cell differentiation ...... 9
1.6 Treatment of Crohn’s disease ...... 11
1.6.1 Traditional Crohn’s disease therapies...... 11 1.6.2 Exclusive enteral nutrition ...... 12
1.7 Microbial community analysis...... 14
1.7.1 Sequencing microbial communities ...... 14 1.7.2 Quantifying microbial community characteristics ...... 15
III 1.8 Study aims ...... 15
2 Methods ...... 17
2.1 Study design ...... 17
2.2 Stool sample data collection ...... 20
2.2.1 DNA sequencing of cohort stool samples ...... 20 2.2.2 Gas chromatography of cohort stool samples ...... 20
2.3 Bioinformatic analysis ...... 20
2.3.1 Taxonomic assignment of 16S rRNA gene sequences...... 21 2.3.2 Community alpha diversity analysis ...... 22 2.3.3 Community beta diversity analysis ...... 23 2.3.4 Taxonomic assignment of whole genome sequences ...... 25 2.3.5 Genetic pathway assignment within whole genome sequences ...... 26 2.3.6 Identification of differentially expressed community tags ...... 26
2.4 Quantification of total bacterial abundance ...... 27
3 Results ...... 28
3.1 Enteral nutrition stringency and duration affect completion ...... 28
3.2 Mock community standards confirm data reliability...... 28
3.3 Gut microbiome alpha diversity...... 29
3.3.1 Alpha diversity is reduced in Crohn’s disease patients ...... 29 3.3.2 Enteral nutrition does not change alpha diversity...... 31
3.4 Bacterial abundance does not change with enteral nutrition ...... 34
3.5 Gut microbiome beta diversity ...... 34
3.5.1 Healthy and Crohn’s disease gut microbiomes are distinguishable...... 34 3.5.2 The gut microbiome changes with exclusive enteral nutrition ...... 37
3.6 Exclusive enteral nutrition perturbs gut Clostridia ...... 42
3.7 Gut microbiome functionality ...... 45
3.7.1 Gut microbiome functionality changes with exclusive enteral nutrition ...... 45 3.7.2 Clostridia drive gut microbiome functionality changes ...... 48
IV 3.8 Gut acetate and butyrate are reduced during exclusive enteral nutrition 51
4 Discussion ...... 55
4.1 Exclusive enteral nutrition has greater effect than partial enteral nutrition 55
4.2 Exclusive enteral nutrition does not remediate drop in alpha diversity associated with Crohn’s disease ...... 55
4.3 Exclusive enteral nutrition alters gut microbiome composition ...... 57
4.4 Exclusive enteral nutrition alters gut microbiome taxonomy...... 58
4.5 Exclusive enteral nutrition alters gut microbiome functionality ...... 60
4.5.1 Exclusive enteral nutrition alters genetic pathway abundance ...... 60 4.5.2 Bacterial flagellin plays a key role in host inflammatory responses ...... 62
4.6 Clostridia class species alter gut microbiome functionality ...... 63
4.6.1 Clostridia class species account for genetic changes within the gut microbiome ...... 63 4.6.2 Clostridium cluster XIVa flagellin is associated with Crohn’s disease ...... 64
4.7 Butyrate production induces the tolerance of commensal Clostridia in the healthy gut ...... 66
4.8 Defining a potential Crohn’s disease model ...... 68
4.9 Exclusive enteral nutrition reduces the abundance of gut flagellated bacteria ...... 71
4.10 Potential for novel Crohn’s disease treatments ...... 72
4.11 Limitations of study ...... 73
4.12 Future directions ...... 74
5 References ...... 76
6 Appendices ...... 99
V List of figures
Figure Description Page
1 Timeline of EN treatment for EEN, PEN, and healthy control 19 groups
2 Alpha diversity is reduced in the gut microbiomes of CD patients 30 relative to healthy people
3 Disease, but not EN treatment, affects alpha diversity 33
4 Prior to EN treatment, the gut microbiomes of CD patients differ 36 from those of healthy people
5 EEN alters the gut microbiomes of CD patients 39
6 PEN does not observably alter the gut microbiomes of CD 40 patients
7 EEN alters the gut microbiomes of healthy people 41
8 EEN alters gut microbiome species abundance 44
9 EEN alters gut microbiome genetic pathway abundance 46
10 EEN alters gut microbiome species-associated genetic pathway 50 abundance
11 EEN alters the concentration of SCFAs present in the gut 53
12 A potential model of Crohn’s disease aetiology 70
S1 Sample rarefaction at a sequencing depth of 15,000 reads allows 101 for optimal alpha diversity measurement across samples
S2 Retention of reads through the dada2 ASV assignment pipeline 103
S3 16S rRNA sequencing and subsequent taxonomic assignment 104 reflects expected composition of mock bacterial communities
S4 CD does not observably alter total gut bacterial numbers 105
VI List of tables
Table Description Page
S1 Ensure Plus enteral formula ingredients 98-99
S2 Sample analysis meta-data 100
S3 Fit of DNA substitution models provided by the phangorn 102 function modelTest
S4 R2 values for multivariate analysis of variances performed by the 106 vegan function adonis
VII List of abbreviations
AIC Akaike information criterion
ASV Amplicon sequence variant
CARD15 Caspase recruitment domain-containing protein 15
CD Crohn’s disease
CI Confidence interval cLP Colonic lamina propria
CPM Counts per million
DC Dendritic cell
EEN Exclusive enteral nutrition
EN Enteral nutrition
GALT Gut-associated lymphoid tissue
GF Germ-free
GI Gastrointestinal
GLM Generalised linear model
HDAC Histone deacetylase
HKY model Hasegawa, Kishino, and Yano model
HKY + G model Hasegawa, Kishino, and Yano model, assuming gamma-distributed rate variation
HMP Human Microbiome Project
IBD Inflammatory bowel disease
IEC Intestinal epithelial cell
IFN-! Interferon-!
IL-10 Interleukin-10
IL-12 Interleukin-12
IL-17 Interleukin-17
KEGG Kyoto Encyclopaedia of Genes and Genomes
VIII KO Knock-out
MetaHIT project Metagenomics of the Human Intestinal Tract project mM Millimolar
NK cell Natural killer cell
NMDS Nonmetric multidimensional scaling
NOD2 Nucleotide-binding oligomerization domain- containing protein 2
OR Odds ratio
OTU Operational taxonomic unit
PAMP Pathogen-associated molecular pattern
PCR Polymerase chain reaction
PEN Partial enteral nutrition
PERMANOVA Permutational multivariate analysis of variance
PRR Pattern recognition receptor pTreg cell Peripheral regulatory T cell
RDP Ribosomal Database Project rRNA Ribosomal ribonucleic acid
RT-qPCR Real-time quantitative polymerase chain reaction
SCFA Short-chain fatty acid
SCID mouse Severe combined immunodeficient mouse
SEC Serological expression cloning
TH1 cell T-helper-1 cell
TH17 cell T-helper-17 cell
TMM Trimmed mean of M-values
TNF-" Tumour necrosis factor-"
Treg cell Regulatory T cell
UC Ulcerative colitis
IX WGS Whole-genome sequencing
WT Wildtype
X 1 Introduction
1.1 Epidemiology of inflammatory bowel disease
Crohn’s disease (CD) and ulcerative colitis (UC) are forms of inflammatory bowel disease (IBD), both characterised by chronic, relapsing inflammation of the gastrointestinal (GI) tract (Podolsky, 2002). Meta-analysis shows high rates of IBD across substantial parts of the Western world, with an observed prevalence greater than 135 CD patients and 198 UC patients per 100,000 people, and observed incidence rates greater than 6.38 CD patients and 7.71 UC patients per 100,000 person-years in these regions (Ng et al., 2018). Work by Gearry et al. (2006) demonstrated even higher IBD rates in Canterbury, New Zealand, with an observed prevalence if 155.2 CD patients and 145.0 UC patients in every 100,000 people, and an observed incidence rate of 16.5 CD patients and 7.6 UC patients per 100,000 person years.
These high IBD prevalence rates reflect a rapid increase in incidence over the course of the 20th century, particularly in the last 50 years (Kaplan and Ng, 2017). While determining the exact causes of this increase in incidence remains challenging, this does indicate an environmental component of IBD aetiology. It has been hypothesised that this increase in IBD incidence may be associated with some aspect of Westernisation of diet and environment (Kaplan and Ng, 2017). With increasing IBD prevalence, implementation of strategies and treatments to manage the increasing disease burden is imperative. As there is no treatment currently available that induces permanent IBD remission, development of further treatments may be a viable way of achieving this goal (Pillai et al., 2019).
1.2 Clinical presentation of inflammatory bowel disease
While exact IBD symptoms are dependent on both the severity and location of inflammation, these typically include chronic diarrhoea and abdominal pain (Hendrickson et al., 2002). Other symptoms may include fever, weight loss, osteoporosis, and stunted growth in children (Hendrickson et al., 2002). Symptoms typically go through periods of dormancy, with subsequent inflammatory flare-ups (Lewis et al., 2004). IBD has also been associated with an increased risk of colorectal cancer,
1 particularly in patients who have suffered from severe and prolonged inflammation (Terzic et al., 2010).
While both CD and UC display similar inflammatory symptoms, some variation in clinical presentation allows distinction. CD inflammation may affect any part of the GI tract, through all layers of the GI wall, while UC inflammation is limited to the inner wall layers of the colon and rectum (Lennard-Jones, 1989). The penetration of this ulceration into the gut wall disrupts epithelial barrier integrity, thus increasing intestinal permeability (Coskun, 2014). CD patients may also display skip lesions, where inflamed sections of the intestine are separated by healthy sections, while inflammation in UC patients is always continuous (Lennard-Jones, 1989). This variation in clinical presentation, along with variation in associated genetic risk factors, suggests variation in aetiology between CD and UC (Jostins et al., 2012). As such, the aetiology of these diseases should be considered in isolation. The focus of this study is exclusively on CD.
1.3 Crohn’s disease associated risk factors 1.3.1 Host genetic factors in Crohn’s disease
While the exact aetiology of CD is currently unknown, CD has been linked to a number of risk factors, both within the host and from the environment. Host factors linked to CD include host genetics, and the host’s gut microbiome (Jostins et al., 2012; Rogler and Hausmann, 2019). The greatest risk factor for developing CD is a family history of IBD. Gearry et al. (2010) demonstrated that 1 family member known to have had IBD significantly increased CD risk (CD odds ratio, OR: 3.06 [95% confidence interval, CI: 2.18 – 4.30]), and more than 1 family member known to have had IBD increased CD risk further (CD OR: 7.41 [95% CI: 3.40 – 16.14]). This may indicate a contribution of either genetic or environmental factors to CD pathogenesis, as family members typically share both.
The contribution of genetic influences to CD aetiology has been demonstrated through repeated investigation of CD concordance in twins. Multiple studies, across multiple populations, have demonstrated that monozygotic twins have a significantly higher rate of CD concordance than dizygotic twins (OR: 4.34 - 25) (Halfvarson et al., 2003; Ng et al., 2012; Spehlmann et al., 2008). Despite a large range of effect sizes, the significantly increased risk of CD concordance in monozygotic twins relative to dizygotic twins
2 exhibited in all these studies suggests that host genetics play a substantial role in CD aetiology, as twins typically share similar environments.
While it is known that host genetics plays a key role in CD aetiology, identifying specific genetic risk factors is challenging. CD has been linked to variation in over 140 different host genetic loci, 110 of which are shared with UC (Jostins et al., 2012). A meta-analysis of host CD risk loci conducted by Jostins et al. (2012) indicated that the most perturbed host processes in CD patients were the regulation of production of specific cytokines, as well as the activation of T-, B-, and natural killer (NK) cells. The cytokines with the most dysregulated production were interferon-γ (IFN-γ), interleukin-12 (IL-12), tumour necrosis factor-α (TNF-α), and interleukin-10 (IL-10) (Jostins et al., 2012). Interleukin- 17 (IL-17) production was also dysregulated (Jostins et al., 2012). However, Jostins et al. (2012) were unable to conclusively determine the effect that this dysregulation had on cytokine production.
Prior host genetic studies have suggested that CD inflammation is resultant from an aggravated response from T-helper-1 (TH1) and T-helper-17 (TH17) cells within the gut mucosal immune system, mediated by the IL-12/IFN-γ/TNF cytokine axis and IL-17 respectively (Shih and Targan, 2008). Jostins et al. (2012) concluded that the patterns observed in their meta-analysis support this hypothesis, and that this increased immune sensitivity would make an aggravated immune response more probable.
CD is also associated with polymorphisms in host genes associated with bacterial sensing. One gene of particular importance encodes nucleotide-binding oligomerization domain- containing protein 2 (NOD2), previously known as caspase recruitment domain- containing protein 15 (CARD15) (Hugot et al., 2001; Ogura et al., 2001). NOD2 belongs to the pattern recognition receptor (PRR) family, and responds to bacterial muramyl dipeptide (Girardin et al., 2003). As a member of the PRR family, NOD2 is known to be involved in the mediation of inflammatory cytokine release (Takeuchi and Akira, 2010). However, the exact role of NOD2 in CD aetiology is currently not well understood (Mukherjee et al., 2018). Despite this uncertainty, associations between polymorphisms in bacterial sensing genes and CD suggest the involvement of bacterial antigens in CD aetiology.
3 When viewed in concordance, studies investigating host genetic factors associated with
CD suggest links between an amplified TH1/TH17 immune response, host bacterial antigens, and CD aetiology. It is widely believed that this amplified immune response is to some component of the gut microbiome (Kostic et al., 2014). As such, environmental factors that alter the gut microbiome may be involved in CD aetiology.
1.3.2 Environmental factors in Crohn’s disease
As classical twin studies comparing monozygotic and dizygotic twins have been utilised to demonstrate a link between host genetics and CD, comparison of CD concordance in dizygotic twin and non-twin siblings suggests an environmental contribution to CD. Bengtson et al. (2010) demonstrated that, while both dizygotic twins and non-twin siblings have a substantially increased rate of CD concordance relative to that expected within the observed Norwegian cohort, this is higher in dizygotic twins (CD OR: 42.4 [95% CI: 29.6 – 55.2]) than non-twin siblings (CD OR: 22.7 [95% CI: 13.3 – 32.1]). As dizygotic twins and non-twin siblings have similar genetic concordances, this suggests that environment also contributes to CD risk. While non-twin siblings would frequently experience similar environments to each other, these would be less similar than those experienced by twins. This difference in environmental variation between twin and non- twin siblings is substantial enough to be detected in this work, and implicated in CD aetiology (Bengtson et al., 2010).
Numerous, wide-ranging environmental factors have been linked to increased CD risk. These include factors which directly alter the gut microbiome, such as antibiotic use at various stages of life. CD risk is increased both when patients have undergone antibiotic treatment before they reach 1 year of age (CD OR: 1.71 [95% CI: 1.41 – 2.08]), and when patients have used antibiotics within the past 2 – 5 years (CD OR: 1.32 [95% CI: 1.05 – 1.65]) (Card et al., 2004; Kronman et al., 2012). Alteration of the gut microbiome by these antibiotic treatments has been hypothesised as the mechanism by which CD risk is increased (Card et al., 2004; Kronman et al., 2012). Alteration of the gut microbiome through the proliferation of bacterial pathogens also increases CD risk, with prior infection by Salmonella (CD OR: 2.5 [95% CI: 1.0 – 6.3]), or Campylobacter (CD OR: 3.3 [95% CI: 1.6 – 7.0]), also observed as CD risk factors (Gradel et al., 2009; Schultz et al., 2017).
4 Environmental factors that alter the gut microbiome through less direct mechanisms have also been linked to increased CD risk. These include smoking (CD OR: 1.99 [95% CI: 1.48 – 2.68]), the use of oral contraceptives (CD OR: 1.85 [95% CI: 1.09 – 3.11]), and reduced plasma vitamin D levels (CD OR: 1.85 [95% CI: 1.01 – 3.33]) (Ananthakrishnan et al., 2012; Gearry et al., 2010). All of these factors have been independently linked to significant alterations of the gut microbiome, which are characteristic of CD-associated risk factors (Khalili et al., 2016; Lee et al., 2018; Waterhouse et al., 2018). While the mechanistic link between these environmental factors and the gut microbiome may not be immediately apparent, this may be through the host TH1/TH17 immune response. Both smoking and oral contraceptive use have been demonstrated to amplify TH1-mediated immune responses, while vitamin D has been demonstrated to suppress TH1-mediated immune responses through induction of regulatory T (Treg) cells (Barrat et al., 2002; de Heens et al., 2009; Khalili et al., 2016). As such, it may be possible for these risk factors to alter the gut microbiome through an amplified, or poorly regulated, host TH1/TH17 inflammatory immune response. This supports the previously described hypothesis that
CD is the result of a TH1/TH17 inflammatory immune response (Jostins et al., 2012; Shih and Targan, 2008).
The dramatic increase in CD incidence over the course of the 20th century further suggests that associated environmental changes play a significant role in CD pathogenesis. The hygiene hypothesis proposes that a combination of reduced infection rates, vaccination, and improved sanitation over time have resulted in loss of sensitivity to important immunomodulatory antigenic signals, thus contributing to the risk of diseases such as CD (Christen and von Herrath, 2005; Koloski et al., 2008). Beyond increased immune sensitivity, a lower microbial exposure over a person’s lifetime may directly influence the gut microbiome, thus also contributing to CD risk in this way (Koloski et al., 2008).
Dietary changes are another factor associated with both sociological development and increased CD risk. It has been proposed that a diet high in total fats predisposes a person to CD (CD OR: 2.86 [95% CI: 1.39 – 5.90]), while a diet high in fibre may have a protective effect (CD OR: 0.59 [95% CI: 0.39 – 0.90]) (Ananthakrishnan et al., 2013; Hou et al., 2011; Sakamoto et al., 2005). These dietary changes alter both the gut microbiome, and the presentation of dietary antigens at the gut. In this way, the spread of the modern “Western” diet may have significant influence over the observed increase in CD incidence.
5 Examination of the broad range of environmental CD risk factors gives insight into the mechanisms by which these may contribute to CD aetiology. This, in turn, supports the widely-held hypothesis that CD is the result of an inappropriate T cell-mediated immune response to the gut microbiome (Kostic et al., 2014).
1.4 The gut microbiome in health and disease 1.4.1 Gut microbiome function
The human microbiome is substantial, containing more than 1013 bacterial cells, similar to the number of human host cells (Sender et al., 2016). This vast community plays numerous important roles in health and disease, through both direct and indirect interactions with the human host. While all exposed surfaces of the human body are colonised by microorganisms, the majority of the human microbiome resides in the GI tract, with 70% of the total human microbiome residing in the colon alone (Jandhyala et al., 2015; Sekirov et al., 2010). This substantial microbial community is supported by both the large available surface area within the GI tract, and the nutrient-rich environment provided by food passing through the host gut (Helander and Fandriks, 2014).
Colonisation of the gut microbiome begins at birth, when the infant is exposed to the mother’s vaginal, faecal, and skin microbiome (Palmer et al., 2007). From this point, the human microbiome evolves over the course of the host’s life, modified by both endogenous and exogenous factors. These factors include host genetics and diet, among many others (David et al., 2014; Goodrich et al., 2014; Rothschild et al., 2018). This combination of selective pressures results in a normal microbiota that varies substantially between hosts, and between body sites on a single host (Falony et al., 2016). The majority of inter-host gut microbiome variability occurs at the genus and species level, with the gut microbiomes of most people dominated by the Bacteroidetes and Firmicutes phyla (Lozupone et al., 2012b).
Large-scale projects, such as the Human Microbiome Project (HMP) and the European MetaHIT (Metagenomics of the Human Intestinal Tract) project, have endeavoured to define the role of the human microbiome in health and disease through multi-“omic” approaches (Huttenhower et al., 2012; Qin et al., 2010). While there is still much work to be done defining mechanisms of interaction between the microbiome and the host,
6 substantial associations have been made between the human microbiome, both inside and outside the gut, and host physiology.
Within the gut, microbiome interactions with host mucosal epithelial and immune cells shape physiological processes such as digestion, energy homeostasis, and development of gut-associated lymphoid tissues (GALTs) (Sommer and Backhed, 2013; Tremaroli and Backhed, 2012). This can occur through extensive microbiome-host interaction across the substantial GI tract surface area (Helander and Fandriks, 2014). The normal gut microflora also has a protective effect, with competitive exclusion by the pre-existing microbes creating a physical barrier against incoming pathogens (Sekirov et al., 2010). Associations have also been made between the gut microbiome and numerous aspects of host physiology outside the gut, including changes in metabolism and behaviour (Sommer and Backhed, 2013).
In order for the gut microbiome to be beneficial to host health, community homeostasis must be maintained. While the healthy adult gut microbiome is believed to be highly resilient to disturbance, frequent perturbation may reduce its ability to recover to its normal state, which may lead to functional changes (Greenhalgh et al., 2016; Lozupone et al., 2012b). Both the balance in the composition of the gut microbiome, and the presence or absence of specific key species, are capable of affecting specific host responses (Rooks and Garrett, 2016). Through this, dysbiosis may cause disease in the host (Lyons and Coopersmith, 2017).
1.4.2 Crohn’s disease and the gut microbiome
It is well accepted that an inappropriate immune response at the host mucosa in response to the gut microbiome is a probable cause of CD (Kostic et al., 2014). This has been demonstrated through both strong correlative studies in humans, and direct links between the gut microbiome and CD in mice (Ewaschuk et al., 2006; Hviid et al., 2011; Schaubeck et al., 2016). However, the mechanism by which the gut microbiome adopts an irregular, pathogenic role in CD cases is not yet well understood.
Although there are substantial differences between the gut microbiomes of mice and humans, experimental mouse model systems provide us with significant insight into host- microbiota interactions (Kostic et al., 2013). More than 66 methods have been developed for emulating CD in mice, which are commonly used to test potential causative CD factors
7 (Mizoguchi, 2012). These methods include chemical induction, cell-transfer, congenial mutant, and genetically engineered models (Mizoguchi, 2012). However, regardless of the method used, CD-like ileitis cannot be induced in germ-free (GF) mice (Schaubeck et al., 2016). When the gut microbiota of wildtype (WT) mice exhibiting CD-like ileitis is transplanted into GF mice, these GF mice then develop CD-like ileitis (Schaubeck et al., 2016). This demonstrates a direct link between CD and the gut microbiome. This link was further demonstrated in the work of Kim et al. (2005), where monoassociation with the normally commensal species Enterococcus faecalis and Escherichia coli induced colitis in IL-10 knock-out (KO) mice, but not in WT mice. These colitis symptoms varied in severity with the utilised bacterial species, and did not occur in response to monoassociation with Pseudomonas fluorescens (Kim et al., 2005). This supports the role of an inappropriate host immune response to the gut microbiome in CD aetiology.
Links between CD and the gut microbiome are also supported in human-based studies, with antibiotic treatment capable of some reduction of CD symptoms (Ewaschuk et al., 2006). Antibiotics used for CD treatment are typically broad-spectrum, including metronidazole and ciprofloxacin (Bernstein, 2013). When considered with the increased CD risk associated with antibiotic use at different life stages, this supports the hypothesis that host microbiome dysbiosis contributes to CD, with antibiotic treatment able to remediate this in some cases (Card et al., 2004; Kronman et al., 2012).
While there is substantial variation between studies, the CD gut microbiome is broadly described as having reduced microbial diversity, reduced Firmicutes abundance, and increased Proteobacteria abundance, relative to that of healthy people (Manichanh et al., 2006; Qin et al., 2010). However, analysis of taxonomic changes with CD is complicated by the substantial gut microbiome variation between people, which makes it challenging to differentiate taxonomic variation related to disease from normal variation (Eckburg et al., 2005). As such, it has been speculated that it may be preferable to primarily investigate the composition, genetic capacity, and interactions within the gut microbiome, as opposed to focussing on individual species (Ley et al., 2008; Tringe et al., 2005). These measures give a broader view of community characteristics. In this way, both interpersonal variation and functional redundancy can be accounted for, and differences in the impact of the gut microbiome on the host can be more clearly assessed.
8 This impact can include direct host interaction with microbial antigens, and immune regulation by microbial metabolites.
1.5 Regulation of immune responses 1.5.1 Regulatory T cells in immunological tolerance
The link between a TH1/TH17 immune response and CD aetiology implies that CD is also
+ linked to Treg cell activity. Treg cells are a sub-population of CD4 T cells that express the transcription factor FoxP3, and are proposed to suppress an effector T cell response through the production of anti-inflammatory cytokines such as IL-10 (Hori et al., 2003;
Sakaguchi et al., 2001; Stewart et al., 2013). Treg cells have been heavily implicated in the tolerance of antigens both within the gut microbiome, and within the diet (Luu et al., 2017).
Kim et al. (2016) demonstrated that, while GF mice had normal numbers of total CD4+ T cells at the colonic lamina propria (cLP), Treg cells were significantly reduced here relative to non-GF mice. As such, it seems likely that antigens from the gut microbiome influence peripheral regulatory T (pTreg) cell differentiation in this area. This association is supported by the substantial accumulation of pTreg cells at the intestine, with none at other body sites (Geem et al., 2016).
Dysfunctional pTreg activity, resulting in an amplified effector T cell response, has been implicated in CD aetiology. This can be observed as a decreased number of pTreg cells, and an increased number of peripheral TH17 cells, in CD patients (Eastaff-Leung et al., 2010).
It has also been demonstrated that adoptive transfer of Treg cells into colitic mouse models reduces inflammation through downregulation of the TH17 response (Harrison et al., 2015).
1.5.2 Short-chain fatty acids modulate regulatory T cell differentiation
The close association of the gut microbiome with the intestinal epithelia allows substantial interaction with the host. Microbial metabolites, including short-chain fatty acids (SCFAs), contribute substantially to this interaction. SCFAs are products of bacterial fermentation, and influence the generation of Treg cells (Morrison and Preston, 2016). SCFAs that are found in particularly high concentrations in the intestinal tract include acetate, propionate, and butyrate (Morrison and Preston, 2016). In the proximal colon,
9 concentrations of SCFAs can range from 70 to 140 mM, while SCFA concentrations in the distal colon can range from 20 to 70 mM (Wong et al., 2006). SCFAs present in the gut are absorbed through the intestinal epithelial cells (IECs) (Sun et al., 2017).
In vitro treatment of dendritic cells (DCs) with butyrate, and to lesser extent propionate, increases DC expression of indoleamine 2,3-dioxygenase 1 and aldehyde dehydrogenase 1A2 (Gurav et al., 2015). The production of these enzymes, alongside other effects that
+ SCFAs exert on DCs, allow DCs a greater capacity to convert naïve T cells into FoxP3 Treg cells, and prevent conversion of naïve T cells into pro-inflammatory T cells (IFN-γ+ T cells) (Gurav et al., 2015).
It has been demonstrated that butyrate activation of the G protein coupled receptor GPR109a in both DCs and macrophages is necessary for maintaining balance between pro- and anti-inflammatory CD4+ T cells (Singh et al., 2014). Both macrophages and DCs incubated with butyrate have shown increases in expression of both Il10 and Aldh1a1, which assist in the differentiation of naïve T cells to Treg cells (Singh et al., 2014). GPR109a KO mice are also more susceptible to colitis and intestinal inflammation (Singh et al., 2014).
SCFAs are also capable of inducing differentiation to Treg cells through direct interaction with naïve T cells (Correa-Oliveira et al., 2016). It has been shown that butyrate induces the differentiation of naïve T cells to pTreg cells both in vitro and in vivo (Arpaia et al., 2013). The mechanism proposed for this SCFA action was through the inhibition of histone deacetylases (HDACs) (Arpaia et al., 2013). The inhibition of HDACs results in enhanced histone H3 acetylation of the FoxP3 locus, thus increasing the expression of this transcription factor (Furusawa et al., 2013). This may then allow differentiation to pTreg cells. By this mechanism, it has been hypothesised that butyrate produced by commensal
Clostridia in the colon induces the differentiation of local pTreg cells, thus reducing inflammatory T cell responses (Furusawa et al., 2013).
The involvement of gut microbiome-produced SCFAs in Treg differentiation, and therefore the regulation of immune responses at the gut, is likely to be related to the uncontrolled immune responses seen in CD patients (Kim et al., 2014). This can be seen in the work of Smith et al. (2013), which demonstrated that SCFA activation of the G protein coupled receptor GPR43, expressed on colonic Treg cells, protected against induced colitis in mice.
10 In this way, a link between gut microbiome SCFA production and CD can be observed. However, to best understand the role of the gut microbiome in CD, other methods of controlling CD inflammations and altering the gut microbiome must first be understood.
1.6 Treatment of Crohn’s disease 1.6.1 Traditional Crohn’s disease therapies
As there is no permanent cure for CD currently available, the primary goals of CD treatment are to induce remission, minimise symptoms, and prevent flare-ups (Feldman et al., 2007). In mild CD cases, mesalamines including mesalazine and sulfasalazine are sometimes used to reduce inflammation, although there is little evidence that these are more effective than placebos (Akobeng and Gardener, 2005). In more severe CD cases, inflammation is reduced using corticosteroids including prednisone, budesonide, and hydrocortisone acetate (Cronin, 2010; Farrell et al., 2003; Greenberg et al., 1994). The observed efficacy of corticosteroid treatment of Crohn’s disease inflammation varies between studies and dosages, with prednisone inducing remission in 60%-92% of active CD patients, and budesonide inducing remission in 47%-69% of active CD patients (Malchow et al., 1984; Mowat et al., 2011; Rezaie et al., 2015; Summers et al., 1979). However, only 44% of CD patients demonstrate prolonged remission 1 year after steroid treatment, while 36% exhibit steroid dependency and 20% exhibit steroid resistance (Munkholm et al., 1994). Corticosteroids prescribed for CD patients can also have significant side effects, including high blood pressure, high blood sugar, osteoporosis, cataracts, a weakened immune system, and Cushing’s syndrome (Barnes and Adcock, 2009; Manson et al., 2009). Because of this, corticosteroids are ideally only prescribed for short periods of time, to treat flare-ups, but frequent patient dependence makes this challenging (Feldman et al., 2007).
Where CD does not respond to corticosteroids, immunomodulators such as methotrexate and azathioprine, or anti-TNF agents such as infliximab and adalimumab, are commonly utilized (Colombel et al., 2010; Colombel et al., 2007; Patel et al., 2014). Methotrexate induces remission in 39%-65% of active CD patients, infliximab induces remission in up to 81% of active CD patients, and adalimumab induces remission in 21%-30% of active CD patients (Feagan et al., 2000; Feagan et al., 1995; Hanauer et al., 2002; Hanauer et al., 2006; Sandborn et al., 2007). However, these can also have significant side effects, with
11 prolonged use of immunomodulators increasing risk of opportunistic infections, and other biologic agents, such as infliximab, losing efficacy with prolonged treatment due to antibody development (Fuentes et al., 2003; Ruemmele et al., 2009). Infliximab treatment is also associated with a risk of local reactions, anaphylaxis, and vasculitis, as well as a small risk of lymphomas (Kandiel et al., 2005; Ruemmele et al., 2009).
Despite some treatments being available, approximately 75% of CD patients will require surgery at some point in their lives (Simillis et al., 2008). This surgery does not provide a cure, but may be required to correct CD complications, or to control symptoms that have remained uncontrollable by other treatment methods. In cases where portions of the intestine have become damaged due to severe inflammation, these may be removed by resection (Simillis et al., 2008). Surgical drainage procedures may also be used to treat abscesses around the anus (Faucheron et al., 1996). However, even with surgical treatment, most patients will eventually relapse, with reoperation rates ranging from 40% to 80% of patients (Bernell et al., 2000; Olaison et al., 1992). The inefficiency and substantial negative side effects of current CD treatments mean that an improved treatment method is required.
1.6.2 Exclusive enteral nutrition
In children, exclusive enteral nutrition (EEN) is commonly used as a CD treatment (Day and Lopez, 2015). EEN is a form of therapeutic nutrition, in which the patient’s diet consists of only the same complete food preparation, usually a liquid enteral formula (Shah and Kellermayer, 2014). Liquid EEN is normally either an elemental or a polymeric formula, with protein in the elemental formula in the form of individual amino acids, and with intact protein present in the polymeric formula (Shah and Kellermayer, 2014).
EEN tends to be safer for patients than other, traditional CD treatments, such as immunomodulators or corticosteroids (Kansal et al., 2013; Manson et al., 2009). It is effective at inducing remission in approximately 85% of paediatric CD cases, and meta- analysis shows that in these paediatric cases EEN is as effective as corticosteroid treatment (Day and Lopez, 2015; Heuschkel et al., 2000). EEN has also been shown to be associated with mucosal healing, while steroid treatments have not (Berni Canani et al., 2006; Borrelli et al., 2006; Rubio et al., 2011). This may mean that EEN could assist in the healing of mucosal damage resultant from CD, but this has not yet been conclusively
12 demonstrated, as patients in these studies frequently received other CD medications alongside EEN. Small cohort studies have demonstrated that rates of CD relapse after EEN are comparable to more traditional CD treatments, such as corticosteroids and anti-TNF agents, with 40%-60% of treated CD patients relapsing within a year of EEN cessation, meaning EEN is not a permanent cure (Duncan et al., 2014; Knight et al., 2005; Takagi et al., 2006).
While EEN has been demonstrated to be very effective at inducing remission in paediatric CD cases, there has been less evidence of this efficacy in adult patients (Ashton et al., 2019). Meta-analysis suggests that this is unlikely to be due to a differing treatment mechanism in adults, but rather due to low adult treatment compliance, due to poor formula taste (Wall et al., 2013). In paediatric CD cases, compliance to the full EEN treatment has been demonstrated to range from 56%-93% of participants (Gavin et al., 2005; Whitten et al., 2012). In a meta-analysis of EEN treatment studies on adult CD patients, full treatment compliance ranged as low as 59% of participants (Wall et al., 2013). Despite this, when adhered to, EEN appears to also be as effective in adult CD patients as corticosteroids, and thus may be a viable option for treatment (Wall et al., 2013). As such, while EEN has not been globally adopted as a first-line treatment for adult CD patients, it is routinely recommended by Japanese clinicians (Takagi et al., 2006).
While initial findings on the efficacy of EEN are generally positive, there is still much to be learned regarding best practices for its administration. This includes determining efficacy of enteral nutrition as an exclusive diet, compared to as part of a slightly more inclusive diet, or partial enteral nutrition (PEN), which allows the consumption of some foods other than the enteral nutrition (EN) formula (Shah and Kellermayer, 2014). Further knowledge of the EN mechanism of action would assist in this optimisation.
Several small case studies have indicated that EEN alters the gut microbiome of CD patients (Leach et al., 2008; Lionetti et al., 2005). However, despite these studies, no single taxon has been conclusively linked to CD, or EEN treatment (Shah and Kellermayer, 2014). Beyond altering an imbalanced gut microbiome in CD patients, paediatric studies have also shown that EEN treatment can downregulate the production of pro- inflammatory cytokines such as TNF-α, IL-1, and IL-6, which are upregulated during CD (Fell et al., 2000; Podolsky, 2002). EEN has also been demonstrated to be associated with
13 mucosal healing, through reduction of permeability in the dysfunctional intestinal epithelia of CD patients (Nahidi et al., 2014; Shah and Kellermayer, 2014).
The mechanism by which EEN is therapeutic in CD cases is not well understood. There have been several hypotheses posed as to how EEN exerts this therapeutic response, including general nutritional repletion, or reduction in inflammatory by-products produced by the provision of complex dietary fats or carbohydrates (Critch et al., 2012; Nahidi et al., 2014). It has also been hypothesised that the EEN therapeutic response is due to a reduction in exposure to antigens, or an improvement in intestinal barrier function (Critch et al., 2012; Nahidi et al., 2014).
1.7 Microbial community analysis 1.7.1 Sequencing microbial communities
As it is impossible to apply in vitro cultivation techniques to many microbial communities, including the gut microbiome, modern studies investigating microbial communities typically utilize DNA sequencing techniques (Amann et al., 1995). These DNA-based analyses have revolutionised the study of microbial communities in numerous environments, and have generated vast databases which may be utilised to give information on numerous microbial species (DeSantis et al., 2006; Quast et al., 2013).
The most common method for observing the composition of microbial communities involves sequencing the ubiquitous gene encoding 16S ribosomal ribonucleic acid (rRNA), an integral component of the prokaryotic 30S ribosomal subunit, within extracted community DNA (Gabashvili et al., 2000; Janda and Abbott, 2007; Yusupov et al., 2001). The ubiquity of the gene encoding 16S rRNA in bacteria and archaea, combined with the presence of both hyper-variable and hyper-conserved regions, make this gene an ideal identifier of bacterial species (Janda and Abbott, 2007; Srinivasan et al., 2015). Here, the hyper-conserved regions allow for universal 16S rRNA gene sequence amplification through polymerase chain reaction (PCR), as this allows PCR primers to bind across bacterial and archaeal species. Subsequently, analysis of the hyper-variable regions by comparison to vast sequence databases allows for taxonomic identification (McDonald et al., 2012; Yilmaz et al., 2014). This taxonomic identification can typically be achieved to the genus level, and sometimes to the species level (Janda and Abbott, 2007).
14 An alternative method for observing the composition of microbial community samples is through whole-genome shotgun sequencing (WGS). Here, the whole genomes of microbes present in community samples are sequenced, as opposed to the 16S rRNA gene in isolation (Tyson et al., 2004). While using shotgun metagenomics is substantially more expensive than sequencing only the 16S rRNA gene, it has several advantages over 16S rRNA gene sequencing (Ranjan et al., 2016). Unlike 16S rRNA gene sequencing, shotgun sequencing can detect both fungi and viruses in a microbial community, as well as bacteria (Ranjan et al., 2016). WGS can also be used to directly analyse the presence of specific functional pathways in a community, whereas this information must be implied from the taxonomic profile when using 16S rRNA gene sequencing (Langille et al., 2013).
Prior to sequencing of microbial communities, these communities must first be sampled. In gut microbiome analysis, the microbial community present in stool samples is commonly sequenced (Panek et al., 2018). This allows for non-invasive observation of gut microbiome composition.
1.7.2 Quantifying microbial community characteristics
When comparing human microbiome samples, there is a huge degree of species-level inter-host variation between microbiomes associated with healthy phenotypes (Lozupone et al., 2012b). This implies that many host responses to the gut microbiome are likely to be linked to the variable functions that bacteria within the gut microbiome perform, as opposed to being directly linked to bacterial species. As such, it seems most appropriate in microbiome studies to analyse changes in community composition and functionality, as opposed to changes in the abundance of individual species. This can include analysis of the ecological properties of the community, such as alpha and beta diversity, as well as genetic functions within the community.
1.8 Study aims
The primary aim of this study is to determine how treatment with EN alters the gut microbiome of adult CD patients. This will include analysis of changes to the ecological properties of the gut microbiome, including changes in both alpha and beta diversity, as well as changes in the abundances of individual taxa. This will test the hypotheses that EN treatment alters the composition of the gut microbiomes of CD patients, and that these gut microbiomes become more like those of healthy people.
15 Changes in genetic functions available within the community samples will also be analysed. This will test the hypotheses that EN treatment alters the functionality of the gut microbiomes of CD patients within their environment, and that the role of these gut microbiomes within the host become more like those of healthy people.
Changes in SCFA amounts present within the community samples will also be analysed. This will test the hypotheses that EN treatment alters SCFA production by the gut microbiomes of CD patients, and that this production becomes more like that of the gut microbiomes of healthy people.
Completing these studies may help define the mechanism by which EN treatment is effective in treating CD. Through this, valuable information may be gained regarding CD aetiology, and how best to treat it.
16 2 Methods
2.1 Study design
The goal of this study was to observe changes to the gut microbiomes of both CD patients and healthy controls that occur over the course of EN treatment. In order to do this, stool samples were collected over EN treatment, from which the microbiome was sequenced and SCFA content was measured.
A cohort of CD patients and healthy controls was recruited and stool samples were collected by the Gearry and Day research groups, at the University of Otago Christchurch. Samples were stored at -80 ℃ until DNA extraction. CD patients aged 16 to 40 years old, with active CD involving the ileum, were included in the cohort. CD patients were excluded if they had taken corticosteroids in the past fortnight, although use of other CD medications was permitted. Patient clinical details, such as participant disease activity and faecal calprotectin concentration, were not available at the time of analysis.
Prior to starting EN treatment, the baseline energy requirements of each patient were calculated, to establish the required daily intake of the EN formula. The prescribed volume of formula was progressively increased over the first 3 days of EN treatment, until the required daily intake was reached. CD patients were divided into 2 treatment groups, those who underwent EEN, and those who underwent PEN. Patients in the EEN group (n = 21) were only permitted to consume Ensure Plus® enteral formula and water over an 8 week treatment period. Ensure Plus enteral formula is a high-energy (1.5 kcal/mL), nutritionally-complete oral preparation produced by Abbott Laboratories, that is suitable as a sole nutritional source. The primary ingredients of the Ensure Plus enteral formula are milk proteins and soy proteins (Table S1). Stool samples were collected from patients in the EEN group at the start of treatment, then every 2 weeks until the end of treatment (week 8), then again after treatment at weeks 12 and 24. This sampling timeline is shown in Figure 1.
Patients in the PEN group (n = 9) were only permitted to consume Ensure Plus enteral formula and water for the first 2 weeks of their 8-week EN treatment time. For the remaining 6 weeks of EN treatment, patients in the PEN group were permitted to consume other foods alongside the Ensure Plus enteral formula. Patients undergoing PEN
17 treatment were permitted to consume one small meal of solid food per day, which could not contain more than 33% of total daily calories. Stool samples were collected from patients in the PEN group at the start of treatment, then every 2 weeks until the end of treatment (week 8), then again after treatment at weeks 12 and 24 (Figure 1).
All healthy controls (n = 17) underwent EEN for 2 weeks. Here, they were only permitted to consume Ensure Plus enteral formula and water for this 2 week treatment period. Stool samples were collected from patients in the healthy control group at the start of treatment, then again at the end of treatment (week 2), then again after treatment at week 6 (Figure 1).
18
Figure 1. Timeline of EN treatment for EEN, PEN, and healthy control groups. Treatments and follow-up stages are represented by timeline colours. Stool sampling timepoints are represented by diamonds. Jumps in time are represented by jagged portions of the timeline.
19 2.2 Stool sample data collection 2.2.1 DNA sequencing of cohort stool samples
Both 16S rRNA gene sequencing and WGS were performed on microbial community DNA extracted from subject stool samples over the course of EN treatment. DNA was extracted from all tested stool samples by Cecilia Wang, at the University of Otago. All stool samples were stored at -80 ℃, then freeze-dried prior to DNA extraction. DNA was extracted from approximately 0.1 g of wet stool sample, weighed to allow for normalisation by both wet and dry weight, using a DNeasy PowerLyzer PowerSoil Kit as specified by the manufacturers (QIAGEN). All stool samples (Table S2) underwent sequencing of the V4 hypervariable region of the 16S rRNA gene at Argonne National Laboratories (Chicago, USA) using 2 x 250 base pair (bp) read length sequencing on Illumina MiSeq, as described by Caporaso et al. (2012). This sequencing run included 3 ZymoBIOMICS Microbial Community DNA Standards (Catalogue No. D6305). A subset of 90 stool samples from subjects in the EEN and healthy control groups collected over the course of EN treatment underwent WGS (Table S2) by Custom Science. Samples were sequenced using 2 x 150 base pair (bp) paired-end reads on Illumina HiSeq.
2.2.2 Gas chromatography of cohort stool samples
The same subset of 90 EEN and healthy control stool samples that underwent WGS (Table S2) also underwent SCFA content measurement by gas chromatography. Gas chromatography of stool samples was performed by the Tannock laboratory, at the University of Otago. SCFA content measurement by gas chromatography was conducted as described by Centanni et al. (2019). SCFA content was measured in approximately 0.25 g of wet stool sample, weighed to allow for normalisation by wet weight. Briefly, samples were derivatised, and extracted SCFA derivatives were detected and quantified using a TRACE 1300 gas chromatograph and a Chromeleon 7 chromatography data system.
2.3 Bioinformatic analysis
Bioinformatic analysis was performed on sample 16S rRNA and whole-genome sequences. All 16S rRNA gene sequences were processed and analysed exclusively in R, while all whole-genome sequences were processed using HUMAnN2 prior to R import (Franzosa et al., 2018). All R-based bioinformatic scripts used in this study are available at: https://gitlab.com/morganlab/students/aleece/tree/master/Code
20 2.3.1 Taxonomic assignment of 16S rRNA gene sequences
The dada2 R package was used to assign amplicon sequence variants (ASVs) to 16S rRNA gene reads across all samples (Callahan et al., 2016). ASVs are similar to operational taxonomic units (OTUs), except ASVs may be resolved exactly, to the level of single- nucleotide differences (Callahan et al., 2017). Taxonomy was then assigned to these ASVs.
16S rRNA gene sequences were provided as forward and reverse FASTQ files. To merge forward and reverse read FASTQ files with low error rates, the low-quality read tails must first be trimmed off. To determine the optimal point to truncate both the forward and reverse reads, the dada2 function plotQualityProfile was used to visualise these reads, for all samples. Here, median sequence nucleobase Phred quality scores were plotted against nucleobase position. The dada2 function filterAndTrim was then used to truncate reads where Phred quality score decreased rapidly. All forward reads were truncated at position 230, with the last 20 nucleobases lost, while all reverse reads were truncated at position 160, with the last 90 nucleobases lost. The filterAndTrim function was also used to filter out low-quality reads. The maximum number of “expected errors”, based on Phred quality score, allowed for a read to pass filtration was 2 each for the forward and reverse reads (Edgar and Flyvbjerg, 2015). Reads were also required to have an assigned nucleobase for every position to pass the filtration step.
All reads within sample FASTQ files were dereplicated, with all identical sequencing reads combined into “unique sequences” with a corresponding abundance. The dada2 function dada was then applied to the dereplicated reads. The dada function is the core sample inference algorithm for dada2, denoising the dereplicated reads to infer “true” ASVs. The dada2 function mergePairs was then used to align and merge the forward and reverse reads to give full sequence contigs. Merged sequences were only output where there was an overlap of at least 12 identical bases between the forward and reverse reads. From these contigs, the dada2 function makeSequenceTable was used to construct an ASV table. All sequences outside of the expected sequence length of 250 – 256 nucleotides were removed. The dada2 function removeBimeraDenovo was then used to remove chimeric sequences. The removeBimeraDenovo function achieves this through recognition of sequences that can be easily constructed from 2 more abundant
21 contributing sequences. The total number of reads passing each stage of the dada2 pipeline to this point was tracked in 6 samples.
The dada2 functions assignTaxonomy and addSpecies were then used to assign genus- and, where possible, species-level taxonomy to the ASVs using the Ribosomal Database Project (RDP) naïve Bayesian classifier method (Wang et al., 2007). Both the assignTaxonomy and addSpecies functions assigned taxonomy based on the SILVA 132 rRNA reference database. The ASV and taxonomic tables produced with dada2, along with all sample metadata, were imported for further analysis with the phyloseq R package (McMurdie and Holmes, 2013).
To determine the reliability of dada2 taxonomic assignment, Spearman’s rank-order correlation was calculated between expected and observed relative species abundances in each of the 3 sequenced ZymoBIOMICS Microbial Community DNA Standards, based on relative abundances defined by the manufacturers (Zymo Research).
2.3.2 Community alpha diversity analysis 2.3.2.1 Alpha diversity metrics
Alpha diversity of 16S rRNA data from stool samples was quantified by observed species richness, Shannon index, and Gini-Simpson index. The species richness of a community is defined as the number of species present in that community (Lande, 1996). Shannon information quantifies entropy, or the degree of uncertainty, within a population, and can therefore be used to quantify community richness based on relative species abundance (Lande, 1996; Shannon and Weaver, 1949). The Simpson concentration, &, is the probability that 2 individuals chosen randomly from a given population belong to the same group (Simpson, 1949). Therefore, 1 − &, otherwise known as the Gini coefficient or Gini-Simpson index, is the probability that 2 individuals randomly chosen from a population belong to different groups, which may also be used as a measure of ecological diversity (Pielou, 1969).
2.3.2.2 Rarefaction curve construction
For observed species richness to be comparable between samples, all samples must first be rarefied to the same sequencing depth. To determine the optimal sequencing depth for sample rarefaction, a rarefaction curve was constructed. The 16S rRNA data from each stool sample were randomly rarefied to a sequencing depth of 1, 10, 100, 1000, and then
22 every 1000 reads until a depth of 100,000 reads was reached. This rarefaction sequence was repeated 10 times per sample. The phyloseq function estimate_richness was used to calculate the observed species richness for all sample rarefactions. The observed species richness for each rarefaction was plotted against tested rarefaction depth (Figure S1). This rarefaction curve was analysed to determine optimal rarefaction depth, balancing reliable observation of species richness with minimal loss of samples. The optimal rarefaction depth for the tested samples was determined to be 15,000 reads.
2.3.2.3 Comparison of community alpha diversity
Alpha diversities within communities were compared across both disease state and EN treatment time. These comparisons were conducted for each of the observed species richness, Shannon index, and Gini-Simpson index measures. All samples in the 16S rRNA class phyloseq object were rarefied to an even sequencing depth of 15,000 reads. The estimate_richness function was then used to calculate observed species richness from the rarefied data set, and Shannon and Gini-Simpson indices from the unrarefied data set, for each sample. Wilcoxon rank sum tests were performed comparing the alpha diversity indices of all samples collected before EN treatment between CD patients and healthy controls (Mann and Whitney, 1947).
Samples were grouped by whether they belonged to the EEN, PEN, or healthy control groups, as well as by whether they were collected before, during, or after EN treatment. Within each sample time point, a Kruskal-Wallis test was performed, comparing groups (Kruskal and Wallis, 1952). Where the Kruskal-Wallis test gave a significant result, pairwise Wilcoxon rank sum tests with Benjamini-Hochberg correction were performed, to directly compare groups (Benjamini and Hochberg, 1995; Mann and Whitney, 1947). Alpha diversities were also compared within group, across time points. Here, identical Kruskal-Wallis tests and a post-hoc pairwise Wilcoxon rank sum test with Benjamini- Hochberg correction were used.
2.3.3 Community beta diversity analysis 2.3.3.1 Beta diversity metrics
Beta diversity between stool samples was quantified in 16S rRNA data by Bray-Curtis dissimilarity, and both weighted and unweighted UniFrac distance. Bray-Curtis dissimilarity quantifies differences between communities based on taxonomic
23 composition, but not the phylogenetic relatedness of observed taxa (Bray and Curtis, 1957). UniFrac distance is another measure of relatedness between communities, but unlike Bray-Curtis dissimilarity, UniFrac distance accounts for the phylogenetic relatedness of species between the given communities (Lozupone and Knight, 2005). This means that closely related taxa between communities will reduce the UniFrac distance between these communities (Lozupone and Knight, 2005). Unweighted UniFrac distance is a qualitative measure, which only considers the presence or absence of species in samples when computing relatedness, while weighted UniFrac distance is a quantitative measure, considering the abundance of observed organisms, as well as their presence or absence (Lozupone and Knight, 2005; Lozupone et al., 2007).
2.3.3.2 Phylogenetic tree construction
Computation of both weighted and unweighted UniFrac distances requires the construction of a phylogenetic tree, to provide information on the phylogenetic relatedness of all species observed in samples. The DECIPHER R package function AlignSeqs was used to align sample DNA sequences (Wright, 2016). The phangorn R package function modelTest was used to determine which DNA substitution model best fits the aligned DNA sequences, to allow for distance between DNA sequences to be computed using maximum likelihood methodology (Table S3) (Felsenstein, 1981; Schliep, 2011). The optimal DNA substitution model was determined based on Akaike information criterion (AIC), with a lower AIC value indicating better model fit, and therefore an optimal substitution model for the available DNA sequences (Akaike, 1974). Based on this analysis, the optimal tested DNA substitution model was determined to be the Hasegawa, Kishino, and Yano (HKY) model, assuming gamma-distributed rate variation (“HKY + G” model) (Hasegawa et al., 1985).
The phangorn function bootstrap.pml was used to bootstrap the “HKY + G” model phylogenetic tree 100 times, with bootstrapped support values representing the frequency of re-observation of the same phylogenetic branch. The phangorn function plotBS was then used to create a “HKY + G” model phylogenetic tree, containing branches with bootstrapped support values greater than 80%. This bootstrapped phylogenetic tree was then imported with phyloseq for further analysis.
24 2.3.3.3 Comparison of community beta diversity clusters
The relationships between disease state, EN treatment, time and stool sample beta diversity were quantified using Kruskal’s nonmetric multidimensional scaling (NMDS) (Kruskal, 1964). Stool sample 16S rRNA data were subset by whether samples were collected before EN treatment, and whether they were collected from the EEN, PEN, or healthy control groups. The vegan R package function metaMDS was used to independently perform NMDS with 20 random starting configurations on the subset of samples collected before EN treatment, and the subsets of samples collected from the EEN, PEN, and healthy control groups (Oksanen et al., 2018). NMDS was utilised with samples compared by Bray-Curtis dissimilarity, weighted UniFrac distance, and unweighted UniFrac distance. The starting configuration that returned the lowest stress response was used for further analysis with all measures, to minimise distortion of data. Samples within the pre-treatment subset were clustered by whether they were collected from CD patients or healthy people, and samples within the EEN, PEN, and healthy control subsets were clustered by whether they were collected before, during, or after EN treatment.
The vegan functions betadisper and permutest were conjointly used to perform permutation tests on cluster dispersion homogeneity, with 999 permutations, within all tested sample NMDS subsets. The vegan permutational multivariate analysis of variance (PERMANOVA) function adonis was then used, with 999 permutations, to determine whether clustering of samples was significant (p < 0.05) within all tested NMDS subsets. Where clustering by samples collection time point was significant within the EEN, PEN, and healthy control subsets, pairwise PERMANOVA with 999 permutations and Benjamini-Hochberg correction was used to directly compare groups.
2.3.4 Taxonomic assignment of whole genome sequences
The BBMap program function demuxbyname was used to demultiplex all WGS reads into separate files, based on each sample’s unique sequence barcode (Bushnell, 2014). MetaPhlAn2 was then used to assign taxonomy to all WGS samples (Segata et al., 2012). Here, sample FASTQ files are input, and a list of detected microbes with their relative abundances are returned. MetaPhlaAn2 assigns taxonomy by the recognition of unique clade-specific microbial marker genes.
25 2.3.5 Genetic pathway assignment within whole genome sequences
The pipeline HUMAnN2 was used to determine the abundance of genetic pathways within each microbial community sample (Franzosa et al., 2018). This includes the total abundance of pathways across all species in a community, and the abundance of pathways within attributable species. HUMAnN2 was used to identify UniRef50 genes present in sample reads, which were then associated with specific organisms based on the MetaPhlAn2 taxonomic profiles. HUMAnN2 was then used to group identified UniRef50 genes into genetic pathways. These UniRef50 pathway names were then translated to Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway names.
2.3.6 Identification of differentially expressed community tags
The R package edgeR was used to analyse changes in the abundance of both individual bacterial species and individual KEGG pathways over the course of EN treatment (Robinson et al., 2010). Samples were grouped based on whether they were collected from the EEN or healthy control groups, and whether they were collected before, during, or after EN treatment. All tags, here defined as either the bacterial species or KEGG pathway to be investigated, were filtered out where not present at a minimum of 1 count per million (CPM) in at least 6 of the samples in each group. Normalisation factors were calculated using the trimmed mean of M-values (TMM) method, and raw library sizes were scaled accordingly (Robinson and Oshlack, 2010). Negative binomial, and adjusted profile log-likelihoods were maximised, and estimates of common, trended and tagwise dispersions were given across all tags. Utilising both scaled tag counts and estimated dispersions, negative binomial generalised linear models (GLMs) were fit for each tag. All tags that had a fold-change less than 1.5 were then removed. The edgeR function decideTestsDGE was then used, with Benjamini-Hochberg correction, to identify tags that were differentially expressed either across time points, or between CD patients and healthy controls pre-treatment (Benjamini and Hochberg, 1995). Differential expression was determined to be significant where P-value was below an alpha level of 0.05.
Identified KEGG pathway tags were grouped by whether they represented the total abundance of a pathway across all species in which it was detected, or the abundance of a pathway in a single species. In all cases where total abundance of a KEGG pathway
26 across all species was significantly differentially expressed, cases of significant differential expression of this same pathway in individual species were identified.
2.4 Quantification of total bacterial abundance
Bacterial 16S rRNA gene copy number in participant stool samples was quantified using real-time quantitative polymerase chain reaction (RT-qPCR). DNA was extracted from a subset of 36 wet stool samples, randomly selected from subjects in the EEN and healthy control groups over the course of EN treatment (Table S2). DNA was extracted from 0.25 g of weighed stool samples using a DNeasy PowerLyzer PowerSoil Kit, as specified by the manufacturers (QIAGEN). The concentration of DNA samples was determined by performing Qubit dsDNA High Sensitivity (HS) Assays, as specified by the manufacturers of the Qubit Fluorometer, Life Technologies. Bacterial copy number was quantified using RT-qPCR on all community DNA samples, as well as 5 10-fold dilutions of an E. faecalis JH2-2 genomic DNA positive control. RT-qPCR was conducted using the reagent SYBR Premix Ex Taq II (Takara Bio) containing the fluorescent dye SYBR Green I, as specified by the manufacturers (Takara Bio). The PCR primer set used here was a universal 16S rRNA gene primer set, amplifying the V3 hypervariable region (forward primer sequence: ACTCCTACGGGAGGCAGCAGT, reverse primer sequence: ATTACCGCGGCTGCTGGC) (Hartman et al., 2009). The known E. faecalis JH2-2 genome size and DNA sample concentration were used to calculate bacterial copy number within this genomic DNA sample and in each of the 10-fold E. faecalis JH2-2 dilutions. These values were used to create a standard curve showing sample CT value, observed during RT-qPCR, against bacterial copy number. This standard curve was then used to extrapolate bacterial copy numbers from each of the community DNA samples, based on CT value observed during RT-qPCR. Bacterial copy numbers were then normalised by initial stool sample weight, and extracted DNA concentration,
27 3 Results
3.1 Enteral nutrition stringency and duration affect completion
To test the compliance of participants within treatment groups to their respective treatments, completion of treatment by participants was assessed. Greater participant treatment completion was observed within both the PEN and healthy control groups than within the EEN group. Rates of participant completion for both treatment and follow-up were 13 of 21 (62%) within the EEN group, 8 of 9 (89%) within the PEN group, and 17 of 17 (100%) within the healthy control group. All participant dropout occurred during EN treatment, as opposed to post-treatment follow-up. The substantial reduction in compliance within the EEN group, relative to the PEN and healthy control groups, suggests that the treatment utilised here is less tolerable to the participants. This is likely because EEN treatment is stricter than that of the PEN group, and occurs over a longer timeframe than that of the healthy control group.
3.2 Mock community standards confirm data reliability
To test the reliability of taxonomic assignment to 16S rRNA gene sequences, both the passage of sequences through the dada2 pipeline, and the taxonomic assignment of mock microbial communities of known composition were assessed. Minimal sequence loss was observed with processing, and mock community samples resembled the expected composition. Within all samples, more than 76% of reads passed through the entirety of the dada2 pipeline, with no major reductions at any individual stage (Figure S2). This suggests that the majority of true sequences variants were retained through this pipeline.
The observed taxonomic composition of each of the 3 ZymoBIOMICS Microbial Community DNA Standards was then compared to the expected taxonomic composition, as specified by the manufacturers (Figure S3; Zymo Research). This comparison demonstrates that observed species abundances within all 3 tested mock community samples resemble expected species abundances (Spearman’s * > 0.83 in all cases; p < 0.0001 in all cases). All 8 of the species expected within the mock communities were observed, in all cases. While there were some unexpected species detected within the mock communities, none of these had a relative abundance greater than 0.02, within any
28 of the mock communities. This suggests that taxonomic assignment of the 16S rRNA gene sequences using dada2 was representative of the sequenced communities. Of the mock community species, only the observed abundance of E. coli differed substantially from expected abundance, with this reduction indicating that dada2 taxonomic assignment of 16S rRNA gene sequences had reduced sensitivity to this species.
3.3 Gut microbiome alpha diversity 3.3.1 Alpha diversity is reduced in Crohn’s disease patients
To test for changes to gut microbiome alpha diversity that occur with CD, alpha diversity was quantified by observed species richness (Figure 2A), Shannon diversity (Figure 2B), and Gini-Simpson diversity (Figure 2C) in 16S rRNA data from stool samples collected prior to EN treatment. Alpha diversity was significantly reduced in CD patients relative to healthy controls, with all utilised measures (p < 0.01 in all cases, Wilcoxon rank sum test). This reduction in CD patient stool alpha diversity suggests a corresponding reduction in gut microbiome alpha diversity. The ubiquity of these results across all utilised measures of alpha diversity suggests that this is a reduction in both the total number of species present in the CD gut microbiome, and the associated entropy.
29
A **
Observed species richness species Observed CD Healthy B ** Shannon index
CD Healthy C ** Gini-Simpson index Gini-Simpson
CD Healthy
Figure 2. Alpha diversity is reduced in the gut microbiomes of CD patients relative to healthy people. Alpha diversity of stool samples from CD patients and healthy controls prior to EN treatment, quantified by A) observed species richness, B) Shannon index, or C) Gini-Simpson index. CD patient and healthy control samples were compared by Wilcoxon rank sum test (** p < 0.01).
30 3.3.2 Enteral nutrition does not change alpha diversity
To test for changes to gut microbiome alpha diversity during EN, alpha diversity was quantified by observed species richness (Figure 3A), Shannon diversity (Figure 3B), and Gini-Simpson diversity (Figure 3C) in 16S rRNA data from stool samples collected over the EN treatment period. While alpha diversity was reduced in CD patients relative to healthy controls across treatment time points, there was no change in alpha diversity over the course of EN treatment.
Prior to EN treatment, the CD patients of the EEN group had significantly reduced alpha diversity relative to the healthy controls by all 3 utilised measures (p < 0.01 in all cases; Wilcoxon rank sum test), while the CD patients of the PEN group had significantly reduced alpha diversity relative to the healthy controls when quantified by observed species richness and Shannon index (p < 0.05 in both cases; Wilcoxon rank sum test), but not by Gini-Simpson index (p > 0.05; Wilcoxon rank sum test). There were no significant differences in alpha diversity between the EEN and PEN groups prior to EN treatment by any utilised measure (p > 0.05 in all cases; Wilcoxon rank sum test).
During EN treatment, the EEN group had significantly reduced alpha diversity relative to the healthy controls by all 3 utilised measures (observed species richness p < 0.001, Shannon index p < 0.01, Gini-Simpson p < 0.05; Wilcoxon rank sum test), while the PEN group had significantly reduced alpha diversity relative to the healthy controls when quantified by observed species richness (p < 0.001; Wilcoxon rank sum test), but not by Shannon index or Gini-Simpson index (p > 0.05 in both cases; Wilcoxon rank sum test). There were no significant differences in alpha diversity between the EEN and PEN groups during EN treatment when quantified by observed species richness or Shannon index (p > 0.05 in both cases; Wilcoxon rank sum test), but there was a significant reduction in Gini-Simpson diversity in the EEN group relative to the PEN group (p < 0.05; Wilcoxon rank sum test).
After EN treatment, both the EEN and PEN groups had significantly reduced alpha diversity relative to the healthy controls when quantified by observed species richness (EEN p < 0.05, PEN p < 0.01; Wilcoxon rank sum test), but not by Shannon index or Gini- Simpson index (p > 0.05 in all cases; Kruskal-Wallis test). There were no significant differences in alpha diversity between the EEN and PEN groups after EN treatment by
31 any utilised measure (p > 0.05 in all cases; Wilcoxon rank sum test, Kruskal-Wallis test). There were no significant changes in alpha diversity between samples collected before, during, or after EN treatment, in any group, with any utilised measure (p > 0.05 in all cases; Kruskal-Wallis test).
Although stool sample alpha diversity is consistently reduced in CD patients relative to healthy controls, the lack of significant change over the course of EN treatment suggests that EN does not alter gut microbiome alpha diversity. The ubiquity of these results across all utilised measures of alpha diversity suggests that EN does not alter either the total number of species present in the gut microbiome, or the associated entropy.
32 A Observed species richness species Observed
Before During After B Time (relative to treatment) Shannon index
Before During After C Time (relative to treatment) Gini-Simpson index Gini-Simpson
Before During After Time (relative to treatment)
Cohort EEN PEN Healthy
Figure 3. Disease, but not EN treatment, affects alpha diversity. Alpha diversity of stool samples from the EEN, PEN, and healthy control groups throughout EN treatment, quantified by A) observed species richness, B) Shannon index, or C) Gini-Simpson index. Groups were compared within observed time points by Kruskal-Wallis test, and, where appropriate, pairwise Wilcoxon rank sum test (ns p > 0.05, * p < 0.05, ** p < 0.01, *** p < 0.001).
33 3.4 Bacterial abundance does not change with enteral nutrition
To test for changes in total gut microbiome bacterial abundance with disease state, and with EEN treatment, RT-qPCR was used to determine the concentration of 16S rRNA gene copies within wet stool samples collected from both CD patients in the EEN group, and healthy controls, over the EEN treatment period (Figure S4). There were no significant differences in 16S rRNA gene copy concentration between the stool samples of the CD patients of the EEN group, and the healthy controls, at any time point (p > 0.05 in all cases; Wilcoxon rank sum test). There were also no significant changes in stool sample 16S rRNA gene copy concentration over the course of EEN treatment, within either the EEN or healthy control groups (p > 0.05 in both cases; Kruskal-Wallis test). The lack of change in 16S rRNA gene copy concentration suggests that EEN does not substantially alter total bacterial abundance within the gut microbiome. However, limited sample availability for RT-qPCR may mean that this analysis has insufficient power to detect minor changes in total bacterial abundance.
3.5 Gut microbiome beta diversity 3.5.1 Healthy and Crohn’s disease gut microbiomes are distinguishable
To test for differences in composition between the gut microbiomes of CD patients and healthy controls, beta diversity was quantified by Bray-Curtis dissimilarity (Figure 4A), weighted UniFrac distance (Figure 4B), and unweighted UniFrac distance (Figure 4C) in 16S rRNA data from stool samples collected from both CD patients and healthy controls prior to EN treatment. Sample beta diversity differed between CD patients and healthy people. There were no significantly heterogeneous cluster dispersions when samples were clustered by disease state, with any utilised measure (p > 0.05 in all cases; betadisper permutation test of group dispersion homogeneity). Clustering of samples by disease state was significant when beta diversity was quantified by all utilised measures (p < 0.01 in all cases; adonis PERMANOVA).
As stool samples collected before EN treatment can be clustered by disease state, this suggests that the gut microbiomes of CD patients differ from those of healthy people. PERMANOVA analysis of clustering identifies significant differences in cluster centroid and dispersion. Because there is no significant heterogeneity in cluster dispersion, centroid location must contribute to the significant differences observed with disease
34 state, indicating true differences in gut microbiome composition between clusters. Analysis of PERMANOVA R2 values (Table S4) shows that these values are low, indicating that variation with disease state represents a small amount of overall community variation. While this compositional change effect size is small, it remains significant with all utilised measures of beta diversity, suggesting that changes to the gut microbiome with disease state include changes to both the taxa that are present, and their relative abundances.
35 A NMDS2
NMDS1 B NMDS2
NMDS1 C NMDS2
NMDS1
Disease state CD Healthy
Figure 4. Prior to EN treatment, the gut microbiomes of CD patients differ from those of healthy people. NMDS ordination of all pre-treatment stool samples from the EEN, PEN, and healthy control groups. Samples are grouped by disease state. Diamonds represent group centroids, and ellipses represent 95% CI. Groups are compared by PERMANOVA analysis. Beta diversity was quantified by A) Bray-Curtis dissimilarity (stress = 0.131; PERMANOVA p < 0.01, R2 = 0.0340), B) weighted UniFrac distance (stress = 0.136; PERMANOVA p < 0.01, R2 = 0.0581), or C) unweighted UniFrac distance (stress = 0.141; PERMANOVA p < 0.01, R2 = 0.0546).
36 3.5.2 The gut microbiome changes with exclusive enteral nutrition
To test for changes to gut microbiome composition during EN, beta diversity was quantified in 16S rRNA data from stool samples collected from the EEN group (Figure 5), PEN group (Figure 6), and healthy control group (Figure 7) over the EN treatment period. EEN was disruptive to the gut microbiomes of both CD patients and healthy people, while PEN was not disruptive to the gut microbiomes of CD patients. Beta diversity was quantified by Bray-Curtis dissimilarity (Figures 5A, 6A, 7A), weighted UniFrac distance (Figures 5B, 6B, 7B), and unweighted UniFrac distance (Figures 5C, 6C, 7C).
There were no significantly heterogeneous cluster dispersions when samples were clustered by whether they were collected before, during, or after EN, in any group, with any utilised measure (p > 0.05 in all cases; betadisper permutation test of group dispersion homogeneity). Clustering of samples by whether they were taken before, during, or after EN was significant in the EEN and healthy control groups when beta diversity was quantified by Bray-Curtis dissimilarity and weighted UniFrac distance (p < 0.01 in all cases; adonis PERMANOVA), but not by unweighted UniFrac distance (p > 0.05 in both cases; adonis PERMANOVA), and was not significant by any utilised measure in the PEN group (p > 0.05 in all cases; adonis PERMANOVA).
In the EEN group, there were significant differences in the clustering of samples collected before and during EEN when beta diversity was quantified by Bray-Curtis dissimilarity (p < 0.05; PERMANOVA), but not by weighted UniFrac distance (p > 0.05; PERMANOVA), while in the healthy group, there were significant differences in the clustering of samples collected before and during EEN when quantified by both Bray-Curtis dissimilarity and weighted UniFrac distance (p < 0.01 in both cases; PERMANOVA). In both the EEN and healthy groups, there were significant differences in the clustering of samples collected during and after EEN when beta diversity was quantified by both Bray-Curtis dissimilarity and weighted UniFrac distance (p < 0.01 in all cases; PERMANOVA). There were no significant differences in the clustering of samples collected before and after EN, in any group, with any utilised measure (p > 0.05 in all cases; PERMANOVA).
As stool samples can be clustered by time point relative to EEN, but not PEN, this suggests that the gut microbiomes of both CD patients and healthy people change with EEN, but not PEN, treatment. The significant changes to samples collected during EEN, combined
37 with a lack of significant differences between samples collected before and after EEN, suggests that these alterations are temporary, and the gut microbiomes of both CD patients and healthy people rebound after treatment. The lack of significant cluster dispersion heterogeneity suggests that these are true changes in gut microbiome composition. Analysis of PERMANOVA R2 values (Table S4) shows larger effect sizes from EEN treatment within the healthy controls than within the CD patients of the EEN group. This suggests that, while EEN has some impact on the CD gut microbiome, it has a greater impact on the gut microbiomes of healthy people. Despite the variations in effect size, the gut microbiome changes with EEN observed in both CD patients and healthy people occurred when beta diversity was quantified by Bray-Curtis dissimilarity and weighted UniFrac distance, but not unweighted UniFrac distance. This suggests that these changes are either driven by changes to abundant taxa, as opposed to rare taxa, or that there is rearrangement in the taxa that are already present, with minimal gain or loss of individual species. As alpha diversity does not change with EN treatment, changes that occur to the gut microbiome are likely to be driven by alterations to the taxa present, as opposed to alterations to total community diversity.
38 A NMDS2
NMDS1 B NMDS2
C NMDS1 NMDS2
NMDS1
Time (relative to treatment) Before During After
Figure 5. EEN alters the gut microbiomes of CD patients. NMDS ordination of all stool samples from the EEN group. Samples are grouped by time point relative to treatment. Diamonds represent group centroids, and ellipses represent 95% CI. Groups are compared by PERMANOVA, and, where appropriate, pairwise PERMANOVA analysis. Beta diversity was quantified by A) Bray-Curtis dissimilarity (stress = 0.158; PERMANOVA p < 0.01, R2 = 0.0294), B) weighted UniFrac distance (stress = 0.162; PERMANOVA p < 0.01, R2 = 0.0402), or C) unweighted UniFrac distance (stress = 0.114; PERMANOVA p > 0.05, R2 = 0.0239).
39 A NMDS2
NMDS1 B NMDS2
NMDS1 C NMDS2
NMDS1
Time (relative to treatment) Before During After
Figure 6. PEN does not observably alter the gut microbiomes of CD patients. NMDS ordination of all stool samples from the PEN group. Samples are grouped by time point relative to treatment. Diamonds represent group centroids, and ellipses represent 95% CI. Groups are compared by PERMANOVA, and, where appropriate, pairwise PERMANOVA analysis. Beta diversity was quantified by A) Bray-Curtis dissimilarity (stress = 0.124; PERMANOVA p > 0.05, R2 = 0.0285), B) weighted UniFrac distance (stress = 0.125; PERMANOVA p > 0.05, R2 = 0.0269), or C) unweighted UniFrac distance (stress = 0.119; PERMANOVA p > 0.05, R2 = 0.0248).
40 A NMDS2
B NMDS1 NMDS2
C NMDS1 NMDS2
NMDS1 Time (relative to treatment) Before During After
Figure 7. EEN alters the gut microbiomes of healthy people. NMDS ordination of all stool samples from the healthy control group. Samples are grouped by time point relative to treatment. Diamonds represent group centroids, and ellipses represent 95% CI. Groups are compared by PERMANOVA, and, where appropriate, pairwise PERMANOVA analysis. Beta diversity was quantified by A) Bray-Curtis dissimilarity (stress = 0.185; PERMANOVA p < 0.01, R2 = 0.0561), B) weighted UniFrac distance (stress = 0.192; PERMANOVA p < 0.01, R2 = 0.0804), or C) unweighted UniFrac distance (stress = 0.168; PERMANOVA p > 0.05, R2 = 0.0488).
41 3.6 Exclusive enteral nutrition perturbs gut Clostridia
To identify changes in gut microbiome species abundances during EEN, suggested by beta diversity changes, species abundances were quantified in WGS data from stool samples collected from the EEN and healthy control groups over the EEN treatment period. EEN significantly perturbed taxa in the gut microbiomes of both CD patients and healthy people, particularly species belonging to the Clostridia class. Species with abundances significantly altered from their pre-treatment state, and with an abundance fold-change greater than 1.5, are determined to be significantly perturbed, and are shown in Figure 8. EEN significantly changed the abundance of 5 species within samples collected from CD patients, and 17 species within samples collected from healthy controls. In the CD patients, 4 species were significantly perturbed during EEN, and 2 species were significantly perturbed after EEN. In the healthy controls, 17 species were significantly perturbed during EEN, and 1 species was significantly perturbed after EEN.
Of the 19 total species significantly perturbed by EEN across both CD patients and healthy controls, 11 belonged to the Clostridia class. Nine of these perturbed Clostridia class species displayed similar responses to EEN between CD patients and healthy controls, with consistent increases and decreases in abundance. The only 2 Clostridia class species that displayed a mixed response to EEN between CD patients and healthy controls were Clostridium nexile and Clostridium symbiosum. Of the 11 perturbed Clostridia class species, 5 were significantly reduced during EEN, 5 were significantly increased during EEN, and 1, C. symbiosum, was not significantly altered during EEN, but was significantly reduced after EEN. Other species perturbed by EEN belonged to the Actinobacteria, Bacteroidia, Negativicutes, Betaproteobacteria, and Gammaproteobacteria classes. Species within the Actinobacteria and Bacteroidia classes displayed consistent patterns of disturbance between CD patients and healthy controls, while species in the Negativicutes, Betaproteobacteria, and Gammaproteobacteria classes did not. The non- Clostridia class species were typically decreased during EEN, with all except Alistipes finegoldii significantly decreased during EEN in either CD patients or healthy people. A. finegoldii abundance was significantly increased in healthy controls during EEN.
When the species that were significantly perturbed over EEN were compared between CD patients and healthy people, only C. symbiosum, E. coli, and the unclassified Acidaminococcus and Ralstonia species were significantly different before EEN treatment.
42 C. symbiosum and E. coli both had significantly increased abundances in CD patients relative to healthy people (C. symbiosum fold-change = 8.47, p < 0.001; E. coli fold-change = 6.53, p < 0.01), while the unclassified Acidaminococcus and Ralstonia species both had significantly reduced abundances in CD patients relative to healthy people (Acidaminococcus fold-change = 0.110, p < 0.001; Ralstonia fold-change = 0.166, p < 0.01). All of these species displayed dissimilar patterns of disturbance between CD patients and healthy controls with EEN. The significant differences in the abundances of these species between CD patients and healthy controls were lost during EEN treatment, as a result of decrease in CD patient E. coli abundance, alongside increase in healthy control C. symbiosum abundance, and decreases in healthy control unclassified Acidaminococcus and Ralstonia abundances. Significant differences in the abundance of these species did not re-emerge after EEN treatment.
The taxonomic changes observed here highlight how EEN alters the gut microbiomes of both CD patients and healthy people. While the impact of EEN appears to be greater in the gut microbiomes of healthy people, with a substantially greater number of species affected than in CD patients, it also appears to be more transient, with more perturbed species returning to their pre-treatment states. The association between species with variable abundances between CD patients and healthy controls and variable responses to EEN suggests that the starting composition of the gut microbiome is likely to play a substantial role in its response to EEN. These taxonomic changes also indicate that species belonging to the Clostridia class contribute substantially to the gut microbiome changes observed with EEN.
43 CD Healthy
Actinobacteria Bifidobacterium B. bifidum *** Prevotella P. copri **** ** Bacteroidia Alistipes A. finegoldii ** Unnamed ph8 ** C. nexile Clostridium **** C. symbiosum *** E. eligens ** Eubacterium E. rectale **** **** E. sp. 3 1 31 **** *** Clostridia Butyrivibrio B. crossotus *** Roseburia R. intestinalis *** R. gnavus * Ruminococcus R. torques ** R. bromii **** Subdoligranulum S. sp. 4 3 54A2FAA *** Acidaminococcus Unclassified Negativicutes *** Dialister D. invisus **** Betaproteobacteria Ralstonia Unclassified *** *** Gammaproteobacteria Escherichia E. coli **** *** Before During After Before During After Time (relative to treatment)
Fold-change (from pre-treatment) -10 -5 0 5 10
Figure 8. EEN alters gut microbiome species abundance. Heatmap of all bacterial species undergoing significant perturbation over EEN treatment time. Showing fold-change from pre-treatment species abundance. Abundance of species is compared across time points by Kruskal-Wallis test, and, where appropriate, pairwise Wilcoxon rank sum test. All significant changes from pre-treatment abundances are indicated on heatmap (* p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001).
44 3.7 Gut microbiome functionality 3.7.1 Gut microbiome functionality changes with exclusive enteral nutrition
To test for changes to the genetic functional capacity of the gut microbiome during EEN, KEGG pathway abundance was quantified in WGS data from stool samples collected from the EEN and healthy control groups over the EEN treatment period. Of the observed KEGG pathways, only the total abundances of the KEGG pathways ko02030 (bacterial chemotaxis), ko02040 (flagellar assembly), and ko05146 (amoebiasis) were significantly altered from their pre-treatment state, with a fold-change greater than 1.5 (Figure 9). These KEGG pathways are determined to be significantly perturbed.
All 3 KEGG pathways that were significantly perturbed with EEN were also significantly less abundant in CD patients than healthy controls prior to EEN treatment (ko02030 p < 0.05, ko02040 p < 0.05, ko05146 p < 0.01; Wilcoxon rank sum test; Figure 9A). None of these KEGG pathways were significantly perturbed in CD patients over EEN treatment (p > 0.05 in all cases; Kruskal-Wallis test; Figures 9B – 9D). In the healthy controls, the relative abundances of all 3 of these KEGG pathways was significantly reduced during treatment from their pre-treatment state (ko02030 p < 0.001, ko02040 p < 0.01, ko05146 p < 0.0001; Wilcoxon rank sum test; Figures 9B – 9D), and significantly increased after treatment from during treatment (ko02030 p < 0.001, ko02040 p < 0.001, ko05146 p < 0.01; Wilcoxon rank sum test; Figures 9B – 9D). There were no significant changes in relative abundance of any tested KEGG pathway from pre-treatment state to after treatment (p > 0.05 in all cases; Wilcoxon rank sum test; Figures 9B – 9D).
The changes in KEGG pathway abundance observed here indicate that the genetic functional capacity of the gut microbiome changes with EEN. This is likely to affect the role that the gut microbiome plays within the host. These changes reflect previously observed changes in gut microbiome taxonomy with EEN, demonstrating that the impact of EEN is transient, and greater in healthy controls than CD patients. While significant changes were only observed in the healthy control group, the same patterns of perturbation and rebound were observed in the gut microbiomes of CD patients, with a smaller effect size. This suggests that EEN alters the gut microbiomes of CD patients and healthy people in similar ways, but at different magnitudes.
45
A B nsns nsns *** ****** * * ** Relative pathway abundance pathway Relative CD Healthy Disease state
C ns ns ** *** Relative pathway abundance pathway Relative
Relative pathway abundance pathway Relative CD Healthy Disease state
D ns ns **** ** Relative pathway abundance pathway Relative ko02030 ko02040 ko05146 CD Healthy KEGG pathway Disease state
Disease state: CD Healthy Time (relative to treatment): Before During After
46
Figure 9. EEN alters gut microbiome genetic pathway abundance. Showing abundance of all KEGG pathways undergoing significant perturbance during EEN treatment. Showing A) pre-treatment abundance of all significantly changing KEGG pathways, or abundance over EEN treatment of KEGG pathways B) ko02030 (bacterial chemotaxis), C) ko02040 (flagellar assembly), or D) ko05146 (amoebiasis). Groups were compared by Wilcoxon rank sum test or Kruskal-Wallis test, where appropriate (ns p > 0.05, * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001).
47 3.7.2 Clostridia drive gut microbiome functionality changes
To identify bacterial species contributing to the observed changes in gut microbiome functionality over EEN, the abundances of KEGG pathways ko02030 (bacterial chemotaxis), ko02040 (flagellar assembly), and ko05146 (amoebiasis) in associated bacterial species were quantified in WGS data from stool samples collected from the EEN and healthy control groups, over the EEN treatment period. Within this subset of all observed species-associated KEGG pathways, all significant changes in abundance over EEN occurred in KEGG pathways in species belonging to the Clostridia class.
Species-associated KEGG pathways with abundances significantly altered from their pre- treatment state, either during or after treatment, and with an abundance fold-change greater than 1.5, are shown in Figure 10 (Wilcoxon rank sum test; p < 0.05 indicates significance). This differs from changes to total KEGG pathway abundance (Figure 9), as KEGG pathway abundance has here been stratified by the bacterial species in which the KEGG pathway is identified. Species were assignable for 54.5% of ko02030 pathway reads, 66.8% of ko02040 pathway reads, and 80.1% of ko05146 pathway reads, across all samples. Of the total species associated with the 3 tested KEGG pathways, 22 of 30 (73%) belonged to the Clostridia class. Other species associated with the tested KEGG pathways belonged to the Gammaproteobacteria, Bacteroidia, Bacilli, Deltaproteobacteria, Epsilonproteobacteria, and Actinobacteria classes.
All species-associated KEGG pathways that changed significantly in abundance with EEN belonged to the Clostridia class (Figure 10). There was substantial overlap between KEGG pathway associations, with significant changes in the abundances of more than 1 tested KEGG pathway observed in 7 of the 12 total associated species. Where more than 1 KEGG pathway associated with a single species changed significantly over EEN, the abundances of these linked KEGG pathways were typically consistent with each other at all time points. The single exception to this occurred in Eubacterium eligens, where, in the gut microbiomes of CD patients, the abundance of E. eligens ko02030 was observed to increase during EEN treatment, while the abundance of E. eligens ko02040 was observed to decrease during EEN treatment.
EEN significantly altered the abundance of 14 species-associated KEGG pathways within samples collected from CD patients, and 16 species-associated KEGG pathways within
48 samples collected from healthy controls. Of the 20 total species-associated KEGG pathways significantly perturbed by EEN, 7 displayed dissimilar patterns of disturbance between the CD patients and the healthy controls, consisting of KEGG pathways from the species E. eligens, Eubacterium siraeum, unnamed Lachnospiraceae sp. 1 4 56FAA, Roseburia hominis, and Flavonifractor plautii. Of the species-associated KEGG pathways significantly perturbed with EEN in CD patients, 8 were significantly reduced and 3 were significantly increased during EEN, while 2 were significantly reduced and 3 were significantly increased after EEN. Of the species-associated KEGG pathways significantly perturbed with EEN in healthy people, 13 were significantly reduced and 3 were significantly increased during EEN, while none were significantly altered after EEN.
The clear link between alteration of KEGG pathway abundance and associated Clostridia class species observed here indicates that EEN alters gut microbiome functionality through perturbation of gut Clostridia. This can be observed in the gut microbiomes of both CD patients and healthy people, with some differences in response, and a more lasting effect observed in CD patients. While the response to EEN is not universal across KEGG pathways in all observed Clostridia class species, patterns of perturbation observed here typically demonstrate a reduction in the abundance of species-associated KEGG pathways during EEN, and a rebound following EEN, as observed previously.
49 Ta x a Pathway CD Healthy
Anaerotruncus A. colihominis ko02030 Clostridiaceae *** Clostridium C. hathewayi ko02040 * ko02030 E. eligens **** *** ko02040 *** **** ko02030 **** **** Eubacteriaceae Eubacterium E. rectale ko02040 *** **** ko02030 E. siraeum * ko05146 ** * ko02030 Butyrivibrio B. crossotus ** ** *** ko02040 ** ** *** 1 4 56FAA ko02040 ** Unnamed ko02030 7 1 58FAA ** ko02040 Lachnospiraceae *** ko02030 R. hominis ** ko02040 ** Roseburia ko02030 *** **** R. intestinalis ko02040 ** **** ko05146 *** Flavonifractor F. plaut ii ko02030 Ruminococcaceae * Ruminococcus R.bromii ko05146 *** **** Before During After Before During After Time (relative to treatment)
Fold-change (from pre-treatment) -10 0 5-5 10
Figure 10. EEN alters gut microbiome species-associated genetic pathway abundance. Heatmap of all species-associated KEGG pathways undergoing significant perturbation over EEN treatment time. Showing fold change from pre-treatment pathway abundance. Abundance of pathways is compared across time points by Kruskal-Wallis test, and, where appropriate, pairwise Wilcoxon rank sum test. All significant changes from pre-treatment abundances are indicated on heatmap (* p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001). 50 3.8 Gut acetate and butyrate are reduced during exclusive enteral nutrition
To test for changes to gut SCFA availability during EEN, gas chromatography was performed on EEN and healthy control group stool samples to determine concentrations of the SCFAs acetate, butyrate, isobutyrate, isovalerate, propionate, and valerate (Figure 11). Stool concentrations of both acetate and butyrate decreased during EEN, then rebounded after EEN. In the CD patients of the EEN group, there was a significant increase in butyrate concentration from during EEN treatment to after treatment (p < 0.05; Wilcoxon rank sum test; Figure 11C). There were no significant changes in CD patient butyrate concentration from either before to during treatment, or before to after treatment (p > 0.05 in both cases; Wilcoxon rank sum test). There were no significant changes in the concentrations of any of the other tested SCFAs in CD patients over EEN treatment (p > 0.05 in all cases; Kruskal-Wallis test; Figures 11B, 11D – 11G). In the healthy controls, there was a significant reduction in the concentrations of both acetate and butyrate from before to during treatment (acetate p < 0.01, butyrate p < 0.001; Wilcoxon rank sum test; Figures 11B, 11C). There was then a significant increase in the concentrations of both acetate and butyrate in the healthy controls from during treatment to after treatment (p > 0.01 in both cases; Wilcoxon rank sum test). There were no significant differences in the concentrations of either acetate or butyrate between before and after EEN treatment in the healthy controls (p > 0.05 in both cases; Wilcoxon rank sum test). There were no significant changes in the concentration of any of the other tested SCFAs in the healthy controls over EEN treatment (p > 0.05 in all cases; Kruskal- Wallis test; Figures 11D – 11G).
Stool concentrations of both isobutyrate and isovalerate prior to EEN treatment were significantly reduced in CD patients relative to healthy controls (p < 0.05 in both cases; Wilcoxon rank sum test; Figure 11A). There were no significant differences in the concentrations of any of the other tested SCFAs between CD patients and healthy controls prior to treatment (p > 0.05 in all cases, Wilcoxon rank sum test).
The changes in stool concentrations of acetate and butyrate observed here suggest that EEN alters the concentrations of these SCFAs within the gut. This may be due to changes in gut microbiome taxonomy and genetic functional capacity, where microbial production
51 of acetate and butyrate may be temporarily reduced. Alternatively, absorbency of acetate and butyrate by the intestinal epithelia may be altered during EEN treatment, thus altering the amount of these SCFAs remaining in the stool. Whether changes in acetate and butyrate concentration occur due to one of these mechanisms, or a combination of both, these changes are still indicative of an alteration in gut functionality that occurs with EEN treatment.
52 A BCD
ns ns ns ns ns ns **** ns * *****
ns ns * * ns ns Community acetate (mMol/kg) acetate Community Community butyrate(mMol/kg) Community Community isobutyrate (mMol/kg) isobutyrate Community CD Healthy CD Healthy CD Healthy EFG
ns ns ns ns ns ns Community SCFA (mMol/kg) SCFA Community Community valerate (mMol/kg) valerate Community Community propionate (mMol/kg) propionate Community Community isovalerate (mMol/kg) isovalerate Community
Acetate Butyrate Valerate CD Healthy CD Healthy CD Healthy Isobutyrate Isovalerate Propionate Cohort CD Healthy Time (relative to treatment) Before During After
53
Figure 11. EEN alters the concentration of SCFAs present in the gut. SCFA concentrations observed in participant stool samples. Showing A) pre-treatment concentration of all tested SCFAs, or concentration over EEN treatment of B) acetate, C) butyrate, D) isobutyrate, E) isovalerate, F) propionate, or G) valerate. Groups were compared by Wilcoxon rank sum test or Kruskal-Wallis test, where appropriate (ns p > 0.05, * p < 0.05, ** p < 0.01, *** p < 0.001).
54 4 Discussion
Observation of both CD patients and healthy controls clearly demonstrates that EEN alters the gut microbiome, and this change may be responsible for the reduction in CD symptoms observed with EN treatment. These alterations include changes to community structure, taxonomy, genetic functional capacity, and metabolic output. These observations may allow a better understanding of CD aetiology, and a better understanding of how CD can be treated.
4.1 Exclusive enteral nutrition has greater effect than partial enteral nutrition
This study demonstrated that EEN alters the gut microbiome, as well as associated functional capacity and SCFA production. However, no significant changes were observed in the PEN group, with any measure. There are several factors that may contribute to the lack of observed change. Firstly, the PEN treatment is less strict than the EEN treatment, and is therefore likely to have a reduced impact, and a smaller effect size. Secondly, with only 9 participants, the PEN group was substantially smaller than both the EEN and the healthy control groups (21 and 17 participants respectively). As such, the gut microbiomes of CD patients within the PEN group may be altered in a small way, but the small number of samples may not allow sufficient power to detect these changes. Therefore, it is impossible to determine the true impact of PEN treatment on CD patients without a greater number of samples. While little information can be gained from the PEN group, both the EEN and healthy control groups offer substantial insight into the mechanisms by which EN may treat CD.
4.2 Exclusive enteral nutrition does not remediate drop in alpha diversity associated with Crohn’s disease
This study demonstrated that CD gut microbiome alpha and beta diversities differ from those of healthy people, prior to EN treatment. Here, CD patients have reduced gut microbiome alpha diversity relative to healthy people. This reduction in alpha diversity has been previously described, both within the gut microbiomes of CD patients, and specifically at areas of inflammation within the CD gut (Kostic et al., 2014; Manichanh et
55 al., 2006; Sepehri et al., 2007). As no changes in total bacterial load were observed with RT-qPCR analysis in this study, either between CD patients and healthy controls or with EEN, this indicates that this reduction in alpha diversity is driven primarily by disturbance of specific taxa, as opposed to a general disruption of all taxa. These reductions in alpha diversity have previously been attributed to reduced diversity within the Firmicutes phylum, which include commensal species belonging to the Clostridia class (Kang et al., 2010). However, it is impossible to determine from this information whether the observed reduction in gut microbiome alpha diversity with CD inflammation contributes to, or is a product of, disease.
Despite the observed reduction in gut microbiome alpha diversity that occurs with CD, this study demonstrated no observable changes in gut microbiome alpha diversity with EN, either in CD patients or healthy people. Previous work investigating changes to the paediatric CD gut microbiome has demonstrated that a decrease in overall alpha diversity occurs during EEN in these cases (Gerasimidis et al., 2014). Gerasimidis et al. (2014) observed EEN treatment for approximately 60 days, similar to the 8 weeks (56 days) observed in this study. It is possible that differences between the adult cohort examined in this study, and the paediatric cohort examined by Gerasimidis et al. (2014), mean that significant reductions in alpha diversity during EEN were not observable here. These may include a greater compliance to EEN in paediatric cases, which is observed in the cohort examined by Gerasimidis et al. (2014), and has been described previously (Wall et al., 2013).
The lack of significant increase in CD gut microbiome alpha diversity during EEN suggests that EEN cannot treat CD through restoration of the full, healthy gut microbiome. Low gut microbiome alpha diversity does not prevent a reduction in disease symptoms, and is therefore unlikely to contribute substantially to disease. As such, it seems probable that the reduction in gut microbiome alpha diversity observed with CD is predominantly a product of disease, as opposed to a contributing factor.
The response of the CD gut microbiome to EEN contrasts with responses to other CD treatments, such as the TNF antagonist infliximab and the IL-12 and IL-23 monoclonal antibody ustekinumab, which are both associated with increases in CD gut microbiome alpha diversity during treatment (Doherty et al., 2018; Wang et al., 2018). As both these drugs directly inhibit the inflammatory response, it is likely that the observed increase in
56 CD gut microbiome alpha diversity with their use is the result of a reduction in the inflammatory response at the intestinal epithelia (Simon et al., 2016). This further suggests that the reduction in gut microbiome alpha diversity that occurs with CD may be predominantly the result of, not the cause of, the CD inflammatory response. This indicates that, for EEN to have a therapeutic effect, it must alter the gut microbiome in other ways. The lack of alpha diversity changes observed with EEN in this study indicates that these changes are likely to be purely taxonomic, with changes in the abundances of different taxa, but no changes to total species numbers.
4.3 Exclusive enteral nutrition alters gut microbiome composition
Analysis of gut microbiome beta diversity in this study indicates significant gut microbiome changes, both between CD patients and healthy people, and over EEN treatment in both groups. While substantial variation in both healthy and CD gut microbiomes makes these changes challenging to observe, they are still significant, if comparatively small. The observed differences between healthy and CD gut microbiomes is consistent with previous work, which has described both the substantial variation in healthy and CD gut microbiomes, and supports the role of a perturbed gut microbiome in CD aetiology (Backhed et al., 2012; Douglas et al., 2018; Pascal et al., 2017; Vila et al., 2018; Zuo and Ng, 2018).
The gut microbiomes of both CD patients and healthy people are observed here to differentiate with EEN treatment, then rebound to approximately their initial state after treatment. This pattern was observed in both groups when beta diversity was quantified by both Bray-Curtis dissimilarity and weighted UniFrac distance, but not when beta diversity was quantified by unweighted UniFrac distance. With similar patterns of differentiation and rebound observed in the gut microbiomes of both the CD patients of the EEN group, and the healthy controls, it appears that EEN alters the gut microbiome by similar mechanisms, regardless of disease state. These alterations can be better understood when the different beta diversity metrics used here are considered.
UniFrac distance accounts for the phylogenetic relatedness of species in a community, while Bray-Curtis dissimilarity does not (Bray and Curtis, 1957; Lozupone and Knight, 2005). Therefore, significant changes with both Bray-Curtis dissimilarity and a UniFrac
57 distance metric indicate that the communities change, and that this change may include significant genetic shifts. Insight can also be gained by comparing measures of UniFrac distance. As weighted UniFrac distance accounts for species abundance, while unweighted UniFrac distance does not, these measures are commonly regarded as being sensitive to changes in overall community structure and rare taxa respectively (Lozupone et al., 2007). As such, the lack of significant changes when beta diversity is quantified by unweighted UniFrac distance suggests that these gut microbiome changes are primarily driven by changes to abundant taxa, not rare taxa. Considered with the lack of observed changes in alpha diversity with EEN, these results clearly indicate that changes to the gut microbiome with EEN involve minimal species gains or losses, but instead involve reordering of the abundances of the species present. Overall, analysis using these 3 beta diversity metrics indicates that the gut microbiomes of the EEN and healthy control groups change with and rebound after EN treatment, with most significant change being to the more abundant taxa.
While EEN alters the gut microbiomes of CD patients and healthy people in similar ways, the amount by which these gut microbiomes are altered differs. Analysis of beta diversity cluster PERMANOVA R2 values indicates that EEN has a greater impact on the gut microbiomes of healthy people, relative to CD patients. This can be seen in a substantially greater differentiation with the onset of EEN treatment in the healthy control group. This difference in response may be due to differences between the gut microbiomes of CD patients and healthy people that exist prior to EN treatment.
4.4 Exclusive enteral nutrition alters gut microbiome taxonomy
Observed changes in the abundance of individual species reflected the changes in gut microbiome beta diversity previously observed in this study. Here, the gut microbiome again differentiates with EEN treatment, and partially rebounds afterwards. This can be observed in the primarily transient changes that occur in the abundances of the perturbed species. An increased response to EEN treatment in the gut microbiomes of healthy people, relative to those of CD patients, is also observable, with an increased number of significantly perturbed species in the healthy controls relative to the CD patients.
58 The majority of the species affected by EEN (11 of 19; 58%) belong to the Clostridia class. As Clostridia class species normally make up between 10% and 40% of the gut microbiome, this appears to be a substantial overrepresentation (Lopetuso et al., 2013). Alteration of abundances of different Clostridia families with EEN has been observed previously, although, as observed in this study, the response of these taxa to EEN varies between species (Kaakoush et al., 2015; MacLellan et al., 2017; Schwerd et al., 2016).
Species perturbed during EEN predominantly show similar responses between CD patients and healthy people. Where a dissimilar response is observed, this can frequently be associated with significant differences in pre-treatment abundance between the CD patients and healthy people. Pre-treatment E. coli abundance is 7-fold higher in CD patients relative to healthy people, and is reduced during EEN in CD patients, but remains largely unaffected in healthy people. The pre-treatment abundances of the unclassified Acidaminococcus and Ralstonia species are respectively 9-fold and 6-fold higher in healthy people relative to CD patients, and are both reduced during EEN in healthy people, but remain largely unaffected in CD patients. This suggests that, while EEN is capable of reducing the abundances of all of these species, the greatest effects are observed where the pre-treatment abundances of these species are high. Contrastingly, pre-treatment C. symbiosum abundance is 8-fold higher in CD patients relative to healthy people, and is increased during EEN in healthy people, but is reduced during and after EEN in CD patients. This suggests that the low C. symbiosum abundance observed in the gut microbiomes of healthy people allows proliferation during EEN, while the high abundance observed in CD patients does not. This C. symbiosum proliferation in the gut microbiomes of healthy people may be the result of C. symbiosum filling niches that have been cleared by EEN.
The increased abundance of mucosal-associated E. coli observed in CD patients relative to healthy people has been extensively documented, however its response to EN has not (Alpern et al., 2006; Darfeuille-Michaud et al., 2004; Kotlowski et al., 2007; Martin et al., 2004; Mylonaki et al., 2005; Swidsinski et al., 2002). The increased C. symbiosum and reduced Acidaminococcus and Ralstonia species abundances observed here in pre- treatment CD patients have also not been validated in other work, and have not previously been observed to alter with EEN (Gatti et al., 2017). However, the substantial variability in both CD and healthy gut microbiomes makes it challenging to conclusively
59 identify individual species that contribute to CD and EN-related gut microbiome changes, leading to frequent inconsistencies between studies (Gatti et al., 2017). When examining disease aetiology related to the gut microbiome, a focus on alteration of gut microbiome functional capacity seems more likely to offer an understanding of potential mechanisms contributing to disease. This also assists in differentiation between changes that are likely to contribute to disease, and those that are likely to result from disease, which may become lost when microbiome analysis is purely taxonomic.
4.5 Exclusive enteral nutrition alters gut microbiome functionality 4.5.1 Exclusive enteral nutrition alters genetic pathway abundance
To determine how EEN alters the genetic functional capacity of the gut microbiome, the abundance of KEGG genetic pathways was observed over the course of EEN treatment. The only 3 KEGG pathways where a significant change in relative abundance was identified with EEN treatment were the flagellar assembly, bacterial chemotaxis, and amoebiasis pathways. The abundance of all of these pathways in healthy controls was reduced during EEN, rebounding after treatment. There were no KEGG pathways that had a significant increase in abundance during EEN. This pattern of differentiation and rebound reflects previously observed changes to the gut microbiome with EEN, both to community structure and taxonomic composition. While there were no significant changes in the relative abundance of these pathways in the gut microbiomes of CD patients with EEN, the same pattern of pathway reduction and rebound was observed here. As all pathways that are significantly changed in abundance over EEN treatment are also significantly reduced in CD patients prior to EN treatment, this may explain the differences in the impact of EEN on these pathways between groups. With a lower starting abundance in the gut microbiomes of CD patients, it is likely that the impact of any mechanism by which EEN reduces the abundance of these pathways will be reduced. As such, the observation of a significant change is less likely. However, while the changes observed are not significant, this does not necessarily mean they are not clinically relevant.
While the identification of the flagellar assembly and bacterial chemotaxis pathways seems reliable, the identification of the amoebiasis pathway does not. This is because the KEGG amoebiasis pathway (ko05146) describes the human immunological response to
60 infection by Entamoeba histolytica. This pathway may only be correctly mapped to eukaryotic organisms, but in this instance it has been mapped to several bacterial species. As such, it is likely that the assignment of this KEGG orthology is a bioinformatic artefact. This may have occurred either with conversion of UniRef50 gene family abundance to KEGG pathway abundance, or with initial UniRef50 gene family assignment. This pathway is of vastly lower abundance than the other pathways detected to change with EEN treatment, which may indicate that the ko05146 pathway is uncommon, and potentially poorly characterised. Poor pathway characterisation may lead to an increased risk of incorrect assignment.
While the incorrect assignment of the ko05146 pathway does cast some doubt on the process of KEGG pathway assignment using HUMAnN2, it is unlikely that either of the 2 pathways observed to change with EEN have been incorrectly assigned. The other 2 pathways were both consistent with their associated bacterial species, and are of substantially higher relative abundance, which may make poor characterisation less likely. The incorrect assignment of the ko05146 pathway does not influence the assignment of bacterial species names, as these were identified during MetaPhlAn2 processing, prior to gene family assignment using HUMAnN2.
Despite the incorrect assignment of the ko05146 pathway, there is consistency in the response of the 3 perturbed KEGG pathways to EEN. This suggests that changes to the abundance of these pathways are linked through changes to the abundance of related taxa. Analysis of species associated with changes in the abundances of these KEGG pathways demonstrates this connection, with 7 of the 12 associated species linked to multiple of the perturbed KEGG pathways.
The consistency of the response of the significantly altered KEGG pathways to EEN allows for a better understanding of the mechanisms by which EEN is effective in treating CD. Here, the abundances of these pathways were significantly lower in the gut microbiomes of CD patients, then reduced further with EEN. This suggests that EEN does not reduce CD symptoms through restoration of the healthy gut microbiome. Instead, it seems more likely that taxa and genetic pathways that are altered in the gut microbiomes of CD patients are predominantly altered in response to CD, and that EEN may be effective through amplification of these responses. To best understand how alterations in the
61 abundance of these pathways may be linked to CD, the role that these pathways play in the gut microbiome must first be understood.
4.5.2 Bacterial flagellin plays a key role in host inflammatory responses
Changes to the relative abundance of both the flagellar assembly pathway (ko02040) and the chemotaxis pathway (ko02030) are likely to have a substantial impact on the overall functionality of the gut microbiome. Flagellin is a key antigenic target in both innate and acquired immune responses. In innate immune responses, flagellin can stimulate toll-like receptor 5 (TLR5) on intestinal epithelial cells (IECs), resulting in the production of macrophage inflammatory protein 3! (MIP3!) and IL-8 (Gewirtz et al., 2001; Rhee et al., 2004). In acquired immune responses, flagellin can induce dendritic cell (DC) maturation, again through stimulation of TLR5 (Means et al., 2003).
Under normal circumstances, TLR5 stimulation by flagellin helps the host protect against a number of flagellated pathogens in different regions of the body (Yoon et al., 2012). The range of pathogens impacted upon is broad, including Salmonella species, pathogenic E. coli, and Pseudomonas aeruginosa (Donnelly and Steiner, 2002; Morris et al., 2009; Murthy et al., 2004). However, the basolateral expression of IEC TLR5 typically protects commensal flagellated bacterial species from a TLR5-mediated inflammatory response, as only species that translocate flagellin across the intestinal epithelia can typically stimulate TLR5 (Gewirtz et al., 2001). Despite this, the role of flagellin as a key microbial pathogen-associated molecular pattern (PAMP) may still be relevant to CD aetiology, and reduction in the relative community abundance of the flagellar assembly pathway by EEN treatment may have a substantial impact on CD, with reduced availability of this pro- inflammatory antigen.
The involvement of an inflammatory response to bacterial flagellin in CD may also explain the reduction in abundance of the flagellar assembly pathway in the gut microbiomes of CD patients prior to EN treatment. It is possible that the CD inflammatory response targets flagellated bacteria, thus decreasing the relative abundance of the flagellin assembly pathway.
The connection between CD and the bacterial chemotaxis pathway is less clear than that with the flagellar assembly pathway. However, it is possible that changes to the abundance of the bacterial chemotaxis pathway with EEN reflect changes to the
62 abundance of taxa that are also associated with the flagellar assembly pathway. To clarify the roles of the flagellar assembly and bacterial chemotaxis pathways within the gut microbiomes of CD patients, the species that contribute to the changes in abundance of these pathways were investigated.
4.6 Clostridia class species alter gut microbiome functionality 4.6.1 Clostridia class species account for genetic changes within the gut microbiome
All of the bacterial species linked to the genetic changes that occur in the gut microbiome with EEN treatment belonged to the Clostridia class. As expected from analysis of the consistent KEGG pathway abundance changes with EEN, there is substantial overlap between the species that contribute to the changes in abundance of these pathways. As expected, where changes in pathway abundance were linked to the same bacterial species, these changes typically reflect each other. This supports the hypothesis that changes in the relative abundance of both the bacterial chemotaxis and unknown pathways may be resultant from changes to the abundance of flagellated bacteria. The single exception to this occurs in E. eligens, where the abundance of the associated KEGG flagellar assembly pathway (ko02040) is substantially reduced during EEN in CD patients, while the abundance of the chemotaxis pathway (ko02030) is substantially increased during EEN in CD patients. It has previously been demonstrated that genes associated with motility and flagellar assembly are non-uniformly detected within the genomes of E. eligens, which may explain the differential response of ko02030 and ko02040 to EEN (Neville et al., 2013). This suggests that flagellated E. eligens are selected against, while non-flagellated E. eligens are selected for, by EEN. This supports the hypothesis that EEN selects against flagellated bacterial species in the gut microbiome.
All bacterial species associated with significant changes to the functional capacity of the gut microbiome with EEN belonged to the Clostridia class. Clostridia typically play a key commensal role within the gut microbiome, and are involved in the regulation of host physiological, metabolic and immune processes (Lopetuso et al., 2013). While the Clostridia class has a high degree of heterogeneity, with substantial class reorganisation occurring with continued analysis, commensal Clostridia within the gut typically display some shared characteristics (Galperin, 2013; Yutin and Galperin, 2013).
63 Commensal gut Clostridia utilise both flagella and chemotaxis to promote adherence to intestinal epithelial cells (Anjuwon-Foster and Tamayo, 2017). Clostridia begin colonisation of a breastfed infant’s gut microbiome in the infant’s first month of life (Roberts et al., 1992). With this colonisation, Clostridia associate closely with the host’s intestinal mucosa, and will eventually compose between 10% and 40% of the gut microbiome (Lopetuso et al., 2013). This close association of commensal Clostridia with the host’s intestinal mucosa allows substantial Clostridia interaction with the host.
The majority of commensal gut Clostridia belong to the phylogenetic Clostridium clusters XIVa and IV (Hold et al., 2002). Clostridium cluster XIVa mostly contains species of the family Lachnospiraceae, while Clostridium cluster IV mostly contains species of the family Ruminococcaceae. While these are defined as Clostridium clusters, this nomenclature is based on historical taxonomic assignment of species belonging to the Clostridia class, and these species are not necessarily of the Clostridium genus (Collins et al., 1994). Analysis of the roles that these phylogenetic clusters play within the gut microbiome allows for a greater understanding of the role of Clostridia in CD.
4.6.2 Clostridium cluster XIVa flagellin is associated with Crohn’s disease
The Clostridia class species that contribute to EEN-related genetic changes in the gut microbiome can be broadly phylogenetically clustered. The species Eubacterium rectale, E. eligens, Roseburia intestinalis, R. hominis, Clostridium hathewayi, and Butyrivibrio crossotus all belong to Clostridium cluster XIVa (Rosero et al., 2016; Stanton and Savage, 1983; Warren et al., 2006; Willems et al., 1996). The species E. siraeum, F. plautii, Anaerotruncus colihominis, and Ruminococcus bromii all belong to Clostridium cluster IV (Carlier et al., 2010; Collins et al., 1994; Moore et al., 1972; Moore et al., 1976). The Lachnospiraceae species 1_4_56FAA and 7_1_58FAA are of unknown cluster, but are most similar to species in cluster XIVa, based on family-level phylogeny.
Of the species linked to EEN-related genetic changes of the gut microbiome, all species belonging to Clostridium cluster XIVa, and of unconfirmed cluster, were linked to changes in the abundance of the flagellar assembly pathway. Conversely, none of the genetic change-linked Clostridium cluster IV species were linked to changes in the abundance of the flagellar assembly pathway. As such, it seems likely that species belonging to Clostridium cluster XIVa contribute substantially to the reductions in total flagellar
64 assembly that occur with both CD and EEN. This is of particular interest, as an inappropriate host immune reaction to Clostridium cluster XIVa flagellin has previously been heavily implicated in CD.
Lodes et al. (2004) identified strong IgG2a and TH1 T cell responses to flagellin in the serum of C3H/HeJBir, Mdr-null, and IL-10-null colitic mice. Here, serological expression cloning (SEC) was used to identify bacterial antigens that were immunoreactive with sera from the colitic mouse strains, but not with that of closely related healthy mice. Of these identified antigens, flagellins from microbial species aligning with Clostridium cluster XIVa were dominant. The XIVa flagellin antigens identified here included CBir1 and Fla- X. The strong anti-flagellin immune response occurred across tested mouse strains, despite the differing mechanisms by which these strains are believed to develop colitis (Lodes et al., 2004). These include defects in immune regulation in IL-10-null mice, and impaired barrier function in Mdr-null mice (Keubler et al., 2015; Resta-Lenert et al., 2005). Further work demonstrates that even murine colitis induced by consumption of dextran sodium sulphate (DSS), which directly ulcerates the gut epithelium, results in heightened serum immunoreactivity to Clostridium-like flagellins (Sanders et al., 2006). Inversely, it was demonstrated that adoptive transfer of flagellin-specific CD4+ T cells to naïve severe combined immunodeficient (SCID) mice induced severe colitis (Lodes et al., 2004).
Lodes et al. (2004) also demonstrated an anti-flagellin immune response in human CD patients. Here, similar to the murine studies, heightened serum immunoreactivity to CBir1 was observed in CD patients. While a small degree of serum immunoreactivity was observed to the dissimilar Salmonella muenchen flagellin, which is similar to E. coli flagellin, this was not observed to differ between CD patients and healthy controls (Lodes et al., 2004). Serum anti-CBir1 antibodies have also been independently associated with CD complications, including small-bowel, internal-penetrating, and fibrostenosing disease features (Targan et al., 2005). This suggests that the inappropriate anti-flagellin immune response observed in CD patients is targeted primarily towards Clostridia.
The work of Lodes et al. (2004) heavily implicates Clostridium cluster XIVa flagellins in CD aetiology. In the context of the results observed in this study, a better understanding of the role that Clostridium cluster XIVa flagellins in the CD gut microbiome can begin to emerge. Here, it can be observed that the alterations to gut microbiome functionality that
65 EEN treatment induces are dominated by reductions in the abundance of Clostridium cluster XIVa flagellins. This reduction follows the reduction in flagellar assembly that already exists within the gut microbiomes of untreated CD patients. In this way, the mechanism by which EEN treatment is able to reduce CD symptoms may be observed.
To fully understand CD aetiology, it is important to understand why CD is more likely to occur in some people than others. Under normal conditions, both Clostridium clusters IV and XIVa exist commensally within the gut. It is essential to understand what host factors may be altered in CD patients. When considering this, the ubiquitous innate immune response to Clostridium cluster XIVa flagellin in colitic mouse strains observed by Lodes et al. (2004), regardless of how colitis was induced, is of particular interest. The primary factor that all colitic mouse strains utilised here would have in common would be impaired intestinal epithelia barrier function (Lodes et al., 2004). As such, it seems likely that host intestinal epithelial barrier function may be involved in some way in the development of an inappropriate host immune response to Clostridium cluster XIVa flagellins.
4.7 Butyrate production induces the tolerance of commensal Clostridia in the healthy gut
Under normal circumstances, commensal Clostridia class species are capable of inducing
+ + their own immunogenic tolerance. This is achieved through induction of CD4 FoxP3 Treg cell accumulation in the cLP (Omenetti and Pizarro, 2015). This accumulation of Treg cells suppresses inflammatory immune responses, and aids in tolerance to the Clostridia (Omenetti and Pizarro, 2015).
The exact mechanisms by which commensal gut microbes induce colonic Treg cell accumulation remain unclear, but the presence of gut SCFAs has been proposed as a contributing factor (Arpaia et al., 2013; Furusawa et al., 2013). Butyrate produced by the gut microbiome influences colonic Treg cell development. Butyrate can participate in Treg cell differentiation directly, by aiding the acetylation of histone H3 in the promoter region, and conserved non-coding regions 1 and 3, of the FoxP3 locus (Furusawa et al.,
2013). This induces FoxP3 transcription, thus differentiating naïve T cells into Treg cells.
Butyrate can also participate in Treg cell differentiation indirectly, by stimulating the DC receptor GPR109a, promoting DC retinoic acid production, and subsequent Treg cell
66 differentiation (Singh et al., 2014). Beyond its role in Treg cell differentiation, in vivo administration of butyrate has been demonstrated to directly supress pro-inflammatory cytokine production by both DCs and macrophages (Arpaia et al., 2013; Chang et al., 2014). This is likely a function of inhibition of HDACs by butyrate (Arpaia et al., 2013; Chang et al., 2014). As Clostridia class species are the key butyrate producers within the gut microbiome, it seems likely that butyrate produced by Clostridia may play some role in controlling inflammatory immune responses to these species (Lopetuso et al., 2013).
Of the 6 SCFAs tested, the amounts of both acetate and butyrate in the participant’s stool changed significantly over EN treatment. In both cases, the amount of these SCFAs in healthy control samples reflect previously observed patterns of significant reduction, then rebound. While neither acetate nor butyrate was significantly changed between CD patients and healthy controls pre-treatment, there was a substantial, non-significant reduction in the amount of butyrate in the pre-treatment CD samples relative to the healthy controls. A similar change was not observed in the amount of pre-treatment sample acetate.
Gut microbiome acetate production is widely distributed across taxonomy, while gut microbiome butyrate production is dominated by species in the Clostridium clusters XIVa and IV (Louis and Flint, 2009; Morrison and Preston, 2016). As such, it is likely that the changes to the amounts of butyrate observed in the participant’s stool are resultant from changes to total Clostridia abundance. It is more challenging to link changes in observed amounts of acetate to specific taxonomic changes.
Crohn’s disease has previously been linked to a reduction in the gut microbiome’s ability to produce butyrate (Marchesi et al., 2007). Based on the results observed in this study, it seems likely that this reduction in butyrate production is the product of a reduction in the abundance of commensal Clostridia that occurs with EEN.
It has previously been demonstrated that administering a butyrate-based treatment is capable of reducing intestinal inflammation in CD patients, at least temporarily (Di Sabatino et al., 2005). However, this treatment method is unlikely to induce permanent remission, as inflammatory antigens would still be present, and additional butyrate would be unlikely to result in sustained Treg cell accumulation. To develop a treatment
67 that induces permanent CD remission, we must first fully understand all the factors that contribute to CD.
4.8 Defining a potential Crohn’s disease model
Based on the results observed in this study, in the context of the scientific literature, we can begin to define a potential model of CD aetiology, as visualised in Figure 12. Lodes et al. (2004) hypothesised that CD is the result of an inappropriate immune response caused by the inherent immunogenicity of Clostridium cluster XIVa flagellin, alongside an underlying genetic susceptibility in the host. The results observed in this study align with this hypothesis. Here, it has been demonstrated that the probable primary effect of EEN treatment on the gut microbiome is to limit the abundance of Clostridium cluster XIVa species, and thus the associated CBir1 and Fla-X flagellin proteins. From this, any inappropriate host inflammatory response to CBir1 and Fla-X is also likely to be reduced.
However, Clostridium cluster XIVa flagellins are not capable of causing CD in isolation. Both CBir1 and Fla-X are present in the gut microbiomes of healthy people, and do not cause disease. As such, it seems likely that CBir1 and Fla-X are only capable of contributing to CD in the presence of other contributing host or environmental factors. The forms these other contributing factors may take are wide-ranging, but they are all capable of either amplifying the innate immune response to CBir1 and Fla-X, or reducing regulation of the innate immune response by Treg cells. Here, it is possible to see how many of the factors proposed to contribute to CD risk may upset the healthy balance.
One example of this is disruption through loss of NOD2 functionality, which may result in an increased rate of Treg cell death. Loss-of-function mutations in NOD2 have previously been demonstrated to be a substantial CD risk factor (Hugot et al., 2001; Ogura et al.,
2001). Rahman et al. (2010) demonstrated that Treg cells expressing fully functional NOD2 had a lesser rate of cell death than those without. It was hypothesised that this was due to a lack of NOD2 stimulation by its ligand, muramyl dipeptide, which may prevent
Fas-mediated apoptosis in Treg cells (Rahman et al., 2010). While NOD2 is still not well understood, these hypotheses allow us to examine the potential ways it could be involved in disruption of the balance of inflammatory and tolerogenic T cell responses to commensal Clostridia class species in the gut.
68 Another example of disruption of the balance between inflammatory and tolerogenic T cell responses can be observed in the wide range of pro-inflammatory host genetic factors associated with increased CD risk (Jostins et al., 2012). These are likely to increase any innate immune response to CBir1 and Fla-X, thus making this immune response more challenging to regulate. During CD, inflammation may be further aggravated by disruption to the intestinal epithelia. MacDonald and Monteleone (2005) hypothesised that increased epithelial permeability with CD ulceration allows for increased passage of microbial antigens into the lamina propria, thus supporting an inflammatory immune response. We can therefore see how host genetic susceptibility may contribute to CD aetiology, whereby effectively any mutation that alters the balance between inflammatory and regulatory T cell responses may contribute to CD risk. Damage to the intestinal epithelia may then support maintained inflammation.
Environmental factors are also likely to play a role in CD aetiology. The gut microbiome, like other ecological communities, is subject to a vast number of selective pressures. As such, directly linking environmental factors that influence the gut microbiome to CD aetiology becomes challenging. However, it still seems likely that any environmental factor resulting in a substantially greater abundance of flagellated cluster XIVa Clostridia in the gut, or disrupting the passage of butyrate through the intestinal epithelia, may contribute to CD aetiology.
69 Healthy
Lamina Treg propria Treg
Regulation Treg
Th1
CBir1 Butyrate Fla-X
Cluster XIVa Intestinal Clostridia lumen CD Lamina propria Treg
Regulation
Th1 Th1 Th1
CBir1 Butyrate Fla-X
Cluster XIVa Intestinal Clostridia lumen
Figure 12. A potential model of Crohn’s disease aetiology. The Clostridium cluster XIVa flagellin proteins CBir1 and Fla-X are present in the gut microbiomes of both healthy people and CD patients, and are capable of stimulating an innate inflammatory immune response. In healthy people, any inappropriate host immune response to commensal gut
Clostridia is regulated by Treg cells. The accumulation of these Treg cells is promoted by butyrate produced by commensal gut Clostridia. In CD patients, this balance is disrupted, either by any factor that increases the host innate immune response to Clostridium cluster
XIVa flagellins, or by any factor that reduces the accumulation of Treg cells. Disruption of this balance will then lead to intestinal inflammation.
70 4.9 Exclusive enteral nutrition reduces the abundance of gut flagellated bacteria
While it appears that the inflammatory response observed in CD patients directly targets species expressing CBir1 and Fla-X, it is unlikely that EEN is this specific. This can be observed in the impact that EEN has on other flagellated bacterial species that do not express CBir1 and Fla-X, such as E. coli (Lodes et al., 2004). While changes in the abundance of these species have not been associated here with changes in the functional capacity of the gut microbiome, they may still offer insight into the mechanism behind EEN. The observed increase in E. coli abundance in CD patients relative to healthy controls suggests that E. coli escapes the CD inflammatory response, which is consistent with its lack of expression of CBir1 and Fla-X. It is possible that the targeting of other flagellated gut bacterial species in CD inflammation allows E. coli to proliferate in the recently vacated niche, specifically at the mucosa, with which both E. coli and Clostridia class species closely associate (Anjuwon-Foster and Tamayo, 2017; Martin et al., 2004). Despite this, E. coli abundance is reduced substantially during EEN. This indicates that EEN broadly reduces the abundance of species that occupy this niche, regardless of whether they express CBir1 and Fla-X, or belong to the Clostridia class.
The observed reduction with EEN in the abundance of flagellated E. eligens, alongside the increase in abundance of presumably non-flagellated E. eligens, also indicates that EEN reduces the abundance of flagellated bacterial species. If this is not a bioinformatic artefact, this may be through some selective pressure against flagellated bacteria in the gut microbiome, or through a selective pressure against mucosal-associated bacteria, as non-flagellated, non-motile bacteria would be unable to efficiently associate with the mucosa. With the limited knowledge available on the mechanisms through which EEN is effective, it is difficult to determine this from the information available here.
The final notable impact that EEN has on species not associated with changes in gut microbiome functional capacity is to C. symbiosum and C. nexile. While both these Clostridium species belong to Clostridia cluster XIVa, they are increased in abundance in CD patients relative to healthy people, suggesting they are capable of escaping the CD inflammatory response (Holdeman and Moore, 1974; Kaneuchi et al., 1976). C. symbiosum is unusually highly resistant to oxidative stress, and inflammatory responses, which may
71 allow it to survive both CD and EEN (Lozupone et al., 2012a). While this has not been demonstrated in C. nexile, it is possible that C. nexile survives CD and EEN through similar mechanisms. If C. symbiosum and C. nexile are capable of avoiding the CD inflammatory response, this would allow them to also proliferate in the niche now vacated by similar species. During EEN, there were minimal observed changes in the abundance of either C. symbiosum or C. nexile in the gut microbiomes of CD patients, while both proliferated in the gut microbiomes of healthy people. Again, this suggests that C. symbiosum and C. nexile are able to escape reduction by EEN, and proliferate within the niche recently vacated in the gut microbiomes of healthy people. The ability of these Clostridia cluster XIVa species to escape EEN treatment may be a limitation of EEN, and further supports the need for novel CD treatments.
4.10 Potential for novel Crohn’s disease treatments
An understanding of CD aetiology potentially allows for the development of novel treatments. While EEN treatment has a number of strengths, it is not flawless. The bad taste in the EEN formula results in treatment compliance rates that can range as low as 59% in adults, which was reflected in the full EEN treatment compliance rate of 62% observed in this study (Wall et al., 2013). EEN treatment also does not induce long-term remission, with 40%-60% of paediatric patients relapsing within a year of EEN treatment (Duncan et al., 2014; Knight et al., 2005; Takagi et al., 2006). As such, it is important to develop alternative CD treatments.
Di Sabatino et al. (2005) demonstrated that oral administration of butyrate had some efficacy in inducing short-term remission in CD patients. This is likely to be effective through promotion of Treg cell accumulation. However, this technique is unlikely to induce long-term remission, as when direct butyrate treatment abates, flagellins will still be present on commensal Clostridia, thus contributing to the renewal of inflammation. A more effective technique may be similar to that demonstrated by Geirnaert et al. (2017), who suggested that butyrate-producing bacteria may be supplemented into the gut microbiomes of CD patients. Here, an in vitro model based on stool samples from CD patients simulated microbial interactions with the gut epithelia. With supplementation with 6 butyrate-producing Clostridia class species, in vitro epithelial barrier integrity was improved (Geirnaert et al., 2017).
72 While work by Geirnaert et al. (2017) is initially promising, and presents strong proof-of- concept for gut microbiome supplementation with butyrate-producing bacteria as a potential CD treatment, the potentially detrimental effects of these bacterial species was not discussed here. When regarding this work in the context of both the findings of this study, and the scientific literature, there seems to be a strong chance that flagellin proteins of these butyrate-producing species may induce an inflammatory response in the CD patient. However, this could potentially be avoided by careful selection of the butyrate-producing species used in this treatment. While Geirnaert et al. (2017) used Clostridia class species belonging to both Clostridium clusters IV and XIVa, using exclusively cluster IV may be the preferable methodology. To date, only Clostridium cluster XIVa flagellin proteins have been identified as antigens likely to cause an inflammatory response in people genetically susceptible to CD (Lodes et al., 2004). As such, it is possible that species belonging to Clostridium cluster IV would be capable of producing the butyrate required to induce a tolerogenic response, without simultaneously increasing the abundance of antigens that result in inflammation. If this is accurate, then probiotic treatment with species belonging to Clostridium cluster IV may be an effective treatment for CD.
4.11 Limitations of study
The study performed here has some limitations. It has been argued that, due to greater passage of lumenal bacterial species into the faecal material relative to mucosal species, stool samples may not be truly representative of the gut microbiome (Durban et al., 2011). However, in our own results, the vast majority of significant changes within taxonomic groups occur in those species that associate closely with the mucosa, including the commensal Clostridia species implicated in disease (Anjuwon-Foster and Tamayo, 2017). This suggests that, regardless of any potential misrepresentation of relative mucosal and lumenal species abundance, changes to mucosal-associated samples were still detected to a sufficient extent. Work by Yasuda et al. (2015) also demonstrates that faecal samples are highly representative of the colonic microbiome of the rhesus macaque, suggesting that the use of faecal samples in this study is reasonable.
The small size of the tested group makes it challenging to truly determine how PEN affects the gut microbiome of CD patients. No significant changes were observed with this treatment in this investigation, but this does not necessarily mean that PEN does not alter
73 the gut microbiome at all. To examine this properly, a larger group would need to be examined, potentially similar in size to the EEN and heathy control groups. There was also suboptimal compliance to the full EEN treatment within the EEN group. As such, the number of stool samples available within the sample upon which analysis could be performed was reduced. Despite this, substantial, significant patterns were still observed within the gut microbiomes of the EEN group with EN treatment. This suggests that the number of stool samples available within this group was sufficient to observe any major gut microbiome changes.
4.12 Future directions
While a causative relationship between Clostridium cluster XIVa flagellin proteins and CD has not yet been demonstrated, it has been heavily implicated in this study. To prove the role of these flagellin proteins in CD, further testing and analysis must be performed. This mechanism could be tested by exposing mice to the isolated CBir1 and Fla-X antigens via enema, and testing for development of colitis. Observing the nature of any subsequent immune response would allow insight into the potential role of CBir1 and Fla-X in inflammation. The response to CBir1 and Fla-X could be assessed across groups of healthy, germ-free, and genetically susceptible mice, such as IL-10 KO mice (Mizoguchi, 2012). This would allow comparison of potential dose-dependent responses, and analysis of factors that increase mouse susceptibility to CBir1- and Fla-X-induced colitis, including prior antigenic exposure and genetic susceptibility. This model could be further tested through treatment with butyrate, to observe if mice with CBir1- or Fla-X-induced colitis recover. Oral administration of butyrate has previously been demonstrated to induce short-term CD remission in humans, and reduction in CBir1- or Fla-X-induced colitis symptoms in mice would demonstrate that butyrate is capable of inducing tolerance to these flagellins (Di Sabatino et al., 2005).
The role of CBir1 and Fla-X in CD-associated inflammation can also be studied further in humans. The amount of CBir1 and Fla-X present in the gut could be observed directly, through quantification of CBir1 and Fla-X in stool samples. This would allow assessment of both antigen quantities over EEN, with remission, and association with flare-ups. The proposed model of CD aetiology could also be tested through observation of changes in serum immunoreactivity to CBir1 and Fla-X with butyrate-based treatment of CD patients. Similarly to the previously proposed murine butyrate treatment tests, this may
74 demonstrate that butyrate is capable of inducing tolerance to CBir1 and Fla-X. Tests like these will help validate the proposed model of CD aetiology. With a full understanding of CD aetiology, it may be possible to develop novel treatments which are capable of inducing permanent remission.
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98 6 Appendices
Ingredients Mass (per 100 mL) Water 77.4 g Carbohydrate sources Maltodextrin 8.42 g Corn syrup 6.1 g Sucrose 5.6 g Stabilisers: E460 (cellulose); 0.16 g E466 (carboxymethyl cellulose) Protein sources Milk protein isolate 3.44 g Calcium caseinate 1.25 g Sodium caseinate 0.94 g Soy protein isolate 0.63 g Fat sources Canola oil 2.98 g Corn oil 1.77 g Lecithin 0.17 g Vitamins Vitamin A 117 µg
Vitamin D3 2.0 µg Vitamin E 2,100 µg
Vitamin K1 12 µg Vitamin C 12,000 µg Folacin (folic acid) 40 µg 200 µg Thiamin (vitamin B1) 270 µg Riboflavin (vitamin B2) 270 µg Vitamin B6 0.55 µg Vitamin B12 Niacin 2,600 µg Pantothenic acid 1,100 µg Biotin 6.0 µg Minerals Sodium citrate Potassium citrate Magnesium chloride Potassium chloride Magnesium phosphate dibasic Calcium phosphate tribasic Potassium phosphate dibasic Ferrous sulphate
99 Zinc sulphate Manganese sulphate Cupric sulphate Sodium molybdate Potassium iodide Chromium chloride Sodium selenate Flavourings
Table S1. Ensure Plus enteral formula ingredients. Based on Ensure Plus datasheet provided by Abbott Laboratories. Ingredients based on vanilla flavour – some minor differences exist between flavours.
100 Sample meta-data Analyses Disease Treatment Patient Complete Missing time WGS SCFA qPCR state points CD EEN 2 Yes 0, 2, 12 0, 2, 12 3 Yes 0, 2, 12 0, 2, 12 24 4 No 6, 8, 12, 24 0, 4 5 No 6, 8, 12, 24 0, 4 6 Yes 0, 2, 12 0, 2, 12 7 Yes 0, 2, 12 0, 2, 12 4, 24 8 Yes 0, 2, 12 0, 2, 12 4, 24 9 Yes 0, 2, 12 0, 2, 12 4, 24 10 Yes 0, 2, 12 0, 2, 12 11 No 4, 6, 8, 12, 24 0 12 No 8, 12, 24 0, 4 14 No 4, 6, 8, 12, 24 0 15 No 4, 6, 8, 12, 24 0 16 No 6, 8, 12, 24 17 Yes 0, 2, 12 0, 2, 12 18 Yes 0, 2, 12 0, 2, 12 19 Yes 0, 2, 12 0, 2, 12 20 Yes 0, 2, 12 0, 2, 12 24 21 Yes 0, 2, 12 0, 2, 12 23 No 4, 6, 8, 12, 24 25 Yes 0, 2, 12 0, 2, 12 24 PEN 50 Yes 51 Yes 52 Yes 53 No 4 55 Yes 56 Yes 59 Yes 61 Yes 62 Yes Healthy EEN 201 Yes 0, 2, 6 0, 2, 6 202 Yes 0, 2, 6 0, 2, 6 0, 2, 6 203 Yes 0, 2, 6 0, 2, 6 0, 2, 6 205 Yes 0, 2, 6 0, 2, 6 206 Yes 0, 2, 6 0, 2, 6 207 Yes 0, 2, 6 0, 2, 6 208 Yes 0, 2, 6 0, 2, 6 209 Yes 0, 2, 6 0, 2, 6 210 Yes 0, 2, 6 0, 2, 6 211 Yes 0, 2, 6 0, 2, 6 0, 2, 6 212 Yes 0, 2, 6 0, 2, 6 0, 2, 6 214 Yes 0, 2, 6 0, 2, 6 0, 2, 6 215 Yes 0, 2, 6 0, 2, 6 0, 2, 6 216 Yes 0, 2, 6 0, 2, 6 217 Yes 0, 2, 6 0, 2, 6 218 Yes 0, 2, 6 0, 2, 6 221 Yes 0, 2, 6 0, 2, 6
Table S2. Sample analysis meta-data. Showing available sample time points (weeks), and samples used for analyses. All samples shown here underwent 16S rRNA sequencing. 101
Observed species richness species Observed
Tested sample depth
Figure S1. Sample rarefaction at a sequencing depth of 15,000 reads allows for optimal alpha diversity measurement across samples. Observed species richness of 16S rRNA data from all available stool samples, rarefied to increasing sequencing depths. The optimal rarefaction depth of 15,000 reads, determined by levelling of observed species richness with sequencing depth, is indicated by the vertical line.
102 DNA substitution Log-likelihood Akaike Bayesian model of fit information information criterion (AIC) criterion (BIC)
JC -151928.8 318079.5 343875.6
JC + I -151711.7 317647.3 343446.9
JC + G -132959.8 280143.6 305943.3
JC + G + I -132944.2 280114.4 305917.7
F81 -151547.8 317323.6 343130.5
F81 + I -151321.6 316873.1 342683.6
F81 + G -132552.9 279335.9 305146.4
F81 + G + I -132439.7 279111.4 304925.5
K80 -148555.4 311334.9 337134.5
K80 + I -148337.3 310900.5 336703.8
K80 + G -129341.8 272909.6 298712.9
K80 + G + I -129326.6 272881.2 298688.1
HKY -148225.0 310680.0 336490.6
HKY + I -147991.9 310215.8 336030.0
HKY + G -128435.0 271102.0 296916.1
HKY + G + I -128452.4 271138.8 296956.6
SYM -147430.3 309092.6 334906.7
SYM + I -147228.7 308691.4 334509.2
SYM + G -128578.7 271391.5 297209.3
SYM + G + I -128565.7 271367.3 297188.7
GTR -147091.4 308420.7 334245.8
GTR + I -146886.7 308013.5 333842.2
GTR + G -128458.1 271156.3 296984.9
GTR + G + I -128452.7 271147.3 296979.6
Table S3. Fit of DNA substitution models provided by the phangorn function modelTest. Showing measures of model fit.
103 Relative abundance Relative
Sample Removed low-quality reads Removed "untrue" ASVs Removed unmerged reads
Removed unexpected-length reads Removed chimeras Utilised reads
Figure S2. Retention of reads through the dada2 ASV assignment pipeline. Relative abundance of read fates through each limiting step of the dada2 pipeline, in all samples. Showing all reads remaining after dada2 processing as “utilised reads”.
104 A
B
C
Figure S3. 16S rRNA sequencing and subsequent taxonomic assignment reflects expected composition of mock bacterial communities. Relative abundance of genera found in ZymoBIOMICS Microbial Community DNA Standards, against expected relative abundances defined by the manufacturers (Zymo Research), observed in A) Mock 1 (Spearman’s ! = 0.859; p < 0.0001), B) Mock 2 (Spearman’s ! = 0.859; p < 0.0001), or C) Mock 3 (Spearman’s ! = 0.838; p < 0.0001). The equivalence point for observed and expected relative abundance is indicated by the line. 105
Figure S4. CD does not observably alter total gut bacterial numbers. Showing stool 16S rRNA concentration (16S rRNA copies / ng DNA / mg stool) of CD patients and healthy people over EEN treatment. CD patient and healthy control samples compared by Wilcoxon rank sum test (ns p > 0.05).
106
Beta diversity metric
Comparison Group Bray-Curtis Weighted UniFrac Unweighted dissimilarity distance UniFrac distance
Disease state Pre-treatment 0.0340 ** 0.0581 ** 0.0546 **
Treatment EEN 0.0294 ** 0.0402 ** 0.0239
time PEN 0.0285 0.0269 0.0248
Healthy 0.0561 ** 0.0804 ** 0.0488
Table S4. R2 values for multivariate analysis of variances performed by the vegan function adonis. Showing R2 values for adonis tests comparing disease state and treatment time within the pre-treatment, EEN, PEN, and healthy control groups. Bold R2 values indicate a significant adonis test (** p < 0.01).
107