bioRxiv preprint doi: https://doi.org/10.1101/2020.05.27.120386; this version posted May 28, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
1 Fecal microbiota transplantation efficacy for Clostridium difficile infection: A
2 trans-kingdom battle between donor and recipient gut microbiomes
3 FMT trans-kingdom battle between donor and recipient
4 Negin Kazemian,a Milad Ramezankhani,a Aarushi Sehgal,b Faizan Muhammad
5 Khalid,a Amir Hossein Zeinali Kalkhoran,c Apurva Narayan,c Gane Ka-Shu
6 Wong,d,e,f Dina Kao,g* Sepideh Pakpoura*
7 aSchool of Engineering, University of British Columbia, Kelowna, BC, Canada
8 bDepartment of Computer Science and Engineering, National Institute of Technology, Hamirpur,
9 Himachal Pradesh, India
10 cDepartment of Computer Science, University of British Columbia, Kelowna, BC, Canada
11 dDepartment of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
12 eDepartment of Medicine, University of Alberta, Edmonton, Alberta, Canada
13 fBGI-Shenzhen, Beishan Industrial Zone, Yantian District, Shenzhen, China
14 gDivision of Gastroenterology, Department of Medicine, University of Alberta, Edmonton,
15 Alberta, Canada
16 * Corresponding authors
17 [email protected] (SP)
18 [email protected] (DK)
19
20
1 bioRxiv preprint doi: https://doi.org/10.1101/2020.05.27.120386; this version posted May 28, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
21 Abstract Fundamental restoration ecology and community ecology theories can help us better
22 understand the underlying mechanisms of fecal microbiota transplantation (FMT) and to better
23 design future microbial therapeutics for recurrent Clostridium difficile infections (rCDI) and other
24 dysbiosis-related conditions. In a single cohort study, stool samples were collected from donors
25 and rCDI patients one week prior to FMT (pre-FMT) as well as from patients one week following
26 FMT (post-FMT). Using metagenomic sequencing and machine learning methods, our results
27 suggested that the FMT outcome is not only dependent on the ecological structure of the
28 recipients, but also the interactions between the donor and recipient microbiomes, both at the
29 taxonomical and functional levels. Importantly, we observed that the presence of specific bacteria
30 in donors (Clostridiodes spp., Desulfovibrio spp., Odoribacter spp. and Oscillibacter spp.) and
31 the absence of specific fungi (Yarrowia spp.) and bacteria (Wigglesworthia spp.) in recipients
32 prior to FMT could accurately predict FMT success. Our results also suggested a series of
33 interlocked mechanisms for FMT success, including the repair of the disturbed gut microbial
34 ecosystem by transient colonization of nexus species followed by secondary succession of bile
35 acid metabolizers, sporulators, and short chain fatty acid producers. Therefore, a better
36 understanding of such mechanisms can be fundamental key elements to develop adaptive,
37 personalized microbial-based strategies for the restoration of the gut ecosystem.
38 Importance There have been a number of studies focusing on understanding the underlying
39 mechanisms in FMT treatment, which can accordingly be used for the optimization of future
40 treatments. However, the current scientific lens has mainly had a uni-kingdom major focus on
41 bacteria, leading to the proposition of the existence of FMT “super-donors”. On the contrary, our
42 preliminary study here suggests that FMT is not necessarily a ‘one stool fits all’ approach and
43 that donor-recipient cross-kingdom microbiota interactions, along with their short-term
2 bioRxiv preprint doi: https://doi.org/10.1101/2020.05.27.120386; this version posted May 28, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
44 fluctuations in the gut, bring profound implications in FMT success. The results
45 also conceptualize a series of interlocked mechanisms for FMT success, including first repairing
46 the disturbed gut microbial ecosystem by transient species, followed by secondary succession of
47 indigenous or exogenous bile acid metabolizers, sporulators, and short chain fatty acid producers.
48 Keywords: fecal microbiota transplantation (FMT), Clostridium difficile infection (CDI), gut
49 microbiome, fecal transplant, community restoration
50 Introduction
51 Antibiotics are the primary treatment method for Clostridium difficile infections (CDI);
52 however, the negative impact on the diversity, composition, and functionality of gut microbiota
53 results in recurrent CDI (rCDI) (1, 2) requiring fecal microbiota transplantation (FMT). FMT is a
54 strategy for the restoration of a disturbed microbial ecosystem and reinstatement of lost microbial
55 functional networks. Although highly effective in the treatment of rCDI as well as promising in
56 several other diseases (2-8), FMT carries infectious and non-infectious risks (9-12). In addition,
57 under each specific disease scenario, it is crucial to understand how microbial ecosystems
58 reassemble overtime after FMT and which microbial strains are the determining factors in this
59 dynamic process. For rCDI treatment, the current scientific lens has mainly had a uni-kingdom
60 major focus on bacteria. It has been suggested that an ideal donor should have high
61 Lachnospiraceae and Ruminococcaceae (13), which are also positively associated with secondary
62 bile acids that inhibit CDI germination (1). Increased Clostridium Scindens in donors has also
63 shown a positive correlation with FMT efficacy and outcomes via the production of secondary
64 bile acids (14). Moreover, FMT restores short chain fatty acids (SCFAs) metabolism, with
65 immune modulatory effects in rCDI patients (15). SCFAs and butyrate producing bacteria have
3 bioRxiv preprint doi: https://doi.org/10.1101/2020.05.27.120386; this version posted May 28, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
66 been found to decrease the induction of proinflammatory cytokines and promote the
67 differentiation of colonic Treg cells, leading to the attenuation of colitis in mice and humans (16,
68 17). In addition, anaerobic, endospore-forming Firmicutes are dominant members of gut
69 microbiota that can produce SCFAs (18), which allow organisms to enter metabolically dormant
70 states that aid in their survival and transmission to new hosts (19). Thus, the oral delivery of
71 SER-109, composed of sporulating bacteria, remains a promising therapeutic approach for rCDI
72 treatment (20, 21). Furthermore, a critical consideration for FMT efficacy and durability is that
73 the microbial consortium of the donors is not the only key player. The existing endogenous
74 microbiome in recipients can also play a significant role in determining the colonization of those
75 exogenous species. For example, focusing on bacterial engraftment, Smillie et al. (22) suggested
76 that selective forces in the patient’s gut (host control), rather than input dose dependence
77 (bacterial abundance in the donor and patient), determines bacterial abundance after FMT and,
78 subsequently, its efficacy. In contrast, a number of studies suggest that FMT success is only
79 dependent on the bacterial diversity and composition of the stool donor, leading to the
80 proposition of the existence of FMT super-donors (3, 23).
81 Beyond the gut bacterium, few studies have examined the role of gut mycobiome and
82 virome on FMT efficacy. For example, Zuo and colleagues found a negative relationship between
83 the abundance of fungi such as Candida albicans in donor stool and FMT efficacy (24). Over the
84 last decade, phages have gained increasing attention for therapeutic use due to their specificity
85 (25). The reduction in the abundance of Caudovirales bacteriophages and an increase in
86 Microviridae abundance, specifically higher abundance of Eel River basin pequenovirus as a
87 potential Proteobacteria predator, were shown to be related to FMT efficacy in CDI patients (26,
88 27). Using targeted refined phage therapy, Nale et al. (28) used a cocktail of four C. difficile
4 bioRxiv preprint doi: https://doi.org/10.1101/2020.05.27.120386; this version posted May 28, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
89 Myoviruses (CDHM1, 2, 5, and 6) to eradicate the CDI in a batch fermentation model, which
90 suggests that a combination of bacteriophages may be needed to treat CDI. More recently, rCDI
91 in five patients was prevented using sterile fecal filtrate, void of live bacteria (29). Contrary to
92 these, a study by Meader et al. (30) showed that bacteriophages alone weren’t sufficient to
93 eradicate CDI. These studies emphasize that in order to uncover mechanisms involved in FMT
94 efficacy, it is fundamental to include the relative contribution of all domains and consider the
95 microbiome-associated ecosystem heterogeneity in both donors and recipients. To this end, we
96 specifically investigated whether FMT super-donors exists for rCDI treatment, or whether the
97 donor-recipient compatibility and short-term fluctuations in the gut microbiomes (a combination
98 of bacteria, fungi, archaea, and viruses) of both donors and recipients have profound implications
99 in FMT success.
100 Results
101 Among recipients, 9/17 patients were successfully treated with a single FMT (53%
102 successful FMT), while 8 patients failed the first FMT and required a second procedure. There
103 was no difference between the two groups in factors of age, sex, or duration of CDI (31).
104 We found no significant difference in alpha diversities of different organisms in stool
105 samples provided by donors used for all patients whether the treatment outcome was successful
106 or not (Kruskal-Wallis test, p>0.05, Fig. 1A-E). For the recipients, no significant differences in
107 alpha diversities were observed between successful and failed pre-FMT samples (Kruskal-Wallis
108 test, Fig. 1). There was a significant increase in the bacterial (Fig. 1A) and fungal (Fig. 1C) alpha
109 diversities (Shannon diversity index) in post-FMT stool samples after successful FMT (Wilcoxon
110 test, adjusted p-value<0.0001 and p-value=0.002, respectively), but not failed ones. No
5 bioRxiv preprint doi: https://doi.org/10.1101/2020.05.27.120386; this version posted May 28, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
111 significant changes in this index were seen post-FMT in archaeal, protozoan, and viral diversities
112 (Fig. 1B, D, E). It is important to note that although no significant changes were observed for
113 viral diversities, the interplay between phages and bacteria in rCDI and FMT treatment is
114 intricate especially due to technical limitations of viral enrichment in biological samples,
115 extraction and sequencing library bias towards dsDNA viruses, and removal of ssDNA and RNA
116 viruses.
117 The microbial composition of donors, recipients pre-FMT, and recipients post-FMT also
118 differed from each other, as measured by the Bray-Curtis based density plots (ANOSIM,
119 R=0.686, p=0.001) (Fig. 1F). Specifically, pre-FMT, the microbial community of recipients
120 deviated from those of healthy individuals based on Bray-Curtis dissimilarities (ANOSIM,
121 R=0.920) while the microbial community structure of successful and failed donors overlapped
122 (ANOSIM, R=0.648) (Fig. 1F). After successful FMT, the recipients’ microbiome composition
123 resembled the donors (ANOSIM, R=0.595). However, the composition of failed FMT recipients
124 pre and post-FMT overlapped (ANOSIM, R= 0.719) (Fig. 1F).
125 Since the restoration of bile acid metabolism and SCFA production is reported to account
126 for FMT efficacy, we then extracted bacterial genera associated with these functions (15, 32), as
127 well as sporulating communities (33). Our results showed that successful and failed FMT donors
128 did not significantly differ in their alpha diversity for bile acid metabolizers, SCFA producers,
129 and sporulators (Fig. 2A, B, C). However, a significant increase in the alpha diversity was
130 observed in successful recipients post-FMT for bile acid metabolizers (Wilcoxon signed-rank
131 test, p=0.003), SCFA producers was observed after successful FMT (Wilcoxon signed-rank test,
132 p= 0.014), and sporulators (Wilcoxon signed-rank test, p=0.015) (Fig. 2A, B, C). The density plot
133 for Bray-Curtis dissimilarity showed that the failed and successful FMT donors were not
6 bioRxiv preprint doi: https://doi.org/10.1101/2020.05.27.120386; this version posted May 28, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
134 significantly different in their SCFA producing and sporulating communities (ANOSIM,
135 R=0.524 and ANOSIM, R=0.524) (Fig. 2E, F), but differed in their bile acid metabolizing
136 community structures (ANOSIM, R=0.828) (Fig. 2D). Specifically, the relative abundance of
137 bacterial bile acid metabolizers including Lactobacillus (associated with deconjugation and
138 esterification of bile salts), Fusobacterium (associated with desulfation of bile salts),
139 Pseudomonas (desulfation of bile salts), and Escherichia (oxidation and epimerization of bile
140 salts) were significantly lower in unsuccessful donor samples (Fig. 3A). Interestingly, intra-
141 variability within donors pertaining to the abundance of bacterial bile acid metabolizers can also
142 be observed (Fig. 3A), which shows that donor composition can vary over time and affect FMT
143 outcome. The successful FMT recipients pre- and post-FMT also differed in their community
144 structure for bile acid metabolizers (ANOSIM, R=0.670), SCFA producers (ANOSIM, R=0.759),
145 and sproulators (ANOSIM, R=0.872) while the failed FMT recipients pre and post-FMT
146 overlapped for the community structure of genera associated with these functions (ANOSIM,
147 R=0.091, R=0.117, and R=0.134, respectively) (Fig. 2D, E, F). Interestingly, the relative
148 abundance of all investigated bacterial bile acid producers was decreased in the failed FMT
149 recipients post-FMT compared to successful FMT recipients (Fig. 3C). Therefore, successful
150 FMT is associated with the colonization of bile acid metabolizing bacteria in the recipients.
151 In addition, we also investigated whether microbial community compositions of donor
152 and recipients before FMT can predict the treatment outcomes. The top 20 features from PCA
153 analysis were selected and employed in the subsequent training process of a classification model,
154 using samples from both donor and recipient pre-FMT at the genus level. Using LOO cross
155 validation, the prediction model was significant (p=0.0099) (Fig. 4A), with the most important
156 genera being Desulfovibrio, Filifactor, Bacillus, Yarrowia, Odoribacter, Wigglesworthia,
7 bioRxiv preprint doi: https://doi.org/10.1101/2020.05.27.120386; this version posted May 28, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
157 Oscillibacter, Intestinimonas, and Clostridiodes (Fig. 4B). Furthermore, in order to visualize the
158 impact of the top features on FMT efficiency, the relative abundance of such features was plotted
159 for donor and recipient samples pre- and post-FMT (Fig. 4C). Interestingly, the fungal genus of
160 Yarrowia, as well as bacterial genus of Wigglesworthia, were significantly higher in pre-FMT
161 failed recipients than pre-FMT successful recipients (Fig. 4C, Kruskal-Wallis, p=0.001 and
162 p=0.002, respectively). The donor samples that contributed to a successful FMT outcome had a
163 higher abundance of Clostridiodes (p=0.002), Desulfovibrio (p=0.004), Odoribacter (p=0.002),
164 and Oscillibacter (p=0.003) compared to failed FMT donors (Fig. 4C), and intra-variability in the
165 relative abundances of these genera for each donor was observed (Fig. S1) when comparing
166 successful and failed samples. It is important to note that, these genera were not detected in
167 successful recipients post-FMT (Fig. S1 and S2), indicating that long-term colonization of these
168 genera in recipients may not be critical for FMT success.
169 When we evaluated our model’s performance against an independent dataset (34, 35),
170 interestingly the model identified similar top features including Odoribacter and Clostrioides;
171 albeit with no statistically significant discriminatory powers. This was expected due to technical
172 variation between studies which overshadowed the biological variation as well as the lack of full
173 consistency between the two studies rooted in the difference in the average age or ethnicity of the
174 two cohorts.
175 Discussion
176 Despite long-term stability and plasticity of healthy and low to moderately disturbed gut
177 systems (36), severely damaged gut ecosystems are not self-renewing; therefore, FMT can help
178 with restoring damaged systems through (a) the recreation of the original ecosystem (e.g., by
8 bioRxiv preprint doi: https://doi.org/10.1101/2020.05.27.120386; this version posted May 28, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
179 autologous FMT) or (b) the construction of an entirely new and alternative ecosystem (e.g., by
180 allogeneic FMT). In our study, we showed that the success of gut ecological recovery through
181 FMT is dependent on several factors, including the donor gut microbiome (the presence of
182 specific bacteria) as well as the pre-FMT recipient gut community structures and recovering
183 habitat (the absence of specific bacteria and fungi) (Fig. 4). In addition, short-term fluctuations in
184 the gut microbiome of both donors and recipients have profound implications in FMT success by
185 producing temporary changes or loss of function (see supplemental materials Fig. S1 and S2; Fig.
186 3). Therefore, the notion of the “super-donor” is oversimplified due to the observed short-term
187 fluctuations, and a recipient’s microbiota may be just as important to consider when predicting
188 treatment outcomes, especially in other dysbiotic conditions such as ulcerative colitis.
189 Our results also showed that a trans-kingdom interaction between bacteria and fungi may
190 be important to consider in FMT outcomes. Considering ecological theories on community
191 construction and recovery after disturbance, we hypothesize that the first step of a successful
192 FMT is the colonization of “nexus species” including members of Desulfovibrio, Odoribacter,
193 Oscillibacter, and Clostridioides genera, as identified in two independent datasets (Fig. 5). These
194 are transient in the community development, but are ecosystem engineers that determine
195 secondary succession trajectories of the ecosystem (Fig. S1 and S2). For example, Odoribacter is
196 a known SCFA producer (37). Thus, its presence in the donor and the initial transfer to recipients
197 may contribute to decreased inflammation (38). In addition, the class Clostridia includes many
198 endospore-forming organisms that have the capacity to produce SCFAs (39, 40), which can
199 induce T regulatory cells and associated anti-inflammatory cytokines (17). Following a
200 successful repair, the secondary succession of endogenous or exogenous bile acid metabolizers
201 can restore microbial diversity (lost commensals) and a variety of ecosystem functions (41).
9 bioRxiv preprint doi: https://doi.org/10.1101/2020.05.27.120386; this version posted May 28, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
202 Namely, when bile acid metabolizers colonize the repaired gut ecosystem, secondary bile acid
203 concentrations, as pleiotropic signaling molecules in the gut, liver, and systemic circulation,
204 increases (42). This process entails the germination of endogenous or exogenous sporulators such
205 as Clostridia and other putative endospore formers, which are considered stress-resistant and are
206 particularly adaptive to cross-host dissemination (19, 43). Aligned with the above hypothesized
207 mechanism, donors that led to a failed FMT had reduced Fusobacterium and Pseudomonas
208 genera, which are both capable of desulfating primary bile acids. When these genera exist,
209 sulfation can reduce primary bile acid toxicity and increase secondary bile acid excretion via
210 urine and feces (44). This reduced desulfation capacity in failed donor samples further
211 perpetuates the already existing disturbed bile acid pool and inhibits successful secondary
212 colonization for functional ecosystem restoration. Moreover, bacterial genera, which can
213 dehydroxylate primary bile acids into secondary bile acids, are also known to produce SCFAs
214 (38). These gut microbiota associated metabolites, especially butyrate, are a main source of
215 energy for colonocytes and can activate G-protein coupled receptors that regulate intestinal
216 motility and inflammation (38, 45). Lack of such genera in donor samples may diminish the
217 therapeutic potential of FMT.
218 However, interestingly, the presence of the Yarrowia and Wigglesworthia genera in pre-
219 FMT recipients can act as a barrier for the establishment of repair or successful secondary
220 colonization for functional ecosystem restoration (Fig. 4C). This can be due to nutrient cycling
221 and carbon uptake elevation by fungal activity. Moreover, Yarrowia lipolytica has been vastly
222 studied as a non-conventional yeast species capable of synthesizing a group of metabolites, in
223 particular lipases and other hydrolytic enzymes (46). These opportunistic fungal pathogens can
224 cause infections in immunocompromised and critically ill patients (47-49). To overcome this
10 bioRxiv preprint doi: https://doi.org/10.1101/2020.05.27.120386; this version posted May 28, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
225 challenge, treatment targeted at these fungal elements prior to FMT may potentially enhance
226 treatment efficacy.
227 In summary, the findings presented herein shed light on potential interlocked mechanisms
228 underlying FMT treatment success, beyond the bacterial community effect. Additionally, FMT is
229 not a ‘one stool fits all’ approach and a more personalized treatment with the inclusion of both
230 donor and patient variables should be taken into consideration to maximize the chance of FMT
231 success. Further knowledge of these factors and mechanisms would be required to optimize FMT
232 treatment for rCDI and possibly other dysbiotic-related diseases. However, the results should not
233 yet be generalized to other patient populations with different demographic characteristics, since
234 our study cohort was small with 88% Caucasian. This signifies the need for larger cohort studies
235 that include patients with diverse demographic characteristics.
236 Materials and Methods
237 Study design and sample collection. Seventeen adult male and female patients who
238 received FMT for rCDI at the University of Alberta Hospital in Edmonton, Alberta, Canada,
239 between October 2012 and November 2014 were included in this study (31). Criteria for
240 receiving FMT were 1) at least 2 recurrent episodes of mild to moderate CDI, or 2) at least 1
241 recurrent episode of CDI requiring hospitalization. This study was approved by the University of
242 Alberta Health Research Ethics Board, and all participants provided written informed consent.
243 All participants received FMT by colonoscopy, with stool samples from unrelated donors
244 registered with the Edmonton FMT program. Donor selection criteria and screening process have
245 been described previously (50). After a failed FMT, each patient received FMT from the same
246 donor or a different donor, depending on donor availability. Patients discontinued antibiotics for
11 bioRxiv preprint doi: https://doi.org/10.1101/2020.05.27.120386; this version posted May 28, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
247 CDI 24 hours prior to FMT and took 4 L of polyethylene glycol-based bowel preparation
248 (GoLYTELY) one day prior to FMT. Stool samples were collected from donors and patients one
249 week prior to FMT (pre-FMT) as well as from patients one week following FMT (post-FMT).
250 Figure 6 shows the number of donors and recipients, as well as the FMT treatment outcomes. It’s
251 important to note that although some donors had provided multiple stool samples, these samples
252 were provided at different time points (minimum of a one-week gap), which were then
253 administered to the recipients (Fig. 6). It has been perceived that the autocorrelation between
254 microbiomes of stool samples of a given donor normally diminishes between 3-5 days (51).
255 Metagenomic data collection. DNA from stool samples were extracted using the Qiagen
256 QIAamp DNA stool kit. Shotgun sequencing for metagenomics was applied using the Nextera
257 XT DNA Sample Preparation Kit, and Illumina MiSeq platform was performed as previously
258 described (31). Host DNA was detected and reads were removed by mapping with the GEM
259 program to the human genome with inclusive parameters (52). A custom Kraken database was
260 built of whole genomes of bacteria, viruses, archaea, eukaryote, and viroids (53). The Bayesian
261 Reestimation of Abundance with KrakEN (Bracken) algorithm was used (kmer length of 30 and
262 read length of 100 bp) to compute the abundance of species in DNA sequences originating from
263 each metagenomic sample (54). Singletons, as well as those taxa occurring in only one or two
264 samples, were removed and abundances of different microbial genera were obtained by
265 collapsing detected taxonomies to the genus level and summing features within the same genera.
266 Subsequently, taxa abundances were normalized by the total number of reads sequenced in each
267 sample.
268 Statistical analysis. The α-diversity (Shannon diversity index) of successful and failed
269 FMT samples were compared for all organisms using the R package Vegan (55). In addition, α-
12 bioRxiv preprint doi: https://doi.org/10.1101/2020.05.27.120386; this version posted May 28, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
270 diversity of bacteria related to bile acid metabolizers (32). SCFA producing genera (15) and
271 sporulators (33) were compared for successful and failed FMT samples. Significant differences in
272 α-diversity were determined using the non-parametric Kruskal-Wallis and Wilcoxon signed-rank
273 test for unpaired and paired samples (pre- and post-FMT samples of recipients), respectively,
274 using Bonferroni correction to adjust the probability. Differences among community structures
275 across samples (β-diversity) were calculated using the Bray-Curtis dissimilarity metric using the
276 R package Vegan and visualized via density plots using custom python scripts (55). Significant
277 differences in β-diversity across donors and recipients were evaluated using analysis of
278 similarities (ANOSIM) (56). Heatmap clustering graphs were constructed using the R pheatmap
279 package to visualize the relative abundance of major bile acid producers in donors and recipients
280 before and after FMT (57).
281 To test whether donor and recipient microbial composition can predict FMT outcome, we
282 trained a Random Forest (RF) model on pre-treatment samples of both donors and recipients at
283 the genus leve (58). The microbial taxa of both donors and recipients constitute the feature space
284 of the model and the following steps were performed using the Python library, Scikit-learn (59).
285 As the features’ count outnumbers that of the test samples, a dimensionality reduction method
286 was implemented so that the trained model avoids overfitting and generalizes better on the test
287 data (60). Thus, the Principal Component Analysis (PCA) was used to exploit the features which
288 describe the principal components the most. The top 20 features from this analysis were selected
289 to be employed in the training process of the RF model. In order to assess how well the trained
290 classifier generalizes in case of unseen data, the Leave One Out (LOO) cross-validation method
291 was employed. In this method, each data point was used once as a test data, while the classifier
292 was trained on the remaining data points. Subsequently, the cross-validation error value was
13 bioRxiv preprint doi: https://doi.org/10.1101/2020.05.27.120386; this version posted May 28, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
293 calculated by averaging all the measured test errors. For each LOO data subset, the Receiver
294 Operating Characteristic (ROC) curve was plotted. Next, the RF classifiers with the highest
295 validation scores were compared by implementing a statistical significance test. Herein,
296 McNemar’s test was used to determine the statistical significance of the difference between the
297 predictive performance of the top RF candidates (61). The RF model identified to be the most
298 precise was then employed to find the most important features in the FMT treatment outcome
299 task. After running the model 100 times, the average Mean Decrease in Impurity (MDI) of the
300 most important features were also calculated (62). Subsequently, the Kruskal-Wallis test with the
301 Bonferroni correction to adjust the probability was utilized to compare the relative abundance of
302 the top important features across the samples. Lastly, in an attempt to evaluate our model’s
303 performance and its generalizability another independent dataset was used (34, 35). This dataset
304 consisted of DNA extracted from 5 fecal samples from 3 donors, and 5 fecal samples from each
305 of 10 FMT recipients: collected at day 0 (pre-FMT) and days 2, 14, 42 and 84 after FMT.
306
307 Supplemental Material
308 Fig. S1 The intra-variability of relative abundance of the top 9 features of donor 1 (D1) samples
309 and their corresponding recipients, pre- and post-FMT leading to successful (A and B) and failed
310 (C and D) FMT outcomes.
311
312 Fig. S2 The intra-variability of relative abundance of the top 9 features of donor 3 (D3) samples
313 and their corresponding recipients, pre- and post-FMT leading to successful (A and B) and failed
314 (C and D) FMT outcomes.
315
316 Acknowledgments
14 bioRxiv preprint doi: https://doi.org/10.1101/2020.05.27.120386; this version posted May 28, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
317 Author Contributions
318 N.K and S.P wrote the first draft of the manuscript. SP assisted in reviewing literature, guided the
319 analysis, and provided intellectual input in the manuscript. M.R, A.S, and A.N aided with the
320 data analysis. N.K, M.R, A.N., G.K.-S.W, D.K, and S.P reviewed and edited the manuscript.
321 F.M.K and A.H.Z.K contributed to the metagenomic sequence handling and processing. N.K,
322 M.R, A.N., G.K.-S.W, D.K, and S.P actively contributed to the critical discussions. The authors
323 read and approved the final manuscript.
324
325 Disclosure of potential conflicts of interest
326 The authors report no potential conflict of interest.
327
328 References 329 330 1. Bajaj J, Savidge T, Kassam Z, Hylemon P, Gillevet P. 2018. Antibiotic-associated 331 disruption of microbiota composition and function in cirrhosis is restored by fecal 332 transplant reply. Hepatology 68:1206-1206. 333 2. Pamer E. 2016. Resurrecting the intestinal microbiota to combat antibiotic-resistant 334 pathogens. Science 352:535-538. 335 3. Moayyedi P, Surette MG, Kim PT, Libertucci J, Wolfe M, Onischi C, Armstrong D, Lee 336 CH. 2015. Fecal microbiota transplantation induces remission in patients with active 337 ulcerative colitis in a randomized controlled trial. Gastroenterology 149:102-109. 338 4. Johnsen PH, Hilpusch F, Cavanagh JP, Leikanger IS, Kolstad C, Valle PC, Goll R. 2018. 339 Faecal microbiota transplantation versus placebo for moderate-to-severe irritable bowel 340 syndrome: A double-blind, randomised, placebo-controlled, parallel-group, single-centre 341 trial. Lancet Gastroenterol 3:17-24. 342 5. Bajaj JS, Kassam Z, Fagan A, Gavis EA, Liu E, Cox IJ, Kheradman R, Gillevet PM. 343 2017. Fecal microbiota transplant from a rational stool donor improves hepatic 344 encephalopathy: A randomized clinical trial. Hepatology 66:1727-1738. 345 6. Kang D, Adams JB, Gregory AC, Borody T, Chittick L, Fasano A, Khoruts A, 346 Krajmalnik-Brown R. 2017. Microbiota transfer therapy alters gut ecosystem and 347 improves gastrointestinal and autism symptoms: An open-label study. Microbiome 5:10. 348 7. He Z, Cui BT, Zhang T, Li P, Long CY, Ji GZ, Zhang FM. 2017. Fecal microbiota 349 transplantation cured epilepsy in a case with crohn's disease: The first report. World J 350 Gastroenterol 23:3565-3568.
15 bioRxiv preprint doi: https://doi.org/10.1101/2020.05.27.120386; this version posted May 28, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
351 8. van Nood E, Vrieze A, Nieuwdorp M, Fuentes S, Zoetendal EG, de Vos WM, Visser CE, 352 Kuijper EJ, Keller JJ. 2013. Duodenal infusion of donor feces for recurrent Clostridium 353 difficile. N Engl J Med 368:407-415. 354 9. DeFilipp Z, Bloom PP, Torres Soto M, Mansour MK, Sater MRA, Huntley MH, Turbett 355 S, Chung RT, Hohmann EL. 2019. Drug-resistant E. coli bacteremia transmitted by fecal 356 microbiota transplant. N Engl J Med 381:2043-2050. 357 10. Alang N, Kelly C. 2015. Weight gain after fecal microbiota transplantation. Open Forum 358 Infect Dis 2:ofv004. 359 11. Schwartz M, Gluck M, Koon S. 2013. Norovirus gastroenteritis after fecal microbiota 360 transplantation for treatment of Clostridium difficile infection despite asymptomatic 361 donors and lack of sick contacts. Am J of Gastroenterol 108:1367. 362 12. Quera R, Espinoza R, Estay C, Rivera D. 2014. Bacteremia as an adverse event of fecal 363 microbiota transplantation in a patient with crohn's disease and recurrent Clostridium 364 difficile infection. J Crohn's Colitis 8:252-253. 365 13. Taur Y, Coyte K, Schluter J, Robilotti E, Figueroa C, Gjonbalaj M, Littmann ER, Ling L, 366 Xavier J. 2018. Reconstitution of the gut microbiota of antibiotic-treated patients by 367 autologous fecal microbiota transplant. Sci Transl Med 10:eaap9489. 368 14. Buffie CG, Bucci V, Stein RR, McKenney PT, Ling L, Gobourne A, No D, Liu H, Parmer 369 EG. 2015. Precision microbiome restoration of bile acid-mediated resistance to 370 Clostridium difficile. Nature 517:205-208. 371 15. Seekatz AM, Theriot CM, Rao K, Chang Y, Freeman AE, Kao JY, Young VB. 2018. 372 Restoration of short chain fatty acid and bile acid metabolism following fecal microbiota 373 transplantation in patients with recurrent Clostridium difficile infection. Anaerobe 53:64- 374 73. 375 16. Crothers J, Kassam Z, Smith M, Phillips M, Vo E, Velez M, Cohn AH, Collins C. 2018. 376 Tu1893 - A double-blind, randomized, placebo-control pilot trial of fecal microbiota 377 transplantation capsules from rationally selected donors in active ulcerative colitis. 378 Gastroenterology 154:S-1050-S-1051. 379 17. Atarashi K, Tanoue T, Oshima K, Suda W, Nagano Y, Nishikawa H, Fukuda S, Saito T, 380 Honda K. 2013. Treg induction by a rationally selected mixture of clostridia strains from 381 the human microbiota. Nature 500:232-236. 382 18. Browne H, Forster S, Anonye B, Kumar N, Neville B, Stares M, Goulding D, Lawley T. 383 2016. Culturing of 'unculturable' human microbiota reveals novel taxa and extensive 384 sporulation. Nature 533:543. 385 19. Kearney SM, Gibbons SM, Poyet M, Gurry T, Bullock K, Allegretti JR, Clish CB, Alm 386 EJ. 2018. Endospores and other lysis-resistant bacteria comprise a widely shared core 387 community within the human microbiota. ISME J 12:2403-2416. 388 20. Khanna S, Pardi DS, Kelly CR, Kraft CS, Dhere T, Henn MR, Lombardo MJ, Hohmann 389 EL. 2016. A novel microbiome therapeutic increases gut microbial diversity and prevents 390 recurrent Clostridium difficile infection. J Infect 214:173-181. 391 21. Gerding DN, Meyer T, Lee C, Cohen SH, Murthy UK, Poirier A, Van Schooneveld TC, 392 Villano S. 2015. Administration of spores of nontoxigenic clostridium difficile strain M3 393 for prevention of recurrent C. difficile infection: A randomized clinical trial. JAMA 394 313:1719-1727. 395 22. Smillie CS, Sauk J, Gevers D, Friedman J, Sung J, Youngster I, Hohmann EL, Alm EJ. 396 2018. Strain tracking reveals the determinants of bacterial engraftment in the human gut 397 following fecal microbiota transplantation. Cell Host Microbe 23:229-240.
16 bioRxiv preprint doi: https://doi.org/10.1101/2020.05.27.120386; this version posted May 28, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
398 23. Paramsothy S, Kamm MA, Kaakoush NO, Walsh AJ, van den Bogaerde J, Samuel D, 399 Leong RWL, Connor S, Ng W, Paramsothy R, Xuan W, Lin E, Mitchell HM, Borody TJ. 400 2017. Multidonor intensive faecal microbiota transplantation for active ulcerative colitis: 401 A randomised placebo-controlled trial. Lancet 389:1218-1228. 402 24. Zuo T, Wong S, Cheung C, Lam K, Lui R, Cheung K, Zhanf F, Ng S. 2018. Gut fungal 403 dysbiosis correlates with reduced efficacy of fecal microbiota transplantation in 404 Clostridium difficile infection. Nature 9:1-11. 405 25. Wittebole X, De Roock S, Opal SM. 2014. A historical overview of bacteriophage 406 therapy as an alternative to antibiotics for the treatment of bacterial pathogens. Virulence 407 5: 226-235. 408 26. Zuo T, Wong SH, Lam K, Lui R, Cheung K, Tang W, Ching JYL, Ng SC. 2017. 409 Bacteriophage transfer during faecal microbiota transplantation in Clostridium difficile 410 infection is associated with treatment outcome. Gut 67:634-643. 411 27. Bryson SJ, Thurber AR, Correa AMS, Orphan VJ, Vega Thurber R. 2015. A novel sister 412 clade to the enterobacteria microviruses (family microviridae) identified in methane seep 413 sediments. Environ Microbiol 17:3708-3721. 414 28. Nale JY, Redgwell TA, Millard A, Clokie MRJ. 2018. Efficacy of an optimised 415 bacteriophage cocktail to clear Clostridium difficile in a batch fermentation model. 416 Antibiotics 7:13. 417 29. Ott SJ, Waetzig GH, Rehman A, Moltzau-Anderson J, Bharti R, Grasis JA, Cassidy L, 418 Schreiber S. 2027. Efficacy of sterile fecal filtrate transfer for treating patients with 419 Clostridium difficile infection. Gastroenterology 152:799-811. 420 30. Meader E, Mayer MJ, Steverding D, Carding SR, Narbad A. 2013. Evaluation of 421 bacteriophage therapy to control Clostridium difficile and toxin production in an in vitro 422 human colon model system. Anaerobe 22:25-30. 423 31. Millan B, Park H, Hotte N, Mathieu O, Burguiere P, Tompkins T, Kao D, Madsen KL. 424 2016. Fecal microbial transplants reduce antibiotic-resistant genes in patients with 425 recurrent Clostridium difficile infection. Clin Infect Dis 62:1479-1486. 426 32. Gérard P. 2013. Metabolism of cholesterol and bile acids by the gut microbiota. 427 Pathogens (Basel, Switzerland) 3:13-24. 428 33. Lagier J, Cadoret F, Raoult D. 2017. Critical microbiological view of SER-109. J Infect 429 215:161-162. 430 34. Li SS, Zhu A, Benes V, Costea PI, Hercog R, Hildebrand F, Huerta-Cepas J, Nieuwdorp 431 M, Salojarvi J, Voigt AY, Zeller G, Sunagawa S, de Vos WM, Bork P. 2015. Durable 432 coexistence of donor and recipient strains after fecal microbiota transplantation. Science 433 352:586-589. 434 35. Vrieze A, Van Nood E, Holleman F, Salojärvi J, Kootte RS, Bartelsman JF, Dallinga- 435 Thie GM, Ackermans MT, Serlie MJ, Oozeer R, Derrien M, Nieuwdorp M. 2012. 436 Transfer of intestinal microbiota from lean donors increases insulin sensitivity in 437 individuals with metabolic syndrome. Gastroenterology 143:913-916.e7. 438 36. David LA, Materna AC, Friedman J, Campos-Baptista MI, Blackburn MC, Perrotta A, 439 Erdman SE, Alm EJ. 2014. Host lifestyle affects human microbiota on daily timescales. 440 Genome Biol 15:R89-R89. 441 37. Goker M, Gronow S, Zeytun A, Nolan M, Lucas S, Lapidus A, Hammon N, Klenk HP. 442 2011. Complete genome sequence of odoribacter splanchnicus type strain (1651/6(T)). 443 Stand Genomic Sci 4:200-209.
17 bioRxiv preprint doi: https://doi.org/10.1101/2020.05.27.120386; this version posted May 28, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
444 38. Ríos-Covián D, Ruas-Madiedo P, Margolles A, Gueimonde M, Reyes-Gavilán dL, Clara 445 G S, N. 2016. Intestinal short chain fatty acids and their link with diet and human health. 446 Front Microbiol 7:185. 447 39. Van den Abbeele P, Belzer C, Goossens M, Kleerebezem M, M. DVW, Thas O, De 448 Weirdt R, Van de Wiele T. 2013. Butyrate-producing clostridium cluster XIVa species 449 specifically colonize mucins in an in vitro gut model. ISME J 7:949-961. 450 40. Paredes-Sabja D, Torres JA, Setlow P, Sarker MR. 2008. Clostridium perfringens spore 451 germination: Characterization of germinants and their receptors. J Bacteriol 190:1190- 452 1201. 453 41. Whisenant SG. 1999. Repairing damaged wildlands: A process-oriented, landscape-scale 454 approach. Cambridge University Press, New York, USA. 455 42. Trauner M, Fickert P, Tilg H. 2013. Bile acids as modulators of gut microbiota linking 456 dietary habits and inflammatory bowel disease: A potentially dangerous liaison. 457 Gastroenterology 144:844-846. 458 43. Filippidou S, Junier P, Junier T, Wunderlin T, Lo C, Li P, Chain PS, Junier P. 2015. 459 Under-detection of endospore-forming firmicutes in metagenomic data. Comput Struct 460 Biotec 13:299. 461 44. Alnouti Y. 2009. Bile acid sulfation: A pathway of bile acid elimination and 462 detoxification. Toxicol Sci 108: 225-246. 463 45. Jung T, Park JH, Jeon W, Han K. 2015. Butyrate modulates bacterial adherence on 464 LS174T human colorectal cells by stimulating mucin secretion and MAPK signaling 465 pathway. Nutr Res Pract 9:343-349. 466 46. Fabiszewska AU, Stolarzewicz IA, Zamojska WM, Białecka-Florjańczyk E. 2014. 467 Carbon source impact on yarrowia lipolytica KKP 379 lipase production. Appl Biochem 468 Microbiol 50:404-410. 469 47. Gouba N, Drancourt M. 2015. Digestive tract mycobiota: A source of infection. Med Mal 470 Infect 45:9-16. 471 48. Boyd AS, Wheless L, Brady BG, Ellis D. 2017. Cutaneous yarrowia lipolytica infection 472 in an immunocompetent woman. JAAD Case Rep 3:219-221. 473 49. Zieniuk B, Fabiszewska A. 2019. Yarrowia lipolytica: A beneficious yeast in 474 biotechnology as a rare opportunistic fungal pathogen: A minireview. World J Microb 475 Biot 35:1-8. 476 50. Kao D, Roach B, Silva M, Beck P, Rioux K, Kaplan GG, Chang HJ, Coward S, Louie T. 477 2017. Effect of oral capsule– vs colonoscopy-delivered fecal microbiota transplantation 478 on recurrent Clostridium difficile infection: A randomized clinical trial. JAMA 318:1985- 479 1993. 480 51. Gibbons S, Kearney S, Smillie C, Alm E. 2017. Two dynamic regimes in the human gut 481 microbiome. Plos Comput Biol 13:e1005364. 482 52. Guo Y, Mahony S, Gifford DK. 2012. High resolution genome wide binding event 483 finding and motif discovery reveals transcription factor spatial binding constraints. Plos 484 Comput Biol 8:e1002638-e1002638. 485 53. Wood DE, Lu J, Langmead B. 2019. Improved metagenomic analysis with kraken 2. 486 Genome Biol 20:257-13. 487 54. Lu J, Breitwieser FP, Thielen P, Salzberg SL. 2017. Bracken: Estimating species 488 abundance in metagenomics data. PeerJ Comput Sci 3:e104.
18 bioRxiv preprint doi: https://doi.org/10.1101/2020.05.27.120386; this version posted May 28, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
489 55. Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, Minchin PR, 490 O’Hara RB, Simpson GL, Wagner H. 2017. Vegan: Community Ecology Package. R 491 package version 2.4-6. https: // CRAN.R-project.org/ package=vegan. 492 56. Clarke KR. 1993. Non-parametric multivariate analyses of changes in community 493 structure. Austral Ecol 18:117-143. 494 57. Kolde R. 2019. pheatmap: Pretty Heatmaps https://cran.r- 495 project.org/web/packages/pheatmap/index.html. 496 58. Cutler DR, Edwards TC, Beard KH, Cutler A, Hess KT, Gibson J, Lawler JJ. 2007. 497 Random forests for classification in ecology. Ecology 88:2783-2792. 498 59. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Duchesnay É. 499 2011. Scikit-learn: Machine learning in python. J Mach Learn Res 12:2825-2830. 500 60. Cao LJ, Chua KS, Chong WK, Lee HP, Gu QM. 2003. A comparison of PCA, KPCA and 501 ICA for dimensionality reduction in support vector machine. Neurocomputing 55:321- 502 336. 503 61. Dietterich TG. 1998. Approximate statistical tests for comparing supervised classification 504 learning algorithms. Neural Comput 10:1895-1923. 505 62. Louppe G, Wehenkel L, Sutera A, Geurts P. Understanding variable importances in 506 forests of randomized trees. In: Neural Information Processing Systems 26 (NIPS 2013); 507 2013; California, US. 508 509 Fig 1 Gut microbial diversity of FMT donors and recipients. The α-diversity (Shannon index) of
510 (A) bacteria, (B) archaea, (C) fungi, (D) protozoa, and (E) viruses of donors, recipients pre- and
511 post-FMT for successful and failed FMT outcomes of rCDI patients. Significant differences were
512 determined using the Kruskal-Wallis and Wilcoxon signed-rank tests for unpaired and paired (i.e.
513 when analysing pre- and post-FMT of recipients) samples, respectively, followed by Bonferroni
514 post-hoc correction. Adjusted p-values were defined at *p<0.05, **p<0.01, ***p<0.001, and
515 ****p<0.0001. The Beta diversity was calculated for all microorganisms (F) using the Bray-
516 Curtis dissimilarity and analyzed using ANOSIM.
517 Fig 2 Gut microbial diversity of bile acid metabolizers, SCFA producers, and sporulators. The α-
518 diversity (Shannon index) of (A) bile acid metabolizers, (B) SCFA producers, and (C)
519 sporulating bacteria of donors, recipients pre- and post-FMT for successful and failed FMT
520 outcomes. Significant differences were determined using the Kruskal-Wallis and Wilcoxon
521 signed-rank tests for unpaired and paired samples, respectively, followed by the Bonferroni post-
19 bioRxiv preprint doi: https://doi.org/10.1101/2020.05.27.120386; this version posted May 28, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
522 hoc correction. Adjusted p-values were defined at *p<0.05, **p<0.01, ***p<0.001, and
523 ****p<0.0001. The Beta diversity was also calculated using the Bray-Curtis distance-based
524 density plots (D, E, F) and analyzed using ANOSIM.
525 Fig 3 Bile acid metabolizers of FMT donors and recipients. Heatmap of bile acid metabolizers of
526 (A) donors (D1-D4), (B) recipients pre-FMT, and (C) recipients post-FMT for successful and
527 failed FMT outcomes. The dendrogram shows clustering based on the relative abundances. The
528 heatmap color (white to dark blue, corresponding to low to high) represents the row z-score of the
529 mean relative abundance values.
530 Fig 4 Machine learning model predicting FMT outcome. The random forest model utilized led to
531 (A) the ROC curve displaying an AUC of 98%, (B) the top 9 most important microbes (Blue
532 ellipses represent bacteria; Yellow ellipse represent fungi) involved in FMT success prediction
533 where the size of the ellipses represents the feature importance magnitudes (average Mean
534 Decrease in Impurity), and (C) the relative abundance (shown on logarithmic scale) for the top 9
535 features of donor, and recipient samples pre- and post-FMT.
536 Fig 5 Multifaceted mechanisms affecting FMT treatment outcome. FMT treatment outcome of
537 (A) successful FMT recipients, and (B) failed FMT recipients. A successful treatment outcome
538 includes the repair of the disturbed gut microbial ecosystem by transient colonization of nexus
539 species followed by secondary succession of bile acid metabolizers, sporulators, and short chain
540 fatty acid producers. A failed treatment outcome may be due to the presence of fungal and
541 bacteria genera including Yarrowia and Wigglesworthia in recipients, minimizing the
542 establishment of repair or successful secondary colonization for functional ecosystem restoration.
20 bioRxiv preprint doi: https://doi.org/10.1101/2020.05.27.120386; this version posted May 28, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
543 Fig 6 The experimental data structure of stool samples collected. Samples were collected from 4
544 donors (D1 to D4) and 17 patients one week prior to FMT (pre-FMT) and patients one week
545 following FMT (post-FMT). For D1 and D3, multiple independent samples were taken for
546 different patients. Green line indicates a successful FMT outcome and a red line indicates a failed
547 FMT outcome.
21 Shannon Diversity Shannon Diversity Shannon Diversity bioRxiv preprint 1 1 3 3 2 2 1 0 0 4 4 3 2 0 4 (E) (C) (A) Successful FMT Successful FMT Successful FMT was notcertifiedbypeerreview)istheauthor/funder.Allrightsreserved.Noreuseallowedwithoutpermission. doi: https://doi.org/10.1101/2020.05.27.120386 *** ** V Bacteria Fungi iruses Failed FMT Failed FMT Failed FMT ; this versionpostedMay28,2020.
Density 0.10 0.15 0.20 0.25 0.05 Shannon Diversity Shannon Diversity 1 1 2 3 2 3 0 4 0 4 0 -0.4 (F) (B) (D) Successful FMT Successful FMT 020.0 -0.2 The copyrightholderforthispreprint(which Bray–Curtis Dissimilarity All Organisms Archaea Protozoa (R=0.686) . . 0.6 0.4 0.2 Failed FMT Failed FMT Alpha Diversity Beta Diversity Recipients - Recipients -BeforeFMT Donors Recipients -BeforeFailedFMT Donoros -SuccessfulFMT Donors -FailedFMT Recipients - Recipients -BeforeSuccessful FMT Recipients - After FailedFMT After -FMT After Successful FMT bioRxiv preprint doi: https://doi.org/10.1101/2020.05.27.120386; this version posted May 28, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
Alpha Diversity (A) Bile Acid Metabolizers (B) (C) 4 SCFA Producers Sporulators Donors *** Recipients Before - FMT 3 Recipients After - FMT * 2 **
1 Shannon Diversity 0
Successful FMT Failed FMT Successful FMT Failed FMT Successful FMT Failed FMT
Beta Diversity (D) Bile Acid Metabolizers (E) SCFA Producers (F) Sporulators (R=0.456) (R=0.542) (R=0.396) Failed FMT Successful FMT 0.90 Donors Before Failed FMT After Failed FMT Before Successful FMT 0.60 Recipients After Successful FMT Density
0.30
0
-0.4 -0.2 0.0 0.2 -0.2 -0.0 0.2 0.4 -0.2 0.0 0.2 Bray–Curtis Dissimilarity Bray–Curtis Dissimilarity Bray–Curtis Dissimilarity bioRxiv preprint doi: https://doi.org/10.1101/2020.05.27.120386; this version posted May 28, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
Donoros - Successful FMT 3 Donors - Failed FMT 2 Recipients - Before Successful FMT 1 0 Recipients - Before Failed FMT −1 Recipients - After Successful FMT −2 Recipients - After Failed FMT −3 (A) (B) (C)
Bacteroides Ruminococcus Ruminococcus Ruminococcus Clostridium Clostridium Clostridium Eubacterium Pseudomonas Bifidobacterium Pseudomonas Fusobacterium Listeria Escherichia Bifidobacterium Lactobacillus Bacteroides Listeria Fusobacterium Lactobacillus Lactobacillus Pseudomonas Fusobacterium Eubacterium Escherichia Bifidobacterium Bacteroides Eubacterium Listeria Eggerthella Eggerthella Eggerthella Escherichia
D1 D3 D2 D1 D3 D4 bioRxiv preprint doi: https://doi.org/10.1101/2020.05.27.120386; this version posted May 28, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
(A) (B) 1.0 Bacillus Yarrowia 0.0466 0.8 0.0457 Filifactor 0.0498 0.6 Odoribacter 0.0454 Clostridioides Intestinimonas 0.0435 0.0438
0.4 Wigglesworthia Oscillibacter 0.0452 0.0447
True Positive Rate True AUC = 0.98 Desulfovibrio 0.2 Luck 0.0518 Mean ROC 0.0
0.0 0.2 0.4 0.6 0.8 1.0 False Positive Rate (C) 1 Failed FMT Successful FMT 0.1 Recipient Before FMT 0.01 Recipient After FMT Donor 0.001
0.0001
Relative Abundance (Log Scale) Abundance (Log Relative 0.00001
0.00000
Bacillus ibacter Filifactor Yarrowia Odor Clostridioides Desulfovibrio Oscillibacter Intestinimonas Wigglesworthia bioRxiv preprint doi: https://doi.org/10.1101/2020.05.27.120386; this version posted May 28, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
A) Clolonization of Perturbation 2o Succession Healthy State Transient Species
Dysbiosis Clostridiodes 2o bile acid Homeostasis Desulfovibrio SCFA Odoribacter Pathogenic Sporulators Colonization Community Oscillibacter Resistance Metabolic Metabolic Function Mutualism
1o bile acid Sporulators
2o bile acid SCFA 2o bile acid SCFA Less Mucus Maintained Layer Mucus Layer Successful FMT Disturbance
B)
Failed Clolonization Perturbation Failed 2O Succession Perturbation of Transient Species
Dysbiosis Dysbiosis Pathogenic Pathogenic Community Community Metabolic Metabolic Function Function Fungi Fungi Wigglesworthia Yarrowia Wigglesworthia Yarrowia
Less Mucus Less Mucus Layer Layer Failed FMT Disturbance bioRxiv preprint doi: https://doi.org/10.1101/2020.05.27.120386; this version posted May 28, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
D1 D2 D3 D4
Donors
Pre-FMT Recipients Post-FMT