Human Gut Microbiota from Autism Spectrum Disorder Promote Behavioral Symptoms in Mice
Authors: Nikki Jamie Cruz
David W Hoyt
Christianne J Lane
Michael J Sweredoski
Rob Knight
Janet K Jansson
Thomas O Metz
Bo Wang
Sarkis K Mazmanian
Gil Sharon
Annie Moradian
Rosa Krajmalnik-Brown
Lisa M Bramer
Elhanan Borenstein
Cecilia Noecker
Erika M Zink
Michael J Gandal
Nancy G Isern
Young-Mo Kim
Cameron P Casey
Dae-Wook Kang
Bryn C Taylor
Carlos Lois
Daniel H Geschwind
Journal: Cell (2019)
Discipline: MOLECULAR BIOLOGY & GENETICS
Keywords: autism; autism spectrum disorder; bacterial metabolites; gut microbiome; gut-brain axis
Affiliations: Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA. Electronic address: ., Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA., Biodesign Swette Center for Environmental Biotechnology, Arizona State University, Tempe, AZ 85287, USA; Biodesign Center for Fundamental and Applied Microbiomics, Arizona State University, Tempe, AZ 85287, USA., Center for Autism Research and Treatment, Program in Neurobehavioral Genetics, Semel Institute, University of California Los Angeles, Los Angeles, CA 90095, USA; Department of Neurology, Semel Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Psychiatry, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA., Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA., Biomedical Sciences Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA., Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA., National Security Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA., Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA., Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA; Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA; Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 6997801, Israel; Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel; Santa Fe Institute, Santa Fe, NM 87501, USA., Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA 92093, USA; Department of Pediatrics, School of Medicine, University of California, San Diego, La Jolla, CA 92093, USA; Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92093, USA; Department of Biongineering, University of California, San Diego, La Jolla, CA 92093, USA., Center for Autism Research and Treatment, Program in Neurobehavioral Genetics, Semel Institute, University of California Los Angeles, Los Angeles, CA 90095, USA; Department of Neurology, Semel Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA., Biodesign Swette Center for Environmental Biotechnology, Arizona State University, Tempe, AZ 85287, USA; Biodesign Center for Fundamental and Applied Microbiomics, Arizona State University, Tempe, AZ 85287, USA; School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ 85287, USA.

Abstract:  Autism spectrum disorder (ASD) manifests as alterations in complex human behaviors including social communication and stereotypies. In addition to genetic risks, the gut microbiome differs between typically developing (TD) and ASD individuals, though it remains unclear whether the microbiome contributes to symptoms. We transplanted gut microbiota from human donors with ASD or TD controls into germ-free mice and reveal that colonization with ASD microbiota is sufficient to induce hallmark autistic behaviors. The brains of mice colonized with ASD microbiota display alternative splicing of ASD-relevant genes. Microbiome and metabolome profiles of mice harboring human microbiota predict that specific bacterial taxa and their metabolites modulate ASD behaviors. Indeed, treatment of an ASD mouse model with candidate microbial metabolites improves behavioral abnormalities and modulates neuronal excitability in the brain. We propose that the gut microbiota regulates behaviors in mice via production of neuroactive metabolites, suggesting that gut-brain connections contribute to the pathophysiology of ASD. 

Retracted: No

PubPeer comments:
 Can the authors comment on how n=4-23 mice per unique donor per sex were determined (Figure S6)? How did the authors decide which donor microbiota would be given to 4 mice, and which donor would be administered to 23 mice? Or 8 - 23 mice per donor per gender (Figure 1E, F, G)? Although the group overall differences in behavior may be statistically-significant, can the authors comment on why mice from the same donor can have completely opposite behavioral outcomes? For example, please see the graph below (comments outside the graph are mine). Where recipient mice from the same donor TD individual can have, for example, either large amount of distance travelled or very little distance traveled. It seems the variation in mice receiving either TD or ASD transplants suggests TD or ASD microbiota will not promote or suppress behavioral symptoms associated with ASD-like behavior in mice--but rather it is more luck of the draw with which mouse has received which microbiota. There it appears that the major claim of the manuscript, that the  human gut microbiota from Autism Spectrum Disorder promote behavioral symptoms in mice appears to be misleading. ![file] ![file] , 
 Fig. 1G. First - how can these samples differ with a p value of better than 0.001? Why is the statistical test not described? And how do you do a chi-squared test on data that are continuous and differently distributed? Second - even if the data are statistically different, which I doubt, who would ascribe any meaning to them? They are only biologically relevant given an extreme explanation of why such extremely similar data can support anything... , 
 Despite the large numbers of mice used for this study, it is important to keep in mind that what is being compared are small numbers of stool samples: 3 control stools (from  Typically Developing subjects) and 5 Autism Spectrum Disorder (ASD) stools were selected from 16 donors. (Could that selection have occurred After the Results were Known?) See table in Fig. 1B. This is therefore a rather preliminary and very low-power design. In general, however, the statistics seem to be a good deal more sophisticated than in previous work from this lab, https: pubpeer.com publications 765CBDC8E738AC8FD5CF0B1A2F6A2E so congratulations on that. The authors seem to have made their data available, which is also to be commended warmly. However, the analysis scripts seem not to be available; it would have been nice to be able to check exactly how the analyses were done, especially how those small donor samples were dealt with. Unfortunately, there doesn t appear to be any discussion in this paper of B. fragilis or L. reuteri, the subjects of previous Microbiome-Autism blockbusters at Cell: https: pubpeer.com publications 52FB330D25504AD87FEBB31CBC3C5D https: pubpeer.com publications 55C3E6C3B85789A8F20E25AFBDC71A The conclusion of this paper is that the microbiome is acting via taurine (amongst other things). Cue pregnant mothers drinking large volumes of Red Bull... However, the results in Fig. 6 on this are all desperately weak - a festival of p-values close to 0.05. I seriously doubt this will replicate. (The authors may wish to look into the difficulties of interpreting p-values.) While this comment has awaited moderation, similar issues have also been raised online: https: twitter.com WiringTheBrain status 1134129415456264193 https: twitter.com DrBrocktagon status 1134381286968053760 https: blogs.sciencemag.org pipeline archives 2019 05 31 autism-mouse-models-for-the-microbiome , 
 The p-value for Fig. 1G is difficult to believe. This is why it would be good for the authors to provide more detail, ideally the actual analysis scripts commands. Guided by the description in the Methods, I tried to set up a linear model and apply it to the data the authors did provide. However, as the transcript of my analysis (in R) shows, either I or the authors have made a mistake, as the p-value I obtain is p = 0.27 not less than 0.001. I haven t checked the other analyses yet, but exact replication will be difficult without proper detail.  QUANTIFICATION AND STATISTICAL ANALYSIS Statistical analysis for behavioral outcomes in fecal transplanted offspring. Comparison of behavioral outcomes between TD Controls and ASD donors were tested using longitudinal linear mixed effects analyses, with test cycles and donors treated as repeated factors. Analyses were performed in SPSS (v 24); a priori alpha = 0.05. All outcomes were tested for normality and transformed as required. Diagonal covariance matrices were used so that intra-cycle and intra-donor correlations were accounted for in the modeling. The donor type (TD versus ASD) was the primary fixed effect measured, and mouse sex was an a priori covariate. ``` library(lme4) d = read.csv(file= Fig1EFGH_subset8.csv , header=T) summary(lmer(OFT_Distance ~ ASD_diagnosis + Gender + (1|Cycle) + (1|Donor), data=d)) Linear mixed model fit by REML [ lmerMod ] Formula: OFT_Distance ~ ASD_diagnosis + Gender + (1 | Cycle) + (1 | Donor) Data: d REML criterion at convergence: 3375.2 Scaled residuals: Min 1Q Median 3Q Max -2.5648 -0.6139 -0.1403 0.4480 3.3025 Random effects: Groups Name Variance Std.Dev. Donor (Intercept) 168021 409.90 Cycle (Intercept) 3043 55.17 Residual 866634 930.93 Number of obs: 206, groups: Donor, 8; Cycle, 2 Fixed effects: Estimate Std. Error t value (Intercept) 4296.8 218.2 19.693 ASD_diagnosisNT 366.3 330.4 1.109 GenderMale -109.6 131.4 -0.835 Correlation of Fixed Effects: (Intr) ASD_NT ASD_dgnssNT -0.576 GenderMale -0.290 -0.017 2*pt(-1.109, 205) [1] 0.2687295 ``` , 
 Several aspects of this paper raise questions about data selection and flexible analysis. The selection of 3 controls from 5 and of 5 ASD from 11 (or 8) has already been mentioned; the criteria for the selection are, I think, not detailed:  Further in-depth studies were performed on a representative subset of fecal samples from three TD and five ASD donors. The data file for Fig. 1EFGH lists numerous behavioural parameters that were measured. It is not clear why the three shown in E, F and G were chosen. Although I haven t yet made exhaustive checks, ASD diagnosis is not a significant factor for several of the other parameters I have tested. Maybe there is a good reason to assume that  Distance traveled is a key ASD indicator in the mouse model, but the authors don t actually make that argument. Nor do they show the retesting of that parameter with taurine in Fig. 6. Instead, they preferred Center Duration . In fact, only marble-burying is common to Figs 1 and 6. , 
 I found this interesting background reading. It makes a strong argument that ASD is very largely genetic. If that s the case, why are we looking for a non-genetic cause? http: www.wiringthebrain.com 2014 10 autism-truth-is-not-out-there.html , 
 The marble burying test is commonly employed to assess repetitive behavior in mice. Although repetitive behavior is one potential trait that may (or may not) afflict an ASD-individual, it should be emphasized that marble burying is used as an approximation of human behavior. Marble burying itself is not a human behavior. Could the authors please clarify why the title of Figure 1 (please see below) states that Marbles Buried (y-axis) is a  human behavior ? ![file] , 
 Thank you for your comments, which we take very seriously. Please be assured we are looking into the matter and will respond shortly. , 
 I have a skeletal analysis of Fig1G here: https: rdoodles.rbind.io 2019 06 reanalyzing-data-from-human-gut-microbiota-from-autism-spectrum-disorder-promote-behavioral-symptoms-in-mice , 
 #9 Can you please try with parametric bootstrap too (pbnm)? Thanks! , 
 `fit - lmer(Dist ~ Diagnosis + Gender + (1 | Cycle) + (1 | Donor), data = df, REML = FALSE)` `fit_null - lmer(Dist ~ Gender + (1 | Cycle) + (1 | Donor), data = df, REML = FALSE)` `pbkrtest::PBmodcomp(fit, fit_null, nsim = 10000, ref = NULL, seed = 123, cl = NULL, details = 0)` Parametric bootstrap test; time: 39.24 sec; samples: 10000 extremes: 2938; Requested samples: 10000 Used samples: 8907 Extremes: 2938 large : Dist ~ Diagnosis + Gender + (1 | Cycle) + (1 | Donor) small : Dist ~ Gender + (1 | Cycle) + (1 | Donor) stat df p.value LRT 1.4974 1 0.2211 PBtest 1.4974 0.3299 `fit - lmer(Dist ~ Diagnosis + Gender + (1 | Cycle) + (1 | Donor), data = df, REML = TRUE)` `fit_null - lmer(Dist ~ Gender + (1 | Cycle) + (1 | Donor), data = df, REML = TRUE)` `pbkrtest::KRmodcomp(fit, fit_null, betaH = 0, details = 0)` F-test with Kenward-Roger approximation; computing time: 0.05 sec. large : Dist ~ Diagnosis + Gender + (1 | Cycle) + (1 | Donor) small : Dist ~ Gender + (1 | Cycle) + (1 | Donor) stat ndf ddf F.scaling p.value Ftest 1.2162 1.0000 5.8081 1 0.3137 , 
 We thank members of the community for a thoughtful discussion regarding our recent publication, reported in doi: 10.1016 j.cell.2019.05.004; PMID: 31150625. In addition, we appreciate the broader comments on the gut microbiota, mouse models, and statistical analyses. We would like to address the following points raised during post-publication: 1. A number of reports, from many groups worldwide, have found differences in the human microbiome (from stool samples) between individuals with autism and typically developing populations (reviewed in PMID: 30251184, with 20 original articles). These studies in humans are unable to resolve the root cause of microbiome changes (potential examples include diet, genetics, etc.), or whether these observed differences have any biological relevance. The goal of our study is to take a next step in this line of existing research, by testing whether colonization of mice with microbiomes from individuals with autism can influence mouse behaviors, compared to controls (typically developing donors). These studies cannot be performed in people, but allow for a functional evaluation of the microbiome in mice, beyond simply reporting differences in microbial profiles. Our experiments do not, and cannot, conclude that the microbiota is causative in autism. In fact, we clearly state in the manuscript that our results do not demonstrate causality. Moreover, our work does not establish a new mouse model, which we have never claimed, but serves to test whether microbiota from ASD individuals can affect mouse behaviors. 2. It is our position that mice do not develop autism, but rather can be used to investigate mouse behaviors that are analogous to those of humans (PMID: 20559336, 27305922). Mouse models therefore enable testing of behaviors, classified by behavioral neuroscientists as being close to those behavioral domains in humans, in controlled systems. In our study, we tested outcomes that are regarded as similar to the main behavioral domains in autism, namely stereotypic repetitive behavior and social interaction. While modeling complex behaviors in mice has limitations, this approach is a widely-accepted tool in behavioral neuroscience and has been used in hundreds of peer-reviewed publications. 3. Statistical analyses, part of Figure 1, specify linear mixed effects modeling of individual mice, nested within donor, and round of testing. Fixed effects included donor ASD diagnosis, mouse sex, and the interaction of these factors. You will note that we also ran models stratified by mouse sex, because DSI was only evident in male mice. Diagonal covariance matrices were specified so that mice sharing donors would be allowed to share variability while maintaining independence from other donors. Likewise, this was also used for testing Round. Because the number of donors was not large, more complex models specifying random effects did not converge and were not stable, and thus we decided to adhere to a parsimonious model to address our research question. We are providing the original SPSS output of the statistical analysis presented in Figures 1E u2013 G for reference; the syntax use for analyses is included therein . Thank you again for the constructive dialogue , 
 Thomas Lumley argues that the degrees of freedom reported in the supplementary information are much greater than the numbers of donors, so something does seem to have gone wrong in the analysis. The upshot for panels E, F and G of Fig. 1 is that marble burying is barely significant, while the other two behaviours probably are not. https: notstatschat.rbind.io 2019 06 16 analysing-the-mouse-autism-data , 
 As #13 says, I think the analysis is inappropriate. Background: Two weeks ago, when there was some discussion on Twitter, I tried to reconstruct the analysis and (after some effort) succeeded. My blog post linked in #13 describes this attempt. Before posting it, I wrote to Gil Sharon to summarise my concerns. This week, he kindly emailed me the information he also posted here at #12. I added an update to what I d written and posted it. The model for the behavioural outcomes described by the SPSS code is not a mixed model in any usual sense. It is a simple fixed-effect regression with sex, ASD status, and their interaction. It does not permit any correlation between mice for the same donor -- that s because individual mice are identified as SUBJECTs in the REPEATED statement. The model allows the *variance* of the residuals to differ by donor, but does not allow the *mean* of the residuals to differ by donor as a random effect of donor would. In opinion It s unfortunate that SPSS labels this a mixed model. For the benefit of R users, this is the code I used to reproduce the F-statistics in Table S4 (all the code, starting from reading the data, is [in my blog post] ) ``` cell$ASD_sum -C(cell$ASD_diagnosis, contr.sum ) cell$Gender_sum -C(cell$Gender, contr.sum ) marbles_gls - gls(MB_buried~ASD_sum*Gender_sum, weights=varIdent(form=~1|Donor),data=cell) anova(marbles_gls, type= marginal ) ``` I fitted several mixed models as well. I didn t have any convergence problems, but for some of the models the point estimate for one of the variance components (for `Round`) was zero, which can look like a convergence problem in some software. None of the mixed models showed any evidence of difference for the DSI and OFT tests and they showed fairly weak evidence of difference for the marble-burying test. The high denominator degrees of freedom mentioned in #12 are a symptom of the problem: the analysis is estimating residual uncertainty using differences between all the mice, and getting a large effective sample size. It should have been estimating residual uncertainty using differences between donors, and getting an effective sample size of about five. The number of donors, rather than the number of mice, is the limiting factor on uncertainty. A model with Donor random effect, as in the caption to Figure 1, would reflect this and show much weaker evidence for behavioural differences. While it s a more minor point, there s also an issue with testing the main effect of ASD status in a model with an interaction. The hypothesis being tested depends on how the variables are coded, and since ASD status and sex are not orthogonal, there s no uniquely well-defined way to isolate the main effect and interaction. The interaction means that ASD status acts differently by sex, so  the effect of ASD status isn t a thing. It s a relatively minor point in this case because ASD status and sex aren t that far from orthogonal and the interaction effects aren t all that strong, and because SPSS defaults to a good coding of factors for handling interactions. , 
 One update on #14. I had thought that `Round*MouseID` was a unique identifier. In fact, there are two pairs with the same combination (I m guessing this is an accident and they aren t really the same mouse). The model still doesn t model correlation within these two mouse pairs, because the default diagonal covariance still requires the correlation to be zero. So the model the authors fitted is equivalent to the one I fitted, but the specifications aren t identical ,