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Altered microbiota composition [20,21,22,23,24] and abnormal levels of microbial metabolites [20, 23, 24] have also been observed in rodent models of ASD: the maternal immune activation (MIA), the diet-induced obesity (DIO) and the valproate environmental models, the BTBR idiopathic model, and the Shank3b-KO genetic model. Further, changes in microbiota composition induced by FMT or probiotic treatment alleviated behavioral alterations in several of these ASD models [20,21,22]. Finally, mice born from mothers transplanted with feces from ASD patients exhibited social behavior deficits, changes in microbiota composition, and abnormal patterns of microbial metabolites [25]. Several microbial metabolites were shown to induce behavioral changes when administered to rodents. Treatment with the short-chain fatty acid (SCFA) propionate induced social interaction deficits, stereotypies, cognitive deficits, and anxiety in rats [17]. The tyrosine degradation product 4-ethylphenylsulfate (4-EPS) and indole induced anxiety in mice [1, 20, 26]. Altogether, these data suggested that dysbiosis could contribute to ASD core and associated symptoms via the production of microbial metabolites.
Here, we show that p-Cresol-treated mice exhibit social behavior deficits and stereotyped/perseverative behaviors, but no changes in anxiety, locomotion, or cognition. This suggests a possible causal relationship between elevated p-Cresol levels and ASD core symptoms. While several other metabolites modified behavior when administered to rodents, none of them selectively induced ASD core symptoms. For example, the SCFA propionate did induce not only social interaction deficits and stereotypies, but also anxiety, hyperlocomotion, and cognitive deficits [17]. Indoles increased social contacts and anxiety, reduced locomotor activity in rats [41], and exacerbated emotional behaviors in chronically stressed mice [42]. 4-EPS increased anxiety and startle reflex in mice but had no impact on social behavior or stereotypies [20]. Frequent ASD comorbidities are hyperactivity, ID, and anxiety disorder [4] and we show that related behavioral domains (locomotor activity, cognition, anxiety) are not impacted by p-Cresol exposure in mice. It appears that propionate, indoles, or 4-EPS induce anxiety, which could interfere with social and cognitive abilities and explain their broader effects. These data collectively suggest that microbial metabolites likely interfere with several dimensions of behavior impacted in ASD, each with its specificities, with p-Cresol selectively impacting social behavior and stereotyped/perseverative behaviors, related to ASD core symptoms.
Some microbial metabolites are ligands to host receptors: 3-indoxylsulfate binds to the aryl-hydrocarbon receptor [51], indole-3-propionate to the xenobiotic sensor pregnane X receptor [52], and propionate to GPR41 and GPR43 [53]. Yet their signaling role has been mainly investigated in metabolic, GI, or autoimmune disorders [51,52,53], and not in ASD. Indoles, SCFA, and their receptors are detected in the brain suggesting that these metabolites could act centrally [26, 54, 55]. However, this hypothesis remains to be tested. While both urinary and fecal p-Cresol levels are increased in p-Cresol-treated mice, serum levels are not, suggesting that p-Cresol does not reach bioactive levels in the brain and does not act centrally per se. Although p-Cresol is produced by bacterial metabolism of dietary tyrosine in the colonic lumen, limited amounts of p-Cresol actually reach the general circulation, as p-Cresol is rapidly uptaken by colonocytes and detoxified by conjugation into p-Cresylsulfate [56, 57]. Most of the remaining circulating fraction of free p-Cresol is also conjugated by hepatocytes into p-Cresylsulfate and p-Cresylglucuronide and excreted in the urine [56, 57]. This may explain why we do not observe elevated serum levels of p-Cresol in our mice. It remains an open question whether p-Cresylsulfate and p-Cresylglucuronide could relay centrally the effects of p-Cresol.
Individual datasets management (taxonomy, abundance, and metadata) was done on Phyloseq [95]. The effects of p-Cresol treatment or FMTp-Cresol vs. FMTControl were assessed using the analysis of composition of microbiomes (ANCOM) method [96]. ANCOM accounts for the compositional nature of 16S rRNA gene sequencing datasets, is very sensitive for datasets >20 samples, and displays superior performances to control for false discovery rate compared to other methods such as DESeq2 [39]. The utility of ANCOM has been demonstrated for preprocessing sparse microbiome data sets with matrix completion to allow compositional ordination and to preserve information about the features driving differences among samples [39]. ANCOM accounts for compositionality using centered log ratio (CLR) analysis and therefore improves inference in microbiota survey data [39]. Recommended ANCOM thresholds were used to identify dysregulated taxa: CLR > 0.2 and W = 0.7. Of note, the p-Cresol versus control dataset consisted of two batches of sequences obtained from two independent biological replicates (n = 15 animals/group/replicate) and the built-in batch effect correction implemented in ANCOM was used to account for batch effect. Based on features identified by ANCOM at each taxonomic level (from ASV to phylum), synthetic cladograms were built using GraPhlAn [97]. 2ff7e9595c
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