(548a) Characterizing Microbial Taxa and Metabolic Pathways Trends Following Microbiota Transfer Therapy in Children with Autism Spectrum Disorder and Gastrointestinal Symptoms

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that is characterized by difficulty in communication, social interaction, and restricted repetitive behaviors. Although it is estimated that 1 in 54 children over the age of 8 are affected by ASD in the United States, much about the underlying mechanisms of these behavioral and neurological symptoms are not well understood (Maenner et al., 2020). It is generally accepted that the onset of ASD pathology is the result of complex interactions between environmental and genetic factors (Cheroni et al., 2020).

Gastrointestinal (GI) symptoms are a common ASD co-occurring condition, with manifestations including constipation, abdominal pain and irritable bowel syndrome. Various studies estimate the prevalence of GI co-occurring conditions among children with ASD to be approximately 47% (Lefter et al., 2020). Consequently, a large body of research has focused on interactions of the gut-brain axis in the context of better understanding ASD pathology (Li & Zhou; 2016). One key result is that the gut microbiota of individuals with ASD, both with and without the presence of GI co-occurring condition, have consistently been observed to be distinct from their typically-developing (TD) peers (Hughes et al., 2018).

The use of microbiota transfer therapy (MTT) has shown considerable promise as a treatment for individuals with both ASD and GI symptoms. Our prior work showed that ASD severity characterized using the Childhood Autism Rating Scale (CARS) and GI symptoms quantified using the average Gastrointestinal Symptom Rating Scale (GSRS) significantly decreased in a cohort of children with ASD and GI issues. These changes for both metrics were observed at the conclusion of 10 weeks of MTT and 8 weeks later (Kang et al., 2017). Furthermore, changes to behavioral and GI symptoms were statistically significant even two years after treatment had ceased (Kang et al., 2019). Also, children who underwent MTT had fecal and plasma metabolic profiles that were close to those of their TD peers (Adams et al., 2019; Qureshi et al., 2020).

This work builds on the analysis of this prior MTT study by examining the changes in biochemical pathways and microbiota taxa diversity up to two years after the conclusion of therapy. Using statistical and machine learning techniques, the significance of these changes were evaluated using a number of different metrics. Furthermore, taxa subgroups significantly correlated with co-occurring condition symptoms were investigated in detail and compared to the trends observed across all taxa examined.

Methods

The performed open-label MTT study consisted of 38 children, aged 7-16 years. The children were divided into both an ASD and a typically developing group. Specifically, 18 children were assigned to the ASD group with diagnosis verified using the Autism Diagnostic Interview-Revised (ADI-R). Also, all 18 children with an ASD diagnosis had moderate to severe GI symptoms which was one of the recruitment criteria. The corresponding TD group consisted of the remaining 20 children.

This study involved 2 weeks of preparing ASD participants for MTT, followed by a one to two-day period of administering a high dose of Standardized Human Gut Microbiota (SHGM). The subsequent component to this therapy involved 8 weeks of low-dose SHGM administered orally. Plasma and fecal samples were collected at five time points. Samples were taken prior to treatment, following the 2 weeks of MTT prep, and at the conclusion of the 8 weeks of low-dose treatment. Post-treatment samples were collected after 8 weeks (n=18) and approximately two years after MTT ceased (n=16).

Gut microbiota was assessed from fecal samples at baseline pre-treatment, 10 weeks post treatment start and two years post treatment using 16S ribosomal RNA (rRNA) gene amplicon sequencing analysis for the ASD cohort. The corresponding TD group was evaluated at baseline as the TD group did not undergo treatment. There were 5,309 distinct taxa that were evaluated initially. Due to limitations of the statistical analysis possible, those that had especially high rates of missing data (e.g., greater than 70% of samples were absent) were excluded from subsequent examination. Shotgun metagenomic sequencing was used to gain a greater understanding of microbiota/metabolic pathways. Over 5,000 Kyoto Encyclopedia of Genes and Genomes Orthology (KO) annotated networks were thus examined. Using univariate testing and classification techniques, the extent to which MTT corresponded with taxa and pathway changes was examined.

Univariate analysis was performed for the taxa and pathway data via hypothesis testing with false discovery rates determined using the leave-one-out approach. Additionally, the area under the receiver operator curve (AUROC) for classifying between the baseline ASD, post-treatment ASD and TD cohorts was determined for each taxa. This metric provides a measure of how well the characteristic or variable in question can classify between two different groups. Subsequent validation of AUROC findings were done using random forest and logistic regression. Additionally, the performance for taxa that had been identified to be significantly correlated to behavioral and GI symptoms were assessed and compared to the overall average trends observed.

Results and Discussion

In total, 4,714 taxa were retained for analysis following the preprocessing step. From among them, 302 and 940 taxa were observed to be significantly different when comparing the baseline ASD group to the Week 10 and 2-year post-treatment group respectively (Table 1). This is indicative of increasing divergence from baseline observation profiles. The conclusion of this interval also corresponded to an average AUROC value of 0.64 across all taxa vs baseline. It was also observed that the taxa profiled more closely resembled that of the TD group at the 10-week post-treatment mark. There was a 24% reduction in taxa that were statistically significant between the ASD baseline and ASD week 10 measurements against the TD group. While there was a very sizable number of significant taxa between the ASD baseline and data collected two years after treatment ceased, a similar shift was also observed when comparing the TD cohort to the ASD data collected two years post treatment (Table 1). Thus, taxa measurements at the MTT 2-year mark were the most distinct group from either baseline ASD or TD observations.

For taxa that had been found to be significantly correlated with CARS and GSRS scores, the changes observed were more pronounced than average. Among this sub cohort of taxa, the mean AUROC value at baseline vs MTT 10 weeks was higher compared to the complete dataset (0.68 vs 0.63) and the proportion that shifted to values more consistent with TD observations post treatment was greater (75% samples vs 68%).

KO metabolomic pathways data similarly showed a trend for ASD AUROC values determined at both 10 weeks and 2 years post-MTT to be increasingly distinct from baseline measurements. However, one main area of difference was that while the taxa data initially trended to be more like the TD cohort at week 10, this shift was not seen in the pathway data. The differences between baseline and week 10 vs TD measurements represented an average change in AUROC of -0.04 for taxa but only -0.01 for pathways.

Conclusion

This work focused on elucidating how MTT affects changes in the microbiota and in metabolic pathways. The reduction in observed statistically significant taxa between the ASD and TD groups initially after treatment underscored the extent to which the microbiota profile between both groups became more alike. This was further shown here using the average AUROC values when comparing both the MTT 10 weeks and baseline ASD measurements to their typically developing counterparts. Those taxa that were significantly correlated with GI and behavioral issues showed a greater change following MTT, which may point to possible mechanisms of action behind the reduction of symptoms observed. Nonetheless, follow-up work is needed, such as examining the change in taxa among a control group and more closely following changes observed in a TD cohort over time.

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