Description

Research Methods Experimental design: We propose to test the effect of two different probiotics and a placebo on gut microbiota and behavior of children with autism. We will employ a randomized longitudinal crossover study design, where each subject will receive the two probiotics and a placebo for two months each over the course of the experiment, for a total study period of six-months. As is typical in a crossover design, the order of the treatments will be randomized to control for carryover effects. We will recruit 45 children with ASD (with the expectation that at least 70% will finish the study) and collect two types of samples for microbiota and phageome analysis (detailed below), diet diaries, and GI and behavior questionnaires for 6 months. As a control, we will study the microbiota of 15 unaffected children. This crossover design allows each individual to act as their own control, which, due to the personalized nature of the human microbiota and the relatively small sample size, is essential to maximize statistical power and independence. Dr. Krajmalnik-Brown, in collaboration with Dr. James Adams (Director of the Autism/Asperger's Research Program at ASU), will lead subject recruitment and sample collection. Assessing Autistic Spectrum Disorders and gastrointestinal symptoms: The enrolled subjects will be between the ages of 3 and 16. We will periodically assess the GI symptoms of the children with a modified version of the GSI questionnaire. Children with ASD will be assessed with the Autism Diagnostics Observation Schedule (ADOS) at the beginning of the study to ensure that they have a diagnosis of autism. We will also conduct surveys documenting dietary patterns to identify potential confounding factors. Questionnaire-based assessments of behavior, including the Aberrant Behavior Checklist (ABC) and Autism Treatment Evaluation Checklist (ATEC), will be used at the end of each treatment phase to assess behavioral changes. Assessing change in microbial communities: We will profile the microbial communities in all samples by sequencing the bacterial small-subunit ribosomal RNA (rRNA), a universal marker gene that is used to characterize the taxonomic composition of a microbial community. We will analyze how the communities change during the treatment study, and in untreated neurotypical children studied over several months. Dr. Caporaso was instrumental in developing this sequencing protocol. Bioinformatics and statistical analysis will be performed using Quantitative Insights into Microbial Ecology, a software package for analyzing microbiota data generated with next-generation sequencing technologies of which Dr. Caporaso is the lead developer. The type of difference testing that will be applied in this study has only rarely been applied to the microbiota to date. Dr. Caporaso is now pioneering advances in this area, and Drs. Caporaso and Krajmalnik-Brown are currently applying these concepts to another human gut microbiota dataset. Observed changes in microbiota diversity will be carefully assessed to uncover associations between the probiotics used, changes in GI symptoms, and changes in behavior. We will additionally track, as needed, changes in the absolute abundance of certain microbial species using quantitative PCR. Assessing change in viral communities: Dr. Sullivans group has been instrumental in developing next-generation-sequencing-based methods for studying the phageome, including driving essential advances in quality control, read assembly, and detecting of functional genes or Open Reading Frames (ORFs). Estimating viral diversity is challenging due to the lack of universally shared genes (there is no universally shared gene, like the bacterial rRNA gene). We therefore use protein cluster composition to assess whole-community diversity and the corresponding protein-cluster-by-sample count tables can be analyzed using QIIME. Drs. Sullivan and Caporaso previously collaborated on analysis of phageome data using QIIME, which is not currently supported by the software. In this project, we will formalize support for phageome analysis with QIIME, including the public release of Dr. Sullivans phage reference sequence data for use with QIIME. This will fill a major gap in phageome analysis. Assessing associations between viral and microbial communities: Deriving new knowledge from massive biological data sets is a central challenge in modern biology. This is Dr. Caporasos area of expertise, and his work on the QIIME software package in particular has enabled many recent high-profile studies of the human microbiota. A current challenge is comparing different omics data types, such as phageome and microbiota data, as there are not easily accessible tools to support integration of these data types. This was highlighted as a major gap in the field at the recent NIH Human Microbiota Science meeting (July, 2013, Bethesda, MD). Prior work in this area by Drs. Caporaso and Sullivan, as described in the previous section, will support the aims of this study and allow us to narrow this gap in the field.
StatusFinished
Effective start/end date10/1/136/30/15

Funding

  • Arizona Board of Regents: $105,300.00

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Microbiota
Autistic Disorder
Viruses
Cross-Over Studies
Probiotics
Software
Checklist
Bacterial Small Ribosome Subunits
Genes
Placebos
Bacterial RNA
Gastrointestinal Microbiome
Bacterial Genes
Child Behavior
Therapeutics
Ecology
Computational Biology
rRNA Genes
Research
Quality Control