Data for: Human Gut Microbiota from Autism Spectrum Disorder Promote Behavioral Symptoms in Mice
Raw data (excluding raw sequencing and metabolomic data) used to construct figures for manuscript. Data is predominantly in csv or tsv format. qza and qzv files are QIIME2 outputs and can be viewed at https://view.qiime2.org/ . mat files are mat lab datasets. Human Gut Microbiota from Autism Spectrum Disorder Promote Behavioral Symptoms in Mice Gil Sharon1,*, Nikki Jamie Cruz1, Dae-Wook Kang2,3,21, Michael J. Gandal4,5,6,7, Bo Wang1, Young-Mo Kim8, Erika M. Zink8, Cameron P. Casey8, Bryn C. Taylor9, Christianne J. Lane10, Lisa M. Bramer11, Nancy G. Isern8, David W. Hoyt8, Cecilia Noecker12, Michael J. Sweredoski1, Annie Moradian1, Elhanan Borenstein12,13,14,15,16, Janet K. Jansson8, Rob Knight17,18,19, Thomas O. Metz8, Carlos Lois1, Daniel H. Geschwind4,5,6, Rosa Krajmalnik-Brown2,3, and Sarkis K. Mazmanian1,20,* 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 and TD controls into germ-free mice, and reveal that colonization with ASD microbiota was 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 affects neuronal excitability in the brain. We propose that the gut microbiome modulates behaviors in mice via production of neuroactive metabolites, suggesting that gut-brain connections contribute to the pathophysiology of ASD.
Steps to reproduce
The following python code can be used to unpack matlab data to a Pandas dataframe (Python 3): import numpy as np from scipy.io import loadmat # this is the SciPy module that loads mat-files import pandas as pd mat = loadmat('caImaging_taurine 5AV.mat') # load mat-file mdata = mat['RawData'] # Data variable in mat file # to get the data out of matlabs data structure, which is an array or arrays: i,j = mdata.shape AllCells = pd.DataFrame() for column in range(j-1): # iterate over days day = mdata[0,column+1].item() group1_data = pd.DataFrame(np.transpose(mdata[1,column+1])) group1_data['DIV'] = day group1_data['Treatment'] = mdata[1,0].item() group1_data['counter'] = range(1,len(group1_data)+1) group2_data = pd.DataFrame(np.transpose(mdata[2,column+1])) group2_data['DIV'] = day group2_data['Treatment'] = mdata[2,0].item() group2_data['counter'] = range(1,len(group2_data)+1) group3_data = pd.DataFrame(np.transpose(mdata[3,column+1])) group3_data['DIV'] = day group3_data['Treatment'] = mdata[3,0].item() group3_data['counter'] = range(1,len(group3_data)+1) AllCells = pd.concat([AllCells, group1_data, group2_data, group3_data]) AllCells.set_index(['DIV','Treatment', 'counter'], inplace=True) AllCells_stacked = AllCells.stack().reset_index() AllCells_stacked.columns = ['DIV', 'Treatment','Cell#', 'Time', 'dF/F0'] AllCells_stacked = AllCells_stacked[AllCells_stacked['dF/F0'] >= 0]