RNA-seq analysis code for the paper "Mechanistic modeling identifies emergent behavior in a synthetic human gut community"
Whereas the composition of the human gut microbiome is relatively well resolved, predictive understanding of its response to perturbations such as diet shifts is still lacking. Here, we followed a bottom-up strategy to explore human gut community dynamics. We established a synthetic community composed of three representative human gut isolates in well-controlled conditions in vitro. We then explored species interactions by performing all mono- and pair-wise fermentation experiments and quantified with a mechanistic community model how well tri-culture dynamics was predicted from mono-culture data. With the model as a reference, we demonstrated that species grown in co-culture behaved differently than in mono-culture and confirmed their altered behavior at the transcriptional level. In addition, we showed with replicate tri-cultures and in simulations that dominance in tri-culture sensitively depends on initial conditions. Our work has important implications for gut microbial community modeling as well as ecological interaction detection from batch cultures.
- scripts: contains the Rscript for the analysis of the RNA-seq data from the paper
- data: contains the functional annotation for the three species studied here (B. hydrogenotrophica, F. prausnitzii and R. intestinalis) as well as the raw count data for all the experiments, as obtained through mapping with
bowtie2+htseq-count - figures: pdfs of the figures generated using the analysis script
- data_output: output tables from the differential expression analysis
- R: auxiliary function(s) required by the main analysis script