Predicting the fate of a microbial community and its member species relies on understanding the nature of their interactions. However, designing simple assays that distinguish between interaction types can be challenging. Here, we performed spent media assays based on the predictions of a mathematical model to decipher the interactions between four bacterial species: Agrobacterium tumefaciens (At), Comamonas testosteroni (Ct), Microbacterium saperdae (Ms) and Ochrobactrum anthropi (Oa). While most experimental results matched model predictions, the behavior of Ct did not: its lag phase was reduced in the pure spent media of At and Ms, but prolonged again when we replenished with our growth medium. Further experiments showed that the growth medium actually delayed the growth of Ct, leading us to suspect that At and Ms could alleviate this inhibitory effect. There was, however, no evidence supporting such "cross-detoxification" and instead, we identified metabolites secreted by At and Ms that were then consumed or "cross-fed" by Ct, shortening its lag phase. Our results highlight that even simple, defined growth media can have inhibitory effects on some species and that such negative effects need to be included in our models. Based on this, we present new guidelines to correctly distinguish between different interaction types, such as cross-detoxification and cross-feeding.

All the data that were used to generate the figures (and supp. figures) of the paper. This includes:

- OD600 / CFUs data

- Metabolomics data

- Chemical quantifications from commercial kits

For the chemical kits, please refer to their protocol from the vendor (exact product numbers are available in the manuscript).