Evidence of the cooperative response of Blattella germanica gut microbiota to antibiotic treatment

• Published in: Mathematical biosciences Vol. 364; p. 109057
• Main Authors: Creus-Martí, Irene, Marín-Miret, Jesús, Moya, Andrés, Santonja, Francisco J.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.10.2023
• Subjects: Antibiotic resistance; Bacterial dynamics; Compositional data; Groups behavior; Longitudinal data; Microbiome modeling; Bacterial dynamics; Groups behavior; Longitudinal data; Microbiome modeling; Antibiotic resistance; Compositional data; Groups behaviour
• ISSN: 0025-5564; 1879-3134
• DOI: 10.1016/j.mbs.2023.109057

Gut microbiota plays a key role in host health under normal conditions. However, bacterial composition can be altered by external factors such as antibiotic (AB) intake. While there are many descriptive publications about the effects of AB on gut microbiota composition after treatment, the dynamics and interactions among the bacterial taxa are still poorly understood. In this work, we performed a longitudinal study of gut microbiome dynamics in B. germanica treated with kanamycin. The AB was supplied in three separate periods, giving the microbiota time to recover between each antibiotic intake. We applied two new statistical models, not focusing on pair-wise interactions, to more realistically study the interactions between groups of bacterial taxa and how some groups affect a single taxon. The first model provides information on the importance of a given genus, and the rest of the community, to define the abundance of that genus. The second model, on the other hand, provides details about the relationship between groups of bacteria, focusing on which community groups affect the taxa. These models help us to identify which bacteria are community-dependent in stress conditions, which taxa might be better adapted than the rest of the community, and which bacteria might be working together within the community to overcome the antibiotic. In addition, these models enable us to identify different bacterial groups that were separated in control conditions but were found together in treated conditions, suggesting that when the environment is more hostile (as it is under antibiotic treatment), the whole community tends to work together. •Two new statistical models are been considered, not focusing on pair-wise interactions, to more realistically study the interactions between groups of bacterial taxa.•These models help us to identify which bacteria are community dependent in stress conditions, which taxa might be better adapted than the rest of the community, and which bacteria might be working together within the community to overcome the antibiotic.•These models enable us to identify different bacterial groups that were separated in control conditions but were found together in treated conditions.