Biology subjects

Arias-Sanchez, F. I.

Publications and source records attributed to Arias-Sanchez, F. I..

3 recordsLinked to original sources

bioRxiv2023

Towards faster identification of MRSA and MSSA: analysis of calorimetric curve parameters from large hospital bacterial collections

There is a need to develop faster yet precise diagnostic tools for the identification of infectious agents and their levels of antimicrobial susceptibility. One such tool is calorimetry, as previous studies show that bacteria display unique signatures of calorimetric/metabolic activity that such signatures can be used for pathogen identification under controlled laboratory conditions. However, the level of variation of these unique signatures remains largely unexplored. Here, we use real-time isothermal microcalorimetry to disentangle the inter- and intra-species metabolic differences between S. aureus and S. epidermidis. We worked with a large collection of bacterial isolates obtained from patients with prosthetic joint infections as well as reference laboratory strains. We analyzed their calorimetric profiles and decomposed the curve parameters derived from them. As a result, we identified six calorimetric parameters that are useful for identification at the intra-species level, with a particular focus on MRSA. Furthermore, we found important differences between the thermograms of ATCC laboratory strains when compared against model thermograms generated from all our clinical isolates. These results indicate that accounting for metabolic variability can impact diagnosis precision. Finally, we discuss our findings and suggest ways to optimize calorimetric diagnostics and treatment approaches.

microbiology↗

bioRxiv2023

Artificial selection optimizes pollutant-degrading bacterial communities

Artificial selection is a promising way to improve microbial community functions, but previous experiments have only shown moderate success. Here, we experimentally evaluate a new method that was inspired by genetic algorithms to artificially select small bacterial communities of known species composition based on their degradation of an industrial pollutant. Starting from 29 randomly generated four-species communities, we repeatedly grew communities for four days, selected the 10 best-degrading communities, and rearranged them into 29 new communities composed of four species of equal ratios whose species compositions resembled those of the most successful communities from the previous round. The best community after 18 such rounds of selection degraded the pollutant better than the best community in the first round. It featured member species that degrade well, species that degrade badly alone but improve community degradation, and free-rider species that did not contribute to community degradation. Most species in the evolved communities did not differ significantly from their ancestors in their phenotype, suggesting that genetic evolution plays a small role at this time scale. These experiments show that artificial selection on microbial communities can work in principle, and inform on how to improve future experiments.

evolutionary biology↗

bioRxiv2023

Novel artificial selection method improves function of simulated microbial communities

There is increasing interest in artificially selecting or breeding microbial communities, but experiments have reported modest success. Here, we develop computational models to simulate two previously known selection methods and compare them to a new "disassembly" method. We evaluate all three methods in their ability to find a community that could efficiently degrade toxins, whereby investment into degradation results in slower growth. Our disassembly method relies on repeatedly competing different communities of known species combinations against one another, while regularly shuffling around their species combinations. This approach allows many species combinations to be explored, thereby maintaining enough between-community diversity for selection to act on, and resulting in communities with high performance. Nevertheless, selection at the community level in our simulations did not counteract selection at the individual level, nor the communities ecological dynamics. Species in our model evolved to invest less into community function and more into growth, but increased growth compensated for reduced investment, such that overall community performance was barely affected by within-species evolution. Within-community ecological dynamics were more of a challenge, as we could control them during the selection process, but community composition and function dropped in the longer term. Our work shows that the strength of disassembly lies mainly in its ability to explore different species combinations, and helps to propose alternative designs for community selection experiments.

evolutionary biology↗