Genetic barriers more than environmental associations explain Serratia marcescens population structure.

Bacterial species often comprise well-separated lineages, likely emerged and maintained by genetic isolation and/or ecological divergence. How these two evolutionary actors interact in the shaping of bacterial population structure is currently not fully understood. In this study, we investigate the genetic and ecological drivers underlying the evolution of Serratia marcescens, an opportunistic pathogen with high genomic flexibility and able to colonise diverse environments. Comparative genomic analyses reveal a population structure composed of five deeply-demarcated genetic clusters with open pan-genome but limited inter-cluster gene flow, partially explained by Restriction-Modification (R-M) systems incompatibility. Furthermore, a large-scale research on hundred-thousands metagenomic datasets reveals only a partial habitat separation of the clusters. Globally, two clusters only show a separate gene composition coherent with ecological adaptations. These results suggest that genetic isolation has preceded ecological adaptations in the shaping of the species diversity, an evolutionary scenario coherent with the Evolutionary Extended Synthesis.

Cultivation and sequencing-free protocol for Serratia marcescens detection and typing.

Serratia marcescens is an opportunistic pathogen that survives in inhospitable environments causing large outbreaks, particularly in neonatal intensive care units (NICUs). Genomic studies revealed that most S. marcescens nosocomial infections are caused by a specific clone (here "Infectious clone"). Whole genome sequencing (WGS) is the only portable method able to identify this clone, but it requires days to obtain results. We present a cultivation-free hypervariable-locus melting typing (HLMT) protocol for the fast detection and typing of S. marcescens, with 100% detection capability on mixed samples and a limit of detection that can reach the 10 genome copies. The protocol was able to identify the S. marcescens infectious clone with 97% specificity and 96% sensitivity when compared to WGS, yielding typing results portable among laboratories. The protocol is a cost and time saving method for S. marcescens detection and typing for large environmental/clinical surveillance screenings, also in low-middle income countries.

Computational protocol to perform a spatiotemporal reconstruction of an epidemic.

Here, we present a computational protocol to perform a spatiotemporal reconstruction of an epidemic. We describe steps for using epidemiological data to depict how the epidemic changes over time and for employing clustering analysis to group geographical units that exhibit similar temporal epidemic progression. We then detail procedures for analyzing the temporal and spatial dynamics of the epidemic within each cluster. This protocol has been developed to be used on historical data but could also be applied to modern epidemiological data. For complete details on the use and execution of this protocol, please refer to Galli et al. (2023).1.