What this Trial Is About

The investigators will accomplish our research aims by collecting cutaneous microbiome samples from 50 persons that have undergone the Osseointegration (OI) surgery at eight timepoints, prospectively. The investigators will also collect control samples to correct for turnovers in species compositions that may naturally occur and to compare the residual limb microbiome to the sound contralateral limb. The investigators will sequence the bacterial community using universal bacterial primers. Using these sequences, The investigators will borrow from ecological theory and calculate the alpha and beta diversity. The alpha diversity will determine the species and abundance of each species that are present, while the beata diversity will allow us to compare how species assemblages and frequencies change between time points. Then, the investigators will take a phylogenetic modeling approach to determine if particular species assemblages correlate with rates of wound healing. The investigators will construct phylogenies from the sequences at the different time points and "paint" the rate of wound healing along the phylogeny (e.g., improved, stagnated, worsened). Using Akaike and Bayesian information criterion, the investigators can determine which phylogenetic model best explains the patterns the investigators see across patients. Lastly, the investigators will quantify soft tissue stability and health and correlate this with the homeostasis of the microbial community. Specifically, the investigators will determine if redundant soft tissue leads to altered microbial communities that can impact the rate of wound healing. Finally, the investigators will further stratify these data to compare microbial communities between the sexes, upper versus lower limbs, and proximal versus distal amputations. This work will allow us to better treat infections after OI surgery and can shed light on wound healing process so that the investigators can better treat limb loss patients and the military community as a whole.

Microbiome Population Adaptation Study