DNA-stable isotope probing (DNA-SIP) identifies marine sponge-associated bacteria actively utilizing dissolved organic matter (DOM)

Sponges possess exceptionally diverse associated microbial communities and play a major role in (re)cycling of dissolved organic matter (DOM) in marine ecosystems. Linking sponge‐associated community structure with DOM utilization is essential to understand host–microbe interactions in the uptake, processing, and exchange of resources. We coupled, for the first time, DNA‐stable isotope probing (DNA‐SIP) with 16S rRNA amplicon sequencing in a sponge holobiont to identify which symbiotic bacterial taxa are metabolically active in DOM uptake. Parallel incubation experiments with the sponge Plakortis angulospiculatus were amended with equimolar quantities of unlabelled ((12)C) and labelled ((13)C) DOM. Seven bacterial amplicon sequence variants (ASVs), belonging to the phyla PAUC34f, Proteobacteria, Poribacteria, Nitrospirae, and Chloroflexi, were identified as the first active consumers of DOM. Our results support the predictions that PAUC34f, Poribacteria, and Chloroflexi are capable of organic matter degradation through heterotrophic carbon metabolism, while Nitrospirae may have a potential mixotrophic metabolism. We present a new analytical application of DNA‐SIP to detect substrate incorporation into a marine holobiont with a complex associated bacterial community and provide new experimental evidence that links the identity of diverse sponge‐associated bacteria to the consumption of DOM

A machine learning approach to explore predictors of graft detachment following posterior lamellar keratoplasty:a nationwide registry study

Machine learning can be used to explore the complex multifactorial patterns underlying postsurgical graft detachment after endothelial corneal transplantation surgery and to evaluate the marginal effect of various practice pattern modulations. We included all posterior lamellar keratoplasty procedures recorded in the Dutch Cornea Transplant Registry from 2015 through 2018 and collected the center-specific practice patterns using a questionnaire. All available data regarding the donor, recipient, surgery, and practice pattern, were coded into 91 factors that might be associated with the occurrence of a graft detachment. In this research, we used three machine learning methods; a regularized logistic regression (lasso), classification tree analysis (CTA), and random forest classification (RFC), to select the most predictive subset of variables for graft detachment. A total of 3647 transplants were included in our analysis and the overall prevalence of graft detachment was 9.9%. In an independent test set the area under the curve for the lasso, CTA, and RFC was 0.70, 0.65, and 0.72, respectively. Identified risk factors included: a Descemet membrane endothelial keratoplasty procedure, prior graft failure, and the use of sulfur hexafluoride gas. Factors with a reduced risk included: performing combined procedures, using pre-cut donor tissue, and a pre-operative laser iridotomy. These results can help surgeons to review their practice patterns and generate hypotheses for empirical research regarding the origins of graft detachments