Diversity of bacterial and fungal endophytic communities presents in the leaf blades of Sinningia magnifica, Sinningia schiffneri and Sinningia speciosa from different cladus of Gesneriaceae family: A comparative analysis in three consecutive years.

Sinningia is a genus of plants of Gesneriaceae family with species native to Brazil and is a source of several classes of bioactive secondary metabolites, such as quinones, terpenoids, flavonoids, and phenylethanoid glycosides. However, the diversity of endophytic microorganisms associated with them and the impact of endophytes on the biosynthesis of bioactive substances is unknown. Therefore, we sought to evaluate the microbial diversity, behavior, and frequency of endophytes in leaves blades of S. magnifica, S. schiffneri, and S. speciosa. These plants were collected in different regions and environments of Brazil and were studied comparatively for three consecutive years. The total DNA obtained from the blades of the plant leaves were sequenced by the Illumina MiSeq platform, followed by bioinformatics analysis to assess the microbial diversity of endophytes associated with each plant species and study year. The results of the taxonomic diversity showed a dynamic microbial community, which contained several bacterial phyla among them, Actinomycetota, Bacteroidota, Bacillota, and Pseudomonadota, and for the fungal phyla Ascomycota and Basidiomycota. Comparing the three years of study, the richness of the genera, over time, was decreasing, with signs of recovery towards the third year. The alpha and beta diversity indices confirm a great phylogenetic richness in the endophytic communities of bacteria and fungi associated with the leaf blades of Sinningia. However, these communities are comparatively little conserved, showing population and taxonomic changes of the microorganisms over time, possibly as a measure of adjustment to environmental conditions, evidencing both its fragility and versatility against the effects of environmental change on its endophytic microbial communities.

Fast discrimination of bacteria using a filter paper-based SERS platform and PLS-DA with uncertainty estimation.

Rapid and reliable identification of bacteria is an important issue in food, medical, forensic, and environmental sciences; however, conventional procedures are time-consuming and often require extensive financial and human resources. Herein, we present a label-free method for bacterial discrimination using surface-enhanced Raman spectroscopy (SERS) and partial least squares discriminant analysis (PLS-DA). Filter paper decorated with gold nanoparticles was fabricated by the dip-coating method and it was utilized as a flexible and highly efficient SERS substrate. Suspensions of bacterial samples from three genera and six species were directly deposited on the filter paper-based SERS substrates before measurements. PLS-DA was successfully employed as a multivariate supervised model to classify and identify bacteria with efficiency, sensitivity, and specificity rates of 100% for all test samples. Variable importance in projection was associated with the presence/absence of some purine metabolites, whereas confidence intervals for each sample in the PLS-DA model were calculated using a resampling bootstrap procedure. Additionally, a potential new species of bacteria was analyzed by the proposed method and the result was in agreement with that obtained via 16S rRNA gene sequence analysis, thereby indicating that the SERS/PLS-DA approach has the potential to be a valuable tool for the discovery of novel bacteria. Graphical abstract This paper describes the discrimination of bacteria at the genus and species levels, after minimal sample preparation, using paper-based SERS substrates and PLS-DA with uncertainty estimation.