Spatial co-occurrence patterns of benthic microbial assemblage in response to trace metals in the Atacama Desert Coastline

Taxonomic and functional microbial communities may respond differently to anthropogenic coastal impacts, but ecological quality monitoring assessments using environmental DNA and RNA (eDNA/eRNA) in response to pollution are poorly understood. In the present study, we investigated the utility of the co-occurrence network approach's to comprehensively explore both structure and potential functions of benthic marine microbial communities and their responses to Cu and Fe fractioning from two sediment deposition coastal zones of northern Chile via 16S rRNA gene metabarcoding. The results revealed substantial differences in the microbial communities, with the predominance of two distinct module hubs based on study zone. This indicates that habitat influences microbial co-occurrence networks. Indeed, the discriminant analysis allowed us to identify keystone taxa with significant differences in eDNA and eRNA comparison between sampled zones, revealing that Beggiatoaceae, Carnobacteriaceae, and Nitrosococcaceae were the primary representatives from Off Loa, whereas Enterobacteriaceae, Corynebacteriaceae, Latescibacteraceae, and Clostridiaceae were the families responsible for the observed changes in Mejillones Bay. The quantitative evidence from the multivariate analyses supports that the benthic microbial assemblages' features were linked to specific environments associated with Cu and Fe fractions, mainly in the Bay. Furthermore, the predicted functional microbial structure suggested that transporters and DNA repair allow the communities to respond to metals and endure the interacting variable environmental factors like dissolved oxygen, temperature, and salinity. Moreover, some active taxa recovered are associated with anthropogenic impact, potentially harboring antibiotic resistance and other threats in the coastal zone. Overall, the method of scoping eRNA in parallel with eDNA applied here has the capacity to significantly enhance the spatial and functional understanding of real-time microbial assemblages and, in turn, would have the potential to increase the acuity of biomonitoring programs key to responding to immediate management needs for the marine environment.

Metabarcoding approaches in amphibian disease ecology: Disentangling the functional contributions of skin bacteria on disease outcome

Molecular technologies have revolutionized the field of wildlife disease ecology, allowing the detection of outbreaks, novel pathogens, and invasive strains. In particular, metabarcoding approaches, defined here as tools used to amplify and sequence universal barcodes from a single sample (e.g., 16S rRNA for bacteria, ITS for fungi, 18S rRNA for eukaryotes), are expanding our traditional view of host-pathogen dynamics by integrating microbial interactions that modulate disease outcome. Here, I provide an analysis from the perspective of the field of amphibian disease ecology, where the emergence of multi-host pathogens has caused global declines and species extinctions. I re-analyzed an experimental mesocosm dataset to infer the functional profiles of the skin microbiomes of coqui frogs (Eleutherodactylus coqui), an amphibian species that is consistently found infected with the fungal pathogen Batrachochytrium dendrobatidis and has high turnover of skin bacteria driven by seasonal shifts. I found that the metabolic activities of microbiomes operate at different capacities depending on the season. Global enrichment of predicted functions was more prominent during the warm-wet season, indicating that microbiomes during the cool-dry season were either depauperate, resistant to new bacterial colonization, or that their functional space was more saturated. These findings suggest important avenues to investigate how microbes regulate population growth and contribute to host physiological processes. Overall, this study highlights the current challenges and future opportunities in the application of metabarcoding to investigate the causes and consequences of disease in wild systems.

Signatures of Mucosal Microbiome in Oral Squamous Cell Carcinoma Identified Using a Random Forest Model

Objective

The aim of this study was to explore the signatures of oral microbiome associated with OSCC using a random forest (RF) model.

Patients and Methods

A total of 24 patients with OSCC were enrolled in the study. The oral microbiome was assessed in cancerous lesions and matched paracancerous tissues from each patient using 16S rRNA gene sequencing. Signatures of mucosal microbiome in OSCC were identified using a RF model.

Results

Significant differences were found between OSCC lesions and matched paracancerous tissues with respect to the microbial profile and composition. Linear discriminant analysis effect size analyses (LEfSe) identified 15 bacteria genera associated with cancerous lesions. Fusobacterium, Treponema, Streptococcus, Peptostreptococcus, Carnobacterium, Tannerella, Parvimonas and Filifactor were enriched. A classifier based on RF model identified a microbial signature comprising 12 bacteria, which was capable of distinguishing cancerous lesions and paracancerous tissues (AUC = 0.82). The network of the oral microbiome in cancerous lesions appeared to be simplified and fragmented. Functional analyses of oral microbiome showed altered functions in amino acid metabolism and increased capacity of glucose utilization in OSCC.

Conclusion

The identified microbial signatures may potentially be used as a biomarker for predicting OSCC or for clinical assessment of oral cancer risk.