The human respiratory tract is colonized by a complex microbial community that helps maintain respiratory health and plays a crucial role in defending the host from infections. Respiratory viruses have been demonstrated to alter microbiota composition, resulting in opportunistic species expansion, and increasing the disease severity and host susceptibility to bacterial co-infections. This study aims to examine the compositional differences in the nasal microbiota between SARS-CoV-2-infected and non-infected patients. We conducted Oxford Nanopore full-length 16S rRNA sequencing on nasal swabs from 94 COVID-19 negative and 85 COVID-19 positive patients collected during the SARS-CoV-2 pandemic in Malta. Our analysis identified significant alpha and beta diversity differences in the nasal microbiota composition among our study groups. We observed a trend toward decreased microbial richness and evenness in the COVID-Positive cohort with and increased abundance of common nasal opportunistic species including Citrobacter koseri, Dolosigranulum pigrum, Haemophilus influenzae, Klebsiella pneumoniae, and Moraxella catarrhalis. The findings from this study are in line with previously published papers identifying key alterations in the nasal microbiota composition associated with SARS-CoV-2 infection. Understanding these microbiome-driven mechanisms could present novel prognostic markers or offer new approaches for disease prevention and treatment.

The SARS-CoV-2 pandemic has led to an urgent need for effective and rapid diagnostic tools. In the present study, we have evaluated the predictive diagnostic potential of nasal microbiota by analyzing microbial community structures at different taxonomic level resolutions—species, genus, family, order, class and phylum—using Random Forest modelling. A total of 179 nasal swabs from COVID-19-positive (n = 85) and COVID-19-negative (n = 94) individuals were sequenced using a full-length 16S rRNA sequencing (Oxford Nanopore) approach. During each iteration of the Random Forest model, the dataset was randomly split into a training set (70%) and a testing set (30%). Model performance improved with finer taxonomic resolution, achieving the highest accuracy at the Species level (AUROC = 0.821 ± 0.059) and a sensitivity of 55.6% at a specificity threshold of 90%. A progressive decline in AUROC and sensitivity was observed at broader taxonomic levels. Furthermore, Beta diversity analysis supported that microbial community structures are more distinct between COVID-19-positive and COVID-19-negative groups at finer taxonomic levels. These findings highlight the potential role of nasal microbiota profiling as a secondary diagnostic tool for COVID-19, particularly at species- and genus-level classification, and underscore the importance of high taxonomic resolution in microbiome-based diagnostics. However, limited by an uneven sample distribution and the lack of medical evaluations, further large-scale studies are needed before the nasal microbiota can be implemented in the clinical diagnostics of COVID-19.

The human gut microbiota (HGM) plays a pivotal role in human health by regulating metabolic, immune, and neurological processes and contributes to the susceptibility to various diseases. The increasing interest in microbiota-targeted therapies has highlighted the need for in vitro models capable of accurately reproducing the composition, function, and ecological dynamics of the intestinal microbial ecosystem. This review examines the methodological factors that determine the biological relevance of HGM in vitro models, emphasizing that reliable experimental outcomes depend not only on the choice of fermentation system (including batch and continuous fermentation systems), but also on the optimization of inoculum preparation, culture media, environmental control, and strategies that preserve microbial stability and reproducibility. We further discuss how these methodological decisions influence the interpretation of experimental results and determine the suitability of each model for different biological applications. The application of these models in dietary, prebiotic, probiotic, and pharmacological interventions is explored, demonstrating their potential to predict microbiota responses and elucidate mechanistic links between microbial activity and host responses. We propose that rigorous methodological standardization, rather than increasing technological complexity alone, represents the principal requirement for generating biologically relevant and reproducible in vitro gut microbiota models. Finally, we discuss emerging host-microbe co-culture systems, along with the integration of multi-omics and computational approaches, which are expected to expand the physiological relevance and translational applicability of these models. Unlike reviews primarily organized around individual fermentation technologies, this review adopts a workflow-based perspective and examines how methodological decisions interact to determine ecological fidelity, functional stability and reproducibility.