Antibiotic Sensitivity and Nasal Microbiome in Patients with Acute Bacterial Rhinosinusitis

OBJECTIVES

Acute rhinosinusitis (ARS) is a common upper respiratory tract infection that is mostly of viral origin. However, little is known about the nasal microbiome profile at presentation and the changes caused by antibiotics in acute bacterial rhinosinusitis (ABRS).

METHODS

This was a prospective single-center study. Overall, 43 ARS patients were screened and were assessed with the symptom questionnaires, nasal endoscopy, and Water's view. Five healthy subjects were recruited as controls. Middle meatal mucus samples were obtained using a cotton swab (for bacterial culture and antimicrobial susceptibility testing) and the suction technique (for 16S rRNA sequencing). After 1 week of antibiotic use (amoxicillin with clavulanic acid), we enrolled 13 patients with ABRS with positive isolates and middle meatal samples for 16S rRNA sequencing were obtained again.

RESULTS

Overall, we demonstrated a significantly lower abundance of the Lactobacillaceae family in ABRS patients than in healthy controls. Resistant ABRS had different characteristics of middle meatal microbiomes when compared to sensitive ABRS as follows: (1) lower proportion of lactic acid bacteria, (2) increased pathogens such as Rhodococcus sp., Massila sp., Acinetobacter sp., and H. influenza, and (3) increased beta diversity. However, no remarkable changes were observed in the middle meatal microbiome after antibiotic use.

CONCLUSION

We showed the roles of Lactobacillaceae in ABRS, and Acinetobacter and Massilia in case of amoxicillin resistance.

LEVEL OF EVIDENCE

3 Laryngoscope, 134:1081-1088, 2024.

Skin microbe-dependent TSLP-ILC2 priming axis in early life is co-opted in allergic inflammation

Although early life colonization of commensal microbes contributes to long-lasting immune imprinting in host tissues, little is known regarding the pathophysiological consequences of postnatal microbial tuning of cutaneous immunity. Here, we show that postnatal exposure to specific skin commensal Staphylococcus lentus (S. lentus) promotes the extent of atopic dermatitis (AD)-like inflammation in adults through priming of group 2 innate lymphoid cells (ILC2s). Early postnatal skin is dynamically populated by discrete subset of primed ILC2s driven by microbiota-dependent induction of thymic stromal lymphopoietin (TSLP) in keratinocytes. Specifically, the indole-3-aldehyde-producing tryptophan metabolic pathway, shared across Staphylococcus species, is involved in TSLP-mediated ILC2 priming. Furthermore, we demonstrate a critical contribution of the early postnatal S. lentus-TSLP-ILC2 priming axis in facilitating AD-like inflammation that is not replicated by later microbial exposure. Thus, our findings highlight the fundamental role of time-dependent neonatal microbial-skin crosstalk in shaping the threshold of innate type 2 immunity co-opted in adulthood.

ViBE: a hierarchical BERT model to identify eukaryotic viruses using metagenome sequencing data

Viruses are ubiquitous in humans and various environments and continually mutate themselves. Identifying viruses in an environment without cultivation is challenging; however, promoting the screening of novel viruses and expanding the knowledge of viral space is essential. Homology-based methods that identify viruses using known viral genomes rely on sequence alignments, making it difficult to capture remote homologs of the known viruses. To accurately capture viral signals from metagenomic samples, models are needed to understand the patterns encoded in the viral genomes. In this study, we developed a hierarchical BERT model named ViBE to detect eukaryotic viruses from metagenome sequencing data and classify them at the order level. We pre-trained ViBE using read-like sequences generated from the virus reference genomes and derived three fine-tuned models that classify paired-end reads to orders for eukaryotic deoxyribonucleic acid viruses and eukaryotic ribonucleic acid viruses. ViBE achieved higher recall than state-of-the-art alignment-based methods while maintaining comparable precision. ViBE outperformed state-of-the-art alignment-free methods for all test cases. The performance of ViBE was also verified using real sequencing datasets, including the vaginal virome.

MegaR: an interactive R package for rapid sample classification and phenotype prediction using metagenome profiles and machine learning

BACKGROUND

Diverse microbiome communities drive biogeochemical processes and evolution of animals in their ecosystems. Many microbiome projects have demonstrated the power of using metagenomics to understand the structures and factors influencing the function of the microbiomes in their environments. In order to characterize the effects from microbiome composition for human health, diseases, and even ecosystems, one must first understand the relationship of microbes and their environment in different samples. Running machine learning model with metagenomic sequencing data is encouraged for this purpose, but it is not an easy task to make an appropriate machine learning model for all diverse metagenomic datasets.

RESULTS

We introduce MegaR, an R Shiny package and web application, to build an unbiased machine learning model effortlessly with interactive visual analysis. The MegaR employs taxonomic profiles from either whole metagenome sequencing or 16S rRNA sequencing data to develop machine learning models and classify the samples into two or more categories. It provides various options for model fine tuning throughout the analysis pipeline such as data processing, multiple machine learning techniques, model validation, and unknown sample prediction that can be used to achieve the highest prediction accuracy possible for any given dataset while still maintaining a user-friendly experience.

CONCLUSIONS

Metagenomic sample classification and phenotype prediction is important particularly when it applies to a diagnostic method for identifying and predicting microbe-related human diseases. MegaR provides various interactive visualizations for user to build an accurate machine-learning model without difficulty. Unknown sample prediction with a properly trained model using MegaR will enhance researchers to identify the sample property in a fast turnaround time.

Data-Driven Modeling for Species-Level Taxonomic Assignment From 16S rRNA: Application to Human Microbiomes

With the emergence of next-generation sequencing (NGS) technology, there have been a large number of metagenomic studies that estimated the bacterial composition via 16S ribosomal RNA (16S rRNA) amplicon sequencing. In particular, subsets of the hypervariable regions in 16S rRNA, such as V1-V2 and V3-V4, are targeted using high-throughput sequencing. The sequences from different taxa are assigned to a specific taxon based on the sequence homology. Since such sequences are highly homologous or identical between species in the same genus, it is challenging to determine the exact species using 16S rRNA sequences only. Therefore, in this study, homologous species groups were defined to obtain maximum resolution related with species using 16S rRNA. For the taxonomic assignment using 16S rRNA, three major 16S rRNA databases are independently used since the lineage of certain bacteria is not consistent among these databases. On the basis of the NCBI taxonomy classification, we re-annotated inconsistent lineage information in three major 16S rRNA databases. For each species, we constructed a consensus sequence model for each hypervariable region and determined homologous species groups that consist of indistinguishable species in terms of sequence homology. Using a k-nearest neighbor method and the species consensus sequence models, the species-level taxonomy was determined. If the species determined is a member of homologous species groups, the species group is assigned instead of a specific species. Notably, the results of the evaluation on our method using simulated and mock datasets showed a high correlation with the real bacterial composition. Furthermore, in the analysis of real microbiome samples, such as salivary and gut microbiome samples, our method successfully performed species-level profiling and identified differences in the bacterial composition between different phenotypic groups.

Functional dynamics of bacterial species in the mouse gut microbiome revealed by metagenomic and metatranscriptomic analyses

Background

Microbial communities of the mouse gut have been extensively studied; however, their functional roles and regulation are yet to be elucidated. Metagenomic and metatranscriptomic analyses may allow us a comprehensive profiling of bacterial composition and functions of the complex gut microbiota. The present study aimed to investigate the active functions of the microbial communities in the murine cecum by analyzing both metagenomic and metatranscriptomic data on specific bacterial species within the microbial communities, in addition to the whole microbiome.

Results

Bacterial composition of the healthy mouse gut microbiome was profiled using the following three different approaches: 16S rRNA-based profiling based on amplicon and shotgun sequencing data, and genome-based profiling based on shotgun sequencing data. Consistently, Bacteroidetes, Firmicutes, and Deferribacteres emerged as the major phyla. Based on NCBI taxonomy, Muribaculaceae, Lachnospiraceae, and Deferribacteraceae were the predominant families identified in each phylum. The genes for carbohydrate metabolism were upregulated in Muribaculaceae, while genes for cofactors and vitamin metabolism and amino acid metabolism were upregulated in Deferribacteraceae. The genes for translation were commonly enhanced in all three families. Notably, combined analysis of metagenomic and metatranscriptomic sequencing data revealed that the functions of translation and metabolism were largely upregulated in all three families in the mouse gut environment. The ratio of the genes in the metagenome and their expression in the metatranscriptome indicated higher expression of carbohydrate metabolism in Muribaculum, Duncaniella, and Mucispirillum.

Conclusions

We demonstrated a fundamental methodology for linking genomic and transcriptomic datasets to examine functional activities of specific bacterial species in a complicated microbial environment. We investigated the normal flora of the mouse gut using three different approaches and identified Muribaculaceae, Lachnospiraceae, and Deferribacteraceae as the predominant families. The functional distribution of these families was reflected in the entire microbiome. By comparing the metagenomic and metatranscriptomic data, we found that the expression rates differed for different functional categories in the mouse gut environment. Application of these methods to track microbial transcription in individuals over time, or before and after administration of a specific stimulus will significantly facilitate future development of diagnostics and treatments.