Mell, J. | Search bioRxiv

bioRxiv2025

Outer membrane vesicles from Bacteroides fragilis contain coding and non-coding small RNA species that modulate inflammatory signaling in intestinal epithelial cells.

Alterations to the community structure and function of the microbiome are associated with changes to host physiology, including immune responses. However, the contribution of microbe-derived RNAs carried by outer membrane vesicles (OMVs) to host immune responses remain unclear. This study investigated the role of OMVs and OMV-associated small RNA (sRNA) species from pathogenic and commensal Bacteroides fragilis (ETBF and NTBF respectively) in eliciting different immune responses from intestinal epithelial cells. To distinguish the differences in the sRNA profiles of the two strains and their OMVs, RNA-seq, qRT-PCR, and northern blotting were conducted to identify enrichment of discrete sRNA species in OMVs, which were also differentially expressed between the two strains. Specifically, both coding and non-coding RNAs were enriched in OMVs from NTBF and ETBF, with BF9343_RS22680 and BF9343_RS17870 being significantly enriched in ETBF OMVs compared to NTBF. To understand the effects of OMVs on pattern recognition receptors, reporter cells of Toll-like receptor (TLR) activation were treated with OMVs, demonstrating activation of TLRs 2, 3, and 7. Treatment of Caco-2 and HT29-MTX cells with OMVs demonstrated increased expression of IL-8. Surprisingly, we discovered that degradation of RNase-accessible RNAs within ETBF OMVs, but not NTBF OMVs, resulted in vesicles with enhanced capacity to stimulate IL-8 expression, indicating that these extravesicular RNAs exert an immunosuppressive effect. This suggests a dual role for OMV-associated RNAs in modulating host immune responses, with implications for both bacterial pathogenesis and therapeutic applications. Graphical Abstract

molecular biology↗

bioRxiv2020

Learning, Visualizing and Exploring 16S rRNA Structure Using an Attention-based Deep Neural Network

Recurrent neural networks (RNNs) with memory (e.g. LSTMs) and attention mechanisms are widely used in natural language processing because they can capture short and long term sequential information for diverse tasks. We propose an integrated deep learning model for microbial DNA sequence data, which exploits convolutional networks, recurrent neural networks, and attention mechanisms to perform sample-associated attribute prediction--phenotype prediction--and extract interesting features, such as informative taxa and predictive k-mer context. In this paper, we develop this novel deep learning approach and evaluate its application to amplicon sequences. We focus on typically short DNA reads of 16s ribosomal RNA (rRNA) marker genes, which identify the heterogeneity of a microbial community sample. Our deep learning approach enables sample-level attribute and taxonomic prediction, with the aim of aiding biological research and supporting medical diagnosis. We demonstrate that our implementation of a novel attention-based deep network architecture, Read2Pheno, achieves read-level phenotypic prediction and, in turn, that aggregating read-level information can robustly predict microbial community properties, host phenotype, and taxonomic classification, with performance comparable to conventional approaches. Most importantly, as a further result of the training process, the network architecture will encode sequences (reads) into dense, meaningful representations: learned embedded vectors output on the intermediate layer of the network model, which can provide biological insight when visualized. Finally, we demonstrate that a model with an attention layer can automatically identify informative regions in sequences/reads which are particularly informative for classification tasks. An implementation of the attention-based deep learning network is available at https://github.com/EESI/sequence_attention.

bioinformatics↗