mSystems最新文献

Systemic inflammatory response syndrome (SIRS) is a severe inflammatory response that can lead to organ dysfunction and death. Modulating the gut microbiome is a promising therapeutic approach for managing SIRS. This study assesses the therapeutic potential of the Xuanfei Baidu (XFBD) formula in treating SIRS. The results showed that XFBD administration effectively reduced mortality rates and inflammation in SIRS mice. Using 16S rRNA sequencing and fecal microbiota transplantation (FMT), we substantiated that the therapeutic effects of XFBD are partly attributed to gut microbiota modulation. We conducted in vitro experiments to accurately assess the gut microbiome remodeling effects of 51 compounds isolated from XFBD. These compounds exhibited varying abilities to induce a microbial structure that closely resembles that of the healthy control group. By quantifying their impact on microbial structure and clustering their regulatory patterns, we devised multiple gut microbiome remodeling compound (GMRC) cocktails. GMRC cocktail C, comprising aucubin, gentiopicroside, syringic acid, gallic acid, p-hydroxybenzaldehyde, para-hydroxybenzoic acid, and isoimperatorin, demonstrated superior efficacy in treating SIRS compared to a single compound or to other cocktails. Finally, in vitro experiments showcased that GMRC cocktail C effectively rebalanced bacteria composition in SIRS patients. This study underscores XFBD's therapeutic potential in SIRS and highlights the importance of innovative treatment approaches for this disease by targeting the gut microbiota.IMPORTANCEDeveloping effective treatment strategies for systemic inflammatory response syndrome (SIRS) is crucial due to its severe and often life-threatening nature. While traditional treatments like dexamethasone have shown efficacy, they also come with significant side effects and limitations. This study makes significant strides by demonstrating that the Xuanfei Baidu (XFBD) formula can substantially reduce mortality rates and inflammation in SIRS mice through effective modulation of the gut microbiota. By quantitatively assessing the impact of 51 compounds derived from XFBD on the gut microbiome, we developed a potent gut microbiome remodeling compound cocktail. This cocktail outperformed individual compounds and other mixtures in efficacy against SIRS. These findings highlight the potential of XFBD as a therapeutic solution for SIRS and underscore the critical role of innovative strategies targeting the gut microbiota in addressing this severe inflammatory condition.

Matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) is widely used in clinical microbiology laboratories for bacterial identification but its use for detection of antimicrobial resistance (AMR) remains limited. Here, we used MALDI-TOF MS with artificial intelligence (AI) approaches to successfully predict AMR in Pseudomonas aeruginosa, a priority pathogen with complex AMR mechanisms. The highest performance was achieved for modern β-lactam/β-lactamase inhibitor drugs, namely, ceftazidime/avibactam and ceftolozane/tazobactam. For these drugs, the model demonstrated area under the receiver operating characteristic curve (AUROC) of 0.869 and 0.856, specificity of 0.925 and 0.897, and sensitivity of 0.731 and 0.714, respectively. As part of this work, we developed dynamic binning, a feature engineering technique that effectively reduces the high-dimensional feature set and has wide-ranging applicability to MALDI-TOF MS data. Compared to conventional feature engineering approaches, the dynamic binning method yielded highest performance in 7 of 10 antimicrobials. Moreover, we showcased the efficacy of transfer learning in enhancing the AUROC performance for 8 of 11 antimicrobials. By assessing the contribution of features to the model's prediction, we identified proteins that may contribute to AMR mechanisms. Our findings demonstrate the potential of combining AI with MALDI-TOF MS as a rapid AMR diagnostic tool for Pseudomonas aeruginosa.IMPORTANCEPseudomonas aeruginosa is a key bacterial pathogen that causes significant global morbidity and mortality. Antimicrobial resistance (AMR) emerges rapidly in P. aeruginosa and is driven by complex mechanisms. Drug-resistant P. aeruginosa is a major challenge in clinical settings due to limited treatment options. Early detection of AMR can guide antibiotic choices, improve patient outcomes, and avoid unnecessary antibiotic use. Matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) is widely used for rapid species identification in clinical microbiology. In this study, we repurposed mass spectra generated by MALDI-TOF and used them as inputs for artificial intelligence approaches to successfully predict AMR in P. aeruginosa for multiple key antibiotic classes. This work represents an important advance toward using MALDI-TOF as a rapid AMR diagnostic for P. aeruginosa in clinical settings.

Characterization of microbial community metabolic output is crucial to understanding their functions. Construction of genome-scale metabolic models from metagenome-assembled genomes (MAG) has enabled prediction of metabolite production by microbial communities, yet little is known about their accuracy. Here, we examined the performance of two approaches for metabolite prediction from metagenomes, one that is MAG-guided and another that is taxonomic reference-guided. We applied both on shotgun metagenomics data from human and environmental samples, and validated findings in the human samples using untargeted metabolomics. We found that in human samples, where taxonomic profiling is optimized and reference genomes are readily available, when number of input taxa was normalized, the reference-guided approach predicted more metabolites than the MAG-guided approach. The two approaches showed significant overlap but each identified metabolites not predicted in the other. Pathway enrichment analyses identified significant differences in inferences derived from data based on the approach, highlighting the need for caution in interpretation. In environmental samples, when the number of input taxa was normalized, the reference-guided approach predicted more metabolites than the MAG-guided approach for total metabolites in both sample types and non-redundant metabolites in seawater samples. Nonetheless, as was observed for the human samples, the approaches overlapped substantially but also predicted metabolites not observed in the other. Our findings report on utility of a complementary input to genome-scale metabolic model construction that is less computationally intensive forgoing MAG assembly and refinement, and that can be applied on shallow shotgun sequencing where MAGs cannot be generated.IMPORTANCELittle is known about the accuracy of genome-scale metabolic models (GEMs) of microbial communities despite their influence on inferring community metabolic outputs and culture conditions. The performance of GEMs for metabolite prediction from metagenomes was assessed by applying two approaches on shotgun metagenomics data from human and environmental samples, and validating findings in the human samples using untargeted metabolomics. The performance of the approach was found to be dependent on sample type, but collectively, the reference-guided approach predicted more metabolites than the MAG-guided approach. Despite the differences, the predictions from the approaches overlapped substantially but each identified metabolites not predicted in the other. We found significant differences in biological inferences based on the approach, with some examples of uniquely enriched pathways in one group being invalidated when using the alternative approach, highlighting the need for caution in interpretation of GEMs.

The human pathogen Pseudomonas aeruginosa, a leading cause of hospital-acquired infections, inhabits and forms sessile antibiotic-resistant communities called biofilms in a wide range of biotic and abiotic environments. In this study, we examined how two global sensory signaling pathways-the RhlR quorum-sensing system and the CbrA/CbrB nutritional adaptation system-intersect to control biofilm development. Previous work has shown that individually these two systems repress biofilm formation. Here, we used biofilm analyses, RNA-seq, and reporter assays to explore the combined effect of information flow through RhlR and CbrA on biofilm development. We find that the ΔrhlRΔcbrA double mutant exhibits a biofilm morphology and an associated transcriptional response distinct from wildtype and the parent ΔrhlR and ΔcbrA mutants indicating codominance of each signaling pathway. The ΔrhlRΔcbrA mutant gains suppressor mutations that allow biofilm expansion; these mutations map to the crc gene resulting in loss of function of the carbon catabolite repression protein Crc. Furthermore, the combined absence of RhlR and CbrA leads to a drastic reduction in the abundance of the Crc antagonist small RNA CrcZ. Thus, CrcZ acts as the molecular convergence point for quorum- and nutrient-sensing cues. We find that in the absence of antagonism by CrcZ, Crc promotes the expression of biofilm matrix components-Pel exopolysaccharide, and CupB and CupC fimbriae. Therefore, this study uncovers a regulatory link between nutritional adaption and quorum sensing with potential implications for anti-biofilm targeting strategies.IMPORTANCEBacteria often form multicellular communities encased in an extracytoplasmic matrix called biofilms. Biofilm development is controlled by various environmental stimuli that are decoded and converted into appropriate cellular responses. To understand how information from two distinct stimuli is integrated, we used biofilm formation in the human pathogen Pseudomonas aeruginosa as a model and studied the intersection of two global sensory signaling pathways-quorum sensing and nutritional adaptation. Global transcriptomics on biofilm cells and reporter assays suggest parallel regulation of biofilms by each pathway that converges on the abundance of a small RNA antagonist of the carbon catabolite repression protein, Crc. We find a new role of Crc as it modulates the expression of biofilm matrix components in response to the environment. These results expand our understanding of the genetic regulatory strategies that allow P. aeruginosa to successfully develop biofilm communities.