mSystems最新文献

Shotgun metagenomics allows comprehensive sampling of the genomic information of microbes in a given environment and is a tool of choice for studying complex microbial systems. Mapping sequencing reads against a set of reference or metagenome-assembled genomes is in principle a simple and powerful approach to define the species-level composition of the microbial community under investigation. However, despite the widespread use of this approach, there is no established way to properly interpret the alignment results, with arbitrary relative abundance thresholds being routinely used to discriminate between present and absent species. Such an approach can be affected by significant biases, especially in the identification of rare species. Therefore, it is important to develop new metrics to overcome these biases. Here, we present Metapresence, a new tool to perform reliable identification of the species in metagenomic samples based on the distribution of mapped reads on the reference genomes. The analysis is based on two metrics describing the breadth of coverage and the genomic distance between consecutive reads. We demonstrate the high precision and wide applicability of the tool using data from various synthetic communities, a real mock community, and the gut microbiome of healthy individuals and antibiotic-associated-diarrhea patients. Overall, our results suggest that the proposed approach has a robust performance in hard-to-analyze microbial communities containing contaminated or closely related genomes in low abundance.IMPORTANCEDespite the prevalent use of genome-centric alignment-based methods to characterize microbial community composition, there lacks a standardized approach for accurately identifying the species within a sample. Currently, arbitrary relative abundance thresholds are commonly employed for this purpose. However, due to the inherent complexity of genome structure and biases associated with genome-centric approaches, this practice tends to be imprecise. Notably, it introduces significant biases, particularly in the identification of rare species. The method presented here addresses these limitations and contributes significantly to overcoming inaccuracies in precisely defining community composition, especially when dealing with rare members.

Although long-read sequencing has enabled obtaining high-quality and complete genomes from metagenomes, many challenges still remain to completely decompose a metagenome into its constituent prokaryotic and viral genomes. This study focuses on decomposing an estuarine metagenome to obtain a more accurate estimate of microbial diversity. To achieve this, we developed a new bead-based DNA extraction method, a novel bin refinement method, and obtained 150 Gbp of Nanopore sequencing. We estimate that there are ~500 bacterial and archaeal species in our sample and obtained 68 high-quality bins (>90% complete, <5% contamination, ≤5 contigs, contig length of >100 kbp, and all ribosomal and tRNA genes). We also obtained many contigs of picoeukaryotes, environmental DNA of larger eukaryotes such as mammals, and complete mitochondrial and chloroplast genomes and detected ~40,000 viral populations. Our analysis indicates that there are only a few strains that comprise most of the species abundances.

Importance: Ocean and estuarine microbiomes play critical roles in global element cycling and ecosystem function. Despite the importance of these microbial communities, many species still have not been cultured in the lab. Environmental sequencing is the primary way the function and population dynamics of these communities can be studied. Long-read sequencing provides an avenue to overcome limitations of short-read technologies to obtain complete microbial genomes but comes with its own technical challenges, such as needed sequencing depth and obtaining high-quality DNA. We present here new sampling and bioinformatics methods to attempt decomposing an estuarine microbiome into its constituent genomes. Our results suggest there are only a few strains that comprise most of the species abundances from viruses to picoeukaryotes, and to fully decompose a metagenome of this diversity requires 1 Tbp of long-read sequencing. We anticipate that as long-read sequencing technologies continue to improve, less sequencing will be needed.

Streptococcus pyogenes is responsible for a range of diseases in humans contributing significantly to morbidity and mortality. Among more than 200 serotypes of S. pyogenes, serotype M1 strains hold the greatest clinical relevance due to their high prevalence in severe human infections. To enhance our understanding of pathogenesis and discovery of potential therapeutic approaches, we have developed the first genome-scale metabolic model (GEM) for a serotype M1 S. pyogenes strain, which we name iYH543. The curation of iYH543 involved cross-referencing a draft GEM of S. pyogenes serotype M1 from the AGORA2 database with gene essentiality and autotrophy data obtained from transposon mutagenesis-based and growth screens. We achieved a 92.6% (503/543 genes) accuracy in predicting gene essentiality and a 95% (19/20 amino acids) accuracy in predicting amino acid auxotrophy. Additionally, Biolog Phenotype microarrays were employed to examine the growth phenotypes of S. pyogenes, which further contributed to the refinement of iYH543. Notably, iYH543 demonstrated 88% accuracy (168/190 carbon sources) in predicting growth on various sole carbon sources. Discrepancies observed between iYH543 and the actual behavior of living S. pyogenes highlighted areas of uncertainty in the current understanding of S. pyogenes metabolism. iYH543 offers novel insights and hypotheses that can guide future research efforts and ultimately inform novel therapeutic strategies.IMPORTANCEGenome-scale models (GEMs) play a crucial role in investigating bacterial metabolism, predicting the effects of inhibiting specific metabolic genes and pathways, and aiding in the identification of potential drug targets. Here, we have developed the first GEM for the S. pyogenes highly virulent serotype, M1, which we name iYH543. The iYH543 achieved high accuracy in predicting gene essentiality. We also show that the knowledge obtained by substituting actual measurement values for iYH543 helps us gain insights that connect metabolism and virulence. iYH543 will serve as a useful tool for rational drug design targeting S. pyogenes metabolism and computational screening to investigate the interplay between inhibiting virulence factor synthesis and growth.

Methicillin-resistant Staphylococcus aureus (MRSA) sequence type 630 (ST630) is a rarely reported lineage worldwide. This study aimed to trace the dissemination of the emerging MRSA ST630 clones in China and investigate their virulence potential. We collected 22 ST630-MRSA isolates from across China and performed whole-genome sequencing analysis and virulence characterization on these isolates. Epidemiological results showed that MRSA ST630 isolates were primarily isolated from pus/wound secretions, mainly originating from Jiangxi province, and carried diverse virulence and drug resistance genes. Staphylococcal cassette chromosome mec type V (SCCmec V) predominated (11/22, 50.0%) among the MRSA ST630 isolates. Interestingly, nearly half (45.5%) of the 22 ST630-MRSA isolates tested lacked intact SCCmec elements. Phylogenetic analysis demonstrated that ST630-MRSA could be divided into two distinct clades, with widespread dissemination mainly in Chinese regions. Five representative isolates were selected for phenotypic assays, including hemolysin activity, real-time fluorescence quantitative PCR, western blot analysis, hydrogen peroxide killing assay, blood killing assay, cell adhesion and invasion assay, and mouse skin abscess model. The results showed that, compared to the USA300-LAC strain, ST630 isolates exhibited particularly strong invasiveness and virulence in the aforementioned phenotypic assays. This study described the emergence of a highly virulent ST630-MRSA lineage and improved our insight into the molecular epidemiology of ST630 clones in China.IMPORTANCEMethicillin-resistant Staphylococcus aureus (MRSA) sequence type 630 (ST630) is an emerging clone with an increasing isolation rate in China. This study raises awareness of the hypervirulent MRSA ST630 clones in China and alerts people to their widespread dissemination. ST630-staphylococcal cassette chromosome mec V is a noteworthy clone in China, and we present the first comprehensive genetic and phenotypic analysis of this lineage. Our findings provide valuable insights for the prevention and control of infections caused by this emerging MRSA clone.

Nontyphoidal Salmonella (NTS) is the main etiological agent of human nontyphoidal salmonellosis. The aim of this study was to analyze the epidemiological characteristics and horizontal transfer mechanisms of antimicrobial resistance (AMR) genes from eight strains of NTS detected in Zhenjiang City, Jiangsu Province, China. Fecal samples from outpatients with food-borne diarrhea were collected in 2022. The NTS isolates were identified, and their susceptibility was tested with the Vitek 2 Compact system. The genomes of the NTS isolates were sequenced with the Illumina NovaSeq platform and Oxford Nanopore Technologies platform. The AMR genes and mobile genetic elements (MGEs) were predicted with the relevant open access resources. Eight strains of NTS were isolated from 153 specimens, and Salmonella Typhimurium ST19 was the most prevalent serotype. The AMR gene with the highest detection rate was AAC(6')-Iaa (10.5%) followed by TEM-1 (7.9%), sul2 (6.6%), and tet(A) (5.3%). Eleven MGEs carrying 34 AMR genes were identified on the chromosomes of 3 of the 8 NTS, including 3 resistance islands, 6 composite transposons (Tns), and 2 integrons. Eighteen plasmids carrying 40 AMR genes were detected in the 8 NTS strains, including 6 mobilizable plasmids, 3 conjugative plasmids, and 9 nontransferable plasmids, 7 of which carried 10 composite Tns and 3 integrons. This study provided a theoretical basis, from a genetic perspective, for the prevention and control of NTS resistance in Zhenjiang City.

Importance: Human nontyphoidal salmonellosis is one of the common causes of bacterial food-borne illnesses, with significant social and economic impacts, especially those caused by invasive multidrug-resistant nontyphoidal Salmonella, which entails high morbidity and mortality. Antimicrobial resistance is mainly mediated by drug resistance genes, and mobile genetic elements play key roles in the capture, accumulation, and dissemination of antimicrobial resistance genes. Therefore, it is necessary to study the epidemiological characteristics and horizontal transfer mechanisms of antimicrobial resistance genes of nontyphoidal Salmonella to prevent the spread of multidrug-resistant nontyphoidal Salmonella.

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.

Targeted high-throughput sequencing (HTS) has revolutionized the way we look at bacterial communities. It can be used for the species-specific detection of bacteria as well as for the determination of the microbiome and resistome and can be applied to samples from almost any environment. However, the results of targeted HTS can be influenced by many factors, which poses a major challenge for its use in clinical diagnostics. In this study, we investigated the impact of the DNA extraction method on the determination of the bacterial microbiome and resistome by targeted HTS using principles from metrology and diagnostics such as repeatability and analytical sensitivity. Sputum samples spiked with Acinetobacter baumannii, Klebsiella pneumoniae, and Pseudomonas aeruginosa at three different concentrations (10³-10⁶ cells/mL) were used. DNA was extracted from each sample on 2 separate days in three replicates each using three different extraction methods based on cetrimonium bromide, magnetic beads, and silica membranes. All three spiked bacteria were detected in sputum, and the DNA extraction method had no significant effect on detection. However, the DNA extraction method had significant effects on the composition of the microbiome and the resistome. The sequencing results were repeatable in the majority of cases. The silica membrane-based DNA extraction kit provided the most repeatable results and the highest diversity of the microbiome and resistome. Targeted HTS has been shown to be a reliable tool for determining the microbiome and resistome; however, the method of DNA extraction should be carefully selected to minimize its impact on the results.

Importance: High-throughput sequencing (HTS) is one of the crucial new technologies that gives us insights into previously hidden parts of microbial communities. The DNA extraction method is an important step that can have a major impact on the results, and understanding this impact is of paramount importance for their reliable interpretation. Our results are of great value for the interpretation of sputum microbiome and resistome results obtained by targeted HTS. Our findings allow for a more rational design of future microbiome studies, which would lead to higher repeatability of results and easier comparison between different laboratories. This could also facilitate the introduction of targeted HTS in clinical microbiology for reliable identification of pathogenic bacteria and testing for antimicrobial resistance (AMR). As AMR is a major threat to public health, the improved methods for determining AMR would bring great benefits to both the healthcare system and society as a whole.

Gut dysbiosis is closely related to dysregulated microRNAs (miRNAs) in the intestinal epithelial cells, which plays an important role in the pathogenesis of Crohn's disease (CD). We investigated the relationship between fecal gut microbiome (GM) and intestinal tissue miRNAs in different stages of pediatric CD. Metagenomic analysis and miRNA sequencing were conducted to examine the GM and intestinal miRNA profiles of CD patients before and after clinical induction therapy and the controls. Twenty-seven newly diagnosed, therapy-naïve pediatric patients with active CD and 11 non-inflammatory bowel disease (IBD) controls were recruited in this study. Among CD patients, 11 patients completed induction treatment and reached clinical remission. Both GM and miRNA profiles were significantly changed between CD patients and controls. Seven key bacteria were identified at species level including Defluviitalea raffinosedens, Thermotalea metallivorans, Roseburia intestinalis, Dorea sp. AGR2135, Escherichia coli, Shigella sonnei, and Salmonella enterica, the exact proportions of which were further validated by real-time quantitative PCR analysis. Eight key miRNAs were also identified including hsa-miR-215-5p, hsa-miR-194-5p, hsa-miR-12135, hsa-miR-509-3-5p, hsa-miR-212-5p, hsa-miR-4448, hsa-miR-501-3p, and hsa-miR-503-5p. The functional enrichment analysis of differential miRNAs indicated the significantly altered cyclin protein, cyclin-dependent protein, and cell cycle pathway. The close interactions between seven key bacteria and eight key miRNAs were further investigated by miRNA target prediction. The association between specific miRNA expressions and key gut bacteria at different stages of CD supported their important roles as potential molecular biomarkers. Understanding the relationship between them will help us to explore the molecular mechanisms of CD.

Importance: Since previous studies have focused on the change of the fecal gut microbiome and intestinal tissue miRNA in pediatric Crohn's disease (CD), the relationship between them in different stages is still not clear. This is the first study to explore the gut microbiota and miRNA and their correlations with the Pediatric Crohn's Disease Activity Index (PCDAI). Crohn's Disease Endoscopic Index of Severity (CDEIS), and calprotectin, by applying two omics approach in three different groups (active CD, CD in remission with exclusive enteral nutrition or infliximab induction therapy, and the healthy controls). Both gut microbiome structure and the miRNA profiles were significantly changed in the different stage of CD. Seven key gut microbiome at species and eight key miRNAs were found, and their close interactions were further fully investigated by miRNA target prediction.

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.