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Respiratory disease (RD) is a worldwide leading threat to the pig industry, but there is still limited understanding of the pathogens associated with swine RD. In this study, we conducted a nationwide genomic surveillance on identifying viruses, bacteria, and antimicrobial resistance genes (ARGs) from the lungs of pigs with RD in China. By performing metatranscriptomic sequencing combined with metagenomic sequencing, we identified 21 viral species belonging to 12 viral families. Among them, porcine reproductive and respiratory syndrome virus, influenza A virus, herpes virus, adenovirus, and parvovirus were commonly identified. However, emerging viruses, such as Getah virus and porcine respiratory coronaviruses, were also characterized. Apart from viruses, a total of 164 bacterial species were identified, with Streptococcus suis, Mycoplasma hyorhinis, Mycoplasma hyopneumoniae, Glaesserella parasuis, and Pasteurella multocida being frequently detected in high abundances. Notably, Escherichia coli, Enterococcus faecalis, Staphylococcus aureus, and Klebsiella pneumoniae were also highly detected. Our further analysis revealed a complex interaction between the identified pathogens in swine RD. We also conducted retrospectively analyses to demonstrate the prevalent viral genotypes or bacterial serotypes associated with swine RD in China. Finally, we identified 48 ARGs, which conferred resistance to 13 predicted antimicrobial classes, and many of these ARGs were significantly associated with a substantial number of mobile genetic elements, including transposons (e.g., tnpAIS1, tnpA1353, int3, and ISCau1) and plasmids (e.g., Col(BS512), Col(YC)]. These findings will contribute to further understanding the etiology, epidemiology, and microbial interactions in swine RD, and may also shed a light on the development of effective vaccines.IMPORTANCEIn this study, we identified viruses and bacteria from the lungs of pigs with RD in China at a nationwide farm scale by performing metatranscriptomic sequencing combined with metagenomic sequencing. We also demonstrated the complex interactions between different viral and/or bacterial species in swine RD. Our work provides a comprehensive knowledge about the etiology, epidemiology, and microbial interactions in swine RD and data reference for the research and development of effective vaccines against the disease.

Average nucleotide identity (ANI) is a widely used metric to estimate genetic relatedness, especially in microbial species delineation. While ANI calculation has been well optimized for bacteria and closely related viral genomes, accurate estimation of ANI below 80%, particularly in large reference data sets, has been challenging due to a lack of accurate and scalable methods. To bridge this gap, we introduce MANIAC, an efficient computational pipeline optimized for estimating ANI and alignment fraction (AF) in viral genomes with divergence around ANI of 70%. Using a rigorous simulation framework, we demonstrate MANIAC's accuracy and scalability compared to existing approaches, even to data sets of hundreds of thousands of viral genomes. Applying MANIAC to a curated data set of complete bacterial dsDNA viruses revealed a multimodal ANI distribution, with a distinct gap around 80%, akin to the bacterial ANI gap (~90%) but shifted, likely due to viral-specific evolutionary processes such as recombination dynamics and mosaicism. We then evaluated ANI and AF as predictors of genus-level taxonomy using a logistic regression model. We found that this model has strong predictive power (PR-AUC = 0.981), but that it works much better for virulent (PR-AUC = 0.997) than temperate (PR-AUC = 0.847) bacterial viruses. This highlights the complexity of taxonomic classification in temperate phages, known for their extensive mosaicism, and cautions against over-reliance on ANI in such cases. MANIAC can be accessed at https://github.com/bioinf-mcb/MANIAC.IMPORTANCEWe introduce a novel computational pipeline called MANIAC, designed to accurately assess average nucleotide identity (ANI) and alignment fraction (AF) between diverse viral genomes, scalable to data sets of over 100k genomes. Using computer simulations and real data analyses, we show that MANIAC could accurately estimate genetic relatedness between pairs of viral genomes of around 60%-70% ANI. We applied MANIAC to investigate the question of ANI discontinuity in bacterial dsDNA viruses, finding evidence for an ANI gap, akin to the one seen in bacteria but around ANI of 80%. We then assessed the ability of ANI and AF to predict taxonomic genus boundaries, finding its strong predictive power in virulent, but not in temperate phages. Our results suggest that bacterial dsDNA viruses may exhibit an ANI threshold (on average around 80%) above which recombination helps maintain population cohesiveness, as previously argued in bacteria.

Marine protists form complex communities that are shaped by environmental and biological ecosystem properties, as well as ecological interactions between organisms. While all of these factors play a role in shaping protistan communities, the specific ways in which these properties and interactions influence protistan communities remain poorly understood. Fourteen years and 9 months of eukaryotic amplicon (18S-V4 rRNA gene) data collected monthly at the San Pedro Ocean Time-series (SPOT) station were used to evaluate the impacts that environmental and biological factors, and protist-protist interactions had on protistan community composition. Statistical analysis of the amplicon data revealed that seasonal patterns in protistan community composition were apparent, but that the environmental data collected through routine time-series sampling efforts could not explain most of the variability that was evident in the communities. To identify some of the protist-protist interactions that may have played a role in shaping protistan communities, ecological networks were constructed using the amplicon data and the network predictions were compared against a database of confirmed protist-protist interactions. The database comparisons revealed hundreds of established parasitic, predator-prey, photosymbiotic, and mutualistic relationships in the networks. Although many interactions were confirmed using the database, these confirmed interactions constituted only 2% of the interactions identified at the SPOT station, highlighting the need to better characterize protist-protist interactions in marine environments. Finally, the network-predicted interactions that were not found in the database were used to identify putative, novel protist-protist interactions that may have played a role in structuring the protistan communities at the SPOT station.

Importance: Network analyses are commonly used to identify some of the ecological interactions that may be occurring between protists in the ocean; however, evaluating predictions obtained from these analyses remains difficult due to the large number of interactions that may be recovered and the limited amount of information available on protist-protist interactions in nature. In this study, ecological network analyses were conducted using data collected at the San Pedro Ocean Time-series (SPOT) station and the network predictions were compared against a database of established protist-protist interactions. These database comparisons revealed hundreds of confirmed protist-protist interactions, and thousands of putative, novel interactions that may be occurring at the SPOT station. The database comparisons carried out in this study provide a new way of evaluating network predictions and highlight the complex, yet critical role that ecological interactions play in shaping protistan community composition in marine ecosystems.

Oral squamous cell carcinoma (OSCC) is a prevalent malignancy in the oral-maxillofacial region with a poor prognosis. Oral microbiomes play a potential role in the pathogenesis of this disease. However, findings from individual studies have been inconsistent, and a comprehensive understanding of OSCC-associated microbiome dysbiosis remains elusive. Here, we conducted a large-scale meta-analysis by integrating 11 publicly available data sets comprising salivary microbiome profiles of OSCC patients and healthy controls. After correcting for batch effects, we observed significantly elevated alpha diversity and distinct beta-diversity patterns in the OSCC salivary microbiome compared to healthy controls. Leveraging random effects models, we identified robust microbial signatures associated with OSCC across data sets, including enrichment of taxa such as Streptococcus, Lactobacillus, Prevotella, Bulleidia moorei, and Haemophilus in OSCC samples. The machine learning models constructed from these microbial markers accurately predicted OSCC status, highlighting their potential as non-invasive diagnostic biomarkers. Intriguingly, our analyses revealed that the age- and gender-associated signatures in normal saliva microbiome were disrupted in the OSCC, suggesting perturbations in the intricate host-microbe interactions. Collectively, our findings uncovered complex alterations in the oral microbiome in OSCC, providing novel insights into disease etiology and paving the way for microbiome-based diagnostic and therapeutic strategies. Given that the salivary microbiome can reflect the overall health status of the host and that saliva sampling is a safe, non-invasive approach, it may be worthwhile to conduct broader screening of the salivary microbiome in high-risk OSCC populations as implications for early detection.

Importance: The oral cavity hosts a diverse microbial community that plays a crucial role in systemic and oral health. Accumulated research has investigated significant differences in the saliva microbiota associated with oral cancer, suggesting that microbiome dysbiosis may contribute to the pathogenesis of oral squamous cell carcinoma (OSCC). However, the specific microbial alterations linked to OSCC remain controversial. This meta-analysis reveals robust salivary microbiome alterations. Machine learning models using differential operational taxonomic units accurately predicted OSCC status, highlighting the potential of the salivary microbiome as a non-invasive diagnostic biomarker. Interestingly, age- and gender-associated signatures in the normal salivary microbiome were disrupted in OSCC, suggesting perturbations in host-microbe interactions.

Gluconeogenesis, the reciprocal pathway of glycolysis, is an energy-consuming process that generates glycolytic intermediates from non-carbohydrate sources. In this study, we demonstrate that robust and efficient gluconeogenesis in bacteria relies on the allosteric inactivation of pyruvate kinase, the enzyme responsible for the irreversible final step of glycolysis. Using the model bacterium Bacillus subtilis as an example, we discovered that pyruvate kinase activity is inhibited during gluconeogenesis via its extra C-terminal domain (ECTD), which is essential for autoinhibition and metabolic regulation. Physiologically, a B. subtilis mutant lacking the ECTD in pyruvate kinase displayed multiple defects under gluconeogenic conditions, including inefficient carbon utilization, slower growth, and decreased resistance to the herbicide glyphosate. These defects were not caused by the phosphoenolpyruvate-pyruvate-oxaloacetate futile cycle. Instead, we identified two major metabolic consequences of pyruvate kinase dysregulation during gluconeogenesis: failure to establish high phosphoenolpyruvate (PEP) concentrations necessary for robust gluconeogenesis and increased carbon overflow into the medium. In silico analysis revealed that, in wild-type cells, an expanded PEP pool enabled by pyruvate kinase inactivation is critical for maintaining the thermodynamic feasibility of gluconeogenesis. Additionally, we discovered that B. subtilis exhibits glyphosate resistance specifically under gluconeogenic conditions, and this resistance depends on the PEP pool expansion resulting from pyruvate kinase inactivation. Our findings underscore the importance of allosteric regulation during gluconeogenesis in coordinating metabolic flux, efficient carbon utilization, and antimicrobial resistance.IMPORTANCEPyruvate kinase catalyzes the final irreversible step in glycolysis and is commonly thought to play a critical role in regulating this pathway. In this study, we identified a constitutively active variant of pyruvate kinase, which did not impact glycolysis but instead led to multiple metabolic defects during gluconeogenesis. Contrary to conventional understanding, these defects were not due to the phosphoenolpyruvate-pyruvate-oxaloacetate futile cycle. Our findings suggest that the defects arose from an insufficient buildup of the phosphoenolpyruvate pool and an increase in carbon overflow metabolism. Overall, this study demonstrates the essential role of pyruvate kinase allosteric regulation during gluconeogenesis in maintaining adequate phosphoenolpyruvate levels, which helps prevent overflow metabolism and enhances the thermodynamic favorability of the pathway. This study also provides a novel link between glyphosate resistance and gluconeogenesis.

A comprehensive optimization of known prokaryotic autotrophic carbon dioxide (CO₂) fixation pathways is presented that evaluates all their possible variants under different environmental conditions. This was achieved through a computational methodology recently developed that considers the trade-offs between energy efficiency (yield) and growth rate, allowing us to evaluate candidate metabolic modifications in silico for microbial conversions. The results revealed the superior configurations in terms of both yield (efficiency) and rate (driving force). The pathways from anaerobic organisms appear to fix carbon at lower net ATP cost than those found in aerobic organisms, and the reverse TCA cycle pathway shows the lowest overall energy cost and maximum adaptability across a broad range of CO₂ and electron donor (H₂) concentrations. The reverse tricarboxylic acid cycle and Wood-Ljungdahl pathways appear highly efficient under a broad range of conditions, while the 3-hydroxypropionate 4-hydroxybutyrate cycle and the 3-hydroxypropionate bicycle appear capable of generating large thermodynamic driving forces at only moderate ATP yield losses.IMPORTANCEBiotechnology can lead to cost-effective processes for capturing carbon dioxide using the natural or genetically engineered metabolic capabilities of microorganisms. However, introducing desirable genetic modifications into microbial strains without compromising their fitness (growth yield and rate) during industrial-scale cultivation remains a challenge. The approach and results presented can guide optimal pathway configurations for enhanced prokaryotic carbon fixation through metabolic engineering. By aligning strain modifications with these theoretically revealed near-optimal pathway configurations, we can optimally engineer strains of good fitness under open culture industrial-scale conditions.