mSystems最新文献

Tuberculosis (TB) remains a major global health challenge. The close relationship between the microbiome and the host is becoming increasingly notable. While studies on the respiratory microbiome in pulmonary tuberculosis (PTB) exist, a comprehensive understanding of microbial characteristics across the entire respiratory tract is still lacking. To address this, we conducted a meta-analysis by integrating data from common and representative respiratory samples. We integrated 16S rRNA data from 11 public datasets encompassing upper respiratory tract specimens (URTs), sputum, and bronchoalveolar lavage fluid (BALF). Ecological patterns were investigated through co-occurrence networks and neutral community modeling, while taxonomic and functional analyses were conducted with QIIME2 and PICRUSt2. The respiratory microbiota in PTB exhibited dynamic variations while sharing common genera, such as Streptococcus, Prevotella, Veillonella, and Neisseria. Alpha diversity was consistently higher in PTB than in healthy controls, with BALF exhibiting the greatest microbial diversity. Several differentially abundant genera were identified among the three sample types, Serratia being almost exclusively detected in BALF. Notably, the microbial interaction network in sputum was more complex and demonstrated the best fit to the neutral community model. Functional predictions highlighted enriched pathways such as peptidoglycan maturation and ABC transporters, and Bacillus was linked to multiple metabolic pathways. Several KO functions were predicted to be more active in URTs and sputum than in BALF. Our multi-scale analysis delineates a niche-specific biogeography of the respiratory microbiome in PTB. By elucidating community assembly and microbe interplay, we position the respiratory microbiota as an active contributor to TB. This work paves the way for novel microbiota-based diagnostics and ecologically informed therapies.

Importance: Pulmonary tuberculosis (PTB) remains a leading cause of global mortality, yet the ecological principles shaping its respiratory microbiome are poorly understood. By integrating 16S rRNA datasets from upper and lower airway specimens, this study provides the first comprehensive meta-analysis of respiratory microbial diversity and function in PTB. We reveal distinct community structures and functional potentials among upper airways, sputum, and bronchoalveolar lavage fluid, driven by niche-specific ecological processes rather than stochastic assembly. These findings establish a baseline framework for interpreting microbial biogeography across the respiratory tract and highlight potential microbial biomarkers for site-specific monitoring and therapeutic targeting in PTB.

Microbial strains engineered for high-titer ethanol production often stop fermenting while substantial substrate remains, limiting industrial performance. We investigated this limitation in engineered strains of Escherichia coli and Thermoanaerobacterium saccharolyticum and the native ethanologen Zymomonas mobilis. By combining high-titer fermentations with intracellular metabolomics, we are able to see how intracellular metabolite concentrations change as product formation stops. We then used max-min driving force (MDF) thermodynamic analysis to understand how these changes in intracellular metabolite levels can limit flux and to identify key enzymes that might be responsible for these limitations. In engineered strains, cessation of ethanol production coincided with strong pyruvate accumulation and MDF values near or below zero at the pyruvate kinase step, implying that the pyruvate consuming enzyme(s) (pyruvate decarboxylase for E. coli and pyruvate ferredoxin oxidoreductase, or associated electron transfer enzymes for T. saccharolyticum) might limit flux. By contrast, Z. mobilis maintained positive driving forces without pyruvate buildup, suggesting that its titer is limited by processes outside central carbon metabolism, such as substrate uptake. These results establish a generalizable framework linking metabolite concentrations to pathway thermodynamics and demonstrate how thermodynamic analysis can diagnose where metabolic constraints emerge during high-titer fermentation.IMPORTANCEHigh-titer fermentation is essential for economically viable biofuel production, yet even extensively-engineered microbes frequently stop producing ethanol before the substrate is exhausted. Furthermore, the causes of titer limitations are often poorly understood. A particular challenge is identifying the location of titer limitations in multi-enzyme pathways. Here, we show that MDF analysis can assist in the interpretation of metabolomic data. These findings provide a systems-level explanation for "stuck" fermentations in bacteria and identify thermodynamic driving force as a quantitative diagnostic metric that reveals where biological design targets emerge for metabolic engineering of ethanol and other bioproducts.

Alzheimer's disease (AD) is associated with the gut microbiota, and identifying reliable gut microbiota biomarkers enhances AD diagnosis. We aim to characterize the gut microbiota in AD patients by integrating data from multiple populations and identifying key candidate gut microbiota indicators with diagnostic value for AD. Public data from studies on AD and gut microbiota were collected, including participants from AD dementia, mild cognitive impairment (MCI), and normal control (NC) groups. Microbiota composition, diversity, and network analyses were used to characterize the gut microbiota of the three groups. Differential bacterial genera identified simultaneously by seven common methods served as candidate indicators. The study included 799 AD dementia, 170 MCI, and 731 NC participants. The AD dementia group demonstrated a lower relative abundance of Bacteroides and Faecalibacterium and lower α-diversity than the MCI and NC groups (P < 0.05). The microbial network density in the AD dementia group was reduced by 1.5% and 1.6% compared with the NC and MCI groups, respectively. We identified 35 bacterial genera as candidate indicators for AD, including first-time reports of RF39 and Oligella. Faecalibacterium was the most important candidate indicator in the overall population, Akkermansia in the Chinese population, Collinsella in the "Turkish and Kazakh" population, and Actinomyces in the "American and Canadian" population. Our findings contribute to the development of non-invasive biomarkers for AD diagnosis and targeted microbiota therapies and provide a valuable reference for selecting specific biomarkers for different application scenarios.

Importance: This study characterized the gut microbiota of Alzheimer's disease (AD) patients and identified candidate indicators for AD diagnosis using a large, multi-population data set. The AD dementia group consistently showed lower α-diversity and a sparser microbiota interaction network than the other groups. We identified 35 bacterial genera as candidate indicators for AD, including first-time reports of RF39 and Oligella. Faecalibacterium was the most important candidate indicator in the overall population, Akkermansia in the Chinese population, Collinsella in the "Turkish and Kazakh" population, and Actinomyces in the "American and Canadian" population. These findings provide a valuable reference for selecting biomarkers for different application scenarios.

The growing threat of antimicrobial resistance necessitates the discovery of novel antibiotics with activity against drug-resistant pathogens. Members of the genus Paenibacillus are a rich source of nonribosomal peptides (NRPs), including well-known antibiotics such as polymyxins, paenibacterin, and tridecaptins. Here, we use a targeted mass spectrometry query language (MassQL)-based approach to identify the NRPs produced by a collection of 227 taxonomically diverse plant-associated Paenibacillus strains, providing detailed insights into their NRP-producing potential. Using MassQL to zoom in specifically on NRPs containing basic amino acids, we discovered a novel family of bacitracins, which we designated paenitracins. The paenitracins are the first bacitracin-type peptides reported in Paenibacillus and are distinguished from canonical bacitracins by three previously unseen amino acid substitutions. The paenitracins exhibit potent activity against gram-positive pathogens, including vancomycin-resistant Enterococcus faecium E155. Our work provides a novel metabolomics-guided and genomics-guided workflow for the discovery of bioactive NRPs as a strategy to prioritize natural product chemical space and accelerate antibiotic discovery.IMPORTANCEMembers of the genus Paenibacillus play an important role in soil ecology, producing a range of important nonribosomal peptides (NRPs). A collection of plant-associated Paenibacillus spp. were analyzed for their phylogenetic and metabolic diversity. We developed a novel discovery pipeline that combines feature-based molecular networking with mass spectrometry query language queries to systematically prioritize bioactive NRPs containing basic amino acids. Thus, we provide a comprehensive genus-wide inventory of NRPs produced by Paenibacillus spp. We thereby identified the paenitracins, a new sub-family of bacitracins active against multidrug-resistant gram-positive pathogens. Our pipeline enables the discovery of novel peptidic natural products to accelerate the prioritization of chemical space for antibiotics.

Depression and obesity are highly comorbid and likely involve common risk factors and pathophysiological mechanisms, which could crosslink to gut microbiome dysfunction. Here, we performed a case-control study with a total of 105 subjects, 43 with major depressive disorder (MDD) and 62 non-depressed controls free from psychiatric comorbidities, to identify gut microbiome signatures associated with MDD and dissect its relation to body mass index (BMI) and lifestyle (diet and exercise). We performed shotgun metagenomics, followed by taxonomic and functional annotations. Using different machine learning methods, we were able to classify subjects into depressed and non-depressed controls with a balanced accuracy of 0.90 and into depressed or non-depressed and normal weight or overweight with a balanced accuracy of 0.78 based solely on taxonomic profiles. We identify novel bacterial taxa associated with depression, including reductions in Butyrivibrio hungatei and Anaerocolumna sedimenticola, and also replicate previously reported associations, such as decreased Faecalibacterium prausnitzii in patients with MDD. Functional annotation of metagenomes shows differences in pathways linked to the synthesis of fundamental nutrients, which have been associated with diet, as well as inflammation. Strikingly, we found an increase in tryptophan degradation and a decrease in queuosine synthesis pathways, both of which are directly related to a decrease in monoaminergic neurotransmitter availability. Additionally, our functional analysis shows that most of the functions that are more abundant in controls than in depressed subjects are encoded by F. prausnitzii. These findings reveal distinct microbial and functional signatures associated with depression, including taxa and pathways linked to neurotransmitter metabolism and independent of other covariates. This suggests that gut microbiome profiling could support diagnosis and the development of gut-directed depression treatments.

Importance: This study identifies gut microbiome signatures that are predictive of major depressive disorder (MDD) and explores their links to body mass index (BMI). We uncover bacterial species and metabolic pathways that are associated with MDD, some of them related to neurotransmitter metabolism and inflammation. Among the differences identified, depletion of Faecalibacterium prausnitzii stands out as an important feature in the MDD microbiome, which suggests the possible use of this species to improve depression symptoms. Importantly, we demonstrate shared microbiome features between MDD and BMI, suggesting common underlying mechanisms. This research not only provides a framework for developing microbiome-based diagnostics but also informs future stratified interventions targeting gut microbial functions to improve mental health outcomes.

Microbiome research using amplicon sequencing of microbial marker genes has surged over the past decade, propelled by protocols for highly multiplexed sequencing with barcoded primer constructs. Newer Illumina platforms like the NovaSeq and NextSeq series significantly outperform older sequencers in terms of reads, output, and runtime. However, these platforms are more prone to index-hopping, which limits the application of protocols designed for older platforms such as the Earth Microbiome Project protocols; hence, there is a need to adapt these established protocols. Here, we present an ultra-high-throughput amplicon library preparation and sequencing protocol (HighALPS) incorporating the capabilities of these newer sequencing platforms, designed for both 16S rRNA gene and fungal internal transcribed spacer domain sequencing. Our results demonstrate good run performance across different sequencing platforms and flow cells, with successful sequencing of mock communities, validating the protocol's effectiveness. The HighALPS library preparation method offers a robust, cost-effective, and ultra-high-throughput solution for microbiome research, compatible with the latest sequencing technologies. This protocol allows multiplexing thousands of samples in a single run at a read depth of tens of millions of sequences per sample.IMPORTANCEMarker gene amplicon sequencing on Illumina devices remains the most commonly used technology to profile microbial communities. Yet, most library preparation protocols are not adapted to harness the capabilities and deal with the caveats of the latest Illumina sequencing platforms, which highly outperform older platforms in terms of speed, quality, and output. Here, we present an ultra-high-throughput, cost-effective, and robust library preparation protocol (HighALPS) optimized to fully leverage the capabilities of the latest Illumina sequencing platforms. The combinatorial unique dual index strategy effectively combats miss-assignment of reads due to index-hopping, which is more prevalent in newer platforms. The HighALPS protocol incorporates technological (e.g., novel sequencing chemistry and lab automation platforms) as well as bioinformatics advances (e.g., denoising algorithms which make triplicate amplifications unnecessary) of the last few years to optimize and streamline library preparation for bacterial and fungal communities.

While numerous deep-branching lineages of Archaea have been found over the last two decades, most of them still remain enigmatic and uncultivated. In their article, Prokofeva et al. report the isolation of two species from a thermoacidophilic lineage previously reported as "Candidatus Marsarchaeota" and propose a renaming to Tardisphaeria (M. I. Prokofeva, A. I. Karaseva, A. S. Tulenkov, A. A. Klyukina, et al., mSystems 10:e00710-25, 2025, https://doi.org/10.1128/msystems.00710-25). Cultivation coupled with genome analysis revealed a strong enrichment and co-occurrence of polysaccharide and sugar metabolisms in these archaea relative to other thermoacidophiles. These polyextreme archaea were also found to make up large abundances (up to 40% relative abundance) in acidic hot springs, indicating they are important for carbon cycling in these environments. These organisms may also host biotechnologically relevant genes for using polysaccharides that are stable at high temperatures and low pH. Overall, these new isolates improve our understanding of archaeal diversity and metabolism and open the door for more studies on these previously inaccessible organisms.

Bifidobacterium adolescentis is one of the most frequently encountered bifidobacterial species present in the adult human gut microbiota, with a prevalence of approximately 60%. Despite its high prevalence, B. adolescentis has not been extensively studied and characterized, and our understanding of its physiological traits, genetic diversity, and potential interactions with other members of the human gut microbiota or with its host is therefore fragmentary. In the current study, a data set comprising 1,682 B. adolescentis genomes was compiled by combining publicly available data and metagenome assemblies from 131 projects to uncover the unique genetic characteristics of this species. A pangenome analysis of B. adolescentis identified 203 clusters of orthologous genes absent from the other five human-associated Bifidobacterium species, six of which were in silico predicted to encode functions unique to this taxon. Furthermore, 2,597 genes were predicted to have been acquired by horizontal gene transfer, including genes encoding extracellular structures involved in interaction with the host and other microorganisms, and phage defense mechanisms against bacteriophages. Detailed phylogenetic analysis revealed seven clusters within the B. adolescentis species, each partially associated with the origin of strain isolation, suggesting phylogenetic differentiation shaped by geographical strain origin. Moreover, a large-scale metagenomic analysis of over 10,000 human gut metagenomes from healthy adults revealed that B. adolescentis co-occurs with 36 putative beneficial commensals and butyrate-producing taxa, highlighting its role as a key bifidobacterial species involved in microbial networking within the adult human gut microbiota.

Importance: To comprehensively explore the biodiversity within a microbial species, the reconstruction of a substantial number of genomes is essential. In this study, we successfully uncovered the genetic diversity of Bifidobacterium adolescentis by retrieving a large number of genomes from human gut metagenomic samples. The complete overview of the B. adolescentis pangenome enabled us to investigate the genetic features that distinguish this gut commensal from other bifidobacterial species residing in the human intestinal microbiota.