mSystems最新文献

The archaeal domain contains organisms that are well-adapted to extreme conditions and changes in their habitat. Post-transcriptional regulation plays a key role in environmental adaptation, including rapid molecular responses to stress conditions. To understand the importance of RNA-based post-transcriptional regulation for these processes, a comprehensive analysis of the presence and processing of regulatory RNAs, as well as their interactions with other RNAs and proteins, is indispensable. Here, we combine the analysis of several RNA sequencing approaches to reveal the presence of a set of novel non-coding RNAs (ncRNAs), their expression in various conditions, processing, and molecular interactions in the transcriptome of Sulfolobus acidocaldarius, a model organism for Archaea. We expand its annotation by 102 intergenic ncRNAs (sRNAs) and 1,048 antisense RNAs (asRNAs), add the location and motifs of over 6,000 transcript processing sites, and determine the interaction of transcripts with Sm-like archaeal proteins (SmAPs), known RNA chaperones involved in RNA-based regulatory systems. We determined the correlation between the expression patterns of asRNAs and their cognate mRNAs, suggesting transcript-based regulation patterns in gene expression, particularly in response to changing environmental conditions. Additionally, we observed differential binding preferences of SmAP1 and SmAP2 toward mRNA and ncRNAs, suggesting a distribution of regulating roles of these chaperones. Finally, we provide an overview of our post-transcriptional data analysis results, optimized for custom exploration, in the form of a web-based transcriptome atlas (https://vicentebr.github.io/Sulfolobus_atlas/).

Importance: Post‑transcriptional regulation is a key control layer in gene expression. Yet, resources integrating antisense RNAs (asRNAs), RNA processing sites, and RNA-protein interactions are scarce for archaeal organisms. Here, we combine multiple RNA‑seq strategies and RIP‑seq to expand the Sulfolobus acidocaldarius transcriptome with 1,048 asRNAs, thousands of transcript processing sites, and the interactomes of the essential RNA chaperones Sm-like archaeal protein (SmAP)1 and SmAP2. Integrating the novel generated data for the re‑analysis of heat‑shock transcriptomics reveals a consistent upregulation of asRNAs and antagonistic expression profiles with their cognate mRNAs. Moreover, our publicly accessible web atlas provides a community platform to explore these datasets and assist in the formulation of new hypotheses about archaeal RNA regulation.

Genome-scale metabolic models (GSMMs) can mechanistically explain phenotypic differences among closely related bacterial strains. However, high-throughput multi-strain reconstructions of GSMMs are still challenging: reference-based methods inherit curated information while missing new contents; alternatively (universe-based), reference-free methods could cover strain-specific reactions, but they disregard curated information. Ideally, references should be curated pan-GSMMs for species (or genus), but their reconstruction is extremely demanding, making them still rare in the literature. Here, Gempipe is presented, a computational tool streamlining the multi-strain reconstruction and analysis of GSMMs, going through the production of a pan-GSMM. Its reconstruction method is hybrid; as an optional reference, GSMM is automatically expanded with extra reactions taken from a reference-free reconstruction. Gempipe also downloads, filters, and annotates genomes; performs in-depth gene recovery; annotates models' contents; and predicts strain-specific capabilities. The companion programming interface includes functions ranging from the (pan-)GSMMs' curation to the multi-strain analysis. Gempipe was validated using multi-strain data sets, showing improved accuracy when compared with state-of-the-art tools. Moreover, metabolic diversities within Limosilactobacillus reuteri were explored, grouping strains into metabolically coherent clusters and systematically predicting health-related metabolites' biosynthesis.IMPORTANCEAvailable genome-scale metabolic model (GSMM) reconstruction tools present major limitations in the context of multi-strain modeling. Gempipe surpasses these limitations by implementing a novel, hybrid reconstruction strategy. Not only does it produce more accurate strain-specific GSMMs, but it also produces pan-GSMMs when the only available reference is a manually curated model for a single strain, which is currently the most common case. With the vast availability of genome sequences, the high-throughput, multi-strain GSMM reconstruction and analysis approach provided by Gempipe will facilitate large-scale studies of exploration and bioprospecting of strain-level bacterial metabolic diversity, moving a step forward in strains' screening and rational selection.

In microbial ecosystems, metabolic interactions are key determinants of species' relative abundance and activity. Given the immense number of possible interactions in microbial communities, their experimental characterization is best guided by testable hypotheses generated through computational predictions. However, widely adopted software tools-such as those utilizing microbial co-occurrence-typically fail to highlight the pathways underlying these interactions. Bridging this gap will require methods that utilize microbial activity data to infer putative target pathways for experimental validation. In this study, we explored a novel approach by applying cross-species co-expression to predict interactions from microbial co-culture RNA-sequencing data. Specifically, we investigated the extent to which co-expression between genes and pathways of different bacterial species can predict competition, cross-feeding, and specialized metabolic interactions. Our analysis of the Mucin and Diet-based Minimal Microbiome (MDb-MM) data yielded results consistent with previous findings and demonstrated the method's potential to identify pathways that are subject to resource competition. Our analysis of the Hitchhikers of the Rhizosphere (THOR) data showed links between related specialized functions, for instance, between antibiotic and multidrug efflux system expression. Additionally, siderophore co-expression and further evidence suggested that increased siderophore production of the Pseudomonas koreensis koreenceine BGC deletion-mutant drives siderophore production in the other community members. In summary, our findings confirm the feasibility of using cross-species co-expression to predict pathways potentially involved in microbe-microbe interactions. We anticipate that the approach will also facilitate the discovery of novel gene functions through their association with other species' metabolic pathways, for example, those involved in antibiotic response.IMPORTANCEAn improved mechanistic understanding of microbial interactions can guide targeted interventions or inform the rational design of microbial communities to optimize them for applications such as pathogen control, food fermentation, and various biochemical processes. Existing methodologies for inferring the mechanisms behind microbial interactions often rely on complex model-building and are, therefore, sensitive to the introduction of biases from the incorporated existing knowledge and model-building assumptions. We highlight the microbial interaction prediction potential of cross-species co-expression analysis, which contrasts with these methods by its data-driven nature. We describe the utility of cross-species co-expression for various types of interactions and thereby inform future studies on use-cases of the approach and the opportunities and pitfalls that can be expected in its application.

Liquid biopsies are transforming oncology, enabling earlier diagnosis, dynamic treatment guidance, and personalized precision medicine, yet current approaches focusing mainly on circulating host cell-free DNA (cfDNA) neglect crucial information within co-existing microbial cell-free DNA (mcfDNA). This review argues for the combined potential of simultaneously analyzing host and microbial signals from samples like blood, specifically focusing on circulating tumor DNA (ctDNA) as the key host component. While ctDNA analysis is already used to guide treatment decisions, the detection of mcfDNA-although present in smaller amounts compared to total cfDNA-offers a distinct and complementary opportunity to identify disease-causing microbes and investigate the host-associated microbiome in the context of cancer. Leveraging machine learning strategies is essential to integrate these multi-view data sets and realize their full potential for enhancing liquid biopsy applications, particularly in early cancer detection.

Pseudomonas aeruginosa is a predominant colonizer of airways in non-cystic fibrosis bronchiectasis (NCFB), yet its adaptive mechanisms remain poorly understood. This study investigates the genetic characteristics, virulence variation, and resistance mechanisms of 66 P. aeruginosa isolates derived from NCFB patients. Whole-genome sequencing revealed extensive genetic diversity, encompassing 53 sequence types and a predominance of the O6 serotype (30/66, 45.5%). Phylogenetic analysis indicated that most NCFB isolates were acquired independently, with limited evidence of transmission. Extensive loss-of-function mutations were identified, with mucA mutations present in 90.6% (29/32) of mucoid and 67.6% (23/34) of non-mucoid isolates. Most mucA mutations were frameshift variants, predominantly at codon 144 (Ala144fs), indicating the selective advantage of this site in driving alginate overproduction during chronic airway infection. Virulence gene profiling demonstrated a highly conserved core repertoire but considerable variability in type VI secretion and pyoverdine systems. Notably, mucoid isolates exhibited significantly higher cefiderocol MICs compared to non-mucoid isolates (P = 0.0073), along with enhanced biofilm formation (P < 0.0001) but reduced virulence in the Galleria mellonella infection model. Mechanistic studies revealed that cefiderocol resistance in mucoid P. aeruginosa was driven by synergistic interactions between alginate overproduction and mutations in iron-uptake regulatory genes, particularly Gly132 frameshift in pirR. Disruption of alginate biosynthesis (ΔalgD) and complementation of pirR in mucoid strains markedly restored cefiderocol susceptibility. These findings highlight the remarkable genomic diversity and adaptive resistance mechanisms of P. aeruginosa in NCFB, providing important insights into its persistence and therapeutic challenges in chronic airway infection.IMPORTANCEUnderstanding the adaptive mechanisms of Pseudomonas aeruginosa in non-cystic fibrosis bronchiectasis (NCFB) is critical for improving treatment strategies. This study reveals substantial genomic diversity and highlights alginate overproduction as a key feature of chronic adaptation. Notably, we uncover a novel resistance mechanism involving synergistic interactions between alginate production and mutations in iron-uptake regulators, particularly pirR. These findings underscore the complex evolutionary pressures shaping P. aeruginosa persistence in NCFB and provide valuable insights into its resistance and virulence balance, offering potential targets for more effective therapeutic interventions.

Soil microorganisms perform biogeochemical processes fundamental to soil functions. Bulk respiration, or microbial metabolic emission of CO₂, is the classic indicator of soil biological activity. However, among the millions of microbes per gram of soil, only 0.1%-2.0% are metabolically active at any given time. Understanding the relationship between bulk soil respiration and microbial activity is complicated by microbes potentially awakening from quiescent states during incubation test periods. Here, we investigated this relationship through parallel measurements of substrate-induced respiration and translationally active cell counts using bioorthogonal non-canonical amino acid tagging (BONCAT). After a 6-h incubation of agricultural soil with glucose, galactose, or only water, active cell counts were positively correlated with respiration rates. As hypothesized, active cell numbers increased rapidly compared to total cell numbers after a 6-h incubation with glucose, suggesting newly activated cells. Additionally, carbon-amended soils respired more than water-only soils with similar active cell counts. This suggested that cells in carbon-rich environments were turning over freshly added carbon faster or metabolizing it less efficiently than those exposed to native substrates only. This study distinguishes for the first time microbial activation in the soil matrix using a translation signal, providing evidence that respiration rates reflect active cell numbers and varied metabolic responses, decoupled from cell growth upon soil wetting and carbon addition. We propose BONCAT as a useful tool to gain mechanistic insights into microbial activation and recommend combining it with tracking substrate incorporation and phylogenetics.IMPORTANCEMany critical ecosystem services provided by soils rely on active microbes, even though most soil microbes are known to be quiescent or dormant much of the time. This study demonstrates that microbes become translationally active within hours after substrate addition and that the correlation between active cell numbers and soil respiration rates varies with the type of substrate. Advancing knowledge in this area will enable better interpretation of bulk soil respiration tests by land managers and inform modeling efforts that relate soil microbial respiration to global carbon dynamics.

Necrotizing soft tissue infections (NSTIs) are rapidly progressive and life-threatening diseases caused by diverse bacterial pathogens. While classical virulence factors, such as toxins and secretion systems, have been extensively characterized, the role of metabolic fitness in supporting bacterial survival within the nutrient-restricted host environment remains underexplored. Edwardsiella tarda, a human-pathogenic bacterium implicated in NSTIs, represents an emerging model for studying non-canonical pathogenic strategies. Here, we employed transposon-directed insertion site sequencing (TraDIS) to identify genes critical for E. tarda survival in a murine soft tissue infection model. A genome-wide screen revealed 41 genes significantly depleted during the infection, including those involved in iron and zinc acquisition (fetB, zupT), vitamin biosynthesis (pdxK, cobA), and polyamine metabolism (speB). Functional assays using defined minimal media demonstrated that supplementation with vitamin B6 or putrescine enhanced bacterial growth, validating their contribution to fitness under nutrient-limited conditions. Our findings indicate that E. tarda pathogenesis is driven not solely by classical virulence factors but also by its ability to acquire essential nutrients and adapt metabolically to host-imposed nutritional stress. This study provides the first genome-wide fitness map for E. tarda during soft tissue infection and reveals new targets for therapeutic intervention that disrupt nutrient acquisition systems. These results also emphasize the broader relevance of metabolic adaptation as a determinant of virulence in invasive bacterial infections.IMPORTANCENecrotizing soft tissue infections (NSTIs) are severe, rapidly progressing bacterial infections with high morbidity and mortality. Although classical virulence factors such as toxins have been widely studied, much less is known about how pathogens adapt metabolically to survive within the nutrient-restricted environment in host tissues. This study uses Edwardsiella tarda, an emerging NSTI pathogen, as a model to identify genes required for in vivo fitness using transposon insertion sequencing. By revealing the critical roles of nutrient acquisition and metabolic adaptation, rather than toxin production alone, this work challenges conventional paradigms of bacterial virulence. Our findings suggest that targeting bacterial nutrient acquisition pathways may offer a novel therapeutic approach to control invasive infections. Furthermore, this study provides the first genome-wide fitness map of E. tarda during soft tissue infection, offering a valuable resource for future research into polymicrobial wound infections and host-pathogen nutrient competition.

Carbapenem-resistant hypervirulent Klebsiella pneumoniae (CR-hvKP) infection is gradually increasing globally. Phage therapy is a viable application as an alternative to antibiotics. However, clinical application of phage therapy is restricted by phage resistance. To further explore the mechanism underlying phage resistance, particularly the difference observed between in vivo and in vitro, we employed a mouse intra-abdominal infection model to assess the antibacterial properties of two lytic phages and further isolate and characterize phage-resistant mutants. We identified that the majority of the mutation sites in the phage-resistant K. pneumoniae mutants were located in the capsular polysaccharide (CPS) gene cluster, as determined through genomic and transcriptomic analysis. However, some K. pneumoniae phage-resistant mutants, including RM01, RM02, and RM12, developed phage resistance by downregulating CPS and the respective transcriptional regulators without any mutations in the CPS gene. In summary, these findings provide further evidence supporting phage therapy, particularly addressing the issue of CR-hvKP infections.IMPORTANCEThe global rise in antibiotic resistance has rekindled interest in utilizing bacteriophage therapy as a potential solution. In this study, we explored the therapeutic potential of two novel bacteriophages, with a focus on their in vivo efficacy using mouse models, and analyzed the probable mechanisms of phage resistance in bacteria. Our results indicated that in a murine infection model, phages JLBP1001 and JLBP1002 for Klebsiella pneumoniae were highly effective, significantly improving mouse survival. We further characterized and analyzed phage-resistant K. pneumoniae isolated from the mice and found that the resistance mechanisms in an in vivo environment are primarily concentrated in the capsular polysaccharide gene cluster. In RM01, RM02, and RM12, putA contributes to phage resistance through point mutations. These insights are important for optimizing phage-based therapies, particularly in the context of multidrug-resistant bacterial infections.