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Photorhabdus is a bacterial genus containing both insect and emerging human pathogens. Most insect-restricted species display temperature restriction, unable to grow above 34°C, while Photorhabdus asymbiotica can grow at 37°C to infect mammalian hosts and cause Photorhabdosis. Metabolic adaptations have been proposed to facilitate the survival of this pathogen at higher temperatures, yet the biological mechanisms underlying these are poorly understood. We have reconstructed an extensively manually curated genome-scale metabolic model of P. asymbiotica (iEC1073, BioModels ID MODEL2309110001), validated through in silico gene knockout and nutrient utilization experiments with an excellent agreement between experimental data and model predictions. Integration of iEC1073 with transcriptomics data obtained for P. asymbiotica at temperatures of 28°C and 37°C allowed the development of temperature-specific reconstructions representing metabolic adaptations the pathogen undergoes when shifting to a higher temperature in a mammalian compared to insect host. Analysis of these temperature-specific reconstructions reveals that nucleotide metabolism is enriched with predicted upregulated and downregulated reactions. iEC1073 could be used as a powerful tool to study the metabolism of P. asymbiotica, in different genetic or environmental conditions.

Importance: Photorhabdus bacterial species contain both human and insect pathogens, and most of these species cannot grow in higher temperatures. However, Photorhabdus asymbiotica, which infects both humans and insects, can grow in higher temperatures and undergoes metabolic adaptations at a temperature of 37°C compared to that of insect body temperature. Therefore, it is important to examine how this bacterial species can metabolically adapt to survive in higher temperatures. In this work, using a mathematical model, we have examined the metabolic shift that takes place when the bacteria switch from growth conditions in 28°C to 37°C. We show that P. asymbiotica potentially experiences predicted temperature-induced metabolic adaptations at 37°C predominantly clustered within the nucleotide metabolism pathway.

Oral microbial dysbiosis has been associated with periodontitis in studies using 16S rRNA gene sequencing analysis. However, this technology is not sufficient to consistently separate the bacterial species to species level, and reproducible oral microbiome signatures are scarce. Obtaining these signatures would significantly enhance our understanding of the underlying pathophysiological processes of this condition and foster the development of improved therapeutic strategies, potentially personalized to individual patients. Here, we sequenced newly collected samples from 24 patients with periodontitis, and we collected available oral microbiome data from 24 samples in patients with periodontitis and from 214 samples in healthy individuals (n = 262). Data were harmonized, and we performed a pooled analysis of individual patient data. By metagenomic sequencing of the plaque microbiome, we found microbial signatures for periodontitis and defined a periodontitis-related complex, composed by the most discriminative bacteria. A simple two-factor decision tree, based on Tannerella forsythia and Fretibacterium fastidiosum, was associated with periodontitis with high accuracy (area under the curve: 0.94). Altogether, we defined robust oral microbiome signatures relevant to the pathophysiology of periodontitis that can help define promising targets for microbiome therapeutic modulation when caring for patients with periodontitis.

Importance: Oral microbial dysbiosis has been associated with periodontitis in studies using 16S rRNA gene sequencing analysis. However, this technology is not sufficient to consistently separate the bacterial species to species level, and reproducible oral microbiome signatures are scarce. Here, using ultra-deep metagenomic sequencing and machine learning tools, we defined a simple two-factor decision tree, based on Tannerella forsythia and Fretibacterium fastidiosum, that was highly associated with periodontitis. Altogether, we defined robust oral microbiome signatures relevant to the pathophysiology of periodontitis that can help define promising targets for microbiome therapeutic modulation when caring for patients with periodontitis.

Carbapenem-resistant hypervirulent Klebsiella pneumoniae (CR-hvKP) strains present a significant global public health threat due to their high mortality rates. This study investigated the genomic characteristics of seven ST11-K1 CR-hvKP isolates harboring highly homologous KPC-2-encoding multidrug-resistance plasmids. The strains were isolated from a Chinese tertiary hospital between 2017 and 2020. Whole-genome sequencing and bioinformatic analysis revealed various antibiotic resistance genes (ARGs) and virulence determinants. The bla_KPC-2-bearing plasmids that contain multiple antibiotic-resistance genes were also identified in these strains. ISfinder and Orifinder were applied to identify insertion sequences (IS) and conjugation-related factors among these bla_KPC-2-bearing plasmids. The bla_KPC-2 was highly consistent in seven bla_KPC-2-bearing plasmids (ISKpn6-bla_KPC-2-ISKpn27-ISYps3-IS26). In addition, we found a region composed of ISIR, Tn5393, and IS26. It was located upstream of the bla_CTX-M-15 gene and presented in six bla_KPC-2-bearing plasmids, with pCR-hvKP221-KPC-P3 as an exception. Conjugation experiments demonstrated the horizontal transfer of resistance plasmids pCR-hvKP128-KPC-P1 and pCR-hvKP132-KPC-P1 across species. Notably, pLVPK-like virulence plasmids carrying virulence gene clusters pCR-hvKP173-Vir-P1, and pCR-hvKP221-Vir-P1 were also detected. A fusional plasmid pCR-hvKP221-Vir-P2, which carries virulence gene clusters and ARGs, was also identified. Five CR-hvKP strains displayed enhanced biofilm formation and high virulence in vivo infection models. Phylogenetic and single nucleotide polymorphism (SNP) analyses indicated a close genetic relationship among the isolates, suggesting a subclade. These findings highlight the complex genetic profiles and potential transmission mechanisms of CR-hvKP strains.

Importance: We reported seven CR-hvKP strains all carried a highly homologous bla_KPC-2 integrated IncFⅡ-resistant plasmid, and two strains harbored virulence plasmids. Conjugation experiments confirmed the transferability of these plasmids, indicating a potential for resistance spread. Phylogenetic analysis clarified the relationship among the CR-hvKP isolates. This study provides insights into the phenotypic and genomic characteristics of seven ST11-K1 CR-hvKP strains. The high prevalence and potential for local outbreaks emphasize the need for effective control measures.

Segregation and mixing shape the structure and functioning of aquatic microbial communities, but their respective roles are challenging to disentangle in field studies. We explored the hypothesis that functional differences and beta diversity among stochastically assembled communities would increase in the absence of dispersal. Contrariwise, we expected biotic selection during homogenizing dispersal to reduce beta and gamma diversity as well as functional variability. This was experimentally addressed by examining the compositional and functional changes of 20 freshwater bacterial assemblages maintained at identical conditions over seven growth cycles for 34 days and subjected to two consecutive dispersal regimes. Initial dispersal limitation generated high beta diversity and led to the repeated emergence of community types that were dominated by particular taxa. Compositional stability and evenness of the community types varied over successive growth cycles, reflecting differences in functional properties. Carbon use efficiency increased during cultivation, with some communities of unique composition outperforming the replicate community types. Homogenizing dispersal led to high compositional similarity and reduced gamma diversity. While a neutral and a competition-based (Elo-rating) model together largely explained community assembly, a pseudomonad disproportionally dominated across communities, possibly due to interaction-related genomic traits. In conclusion, microbial assemblages stochastically generated by dispersal limitation can be gradually "refined" into distinct community types by subsequent deterministic processes. Segregation of communities represented an insurance mechanism for highly productive but competitively weak microbial taxa that were excluded during community coalescence.

Importance: We experimentally assessed the compositional and functional responses of freshwater bacterial assemblages exposed to two consecutive dispersal-related events (dispersal limitation and homogenizing dispersal) under identical growth conditions. While segregation led to a decreased local diversity, high beta diversity sustained regional diversity and functional variability. In contrast, homogenizing dispersal reduced the species pool and functional variability of the metacommunity. Our findings highlight the role of dispersal in regulating both diversity and functional variability of aquatic microbial metacommunities, thereby providing crucial insight to predict changes in ecosystem functioning.

Microbial transformation is a favored approach for environmental remediation. However, the effectiveness of microbial remediation has been limited by the lack of chassis cells with satisfactory contaminant degradation performance. Pseudomonas putida B6-2, with a wide substrate spectrum and high solvent tolerance, is a chassis strain with great potential for application in environmental remediation. Here, guided by bioinformatic analyses and genome-scale metabolic model (GEM) predictions, we successfully optimized P. putida B6-2 by rationally reducing its nonessential genetic components and generating a more robust genome-streamlined strain, P. putida BGR4. Several improvements were observed compared with the original P. putida B6-2 strain, including a 1.4 × 10⁵-fold increase in electroporation efficiency, an 8.3-fold increase in conjugation efficiency, improved glycerol utilization capability, and increased phenol utilization after heterologous expression of the phenol monooxygenase encoded by dmpKLMNOP. Additionally, P. putida BGR4 exhibited enhanced tolerance to several stressors, including starvation, oxidative stress, and DNA damage. Transcriptomic analysis revealed that genome streamlining led to the upregulation of genes involved in the "carbon metabolism" and "tricarboxylic acid cycle" pathways in P. putida BGR4, which likely contributed to the superior phenotype of P. putida BGR4 in terms of carbon source utilization and contaminant degradation capabilities. Furthermore, the absence of four prophages was identified as a potential cause of the enhanced stress resistance observed in P. putida BGR4. Overall, we developed a combined genome-streamlining strategy involving bioinformatic analyses and GEM predictions and generated a more robust chassis strain, P. putida BGR4, which expands the repertoire of chassis cells for environmental remediation.IMPORTANCEDespite the development of many chassis cells, there is still a lack of robust chassis cells with satisfactory contaminant degradation performance. Targeted genome streamlining is an effective way to provide powerful chassis cells. However, genome streamlining does not always lead to the improved phenotypes of genome-streamlined chassis cells. In this research, a novel procedure that combined bioinformatic analyses and GEM predictions was proposed to guide genome streamlining and predict the effects of genome streamlining. This genome streamlining procedure was successfully applied to Pseudomonas putida B6-2, which was a chassis cell with great potential for application in environmental remediation and resulted in the generation of a more robust chassis cell, P. putida BGR4, thereby providing a superior chassis cell for efficient and sustainable environmental remediation and a valuable framework for guiding the genome streamlining of strains for other applications.

Legionella pneumophila uses over 300 translocated effector proteins to rewire host cells during infection and create a replicative niche for intracellular growth. To date, several studies have identified L. pneumophila effectors that indirectly and directly regulate the activity of other effectors, providing an additional layer of regulatory complexity. Among these are "metaeffectors," a special class of effectors that regulate the activity of other effectors once inside the host. A defining feature of metaeffectors is direct, physical interaction with a target effector. Metaeffector identification, to date, has depended on phenotypes in heterologous systems and experimental serendipity. Using a multiplexed, recombinant barcode-based yeast two-hybrid technology we screened for protein-protein interactions among all L. pneumophila effectors and 28 components of the Dot/Icm type IV secretion system (>167,000 protein combinations). Of the 52 protein interactions identified by this approach, 44 are novel protein interactions, including 10 novel effector-effector interactions (doubling the number of known effector-effector interactions).

Importance: Secreted bacterial effector proteins are typically viewed as modulators of host activity, entering the host cytosol to physically interact with and modify the activity of one or more host proteins in support of infection. A growing body of evidence suggests that a subset of effectors primarily function to modify the activities of other effectors inside the host. These "effectors of effectors" or metaeffectors are often identified through experimental serendipity during the study of canonical effector function against the host. We previously performed the first global effector-wide genetic interaction screen for metaeffectors within the arsenal of Legionella pneumophila, an intracellular bacterial pathogen with over 300 effectors. Here, using a high-throughput, scalable methodology, we present the first global interaction network of physical interactions between L. pneumophila effectors. This data set serves as a complementary resource to identify and understand both the scope and nature of non-canonical effector activity within this important human pathogen.