mSystems

Journal: mSystems

Publisher

American Society for Microbiology

ISSN

2379-5077

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Publications 1 - 10 of 22

Collateral Sensitivity Interactions between Antibiotics Depend on Local Abiotic Conditions
Allen, Richard C.; Pfrunder-Cardozo, Katia R.; Hall, Alex R. (2021)
mSystems
Mutations conferring resistance to one antibiotic can increase (cross-resistance) or decrease (collateral sensitivity) resistance to others. Antibiotic combinations displaying collateral sensitivity could be used in treatments that slow resistance evolution. However, lab-to-clinic translation requires understanding whether collateral effects are robust across different environmental conditions. Here, we isolated and characterized resistant mutants of Escherichia coli using five antibiotics, before measuring collateral effects on resistance to other paired antibiotics. During both isolation and phenotyping, we varied conditions in ways relevant in nature (pH, temperature, and bile). This revealed that local abiotic conditions modified expression of resistance against both the antibiotic used during isolation and other antibiotics. Consequently, local conditions influenced collateral sensitivity in two ways: By favoring different sets of mutants (with different collateral sensitivities) and by modifying expression of collateral effects for individual mutants. These results place collateral sensitivity in the context of environmental variation, with important implications for translation to real-world applications.
Lactate Metabolism Is Strongly Modulated by Fecal Inoculum, pH, and Retention Time in PolyFermS Continuous Colonic Fermentation Models Mimicking Young Infant Proximal Colon
Pham, Van Thanh; Chassard, Christophe; Rifa, Etienne; et al. (2019)
mSystems
The metabolism of lactate impacts infant gut health and may lead to acute accumulation of lactate and/or H2 associated with pain and crying of colicky infants. Because gut microbiota studies are limited due to ethical and safety concerns, in vitro fermentation models were developed as powerful tools to assess effects of environmental conditions on the gut microbiota. In this study, we established a continuous colonic fermentation model (PolyFermS), inoculated with immobilized fecal microbiota and mimicking the proximal colon of 2-month-old infants. We investigated the effects of pH and retention time (RT) on lactate metabolism and of lactate-utilizing bacteria (LUB) exhibiting little or no H2 production. We observed that a drop in pH from 6.0 to 5.0 increased the number of lactate-producing bacteria (LPB) and decreased LUB concomitantly with lactate accumulation. Increasing RT from 5 to 10 h at pH 5.0 resulted in complete lactate consumption associated with increased LUB. Supplementation with dl-lactate (60 mM) to mimic lactate accumulation promoted propionate and butyrate production with no effect on acetate production. We further demonstrated that lactate-utilizing Propionibacterium avidum was able to colonize the reactors 4 days after spiking, suggesting its ability to compete with other lactate-utilizing bacteria producing H2. In conclusion, we showed that PolyFermS is a suitable model for mimicking young infant colonic microbiota. We report for the first time pH and RT as strong drivers for composition and metabolic activity of infant gut microbiota, especially for the metabolism of lactate, which is a key intermediate product for ecology and infant health. IMPORTANCE: The metabolism of lactate is important for infant gut health and may lead to acute lactate and/or H2 accumulation, pain, and crying as observed in colicky infants. Functional human studies often faced ethical challenges due to invasive medical procedures; thus, in this study, we implemented PolyFermS fermentation models to mimic the infant proximal colon, which were inoculated with immobilized fecal microbiota of two 2-month-old infants. We investigated the impact of pH, retention time, and accumulation of dl-lactate on microbiota composition and metabolic activity. We found that a drop in pH from 6.0 to 5.0 led to increased LPB and decreased LUB concomitantly with lactate accumulation. Increasing the RT resulted in complete lactate consumption associated with increased LUB. Our data highlight for the first time the impact of key abiotic factors on the metabolism of lactate, which is an important intermediate product for ecology and infant health.
q2-longitudinal: Longitudinal and Paired-Sample Analyses of Microbiome Data
Bokulich, Nicholas; Dillon, Matthew R.; Zhang, Yilong; et al. (2018)
mSystems
Studies of host-associated and environmental microbiomes often incorporate longitudinal sampling or paired samples in their experimental design. Longitudinal sampling provides valuable information about temporal trends and subject/population heterogeneity, offering advantages over cross-sectional and pre-post study designs. To support the needs of microbiome researchers performing longitudinal studies, we developed q2-longitudinal, a software plugin for the QIIME 2 microbiome analysis platform (https://qiime2.org). The q2-longitudinal plugin incorporates multiple methods for analysis of longitudinal and paired-sample data, including interactive plotting, linear mixed-effects models, paired differences and distances, microbial interdependence testing, first differencing, longitudinal feature selection, and volatility analyses. The q2-longitudinal package (https://github.com/qiime2/q2-longitudinal) is open-source software released under a 3-clause Berkeley Software Distribution (BSD) license and is freely available, including for commercial use. IMPORTANCE Longitudinal sampling provides valuable information about temporal trends and subject/population heterogeneity. We describe q2-longitudinal, a software plugin for longitudinal analysis of microbiome data sets in QIIME 2. The availability of longitudinal statistics and visualizations in the QIIME 2 framework will make the analysis of longitudinal data more accessible to microbiome researchers.
Integrating sequence composition information into microbial diversity analyses with k-mer frequency counting
Bokulich, Nicholas A. (2025)
mSystems
k-mer frequency information in biological sequences is used for a wide range of applications, including taxonomy classification, sequence similarity estimation, and supervised learning. However, in spite of its widespread utility, k-mer counting has been largely neglected for diversity estimation. This work examines the application of k-mer counting for alpha and beta diversity as well as supervised classification from microbiome marker-gene sequencing data sets (16S rRNA gene and full-length fungal internal transcribed spacer [ITS] sequences). Results demonstrate a close correspondence with phylogenetically aware diversity metrics, and advantages for using k-mer-based metrics for measuring microbial biodiversity in microbiome sequencing surveys. k-mer counting appears to be a suitable and efficient strategy for feature processing prior to diversity estimation as well as supervised learning in microbiome surveys. This allows the incorporation of subsequence-level information into diversity estimation without the computational cost of pairwise sequence alignment. k-mer counting is proposed as a complementary approach for feature processing prior to diversity estimation and supervised learning analyses, enabling large-scale reference-free profiling of micro biomes in biogeography, ecology, and biomedical data. A method for k-mer counting from marker-gene sequence data is implemented in the QIIME 2 plugin q2-kmerizer (https://github.com/bokulich-lab/q2-kmerizer).
Enhancing Microbiome Research through Genome-Scale Metabolic Modeling
Ankrah, Nana Y. D.; Bernstein, David B.; Biggs, Matthew; et al. (2021)
mSystems
Construction and analysis of genome-scale metabolic models (GEMs) is a well-established systems biology approach that can be used to predict metabolic and growth phenotypes. The ability of GEMs to produce mechanistic insight into microbial ecological processes makes them appealing tools that can open a range of exciting opportunities in microbiome research. Here, we briefly outline these opportunities, present current rate-limiting challenges for the trustworthy application of GEMs to microbiome research, and suggest approaches for moving the field forward.
Planktonic and Sessile Artificial Colonic Microbiota Harbor Distinct Composition and Reestablish Differently upon Frozen and Freeze-Dried Long-Term Storage
Bircher, Lea; Schwab, Clarissa; Geirnaert, Annelies; et al. (2020)
mSystems
Biofilm-associated, sessile communities represent the major bacterial lifestyle, whereas planktonic cells mainly appear during initial colonization of new surfaces. Previous research, mainly performed with pathogens, demonstrated increased environmental stress tolerance of biofilm-growing compared to planktonic bacteria. The lifestyle-specific stress response of colonic microbiota, both natural and fermentation produced, has not been addressed before. Planktonic and sessile “artificial” colonic microbiota delivered by PolyFermS continuous fermentation models can provide a controllable and reproducible alternative to fecal transplantation in treating gastrointestinal disorders. We therefore characterized planktonic and sessile microbiota produced in two PolyFermS models inoculated with immobilized fecal microbiota and comparatively tested their levels of tolerance of frozen storage (–80°C) and freeze-dried storage (4°C) for 9 months to mimic preservation strategies for therapeutic applications. Sessile microbiota harbored next to shared taxa a unique community distinguishable from planktonic microbiota. Synergistetes and Proteobacteria were highly represented in sessile microbiota, while Firmicutes were more abundant in planktonic microbiota. The community structure and metabolic activity of both microbiota, monitored during standardized reactivation batch fermentations, were better preserved after frozen storage than dried storage, indicated by higher Bray-Curtis similarity and enhanced recovery of metabolite production. For both lifestyles, reestablishment of Bacteroidaceae was impaired after frozen and dried storage along with reduced propionate formation. In contrast, butyrate production was maintained after reactivation despite compositional rearrangements within the butyrate-producing community. Unexpectedly, the rate of recovery of metabolite production was lower after preservation of sessile than planktonic microbiota. We speculate that higher functional dependencies between microbes might have led to the lower stress tolerance of sessile than planktonic microbiota.
Microaerobic Lifestyle at Nanomolar O-2 Concentrations Mediated by Low-Affinity Terminal Oxidases in Abundant Soil Bacteria
Trojan, Daniela; Garcia-Robledo, Emilio; Meier, Dimitri; et al. (2021)
mSystems
High-affinity terminal oxidases (TOs) are believed to permit microbial respiration at low oxygen (O-2) levels. Genes encoding such oxidases are widespread, and their existence in microbial genomes is taken as an indicator for microaerobic respiration. We combined respiratory kinetics determined via highly sensitive optical trace O-2 sensors, genomics, and transcriptomics to test the hypothesis that high-affinity TOs are a prerequisite to respire micro- and nanooxic concentrations of O-2 in environmentally relevant model soil organisms: acidobacteria. Members of the Acidobacteria harbor branched respiratory chains terminating in low-affinity (caa(3)-type cytochrome c oxidases) as well as high-affinity (cbb(3)-type cytochrome c oxidases and/or bd-type quinol oxidases) TOs, potentially enabling them to cope with varying O(2 )concentrations. The measured apparent K-m (K-m(app())) values for O(2 )of selected strains ranged from 37 to 288 nmol O(2 )liter(-1), comparable to values previously assigned to low-affinity TOs. Surprisingly, we could not detect the expression of the conventional high-affinity TO (cbb3 type) at micro- and nanomolar O(2 )concentrations but detected the expression of low-affinity TOs. To the best of our knowledge, this is the first observation of microaerobic respiration imparted by low-affinity TOs at O-2 concentrations as low as 1 nM. This challenges the standing hypothesis that a microaerobic lifestyle is exclusively imparted by the presence of high-affinity TOs. As low-affinity TOs are more efficient at generating ATP than high-affinity TOs, their utilization could provide a great benefit, even at low-nanomolar O(2 )levels. Our findings highlight energy conservation strategies that could promote the success of Acidobacteria in soil but might also be important for as-yet-unrevealed microorganisms.
Alteromonas myovirus v22 represents a new genus of marine bacteriophages requiring a tail fiber chaperone for host recognition
Gonzalez-Serrano, Rafael; Dunne, Matthew; Rosselli, Riccardo; et al. (2020)
mSystems
Marine phages play a variety of critical roles in regulating the microbial composition of our oceans. Despite constituting the majority of genetic diversity within these environments, there are relatively few isolates with complete genome sequences or in-depth analyses of their host interaction mechanisms, such as characterization of their receptor binding proteins (RBPs). Here, we present the 92,760-bp genome of the Alteromonas-targeting phage V22. Genomic and morphological analyses identify V22 as a myovirus; however, due to a lack of sequence similarity to any other known myoviruses, we propose that V22 be classified as the type phage of a new Myoalterovirus genus within the Myoviridae family. V22 shows gene homology and synteny with two different subfamilies of phages infecting enterobacteria, specifically within the structural region of its genome. To improve our understanding of the V22 adsorption process, we identified putative RBPs (gp23, gp24, and gp26) and tested their ability to decorate the V22 propagation strain, Alteromonas mediterranea PT11, as recombinant green fluorescent protein (GFP)-tagged constructs. Only GFP-gp26 was capable of bacterial recognition and identified as the V22 RBP. Interestingly, production of functional GFP-gp26 required coexpression with the downstream protein gp27. GFP-gp26 could be expressed alone but was incapable of host recognition. By combining size-exclusion chromatography with fluorescence microscopy, we reveal how gp27 is not a component of the final RBP complex but instead is identified as a new type of phage-encoded intermolecular chaperone that is essential for maturation of the gp26 RBP.
A Genome-Scale Metabolic Model of Marine Heterotroph Vibrio splendidus Strain 1A01
Iffland-Stettner, Arion; Okano, Hiroyuki; Gralka, Matti; et al. (2023)
mSystems
While Vibrio splendidus is best known as an opportunistic pathogen in oysters, Vibrio splendidus strain 1A01 was first identified as an early colonizer of synthetic chitin particles incubated in seawater. To gain a better understanding of its metabolism, a genome-scale metabolic model (GSMM) of V. splendidus 1A01 was reconstructed. GSMMs enable us to simulate all metabolic reactions in a bacterial cell using flux balance analysis. A draft model was built using an automated pipeline from BioCyc. Manual curation was then performed based on experimental data, in part by gap-filling metabolic pathways and tailoring the model's biomass reaction to V. splendidus 1A01. The challenges of building a metabolic model for a marine microorganism like V. splendidus 1A01 are described.IMPORTANCE A genome-scale metabolic model of V. splendidus 1A01 was reconstructed in this work. We offer solutions to the technical problems associated with model reconstruction for a marine bacterial strain like V. splendidus 1A01, which arise largely from the high salt concentration found in both seawater and culture media that simulate seawater.
Integration of Metabolomics and Transcriptomics Reveals a Complex Diet of Mycobacterium tuberculosis during Early Macrophage Infection
Zimmermann, Michael; Kogadeeva, Maria; Gengenbacher, Martin; et al. (2017)
mSystems
Nutrient acquisition from the host environment is crucial for the survival of intracellular pathogens, but conceptual and technical challenges limit our knowledge of pathogen diets. To overcome some of these technical roadblocks, we exploited an experimentally accessible model for early infection of human macrophages by Mycobacterium tuberculosis, the etiological agent of tuberculosis, to study host-pathogen interactions with a multi-omics approach. We collected metabolomics and complete transcriptome RNA sequencing (dual RNA-seq) data of the infected macrophages, integrated them in a genome-wide reaction pair network, and identified metabolic subnetworks in host cells and M. tuberculosis that are modularly regulated during infection. Up- and downregulation of these metabolic subnetworks suggested that the pathogen utilizes a wide range of host-derived compounds, concomitant with the measured metabolic and transcriptional changes in both bacteria and host. To quantify metabolic interactions between the host and intracellular pathogen, we used a combined genome-scale model of macrophage and M. tuberculosis metabolism constrained by the dual RNA-seq data. Metabolic flux balance analysis predicted coutilization of a total of 33 different carbon sources and enabled us to distinguish between the pathogen’s substrates directly used as biomass precursors and the ones further metabolized to gain energy or to synthesize building blocks. This multiple-substrate fueling confers high robustness to interventions with the pathogen’s metabolism. The presented approach combining multi-omics data as a starting point to simulate system-wide host-pathogen metabolic interactions is a useful tool to better understand the intracellular lifestyle of pathogens and their metabolic robustness and resistance to metabolic interventions.

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