High-throughput dilution-based growth method enables time-resolved exo-metabolomics of Pseudomonas putida and Pseudomonas aeruginosa

Mutant fate in spatially structured populations on graphs: Connecting models to experiments

In nature, most microbial populations have complex spatial structures that can affect their evolution. Evolutionary graph theory predicts that some spatial structures modelled by placing individuals on the nodes of a graph affect the probability that a mutant will fix. Evolution experiments are beginning to explicitly address the impact of graph structures on mutant fixation. However, the assumptions of evolutionary graph theory differ from the conditions of modern evolution experiments, making the comparison between theory and experiment challenging. Here, we aim to bridge this gap by using our new model of spatially structured populations. This model considers connected subpopulations that lie on the nodes of a graph, and allows asymmetric migrations. It can handle large populations, and explicitly models serial passage events with migrations, thus closely mimicking experimental conditions. We analyze recent experiments in light of this model. We suggest useful parameter regimes for future experiments, and we make quantitative predictions for these experiments. In particular, we propose experiments to directly test our recent prediction that the star graph with asymmetric migrations suppresses natural selection and can accelerate mutant fixation or extinction, compared to a well-mixed population.

Cultivating efficiency: high-throughput growth analysis of anaerobic bacteria in compact microplate readers

Anaerobic microbes play crucial roles in environmental processes, industry, and human health. Traditional methods for monitoring the growth of anaerobes, including plate counts or subsampling broth cultures for optical density measurements, are time and resource-intensive. The advent of microplate readers revolutionized bacterial growth studies by enabling high-throughput and real-time monitoring of microbial growth kinetics. Yet, their use in anaerobic microbiology has remained limited. Here, we present a workflow for using small-footprint microplate readers and the Growthcurver R package to analyze the kinetic growth metrics of anaerobic bacteria. We benchmarked the small-footprint Cerillo Stratus microplate reader against a BioTek Synergy HTX microplate reader in aerobic conditions using Escherichia coli DSM 28618 cultures. The growth rates and carrying capacities obtained from the two readers were statistically indistinguishable. However, the area under the logistic curve was significantly higher in cultures monitored by the Stratus reader. We used the Stratus to quantify the growth responses of anaerobically grown E. coli and Clostridium bolteae DSM 29485 to different doses of the toxin sodium arsenite. The growth of E. coli and C. bolteae was sensitive to arsenite doses of 1.3 µM and 0.4 µM, respectively. Complete inhibition of growth was achieved at 38 µM arsenite for C. bolteae and 338 µM in E. coli. These results show that the Stratus performs similarly to a leading brand of microplate reader and can be reliably used in anaerobic conditions. We discuss the advantages of the small format microplate readers and our experiences with the Stratus.

IMPORTANCE

We present a workflow that facilitates the production and analysis of growth curves for anaerobic microbes using small-footprint microplate readers and an R script. This workflow is a cost and space-effective solution to most high-throughput solutions for collecting growth data from anaerobic microbes. This technology can be used for applications where high throughput would advance discovery, including microbial isolation, bioprospecting, co-culturing, host-microbe interactions, and drug/toxin-microbial interactions.

Identifying and mathematically modeling the time-course of extracellular metabolic markers associated with resistance to ceftolozane/tazobactam in Pseudomonas aeruginosa

Extracellular bacterial metabolites have potential as markers of bacterial growth and resistance emergence but have not been evaluated in dynamic in vitro studies. We investigated the dynamic metabolomic footprint of a multidrug-resistant hypermutable Pseudomonas aeruginosa isolate exposed to ceftolozane/tazobactam as continuous infusion (4.5 g/day, 9 g/day) in a hollow-fiber infection model over 7-9 days in biological replicates (n = 5). Bacterial samples were collected at 0, 7, 23, 47, 71, 95, 143, 167, 191, and 215 h, the supernatant quenched, and extracellular metabolites extracted. Metabolites were analyzed via untargeted metabolomics, including hierarchical clustering and correlation with quantified total and resistant bacterial populations. The time-courses of five (of 1,921 detected) metabolites from enriched pathways were mathematically modeled. Absorbed L-arginine and secreted L-ornithine were highly correlated with the total bacterial population (r -0.79 and 0.82, respectively, P<0.0001). Ribose-5-phosphate, sedoheptulose-7-phosphate, and trehalose-6-phosphate correlated with the resistant subpopulation (0.64, 0.64, and 0.67, respectively, P<0.0001) and were likely secreted due to resistant growth overcoming oxidative and osmotic stress induced by ceftolozane/tazobactam. Using pharmacokinetic/pharmacodynamic-based transduction models, these metabolites were successfully modeled based on the total or resistant bacterial populations. The models well described the abundance of each metabolite across the differing time-course profiles of biological replicates, based on bacterial killing and, importantly, resistant regrowth. These proof-of-concept studies suggest that further exploration is warranted to determine the generalizability of these findings. The metabolites modeled here are not exclusive to bacteria. Future studies may use this approach to identify bacteria-specific metabolites correlating with resistance, which would ultimately be extremely useful for clinical translation.

Cultivating efficiency: High-throughput growth analysis of anaerobic bacteria in compact microplate readers

Anaerobic microbes play crucial roles in environmental processes, industry, and human health. Traditional methods for monitoring the growth of anaerobes, including plate counts or subsampling broth cultures for optical density measurements, are time and resource intensive. The advent of microplate readers revolutionized bacterial growth studies by enabling high-throughput and real-time monitoring of microbial growth kinetics but their use in anaerobic microbiology has remained limited. Here, we present a workflow for using small-footprint microplate readers and the Growthcurver R package to analyze the kinetic growth metrics of anaerobic bacteria. We benchmarked the small-footprint Cerillo Stratus microplate reader against a BioTek Synergy HTX microplate reader in aerobic conditions using Escherichia coli DSM 28618 cultures. The growth rates and carrying capacities obtained from the two readers were statistically indistinguishable. However, the area under the logistic curve was significantly higher in cultures monitored by the Stratus reader. We used the Stratus to quantify the growth responses of anaerobically grown E. coli and Clostridium bolteae DSM 29485 to different doses of the toxin sodium arsenite. The growth of E. coli and C. bolteae was sensitive to arsenite doses of 1.3 μM and 0.4 μM, respectively. Complete inhibition of growth was achieved at 38 μM arsenite for C. bolteae, and 338 μM in E. coli. These results show that the Stratus performs similarly to a leading brand of microplate reader and can be reliably used in anaerobic conditions. We discuss the advantages of the small format microplate readers and our experiences with the Stratus.

Time-resolved, deuterium-based fluxomics uncovers the hierarchy and dynamics of sugar processing by Pseudomonas putida

Pseudomonas putida, a microbial host widely adopted for metabolic engineering, processes glucose through convergent peripheral pathways that ultimately yield 6-phosphogluconate. The periplasmic gluconate shunt (PGS), composed by glucose and gluconate dehydrogenases, sequentially transforms glucose into gluconate and 2-ketogluconate. Although the secretion of these organic acids by P. putida has been extensively recognized, the mechanism and spatiotemporal regulation of the PGS remained elusive thus far. To address this challenge, we adopted a dynamic 13C- and 2H-metabolic flux analysis strategy, termed D-fluxomics. D-fluxomics demonstrated that the PGS underscores a highly dynamic metabolic architecture in glucose-dependent batch cultures of P. putida, characterized by hierarchical carbon uptake by the PGS throughout the cultivation. Additionally, we show that gluconate and 2-ketogluconate accumulation and consumption can be solely explained as a result of the interplay between growth rate-coupled and decoupled metabolic fluxes. As a consequence, the formation of these acids in the PGS is inversely correlated to the bacterial growth rate-unlike the widely studied overflow metabolism of Escherichia coli and yeast. Our findings, which underline survival strategies of soil bacteria thriving in their natural environments, open new avenues for engineering P. putida towards efficient, sugar-based bioprocesses.

High-throughput Saccharomyces cerevisiae cultivation method for credentialing-based untargeted metabolomics

Identifying metabolites in model organisms is critical for many areas of biology, including unravelling disease aetiology or elucidating functions of putative enzymes. Even now, hundreds of predicted metabolic genes in Saccharomyces cerevisiae remain uncharacterized, indicating that our understanding of metabolism is far from complete even in well-characterized organisms. While untargeted high-resolution mass spectrometry (HRMS) enables the detection of thousands of features per analysis, many of these have a non-biological origin. Stable isotope labelling (SIL) approaches can serve as credentialing strategies to distinguish biologically relevant features from background signals, but implementing these experiments at large scale remains challenging. Here, we developed a SIL-based approach for high-throughput untargeted metabolomics in S. cerevisiae, including deep-48 well format-based cultivation and metabolite extraction, building on the peak annotation and verification engine (PAVE) tool. Aqueous and nonpolar extracts were analysed using HILIC and RP liquid chromatography, respectively, coupled to Orbitrap Q Exactive HF mass spectrometry. Of the approximately 37,000 total detected features, only 3-7% of the features were credentialed and used for data analysis with open-source software such as MS-DIAL, MetFrag, Shinyscreen, SIRIUS CSI:FingerID, and MetaboAnalyst, leading to the successful annotation of 198 metabolites using MS² database matching. Comparable metabolic profiles were observed for wild-type and sdh1Δ yeast strains grown in deep-48 well plates versus the classical shake flask format, including the expected increase in intracellular succinate concentration in the sdh1Δ strain. The described approach enables high-throughput yeast cultivation and credentialing-based untargeted metabolomics, providing a means to efficiently perform molecular phenotypic screens and help complete metabolic networks.

An automated workflow for multi-omics screening of microbial model organisms

Multi-omics datasets are becoming of key importance to drive discovery in fundamental research as much as generating knowledge for applied biotechnology. However, the construction of such large datasets is usually time-consuming and expensive. Automation might enable to overcome these issues by streamlining workflows from sample generation to data analysis. Here, we describe the construction of a complex workflow for the generation of high-throughput microbial multi-omics datasets. The workflow comprises a custom-built platform for automated cultivation and sampling of microbes, sample preparation protocols, analytical methods for sample analysis and automated scripts for raw data processing. We demonstrate possibilities and limitations of such workflow in generating data for three biotechnologically relevant model organisms, namely Escherichia coli, Saccharomyces cerevisiae, and Pseudomonas putida.

Sample Preparation in Microbial Metabolomics: Advances and Challenges

Microbial metabolomics has gained significant interest as it reflects the physiological state of microorganisms. Due to the great variability of biological organisms, in terms of physicochemical characteristics and variable range of concentration of metabolites, the choice of sample preparation methods is a crucial step in the metabolomics workflow and will reflect on the quality and reliability of the results generated. The procedures applied to the preparation of microbial samples will vary according to the type of microorganism studied, the metabolomics approach (untargeted or targeted), and the analytical platform of choice. This chapter aims to provide an overview of the sample preparation workflow for microbial metabolomics, highlighting the pre-analytical factors associated with cultivation, harvesting, metabolic quenching, and extraction. Discussions focus on obtaining intracellular and extracellular metabolites. Finally, we introduced advanced sample preparation methods based on automated systems.

Towards high-throughput screening (HTS) of polyhydroxyalkanoate (PHA) production via Fourier transform infrared (FTIR) spectroscopy of Halomonas sp. R5-57 and Pseudomonas sp. MR4-99

High-throughput screening (HTS) methods for characterization of microbial production of polyhydroxyalkanoates (PHA) are currently under investigated, despite the advent of such systems in related fields. In this study, phenotypic microarray by Biolog PM1 screening of Halomonas sp. R5-57 and Pseudomonas sp. MR4-99 identified 49 and 54 carbon substrates to be metabolized by these bacteria, respectively. Growth on 15 (Halomonas sp. R5-57) and 14 (Pseudomonas sp. MR4-99) carbon substrates was subsequently characterized in 96-well plates using medium with low nitrogen concentration. Bacterial cells were then harvested and analyzed for putative PHA production using two different Fourier transform infrared spectroscopy (FTIR) systems. The FTIR spectra obtained from both strains contained carbonyl-ester peaks indicative of PHA production. Strain specific differences in the carbonyl-ester peak wavenumber indicated that the PHA side chain configuration differed between the two strains. Confirmation of short chain length PHA (scl-PHA) accumulation in Halomonas sp. R5-57 and medium chain length PHA (mcl-PHA) in Pseudomonas sp. MR4-99 was done using Gas Chromatography-Flame Ionization Detector (GC-FID) analysis after upscaling to 50 mL cultures supplemented with glycerol and gluconate. The strain specific PHA side chain configurations were also found in FTIR spectra of the 50 mL cultures. This supports the hypothesis that PHA was also produced in the cells cultivated in 96-well plates, and that the HTS approach is suitable for analysis of PHA production in bacteria. However, the carbonyl-ester peaks detected by FTIR are only indicative of PHA production in the small-scale cultures, and appropriate calibration and prediction models based on combining FTIR and GC-FID data needs to be developed and optimized by performing more extensive screenings and multivariate analyses.

Tuning a high performing multiplexed-CRISPRi Pseudomonas putida strain to further enhance indigoidine production

In this study, a 14-gene edited Pseudomonas putida KT2440 strain for heterologous indigoidine production was examined using three distinct omic datasets. Transcriptomic data indicated that CRISPR/dCpf1-interference (CRISPRi) mediated multiplex repression caused global gene expression changes, implying potential undesirable changes in metabolic flux. ¹³C-metabolic flux analysis (¹³C-MFA) revealed that the core P. putida flux network after CRISPRi repression was conserved, with moderate reduction of TCA cycle and pyruvate shunt activity along with glyoxylate shunt activation during glucose catabolism. Metabolomic results identified a change in intracellular TCA metabolites and extracellular metabolite secretion profiles (sugars and succinate overflow) in the engineered strains. These omic analyses guided further strain engineering, with a random mutagenesis screen first identifying an optimal ribosome binding site (RBS) for Cpf1 that enabled stronger product-substrate pairing (1.6-fold increase). Then, deletion strains were constructed with excision of the PHA operon (ΔphaAZC-IID) resulting in a 2.2-fold increase in indigoidine titer over the optimized Cpf1-RBS construct at the end of the growth phase (∼6 h). The maximum indigoidine titer (at 72 h) in the ΔphaAZC-IID strain had a 1.5-fold and 1.8-fold increase compared to the optimized Cpf1-RBS construct and the original strain, respectively. Overall, this study demonstrated that integration of omic data types is essential for understanding responses to complex metabolic engineering designs and directly quantified the effect of such modifications on central metabolism.

A synthetic C2 auxotroph of Pseudomonas putida for evolutionary engineering of alternative sugar catabolic routes

Acetyl-coenzyme A (AcCoA) is a metabolic hub in virtually all living cells, serving as both a key precursor of essential biomass components and a metabolic sink for catabolic pathways for a large variety of substrates. Owing to this dual role, tight growth-production coupling schemes can be implemented around the AcCoA node. Building on this concept, a synthetic C2 auxotrophy was implemented in the platform bacterium Pseudomonas putida through an in silico-informed engineering approach. A growth-coupling strategy, driven by AcCoA demand, allowed for direct selection of an alternative sugar assimilation route-the phosphoketolase (PKT) shunt from bifidobacteria. Adaptive laboratory evolution forced the synthetic P. putida auxotroph to rewire its metabolic network to restore C2 prototrophy via the PKT shunt. Large-scale structural chromosome rearrangements were identified as possible mechanisms for adjusting the network-wide proteome profile, resulting in improved PKT-dependent growth phenotypes. ¹³C-based metabolic flux analysis revealed an even split between the native Entner-Doudoroff pathway and the synthetic PKT bypass for glucose processing, leading to enhanced carbon conservation. These results demonstrate that the P. putida metabolism can be radically rewired to incorporate a synthetic C2 metabolism, creating novel network connectivities and highlighting the importance of unconventional engineering strategies to support efficient microbial production.