Salmonella typhimurium and Escherichia coli dissimilarity: Closely related bacteria with distinct metabolic profiles

Curcumin Stimulates the Overexpression of Virulence Factors in Salmonella enterica Serovar Typhimurium: In Vitro and Animal Model Studies

Salmonella spp. is one of the most common food poisoning pathogens and the main cause of diarrheal diseases in humans in developing countries. The increased Salmonella resistance to antimicrobials has led to the search for new alternatives, including natural compounds such as curcumin, which has already demonstrated a bactericidal effect; however, in Gram-negatives, there is much controversy about this effect, as it is highly variable. In this study, we aimed to verify the antibacterial activity of curcumin against the Salmonella enterica serovar Typhimurium growth rate, virulence, and pathogenicity. The strain was exposed to 110, 220 or 330 µg/mL curcumin, and by complementary methods (spectrophotometric, pour plate and MTT assays), we determined its antibacterial activity. To elucidate whether curcumin regulates the expression of virulence genes, Salmonella invA, fliC and siiE genes were investigated by quantitative real-time reverse transcription (qRT-PCR). Furthermore, to explore the effect of curcumin on the pathogenesis process in vivo, a Caenorhabditis elegans infection model was employed. No antibacterial activity was observed, even at higher concentrations of curcumin. All concentrations of curcumin caused overgrowth (35−69%) and increased the pathogenicity of the bacterial strain through the overexpression of virulence factors. The latter coincided with a significant reduction in both the lifespan and survival time of C. elegans when fed with curcumin-treated bacteria. Our data provide relevant information that may support the selective antibacterial effects of curcumin to reconsider the indiscriminate use of this phytochemical, especially in outbreaks of pathogenic Gram-negative bacteria.

CAMDLES: CFD-DEM Simulation of Microbial Communities in Spaceflight and Artificial Microgravity

We present CAMDLES (CFD-DEM Artificial Microgravity Developments for Living Ecosystem Simulation), an extension of CFDEM^®Coupling to model biological flows, growth, and mass transfer in artificial microgravity devices. For microbes that accompany humans into space, microgravity-induced alterations in the fluid environment are likely to be a major factor in the microbial experience of spaceflight. Computational modeling is needed to investigate how well ground-based microgravity simulation methods replicate that experience. CAMDLES incorporates agent-based modeling to study inter-species metabolite transport within microbial communities in rotating wall vessel bioreactors (RWVs). Preexisting CFD modeling of RWVs has not yet incorporated growth; CAMDLES employs the simultaneous modeling of biological, chemical, and mechanical processes in a micro-scale rotating reference frame environment. Simulation mass transfer calculations were correlated with Monod dynamic parameters to predict relative growth rates between artificial microgravity, spaceflight microgravity, and 1 g conditions. By simulating a microbial model community of metabolically cooperative strains of Escherichia coli and Salmonella enterica, we found that the greatest difference between microgravity and an RWV or 1 g gravity was when species colocalized in dense aggregates. We also investigated the influence of other features of the system on growth, such as spatial distribution, product yields, and diffusivity. Our simulation provides a basis for future laboratory experiments using this community for investigation in artificial microgravity and spaceflight microgravity. More broadly, our development of these models creates a framework for novel hypothesis generation and design of biological experiments with RWVs, coupling the effects of RWV size, rotation rate, and mass transport directly to bacterial growth in microbial communities.

Deep Learning for Rapid Identification of Microbes Using Metabolomics Profiles

Rapid detection of viable microbes remains a challenge in fields such as microbial food safety. We here present the application of deep learning algorithms to the rapid detection of pathogenic and non-pathogenic microbes using metabolomics data. Microbes were incubated for 4 h in a protein-free defined medium, followed by 1D ¹H nuclear magnetic resonance (NMR) spectroscopy measurements. NMR spectra were analyzed by spectral binning in an untargeted metabolomics approach. We trained multilayer ("deep") artificial neural networks (ANN) on the data and used the resulting models to predict spectra of unknown microbes. ANN predicted unknown microbes in this laboratory setting with an average accuracy of 99.2% when using a simple feature selection method. We also describe learning behavior of the employed ANN and the optimization strategies that worked well with these networks for our datasets. Performance was compared to other current data analysis methods, and ANN consistently scored higher than random forest models and support vector machines, highlighting the potential of deep learning in metabolomics data analysis.

Metabolomics Reveal Potential Natural Substrates of AcrB in Escherichia coli and Salmonella enterica Serovar Typhimurium

Multidrug-resistant Gram-negative bacteria pose a global threat to human health. The AcrB efflux pump confers inherent and evolved drug resistance to Enterobacterales, including Escherichia coli and Salmonella enterica serovar Typhimurium.

In the fight against antibiotic resistance, drugs that target resistance mechanisms in bacteria can be used to restore the therapeutic effectiveness of antibiotics. The multidrug resistance efflux complex AcrAB-TolC is the most clinically relevant efflux pump in Enterobacterales and is a target for drug discovery. Inhibition of the pump protein AcrB allows the intracellular accumulation of a wide variety of antibiotics, effectively restoring their therapeutic potency. To facilitate the development of AcrB efflux inhibitors, it is desirable to discover the native substrates of the pump, as these could be chemically modified to become inhibitors. We analyzed the native substrate profile of AcrB in Escherichia coli MG1655 and Salmonella enterica serovar Typhimurium SL1344 using an untargeted metabolomics approach. We analyzed the endo- and exometabolome of the wild-type strain and their respective AcrB loss-of-function mutants (AcrB D408A) to determine the metabolites that are native substrates of AcrB. Although there is 95% homology between the AcrB proteins of S. Typhimurium and E. coli, we observed mostly different metabolic responses in the exometabolomes of the S. Typhimurium and E. coli AcrB D408A mutants relative to those in the wild type, potentially indicating a differential metabolic adaptation to the same mutation in these two species. Additionally, we uncovered metabolite classes that could be involved in virulence of S. Typhimurium and a potential natural substrate of AcrB common to both species.

Acetate as a potential feedstock for the production of value-added chemicals: Metabolism and applications

Acetate is regarded as a promising carbon feedstock in biological production owing to its possible derivation from C₁ gases such as CO, CO₂ and methane. To best use of acetate, comprehensive understanding of acetate metabolisms from genes and enzymes to pathways and regulations is needed. This review aims to provide an overview on the potential of acetate as carbon feedstock for industrial biotechnology. Biochemical, microbial and biotechnological aspects of acetate metabolism are described. Especially, the current state-of-the art in the production of value-added chemicals from acetate is summarized. Challenges and future perspectives are also provided.

Rise of the killer plants: investigating the antimicrobial activity of Australian plants to enhance biofilter-mediated pathogen removal

BACKGROUND

Biofilters are soil-plant based passive stormwater treatment systems which demonstrate promising, although inconsistent, removal of faecal microorganisms. Antimicrobial-producing plants represent a safe, inexpensive yet under-researched biofilter design component that may enhance treatment reliability. The mechanisms underlying plant-mediated microbial removal in biofilters have not been fully elucidated, particularly with respect to antimicrobial production. The aim of this study was therefore to inform biofilter vegetation selection guidelines for optimal pathogen treatment by conducting antimicrobial screening of biofilter-suitable plant species. This involved: (1) selecting native plants suitable for biofilters (17 species) in a Victorian context (southeast Australia); and (2) conducting antimicrobial susceptibility testing of selected plant methanolic extracts (≥ 5 biological replicates/species; 86 total) against reference stormwater faecal bacteria (Salmonella enterica subsp. enterica ser. Typhimurium, Enterococcus faecalis and Escherichia coli).

RESULTS

The present study represents the first report on the inhibitory activity of polar alcoholic extracts from multiple tested species. Extracts of plants in the Myrtaceae family, reputed for their production of antimicrobial oils, demonstrated significantly lower minimum inhibitory concentrations (MICs) than non-myrtaceous candidates (p < 0.0001). Melaleuca fulgens (median MIC: 8 mg/mL; range: [4-16 mg/mL]), Callistemon viminalis (16 mg/mL, [2-16 mg/mL]) and Leptospermum lanigerum (8 mg/mL, [4-16 mg/mL]) exhibited the strongest inhibitory activity against the selected bacteria (p < 0.05 compared to each tested non-myrtaceous candidate). In contrast, the Australian biofilter gold standard Carex appressa demonstrated eight-fold lower activity than the highest performer M. fulgens (64 mg/mL, [32-64 mg/mL]).

CONCLUSION

Our results suggest that myrtaceous plants, particularly M. fulgens, may be more effective than the current vegetation gold standard in mediating antibiosis and thus improving pathogen treatment within biofilters. Further investigation of these plants in biofilter contexts is recommended to refine biofilter vegetation selection guidelines.

A Comparative Analysis of the Metabolomic Response of Electron Beam Inactivated E. coli O26:H11 and Salmonella Typhimurium ATCC 13311

Ionizing radiation such as Electron beam (EB) and gamma irradiation inactivate microbial cells preventing their multiplication. These cells, however, are structurally intact and appear to have residual metabolic activity. We were interested in understanding the metabolic pathways that were still functional in EB-inactivated cells. Therefore, the primary objective of this study was to compare the metabolites accumulating in EB-inactivated pathogens E. coli 026:H11 and S. Typhimurium immediately after EB inactivation and 24 h post inactivation. Defined aliquots (109 CFU/mL) of E. coli O26-H11 (TW 1597) and S. Typhimurium (ATCC 13311) suspended in phosphate-buffered saline were exposed to lethal EB doses of 3 kGy and 2 kGy, respectively. Complete inactivation (inability of cells to multiply) was confirmed by traditional plating methods. An untargeted analysis of the primary metabolites accumulating in un-irradiated (control) cells, EB-inactivated cells immediately after irradiation, and EB-inactivated cells that were incubated at room temperature for 24 h post EB inactivation was performed using gas chromatography/mass spectrometry. A total of 349 different metabolites were detected in the EB-inactivated S. Typhimurium and E. coli O26:H11 cells, out of which, only 50% were identifiable. In S. Typhimurium, 98 metabolites were expressed at statistically different concentrations (P < 0.05) between the three treatment groups. In E. coli O26:H11, 63 metabolites were expressed at statistically different concentrations (P < 0.05) between the three treatment groups. In both these pathogens, the β-alanine, alanine, aspartate, and glutamate metabolic pathways were significantly impacted (P < 0.01). Furthermore, the metabolomic changes in EB-inactivated cells were amplified significantly after 24 h storage at room temperature in phosphate-buffered saline. These results suggest that EB-inactivated cells are very metabolically active and, therefore, the term Metabolically Active yet Non-culturable is an apt term describing EB-inactivated bacterial cells.

Mapping Salmonella typhimurium pathways using 13C metabolic flux analysis

In the last years, Salmonella has been extensively studied not only due to its importance as a pathogen, but also as a host to produce pharmaceutical compounds. However, the full exploitation of Salmonella as a platform for bioproduct delivery has been hampered by the lack of information about its metabolism. Genome-scale metabolic models can be valuable tools to delineate metabolic engineering strategies as long as they closely represent the actual metabolism of the target organism. In the present study, a ¹³C-MFA approach was applied to map the fluxes at the central carbon pathways of S. typhimurium LT2 growing at glucose-limited chemostat cultures. The experiments were carried out in a 2L bioreactor, using defined medium enriched with 20% ¹³C-labeled glucose. Metabolic flux distributions in central carbon pathways of S. typhimurium LT2 were estimated using OpenFLUX2 based on the labeling pattern of biomass protein hydrolysates together with biomass composition. The results suggested that pentose phosphate is used to catabolize glucose, with minor fluxes through glycolysis. In silico simulations, using Optflux and pFBA as simulation method, allowed to study the performance of the genome-scale metabolic model. In general, the accuracy of in silico simulations was improved by the superimposition of estimated intracellular fluxes to the existing genome-scale metabolic model, showing a better fitting to the experimental extracellular fluxes, whereas the intracellular fluxes of pentose phosphate and anaplerotic reactions were poorly described.

Disposable all-printed electronic biosensor for instantaneous detection and classification of pathogens

A novel disposable all-printed electronic biosensor is proposed for a fast detection and classification of bacteria. This biosensor is applied to classify three types of popular pathogens: Salmonella typhimurium, and the Escherichia coli strains JM109 and DH5-α. The proposed sensor consists of inter-digital silver electrodes fabricated through an inkjet material printer and silver nanowires uniformly decorated on the electrodes through the electrohydrodynamic technique on a polyamide based polyethylene terephthalate substrate. The best sensitivity of the proposed sensor is achieved at 200 µm teeth spaces of the inter-digital electrodes along the density of the silver nanowires at 30 × 10³/mm². The biosensor operates on ±2.5 V and gives the impedance value against each bacteria type in 8 min after sample injection. The sample data are measured through an impedance analyzer and analyzed through pattern recognition methods such as linear discriminate analysis, maximum likelihood, and back propagation artificial neural network to classify each type of bacteria. A perfect classification and cross-validation is achieved by using the unique fingerprints extracted from the proposed biosensor through all the applied classifiers. The overall experimental results demonstrate that the proposed disposable all-printed biosensor is applicable for the rapid detection and classification of pathogens.

Novel Two-Step Hierarchical Screening of Mutant Pools Reveals Mutants under Selection in Chicks

Contaminated chicken/egg products are major sources of human salmonellosis, yet the strategies used by Salmonella to colonize chickens are poorly understood. We applied a novel two-step hierarchical procedure to identify new genes important for colonization and persistence of Salmonella enterica serotype Typhimurium in chickens. A library of 182 S. Typhimurium mutants each containing a targeted deletion of a group of contiguous genes (for a total of 2,069 genes deleted) was used to identify regions under selection at 1, 3, and 9 days postinfection in chicks. Mutants in 11 regions were under selection at all assayed times (colonization mutants), and mutants in 15 regions were under selection only at day 9 (persistence mutants). We assembled a pool of 92 mutants, each deleted for a single gene, representing nearly all genes in nine regions under selection. Twelve single gene deletion mutants were under selection in this assay, and we confirmed 6 of 9 of these candidate mutants via competitive infections and complementation analysis in chicks. STM0580, STM1295, STM1297, STM3612, STM3615, and STM3734 are needed for Salmonella to colonize and persist in chicks and were not previously associated with this ability. One of these key genes, STM1297 (selD), is required for anaerobic growth and supports the ability to utilize formate under these conditions, suggesting that metabolism of formate is important during infection. We report a hierarchical screening strategy to interrogate large portions of the genome during infection of animals using pools of mutants of low complexity. Using this strategy, we identified six genes not previously known to be needed during infection in chicks, and one of these (STM1297) suggests an important role for formate metabolism during infection.

In Silico Constraint-Based Strain Optimization Methods: the Quest for Optimal Cell Factories

Shifting from chemical to biotechnological processes is one of the cornerstones of 21st century industry. The production of a great range of chemicals via biotechnological means is a key challenge on the way toward a bio-based economy. However, this shift is occurring at a pace slower than initially expected. The development of efficient cell factories that allow for competitive production yields is of paramount importance for this leap to happen. Constraint-based models of metabolism, together with in silico strain design algorithms, promise to reveal insights into the best genetic design strategies, a step further toward achieving that goal. In this work, a thorough analysis of the main in silico constraint-based strain design strategies and algorithms is presented, their application in real-world case studies is analyzed, and a path for the future is discussed.