Characterizing Aquifer Heterogeneity Using Bacterial and Bacteriophage Tracers

Gravel aquifers act as important potable water sources in central western Europe yet they are subject to numerous contamination pressures. Compositional and textural heterogeneity makes protection zone delineation around groundwater supplies in these units challenging; artificial tracer testing aids characterization. This paper reappraises previous tracer test results in light of new geological and microbiological data. Comparative passive gradient testing, using a fluorescent solute (Uranine), virus (H40/1 bacteriophage), and comparably sized bacterial tracers Escherichia coli and Pseudomonas putida, was used to investigate a calcareous gravel aquifer’s ability to remove microbiological contaminants at a test site near Munich, Germany. Test results revealed E. coli relative recoveries could exceed those of H40/1 at monitoring wells 10 m and 20 m from an injection well by almost four times; P. putida recoveries varied by a factor of up to three between wells. Application of filtration theory suggested greater attenuation of H40/1 relative to similarly charged E. coli occurred due to differences in microorganism size, while estimated collision efficiencies appeared comparable. By contrast, more positively charged P. putida experienced greater attenuation at one monitoring point, while lower attenuation rates at the second location indicated the influence of geochemical heterogeneity. Test findings proved consistent with observations from nearby fresh outcrops that suggested thin open framework gravel beds dominated mass transport in the aquifer, while discrete intervals containing stained clasts reflect localized geochemical heterogeneity. Study results highlight the utility of reconciling outcrop observations with artificial tracer test responses, using microbiological tracers with well-defined properties, to characterize aquifer heterogeneity.

ArnT proteins that catalyse the glycosylation of lipopolysaccharide share common features with bacterial N-oligosaccharyltransferases

ArnT is a glycosyltransferase that catalyses the addition of 4-amino-4-deoxy-L-arabinose (L-Ara4N) to the lipid A moiety of the lipopolysaccharide. This is a critical modification enabling bacteria to resist killing by antimicrobial peptides. ArnT is an integral inner membrane protein consisting of 13 predicted transmembrane helices and a large periplasmic C-terminal domain. We report here the identification of a functional motif with a canonical consensus sequence DEXRYAX(5)MX(3)GXWX(9)YFEKPX(4)W spanning the first periplasmic loop, which is highly conserved in all ArnT proteins examined. Site-directed mutagenesis demonstrated the contribution of this motif in ArnT function, suggesting that these proteins have a common mechanism. We also demonstrate that the Burkholderia cenocepacia and Salmonella enterica serovar Typhimurium ArnT C-terminal domain is required for polymyxin B resistance in vivo. Deletion of the C-terminal domain in B. cenocepacia ArnT resulted in a protein with significantly reduced in vitro binding to a lipid A fluorescent substrate and unable to catalyse lipid A modification with L-Ara4N. An in silico predicted structural model of ArnT strongly resembled the tertiary structure of Campylobacter lari PglB, a bacterial oligosaccharyltransferase involved in protein N-glycosylation. Therefore, distantly related oligosaccharyltransferases from ArnT and PglB families operating on lipid and polypeptide substrates, respectively, share unexpected structural similarity that could not be predicted from direct amino acid sequence comparisons. We propose that lipid A and protein glycosylation enzymes share a conserved catalytic mechanism despite their evolutionary divergence.

Interferon-γ stimulates CD14, TLR2 and TLR4 mRNA expression in gingival fibroblasts increasing responsiveness to bacterial challenge

Objective: To investigate the potential effects of IFN-y on the responsiveness of human gingival fibroblasts to bacterial challenge.
Design :mRNA and protein expression of CD14, TLR2 and TLR4 in human gingival fibroblasts was detected by quantitative polymerase chain reaction (Q-PCR) and flow cytometry. The effect of preincubation with IFN-y on subsequent bacterial LPS-induced expression of IL-6 and IL-8 by gingival fibroblasts was determined by ELISA. Bacterial LPS-induced IκBα degradation in human gingival fibroblasts was investigated by western blot.
Results: Human gingival fibroblasts express CD14, TLR2 and TLR4 mRNAs. IFN-y, but not IL-1B, induced mRNA expression of all three receptors and the expression of membrane bound CD14 protein. Pre-incubation of fibroblasts with IFN-y and subsequent stimulation with Escherichia coli LPS or Porphyromonas gingivalis LPS led to increased production of IL-6 and IL-8. LPS-induced pro-inflammatory cytokine production was abrogated by a blocking antibody to CD14. Both E. coli LPS and P. gingivalis LPS induced IκBα degradation in human gingival fibroblasts.
Conclusion: Our data indicate that IFN-y primes human gingival fibroblasts, through the upregulation of CD14 expression, which results in increased responsiveness to bacterial LPS challenge, as determined by pro-inflammatory cytokine production.

Use of bacterial whole-genome sequencing to investigate local persistence and spread in bovine tuberculosis

Mycobacterium bovis is the causal agent of bovine tuberculosis, one of the most important diseases currently facing the UK cattle industry. Here, we use high-density whole genome sequencing (WGS) in a defined sub-population of M. bovis in 145 cattle across 66 herd breakdowns to gain insights into local spread and persistence. We show that despite low divergence among isolates, WGS can in principle expose contributions of under-sampled host populations to M. bovis transmission. However, we demonstrate that in our data such a signal is due to molecular type switching, which had been previously undocumented for M. bovis. Isolates from farms with a known history of direct cattle movement between them did not show a statistical signal of higher genetic similarity. Despite an overall signal of genetic isolation by distance, genetic distances also showed no apparent relationship with spatial distance among affected farms over distances <5 km. Using simulations, we find that even over the brief evolutionary timescale covered by our data, Bayesian phylogeographic approaches are feasible. Applying such approaches showed that M. bovis dispersal in this system is heterogeneous but slow overall, averaging 2 km/year. These results confirm that widespread application of WGS to M. bovis will bring novel and important insights into the dynamics of M. bovis spread and persistence, but that the current questions most pertinent to control will be best addressed using approaches that more directly integrate WGS with additional epidemiological data.

Abundance and diversity of sedimentary bacterial communities in a coastal productive setting in the Western Irish Sea

The bacterial community composition and biomass abundance from a depositional mud belt in the western Irish Sea and regional sands were investigated by phospholipid ester-linked fatty acid profiling, denaturing gradient gel electrophoresis and barcoded pyrosequencing of 16S rRNA genes. The study area varied by water depth (12-111 m), organic carbon content (0.09-1.57% TOC), grain size, hydrographic regime (well-mixed vs. stratified), and water column phytodetrital input (represented by algal polyunsaturated PLFA). The relative abundance of bacterial-derived PLFA (sum of methyl-branched, cyclopropyl and odd-carbon number PLFA) was positively correlated with fine-grained sediment, and was highest in the depositional mud belt. A strong association between bacterial biomass and eukaryote primary production was suggested based on observed positive correlations with total nitrogen and algal polyunsaturated fatty acids. In addition, 16S rRNA genes affiliated to the classes Clostridia and Flavobacteria represented a major proportion of total 16S rRNA gene sequences. This suggests that benthic bacterial communities are also important degraders of phytodetrital organic matter and closely coupled to water column productivity in the western Irish Sea.

Potential strategies for the eradication of multi-drug resistant Gram-negative bacterial infections

Antimicrobial resistance is one of the leading threats to society. The increasing burden of multidrug-resistant Gram-negative infection is particularly concerning as such bacteria are demonstrating resistance to nearly all currently licensed therapies. Various strategies have been hypothesized to treat multidrug-resistant Gram-negative infections including: targeting the Gram-negative outer membrane; neutralization of lipopolysaccharide; inhibition of bacterial efflux pumps and prevention of protein folding. Silver and silver nanoparticles, fusogenic liposomes and nanotubes are potential strategies for extending the activity of licensed, Gram-positive selective, antibiotics to Gram-negatives. This may serve as a strategy to fill the current void in pharmaceutical development in the short term. This review outlines the most promising strategies that could be implemented to solve the threat of multidrug-resistant Gram-negative infections

Development of polymeric materials to inhibit bacterial quorum sensing

Bacterial infections are an increasing problem for human health. In fact, an increasing number of infections are caused by bacteria that are resistant to most antibiotics and their combinations. Therefore, the scientific community is currently searching for new solutions to fight bacteria and infectious diseases, without promoting antimicrobial resistance. One of the most promising strategies is the disruption or attenuation of bacterial Quorum Sensing (QS), a refined system that bacteria use to communicate. In a QS event, bacteria produce and release specific small chemicals, signal molecules - autoinducers (AIs) - into the environment. At the same time that bacterial population grows, the concentration of AIs in the bacterial environment increases. When a threshold concentration of AIs is reached, bacterial cells respond to it by altering their gene expression profile. AIs regulate gene expression as a function of cell population density. Phenotypes mediated by QS (QSphenotypes) include virulence factors, toxin production, antibiotic resistance and biofilm formation. In this work, two polymeric materials (linear polymers and molecularly imprinted nanoparticles) were developed and their ability to attenuate QS was evaluated. Both types of polymers should to be able to adsorb bacterial signal molecules, limiting their availability in the extracellular environment, with expected disruption of QS. Linear polymers were composed by one of two monomers (itaconic acid and methacrylic acid), which are known to possess strong interactions with the bacterial signal molecules. Molecularly imprinted polymer nanoparticles (MIP NPs) are particles with recognition capabilities for the analyte of interest. This ability is attained by including the target analyte at the synthesis stage. Vibrio fischeri and Aeromonas hydrophila were used as model species for the study. Both the linear polymers and MIP NPs, tested free in solutions and coated to surfaces, showed ability to disrupt QS by decreasing bioluminescence of V. fischeri and biofilm formation of A. hydrophila. No significant effect on bacterial growth was detected. The cytotoxicity of the two types of polymers to a fibroblast-like cell line (Vero cells) was also tested in order to evaluate their safety. The results showed that both the linear polymers and MIP NPs were not cytotoxic in the testing conditions. In conclusion, the results reported in this thesis, show that the polymers developed are a promising strategy to disrupt QS and reduce bacterial infection and resistance. In addition, due to their low toxicity, solubility and easy integration by surface coating, the polymers have potential for applications in scenarios where bacterial infection is a problem: medicine, pharmaceutical, food industry and in agriculture or aquaculture.

Development of infrared spectroscopy for assessing bacterial quality in foods

Rapid and specific detection of foodborne bacteria that can cause food spoilage or illness associated to its consumption is an increasingly important task in food industry. Bacterial detection, identification, and classification are generally performed using traditional methods based on biochemical or serological tests and the molecular methods based on DNA or RNA fingerprints. However, these methodologies are expensive, time consuming and laborious. Infrared spectroscopy is a reliable, rapid, and economic technique which could be explored as a tool for bacterial analysis in the food industry. In this thesis it was evaluated the potential of IR spectroscopy to study the bacterial quality of foods. In Chapter 2, it was developed a calibration model that successfully allowed to predict the bacterial concentration of naturally contaminated cooked ham samples kept at refrigeration temperature during 8 days. In this part, it was developed the methodology that allowed the best reproducibility of spectra from bacteria colonies with minimal sample preparation, which was used in the subsequent work. Several attempts trying different resolutions and number of scans in the IR were made. A spectral resolution of 4 cm-1, with 32 scans were the settings that allowed the best results. Subsequently, in Chapter 3, it was made an attempt to identify 22 different foodborne bacterial genera/species using IR spectroscopy coupled with multivariate analysis. The principal component analysis, used as an exploratory technique, allowed to form distinct groups, each one corresponding to a different genus, in most of the cases. Then, a hierarchical cluster analysis was performed to further analyse the group formation and the possibility of distinction between species of the same bacterial genus. It was observed that IR spectroscopy not only is suitable to the distinction of the different genera, but also to differentiate species of the same genus, with the simultaneous use of principal component analysis and cluster analysis techniques. The utilization of IR spectroscopy and multivariate statistical analysis were also investigated in Chapter 4, in order to confirm the presence of Listeria monocytogenes and Salmonella spp. isolated from contaminated foods, after growth in selective medium. This would allow to substitute the traditional biochemical and serological methods that are used to confirm these pathogens and that delay the obtainment of the results up to 2 days. The obtained results allowed the distinction of 3 different Listeria species and the distinction of Salmonella spp. from other bacteria that can be mistaken with them. Finally, in chapter 5, high pressure processing, an emerging methodology that permits to produce microbiologically safe foods and extend their shelf-life, was applied to 12 foodborne bacteria to determine their resistance and the effects of pressure in cells. A treatment of 300 MPa, during 15 minutes at room temperature was applied. Gram-negative bacteria were inactivated to undetectable levels and Gram-positive showed different resistances. Bacillus cereus and Staphylococcus aureus decreased only 2 logs and Listeria innocua decreased about 5 logs. IR spectroscopy was performed in bacterial colonies before and after HPP in order to investigate the alterations of the cellular compounds. It was found that high pressure alters bands assigned to some cellular components as proteins, lipids, oligopolysaccharides, phosphate groups from the cell wall and nucleic acids, suggesting disruption of the cell envelopes. In this work, bacterial quantification and classification, as well as assessment of cellular compounds modification with high pressure processing were successfully performed. Taking this into account, it was showed that IR spectroscopy is a very promising technique to analyse bacteria in a simple and inexpensive manner.

Genetic programming and bacterial algorithm for neural networks and fuzzy systems design

In the field of control systems it is common to use techniques based on model adaptation to carry out control for plants for which mathematical analysis may be intricate. Increasing interest in biologically inspired learning algorithms for control techniques such as Artificial Neural Networks and Fuzzy Systems is in progress. In this line, this paper gives a perspective on the quality of results given by two different biologically connected learning algorithms for the design of B-spline neural networks (BNN) and fuzzy systems (FS). One approach used is the Genetic Programming (GP) for BNN design and the other is the Bacterial Evolutionary Algorithm (BEA) applied for fuzzy rule extraction. Also, the facility to incorporate a multi-objective approach to the GP algorithm is outlined, enabling the designer to obtain models more adequate for their intended use.

Genetic and bacterial programming for B-Spline neural networks design

The design phase of B-spline neural networks is a highly computationally complex task. Existent heuristics have been found to be highly dependent on the initial conditions employed. Increasing interest in biologically inspired learning algorithms for control techniques such as Artificial Neural Networks and Fuzzy Systems is in progress. In this paper, the Bacterial Programming approach is presented, which is based on the replication of the microbial evolution phenomenon. This technique produces an efficient topology search, obtaining additionally more consistent solutions.

Design of B-spline neural networks using a bacterial programming approach

The design phase of B-spline neural networks represents a very high computational task. For this purpose, heuristics have been developed, but have been shown to be dependent on the initial conditions employed. In this paper a new technique, Bacterial Programming, is proposed, whose principles are based on the replication of the microbial evolution phenomenon. The performance of this approach is illustrated and compared with existing alternatives.

Bacterial algorithm applied for fuzzy rule extraction

This paper presents a method of using the so-colled "bacterial algorithm" (4,5) for extracting a fuzzy rule base from a training set. The bewly proposed bacterial evolutionary algorithm (BEA) is shown. In our application one bacterium corresponds to a fuzzy rule system.

Bacterial peroxide forming enzymes

Dissertação de mestrado, Engenharia Biológica, Faculdade de Ciências e Tecnologia, Universidade do Algarve; Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, 2015