The effects of sodium hypochlorite against selected drinking water-isolated bacteria in planktonic and sessile states

Chlorine is the most commonly used agent for general disinfection, particularly for microbial growth control in drinking water distribution systems. The goals of this study were to understand the effects of chlorine, as sodium hypochlorite (NaOCl), on bacterial membrane physicochemical properties (surface charge, surface tension and hydrophobicity) and on motility of two emerging pathogens isolated from drinking water, Acinetobacter calcoaceticus and Stenotrophomonas maltophilia. The effects of NaOCl on the control of single and dual-species monolayer adhered bacteria (2 h incubation) and biofilms (24 h incubation) was also assessed. NaOCl caused significant changes on the surface hydrophobicity and motility of A. calcoaceticus, but not of S. maltophilia. Planktonic and sessile S. maltophilia were significantly more resistant to NaOCl than A. calcoaceticus. Monolayer adhered co-cultures of A. calcoaceticus-S. maltophilia were more resilient than the single species. Oppositely, dual species biofilms were more susceptible to NaOCl than their single species counterparts. In general, biofilm removal and killing demonstrated to be distinct phenomena: total bacterial viability reduction was achieved even if NaOCl at the higher concentrations had a reduced removal efficacy, allowing biofilm reseed. In conclusion, understanding the antimicrobial susceptibility of microorganisms to NaOCl can contribute to the design of effective biofilm control strategies targeting key microorganisms, such as S. maltophilia, and guarantying safe and high-quality drinking water. Moreover, the results reinforce that biofilms should be regarded as chronic contaminants of drinking water distribution systems and accurate methods are needed to quantify their presence as well as strategies complementary/alternative to NaOCl are required to effectively control the microbiological quality of drinking water.

Metabolic engineering with multi-objective optimization of kinetic models

Kinetic models have a great potential for metabolic engineering applications. They can be used for testing which genetic and regulatory modifications can increase the production of metabolites of interest, while simultaneously monitoring other key functions of the host organism. This work presents a methodology for increasing productivity in biotechnological processes exploiting dynamic models. It uses multi-objective dynamic optimization to identify the combination of targets (enzymatic modifications) and the degree of up- or down-regulation that must be performed in order to optimize a set of pre-defined performance metrics subject to process constraints. The capabilities of the approach are demonstrated on a realistic and computationally challenging application: a large-scale metabolic model of Chinese Hamster Ovary cells (CHO), which are used for antibody production in a fed-batch process. The proposed methodology manages to provide a sustained and robust growth in CHO cells, increasing productivity while simultaneously increasing biomass production, product titer, and keeping the concentrations of lactate and ammonia at low values. The approach presented here can be used for optimizing metabolic models by finding the best combination of targets and their optimal level of up/down-regulation. Furthermore, it can accommodate additional trade-offs and constraints with great flexibility.

Development of computational and experimental methods for biomass composition and evaluation of its impact in genome-scale models prediction

The use of genome-scale metabolic models has been rapidly increasing in fields such as metabolic engineering. An important part of a metabolic model is the biomass equation since this reaction will ultimately determine the predictive capacity of the model in terms of essentiality and flux distributions. Thus, in order to obtain a reliable metabolic model the biomass precursors and their coefficients must be as precise as possible. Ideally, determination of the biomass composition would be performed experimentally, but when no experimental data are available this is established by approximation to closely related organisms. Computational methods however, can extract some information from the genome such as amino acid and nucleotide compositions. The main objectives of this study were to compare the biomass composition of several organisms and to evaluate how biomass precursor coefficients affected the predictability of several genome-scale metabolic models by comparing predictions with experimental data in literature. For that, the biomass macromolecular composition was experimentally determined and the amino acid composition was both experimentally and computationally estimated for several organisms. Sensitivity analysis studies were also performed with the Escherichia coli iAF1260 metabolic model concerning specific growth rates and flux distributions. The results obtained suggest that the macromolecular composition is conserved among related organisms. Contrasting, experimental data for amino acid composition seem to have no similarities for related organisms. It was also observed that the impact of macromolecular composition on specific growth rates and flux distributions is larger than the impact of amino acid composition, even when data from closely related organisms are used.

Growth of sulfate reducing bacteria on the methanogenic inhibitor BES

The metabolism of methanogenic archaea is inhibited by 2-bromoethanesulfonate (BES). Methane production is blocked because BES is an analog of methyl-coenzyme M and competes with this key molecule in the last step of methanogenesis. For this reason, BES is commonly used in several studies to avoid growth of acetoclastic and hydrogenotrophic methanogens [1]. Despite its effectiveness as methanogenic inhibitor, BES was found to alter microbial communities’ structure, to inhibit the metabolism of non-methanogenic microorganisms and to stimulate homoacetogenic metabolism [2,3]. Even though sulfonates have been reported as electron acceptors for sulfate- and sulfite-reducing bacteria (SRB), only one study described the reduction of BES by complex microbial communities [4]. In this work, a sulfate-reducing bacterium belonging to Desulfovibrio genus (98 % identity at the 16S rRNA gene level with Desulfovibrio aminophilus) was isolated from anaerobic sludge after several successive transfers in anaerobic medium containing BES as sole substrate. Sulfate was not supplemented to the anaerobic growth medium. This microorganism was able to grow under the following conditions: on BES plus H2/CO2 in bicarbonate buffered medium; on BES without H2/CO2 in bicarbonate buffered medium; and on BES in phosphate buffered medium. The main products of BES utilization were sulfide and acetate, the former was produced by the reduction of sulfur from the sulfonate moiety of BES and the latter likely originated from the carbon backbone of the BES molecule. BES was found, in this study, to represent not only an alternative electron acceptor but also to serve as electron donor, and sole carbon and energy source, supporting growth of a Desulfovibrio sp. obtained in pure culture. This is the first study that reports growth of SRB with BES as electron donor and electron acceptor, showing that the methanogenic inhibitor is a substrate for anaerobic growth.

Tapping the wealth of microbial data in high-throughput metabolic model reconstruction

Genome-scale metabolic models are valuable tools in the metabolic engineering process, based on the ability of these models to integrate diverse sources of data to produce global predictions of organism behavior. At the most basic level, these models require only a genome sequence to construct, and once built, they may be used to predict essential genes, culture conditions, pathway utilization, and the modifications required to enhance a desired organism behavior. In this chapter, we address two key challenges associated with the reconstruction of metabolic models: (a) leveraging existing knowledge of microbiology, biochemistry, and available omics data to produce the best possible model; and (b) applying available tools and data to automate the reconstruction process. We consider these challenges as we progress through the model reconstruction process, beginning with genome assembly, and culminating in the integration of constraints to capture the impact of transcriptional regulation. We divide the reconstruction process into ten distinct steps: (1) genome assembly from sequenced reads; (2) automated structural and functional annotation; (3) phylogenetic tree-based curation of genome annotations; (4) assembly and standardization of biochemistry database; (5) genome-scale metabolic reconstruction; (6) generation of core metabolic model; (7) generation of biomass composition reaction; (8) completion of draft metabolic model; (9) curation of metabolic model; and (10) integration of regulatory constraints. Each of these ten steps is documented in detail.

Study of the retinoic acid regulated genes in the rodent hypothalamus controlling growth and appetite and the potential for the epigenetic control

Dissertação de mestrado em Bioquímica (área de especialização em Biomedicina)

Characterization of microbial populations in a wastewater treatment plant focusing on Staphylococcus aureus and Pseudomonas aeruginosa

Dissertação de mestrado em Bioengenharia