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Selective drugs with a relatively narrow spectrum can reduce the side effects of treatments compared to broad-spectrum antibiotics by specifically targeting the pathogens responsible for infection. Furthermore, combating an infectious pathogen, especially a drug-resistant microorganism, is more efficient by attacking multiple targets. Here, we combined synthetic lethality with selective drug targeting to identify multi-target and organism-specific potential drug candidates by systematically analyzing the genome-scale metabolic models of six different microorganisms. By considering microorganisms as targeted or conserved in groups ranging from one to six members, we designed 665 individual case studies. For each case, we identified single essential reactions as well as double, triple, and quadruple synthetic lethal reaction sets that are lethal for targeted microorganisms and neutral for conserved ones. As expected, the number of obtained solutions for each case depends on the genomic similarity between the studied microorganisms. Mapping the identified potential drug targets to their corresponding pathways highlighted the importance of key subsystems such as cell envelope biosynthesis, glycerophospholipid metabolism, membrane lipid metabolism, and the nucleotide salvage pathway. To assist in the validation and further investigation of our proposed potential drug targets, we introduced two sets of targets that can theoretically address a substantial portion of the 665 cases. We expect that the obtained solutions provide valuable insights into designing narrow-spectrum drugs that selectively cause system-wide damage only to the target microorganisms.

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Overproduction of desired native or nonnative biochemical(s) in (micro)organisms can be achieved through metabolic engineering. Appropriate rewiring of cell metabolism is performed by making rational changes such as insertion, up-/down-regulation and knockout of genes and consequently metabolic reactions. Finding appropriate targets (including proper sets of reactions to be knocked out) for metabolic engineering to design optimal production strains has been the goal of a number of computational algorithms. We developed FastKnock, an efficient next-generation algorithm for identifying all possible knockout strategies (with a predefined maximum number of reaction deletions) for the growth-coupled overproduction of biochemical(s) of interest. We achieve this by developing a special depth-first traversal algorithm that allows us to prune the search space significantly. This leads to a drastic reduction in execution time. We evaluate the performance of the FastKnock algorithm using various Escherichia coli genome-scale metabolic models in different conditions (minimal and rich mediums) for the overproduction of a number of desired metabolites. FastKnock efficiently prunes the search space to less than 0.2% for quadruple- and 0.02% for quintuple-reaction knockouts. Compared to the classic approaches such as OptKnock and the state-of-the-art techniques such as MCSEnumerator methods, FastKnock found many more beneficial and important practical solutions. The availability of all the solutions provides the opportunity to further characterize, rank and select the most appropriate intervention strategy based on any desired evaluation index. Our implementation of the FastKnock method in Python is publicly available at https://github.com/leilahsn/FastKnock .

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Overproduction of desired native or nonnative biochemical(s) in (micro)organisms can be achieved through metabolic engineering. Appropriate rewiring of cell metabolism is performed making rational changes such as insertion, up-/down-regulation and knockout of genes and consequently metabolic reactions. Finding appropriate targets (including proper sets of reactions to be knocked out) for metabolic engineering to design optimal production strains has been the goal of a number of computational algorithms. We developed FastKnock, an efficient next-generation algorithm for identifying all possible knockout strategies for the growth-coupled overproduction of biochemical(s) of interest. We achieve this by developing a special depth-first traversal algorithm that allows us to prune the search space significantly. This leads to a drastic reduction in execution time. We evaluate the performance of the FastKnock algorithm using three Escherichia coli genome-scale metabolic models in different conditions (minimal and rich mediums) for the overproduction of a number of desired metabolites. FastKnock efficiently prunes the search space to less than 0.2% for quadruple and 0.02% for quintuple-reaction knockouts. Compared to the classic approaches such as OptKnock and the state-of-the-art techniques such as MCSEnumerator methods, FastKnock found many more useful and important practical solutions. The availability of all the solutions provides the opportunity to further characterize and select the most appropriate intervention strategy based on any desired evaluation index. Our implementation of the FastKnock method in Python is publicly available at https://github.com/leilahsn/FastKnock.

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The multidrug resistance of numerous pathogenic microorganisms is a serious challenge that raises global healthcare concerns. Multi-target medications and combinatorial therapeutics are much more effective than single-target drugs due to their synergistic impact on the systematic activities of microorganisms. Designing efficient combinatorial therapeutics can benefit from identification of synthetic lethals (SLs). An SL is a set of non-essential targets (i.e., reactions or genes) that prevent the proliferation of a microorganism when they are "knocked out" simultaneously. To facilitate the identification of SLs, we introduce Rapid-SL, a new multimodal implementation of the Fast-SL method, using the depth-first search algorithm. The advantages of Rapid-SL over Fast-SL include: (a) the enumeration of all SLs that have an arbitrary cardinality, (b) a shorter runtime due to search space reduction, (c) embarrassingly parallel computations, and (d) the targeted identification of SLs. Targeted identification is important because the enumeration of higher order SLs demands the examination of too many reaction sets. Accordingly, we present specific applications of Rapid-SL for the efficient targeted identification of SLs. In particular, we found up to 67% of all quadruple SLs by investigating about 1% of the search space. Furthermore, 307 sextuples, 476 septuples, and over 9000 octuples are found for Escherichia coli genome-scale model, iAF1260.

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The aim of the present study was to investigate the effect of ultrasonic treatment (25 kHz) on biosurfactant production by Lactobacillus plantarum ATCC 8014. The impacts of the ultrasonication (with a frequency of 25 kHz and power of 7.4 W for 30 min time duration) were examined at different stages of the fermentation process to obtain the optimum stimulation instant(s). The optimum scenario was found to be one-time sonication at the 12th hour of fermentation which can be beneficial from an economic point of view (compared with multiple applications of sonication). Ultrasonic treatment at this time resulted in enhancement of the productivities of biomass (4.5 g/L) and biosurfactant (2.01 g/L) which was almost 1.3 times higher than those of the non-sonicated control samples. According to our results, it was clearly observed that glucose consumption increased after ultrasonic treatment representing the improved substrate uptake and progression of the cellular metabolism. Furthermore, the transmission electron microscopic images immediately after sonication clarified the pore formation on the cell surfaces. The results also indicated the enhancement of plasma membrane permeability of the sonicated cells. Fourier transform infrared spectroscopy and scanning electron microscopy coupled with energy dispersive x-ray spectroscopy analyses also disclosed respectively no structural differences before and after ultrasonic exposure in the produced biosurfactant and bacterial cell membrane. The biosurfactant was characterized to be a mixture of carbohydrate (28%), protein (23%) and lipid (specified by gas chromatography-mass spectrometry) known as glycolipoprotein. The sustainable critical micelle concentration and the stability of the synthesized biosurfactant can feature its potential applicability in various processes in the food and pharmaceutical industries.

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An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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The present study focused on producing and characterizing a type of biosurfactant (BS) derived from lactic acid bacteria (LAB) and its potential applications in pharmaceutical and food industries due to the preference of employing nonpathogenic organisms in bioprocesses. To this aim, several screening approaches were applied to identify an efficient BS-producing strain from a set of LAB, and Pediococcus dextrinicus SHU1593 was selected as the most operative one. The BS produced by P. dextrinicus was isolated and structurally characterized as a lipoprotein with an approximately equal ratio of lipids (~52% (w/w)) and proteins (47% (w/w)). It reduced the surface tension (ST) of phosphate-buffered saline (PBS) from 72.80 ± 0.10 to 39.01 ± 0.32 mN/m. The results also indicated the potential of developing low-cost strategies aimed at the production of efficient LAB-derived BSs which are structurally and quantitatively similar to the ones obtained from conventional media. Finally, given the physical and functional characterization (i.e. critical micelle concentration (CMC), emulsification index (%E24), stability, as well as antimicrobial and anti-adhesive activities) of the BS produced in the present study, it can be introduced as a promising candidate to be employed in plenty of areas in pharmaceutical and food industries.

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Rhamnolipids (RLs) produced by the opportunistic human pathogen Pseudomonas aeruginosa are considered as potential candidates for the next generation of surfactants. Large-scale production of RLs depends on progress in strain engineering, medium design, operating strategies, and purification procedures. In this work, the rhlAB genes extracted from a mono_RLs_producing strain of P. aeruginosa (ATCC 9027) were introduced to an appropriate safety host Pseudomonas putida KT2440. The capability of the recombinant strain was evaluated in various media. As a prerequisite for optimal medium design, a set of 32 experiments was performed in two steps for screening a number of macro-nutritional compounds. In the experiments, a two-level fractional factorial design resolution IV was followed by a two-level full factorial one. By means of this approach, it was observed that glycerol, yeast extract, and peptone have significant positive influence on recombinant RLs production while the yeast extract/peptone two-factor and glycerol/yeast extract/peptone three-factor interactions have considerable negative effects. A wide range of variation from 0 to 570 mg/l was obtained for RLs production during the screening experiments indicating the importance of medium optimization. The results point out the opportunity for possible higher yields of RLs through further screening, mixture/combined mixture designs, and high-cell-density cultivations.

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Lactoferrin (Lf) is an iron-binding multi-functional glycoprotein which has numerous physiological functions such as iron transportation, anti-microbial activity and immune response. In this study, different in silico approaches were exploited to investigate Lf protein properties in a number of mammalian species. Results showed that the iron-binding site, DNA and RNA-binding sites, signal peptides and transferrin motifs in the Lf structure were highly conserved. Examined sequences showed three conserved motifs which were repeated twice in the Lf structure, demonstrating ancient duplication events in its gene. Also, results suggest that the functional domains in mammalian Lf proteins are Zinc finger, Tubulin/FtsZ, GTPase, α/ß hydrolase and Zinc knuckle. The potential site for nucleic acid binding and the major DNA and RNA- binding sites in this protein were found in the lactoferricin (Lfc) fragment. Due to its high positive charge, Lf is able to bind a large number of compounds. Our analysis also revealed that the interactions between Lf and ITLN1, LYZ, CSN2, and CD14 proteins played an important role in the protective activities of Lf. Analysis for the prediction of secondary structures indicated that high amounts of α-helix, ß-strand and ß-sheet were present in Lf. The high degree of conservation among mammalian Lf proteins indicates that there is a close relationship between these proteins, reflecting their important role.