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Abstract 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.

In today's industrial landscape, energy management, process modification, and reduction of atmospheric concentrations of pollutants and safety risks have become paramount. This focus is driven by the need to address environmental concerns, economic efficiency, and the global energy and climate change crisis. In gas refineries, incinerators are widely used to convert deadly and environmentally polluting acid gases into less hazardous gases. Therefore, improving incinerator performance can significantly impact environmental, economic, and energy aspects. According to the results of an energy management study at the domestic gas processing plant, the acid gas incineration unit was identified as a significant energy use. Therefore, based on the effects of the performance of this incinerator from environmental and energy points of view, the mentioned unit was prioritized for modification in this work. For this purpose, incinerator performance was assessed using Promax simulation, and Hazard and Operability (HAZOP) analysis was employed to identify potential hazards. The simulations revealed that acid gas residence time was 0.81s, longer than the 0.6s initial design with the damper in place. This suggests damper removal is feasible. Removing the damper reduces residence time and lowers incinerator temperature, especially during startup. Therefore, temperature was considered as the keyword in the HAZOP study, and a number of recommendations were proposed to eliminate or mitigate the risks of system modification. Furthermore, the assistance of results obtained from energy management based on ISO 50001:2018 standards confirm improvements in energy efficiency and fuel consumption, which have positive economic and environmental impacts. Moreover, the study employs a Life Cycle Assessment (LCA) approach using SimaPro Software 9.5.0.1 and the CML-baseline method (Centrum voor Milieukunde Leiden) for environmental impact assessment. The results reveal that, across ten environmental impact categories, the modified project exhibits significantly reduced environmental impacts compared to its original state.

Abstract 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 .

Abstract 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.

Abstract 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.

Abstract Purpose Wheat straw is an agricultural waste which can be used as a cost effective animal feed. However, high hemicellulose and phytic acid content in wheat straw prevents it as a primary feed choice. Utilization of wheat straw in solid-state fermentation may result in wheat straw valorization and enzyme production. In this study, phytase production in solid-state fermentation of wheat straw using Aspergillus ficuum and valorization of wheat straw were evaluated. Methods A two-step experimental design procedure was employed for screening and optimization of influencing factors on phytase production. Effects of different nutritional and environmental factors were investigated by one factor at the time method (OFATM). To reach higher amounts of phytase, response surface methodology (RSM) was employed to optimize phytase production as a function of three of the most effective factors. Results Optimization of the significant parameters resulted in an increase in the phytase activity from 0.74 ± 0.12 to a maximum of 16.46 ± 0.56 Units per gram dry substrate (U gds−1). The high degree of the fungal phytase activity on wheat straw resulted in the decrease in phytic acid content by 57.4%, as compared to the untreated sample. Scanning electron microscopy (SEM) and FTIR structural analysis showed intensive fungal growth on wheat straw, and partial removal of hemicelluloses, lignin and phytic acid. Conclusion The study demonstrated the feasibility of wheat straw utilization in solid-state fermentation using Aspergillus ficuum toward the production of phytase and valorization of wheat straw as an animal feed.

Surface tension as an important characteristic in much scientific and technological research is a function of liquid materials’ physical properties. Thus, it is desirable to have an accurate correlation between effective parameters and surface tension. This study investigates the applicability of artificial neural networks as an efficient tool for the prediction of pure organic compounds’ surface tensions for a wide range of temperatures. The experimental data gathered for training and verification of the network are related to a wide variety of materials such as alkanes, alkenes, aromatics, and sulfur, chlorine, fluorine, and nitrogen containing compounds. The most accurate network among several constructed configurations has one hidden layer with 20 neurons. The average absolute deviation percentage obtained for 1048 data points related to 82 compounds is 1.57%. The results demonstrate that the multilayer perceptron network could be an appropriate lookup table for the determination of surface tension as a function of physical properties. [ABSTRACT FROM AUTHOR]