In silico feasibility of novel biodegradation pathways for 1,2,4-trichlorobenzene

Imidacloprid degrading efficiency of Pseudomonas plecoglossicida MBSB-12 isolated from pesticide contaminated tea garden soil of Assam

Long-term use of toxic pesticides in agricultural grounds has led to adverse effects on the environment and human health. Microbe-mediated biodegradation of pollutants is considered an effective strategy for the removal of contaminants in agricultural and environmental sustainability. Imidacloprid, a neonicotinoid class of pesticides, was widely applied insecticide in the control of pests in agricultural fields including the tea gardens of Assam. Here, native bacteria from imidacloprid contaminating tea garden soils were isolated and screened for imidacloprid degradation efficiency under laboratory conditions. Out of the 30 bacterial isolates, 4 were found to tolerate high concentrations of imidacloprid (25,000 ppm), one of which isolate MBSB-12 showed the highest efficiency for imidacloprid tolerance and utilization as the sole carbon source. Morphological, biochemical, and 16 S ribosomal RNA gene sequencing-based characterization revealed the isolate as Pseudomonas plecoglossicida MBSB-12. The isolate reduced 87% of extractable imidacloprid from the treated soil in 90 days compared to the control soil (without bacterial treatment). High-Resolution Mass Spectrometry (HRMS) analysis indicated imidacloprid breakdown to comparatively less harmful products viz., imidacloprid guanidine olefin [m/z = 209.0510 (M + H)⁺], imidacloprid urea [m/z = 212.0502 (M + H)⁺] and a dechlorinated degraded product of imidacloprid with m/z value 175.0900 (M + H)⁺. Further investigation on the molecular machinery of P. plecoglossicida MBSB-12 involved in the degradation of imidacloprid is expected to provide a better understanding of the degradation pathway.

Review and Perspectives of the Use of Alginate as a Polymer Matrix for Microorganisms Applied in Agro-Industry

Alginate is a polysaccharide with the property of forming hydrogels, which is economic production, zero toxicity, and biocompatibility. In the agro-industry, alginate is used as a super absorbent polymer, coating seeds, fruits, and vegetables and as a carrier of bacteria and fungi as plant-growth promoters and biocontrol. The latter has a high impact on agriculture since the implementation of microorganisms in a polymer matrix improves soil quality; plant nutrition, and is functional as a preventive measure for the appearance of phytopathogenic. Additionally, it minimizes losses of foods due to wrong post-harvest handling. In this review, we provide an overview of physicochemical properties of alginate, some methods for preparation and modification of capsules and coatings, to finally describe its application in agro-industry as a matrix of plant-growth-promoting microorganisms, its effectiveness in cultivation and post-harvest, and its effect on the environment, as well as the prospects for future agro-industrial applications.

Biotechnological tools to elucidate the mechanism of pesticide degradation in the environment

Pesticides are widely used in agriculture, households, and industries; however, they have caused severe negative effects on the environment and human health. To clean up pesticide contaminated sites, various technological strategies, i.e. physicochemical and biological, are currently being used throughout the world. Biological approaches have proven to be a viable method for decontaminating pesticide-contaminated soils and water environments. The biological process eliminates contaminants by utilizing microorganisms' catabolic ability. Pesticide degradation rates are influenced by a variety of factors, including the pesticide's structure, concentration, solubility in water, soil type, land use pattern, and microbial activity in the soil. There is currently a knowledge gap in this field of study because researchers are unable to gather collective information on the factors affecting microbial growth, metabolic pathways, optimal conditions for degradation, and genomic, transcriptomic, and proteomic changes caused by pesticide stress on the microbial communities. The use of advanced tools and omics technology in research can bridge the existing gap in our knowledge regarding the bioremediation of pesticides. This review provides new insights on the research gaps and offers potential solutions for pesticide removal from the environment through the use of various microbe-mediated technologies.

In silico analytical toolset for predictive degradation and toxicity of hazardous pollutants in water sources

Different phenolic compounds, including multimeric lignin derivatives in the β-O-4 form, are among the most prevalent compounds in wastewater, often generated from paper industries. Relatively small concentrations of lignin are hazardous to aquatic organisms and can trigger severe environmental hazards. Herein, we present a predictive toolset to insight the induced toxic hazards prediction, and their Lignin peroxidase (LiP)-assisted degradation mechanism of selected multimeric lignin model compounds. T.E.ST and Toxtree toolset were deployed for toxic hazards estimation in different endpoints. To minimize the concerning hazards, we screened multimeric compounds for binding affinity with LiP. The binding affinity was found to be significantly lower than the reference compound. An Extra precision (XP) Glide score of -6.796 kcal/mol was found for dimer (guaiacyl 4-O-5 guaiacyl) complex as lowest compared to reference compound (-4.007 kcal/mol). The active site residues ASP-153, HIP-226, VAL-227, ARG-244, GLU-215, 239, PHE-261 were identified as site-specific key binding AA residues actively involved with corresponding ligands, forming Hydrophobic, H-Bond, π-Stacking, π-π type interactions. The DESMOND-assisted molecular dynamics simulation's (MDS) trajectories of protein-ligand revealed the considerable binding behavior and attained stability and system equilibrium state. Such theoretical and predictive conclusions indicted the feasibility of LiP assisted sustainable mitigation of lignin-based compounds, and such could be used to protect the environment from the potential hazards posed by recognized similar pollutants.

Port Sediments: Problem or Resource? A Review Concerning the Treatment and Decontamination of Port Sediments by Fungi and Bacteria

Contamination of marine sediments by organic and/or inorganic compounds represents one of the most critical problems in marine environments. This issue affects not only biodiversity but also ecosystems, with negative impacts on sea water quality. The scientific community and the European Commission have recently discussed marine environment and ecosystem protection and restoration by sustainable green technologies among the main objectives of their scientific programmes. One of the primary goals of sustainable restoration and remediation of contaminated marine sediments is research regarding new biotechnologies employable in the decontamination of marine sediments, to consider sediments as a resource in many fields such as industry. In this context, microorganisms—in particular, fungi and bacteria—play a central and crucial role as the best tools of sustainable and green remediation processes. This review, carried out in the framework of the Interreg IT-FR Maritime GEREMIA Project, collects and shows the bioremediation and mycoremediation studies carried out on marine sediments contaminated with ecotoxic metals and organic pollutants. This work evidences the potentialities and limiting factors of these biotechnologies and outlines the possible future scenarios of the bioremediation of marine sediments, and also highlights the opportunities of an integrated approach that involves fungi and bacteria together.

A navigation guide of synthetic biology tools for Pseudomonas putida

Pseudomonas putida is a microbial chassis of huge potential for industrial and environmental biotechnology, owing to its remarkable metabolic versatility and ability to sustain difficult redox reactions and operational stresses, among other attractive characteristics. A wealth of genetic and in silico tools have been developed to enable the unravelling of its physiology and improvement of its performance. However, the rise of this microbe as a promising platform for biotechnological applications has resulted in diversification of tools and methods rather than standardization and convergence. As a consequence, multiple tools for the same purpose have been generated, whilst most of them have not been embraced by the scientific community, which has led to compartmentalization and inefficient use of resources. Inspired by this and by the substantial increase in popularity of P. putida, we aim herein to bring together and assess all currently available (wet and dry) synthetic biology tools specific for this microbe, focusing on the last 5 years. We provide information on the principles, functionality, advantages and limitations, with special focus on their use in metabolic engineering. Additionally, we compare the tool portfolio for P. putida with those for other bacterial chassis and discuss potential future directions for tool development. Therefore, this review is intended as a reference guide for experts and new 'users' of this promising chassis.

Biodegradation of the fungicide Pyraclostrobin by bacteria from orange cultivation plots

The pesticides belonging the strobilurin group are among the most common contaminants in the environment. In this work, biodegradation studies of the strobilurin fungicide Pyraclostrobin by bacteria from orange cultivation plots were performed aiming to contribute with the development of a bioremediation method. Experiments were performed in triplicate with validated methods, and optimization was performed by Central Composite Design and Response Surface Methodology. The strains were evaluated in liquid nutrient medium containing 100 mg L^-1 of Pyraclostrobin, and decreased concentrations of 61.5 to 100.5 mg L^-1 were determined after 5 days at 37 °C and 130 rpm, showing the importance of strain selection. When the five most efficient strains (Bacillus sp. CSA-13, Paenibacillus alvei CBMAI2221, Bacillus sp. CBMAI2222, Bacillus safensis CBMAI2220 and Bacillus aryabhattai CBMAI2223) were used in consortia, synergistic and antagonistic effects were observed accordingly to the employed combination of bacteria, resulting in 64.2 ± 3.9 to 95.4 ± 4.9 mg L^-1 residual Pyraclostrobin. In addition, the formation of 1-(4-chlorophenyl)-1H-pyrazol-3-ol was quantified (0.59-0.01 mg L^-1), and a new biodegradation pathway was proposed with 15 identified metabolites. Experiments were also performed in soil under controlled conditions (30 °C, 0-28 days, 100 mg kg^-1 pesticide), and the native microbiome reduced the pesticide concentration to 70.4 ± 2.3 mg L^-1, whereas the inoculation of an efficient bacterial consortium promoted clearly better results, 57.2 ± 3.9 mg L^-1 residual Pyraclostrobin. This suggests that the introduction of these strains in soil in a bioaugmentation process increases decontamination. However, the native microbiome is important for a more efficient bioremediation.

Bacillus aryabhattai FACU: A promising bacterial strain capable of manipulate the glyphosate herbicide residues

Glyphosate is a commonly used organophosphate herbicide that has an adverse impact on humans, mammals and soil microbial ecosystems. The redundant utilize of glyphosate to control weed growth cause the pollution of the soil environment by this chemical. The discharge of glyphosate in the agricultural drainage can also cause serious environmental damage and water pollution problems. Therefore, it is important to develop methods for enhancing glyphosate degradation in the soil through bioremediation. In this study, thirty bacterial isolates were selected from an agro-industrial zone located in Sadat City of Monufia Governorate, Egypt. The isolates were able to grow in LB medium supplemented with 7.2 mg/ml glyphosate. Ten isolates only had the ability to grow in a medium containing different concentrations of glyphosate (50, 100, 150, 200 and 250 mg/ml). The FACU3 bacterial isolate showed the highest CFU in the different concentrations of glyphosate. The FACU3 isolate was Gram-positive, spore-forming and rod-shape bacteria. Based on API 50 CHB/E medium kit, biochemical properties and 16S rRNA gene sequencing, the FACU3 isolate was identified as Bacillus aryabhattai. Different bioinformatics tools, including multiple sequence alignment (MSA), basic local alignment search tool (BLAST) and primer alignment, were used to design specific primers for goxB gene amplification and isolation. The goxB gene encodes FAD-dependent glyphosate oxidase enzyme that responsible for biodegradation process. The selected primers were successfully used to amplify the goxB gene from Bacillus aryabhattai FACU3. The results indicated that the Bacillus aryabhattai FACU3 can be utilized in glyphosate-contaminated environments for bioremediation. According to our knowledge, this is the first time to isolate of FAD-dependent glyphosate oxidase (goxB) gene from Bacillus aryabhattai.

Microbial biofilm ecology, in silico study of quorum sensing receptor-ligand interactions and biofilm mediated bioremediation

Biofilms are structured microbial communities of single or multiple populations in which microbial cells adhere to a surface and get embedded in extracellular polymeric substances (EPS). This review attempts to explain biofilm architecture, development phases, and forces that drive bacteria to promote biofilm mode of growth. Bacterial chemical communication, also known as Quorum sensing (QS), which involves the production, detection, and response to small molecules called autoinducers, is highlighted. The review also provides a brief outline of interspecies and intraspecies cell-cell communication. Additionally, we have performed docking studies using Discovery Studio 4.0, which has enabled our understanding of the prominent interactions between autoinducers and their receptors in different bacterial species while also scoring their interaction energies. Receptors, such as LuxN (Phosphoreceiver domain and RecA domain), LuxP, and LuxR, interacted with their ligands (AI-1, AI-2, and AHL) with a CDocker interaction energy of - 31.6083 kcal/mole; - 34.5821 kcal/mole, - 48.2226 kcal/mole and - 41.5885 kcal/mole, respectively. Since biofilms are ideal for the remediation of contaminants due to their high microbial biomass and their potential to immobilize pollutants, this article also provides an overview of biofilm-mediated bioremediation.

Wheat phyllosphere yeasts degrade propiconazole

BACKGROUND

Yeasts, which are ubiquitous in agroecosystems, are known to degrade various xenobiotics. The aim of this study was to analyze the effect of fungicides on the abundance of natural yeast communities colonizing winter wheat leaves, to evaluate the sensitivity of yeast isolates to fungicides in vivo, and to select yeasts that degrade propiconazole.

RESULTS

Fungicides applied during the growing season generally did not affect the counts of endophytic yeasts colonizing wheat leaves. Propiconazole and a commercial mixture of flusilazole and carbendazim decreased the counts of epiphytic yeasts, but the size of the yeast community was restored after 10 days. Epoxiconazole and a commercial mixture of fluoxastrobin and prothioconazole clearly stimulated epiphyte growth. The predominant species isolated from leaves were Aureobasidium pullulans and Rhodotorula glutinis. In the disk diffusion test, 14 out of 75 yeast isolates were not sensitive to any of the tested fungicides. After 48 h of incubation in an aqueous solution of propiconazole, the Rhodotorula glutinis Rg 55 isolate degraded the fungicide in 75%. Isolates Rh. glutinis Rg 92 and Rg 55 minimized the phytotoxic effects of propiconazole under greenhouse conditions. The first isolate contributed to an increase in the dry matter content of wheat seedlings, whereas the other reduced the severity of chlorosis.

CONCLUSION

Not sensitivity of many yeast colonizing wheat leaves on the fungicides and the potential of isolate Rhodotorula glutinis Rg 55 to degrade of propiconazole was established. Yeast may partially eliminate the ecologically negative effect of fungicides.

PestLCI 2.0 sensitivity to soil variations for the evaluation of pesticide distribution in Life Cycle Assessment studies

Pesticides are commonly applied in conventional agricultural systems, but they can lead to serious environmental contamination. The calculation of on-field pesticide emissions in Life Cycle Assessment (LCA) studies is challenging, because of the difficulty in the calculation of the fate of pesticides and, therefore, several literature approaches based on different dispersion models have been developed. PestLCI 2.0 model can provide simultaneous assessment of the emission fractions of a pesticide to air, surface water and groundwater based on many parameters. The goal of this study is to exploit the extent of PestLCI 2.0 sensitivity to soil variations, with the ultimate goal of increasing the robustness of the modelling of pesticide emissions in LCA studies. The model was applied to maize cultivation in an experimental farm in Northern Italy, considering three tests, which evaluated the distribution of pesticides among environmental compartments obtained considering different soil types. Results show that small variations in soil characteristics lead to great variation of PestLCI 2.0, with a significance that depends on the type of environmental compartment. The compartment most affected by soil variations was groundwater, whereas surface waters were dominated by meteorological conditions, pesticides' physical and chemical properties and wind drift, which are independent from soil characteristics. Therefore, the use of specific soil data in PestLCI 2.0 results in the availability of a comprehensive set of emission data in the different compartments, which represents a relevant input for the inventory phase of LCA studies and can increase their robustness. Nevertheless, PestLCI 2.0 requires a great effort for the data collection and a specific expertise in soil science for interpreting the results. Moreover, characterization factors for pesticide groundwater emissions should be developed, in order to exploit these detailed results in the impact assessment phase, Finally, the study provides further insights into future improvement of PestLCI 2.0.

Perspectives of Microbial Inoculation for Sustainable Development and Environmental Management

How to sustainably feed a growing global population is a question still without an answer. Particularly farmers, to increase production, tend to apply more fertilizers and pesticides, a trend especially predominant in developing countries. Another challenge is that industrialization and other human activities produce pollutants, which accumulate in soils or aquatic environments, contaminating them. Not only is human well-being at risk, but also environmental health. Currently, recycling, land-filling, incineration and pyrolysis are being used to reduce the concentration of toxic pollutants from contaminated sites, but too have adverse effects on the environment, producing even more resistant and highly toxic intermediate compounds. Moreover, these methods are expensive, and are difficult to execute for soil, water, and air decontamination. Alternatively, green technologies are currently being developed to degrade toxic pollutants. This review provides an overview of current research on microbial inoculation as a way to either replace or reduce the use of agrochemicals and clean environments heavily affected by pollution. Microorganism-based inoculants that enhance nutrient uptake, promote crop growth, or protect plants from pests and diseases can replace agrochemicals in food production. Several examples of how biofertilizers and biopesticides enhance crop production are discussed. Plant roots can be colonized by a variety of favorable species and genera that promote plant growth. Microbial interventions can also be used to clean contaminated sites from accumulated pesticides, heavy metals, polyaromatic hydrocarbons, and other industrial effluents. The potential of and key processes used by microorganisms for sustainable development and environmental management are discussed in this review, followed by their future prospects.

Molecular Evolutionary Analysis of the HCRTR Gene Family in Vertebrates

Hypocretin system is composed of hypocretins (hcrts) and their receptors (hcrtrs), which has multiple vital functions. Hypocretins work via hypocretin receptors and it is reported that functional differentiation occurred in hcrtrs. It is necessary to figure out the evolution process of hypocretin receptors. In our study, we adopt a comprehensive approach and various bioinformatics tools to analyse the evolution process of HCRTR gene family. It turns out that the second round of whole genome duplication in early vertebrate ancestry and the independent round in fish ancestry may contribute to the diversity of HCRTR gene family. HCRTR1 of fishes and mammals are not the same receptor, which means that there are three members in the family. HCRTR2 is proved to be the most ancient one in HCRTR gene family. After duplication events, the structure of HCRTR1 diverged from HCRTR2 owing to relaxed selective pressure. Negative selection is the predominant evolutionary force acting on the HCRTR gene family but HCRTR1 of mammals is found to be subjected to positive selection. Our study gains insight into the molecular evolution process of HCRTR gene family, which contributes to the further study of the system.

Review of NAD(P)H-dependent oxidoreductases: Properties, engineering and application

NAD(P)H-dependent oxidoreductases catalyze the reduction or oxidation of a substrate coupled to the oxidation or reduction, respectively, of a nicotinamide adenine dinucleotide cofactor NAD(P)H or NAD(P)⁺. NAD(P)H-dependent oxidoreductases catalyze a large variety of reactions and play a pivotal role in many central metabolic pathways. Due to the high activity, regiospecificity and stereospecificity with which they catalyze redox reactions, they have been used as key components in a wide range of applications, including substrate utilization, the synthesis of chemicals, biodegradation and detoxification. There is great interest in tailoring NAD(P)H-dependent oxidoreductases to make them more suitable for particular applications. Here, we review the main properties and classes of NAD(P)H-dependent oxidoreductases, the types of reactions they catalyze, some of the main protein engineering techniques used to modify their properties and some interesting examples of their modification and application.

Isolation and Characterization of 2,4-D Butyl Ester Degrading Acinetobacter sp. ZX02 from a Chinese Ginger Cultivated Soil

Strain ZX02 was isolated from Chinese ginger cultivated soil contaminated with various pesticides, which could utilize 2,4-dichlorophenoxyacetic acid butyl ester (2,4-D butyl ester) as the sole carbon source. On the basis of the sequence analysis of 16S rRNA gene as well as the morphological, biochemical, and physiological characteristics of strain ZX02, the organism belonged to Gram-negative bacterium and was identified as Acinetobacter sp. ZX02. The strain ZX02 showed a remarkable performance in 2,4-D butyl ester degradation (100% removal in <96 h) in pure culture. Strain ZX02 was sensitive to tetracycline and resistant to amoxicillin and chloramphenicol in an antibiotic sensitivity test. The curing study indicates that the gene for degradation of 2,4-D butyl ester was encoded on a single plasmid of 23 kb. The gene encoding resistance to polymixin B sulfate was also located on this plasmid. On the basis of its greater biodegradation activity, this bacterium is a potential candidate as a bioremediation agent in soils contaminated with 2,4-D butyl ester.

Integrated Framework for Identifying Toxic Transformation Products in Complex Environmental Mixtures

Complex environmental mixtures consist of hundreds to thousands of unknown and unregulated organic compounds that may have toxicological relevance, including transformation products (TPs) of anthropogenic organic pollutants. Non-targeted analysis and suspect screening analysis offer analytical approaches for potentially identifying these toxic transformation products. However, additional tools and strategies are needed in order to reduce the number of chemicals of interest and focus analytical efforts on chemicals that may pose risks to humans and the environment. This brief review highlights recent developments in this field and suggests an integrated framework that incorporates complementary instrumental techniques, computational chemistry, and toxicity analysis, for prioritizing and identifying toxic TPs in the environment.

Biomolecular Network-Based Synergistic Drug Combination Discovery

Drug combination is a powerful and promising approach for complex disease therapy such as cancer and cardiovascular disease. However, the number of synergistic drug combinations approved by the Food and Drug Administration is very small. To bridge the gap between urgent need and low yield, researchers have constructed various models to identify synergistic drug combinations. Among these models, biomolecular network-based model is outstanding because of its ability to reflect and illustrate the relationships among drugs, disease-related genes, therapeutic targets, and disease-specific signaling pathways as a system. In this review, we analyzed and classified models for synergistic drug combination prediction in recent decade according to their respective algorithms. Besides, we collected useful resources including databases and analysis tools for synergistic drug combination prediction. It should provide a quick resource for computational biologists who work with network medicine or synergistic drug combination designing.

ATLAS of Biochemistry: A Repository of All Possible Biochemical Reactions for Synthetic Biology and Metabolic Engineering Studies

Because the complexity of metabolism cannot be intuitively understood or analyzed, computational methods are indispensable for studying biochemistry and deepening our understanding of cellular metabolism to promote new discoveries. We used the computational framework BNICE.ch along with cheminformatic tools to assemble the whole theoretical reactome from the known metabolome through expansion of the known biochemistry presented in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. We constructed the ATLAS of Biochemistry, a database of all theoretical biochemical reactions based on known biochemical principles and compounds. ATLAS includes more than 130 000 hypothetical enzymatic reactions that connect two or more KEGG metabolites through novel enzymatic reactions that have never been reported to occur in living organisms. Moreover, ATLAS reactions integrate 42% of KEGG metabolites that are not currently present in any KEGG reaction into one or more novel enzymatic reactions. The generated repository of information is organized in a Web-based database ( http://lcsb-databases.epfl.ch/atlas/ ) that allows the user to search for all possible routes from any substrate compound to any product. The resulting pathways involve known and novel enzymatic steps that may indicate unidentified enzymatic activities and provide potential targets for protein engineering. Our approach of introducing novel biochemistry into pathway design and associated databases will be important for synthetic biology and metabolic engineering.

Application of synthetic biology for production of chemicals in yeast Saccharomyces cerevisiae

Synthetic biology and metabolic engineering enable generation of novel cell factories that efficiently convert renewable feedstocks into biofuels, bulk, and fine chemicals, thus creating the basis for biosustainable economy independent on fossil resources. While over a hundred proof-of-concept chemicals have been made in yeast, only a very small fraction of those has reached commercial-scale production so far. The limiting factor is the high research cost associated with the development of a robust cell factory that can produce the desired chemical at high titer, rate, and yield. Synthetic biology has the potential to bring down this cost by improving our ability to predictably engineer biological systems. This review highlights synthetic biology applications for design, assembly, and optimization of non-native biochemical pathways in baker's yeast Saccharomyces cerevisiae We describe computational tools for the prediction of biochemical pathways, molecular biology methods for assembly of DNA parts into pathways, and for introducing the pathways into the host, and finally approaches for optimizing performance of the introduced pathways.

A review of metabolic and enzymatic engineering strategies for designing and optimizing performance of microbial cell factories

Microbial cell factories (MCFs) are of considerable interest to convert low value renewable substrates to biofuels and high value chemicals. This review highlights the progress of computational models for the rational design of an MCF to produce a target bio-commodity. In particular, the rational design of an MCF involves: (i) product selection, (ii) de novo biosynthetic pathway identification (i.e., rational, heterologous, or artificial), (iii) MCF chassis selection, (iv) enzyme engineering of promiscuity to enable the formation of new products, and (v) metabolic engineering to ensure optimal use of the pathway by the MCF host. Computational tools such as (i) de novo biosynthetic pathway builders, (ii) docking, (iii) molecular dynamics (MD) and steered MD (SMD), and (iv) genome-scale metabolic flux modeling all play critical roles in the rational design of an MCF. Genome-scale metabolic flux models are of considerable use to the design process since they can reveal metabolic capabilities of MCF hosts. These can be used for host selection as well as optimizing precursors and cofactors of artificial de novo biosynthetic pathways. In addition, recent advances in genome-scale modeling have enabled the derivation of metabolic engineering strategies, which can be implemented using the genomic tools reviewed here as well.

A hadoop-based method to predict potential effective drug combination

Combination drugs that impact multiple targets simultaneously are promising candidates for combating complex diseases due to their improved efficacy and reduced side effects. However, exhaustive screening of all possible drug combinations is extremely time-consuming and impractical. Here, we present a novel Hadoop-based approach to predict drug combinations by taking advantage of the MapReduce programming model, which leads to an improvement of scalability of the prediction algorithm. By integrating the gene expression data of multiple drugs, we constructed data preprocessing and the support vector machines and naïve Bayesian classifiers on Hadoop for prediction of drug combinations. The experimental results suggest that our Hadoop-based model achieves much higher efficiency in the big data processing steps with satisfactory performance. We believed that our proposed approach can help accelerate the prediction of potential effective drugs with the increasing of the combination number at an exponential rate in future. The source code and datasets are available upon request.

Computational evaluation of factors governing catalytic 2-keto acid decarboxylation

Recent advances in computational approaches for creating pathways for novel biochemical reactions has motivated the development of approaches for identifying enzyme-substrate pairs that are attractive candidates for effecting catalysis. We present an improved structural-based strategy to probe and study enzyme-substrate binding based on binding geometry, energy, and molecule characteristics, which allows for in silico screening of structural features that imbue higher catalytic potential with specific substrates. The strategy is demonstrated using 2-keto acid decarboxylation with various pairs of 2-keto acids and enzymes. We show that this approach fitted experimental values for a wide range of 2-keto acid decarboxylases for different 2-keto acid substrates. In addition, we show that the structure-based methods can be used to select specific enzymes that may be promising candidates to catalyze decarboxylation of certain 2-keto acids. The key features and principles of the candidate enzymes evaluated by the strategy can be used to design novel biosynthesis pathways, to guide enzymatic mutation or to guide biomimetic catalyst design.

Computational tools for modeling xenometabolism of the human gut microbiota

The gut microbiota is increasingly being recognized as a key site of metabolism for drugs and other xenobiotic compounds that are relevant to human health. The molecular complexity of the gut microbiota revealed by recent metagenomics studies has highlighted the need as well as the challenges for system-level modeling of xenobiotic metabolism in the gut. Here, we outline the possible pathways through which the gut microbiota can modify xenobiotics and review the available computational tools towards modeling complex xenometabolic processes. We put these diverse computational tools and relevant experimental findings into a unified perspective towards building holistic models of xenobiotic metabolism in the gut.

Prediction of effective drug combinations by chemical interaction, protein interaction and target enrichment of KEGG pathways

Drug combinatorial therapy could be more effective in treating some complex diseases than single agents due to better efficacy and reduced side effects. Although some drug combinations are being used, their underlying molecular mechanisms are still poorly understood. Therefore, it is of great interest to deduce a novel drug combination by their molecular mechanisms in a robust and rigorous way. This paper attempts to predict effective drug combinations by a combined consideration of: (1) chemical interaction between drugs, (2) protein interactions between drugs' targets, and (3) target enrichment of KEGG pathways. A benchmark dataset was constructed, consisting of 121 confirmed effective combinations and 605 random combinations. Each drug combination was represented by 465 features derived from the aforementioned three properties. Some feature selection techniques, including Minimum Redundancy Maximum Relevance and Incremental Feature Selection, were adopted to extract the key features. Random forest model was built with its performance evaluated by 5-fold cross-validation. As a result, 55 key features providing the best prediction result were selected. These important features may help to gain insights into the mechanisms of drug combinations, and the proposed prediction model could become a useful tool for screening possible drug combinations.

Computational tools for guided discovery and engineering of metabolic pathways

With a high demand for increasingly diverse chemicals, as well as sustainable synthesis for many existing chemicals, the chemical industry is increasingly looking to biosynthesis. The majority of biosynthesis examples of useful chemicals are either native metabolites made by an organism or the heterologous expression of known metabolic pathways into a more amenable host. For chemicals that no known biosynthetic route exists, engineers are increasingly relying on automated computational algorithms, as described here, to identify potential metabolic pathways. In this chapter, we review a broad range of approaches to predict novel metabolic pathways. Broadly, these can rely on biochemical databases to assemble known reactions into a new pathway or rely on generalized biochemical rules to predict unobserved enzymatic reactions that are likely feasible. Many programs are freely available and immediately useable by non-computationally experienced scientists.

A systems biology approach to uncovering pharmacological synergy in herbal medicines with applications to cardiovascular disease

Background. Clinical trials reveal that multiherb prescriptions of herbal medicine often exhibit pharmacological and therapeutic superiority in comparison to isolated single constituents. However, the synergistic mechanisms underlying this remain elusive. To address this question, a novel systems biology model integrating oral bioavailability and drug-likeness screening, target identification, and network pharmacology method has been constructed and applied to four clinically widely used herbs Radix Astragali Mongolici, Radix Puerariae Lobatae, Radix Ophiopogonis Japonici, and Radix Salviae Miltiorrhiza which exert synergistic effects of combined treatment of cardiovascular disease (CVD). Results. The results show that the structural properties of molecules in four herbs have substantial differences, and each herb can interact with significant target proteins related to CVD. Moreover, the bioactive ingredients from different herbs potentially act on the same molecular target (multiple-drug-one-target) and/or the functionally diverse targets but with potentially clinically relevant associations (multiple-drug-multiple-target-one-disease). From a molecular/systematic level, this explains why the herbs within a concoction could mutually enhance pharmacological synergy on a disease. Conclusions. The present work provides a new strategy not only for the understanding of pharmacological synergy in herbal medicine, but also for the rational discovery of potent drug/herb combinations that are individually subtherapeutic.

Mathematical optimization applications in metabolic networks

Genome-scale metabolic models are increasingly becoming available for a variety of microorganisms. This has spurred the development of a wide array of computational tools, and in particular, mathematical optimization approaches, to assist in fundamental metabolic network analyses and redesign efforts. This review highlights a number of optimization-based frameworks developed towards addressing challenges in the analysis and engineering of metabolic networks. In particular, three major types of studies are covered here including exploring model predictions, correction and improvement of models of metabolism, and redesign of metabolic networks for the targeted overproduction of a desired compound. Overall, the methods reviewed in this paper highlight the diversity of queries, breadth of questions and complexity of redesigns that are amenable to mathematical optimization strategies.

Use of an uncertainty analysis for genome-scale models as a prediction tool for microbial growth processes in subsurface environments

The integration of genome-scale, constraint-based models of microbial cell function into simulations of contaminant transport and fate in complex groundwater systems is a promising approach to help characterize the metabolic activities of microorganisms in natural environments. In constraint-based modeling, the specific uptake flux rates of external metabolites are usually determined by Michaelis-Menten kinetic theory. However, extensive data sets based on experimentally measured values are not always available. In this study, a genome-scale model of Pseudomonas putida was used to study the key issue of uncertainty arising from the parametrization of the influx of two growth-limiting substrates: oxygen and toluene. The results showed that simulated growth rates are highly sensitive to substrate affinity constants and that uncertainties in specific substrate uptake rates have a significant influence on the variability of simulated microbial growth. Michaelis-Menten kinetic theory does not, therefore, seem to be appropriate for descriptions of substrate uptake processes in the genome-scale model of P. putida. Microbial growth rates of P. putida in subsurface environments can only be accurately predicted if the processes of complex substrate transport and microbial uptake regulation are sufficiently understood in natural environments and if data-driven uptake flux constraints can be applied.

Computational tools for the synthetic design of biochemical pathways

As the field of synthetic biology is developing, the prospects for de novo design of biosynthetic pathways are becoming more and more realistic. Hence, there is an increasing need for computational tools that can support these efforts. A range of algorithms has been developed that can be used to identify all possible metabolic pathways and their corresponding enzymatic parts. These can then be ranked according to various properties and modelled in an organism-specific context. Finally, design software can aid the biologist in the integration of a selected pathway into smartly regulated transcriptional units. Here, we review key existing tools and offer suggestions for how informatics can help to shape the future of synthetic microbiology.

Flux balance analysis for ethylene formation in genetically engineered Saccharomyces cerevisiae

Biosynthesis of ethylene (ethene) is mainly performed by plants and some bacteria and fungi, via two distinct metabolic routes. Plants use two steps, starting with S-adenosylmethionine, while the ethylene-forming microbes perform an oxygen dependent reaction using 2-oxoglutarate and arginine. Introduction of these systems into Saccharomyces cerevisiae was studied in silico. The reactions were added to a metabolic network of yeast and flux over the two networks was optimised for maximal ethylene formation. The maximal ethylene yields obtained for the two systems were similar in the range of 7-8 mol ethylene/10 mol glucose. The microbial metabolic network was used for testing different strategies to increase the ethylene formation. It was suggested that supplementation of exogenous proline, using a solely NAD-coupled glutamate dehydrogenase, and using glutamate as the nitrogen source, could increase the ethylene formation. Comparison of these in silico results with published experimental data for yeast expressing the microbial system confirmed an increased ethylene formation when changing nitrogen source from ammonium to glutamate. The theoretical analysis methods indicated a much higher maximal yield per glucose for ethylene than was experimentally observed. However, such high ethylene yields could only be obtained with a concomitant very high respiration (per glucose). Accordingly, when ethylene production was optimised under the additional constraint of restricted respiratory capacity (i.e. limited to experimentally measured values) the theoretical maximal ethylene yield was much lower at 0.2/10 mol glucose, and closer to the experimentally observed values.