Set Of Metabolic Pathways Research Articles

Analysis of the probiotic activity of Bacillus velezensis RT-26 strain isolated from reindeer rumen by whole-genome sequencing

The paper analyses the properties of Bacillus velezensis RT-26, a probiotic strain isolated from reindeer rumen, which has high activity towards fiber degradation, against bacterial and fungal pathogens. The analysis was performed using whole-genome sequencing of the strain using the Illumina platform. The study revealed that strain RT-26 possessed a complete set of metabolic pathways, including glycolysis, the tricarboxylic acid cycle, and the pentose phosphate pathway. 411 genes were involved in carbohydrate metabolism in the strain genome, 229 genes were related to vitamin and coenzyme metabolism, 149 genes were involved in fatty acid metabolism. The synthesis pathways of various amino acids, most B vitamins (thiamine, riboflavin, nicotiamide, vitamin B5) were identified in the genome. A complete pathway for synthesis of the dipeptide antibiotic bacilisin was detected in the strain. In addition, the strain is capable of synthesizing class A beta-lactamase. No genes responsible for the degradation of mycotoxins and xenobiotics were detected in the genome of the strain studied. A number of glycosyl hydrolase families were detected in the strain genome: GH 1, 3, 4, 5, 6, 11, 13, 16, 18, 20, 23, 26, 28, 30, 32, 43, 46, 51, 53, 68, 68, 73, 101, 109, 126. Carbohydrate-binding proteins were of the SVM 50 family. Glycosyltransferases were of GT 1, 2, 4, 8, 26, 28, 30, 51, 83 families. In the genome of Bacillus velezensis strain RT-26, cellulases related to families GH 5, 6, 26, 51, chitinases related to families GH 18 and 23, and xylanases related to families GH 1, 3, 4, 16, 30, 43 were found. Thus, strain B. velezensis RT-26 has several phenotypically and genotypically proven properties that can characterize it as a good probiotic microorganism.

Abstract 2341: Metabolomic analysis reveals unique biochemical signatures associated with microbial dysbiosis, inflammation and energy metabolism in breast tissues of obese and nonobese black and white breast cancer patients

Abstract Black women are more likely to be diagnosed with late-stage breast cancer than white women and have the lowest probability of cause-specific survival. Obesity is associated with increased breast cancer risk and deaths in black women. Insulin-resistant obesity features chronic systemic and local inflammation of fat, which has been linked to breast cancer outcomes. The breast also consists of a mucosal immune system that provides a complex mechanical barrier and an inherent defense against pathogens. Growing evidence shows that an imbalance in the microbiome due to inflammation may give rise to cancer development. However, there is a lack of data on the role of the microbiome in breast tumors from black women. Using a global metabolomics approach, we investigated normal (n= 24) and breast cancer (n=27) tissue from black and white women to identify microbial metabolites and metabolomic signatures that differ by breast tumor subtype and obesity status. Principal component analysis and hierarchical clustering of the global metabolite profiling data revealed separation between the metabolic signatures of normal and breast tumor tissue. Random forest analysis also revealed a unique biochemical signature in normal versus breast tumors by obesity status and breast tumor subtype, including triple negative breast tumors. Our data show that obesity associates with a unique set of metabolic pathways that facilitate the handling of inflammation, protein catabolism and tissue remodeling, and energy metabolism, which may be regulated by microbial processing in breast tumor tissue. Citation Format: Athena Starlard-Davenport, Alana Smith, Elizabeth Tolley, Lu Lu. Metabolomic analysis reveals unique biochemical signatures associated with microbial dysbiosis, inflammation and energy metabolism in breast tissues of obese and nonobese black and white breast cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2341.

A comparative proteomic approach using metabolic pathways for the identification of potential drug targets against Helicobacter pylori.

Helicobacter pylori is the most highlighted pathogen across the globe especially in developing countries. Severe gastric problems like ulcers, cancers are associated with H. pylori and its prevalence is widespread. Evolution in the genome and cross-resistance with different antibiotics are the major reason of its survival and pandemic resistance against current regimens. To prioritize potential drug target against H. pylori by comparing metabolic pathways of its available strains. We used various computational tools to extract metabolic sets of all available (61) strains of H. pylori and performed pan genomics and subtractive genomics analysis to prioritize potential drug target. Additionally, the protein interaction and detailed structure-based studies were performed for further characterization of protein. We found 41 strains showing similar set of metabolic pathways. However, 19 strains were found with unique set of metabolic pathways. The metabolic set of these 19 strains revealed 83 unique proteins and BLAST against human proteome further funneled them to 38 non-homologous proteins. The druggability and essentiality testing further converged our findings to a single unique protein as a potential drug target against H. pylori. We prioritized one protein-based drug target which upon subject to applied protocol was found as close homolog of the Saccharopine dehydrogenase. Our study has opened further avenues of research regarding the discovery of new drug targets against H. pylori.

13C metabolic flux analysis of three divergent extremely thermophilic bacteria: Geobacillus sp. LC300, Thermus thermophilus HB8, and Rhodothermus marinus DSM 4252

Thermophilic organisms are being increasingly investigated and applied in metabolic engineering and biotechnology. The distinct metabolic and physiological characteristics of thermophiles, including broad substrate range and high uptake rates, coupled with recent advances in genetic tool development, present unique opportunities for strain engineering. However, poor understanding of the cellular physiology and metabolism of thermophiles has limited the application of systems biology and metabolic engineering tools to these organisms. To address this concern, we applied high resolution 13C metabolic flux analysis to quantify fluxes for three divergent extremely thermophilic bacteria from separate phyla: Geobacillus sp. LC300, Thermus thermophilus HB8, and Rhodothermus marinus DSM 4252. We performed 18 parallel labeling experiments, using all singly labeled glucose tracers for each strain, reconstructed and validated metabolic network models, measured biomass composition, and quantified precise metabolic fluxes for each organism. In the process, we resolved many uncertainties regarding gaps in pathway reconstructions and elucidated how these organisms maintain redox balance and generate energy. Overall, we found that the metabolisms of the three thermophiles were highly distinct, suggesting that adaptation to growth at high temperatures did not favor any particular set of metabolic pathways. All three strains relied heavily on glycolysis and TCA cycle to generate key cellular precursors and cofactors. None of the investigated organisms utilized the Entner-Doudoroff pathway and only one strain had an active oxidative pentose phosphate pathway. Taken together, the results from this study provide a solid foundation for future model building and engineering efforts with these and related thermophiles.

Natural light-micro aerobic condition for PSB wastewater treatment: a flexible, simple, and effective resource recovery wastewater treatment process

ABSTRACTPhotosynthetic bacteria (PSB) have two sets of metabolic pathways. They can degrade pollutants through light metabolic under light-anaerobic or oxygen metabolic pathways under dark-aerobic conditions. Both metabolisms function under natural light-microaerobic condition, which demands less energy input. This work investigated the characteristics of PSB wastewater treatment process under that condition. Results showed that PSB had very strong adaptability to chemical oxygen demand (COD) concentration; with F/M of 5.2–248.5 mg-COD/mg-biomass, the biomass increased three times and COD removal reached above 91.5%. PSB had both advantages of oxygen metabolism in COD removal and light metabolism in resource recovery under natural light-microaerobic condition. For pollutants’ degradation, COD, total organic carbon, nitrogen, and phosphorus removal reached 96.2%, 91.0%, 70.5%, and 92.7%, respectively. For resource recovery, 74.2% of C in wastewater was transformed into biomass. Especially, coexistence of light and oxygen promote N recovery ratio to 70.9%, higher than with the other two conditions. Further, 93.7% of N-removed was synthesized into biomass. Finally, CO2 emission reduced by 62.6% compared with the traditional process. PSB wastewater treatment under this condition is energy-saving, highly effective, and environment friendly, and can achieve pollution control and resource recovery.

Coupling solid phase microextraction to complementary separation platforms for metabotyping of E. coli metabolome in response to natural antibacterial agents

Essential oils are known to possess antimicrobial activity; thus, their use has played an important role over the years in medicine and for food preservation purposes. The effect of clove oil and its major constituents as bactericidal agents on the global metabolic profiling of E. coli bacteria was assessed by means of metabolic alterations, using solid phase microextraction (SPME) as a sample preparation method coupled to complementary analytical platforms. E. Coli cultures treated with clove oil and its major individual components were sampled by HS-SPME-GCxGC-ToF/MS and SPME-UPLC–MS. Full factorial design was applied in order to estimate the most effective antibacterial agent towards E. coli. Central composite design and factorial design were applied to investigate parameters influencing metabolite coverage and efficiency by SPME. The metabolic profile, including 500 metabolites identified by LC–MS and 789 components detected by GCxGC-ToF/MS, 125 of which were identified as dysregulated metabolites, revealed changes in the metabolome provoked by the antibacterial activity of clove oil, and in particular its major constituent eugenol. Analyses of individual components selected using orthogonal projections to latent structures discriminant analysis showed a neat differentiation between control samples in comparison to treated samples in various sets of metabolic pathways. The combination of a sample preparation method capable of providing cleaner extracts coupled to different analytical platforms was successful in uncovering changes in metabolic pathways associated with lipids biodegradation, changes in the TCA cycle, amino acids, and enzyme inhibitors in response to antibacterial treatment.

Metabolic network prediction through pairwise rational kernels.

BackgroundMetabolic networks are represented by the set of metabolic pathways. Metabolic pathways are a series of biochemical reactions, in which the product (output) from one reaction serves as the substrate (input) to another reaction. Many pathways remain incompletely characterized. One of the major challenges of computational biology is to obtain better models of metabolic pathways. Existing models are dependent on the annotation of the genes. This propagates error accumulation when the pathways are predicted by incorrectly annotated genes. Pairwise classification methods are supervised learning methods used to classify new pair of entities. Some of these classification methods, e.g., Pairwise Support Vector Machines (SVMs), use pairwise kernels. Pairwise kernels describe similarity measures between two pairs of entities. Using pairwise kernels to handle sequence data requires long processing times and large storage. Rational kernels are kernels based on weighted finite-state transducers that represent similarity measures between sequences or automata. They have been effectively used in problems that handle large amount of sequence information such as protein essentiality, natural language processing and machine translations.ResultsWe create a new family of pairwise kernels using weighted finite-state transducers (called Pairwise Rational Kernel (PRK)) to predict metabolic pathways from a variety of biological data. PRKs take advantage of the simpler representations and faster algorithms of transducers. Because raw sequence data can be used, the predictor model avoids the errors introduced by incorrect gene annotations. We then developed several experiments with PRKs and Pairwise SVM to validate our methods using the metabolic network of Saccharomyces cerevisiae. As a result, when PRKs are used, our method executes faster in comparison with other pairwise kernels. Also, when we use PRKs combined with other simple kernels that include evolutionary information, the accuracy values have been improved, while maintaining lower construction and execution times.ConclusionsThe power of using kernels is that almost any sort of data can be represented using kernels. Therefore, completely disparate types of data can be combined to add power to kernel-based machine learning methods. When we compared our proposal using PRKs with other similar kernel, the execution times were decreased, with no compromise of accuracy. We also proved that by combining PRKs with other kernels that include evolutionary information, the accuracy can also also be improved. As our proposal can use any type of sequence data, genes do not need to be properly annotated, avoiding accumulation errors because of incorrect previous annotations.

Computational prediction of human metabolic pathways from the complete human genome

A computation pathway analysis of the human genome is presented that assigns enzymes encoded by the genome to predicted metabolic pathways. This analysis provides a genome-based view of human nutrition.