The present study intends to evaluate the potential of co-digestion for utilizing Organic fraction of Municipal Solid Waste (OFMSW) and sewage sludge (SS) for enhanced biogas production. Metagenomic analysis was performed to identify the dominant bacteria, archaea and fungi, changes in their communities with time and their functional roles during the course of anaerobic digestion (AD). The cumulative biogas yield of 586.2 mL biogas/gVS with the highest methane concentration of 69.5% was observed under an optimum ratio of OFMSW:SS (40:60 w/w). Bacteria and fungi were found to be majorly involved in hydrolysis and initial stages of AD. Probably, the most common archaea Methanosarsina sp. primarily followed the acetoclastic pathway. The hydrogenotrophic pathway was less followed as indicated by the reduction in abundance of syntrophic acetate oxidizers. An adequate understanding of microbial communities is important to manipulate and inoculate the specific microbial consortia to maximize CH4 production through AD.
Matched MeSH terms: Metabolic Networks and Pathways
Staphylococcus aureus has become a serious concern in hospitals and community due to rapid adaptation to existing antimicrobial agents. Betulinaldehyde [3β-hydroxy-20(29)-lupen-28-al (BE)] belongs to pentacyclic triterpenoids that are based on a 30-carbon skeleton comprising four six-membered rings and one five-membered ring. In a preliminary study, BE exhibited antimicrobial activity against reference strains of methicillin-resistant and methicillin-sensitive S. aureus. However, the response mechanism of S. aureus to this compound is not known. In this study, the global gene expression patterns of both the reference strains in response to sub-inhibitory concentrations of BE were analyzed using DNA microarray to identify gene targets, particularly essential targets in novel pathways, i.e. not targeted by currently used antibiotics, or novel targets in existing pathways. The transcriptome analysis revealed repression of genes in the aminoacyl-tRNA synthetase and ribosome pathways in both the reference strains. Other pathways such as cell division, two-component systems, ABC transporters, fatty acid biosynthesis and peptidoglycan biosynthesis were affected only in the reference strain of methicillin-resistant S. aureus. The findings suggest that BE regulates multiple desirable targets which could be further explored in the development of therapeutic agents for the treatment of S. aureus infections.
Matched MeSH terms: Metabolic Networks and Pathways/genetics
Helicobacter pylori colonizes almost half of the human population worldwide. H. pylori strains are genetically diverse, and the specific genotypes are associated with various clinical manifestations including gastric adenocarcinoma, peptic ulcer disease (PUD), and nonulcer dyspepsia (NUD). However, our current knowledge of the H. pylori metabolism is limited. To understand the metabolic differences among H. pylori strains, we investigated four Malaysian H. pylori clinical strains, which had been previously sequenced, and a standard strain, H. pylori J99, at the phenotypic level.
Matched MeSH terms: Metabolic Networks and Pathways*
Glycogenes regulate a large number of biological processes such as cancer and development. In this work, we created an interaction network of 923 glycogenes to detect potential hubs from different mouse tissues using RNA-Seq data. DAVID functional cluster analysis revealed enrichment of immune response, glycoprotein and cholesterol metabolic processes. We also explored nsSNPs that may modify the expression and function of identified hubs using computational methods. We observe that the number of nsSNPs predicted by any two methods to affect protein function is 4, 7 and 2 for FLT1, NID2 and TNFRSF1B. Residues in the native and mutant proteins were analyzed for solvent accessibility and secondary structure change. Analysis of hubs can help in determining their degree of conservation and understanding their functions in biological processes. The nsSNPs proposed in this work may be further targeted through experimental methods for understanding structural and functional relationships of hub mutants.
Matched MeSH terms: Metabolic Networks and Pathways/genetics
Predicting the distribution of metabolic fluxes in biochemical networks is of major interest in systems biology. Several databases provide metabolic reconstructions for different organisms. Software to analyze flux distributions exists, among others for the proprietary MATLAB environment. Given the large user community for the R computing environment, a simple implementation of flux analysis in R appears desirable and will facilitate easy interaction with computational tools to handle gene expression data. We extended the R software package BiGGR, an implementation of metabolic flux analysis in R. BiGGR makes use of public metabolic reconstruction databases, and contains the BiGG database and the reconstruction of human metabolism Recon2 as Systems Biology Markup Language (SBML) objects. Models can be assembled by querying the databases for pathways, genes or reactions of interest. Fluxes can then be estimated by maximization or minimization of an objective function using linear inverse modeling algorithms. Furthermore, BiGGR provides functionality to quantify the uncertainty in flux estimates by sampling the constrained multidimensional flux space. As a result, ensembles of possible flux configurations are constructed that agree with measured data within precision limits. BiGGR also features automatic visualization of selected parts of metabolic networks using hypergraphs, with hyperedge widths proportional to estimated flux values. BiGGR supports import and export of models encoded in SBML and is therefore interoperable with different modeling and analysis tools. As an application example, we calculated the flux distribution in healthy human brain using a model of central carbon metabolism. We introduce a new algorithm termed Least-squares with equalities and inequalities Flux Balance Analysis (Lsei-FBA) to predict flux changes from gene expression changes, for instance during disease. Our estimates of brain metabolic flux pattern with Lsei-FBA for Alzheimer's disease agree with independent measurements of cerebral metabolism in patients. This second version of BiGGR is available from Bioconductor.
Matched MeSH terms: Metabolic Networks and Pathways*
Pasteurella multocida causes respiratory infectious diseases in a multitude of birds and mammals. A number of virulence-associated genes were reported across different strains of P. multocida, including those involved in the iron transport and metabolism. Comparative iron-associated genes of P. multocida among different animal hosts towards their interaction networks have not been fully revealed. Therefore, this study aimed to identify the iron-associated genes from core- and pan-genomes of fourteen P. multocida strains and to construct iron-associated protein interaction networks using genome-scale network analysis which might be associated with the virulence. Results showed that these fourteen strains had 1587 genes in the core-genome and 3400 genes constituting their pan-genome. Out of these, 2651 genes associated with iron transport and metabolism were selected to construct the protein interaction networks and 361 genes were incorporated into the iron-associated protein interaction network (iPIN) consisting of nine different iron-associated functional modules. After comparing with the virulence factor database (VFDB), 21 virulence-associated proteins were determined and 11 of these belonged to the heme biosynthesis module. From this study, the core heme biosynthesis module and the core outer membrane hemoglobin receptor HgbA were proposed as candidate targets to design novel antibiotics and vaccines for preventing pasteurellosis across the serotypes or animal hosts for enhanced precision agriculture to ensure sustainability in food security.
Matched MeSH terms: Metabolic Networks and Pathways/genetics
The genus Meridianimaribacter is one of the least-studied genera within Cytophaga-Flavobacteria. To date, no genomic analysis of Meridianimaribacter has been reported. In this study, Meridianimaribacter sp. strain CL38, a lignocellulose degrading halophile was isolated from mangrove soil. The genome of strain CL38 was sequenced and analyzed. The assembled genome contains 17 contigs with 3.33 Mbp, a GC content of 33.13% and a total of 2982 genes predicted. Lignocellulose degrading enzymes such as cellulases (GH3, 5, 9, 16, 74 and 144), xylanases (GH43 and CE4) and mannanases (GH5, 26, 27 and 130) are encoded in the genome. Furthermore, strain CL38 demonstrated its ability to decompose empty fruit bunch, a lignocellulosic waste residue arising from palm oil industry. The genome information coupled with experimental studies confirmed the ability of strain CL38 to degrade lignocellulosic biomass. Therefore, Meridianimaribacter sp. strain CL38, with its halotolerance, could be useful for seawater based lignocellulosic biorefining.
Matched MeSH terms: Metabolic Networks and Pathways/genetics
Anoxybacillus sp. SK 3-4 is a Gram-positive, rod-shaped bacterium and a member of family Bacillaceae. We had previously reported that the strain is an aluminum resistant thermophilic bacterium. This is the first report to provide a detailed analysis of the global transcriptional response of Anoxybacillus when the cells were exposed to 600 mg L(-1) of aluminum. The transcriptome was sequenced using Illumina MiSeq sequencer. Total of 708 genes were differentially expressed (fold change >2.00) with 316 genes were up-regulated while 347 genes were down-regulated, in comparing to control with no aluminum added in the culture. Based on Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, the majority of genes encoding for cell metabolism such as glycolysis, sulfur metabolism, cysteine and methionine metabolism were up-regulated; while most of the gene associated with tricarboxylic acid cycle (TCA cycle) and valine, leucine and isoleucine metabolism were down-regulated. In addition, a significant number of the genes encoding ABC transporters, metal ions transporters, and some stress response proteins were also differentially expressed following aluminum exposure. The findings provide further insight and help us to understand on the resistance of Anoxybacillus sp. SK 3-4 toward aluminium.
Matched MeSH terms: Metabolic Networks and Pathways/genetics
Pandoraea vervacti DSM 23571(T) is an oxalate metabolizing bacterium isolated from an uncultivated field soil in Mugla, Turkey. Here, we present the first complete genome sequence of P. vervacti DSM 23571(T). A complete pathway for degradation of oxalate was revealed from the genome analysis. These data are important to path new opportunities for genetic engineering in the field of biotechnology.
Matched MeSH terms: Metabolic Networks and Pathways/genetics
Evaluation of transcriptome data in combination with QTL information has been applied in many crops to study the expression of genes responsible for specific phenotypes. In oil palm, the mesocarp oil extracted from E. oleifera × E. guineensis interspecific hybrids is known to have lower palmitic acid (C16:0) content compared to pure African palms. The present study demonstrates the effectiveness of transcriptome data in revealing the expression profiles of genes in the fatty acid (FA) and triacylglycerol (TAG) biosynthesis processes in interspecific hybrids. The transcriptome assembly yielded 43,920 putative genes of which a large proportion were homologous to known genes in the public databases. Most of the genes encoding key enzymes involved in the FA and TAG synthesis pathways were identified. Of these, 27, including two candidate genes located within the QTL associated with C16:0 content, showed differential expression between developmental stages, populations and/or palms with contrasting C16:0 content. Further evaluation using quantitative real-time PCR revealed that differentially expressed patterns are generally consistent with those observed in the transcriptome data. Our results also suggest that different isoforms are likely to be responsible for some of the variation observed in FA composition of interspecific hybrids.
Matched MeSH terms: Metabolic Networks and Pathways/genetics
Lactic acid bacteria constitute a diverse group of industrially significant, safe microorganisms that are primarily used as starter cultures and probiotics, and are also being developed as production systems in industrial biotechnology for biocatalysis and transformation of renewable feedstocks to commodity- and high-value chemicals, and health products. Development of strains, which was initially based mainly on natural approaches, is also achieved by metabolic engineering that has been facilitated by the availability of genome sequences and genetic tools for transformation of some of the bacterial strains. The aim of this paper is to provide a brief overview of the potential of lactic acid bacteria as biological catalysts for production of different organic compounds for food and non-food sectors based on their diversity, metabolic- and stress tolerance features, as well as the use of genetic/metabolic engineering tools for enhancing their capabilities.
Matched MeSH terms: Metabolic Networks and Pathways/genetics
The growing number of multidrug-resistant pathogenic bacteria is becoming a world leading challenge for the scientific community and for public health. However, advances in high-throughput technologies and whole-genome sequencing of bacterial pathogens make the construction of bacterial genome-scale metabolic models (GEMs) increasingly realistic. The use of GEMs as an alternative platforms will expedite identification of novel unconditionally essential genes and enzymes of target organisms with existing and forthcoming GEMs. This approach will follow the existing protocol for construction of high-quality GEMs, which could ultimately reduce the time, cost and labor-intensive processes involved in identification of novel antimicrobial drug targets in drug discovery pipelines. We discuss the current impact of existing GEMs of selected multidrug-resistant pathogenic bacteria for identification of novel antimicrobial drug targets and the challenges of closing the gap between genome-scale metabolic modeling and conventional experimental trial-and-error approaches in drug discovery pipelines.
Matched MeSH terms: Metabolic Networks and Pathways/genetics*
Streptomycetes remain as one of the important sources for bioactive products. Isolated from the mangrove forest, Streptomyces gilvigriseus MUSC 26T was previously characterised as a novel streptomycete. The high quality draft genome of MUSC 26T contained 5,213,277bp with G+C content of 73.0%. Through genome mining, several gene clusters associated with secondary metabolites production were revealed in the genome of MUSC 26T. These findings call for further investigations into the potential exploitation of the strain for production of pharmaceutically important compounds.
Matched MeSH terms: Metabolic Networks and Pathways/genetics
The white-rot fungus Pleurotus eryngii F032 showed the capability to degrade a three fused-ring aromatic hydrocarbons fluorene. The elimination of fluorene through sorption was also investigated. Enzyme production is accompanied by an increase in biomass of P. eryngii F032 during degradation process. The fungus totally degraded fluorine within 23 d at 10-mg l(-1) solution. Fluorene degradation was affected with initial fluorene concentrations. The highest enzyme activity was shown by laccase in the 10-mg l(-1) culture after 30 d of incubation (1620 U l(-1)). Few activities of enzymes were observed in the fungal cell at the varying concentration of fluorene. Three metabolic were detected and separated in ethylacetate extract, after isolated by column chromatography. The metabolites, 9-fluorenone, phthalic acid, and benzoic acid were identified using UV-vis spectrophotometer and gas chromatography-mass spectrometry (GC-MS). The results show the presence of a complex mechanism for the regulation of fluorene-degrading enzymes.
Matched MeSH terms: Metabolic Networks and Pathways
Personalized medicine, with the aim of safely, effectively, and cost-effectively targeting treatment to a prespecified patient population, has always been a long-time goal within health care. It is often argued that personalizing treatment will inevitably improve clinical outcomes for patients and help achieve more effective use of health care resources. Demand is increasing for demonstrable evidence of clinical and cost-effectiveness to support the use of personalized medicine in health care. This paper begins with an overview of the existing challenges in conducting economic evaluations of genetics- and genomics-targeted technologies, as an example of personalized medicine. Our paper illustrates the complexity of the challenges faced by these technologies by highlighting the variations in the issues faced by diagnostic tests for somatic variations, generally referring to genetic variation in a tumor, and germline variations, generally referring to inherited genetic variation in enzymes involved in drug metabolic pathways. These tests are typically aimed at stratifying patient populations into subgroups on the basis of clinical effectiveness (response) or safety (avoidance of adverse events). The paper summarizes the data requirements for economic evaluations of genetics and genomics-based technologies while outlining that the main challenges relating to data requirements revolve around the availability and quality of existing data. We conclude by discussing current developments aimed to address the challenges of assessing the cost-effectiveness of genetics and genomics-based technologies, which revolve around two central issues that are interlinked: the need to adapt available evaluation methods and identifying who is responsible for generating evidence for these technologies.
Matched MeSH terms: Metabolic Networks and Pathways
Mathematical modelling is fundamental to understand the dynamic behavior and regulation of the biochemical metabolisms and pathways that are found in biological systems. Pathways are used to describe complex processes that involve many parameters. It is important to have an accurate and complete set of parameters that describe the characteristics of a given model. However, measuring these parameters is typically difficult and even impossible in some cases. Furthermore, the experimental data are often incomplete and also suffer from experimental noise. These shortcomings make it challenging to identify the best-fit parameters that can represent the actual biological processes involved in biological systems. Computational approaches are required to estimate these parameters. The estimation is converted into multimodal optimization problems that require a global optimization algorithm that can avoid local solutions. These local solutions can lead to a bad fit when calibrating with a model. Although the model itself can potentially match a set of experimental data, a high-performance estimation algorithm is required to improve the quality of the solutions. This paper describes an improved hybrid of particle swarm optimization and the gravitational search algorithm (IPSOGSA) to improve the efficiency of a global optimum (the best set of kinetic parameter values) search. The findings suggest that the proposed algorithm is capable of narrowing down the search space by exploiting the feasible solution areas. Hence, the proposed algorithm is able to achieve a near-optimal set of parameters at a fast convergence speed. The proposed algorithm was tested and evaluated based on two aspartate pathways that were obtained from the BioModels Database. The results show that the proposed algorithm outperformed other standard optimization algorithms in terms of accuracy and near-optimal kinetic parameter estimation. Nevertheless, the proposed algorithm is only expected to work well in small scale systems. In addition, the results of this study can be used to estimate kinetic parameter values in the stage of model selection for different experimental conditions.
Matched MeSH terms: Metabolic Networks and Pathways
Seaweeds survive in marine waters with high sulfate concentration compared to those living at freshwater habitats. The cell wall polymer of Gracilaria spp. which supplies more than 50% of the world agar is heavily sulfated. Since sulfation reduces the agar quality, it is interesting to investigate the effects of sulfate deprivation on the sulfate contents of seaweed and agar, as well as the metabolic pathways of these seaweeds. In this study, two agarophytes G. changii and G. salicornia were treated under sulfate deprivation for 5 days. The sulfate contents in the seaweed/agar were generally lower in sulfate-deprivated samples compared to those in the controls, but the differences were only statistically significant for seaweed sample of G. changii and agar sample of G. salicornia. RNA sequencing (RNA-Seq) of sulfate-deprivated and untreated seaweed samples revealed 1,292 and 3,439 differentially expressed genes (DEGs; ≥1.5-fold) in sulfate-deprivated G. changii and G. salicornia, respectively, compared to their respective controls. Among the annotated DEGs were genes involved in putative agar biosynthesis, sulfur metabolism, metabolism of sulfur-containing amino acids, carbon metabolism and oxidative stress. These findings shed light on the sulfate deprivation responses in agarophytes and help to identify candidate genes involved in agar biosynthesis.
Matched MeSH terms: Metabolic Networks and Pathways
The subterranean termite Globitermus sulphureus is an important Southeast Asian pest with limited genomic resources that causes damages to agriculture crops and building structures. Therefore, the main goal of this study was to survey the G. sulphureus transcriptome composition. Here, we performed de novo transcriptome for G. sulphureus workers' heads using Illumina HiSeq paired-end sequencing technology. A total of 88, 639, 408 clean reads were collected and assembled into 243, 057 transcripts and 193, 344 putative genes. The transcripts were annotated with the Trinotate pipeline. In total, 27, 061 transcripts were successfully annotated using BLASTX against the SwissProt database and 17, 816 genes were assigned to 47, 598 GO terms. We classified 14, 223 transcripts into COG classification, resulting in 25 groups of functional annotations. Next, a total of 12, 194 genes were matched in the KEGG pathway and 392 metabolic pathways were predicted based on the annotation. Moreover, we detected two endogenous cellulases in the sequences. The RT-qPCR analysis showed that there were significant differences in the expression levels of two genes β-glucosidase and endo-β-1,4-glucanase between worker and soldier heads of G. sulphureus. This is the first study to characterize the complete head transcriptome of a higher termite G. sulphureus using a high-throughput sequencing. Our study may provide an overview and comprehensive molecular resource for comparative studies of the transcriptomics and genomics of termites.
Matched MeSH terms: Metabolic Networks and Pathways