HUMAnN2 is a system for accelerated functional profiling of shotgun metagenomic and metatranscriptomic (“meta’omic”) sequencing from host- and environmentally-associated microbial communities. HUMAnN2 implements a tiered search strategy comprised of three search phases (tiers). In the first search tier, the meta’omic sample is rapidly screened to identify known species in the underlying community. This information is then used to construct a custom gene sequence database for the sample by concatenating precomputied, functionally annotated pangenomes of detected species. In the second search tier, the entire sample is aligned against this database, yielding i) per-species, per-gene alignment statistics and ii) a collection of unmapped reads. In the final search tier, unmapped reads are aligned against a user-specified (typically comprehensive and nonredundant) protein database by translated search, yielding i) taxonomically unclassified per-gene alignment statistics and ii) a collection of novel reads. Per-gene alignment statistics are weighted based on alignment quality, coverage, and sequence length to yield gene abundance values i) for the community and ii) stratified according to per-species and “unclassified” contributions. Gene abundance values are finally applied to metabolic network reconstruction to identity and quantify pathways in the community (also stratified according to per-species and “unclassified” contributions). These processes, including the underlying databases and search parameters, are expanded in detail below.
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Physiology
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Molecular Function
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Metabolic Networks
Metabolic Networks
Metabolic Networks are complex systems of interconnected biochemical reactions and pathways that govern the essential metabolic processes within living organisms.
These networks enable the efficient conversion of nutrients into energy, the synthesis of crucial biomolecules, and the regulation of diverse physiological functions.
Understading the dynamics and regulation of metabolic networks is crucial for advancing fields such as medicine, biotechnology, and systems biology.
This MeSH term provides a concise overview of the key concepts and applications related to these dynamic, self-organizing biological systems.
These networks enable the efficient conversion of nutrients into energy, the synthesis of crucial biomolecules, and the regulation of diverse physiological functions.
Understading the dynamics and regulation of metabolic networks is crucial for advancing fields such as medicine, biotechnology, and systems biology.
This MeSH term provides a concise overview of the key concepts and applications related to these dynamic, self-organizing biological systems.
Most cited protocols related to «Metabolic Networks»
Genes
Metabolic Networks
Metagenome
Microbial Community
Reconstructive Surgical Procedures
A stoichiometric matrix, S (m × n), is constructed where m is the number of metabolites and n the number of reactions. Each column of S specifies the stoichiometry of the metabolites in a given reaction from the metabolic network. Mass balance equations can be written for each metabolite by taking the dot product of a row in S , corresponding to a particular metabolite, and a vector, v , containing the values of the fluxes through all reactions in the network. A system of mass balance equations for all the metabolites can be represented as follows:
X is a concentration vector of length m, and v is a flux vector of length n. At steady-state, the time derivatives of metabolite concentrations are zero, and equation (1) can be simplified to:
S • v = 0
It follows that in order for a flux vectorv to satisfy this relationship, the rate of production must equal the rate of consumption for each metabolite. Application of additional constraints further reduces the number of allowable flux distributions, v .
Limits on the range of individual flux values can further reduce the number of allowable solutions. These constraints have the form:
α ≤ vi≤ β
where α and β are the lower and upper limits, respectively. Maximum flux values (β) can be estimated based on enzymatic capacity limitations or, for the case of exchange reactions, measured maximal uptake rates can be used. Thermodynamic constraints, regarding the reversibility or irreversibility of a reaction, can be applied by setting the α for the corresponding flux to zero if the reaction is irreversible.
These constraints are not sufficient to shrink the original solution space to a single solution. Instead a number of solutions remain which make up the allowable solution space. Linear optimization can be used to find the solution that maximizes a particular objective function. Some examples of objective functions include the production of ATP, NADH, NADPH or a particular metabolite. An objective function with a combination of the metabolic precursors, energy and redox potential required for the production of biomass has proven useful in predicting in vivo cellular behavior [9 (link),10 (link),25 (link),26 ].
It follows that in order for a flux vector
Limits on the range of individual flux values can further reduce the number of allowable solutions. These constraints have the form:
α ≤ vi≤ β
where α and β are the lower and upper limits, respectively. Maximum flux values (β) can be estimated based on enzymatic capacity limitations or, for the case of exchange reactions, measured maximal uptake rates can be used. Thermodynamic constraints, regarding the reversibility or irreversibility of a reaction, can be applied by setting the α for the corresponding flux to zero if the reaction is irreversible.
These constraints are not sufficient to shrink the original solution space to a single solution. Instead a number of solutions remain which make up the allowable solution space. Linear optimization can be used to find the solution that maximizes a particular objective function. Some examples of objective functions include the production of ATP, NADH, NADPH or a particular metabolite. An objective function with a combination of the metabolic precursors, energy and redox potential required for the production of biomass has proven useful in predicting in vivo cellular behavior [9 (link),10 (link),25 (link),26 ].
Cells
Cloning Vectors
derivatives
Enzymes
Metabolic Networks
NADH
NADP
Oxidation-Reduction
The reconstruction process has also been previously outlined (Feist et al, 2006 (link); Reed et al, 2006a (link)). Here, we provide certain details specific to this work. Starting from the metabolic network for iJR904 (Reed et al, 2003 (link)), additional reactions were added to the network based on E. coli-specific biochemical characterization studies (see Supplementary information for a complete list of references) and other reactions were removed (see Results). This process was aided by comparing the content of iJR904 with the EcoCyc database (see below). The E. coli genome annotation (Riley et al, 2006 (link)) was used as a citation source for biochemical characterization studies and a framework upon which translated metabolic proteins, and subsequently reactions, were assigned to form gene to protein to reaction (GPR) assignments. The SimPheny™ (Genomatica Inc., San Diego, CA) software platform was used to build the reconstruction. For each reaction entered into the reconstruction, the involved metabolites were characterized according to their chemical formula and charge determined using their pKa value for a pH of 7.2. Metabolite charge was determined using its pKa value(s). When the metabolite pKa was not available, charge was determined using the pKa of ionizable groups present in a metabolite (http://www.chemaxon.com/product/pka.html ). All of the reactions entered into the network were designated as enzymatically catalyzed reactions or spontaneous reactions, were both elementally and charged balanced and are either reversible or irreversible. Reversibility was determined first from primary literature for each particular enzyme/reaction, if available (see Supplementary information for references). Additionally, general heuristic rules, like those applied by Kümmel et al (2006b) , were used to enter reversibility using knowledge about the physiological direction of a reaction in a pathway (sometimes including regulatory knowledge) and/or basic thermodynamic information (such as reactions hydrolyzing high-energy phosphate bonds are almost always irreversible). Furthermore, a thermodynamic analysis of reversibility was utilized to assign the directionality of some reactions (see above).
Enzymes
Escherichia coli
Gene Products, Protein
Genome
Metabolic Networks
Phosphates
physiology
Proteins
Reconstructive Surgical Procedures
Homo sapiens
Metabolic Networks
Reconstructive Surgical Procedures
Interaction data was gathered from a number of different published high-throughput datasets and published databases [2–5 (link),27 (link),34 (link),45 (link),46 (link)]. Independent genomic features and Bayesian integration were used to eliminate noise from the dataset [23 (link),43 (link)]. Different datasets (e.g., the FYI [Vidal et al.] [21 (link)] or the DIP core [Eisenberg et al.] [44 (link)]) exhibit the same behavior (see Figures S1 and S4 A). To avoid biases from large complexes (i.e., the ribosome and the proteasome), we repeated our calculations after removing both these complexes (see Figures S2 and S4 B). The regulatory network was created by combining five different datasets [1 (link),2 (link),22 ,34 (link),35 (link),47 (link)]. We excluded DNA-binding enzymes (e.g., PolIII) from the regulatory network. The essential genes in yeast genome were determined experimentally through a PCR-based gene-deletion method [36 (link)]. The metabolic network was taken from the Kyoto Encyclopedia of Genes and Genomes (KEGG) [48 (link)] and all proteins that share a metabolite were considered linked. The genetic network data was downloaded from the GRID [49 (link)] and consists of several large-scale screens of genetic interactions [30 (link),50 (link)]. Expression data was taken from the Rosetta compendium expression dataset [51 (link)]. All datasets and the calculated betweenness of each protein node within these networks are available at http://www.gersteinlab.org/proj/bottleneck . Because most of these networks are far from complete, we will update the networks and, more important, the associated betweenness of each node as they grow in size in the future.
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Enzymes
Gene Deletion
Gene Regulatory Networks
Genes
Genes, Essential
Genome
Metabolic Networks
Multicatalytic Endopeptidase Complex
Proteins
Ribosomes
Saccharomyces cerevisiae
Screenings, Genetic
Most recents protocols related to «Metabolic Networks»
Genomic DNA was extracted from bacteria using a Nucleospin Microbial DNA kit (Thermo Fischer Scientific) as per manufacturer instructions. Further, the QIASeq FX DNA Library Kit (Qiagen) prepared genomic libraries for sequencing. L. amnigena PTJIIT1005 genome was sequenced on NGS (Next Generation Sequencing) Illumina NovaSeq6000 Platform by Redcliffe Lifetech, Noida. A total of 9,365,132 raw reads were obtained; 8,596,940 Illumina reads were de novo assembled using Unicycler (version 0.4.4). The assembled genome sequence was annotated by the tool Prokka 1.12. The complete genome sequence was submitted to NCBI.
Average Nucleotide Identity (ANI) [17 ] measures nucleotide-level genome similarity between the coding regions of two genomes. The complete genome sequence was submitted in FASTA format as an input file. This tool gives the similarity index percentage [18 (link)]. ANI is computed using the formula [19 ]:
Genome annotation of Lelliottia amnigena was done by RAST (Rapid Annotation using Subsystems Technology), PATRIC (The Pathosystems Resource Integration Center), and PGAP (Prokaryotic Genome Annotation Pipeline). Assembled genome sequence was submitted in RAST in FASTA format as input files, assigned functions to the genes. It also predicted the subsystems which were represented in the genome. By using this information, it reconstructs the metabolic network and makes the output file easily downloadable. Similarly, contigs were submitted in PATRIC as input files which provided annotation, subsystem summary, phylogenetic tree, and pathways. NCBI PGAP was used to annotate the bacterial genome where the complete genomic sequence was submitted in FASTA format as an input file, and it predicted the protein-coding regions and functional genome units like tRNAs, rRNA, pseudogenes, transposons, and mobile elements.
Average Nucleotide Identity (ANI) [17 ] measures nucleotide-level genome similarity between the coding regions of two genomes. The complete genome sequence was submitted in FASTA format as an input file. This tool gives the similarity index percentage [18 (link)]. ANI is computed using the formula [19 ]:
Genome annotation of Lelliottia amnigena was done by RAST (Rapid Annotation using Subsystems Technology), PATRIC (The Pathosystems Resource Integration Center), and PGAP (Prokaryotic Genome Annotation Pipeline). Assembled genome sequence was submitted in RAST in FASTA format as input files, assigned functions to the genes. It also predicted the subsystems which were represented in the genome. By using this information, it reconstructs the metabolic network and makes the output file easily downloadable. Similarly, contigs were submitted in PATRIC as input files which provided annotation, subsystem summary, phylogenetic tree, and pathways. NCBI PGAP was used to annotate the bacterial genome where the complete genomic sequence was submitted in FASTA format as an input file, and it predicted the protein-coding regions and functional genome units like tRNAs, rRNA, pseudogenes, transposons, and mobile elements.
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Bacteria
DNA Library
Genome
Genome, Bacterial
Genomic Library
Jumping Genes
Lelliottia amnigena
Metabolic Networks
Nucleotides
Open Reading Frames
Operator, Genetic
Prokaryotic Cells
Pseudogenes
Radioallergosorbent Test
Ribosomal RNA
Transfer RNA
Total microbial DNA were extracted using the QIAamp PowerFecal Pro DNA Kit (Cat#51804, QIAGEN). DNA concentration was measured. 1 μg DNA per sample was used as input. Sequencing libraries were generated using NEBNext® Ultra™ DNA Library Prep Kit (Cat# E7370L, NEB). DNA samples were fragmented by sonication to 350 bp, which were end-polished, A-tailed, and ligated. PCR products were purified. The clustering of the index-coded samples was performed on a cBot Cluster Generation System, and then sequenced on an Illumina Novaseq 6000 platform by Novogene (Novogene Tianjin, China).
QC process including trimming of low-quality bases, masking of human DNA contamination, and removal of duplicated reads were performed by using kneaddata (version v0.6.1). Human DNA contamination was identified by aligning all raw reads to the human reference genome (hg19) using bowtie2 (version 2.3.5.1). Taxonomic annotation of metagenome and the abundance quantification were performed by MetaPhlAn (version 2.0). Relative abundance of each clade was calculated at six levels (L2: phylum, L3: class, L4: order, L5: family, L6: genus, L7: species). Functional annotations were performed by using the data files from the HMP Unified Metabolic Analysis Network 3.0 (HUMAnN 3.0)74 (link). The clean paired-end sequencing data were merged into a single fastq file. The HUMAnN 3.0 toolkit was run by using the “humann–input myseq*.fq–output humann3/–threads 32–memory-use maximum -r -v” command, which calls Bowtie275 (link) to compare nucleic acid sequence and calls DIAMOND76 (link) to compare protein sequences to complete gene and protein function annotation to obtain KEGG pathway annotation. Differences in bacterial abundance and functional pathway were analyzed using MaAslin277 (link). Richness indices were calculated using the R Community Ecology Package vegan. Weighted Unifrac distance was calculated using Metaphlan3 R script “Unifrac_distance.r” and root-tree file “mpa_v30_CHOCOPhlAn_201901_species_tree.nwk”. The PCoA results were calculated and visualized using R build-in functions and the plot3D R package. The ANOSIM test was used to calculate the significance of dissimilarity using the R Community Ecology Package vegan. Pearson correlation and P values were evaluated using the rcorr function in the Hmisc R package.
QC process including trimming of low-quality bases, masking of human DNA contamination, and removal of duplicated reads were performed by using kneaddata (version v0.6.1). Human DNA contamination was identified by aligning all raw reads to the human reference genome (hg19) using bowtie2 (version 2.3.5.1). Taxonomic annotation of metagenome and the abundance quantification were performed by MetaPhlAn (version 2.0). Relative abundance of each clade was calculated at six levels (L2: phylum, L3: class, L4: order, L5: family, L6: genus, L7: species). Functional annotations were performed by using the data files from the HMP Unified Metabolic Analysis Network 3.0 (HUMAnN 3.0)74 (link). The clean paired-end sequencing data were merged into a single fastq file. The HUMAnN 3.0 toolkit was run by using the “humann–input myseq*.fq–output humann3/–threads 32–memory-use maximum -r -v” command, which calls Bowtie275 (link) to compare nucleic acid sequence and calls DIAMOND76 (link) to compare protein sequences to complete gene and protein function annotation to obtain KEGG pathway annotation. Differences in bacterial abundance and functional pathway were analyzed using MaAslin277 (link). Richness indices were calculated using the R Community Ecology Package vegan. Weighted Unifrac distance was calculated using Metaphlan3 R script “Unifrac_distance.r” and root-tree file “mpa_v30_CHOCOPhlAn_201901_species_tree.nwk”. The PCoA results were calculated and visualized using R build-in functions and the plot3D R package. The ANOSIM test was used to calculate the significance of dissimilarity using the R Community Ecology Package vegan. Pearson correlation and P values were evaluated using the rcorr function in the Hmisc R package.
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Amino Acid Sequence
Bacteria
Base Sequence
DNA Contamination
DNA Library
Genes
Genome, Human
Homo sapiens
Memory
Metabolic Networks
Metagenome
Plant Roots
Protein Annotation
Trees
Vegan
Original data collected by the UPLC-QTOF-MS system were processed by Profile Analysis 2.1 (Bruker) for the recognition, calibration, alignment, and normalization of peaks, and then converted into TXT format. To acquire more complete metabolic profiles, the obtained data in the positive and negative modes were merged and imported into SIMCA-P 14.0 (Umetrics, Stockholm, Sweden) for multivariate statistical analysis.
Principal component analysis (PCA) is an unsupervised method of pattern recognition. PCA can reveal visually the natural grouping of samples (Xu et al., 2017 (link); Hu et al., 2020 (link)). Each point represents a sample in a PCA score plot, which is convenient for the removal of outliers (Zhou et al., 2017 (link)). Whereas, orthogonal projections to latent structures discriminant analysis (OPLS-DA) is carried out for supervised regression modeling and identifies potential differential biomarkers between groups (Wang, T. et al., 2019 (link)). The values of R2Y and Q2 are key indices to evaluate the fitting quality and predictability of OPLS-DA models. The values of Q2 were larger than 0.5 and the difference between R2Y and Q2 was less than 0.3, suggesting superior quality of our models. (Wang, X. et al., 2020 (link)).
Variable importance in projection (VIP) is implemented to measure the contribution to sample classification (Yuan, Z. et al., 2020 (link)). Coupled with VIP values (VIP >3) and the Student’s t-test (p < 0.05), the m/z of significantly altered metabolites was filtered preliminarily. According to the retention time, an accurate molecular weight was determined through DataAnalysis 4.4 software (Bruker). And next, biomarker (error <5 ppm) were identified through the Human Metabolome Database (www.hmdb.ca ) and Metaboanalyst (www.metaboanalyst.ca/ ) based on their retention time, accurate molecular weights and MS/MS fragments of ions (Zhou et al., 2019 (link); Li et al., 2022 (link)). Hierarchical clustering analysis (HCA) of biomarkers was carried out and corresponding heatmaps were acquired using Mev 4.8.0 (MeV, United States). Finally, a network diagram of perturbed metabolic pathways in the PE model involving intervention by DS, DS-Pol, DS-Oli, DS-FG, DS-FA, or DS-FO was created using the Kyoto Encyclopedia of Genes and Genomes (KEGG; www.kegg.jp/ ), MetaboAnalyst, and MBRole (http://csbg.cnb.csic.es/mbrole2/ ) databases.
Principal component analysis (PCA) is an unsupervised method of pattern recognition. PCA can reveal visually the natural grouping of samples (Xu et al., 2017 (link); Hu et al., 2020 (link)). Each point represents a sample in a PCA score plot, which is convenient for the removal of outliers (Zhou et al., 2017 (link)). Whereas, orthogonal projections to latent structures discriminant analysis (OPLS-DA) is carried out for supervised regression modeling and identifies potential differential biomarkers between groups (Wang, T. et al., 2019 (link)). The values of R2Y and Q2 are key indices to evaluate the fitting quality and predictability of OPLS-DA models. The values of Q2 were larger than 0.5 and the difference between R2Y and Q2 was less than 0.3, suggesting superior quality of our models. (Wang, X. et al., 2020 (link)).
Variable importance in projection (VIP) is implemented to measure the contribution to sample classification (Yuan, Z. et al., 2020 (link)). Coupled with VIP values (VIP >3) and the Student’s t-test (p < 0.05), the m/z of significantly altered metabolites was filtered preliminarily. According to the retention time, an accurate molecular weight was determined through DataAnalysis 4.4 software (Bruker). And next, biomarker (error <5 ppm) were identified through the Human Metabolome Database (
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21-hydroxy-9beta,10alpha-pregna-5,7-diene-3-ol-20-one
Biological Markers
Genes
Genome
Homo sapiens
Ions
Metabolic Networks
Metabolic Profile
Metabolome
Retention (Psychology)
Student
Tandem Mass Spectrometry
Categorical data were expressed as frequency or percentage, and statistical comparison between groups was carried out using Pearson’s chi-squared test or Fisher’s exact test. The Shapiro–Wilk test was used to test the normal distribution of continuous variables, which were expressed as mean ± standard deviation if normally distributed and as median (25th percentile, 75th percentile) if not normally distributed. Statistical comparison was performed using the Student t-test or Mann–Whitney U test for continuous variables, where appropriate. Prior to statistical analysis of the FF metabolome, metabolite concentration was transformed by log2 scale and Pareto scaling to establish Gaussian distribution for this dataset. Partial least squares discriminant analysis (PLS-DA) and model validation were performed by MetaboAnalyst 5.0 (https://www.metaboanalyst.ca/ ). Since clinical characteristics associated with IVF outcomes were matched between the GH and control groups, Student t-test and false discovery rate were implemented to calculate the significance of FF metabolites between two groups using R software. Only both two-tailed p-values and q-values less than 0.05 and 0.1 respectively were considered statistically significant. The areas under the receiver operating characteristic (ROC) curve were performed using pROC R package [34 (link)]. The forest plot displaying the Pearson correlation between the number of oocytes retrieved and the levels of differential metabolites was illustrated using the ggplot2 R package [35 ]. The Sankey diagram, which links differential metabolites into their KEGG metabolic pathways, was performed by the Online website https://www.omicstudio.cn/ . The metabolic network was illustrated based on the KEGG global metabolism map using MetaboAbalyst 5.0.
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Forests
Metabolic Networks
Metabolism
Metabolome
Ovum
Student
Metabolomics data were first processed using an in-house R script. Processed data were first inspected for sample clustering and variance difference via Principal Component Analysis (PCA) and Partial Least Square Discriminant Analysis (PLSDA), in which PLSDA plots were generated using the R package mixOmics v6.18.1 [28 (link)]. Raw p-values were adjusted using the Benjamini–Hochberg correction and then tested for significance using the Welch method for metabolite profiling using the R package Omu v1.0.6 [29 (link)]. Differential metabolite analysis between the TNFα- and TGFβ2-treated samples compared to control (untreated) samples for both cell and media metabolites was generated by the R package Omu using the count_fold_change function, with the log FoldChange (FC) >1.5 or <−1.5 (TNFα-treated H-RPE vs. control H-RPE), logFC > 1.0, and logFC < −1.0 (TGFβ2-treated H-RPE vs. control H-RPE), with a Benjamini–Hochberg adjusted p-value cut off < 0.05 [29 (link)].
The Enriched Pathway Network Analysis was generated using the KEGG database of Homo sapiens [30 (link)], which was loaded locally using the functions buildGraphFromKEGGREST() and buildDataFromGraph() in the R package FELLA v1.14.0 [31 (link)]. The lists of metabolites that were significantly different from the TNFα-treated vs. control and TGFβ2-treated vs. control were extracted. The KEGG compound hierarchy was assigned to the extracted list of metabolites for the two comparisons using the function defineCompounds() in FELLA by mapping the metabolite compounds against the loaded database. The KEGG-assigned metabolomics data were then used as input for pathway enrichment analysis using the undirected heat diffusion model followed by statistical normalization using Z-scores for sub-network analysis in the R package FELLA v1.14.0 [31 (link)]. For the pathway enrichment analysis, metabolites with logFC > 1.5 or <−1.5 (TNFα-treated vs. control), logFC > 1.0, and logFC < −1.0 (TGFβ2-treated vs. control), with a Benjamini–Hochberg adjusted p-value < 0.05 were included. The purpose of two different fold-change cutoffs was due to differences in metabolite changes between TNFα-treated and TGFβ2-treated H-RPE, enabling the capture of sufficient differential metabolites in each group for generating a comprehensive network analysis. The enrichment analysis outputs were then mapped to the Homo sapiens (hsa) KEGG graphs and subsequently used for network analysis. Optimal visualization of the metabolic network graphs was generated with the number of nodes limit (nlimit) of 250 for TNFα-treated H-RPE vs. control and nlimit of 160 for TGFβ2-treated H-RPE vs. control using the generateResultsGraph() in FELLA. KEGG IDs unmapped to the KEGG graphs were retrieved and searched against the KEGG pathway database (https://www.genome.jp/kegg/pathway.html , accessed on 28 December 2022).
The Enriched Pathway Network Analysis was generated using the KEGG database of Homo sapiens [30 (link)], which was loaded locally using the functions buildGraphFromKEGGREST() and buildDataFromGraph() in the R package FELLA v1.14.0 [31 (link)]. The lists of metabolites that were significantly different from the TNFα-treated vs. control and TGFβ2-treated vs. control were extracted. The KEGG compound hierarchy was assigned to the extracted list of metabolites for the two comparisons using the function defineCompounds() in FELLA by mapping the metabolite compounds against the loaded database. The KEGG-assigned metabolomics data were then used as input for pathway enrichment analysis using the undirected heat diffusion model followed by statistical normalization using Z-scores for sub-network analysis in the R package FELLA v1.14.0 [31 (link)]. For the pathway enrichment analysis, metabolites with logFC > 1.5 or <−1.5 (TNFα-treated vs. control), logFC > 1.0, and logFC < −1.0 (TGFβ2-treated vs. control), with a Benjamini–Hochberg adjusted p-value < 0.05 were included. The purpose of two different fold-change cutoffs was due to differences in metabolite changes between TNFα-treated and TGFβ2-treated H-RPE, enabling the capture of sufficient differential metabolites in each group for generating a comprehensive network analysis. The enrichment analysis outputs were then mapped to the Homo sapiens (hsa) KEGG graphs and subsequently used for network analysis. Optimal visualization of the metabolic network graphs was generated with the number of nodes limit (nlimit) of 250 for TNFα-treated H-RPE vs. control and nlimit of 160 for TGFβ2-treated H-RPE vs. control using the generateResultsGraph() in FELLA. KEGG IDs unmapped to the KEGG graphs were retrieved and searched against the KEGG pathway database (
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Cells
Genome
Homo sapiens
Metabolic Networks
Thermal Diffusion
Tumor Necrosis Factor-alpha
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More about "Metabolic Networks"
Metabolic networks are complex, interconnected systems of biochemical reactions and pathways that govern the essential metabolic processes within living organisms.
These dynamic, self-organizing biological systems enable the efficient conversion of nutrients into energy, the synthesis of crucial biomolecules, and the regulation of diverse physiological functions.
Understanding the regulation and dynamics of metabolic pathways is crucial for advancing fields such as medicine, biotechnology, and systems biology.
Researchers can leverage powerful tools like Ingenuity Pathway Analysis (IPA), MATLAB, and HiSeq 4000 sequencing to study these networks and uncover new insights.
Metabolic networks can be analyzed using a variety of techniques, including HPLC, JMP software, and IPA Analysis Match CL.
By combining cutting-edge technologies with advanced computational methods, scientists can elucidate the intricate mechanisms underlying metabolic processes and unlock new possibilities in areas like personalized medicine and industrial biotech.
Whether you're using MATLAB R2012b or exploring the latest in HiSeq 2500 sequencing, understanding metabolic networks is essential for driving scientific progress and addressing global challenges.
Embark on your journey of metabolic discovery with the power of AI-driven platforms like PubCompare.ai, which can help you optimize your research protocols and enhance reproducibility.
These dynamic, self-organizing biological systems enable the efficient conversion of nutrients into energy, the synthesis of crucial biomolecules, and the regulation of diverse physiological functions.
Understanding the regulation and dynamics of metabolic pathways is crucial for advancing fields such as medicine, biotechnology, and systems biology.
Researchers can leverage powerful tools like Ingenuity Pathway Analysis (IPA), MATLAB, and HiSeq 4000 sequencing to study these networks and uncover new insights.
Metabolic networks can be analyzed using a variety of techniques, including HPLC, JMP software, and IPA Analysis Match CL.
By combining cutting-edge technologies with advanced computational methods, scientists can elucidate the intricate mechanisms underlying metabolic processes and unlock new possibilities in areas like personalized medicine and industrial biotech.
Whether you're using MATLAB R2012b or exploring the latest in HiSeq 2500 sequencing, understanding metabolic networks is essential for driving scientific progress and addressing global challenges.
Embark on your journey of metabolic discovery with the power of AI-driven platforms like PubCompare.ai, which can help you optimize your research protocols and enhance reproducibility.