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Biosynthetic Pathways

Biosynthetic pathways refer to the series of enzymatic reactions that living organisms use to produce specific molecules, such as proteins, lipids, and carbohydrates.
These pathways are critical for the synthesis of essential biomolecules and play a key role in numerous biological processes.
Researchers studying biosynthetic pathways often utilize comperative analysis tools like PubCompare.ai to optimize their workflows, locate relevant protocols, and identify the most accurate and reproducible methods.
By leveraging AI-driven comparisons, scientists can enhance the efficiency and reliability of their biosynthetic pathways research.

Most cited protocols related to «Biosynthetic Pathways»

To undertake negative selection RNAi screening in solid tumours, pools of MCF10DCIS.com cells expressing an shRNA library were injected into the 4th mammary fat pad of immunocompromised mice and allowed to form tumours. Abundances of shRNAs in the tumours was determined using massively parallel sequencing and compared to shRNA abundance in the injected cells. Genes targeted by shRNAs that were significantly depleted during tumour growth were considered hits and prioritized by analyzing gene copy number data from human tumours and cancer cell lines. Lentiviral shRNAs were used to suppress PHGDH expression in breast cancer cell lines with and without PHGDH genomic amplification. Serine synthesis pathway activity and anaplerosis were measured via flux analyses utilizing isotopically labeled molecules.
Publication 2011
Biosynthetic Pathways Breast cDNA Library Cell Lines Cells Genes Genome Homo sapiens Malignant Neoplasms MCF-7 Cells Mus Neoplasms Pad, Fat RNA Interference Serine Short Hairpin RNA
To undertake negative selection RNAi screening in solid tumours, pools of MCF10DCIS.com cells expressing an shRNA library were injected into the 4th mammary fat pad of immunocompromised mice and allowed to form tumours. Abundances of shRNAs in the tumours was determined using massively parallel sequencing and compared to shRNA abundance in the injected cells. Genes targeted by shRNAs that were significantly depleted during tumour growth were considered hits and prioritized by analyzing gene copy number data from human tumours and cancer cell lines. Lentiviral shRNAs were used to suppress PHGDH expression in breast cancer cell lines with and without PHGDH genomic amplification. Serine synthesis pathway activity and anaplerosis were measured via flux analyses utilizing isotopically labeled molecules.
Publication 2011
Biosynthetic Pathways Breast cDNA Library Cell Lines Cells Genes Genome Homo sapiens Malignant Neoplasms MCF-7 Cells Mus Neoplasms Pad, Fat RNA Interference Serine Short Hairpin RNA
In addition to the secondary metabolite cluster types supported in the original release of antiSMASH (type I, II and III polyketides, non-ribosomal peptides, terpenes, lantipeptides, bacteriocins, aminoglycosides/aminocyclitols, β-lactams, aminocoumarins, indoles, butyrolactones, ectoines, siderophores, phosphoglycolipids, melanins and a generic class of clusters encoding unusual secondary metabolite biosynthesis genes), version 2.0 adds support for oligosaccharide antibiotics, phenazines, thiopeptides, homoserine lactones, phosphonates and furans. The cluster detection uses the same pHMM rule-based approach as the initial release (17 (link)): in short, the pHMMs are used to detect signature proteins or protein domains that are characteristic for the respective secondary metabolite biosynthetic pathway. Some pHMMs were obtained from PFAM or TIGRFAM. If no suitable pHMMs were available from these databases, custom pHMMs were constructed based on manually curated seed alignments (Supplementary Table S1). These are composed of protein sequences of experimentally characterized biosynthetic enzymes described in literature, as well as their close homologs found in gene clusters from the same type. The models were curated by manually inspecting the output of searches against the non-redundant (nr) database of protein sequences. The seed alignments are available online at http://antismash.secondarymetabolites.org/download.html#extras. After scanning the genome with the pHMM library, antiSMASH evaluates all hits using a set of rules (Supplementary Table S2) that describe the different cluster types. Unlike the hard-coded rules in the initial release of antiSMASH, the detection rules and profile lists are now located in editable TXT files, making it easy for users to add and modify cluster rules in the stand-alone version, e.g. to accommodate newly discovered or proprietary compound classes without code changes. The results of gene cluster predictions by antiSMASH are continuously checked on new data arising from research performed throughout the natural products community, and pHMMs and their cut-offs are regularly updated when either false positives or false negatives become apparent.
The profile-based detection of secondary metabolite clusters has now been augmented by a tighter integration of the generalized PFAM (22 (link)) domain-based ClusterFinder algorithm (Cimermancic et al., in preparation) already included in version 1.0 of antiSMASH. This algorithm performs probabilistic inference of gene clusters by identifying genomic regions with unusually high frequencies of secondary metabolism-associated PFAM domains, and it was designed to detect ‘classical’ as well as less typical and even novel classes of secondary metabolite gene clusters. While antiSMASH 1.0 only generated the output of this algorithm in a static image, version 2.0 displays these additional putative gene clusters along with the other gene clusters in the HTML output. A key advantage of this is that these putative gene clusters will now also be included in the subsequent (Sub)ClusterBlast analyses.
Publication 2013
Amino Acid Sequence Aminocoumarins Aminoglycosides Anabolism Antibiotics Bacteriocins Biosynthetic Pathways Childbirth Classes Enzymes Furans Gene Clusters Generic Drugs Genes Genome Genomic Library homoserine lactone Indoles Lactams Melanins Natural Products Oligosaccharides Peptides Phenazines Phosphonates Polyketides Prognosis Protein Domain Proteins Ribosomes Secondary Metabolism Siderophores Terpenes
KS and C domains were extracted from select PKS and NRPS genes associated with experimentally characterized biosynthetic pathways using the online program NRPS-PKS (http://www.nii.res.in/searchall.html) [19] (link), [21] (link). The pathways selected include representatives of the currently known enzyme architectures and functions associated with type I and II PKSs and NRPSs and thus this database is not meant to be comprehensive. The biochemical function and enzyme architecture of each domain was manually confirmed by analysis of the associated domain string and secondary metabolic product. Based on these results, each sequence was preliminarily assigned to a domain class. The compound produced by the associated pathway, the literature reference including PubMed ID, and the gene accession number was also recorded for each domain.
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Publication 2012
Biosynthetic Pathways Enzymes Genes Secondary Metabolism
Source catalogs are processed and loaded into the database (2D only) as follows. We harvest tagged values in selected source SDF files. Name and CAS numbers are loaded into a synonyms table, while selected bioactivity and other selected data are stored in a provided_values table. We convert SDF to SMILES98 using RDKit and take the largest organic part of the compound (desalting), enumerating up to four stereoisomers from stereochemically ambiguous SMILES using OEChem TK version 1.7 (OpenEye Scientific Software, Santa Fe, NM). Because of the combinatorial problem of ambiguous stereocenters in sterols, we used SMARTS filters to prioritize the most probable implied stereoisomers based on biosynthetic pathways. (Prof. Leslie Kuhn, private communication.99 The SMILES are neutralized with mitools (molinspiration.com), which also filters out incorrectly coded molecules well. Molecules are loaded using Python/RDKit scripts by attempting to map them to existing ZINC IDs, or creating new ZINC substances as necessary, as well as any additional required datastructures. InChI and InChIkeys are calculated on loading, and the InChIkey is used as a unique constraint in the database. 512 bit Morgan fingerprints with radius 2 (effectively ECFP4) are calculated for each molecule using RDKit.99
Publication 2015
Biosynthetic Pathways Organic Chemicals Phytosterols Python Radius Stereoisomers Zinc

Most recents protocols related to «Biosynthetic Pathways»

Example 2

PAO1, the parent strain of PGN5, is a wild-type P. aeruginosa strain that produces relatively small amounts of alginate and exhibits a non-mucoid phenotype; thus, PGN5 is also non-mucoid when cultured (FIG. 3A). In PAO1, the alginate biosynthetic operon, which contains genes required for alginate production, is negatively regulated. Activation of this operon leads to alginate production and a mucoid phenotype. For example, over-expression of mucE, an activator of the alginate biosynthetic pathway, induces a strong mucoid phenotype in the PAO1 strain (e.g., P. aeruginosa strain VE2; FIG. 3B). The plasmid pUCP20-pGm-mucE, which constitutively over-expresses MucE, was used to test whether the genetically-modified PGN5 strain could produce alginate. Indeed, the presence of this plasmid in PGN5 (PGN5+mucE) induced a mucoid phenotype (FIG. 3B). To measure the amount of alginate produced by PGN5+mucE on a cellular level, a standard carbazole assay was performed, which showed that the PGN5+mucE and VE2 (i.e., PAO1+mucE) strains produce comparable amounts of alginate (FIG. 3C; 80-120 g/L wet weight).

To examine whether the alginate produced by PGN5+mucE was similar in composition to alginate produced by VE2, HPLC was performed to compare the M and G content of alginate produced by each strain. The chromatograms obtained from alginate prepared from VE2 and PGN5+mucE were identical (FIG. 3D), and the M:G ratios were comparable to a commercial alginate control (data not shown). To confirm that the physical properties of VE2 and PGN5+mucE alginates were also similar, alginate gels were prepared from alginate produced by each strain and the viscosity and yield stress was measured. The viscosities of VE2 and PGN5+mucE alginate gels were comparable at 73.58 and 72.12 mPa, respectively (FIG. 3E). Similarly, the yield stress of VE2 and PGN5+mucE alginate gels were comparable at 47.34 and 47.16 Pa, respectively (FIG. 3G).

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Patent 2024
Alginate Alginates Anabolism Biological Assay Biosynthetic Pathways carbazole Cells Gels Genes High-Performance Liquid Chromatographies Operon Parent Phenotype Physical Processes Plasmids Pseudomonas aeruginosa Strains Viscosity
Not available on PMC !

Example 2

Expressed and purified dihydropteroate synthase (DHPS) from S. aureus (saDHPS) was cloned. DHPS is the enzyme that installs PABA (p-aminobenzoic acid) in the folate biosynthesis pathway (Scheme 2). It has been demonstrated that the PABA analog PAS (2-aminosalicylate) is incorporated into folic acid in M. tuberculosis (Chakraborty, S. et al. 2013), suggesting that PAS is a substrate for DHPS. Using a coupled assay, it was determined that the kinetic parameters for saDHPS with PABA, PAS and F-PABA. Importantly, all three compounds have similar kcat and Km values indicating that F-PABA is an alternative substrate for saDHPS. Since PAS is an antibacterial compound whose mechanism of action may be related for the ability of this compound to compete with PABA for DHPS, we determined the antibacterial activity and cytotoxicity of F-PABA for several bacterial species as well as Vero cells. In each case no growth inhibition was observed up to 200 μg/ml. Unlike PAA, 2-F-PABA has no antibacterial activity (Table 1).

[Figure (not displayed)]

TABLE 1
MIC (μg/ml)
2-F-PABAPAS
M. tuberculosis>1000.08
S. aureus>200>200
E. coli>200>200

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Patent 2024
4-Aminobenzoic Acid Anti-Bacterial Agents Bacteria Biological Assay Biosynthetic Pathways Cells Cytotoxin Dihydropteroate Synthase Drug Kinetics Enzymes Escherichia coli Folate Folic Acid Kinetics Mammals Mycobacterium tuberculosis Psychological Inhibition Vero Cells

Example 1

Since the biosynthetic pathway of anatabine and its associated genes is not completely known, a novel genetic variation was created in a population of tobacco plants to identify plants that have a significantly reduced ability to biosynthesize anatabine. These plants very likely have a mutated non-functional gene, critical for anatabine biosynthesis.

A population of the Flue-cured variety “401” was used in these experiments. Approximately 5000 seeds were treated with 0.6% ethyl methane sulfonate and germinated. M1 plants were grown in the field and M2 seeds were collected. Fifteen hundred M2 seeds were germinated and grown in 4-inch pots. At 50% flowering stage, plants were topped. Leaf samples were collected 2 weeks after topping and the samples screened for anatabine levels using high performance thin layer chromatography (HP-TLC) and gas chromatography.

After screening for alkaloids, two Flue Cured (FC) 401 ultra-low anatabine (ULA) lines were selected for trait development. It is noted that the amount of nicotine in both ULA lines is unchanged.

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Patent 2024
Alkaloids Anabolism anatabine Biosynthetic Pathways Ethyl Methanesulfonate Gas Chromatography Genes Genetic Diversity Marijuana Abuse Mutagenesis Nicotiana tabacum Nicotine Plant Embryos Plant Leaves Plants Thin Layer Chromatography
Not available on PMC !

Example 10

The relative contribution of ADC and ODC to putrescine biosynthesis was evaluated by measuring the activity of each enzyme in the leaves (leaf 23) and roots of the NA and LA plants at topping and harvest. ADC and ODC activity varied in an organ-specific and developmental stage-specific manner in both lines (FIG. 4). Whereas ADC activity was high in the leaves but minimal in the roots of both lines, ODC activity was higher in the younger leaves and roots, indicating that ODC is mainly responsible for putrescine biosynthesis in the roots. ADC activity was significantly higher (1.4-fold, p<0.05) in the leaves of the LA plants compared to the NA controls at topping and harvest (FIG. 4A). Similarly, ODC activity was significantly higher (p<0.05) in the LA plants compared to the NA controls in the roots at topping (1.8-fold) and at harvest (1.7-fold), and in young leaves at topping (1.5-fold) (FIG. 4B).

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Patent 2024
Anabolism Biosynthetic Pathways enzyme activity Plant Roots Plants Polyamines Putrescine
Pearson’s correlation analysis was conducted by calculating the correlation coefficient between anthocyanin content and the expression of DEGs enriched in the flavonoid–anthocyanin biosynthesis pathway (ko00941 and ko00942). Furthermore, R2R3-MYB and bHLH TFs with differential expression levels were used to perform a correlation analysis with differentially accumulated anthocyanins. Interaction networks between DEGs and differentially accumulated anthocyanins were visualized using Cytoscape 2.8.2 (Cho et al., 2016 (link)).
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Publication 2023
Anthocyanins Biosynthetic Pathways Flavonoids

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More about "Biosynthetic Pathways"

Biosynthetic pathways, also known as metabolic pathways, refer to the series of enzymatic reactions that living organisms use to produce essential biomolecules such as proteins, lipids, and carbohydrates.
These pathways play a crucial role in numerous biological processes, including energy production, signal transduction, and cellular structure formation.
Researchers studying biosynthetic pathways often utilize comparative analysis tools like PubCompare.ai to optimize their workflows, locate relevant protocols, and identify the most accurate and reproducible methods.
By leveraging AI-driven comparisons, scientists can enhance the efficiency and reliability of their biosynthetic pathways research.
One key technique used in biosynthetic pathways research is qPCR (quantitative Polymerase Chain Reaction), which allows for the amplification and quantification of specific DNA or RNA sequences.
The StepOnePlus Real-Time PCR System and the CFX96 Real-Time System are examples of qPCR instruments that can be used to study gene expression and other molecular processes involved in biosynthetic pathways.
Another important tool is mass spectrometry, which can be used to identify and quantify the biomolecules produced through biosynthetic pathways.
The 4800 MALDI-TOF/TOF mass spectrometer is a powerful instrument that can be utilized for this purpose.
Additionally, researchers may use reagents like TRIzol and the RevertAid First Strand cDNA Synthesis Kit to extract and prepare RNA and cDNA samples for analysis.
The ChamQ Universal SYBR qPCR Master Mix and the SYBR Premix Ex Taq II can then be used to perform qPCR on these samples.
Data analysis is a crucial aspect of biosynthetic pathways research, and tools like the Data Explorer 4.9 Software can be used to analyze and visualize the data generated from experiments.
The MiSeq and HiSeq 2000 platforms are also commonly used in this field for high-throughput sequencing of DNA and RNA samples.
By leveraging these various techniques and tools, researchers can gain a deeper understanding of the complex and dynamic biosynthetic pathways that are essential for the survival and function of living organisms.