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Siderophores

Siderophores are small, high-affinity iron-chelating compounds secreted by microorganisms, such as bacteria and fungi, to facilitate iron uptake.
These molecules play a crucial role in the acquisition and transport of ferric iron, an essential nutrient for many organisms.
Siderophores bind to iron(III) with exceptional strength, allowing them to retrieve this otherwise insoluble nutrient from the environment.
This process is particularly important in iron-limited conditions, where siderophores provide a competitive advantage for microbial growth and survival.
Siderophores have a wide range of applications in areas such as agriculture, bioremediation, and medicine, making them an important subject of study in the field of microbiology and biochemistry.
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Most cited protocols related to «Siderophores»

Expanded Secondary Metabolite Detection in antiSMASH 2.0

Cited 25 times

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.

Blin K., Medema M.H., Kazempour D., Fischbach M.A., Breitling R., Takano E, & Weber T. (2013). antiSMASH 2.0—a versatile platform for genome mining of secondary metabolite producers. Nucleic Acids Research, 41(Web Server issue), W204-W212.

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

Identifying Protein Domains in Fungal SM Clusters

Cited 23 times

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Publication 2010

Anabolism Enzymes Genes Genome Gliotoxin Melanins Membrane Transport Proteins Polyketides Protein Domain pseurotin Siderophores Transcription, Genetic Vertebral Column

Quantitative Siderophore Estimation

Cited 22 times

Quantitative estimation of siderophore was done by taking supernatant of bacterial cultures grown in LB broth medium (Hu and Xu 2011 ). For this, 1 ml broth was taken in 1.5 ml centrifuge tube (Thomas Scientific, US) (one for each bacterial culture) and after sterilization inoculated with 10 µl of freshly grown bacterial culture (10⁸ colony forming units (cfu) per ml). Four replicates (tubes) were taken for each strain. Apart from this, control tube (un-inoculated broth) was also maintained. After incubation at 28 °C for 48 h, bacterial cultures were centrifuged at 10,000 rpm for 10 min, cell pellets were discarded, and supernatant was used to estimate siderophore. Supernatant (0.5 ml) of each bacterial culture was mixed with 0.5 ml CAS reagent and after 20 min optical density was taken at 630 nm (Spectrophotometer: Thermo Scientific, Evolution 201). Siderophore produced by strains was measured in percent siderophore unit (psu) which was calculated according to the following formula (Payne 1993 (link)):

Siderophore production (psu) = \frac{(A_{r} - A_{s}) \times 100}{A_{r}},

where A_r = absorbance of reference (CAS solution and un-inoculated broth), and A_s = absorbance of sample (CAS solution and cell-free supernatant of sample).

Arora N.K, & Verma M. (2017). Modified microplate method for rapid and efficient estimation of siderophore produced by bacteria. 3 Biotech, 7(6), 381.

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Publication 2017

Bacteria Biological Evolution Cells Pellets, Drug Siderophores Sterilization Strains

Comprehensive Biosynthetic Gene Cluster Detection

Cited 14 times

Using the HMMer3 tool (http://hmmer.janelia.org/), the amino acid sequence translations of all protein-encoding genes are searched with profile Hidden Markov Models (pHMMs) based on multiple sequence alignments of experimentally characterized signature proteins or protein domains (proteins, protein subtypes or protein domains which are each exclusively present in a certain type of biosynthetic gene clusters). Using both existing pHMMs (5 (link),11–13 ) and new pHMMs from seed alignments, we constructed a library of models specific for type I, II and III PK, NRP, terpene, lantibiotic, bacteriocin, aminoglycoside/aminocyclitol, beta-lactam, aminocoumarin, indole, butyrolactone, ectoine, siderophore, phosphoglycolipid, melanin and aminoglycoside biosynthesis signature genes. Additionally, we constructed a number of pHMMs specific for false positives, such as the different types of fatty acid synthases which show homology to PKSs. The final detection stage operates a filtering logic of negative and positive pHMMs and their cut-offs. The logic is based on knowledge of the minimal core components of each gene cluster type taken from the scientific literature. The cut-offs were determined by manual studies of the pHMM results when run against the NCBI non-redundant (nr) protein sequence database (ftp://ftp.ncbi.nlm.nih.gov/blast/db). All technical details on the pHMM library and the detection rules are available in Supplementary Tables S1 and S2, respectively.
Gene clusters are defined by locating clusters of signature gene pHMM hits spaced within <10 kb mutual distance. To include flanking accessory genes, gene clusters are extended by 5, 10 or 20 kb on each side of the last signature gene pHMM hit, depending on the gene cluster type detected. As a consequence of this greedy methodology, gene clusters that are spaced very closely together may be merged into ‘superclusters’. These gene clusters are indicated in the output as ‘hybrid clusters’; they may either represent a single gene cluster which produces a hybrid compound that combines two or more chemical scaffold types, or they may represent two separate gene clusters which just happen to be spaced very closely together.

Medema M.H., Blin K., Cimermancic P., de Jager V., Zakrzewski P., Fischbach M.A., Weber T., Takano E, & Breitling R. (2011). antiSMASH: rapid identification, annotation and analysis of secondary metabolite biosynthesis gene clusters in bacterial and fungal genome sequences. Nucleic Acids Research, 39(Web Server issue), W339-W346.

Publication 2011

Amino Acid Sequence Aminoglycosides Anabolism Bacteriocins beta-Lactams DNA Library ectoine Gene Clusters Gene Products, Protein Genes Hybrids indole Lantibiotics Melanins Protein Biosynthesis Proteins Siderophores Synthase, Fatty Acid Terpenes

Comprehensive Microbial Secondary Metabolite Prediction

Cited 13 times

We used PRISM 4 and antiSMASH 5 to predict the chemical structures of secondary metabolites encoded within 3759 complete bacterial genomes and 6362 metagenome-assembled genomes (MAGs). All bacterial genomes with an assembly level of ‘Complete’ were downloaded from NCBI Genome, and a set of dereplicated genomes as determined by the Genome Taxonomy Database^{15 (link)} were retained to mitigate the impact of highly similar genomes on our analysis. Similarly, a set of 7902 MAGs^{23 (link)} was obtained from NCBI BioProject (accession PRJNA348753) and the subset of dereplicated genomes was retained. Detected BGCs were matched between PRISM and antiSMASH if their nucleotide sequence overlapped over any range. A small number of PRISM BGC types were mapped to more than one antiSMASH BGC type, including aminoglycosides (‘amglyccycl’ and ‘oligosaccharide’), type I polyketides (‘t1pks’ and ‘transatpks’), and RiPPs (‘bottromycin’, ‘cyanobactin’, ‘glycocin’, ‘head_to_tail’, ‘LAP’, ‘lantipeptide’, ‘lassopeptide’, ‘linaridin’, ‘microviridin’, ‘proteusin’, ‘sactipeptide’, and ‘thiopeptide’). The “hybrid” category encompassed all BGCs assigned any combination of two or more cluster types, i.e., it was not limited to hybrid NRPS-PKS BGCs. The “other” category encompassed aryl polyenes, bacteriocins, butyrolactones, ectoines, furans, homoserine lactones, ladderanes, melanins, N-acyl amino acids, NRPS-independent siderophores, phenazines, phosphoglycolipids, resorcinols, stilbenes, terpenes, and type III polyketides. Producing organism taxonomy was based on genome phylogeny and retrieved from the Genome Taxonomy Database^{15 (link)}.
Cheminformatic metrics, including molecular weight, number of hydrogen bond donors and acceptors, octanol-water partition coefficients, and Bertz topological complexity, were calculated in RDKit. Both platforms occasionally generated very small, non-specific structure predictions (for example, a single unspecified amino acid or a single malonyl unit) that did not provide actionable information about the chemical structure of the encoded product; to remove these from consideration, we applied a molecular weight filter to remove structures under 100 Da output by either platform. To evaluate the internal structural diversity of each set of predicted structures, we computed the distribution of pairwise Tcs for each set⁴⁵, taking the median pairwise Tc instead of the mean as a summary statistic to ensure robustness against outliers. Structural similarity to known natural products was assessed using the RDKit implementation of the ‘natural product-likeness’ score^{22 (link)}, and by the median Tc between predicted structures and the known secondary metabolite structures deposited in the NP Atlas database^{46 (link)}.

Skinnider M.A., Johnston C.W., Gunabalasingam M., Merwin N.J., Kieliszek A.M., MacLellan R.J., Li H., Ranieri M.R., Webster A.L., Cao M.P., Pfeifle A., Spencer N., To Q.H., Wallace D.P., Dejong C.A, & Magarvey N.A. (2020). Comprehensive prediction of secondary metabolite structure and biological activity from microbial genome sequences. Nature Communications, 11, 6058.

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Publication 2020

Amino Acids Aminoglycosides Bacteriocins Base Sequence bottromycin cyanobactins Donors Furans Genome Genome, Bacterial Head homoserine lactone Hybrids Hydrogen Bonds Melanins Metagenome Natural Products Octanols Oligosaccharides Phenazines Polyenes Polyketides prisma Prokaryotic Cells Resorcinols Secondary Metabolism Siderophores Stilbenes Tail Terpenes

Most recents protocols related to «Siderophores»

Enzymatic Characterization of Marmoricola sp.

To determine hydrolysis of starch, urea, Tween 20, 40, 60, and 80, the isolate was cultured on TSA at 30°C for a week, as described by Cowan and Steel [37 ]. The enzyme activity was evaluated using API ZYM kit, API 20NE kit (bioMérieux, France), and acid production was tested API 50CH test (bioMérieux, France) according to the manufacturer’s instructions at 30°C for 2 days. The type strains, M. bovistercoris NEAU-LLE^T and M. pseudoresistence CC-5209^T, which are related to KUDC0405^T, were analyzed under the same conditions. The cell wall peptidoglycan was analyzed using an amino acid analyzer (L-8900; Hitachi, Japan). To analyze the polar lipids, two-dimensional thin layer chromatography (TLC) analysis were used according to Minnikin et al. [38 (link)]. The fatty acid composition analysis was performed using the Microbial Identification System from cells of the strain KUDC0405^T, and reference strains were incubated on TSA at 30°C for 7 days. To determine siderophore production by strain KUDC0405^T, chrome azurol S (CAS) media were used as previously described [39 (link), 40 (link)].

Hwang Y.J., Lee S.Y., Son J.S., Youn J.S., Lee W., Shin J.H., Lee M.H, & Ghim S.Y. (2023). Microbacterium elymi sp. nov., Isolated from the Rhizospheric Soil of Elymus tsukushiensis, a Plant Native to the Dokdo Islands, Republic of Korea. Journal of Microbiology and Biotechnology, 33(2), 188-194.

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Publication 2023

Acids Amino Acids Cell Wall chrome azurol S enzyme activity Fatty Acids Hydrolysis Lipids Peptidoglycan Siderophores Starch Steel Strains Thin Layer Chromatography Tween 20 Urea

Automated Klebsiella Genome Analysis

Genome assemblies were uploaded to Pathogenwatch v2.3.1 [46 (link)] where Kleborate v2.2 [47 (link)] and Kaptive v2.0 [17 (link)] were automatically deployed to call multi-locus sequence types (STs) using the seven-locus scheme [65 (link)], capsular polysaccharide (K) and lipopolysaccharide O locus types, and serotype predictions, acquired virulence traits including the siderophores aerobactin (iuc), yersiniabactin (ybt) and salmochelin (iro), the genotoxin colibactin (clb) and the hypermucoidy locus (rmpADC). Pathogenwatch also deploys Kleborate to identify established AMR determinants (acquired genes and chromosomal mutations) [47 (link)] for the following antimicrobial classes: aminoglycosides, carbapenems, third-generation cephalosporins, third-generation cephalosporins plus β-lactamase inhibitors, colistin, fluoroquinolones, fosfomycin, penicillins, penicillins + β-lactamase inhibitors, phenicols, sulfonamides, tetracyclines, tigecycline and trimethoprim.

Foster-Nyarko E., Cottingham H., Wick R.R., Judd L.M., Lam M.M., Wyres K.L., Stanton T.D., Tsang K.K., David S., Aanensen D.M., Brisse S, & Holt K.E. (2023). Nanopore-only assemblies for genomic surveillance of the global priority drug-resistant pathogen, Klebsiella pneumoniae. Microbial Genomics, 9(2), mgen000936.

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Publication 2023

aerobactin Aminoglycosides beta-Lactamase Inhibitors Capsule Carbapenems Cephalosporins Chromosomes colibactin Colistin Fluoroquinolones Fosfomycin Genes Genome Lipopolysaccharides Microbicides Mutagens Mutation Penicillins Polysaccharides salmochelin Siderophores Sulfonamides Tetracyclines Tigecycline Trimethoprim Virulence yersiniabactin

Structural Characterization of Chryseochelin

UHPLC–HR–MS was applied to determine accurate masses of siderophores and to predict their molecular formula. MS/MS fragmentation spectra were collected with the conditions described earlier. NMR data of purified chryseochelin A were measured in D₂O on a Bruker AV-600 MHz instrument equipped with a TCI CryoProbe. Thereby, the following experiments were included: ¹H, ¹³C, distortionless enhancement by polarization transfer (DEPT) 90 and 135, heteronuclear single quantum correlation spectroscopy (HSQC), correlation spectroscopy (COSY), heteronuclear multiple bond correlation (HMBC), total correlation spectroscopy (TOCSY), and nuclear overhauser and exchange spectroscopy (NOESY). Ultraviolet-visible (UV/VIS) spectra of chryseochelin A, its ferric complex and photoproduct were recorded on a NanoDrop 2000c spectrophotometer (Thermo Scientific).

Rehm K., Vollenweider V., Gu S., Friman V.P., Kümmerli R., Wei Z, & Bigler L. (2023). Chryseochelins—structural characterization of novel citrate-based siderophores produced by plant protecting Chryseobacterium spp.. Metallomics: Integrated Biometal Science, 15(3), mfad008.

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Publication 2023

Siderophores Spectrum Analysis Tandem Mass Spectrometry

Purification of Siderophore Chryseochelin A

A total of 450 mL bacterial supernatant was prepared under iron limited conditions as described in the previous section. In a first step, the supernatant was frozen by liquid nitrogen and then lyophilized overnight. The dried residue was reconstituted in 80 mL MeOH and the solution was filtered over a filter paper. Afterward, the organic solvent was evaporated under a gentle stream of nitrogen. The dried extract was then dissolved in 5 mL ultrapure H₂O. This solution containing the siderophore chryseochelin A was centrifuged for 10 min (4°C, 14 000 rpm) and transferred into a new Eppendorf tube. The crude siderophore was then purified twice by reversed-phase high pressure liquid chromatography (RP–HPLC) on a preparative Triat C18 column (150 × 30 mm, 5 μm, YMC-Actus) and then on an analytical CORTECS C18 column (150 × 4.6 mm, 2.7 μm, Waters) while monitoring the absorption wavelength at 270 nm. Detailed chromatographic conditions can be found in the supplementary (Supplementary Figs. S1 and S2). The final pure siderophore (3.1 mg), chryseochelin A, was stored as a white powder at −20°C.

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Publication 2023

Bacteria Chromatography Figs Freezing High-Performance Liquid Chromatographies Iron Metabolism Disorders Nitrogen Powder Siderophores Solvents

Siderophore-Producing Chryseobacterium Inhibit R. solanacearum

The three Chryseobacterium isolates, CHR2, CHR5, and CHR6, are representative members of a collection of 120 Chryseobacterium spp. sampled from the soil of tomato fields in China. The detailed description of the sampling, isolation, and characterization can be found in the work of Gu et al.^{26 (link)} Using the chrome azurol S assay, it was demonstrated that the bacterial strains produced siderophores and that their siderophore-containing supernatants were capable of inhibiting the plant pathogen R. solanacearum under iron-limited conditions.^{26 (link)} Genus-level taxonomic identification was obtained by sequencing the 16S rRNA gene using the universal primers F27 (5′-AGAGTTTGATCATGGCTCAG-3′) and R1492 (5′-TACGGTTACCTTGTTACGACTT-3′).
The plant-pathogenic strain R. solanacearum K60 was used for the dose–response experiments.^{28 (link)} Experiments were carried out in the laboratory of Prof. Leo Eberl (Department of Plant and Microbial Biology, University of Zurich) who has the necessary authorization to work with this pathogen.

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Publication 2023

Bacteria Biological Assay Cardiac Arrest chrome azurol S Chryseobacterium Genes Iron Iron Metabolism Disorders isolation Lycopersicon esculentum Oligonucleotide Primers Pathogenicity Plant Diseases Plants Ribosomal RNA Genes RNA, Ribosomal, 16S Siderophores Strains

Top products related to «Siderophores»

Ferrichrome by Merck Group

Sourced in United States

Ferrichrome is a laboratory product manufactured by Merck Group. It is a siderophore, a type of molecule that binds to and transports iron. Ferrichrome facilitates the uptake and utilization of iron by living organisms.

Deferoxamine mesylate salt by Merck Group

Sourced in United States, Germany

Deferoxamine mesylate salt is a pharmaceutical compound used as a chelating agent. Its core function is to bind and remove excess iron from the body in conditions such as iron overload.

Fecl3 6h2o by Merck Group

Sourced in United States, Germany, Spain, India, China, Switzerland, Singapore, United Kingdom, Denmark, France, Italy

FeCl3·6H2O is a chemical compound that consists of ferric chloride (FeCl3) crystalized with six water molecules (6H2O). It is a common inorganic compound used in various laboratory applications.

Millipore filter by Merck Group

Sourced in United States, Germany, United Kingdom, Ireland, Portugal, China, France, Italy, India, Japan

The Millipore filter is a membrane filtration device used for the separation and purification of various substances, such as liquids, gases, and particles. It employs a porous membrane to trap and remove unwanted components from the sample, allowing the desired substance to pass through. The filter is designed to provide efficient and reliable filtration, ensuring the quality and purity of the filtered material.

Forane by Abbott

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Forane is a brand of laboratory equipment offered by Abbott. It serves as an anesthetic agent used in surgical procedures. The core function of Forane is to provide anesthesia and pain relief during medical operations.

Enterobactin by Merck Group

Sourced in Germany

Enterobactin is a laboratory product developed by Merck Group. It is a siderophore, a type of small molecule that chelates and transports iron. Enterobactin's core function is to facilitate the acquisition of iron, an essential nutrient, by microorganisms.

Multiscreen hts filter plates by Merck Group

Sourced in Germany, United States, Ireland

MultiScreen HTS filter plates are a type of laboratory equipment designed for high-throughput screening (HTS) applications. They are used to separate and filter samples in a multi-well format, facilitating rapid processing and analysis of multiple samples simultaneously.

2 2 dipyridyl by Merck Group

Sourced in United States, Germany

2,2′-dipyridyl is a chemical compound commonly used as a laboratory reagent. It serves as a chelating agent, capable of forming stable complexes with various metal ions. This property makes it a useful tool in analytical and synthetic chemistry applications.

Hdtma by Merck Group

Sourced in Germany

HDTMA is a laboratory chemical used as a surfactant and reagent in various analytical and experimental applications. It is a quaternary ammonium compound that acts as a cationic surfactant. The core function of HDTMA is to serve as a wetting agent, emulsifier, and phase transfer catalyst in laboratory procedures.

More about "Siderophores"

Ferrichrome, Deferoxamine mesylate salt, FeCl3·6H2O, Millipore filter, Forane, Enterobactin, MultiScreen HTS filter plates, 2,2′-dipyridyl, HDTMA