The server requires a multiple sequence alignment of proteins and the corresponding DNA sequences as input. The internal action of the program can be divided into three main steps: (i) upload the protein sequence alignment and DNA sequences, (ii) reverse translation, i.e. conversion of the protein sequences into the corresponding DNA sequences in the form of regular expression patterns and (iii) generation of the codon alignment. In the second step, each protein sequence is converted into DNA sequence of a regular expression. For example, a short peptide sequence, MDP, is reverse-translated into a regular expression pattern of the DNA sequence as (A(U∣T)G)(GA(U∣T∣C∣Y))(CC.) . For frame shifts, we adapted the notation used in GeneWise (6 (link)): if an insertion or deletion is found in the coding region, it is represented by the number of nucleic acid residues at that site instead of an amino acid code. For example, M2P indicates that there is 1 nt deletion between methionine and proline. With this notation, it is easy to convert the peptide sequence into a regular expression pattern, in this case (A(U∣T)G)..(CC.) . After converting into a regular expression pattern, the input DNA sequence is searched with the pattern to obtain the corresponding coding region. Unmatched DNA sequence regions are discarded. The pattern matching has been designed to be tolerant of mismatches. This was achieved by extending 10 amino acid regular expression matches in both directions until the entire coding region of the input DNA sequence is covered. The regions between the extended fragments and those not covered by the extension are taken as mismatches, and reported, if any, in the output. In the third step, the protein sequence alignment is converted into the corresponding codon alignment by replacing each amino acid residue with the corresponding codon sequence.
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Chemicals & Drugs
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Amino Acid
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Methionine
Methionine
Methionine is an essential amino acid that plays a crucial role in various biological processes.
It is involved in protein synthesis, methylation reactions, and cellular metabolism.
Methionine is also a precursor for the synthesis of other important compounds, such as cysteine, taurine, and the antioxidant glutathione.
Deficiencies in methionine can lead to health issues, including stunted growth, liver damage, and neurological disorders.
Researchers studying methionine can leverage PubCompare.ai's AI-driven platform to easily locate the best protocols from literature, preprints, and patents, while conducting accurate comparisons to enhance reproducibility and accuarcy.
This tool can help streamline methionine research and optimize the identification of the most effective protocols.
It is involved in protein synthesis, methylation reactions, and cellular metabolism.
Methionine is also a precursor for the synthesis of other important compounds, such as cysteine, taurine, and the antioxidant glutathione.
Deficiencies in methionine can lead to health issues, including stunted growth, liver damage, and neurological disorders.
Researchers studying methionine can leverage PubCompare.ai's AI-driven platform to easily locate the best protocols from literature, preprints, and patents, while conducting accurate comparisons to enhance reproducibility and accuarcy.
This tool can help streamline methionine research and optimize the identification of the most effective protocols.
Most cited protocols related to «Methionine»
Amino Acids
Amino Acid Sequence
Codon
Deletion Mutation
DNA Sequence
Exons
Methionine
Nucleic Acids
Peptides
Proline
Proteins
Reading Frames
Sequence Alignment
Following
data acquisition, Thermo RAW files were processed using
a series of software tools that were developed in-house. First the
RAW files were converted to mzXML using a custom version of ReAdW.exe
(http://sashimi.svn.sourceforge.net/viewvc/sashimi/ ) that
had been modified to export ion accumulation times and FT peak noise.
During this initial processing we also corrected any erroneous assignments
of monoisotopic m/z. Using Sequest,24 (link) MS2 spectra were searched against the human
UniProt database (downloaded on 08/02/2011), supplemented with the
sequences of common contaminating proteins such as trypsin. This forward
database was followed by a decoy component, which included all target
protein sequences in reversed order.
Searches were performed
using a 50 ppm precursor ion tolerance.25 (link) When searching Orbitrap MS2 data, we used 0.02 Th fragment ion tolerance.
The fragment ion tolerance was set to 1.0 Th when searching ITMS2
data. Only peptide sequences with both termini consistent with the
protease specificity of LysC were considered in the database search,
and up to two missed cleavages were accepted. TMT tags on lysine residues
and peptide N-termini (+ 229.162932 Da) and carbamidomethylation of
cysteine residues (+ 57.02146 Da) were set as static modifications,
while oxidation of methionine residues (+ 15.99492 Da) was treated
as a variable modification. An MS2 spectral assignment false discovery
rate of less than 1% was achieved by applying the target-decoy strategy.26 (link) Filtering was performed using linear discriminant
analysis as described previously27 (link) to create
one composite score from the following peptide ion and MS2 spectra
properties: Sequest parameters XCorr and unique ΔCn, peptide
length and charge state, and precursor ion mass accuracy. The resulting
discriminant scores were used to sort peptides prior to filtering
to a 1% FDR, and the probability that each peptide-spectral-match
was correct was calculated using the posterior error histogram.
Following spectral assignment, peptides were assembled into proteins
and proteins were further filtered based on the combined probabilities
of their constituent peptides to a final FDR of 1%. In cases of redundancy,
shared peptides were assigned to the protein sequence with the most
matching peptides, thus adhering to principles of parsimony.28
data acquisition, Thermo RAW files were processed using
a series of software tools that were developed in-house. First the
RAW files were converted to mzXML using a custom version of ReAdW.exe
(
had been modified to export ion accumulation times and FT peak noise.
During this initial processing we also corrected any erroneous assignments
of monoisotopic m/z. Using Sequest,24 (link) MS2 spectra were searched against the human
UniProt database (downloaded on 08/02/2011), supplemented with the
sequences of common contaminating proteins such as trypsin. This forward
database was followed by a decoy component, which included all target
protein sequences in reversed order.
Searches were performed
using a 50 ppm precursor ion tolerance.25 (link) When searching Orbitrap MS2 data, we used 0.02 Th fragment ion tolerance.
The fragment ion tolerance was set to 1.0 Th when searching ITMS2
data. Only peptide sequences with both termini consistent with the
protease specificity of LysC were considered in the database search,
and up to two missed cleavages were accepted. TMT tags on lysine residues
and peptide N-termini (+ 229.162932 Da) and carbamidomethylation of
cysteine residues (+ 57.02146 Da) were set as static modifications,
while oxidation of methionine residues (+ 15.99492 Da) was treated
as a variable modification. An MS2 spectral assignment false discovery
rate of less than 1% was achieved by applying the target-decoy strategy.26 (link) Filtering was performed using linear discriminant
analysis as described previously27 (link) to create
one composite score from the following peptide ion and MS2 spectra
properties: Sequest parameters XCorr and unique ΔCn, peptide
length and charge state, and precursor ion mass accuracy. The resulting
discriminant scores were used to sort peptides prior to filtering
to a 1% FDR, and the probability that each peptide-spectral-match
was correct was calculated using the posterior error histogram.
Following spectral assignment, peptides were assembled into proteins
and proteins were further filtered based on the combined probabilities
of their constituent peptides to a final FDR of 1%. In cases of redundancy,
shared peptides were assigned to the protein sequence with the most
matching peptides, thus adhering to principles of parsimony.28
Amino Acid Sequence
Cytokinesis
Immune Tolerance
Lysine
Methionine
Peptides
Proteins
Trypsin
tyrosyl-alanyl-glycine
Cysteine
Homo sapiens
Immune Tolerance
Methionine
NR4A2 protein, human
Peptides
Proteins
Staphylococcal Protein A
The DIA data were analyzed with Spectronaut 5, a mass spectrometer vendor-independent software from Biognosys. The default settings were used for the Spectronaut search. Retention time prediction type was set to dynamic iRT (correction factor for window 1). Decoy generation was set to scrambled (no decoy limit). Interference correction on MS2 level was enabled. The false discovery rate (FDR) was set to 1% at peptide level. The DDA spectra were analyzed with the MaxQuant Version 1.4.1.2 analysis software using default settings with the following alterations (29 (link)). The minimal peptide length was set to 6. Search criteria included carbamidomethylation of cysteine as a fixed modification, oxidation of methionine and acetyl (protein N terminus) as variable modifications. The mass tolerance for the precursor was 4.5 ppm and for the fragment ions was 20 ppm. The DDA files were searched against the human UniProt fasta database (state 29.04.2013, 20,254 entries), the spike in proteins (12 entries), and the Biognosys iRT peptide sequences (11 entries). The identifications were filtered to satisfy FDR of 1% on peptide and protein level.
Full text: Click here
Cysteine
Homo sapiens
Immune Tolerance
Ions
Methionine
M protein, multiple myeloma
nucleoprotein, Measles virus
Peptides
Proteins
Retention (Psychology)
Acids
Amber
Cells
Chlorides
Cuboid Bone
Electrostatics
Face
Friction
hen egg lysozyme
Ions
Methionine
Molecular Dynamics
Peptides
Pressure
Proteins
Sodium
Ubiquitin
Most recents protocols related to «Methionine»
Example 2
A. Seed Treatment with Isolated Microbe
In this example, an isolated microbe from Tables 1-3 will be applied as a seed coating to seeds of corn (Zea mays). Upon applying the isolated microbe as a seed coating, the corn will be planted and cultivated in the standard manner.
A control plot of corn seeds, which did not have the isolated microbe applied as a seed coating, will also be planted.
It is expected that the corn plants grown from the seeds treated with the seed coating will exhibit a quantifiably higher biomass than the control corn plants.
The biomass from the treated plants may be about 1-10% higher, 10-20% higher, 20-30% higher, 30-40% higher, 40-50% higher, 50-60% higher, 60-70% higher, 70-80% higher, 80-90% higher, or more.
The biomass from the treated plants may equate to about a 1 bushel per acre increase over the controls, or a 2 bushel per acre increase, or a 3 bushel per acre increase, or a 4 bushel per acre increase, or a 5 bushel per acre increase, or more.
In some aspects, the biomass increase is statistically significant. In other aspects, the biomass increase is not statistically significant, but is still quantifiable.
B. Seed Treatment with Microbial Consortia
In this example, a microbial consortium, comprising at least two microbes from Tables 1-3 will be applied as a seed coating to seeds of corn (Zea mays). Upon applying the microbial consortium as a seed coating, the corn will be planted and cultivated in the standard manner.
A control plot of corn seeds, which did not have the microbial consortium applied as a seed coating, will also be planted.
It is expected that the corn plants grown from the seeds treated with the seed coating will exhibit a quantifiably higher biomass than the control corn plants.
The biomass from the treated plants may be about 1-10% higher, 10-20% higher, 20-30% higher, 30-40% higher, 40-50% higher, 50-60% higher, 60-70% higher, 70-80% higher, 80-90% higher, or more.
The biomass from the treated plants may equate to about a 1 bushel per acre increase over the controls, or a 2 bushel per acre increase, or a 3 bushel per acre increase, or a 4 bushel per acre increase, or a 5 bushel per acre increase, or more.
In some aspects, the biomass increase is statistically significant. In other aspects, the biomass increase is not statistically significant, but is still quantifiable.
C. Treatment with Agricultural Composition Comprising Isolated Microbe
In this example, an isolated microbe from Tables 1-3 will be applied as an agricultural composition, administered to the corn seed at the time of sowing.
For example, it is anticipated that a farmer will apply the agricultural composition to the corn seeds simultaneously upon planting the seeds into the field. This can be accomplished, for example, by applying the agricultural composition to a hopper/bulk tank on a standard 16 row planter, which contains the corn seeds and which is configured to plant the same into rows. Alternatively, the agricultural composition can be contained in a separate bulk tank on the planter and sprayed into the rows upon planting the corn seed.
A control plot of corn seeds, which are not administered the agricultural composition, will also be planted.
It is expected that the corn plants grown from the seeds treated with the agricultural composition will exhibit a quantifiably higher biomass than the control corn plants.
The biomass from the treated plants may be about 1-10% higher, 10-20% higher, 20-30% higher, 30-40% higher, 40-50% higher, 50-60% higher, 60-70% higher, 70-80% higher, 80-90% higher, or more.
The biomass from the treated plants may equate to about a 1 bushel per acre increase over the controls, or a 2 bushel per acre increase, or a 3 bushel per acre increase, or a 4 bushel per acre increase, or a 5 bushel per acre increase, or more.
In some aspects, the biomass increase is statistically significant. In other aspects, the biomass increase is not statistically significant, but is still quantifiable.
D. Treatment with Agricultural Composition Comprising Microbial Consortia
In this example, a microbial consortium, comprising at least two microbes from Tables 1-3 will be applied as an agricultural composition, administered to the corn seed at the time of sowing.
For example, it is anticipated that a farmer will apply the agricultural composition to the corn seeds simultaneously upon planting the seeds into the field. This can be accomplished, for example, by applying the agricultural composition to a hopper/bulk tank on a standard 16 row planter, which contains the corn seeds and which is configured to plant the same into rows. Alternatively, the agricultural composition can be contained in a separate bulk tank on the planter and sprayed into the rows upon planting the corn seed.
A control plot of corn seeds, which are not administered the agricultural composition, will also be planted.
It is expected that the corn plants grown from the seeds treated with the agricultural composition will exhibit a quantifiably higher biomass than the control corn plants.
The biomass from the treated plants may be about 1-10% higher, 10-20% higher, 20-30% higher, 30-40% higher, 40-50% higher, 50-60% higher, 60-70% higher, 70-80% higher, 80-90% higher, or more.
The biomass from the treated plants may equate to about a 1 bushel per acre increase over the controls, or a 2 bushel per acre increase, or a 3 bushel per acre increase, or a 4 bushel per acre increase, or a 5 bushel per acre increase, or more.
In some aspects, the biomass increase is statistically significant. In other aspects, the biomass increase is not statistically significant, but is still quantifiable.
A. Seed Treatment with Isolated Microbe
In this example, an isolated microbe from Tables 1-3 will be applied as a seed coating to seeds of corn (Zea mays). Upon applying the isolated microbe as a seed coating, the corn will be planted and cultivated in the standard manner.
A control plot of corn seeds, which did not have the isolated microbe applied as a seed coating, will also be planted.
It is expected that the corn plants grown from the seeds treated with the seed coating will exhibit a quantifiable and superior ability to tolerate drought conditions and/or exhibit superior water use efficiency, as compared to the control corn plants.
The drought tolerance and/or water use efficiency can be based on any number of standard tests from the art, e.g leaf water retention, turgor loss point, rate of photosynthesis, leaf color and other phenotypic indications of drought stress, yield performance, and various root morphological and growth patterns.
B. Seed Treatment with Microbial Consortia
In this example, a microbial consortium, comprising at least two microbes from Tables 1-3 will be applied as a seed coating to seeds of corn (Zea mays). Upon applying the microbial consortium as a seed coating, the corn will be planted and cultivated in the standard manner.
A control plot of corn seeds, which did not have the microbial consortium applied as a seed coating, will also be planted.
It is expected that the corn plants grown from the seeds treated with the seed coating will exhibit a quantifiable and superior ability to tolerate drought conditions and/or exhibit superior water use efficiency, as compared to the control corn plants.
The drought tolerance and/or water use efficiency can be based on any number of standard tests from the art, e.g leaf water retention, turgor loss point, rate of photosynthesis, leaf color and other phenotypic indications of drought stress, yield performance, and various root morphological and growth patterns.
C. Treatment with Agricultural Composition Comprising Isolated Microbe
In this example, an isolated microbe from Tables 1-3 will be applied as an agricultural composition, administered to the corn seed at the time of sowing.
For example, it is anticipated that a farmer will apply the agricultural composition to the corn seeds simultaneously upon planting the seeds into the field. This can be accomplished, for example, by applying the agricultural composition to a hopper/bulk tank on a standard 16 row planter, which contains the corn seeds and which is configured to plant the same into rows. Alternatively, the agricultural composition can be contained in a separate bulk tank on the planter and sprayed into the rows upon planting the corn seed.
A control plot of corn seeds, which are not administered the agricultural composition, will also be planted.
It is expected that the corn plants grown from the seeds treated with the with the agricultural composition will exhibit a quantifiable and superior ability to tolerate drought conditions and/or exhibit superior water use efficiency, as compared to the control corn plants.
The drought tolerance and/or water use efficiency can be based on any number of standard tests from the art, e.g leaf water retention, turgor loss point, rate of photosynthesis, leaf color and other phenotypic indications of drought stress, yield performance, and various root morphological and growth patterns.
D. Treatment with Agricultural Composition Comprising Microbial Consortia
In this example, a microbial consortium, comprising at least two microbes from Tables 1-3 will be applied as an agricultural composition, administered to the corn seed at the time of sowing.
For example, it is anticipated that a farmer will apply the agricultural composition to the corn seeds simultaneously upon planting the seeds into the field. This can be accomplished, for example, by applying the agricultural composition to a hopper/bulk tank on a standard 16 row planter, which contains the corn seeds and which is configured to plant the same into rows. Alternatively, the agricultural composition can be contained in a separate bulk tank on the planter and sprayed into the rows upon planting the corn seed.
A control plot of corn seeds, which are not administered the agricultural composition, will also be planted.
It is expected that the corn plants grown from the seeds treated with the with the agricultural composition will exhibit a quantifiable and superior ability to tolerate drought conditions and/or exhibit superior water use efficiency, as compared to the control corn plants.
The drought tolerance and/or water use efficiency can be based on any number of standard tests from the art, e.g leaf water retention, turgor loss point, rate of photosynthesis, leaf color and other phenotypic indications of drought stress, yield performance, and various root morphological and growth patterns.
A. Seed Treatment with Isolated Microbe
In this example, an isolated microbe from Tables 1-3 will be applied as a seed coating to seeds of corn (Zea mays). Upon applying the isolated microbe as a seed coating, the corn will be planted and cultivated in the standard manner.
A control plot of corn seeds, which did not have the isolated microbe applied as a seed coating, will also be planted.
It is expected that the corn plants grown from the seeds treated with the seed coating will exhibit a quantifiable and superior ability to utilize nitrogen, as compared to the control corn plants.
The nitrogen use efficiency can be quantified by recording a measurable change in any of the main nitrogen metabolic pool sizes in the assimilation pathways (e.g., a measurable change in one or more of the following: nitrate, nitrite, ammonia, glutamic acid, aspartic acid, glutamine, asparagine, lysine, leucine, threonine, methionine, glycine, tryptophan, tyrosine, total protein content of a plant part, total nitrogen content of a plant part, and/or chlorophyll content), or where the treated plant is shown to provide the same or elevated biomass or harvestable yield at lower nitrogen fertilization levels compared to the control plant, or where the treated plant is shown to provide elevated biomass or harvestable yields at the same nitrogen fertilization levels compared to a control plant.
B. Seed Treatment with Microbial Consortia
In this example, a microbial consortium, comprising at least two microbes from Tables 1-3 will be applied as a seed coating to seeds of corn (Zea mays). Upon applying the microbial consortium as a seed coating, the corn will be planted and cultivated in the standard manner.
A control plot of corn seeds, which did not have the microbial consortium applied as a seed coating, will also be planted.
It is expected that the corn plants grown from the seeds treated with the seed coating will exhibit a quantifiable and superior ability to utilize nitrogen, as compared to the control corn plants.
The nitrogen use efficiency can be quantified by recording a measurable change in any of the main nitrogen metabolic pool sizes in the assimilation pathways (e.g., a measurable change in one or more of the following: nitrate, nitrite, ammonia, glutamic acid, aspartic acid, glutamine, asparagine, lysine, leucine, threonine, methionine, glycine, tryptophan, tyrosine, total protein content of a plant part, total nitrogen content of a plant part, and/or chlorophyll content), or where the treated plant is shown to provide the same or elevated biomass or harvestable yield at lower nitrogen fertilization levels compared to the control plant, or where the treated plant is shown to provide elevated biomass or harvestable yields at the same nitrogen fertilization levels compared to a control plant.
C. Treatment with Agricultural Composition Comprising Isolated Microbe
In this example, an isolated microbe from Tables 1-3 will be applied as an agricultural composition, administered to the corn seed at the time of sowing.
For example, it is anticipated that a farmer will apply the agricultural composition to the corn seeds simultaneously upon planting the seeds into the field. This can be accomplished, for example, by applying the agricultural composition to a hopper/bulk tank on a standard 16 row planter, which contains the corn seeds and which is configured to plant the same into rows. Alternatively, the agricultural composition can be contained in a separate bulk tank on the planter and sprayed into the rows upon planting the corn seed.
A control plot of corn seeds, which are not administered the agricultural composition, will also be planted.
It is expected that the corn plants grown from the seeds treated with the agricultural composition will exhibit a quantifiable and superior ability to utilize nitrogen, as compared to the control corn plants.
The nitrogen use efficiency can be quantified by recording a measurable change in any of the main nitrogen metabolic pool sizes in the assimilation pathways (e.g., a measurable change in one or more of the following: nitrate, nitrite, ammonia, glutamic acid, aspartic acid, glutamine, asparagine, lysine, leucine, threonine, methionine, glycine, tryptophan, tyrosine, total protein content of a plant part, total nitrogen content of a plant part, and/or chlorophyll content), or where the treated plant is shown to provide the same or elevated biomass or harvestable yield at lower nitrogen fertilization levels compared to the control plant, or where the treated plant is shown to provide elevated biomass or harvestable yields at the same nitrogen fertilization levels compared to a control plant.
D. Treatment with Agricultural Composition Comprising Microbial Consortia
In this example, a microbial consortium, comprising at least two microbes from Tables 1-3 will be applied as an agricultural composition, administered to the corn seed at the time of sowing.
For example, it is anticipated that a farmer will apply the agricultural composition to the corn seeds simultaneously upon planting the seeds into the field. This can be accomplished, for example, by applying the agricultural composition to a hopper/bulk tank on a standard 16 row planter, which contains the corn seeds and which is configured to plant the same into rows. Alternatively, the agricultural composition can be contained in a separate bulk tank on the planter and sprayed into the rows upon planting the corn seed.
A control plot of corn seeds, which are not administered the agricultural composition, will also be planted.
It is expected that the corn plants grown from the seeds treated with the agricultural composition will exhibit a quantifiable and superior ability to utilize nitrogen, as compared to the control corn plants.
The nitrogen use efficiency can be quantified by recording a measurable change in any of the main nitrogen metabolic pool sizes in the assimilation pathways (e.g., a measurable change in one or more of the following: nitrate, nitrite, ammonia, glutamic acid, aspartic acid, glutamine, asparagine, lysine, leucine, threonine, methionine, glycine, tryptophan, tyrosine, total protein content of a plant part, total nitrogen content of a plant part, and/or chlorophyll content), or where the treated plant is shown to provide the same or elevated biomass or harvestable yield at lower nitrogen fertilization levels compared to the control plant, or where the treated plant is shown to provide elevated biomass or harvestable yields at the same nitrogen fertilization levels compared to a control plant.
The inoculants were prepared from isolates grown as spread plates on R2A incubated at 25° C. for 48 to 72 hours. Colonies were harvested by blending with sterile distilled water (SDW) which was then transferred into sterile containers. Serial dilutions of the harvested cells were plated and incubated at 25° C. for 24 hours to estimate the number of colony forming units (CFU) in each suspension. Dilutions were prepared using individual isolates or blends of isolates (consortia) to deliver 1×105 cfu/microbe/seed and seeds inoculated by either imbibition in the liquid suspension or by overtreatment with 5% vegetable gum and oil.
Seeds corresponding to the plants of table 15 were planted within 24 to 48 hours of treatment in agricultural soil, potting media or inert growing media. Plants were grown in small pots (28 mL to 200 mL) in either a controlled environment or in a greenhouse. Chamber photoperiod was set to 16 hours for all experiments on all species. Air temperature was typically maintained between 22-24° C.
Unless otherwise stated, all plants were watered with tap water 2 to 3 times weekly. Growth conditions were varied according to the trait of interest and included manipulation of applied fertilizer, watering regime and salt stress as follows:
-
- Low N—seeds planted in soil potting media or inert growing media with no applied N fertilizer
- Moderate N—seeds planted in soil or growing media supplemented with commercial N fertilizer to equivalent of 135 kg/ha applied N
- Insol P—seeds planted in potting media or inert growth substrate and watered with quarter strength Pikovskaya's liquid medium containing tri-calcium phosphate as the only form phosphate fertilizer.
- Cold Stress—seeds planted in soil, potting media or inert growing media and incubated at 10° C. for one week before being transferred to the plant growth room.
- Salt stress—seeds planted in soil, potting media or inert growing media and watered with a solution containing between 100 to 200 mg/L NaCl.
Untreated (no applied microbe) controls were prepared for each experiment. Plants were randomized on trays throughout the growth environment. Between 10 and 30 replicate plants were prepared for each treatment in each experiment. Phenotypes were measured during early vegetative growth, typically before the V3 developmental stage and between 3 and 6 weeks after sowing. Foliage was cut and weighed. Roots were washed, blotted dry and weighed. Results indicate performance of treatments against the untreated control.
The data presented in table 15 describes the efficacy with which a microbial species or strain can change a phenotype of interest relative to a control run in the same experiment. Phenotypes measured were shoot fresh weight and root fresh weight for plants growing either in the absence of presence of a stress (assay). For each microbe species, an overall efficacy score indicates the percentage of times a strain of that species increased a both shoot and root fresh weight in independent evaluations. For each species, the specifics of each independent assay is given, providing a strain ID (strain) and the crop species the assay was performed on (crop). For each independent assay the percentage increase in shoot and root fresh weight over the controls is given.
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Ammonia
Asparagine
Aspartic Acid
Biological Assay
Bosea thiooxidans
Calcium Phosphates
Capsicum
Cells
Chlorophyll
Cold Shock Stress
Cold Temperature
Crop, Avian
Dietary Fiber
DNA Replication
Droughts
Drought Tolerance
Embryophyta
Environment, Controlled
Farmers
Fertilization
Glutamic Acid
Glutamine
Glycine
Growth Disorders
Herbaspirillum
Herbaspirillum huttiense
Leucine
Lolium
Lycopersicon esculentum
Lysine
Maize
Massilia niastensis
Methionine
Microbial Consortia
Nitrates
Nitrites
Nitrogen
Novosphingobium rosa
Paenibacillus
Paenibacillus amylolyticus
Pantoea agglomerans
Pantoea vagans
Phenotype
Phosphates
Photosynthesis
Plant Development
Plant Embryos
Plant Leaves
Plant Proteins
Plant Roots
Plants
Polaromonas ginsengisoli
Pseudoduganella violaceinigra
Pseudomonas
Pseudomonas fluorescens
Rahnella
Rahnella aquatilis
Retention (Psychology)
Rhodococcus erythropolis
Rosa
Salt Stress
Sodium Chloride
Sodium Chloride, Dietary
Stenotrophomonas chelatiphaga
Stenotrophomonas maltophilia
Stenotrophomonas rhizophila
Stenotrophomonas terrae
Sterility, Reproductive
Strains
Technique, Dilution
Threonine
Triticum aestivum
Tryptophan
Tyrosine
Vegetables
Zea mays
Example 10
The objective of this study was to develop an acute model of homocystinuria in nonhuman primates. Male cynomolgus monkeys of approximately 2-5 years of age (average weight of 3.4 kg) were fasted overnight and orally administered a methionine load at 100 or 300 mg/kg, and plasma was collected at 0-, 0.5-, 1-, 2-, 4-, 6-, and 24-hours post-dose for methionine and total homocysteine measurements by LC-MS/MS.
Oral administration of methionine (100 or 300 mg/kg) resulted in a dose-dependent increase in plasma methionine levels, with peak concentration recorded at 30 minutes and 1 hour post dose for 100 mg/kg and 300 mg/kg, respectively (
Full text: Click here
Administration, Oral
Homocysteine
Homocystinuria
Macaca fascicularis
Males
Methionine
Plasma
Primates
Tandem Mass Spectrometry
Example 13
The objectives of this study were to determine (1) whether enterorecirculation of methionine occurs, and (2) whether orally-administered SYNB1353 can consume peripherally administered (IP) labeled methionine in mice.
In a first study, healthy male C57BL/6 mice (n=3/group) were fasted overnight and received a single IP dose of D4-methionine (100 mg/kg). Blood and gut effluents (SI, cecum or colon) were collected at 0, 0.5, 1, or 2 hours post dosing for D4-methionine measurements. Results shown in
In a second study, healthy male C57BL/6 mice (n=10-18/group) were fasted overnight and received a single IP dose of D4-Met (100 mg/kg) followed by 2 doses of SYNB1353 PO 0.5 and 1.5 hours later. Blood and urine were collected for D4-Met, D4-tHcy and D4-3-MTP measurements. Results are shown in
Full text: Click here
BLOOD
Cecum
Colon
Escherichia coli
Figs
Homocysteine
Males
Methionine
Mice, House
Mice, Inbred C57BL
Plasma
Strains
Urine
Example 8
SYNB1353 comprises a metP gene, metDC gene, and deletion of the yjeH gene, as shown in
SYNB1353 and SYN094 were grown and activated in a bioreactor following optimized processes intended to be used for the scale-up of drug product. Activated cell batches were resuspended to the specified live cell count in assay media, and cells were statically incubated at 37° C. Supernatants were collected at defined timepoints, and the quantity of each analyte (methionine and 3-MTP) in each sample was determined by liquid chromatography mass spectrometry (LC-MS/MS). As observed in
In vitro Met consumption assays, as described above, show consumption of methionine and production of 3-MTP by SYNB1353 and not the EcN control (
Full text: Click here
Biological Assay
Bioreactors
Cells
Gene Deletion
Genes
Liquid Chromatography
Mass Spectrometry
Methionine
Pharmaceutical Preparations
Strains
Example 14
Cystinuria is a genetic disorder of amino acid import in the kidney characterized by excessive excretion of cystine, and dibasic amino acids (ornitihine, lysine, and arginine) in the urine, and cystine stone formation in the urinary tract.
The potential of a methionine consuming strain described herein to treat, prevent, or reduce cystinuria was evaluated by analyzing the effect of a methionine restricted diet in a Slc3a1 knockout (KO) mouse model for cystinuria. Slc3a1 KO mice were subjected to a reduction in the methionine content of diet from the standard 0.62% to 0.12% for eight weeks, and cysteine as well as cystine levels in urine and plasma, and stone formation in the bladder were evaluated according to a scheme shown in
Cystine stone formation was not observed in any of the twelve mice on the low-methionine diet. In contrast, bladder stones were observed in nine out of twelve mice (75%) on the 0.62% diet. Time of stone formation ranged from 2-8 weeks following diet treatment.
These data suggest that a treatment resulting in a reduction in plasma or urinary methionine, e.g., administration of a methionine-consuming strain described herein, is a promising approach for the treatment of cystinuria.
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Amino Acid Metabolism, Inborn Errors
Amino Acids, Diamino
Arginine
Calculi
Calculus, Bladder
Cysteine
Cystine
Cystinuria
Diet
Dietary Restriction
Genes, vif
Kidney
Lysine
Methionine
Mice, Knockout
Mus
Plasma
Strains
Urinary Calculi
Urine
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More about "Methionine"
Methionine, an essential amino acid, is a crucial player in various biological processes.
It's involved in protein synthesis, methylation reactions, and cellular metabolism, serving as a precursor for the synthesis of other important compounds like cysteine, taurine, and the antioxidant glutathione.
Deficiencies in methionine can lead to health issues, including stunted growth, liver damage, and neurological disorders.
Researchers studying methionine can leverage powerful tools like Proteome Discoverer, Mascot, and Proteome Discoverer 2.2 to streamline their research.
These platforms allow for the accurate identification and quantification of methionine-containing proteins, helping to elucidate its role in biological systems.
The use of [35S]-methionine, a radiolabeled form of the amino acid, can provide insights into methionine metabolism and incorporation into proteins.
The Mascot search engine, including the latest version Mascot 2.4, is a widely used tool for the identification of proteins, including those containing methionine.
For in vitro studies, the TNT Quick Coupled Transcription/Translation System can be utilized to study the synthesis of methionine-containing proteins.
Proteome Discoverer 1.4 is another valuable tool for the analysis of methionine-related proteins and their post-translational modifications.
PubCompare.ai's AI-driven platform can further enhance methionine research by allowing researchers to easily locate the best protocols from literature, preprints, and patents, while conducting accurate comparisons to improve reproducibility and accuracy.
This streamlined approach can help scientists optimize their methionine studies and make groundbreaking discoveries.
It's involved in protein synthesis, methylation reactions, and cellular metabolism, serving as a precursor for the synthesis of other important compounds like cysteine, taurine, and the antioxidant glutathione.
Deficiencies in methionine can lead to health issues, including stunted growth, liver damage, and neurological disorders.
Researchers studying methionine can leverage powerful tools like Proteome Discoverer, Mascot, and Proteome Discoverer 2.2 to streamline their research.
These platforms allow for the accurate identification and quantification of methionine-containing proteins, helping to elucidate its role in biological systems.
The use of [35S]-methionine, a radiolabeled form of the amino acid, can provide insights into methionine metabolism and incorporation into proteins.
The Mascot search engine, including the latest version Mascot 2.4, is a widely used tool for the identification of proteins, including those containing methionine.
For in vitro studies, the TNT Quick Coupled Transcription/Translation System can be utilized to study the synthesis of methionine-containing proteins.
Proteome Discoverer 1.4 is another valuable tool for the analysis of methionine-related proteins and their post-translational modifications.
PubCompare.ai's AI-driven platform can further enhance methionine research by allowing researchers to easily locate the best protocols from literature, preprints, and patents, while conducting accurate comparisons to improve reproducibility and accuracy.
This streamlined approach can help scientists optimize their methionine studies and make groundbreaking discoveries.