The full details of the implementation of the INNO-BIA Alz Bio3 immunoassay reagents on the Luminex analytical platform are described elsewhere [29 (link), 42 ]. Monoclonal antibodies (mAbs) are used in this assay, and the production process for the immunoassay kits includes in current production processes assurance of lot-to-lot consistency. These tests are relative quantitative assays for CSF Aβ1–42, t-tau and p-tau181 since no international reference standards for the analytes prepared in CSF are available. Each participating center used the same INNO-BIA AlzBio3 immunoassay kit (assay lot # 157353 and calibrator lot # 157379), provided for the study by Innogenetics, Ghent, Belgium. The kit reagents include a mixture of three xMAP color-coded carboxylated microspheres, each containing a bead set coupled with well-characterized capture mAbs specific for Aβ1–42 (4D7A3; bead region 56), t-tau (AT120; bead region 2) or p-tau181 (AT270; bead region 69), and a vial with analyte-specific biotinylated detector mAbs (3D6 for Aβ1–42 and HT7 for t-tau or p-tau181). Ready-to-use vials containing pre-determined calibrator concentrations for the three analytes were provided. Calibration curves were produced for each biomarker using aqueous buffered solutions that contained the combination of three bio-markers at concentrations ranging from 56 to 1,948 pg/mL for recombinant t-tau, 27–1,574 pg/mL for synthetic Aβ1–42 and 8–230 pg/mL for a synthetic tau peptide phosphorylated at the threonine 181 position (the p-tau181 standard; numbering according to the longest tau isoforms [13 (link)]). In addition to the calibrators, the immunoassay kit includes two quality control samples, produced in aqueous diluent, with pre-defined acceptable concentration ranges for the three biomarkers.
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Chemicals & Drugs
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Amino Acid
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Threonine
Threonine
Threonine is an essential amino acid that plays a crucial role in protein synthesis and various metabolic processes.
It is involved in the formation of collagen and elastin, contributing to the health of skin, hair, and connective tissues.
Threonine also supports immune function and participates in the metabolism of fats and carbohydrates.
Dietary sources of threonine include meat, dairy products, eggs, lgeumes, and certain grains.
Deficiencies in threonine can lead to impaired growth, skin lesions, and reduced immune response.
Understanding the importance of threonine in human health and its applications in research is essential for advancing scientific understanding and developing effective therapies.
It is involved in the formation of collagen and elastin, contributing to the health of skin, hair, and connective tissues.
Threonine also supports immune function and participates in the metabolism of fats and carbohydrates.
Dietary sources of threonine include meat, dairy products, eggs, lgeumes, and certain grains.
Deficiencies in threonine can lead to impaired growth, skin lesions, and reduced immune response.
Understanding the importance of threonine in human health and its applications in research is essential for advancing scientific understanding and developing effective therapies.
Most cited protocols related to «Threonine»
Biological Assay
Biological Markers
Immunoassay
Microspheres
Monoclonal Antibodies
Peptides
Protein Isoforms
Threonine
HEK-293 cells were transfected with either empty pcDNA3 vector (Invitrogen, Carlsbad, CA, USA) or pcDNA3 vector containing full-length ABCG2 coding either an arginine, threonine or glycine for amino-acid 482. Expression of ABCG2 in the transfectants was enforced by selection in G418 (Invitrogen, Carlsbad, CA, USA). Stable transfectants were maintained in Eagle's minimum essential medium (ATCC, Manassas, VA, USA) supplemented with 10% FCS, penicillin, and streptomycin with G418 at a concentration of 2 mg ml−1. Clones were preliminarily screened for ABCG2 expression by examining the ability of the cells to efflux BODIPY-prazosin in a flow cytometry-based assay. The ABCG2 sequence was subsequently verified in the clones examined here.
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Amino Acids
antibiotic G 418
Arginine
Biological Assay
BODIPY
Clone Cells
Cloning Vectors
Flow Cytometry
Glycine
HEK293 Cells
Penicillins
Prazosin
Streptomycin
Threonine
All MD simulations were
performed with NAMD40 (link) employing CHARMM-formatted
parameter files41 (link) for all force fields
tested, which are provided in theSupporting Information . For all simulations, a temperature of 300 K and pressure of 1 atm
were maintained with a Nose–Hoover Langevin piston barostat
with a piston period of 100 fs and a piston dampening time scale of
50 fs and a Langevin thermostat with a damping coefficient of 1 ps–1. Nonbonded cutoffs were employed at 11 Å with
a smoothing function starting at 9 Å, with particle mesh Ewald
used to treat long-range electrostatics. The systems were solvated
in cubic water boxes with edge lengths ranging from 25 to 58 Å.
Sodium and chloride ions were added to neutralize the charges in the
system and provide approximately a 150 mM concentration of salt. A
2 fs time step was employed with the use of SHAKE and SETTLE.
Triplicate 205 ns simulations were run for an unblocked alanine pentapeptide
(Ala5) with and glycine tripeptide (Gly3) with
protonated C-termini with the first 5 ns discarded as equilibration.
The remaining amino acids, with the exception of proline, were simulated
for 205 ns as blocked dipeptides, again in triplicate with the first
5 ns discarded as equilibration. Values and error bars throughout
the paper represent the mean and standard deviation of the calculated
quantities from the triplicate runs. Ala5 and Gly3 simulations were run with each of the four weighting temperatures
examined in this work, as well as the previous OPLS-AA and OPLS-AA/L
force field. Dipeptide simulations were performed with OPLS-AA, OPLS-AA/L,
and the new parameters optimized at 2000 K. As each system was studied
for 600 ns with at least three different force fields, over 50 μs
of validating simulations have been executed. In analyzing the molecular
dynamics simulations for the short alanine and glycine peptides, the
definitions of secondary structure, the three sets of Karplus parameters
for calculating J couplings, and the experimental
error values used to calculate χ2 from Best et al.42 (link) were employed. For the dipeptide simulations,
only the first set of Karplus parameters, that of Hu and Bax,43 was employed. χ1 rotamer populations
were determined by dividing the range of χ1 values
into three equal sized bins, corresponding to the p (+60°), t
(180°) and m (−60°) conformers. Definitions of p,
t, and m for valine, isoleucine, and threonine were adopted from the
work of Dunbrak and co-workers27 (link) and are
depicted in Figure1 .
The proteins ubiquitin and GB3 were started from the PDB
structures 1UBQ(44 (link)) and 1P7E(45 (link)) and gradually
heated to 300 K over
400 ps before 205 ns simulations were run. Both the heating period
and the first 5 ns were discarded as equilibration, and simulations
were performed in triplicate for each protein. All other simulation
parameters were identical to those used for the dipeptides. For calculation
of backbone J couplings of the full protein, both
the 1997 empirical Karplus parameters43 used for the dipeptides and another empirical model developed from
work with GB346 (link) are employed. Side chain J couplings were calculated for couplings to methyl side
chains with the set of Karplus parameters developed by Vögeli
et al.,46 (link) while all other couplings employed
Karplus parameters from Perez et al.48 (link)
performed with NAMD40 (link) employing CHARMM-formatted
parameter files41 (link) for all force fields
tested, which are provided in the
were maintained with a Nose–Hoover Langevin piston barostat
with a piston period of 100 fs and a piston dampening time scale of
50 fs and a Langevin thermostat with a damping coefficient of 1 ps–1. Nonbonded cutoffs were employed at 11 Å with
a smoothing function starting at 9 Å, with particle mesh Ewald
used to treat long-range electrostatics. The systems were solvated
in cubic water boxes with edge lengths ranging from 25 to 58 Å.
Sodium and chloride ions were added to neutralize the charges in the
system and provide approximately a 150 mM concentration of salt. A
2 fs time step was employed with the use of SHAKE and SETTLE.
Triplicate 205 ns simulations were run for an unblocked alanine pentapeptide
(Ala5) with and glycine tripeptide (Gly3) with
protonated C-termini with the first 5 ns discarded as equilibration.
The remaining amino acids, with the exception of proline, were simulated
for 205 ns as blocked dipeptides, again in triplicate with the first
5 ns discarded as equilibration. Values and error bars throughout
the paper represent the mean and standard deviation of the calculated
quantities from the triplicate runs. Ala5 and Gly3 simulations were run with each of the four weighting temperatures
examined in this work, as well as the previous OPLS-AA and OPLS-AA/L
force field. Dipeptide simulations were performed with OPLS-AA, OPLS-AA/L,
and the new parameters optimized at 2000 K. As each system was studied
for 600 ns with at least three different force fields, over 50 μs
of validating simulations have been executed. In analyzing the molecular
dynamics simulations for the short alanine and glycine peptides, the
definitions of secondary structure, the three sets of Karplus parameters
for calculating J couplings, and the experimental
error values used to calculate χ2 from Best et al.42 (link) were employed. For the dipeptide simulations,
only the first set of Karplus parameters, that of Hu and Bax,43 was employed. χ1 rotamer populations
were determined by dividing the range of χ1 values
into three equal sized bins, corresponding to the p (+60°), t
(180°) and m (−60°) conformers. Definitions of p,
t, and m for valine, isoleucine, and threonine were adopted from the
work of Dunbrak and co-workers27 (link) and are
depicted in Figure
The proteins ubiquitin and GB3 were started from the PDB
structures 1UBQ(44 (link)) and 1P7E(45 (link)) and gradually
heated to 300 K over
400 ps before 205 ns simulations were run. Both the heating period
and the first 5 ns were discarded as equilibration, and simulations
were performed in triplicate for each protein. All other simulation
parameters were identical to those used for the dipeptides. For calculation
of backbone J couplings of the full protein, both
the 1997 empirical Karplus parameters43 used for the dipeptides and another empirical model developed from
work with GB346 (link) are employed. Side chain J couplings were calculated for couplings to methyl side
chains with the set of Karplus parameters developed by Vögeli
et al.,46 (link) while all other couplings employed
Karplus parameters from Perez et al.48 (link)
A-A-1 antibiotic
A 300
Alanine
Amino Acids
Chlorides
Cuboid Bone
Dipeptides
Electrostatics
Glycine
Ions
Isoleucine
Nose
Peptides
Pressure
Proline
Proteins
Sodium
Sodium Chloride
Threonine
Tremor
Ubiquitin
Valine
Vertebral Column
The PhosphoBase (6 (link)) consists of 1883 experimentally verified phosphorylation sites within 597 protein entries. The number of serine, threonine and tyrosine sites is 984, 246 and 653, respectively. Swiss-Prot (7 (link)) (release 45 of October 2004) maintains 163 500 protein entries, of which 3614 have phosphorylation annotation. Among these entries, the number of serine, threonine and tyrosine sites was 1005, 281 and 321, respectively. Generally, the serine, threonine and tyrosine, which are not annotated as phosphorylation residues, within the experimentally validated phosphorylated proteins, are selected as negative sets, i.e. the non-phosphorylated sites. Therefore, two negative (non-phosphorylated) datasets were obtained from the PhosphoBase and Swiss-Prot based on the phosphorylation annotation. Because of the absence of good negative dataset exists for non-phosphorylated sites, the residues that had not been previously annotated as phosphorylated in phosphorylation annotated proteins were chosen as a reflection of more general non-phosphorylated sites. Supplementary Table S1 summarizes the statistics of kinase-specific phosphorylated sites used for learning models in the proposed application. This work confirms the existence of two major protein kinases phosphorylating either at serine/threonine residues or at tyrosine residues.
Figure 1 depicts a flowchart of the proposed method. Phosphorylated sites were first extracted as positive sets; non-phosphorylated sites were extracted as negative sets, and the catalytic kinase annotations were obtained from PhosphoBase and Swiss-Prot. The positive sets were then categorized by catalytic kinases. Alternatively, in larger positive groups, the sequences of the phosphorylated sites can be clustered into subgroups by maximal dependence decomposition (MDD) (8 (link)). The MDD was first applied in nucleotides and is a recursive process to divide a sequence set into tree-like subgroups based on the positional dependency of the sequences. Here, we applied the MDD to group protein phosphorylation substrates into subgroups. As the example given in Figure 1 , 232 phosphorylation serine substrates are grouped into subgroups. When applying MDD to cluster the sequences of a positive set, a parameter, i.e. the minimum-cluster-size, should be set. If the size of a subgroup is less than the minimum-cluster-size, the subgroup is terminated to be divided. The MDD process terminates until all the subgroup sizes are less than the minimum-cluster-size.
Thereupon, the concept of the profile HMM was adopted to learn computational models from positive sets of phosphorylation sites. To evaluate the learned models, k-fold cross-validation and leave-one-out cross-validation were performed on them. After evaluating the models, the model with highest accuracy in each dataset was chosen.
For each kinase-specific positive set of the phosphorylated sites, the best performed model is selected and used to identify the phosphorylation sites within the input protein sequences by HMMsearch (9 (link)). To search the hits of a model, HMMER returns both a HMMER bit score and an expectation value (E-value). The HMMER bit score is used as the criterion to define a HMM match. We select the HMMER score as the criterion to define a HMM match. A search of a model with the HMMER score greater than the threshold t is defined as a positive prediction, i.e. a HMM recognizes a phosphorylation site. The threshold t of each model is decided by maximizing the accuracy measure during a variety of cross-validations with the HMM bit score value range from 0 to −10. For example, Supplementary Figure S1 depicts the optimization of the threshold of the HMM bit scores in the S_PKA model. The threshold of the S_PKA model is set to −4.5 to maximize the accuracy measure of the model.
When considering a MDD-clustered dataset, for example, MDD-clustered PKA catalytic serine (S_PKA), the HMMs are trained separately from the subgroups of the phosphorylated sites resulted by MDD. Each model is used to search in the given protein sequences for the phosphorylated sites. A positive prediction of a model group is defined by at least one of the models that makes a positive prediction, whereas a negative prediction is defined as all the models that make negative predictions.
Thereupon, the concept of the profile HMM was adopted to learn computational models from positive sets of phosphorylation sites. To evaluate the learned models, k-fold cross-validation and leave-one-out cross-validation were performed on them. After evaluating the models, the model with highest accuracy in each dataset was chosen.
For each kinase-specific positive set of the phosphorylated sites, the best performed model is selected and used to identify the phosphorylation sites within the input protein sequences by HMMsearch (9 (link)). To search the hits of a model, HMMER returns both a HMMER bit score and an expectation value (E-value). The HMMER bit score is used as the criterion to define a HMM match. We select the HMMER score as the criterion to define a HMM match. A search of a model with the HMMER score greater than the threshold t is defined as a positive prediction, i.e. a HMM recognizes a phosphorylation site. The threshold t of each model is decided by maximizing the accuracy measure during a variety of cross-validations with the HMM bit score value range from 0 to −10. For example, Supplementary Figure S1 depicts the optimization of the threshold of the HMM bit scores in the S_PKA model. The threshold of the S_PKA model is set to −4.5 to maximize the accuracy measure of the model.
When considering a MDD-clustered dataset, for example, MDD-clustered PKA catalytic serine (S_PKA), the HMMs are trained separately from the subgroups of the phosphorylated sites resulted by MDD. Each model is used to search in the given protein sequences for the phosphorylated sites. A positive prediction of a model group is defined by at least one of the models that makes a positive prediction, whereas a negative prediction is defined as all the models that make negative predictions.
Amino Acid Sequence
Base Sequence
Catalysis
Exhaling
Hypertelorism, Severe, With Midface Prominence, Myopia, Mental Retardation, And Bone Fragility
Nucleotides
Phosphorylation
Phosphotransferases
Protein Kinases
Proteins
Reflex
Serine
Threonine
Trees
Tyrosine
A-A-1 antibiotic
A 300
Alanine
Amino Acids
Chlorides
Cuboid Bone
Dipeptides
Electrostatics
Glycine
Ions
Isoleucine
Nose
Peptides
polysucrose-400
Population Group
Pressure
Proline
Proteins
Sodium
Sodium Chloride
Threonine
Tremor
Ubiquitin
Valine
Vertebral Column
Most recents protocols related to «Threonine»
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 5
The SpyTag002-MBP fusion has a reaction rate of 0.40 μM−1 min−1 with SpyCatcher002. We surprisingly determined that the reaction rate could be further improved by introducing additional modifications to the SpyTag002 peptide.
Substitution of the threonine residue at position 3 of SpyTag002 (SEQ ID NO: 3) with histidine, i.e. reversion to the residue at the equivalent position in SpyTag, resulted in a peptide (SEQ ID NO: 4) with a reaction rate of 0.53-0.55 μM−1 min−1, i.e. about a 35% increase in activity (
Modification of the improved peptide to include arginine and glycine residues at the N-terminus (SEQ ID NO: 5) more than doubled the reaction rate to 1.21 μM−1 min−1 (
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Arginine
Glycine
Histidine
Peptides
Threonine
Example 3
Several other substitutions at amino acid site 63 were produced to compare to the PCV2b ORF BDH native strain. The results from the evaluation of the PCV2b ORF2 BDH mutant constructs are shown in
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Amino Acids
Amino Acid Substitution
Arginine
Aspartate
Cells
Figs
Glutamine
Glycine
Mutant Proteins
Mutation
Strains
Threonine
Virion
HPLC-ESI-MS/MS was performed in positive ion mode on a Thermo Fisher Scientific Orbitrap Fusion Lumos tribrid mass spectrometer fitted with an EASY-Spray Source as previously described (Parker et al., 2019 (link)). NanoLC was performed using a Thermo Fisher Scientific UltiMate 3000 RSLCnano System with an EASY Spray C18 LC column (Thermo Fisher Scientific, 50 cm × 75 μm inner diameter, packed with PepMap RSLC C18 material, 2 μm, cat. # ES803); loading phase for 15 min; mobile phase, linear gradient of 1–47% ACN in 0.1% FA for 106 min, followed by a step to 95% ACN in 0.1% FA over 5 min, hold 10 min, and then a step to 1% ACN in 0.1% FA over 1 min and a final hold for 19 min (total run 156 min); Buffer A = 100% H2O in 0.1% FA; Buffer B = 80% ACN in 0.1% FA; flow rate, 250–300 nl/min. All solvents were liquid chromatography mass spectrometry grade. Spectra were acquired using XCalibur, version 2.1.0 (Thermo Fisher Scientific). A “top 15” data-dependent MS/MS analysis was performed (acquisition of a full scan spectrum followed by collision-induced dissociation mass spectra of the 15 most abundant ions in the survey scan). Dynamic exclusion was enabled with a repeat count of 1, a repeat duration of 30 s, an exclusion list size of 500, and an exclusion duration of 40 s. Tandem mass spectra were extracted from Xcalibur ‘RAW’ files and charge states were assigned using the ProteoWizard 2.1.x msConvert script using the default parameters. The fragment mass spectra were searched against the 2016 Mus musculus SwissProt database (16838 entries) using Mascot (Matrix Science; version 2.4) using the default probability cut-off score. The search variables that were used were: 10 ppm mass tolerance for precursor ion masses and 0.5 Da for product ion masses; digestion with trypsin; a maximum of two missed tryptic cleavages; variable modifications of oxidation of methionine and phosphorylation of serine, threonine, and tyrosine. Cross-correlation of Mascot search results with X! Tandem was accomplished with Scaffold (version Scaffold_4.8.7; Proteome Software). Probability assessment of peptide assignments and protein identifications were made using Scaffold. Only peptides with ≥95% probability were considered.
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Buffers
Cytokinesis
Digestion
High-Performance Liquid Chromatographies
Immune Tolerance
Liquid Chromatography
Mass Spectrometry
Methionine
Mice, House
Peptides
Phosphorylation
Proteins
Proteome
Radionuclide Imaging
Serine
Solvents
Tandem Mass Spectrometry
Threonine
Trypsin
Tyrosine
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Alanine
Albumins
Ammonia
Amylase
Ascorbic Acid
Aspartic Acid
Biological Assay
Buffers
Calcium chloride
Cysteine
Glutamic Acid
Glycine
Histidine
Homo sapiens
Isoleucine
Leucine
Lysine
Magnesium Chloride
Methionine
Phenylalanine
Potassium Chloride
potassium phosphate, monobasic
Proline
Rivers
Saliva, Artificial
Serine
Serum
Sodium Chloride
sodium phosphate, monobasic
Technique, Dilution
tecogalan sodium
Threonine
Tryptophan
Tyrosine
Urea
Valine
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L-threonine is a laboratory-grade amino acid used as a chemical building block in various applications. It serves as a precursor for the synthesis of other biomolecules and is commonly used in biochemical research and analysis.
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Threonine is a laboratory equipment product that is used in the analysis and quantification of the amino acid threonine. It functions as a tool for researchers and scientists to measure and study the presence and levels of threonine in various samples.
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Alanine is a laboratory equipment product manufactured by Merck Group. It is an amino acid commonly used in biochemical and analytical applications.
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Valine is a laboratory chemical used as an essential amino acid in cell culture and biochemical research applications. It serves as a building block for proteins and plays a role in various metabolic processes. Valine is commonly used in cell culture media formulations to support cell growth and proliferation.
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More about "Threonine"
Threonine is an essential amino acid that plays a crucial role in protein synthesis and various metabolic processes.
It is involved in the formation of collagen and elastin, contributing to the health of skin, hair, and connective tissues.
Threonine, also known as L-threonine, supports immune function and participates in the metabolism of fats and carbohydrates.
Dietary sources of this important amino acid include meat, dairy products, eggs, legumes, and certain grains.
Deficiencies in threonine can lead to impaired growth, skin lesions, and reduced immune response.
Understanding the importance of this amino acid in human health and its applications in research is essential for advancing scientific understanding and developing effective therapies.
Proteome Discoverer and Mascot are tools used in the analysis and identification of proteins, including those containing threonine.
Related amino acids like phenylalanine, alanine, glycine, and valine also play important roles in the body and can be found in similar food sources.
Proper nutrition and supplementation with threonine and other essential amino acids can help support overall health and well-being.
By expanding our knowledge of threonine and its metabolic functions, researchers can develop new strategies to optimize human health and address deficiencies or disorders related to this key nutrient.
It is involved in the formation of collagen and elastin, contributing to the health of skin, hair, and connective tissues.
Threonine, also known as L-threonine, supports immune function and participates in the metabolism of fats and carbohydrates.
Dietary sources of this important amino acid include meat, dairy products, eggs, legumes, and certain grains.
Deficiencies in threonine can lead to impaired growth, skin lesions, and reduced immune response.
Understanding the importance of this amino acid in human health and its applications in research is essential for advancing scientific understanding and developing effective therapies.
Proteome Discoverer and Mascot are tools used in the analysis and identification of proteins, including those containing threonine.
Related amino acids like phenylalanine, alanine, glycine, and valine also play important roles in the body and can be found in similar food sources.
Proper nutrition and supplementation with threonine and other essential amino acids can help support overall health and well-being.
By expanding our knowledge of threonine and its metabolic functions, researchers can develop new strategies to optimize human health and address deficiencies or disorders related to this key nutrient.