The distribution of the 90K SNPs across populations was assessed in the diverse panel of 726 accessions including tetraploid and hexaploid landraces (Table S14). A total of eight bi-parental doubled-haploid mapping populations were used to order SNPs along chromosomes: BT-Schomburgk × AUS33384 (CIGM92.1712), Young × AUS33414 (CIGM93.238), Chara × Glenlea, W7984 × Opata M85, Sundor × AUS30604, Westonia × Kauz, Avalon × Cadenza and Savannah × Rialto. Ditelosomic lines for Chinese Spring wheat (Kimber and Sears, 1968 ) were used to test the accuracy of clustering and assign the consensus genetic map linkage groups to wheat chromosomes. For cluster file development for hexaploid wheat, 2473 bread wheat lines comprising 1979 worldwide wheat accessions and 494 F4 progeny from a nested association mapping population were used. The F4 lines were included to provide a sufficient number of heterozygous individuals for the majority of SNPs to ensure correct clustering of the heterozygous SNP alleles. For cluster file development in durum wheat, diverse accessions from a worldwide durum panel, recombinant inbred lines from a four-way cross of (Neodur × Claudio) × (Colosseo × Rascon37/Tarro2/Rascon37), six F1 samples (Dylan × Normanno; Tiziana × Normanno; Dupri × Normanno; Achille × Normanno; Strongfield × Saragolla; Kofa × Claudio) and the corresponding nine F1 parental lines were used.
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Living Beings
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Plant
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Triticum aestivum
Triticum aestivum
Triticum aestivum (common wheat) is a widely cultivated cereal grain species belonging to the Poaceae family.
It is a staple food crop grown globally for its edible seeds, which are used to produce flour, bread, pasta, and other wheat-based products.
Triticum aestivum is characterized by its tall, annual grass-like stems, narrow leaves, and spike-like inflorescences that produce the distinctive wheat kernels.
This species is known for its adaptability to diverse climates and soil conditions, making it an important contributor to food security and economic development worldwide.
Researchers can discover optimized protocols for studying Triticum aestivum using the PubCompare.ai platform, which leverages AI-driven comparisons to identify the most effective and reproducible research methods from the literature, pre-prints, and patents, saving time and enhancing the quality of their investigations.
It is a staple food crop grown globally for its edible seeds, which are used to produce flour, bread, pasta, and other wheat-based products.
Triticum aestivum is characterized by its tall, annual grass-like stems, narrow leaves, and spike-like inflorescences that produce the distinctive wheat kernels.
This species is known for its adaptability to diverse climates and soil conditions, making it an important contributor to food security and economic development worldwide.
Researchers can discover optimized protocols for studying Triticum aestivum using the PubCompare.ai platform, which leverages AI-driven comparisons to identify the most effective and reproducible research methods from the literature, pre-prints, and patents, saving time and enhancing the quality of their investigations.
Most cited protocols related to «Triticum aestivum»
Alleles
Bread
Chara
Chinese
Chromosomes
Durum Wheat
Heterozygote
Linkage, Genetic
Parent
Tetraploidy
Triticum aestivum
We collected GBS data from a collection of 1995 accessions from the genus Malus from the US Department of Agriculture apple germplasm repository in Geneva, NY. The samples were processed with two different restriction enzymes (ApeKI, PstI/EcoT22I) in separate GBS libraries and were sequenced using Illumina Hi-Sequation 2000 technology. Genotypes were called using a custom GBS pipeline described in Gardner et al. (2014) (link). Briefly, 100-bp reads generated from both enzymes were aligned to the Malus domestica reference genome version 1.0 (Velasco et al. 2010 (link)) using the default parameters in BWA (Li and Durbin 2009 (link)). Genotypes were called using GATK (McKenna et al. 2010 (link)) with a minimum of eight reads supporting each genotype. The final genotype matrix was filtered to contain only samples from the domesticated apple, Malus domestica, and ≤20% missing data per SNP and per sample. SNPs with a minor allele frequency (MAF) of <0.01 were then discarded. Finally, the data were pruned to exclude clonal relationships: if two or more samples had IBD >0.9, they were considered clones and the sample with the least amount of missing data from the group was retained. This resulted in a dataset of 711 samples and 8404 SNPs.
To test the accuracy of our imputation method we created a “masked” dataset by setting 10,000 random genotypes to missing. This created “truth known” genotypes to which our imputed genotype calls were compared. We limited our testing to 10,000 masked genotypes, which represents 0.17% of the genotype matrix, in order to maintain a dataset with a reasonable amount of missing data while providing enough masked genotypes to be able to estimate imputation accuracy.
Biased allele frequency in imputed data has been shown to affect downstream analyses (Han et al. 2014 (link)). To determine how well each imputation method estimates allele frequencies, we filtered the genotype matrix to contain no missing data. This resulted in a matrix containing 1001 SNPs from 459 samples (Figure S2 ). We masked and then imputed 20% (91,952 genotypes) of the genotypes at random and compared the allele frequency estimates from the imputed data to the allele frequency estimates from the complete genotype matrix. As most imputation methods make use of other SNPs to aid imputation, we imputed using all 8404 SNPs in the dataset so as to provide more information to these methods. We then restrict our analysis to the 1001 complete SNPs.
We also tested the performance of our method on genome-wide SNP data from maize and grape. The maize data were downloaded from the International Maize and Wheat Improvement Center (Hearne et al. 2014 ). We reduced the data to biallelic SNPs with <20% missing data and a MAF >1% and then discarded samples with >20% missing data. This resulted in 43,696 SNPs from 4300 samples.
To generate the grape dataset we collected GBS data from a collection of diverse samples from the genus Vitis including commercial Vitis vinifera varieties, hybrids and wild accessions from the USDA grape germplasm collection. The samples were processed with two different restriction enzymes (HindIII/BfaI, HindIII/MseI) and were sequenced using Illumina Hi-Sequation 2000 technology. We then used the 12X grape reference genome (Jaillon et al. 2007 (link); Adam-Blondon et al. 2011 ) and the Tassel / BWA version 4 pipeline to generate a genotype matrix (Li and Durbin 2009 (link); Glaubitz et al. 2014 (link)). Default parameters were used at each stage except for the SNP output stage where we filtered for biallelic SNPs. We then removed any genotypes with fewer than eight supporting reads using vcftools (Danecek et al. 2011 (link)). Using PLINK (Purcell et al. 2007 (link)), we removed SNPs with >20% missing data before removing samples with >20% missing data. We then removed SNPs with excess heterozygosity (failed a Hardy−Weinberg equilibrium test with a p-value < 0.001) and finally SNPs with a MAF < 0.01. This created a dataset of 8506 SNPs and 77 samples.
To test the accuracy of our imputation method we created a “masked” dataset by setting 10,000 random genotypes to missing. This created “truth known” genotypes to which our imputed genotype calls were compared. We limited our testing to 10,000 masked genotypes, which represents 0.17% of the genotype matrix, in order to maintain a dataset with a reasonable amount of missing data while providing enough masked genotypes to be able to estimate imputation accuracy.
Biased allele frequency in imputed data has been shown to affect downstream analyses (Han et al. 2014 (link)). To determine how well each imputation method estimates allele frequencies, we filtered the genotype matrix to contain no missing data. This resulted in a matrix containing 1001 SNPs from 459 samples (
We also tested the performance of our method on genome-wide SNP data from maize and grape. The maize data were downloaded from the International Maize and Wheat Improvement Center (Hearne et al. 2014 ). We reduced the data to biallelic SNPs with <20% missing data and a MAF >1% and then discarded samples with >20% missing data. This resulted in 43,696 SNPs from 4300 samples.
To generate the grape dataset we collected GBS data from a collection of diverse samples from the genus Vitis including commercial Vitis vinifera varieties, hybrids and wild accessions from the USDA grape germplasm collection. The samples were processed with two different restriction enzymes (HindIII/BfaI, HindIII/MseI) and were sequenced using Illumina Hi-Sequation 2000 technology. We then used the 12X grape reference genome (Jaillon et al. 2007 (link); Adam-Blondon et al. 2011 ) and the Tassel / BWA version 4 pipeline to generate a genotype matrix (Li and Durbin 2009 (link); Glaubitz et al. 2014 (link)). Default parameters were used at each stage except for the SNP output stage where we filtered for biallelic SNPs. We then removed any genotypes with fewer than eight supporting reads using vcftools (Danecek et al. 2011 (link)). Using PLINK (Purcell et al. 2007 (link)), we removed SNPs with >20% missing data before removing samples with >20% missing data. We then removed SNPs with excess heterozygosity (failed a Hardy−Weinberg equilibrium test with a p-value < 0.001) and finally SNPs with a MAF < 0.01. This created a dataset of 8506 SNPs and 77 samples.
Clone Cells
DNA Restriction Enzymes
Enzymes
Genome
Genotype
Grapes
Heterozygote
Hybrids
Maize
Malus
Malus domestica
Single Nucleotide Polymorphism
Tassel
Triticum aestivum
Vitis
To detect small sequencing errors (single nucleotides and indels of 1–4 bases) in the newly assembled reference genome, Illumina Genome Analyzer II/IIx generated reads were used. The genomes of two different individuals of Nipponbare were independently re-sequenced at NIAS and CSHL. Low quality bases (http://hannonlab.cshl.edu/fastx_toolkit/). Reads that were <32 bp in length were discarded for further analyses. If only one read of a paired-end read set was discarded in these preprocessing steps, the other read was regarded as a single-end read and named "unpaired." All qualified reads were aligned to the reference genome using BWA v0.5.8a with default options (Li and Durbin 2009 (link)). The NIAS single-end reads and CSHL unpaired reads were aligned in the single-end mode using the BWA command “samse”. The CSHL paired-end reads were aligned in the paired-end mode using the BWA command “sampe”. The reads that matched to multiple genomic positions were discarded. A pile up alignment file of all uniquely mapped reads with a mapping quality value of ≥20 was generated using SAMtools v1.8 (Li et al. 2009 (link)). To avoid erroneous detection of variants, only sites with a read depth of 10 or more were selected.
By comparing the Illumina reads with the reference genome, each aligned site was first classified into four categories: "reference type (R)," "non-reference type (N)," "allelic (A)," and "low depth (L)" for each of three sets (NIAS, CSHL and NIAS + CSHL) (Additional file7 ). If a site had less than 10 reads, the site was "low depth (L)," which means we were unable to assess the site due to low sampling. If ≥80% of the reads were identical to the reference base, the site was classified as "reference type (R)". If ≥80% of the reads were discordant with the reference base, the site was classified as "non-reference type (N)". If there were two alleles with ≥40% read support, the site was classified as "allelic (A)". Since we have two data sets from NIAS and CSHL, the classifications of the three sets (NIAS, CSHL and NIAS + CSHL) were combined and reexamined to decide the genotype for each site (Additional file 7 ): "reference type", "sequencing error (Additional file 9 )", "alleles between individuals” (Additional file 10 ), "alleles within individuals” (Additional file 11 ), and "low depth". SNPs classified as allelic variations were annotated based on the RAP-DB gene models using SnpEff v. 3.1 (Cingolani et al. 2012 (link)) (Additional file 12 ).
The genome of the same NIAS individual used in the Illumina re-sequencing was sequenced using the Roche GS FLX platform. Low quality bases (http://www.repeatmasker.org/) with the MIPS Repeat Element Database (mips-REdat) version 4.3 (http://mips.helmholtz-muenchen.de/plant/genomes.jsp ; Spannagl et al. 2007 (link)) and the Triticeae Repeat Sequence Database release 10 (http://wheat.pw.usda.gov/ITMI/Repeats/ ). All preprocessed reads were aligned to the reference genome using Megablast (version 2.2.24) with the following options: -F 'm D' -U T -e 1e-10 (Zhang et al. 2000 (link)).
By comparing the Illumina reads with the reference genome, each aligned site was first classified into four categories: "reference type (R)," "non-reference type (N)," "allelic (A)," and "low depth (L)" for each of three sets (NIAS, CSHL and NIAS + CSHL) (Additional file
The genome of the same NIAS individual used in the Illumina re-sequencing was sequenced using the Roche GS FLX platform. Low quality bases (
Alleles
Genes, vpr
Genome
Genome, Plant
Genotype
Hemorrhoids
INDEL Mutation
Macrophage Inflammatory Protein-1
MPIF-1 protein, human
Nucleotides
Single Nucleotide Polymorphism
Triticum aestivum
We performed genomic breeding value estimation (GEBV) and hybrid prediction with wheat data, and the results were compared to other genomic selection and mixed model software, including rrBLUP [13 ], ASReml [21 ], regress (used by synbreed as well) [17 ,18 (link)], EMMREML [19 ], MCMCglmm [15 (link)], and BGLR [16 (link)]. We used the wheat data contained in the R package BGLR consisting of 599 inbred lines genotyped with 1279 diversity array technology (DArT) markers [16 (link)]. Phenotypic data consisted of grain yield (GY) for the 599 lines from the historical CIMMYT's Global Wheat Program evaluated in four mega-environments.
From the 599 wheat lines, 179,101 distinct single crosses can be performed. Kinship-based BLUP prediction for the 599 lines were obtained using rrBLUP (ridge regression), ASReml (average information), regress (Newton-Raphson), EMMREML (modified EMMA), BGLR (using the Reproducing kernel Hilbert space [RKHS] kernel), MCMCglmm (Gibbs sampling) and the three algorithms implemented in sommer (AI, EM, and EMMA). Similarity among BLUPs using all software was performed in R and displayed in tables and figures [26 ]. The genomic estimated breeding values (GEBV) for each of the 599 inbred lines was used to predict the performance of possible crosses as the average among the breeding value of the parental lines. The mixed model fitted has the form:
with variance:
and the mixed model equations for this model are:
Here,G = K σ2u, is the variance covariance matrix of the random effect u, from a multivariate normal distribution u ~ MVN(0, K σ2u), K being, in the genomics context, the additive or genomic relationship matrix (A or A g). X and Z are incidence matrices for fixed and random effects respectively, and R is the matrix for residuals (here I σ2e). A mixed model with a single variance component other than the error (σ2e) can be used to estimate the genetic variance (σ2u) along with genotype BLUPs to exploit the genetic relationships between individuals coded in K (A ). The genomic relationship matrix was constructed according to VanRaden where K = ZZ ’/2Σpi(1-pi) [27 (link)]. Genotype BLUPs were calculated and considered equal to the GEBV and these were used to predict the performance of the 179,101 possible crosses as the average of parental genomic breeding values. We fitted this model using the sommer package by specifying the incidence and variance-covariance matrices and using the three algorithms implemented (AI, EM, EMMA). In addition, a five-fold cross validation was performed to calculate the predictive correlation for grain yield in the four mega environments available for the wheat data using the sommer package. In addition, heritability was estimated as h2 = σ2u / σ2u + σ2e.
From the 599 wheat lines, 179,101 distinct single crosses can be performed. Kinship-based BLUP prediction for the 599 lines were obtained using rrBLUP (ridge regression), ASReml (average information), regress (Newton-Raphson), EMMREML (modified EMMA), BGLR (using the Reproducing kernel Hilbert space [RKHS] kernel), MCMCglmm (Gibbs sampling) and the three algorithms implemented in sommer (AI, EM, and EMMA). Similarity among BLUPs using all software was performed in R and displayed in tables and figures [26 ]. The genomic estimated breeding values (GEBV) for each of the 599 inbred lines was used to predict the performance of possible crosses as the average among the breeding value of the parental lines. The mixed model fitted has the form:
with variance:
and the mixed model equations for this model are:
Here,
Cereals
Genetic Diversity
Genome
Genotype
Hybrids
Parent
Phenotype
Reproduction
Triticum aestivum
A high-quality gene set for wheat was generated using a custom pipeline integrating wheat-specific transcriptomic data, protein similarity, and evidence-guided gene predictions generated with AUGUSTUS (Stanke and Morgenstern 2005 (link)). Full methods are in Supplemental Information S8 . RNA-seq reads (ERP004714, ERP004505, and 250-bp PE strand-specific reads from six different tissues) were assembled using four alternative assembly methods (Trapnell et al. 2010 (link); Haas et al. 2013 (link); Pertea et al. 2015 (link); Song et al. 2016 (link)) and integrated with PacBio transcripts into a coherent and nonredundant set of models using Mikado (https://github.com/lucventurini/mikado ). PacBio reads were then classified according to protein similarity and a subset of high-quality (e.g., full length, canonical splicing, nonredundant) transcripts used to train an AUGUSTUS wheat-specific gene prediction model. AUGUSTUS was then used to generate a first draft of the genome annotation, using as input Mikado-filtered transcript models, reliable junctions identified with Portcullis (https://github.com/maplesond/portcullis ), and peptide alignments of proteins from five close wheat relatives (B. distachyon, maize, rice, S. bicolor, and S. italica). This draft annotation was refined by correcting probable gene fusions, missing loci and alternative splice variants. The annotation was functionally annotated, and all loci were assigned a confidence rank based on their similarity to known proteins and their agreement with transcriptome data.
Gene Expression Profiling
Gene Fusion
Genes
Genome
Maize
Peptides
Proteins
Rice
RNA-Seq
Tissues
Transcriptome
Triticum aestivum
Most recents protocols related to «Triticum aestivum»
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.
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 6
Both RGA 1 and RGA 2 gene functions can be validated with different methods well known in the art. Genetic transformation of a susceptible wheat cultivar overexpressing RGA 1 or RGA 2 under different promoters can be obtained and tested for their ability to confer OWBM resistance in glass-house conditions or in the field.
Validation can also be achieved by mutagenesis with methods known from skilled person in the art, with for example, EMS treatment. The validation consists of obtaining several independent “loss-of-resistance” mutants derived from the EMS treatment of a resistant wheat cultivar and further identifying mutations within the candidate gene; thus confirming the resistance function of the gene. For example, such method is described by Periyannan et al. (2013) used to identify the wheat stem rust resistance gene Sr33.
Disease Resistance
Genes
Mutagenesis
Mutation
Operator, Genetic
Stem, Plant
Transformation, Genetic
Triticum aestivum
Example 1
Wheat straw was first air dried, then oven dried in an oven at 105° C. The dried wheat straw was then ground in a hammer mill to particles sizes as high as 0.5 mm and sieved. The pyrolyzer was then operated to temperatures between 400° C. and 600° C. and 50 grams of wheat straw powder was fed into the pyrolyzer chamber. The pyrolyzer was operated for 2 hours to 2.5 hours for each batch experiment before feeding the next batch (50 g of wheat straw) into the pyrolyzer chamber. The biochar was collected from the cup holder of the pyrolyzer. Nitrogen was continuously flowing through the pyrolyzer during the preparation of biochar to provide an inert environment. For the loading of the biochar, 08 grams of biochar was provided in an emerald flask and 02 grams of copper sulfate (CuSO4) was added to that. 40 grams of deionized water was then added into the flask to form a solution and the solution was stirred for 45 minutes at 50° C. Afterwards, the solution was heated in an oven until a wet biochar was achieved. Then, the wet biochar was placed in a muffle furnace at 600° C. for 2 hours at a heating rate of 2° C./sec. The formation of copper augmented biochar was confirmed through XRD and FTIR analysis before the copper augmented biochar was used for chromium removal experiments.
biochar
Chromium
Copper
Nitrogen
Powder
Spectroscopy, Fourier Transform Infrared
Sulfate, Copper
Triticum aestivum
Example 18
Thin pancakes were made from thin pancake batter comprising the following ingredients:
The thin pancakes were made in a frying pan into which batter was placed and cooked at ordinary temperature and time according to the recipe. The thin pancakes were comparable or superior in quality and taste compared to thin pancakes made using traditional all-purpose or cake flour and had superior nutrition profile.
Flour
Food
MCTS1 protein, human
Plants
Sodium Chloride
Taste
Triticum aestivum
Example 16
Bread was made from bread dough comprising the following ingredients:
Bread loaves were made by kneading the bread dough as usual, letting it rise, beating the raised dough down, placing the dough into baking pans, allowing the dough to rise a second time, and baked at ordinary temperature and time in an oven according to the recipe. The bread wase comparable or superior in quality and taste compared to bread made using traditional all-purpose or bread flour and had superior nutrition profile.
Bread
Butter
Carbohydrates
Flour
Food
MCT-21
Milk, Cow's
Plants
Powder
Saccharomyces cerevisiae
Sodium Chloride
Taste
Triticum aestivum
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TRIzol reagent is a monophasic solution of phenol, guanidine isothiocyanate, and other proprietary components designed for the isolation of total RNA, DNA, and proteins from a variety of biological samples. The reagent maintains the integrity of the RNA while disrupting cells and dissolving cell components.
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The HiSeq 2500 is a high-throughput DNA sequencing system designed for a wide range of applications, including whole-genome sequencing, targeted sequencing, and transcriptome analysis. The system utilizes Illumina's proprietary sequencing-by-synthesis technology to generate high-quality sequencing data with speed and accuracy.
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The BX51 microscope is an optical microscope designed for a variety of laboratory applications. It features a modular design and offers various illumination and observation methods to accommodate different sample types and research needs.
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More about "Triticum aestivum"
Triticum aestivum, commonly known as common wheat, is a widely cultivated cereal grain species belonging to the Poaceae family.
It is a staple food crop grown globally for its edible seeds, which are used to produce flour, bread, pasta, and other wheat-based products.
Triticum aestivum is characterized by its tall, annual grass-like stems, narrow leaves, and spike-like inflorescences that produce the distinctive wheat kernels.
This species is known for its adaptability to diverse climates and soil conditions, making it an important contributor to food security and economic development worldwide.
Researchers can leverage various tools and techniques to study Triticum aestivum, such as the TRIzol reagent for RNA extraction, the RNeasy Plant Mini Kit and DNeasy Plant Mini Kit for nucleic acid purification, the TNT SP6 High-Yield Wheat Germ Protein Expression System for protein expression, and the HiSeq 2500 platform for high-throughput sequencing.
Microscopy techniques, like the BX51 microscope, can be used to examine the plant's morphology.
Additionally, the Agilent 2100 Bioanalyzer can be employed for quality assessment of extracted nucleic acids, while the FastQuant RT Kit can facilitate cDNA synthesis for downstream applications.
Wheat-specific biomolecules, such as arabinoxylan, are also of interest for researchers investigating this important cereal crop.
By utilizing these tools and techniques, scientists can discover optimized protocols for studying Triticum aestivum using the PubCompare.ai platform, which leverages AI-driven comparisons to identify the most effective and reproducible research methods from the literature, pre-prints, and patents, saving time and enhancing the quality of their investigations.
It is a staple food crop grown globally for its edible seeds, which are used to produce flour, bread, pasta, and other wheat-based products.
Triticum aestivum is characterized by its tall, annual grass-like stems, narrow leaves, and spike-like inflorescences that produce the distinctive wheat kernels.
This species is known for its adaptability to diverse climates and soil conditions, making it an important contributor to food security and economic development worldwide.
Researchers can leverage various tools and techniques to study Triticum aestivum, such as the TRIzol reagent for RNA extraction, the RNeasy Plant Mini Kit and DNeasy Plant Mini Kit for nucleic acid purification, the TNT SP6 High-Yield Wheat Germ Protein Expression System for protein expression, and the HiSeq 2500 platform for high-throughput sequencing.
Microscopy techniques, like the BX51 microscope, can be used to examine the plant's morphology.
Additionally, the Agilent 2100 Bioanalyzer can be employed for quality assessment of extracted nucleic acids, while the FastQuant RT Kit can facilitate cDNA synthesis for downstream applications.
Wheat-specific biomolecules, such as arabinoxylan, are also of interest for researchers investigating this important cereal crop.
By utilizing these tools and techniques, scientists can discover optimized protocols for studying Triticum aestivum using the PubCompare.ai platform, which leverages AI-driven comparisons to identify the most effective and reproducible research methods from the literature, pre-prints, and patents, saving time and enhancing the quality of their investigations.