Log In Sign up
The largest database of trusted experimental protocols
> Physiology > Organism Attribute > Phenotype

Phenotype

Phenotype refers to the observable physical or biochemical characteristics of an organism, as determined by both genetic and environmental factors.
It represents the expression of a genotype in a specific environment.
Phenotypes can include morphological, physiological, behavioral, and biochemical traits, and are the result of the interaction between an organism's genes and its surroundings.
Understanding phenotypes is crucial for research in genetics, developmental biology, and personalized medicine.
PubCompare.ai's AI-driven platform can help identify the most effective approaches for phentoype optimization needs, enhancing research reproducibility by locating the best protocols from literature, preprints, and patents.

Most cited protocols related to «Phenotype»

1
Hallmark Gene Refinement Protocol

Protocol full text hidden due to copyright restrictions

Open the protocol to access the free full text link

Publication 2015
Cloning Vectors Discrimination, Psychology Gene Expression Genes Lanugo Phenotype Stem, Plant
2
Verification of Antimicrobial Resistance Genes
Verification of the databases was made by testing ResFinder with the 1862 GenBank files from which the genes were collected, to verify that the method would find all genes with ID = 100%.
Short sequence reads from 23 isolates of five different species, Escherichia coli, Klebsiella pneumoniae, Salmonella enterica, Staphylococcus aureus and Vibrio cholerae, were also submitted to ResFinder. All 23 isolates had been sequenced on the Illumina platform using paired-end reads. A ResFinder threshold of ID = 98.00% was selected, as previous tests of ResFinder had shown that a threshold lower than this gives too much noise (e.g. fragments of genes). Phenotypic antimicrobial susceptibility testing was determined as MIC determinations, as previously described.8 (link)With ‘(chromosome and plasmid)(multi-drug or antimicrobial or antibiotic)(resistant or resistance) pathogen’ as search criteria, one isolate from each species with completely sequenced and assembled, and chromosome and plasmid data were collected from the NCBI Genomes database (http://www.ncbi.nlm.nih.gov/genome). This resulted in 30 isolates, from 30 different species, containing 85 chromosome/plasmid sequences. All sequences were run through all databases in ResFinder with a selected threshold of ID = 98.00%.
Zankari E., Hasman H., Cosentino S., Vestergaard M., Rasmussen S., Lund O., Aarestrup F.M, & Larsen M.V. (2012). Identification of acquired antimicrobial resistance genes. Journal of Antimicrobial Chemotherapy, 67(11), 2640-2644.
Publication 2012
Antibiotics Chromosomes Escherichia coli Genes Genome Klebsiella pneumoniae Microbicides Pathogenicity Pharmaceutical Preparations Phenotype Plasmids Salmonella enterica Staphylococcus aureus Susceptibility, Disease Vibrio cholerae
3
Genome-Wide Association Study Protocol
A detailed description of materials and methods is given in Methods. The work-flow and organization of the project are given in Supplementary Fig. 16. Case series came from previously established collections with nationally representative recruitment: 2,000 samples were genotyped for each. The control samples came from two sources: half from the 1958 Birth Cohort and the remainder from a new UK Blood Service sample. The latter collection was established specifically for this study and is a UK national repository of anonymized DNA samples from 3,622 consenting blood donors. The vast majority of subjects were self-reported as of European Caucasian ancestry. All DNA samples were requantified and tested for degradation and PCR amplification. Genotyping was performed using GeneChip 500K arrays at the Affymetrix Services Lab (California): arrays not passing the 93% call rate threshold at P=0.33 with the Dynamic Model algorithm were repeated. CEL (cell intensity) files were transferred to WTCCC for quantile normalization, and genotypes called using a new genotyping algorithm, CHIAMO, developed for this project. QC/QA measures included sample call rate, overall heterozygosity and evidence of non-European ancestry (809 samples excluded; 16,179 retained for analysis). SNPs were excluded from analysis because of missing data rates, departures from Hardy-Weinberg equilibrium and other metrics (31,011 excluded; 469,557 retained). Standard 1-d.f. and 2-d.f. tests of case-control association were supplemented with bayesian approaches, multilocus methods (data imputation) and analyses with combined data sets, either as additional cases (to detect variants influencing multiple phenotypes) or as an expanded reference group (to increase power). Results for each SNP for all analyses reported will be available from http://www.wtccc.org.uk, as will details allowing other researchers to apply for access to WTCCC genotype data. Software packages developed within the WTCCC are available on request (see Methods for details).
Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. (2007). Nature, 447(7145), 661-678.
Publication 2007
Birth Cohort BLOOD Caucasoid Races Cells DNA, A-Form Donor, Blood Europeans Gene Chips Genotype Heterozygote Phenotype
4
Identifying Relevant Datasets for Hallmark Refinement

Protocol full text hidden due to copyright restrictions

Open the protocol to access the free full text link

Publication 2015
Biopharmaceuticals Genes Genetic Heterogeneity Homo sapiens Mice, Laboratory Phenotype
5
Customizing Leukocyte Signature Matrices for Tumor Profiling
In the following two sections, we describe how to create a custom leukocyte signature matrix and apply it to study cellular heterogeneity and TIL survival associations in melanoma tumors profiled by The Cancer Genome Atlas (TCGA). Readers can follow along by creating ‘LM6’, a leukocyte RNA-Seq signature matrix comprised of six peripheral blood immune subsets (B cells, CD8 T cells, CD4 T cells, NK cells, monocytes/macrophages, neutrophils; GSE60424 [20 ]). Key input files are provided on the CIBERSORT website (‘Menu>Download’).
A custom signature file can be created by uploading the Reference sample file and the Phenotype classes file (section 3.3.2) to the online CIBERSORT application (SeeFigure 2) or can be created using the downloadable Java package. To build a custom gene signature matrix with the latter, the user should download the Java package from the CIBERSORT website and place all relevant files under the package folder. To link Java with R, run the following in R:
Within R:

> library(Rserve)

> Rserve(args=“–no-save”)

Command line:

> java -Xmx3g -Xms3g -jar CIBERSORT.jar -M Mixture_file -P Reference_sample_file -c phenotype_class_file -f

The last argument (-f) will eliminate non-hematopoietic genes from the signature matrix and is generally recommended for signature matrices tailored to leukocyte deconvolution. The user can also run this step on the website by choosing the corresponding reference sample file and phenotype class file (seeFigure 2). The CIBERSORT website will generate a gene signature matrix located under ‘Uploaded Files’ for future download.
Following signature matrix creation, quality control measures should be taken to ensure robust performance (see ‘Calibration of in silico TIL profiling methods’ in Newman et al.) [17 (link)]. Factors that can adversely affect signature matrix performance include poor input data quality, significant deviations in gene expression between cell types that reside in different tissue compartments (e.g., blood versus tissue), and cell populations with statistically indistinguishable expression patterns. Manual filtering of poorly performing genes in the signature matrix (e.g., genes expressed highly in the tumor of interest) may improve performance.
To benchmark our custom leukocyte matrix (LM6), we compared it to LM22 using a set of TCGA lung squamous cell carcinoma tumors profiled by RNA-Seq and microarray (n = 130 pairs). Deconvolution results were significantly correlated for all cell subsets shared between the two signature matrices (P < 0.0001). Notably, since LM6 was derived from leukocytes isolated from peripheral blood [20 ,21 (link)], we restricted the CD4 T cell comparison to naïve and resting memory CD4 T cells in LM22. Once validation is complete, a CIBERSORT signature matrix can be broadly applied to mixture samples as described in section 3.3 (e.g., SeeFigure 4).
Chen B., Khodadoust M.S., Liu C.L., Newman A.M, & Alizadeh A.A. (2018). Profiling tumor infiltrating immune cells with CIBERSORT. Methods in molecular biology (Clifton, N.J.), 1711, 243-259.
Publication 2018
B-Lymphocytes BLOOD CD4 Positive T Lymphocytes CD8-Positive T-Lymphocytes cDNA Library Cells Genes, vif Genetic Diversity Genetic Heterogeneity Hematopoietic System Leukocytes Lung Neoplasms Macrophage Malignant Neoplasms Melanoma Memory Microarray Analysis Monocytes Natural Killer Cells Neoplasms Neutrophil Phenotype Population Group RNA-Seq RNA Motifs Squamous Cell Carcinoma Strains Tissues

Most recents protocols related to «Phenotype»

1
Overexpression of Ceres cDNA 12723147 Confers Drought Tolerance
Not available on PMC !

Example 6

Ceres cDNA 12723147 encodes an Arabidopsis putative aldo/keto reductase. Ectopic expression of Ceres cDNA 12723147 under the control of the CaMV35S promoter induces the following phenotypes:

    • Germination on high concentrations of polyethylene glycol (PEG), mannitol and abscissic acid (ABA).
    • Continued growth on high concentration of PEG, mannitol and ABA.
      Generation and Phenotypic Evaluation of T1 Lines Containing 35S::cDNA 12723147.

Wild-type Arabidopsis Wassilewskija (WS) plants were transformed with a Ti plasmid containing cDNA 12723147 in the sense orientation relative to the CaMV35S constitutive promoter. The Ti plasmid vector used for this construct, CRS338, contains PAT and confers herbicide resistance to transformed plants. Ten independently transformed events were selected and evaluated for their qualitative phenotype in the T1 generation. No positive or negative phenotypes were observed in the T1 plants.

Screens of Superpools on High PEG, Mannitol, and ABA as Surrogate Screens for Drought Tolerance.

Seeds from 13 superpools (1,200 T2 seeds from each superpool) from the CaMV35S or 32449 over-expression lines were tested on 3 drought surrogate screens (high concentrations of PEG, mannitol, and ABA) as described above. T3 seeds were collected from the resistant plants and analyzed for resistance on all three surrogate drought screens.

Once cDNA 12723147 was identified in resistant plants from each of the three surrogate drought screens, the five individual T2 events containing this cDNA (SR01013) were screened on high PEG, mannitol, and ABA to identify events with the resistance phenotype.

Superpools (SP) are referred to as SP1, SP2 and so on. The letter following the hyphen refers to the screen (P=PEG, M=mannitol, and A=ABA) and the number following the letter refers to a number assigned to each plant obtained from that screen on that superpool. For example, SP1-M18 is the 18th plant isolated from a mannitol screen of Superpool 1.

Qualitative and Quantitative Analysis of 2 Independent Events Representing 35S::cDNA 12659859 (SR01010) on PEG, Mannitol and ABA

To identify two independent events of 35S::cDNA 12659859 showing PEG, mannitol, and ABA resistance, 36 seedlings from each of two events, SR01013-01 and -02 were screened as previously described. BastaR segregation was assessed to verify that the lines contained a single insert segregating in a 3:1 (R:S) ratio as calculated by a chi-square test (Table 6-1). Both lines (01 and 02) segregated for a single insert in the T2 generation (Table 1)

TABLE 6-1
BastaR segregation for SR01013 individual events
Probability
EventResistantSensitiveTotalof Chi-test*
SR01013-01305350.14323
SR01013-02306360.24821
SR01013-01-3341360.00248**
SR01013-02-2320320.00109**
*Chi-test to determine whether actual ratio of resistant to sensitive differs form the expected 3:1 ratio.
**Significantly different than a 3:1 (R:S) ratio

Lines SR01013-01 and -02 were chosen as the two events because they had a strong and consistent resistance to PEG, mannitol and ABA. The controls were sown the same day and in the same plate as the individual lines. The PEG (Tables 6-2 and 6-3), mannitol (Tables 6-4 and 6-5) and ABA (Tables 6-6 and 6-7) segregation ratios observed for SR01013-01 and -02 are consistent with the presence of single insert as demonstrated by chi-square, similar to what we observed for BastaR resistance (Table 6-1).

The progeny from one resistant T2 plant from each of these two events were tested in the same manner as the T2. Resistance to PEG, mannitol and ABA was also observed in the T3 generation. Taken together, the segregation of resistant seedlings containing cDNA 12723147 from two events on all three drought surrogate screens and the inheritance of this resistance in a subsequent generation, provide strong evidence that cDNA 12723147 when over-expressed can provide tolerance to drought.

TABLE 6-2
Chi-square analysis assuming a 3:1 (R:S) ratio for progeny of
SR01013-01T2 containing 35S::cDNA 12723147 on PEG.
Probability
EventObservedExpectedχ2of Chi-Test
PEG Resistant22270.9260.054
PEG Sensitive1492.778
36363.704

TABLE 6-3
Chi-square analysis assuming a 3:1 (R:S) ratio for progeny of
SR01013-02 T2 containing 35S::cDNA 12723147 on PEG.
Probability
EventObservedExpectedχ2of Chi-Test
PEG Resistant26270.037.700
PEG Sensitive109.111
3636.148

TABLE 6-4
Chi-square analysis assuming a 3:1 (R:S) ratio for progeny of
SR01013-01 T2 containing 35S::cDNA 12723147 on mannitol.
Probability
EventObservedExpectedχ2of Chi-Test
Mannitol Resistant2827.037.700
Mannitol Sensitive89.111
3636.148

TABLE 6-5
Chi-square analysis assuming a 3:1 (R:S) ratio for progeny of
SR01013-02 T2 containing 35S::cDNA 12723147 on mannitol.
Probability
EventObservedExpectedχ2of Chi-Test
Mannitol Resistant18273.0005
Mannitol Sensitive1899
363612

TABLE 6-6
Chi-square analysis assuming a 3:1 (R:S) ratio for progeny of
SR01013-02 T2 containing 35S::cDNA 12723147 on ABA.
EventObservedExpectedχ2Probability
ABA Resistant1324 5.0427.098
ABA Sensitive19 815.125
323220.167

TABLE 6-7
Chi-square analysis assuming a 3:1 (R:S) ratio for progeny of
SR01013-02 T2 containing 35S::cDNA 12723147 on ABA.
EventObservedExpectedχ2Probability
ABA Resistant1324 5.0427.098
ABA Sensitive19 815.125
323220.167
FIG. 5 provides the results of the consensus sequence (SEQ ID NOs: 178-200) analysis based on Ceres cDNA 12723147.

US11859195B2. Nucleotide sequences and polypeptides encoded thereby useful for modifying plant characteristics (2024-01-02). CERES, INC. [US]. Inventors: Cory Christensen [US], Nestor Apuya [US], Kenneth A. Feldmann [US].
Patent 2024
14-3-3 Proteins Abscisic Acid Aldo-Keto Reductase Arabidopsis CERE Cloning Vectors Consensus Sequence DNA, Complementary Droughts Drought Tolerance Ectopic Gene Expression Germination Herbicide Resistance Mannitol Pattern, Inheritance Phenotype Plant Embryos Plants Plant Tumor-Inducing Plasmids Polyethylene Glycols Seedlings
2
Engineered Alginate Production in P. aeruginosa
Not available on PMC !

Example 2

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

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

US11873477B2. Bacterial cultures and methods for production of alginate (2024-01-16). Marshall University Research Corporation [US]. Inventors: Hongwei D. Yu [US], Meagan E. Valentine [US], Richard M. Niles [US], Thomas Ryan Withers [US], Brandon D. Kirby [US].
Patent 2024
Alginate Alginates Anabolism Biological Assay Biosynthetic Pathways carbazole Cells Gels Genes High-Performance Liquid Chromatographies Operon Parent Phenotype Physical Processes Plasmids Pseudomonas aeruginosa Strains Viscosity
3
Microbial Seed Treatments for Enhanced Crop Performance
Not available on PMC !

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.

TABLE 15
StrainShootRoot
Microbe sp.IDCropAssayIOC (%)IOC (%)
Bosea thiooxidans123EfficacyEfficacy
overall100%100%
Bosea thiooxidans54522WheatEarly vigor - insol P30-40 —
Bosea thiooxidans54522RyegrassEarly vigor50-60 50-60 
Bosea thiooxidans54522RyegrassEarly vigor - moderate P0-100-10
Duganella violaceinigra111EfficacyEfficacy
overall100%100%
Duganella violaceinigra66361TomatoEarly vigor0-100-10
Duganella violaceinigra66361TomatoEarly vigor30-40 40-50 
Duganella violaceinigra66361TomatoEarly vigor20-30 20-30 
Herbaspirillum huttiense222Efficacy—
overall100%
Herbaspirillum huttiense54487WheatEarly vigor - insol P30-40 —
Herbaspirillum huttiense60507MaizeEarly vigor - salt stress0-100-10
Janthinobacterium sp.222Efficacy—
Overall100%
Janthinobacterium sp.54456WheatEarly vigor - insol P30-40 —
Janthinobacterium sp.54456WheatEarly vigor - insol P0-10—
Janthinobacterium sp.63491RyegrassEarly vigor - drought0-100-10
stress
Massilia niastensis112EfficacyEfficacy
overall80%80%
Massilia niastensis55184WheatEarly vigor - salt stress0-1020-30 
Massilia niastensis55184WinterEarly vigor - cold stress0-1010-20 
wheat
Massilia niastensis55184WinterEarly vigor - cold stress20-30 20-30 
wheat
Massilia niastensis55184WinterEarly vigor - cold stress10-20 10-20 
wheat
Massilia niastensis55184WinterEarly vigor - cold stress<0<0
wheat
Novosphingobium rosa211EfficacyEfficacy
overall100%100%
Novosphingobium rosa65589MaizeEarly vigor - cold stress0-100-10
Novosphingobium rosa65619MaizeEarly vigor - cold stress0-100-10
Paenibacillus amylolyticus111EfficacyEfficacy
overall100%100%
Paenibacillus amylolyticus66316TomatoEarly vigor0-100-10
Paenibacillus amylolyticus66316TomatoEarly vigor10-20 10-20 
Paenibacillus amylolyticus66316TomatoEarly vigor0-100-10
Pantoea agglomerans323EfficacyEfficacy
33%50%
Pantoea agglomerans54499WheatEarly vigor - insol P40-50 —
Pantoea agglomerans57547MaizeEarly vigor - low N<00-10
Pantoea vagans55529MaizeEarly vigor<0<0
(formerly P. agglomerans)
Polaromonas ginsengisoli111EfficacyEfficacy
66%100%
Polaromonas ginsengisoli66373TomatoEarly vigor0-100-10
Polaromonas ginsengisoli66373TomatoEarly vigor20-30 30-40 
Polaromonas ginsengisoli66373TomatoEarly vigor<010-20 
Pseudomonas fluorescens122Efficacy—
100%
Pseudomonas fluorescens54480WheatEarly vigor - insol P>100 —
Pseudomonas fluorescens56530MaizeEarly vigor - moderate N0-10—
Rahnella aquatilis334EfficacyEfficacy
80%63%
Rahnella aquatilis56532MaizeEarly vigor - moderate N10-20 —
Rahnella aquatilis56532MaizeEarly vigor - moderate N0-100-10
Rahnella aquatilis56532WheatEarly vigor - cold stress0-1010-20 
Rahnella aquatilis56532WheatEarly vigor - cold stress<00-10
Rahnella aquatilis56532WheatEarly vigor - cold stress10-20 <0
Rahnella aquatilis57157RyegrassEarly vigor<0—
Rahnella aquatilis57157MaizeEarly vigor - low N0-100-10
Rahnella aquatilis57157MaizeEarly vigor - low N0-10<0
Rahnella aquatilis58013MaizeEarly vigor0-1010-20 
Rahnella aquatilis58013MaizeEarly vigor - low N0-10<0
Rhodococcus erythropolis313Efficacy—
66%
Rhodococcus erythropolis54093MaizeEarly vigor - low N40-50 —
Rhodococcus erythropolis54299MaizeEarly vigor - insol P>100 —
Rhodococcus erythropolis54299MaizeEarly vigor<0<0
Stenotrophomonas chelatiphaga611EfficacyEfficacy
60%60%
Stenotrophomonas chelatiphaga54952MaizeEarly vigor0-100-10
Stenotrophomonas chelatiphaga47207MaizeEarly vigor<0 0
Stenotrophomonas chelatiphaga64212MaizeEarly vigor0-1010-20 
Stenotrophomonas chelatiphaga64208MaizeEarly vigor0-100-10
Stenotrophomonas chelatiphaga58264MaizeEarly vigor<0<0
Stenotrophomonas maltophilia612EfficacyEfficacy
43%66%
Stenotrophomonas maltophilia54073MaizeEarly vigor - low N50-60 —
Stenotrophomonas maltophilia54073MaizeEarly vigor<00-10
Stenotrophomonas maltophilia56181MaizeEarly vigor0-10<0
Stenotrophomonas maltophilia54999MaizeEarly vigor0-100-10
Stenotrophomonas maltophilia54850MaizeEarly vigor 00-10
Stenotrophomonas maltophilia54841MaizeEarly vigor<00-10
Stenotrophomonas maltophilia46856MaizeEarly vigor<0<0
Stenotrophomonas rhizophila811EfficacyEfficacy
12.5%37.5%
Stenotrophomonas rhizophila50839MaizeEarly vigor<0<0
Stenotrophomonas rhizophila48183MaizeEarly vigor<0<0
Stenotrophomonas rhizophila45125MaizeEarly vigor<0<0
Stenotrophomonas rhizophila46120MaizeEarly vigor<00-10
Stenotrophomonas rhizophila46012MaizeEarly vigor<0<0
Stenotrophomonas rhizophila51718MaizeEarly vigor0-100-10
Stenotrophomonas rhizophila66478MaizeEarly vigor<0<0
Stenotrophomonas rhizophila65303MaizeEarly vigor<00-10
Stenotrophomonas terrae221EfficacyEfficacy
50%50%
Stenotrophomonas terrae68741MaizeEarly vigor<0<0
Stenotrophomonas terrae68599MaizeEarly vigor<00-10
Stenotrophomonas terrae68599Capsicum *Early vigor20-30 20-30 
Stenotrophomonas terrae68741Capsicum *Early vigor10-20 20-30 

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.

US11871752B2. Agriculturally beneficial microbes, microbial compositions, and consortia (2024-01-16). BIOCONSORTIA, INC. [US]. Inventors: Peter Wigley [NZ], Susan Turner [US], Caroline George [NZ], Thomas Williams [US], Kelly Roberts [US], Graham Hymus [US], Kelvin Lau [NZ].
Patent 2024
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
4
Dictyostelium Strains Suppress Soft Rot Pathogens
Not available on PMC !

Example 1

119 Dicty strains were screened for their ability to feed on Dickeya (Dd) or Pectobacterium (Pcc) at 10° C. This assay was performed by inoculating Dd or Pcc on a low nutrient medium (SM2 agar) that supports both bacterial and Dicty growth. Dicty spores from individual strains were then inoculated on top of the bacterial growth and incubated at 10° C. to mimic potato storage temperatures. Dicty strains that successfully fed on Dd or Pcc created visible clearings in the lawn of bacterial growth and ultimately produced sporangia (fruiting bodies) that rose from the agar surface. An example of the phenotype that was considered successful clearing of bacteria is shown in FIG. 3A. From this initial screen, 36 Dicty strains that were capable of feeding on both Dd and Pcc at 10° C. were identified (FIG. 1B).

Of the 36 strains capable of feeding on both Dd and Pcc, 34 came from the Group 4 Dictyostelids (FIG. 1). This group includes D. discoideum, D. giganteum, D. minutum, D. mucoroides, D. purpureum, and D. sphaerocephalum (72). The results indicate that this group is particularly enriched in Dd and Pcc-feeding strains.

A further experiment was performed to identify Dicty species capable of feeding on biofilms of Dd and Pcc. Microporous polycarbonate membranes (MPMs) are widely reported to support biofilm formation of numerous Enterobacteriaceae species (2, 63, 70, 71). It was determined if Dd and Pcc formed biofilms on MPMs and determined if Dicty strains were capable of feeding on these biofilms. Membranes were placed on top of SM2 agar to provide Dd and Pcc with nutrients for growth. Bacteria were then inoculated on the surface of the MPMs and growth was monitored over the course of 1 week by washing bacteria off the membranes and performing dilution plating for colony counting. Growth of both bacterial strains plateaued around 4 dpi (FIG. 2).

From these results, it was determined that the best time to collect inoculated MPMs for biofilm analysis was at 2 dpi. Scanning electron microscopy (SEM) is commonly used to confirm biofilm formation by detecting extracellular polymeric substance (EPS) that forms the biofilm matrix (2). Samples of Dd and Pcc after 2 days of growth on MPMs in the presence and absence of Dicty are analyzed using SEM.

19 Dicty strains identified as active were tested for their ability to feed on Dd and Pcc growing on MPMs. These experiments were performed by establishing Dd and Pcc growth on MPMs overlaid on SM2 agar at 37° C. for 24 hr. Dicty spores were then applied to the center of bacterial growth in a 5 uL drop containing 1000 spores. Bacteria and Dicty were incubated at 10° C. for 2 weeks before remaining bacteria were washed off and colonies were counted. Representative images of Dicty growing on Dd and Pcc on MPMs are shown in FIG. 3A.

No Dicty strains produced a statistically significant reduction in Dd viability compared to the non-treated control. However, treating Dd lawns with Cohen 36, Cohen 9, WS-15, WS-20, and WS-69 consistently reduced the number of viable bacteria by approximately 100,000-fold compared to the non-treated control (FIG. 3B). Cohen 9 was the only Dicty strain that produced a statistically significant reduction in viability of Pcc compared to the non-treated control (FIG. 3C). Other Dicty strains capable of reducing the number of viable Pcc by at least 100,000-fold were Cohen 35, Cohen 36, WS-647, and WS-69 (FIG. 3C).

It was observed that Dicty strains Cohen 9, Cohen 36, and WS-69 were capable of feeding on both Dd and Pcc when these bacteria were cultured on SM2 agar and MPMs (FIGS. 1 and 3). These strains were also particularly effective feeders as all three reduced the number of viable Dd and Pcc on MPMs at 10° C. by 100,000-fold compared to the non-treated control (FIGS. 3B and 3C).

To determine if these strains could suppress soft rot development on seed potato tubers, tubers were tab-inoculated with Dd or Pcc and treated with spores from each Dicty strain. Seed potatoes were surface-sterilized and punctured using a sterile screw to a depth of 1.5 mm. Overnight cultures of Dd and Pcc were suspended in 10 mM potassium phosphate buffer, diluted to an OD600 of approximately 0.003, and administered as a 5 μL drop into the wound. Next, 5 of a Dicty spore suspension (100,000 spores) was added to the wound. Inoculated seed potatoes were placed in a plastic container with moist paper towels and were misted with water twice a day to maintain a high humidity. After 3 days at room temperature, seed potatoes were sliced in half and the area of macerated tissue was quantified using ImageJ.

All three strains reduced the severity of soft rot caused by Dd and Pcc (FIG. 4). Cohen 36 was the most effective strain on both Dd and Pcc: reducing the area of tissue maceration by 60% and 35%, respectively (FIG. 4B). Treating seed potatoes with WS-69 reduced the area of tissue maceration by 50% and 30% for Dd and Pcc, respectively (FIG. 4B). Finally, Cohen 9 was the least effective, but still able to reduce tissue maceration caused by Dd and Pcc by 25% and 20%, respectively (FIG. 4B).

FIG. 7 shows that three Dicty isolates control Dd and Pcc in seed tubers (at 25° C.). Two sets of data from different weeks were normalized to the Dickeya or Pectobacterium only bacterial control. The average area of macerated potato tissue measured in mm2 was set as “1” or “100%”. The average of all the other treatments including Dicty were divided by bacteria only control and multiplied by 100 to obtain a percentage. Each set contained 5 tubers per treatment.

Dicty should be capable of sporulating at temperatures as cold as 10° C. on a potato surface if they are applied as a one-time pre-planting or post-harvest treatment. Sporulation was assessed by inoculating small potato discs (5×6 mm) with 10 μL of Dd or Pcc suspensions at an OD600 of 3×10−5 and Dicty spores at a concentration of 1×107 spores/mL. Potato discs were kept in a covered 96-well plate for two weeks at 10° C. followed by visual inspection for son using a dissecting microscope. Representative images of a strain producing many sori (WS-517) and a strain producing few sori (WS-69) are shown in FIG. 5. Of the 11 strains evaluated, only Cohen 9 and WS-20 were unable to sporulate in the presence of both pathogens (Table 1).

TABLE 1
Assessment of Dicty sporulation at 10° C. on potato
in the presence of Dd or Pcc. A (✓) indicates sori
have been observed while a ( [Figure (not displayed)]  ) means they have not.
Dicty strainDdPcc
Cohen 9✓[Figure (not displayed)]
Cohen 36✓✓
WS-69✓✓
WS-517✓✓
WS-588✓✓
WS-606✓✓
WS-15✓✓
WS-20[Figure (not displayed)] ✓
DC-7✓✓
DC-61✓✓
WS-116d✓✓

Example 2

This example describes the use of a high throughput screening assay to identify Dicty strains from Alaska (e.g., BAC10A, BAF6A, BAC3A, NW2, KB4A (ATCC® MYA-4262™) SO8B, SO3A, BAF9B, IC2A (ATCC® MYA-4259™), AK1A1 (ATCC® MYA-4272™) PBF4B (ATCC® MYA-4263), PBF8B, BSB1A, SO5B (ATCC® MYA-4249), PBF3C, PBF6B, NW2B, NW10B (ATCC® MYA-4271™), PBF9A, IC5A (ATCC® MYA-4256TH), ABC8A (ATCC® MYA-4260), NW16B, ABC10B, ABB6B (ATCC® MYA-4261), BA4A (ATCC® MYA-4252), AKK5A, AKK52C, HP4 (ATCC® MYA-4286), HP8 (ATCC® MYA-4284), or NW9A) that feed on Dd and Pcc at 10° C. on potatoes.

Results from 11 Dicty strains screened against Dd at 10° C. are presented in FIG. 6. Data was analyzed for significance using a one-way analysis of variance (ANOVA; alpha =0.05) with Tukey's honest significant difference (HSD) test to compare means between the treatments and the No Dicty control. A reduction in Dd proliferation when potato discs were treated with Dicty strains Cohen 9, Cohen 36, WS-15, Maryland 18a, BAF6A, NW2, and SO3A.

The Alaskan Dicty strains, and those identified in Example 1, are further tested against coinfections of Dd and Pcc. It is useful to identify Dicty strains that can suppress Dd and Pcc coinfections as these two pathogens have been isolated together from diseased potatoes (15). The ability of Dicty strains with different feeding preferences (Dd vs. Pcc) to complement each other when administered as a cotreatment is assayed.

US11864559B2. Therapeutic amoeba and uses thereof (2024-01-09). AmebaGone, LLC [US]. Inventors: Marcin Filutowicz [US], Ryan Kessens [US], Amy Jancewicz [US].
Patent 2024
A-A-1 antibiotic Agar Amoeba Bacteria Biofilms Buffers Coinfection Cold Temperature Combined Modality Therapy Dickeya Dictyosteliida Enterobacteriaceae Extracellular Polymeric Substance Matrix Extracellular Polymeric Substances High-Throughput Screening Assays Human Body Humidity Microscopy neuro-oncological ventral antigen 2, human Nutrients Pathogenicity Pectobacterium Phenotype Plant Tubers polycarbonate potassium phosphate Scanning Electron Microscopy Solanum tuberosum Sporangia Spores Sterility, Reproductive Strains Technique, Dilution Tissue, Membrane Tissues Wounds
5
Bleomycin-Induced Trabecular Meshwork Senescence
Not available on PMC !

Example 8

Administration of bleomycin, a DNA damaging agent, to the anterior chamber of the mouse or rabbit eye leads to cellular senescence, as detected by the induction of p16 transcript in the trabecular meshwork.

To induce a senescent phenotype in the trabecular meshwork in vivo, C57Bl/6 mice (aged 8 to 10 weeks) were injected intracamerally with 2 μL of 0.0075 U bleomycin sulfate. In the rabbit, 30 μL of 0.0075 U bleomycin sulfate were injected intracamerally in New Zealand white rabbits. Eyes were enucleated 14 days post-bleomycin injury and TM-enriched samples were micro-dissected. To determine change in senescent cells, RNA was isolated from TM and qPCR analysis was done to assess the effect of bleomycin on p16 mRNA levels.

FIGS. 12A and 12B show elevated relative expression of p16 at 14 days after intracameral (IC) injection of bleomycin in the right (OD) eye relative to the PBS-injected left (OS) eye of the test animals. This model can also be used to assess whether a test compound is pharmacologically capable of reducing or ameliorating the increased intraocular pressure that is a hallmark of primary open angle glaucoma (POAG).

US11865123B2. Methods of inhibiting pathological angiogenesis (2024-01-09). Unity Biotechnology, Inc. [US]. Inventors: Pam Tsuruda [US], Jill Hopkins [US], Harry Sweigard [US], Yan Poon [US], Jamie Dananberg [US], Daniel Marquess [US], Nathaniel David [US].
Patent 2024
Animal Model Animals Bleomycin Cellular Senescence Chambers, Anterior DNA, A-Form Figs Glaucoma Glaucoma, Primary Open Angle indium-bleomycin Injuries Mice, Inbred C57BL Mus New Zealand Rabbits Phenotype Rabbits RNA, Messenger Sulfate, Bleomycin Tonometry, Ocular Trabecular Meshwork

Top products related to «Phenotype»

1
Fetal bovine serum (fbs) by Thermo Fisher Scientific
Sourced in United States, China, United Kingdom, Germany, Australia, Japan, Canada, Italy, France, Switzerland, New Zealand, Brazil, Belgium, India, Spain, Israel, Austria, Poland, Ireland, Sweden, Macao, Netherlands, Denmark, Cameroon, Singapore, Portugal, Argentina, Holy See (Vatican City State), Morocco, Uruguay, Mexico, Thailand, Sao Tome and Principe, Hungary, Panama, Hong Kong, Norway, United Arab Emirates, Czechia, Russian Federation, Chile, Moldova, Republic of, Gabon, Palestine, State of, Saudi Arabia, Senegal
Fetal Bovine Serum (FBS) is a cell culture supplement derived from the blood of bovine fetuses. FBS provides a source of proteins, growth factors, and other components that support the growth and maintenance of various cell types in in vitro cell culture applications.
2
Penicillin streptomycin by Thermo Fisher Scientific
Sourced in United States, Germany, United Kingdom, China, Canada, France, Japan, Australia, Switzerland, Israel, Italy, Belgium, Austria, Spain, Gabon, Ireland, New Zealand, Sweden, Netherlands, Denmark, Brazil, Macao, India, Singapore, Poland, Argentina, Cameroon, Uruguay, Morocco, Panama, Colombia, Holy See (Vatican City State), Hungary, Norway, Portugal, Mexico, Thailand, Palestine, State of, Finland, Moldova, Republic of, Jamaica, Czechia
Penicillin/streptomycin is a commonly used antibiotic solution for cell culture applications. It contains a combination of penicillin and streptomycin, which are broad-spectrum antibiotics that inhibit the growth of both Gram-positive and Gram-negative bacteria.
3
Facscanto 2 by BD
Sourced in United States, Germany, United Kingdom, Belgium, China, Australia, France, Japan, Italy, Spain, Switzerland, Canada, Uruguay, Netherlands, Czechia, Jersey, Brazil, Denmark, Singapore, Austria, India, Panama
The FACSCanto II is a flow cytometer instrument designed for multi-parameter analysis of single cells. It features a solid-state diode laser and up to four fluorescence detectors for simultaneous measurement of multiple cellular parameters.
4
Facscalibur by BD
Sourced in United States, Germany, United Kingdom, China, Canada, Japan, Italy, France, Belgium, Switzerland, Singapore, Uruguay, Australia, Spain, Poland, India, Austria, Denmark, Netherlands, Jersey, Finland, Sweden
The FACSCalibur is a flow cytometry system designed for multi-parameter analysis of cells and other particles. It features a blue (488 nm) and a red (635 nm) laser for excitation of fluorescent dyes. The instrument is capable of detecting forward scatter, side scatter, and up to four fluorescent parameters simultaneously.
5
Dulbecco modified eagle medium (dmem) by Thermo Fisher Scientific
Sourced in United States, China, United Kingdom, Germany, France, Australia, Canada, Japan, Italy, Switzerland, Belgium, Austria, Spain, Israel, New Zealand, Ireland, Denmark, India, Poland, Sweden, Argentina, Netherlands, Brazil, Macao, Singapore, Sao Tome and Principe, Cameroon, Hong Kong, Portugal, Morocco, Hungary, Finland, Puerto Rico, Holy See (Vatican City State), Gabon, Bulgaria, Norway, Jamaica
DMEM (Dulbecco's Modified Eagle's Medium) is a cell culture medium formulated to support the growth and maintenance of a variety of cell types, including mammalian cells. It provides essential nutrients, amino acids, vitamins, and other components necessary for cell proliferation and survival in an in vitro environment.
6
Penicillin by Thermo Fisher Scientific
Sourced in United States, United Kingdom, Germany, China, France, Canada, Japan, Australia, Switzerland, Italy, Israel, Belgium, Austria, Spain, Brazil, Netherlands, Gabon, Denmark, Poland, Ireland, New Zealand, Sweden, Argentina, India, Macao, Uruguay, Portugal, Holy See (Vatican City State), Czechia, Singapore, Panama, Thailand, Moldova, Republic of, Finland, Morocco
Penicillin is a type of antibiotic used in laboratory settings. It is a broad-spectrum antimicrobial agent effective against a variety of bacteria. Penicillin functions by disrupting the bacterial cell wall, leading to cell death.
7
Streptomycin by Thermo Fisher Scientific
Sourced in United States, United Kingdom, Germany, China, France, Canada, Australia, Japan, Switzerland, Italy, Belgium, Israel, Austria, Spain, Netherlands, Poland, Brazil, Denmark, Argentina, Sweden, New Zealand, Ireland, India, Gabon, Macao, Portugal, Czechia, Singapore, Norway, Thailand, Uruguay, Moldova, Republic of, Finland, Panama
Streptomycin is a broad-spectrum antibiotic used in laboratory settings. It functions as a protein synthesis inhibitor, targeting the 30S subunit of bacterial ribosomes, which plays a crucial role in the translation of genetic information into proteins. Streptomycin is commonly used in microbiological research and applications that require selective inhibition of bacterial growth.
8
Sas 9 by SAS Institute
Sourced in United States, Austria, Japan, Belgium, United Kingdom, Cameroon, China, Denmark, Canada, Israel, New Caledonia, Germany, Poland, India, France, Ireland, Australia
SAS 9.4 is an integrated software suite for advanced analytics, data management, and business intelligence. It provides a comprehensive platform for data analysis, modeling, and reporting. SAS 9.4 offers a wide range of capabilities, including data manipulation, statistical analysis, predictive modeling, and visual data exploration.
9
Rpmi 1640 by Thermo Fisher Scientific
Sourced in United States, China, United Kingdom, Germany, France, Canada, Japan, Australia, Italy, Switzerland, Belgium, New Zealand, Austria, Netherlands, Israel, Sweden, Denmark, India, Ireland, Spain, Brazil, Norway, Argentina, Macao, Poland, Holy See (Vatican City State), Mexico, Hong Kong, Portugal, Cameroon
RPMI 1640 is a common cell culture medium used for the in vitro cultivation of a variety of cells, including human and animal cells. It provides a balanced salt solution and a source of essential nutrients and growth factors to support cell growth and proliferation.
10
Rpmi 1640 medium by Thermo Fisher Scientific
Sourced in United States, China, Germany, United Kingdom, Japan, France, Canada, Australia, Italy, Switzerland, Belgium, New Zealand, Spain, Israel, Sweden, Denmark, Macao, Brazil, Ireland, India, Austria, Netherlands, Holy See (Vatican City State), Poland, Norway, Cameroon, Hong Kong, Morocco, Singapore, Thailand, Argentina, Taiwan, Province of China, Palestine, State of, Finland, Colombia, United Arab Emirates
RPMI 1640 medium is a commonly used cell culture medium developed at Roswell Park Memorial Institute. It is a balanced salt solution that provides essential nutrients, vitamins, and amino acids to support the growth and maintenance of a variety of cell types in vitro.

More about "Phenotype"

Phenotype refers to the observable physical, biochemical, physiological, behavioral, and morphological characteristics of an organism, which are determined by both genetic and environmental factors.
It represents the expression of a genotype in a specific environment, resulting from the interaction between an organism's genes and its surroundings.
Understanding phenotypes is crucial for research in genetics, developmental biology, and personalized medicine.
Phenotypes can be influenced by various factors, including gene expression, epigenetics, and environmental conditions.
For example, the phenotype of a cell or organism may be affected by the culture conditions, such as the media (e.g., RPMI 1640, DMEM) and the presence of growth factors (e.g., penicillin, streptomycin).
Techniques like flow cytometry (e.g., FACSCanto II, FACSCalibur) can be used to analyze and quantify specific phenotypic characteristics, such as cell surface markers or intracellular proteins.
Data analysis tools, like SAS 9.4, can further help researchers understand and interpret phenotypic data.
PubCompare.ai's AI-driven platform can enhance research reproducibility by helping scientists identify the most effective approaches for phenotype optimization needs.
By leveraging AI-powered comparisons, researchers can locate the best protocols from literature, preprints, and patents, leading to more robust and reliable phenotypic studies.