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Plant Proteins

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Most cited protocols related to «Plant Proteins»

We extracted small toxins (proteins/peptides) from different databases and studies that include ATDB [15] (link), Arachno-Server [19] (link), Conoserver [20] (link), DBETH [16] (link), BTXpred [17] (link), NTXpred [18] (link), and SwissProt [21] (link). We removed all proteins/peptides having more than 35 residues or any non-natural amino acid. As a result, 1805 unique toxic proteins/peptides were obtained. By employing the similar criteria, toxic proteins/peptides were also searched in SwissProt database using keyword KW800 (keyword 800 stands for toxin as molecular functions). A total of 803 toxic proteins, having length less than 35 amino acids were obtained. It is possible that many toxic peptides obtained from various databases could also be present in SwissProt. Therefore, identical toxic proteins/peptides were removed and finally we got 303 unique toxic proteins/peptides from SwissProt. These proteins/peptides were considered as toxic peptides or positive examples. Though it is possible to extract well-annotated or experimentally validated toxic peptides, but it is difficult to obtained non-toxic peptides. Therefore, to create a negative dataset, we have searched protein/peptide sequences in UniProt using keywords NOT KW800 NOT KW20 (keyword 800 and 20 stand for toxin and allergen as molecular functions). Proteins/peptide sequences having length less than 35 amino acids were extracted. After removing sequences with non-natural amino acids, two types of negative datasets were created; first dataset consists of 3893 sequences from SwissProt (NOT KW800 NOT KW20) and second dataset consists of 13541 sequences from TrEMBL (keyword NOT KW800 AND KW33090) [21] (link). While searching non-toxins in TrEMBL, additional keyword plant proteins were applied as search criteria as most of the plants are edible and therefore, the probability of plant proteins/peptides to be toxic is very low. Above toxic and non-toxic peptides/proteins were used to generate various datasets for training, testing and evaluating our models developed for predicting toxicity of peptides (Figure 1). Following is the brief description of these datasets:
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Publication 2013
Allergens Amino Acids Amino Acid Sequence Peptides Plant Proteins Plants Proteins Staphylococcal Protein A Toxins, Biological
Protein sequences were collected from the Swiss-Prot database at http://www.ebi.ac.uk/swissprot/. The detailed procedures are basically
the same as those elaborated in [13] (link); the only differences are as follows.
(1) To get the updated benchmark dataset, instead of version 49.3 of the
Swiss-Prot database, the version 55.3 released on 29-Apr-2008 was adopted.
(2) In order to make the new predictor also able to deal with proteins
having two or more location sites, the multiplex proteins are no longer excluded in this
study. Actually, according to a statistical analysis on the current database, about
8% of plant proteins were found located in more than one location.
After strictly following the aforementioned procedures, we finally obtained a benchmark
dataset containing 978 different protein sequences, which are distributed
among 12 subcellular locations (Fig. 1); i.e., where represents the subset for the subcellular location of cell membrane, for cell wall, for chloroplast, and so forth; while represents the symbol for “union” in the set
theory. A breakdown of the 978 plant proteins in the benchmark dataset according to their 12 location sites is given in Table 1. To avoid redundancy and homology bias, none of the proteins in has pairwise sequence identity to any other in a same subset. The
corresponding accession numbers and protein sequences are given in Table S1.
Since some proteins in may occur in two or more locations, it is instructive to introduce the
concept of “locative protein” [23] (link), as briefed as follows. A
protein coexisting at two different location sites will be counted as 2 locative
proteins even though the two are with completely the same sequence; if
coexisting at three sites, 3 locative proteins; and so forth. Thus, it follows where is the number of total locative proteins, the number of total different protein sequences, the number of proteins with one location, the number of proteins with two locations, and so forth;
while is the number of total subcellular location sites concerned (for the
current case, as shown in Fig. 1).
For the current 978 different protein sequences, 904 occur in one subcellular location,
71 in two locations, 3 in three locations, and none in four or more locations.
Substituting these data into Eq.2, we have which is fully consistent with the figures in Table 1 and the data in Table S1.
To develop a powerful method for predicting protein subcellular localization, it is very
important to formulate the sample of a protein in terms of the core features that are
intrinsically correlated with its localization in a cell. To realize this, the strategy
by integrating the GO representation and PseAAC representation was adopted in the
original Plant-PLoc [13] (link). In this study, the essence of such a strategy will be
still kept. However, in order to overcome the four shortcomings as mentioned in Introduction for Plant-PLoc [13] (link), a completely different
combination approach has been developed, as described below.
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Publication 2010
Amino Acid Sequence Catabolism Cells Cell Wall Chloroplasts Plant Proteins Plants Proteins Staphylococcal Protein A Teaching
Eukaryotic proteins are processed using the general pipeline depicted in Figure 1. The pipeline is organized as a directed rooted computational graph where each node corresponds to the execution of a specific tool. The graph root is the query protein sequence, while leaves correspond to predicted subcellular localizations, here represented as GO terms of the cellular component ontology. A path from the root to one leaf is determined by the outcomes of the different tools. In Figure 1, GO terms and tools highlighted in green are only applied for plant proteins.
At the very first level, the query sequence is scanned for the presence of signal peptide using the DeepSig predictor (4 ). If the signal sequence is found (suggesting the sorting of the protein through the secretory pathway), the mature protein sequence is determined by cleaving the predicted signal peptide. The resulting mature sequence is then analyzed by the subsequent tools. Firstly, PredGPI (6 (link)) determines the presence of GPI-anchors. If an anchor is found, the sequence is classified as Membrane anchored component (GO:0046658). Otherwise, the sequence is filtered for the presence of α-helical TransMembrane (TM) domains using ENSEMBLE3.0 (7 (link)). If at least one TM domain is found, the protein is predicted as membrane protein and passed to MemLoci (10 (link)), which predicts the final membrane protein localization that includes: Endomembrane system (GO:00112505), Plasma membrane (GO:0005886) and Organelle membrane (GO:0031090). If no TM domain is found, the protein is predicted to be localized in the Extracellular space (GO:0005615).
Proteins not directed to the secretory pathway (as predicted with DeepSig) are analyzed for their potential organelle localization using TPpred3 (5 (link)), which predicts the presence of organelle-targeting peptides and distinguishes between mitochondrial and chloroplast sorting for plant proteins.
If no targeting peptide is detected with TPpred3, ENSEMBLE3.0 is used to discriminate membrane from globular proteins: MemLoci or BaCelLo (9 (link)) are hence applied to predict localization of membrane and globular protein, respectively. In particular, BaCelLo is able to distinguish among five different cellular compartments (four in case of animal or fungi proteins): Nucleus (GO:0005634), Cytoplasm (GO:0005737), Extracellular space (GO:0005615), Mitochondrion (GO:0005739) and, for plant proteins, Chloroplast (GO:0009507). Moreover, since BaCelLo adopts different optimized models for animals and fungi, information about the taxonomic origin of the input is also provided as a parameter to the predictor.
When a mitochondrial targeting signal is detected, this is cleaved-off to determine the mature protein sequence. ENSEMBLE3.0 is then used to determine whether the mature protein is localized into a Mitochondrial membrane (GO:0031966) or, more generally, into the Mitochondrion (GO:0005739).
For plant proteins, TPpred3 is also able to distinguish potential chloroplast-targeting peptides. If detected, they are cleaved and the sequence submitted to SChloro (11 (link)) that discriminates six different sub-chloroplast localizations: Outer membrane (GO:0009707), Inner membrane (GO:0009706), Plastoglobule (GO:0010287), Thylakoid lumen (GO:0009543), Thylakoid membrane (GO:0009535) and Stroma (GO:0009570).
Overall BUSCA is able to predict sixteen different compartments for plants and nine for animals and fungi.
Publication 2018
Amino Acid Sequence Animal Model Animals Cell Nucleus Cells Cellular Structures Chloroplasts Cytoplasm Eukaryotic Cells Extracellular Space Eye Fungal Proteins Fungi Helix (Snails) Membrane Proteins Mitochondria Mitochondrial Membranes Organelles Peptides Plant Leaves Plant Proteins Plant Roots Plants Plasma Membrane Proteins Reproduction Secretory Pathway Signal Peptides Strains Thylakoid Membrane Thylakoids Tissue, Membrane
For assessment of GeneMark-EP as well as ProtHint accuracy, we selected annotated genomes from diverse clades: fungi, worms, plants, insects and vertebrae (Table 1). The genome length varied from <100 Mb (Neurospora crassa) to >1.3 Gb (Danio rerio). With exception of Solanum lycopersicum, a species representing large genome plants important for economy, all selected species are model organisms whose genomes presumably have high-quality annotation. To assess accuracy of gene prediction made for model species, we compared genes predicted and annotated on a whole genome scale. In case of S. lycopersicum, we used a limited set of genes, validated by available RNA-Seq data. In all genomic datasets, contigs not assigned to any chromosome were excluded from the analysis as well as genomes of organelles.
We used OrthoDB v10 protein database (23 (link)) as an all-inclusive source of protein sequences. Still, for generating protein hints for particular species we used subsets of OrthoDB: plant proteins for gene prediction in Arabidopsis thaliana, arthropod proteins for gene prediction in Drosophila melanogaster, etc. (Table 2).
As an additional test set, we used annotation of major protein isoforms available in the APPRIS database (24 (link)); this assessment was done for C. elegans, D. melanogaster and D. rerio (Supplementary Table S1). Arguably, accuracy of prediction of major isoforms is of significant interest, since in a gene locus the major isoform was observed to be expressed in higher volume than other (minor) isoforms (24 (link)).
Publication 2020
Amino Acid Sequence Arabidopsis thalianas Arthropod Proteins Arthropods Chromosomes Drosophila melanogaster Fungi Gene Products, Protein Genes Genes, Plant Genes, vif Genetic Loci Genome Helminths Inclusion Bodies Insecta Lycopersicon esculentum Neurospora crassa Organelles Plant Proteins Plants Protein Annotation Protein Isoforms Proteins RNA-Seq Vertebra Zebrafish
The core of pathogen recognition genes analysis and gene orthology (DRAGO 2) pipeline consists of a PERL script that predicts putative PRGs within a plant transcriptome/proteome FASTA file (Figure 1). In a first round, DRAGO 2 was executed with the cloned PRG FASTA file as an input to define the normalization value and the minimum score thresholds. Specifically, the previously created 60 HMM modules are used by DRAGO 2 to detect LRR, Kinase, NBS and TIR domains and compute the alignment score of the different hits based on a BLOSUM62 matrix. The normalization value is the absolute smallest similarity score found among the input sequences considering all domains. The minimum score thresholds are calculated from the smallest similarity score reported in a specific domain among the input sequences. Once these values are known, DRAGO 2 can be launched on any transcriptome or proteome. Apart from detecting the mentioned domains, DRAGO 2 is also able to detect CC domains and TM domains using COILS 2.2 program (19 (link)) and TMHMM 2.0c program (20 (link)). DRAGO 2 generates two output files: a numeric matrix that represents the similarity score of every single protein input to each HMM profile, and a JSON or TSB format file with the domain name, start position, end position, resistance class and identification for every putative plant resistance protein.
Publication 2017
Genes Pathogenicity Phosphotransferases Plant Proteins Plants Proteins Proteome Transcriptome

Most recents protocols related to «Plant Proteins»

Example 1

a. Materials and Methods

i. Vector Construction

1. Virus-Like Particle

As most broadly neutralizing HPV antibodies are derived from the highly conserved N-terminal region of L2, amino acids 14-122 of HPV16 L2 were used to create HBc VLPs. L2 with flanking linker regions was inserted into the tip of the a-helical spike of an HBc gene copy which was fused to another copy of HBc lacking the L2 insert. This arrangement allows the formation of HBc dimers that contain only a single copy of L2, increasing VLP stability (Peyret et al. 2015). This heterodimer is referred to as HBche-L2. A dicot plant-optimized HPV16 L2 coding sequence was designed based upon the sequence of GenBank Accession No. CAC51368.1 and synthesized in vitro using synthetic oligonucleotides by the method described (Stemmer et al., 1995). The plant-optimized L2 nucleotide sequence encoding residues 1-473 is posted at GenBank Accession No. KC330735. PCR end-tailoring was used to insert Xbal and SpeI sites flanking the L2 aa 14-122 using primers L2-14-Xba-F (SEQ ID NO. 1: CGTCTAGAGTCCGCAACCCAACTTTACAAG) and L2-122-Spe-R (SEQ ID NO. 2: G GGACTAGTTGGGGCACCAGCATC). The SpeI site was fused to a sequence encoding a 6His tag, and the resulting fusion was cloned into a geminiviral replicon vector (Diamos, 2016) to produce pBYe3R2K2Mc-L2(14-122)6H.

The HBche heterodimer VLP system was adapted from Peyret et al (2015). Using the plant optimized HBc gene (Huang et al., 2009), inventors constructed a DNA sequence encoding a dimer comprising HBc aa 1-149, a linker (G2S)5G (SEQ ID NO. 39), HBc aa 1-77, a linker GT(G4S)2 (SEQ ID NO. 40), HPV-16 L2 aa 14-122, a linker (GGS)2GSSGGSGG (SEQ ID NO. 41), and HBc aa 78-176. The dimer sequence was generated using multiple PCR steps including overlap extensions and insertion of BamHI and SpeI restriction sites flanking the L2 aa 14-122, using primers L2-14-Bam-F (SEQ ID NO. 3: CAGGATCCGCAACC CAACTTTACAAGAC) and L2-122-Spe-R (SEQ ID NO. 2). The HBche-L2 coding sequence was inserted into a geminiviral replicon binary vector pBYR2eK2M (FIG. 3), which includes the following elements: CaMV 35S promoter with duplicated enhancer (Huang et al., 2009), 5′ UTR of N. benthamiana psaK2 gene (Diamos et al., 2016), intron-containing 3′ UTR and terminator of tobacco extensin (Rosenthal et al, 2018), CaMV 35S 3′ terminator (Rosenthal et al, 2018), and Rb7 matrix attachment region (Diamos et al., 2016).

2. Recombinant Immune Complex

The recombinant immune complex (RIC) vector was adapted from Kim et al., (2015). The HPV-16 L2 (aa 14-122) segment was inserted into the BamHI and SpeI sites of the gene encoding humanized mAb 6D8 heavy chain, resulting in 6D8 epitope-tagged L2. The heavy chain fusion was inserted into an expression cassette linked to a 6D8 kappa chain expression cassette, all inserted into a geminiviral replicon binary vector (FIG. 3, RIC vector). Both cassettes contain CaMV 35S promoter with duplicated enhancer (Huang et al., 2009), 5′ UTR of N. benthamiana psaK2 gene (Diamos et al., 2016), intron-containing 3′ UTR and terminator of tobacco extensin (Rosenthal et al, 2018), and Rb7 matrix attachment region (Diamos et al., 2016).

ii. Agroinfiltration of Nicotiana benthamiana Leaves

Binary vectors were separately introduced into Agrobacterium tumefaciens EHA105 by electroporation. The resulting strains were verified by restriction digestion or PCR, grown overnight at 30° C., and used to infiltrate leaves of 5- to 6-week-old N. benthamiana maintained at 23-25° C. Briefly, the bacteria were pelleted by centrifugation for 5 minutes at 5,000 g and then resuspended in infiltration buffer (10 mM 2-(N-morpholino)ethanesulfonic acid (MES), pH 5.5 and 10 mM MgSO4) to OD600=0.2, unless otherwise described. The resulting bacterial suspensions were injected by using a syringe without needle into leaves through a small puncture (Huang et al. 2004). Plant tissue was harvested after 5 DPI, or as stated for each experiment. Leaves producing GFP were photographed under UV illumination generated by a B-100AP lamp (UVP, Upland, CA).

iii. Protein Extraction

Total protein extract was obtained by homogenizing agroinfiltrated leaf samples with 1:5 (w:v) ice cold extraction buffer (25 mM sodium phosphate, pH 7.4, 100 mM NaCl, 1 mM EDTA, 0.1% Triton X-100, 10 mg/mL sodium ascorbate, 0.3 mg/mL PMSF) using a Bullet Blender machine (Next Advance, Averill Park, NY) following the manufacturer's instruction. To enhance solubility, homogenized tissue was rotated at room temperature or 4° C. for 30 minutes. The crude plant extract was clarified by centrifugation at 13,000 g for 10 minutes at 4° C. Necrotic leaf tissue has reduced water weight, which can lead to inaccurate measurements based on leaf mass. Therefore, extracts were normalized based on total protein content by Bradford protein assay kit (Bio-Rad) with bovine serum albumin as standard.

iv. SDS-PAGE and Western Blot

Clarified plant protein extract was mixed with sample buffer (50 mM Tris-HCl, pH 6.8, 2% SDS, 10% glycerol, 0.02% bromophenol blue) and separated on 4-15% polyacrylamide gels (Bio-Rad). For reducing conditions, 0.5M DTT was added, and the samples were boiled for 10 minutes prior to loading. Polyacrylamide gels were either transferred to a PVDF membrane or stained with Coomassie stain (Bio-Rad) following the manufacturer's instructions. For L2 detection, the protein transferred membranes were blocked with 5% dry milk in PBST (PBS with 0.05% tween-20) overnight at 4° C. and probed with polyclonal rabbit anti-L2 diluted 1:5000 in 1% PBSTM, followed by goat anti-rabbit horseradish peroxidase conjugate (Sigma). Bound antibody was detected with ECL reagent (Amersham).

v. Immunization of Mice and Sample Collection

All animals were handled in accordance to the Animal Welfare Act and Arizona State University IACUC. Female BALB/C mice, 6-8 weeks old, were immunized subcutaneously with purified plant-expressed L2 (14-122), HBche-L2 VLP, L2 RIC, or PBS mixed 1:1 with Imject® Alum (Thermo Scientific, Rockford, IL). In all treatment groups, the total weight of antigen was set to deliver an equivalent 5 μg of L2. Doses were given on days 0, 21, and 42. Serum collection was done as described (Santi et al. 2008) by submandibular bleed on days 0, 21, 42, and 63.

vi. Antibody Measurements

Mouse antibody titers were measured by ELISA. Bacterially-expressed L2 (amino acids 11-128) was bound to 96-well high-binding polystyrene plates (Corning), and the plates were blocked with 5% nonfat dry milk in PBST. After washing the wells with PBST (PBS with 0.05% Tween 20), the diluted mouse sera were added and incubated. Mouse antibodies were detected by incubation with polyclonal goat anti-mouse IgG-horseradish peroxidase conjugate (Sigma). The plate was developed with TMB substrate (Pierce) and the absorbance was read at 450 nm. Endpoint titers were taken as the reciprocal of the lowest dilution which produced an OD450 reading twice the background. IgG1 and IgG2a antibodies were measured with goat-anti mouse IgG1 or IgG2a horseradish peroxidase conjugate.

vii. Electron Microscopy

Purified samples of HBche or HBche-L2 were initially incubated on 75/300 mesh grids coated with formvar. Following incubation, samples were briefly washed twice with deionized water then negatively stained with 2% aqueous uranyl acetate. Transmission electron microscopy was performed with a Phillips CM-12 microscope, and images were acquired with a Gatan model 791 CCD camera.

viii. Statistical Analysis

The significance of vaccine treatments and virus neutralization was measured by non-parametric Mann-Whitney test using GraphPad prism software. Two stars (**) indicates p values <0.05. Three stars (***) indicates p values <0.001.

b. Design and Expression of HBc VLPs and RIC Displaying HPV16 L2

BeYDV plant expression vectors (FIG. 3) expressing either the target VLP HBche-L2, or L2 and HBche alone as controls, were agroinfiltrated into the leaves of N. benthamiana and analyzed for VLP production. After 4-5 days post infiltration (DPI), leaves displayed only minor signs of tissue necrosis, indicating that the VLP was well-tolerated by the plants (FIG. 4A). Leaf extracts analyzed by reducing SDS-PAGE showed an abundant band near the predicted size of 51 kDa for HBche-L2, just above the large subunit of rubisco (RbcL). HBche was detected around the predicted size of 38 kDa (FIG. 4B). Western blot probed with anti-L2 polyclonal serum detected a band for HBche-L2 at ˜51 kDa (FIG. 4B). These results indicate that this plant system is capable of producing high levels of L2-containing HBc VLP.

To express L2-containing MC, amino acids 14-122 of HPV16 L2 were fused with linker to the C-terminus of the 6D8 antibody heavy chain and tagged with the 6D8 epitope (Kim et al. 2015). A BeYDV vector (FIG. 3) expressing both the L2-fused 6D8 heavy chain and the light chain was agroinfiltrated into leaves of N. benthamiana and analyzed for RIC production. To create more homogenous human-type glycosylation, which has been shown to improve antibody Fc receptor binding in vivo, transgenic plants silenced for xylosyltransferase and fucosyltransferase were employed (Castilho and Steinkellner 2012). By western blot, high molecular weight bands >150 kDa suggestive of RIC formation were observed (FIG. 4C). Expression of soluble L2 RIC was lower than HBche-L2 due to relatively poor solubility of the RIC (FIG. 4C).

After rigorous genetic optimization, the N. benthamiana system is capable of producing very high levels of recombinant protein, up to 30-50% of the total soluble plant protein, in 4-5 days (Diamos et al. 2016). Using this system, we produced and purified milligram quantities of fully assembled and potently immunogenic HBc VLPs displaying HPV L2 through a simple one-step purification process (FIGS. 4A-4C and 6).

c. Purification and Characterization of HBche-L2 and L2 RIC

To assess the assembly of HBc-L2 VLP, clarified plant extracts containing either HBche-L2 or HBche were analyzed by sucrose gradient sedimentation. HBche-L2 sedimented largely with HBche, which is known to form VLP, though a small increase in density was observed with HBche-L2, perhaps due to the incorporation of L2 into the virus particle (FIG. 5A). To demonstrate particle formation, sucrose fractions were examined by electron microscopy. Both HBche and HBche-L2 formed ˜30 nm particles, although the appearance of HBche-L2 VLP suggested slightly larger, fuller particles (FIGS. 5C and 5D). As most plant proteins do not sediment with VLP, pooling peak sucrose fractions resulted in >95% pure HBche-L2 (FIG. 5B), yielding sufficient antigen (>3 mg) for vaccination from a single plant leaf.

L2 RIC was purified from plant tissue by protein G affinity chromatography. By SDS-PAGE, an appropriately sized band was visible >150 kDa that was highly pure (FIG. 5B). Western blot confirmed the presence of L2 in this band, indicating proper RIC formation (FIG. 5B). L2 RIC bound to human complement C1q receptor with substantially higher affinity compared to free human IgG standard, suggesting proper immune complex formation (FIG. 5E).

d. Mouse Immunization with HBche-L2 and L2 RIC

Groups of Balb/c mice (n=8) were immunized, using alum as adjuvant, with three doses each of 5 μg L2 delivered as either L2 alone, HBche-L2 VLP, L2 RIC, or a combination of half VLP and half RIC. VLP and RIC, alone or combined, greatly enhanced antibody titers compared to L2 alone by more than an order of magnitude at all time points tested (FIG. 6). After one or two doses, the combined VLP/RIC treatment group outperformed both the VLP or RIC groups, reaching mean endpoint titers of >200,000, which represent a 700-fold increase over immunization with L2 alone (FIG. 6). After the third dose, both the VLP and combined VLP/RIC groups reached endpoint titers >1,300,000, a 2-fold increase over the RIC alone group. To determine the antibody subtypes produced by each treatment group, sera were assayed for L2-binding IgG1 and IgG2a. All four groups produced predominately IgG1 (FIG. 7, note dilutions). However, RIC and especially VLP-containing groups had an elevated ratio of IgG2a:IgG1 (>3-fold) compared to L2 alone (FIG. 7).

In vitro neutralization of HPV16 pseudovirions showed that the VLP and RIC groups greatly enhanced neutralization compared to L2 alone (FIG. 5, p<0.001). Additionally, VLP and RIC combined further enhanced neutralization activity ($5-fold, p<0.05) compared to either antigen alone, supporting the strong synergistic effect of delivering L2 by both platforms simultaneously.

In this study, by displaying amino acids 11-128 on the surface of plant-produced HBc VLPs, L2 antibody titers as high as those seen with L1 vaccines were generated (FIG. 6). Mice immunized with L2 alone had highly variable antibody titers, with titers spanning two orders of magnitude. By contrast, the other groups had much more homogenous antibody responses, especially the VLP-containing groups, which had no animals below an endpoint titer of 1:1,000,000 (FIG. 6). These results underscore the potential of HBc VLP and RIC to provide consistently potent immune responses against L2. Moreover, significant synergy of VLP and RIC systems was observed when the systems were delivered together, after one or two doses (FIG. 6). Since equivalent amounts of L2 were delivered with each dose, the enhanced antibody titer did not result from higher L2 doses. Rather, these data suggest that higher L2-specific antibody production may be due to augmented stimulation of L2-specific B cells by T-helper cells that were primed by RIC-induced antigen presenting cells. Although treatment with VLP and RIC alone reached similar endpoint titers as the combined VLP/RIC group after 3 doses, virus neutralization was substantially higher (>5-fold) in the combined group (FIG. 8). Together, these data indicate unique synergy exists when VLP and RIC are delivered together. Inventors have observed similarly significant synergistic enhancement of immunogenicity for a variety of other antigens.

Mice immunized with L2 alone had highly variable antibody titers, with titers spanning two orders of magnitude. By contrast, the VLP and VLP/RIC groups had much more homogenous antibody responses, with no animals below an endpoint titer of 1:1,000,000 (FIG. 6). These results underscore the potential of HBc VLP and RIC to provide consistently potent immune responses against L2.

Fc gamma receptors are present on immune cells and strongly impact antibody effector functions such as antibody-dependent cell-mediated cytotoxicity and complement-dependent cytotoxicity (Jefferis 2009). In mice, these interactions are controlled in part by IgG subtypes. IgG1 is associated with a Th2 response and has limited effector functions. By contrast, IgG2a is associated with a Th1 response and more strongly binds complement components (Neuberger and Raj ewsky 1981) and Fc receptors (Radaev 2002), enhancing effector functions and opsonophagocytosis by macrophages (Takai et al. 1994). Immunization with L2 alone was found to produce low levels of IgG2a, however immunization with RIC and VLP produced significant increases in IgG2a titers. VLP-containing groups in particular showed a 3-fold increase in the ratio of IgG2a to IgG1 antibodies (FIG. 7). Importantly, production of IgG2a is associated with successful clearance of a plethora of viral pathogens (Coutelier et al. 1988; Gerhard et al. 1997; Wilson et al. 2000; Markine-Goriaynoff and Coutelier 2002).

The glycosylation state of the Fc receptor also plays an important role in antibody function. Advances in glycoengineering have led to the development of transgenic plants with silenced fucosyl- and xylosyl-transferase genes capable of producing recombinant proteins with authentic human N-glycosylation (Strasser et al. 2008). Antibodies produced in this manner have more homogenous glycoforms, resulting in improved interaction with Fc gamma and complement receptors compared to the otherwise identical antibodies produced in mammalian cell culture systems (Zeitlin et al. 2011; Hiatt et al. 2014; Strasser et al. 2014; Marusic et al. 2017). As the known mechanisms by which RIC vaccines increase immunogenicity of an antigen depend in part on Fc and complement receptor binding, HPV L2 RIC were produced in transgenic plants with silenced fucosyl- and xylosyl-transferase. Consistent with these data, we found that L2 RIC strongly enhanced the immunogenicity of L2 (FIG. 6). However, yield suffered from insolubility of the RIC (FIG. 4C). We found that the 11-128 segment of L2 expresses very poorly on its own in plants and may be a contributing factor to poor L2 RIC yield. Importantly, we have produced very high yields of RIC with different antigen fusions. Thus, in some aspects, antibody fusion with a shorter segment of L2 could substantially improve the yield of L2 RIC.

e. Neutralization of HPV Pseudovirions

Neutralization of papilloma pseudoviruses (HPV 16, 18, and 58) with sera from mice immunized IP with HBc-L2 VLP and L2(11-128) showed neutralization of HPV 16 at titers of 400-1600 and 200-800, respectively (Table 1). More mice IP-immunized with HBc-L2 VLP had antisera that cross-neutralized HPV 18 and HPV 58 pseudoviruses, compared with mice immunized with L2(11-128). Anti-HBc-L2 VLP sera neutralized HPV 18 at titers of 400 and HPV 58 at titers ranging from 400-800 (Table 1), while anti-L2(11-128) sera neutralized HPV 18 at a titer of 200 and HPV 58 at a titer of 400 (Table 1). None of the sera from intranasal-immunized mice demonstrated neutralizing activity, consistent with lower anti-L2 titers for intranasal than for intraperitoneal immunized mice.

TABLE 1
L2-specific serum IgG and pseudovirus neutralization
titers from IP immunized mice
Neutralization of Pseudoviruses
ImmunogenSerum IgGHPV 16HPV 18HPV 58
HBc-L2>50,000 400
~70,0001600400400
>80,0001600400800
L2 (11-128)~8000 200
~12,000 400
~50,000 800200400

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Patent 2024
3' Untranslated Regions 5' Untranslated Regions AA 149 Agrobacterium tumefaciens aluminum potassium sulfate aluminum sulfate Amino Acids Animals Animals, Transgenic Antibodies Antibody Formation Antigen-Presenting Cells Antigens B-Lymphocytes Bacteria Bromphenol Blue Buffers Cell Culture Techniques Cells Centrifugation Chromatography, Affinity Cloning Vectors Cold Temperature Combined Modality Therapy complement 1q receptor Complement Receptor Complex, Immune Complex Extracts Cytotoxicities, Antibody-Dependent Cell Cytotoxin Digestion DNA, A-Form DNA Sequence Edetic Acid Electron Microscopy Electroporation Enzyme-Linked Immunosorbent Assay Epitopes ethane sulfonate Fc Receptor Females Formvar Fucosyltransferase G-substrate Gamma Rays Genes Genes, vif Glycerin Goat Helix (Snails) Helper-Inducer T-Lymphocyte Homo sapiens Homozygote Horseradish Peroxidase Human papillomavirus 16 Human papillomavirus 18 Human Papilloma Virus Vaccine IGG-horseradish peroxidase IgG1 IgG2A Immune Sera Immunoglobulin Heavy Chains Immunoglobulins Immunologic Factors Institutional Animal Care and Use Committees Introns Inventors L2 protein, Human papillomavirus type 16 Light Macrophage Mammals Matrix Attachment Regions Mice, Inbred BALB C Microscopy Milk, Cow's Morpholinos Mus Necrosis Needles Nicotiana Oligonucleotide Primers Oligonucleotides Open Reading Frames Opsonophagocytosis Papilloma Pathogenicity Plant Development Plant Extracts Plant Leaves Plant Proteins Plants Plants, Transgenic polyacrylamide gels Polystyrenes polyvinylidene fluoride prisma Protein Glycosylation Proteins Punctures Rabbits Receptors, IgG Recombinant Proteins Replicon Reproduction Response, Immune Ribulose-Bisphosphate Carboxylase Large Subunit Satellite Viruses SDS-PAGE Serum Serum Albumin, Bovine Sodium Ascorbate Sodium Chloride sodium phosphate Specimen Collection Stars, Celestial Strains Sucrose Sulfate, Magnesium Syringes System, Immune Technique, Dilution Tissue, Membrane Tissues Transferase Transmission Electron Microscopy Triton X-100 Tromethamine Tween 20 Ultraviolet Rays uranyl acetate Vaccination Vaccines Vaccines, Recombinant Virion Viroids Virus Vision Western Blotting xylosyltransferase

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.

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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

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Publication 2023
Anti-Antibodies Arabidopsis GAPDH protein, human Plant Proteins Seedlings Sterility, Reproductive Technique, Dilution
Flavonoid biosynthesis proteins from A. thaliana were used to search for homologs in S. cerevisiae using DELTA-BLAST with an e-value < 1e-15. To search for CHI homologs in yeasts, a HMMER profile was constructed using S. cerevisiae Aim18p and Aim46p and used to conduct a HMMER search 332 annotated budding yeast genomes (15 (link)) to identify hits with score >50 and e-value <0.001. To search for CHI homologs in more divergent fungi, tBLASTn was used to identify hits in over 700 unannotated Ascomycota genomes (16 (link)) with e-value <1e-6. The full plant and fungal CHI homolog phylogeny was built by adding the fungal sequences to a structural alignment of plant CHI homolog sequences (7 (link)) using the —add function of MAFFT (55 (link)) using default settings. The resulting alignment was truncated to the regions matching the initial structural alignment that included the CHI domain and filtered to sites with less than 50% gaps using trimAl (56 (link)). A phylogenetic tree was constructed using fasttree (57 ) with the settings (d -lg -gamma -spr 4 -slownni -mlacc 2), midpoint rooted, and edited to collapse clades using iTOL (58 (link)). The absence of any CHI homologs in metazoan lineages was determined by repeated search attempts using both blastp and DELTA-BLAST at permissive e-value thresholds restricted to relevant taxonomic groups. The determination that the single CHI homolog in the published genome of S. arboricola was due to a fusion of paralogs was made by aligning the coding sequence of all hits from Saccharomyces species via MAFFT using default settings (55 (link)) and looking for signatures of recombination between AIM18 and AIM46 homologs in the S. arboricola gene using RDP4 (59 (link)). A single recombination event was found in this sequence (all tests of recombination were significant at p<1e-13), which supports a fusion with AIM46 providing roughly the 5′ 60% and AIM18 the final 3′ 40% of the sequence.
A subset of protein sequences of plant and fungal CHIs were aligned via MAFFT using default settings (55 (link)). Protein sequence alignments were visualized using Jalview 2.11.2.5 and colored by sequence conservation (60 (link)). Mid-point–rooted phylogenetic trees of plant and fungal CHIs were generated through the Phylogeny.fr “one click” mode workflow (http://phylogeny.lirmm.fr/) using default settings (61 , 62 (link), 63 (link), 64 (link), 65 ). Alignments and phylogenetic trees were exported as svg files and annotated in Adobe Illustrator 26.5.
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Publication 2023
Amino Acid Sequence Ascomycetes Flavonoids Fungi Gamma Rays Genes Genome Homologous Sequences Open Reading Frames Plant Proteins Plants Protein Biosynthesis Proteins Recombination, Genetic Saccharomyces Saccharomyces cerevisiae Saccharomycetales Sequence Alignment Shock SP3 protein, human Strains Yeasts
The potential orthologs of AtSWEET11 and AtSWEET12 were identified by comparing the protein sequence against the proteomes of 39 economically important plant species from 20 different families. SWEET protein sequences were retrieved from EnsemblPlants database (http://plants.ensembl.org/). The best five hits for the orthologs of each plant species were again compared against the Arabidopsis proteome. The best three hits were then examined for the presence of MtN3_slv domain (IPR018179) using Pfam database (http://pfam.xfam.org/) and Conserved Domain Database (CDD) (http://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi) and also checked for the presence of transmembrane helices (TMHs) in protein sequences using TMHMM Server v.2.0 (http://www.cbs.dtu.dk/services/TMHMM/) with default parameters. Finally, the first best hit obtained after passing through these steps was considered as a potential orthologs of AtSWEET11 and AtSWEET12 for each plant species.
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Publication 2023
Amino Acid Sequence Arabidopsis Helix (Snails) Plant Proteins Plants Proteome

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PVDF membranes are a type of laboratory equipment used for a variety of applications. They are made from polyvinylidene fluoride (PVDF), a durable and chemically resistant material. PVDF membranes are known for their high mechanical strength, thermal stability, and resistance to a wide range of chemicals. They are commonly used in various filtration, separation, and analysis processes in scientific and research settings.
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The Pierce BCA Protein Assay Kit is a colorimetric-based method for the quantification of total protein in a sample. It utilizes the bicinchoninic acid (BCA) reaction, where proteins reduce Cu2+ to Cu+ in an alkaline environment, and the resulting purple-colored reaction is measured spectrophotometrically.
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PVDF membranes are a type of laboratory equipment used for protein transfer and detection in Western blot analysis. They provide a stable and durable surface for the immobilization of proteins, enabling effective identification and quantification of target proteins in complex biological samples.
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MG132 is a proteasome inhibitor, a type of laboratory reagent used in research applications. It functions by blocking the activity of the proteasome, a complex of enzymes responsible for the degradation of proteins within cells. MG132 is commonly used in cell biology and biochemistry studies to investigate the role of the proteasome in various cellular processes.

More about "Plant Proteins"

Plant-Derived Proteins: Unlocking the Potential for Groundbreaking Research Explore the captivating world of plant-derived proteins and unlock new possibilities for your research.
These versatile biomolecules, often referred to as phytoproteins or vegetable proteins, are gaining increasing attention due to their diverse applications and potential benefits.
Delve into the vast array of plant protein sources, from common crops like soy, wheat, and corn to lesser-known alternatives like quinoa, chia, and hemp.
Each plant species offers a unique protein profile, presenting researchers with a treasure trove of opportunities to discover novel functionalities and applications.
Optimize your plant protein extraction and purification processes with specialized kits like the Plant Total Protein Extraction Kit, Plant Protein Extraction Kit, and P-PER plant protein extraction kit.
These tools streamline the extraction process, ensuring high-quality samples and preserving the integrity of your precious plant-derived proteins.
Enhance the accuracy and reproducibility of your experiments by leveraging advanced techniques like the Pierce BCA Protein Assay Kit.
This reliable method allows you to precisely quantify the protein content in your samples, empowering you to make informed decisions and draw meaningful conclusions.
Protect your plant protein samples from unwanted degradation with the help of protease inhibitor cocktails.
These specialized formulations safeguard your precious biomolecules, enabling you to maintain their structural and functional integrity throughout your research.
Explore the world of plant proteins with the power of PubCompare.ai, the AI-driven platform that revolutionizes your literature search and protocol optimization.
Easily locate the best protocols from published literature, preprints, and patents, and discover the most reliable and effective methods to elevate your plant protein research.
Embark on your plant protein journey with confidence, armed with the knowledge and tools to unlock new frontiers in your field.
Elevate your research, push the boundaries of discovery, and make a lasting impact with the power of plant-derived proteins.