Christopher M. Kearney

A tobamovirus expression vector for agroinfection of legumes and Nicotiana.

The highest recombinant protein expression levels in plants have been achieved using tobacco mosaic virus (TMV) vectors via agroinoculation of the tobacco, Nicotiana benthamiana. These vectors have been utilized for pharmaceutical protein production and also can serve as rapid gene expression screens for proteonomics. We have constructed a similar vector based on the legume-infecting tobamovirus, sunn hemp mosaic virus (SHMV), by deleting the coat protein gene (SHMV eliminate coat protein gene or SHEC). SHEC/GFP co-agroinoculated with a 35S/p19 binary yielded 600 microg GFP/gfw (25% TSP) in N. benthamiana. In the absence of p19, SHEC/GFP expression was nearly eliminated. SHEC also yielded strong GUS production in agroinoculated Medicago trunculata, Pinto bean, cowpea, pea and lentil even without the aid of systemic infection. A full-length version (SHAC, SHMV alternate coat protein) was created by adding to SHEC the coat protein subgenomic promoter and ORF from the tobamovirus, tobacco mild green mottle virus (TMGMV). SHAC induced a slowly developing, symptomless infection of N. benthamiana and may be of use as a virus induced gene silencing (VIGS) vector.

Plant-Expressed Recombinant Mountain Cedar Allergen Jun a 1 Is Allergenic and Has Limited Pectate Lyase Activity

Background: Mountain cedar (Juniperus ashei) pollen commonly causes a winter time allergic rhinitis in the central USA. Jun a 1 is the dominant allergenic protein, but biologically active recombinant Jun a 1 has not been successfully expressed, despite numerous attempts with several expression systems. Method: Jun a 1 cDNA was inserted into a tobacco mosaic virus vector and transferred to Agrobacterium tumefaciens. Bacteria were syringe-inoculated into leaves of Nicotiana benthamiana (agroinoculation). The interstitial (apoplastic) fluid containing Jun a 1 was isolated. The recombinant protein was analyzed by SDS-PAGE, N-terminal sequencing and MALDI-TOF to confirm identity. Immunogenicity was examined with IgE from allergic patient’s sera, mouse monoclonal anti-Jun a 1 antibodies, IgE-binding inhibition and by degranulation of RBL SX-38 cells sensitized with sera from allergic patients. Pectate lyase activity was assayed by capillary zone electrophoresis and mass spectrometry analysis. Results: Recombinant Jun a 1 was recovered in good quantity (100 µg/g leaf material), was confirmed as Jun a 1, bound IgE from sera from cedar hypersensitive patients and inhibited IgE binding to native Jun a 1. Jun a 1 mutants were created and their pectate lyase activity quantified. For the first time, Jun a 1 pectate lyase activity was demonstrated, which may explain the necrosis seen on host plants, which was similar to that of control plants expressing banana pectate lyase. Conclusions: A means of producing recombinant Jun a 1 is now available for structure/function studies and potentially for diagnostic and therapeutic uses.

Chimeric Flock House virus protein A with endoplasmic reticulum-targeting domain enhances viral replication and virus-like particle trans-encapsidation in plants

Flock House virus (FHV) RNA can be trans-encapsidated, entirely in planta, by tobacco mosaic virus coat protein to form virus-like particles (VLPs). Vaccination with these VLPs leads to strong antigen expression in mice and immune-activation. We hypothesize that creating an additional cellular site for replication and/or trans-encapsidation might significantly improve the final output of trans-encapsidated product. FHV protein A was engineered to target the endoplasmic reticulum (ER) via a heterologous tobacco etch virus ER-targeting domain, and was expressed in cis or in trans relative to the replicating FHV RNA1. A strong increase in marker gene expression in plants was noted when ER-targeted protein A was supplied in trans. RNA fluorescence in situ hybridization revealed RNA1 replication in both the mitochondria and ER, and total RNA1 accumulation was increased. In support of our hypothesis, VLP yield was increased significantly by the addition of this single genetic component to the inoculum.

Plant Expression of Trans-Encapsidated Viral Nanoparticle Vaccines with Animal RNA Replicons

In this protocol, we outline how to produce a live viral nanoparticle vaccine in a biosafety level 1 (BSL1) environment. An animal viral vector RNA encapsidated with tobacco mosaic virus (TMV) coat protein can be fully assembled in planta. Agrobacterium cultures containing each component are inoculated together into tobacco leaves and the self-assembled hybrid nanoparticle vaccine is harvested 4 days later and purified with a simple PEG precipitation. The viral RNA delivery vector is derived from the BSL1 insect virus, Flock House virus (FHV), and replicates in human and animal cells but does not spread systemically. A polyethylene glycol purification protocol is also provided to collect and purify these vaccines for immunological tests.

Protein classification using modified n-grams and skip-grams

Motivation Classification by supervised machine learning greatly facilitates the annotation of protein characteristics from their primary sequence. However, the feature generation step in this process requires detailed knowledge of attributes used to classify the proteins. Lack of this knowledge risks the selection of irrelevant features, resulting in a faulty model. In this study, we introduce a supervised protein classification method with a novel means of automating the work-intensive feature generation step via a Natural Language Processing (NLP)-dependent model, using a modified combination of n-grams and skip-grams (m-NGSG). Results A meta-comparison of cross-validation accuracy with twelve training datasets from nine different published studies demonstrates a consistent increase in accuracy of m-NGSG when compared to contemporary classification and feature generation models. We expect this model to accelerate the classification of proteins from primary sequence data and increase the accessibility of protein characteristic prediction to a broader range of scientists. Availability and implementation m-NGSG is freely available at Bitbucket: https://bitbucket.org/sm_islam/mngsg/src. A web server is available at watson.ecs.baylor.edu/ngsg. Contact erich_baker@baylor.edu. Supplementary information Supplementary data are available at Bioinformatics online.

Protein Classification using Modified N-Gram and Skip-Gram Models: Extended Abstract

Machine Learning (ML)-based classification of protein characteristics from primary sequences is an important tool for exploring candidate proteins in targeted drug discovery, mutational analysis, and functional identification. However, ML feature selection requires extensive manual curation and knowledge of protein chemistry, interactions, and micro-environment of the proteins of interest. Current approaches include amino acid composition strategies, specific motif analysis or Quantitative Structure-Activity Relationship (QSAR)-based feature generation methods. In contrast, we propose an automated generalized feature generation method based on Natural Language Processing (NLP), using a modified combination of N-Gram and Skip-Gram models (m-NGSG). Optimal parameters are selected using an adapted grid search algorithm, enabling a high-throughput global application of our approach. A meta-comparison of logistic regression mediated classification approaches exploiting m-NGSG with other published models illustrates enhanced functional and structural binary and multi-class classification accuracy in every instance. The lack of dependence on detailed physicochemical knowledge makes the m-NGSG approach ideal for the exploration of protein characteristics recalcitrant to previous approaches without any loss in predictive accuracy. A further test on prediction quality of m-NGSG on cationic channel blockers with 70% sequence identity from Arthropods demonstrated 94.10% and 92.30% accuracy on the training and test set, respectively. The latter study demonstrates the applicability of m-NGSG model on a functional classification of proteins employing a novel dataset.Thus, without the requirement of expert intervention for optimal feature selection, it is hoped that this automated feature generation approach will reduce the time needed to employ ML classification strategies for prediction of protein characteristics.

Pathogenesis-related proteins induced by agroinoculation-associated cell wall weakening can be obviated by spray-on inoculation or mannitol ex vivo culture

Agroinoculation transient expression systems are commonly accompanied with elevated pathogenesis-related (PR) protein production and leaf necrosis. We identified the major PR proteins in Nicotiana benthamiana in response to agroinoculation and determined that their occurrence was mainly due to agrobacterium infection and the method of inoculation, rather than due to viral vectors overexpressing foreign proteins. A spray-on inoculation method was optimized in this research and used to obviate the leaf necrosis and PR proteins induced by agrobacterium. Subsequently, this method also increased the yield and purity of the protein-of-interest. A further investigation of PR protein induction by a necrosis-inducing protein, Jun a 1, suggested that the plant pathogenic response was related to biochemical integrity of plant cell wall, which was also confirmed by an osmo-stabilizing mannitol ex vivo culture experiment. These findings provide insight into the response of plants to agroinoculation and suggested a connection between cell wall weakening and PR protein elicitation.

Protein classification using modified n-gram and skip-gram models

Motivation Classification by supervised machine learning greatly facilitates the annotation of protein characteristics from their primary sequence. However, the feature generation step in this process requires detailed knowledge of attributes used to classify the proteins. Lack of this knowledge risks the selection of irrelevant features, resulting in a faulty model. In this study, we introduce a means of automating the work-intensive feature generation step via a Natural Language Processing (NLP)-dependent model, using a modified combination of N-Gram and Skip-Gram models (m-NGSG). Results A meta-comparison of cross validation accuracy with twelve training datasets from nine different published studies demonstrates a consistent increase in accuracy of m-NGSG when compared to contemporary classification and feature generation models. We expect this model to accelerate the classification of proteins from primary sequence data and increase the accessibility of protein prediction to a broader range of scientists. Availability m-NGSG is freely available at Bitbucket: https://bitbucket.org/smislam/mngsg/src Supplements link to supplementary documents Contact Erich_Baker@baylor.edu

Classes, Databases, and Prediction Methods of Pharmaceutically and Commercially Important Cystine-Stabilized Peptides

Cystine-stabilized peptides represent a large family of peptides characterized by high structural stability and bactericidal, fungicidal, or insecticidal properties. Found throughout a wide range of taxa, this broad and functionally important family can be subclassified into distinct groups dependent upon their number and type of cystine bonding patters, tertiary structures, and/or their species of origin. Furthermore, the annotation of proteins related to the cystine-stabilized family are under-represented in the literature due to their difficulty of isolation and identification. As a result, there are several recent attempts to collate them into data resources and build analytic tools for their dynamic prediction. Ultimately, the identification and delivery of new members of this family will lead to their growing inclusion into the repertoire of commercial viable alternatives to antibiotics and environmentally safe insecticides. This review of the literature and current state of cystine-stabilized peptide biology is aimed to better describe peptide subfamilies, identify databases and analytics resources associated with specific cystine-stabilized peptides, and highlight their current commercial success.

Nectar protein content and attractiveness to Aedes aegypti and Culex pipiens in plants with nectar/insect associations

We chose five easily propagated garden plants previously shown to be attractive to mosquitoes, ants or other insects and tested them for attractiveness to Culex pipiens and Aedes aegypti. Long term imbibition was tested by survival on each plant species. Both mosquito species survived best on Impatiens walleriana, the common garden impatiens, followed by Asclepias curassavica, Campsis radicans and Passiflora edulis, which sponsored survival as well as the 10% sucrose control. Immediate preference for imbibition was tested with nectar dyed in situ on each plant. In addition, competition studies were performed with one dyed plant species in the presence of five undyed plant species to simulate a garden setting. In both preference studies I. walleriana proved superior. Nectar from all plants was then screened for nectar protein content by SDS-PAGE, with great variability being found between species, but with I. walleriana producing the highest levels. The data suggest that I. walleriana may have value as a model plant for subsequent studies exploring nectar delivery of transgenic mosquitocidal proteins.