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Record W2336354963 · doi:10.1186/1753-6561-9-s9-p49

Characterization of Alternative Promoters to Stagger and Control Protein Expression in the Baculovirus-Insect Cell System: From Intracellular Reporter Proteins to Fluorescent Influenza Virus-like Particles

2015· article· en· W2336354963 on OpenAlexafffundabout
Steve George, Marc G. Aucoin

Bibliographic record

VenueBMC Proceedings · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicViral Infectious Diseases and Gene Expression in Insects
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGreen fluorescent proteinIntracellularVirusCell biologyBaculoviridaeReporter geneCharacterization (materials science)VirologyFluorescenceFluorescent proteinBiologyGene expressionGeneNanotechnologyBiochemistryMaterials scienceRecombinant DNASpodopteraPhysics

Abstract

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Background The Baculovirus Expression Vector System (BEVS) is increasingly used for protein production in both industry and academia, and much work has been conducted to improve this system. The baculovirus infection of an insect cell sets up a sophisticated and complex series of gene expression events that are very tightly temporally regulated. The study of this system has progressed to such an extent that many control elements, such as activators, enhancers, and promoters involved in this process have been discovered and characterized to some extent, as reviewed in [1]. These control elements can be used to regulate the expression of heterologous genes, in order to move beyond “brute force” expression of large amounts of protein within insect cells. It enables researchers to set up a pre-planned series of expression events of multiple genes within one cell, and to essentially “program” gene expression by modifying the baculovirus genome. While some groups have investigated this, a systematic study of control elements and how expression from a single gene affects expression from other heterologous genes, has not been conducted thus far. This study characterizes gene expression from several baculovirus promoters for the production of proteins and virus-like particles, and examines interaction effects when promoters drive expression of genes at different times and at different levels. Materials and Methods Two sets of protein coding genes were investigated. Both sets of constructs were arranged such that one gene was always under the control of the very strong polyhedron (polh) promoter, while the other gene was under the control of the early ie1, late basic, gp64orvcath, or the very late p10 promoters. The first set of proteins examined consisted of two easily traceable fluorescent proteins requiring minimal post-translational processing: the enhanced green fluorescent protein (eGFP, herein referred to as GFP) and a red fluorescent protein (DsRed2 herein referred to as RFP). The RFP gene was always under the control of the polh promoter while GFP was placed downstream of one of the other five promoters [2].The second set of proteins studied were fusions of influenza A virus proteins. More specifically, human influenza A/PR/8/34 hemagglutinin (HA) and matrix (M1) proteins were fused to eGFP(HAGFP) and DsRed2 (M1RFP) respectively. The M1RFP gene was always under the control of the polh promoter while HAGFP was placed downstream of one of the other five promoters. Sf9 cells were infected at a cell density of 1 × 106 cells/mL and at a multiplicity of infection of 5. Cells were examined by light and fluorescence microscopy, as well as by flow cytometry. Virus-like particles were recovered from infected cell culture supernatants by subjecting the supernatants to iodixanol gradient ultracentrifugation as previously described in [3]. Virus-like particles were characterized by flow cytometry using a method similar to that described in [4],by negative stain * Correspondence: marc.aucoin@uwaterloo.ca Department of Chemical Engineering, University of Waterloo, Waterloo, Ontario, N2L3G1, Canada George and Aucoin BMC Proceedings 2015, 9(Suppl 9):P49 http://www.biomedcentral.com/1753-6561/9/S9/P49

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.584

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.243
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2015
Admission routes3
Has abstractyes

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