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Record W4394820674 · doi:10.1038/s41598-024-59266-2

Chromatography affinity resin with photosynthetically-sourced protein A ligand

2024· article· en· W4394820674 on OpenAlexafffund
Nisha A. Owens, Pieter H. Anborgh, Igor Kolotilin

Bibliographic record

VenueScientific Reports · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicTransgenic Plants and Applications
Canadian institutionsBiotechnology Research InstituteFanshawe College
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAffinity chromatographyLigand (biochemistry)ChemistryChromatographyBiochemistryReceptor

Abstract

fetched live from OpenAlex

Green, photosynthesizing plants can be proficiently used as cost-effective, single-use, fully biodegradable bioreactors for environmentally-friendly production of a variety of valuable recombinant proteins. Being near-infinitely scalable and most energy-efficient in generating biomass, plants represent profoundly valid alternatives to conventionally used stationary fermenters. To validate this, we produced a plastome-engineered tobacco bioreactor line expressing a recombinant variant of the protein A from Staphylococcus aureus, an affinity ligand widely useful in antibody purification processes, reaching accumulation levels up to ~ 250 mg per 1 kg of fresh leaf biomass. Chromatography resin manufactured from photosynthetically-sourced recombinant protein A ligand conjugated to agarose beads demonstrated the innate pH-driven ability to bind and elute IgG-type antibodies and allowed one-step efficient purification of functional monoclonal antibodies from the supernatants of the producing hybridomas. The results of this study emphasize the versatility of plant-based recombinant protein production and illustrate its vast potential in reducing the cost of diverse biotechnological applications, particularly the downstream processing and purification of monoclonal antibodies.

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 score0.230
Threshold uncertainty score0.342

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 head0.006
GPT teacher head0.222
Teacher spread0.215 · 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

Citations4
Published2024
Admission routes2
Has abstractyes

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