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Record W2001359008 · doi:10.1139/w11-007

Use of protein microarray to identify gene expression changes of<i>Yersinia pestis</i>at different temperatures

2011· article· en· W2001359008 on OpenAlexvenueno aff
Bei Li, Yafang Tan, Jingyu Guo, Baizhong Cui, Zuyun Wang, Hu Wang, Lei Zhou, Zhaobiao Guo, Ziwen Zhu, Zongmin Du, Ruifu Yang

Bibliographic record

VenueCanadian Journal of Microbiology · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicYersinia bacterium, plague, ectoparasites research
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsYersinia pestisVirulenceBiologyMicroarrayGeneMicrobiologyGene expression profilingMicroarray analysis techniquesPlague (disease)Gene expressionAntigenAntibodyMolecular biologyGenetics

Abstract

fetched live from OpenAlex

Yersinia pestis is a bacterium that is transmitted between fleas, which have a body temperature of 26 °C, and mammalian hosts, which have a body temperature of 37 °C. To adapt to the temperature shift, phenotype variations, including virulence, occur. In this study, an antigen microarray including 218 proteins of Y. pestis was used to evaluate antibody responses in a pooled plague serum that was unadsorbed, adsorbed by Y. pestis cultivated at 26 °C, or adsorbed by Y. pestis cultivated at 26 and 37 °C to identify protein expression changes during the temperature shift. We identified 12 proteins as being expressed at 37 °C but not at 26 °C, or expressed at significantly higher levels at 37 °C than at 26 °C. The antibodies against 7 proteins in the serum adsorbed by Y. pestis cultivated at 26 and 37 °C remained positive, suggesting that they were not expressed on the surface of Y. pestis in LB broth in vitro or specifically expressed in vivo. This study proved that protein microarray and antibody profiling comprise a promising technique for monitoring gene expression at the protein level and for better understanding pathogenicity, to find new vaccine targets against plague.

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 score0.013
Threshold uncertainty score0.647

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 head0.031
GPT teacher head0.260
Teacher spread0.229 · 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

Citations9
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of MicrobiologySame topicYersinia bacterium, plague, ectoparasites researchFrench-language works237,207