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Record W2291022936 · doi:10.1186/s12864-016-2548-y

An iNTT system for the large-scale screening of differentially expressed, nuclear-targeted proteins: cold-treatment-induced nucleoproteins in Rye (Secale cereale L.)

2016· article· en· W2291022936 on OpenAlexaff
Xinyou Cao, Xueyan Chen, Liu YangNa, Zhao‐Shi Xu, Liancheng Li, Yongbin Zhou, Jianjun Liu, Zhao Zhen, Ming Chen, You‐Zhi Ma

Bibliographic record

VenueBMC Genomics · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNuclear Structure and Function
Canadian institutionsMinistry of Agriculture
FundersNational Key Project for Research on Transgenic BiologyShandong Academy of Agricultural SciencesNational Natural Science Foundation of China
KeywordsSecaleBiologyNucleoproteinProteomicsDNA microarrayGeneticsScale (ratio)Computational biologyDNABotanyGene expressionGene

Abstract

fetched live from OpenAlex

BACKGROUND: Nuclear proteins play critical roles in regulating mRNA transcription and processing, DNA replication, and epigenetic genome modification. Therefore, the ability to monitor changes in nuclear proteins is helpful not only to identify important regulatory proteins but also to study the mechanisms of actions of nuclear proteins. However, no effective methods have been developed yet. Rye is strongly resistant to various biotic and abiotic stresses; however, few genes have been functionally characterized to date due to the complexity of its genome and a lack of genomic sequence information. RESULTS: We developed an integrative Nuclear Transportation Trap (iNTT) system that includes an improved nuclear transportation trap and utilizes the "after suppression subtraction" method. Oligonucleotides encoding a nuclear localization signal (NLS) or a transcription factor, GmAREB, were inserted into pLexAD or pLexAD-NES, respectively, and then transformed into yeast cells (EGY48). We showed that the pLexAD vector expressing a cDNA library in the iNTT system was more efficient for screening than the vector pLexAD-NES, which has previously been used in an NTT system. We used the iNTT system to screen a cDNA library of cold-treated rye. A total of 241 unique genes were identified, including 169 differentially expressed proteins; of these, 106 were of known and 63 were of unknown function. Moreover, 82 genes (49 %) among the 169 differentially expressed genes were predicted to contain an NLS domain. Thirty-three (31 %) of the 106 functionally known proteins have DNA-binding activity. To test the specificity of the nuclear proteins identified using the iNTT screen, four of the proteins differentially expressed in response to temperature stress, ScT1 (a heat shock protein), ScT36 (a MYB-like transcription factor), ScT133 (an ERF-like transcription factor) and ScT196 (a protein of unknown function), were studied in more depth. These proteins were shown to exclusively localize to the nucleus, and their expression levels were increased in response to low-temperature stress. To identify the function of these screened nuclear proteins, ScT1- and ScT36-transgenic Arabidopsis plants were constructed, and ScT1 or ScT36 overexpression was found to enhance tolerance to high-temperature or freezing stresses, respectively. CONCLUSIONS: The newly developed iNTT system provides an effective method for identifying nuclear-targeted proteins and monitoring induced expression levels. ScT1 and ScT36 might be good candidate genes for improving the stress tolerance of plants by genetic transformation.

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 score0.024
Threshold uncertainty score0.491

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 head0.013
GPT teacher head0.224
Teacher spread0.210 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

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