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Record W2285170096 · doi:10.1186/s12864-016-2476-x

Analysis of weighted co-regulatory networks in maize provides insights into new genes and regulatory mechanisms related to inositol phosphate metabolism

2016· article· en· W2285170096 on OpenAlexaff
Shaojun Zhang, Wenzhu Yang, Qianqian Zhao, Xiaojin Zhou, Ling Jiang, Shuai Ma, Xiaoqing Liu, Ye Li, Chunyi Zhang, Yunliu Fan, Rumei Chen

Bibliographic record

VenueBMC Genomics · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPhytase and its Applications
Canadian institutionsBiotechnology Research Institute
FundersNational Key Research and Development Program of China
KeywordsBiologyWRKY protein domainPhytic acidGeneSignal transductionInositolTranscription factorArabidopsisRegulation of gene expressionGene expressionMetabolic pathwayBiochemistryGeneticsArabidopsis thalianaCell biologyTranscriptomeReceptorMutant

Abstract

fetched live from OpenAlex

BACKGROUND: D-myo-inositol phosphates (IPs) are a series of phosphate esters. Myo-inositol hexakisphosphate (phytic acid, IP6) is the most abundant IP and has negative effects on animal and human nutrition. IPs play important roles in plant development, stress responses, and signal transduction. However, the metabolic pathways and possible regulatory mechanisms of IPs in maize are unclear. In this study, the B73 (high in phytic acid) and Qi319 (low in phytic acid) lines were selected for RNA-Seq analysis from 427 inbred lines based on a screening of IP levels. By integrating the metabolite data with the RNA-Seq data at three different kernel developmental stages (12, 21 and 30 days after pollination), co-regulatory networks were constructed to explore IP metabolism and its interactions with other pathways. RESULTS: Differentially expressed gene analyses showed that the expression of MIPS and ITPK was related to differences in IP metabolism in Qi319 and B73. Moreover, WRKY and ethylene-responsive transcription factors (TFs) were common among the differentially expressed TFs, and are likely to be involved in the regulation of IP metabolism. Six co-regulatory networks were constructed, and three were chosen for further analysis. Based on network analyses, we proposed that the GA pathway interacts with the IP pathway through the ubiquitination pathway, and that Ca(2+) signaling functions as a bridge between IPs and other pathways. IP pools were found to be transported by specific ATP-binding cassette (ABC) transporters. Finally, three candidate genes (Mf3, DH2 and CB5) were identified and validated using Arabidopsis lines with mutations in orthologous genes or RNA interference (RNAi)-transgenic maize lines. Some mutant or RNAi lines exhibited seeds with a low-phytic-acid phenotype, indicating perturbation of IP metabolism. Mf3 likely encodes an enzyme involved in IP synthesis, DH2 encodes a transporter responsible for IP transport across organs and CB5 encodes a transporter involved in IP co-transport into vesicles. CONCLUSIONS: This study provides new insights into IP metabolism and regulation, and facilitates our development of a better understanding of the functions of IPs and how they interact with other pathways involved in plant development and stress responses. Three new genes were discovered and preliminarily validated, thereby increasing our knowledge of IP metabolism.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.750
Threshold uncertainty score0.243

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.011
GPT teacher head0.213
Teacher spread0.202 · 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

Citations27
Published2016
Admission routes1
Has abstractyes

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