Dataset Information

Development of Molecular Markers for Iron Metabolism Related Genes in Lentil and Their Expression Analysis under Excess Iron Stress.

ABSTRACT: Multiple genes and transcription factors are involved in the uptake and translocation of iron in plants from soil. The sequence information about iron uptake and translocation related genes is largely unknown in lentil (Lens culinaris Medik.). This study was designed to develop iron metabolism related molecular markers for Ferritin-1, BHLH-1 (Basic helix loop helix), or FER-like transcription factor protein and IRT-1 (Iron related transporter) genes using genome synteny with barrel medic (Medicago truncatula). The second objective of this study was to analyze differential gene expression under excess iron over time (2 h, 8 h, 24 h). Specific molecular markers were developed for iron metabolism related genes (Ferritin-1, BHLH-1, IRT-1) and validated in lentil. Gene specific markers for Ferritin-1 and IRT-1 were used for quantitative PCR (qPCR) studies based on their amplification efficiency. Significant differential expression of Ferritin-1 and IRT-1 was observed under excess iron conditions through qPCR based gene expression analysis. Regulation of iron uptake and translocation in lentil needs further characterization. Greater emphasis should be given to development of conditions simulating field conditions under external iron supply and considering adult plant physiology.

SUBMITTER: Sen Gupta D

PROVIDER: S-EPMC5390492 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Development of Molecular Markers for Iron Metabolism Related Genes in Lentil and Their Expression Analysis under Excess Iron Stress.

Sen Gupta Debjyoti D McPhee Kevin K Kumar Shiv S

Frontiers in plant science 20170413

Multiple genes and transcription factors are involved in the uptake and translocation of iron in plants from soil. The sequence information about iron uptake and translocation related genes is largely unknown in lentil (Lens culinaris Medik.). This study was designed to develop iron metabolism related molecular markers for Ferritin-1, BHLH-1 (Basic helix loop helix), or FER-like transcription factor protein and IRT-1 (Iron related transporter) genes using genome synteny with ...[more]

PMID: 28450880

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Project description:Lower-grade glioma (LGG) is characterized by genetic and transcriptional heterogeneity, and a dismal prognosis. Iron metabolism is considered central for glioma tumorigenesis, tumor progression and tumor microenvironment, although key iron metabolism-related genes are unclear. Here we developed and validated an iron metabolism-related gene signature LGG prognosis. RNA-sequence and clinicopathological data from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) were downloaded. Prognostic iron metabolism-related genes were screened and used to construct a risk-score model via differential gene expression analysis, univariate Cox analysis, and the Least Absolute Shrinkage and Selection Operator (LASSO)-regression algorithm. All LGG patients were stratified into high- and low-risk groups, based on the risk score. The prognostic significance of the risk-score model in the TCGA and CGGA cohorts was evaluated with Kaplan-Meier (KM) survival and receiver operating characteristic (ROC) curve analysis. Risk- score distributions in subgroups were stratified by age, gender, the World Health Organization (WHO) grade, isocitrate dehydrogenase 1 (IDH1) mutation status, the O6-methylguanine-DNA methyl-transferase (MGMT) promoter-methylation status, and the 1p/19q co-deletion status. Furthermore, a nomogram model with a risk score was developed, and its predictive performance was validated with the TCGA and CGGA cohorts. Additionally, the gene set enrichment analysis (GSEA) identified signaling pathways and pathological processes enriched in the high-risk group. Finally, immune infiltration and immune checkpoint analysis were utilized to investigate the tumor microenvironment characteristics related to the risk score. We identified a prognostic 15-gene iron metabolism-related signature and constructed a risk-score model. High risk scores were associated with an age of > 40, wild-type IDH1, a WHO grade of III, an unmethylated MGMT promoter, and 1p/19q non-codeletion. ROC analysis indicated that the risk-score model accurately predicted 1-, 3-, and 5-year overall survival rates of LGG patients in the both TCGA and CGGA cohorts. KM analysis showed that the high-risk group had a much lower overall survival than the low-risk group (P < 0.0001). The nomogram model showed a strong ability to predict the overall survival of LGG patients in the TCGA and CGGA cohorts. GSEA analysis indicated that inflammatory responses, tumor-associated pathways, and pathological processes were enriched in high-risk group. Moreover, a high risk score correlated with the infiltration immune cells (dendritic cells, macrophages, CD4+ T cells, and B cells) and expression of immune checkpoint (PD1, PDL1, TIM3, and CD48). Our prognostic model was based on iron metabolism-related genes in LGG, can potentially aid in LGG prognosis, and provides potential targets against gliomas.

Dataset Information

Development of Molecular Markers for Iron Metabolism Related Genes in Lentil and Their Expression Analysis under Excess Iron Stress.

Publications

Development of Molecular Markers for Iron Metabolism Related Genes in Lentil and Their Expression Analysis under Excess Iron Stress.

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