DataSheet_1_Genome-wide characterization and expression analysis of MADS-box transcription factor gene family in Perilla frutescens.pdf

MADS-box transcription factors are widely involved in the regulation of plant growth, developmental processes, and response to abiotic stresses. Perilla frutescens, a versatile plant, is not only used for food and medicine but also serves as an economical oil crop. However, the MADS-box transcription factor family in P. frutescens is still largely unexplored. In this study, a total of 93 PfMADS genes were identified in P. frutescens genome. These genes, including 37 Type I and 56 Type II members, were randomly distributed across 20 chromosomes and 2 scaffold regions. Type II PfMADS proteins were found to contain a greater number of motifs, indicating more complex structures and diverse functions. Expression analysis revealed that most PfMADS genes (more than 76 members) exhibited widely expression model in almost all tissues. The further analysis indicated that there was strong correlation between some MIKCC-type PfMADS genes and key genes involved in lipid synthesis and flavonoid metabolism, which implied that these PfMADS genes might play important regulatory role in the above two pathways. It was further verified that PfMADS47 can effectively mediate the regulation of lipid synthesis in Chlamydomonas reinhardtii transformants. Using cis-acting element analysis and qRT-PCR technology, the potential functions of six MIKCC-type PfMADS genes in response to abiotic stresses, especially cold and drought, were studied. Altogether, this study is the first genome-wide analysis of PfMADS. This result further supports functional and evolutionary studies of PfMADS gene family and serves as a benchmark for related P. frutescens breeding studies.</p

Image_1_Genome-wide characterization and expression analysis of MADS-box transcription factor gene family in Perilla frutescens.jpeg

MADS-box transcription factors are widely involved in the regulation of plant growth, developmental processes, and response to abiotic stresses. Perilla frutescens, a versatile plant, is not only used for food and medicine but also serves as an economical oil crop. However, the MADS-box transcription factor family in P. frutescens is still largely unexplored. In this study, a total of 93 PfMADS genes were identified in P. frutescens genome. These genes, including 37 Type I and 56 Type II members, were randomly distributed across 20 chromosomes and 2 scaffold regions. Type II PfMADS proteins were found to contain a greater number of motifs, indicating more complex structures and diverse functions. Expression analysis revealed that most PfMADS genes (more than 76 members) exhibited widely expression model in almost all tissues. The further analysis indicated that there was strong correlation between some MIKCC-type PfMADS genes and key genes involved in lipid synthesis and flavonoid metabolism, which implied that these PfMADS genes might play important regulatory role in the above two pathways. It was further verified that PfMADS47 can effectively mediate the regulation of lipid synthesis in Chlamydomonas reinhardtii transformants. Using cis-acting element analysis and qRT-PCR technology, the potential functions of six MIKCC-type PfMADS genes in response to abiotic stresses, especially cold and drought, were studied. Altogether, this study is the first genome-wide analysis of PfMADS. This result further supports functional and evolutionary studies of PfMADS gene family and serves as a benchmark for related P. frutescens breeding studies.</p

DataSheet_3_Genome-wide characterization and expression analysis of MADS-box transcription factor gene family in Perilla frutescens.pdf

MADS-box transcription factors are widely involved in the regulation of plant growth, developmental processes, and response to abiotic stresses. Perilla frutescens, a versatile plant, is not only used for food and medicine but also serves as an economical oil crop. However, the MADS-box transcription factor family in P. frutescens is still largely unexplored. In this study, a total of 93 PfMADS genes were identified in P. frutescens genome. These genes, including 37 Type I and 56 Type II members, were randomly distributed across 20 chromosomes and 2 scaffold regions. Type II PfMADS proteins were found to contain a greater number of motifs, indicating more complex structures and diverse functions. Expression analysis revealed that most PfMADS genes (more than 76 members) exhibited widely expression model in almost all tissues. The further analysis indicated that there was strong correlation between some MIKCC-type PfMADS genes and key genes involved in lipid synthesis and flavonoid metabolism, which implied that these PfMADS genes might play important regulatory role in the above two pathways. It was further verified that PfMADS47 can effectively mediate the regulation of lipid synthesis in Chlamydomonas reinhardtii transformants. Using cis-acting element analysis and qRT-PCR technology, the potential functions of six MIKCC-type PfMADS genes in response to abiotic stresses, especially cold and drought, were studied. Altogether, this study is the first genome-wide analysis of PfMADS. This result further supports functional and evolutionary studies of PfMADS gene family and serves as a benchmark for related P. frutescens breeding studies.</p

DataSheet_2_Genome-wide characterization and expression analysis of MADS-box transcription factor gene family in Perilla frutescens.pdf

MADS-box transcription factors are widely involved in the regulation of plant growth, developmental processes, and response to abiotic stresses. Perilla frutescens, a versatile plant, is not only used for food and medicine but also serves as an economical oil crop. However, the MADS-box transcription factor family in P. frutescens is still largely unexplored. In this study, a total of 93 PfMADS genes were identified in P. frutescens genome. These genes, including 37 Type I and 56 Type II members, were randomly distributed across 20 chromosomes and 2 scaffold regions. Type II PfMADS proteins were found to contain a greater number of motifs, indicating more complex structures and diverse functions. Expression analysis revealed that most PfMADS genes (more than 76 members) exhibited widely expression model in almost all tissues. The further analysis indicated that there was strong correlation between some MIKCC-type PfMADS genes and key genes involved in lipid synthesis and flavonoid metabolism, which implied that these PfMADS genes might play important regulatory role in the above two pathways. It was further verified that PfMADS47 can effectively mediate the regulation of lipid synthesis in Chlamydomonas reinhardtii transformants. Using cis-acting element analysis and qRT-PCR technology, the potential functions of six MIKCC-type PfMADS genes in response to abiotic stresses, especially cold and drought, were studied. Altogether, this study is the first genome-wide analysis of PfMADS. This result further supports functional and evolutionary studies of PfMADS gene family and serves as a benchmark for related P. frutescens breeding studies.</p

Dietary Information Improves Model Performance and Predictive Ability of a Noninvasive Type 2 Diabetes Risk Model

<div><p>There is no diabetes risk model that includes dietary predictors in Asia. We sought to develop a diet-containing noninvasive diabetes risk model in Northern China and to evaluate whether dietary predictors can improve model performance and predictive ability. Cross-sectional data for 9,734 adults aged 20–74 years old were used as the derivation data, and results obtained for a cohort of 4,515 adults with 4.2 years of follow-up were used as the validation data. We used a logistic regression model to develop a diet-containing noninvasive risk model. Akaike’s information criterion (AIC), area under curve (AUC), integrated discrimination improvements (IDI), net classification improvement (NRI) and calibration statistics were calculated to explicitly assess the effect of dietary predictors on a diabetes risk model. A diet-containing type 2 diabetes risk model was developed. The significant dietary predictors including the consumption of staple foods, livestock, eggs, potato, dairy products, fresh fruit and vegetables were included in the risk model. Dietary predictors improved the noninvasive diabetes risk model with a significant increase in the AUC (delta AUC = 0.03, <i>P</i><0.001), an increase in relative IDI (24.6%, <i>P</i>-value for IDI <0.001), an increase in NRI (category-free NRI = 0.155, <i>P</i><0.001), an increase in sensitivity of the model with 7.3% and a decrease in AIC (delta AIC = 199.5). The results of the validation data were similar to the derivation data. The calibration of the diet-containing diabetes risk model was better than that of the risk model without dietary predictors in the validation data. Dietary information improves model performance and predictive ability of noninvasive type 2 diabetes risk model based on classic risk factors. Dietary information may be useful for developing a noninvasive diabetes risk model.</p></div

The Hosmer-Lemeshow Goodness-of-fit Test for the Diet-containing Diabetes Risk Model and Classic Noninvasive Diabetes Risk Model.

<p>X-axes indicate the deciles of the predicted risk of type 2 diabetes, and y-axes indicate the probability of type 2 diabetes events. <i>P</i>-values from χ² statistics calculated to compare the difference between the predicted and the actual incidence of type 2 diabetes.</p

The Detailed Parameters of the Risk Model Evaluation that Dietary Predictors Improved in the Derivation Data.

<p>The Detailed Parameters of the Risk Model Evaluation that Dietary Predictors Improved in the Derivation Data.</p

Dietary Information Improves Model Performance and Predictive Ability of a Noninvasive Type 2 Diabetes Risk Model - Fig 2

<p><b>Estimated 4.2 years cumulative incidence of type 2 diabetes by quintile of risk scores of the classic noninvasive risk model (A) and diet-containing risk model (B).</b> X-axes indicate the quintiles of the two risk scores, and y-axes indicate the 4.2 years cumulative incidence of type 2 diabetes. <i>P</i>-values from χ² statistics calculated to compare the difference of the incidence of type 2 diabetes across the quintiles of the two risk scores.</p

Characteristics of Subjects in the Derivation and Validation Data.

<p>Characteristics of Subjects in the Derivation and Validation Data.</p

Risk Scores Based on the Classic Noninvasive Risk Model and the Diet-containing Risk Model for Type 2 Diabetes Risk in the derivation Data.

<p>Risk Scores Based on the Classic Noninvasive Risk Model and the Diet-containing Risk Model for Type 2 Diabetes Risk in the derivation Data.</p