Comparative transcriptome analysis reveals differentially expressed genes related to the tissue-specific accumulation of anthocyanins in pericarp and aleurone layer for maize

Li, Tingchun; Zhang, Wei; Yang, Huaying; Dong, Qing; Ren, Jie; Fan, Honghong; Zhang, Xin; Zhou, Yingbing

doi:10.1038/s41598-018-37697-y

Cited by 34 publications

(28 citation statements)

References 36 publications

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“…To evaluate this same utility in the phenotype similarity networks we generated using computational methods and to compare their utility to that of the network from Oellrich, Walls et al (2015) generated using hand-curated EQ statements, we first expanded the set of maize anthocyanin path-way genes to include those present in the description of the pathway given by Li et al, (2019), and listed in Supplementary Table 1 of that publication. Of those genes, 10 are present in the Oellrich, Walls et al (2015) dataset (Table 4).…”

Section: Resultsmentioning

confidence: 99%

Automated methods enable direct computation on phenotypic descriptions for novel candidate gene prediction

Braun

Lawrence‐Dill

2019

Preprint

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1 Abstract 1 Natural language descriptions of plant phenotypes are a rich source of information for genetics and 2 genomics research. We computationally translated descriptions of plant phenotypes into structured 3 representations that can be analyzed to identify biologically meaningful associations. These repre-4 sentations include the EQ (Entity-Quality) formalism, which uses terms from biological ontologies 5 to represent phenotypes in a standardized, semantically-rich format, as well as numerical vector 6 representations generated using Natural Language Processing (NLP) methods (such as the bag-of-7 words approach and document embedding). We compared resulting phenotype similarity measures 8 to those derived from manually curated data to determine the performance of each method. Com-9 putationally derived EQ and vector representations were comparably successful in recapitulating 10 biological truth to representations created through manual EQ statement curation. Moreover, NLP 11 methods for generating vector representations of phenotypes are scalable to large quantities of text 12 because they require no human input. These results indicate that it is now possible to compu-13 tationally and automatically produce and populate large-scale information resources that enable 14 researchers to query phenotypic descriptions directly. 15 2 Background 16 Phenotypes encompass a wealth of important and useful information about plants, potentially 17including states related to fitness, disease, and agricultural value. They comprise the material on 18 which natural and artificial selection act to increase fitness or to achieve desired traits, respectively. 19 Determining which genes are associated with traits of interest and understanding the nature of 20 these relationships is crucial for manipulating phenotypes. When causal alleles for phenotypes of 21 1 interest are identified, they can be selected for in populations, targeted for deletion, or employed 22 as transgenes to introduce desirable traits within and across species. The process of identifying 23 candidate genes and specific alleles associated with a trait of interest is called candidate gene 24 prediction. 25 Genes with similar sequences often share biological functions and therefore can create similar 26 phenotypes. This is one reason sequence similarity search algorithms like BLAST are so useful 27 for candidate gene prediction (Altschul et al., 1990). However, similar phenotypes can also be 28 attributed to the function of genes that have no sequence similarity. This is how protein-coding 29 genes that are involved in different steps of the same metabolic pathway or transcription factors 30 involved in regulating gene expression contribute to shared phenotypes. For example, knocking 31 out any one of the many genes involved in the maize anthocyanin pathway can result in pigment 32 changes (reviewed in Sharma et al., 2011). This concept is modelled in Figure 1, where, notably, 33 the sequence-based search with Gene 1 as a query can only return gen...

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Section: Resultsmentioning

confidence: 99%

Automated methods enable direct computation on phenotypic descriptions for novel candidate gene prediction

Braun

Lawrence‐Dill

2019

Preprint

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“…To evaluate this same utility in the phenotype similarity networks we generated using computational methods and to compare their utility to that of the network from Oellrich, Walls et al (2015) generated using hand-curated EQ statements, we first expanded the set of maize anthocyanin pathway genes to include those present in the description of the pathway given by Li et al (2019), and listed in Supplementary Table 1 of that publication. Of those genes, 10 are present in the Oellrich, Walls et al (2015) dataset (Table 4).…”

Section: Computational Methods Outperform Hand-curation For Recoverinmentioning

confidence: 99%

Automated Methods Enable Direct Computation on Phenotypic Descriptions for Novel Candidate Gene Prediction

Braun

Lawrence‐Dill

2020

Front. Plant Sci.

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Natural language descriptions of plant phenotypes are a rich source of information for genetics and genomics research. We computationally translated descriptions of plant phenotypes into structured representations that can be analyzed to identify biologically meaningful associations. These representations include the entity-quality (EQ) formalism, which uses terms from biological ontologies to represent phenotypes in a standardized, semantically rich format, as well as numerical vector representations generated using natural language processing (NLP) methods (such as the bag-of-words approach and document embedding). We compared resulting phenotype similarity measures to those derived from manually curated data to determine the performance of each method. Computationally derived EQ and vector representations were comparably successful in recapitulating biological truth to representations created through manual EQ statement curation. Moreover, NLP methods for generating vector representations of phenotypes are scalable to large quantities of text because they require no human input. These results indicate that it is now possible to computationally and automatically produce and populate large-scale information resources that enable researchers to query phenotypic descriptions directly.

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“…This allelic combination can activate mainly the synthesis of cyanidin-3-O-glucoside in the pericarp and some tissues of the maize plant. Whereas r1/c1 combination is required for the accumulation of pel-argonidin-3-O-glucoside in the aleurone layer [Cone, 2007;Sharma et al, 2011;Li et al, 2019].…”

Section: Biosynthesis Of Anthocyanins In Pigmented Maizementioning

confidence: 99%

Pigmented Maize (Zea mays L.) Contains Anthocyanins with Potential Therapeutic Action Against Oxidative Stress - A Review

Magaña-Cerino¹,

Peniche-Pavía²,

Tiessen³

et al. 2019

Pol. J. Food Nutr. Sci.

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Different maize (Zea mays L.) varieties have been used for thousands of years as a healthy food source in Mesoamerica including pigmented maize. Maize ingestion could contribute to the reduction in the rate of non-communicable diseases and, in turn, to its function as an adjuvant in their management. These diseases are mainly associated with oxidative stress, which is characterized by a redox cell imbalance produced due to pro-oxidant molecules accumulation, inducing irreversible damages. Although the endogenous antioxidant defense system is effi cient, exogenous antioxidants are necessary to help to prevent this damage. Bioactive compounds, like anthocyanins, contained in dietary plants exert a major activity against oxidative stress. Could the maize anthocyanins play a curative, preventive or complementary role in the treatment of chronic diseases? Here, we describe the occurrence of anthocyanins from pigmented maize and their chemical structures. Furthermore, the biosynthesis, bioavailability, and stability are also summarized. Finally, many in vitro and in vivo studies of maize anthocyanins are discussed that demonstrated their nutraceutical potential, antioxidant capacity, and other biological effects. Given the importance of the biological properties of maize anthocyanins, it is necessary to understand the current knowledge and propose further research or clinical studies which allows us to better elucidate the biological mechanism of maize anthocyanins derivatives of several varieties and processes of cooking and combination with other ingredients to enhance their nutritional and health benefi ts.

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Comparative transcriptome analysis reveals differentially expressed genes related to the tissue-specific accumulation of anthocyanins in pericarp and aleurone layer for maize

Cited by 34 publications

References 36 publications

Automated methods enable direct computation on phenotypic descriptions for novel candidate gene prediction

Automated methods enable direct computation on phenotypic descriptions for novel candidate gene prediction

Automated Methods Enable Direct Computation on Phenotypic Descriptions for Novel Candidate Gene Prediction

Pigmented Maize (Zea mays L.) Contains Anthocyanins with Potential Therapeutic Action Against Oxidative Stress - A Review

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