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1.
Int J Mol Sci ; 25(15)2024 Jul 25.
Artículo en Inglés | MEDLINE | ID: mdl-39125685

RESUMEN

Transcription factors (TFs) are crucial pre-transcriptional regulatory mechanisms that can modulate the expression of downstream genes by binding to their promoter regions. DOF (DNA binding with One Finger) proteins are a unique class of TFs with extensive roles in plant growth and development. Our previous research indicated that iron content varies among bamboo leaves of different colors. However, to our knowledge, genes related to iron metabolism pathways in bamboo species have not yet been studied. Therefore, in the current study, we identified iron metabolism related (IMR) genes in bamboo and determined the TFs that significantly influence them. Among these, DOFs were found to have widespread effects and potentially significant impacts on their expression. We identified specific DOF members in Dendrocalamus latiflorus with binding abilities through homology with Arabidopsis DOF proteins, and established connections between some of these members and IMR genes using RNA-seq data. Additionally, molecular docking confirmed the binding interactions between these DlDOFs and the DOF binding sites in the promoter regions of IMR genes. The co-expression relationship between the two gene sets was further validated using q-PCR experiments. This study paves the way for research into iron metabolism pathways in bamboo and lays the foundation for understanding the role of DOF TFs in D. latiflorus.


Asunto(s)
Regulación de la Expresión Génica de las Plantas , Hierro , Hojas de la Planta , Proteínas de Plantas , Factores de Transcripción , Hojas de la Planta/metabolismo , Hojas de la Planta/genética , Hierro/metabolismo , Factores de Transcripción/metabolismo , Factores de Transcripción/genética , Proteínas de Plantas/genética , Proteínas de Plantas/metabolismo , Regiones Promotoras Genéticas , Simulación del Acoplamiento Molecular , Poaceae/genética , Poaceae/metabolismo
2.
Curr Issues Mol Biol ; 46(2): 1503-1515, 2024 Feb 14.
Artículo en Inglés | MEDLINE | ID: mdl-38392215

RESUMEN

The diversity of leaf characteristics, particularly leaf color, underscores a pivotal area of inquiry within plant science. The synthesis and functionality of chlorophyll, crucial for photosynthesis, largely dictate leaf coloration, with varying concentrations imparting different shades of green. Complex gene interactions regulate the synthesis and degradation of chlorophyll, and disruptions in these pathways can result in abnormal chlorophyll production, thereby affecting leaf pigmentation. This study focuses on Bambusa multiplex f. silverstripe, a natural variant distinguished by a spectrum of leaf colors, such as green, white, and green-white, attributed to genetic variations influencing gene expression. By examining the physiological and molecular mechanisms underlying chlorophyll anomalies and genetic factors in Silverstripe, this research sheds light on the intricate gene interactions and regulatory networks that contribute to leaf color diversity. The investigation includes the measurement of photosynthetic pigments and nutrient concentrations across different leaf color types, alongside transcriptomic analyses for identifying differentially expressed genes. The role of key genes in pathways such as ALA biosynthesis, chlorophyll synthesis, photosynthesis, and sugar metabolism is explored, offering critical insights for advancing research and plant breeding practices.

3.
Front Plant Sci ; 14: 1309744, 2023.
Artículo en Inglés | MEDLINE | ID: mdl-38146270
4.
Front Plant Sci ; 14: 1260856, 2023.
Artículo en Inglés | MEDLINE | ID: mdl-37908839

RESUMEN

Cupin_1 domain-containing protein (CDP) family, which is a member of the cupin superfamily with the most diverse functions in plants, has been found to be involved in hormone pathways that are closely related to rhizome sprouting (RS), a vital form of asexual reproduction in plants. Ma bamboo is a typical clumping bamboo, which mainly reproduces by RS. In this study, we identified and characterized 53 Dendrocalamus latiflorus CDP genes and divided them into seven subfamilies. Comparing the genetic structures among subfamilies showed a relatively conserved gene structure within each subfamily, and the number of cupin_1 domains affected the conservation among D. latiflorus CDP genes. Gene collinearity results showed that segmental duplication and tandem duplication both contributed to the expansion of D. latiflorus CDP genes, and lineage-specific gene duplication was an important factor influencing the evolution of CDP genes. Expression patterns showed that CDP genes generally had higher expression levels in germinating underground buds, indicating that they might play important roles in promoting shoot sprouting. Transcription factor binding site prediction and co-expression network analysis indicated that D. latiflorus CDPs were regulated by a large number of transcription factors, and collectively participated in rhizome buds and shoot development. This study significantly provided new insights into the evolutionary patterns and molecular functions of CDP genes, and laid a foundation for further studying the regulatory mechanisms of plant rhizome sprouting.

5.
Artículo en Inglés | MEDLINE | ID: mdl-36215367

RESUMEN

We propose Recognition as Part Composition (RPC), an image encoding approach inspired by human cognition. It is based on the cognitive theory that humans recognize complex objects by components, and that they build a small compact vocabulary of concepts to represent each instance with. RPC encodes images by first decomposing them into salient parts, and then encoding each part as a mixture of a small number of prototypes, each representing a certain concept. We find that this type of learning inspired by human cognition can overcome hurdles faced by deep convolutional networks in low-shot generalization tasks, like zero-shot learning, few-shot learning and unsupervised domain adaptation. Furthermore, we find a classifier using an RPC image encoder is fairly robust to adversarial attacks, that deep neural networks are known to be prone to. Given that our image encoding principle is based on human cognition, one would expect the encodings to be interpretable by humans, which we find to be the case via crowd-sourcing experiments. Finally, we propose an application of these interpretable encodings in the form of generating synthetic attribute annotations for evaluating zero-shot learning methods on new datasets.

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