Interplay of phytohormones and epigenetic regulation: A recipe for plant development and plasticity

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Both phytohormone signaling and epigenetic mechanisms have long been known to play crucial roles in plant development and plasticity in response to ambient stimuli. Indeed, diverse signaling pathways mediated by phytohormones and epigenetic processes integrate multiple upstream signals to regulate various plant traits. Emerging evidence indicates that phytohormones and epigenetic processes interact at multiple levels. In this review, we summarize the current knowledge of the interplay between phytohormones and epigenetic processes from the perspective of phytohormone biology. We also review chemical regulators used in epigenetic studies and propose strategies for developing novel regulators using multidisciplinary approaches.

Related collections

Most cited references 222

Record: found
Abstract: found
Article: found

Is Open Access

Highly accurate protein structure prediction with AlphaFold

John Jumper, Richard Evans, Alexander Pritzel … (2021)

Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.

0 comments Cited 8583 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors.

Kazutoshi Takahashi, Shinya Yamanaka (2006)

Differentiated cells can be reprogrammed to an embryonic-like state by transfer of nuclear contents into oocytes or by fusion with embryonic stem (ES) cells. Little is known about factors that induce this reprogramming. Here, we demonstrate induction of pluripotent stem cells from mouse embryonic or adult fibroblasts by introducing four factors, Oct3/4, Sox2, c-Myc, and Klf4, under ES cell culture conditions. Unexpectedly, Nanog was dispensable. These cells, which we designated iPS (induced pluripotent stem) cells, exhibit the morphology and growth properties of ES cells and express ES cell marker genes. Subcutaneous transplantation of iPS cells into nude mice resulted in tumors containing a variety of tissues from all three germ layers. Following injection into blastocysts, iPS cells contributed to mouse embryonic development. These data demonstrate that pluripotent stem cells can be directly generated from fibroblast cultures by the addition of only a few defined factors.

0 comments Cited 2656 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Induction of pluripotent stem cells from adult human fibroblasts by defined factors.

Kazutoshi Takahashi, Koji TANABE, Mari Ohnuki … (2007)

Successful reprogramming of differentiated human somatic cells into a pluripotent state would allow creation of patient- and disease-specific stem cells. We previously reported generation of induced pluripotent stem (iPS) cells, capable of germline transmission, from mouse somatic cells by transduction of four defined transcription factors. Here, we demonstrate the generation of iPS cells from adult human dermal fibroblasts with the same four factors: Oct3/4, Sox2, Klf4, and c-Myc. Human iPS cells were similar to human embryonic stem (ES) cells in morphology, proliferation, surface antigens, gene expression, epigenetic status of pluripotent cell-specific genes, and telomerase activity. Furthermore, these cells could differentiate into cell types of the three germ layers in vitro and in teratomas. These findings demonstrate that iPS cells can be generated from adult human fibroblasts.

0 comments Cited 2076 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Journal

Title: Journal of Integrative Plant Biology

Abbreviated Title: JIPB

Publisher: Wiley

ISSN (Print): 1672-9072

ISSN (Electronic): 1744-7909

Publication date Created: February 2023

Publication date (Electronic): December 31 2022

Publication date (Print): February 2023

Volume: 65

Issue: 2

Pages: 381-398

Affiliations

[1 ]Institute of Plant and Food Science, Key Laboratory of Molecular Design for Plant Cell Factory of Guangdong Higher Education Institutes, Department of Biology, School of Life Sciences Southern University of Science and Technology (SUSTech) Shenzhen 518055 China