Arabidopsis RETINOBLASTOMA RELATED directly regulates DNA damage responses through functions beyond cell cycle control

The rapidly proliferating cells in plant meristems must be protected from genome damage. Here, we show that the regulatory role of the Arabidopsis RETINOBLASTOMA RELATED (RBR) in cell proliferation can be separated from a novel function in safeguarding genome integrity. Upon DNA damage, RBR and its binding partner E2FA are recruited to heterochromatic γH2AX-labelled DNA damage foci in an ATM- and ATR-dependent manner. These γH2AX-labelled DNA lesions are more dispersedly occupied by the conserved repair protein, AtBRCA1, which can also co-localise with RBR foci. RBR and AtBRCA1 physically interact in vitro and in planta. Genetic interaction between the RBR-silenced amiRBR and Atbrca1 mutants suggests that RBR and AtBRCA1 may function together in maintaining genome integrity. Together with E2FA, RBR is directly involved in the transcriptional DNA damage response as well as in the cell death pathway that is independent of SOG1, the plant functional analogue of p53. Thus, plant homologs and analogues of major mammalian tumour suppressor proteins form a regulatory network that coordinates cell proliferation with cell and genome integrity

Probing the roles of LRR RLK genes in Arabidopsis thaliana roots using a custom T-DNA insertion set

Leucine-rich repeat receptor-like protein kinases (LRR RLKs) represent the largest group of Arabidopsis RLKs with approximately 235 members. A minority of these LRR RLKs have been assigned to diverse roles in development, pathogen resistance and hormone perception. Using a reverse genetics approach, a collection of homozygous T-DNA insertion lines for 69 root expressed LRR RLK genes was screened for root developmental defects and altered response after exposure to environmental, hormonal/chemical and abiotic stress. The obtained data demonstrate that LRR RLKs play a role in a wide variety of signal transduction pathways related to hormone and abiotic stress responses. The described collection of T-DNA insertion mutants provides a valuable tool for future research into the function of LRR RLK genes

Similarities between plant traits based on their connection to underlying gene functions

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Understanding of phenotypes and their genetic basis is a major focus in current plant biology. Large amounts of phenotype data are being generated, both for macroscopic phenotypes such as size or yield, and for molecular phenotypes such as expression levels and metabolite levels. More insight in the underlying genetic and molecular mechanisms that influence phenotypes will enable a better understanding of how various phenotypes are related to each other. This will be a major step forward in understanding plant biology, with immediate value for plant breeding and academic plant research. Currently the genetic basis of most phenotypes remains however to be discovered, and the relatedness of different traits is unclear. We here present a novel approach to connect phenotypes to underlying biological processes and molecular functions. These connections define similarities between different types of phenotypes. The approach starts by using Quantitative Trait Locus (QTL) data, which are abundantly available for many phenotypes of interest. Overrepresentation analysis of gene functions based on Gene Ontology term enrichment across multiple QTL regions for a given phenotype, be it macroscopic or molecular, results in a small set of biological processes and molecular functions for each phenotype. Subsequently, similarity between different phenotypes can be defined in terms of these gene functions. Using publicly available rice data as example, a close relationship with defined molecular phenotypes is demonstrated for many macroscopic phenotypes. This includes for example a link between ‘leaf senescence’ and ‘aspartic acid’, as well as between ‘days to maturity’ and ‘choline’. Relationships between macroscopic and molecular phenotypes may result in more efficient marker-assisted breeding and are likely to direct future research aimed at a better understanding of plant phenotypes

Similarities between plant traits based on their connection to underlying gene functions

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Understanding of phenotypes and their genetic basis is a major focus in current plant biology. Large amounts of phenotype data are being generated, both for macroscopic phenotypes such as size or yield, and for molecular phenotypes such as expression levels and metabolite levels. More insight in the underlying genetic and molecular mechanisms that influence phenotypes will enable a better understanding of how various phenotypes are related to each other. This will be a major step forward in understanding plant biology, with immediate value for plant breeding and academic plant research. Currently the genetic basis of most phenotypes remains however to be discovered, and the relatedness of different traits is unclear. We here present a novel approach to connect phenotypes to underlying biological processes and molecular functions. These connections define similarities between different types of phenotypes. The approach starts by using Quantitative Trait Locus (QTL) data, which are abundantly available for many phenotypes of interest. Overrepresentation analysis of gene functions based on Gene Ontology term enrichment across multiple QTL regions for a given phenotype, be it macroscopic or molecular, results in a small set of biological processes and molecular functions for each phenotype. Subsequently, similarity between different phenotypes can be defined in terms of these gene functions. Using publicly available rice data as example, a close relationship with defined molecular phenotypes is demonstrated for many macroscopic phenotypes. This includes for example a link between ‘leaf senescence’ and ‘aspartic acid’, as well as between ‘days to maturity’ and ‘choline’. Relationships between macroscopic and molecular phenotypes may result in more efficient marker-assisted breeding and are likely to direct future research aimed at a better understanding of plant phenotypes

Connections with different biological process terms for three macroscopic traits (red) and two expression traits (green).

<p>Macroscopic traits included are ‘root volume’ (red triangles), ‘days to heading’ (red circles) and ‘days to maturity’ (red star). Expression traits are LOC_Os01g10504 (green circles) and LOC_Os04g30210 (green triangles). At each symbol, the biological process (BP) term connected to that trait is given. REVIGO [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0182097#pone.0182097.ref044" target="_blank">44</a>] was applied to obtain a plot in which the distance between symbols indicates dissimilarity between BP terms. In this plot, the x- and y-axis do not have a direct meaning, but for each point, the x- and y-coordinates are chosen so that terms with a high semantic similarity are close to each other in the plot.</p