Christopher N. Topp

Repression of Flowering in Arabidopsis Requires Activation of FLOWERING LOCUS C Expression by the Histone Variant H2A.Z

The histone variant H2A.Z has been implicated in numerous chromatin-mediated processes, including transcriptional activation, euchromatin maintenance, and heterochromatin formation. In yeast and humans, H2A.Z is deposited into chromatin by a conserved protein complex known as SWR1 and SRCAP, respectively. Here, we show that mutations in the Arabidopsis thaliana homologs of two components of this complex, ACTIN-RELATED PROTEIN6 (ARP6) and PHOTOPERIOD-INDEPENDENT EARLY FLOWERING1 (PIE1), produce similar developmental phenotypes and result in the misregulation of a common set of genes. Using H2A.Z-specific antibodies, we demonstrate that ARP6 and PIE1 are required for the deposition of H2A.Z at multiple loci, including the FLOWERING LOCUS C (FLC) gene, a central repressor of the transition to flowering. Loss of H2A.Z from chromatin in arp6 and pie1 mutants results in reduced FLC expression and premature flowering, indicating that this histone variant is required for high-level expression of FLC. In addition to defining a novel mechanism for the regulation of FLC expression, these results support the existence of a SWR1-like complex in Arabidopsis and show that H2A.Z can potentiate transcriptional activation in plants. The finding that H2A.Z remains associated with chromatin throughout mitosis suggests that it may serve an epigenetic memory function by marking active genes and poising silenced genes for reactivation.

3D phenotyping and quantitative trait locus mapping identify core regions of the rice genome controlling root architecture

Significance Improving the efficiency of root systems should result in crop varieties with better yields, requiring fewer chemical inputs, and that can grow in harsher environments. Little is known about the genetic factors that condition root growth because of roots’ complex shapes, the opacity of soil, and environmental influences. We designed a 3D root imaging and analysis platform and used it to identify regions of the rice genome that control several different aspects of root system growth. The results of this study should inform future efforts to enhance root architecture for agricultural benefit. Identification of genes that control root system architecture in crop plants requires innovations that enable high-throughput and accurate measurements of root system architecture through time. We demonstrate the ability of a semiautomated 3D in vivo imaging and digital phenotyping pipeline to interrogate the quantitative genetic basis of root system growth in a rice biparental mapping population, Bala × Azucena. We phenotyped >1,400 3D root models and >57,000 2D images for a suite of 25 traits that quantified the distribution, shape, extent of exploration, and the intrinsic size of root networks at days 12, 14, and 16 of growth in a gellan gum medium. From these data we identified 89 quantitative trait loci, some of which correspond to those found previously in soil-grown plants, and provide evidence for genetic tradeoffs in root growth allocations, such as between the extent and thoroughness of exploration. We also developed a multivariate method for generating and mapping central root architecture phenotypes and used it to identify five major quantitative trait loci (r2 = 24–37%), two of which were not identified by our univariate analysis. Our imaging and analytical platform provides a means to identify genes with high potential for improving root traits and agronomic qualities of crops.

GiA Roots: software for the high throughput analysis of plant root system architecture

BackgroundCharacterizing root system architecture (RSA) is essential to understanding the development and function of vascular plants. Identifying RSA-associated genes also represents an underexplored opportunity for crop improvement. Software tools are needed to accelerate the pace at which quantitative traits of RSA are estimated from images of root networks.ResultsWe have developed GiA Roots (General Image Analysis of Roots), a semi-automated software tool designed specifically for the high-throughput analysis of root system images. GiA Roots includes user-assisted algorithms to distinguish root from background and a fully automated pipeline that extracts dozens of root system phenotypes. Quantitative information on each phenotype, along with intermediate steps for full reproducibility, is returned to the end-user for downstream analysis. GiA Roots has a GUI front end and a command-line interface for interweaving the software into large-scale workflows. GiA Roots can also be extended to estimate novel phenotypes specified by the end-user.ConclusionsWe demonstrate the use of GiA Roots on a set of 2393 images of rice roots representing 12 genotypes from the species Oryza sativa. We validate trait measurements against prior analyses of this image set that demonstrated that RSA traits are likely heritable and associated with genotypic differences. Moreover, we demonstrate that GiA Roots is extensible and an end-user can add functionality so that GiA Roots can estimate novel RSA traits. In summary, we show that the software can function as an efficient tool as part of a workflow to move from large numbers of root images to downstream analysis.

DNA Binding of Centromere Protein C (CENPC) Is Stabilized by Single-Stranded RNA

Centromeres are the attachment points between the genome and the cytoskeleton: centromeres bind to kinetochores, which in turn bind to spindles and move chromosomes. Paradoxically, the DNA sequence of centromeres has little or no role in perpetuating kinetochores. As such they are striking examples of genetic information being transmitted in a manner that is independent of DNA sequence (epigenetically). It has been found that RNA transcribed from centromeres remains bound within the kinetochore region, and this local population of RNA is thought to be part of the epigenetic marking system. Here we carried out a genetic and biochemical study of maize CENPC, a key inner kinetochore protein. We show that DNA binding is conferred by a localized region 122 amino acids long, and that the DNA-binding reaction is exquisitely sensitive to single-stranded RNA. Long, single-stranded nucleic acids strongly promote the binding of CENPC to DNA, and the types of RNAs that stabilize DNA binding match in size and character the RNAs present on kinetochores in vivo. Removal or replacement of the binding module with HIV integrase binding domain causes a partial delocalization of CENPC in vivo. The data suggest that centromeric RNA helps to recruit CENPC to the inner kinetochore by altering its DNA binding characteristics.

Identification of a Maize Neocentromere in an Oat-Maize Addition Line

We report a neocentromere event on maize chromosome 3 that occurred due to chromosome breakage. The neocentromere lies on a fragment of the short arm that lacks the primary centromere DNA elements, CentC and CRM. It is transmitted in the genomic background of oat via a new centromere (and kinetochore), as shown by immunolocalization of the oat CENH3 protein. Despite normal transmission of the maize fragment in most progeny, neocentromeres appear to vary in size within the same tissue, as shown by fluorescent measurements. A secondary truncation in one line lowered mitotic transmission to 3% and precipitously reduced the size of the chromosome. The results support the view that neocentromere formation is generally associated with major genomic disturbances such as wide species crosses or deletion of an existing centromere. The data further suggest that new centromeres may undergo a period of instability that is corrected over a period of several generations.

Transformation of rice with long DNA-segments consisting of random genomic DNA or centromere-specific DNA

Rice was transformed with either long DNA-segments of random genomic DNA from rice, or centromere-specific DNA sequences from either maize or rice. Despite the repetitive nature of the transgenic DNA sequences, the centromere-specific sequences were inserted largely intact and behave as simple Mendelian units. Between 4 and 5% of bombarded callus clusters were transformed when bombarded with just pCAMBIA 1305.2. Frequency of recovery dropped to 2–3% when BACs with random genomic inserts were co-bombarded with pCAMBIA, and fell to less than 1% when BACs with centromeric DNA inserts and pCAMBIA were co-bombarded. A similar effect was noted on regeneration frequency. Differences in transformation ability, regeneration and behavior of plants transgenic for BACs with random genomic DNA inserts, as compared to those with centromeric DNA inserts, suggests functional differences between these two types of DNA.

The quest for understanding phenotypic variation via integrated approaches in the field environment

Field-based, high-throughput phenotyping enables the detailed characterization of plant populations under relevant conditions, providing valuable biological insight into the life history of plants.

Distinct influences of tandem repeats and retrotransposons on CENH3 nucleosome positioning

BackgroundUnique structural characteristics of centromere chromatin enable it to support assembly of the kinetochore and its associated tensions. The histone H3 variant CENH3 (centromeric histone H3) is viewed as the key element of centromere chromatin and its interaction with centromere DNA is epigenetic in that its localization to centromeres is not sequence-dependent.ResultsIn order to investigate what influence the DNA sequence exerts on CENH3 chromatin structure, we examined CENH3 nucleosome footprints on maize centromere DNA. We found a predominant average nucleosome spacing pattern of roughly 190-bp intervals, which was also the dominant arrangement for nucleosomes genome-wide. For CENH3-containing nucleosomes, distinct modes of nucleosome positioning were evident within that general spacing constraint. Over arrays of the major ~156-bp centromeric satellite sequence (tandem repeat) CentC, nucleosomes were not positioned in register with CentC monomers but in conformity with a striking ~10-bp periodicity of AA/TT dimers within the sequence. In contrast, nucleosomes on a class of centromeric retrotransposon (CRM2) lacked a detectable AA/TT periodicity but exhibited tightly phased positioning.ConclusionsThese data support a model in which general chromatin factors independent of both DNA sequence and CENH3 enforce roughly uniform centromeric nucleosome spacing while allowing flexibility in the mode in which nucleosomes are positioned. In the case of tandem repeat DNA, the natural bending effects related to AA/TT periodicity produce an energetically-favourable arrangement consistent with conformationally rigid nucleosomes and stable chromatin at centromeres.

Quantitative Trait Locus Mapping Reveals Regions of the Maize Genome Controlling Root System Architecture

Phenotypic variation of maize root system architecture highlights a genetic trade-off between small compact and large, exploratory root systems. The quest to determine the genetic basis of root system architecture (RSA) has been greatly facilitated by recent developments in root phenotyping techniques. Methods that are accurate, high throughput, and control for environmental factors are especially attractive for quantitative trait locus mapping. Here, we describe the adaptation of a nondestructive in vivo gel-based root imaging platform for use in maize (Zea mays). We identify a large number of contrasting RSA traits among 25 founder lines of the maize nested association mapping population and locate 102 quantitative trait loci using the B73 (compact RSA) × Ki3 (exploratory RSA) mapping population. Our results suggest that a phenotypic tradeoff exists between small, compact RSA and large, exploratory RSA.

Revealing plant cryptotypes: defining meaningful phenotypes among infinite traits

The plant phenotype is infinite. Plants vary morphologically and molecularly over developmental time, in response to the environment, and genetically. Exhaustive phenotyping remains not only out of reach, but is also the limiting factor to interpreting the wealth of genetic information currently available. Although phenotyping methods are always improving, an impasse remains: even if we could measure the entirety of phenotype, how would we interpret it? We propose the concept of cryptotype to describe latent, multivariate phenotypes that maximize the separation of a priori classes. Whether the infinite points comprising a leaf outline or shape descriptors defining root architecture, statistical methods to discern the quantitative essence of an organism will be required as we approach measuring the totality of phenotype.