Rapid Identification of a Natural Knockout Allele of ARMADILLO REPEAT-CONTAINING KINESIN1 That Causes Root Hair Branching by Mapping-By-Sequencing

In Arabidopsis (Arabidopsis thaliana), branched root hairs are an indicator of defects in root hair tip growth. Among 62 accessions, one accession (Heiligkreuztal2 [HKT2.4]) displayed branched root hairs, suggesting that this accession carries a mutation in a gene of importance for tip growth. We determined 200- to 300-kb mapping intervals using a mapping-by-sequencing approach of F2 pools from crossings of HKT2.4 with three different accessions. The intersection of these mapping intervals was 80 kb in size featuring not more than 36 HKT2.4-specific single nucleotide polymorphisms, only two of which changed the coding potential of genes. Among them, we identified the causative single nucleotide polymorphism changing a splicing site in ARMADILLO REPEAT-CONTAINING KINESIN1. The applied strategies have the potential to complement statistical methods in high-throughput phenotyping studies using different natural accessions to identify causative genes for distinct phenotypes represented by only one or a few accessions.Root hairs are tubular tip outgrowths of single root epidermal cells (trichoblasts). They are an excellent genetic system and serve as a model to study the molecular components regulating tip growth (Carol and Dolan, 2002; Samaj et al., 2004; Lee and Yang, 2008). One of the main regulators of tip growth in root hairs is the small G protein RHO OF PLANTS2 (ROP2; Jones et al., 2002; Payne and Grierson, 2009). ROP2 determines the position of root hairs in incipient epidermal root hair cells and remains localized in the emerging tip during root hair tip growth (Molendijk et al., 2001; Jones et al., 2002). In addition, other factors have been identified to be important for growth and its directionality including phosphoinositides, cytoplasmic [Ca²⁺] gradients and their oscillation, reactive oxygen species, the RAB GTPase homolog A4B, and the cytoskeleton (Foreman et al., 2003; Preuss et al., 2004, 2006; Carol et al., 2005; Thole et al., 2008; Heilmann, 2009).Defects in essential processes for the establishment and maintenance of tip growth lead to deviations in root hair morphology such as branching and waviness (Samaj et al., 2004; Lee and Yang, 2008). Both the microtubules (MTs) and actin are important regulators of tip growth with MTs maintaining one growth point (Bibikova et al., 1999; Miller et al., 1999; Baluska et al., 2000). The latter is evident from the finding that artificially induced [Ca²⁺] gradients can induce additional growth tips when MTs are destroyed by drug treatments (Bibikova et al., 1999).Although the genetic and molecular analysis revealed a well-understood working model for root hair growth, little is known about the natural variation of the underlying processes. Which are the adaptive processes of relevance to specific environmental cues and which have already been selected for in natural accessions? One way to address this question is to link genotype and phenotype by association mapping using various Arabidopsis (Arabidopsis thaliana) accessions. This is greatly facilitated by the 1001 Genomes Project (http://1001genomes.org), providing an increasing number of sequenced accessions. In some cases, phenotypes are only found in one or a few accessions. When the minor allele frequency (AF) is low, the identification of such rare causative alleles with genome-wide association mapping studies is challenging because they cannot be discriminated from false positives (e.g. sequencing errors or synthetic associations; Korte and Farlow, 2013), they are not detectable because of chosen thresholds, or they do not support a statistically significant value (Cantor et al., 2010). Other time-consuming approaches with low mapping resolution, such as quantitative trait loci mapping, need to be followed to identify the causative gene. For this, mapping-by-sequencing, which was originally developed for the identification of mutagen-induced changes in model species (Schneeberger et al., 2009b), can help to rapidly identify causal polymorphisms including nonmodel and nonreference strains (Nordström et al., 2013; Takagi et al., 2013). The resolution of mapping-by-sequencing experiments in Arabidopsis mapping populations is typically between multiple hundreds of kilobase pairs up to a few megabase pairs (James et al., 2013). Although intervals of this size allow the identification of causal mutations in forward genetic screens, they are problematic for the analysis of diverse Arabidopsis accessions because the single nucleotide polymorphism (SNP) density is very high; consequently, hundreds of polymorphisms have to be considered.Here we report on a modification of the mapping-by-sequencing strategy providing a shortcut from distinct, monogenic accession-specific phenotypes to the causative SNP. When studying root hair morphology in 62 accessions for which the genome sequences were released by the 1001 Genomes Project (Cao et al., 2011), we found one accession (Heiligkreuztal2 [HKT2.4]) in which almost all root hairs were branched. To identify the causative gene, we used an approach based on mapping-by-sequencing. Instead of one outcross, we used outcrosses with three different accessions. We selected F2 seedlings exhibiting the distinct, monogenic, recessive root hair branching phenotype for sequencing. Combining the intersection of the three resulting mapping intervals with a selection for accession-specific SNPs revealed two primary candidate genes responsible for the root phenotype. We demonstrate that the causative SNP renders a splicing site in ARMADILLO REPEAT-CONTAINING KINESIN1 (ARK1) inactive and therefore leads to a defective ARK1/MORPHOGENESIS OF ROOT HAIR2 (MRH2) protein that is thought to coordinate actin microfilaments and MTs during tip growth of root hairs (Yang et al., 2007; Yoo and Blancaflor, 2013).     

Quantitative trait loci controlling leaf venation in Arabidopsis

Leaf veins provide the mechanical support and are responsible for the transport of nutrients and water to the plant. High vein density is a prerequisite for plants to have C4 photosynthesis. We investigated the genetic variation and genetic architecture of leaf venation traits within the species Arabidopsis thaliana using natural variation. Leaf venation traits, including leaf vein density (LVD) were analysed in 66 worldwide accessions and 399 lines of the multi‐parent advanced generation intercross population. It was shown that there is no correlation between LVD and photosynthesis parameters within A. thaliana. Association mapping was performed for LVD and identified 16 and 17 putative quantitative trait loci (QTLs) in the multi‐parent advanced generation intercross and worldwide sets, respectively. There was no overlap between the identified QTLs suggesting that many genes can affect the traits. In addition, linkage mapping was performed using two biparental recombinant inbred line populations. Combining linkage and association mapping revealed seven candidate genes. For one of the candidate genes, RCI2c, we demonstrated its function in leaf venation patterning.     

phenoVein—A Tool for Leaf Vein Segmentation and Analysis

Precise measurements of leaf vein traits are an important aspect of plant phenotyping for ecological and genetic research. Here, we present a powerful and user-friendly image analysis tool named phenoVein. It is dedicated to automated segmenting and analyzing of leaf veins in images acquired with different imaging modalities (microscope, macrophotography, etc.), including options for comfortable manual correction. Advanced image filtering emphasizes veins from the background and compensates for local brightness inhomogeneities. The most important traits being calculated are total vein length, vein density, piecewise vein lengths and widths, areole area, and skeleton graph statistics, like the number of branching or ending points. For the determination of vein widths, a model-based vein edge estimation approach has been implemented. Validation was performed for the measurement of vein length, vein width, and vein density of Arabidopsis (Arabidopsis thaliana), proving the reliability of phenoVein. We demonstrate the power of phenoVein on a set of previously described vein structure mutants of Arabidopsis (hemivenata, ondulata3, and asymmetric leaves2-101) compared with wild-type accessions Columbia-0 and Landsberg erecta-0. phenoVein is freely available as open-source software.Leaf veins are an important aspect of leaf structure and responsible for both the mechanical support of leaves and the long-distance transport of water, nutrients, and photoassimilates (Onoda et al., 2011; Malinowski, 2013). The molecular mechanisms by which vascular tissues acquire their identities are yet largely unknown (Roschzttardtz et al., 2014), and there is high interest in analyzing and evaluating traits of veins or leaf venation networks and their genetic regulation. The impact of vein density on photosynthesis is a major investigated topic (Sack and Scoffoni, 2013). During the last decade, a positive correlation between leaf venation and photosynthesis has been observed (Sack and Holbrook, 2006; Brodribb et al., 2007). An optimization of photosynthetic rates was shown to occur by spatial coordination between leaf vein and stomatal densities (Zhang et al., 2012; Carins Murphy et al., 2014; Fiorin et al., 2015). Additionally, there is interest in the impact of vein density on interveinal distances (Dengler et al., 1994; McKown and Dengler, 2009) and the effect of climate, habitat, or growth form on vein density (Sack and Scoffoni, 2013; Scoffoni et al., 2015) or vein width with respect to leaf hydraulic conductance (Feild and Brodribb, 2013; Xiong et al., 2015). Other researchers are particularly interested in the evolution from C3 to C4 plants, which requires higher vein density (Gowik and Westhoff, 2011) and led to selecting for variation of vein density within species (e.g. in a mutant collection by Feldman et al., 2014).Leaf venation studies analyzing traits of veins and venation networks are generally performed on microscopic images of leaves that are properly cleared after harvest. For very small leaves, e.g. the cotyledons or the first leaves (leaves 2–5) of Arabidopsis (Arabidopsis thaliana), basic traits, such as total vein length or vein density (vein length per leaf area), can be achieved manually. However, for larger leaves, manual vein segmentation may become tedious, and at least partially automated analysis is needed for studies on large series of leaf collections. Furthermore, the quantification of vein widths and in particular mean values of vein width of certain vein pieces of interest can hardly be achieved manually. Dedicated image processing tools are, therefore, needed to support researchers for fast and reliable data analysis.A number of software tools have been published that are either specifically made or adapted to analyze leaf veins. These programs have some common properties, like image processing functionalities for vein/areole segmentation and trait extraction. However, they differ in handling strategies or vein parameter analysis methods. A general overview on plant image analysis tools is collected in an online database at http://www.plant-image-analysis.org (Lobet et al., 2013). Programs allowing automated or semiautomated analysis of leaf venation parameters are, for example, a method to extract leaf venation patterns (Rolland-Lagan et al., 2009), the leaf extraction and analysis framework graphical user interface LeafGUI (Price et al., 2011), the leaf image analysis interface LIMANI (Dhondt et al., 2012), the user-interactive vessel generation analysis tool VESGEN (Vickerman et al., 2009; Parsons-Wingerter et al., 2014), and the software network extraction from images NEFI (Dirnberger et al., 2015). Nevertheless, for the analysis of large-scale leaf vein phenotyping experiments, there are certain needs that are only partly covered by each of the approaches and programs mentioned above. Specifically, the following properties are needed: (1) automated vein segmentation with optional manual correction; (2) invariance of the segmentation procedure to inhomogeneous illumination or brightness variations in the leaf image; (3) automated determination of total vein length and projected leaf area; (4) a well-defined and automated determination of vein widths, which is, as far as possible, independent of user chosen thresholds; (5) ability to process large high-resolution images of whole leafs; and (6) full transparency of the source code as well as offline availability of the tool. To provide these functionalities, we developed the user-friendly analysis tool phenoVein. It features automated leaf vein segmentation based on advanced image filtering techniques and includes determination of various vein traits, particularly a model-based vein width estimation. phenoVein allows easy and fast visual control and manual correction on the automatically achieved skeleton of the veins enabled by a real-time overlay of the segmented leaf vein structures on the original image. The length measurement algorithm of phenoVein was validated against complete manual segmentation. We evaluated the impact of image resolution on the results, which has recently been discussed (Price et al., 2014; Sack et al., 2014), and tested whether the orientation (angle) of a leaf on an image may affect the results as suspected from image analysis theory on binary skeleton length measurements (Russ, 2011). To show the powerful phenotyping capabilities of phenoVein, we analyzed the venation traits of leaves of Arabidopsis at different developmental stages (cotyledons, pooled leaves 1 + 2, and leaf 6) harvested from previously described venation mutants and corresponding wild-type lines: asymmetric leaves2-101 (as2-101), ondulata3 (ond3), and hemivenata2 (hve-2) versus Columbia-0 (Col-0) and Landsberg erecta-0 (Ler-0; Semiarti et al., 2001; Alonso-Peral et al., 2006; Robles et al., 2010; Pérez-Pérez et al., 2011). We offer the source code of phenoVein to the public as open-source software that can be further adapted or improved (for details, see “Materials and Methods”).     

Identification and characterization of mutants capable of rapid seed germination at 10 degrees C from activation-tagged lines of Arabidopsis thaliana

Five cold temperature germinating (ctg) mutants, completing germination at 10 degrees C faster than wild type, have been recovered from activation-tagged populations of Arabidopsis thaliana. Three (ctg10-D, 41-D, and 144-D) were tagged and segregated 3:1 for BASTA resistance in the F2 when crossed with wild type. None of the tagged ctg mutants was disturbed in sensitivity to abscisic acid or glucose but all were less sensitive to GA4+7 and osmoticum. The other two mutants (ctg156 and ctg225) were recessive, BASTA sensitive, and exhibited a transparent testa (tt) phenotype. They were more sensitive to abscisic acid, paclobutrazol, and GA4+7 than wild type but had similar sensitivity to osmoticum. Dimethylaminocinnamaldehyde staining of seeds from the two tt mutants, compared with stained seeds from the publicly available tt lines 1-10, suggested that ctg156 was a new allele of tt1, while ctg225 was similar to tt7-1. However, reciprocal crosses determined that ctg156 was not allelic to tt1 while ctg225 was a new allele of tt7. When the gene was sequenced from ctg225 it was missing 10 bp in the second exon, resulting in the incorporation of two spurious amino acids (G282E and D283A) followed by a stop. The screen successfully recovered mutants completing germination faster than wild type at 10 degrees C.     

Pooled‐matrix protein interaction screens using Barcode Fusion Genetics

High‐throughput binary protein interaction mapping is continuing to extend our understanding of cellular function and disease mechanisms. However, we remain one or two orders of magnitude away from a complete interaction map for humans and other major model organisms. Completion will require screening at substantially larger scales with many complementary assays, requiring further efficiency gains in proteome‐scale interaction mapping. Here, we report Barcode Fusion Genetics‐Yeast Two‐Hybrid (BFG‐Y2H), by which a full matrix of protein pairs can be screened in a single multiplexed strain pool. BFG‐Y2H uses Cre recombination to fuse DNA barcodes from distinct plasmids, generating chimeric protein‐pair barcodes that can be quantified via next‐generation sequencing. We applied BFG‐Y2H to four different matrices ranging in scale from ~25 K to 2.5 M protein pairs. The results show that BFG‐Y2H increases the efficiency of protein matrix screening, with quality that is on par with state‐of‐the‐art Y2H methods.