Plant disease resistance is largely governed by complex genetic architecture. In maize, few disease resistance loci have been characterized. Near-isogenic lines are a powerful genetic tool to dissect quantitative trait loci. We analyzed an introgression library of maize (Zea mays) near-isogenic lines, termed a nested near-isogenic line library for resistance to northern leaf blight caused by the fungal pathogen Setosphaeria turcica. The population was comprised of 412 BC5F4 near-isogenic lines that originated from 18 diverse donor parents and a common recurrent parent, B73. Single nucleotide polymorphisms identified through genotyping by sequencing were used to define introgressions and for association analysis. Near isogenic lines that conferred resistance and susceptibility to northern leaf blight were comprised of introgressions that overlapped known northern leaf blight quantitative trait loci. Genome-wide association analysis and stepwise regression further resolved five quantitative trait loci regions, and implicated several candidate genes, including Liguleless1, a key determinant of leaf architecture in cereals. Two independently-derived mutant alleles of liguleless1 inoculated with S. turcica showed enhanced susceptibility to northern leaf blight. In the maize nested association mapping population, leaf angle was positively correlated with resistance to northern leaf blight in five recombinant inbred line populations, and negatively correlated with northern leaf blight in four recombinant inbred line populations. This study demonstrates the power of an introgression library combined with high density marker coverage to resolve quantitative trait loci. Furthermore, the role of liguleless1 in leaf architecture and in resistance to northern leaf blight has important applications in crop improvement.

The growth and division of cells in plant leaves is highly dynamic in time and space, all while the cells cannot move relative to their neighbors. Given these constraints, models predict that long range regulatory systems must exist to maintain flat forms. Juxtaposed microRNA (miRNA) networks could serve as one of these regulatory systems. One of these miRNAs, miR319 is thought to be expressed from the base of leaves and to promote growth by degrading class II TCP transcription factor mRNAs. A miR319 overexpression mutant,jagged and wavy (jaw-D)exhibits rippling and undulating leaves, consistent with biomechanical predictions that without genetic spatial coordination, tissues will deform. It has been theorized thatjaw-Drippling results from overgrowth at the margins, however this does not fully address how miR319 expression from the base of wild-type (WT) leaves allows them to flatten. Here, we track the growth, cell division and cell maturation in live WT andjaw-Dleaves to ask how miR319 expression in WT promotes flattening. This data revealed the importance of spatially distinct growth, division and differentiation patterns in WT leaves, which are missing injaw-D. We propose that WT leaf cells respond to differentiation cues to dynamically re-orient growth in specific tissue locations and regulate flattening.

Aldehyde dehydrogenases are versatile enzymes that serve a range of biochemical functions.  Although traditionally considered metabolic housekeeping enzymes because of their ability to detoxify reactive aldehydes, like those generated from lipid peroxidation damage, the contributions of these enzymes to other biological processes are widespread.  For example, the plant pathogen Pseudomonas syringae strain PtoDC3000 uses an indole-3-acetaldehyde dehydrogenase to synthesize the phytohormone indole-3-acetic acid to elude host responses. Here we investigate the biochemical function of AldC from PtoDC3000.  Analysis of the substrate profile of AldC suggests that this enzyme functions as a long-chain aliphatic aldehyde dehydrogenase.  The 2.5 Å resolution x-ray crystal of the AldC C291A mutant in a dead-end complex with octanal and NAD+ reveals an apolar binding site primed for aliphatic aldehyde substrate recognition.  Functional characterization of site-directed mutants targeting the substrate and NAD(H) binding sites identify key residues in the active site for ligand interactions, including those in the 'aromatic box' that define the aldehyde binding site.  Overall, this study provides molecular insight for understanding the evolution of the prokaryotic aldehyde dehydrogenase superfamily and their diversity of function.

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Modeling has become a popular tool for inquiry and discovery across biological disciplines. Models allow biologists to probe complex questions and to guide experimentation. Modeling literacy among biologists, however, has not always kept pace with the rise in popularity of these techniques and the relevant advances in modeling theory. The result is a lack of understanding that inhibits communication and ultimately, progress in data gathering and analysis. In an effort to help bridge this gap, we present a blueprint that will empower biologists to interrogate and apply models in their field. We demonstrate the applicability of this blueprint in two case studies from distinct subdisciplines of biology; developmental-biomechanics and evolutionary biology. The models used in these fields vary from summarizing dynamical mechanisms to making statistical inferences, demonstrating the breadth of the utility of models to explore biological phenomena.

Background Live imaging is the gold standard for determining how cells give rise to organs. However, tracking many cells across whole organs over large developmental time windows is extremely challenging. In this work, we provide a comparably simple method for confocal live imaging entire Arabidopsis thaliana first leaves across early development. Our imaging method works for both wild-type leaves and the complex curved leaves of the jaw-1D mutant. Results We find that dissecting the cotyledons, affixing a coverslip above the samples and mounting samples with perfluorodecalin yields optimal imaging series for robust cellular and organ level analysis. We provide details of our complementary image processing steps in MorphoGraphX software for segmenting, tracking lineages, and measuring a suite of cellular properties. We also provide MorphoGraphX image processing scripts we developed to automate analysis of segmented images and data presentation. Conclusions Our imaging techniques and processing steps combine into a robust imaging pipeline. With this pipeline we are able to examine important nuances in the cellular growth and differentiation of jaw-D versus WT leaves that have not been demonstrated before. Our pipeline is approachable and easy to use for leaf development live imaging.

Live imaging is the gold standard for determining how cellular development gives rise to organs. However, tracking all individual cells across whole organs over large developmental time windows is extremely challenging. In this work, we provide a comparably simple method for confocal live imaging of Arabidopsis thaliana first leaves across early development. Our imaging method works for both wild-type leaves and the complex curved leaves of the jaw-1D mutant. We find that dissecting the cotyledons, affixing a coverslip above the samples and mounting samples with perfluorodecalin yields optimal imaging series for robust cellular and organ level analysis. We provide details of our complementary image processing steps in MorphGraphX software for segmenting cells, tracking the cell lineages, and measuring a suite of cellular growth properties. We also provide MorphoGraphX image processing scripts that we developed to automate analysis of segmented images and data presentation. Our imaging techniques and processing steps combine into a robust imaging pipeline. With this pipeline we are able to examine important nuances in the cellular growth and differentiation ofjaw-Dversus WT leaves that have not been demonstrated before. Our pipeline is a practical starting place for researchers new to live imaging plant leaves, but also to anyone interested in improving the throughput and reliability of their live imaging process.

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