Charlie Hodgman

Unraveling the Evolution of Auxin Signaling

Auxin signaling is central to plant growth and development, yet hardly anything is known about its evolutionary origin. While the presence of key players in auxin signaling has been analyzed in various land plant species, similar analyses in the green algal lineages are lacking. Here, we survey the key players in auxin biology in the available genomes of Chlorophyta species. We found that the genetic potential for auxin biosynthesis and AUXIN1 (AUX1)/LIKE AUX1- and P-GLYCOPROTEIN/ATP-BINDING CASSETTE subfamily B-dependent transport is already present in several single-celled and colony-forming Chlorophyta species. In addition, our analysis of expressed sequence tag libraries from Coleochaete orbicularis and Spirogyra pratensis, green algae of the Streptophyta clade that are evolutionarily closer to the land plants than those of the Chlorophyta clade, revealed the presence of partial AUXIN RESPONSE FACTORs and/or AUXIN/INDOLE-3-ACETIC ACID proteins (the key factors in auxin signaling) and PIN-FORMED-like proteins (the best-characterized auxin-efflux carriers). While the identification of these possible AUXIN RESPONSE FACTOR- and AUXIN/INDOLE-3-ACETIC ACID precursors and putative PIN-FORMED orthologs calls for a deeper investigation of their evolution after sequencing more intermediate genomes, it emphasizes that the canonical auxin response machinery and auxin transport mechanisms were, at least in part, already present before plants “moved” to land habitats.

Tackling Drought Stress: RECEPTOR-LIKE KINASES Present New Approaches

Global climate change and a growing population require tackling the reduction in arable land and improving biomass production and seed yield per area under varying conditions. One of these conditions is suboptimal water availability. Here, we review some of the classical approaches to dealing with plant response to drought stress and we evaluate how research on RECEPTOR-LIKE KINASES (RLKs) can contribute to improving plant performance under drought stress. RLKs are considered as key regulators of plant architecture and growth behavior, but they also function in defense and stress responses. The available literature and analyses of available transcript profiling data indeed suggest that RLKs can play an important role in optimizing plant responses to drought stress. In addition, RLK pathways are ideal targets for nontransgenic approaches, such as synthetic molecules, providing a novel strategy to manipulate their activity and supporting translational studies from model species, such as Arabidopsis thaliana, to economically useful crops.

Parameter Estimation Using Metaheuristics in Systems Biology: A Comprehensive Review

This paper gives a comprehensive review of the application of metaheuristics to optimization problems in systems biology, mainly focusing on the parameter estimation problem (also called the inverse problem or model calibration). It is intended for either the system biologist who wishes to learn more about the various optimization techniques available and/or the metaheuristic optimizer who is interested in applying such techniques to problems in systems biology. First, the parameter estimation problems emerging from different areas of systems biology are described from the point of view of machine learning. Brief descriptions of various metaheuristics developed for these problems follow, along with outlines of their advantages and disadvantages. Several important issues in applying metaheuristics to the systems biology modeling problem are addressed, including the reliability and identifiability of model parameters, optimal design of experiments, and so on. Finally, we highlight some possible future research directions in this field.

Network Inference Analysis Identifies an APRR2-Like Gene Linked to Pigment Accumulation in Tomato and Pepper Fruits

A likely regulator of tomato ripening is identified from a gene network, its function is validated in transgenic plants, and an orthologous gene is shown to play a similar role in pepper. Carotenoids represent some of the most important secondary metabolites in the human diet, and tomato (Solanum lycopersicum) is a rich source of these health-promoting compounds. In this work, a novel and fruit-related regulator of pigment accumulation in tomato has been identified by artificial neural network inference analysis and its function validated in transgenic plants. A tomato fruit gene regulatory network was generated using artificial neural network inference analysis and transcription factor gene expression profiles derived from fruits sampled at various points during development and ripening. One of the transcription factor gene expression profiles with a sequence related to an Arabidopsis (Arabidopsis thaliana) ARABIDOPSIS PSEUDO RESPONSE REGULATOR2-LIKE gene (APRR2-Like) was up-regulated at the breaker stage in wild-type tomato fruits and, when overexpressed in transgenic lines, increased plastid number, area, and pigment content, enhancing the levels of chlorophyll in immature unripe fruits and carotenoids in red ripe fruits. Analysis of the transcriptome of transgenic lines overexpressing the tomato APPR2-Like gene revealed up-regulation of several ripening-related genes in the overexpression lines, providing a link between the expression of this tomato gene and the ripening process. A putative ortholog of the tomato APPR2-Like gene in sweet pepper (Capsicum annuum) was associated with pigment accumulation in fruit tissues. We conclude that the function of this gene is conserved across taxa and that it encodes a protein that has an important role in ripening.

OsCAND1 Is Required for Crown Root Emergence in Rice

Crown roots are main components of the fibrous root system and important for crops to anchor and absorb water and nutrition. To understand the molecular mechanisms of crown root formation, we isolated a rice mutant defective in crown root emergence designated as Oscand1 (named after the Arabidopsis homologous gene AtCAND1). The defect of visible crown root in the Oscand1 mutant is the result of cessation of the G2/M cell cycle transition in the crown root meristem. Map-based cloning revealed that OsCAND1 is a homolog of Arabidopsis CAND1. During crown root primordium development, the expression of OsCAND1 is confined to the root cap after the establishment of fundamental organization. The transgenic plants harboring DR5::GUS showed that auxin signaling in crown root tip is abnormal in the mutant. Exogenous auxin application can partially rescue the defect of crown root development in Oscand1. Taken together, these data show that OsCAND1 is involved in auxin signaling to maintain the G2/M cell cycle transition in crown root meristem and, consequently, the emergence of crown root. Our findings provide new information about the molecular regulation of the emergence of crown root in rice.

DNA Methylation and Chromatin Regulation during Fleshy Fruit Development and Ripening

Fruit ripening is a developmental process that results in the leaf-like carpel organ of the flower becoming a mature ovary primed for dispersal of the seeds. Ripening in fleshy fruits involves a profound metabolic phase change that is under strict hormonal and genetic control. This work reviews recent developments in our understanding of the epigenetic regulation of fruit ripening. We start by describing the current state of the art about processes involved in histone post-translational modifications and the remodeling of chromatin structure and their impact on fruit development and ripening. However, the focus of the review is the consequences of changes in DNA methylation levels on the expression of ripening-related genes. This includes those changes that result in heritable phenotypic variation in the absence of DNA sequence alterations, and the mechanisms for their initiation and maintenance. The majority of the studies described in the literature involve work on tomato, but evidence is emerging that ripening in other fruit species may also be under epigenetic control. We discuss how epigenetic differences may provide new targets for breeding and crop improvement.

A machine learning heuristic to identify biologically relevant and minimal biomarker panels from omics data

BackgroundInvestigations into novel biomarkers using omics techniques generate large amounts of data. Due to their size and numbers of attributes, these data are suitable for analysis with machine learning methods. A key component of typical machine learning pipelines for omics data is feature selection, which is used to reduce the raw high-dimensional data into a tractable number of features. Feature selection needs to balance the objective of using as few features as possible, while maintaining high predictive power. This balance is crucial when the goal of data analysis is the identification of highly accurate but small panels of biomarkers with potential clinical utility. In this paper we propose a heuristic for the selection of very small feature subsets, via an iterative feature elimination process that is guided by rule-based machine learning, called RGIFE (Rule-guided Iterative Feature Elimination). We use this heuristic to identify putative biomarkers of osteoarthritis (OA), articular cartilage degradation and synovial inflammation, using both proteomic and transcriptomic datasets.Results and discussionOur RGIFE heuristic increased the classification accuracies achieved for all datasets when no feature selection is used, and performed well in a comparison with other feature selection methods. Using this method the datasets were reduced to a smaller number of genes or proteins, including those known to be relevant to OA, cartilage degradation and joint inflammation. The results have shown the RGIFE feature reduction method to be suitable for analysing both proteomic and transcriptomics data. Methods that generate large ‘omics’ datasets are increasingly being used in the area of rheumatology.ConclusionsFeature reduction methods are advantageous for the analysis of omics data in the field of rheumatology, as the applications of such techniques are likely to result in improvements in diagnosis, treatment and drug discovery.

Modulation of Arabidopsis and monocot root architecture by CLAVATA3/EMBRYO SURROUNDING REGION 26 peptide

Highlight CLE26 plays an important role in regulating A. thaliana and monocot root architecture, and interacts with auxin signalling.

Assessing the functional coherence of modules found in multiple-evidence networks from Arabidopsis

BackgroundCombining multiple evidence-types from different information sources has the potential to reveal new relationships in biological systems. The integrated information can be represented as a relationship network, and clustering the network can suggest possible functional modules. The value of such modules for gaining insight into the underlying biological processes depends on their functional coherence. The challenges that we wish to address are to define and quantify the functional coherence of modules in relationship networks, so that they can be used to infer function of as yet unannotated proteins, to discover previously unknown roles of proteins in diseases as well as for better understanding of the regulation and interrelationship between different elements of complex biological systems.ResultsWe have defined the functional coherence of modules with respect to the Gene Ontology (GO) by considering two complementary aspects: (i) the fragmentation of the GO functional categories into the different modules and (ii) the most representative functions of the modules. We have proposed a set of metrics to evaluate these two aspects and demonstrated their utility in Arabidopsis thaliana. We selected 2355 proteins for which experimentally established protein-protein interaction (PPI) data were available. From these we have constructed five relationship networks, four based on single types of data: PPI, co-expression, co-occurrence of protein names in scientific literature abstracts and sequence similarity and a fifth one combining these four evidence types. The ability of these networks to suggest biologically meaningful grouping of proteins was explored by applying Markov clustering and then by measuring the functional coherence of the clusters.ConclusionsRelationship networks integrating multiple evidence-types are biologically informative and allow more proteins to be assigned to a putative functional module. Using additional evidence types concentrates the functional annotations in a smaller number of modules without unduly compromising their consistency. These results indicate that integration of more data sources improves the ability to uncover functional association between proteins, both by allowing more proteins to be linked and producing a network where modular structure more closely reflects the hierarchy in the gene ontology.

Genome-wide mapping of transcriptional enhancer candidates using DNA and chromatin features in maize

BackgroundWhile most cells in multicellular organisms carry the same genetic information, in each cell type only a subset of genes is being transcribed. Such differentiation in gene expression depends, for a large part, on the activation and repression of regulatory sequences, including transcriptional enhancers. Transcriptional enhancers can be located tens of kilobases from their target genes, but display characteristic chromatin and DNA features, allowing their identification by genome-wide profiling. Here we show that integration of chromatin characteristics can be applied to predict distal enhancer candidates in Zea mays, thereby providing a basis for a better understanding of gene regulation in this important crop plant.ResultTo predict transcriptional enhancers in the crop plant maize (Zea mays L. ssp. mays), we integrated available genome-wide DNA methylation data with newly generated maps for chromatin accessibility and histone 3 lysine 9 acetylation (H3K9ac) enrichment in young seedling and husk tissue. Approximately 1500 intergenic regions, displaying low DNA methylation, high chromatin accessibility and H3K9ac enrichment, were classified as enhancer candidates. Based on their chromatin profiles, candidate sequences can be classified into four subcategories. Tissue-specificity of enhancer candidates is defined based on the tissues in which they are identified and putative target genes are assigned based on tissue-specific expression patterns of flanking genes.ConclusionsOur method identifies three previously identified distal enhancers in maize, validating the new set of enhancer candidates and enlarging the toolbox for the functional characterization of gene regulation in the highly repetitive maize genome.