DNA methylation can play important roles in the regulation of transposable elements and genes. A collection of mutant alleles for 11 maize (Zea mays) genes predicted to play roles in controlling DNA methylation were isolated through forward-or reverse-genetic approaches. Low-coverage whole-genome bisulfite sequencing and high-coverage sequence-capture bisulfite sequencing were applied to mutant lines to determine context-and locus-specific effects of these mutations on DNA methylation profiles. Plants containing mutant alleles for components of the RNA-directed DNA methylation pathway exhibit loss of CHH methylation at many loci as well as CG and CHG methylation at a small number of loci. Plants containing loss-of-function alleles for chromomethylase (CMT) genes exhibit strong genome-wide reductions in CHG methylation and some locus-specific loss of CHH methylation. In an attempt to identify stocks with stronger reductions in DNA methylation levels than provided by single gene mutations, we performed crosses to create double mutants for the maize CMT3 orthologs, Zmet2 and Zmet5, and for the maize DDM1 orthologs, Chr101 and Chr106. While loss-of-function alleles are viable as single gene mutants, the double mutants were not recovered, suggesting that severe perturbations of the maize methylome may have stronger deleterious phenotypic effects than in Arabidopsis thaliana.

Inferring phenotypic outcomes from genomic features is both a promise and challenge for systems biology. Using gene expression data to predict phenotypic outcomes, and functionally validating the genes with predictive powers are two challenges we address in this study. We applied an evolutionarily informed machine learning approach to predict phenotypes based on transcriptome responses shared both within and across species. Specifically, we exploited the phenotypic diversity in nitrogen use efficiency and evolutionarily conserved transcriptome responses to nitrogen treatments across Arabidopsis accessions and maize varieties. We demonstrate that using evolutionarily conserved nitrogen responsive genes is a biologically principled approach to reduce the feature dimensionality in machine learning that ultimately improved the predictive power of our gene-to-trait models. Further, we functionally validated seven candidate transcription factors with predictive power for NUE outcomes in Arabidopsis and one in maize. Moreover, application of our evolutionarily informed pipeline to other species including rice and mice models underscores its potential to uncover genes affecting any physiological or clinical traits of interest across biology, agriculture, or medicine.

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BackgroundTranscription factors (TFs) are proteins that can bind to DNA sequences and regulate gene expression. Many TFs are master regulators in cells that contribute to tissue-specific and cell-type-specific gene expression patterns in eukaryotes. Maize has been a model organism for over one hundred years, but little is known about its tissue-specific gene regulation through TFs. In this study, we used a network approach to elucidate gene regulatory networks (GRNs) in four tissues (leaf, root, SAM and seed) in maize. We utilized GENIE3, a machine-learning algorithm combined with large quantity of RNA-Seq expression data to construct four tissue-specific GRNs. Unlike some other techniques, this approach is not limited by high-quality Position Weighed Matrix (PWM), and can therefore predict GRNs for over 2000 TFs in maize.ResultsAlthough many TFs were expressed across multiple tissues, a multi-tiered analysis predicted tissue-specific regulatory functions for many transcription factors. Some well-studied TFs emerged within the four tissue-specific GRNs, and the GRN predictions matched expectations based upon published results for many of these examples. Our GRNs were also validated by ChIP-Seq datasets (KN1, FEA4 and O2). Key TFs were identified for each tissue and matched expectations for key regulators in each tissue, including GO enrichment and identity with known regulatory factors for that tissue. We also found functional modules in each network by clustering analysis with the MCL algorithm.ConclusionsBy combining publicly available genome-wide expression data and network analysis, we can uncover GRNs at tissue-level resolution in maize. Since ChIP-Seq and PWMs are still limited in several model organisms, our study provides a uniform platform that can be adapted to any species with genome-wide expression data to construct GRNs. We also present a publicly available database, maize tissue-specific GRN (mGRN, https://www.bio.fsu.edu/mcginnislab/mgrn/), for easy querying. All source code and data are available at Github (https://github.com/timedreamer/maize_tissue-specific_GRN).Electronic supplementary materialThe online version of this article (10.1186/s12870-018-1329-y) contains supplementary material, which is available to authorized users.

A series of Mo thiolate complexes with the formula Mo[HB(Me2pz)3](NO)(SR)2, R = Et (1), Bun (2), CH2CONHCH3 (3), CH2CON(CH3)2 (4), C2H4CONHCH3 (5), and C2H4CON(CH3)2 ( 6), have been studied using the methods of cyclic voltammetry, IR and resonance Raman spectroscopy, and for 3 and 5, X-ray crystallography. The polar groups of the thiolate ligands exert an influence on the redox potentials reflected in the E\/2 series for the Mo2+/Mo3+ redox couple recorded in CH3CN: 2, -0.

ORCID IDs: 0000-0002-6182-800X (J.H.); 0000-0001-6612-3570 (S.V.); 0000-0002-9564-8146 (K.M.M.).With the emergence of massively parallel sequencing, genomewide expression data production has reached an unprecedented level. This abundance of data has greatly facilitated maize research, but may not be amenable to traditional analysis techniques that were optimized for other data types. Using publicly available data, a gene coexpression network (GCN) can be constructed and used for gene function prediction, candidate gene selection, and improving understanding of regulatory pathways. Several GCN studies have been done in maize (Zea mays), mostly using microarray datasets. To build an optimal GCN from plant materials RNA-Seq data, parameters for expression data normalization and network inference were evaluated. A comprehensive evaluation of these two parameters and a ranked aggregation strategy on network performance, using libraries from 1266 maize samples, were conducted. Three normalization methods and 10 inference methods, including six correlation and four mutual information methods, were tested. The three normalization methods had very similar performance. For network inference, correlation methods performed better than mutual information methods at some genes. Increasing sample size also had a positive effect on GCN. Aggregating single networks together resulted in improved performance compared to single networks.Maize (Zea mays) is the most widely produced crop in United States, and U.S. agriculture accounted for 36% of world maize production in 2015 (USDA, 2016). Maize has also been in the center of genetics research for more than 100 years, including McClintock's pioneering work with transposable elements (reviewed by McClintock, 1983;Fedoroff, 2012). Due to recent technological advances in nucleic acid sequencing and the availability of the maize genome sequence (Schnable et al., 2009), maize genomics research has been greatly expedited.RNA-sequencing (RNA-Seq) has become the favored technique for detecting genomewide expression patterns. RNA-Seq has some advantages over microarray analysis of gene expression, including single base-pair resolution, detection of novel transcripts, and the ability to analyze transcript abundance without existing genome information (reviewed by Wang et al., 2009;Han et al., 2015;Conesa et al., 2016). RNA-Seq data provides information about single nucleotide polymorphisms, which facilitates genomewide association studies (Fu et al., 2013;Li et al., 2013a;Lonsdale et al., 2013;Fadista et al., 2014). Because of its widespread adaptability, greater than 5000 Illumina platform Maize RNA-Seq libraries (Fig. 1A) are available in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) database (Leinonen et al., 2010), adding to the body of data that can be used to study the maize genome.The maize genome is large and heterogeneous, and the genome annotation is still far from complete (Cigan et al., 2005;Ficklin and Feltus, 2011). Although recent work has...

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42 43 44 45 Acknowledgments 46 3 We are grateful to Nathan Springer for providing the SeqCap DNA methylation data. 47 This work was funded by the National Science Foundation funds to K.M.M (CMB-48 035919) and start-up funds from the University of Washington Bothell School of STEM 49 to T.F.M. P.A.C. was supported by a grant from NSF (IOS-1802848). 50 51 Significance statement 52 MOP1-dependent gene expression changes in response to ABA were identified as 53 having synergistic and combinatorial direct and indirect effects. Overlapping regulatory 54 networks were uncovered, reinforcing the idea that epigenetic regulation is crucial to 55 plant response and adaptation to abiotic stress. 56 57 58 4 ABSTRACT 59 60 Plants are subjected to extreme environmental conditions and must adapt rapidly. The 61 phytohormone abscisic acid (ABA) accumulates during abiotic stress, signaling 62 transcriptional changes that trigger physiological responses. Epigenetic modifications 63 often facilitate transcription, particularly at genes exhibiting temporal, tissue-specific and 64 environmentally-induced expression. In maize (Zea mays), MEDIATOR OF 65 PARAMUTATION 1 (MOP1) is required for progression of an RNA-dependent 66 epigenetic pathway that regulates transcriptional silencing of loci genomewide. MOP1 67 function has been previously correlated with genomic regions adjoining particular types 68 of transposable elements and genic regions, suggesting that this regulatory pathway 69 functions to maintain distinct transcriptional activities within genomic spaces, and that 70 loss of MOP1 may modify the responsiveness of some loci to other regulatory 71 pathways. As critical regulators of gene expression, MOP1 and ABA pathways each 72 regulate specific genes. To determine whether loss of MOP1 impacts ABA-responsive 73 gene expression in maize, mop1-1 and Mop1 homozygous seedlings were subjected to 74 exogenous ABA and RNA-sequencing. A total of 3,242 differentially expressed genes 75 (DEGs) were identified in four pairwise comparisons. Overall, ABA-induced changes in 76 gene expression were enhanced in mop1-1 homozygous plants. The highest number of 77 DEGs were identified in ABA-induced mop1-1 mutants, including many transcription 78 factors; this suggests combinatorial regulatory scenarios including direct and indirect 79 transcriptional responses to genetic disruption (mop1-1) and/or stimulus-induction of a 80 hierarchical, cascading network of responsive genes. Additionally, a modest increase in 81 5 CHH methylation at putative MOP1-RdDM loci in response to ABA was observed in 82 some genotypes, suggesting that epigenetic variation might influence environmentally-83 induced transcriptional responses in maize. 84 85 INTRODUCTION 86 87As sessile organisms, plants must adapt rapidly to fluctuating and often extreme abiotic 88 stress conditions that negatively impact crop productivity and yield, such as water 89 deprivation/drought, high salinity, nutrient deficiency and extreme temperatures. In 90 addition to its role in plant develop...