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Four statistical selection methods for inferring transcription factor (TF)–target gene (TG) pairs were developed by coupling mean squared error (MSE) or Huber loss function, with elastic net (ENET) or least absolute shrinkage and selection operator (Lasso) penalty. Two methods were also developed for inferring pathway gene regulatory networks (GRNs) by combining Huber or MSE loss function with a network (Net)-based penalty. To solve these regressions, we ameliorated an accelerated proximal gradient descent (APGD) algorithm to optimize parameter selection processes, resulting in an equally effective but much faster algorithm than the commonly used convex optimization solver. The synthetic data generated in a general setting was used to test four TF–TG identification methods, ENET-based methods performed better than Lasso-based methods. Synthetic data generated from two network settings was used to test Huber-Net and MSE-Net, which outperformed all other methods. The TF–TG identification methods were also tested with SND1 and gl3 overexpression transcriptomic data, Huber-ENET and MSE-ENET outperformed all other methods when genome-wide predictions were performed. The TF–TG identification methods fill the gap of lacking a method for genome-wide TG prediction of a TF, and potential for validating ChIP/DAP-seq results, while the two Net-based methods are instrumental for predicting pathway GRNs.

 

Goss's wilt, caused by the Gram-positive actinobacterium Clavibacter nebraskensis, is an important bacterial disease of maize. The molecular and genetic mechanisms of resistance to the bacterium, or, in general, Gram-positive bacteria causing plant diseases, remain poorly understood. Here, we examined the genetic basis of Goss's wilt through differential gene expression, standard genome-wide association mapping (GWAS), extreme phenotype (XP) GWAS using highly resistant (R) and highly susceptible (S) lines, and quantitative trait locus (QTL) mapping using 3 bi-parental populations, identifying 11 disease association loci. Three loci were validated using near-isogenic lines or recombinant inbred lines. Our analysis indicates that Goss's wilt resistance is highly complex and major resistance genes are not commonly present. RNA sequencing of samples separately pooled from R and S lines with or without bacterial inoculation was performed, enabling identification of common and differential gene responses in R and S lines. Based on expression, in both R and S lines, the photosynthesis pathway was silenced upon infection, while stress-responsive pathways and phytohormone pathways, namely, abscisic acid, auxin, ethylene, jasmonate, and gibberellin, were markedly activated. In addition, 65 genes showed differential responses (up- or down-regulated) to infection in R and S lines. Combining genetic mapping and transcriptional data, individual candidate genes conferring Goss's wilt resistance were identified. Collectively, aspects of the genetic architecture of Goss's wilt resistance were revealed, providing foundational data for mechanistic studies.

 

Understanding gene regulatory networks is essential to elucidate developmental processes and environmental responses. Here, we studied regulation of a maize (Zea mays) transcription factor gene using designer transcription activator-like effectors (dTALes), which are synthetic Type III TALes of the bacterial genus Xanthomonas and serve as inducers of disease susceptibility gene transcription in host cells. The maize pathogen Xanthomonas vasicola pv. vasculorum was used to introduce 2 independent dTALes into maize cells to induced expression of the gene glossy3 (gl3), which encodes a MYB transcription factor involved in biosynthesis of cuticular wax. RNA-seq analysis of leaf samples identified, in addition to gl3, 146 genes altered in expression by the 2 dTALes. Nine of the 10 genes known to be involved in cuticular wax biosynthesis were upregulated by at least 1 of the 2 dTALes. A gene previously unknown to be associated with gl3, Zm00001d017418, which encodes aldehyde dehydrogenase, was also expressed in a dTALe-dependent manner. A chemically induced mutant and a CRISPR-Cas9 mutant of Zm00001d017418 both exhibited glossy leaf phenotypes, indicating that Zm00001d017418 is involved in biosynthesis of cuticular waxes. Bacterial protein delivery of dTALes proved to be a straightforward and practical approach for the analysis and discovery of pathway-specific genes in maize.

 

Abstract Genome sequences provide genomic maps with a single-base resolution for exploring genetic contents. Sequencing technologies, particularly long reads, have revolutionized genome assemblies for producing highly continuous genome sequences. However, current long-read sequencing technologies generate inaccurate reads that contain many errors. Some errors are retained in assembled sequences, which are typically not completely corrected by using either long reads or more accurate short reads. The issue commonly exists, but few tools are dedicated for computing error rates or determining error locations. In this study, we developed a novel approach, referred to as k-mer abundance difference (KAD), to compare the inferred copy number of each k-mer indicated by short reads and the observed copy number in the assembly. Simple KAD metrics enable to classify k-mers into categories that reflect the quality of the assembly. Specifically, the KAD method can be used to identify base errors and estimate the overall error rate. In addition, sequence insertion and deletion as well as sequence redundancy can also be detected. Collectively, KAD is valuable for quality evaluation of genome assemblies and, potentially, provides a diagnostic tool to aid in precise error correction. KAD software has been developed to facilitate public uses. 

Drought stress is an important crop yield limiting factor worldwide. Plant physiological responses to drought stress are driven by changes in gene expression. While drought-responsive genes (DRGs) have been identified in maize, regulation patterns of gene expression during progressive water deficits remain to be elucidated. In this study, we generated time-series transcriptomic data from the maize inbred line B73 under well-watered and drought conditions. Comparisons between the two conditions identified 8,626 DRGs and the stages (early, middle, and late drought) at which DRGs occurred. Different functional groups of genes were regulated at the three stages. Specifically, early and middle DRGs display higher copy number variation among diverse Zea mays lines, and they exhibited stronger associations with drought tolerance as compared to late DRGs. In addition, correlation of expression between small RNAs (sRNAs) and DRGs from the same samples identified 201 negatively sRNA/DRG correlated pairs, including genes showing high levels of association with drought tolerance, such as two glutamine synthetase genes, gln2 and gln6 . The characterization of dynamic gene responses to progressive drought stresses indicates important adaptive roles of early and middle DRGs, as well as roles played by sRNAs in gene expression regulation upon drought stress. 

The maize inbred line A188 is an attractive model for elucidation of gene function and improvement due to its high embryogenic capacity and many contrasting traits to the first maize reference genome, B73, and other elite lines. The lack of a genome assembly of A188 limits its use as a model for functional studies.

Here, we present a chromosome-level genome assembly of A188 using long reads and optical maps. Comparison of A188 with B73 using both whole-genome alignments and read depths from sequencing reads identify approximately 1.1 Gb of syntenic sequences as well as extensive structural variation, including a 1.8-Mb duplication containing the Gametophyte factor1 locus for unilateral cross-incompatibility, and six inversions of 0.7 Mb or greater. Increased copy number of carotenoid cleavage dioxygenase 1 (ccd1) in A188 is associated with elevated expression during seed development. Highccd1expression in seeds together with low expression of yellow endosperm 1 (y1) reduces carotenoid accumulation, accounting for the white seed phenotype of A188. Furthermore, transcriptome and epigenome analyses reveal enhanced expression of defense pathways and altered DNA methylation patterns of the embryonic callus.

The A188 genome assembly provides a high-resolution sequence for a complex genome species and a foundational resource for analyses of genome variation and gene function in maize. The genome, in comparison to B73, contains extensive intra-species structural variations and other genetic differences. Expression and network analyses identify discrete profiles for embryonic callus and other tissues.

 

Clusters of nitrogen‐ and carbon‐coordinated transition metals dispersed in a carbon matrix (e. g., Fe−N−C) have emerged as an inexpensive class of electrocatalysts for the oxygen reduction reaction (ORR). Here, it was shown that optimizing the interaction between the nitrogen‐coordinated transition metal clusters embedded in a more stable and corrosion‐resistant carbide matrix yielded an ORR electrocatalyst with enhanced activity and stability compared to Fe−N−C catalysts. Utilizing first‐principles calculations, an electrostatics‐based descriptor of catalytic activity was identified, and nitrogen‐coordinated iron (FeN₄) clusters embedded in a TiC matrix were predicted to be an efficient platinum‐group metal (PGM)‐free ORR electrocatalyst. Guided by theory, selected catalyst formulations were synthesized, and it was demonstrated that the experimentally observed trends in activity fell exactly in line with the descriptor‐derived theoretical predictions. The Fe−N−TiC catalyst exhibited enhanced activity (20 %) and durability (3.5‐fold improvement) compared to a traditional Fe−N−C catalyst. It was posited that the electrostatics‐based descriptor provides a powerful platform for the design of active and stable PGM‐free electrocatalysts and heterogenous single‐atom catalysts for other electrochemical reactions.

 

Artificial polariton bandgaps at infrared frequencies are investigated by exploiting the strong coupling of electromagnetic waves with induced electric dipoles in two‐dimensional (2D) indium tin oxide nanorod arrays (ITO‐NRAs). The electric dipoles originate from the collective oscillations of free electrons within the individual ITO nanorods undergoing plasmonic resonance. Controlling the near‐field interactions among the neighboring electric dipoles allows for manipulation of the collective polariton modes that are manifested as a polariton bandgap. A theoretical model is developed to understand the coupled phenomena underlying the unique characteristics of plasmon–polariton bandgaps. With high‐degree geometric control of the ITO‐NRAs, it is experimentally demonstrated that reducing the spacing between ITO nanorods in a square array strengthens the near‐field interactions and thus results in a redshift as well as broadening of the polariton bandgap. Furthermore, arranging ITO‐NRAs in a rectangular lattice breaks the symmetry with respect to the principle axis, which leads to a splitting of the collective polariton modes owing to the competition between the quasi‐longitudinally and quasi‐transversely coupled plasmon–polariton modes. The work highlights the use of a classical dipole coupling method for scaling polariton bandgaps to the infrared in artificial plasmonic lattices, thereby offering a new design dimension for infrared sensing, absorbers, and optical communications.