The Ga1 locus of the genus Zea is associated with novel genome structures derived from multiple, independent nonhomologous recombination events.

The Ga1 locus controls cross-incompatibility between field corn and popcorn. The Ga1-S haplotype contains 2 types of pectin methylesterase (PME) genes, ZmPme3 and several copies of ZmGa1P that are expressed in silk and pollen, respectively. The ga1 haplotype contains nonfunctional tandem repeat sequences related to ZmPme3 and ZmGa1P. This haplotype can cross-pollinate freely and is widely present in field corn. The primary objective of this study is to characterize the repeat sequences from a diverse collection of maize and teosinte lines and use this information to understand the evolution of the Ga1 locus. First, we characterized the complexity of the Ga1 genome region in high-quality maize genome assemblies that led to their categorization into 5 groups based on the number and type of PME-like sequences found at this region. Second, we studied duplication events that led to the ga1 and Ga1-S repeats using maximum likelihood phylogenetic reconstruction. Divergence estimates of the ga1 haplotype suggest that the duplication events occurred more than 600 KYA whereas those in Ga1-S occurred at 3 time points, i.e. >600, ∼260, and ∼100 KYA. These estimates suggest that the ga1 and Ga1-S tandem duplication events occurred independently. Finally, analysis of ZmPme3 and ZmGa1P homologs in Zea and Tripsacum genomes suggests that ga1 and Ga1-S repeats originated from an ancestral pair of PME genes that duplicated and diverged through 2 evolutionary branches prior to the domestication of maize.

The maize Ga1-s allele confers protection against ga1 pollen in popcorn and dent corn.

Because corn pollen can be carried great distances by wind, maintaining genetic purity of corn grain is challenging. The challenge is substantially reduced in popcorn, which carries the Ga1-s allele preventing pollination by ga1 plants, which include the vast majority of non-popcorn commercial maize varieties in the U.S.. Ga1-s can be transferred into dent corn but the effectiveness of the Ga1-s allele in popcorn and dent corn has never been compared, which is important because each are regulated differently regarding GMO contamination. We compared pollen exclusion of commercial popcorn hybrids, Ga1-s dent corn hybrids and normal dent corn hybrids for their ability to exclude ga1 pollen using a sensitive field-based assay. While both popcorn and Ga1-s dent corn had significantly better pollen exclusion than normal dent corn, popcorn was significantly better than Ga1-s dent corn on average. Some Ga1-s dent hybrids excluded as well or better than some popcorn lines suggesting that identification of hybrids comparable to popcorn is possible. The information in this study will support revised gene purity regulations potentially decreasing costs and increasing genetic purity of organic corn.

Insights into the molecular control of cross-incompatibility in Zea mays.

Gametophytic cross-incompatibility systems in corn have been the subject of genetic studies for more than a century. They have tremendous economic potential as a genetic mechanism for controlling fertilization without controlling pollination. Three major genetically distinct and functionally equivalent cross-incompatibility systems exist in Zea mays: Ga1, Tcb1, and Ga2. All three confer reproductive isolation between maize or teosinte varieties with different haplotypes at any one locus. These loci confer genetically separable functions to the silk and pollen: a female function that allows the silk to block fertilization by non-self-type pollen and a male function that overcomes the block of the female function from the same locus. Identification of some of these genes has shed light on the reproductive isolation they confer. The identification of both male and female factors as pectin methylesterases reveals the importance of pectin methylesterase activity in controlling the decision between pollen acceptance versus rejection, possibly by regulating the degree of methylesterification of the pollen tube cell wall. The appropriate level and spatial distribution of pectin methylesterification is critical for pollen tube growth and is affected by both pectin methylesterases and pectin methylesterase inhibitors. We present a molecular model that explains how cross-incompatibility systems may function that can be tested in Zea and uncharacterized cross-incompatibility systems. Molecular characterization of these loci in conjunction with further refinement of the underlying molecular and cellular mechanisms will allow researchers to bring new and powerful tools to bear on understanding reproductive isolation in Zea mays and related species.

Examining Short-Term Responses to a Long-Term Problem: RNA-Seq Analyses of Iron Deficiency Chlorosis Tolerant Soybean.

Iron deficiency chlorosis (IDC) is a global crop production problem, significantly impacting yield. However, most IDC studies have focused on model species, not agronomically important crops. Soybean is the second largest crop grown in the United States, yet the calcareous soils across most of the upper U.S. Midwest limit soybean growth and profitability. To understand early soybean iron stress responses, we conducted whole genome expression analyses (RNA-sequencing) of leaf and root tissue from the iron efficient soybean (Glycine max) cultivar Clark, at 30, 60 and 120 min after transfer to iron stress conditions. We identified over 10,000 differentially expressed genes (DEGs), with the number of DEGs increasing over time in leaves, but decreasing over time in roots. To investigate these responses, we clustered our expression data across time to identify suites of genes, their biological functions, and the transcription factors (TFs) that regulate their expression. These analyses reveal the hallmarks of the soybean iron stress response (iron uptake and homeostasis, defense, and DNA replication and methylation) can be detected within 30 min. Furthermore, they suggest root to shoot signaling initiates early iron stress responses representing a novel paradigm for crop stress adaptations.

Deconstructing the genetic architecture of iron deficiency chlorosis in soybean using genome-wide approaches.

BACKGROUND: Iron (Fe) is an essential micronutrient for plant growth and development. Iron deficiency chlorosis (IDC), caused by calcareous soils or high soil pH, can limit iron availability, negatively affecting soybean (Glycine max) yield. This study leverages genome-wide association study (GWAS) and a genome-wide epistatic study (GWES) with previous gene expression studies to identify regions of the soybean genome important in iron deficiency tolerance. RESULTS: A GWAS and a GWES were performed using 460 diverse soybean PI lines from 27 countries, in field and hydroponic iron stress conditions, using more than 36,000 single nucleotide polymorphism (SNP) markers. Combining this approach with available RNA-sequencing data identified significant markers, genomic regions, and novel genes associated with or responding to iron deficiency. Sixty-nine genomic regions associated with IDC tolerance were identified across 19 chromosomes via the GWAS, including the major-effect quantitative trait locus (QTL) on chromosome Gm03. Cluster analysis of significant SNPs in this region deconstructed this historically prominent QTL into four distinct linkage blocks, enabling the identification of multiple candidate genes for iron chlorosis tolerance. The complementary GWES identified SNPs in this region interacting with nine other genomic regions, providing the first evidence of epistatic interactions impacting iron deficiency tolerance. CONCLUSIONS: This study demonstrates that integrating cutting edge genome wide association (GWA), genome wide epistasis (GWE), and gene expression studies is a powerful strategy to identify novel iron tolerance QTL and candidate loci from diverse germplasm. Crops, unlike model species, have undergone selection for thousands of years, constraining and/or enhancing stress responses. Leveraging genomics-enabled approaches to study these adaptations is essential for future crop improvement.

Visualization methods for differential expression analysis.

BACKGROUND: Despite the availability of many ready-made testing software, reliable detection of differentially expressed genes in RNA-seq data is not a trivial task. Even though the data collection is considered high-throughput, data analysis has intricacies that require careful human attention. Researchers should use modern data analysis techniques that incorporate visual feedback to verify the appropriateness of their models. While some RNA-seq packages provide static visualization tools, their capabilities should be expanded and their meaningfulness should be explicitly demonstrated to users. RESULTS: In this paper, we 1) introduce new interactive RNA-seq visualization tools, 2) compile a collection of examples that demonstrate to biologists why visualization should be an integral component of differential expression analysis. We use public RNA-seq datasets to show that our new visualization tools can detect normalization issues, differential expression designation problems, and common analysis errors. We also show that our new visualization tools can identify genes of interest in ways undetectable with models. Our R package "bigPint" includes the plotting tools introduced in this paper, many of which are unique additions to what is currently available. The "bigPint" website is located at https://lindsayrutter.github.io/bigPint and contains short vignette articles that introduce new users to our package, all written in reproducible code. CONCLUSIONS: We emphasize that interactive graphics should be an indispensable component of modern RNA-seq analysis, which is currently not the case. This paper and its corresponding software aim to persuade 1) users to slightly modify their differential expression analyses by incorporating statistical graphics into their usual analysis pipelines, 2) developers to create additional complex and interactive plotting methods for RNA-seq data, possibly using lessons learned from our open-source codes. We hope our work will serve a small part in upgrading the RNA-seq analysis world into one that more wholistically extracts biological information using both models and visuals.

A Pectin Methylesterase ZmPme3 Is Expressed in Gametophyte factor1-s (Ga1-s) Silks and Maps to that Locus in Maize (Zea mays L.).

The ga1 locus of maize confers unilateral cross incompatibility, preventing cross pollination between females carrying the incompatible Ga1-s allele and males not carrying a corresponding compatible allele. To characterize this system at the molecular level, we carried out a transcript profiling experiment in which silks from near isogenic lines carrying the Ga1-s and ga1 alleles were compared. While several differentially expressed genes were identified, only one mapped to the known location of ga1. This gene is a pectin methylesterase (PME), which we designated as ZmPme3, and is present and expressed only in Ga1-s genotypes. While a functional ZmPME3 is not present in the ga1 genotypes examined, a pectin methylesterase gene cluster is found in ga1 genotypes. The gene cluster in W22 contains 58 tandem full-length or partial PME pseudo genes. These data combined with a wealth of previously published data on the involvement of PMEs in pollen tube growth suggest a role for cell wall modification enzymes in the pollen exclusion component of Ga1-s gametophytic incompatibility. Consistent with this role, a third allele which lacks the female function of Ga1-s, Ga1-m, has a mutationally inactivated version of ZmPme3.

Identification of candidate genes involved in early iron deficiency chlorosis signaling in soybean (Glycine max) roots and leaves.

BACKGROUND: Iron is an essential micronutrient for all living things, required in plants for photosynthesis, respiration and metabolism. A lack of bioavailable iron in soil leads to iron deficiency chlorosis (IDC), causing a reduction in photosynthesis and interveinal yellowing of leaves. Soybeans (Glycine max (L.) Merr.) grown in high pH soils often suffer from IDC, resulting in substantial yield losses. Iron efficient soybean cultivars maintain photosynthesis and have higher yields under IDC-promoting conditions than inefficient cultivars. RESULTS: To capture signaling between roots and leaves and identify genes acting early in the iron efficient cultivar Clark, we conducted a RNA-Seq study at one and six hours after replacing iron sufficient hydroponic media (100 µM iron(III) nitrate nonahydrate) with iron deficient media (50 µM iron(III) nitrate nonahydrate). At one hour of iron stress, few genes were differentially expressed in leaves but many were already changing expression in roots. By six hours, more genes were differentially expressed in the leaves, and a massive shift was observed in the direction of gene expression in both roots and leaves. Further, there was little overlap in differentially expressed genes identified in each tissue and time point. CONCLUSIONS: Genes involved in hormone signaling, regulation of DNA replication and iron uptake utilization are key aspects of the early iron-efficiency response. We observed dynamic gene expression differences between roots and leaves, suggesting the involvement of many transcription factors in eliciting rapid changes in gene expression. In roots, genes involved iron uptake and development of Casparian strips were induced one hour after iron stress. In leaves, genes involved in DNA replication and sugar signaling responded to iron deficiency. The differentially expressed genes (DEGs) and signaling components identified here represent new targets for soybean improvement.

Determination of transgene copy number by real-time quantitative PCR.

Efficient methods to characterize transgenic plants are important to quickly understand the state of the transformant. Determining transgene copy number is an important step in transformant characterization and can differentiate between complex and simple transformation events. This knowledge can be extremely useful when determining what future experiments and uses the transgenic lines can be utilized for. The method described here uses real-time quantitative PCR to determine the transgene copy number present in the genome of the transformant. Specifically, this method measures the relative transgene copy number by comparing it with an endogenous gene with a known copy number. This method is a quick alternative to the Southern blot, a method that is commonly used to determine gene copy number, and is effective when screening large numbers of transformants.