Tissue-specific gene expression and protein abundance patterns are associated with fractionation bias in maize

Variable expression of cyanide detoxification and tolerance genes in cyanogenic and acyanogenic white clover (Trifolium repens)

PREMISE

β-Cyanoalanine synthase (β-CAS) and alternative oxidase (AOX) play important roles in the ability of plants to detoxify and tolerate hydrogen cyanide (HCN). These functions are critical for all plants because HCN is produced at low levels during basic metabolic processes, and especially for cyanogenic species, which release high levels of HCN following tissue damage. However, expression of β-CAS and Aox genes has not been examined in cyanogenic species, nor compared between cyanogenic and acyanogenic genotypes within a species.

METHODS

We used a natural polymorphism for cyanogenesis in white clover to examine β-CAS and Aox gene expression in relation to cyanogenesis-associated HCN exposure. We identified all β-CAS and Aox gene copies present in the genome, including members of the Aox1, Aox2a, and Aox2d subfamilies previously reported in legumes. Expression levels were compared between cyanogenic and acyanogenic genotypes and between damaged and undamaged leaf tissue.

RESULTS

β-CAS and Aox2a expression was differentially elevated in cyanogenic genotypes, and tissue damage was not required to induce this increased expression. Aox2d, in contrast, appeared to be upregulated as a generalized wounding response.

CONCLUSIONS

These findings suggest a heightened constitutive role for HCN detoxification (via elevated β-CAS expression) and HCN-toxicity mitigation (via elevated Aox2a expression) in plants that are capable of cyanogenesis. As such, freezing-induced cyanide autotoxicity is unlikely to be the primary selective factor in the evolution of climate-associated cyanogenesis clines.

Design, execution, and interpretation of plant RNA-seq analyses

Genomics has transformed our understanding of the genetic architecture of traits and the genetic variation present in plants. Here, we present a review of how RNA-seq can be performed to tackle research challenges addressed by plant sciences. We discuss the importance of experimental design in RNA-seq, including considerations for sampling and replication, to avoid pitfalls and wasted resources. Approaches for processing RNA-seq data include quality control and counting features, and we describe common approaches and variations. Though differential gene expression analysis is the most common analysis of RNA-seq data, we review multiple methods for assessing gene expression, including detecting allele-specific gene expression and building co-expression networks. With the production of more RNA-seq data, strategies for integrating these data into genetic mapping pipelines is of increased interest. Finally, special considerations for RNA-seq analysis and interpretation in plants are needed, due to the high genome complexity common across plants. By incorporating informed decisions throughout an RNA-seq experiment, we can increase the knowledge gained.

A pan-grass transcriptome reveals patterns of cellular divergence in crops

Different plant species within the grasses were parallel targets of domestication, giving rise to crops with distinct evolutionary histories and traits1. Key traits that distinguish these species are mediated by specialized cell types2. Here we compare the transcriptomes of root cells in three grass species-Zea mays, Sorghum bicolor and Setaria viridis. We show that single-cell and single-nucleus RNA sequencing provide complementary readouts of cell identity in dicots and monocots, warranting a combined analysis. Cell types were mapped across species to identify robust, orthologous marker genes. The comparative cellular analysis shows that the transcriptomes of some cell types diverged more rapidly than those of others-driven, in part, by recruitment of gene modules from other cell types. The data also show that a recent whole-genome duplication provides a rich source of new, highly localized gene expression domains that favour fast-evolving cell types. Together, the cell-by-cell comparative analysis shows how fine-scale cellular profiling can extract conserved modules from a pan transcriptome and provide insight on the evolution of cells that mediate key functions in crops.

The Heterogeneity in the Landscape of Gene Dominance in Maize is Accompanied by Unique Chromatin Environments

Subgenome dominance after whole-genome duplication (WGD) has been observed in many plant species. However, the degree to which the chromatin environment affects this bias has not been explored. Here, we compared the dominant subgenome (maize1) and the recessive subgenome (maize2) with respect to patterns of sequence substitutions, genes expression, transposable element accumulation, small interfering RNAs, DNA methylation, histone modifications, and accessible chromatin regions (ACRs). Our data show that the degree of bias between subgenomes for all the measured variables does not vary significantly when both of the WGD genes are located in pericentromeric regions. Our data further indicate that the location of maize1 genes in chromosomal arms is pivotal for maize1 to maintain its dominance, but location has a less effect on maize2 homoeologs. In addition to homoeologous genes, we compared ACRs, which often harbor cis-regulatory elements, between the two subgenomes and demonstrate that maize1 ACRs have a higher level of chromatin accessibility, a lower level of sequence substitution, and are enriched in chromosomal arms. Furthermore, we find that a loss of maize1 ACRs near their nearby genes is associated with a reduction in purifying selection and expression of maize1 genes relative to their maize2 homoeologs. Taken together, our data suggest that chromatin environment and cis-regulatory elements are important determinants shaping the divergence and evolution of duplicated genes.

Predicting Tissue-Specific mRNA and Protein Abundance in Maize: A Machine Learning Approach

Machine learning and modeling approaches have been used to classify protein sequences for a broad set of tasks including predicting protein function, structure, expression, and localization. Some recent studies have successfully predicted whether a given gene is expressed as mRNA or even translated to proteins potentially, but given that not all genes are expressed in every condition and tissue, the challenge remains to predict condition-specific expression. To address this gap, we developed a machine learning approach to predict tissue-specific gene expression across 23 different tissues in maize, solely based on DNA promoter and protein sequences. For class labels, we defined high and low expression levels for mRNA and protein abundance and optimized classifiers by systematically exploring various methods and combinations of k-mer sequences in a two-phase approach. In the first phase, we developed Markov model classifiers for each tissue and built a feature vector based on the predictions. In the second phase, the feature vector was used as an input to a Bayesian network for final classification. Our results show that these methods can achieve high classification accuracy of up to 95% for predicting gene expression for individual tissues. By relying on sequence alone, our method works in settings where costly experimental data are unavailable and reveals useful insights into the functional, evolutionary, and regulatory characteristics of genes.

qTeller: a tool for comparative multi-genomic gene expression analysis

MOTIVATION

Over the last decade, RNA-Seq whole-genome sequencing has become a widely used method for measuring and understanding transcriptome-level changes in gene expression. Since RNA-Seq is relatively inexpensive, it can be used on multiple genomes to evaluate gene expression across many different conditions, tissues and cell types. Although many tools exist to map and compare RNA-Seq at the genomics level, few web-based tools are dedicated to making data generated for individual genomic analysis accessible and reusable at a gene-level scale for comparative analysis between genes, across different genomes and meta-analyses.

RESULTS

To address this challenge, we revamped the comparative gene expression tool qTeller to take advantage of the growing number of public RNA-Seq datasets. qTeller allows users to evaluate gene expression data in a defined genomic interval and also perform two-gene comparisons across multiple user-chosen tissues. Though previously unpublished, qTeller has been cited extensively in the scientific literature, demonstrating its importance to researchers. Our new version of qTeller now supports multiple genomes for intergenomic comparisons, and includes capabilities for both mRNA and protein abundance datasets. Other new features include support for additional data formats, modernized interface and back-end database and an optimized framework for adoption by other organisms' databases.

AVAILABILITY AND IMPLEMENTATION

The source code for qTeller is open-source and available through GitHub (https://github.com/Maize-Genetics-and-Genomics-Database/qTeller). A maize instance of qTeller is available at the Maize Genetics and Genomics database (MaizeGDB) (https://qteller.maizegdb.org/), where we have mapped over 200 unique datasets from GenBank across 27 maize genomes.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Reconstructing the evolutionary history of the oxytocin and vasotocin receptor gene family: Insights on whole genome duplication scenarios

Vertebrate genome evolution remains a hotly debated topic, specifically as regards the number and the timing of putative rounds of whole genome duplication events. In this study, I sought to shed light to this conundrum through assessing the evolutionary history of the oxytocin/vasotocin receptor family. I performed ancestral analyses of the genomic segments containing oxytocin and vasotocin receptors (OTR-VTRs) by mapping them back to the reconstructed ancestral vertebrate/chordate karyotypes reported in five independent studies (Nakatani et al., 2007; Putnam et al., 2008; Smith and Keinath, 2015; Smith et al., 2018; Simakov et al., 2020) and found that two alternative scenarios can account for their evolution: one consistent with one round of whole genome duplication in the common ancestor of lampreys and gnathostomes, followed by segmental duplications in both lineages, and another consistent with two rounds of whole genome duplication, with the first occurring in the gnathostome-lamprey ancestor and the second in the jawed vertebrate ancestor. Combining the data reported here with synteny and phylogeny data reported in our previous study (Theofanopoulou et al., 2021), I put forward that a single round of whole genome duplication scenario is more consistent with the synteny and evolution of chromosomes where OTR-VTRs are encountered, without excluding the possibility of a scenario including two rounds of whole genome duplication. Although the analysis of one gene family is not able to capture the full complexity of vertebrate genome evolution, this study can provide solid insight, since the gene family used here has been meticulously analyzed for its genes' orthologous and paralogous relationships across species using high quality genomes.