Review: Climate change impacts on food security- focus on perennial cropping systems and nutritional value.

The characterization of the genetic basis of maize (Zea mays) leaf development may support breeding efforts to obtain plants with higher vigor and productivity. In this study, a mapping panel of 197 biparental and multiparental maize recombinant inbred lines (RILs) was analyzed for multiple leaf traits at the seedling stage. RNA sequencing was used to estimate the transcription levels of 29 573 gene models in RILs and to derive 373 769 single nucleotide polymorphisms (SNPs), and a forward genetics approach combining these data was used to pinpoint candidate genes involved in leaf development. First, leaf traits were correlated with gene expression levels to identify transcript-trait correlations. Then, leaf traits were associated with SNPs in a genome-wide association (GWA) study. An expression quantitative trait locus mapping approach was followed to associate SNPs with gene expression levels, prioritizing candidate genes identified based on transcript-trait correlations and GWAs. Finally, a network analysis was conducted to cluster all transcripts in 38 co-expression modules. By integrating forward genetics approaches, we identified 25 candidate genes highly enriched for specific functional categories, providing evidence supporting the role of vacuolar proton pumps, cell wall effectors, and vesicular traffic controllers in leaf growth. These results tackle the complexity of leaf trait determination and may support precision breeding in maize.

A forward genetics approach integrating genome-wide association study and expression quantitative trait locus mapping to dissect leaf development in maize (Zea mays).

Abstract Specialized metabolites not only play important roles in biotic and abiotic stress adaptation of tea plants (Camellia sinensis (L.) O. Kuntze) but also contribute to the unique flavor of tea, the most important nonalcoholic beverage. However, the molecular networks and major genes that regulate specialized metabolites in tea plants are not well understood. Here, we constructed a population-level pan-transcriptome of the tea plant leaf using second-leaf transcriptome data from 134 accessions to investigate global expression differences in the population, expression presence or absence variations (ePAVs), and differentially expressed genes (DEGs) between pure Camellia sinensis var. assamica (CSA) and pure Camellia sinensis var. sinensis (CSS) accessions. Next, we used a genome-wide association study, a quantitative trait transcript study, and a transcriptome-wide association study to integrate genotypes, accumulation levels of specialized metabolites, and expression levels of pan-transcriptome genes to identify candidate regulatory genes for flavor-related metabolites and to construct a regulatory network for specialized metabolites in tea plants. The pan-transcriptome contains 30 482 expressed genes, 4940 and 5506 of which were newly annotated from a de novo transcriptome assembly without a reference and a genome reference-based assembly, respectively. DEGs and ePAVs indicated that CSA and CSS were clearly differentiated at the population transcriptome level, and they were closely related to abiotic tolerance and secondary metabolite synthesis phenotypes of CSA and CSS based on gene annotations. The regulatory network contained 212 specialized metabolites, 3843 candidate genes, and 3407 eQTLs, highlighting many pleiotropic candidate genes, candidate gene-rich eQTLs, and potential regulators of specialized metabolites. These included important transcription factors in the AP2/ERF-ERF, MYB, WD40, and bHLH families. CsTGY14G0001296, an ortholog of AtANS, appeared to be directly related to variation in proanthocyanins in the tea plant population, and the CsTGY11G0002074 gene encoding F3′5′H was found to contribute to the biased distribution of catechins between pure CSAs and pure CSSs. Together, these results provide a new understanding of the metabolite diversity in tea plants and offer new insights for more effective breeding of better-flavored tea varieties.

Pan-transcriptome assembly combined with multiple association analysis provides new insights into the regulatory network of specialized metabolites in the tea plant Camellia sinensis

Abstract Background Transcription bridges genetic information and phenotypes. Here, we evaluated how changes in transcriptional regulation enable maize ( Zea mays ), a crop originally domesticated in the tropics, to adapt to temperate environments. Result We generated 572 unique RNA-seq datasets from the roots of 340 maize genotypes. Genes involved in core processes such as cell division, chromosome organization and cytoskeleton organization showed lower heritability of gene expression, while genes involved in anti-oxidation activity exhibited higher expression heritability. An expression genome-wide association study (eGWAS) identified 19,602 expression quantitative trait loci (eQTLs) associated with the expression of 11,444 genes. A GWAS for alternative splicing identified 49,897 splicing QTLs (sQTLs) for 7614 genes. Genes harboring both cis -eQTLs and cis -sQTLs in linkage disequilibrium were disproportionately likely to encode transcription factors or were annotated as responding to one or more stresses. Independent component analysis of gene expression data identified loci regulating co-expression modules involved in oxidation reduction, response to water deprivation, plastid biogenesis, protein biogenesis, and plant-pathogen interaction. Several genes involved in cell proliferation, flower development, DNA replication, and gene silencing showed lower gene expression variation explained by genetic factors between temperate and tropical maize lines. A GWAS of 27 previously published phenotypes identified several candidate genes overlapping with genomic intervals showing signatures of selection during adaptation to temperate environments. Conclusion Our results illustrate how maize transcriptional regulatory networks enable changes in transcriptional regulation to adapt to temperate regions. 

/pdf/a-role-for-heritable-transcriptomic-variation-in-maize-35kkuz8s.pdf

A role for heritable transcriptomic variation in maize adaptation to temperate environments

Asparagine synthetase catalyses the transfer of an amino group from glutamine to aspartate to form glutamate and asparagine. The accumulation of free (non-protein) asparagine in crops has implications for food safety because free asparagine is the precursor for acrylamide, a carcinogenic contaminant that forms during high-temperature cooking and processing. Here we review publicly-available genome data for asparagine synthetase genes from species of the Pooideae subfamily, including bread wheat and related wheat species (Triticum and Aegilops spp.), barley (Hordeum vulgare) and rye (Secale cereale) of the Triticeae tribe. Also from the Pooideae subfamily: brachypodium (Brachypodium dystachion) of the Brachypodiae tribe. More diverse species are also included, comprising sorghum (Sorghum bicolor) and maize (Zea mays) of the Panicoideae subfamily, and rice (Oryza sativa) of the Ehrhartoideae subfamily. The asparagine synthetase gene families of the Triticeae species each comprise five genes per genome, with the genes assigned to four groups: 1, 2, 3 (subdivided into 3.1 and 3.2) and 4. Each species has a single gene per genome in each group, except that some bread wheat varieties (genomes AABBDD) and emmer wheat (Triticum dicoccoides; genomes AABB) lack a group 2 gene in the B genome. This raises questions about the ancestry of cultivated pasta wheat and the B genome donor of bread wheat, suggesting that the hybridisation event that gave rise to hexaploid bread wheat occurred more than once. In phylogenetic analyses, genes from the other species cluster with the Triticeae genes, but brachypodium, sorghum and maize lack a group 2 gene, while rice has only two genes, one group 3 and one group 4. This means that TaASN2, the most highly expressed asparagine synthetase gene in wheat grain, has no equivalent in maize, rice, sorghum or brachypodium. An evolutionary pathway is proposed in which a series of gene duplications gave rise to the five genes found in modern Triticeae species.

Cereal asparagine synthetase genes.

In maize, kernel traits strongly impact overall grain yields, and it is known that sophisticated spatiotemporal programs of gene expression coordinate kernel development, so advancing our knowledge of kernel development can help efforts to improve grain yields. Here, using phenotype, genotype and transcriptomics data of maize kernels at 5 and 15 days after pollination (DAP) for a large association mapping panel, we employed multiple quantitative genetics approaches-genome-wide association studies (GWAS) as well as expression quantitative trait loci (eQTL) and quantitative trait transcript (QTT) analyses-to gain insights about molecular genetic basis of kernel development in maize. This resulted in the identification of 137 putative kernel length-related genes at 5 DAP, of which 43 are located in previously reported QTL regions. Strikingly, we identified an eQTL that overlaps the locus encoding a maize homolog of the recently described m6 A methylation reader protein ECT2 from Arabidopsis; this putative epi eQTL is associated with 53 genes and may represent a master epi-transcriptomic regulator of kernel development. Notably, among the genes associated with this epi eQTL, 10 are for the main storage proteins in the maize endosperm (zeins) and two are known regulators of zein expression or endosperm development (Opaque2 and ZmICE1). Collectively, beyond cataloging and characterizing genomic attributes of a large number of eQTL associated with kernel development in maize, our study highlights how an eQTL approach can bolster the impact of both GWAS and QTT studies and can drive insights about the basic biology of plants.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/tpj.14193

Kernel size-related genes revealed by an integrated eQTL analysis during early maize kernel development.

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Jing Wang

Author Tools

Chat about Author

Papers

Kernel size-related genes revealed by an integrated eQTL analysis during early maize kernel development.