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Mmbando GS. The link between changing in host carbon allocation and resistance to Magnaporthe oryzae: a possible tactic for mitigating the rice blast fungus. PLANT SIGNALING & BEHAVIOR 2024; 19:2326870. [PMID: 38465846 PMCID: PMC10936674 DOI: 10.1080/15592324.2024.2326870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 02/29/2024] [Indexed: 03/12/2024]
Abstract
One of the most destructive diseases affecting rice is rice blast, which is brought on by the rice blast fungus Magnaporthe oryzae. The preventive measures, however, are not well established. To effectively reduce the negative effects of rice blasts on crop yields, it is imperative to comprehend the dynamic interactions between pathogen resistance and patterns of host carbon allocation. This review explores the relationship between variations in carbon allocation and rice plants' ability to withstand the damaging effects of M. oryzae. The review highlights potential strategies for altering host carbon allocation including transgenic, selective breeding, crop rotation, and nutrient management practices as a promising avenue for enhancing rice blast resistance. This study advances our knowledge of the interaction between plants' carbon allocation and M. oryzae resistance and provides stakeholders and farmers with practical guidance on mitigating the adverse effects of the rice blast globally. This information may be used in the future to create varieties that are resistant to M. oryzae.
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Affiliation(s)
- Gideon Sadikiel Mmbando
- Department of Biology, College of Natural and Mathematical Sciences, University of Dodoma, Dodoma, Tanzania
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Ngidi A, Shimelis H, Abady S, Chaplot V, Figlan S. Genetic variation and association of yield, yield components, and carbon storage in sorghum (Sorghum bicolor [L.] Moench) genotypes. BMC Genom Data 2024; 25:74. [PMID: 39090581 PMCID: PMC11295504 DOI: 10.1186/s12863-024-01256-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2024] [Accepted: 07/22/2024] [Indexed: 08/04/2024] Open
Abstract
Trait heritability and the response to selection depend on genetic variation, a prerequisite to developing sorghum varieties with desirable agronomic traits and high carbon sequestration for sustainable crop production and soil health. The present study aimed to assess the extent of genetic variability and associations among agronomic and carbon storage traits in selected sorghum genotypes to identify the best candidates for production or breeding. Fifty genotypes were evaluated at Ukulinga, Bethlehem and Silverton sites in South Africa during the 2022/23 growing season. The following agronomic and carbon storage traits were collected: days to 50% heading (DTH), days to 50% maturity (DTM), plant height (PH), total plant biomass (PB), shoot biomass (SB), root biomass (RB), root-to-shoot biomass ratio (RS), grain yield (GY), harvest index (HI), shoot carbon content (SCc), root carbon content (RCc), grain carbon content (GCc), total plant carbon stock (PCs), shoot carbon stock (SCs), root carbon stock (RCs), and root-to-shoot carbon stock ratio (RCs/SCs), and grain carbon stock (GCs). Higher genotypic coefficient of variations (GCVs) were recorded for GY at 45.92%, RB (39.24%), RCs/SCs (38.45), and RCs (34.62). Higher phenotypic coefficient of variations (PCVs) were recorded for PH (68.91%), followed by GY (51.8%), RB (50.51%), RS (41.96%), RCs/SCs (44.90%), and GCs (41.90%). High broad-sense heritability and genetic advance were recorded for HI (83.76 and 24.53%), GY (78.59 and 9.98%), PB (74.14 and 13.18%) and PCs (53.63 and 37.57%), respectively, suggesting a marked genetic contribution to the traits. Grain yield exhibited positive association with HI (r = 0.76; r = 0.79), DTH (r = 0.13; r = 0.31), PH (r = 0.1; r = 0.27), PB (r = 0.01; r = 0.02), RB (r = 0.05; r = 0.06) based on genotypic and phenotypic correlations, respectively. Further, the path analysis revealed significant positive direct effects of SB (0.607) and RB (0.456) on GY. The RS exerted a positive and significant indirect effect (0.229) on grain yield through SB. The study revealed that PB, SB, RB, RS, RCs, and RCs/SCs are the principal traits when selecting sorghum genotypes with high yield and carbon storage capacity.
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Affiliation(s)
- Asande Ngidi
- African Centre for Crop Improvement, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Private Bag X01, Scottsville, Pietermaritzburg, 3209, South Africa
| | - Hussein Shimelis
- African Centre for Crop Improvement, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Private Bag X01, Scottsville, Pietermaritzburg, 3209, South Africa
| | - Seltene Abady
- African Centre for Crop Improvement, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Private Bag X01, Scottsville, Pietermaritzburg, 3209, South Africa
| | - Vincent Chaplot
- School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Private Bag X01, Scottsville, Pietermaritzburg, 3209, South Africa
- Laboratory of Oceanography and Climate, Experiments and Numerical Approaches (LOCEAN), UMR 7159, IRD/C NRS/UPMC/MNHN, IPSL, Paris, 75005, France
| | - Sandiswa Figlan
- Department of Agriculture and Animal Health, University of South Africa, Florida, 1709, South Africa.
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Dwivedi SL, Heslop-Harrison P, Amas J, Ortiz R, Edwards D. Epistasis and pleiotropy-induced variation for plant breeding. PLANT BIOTECHNOLOGY JOURNAL 2024. [PMID: 38875130 DOI: 10.1111/pbi.14405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Revised: 05/07/2024] [Accepted: 05/24/2024] [Indexed: 06/16/2024]
Abstract
Epistasis refers to nonallelic interaction between genes that cause bias in estimates of genetic parameters for a phenotype with interactions of two or more genes affecting the same trait. Partitioning of epistatic effects allows true estimation of the genetic parameters affecting phenotypes. Multigenic variation plays a central role in the evolution of complex characteristics, among which pleiotropy, where a single gene affects several phenotypic characters, has a large influence. While pleiotropic interactions provide functional specificity, they increase the challenge of gene discovery and functional analysis. Overcoming pleiotropy-based phenotypic trade-offs offers potential for assisting breeding for complex traits. Modelling higher order nonallelic epistatic interaction, pleiotropy and non-pleiotropy-induced variation, and genotype × environment interaction in genomic selection may provide new paths to increase the productivity and stress tolerance for next generation of crop cultivars. Advances in statistical models, software and algorithm developments, and genomic research have facilitated dissecting the nature and extent of pleiotropy and epistasis. We overview emerging approaches to exploit positive (and avoid negative) epistatic and pleiotropic interactions in a plant breeding context, including developing avenues of artificial intelligence, novel exploitation of large-scale genomics and phenomics data, and involvement of genes with minor effects to analyse epistatic interactions and pleiotropic quantitative trait loci, including missing heritability.
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Affiliation(s)
| | - Pat Heslop-Harrison
- Key Laboratory of Plant Resources Conservation and Sustainable Utilization, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, China
- Department of Genetics and Genome Biology, Institute for Environmental Futures, University of Leicester, Leicester, UK
| | - Junrey Amas
- Centre for Applied Bioinformatics, School of Biological Sciences, University of Western Australia, Perth, WA, Australia
| | - Rodomiro Ortiz
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden
| | - David Edwards
- Centre for Applied Bioinformatics, School of Biological Sciences, University of Western Australia, Perth, WA, Australia
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Boatwright JL, Sapkota S, Kresovich S. Functional genomic effects of indels using Bayesian genome-phenome wide association studies in sorghum. Front Genet 2023; 14:1143395. [PMID: 37065477 PMCID: PMC10102435 DOI: 10.3389/fgene.2023.1143395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 03/20/2023] [Indexed: 04/03/2023] Open
Abstract
High-throughput genomic and phenomic data have enhanced the ability to detect genotype-to-phenotype associations that can resolve broad pleiotropic effects of mutations on plant phenotypes. As the scale of genotyping and phenotyping has advanced, rigorous methodologies have been developed to accommodate larger datasets and maintain statistical precision. However, determining the functional effects of associated genes/loci is expensive and limited due to the complexity associated with cloning and subsequent characterization. Here, we utilized phenomic imputation of a multi-year, multi-environment dataset using PHENIX which imputes missing data using kinship and correlated traits, and we screened insertions and deletions (InDels) from the recently whole-genome sequenced Sorghum Association Panel for putative loss-of-function effects. Candidate loci from genome-wide association results were screened for potential loss of function using a Bayesian Genome-Phenome Wide Association Study (BGPWAS) model across both functionally characterized and uncharacterized loci. Our approach is designed to facilitate in silico validation of associations beyond traditional candidate gene and literature-search approaches and to facilitate the identification of putative variants for functional analysis and reduce the incidence of false-positive candidates in current functional validation methods. Using this Bayesian GPWAS model, we identified associations for previously characterized genes with known loss-of-function alleles, specific genes falling within known quantitative trait loci, and genes without any previous genome-wide associations while additionally detecting putative pleiotropic effects. In particular, we were able to identify the major tannin haplotypes at the Tan1 locus and effects of InDels on the protein folding. Depending on the haplotype present, heterodimer formation with Tan2 was significantly affected. We also identified major effect InDels in Dw2 and Ma1, where proteins were truncated due to frameshift mutations that resulted in early stop codons. These truncated proteins also lost most of their functional domains, suggesting that these indels likely result in loss of function. Here, we show that the Bayesian GPWAS model is able to identify loss-of-function alleles that can have significant effects upon protein structure and folding as well as multimer formation. Our approach to characterize loss-of-function mutations and their functional repercussions will facilitate precision genomics and breeding by identifying key targets for gene editing and trait integration.
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Affiliation(s)
- J. Lucas Boatwright
- Department of Plant and Environmental Sciences, Clemson University, Clemson, SC, United States
- Advanced Plant Technology, Clemson University, Clemson, SC, United States
- *Correspondence: J. Lucas Boatwright,
| | - Sirjan Sapkota
- Advanced Plant Technology, Clemson University, Clemson, SC, United States
| | - Stephen Kresovich
- Department of Plant and Environmental Sciences, Clemson University, Clemson, SC, United States
- Advanced Plant Technology, Clemson University, Clemson, SC, United States
- Feed the Future Innovation Lab for Crop Improvement, Cornell University, Ithaca, NY, United States
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Voelker WG, Krishnan K, Chougule K, Alexander LC, Lu Z, Olson A, Ware D, Songsomboon K, Ponce C, Brenton ZW, Boatwright JL, Cooper EA. Ten new high-quality genome assemblies for diverse bioenergy sorghum genotypes. FRONTIERS IN PLANT SCIENCE 2023; 13:1040909. [PMID: 36684744 PMCID: PMC9846640 DOI: 10.3389/fpls.2022.1040909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 12/09/2022] [Indexed: 06/17/2023]
Abstract
INTRODUCTION Sorghum (Sorghum bicolor (L.) Moench) is an agriculturally and economically important staple crop that has immense potential as a bioenergy feedstock due to its relatively high productivity on marginal lands. To capitalize on and further improve sorghum as a potential source of sustainable biofuel, it is essential to understand the genomic mechanisms underlying complex traits related to yield, composition, and environmental adaptations. METHODS Expanding on a recently developed mapping population, we generated de novo genome assemblies for 10 parental genotypes from this population and identified a comprehensive set of over 24 thousand large structural variants (SVs) and over 10.5 million single nucleotide polymorphisms (SNPs). RESULTS We show that SVs and nonsynonymous SNPs are enriched in different gene categories, emphasizing the need for long read sequencing in crop species to identify novel variation. Furthermore, we highlight SVs and SNPs occurring in genes and pathways with known associations to critical bioenergy-related phenotypes and characterize the landscape of genetic differences between sweet and cellulosic genotypes. DISCUSSION These resources can be integrated into both ongoing and future mapping and trait discovery for sorghum and its myriad uses including food, feed, bioenergy, and increasingly as a carbon dioxide removal mechanism.
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Affiliation(s)
- William G. Voelker
- Dept. of Bioinformatics & Genomics, University of North Carolina at Charlotte, Charlotte, NC, United States
- North Carolina Research Campus, Kannapolis, NC, United States
| | - Krittika Krishnan
- Dept. of Bioinformatics & Genomics, University of North Carolina at Charlotte, Charlotte, NC, United States
- North Carolina Research Campus, Kannapolis, NC, United States
| | - Kapeel Chougule
- Cold Spring Harbor Research Laboratory, Cold Spring Harbor, NY, United States
| | - Louie C. Alexander
- Dept. of Bioinformatics & Genomics, University of North Carolina at Charlotte, Charlotte, NC, United States
- North Carolina Research Campus, Kannapolis, NC, United States
| | - Zhenyuan Lu
- Cold Spring Harbor Research Laboratory, Cold Spring Harbor, NY, United States
| | - Andrew Olson
- Cold Spring Harbor Research Laboratory, Cold Spring Harbor, NY, United States
| | - Doreen Ware
- Cold Spring Harbor Research Laboratory, Cold Spring Harbor, NY, United States
- United States Department of Agriculture - Agricultural Research Service in the North Atlantic Area (USDA-ARS NAA), Robert W. Holley Center for Agriculture and Health, Ithaca, NY, United States
| | - Kittikun Songsomboon
- Dept. of Bioinformatics & Genomics, University of North Carolina at Charlotte, Charlotte, NC, United States
- North Carolina Research Campus, Kannapolis, NC, United States
| | - Cristian Ponce
- Dept. of Bioinformatics & Genomics, University of North Carolina at Charlotte, Charlotte, NC, United States
- North Carolina Research Campus, Kannapolis, NC, United States
| | - Zachary W. Brenton
- Carolina Seed Systems, Darlington, SC, United States
- Advanced Plant Technology, Clemson University, Clemson, SC, United States
| | - J. Lucas Boatwright
- Advanced Plant Technology, Clemson University, Clemson, SC, United States
- Dept. of Plant and Environmental Sciences, Clemson University, Clemson, SC, United States
| | - Elizabeth A. Cooper
- Dept. of Bioinformatics & Genomics, University of North Carolina at Charlotte, Charlotte, NC, United States
- North Carolina Research Campus, Kannapolis, NC, United States
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