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, 13 (3), e0192678

Cross-species Multiple Environmental Stress Responses: An Integrated Approach to Identify Candidate Genes for Multiple Stress Tolerance in Sorghum (Sorghum Bicolor (L.) Moench) and Related Model Species


Cross-species Multiple Environmental Stress Responses: An Integrated Approach to Identify Candidate Genes for Multiple Stress Tolerance in Sorghum (Sorghum Bicolor (L.) Moench) and Related Model Species

Adugna Abdi Woldesemayat et al. PLoS One.

Erratum in


Background: Crop response to the changing climate and unpredictable effects of global warming with adverse conditions such as drought stress has brought concerns about food security to the fore; crop yield loss is a major cause of concern in this regard. Identification of genes with multiple responses across environmental stresses is the genetic foundation that leads to crop adaptation to environmental perturbations.

Methods: In this paper, we introduce an integrated approach to assess candidate genes for multiple stress responses across-species. The approach combines ontology based semantic data integration with expression profiling, comparative genomics, phylogenomics, functional gene enrichment and gene enrichment network analysis to identify genes associated with plant stress phenotypes. Five different ontologies, viz., Gene Ontology (GO), Trait Ontology (TO), Plant Ontology (PO), Growth Ontology (GRO) and Environment Ontology (EO) were used to semantically integrate drought related information.

Results: Target genes linked to Quantitative Trait Loci (QTLs) controlling yield and stress tolerance in sorghum (Sorghum bicolor (L.) Moench) and closely related species were identified. Based on the enriched GO terms of the biological processes, 1116 sorghum genes with potential responses to 5 different stresses, such as drought (18%), salt (32%), cold (20%), heat (8%) and oxidative stress (25%) were identified to be over-expressed. Out of 169 sorghum drought responsive QTLs associated genes that were identified based on expression datasets, 56% were shown to have multiple stress responses. On the other hand, out of 168 additional genes that have been evaluated for orthologous pairs, 90% were conserved across species for drought tolerance. Over 50% of identified maize and rice genes were responsive to drought and salt stresses and were co-located within multifunctional QTLs. Among the total identified multi-stress responsive genes, 272 targets were shown to be co-localized within QTLs associated with different traits that are responsive to multiple stresses. Ontology mapping was used to validate the identified genes, while reconstruction of the phylogenetic tree was instrumental to infer the evolutionary relationship of the sorghum orthologs. The results also show specific genes responsible for various interrelated components of drought response mechanism such as drought tolerance, drought avoidance and drought escape.

Conclusions: We submit that this approach is novel and to our knowledge, has not been used previously in any other research; it enables us to perform cross-species queries for genes that are likely to be associated with multiple stress tolerance, as a means to identify novel targets for engineering stress resistance in sorghum and possibly, in other crop species.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.


Fig 1
Fig 1. Work-flow for gene-phenotype association across-species and stresses.
This figure demonstrates the work flow for the gene-phenotype association in sorghum stress tolerance across species by comparing sorghum drought responsive genes with orthologs in maize, rice and Arabidopsis and across stresses by comparing these genes against salt, cold, heat and oxidative stresses. The Gramene database was used in identification of sorghum genes associated with stress phenotypes based on known stress related ontology terms for each identified plant ontology. Ensembl BioMart was used to get sorghum orthologs having transitive association with known drought regulated functions from related species. The work flow provides a protocol for a step-by-step screening procedure to identify promising gene-sets for multiple stress tolerance across species: 1) The protocol identifies plant ontologies to query genes and detects if the genes belong to the sorghum gene association or to the orthologous group. Where there is no direct sorghum gene association, the protocol looks for orthologous group. Only those genes with these features were retained, and others were discarded. 2) The genes that were not supported by the relevant ontology terms in each ontology group were again rejected and only those with drought and associated ontology terms were screened for the next step. Once merged from all ontology groups, only unique genes were captured by removing the duplicates. 3) Among these, only those which were supported by all ontology groups were used for functional GO enrichment analysis and all others were discarded. 4) Functional GO enrichment analysis based on the P-value, FDR < 0.01 were used to screen the genes associated with stresses under investigation. Only those which satisfied this threshold value were selected as the candidates for the next step. 5) Comparative analysis across species and across traits was undertaken based on the above selected candidates. Sorghum specific and orthologous genes with multi-stress responses were combined with enrichment network and expression profiling for integrative analysis. Sorghum orthologs in other species were selected for which phylogenetic analysis was done. Key to legend: * Response to oxidative stress; ** Drought tolerance.
Fig 2
Fig 2. Ontology map for sorghum gene association to multiple drought related terms.
The figure shows sorghum genes directly and transitively associated to multiple drought related terms based on functional ontologies. The information from EO, TO, GRO, PO and GO was used to investigate sorghum genes and orthologs in rice and maize associated with stress response. The map represents sorghum specific features for displaying class hierarchy against the ontologies under consideration and the orthologous genes from maize and rice. The hierarchical structure was designed to show multiparental relationships of sorghum genes with different ontology categories without including direct class hierarchy between maize or rice genes to the ontologies. This reveals the occurrence of multi-stress responsive sorghum specific genes and orthologous groups which are associated with GO cellular components for their localization. While the molecular functions and the biological processes of the sorghum specific genes and the orthologs are conserved, the ontology supports all these biological realities.
Fig 3
Fig 3. Heat map showing differential gene expression.
This figure shows gene expression profiles based on sorghum drought stressed root and shoot tissues (a), the 22 most abundant GO terms enriched in maize leaf and ovary tissues under drought stress with the corresponding up-regulated maize genes and their respective sorghum orthologs expression patterns (b). Similarly, the figure shows the 22 common GO terms enriched in rice leaf and shoot under salt stress showing the corresponding up-regulated rice genes and their corresponding sorghum orthologs expression patterns (c). Sorghum orthologs expression patterns were added in (b) and (c) to show visual comparison of expression profiles for transitively associated genes between sorghum and maize and sorghum and rice separately. Parametric analysis of gene set enrichment was determined by the T-statistics based clustering frequency using MeV 4.48 [52], an R based software. The rows represent the genes (a), GO-terms and corresponding genes and orthologs and GO annotation (b) and (c), whereas the columns represent the biological samples. While the red color denotes the up-regulation, the green shows down-regulation of the genes in all the clustering panels. Hierarchical clustering, for instance show the patterns of expression in (a), by grouping the most up-regulated sorghum genes in the upper right corner, middle and lower left corner.
Fig 4
Fig 4. Venn diagram to show functional cross-talk and specificity of genes for drought tolerance and other stresses.
Sorghum genes are shown in association with drought, salt, cold and heat stress related ontology terms of the biological process based on the datasets originated from Gramene database (a) and those in association with drought, ABA, cold and salt stresses based on sequence similarity search using expression dataset (b). Similarly, sorghum genes associated with ontology-terms of the biological process related to stress other than drought (salt, cold and heat and reactive oxygen species) based on data from Gramene database (c) are presented. The numbers displayed in the Venn diagram correspond to the number of genes. The superimposed regions of all circles show the number of genes shared in all the four species. The peripheral parts that don't overlap between circles show unique genes responsive to the respective stresses. Key to legend: RC—Response to cold; RH—Response to heat; ROS—Response to osmotic stress; RROS—Response to reactive oxygen species.
Fig 5
Fig 5. Venn diagram showing distribution of shared drought responsive genes among species and specific genes to sorghum.
The figure shows a distribution of sorghum orthologous genes in the other 3 related species in association with drought related ontology terms based on existing data for known genes in Gramene database (a) and based on sequence similarity search using expression dataset (b). The numbers displayed in the Venn diagram correspond to the number of genes. Superimposed regions of all circles show the number of genes shared in all species under investigation. Overlapping regions between any 3 species indicate shared gene loci and functional conservation between the 3 of the 4 species while the shared regions between any 2 species involved show the shared gene loci and functional conservation in the 2 species. Parts that don't overlap between circles show unique drought responsive genes for each species.
Fig 6
Fig 6. Circular representation of the polar formatted phylogenetic tree of the sorghum specific and orthologous genes.
Group of genes were color-coded by orthology group identified for drought response in the other species evolutionarily related to sorghum. The tree represents labels that were aligned with default leaf sorting. Branches represent evolutionarily related ortholog clades. Branch lengths for which 'ignored' setting was adjusted were represented each by the numbers in decimal and the bootstrap values in absolute numbers (S10 Table). The tree was reconstructed after removing the gaps using a bootstrap support of the 1,000 replicates to show the frequency of each internal node, clades in the tree. The red circular bootstrap symbol was used to indicate the bootstrap supported clades based on the values within the range of 100 (small dot)– 1000 (large dot) iterative replicates, where more than 75% of the clades showed the bootstrap above the commonly known threshold value (70%). The clades with the bootstrap values less than 5% were removed from the tree. The values for the robust bootstrap support were given in S10 Table. Key to legend for the colored ranges: SOA, Sorghum orthologs in Arabidopsis; SOM, sorghum orthologs in maize; SOMA, shared sorghum orthologs in maize and Arabidopsis; SOMR, shared sorghum orthologs in maize and rice; SOMRA, shared sorghum orthologs in maize, rice and Arabidopsis; SOR, sorghum orthologs in rice; SORA, shared sorghum orthologs in rice and Aabidopsis; Sorghum, sorghum specific genes; Sorghum_SOA, shared sorghum specific and sorghum orthologs in Arabidopsis; Sorghum_SOMA, shared sorghum specific and sorghum orthologs in maize and Arabidopsis; Sorghum_SOMR, shared sorghum specific and sorghum orthologs in maize and rice; Sorghum_SOMRA, shared sorghum specific and sorghum orthologs in maize, rice and Arabidopsis; Sorghum_SOR, shared sorghum specific and sorghum orthologs in rice; Sorghum_SORA, shared sorghum specific and sorghum orthologs in rice and Arabidopsis.
Fig 7
Fig 7. Venn diagram showing the sorghum orthologous genes identified in different plant ontology categories.
The diagram represents the distribution of gene association related to the five drought-associated plant ontology terms. The numbers in the overlapping portions represent the number of gene contributions shared by 1 or more ontology categories with the genes positioned in the center represented by all ontologies, while those shown on the peripheral portion represent the number of genes specific to each respective ontology category.
Fig 8
Fig 8. Enrichment network map for selected sorghum drought responsive genes.
Gene enrichment maps for selected 50 sorghum drought associated enriched GO-terms (a) and the corresponding genes (b) depict the biological networks of the genes that are involved in the regulation of cross talk in response to multiple stresses. Nodes denote a group of genes (gene-sets) or group of GO-terms and edges represent GO defined relations. The threshold level of the enrichment significance determines the appearance of the group of genes on the enrichment network map. The intensity of the node represents the level of significance of the enrichment and the size of the node correlates with the size of significantly enriched gene set that overlaps or makes the group of up-regulated enrichment. The p-value is included in the label of the nodes to indicate the level of enrichment significance. The network explains the corresponding gene function defined by the enriched GO-terms in the particular GO-category. The position of the nodes for the enriched gene set is not necessarily correlated with the position of the corresponding enriched set of GO-terms. The color usage for the node and edge is an arbitrary selection for proper contrast.

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This work is based on the research supported by the South African Research Chairs Initiative of the Department of Science and Technology ( and National Research Foundation of South Africa ( AC received funding. The University of the Western Cape and the University of South Africa also provided financial support. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.