Abstract
Background
Tef (Eragrostis tef) is a C4 plant known for its tiny, nutritious, and gluten-free grains. It contains higher levels of protein, vitamins, and essential minerals like calcium (Ca), iron (Fe), copper (Cu), and zinc (Zn) than common cereals. Tef is cultivated in diverse ecological zones under diverse climatic conditions. Studies have shown that tef has great diversity in withstanding environmental challenges such as drought. Drought is a major abiotic stress severely affecting crop productivity and becoming a bottleneck to global food security. Here, we used in silico-based functional genomic analysis to identify drought-responsive genes in tef and validated their expression using quantitative RT-PCR.
Results
We identified about 729 drought-responsive genes so far reported in six crop plants, including rice, wheat, maize, barley, sorghum, pearl millet, and the model plant Arabidopsis, and reported 20 genes having high-level of GO terms related to drought, and significantly enriched in several biological and molecular function categories. These genes were found to play diverse roles, including water and fluid transport, resistance to high salt, cold, and drought stress, abscisic acid (ABA) signaling, de novo DNA methylation, and transcriptional regulation in tef and other crops. Our analysis revealed substantial differences in the conserved domains of some tef genes from well-studied rice orthologs. We further analyzed the expression of sixteen tef orthologs using quantitative RT-PCR in response to PEG-induced osmotic stress.
Conclusions
The findings showed differential regulation of some drought-responsive genes in shoots, roots, or both tissues. Hence, the genes identified in this study may be promising candidates for trait improvement in crops via transgenic or gene-editing technologies.
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Introduction
Climate changes and increased water scarcity in some regions challenge global food security and threaten the food supply for the ever-growing global population [1,2,3]. To feed such a growing population, global agricultural production might need to increase by 60–110% [4, 5]. Field crops continuously experience fluctuations in environmental conditions and are often exposed to abiotic stresses such as drought, salinity, excess light, high/low temperatures, and nutrient imbalances [6, 7]. The capability of plants to respond to abiotic stress is associated with the plasticity and adaptability of their traits to the fluctuating conditions of water availability [8].
Drought is a major abiotic stress that severely affects crop production and productivity [9]. Drought affects nutrient availability, plant growth, and survival [10]. It is a complex phenomenon that can be classified into agricultural, metrological, and hydrological components [11]. A hydrological drought is associated with a deficiency in the water supply volume, a meteorological drought encompasses the degree of dryness and the duration of the dry period, and an agricultural drought results from a shortage of available water for plant growth [11]. Studies indicate that more than 30% of the world’s agricultural land is subjected to drought of which 14% is an extreme one [12, 13].
To adapt to various environmental conditions on earth, plant species have evolved C3, C4, and Crassulacean Acid Metabolism (CAM) photosynthetic systems [14]. CAM and C4 photosynthesis were thought to have evolved from the classical C3 photosynthetic pathway around 20–30 million years ago [15]. CAM and C4 photosynthetic processes achieve increased water use efficiency by concentrating CO2 at the C-fixation site of the dark reactions of photosynthesis [16]. The C3 photosynthesis is a one-stage process that produces a three-carbon compound (3-phosphoglyceric acid) via the Calvin Benson-Bassham (CBB) cycle, while C4 and CAM photosynthesis are two-stage processes, with the first CO2 fixation stage generating a four-carbon compound malate, followed by decarboxylation of malate, releasing CO2 to be refixed through the CBB cycle [16]. To boost crop resilience to global warming and to increase crop yields, efforts are ongoing to engineer C4 and CAM traits into C3 crop species [17,18,19].
Developing elite crop germplasms is one of the approaches used to mitigate the impact of drought and promote underutilized crop species that have the potential to enhance food security under unfavorable environmental conditions. Exploiting the large gene pool of underutilized crop plants would provide a more diversified agricultural system and an alternative healthy food resource, ensuring food, and nutritional security [20]. However, the world still relies on a limited number of food crops mostly C3 cereals like wheat (Triticum aestivum), barley (Hordeum vulgare) and rice (Oryza sativa), and very few C4 cereals such as pearl millet (Pennisetum glaucum), maize (Zea mays), and sorghum (Sorghum bicolor) [21]. However, there are still several drought-tolerant and underutilized crops like tef (E. tef) that could be an alternative source of food, feed, and energy.
Tef is a tetraploid (2n = 4x = 20) self-pollinated crop [22]. The genus Eragrostis comprises about 350 species of which tef is the only species cultivated for human consumption as gluten-free grain [23]. Tef is a staple crop in Ethiopia and Eritrea for about 80 million people [24, 25] where it is most widely produced. In Ethiopia, tef was produced on about three million hectares of land in the 2021/22 cropping season and accounted for a yield estimate of about 5.7 million metric tons [26]. Most accessions of tef including their wild relatives grow under a wide range of ecological conditions, ranging from sea level to 3000 m above sea level (m.a.s.l) [27, 28]. Over the last few decades, domestication, and cultivation of tef has been taking place in several other countries including South Africa, Australia, India, USA, China, Netherlands, and Israel for its healthy grains as well as forage grass [27, 28]. Studies have shown that tef is high in protein, vitamins, and essential minerals like calcium, iron, copper, and zinc as compared to other cereal grains such as wheat, maize, barley, and sorghum [29,30,31,32] and becoming globally popular due to its attractive nutritional profiles.
Despite tef’s potential as a nutritious and healthy crop, its productivity is limited due to various factors including the lack of modern farming technologies, susceptibility to lodging (permanent bending of the stem from the upright position), soil acidity, salinity, and terminal drought [33]. Tef is an ‘orphan crops’ that has not benefitted from genetic improvement programs [31]. Its yield also remained very low with a national average yield below 1.75 t/ha in Ethiopia [34]. Tef seed is one of the smallest grains in the world with a length of about 1.0 mm and a width of about 0.60 mm [25]. Tef is moderately drought tolerant when compared to its wild progenitor Eragrostis pilosa [25]. It is reported that the yield loss due to moderate to severe drought from booting to grain filling stages was 35%—52% [35, 36]. A yield reduction of 69 to 77% has also been documented due to drought at the anthesis stage [37]. In Ethiopia, several germplasm screening projects were conducted and some promising tef varieties have been identified [38,39,40,41,42]. Furthermore, seven differentially expressed miRNAs linked to drought tolerance in tef were reported [43]. An attempt to improve drought tolerance using ethyl methyl sulfonate (EMS) based chemical mutagenesis [44] generated two early drought-tolerant (dtt2 and dtt13) and three terminal drought-tolerant lines (tdt9, tdt15, and tdt19) tef varieties that have potential for trait improvement through breeding. Hence, a comprehensive understanding of the impact of drought and associated stresses is critical for developing climate-resilient crops that can adapt to changing climatic conditions.
As more than 1000 whole genome sequence data are available comprising about 788 plant species in the last two decades [45], in silico analysis and identification of candidate genes implicated in several agronomic traits, including drought, is becoming handier. Similarly, several bioinformatics tools capable of analyzing the role of genes and gene products are becoming available. In recent years, many research articles are utilizing in silico analysis for gene identification because it is cost-effective, fast, and does not need sophisticated equipment. In silico analysis is having a great impact on shortening the lengthy classical and laborious wet lab experimentation. It has been used to identify differentially regulated drought-responsive genes in a number of plant samples [46,47,48]. As most studies conducted so far were based on the identification of one or a few genes, attempts to identify a large array of genes from the whole transcriptome are limited. For instance, despite the availability of the draft genome sequence of tef, in silico gene identification so far focused on a few selected transcriptional factors (TFs) rather than utilizing a large array of drought-responsive genes from a range of plant species [49,50,51,52,53,54]. Some research articles published in the last two years on in silico analysis of drought-responsive gene identification in different crops are summarized in Table 1 below. In this paper, were performed in silico analyses and identified 20 potential candidate drought-responsive genes in the tef genome based on ortholog genes reported in related grass species and the model plant Arabidopsis thaliana. Furthermore, quantitative qPCR was used to validate the expression of putative drought-responsive genes in tef.
Methods
Identification of the drought-responsive genetic elements
To identify drought-responsive coding and regulatory elements in tef, sequences were retrieved were retrieved from two databases: Drought Stress Gene Database [65] and CrealESTDb which is a resource for abiotic stress-responsive annotated ESTs [63]. Furthermore, we retrieved 175 previously reported drought-responsive genes in tef [66] from the NCBI database. We also downloaded 889 novel abscisic acid, stress, and ripening (ASR) EST recently reported in pearl millet that were isolated from drought stress-responsive suppression subtractive hybridization (SSH library) and reported to confer multiple abiotic stress tolerance in transgenic Arabidopsis [67]. For drought-responsive genes, the nucleotide sequences were retrieved from the NCBI. Overall, drought-responsive genes that were reported in eight plant species, including tef using rice microarray, Arabidopsis, maize, sorghum, barley, wheat, and pearl millet, were used in our in silico analysis.
Mapping of drought-responsive ortholog genes in the tef genome
To identify drought-responsive gene signatures in the tef genome, we used CoGeBLAST (https://genomevolution.org/coge/CoGeBlast.pl) with genome ID 50954 [68]. Before the homology search, the E-value in CoGe BLAST was set to 1E−30 to generate alignment with strong matches and to minimize the inclusion of sequences with low homology. Using drought-responsive genes from the Drought Stress Gene Database [65], 70 genes having strong homology (E-value < 1E−30) in the tef genome were selected for further analysis. Using the sorghum and maize drought-tolerant genes deposited in CrealESTDb, 86 gene signatures with strong matches in the tef genome were also selected for further analysis. Moreover, out of 175 genes previously reported in tef, 68 genes with top hits were selected. European Nucleotide Archive-European Molecular Biology Language (ENA-EMBL) contained 889 pearl millet EST database [67] of which 505 EST having strong homology with the tef genome were also used for further analysis (Supplementary Table 1). In total, 729 genes and gene elements were used in the analysis. Figure 1 illustrates the overall flowchart from gene retrieval to the identification of highly enriched genes with high-level GO terms and their potential use in future breeding programs.
An overall flowchart illustrating the steps from retrieval to gene identification of enriched genes with high level GO terms
Genetic relatedness of drought-responsive genes and genes with high-level GO term
High-level gene ontology terms represent highly enriched genes in three categories: Biological Process (BP), Molecular Function (MF), and Cellular Component (CC). To determine the phylogenetic relationship between the genes, we used 233 sequences that were identified in the E. tef genome. Multiple sequence alignment was performed using CLUSTALW, and the phylogenetic tree was constructed using the Neighbor Joining method [69]. The text tree file was then imported to Interactive Tree Of Life (iTOL) v5 [70] for phylogenetic tree display and annotation. For the genetic relationship of top genes, we used CDS version of homologous sequences from rice, finger millet (Eleusine coracana), and foxtail millet (Setaria italica). Finger millet and foxtail millet are C4 drought-tolerant plants like tef while rice is a C3 crop.
Functional annotation and enrichment analysis of drought-responsive genes
For functional annotation and enrichment analysis, we first converted all sequences to Entrez ID and used Database for Annotation, Visualization, and Integrated Discovery (DAVID) [71], ShinyGO 0.76 [72], and the latest BLAST2GO version of OmicsBox2.1.14 software [73]. Functional annotation of 224 genes was performed by DAVID and ShinyGO while the 505 raw ESTs were functionally annotated using BLAST2GO.
Clustering of genes, pathway analysis, and protein interaction network analysis
For extracting and clustering of top genes, FDR (false discovery rate; FDR < 0.05) score, enrichment score, p-value, and kappa coefficient were used. Using the kappa coefficient, highly enriched genes were reclassified into very high, high, and moderate enrichment categories. The KEGG analysis was used to identify major pathways that are regulated by the drought-responsive genes. The protein–protein interaction (PPIs) networks of genes with high-level GO terms were computed according to Ge et al. [72].
Identification of conserved domains in genes with high-level GO term
To identify conserved domains in genes with high-level GO terms, we downloaded homologs of rice and foxtail millet from NCBI (https://www.ncbi.nlm.nih.gov/) and finger millet from Phytozome 13 ( https://phytozome-next.jgi.doe.gov/). Foxtail millet and finger millet are C4 grasses closely related to tef, whilst rice is a well-studied C3 species. The CDS version of all the genes were predicted by FGENESH online tool (http://www.softberry.com). Conserved domains of the CDS were identified using NCBI conserved domain identification tool [74].
Drought treatment and gene expression analysis of selected candidate genes
To analyze the expression of drought responsive in tef, hydroponics experiment was conducted using the reference cultivar Dabi obtained from U.S. Germplasm Resources Information Network (GRIN). Briefly, 50 seeds were first washed with autoclaved millipore water in 1.5 microcentrifuge tubes. After the water was removed, the seeds were surface sterilized using 50% bleach for 8 min under continuous agitation. The bleach solution was removed, and the seeds were rinsed four times using autoclaved millipore water. Seeds were then germinated on moist filter paper for five days and the seedlings were transferred to ¼ Hoagland solution for six more days. Eleven-day-old seedling were then assigned to fresh ¼ Hoagland solution with or without 20% PEG8000 (Phytotechnology Laboratories, Lenexa, USA) which was optimized for this experiment. The PEG solution was used to impose osmotic stress in the hydroponic solution at pH 5.6. For the control samples, only ¼ Hoagland solution without PEG was used throughout the experiment. Seedlings were harvested 30 h after PEG treatment; root and shoots were separated; plant tissues were frozen in liquid nitrogen at harvest and stored in -80 °C until use. Total RNA was extracted using GeneJET Plant RNA Purification Mini Kit (Thermo Scientific). cDNA was synthesized using High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems™).
For gene expression analysis using quantitative qPCR, primers were designed for sixteen candidate transcripts (EtWOX9, EtZIP1, EtbZIP23, EtNAC2, EtDREB1A, ETDREB1C, EtDREB2A, EtPIP1-1, EtPIP1-3, EtPIP2-2, EtCPK21, EtNRT1, EtSAP8, EtMATE1, EtDRM3 and EtCPP1) using primer3plus (https://www.primer3plus.com/) (Table 2). The qPCR was performed by QuantStudio3 (Applied Biosystems) using 1X PowerUp SYBR Green master mix, 0.5 µM of forward and reverse primers and 10 ng of cDNA (1 µl) in a total volume of 20 µl. The PCR condition was 2 min initial denaturation at 95 °C, 15 s denaturation at 95 °C, 30 s annealing at 57 °C, and 1 min extension at 72 °C. The protein phosphatase 2A (PP2A) gene was used as a control as it was reported to display maximum stability under abiotic stress conditions [75]. Statistical analysis of the relative quantification data was performed from six biological replicates and three technical replicates using GraphPad Prism (GraphPad Software 8.0.1) software [76].
Results
Phylogenetic analysis and mapping pattern of drought-responsive genes
In the phylogenetic tree shown in Fig. 2, a total of 233 tef homologs of drought-responsive genes were identified based on rice, wheat, maize, sorghum, barley, pearl millet and Arabidopsis, and phylogenetic tree was constructed using the Neighbor Joining method. A total of 19 distinct cluster groups were detected (Fig. 2). The clustering pattern was based on the putative functions of the genes across the plant species analyzed. Clustering also shows the presence of diverse drought responsive genes in the tef genome. Many genes were clustered in sub-cluster 16 (22 genes), and 4 (20 genes). Cluster 13 has only five genes followed by cluster 10 with seven genes.
Phylogenetic analysis of drought responsive genes identified in tef. The first two letters in the descriptions represent initials of the genus and species name of each plant species. Os, Oryza sativa; At, Arabidopsis thaliana; Zm, Zea mays; Sb, Sorghum bicolor; Hv, Hordeum vulgare; Ta, Triticum aestivum; and Pm, Pennisetum glaucum
In the second phylogenetic tree (Fig. 3), the CDS version of genes with high level gene ontology terms representing Eragrostis tef (Et), Oryza sativa (Os), Setaria italica (Si) and Eleusine coracana (Ec) were used with ribulose bisphosphate carboxylase/oxygenase activase gene (GI: 101206383) as our group sequence (Fig. 3). The top 20 highly enriched genes were clustered into three major groups based on the neighbor-joining tree-building method. Cluster I contains nine gene families including TFs such as EtbZIP1-1, EtbZIP-23, EtCPK-21, EtDREB1C, EtDREB1A, EtAHL-23, and EtCPP1. Cluster II contains seven gene families (EtPIP1-1, EtPIP-1–2, EtPIP2-2, EtNRT1, EtMATE, EtNAC2 and EtSAP8), and Cluster III contains four gene families including EtWOX9, EtDREB2A, EtDRM, and Glyco-transf-17.
Unscaled NJ tree of 20 CDS of genes with high level GO term compared across four crop plants. Et, Eragrostis tef; Os, Oryza sativa; Si, Seteria italica, and Ec, Eleusine coracana
The mapping of 253 orthologous drought-responsive genes and their chromosome distribution on the tef genome are presented in Supplementary Fig. 1. Many pearl millet coding genes (505 ESTs) have shown strong identity (> 98%) and homology signal (E = 0.00) with the tef genome. Most of the ESTs from pearl millet were mapped to conting_123 of the tef genome while large arrays of other genes were unevenly mapped to different chromosomes.
Functional enrichment analysis of drought-responsive genes
As stated in the previous section, a total of 729 drought-responsive genes and ESTs were identified in the tef genome. Of these, 224 genes (Supplementary Table 2) and 29 ESTs that were functionally annotated (Supplementary Table 3) were used for further analysis. From the 224 genes with high homology submitted to DAVID, about 73.4% (160 genes) were functionally annotated with GO term direct at the molecular level (Supplementary Table 4), and about 59.6% (130 genes) were annotated to play a role in known drought-related biological processes (Supplementary Table 5).
In addition to GO term direct, we conducted further annotation and functional enrichment analysis using UP_KW_Biological_Process and UP_KW_Molecular_Function in DAVID to determine genes specifically enriched in both terms. About 87 genes (38.8%) were enriched in different biological processes like abscisic acid signaling pathway, auxin and abscisic acid biosynthesis, auxin signaling pathway, transport, and other biological processes (Supplementary Table 6). Eight of these genes were strongly enriched in stress response (p = 4.3E−05) in the biological process. Using UP_KW_Molecular_Function, we found 102 genes (45.5%) enriched in a number of molecular functions category including activator, DNA-binding, acyltransferase, transferase, aspartyl esterase, hydrolase, chaperone, chromatin regulator, developmental protein, serine/threonine-protein kinase, dioxygena, methyltransferase, glycosidase, glycosyltransferase, protein phosphatase, ion channel, potassium channel, voltage-gated channel, chloride channel, kinase, isomerase, monooxygenase, and oxidoreductase (Supplementary Table 7). Out of these genes, nine were highly enriched in activation (p = 0.006) and DNA binding (p = 0.02). The Up-tissue analysis report indicated that the spatial and temporal expression of these genes can be in leaves and roots, and expression can start at the seedling stage (Supplementary Table 8).
Functional annotation of the 505 pearl millet ESTs that showed the highest identity score (> 90%) and low E-Value (< 1E−50) when mapped to the tef genome was conducted by Blast2GO software (with E value ≤ 1E−50) as they lack official gene ID. Blast2GO conducts GO annotation and functions enrichment analysis by directly comparing sequences to proteins with known functions in available databases using InterPro scan algorithm. From these ESTs, we found 29 (5.7%) (Supplementary Table 3) functionally annotated ESTs distributed in different crops. Most of the ESTs were mapped to Setaria italica followed by Vigna unguiculata. The GO biological process terms identified using Blast2GO are cellular metabolic process, primary metabolic process, organic substance metabolic process, nitrogen compound metabolic process, biosynthesis, regulation of the cellular process, and methylation (Supplementary Figure 2A). The major GO molecular terms identified are ion binding, heterologous compound binding, organic cyclic compound binding, oxidoreductase activity, and metal cluster binding (Supplementary Figure 2B). Overall, about 25 ESTs were known to be involved in cellular metabolic processes while about eight were found to play a role in metal ion binding at the molecular level.
To confirm the accuracy of the GO analysis and the functional annotation obtained from DAVID for multiple crop species, we used single model species Arabidopsis and then analyzed the GO term enrichment by ShinyGo software (Supplementary Table 9). The highly enriched genes were shown to have a role in response to abiotic stresses and chemical stimuli (cold, salinity, water deprivation, Osmotic stress, and heat), metabolic process, regulation of the biological process, regulation of the cellular process, primary metabolic process, organic substance metabolic process and response to oxygen-containing compound at the biological process. Figure 4 shows genes that are highly enriched in the biological processes category using Arabidopsis as a model. Some of the top genes with molecular roles identified using ShinyGo software are predicted to have roles in the binding of biomolecules and catalytic activity, abscisic acid 8-hydroxylase activity, calcium-dependent protein serine/threonine kinase activity and calcium ion binding activity (Supplementary Table 10). Figure 5 shows a cluster relationship of genes that are highly enriched in different molecular functions using Arabidopsis as a model and a statistically significant p-value (< 0.002). Hence, the outcome from both softwares is comparable even though DAVID appears to be more appropriate for functionally annotating genes from multiple species while ShinyGO is more appropriate for annotating genes from a single species.
Number of genes that are strongly enriched in different categories of Biological Processes GO term using Arabidopsis as a model (p ≤ 1.72E−05; FDR ≤ 0.001)
Functional cluster of genes strongly enriched in different categories of Molecular Function GO term at FDR < 0.002. Size of the bubble is proportional with the number of genes in each category. The numbers before the description indicates FDR values
Hierarchical clustering of highly enriched drought-responsive genes
Test-based hierarchical clustering helps to organize genes based on their level of significance and enrichment in known biological processes and molecular function. For hierarchical clustering, we used DAVID software to cluster highly enriched drought-responsive genes. Our analysis showed that the DAVID software gene set enrichment score clusters the genes enriched in different molecular functions into two major cluster groups. Cluster I (Enrichment score = 1.33) contain eight genes (EtWOX9, EtbZIP-1, EtbZIP23, EtNAC2, EtDREB1C, EtAHL-23, ETDREB1A and EtDREB2A). The second cluster (Enrichment score = 0.78) contains 21 genes having different functional roles at the molecular level (Table 3 and Supplementary Table 11). We further used Kappa coefficient values to classify functionally enriched genes into very high, high, and moderate enrichment (Table 3 and Supplementary Tables 11 and 12). Based on the Kappa value, the genes were clustered with strong functional enrichment into two: Cluster I and II containing 21 and 32 genes, respectively (Supplementary Table 12). Cluster I contain two genes which are dehydration-responsive element-binding protein 1C (LOC4339974, DREB1C) and dehydration-responsive element-binding protein 1A-like (LOC4347620, DREB1A) that were classified with very high Kappa value (> 0.80). The other genes in cluster I that were classified with high kappa values include dehydration-responsive element-binding protein 2A-like (LOC4324418, DREB2A), C-repeat/DRE binding factor 2 (CBF2), NAC domain-containing protein 2-like(LOC4334553), AT-hook motif nuclear-localized protein 23 (LOC4344714, AHL-23), light-inducible protein CPRF2 (LOC8061169), C-repeat/DRE binding factor 1(CBF1), bZIP transcription factor 23-like (LOC4330838), WUSCHEL-related homeobox 9-like (LOC4324824, WOX9), cyclic dof factor 2 (LOC8078579), cyclic dof factor 1 (LOC8082122), bZIP transcriptional factor 68-like (LOC110429775), and octopine synthase (ocs) element-binding factor 1 (LOC4326871). About twelve genes with different molecular functions were classified with high Kappa values (> 0.50) (Supplementary Table 12). The second cluster of kappa values contains 21 genes of which five are uncharacterized genes. In this cluster, six genes have very high Kappa values (> 0.76) four of which are uncharacterized (Table 3 and Supplementary Table 12).
Pathway analysis of functionally enriched genes
To predict the pathways regulated by the drought-responsive genes identified in this study, we performed the KEGG pathway analysis. Seventy-two of the genes identified in our analysis were predicted to have a role in known cellular pathways including plant hormone signal transduction pathway, carotenoid biosynthesis, MAPK signaling pathway, plant-pathogen interaction, biosynthesis of amino acids and secondary metabolites including sesquiterpenoid and triterpenoid, ABC transporters, biosynthesis, oxidative phosphorylation, carbon fixation and ubiquitin-mediated proteolysis pathway (Supplementary Table 13A & B). Studies have shown that these pathways are implicated in drought stress tolerance [77,78,79].
Candidate genes with potentials to mitigate drought-stress
Genes strongly enriched in known molecular function and biological processes supported by high-level GO terms have the potential to be utilized in enhancing drought tolerance in plants. Out of the 729 genes of tef initially mapped, we identified a total of 20 candidate genes that were predicted to have a major role in abiotic stress tolerance including drought. The list of these genes with their gene symbol, Entrez ID, and putative functional role is shown in Table 4. Based on the in-silico analysis, it can be concluded that these genes are possible candidates for future breeding programs to improve drought tolerance. Table 4 shows 20 genes with high-level GO terms and Fig. 6 depicts a fold enrichment analysis of these genes in the high-level GO category. Most genes were predicted to play a role in a number of biological activities and may not be limited to a single process. For instance, in our present analysis the bZIP family genes were predicted to have an association with 13 out of 20 high-level biological categories (Supplementary Table 14).
High level GO category of selected drought responsive genes with predicted fold enrichment
Network analysis of selected candidate genes with high level GO terms
To predict gene–gene and protein–protein interaction network of the selected 20 candidate genes, network analysis was performed at gene and protein levels. A gene interaction network is a set of genes (nodes) connected by edges representing functional relationships among these genes. Genes are thought to have either a physical interaction through their translation products (proteins), or one of the genes alters or affects the activity of another gene of interest [80]. The functional products of genes work together to accomplish a particular function, and they often physically interact with each other to carry out a more complex biological process. Figure 7A shows gene to gene interaction network of selected candidate genes. Accordingly, genes may interact directly or indirectly with one another. For instance, genes predicted to be involved in water transport were found to directly interact with eight other genes, including those involved in fluid transport, water deprivation, abiotic stresses, chemical stimuli, oxygen-containing compounds, inorganic substrates, stresses, and genes responsive to acid chemicals (Fig. 7A).
Gene–gene and protein–protein interaction network of selected candidate genes. A Each node represents an enriched GO term. Related GO terms are connected by a line, whose thickness reflects percent of overlapping genes. The size of the node corresponds to number of genes. Darker nodes represent more significantly enriched gene sets. Bigger nodes represent larger gene sets. Thicker edges represent more overlapped genes. B Represent protein–protein interaction. Line thickness represent level of interaction (thicker line for strong interactions)
Similarly, protein–protein interaction (PPI) network of selected genes was also conducted (Fig. 7B). Proteins usually interact with one another or with other molecules like DNA or RNA to mediate metabolic and signaling pathways, cellular processes, and organismal systems [81]. For example, the PIP1-1, PIP2-2, and bZIP-1 proteins are predicted to strongly interact with one another, but not with the PIP1-3 protein. The PIP1-3 protein was predicted to physically interact with the SAP8 protein. DREB1A and DREB1C are also predicted to directly interact with NAC2 and bZIP23 proteins.
Analysis of conserved domains in selected candidate genes
To determine the nature and putative roles of conserved domains present in the selected 20 genes in tef, we compared their domains with genes from widely studied japonica rice. Of the 20 genes, 12 have comparable conserved and binding sites in both rice and tef, suggesting functional similarity between a C3 and a C4 plant. However, we found distinct differences for the remaining eight genes. Dehydration-responsive element binding protein 1C (DREB1C) of tef has a shorter APETALA2 (AP2) binding site (31 amino acids) compared to rice (60 amino acid) (data not shown). Likewise, the tef TF bZIP-1 (32 amino acids) has a shorter DNA binding domain compared to rice (38 amino acids). However, the nature and roles of conserved amino acids in bZIP-1 are similar in both tef and rice.
On the other hand, tef has four AP2 conserved domains with 60–68 amino acids on the same TF DREB1A while DREB1A of rice has only one AP2 conserved domain (Fig. 8). In tef, CPK21 gene binding domain (STKc_CAMK) is twofold longer (279 amino acids) than the rice homolog (138 amino acids). In addition to STKc_CAMK domain, CPK21 gene of tef has another Ca2+ binding domain (FRQ1 domain with 152 amino acids). The PIP1-1 and PIP2-2 tef homologs have three-fold longer conserved domain as compared to the rice homolog. Another interesting difference was the presence of three copies of chemical substrate Major Facilitator Superfamily (MSF) with single transcriptional machinery in the tef NRT1 gene while only one MFS domain was detected for the rice NRT1. The conserved domain of GNT3, a beta-1,4-mannosyl-glycoprotein 4-beta-N-acetylglucosaminyltransferase of tef is also longer (348 amino acids) than that of rice (222 amino acids). The description of the candidate genes and their conserved domains is illustrated in Table 5.
Conserved domain analysis of selected genes. A Rice DREB1A with single AP2 binding site, and B Tef DREB1A with four structurally related copes within single transcriptional machinery. The amino acids in bold are predicted significant binding sites
Quantitative RT-PCR validation of selected drought-responsive genes
Quantitative RT-PCR was performed to validate the expression of selected candidate drought-responsive genes in tef. We analyzed the expression of 16 genes in both shoot and root in response to osmotic stress induced by PEG8000. As shown in Fig. 9, the expression of 14 candidate genes was deferentially regulated in shoots, roots or both tissues by PEG-treatment. In shoots, the expression of EtbZIP23, EtNAC2, EtDREB1A, EtDREB1C, EtDREB2A, EtMATE and EtPIP1-3 (Fig. 9B, D, E, F, G, I, and K, respectively) was upregulated while in roots, the expression of EtWOX9, EtbZIP23, EtCIPK21, EtMATE, EtPIP1-1, EtPIP2-2 and EtDRM3 (Fig. 9A, B, H, I, J, L and M, respectively) were upregulated by PEG treatment. Whereas EtZIP1 (Fig. 9C) and EtNRT1 (Fig. 9O) were significantly downregulated in roots while EtWOX9 and EtPIP2-2 were significantly downregulated in shoots by PEG induced osmotic stress. In shoots, the expression of EtbZIP1, EtCIPK21, EtPIP1-1, EtDRM3, EtSAP8, EtNRT1 and EtV5B (Fig. 9C, H, J, M, N, O, and P, respectively) was not significantly affected by the PEG treatment while the expression of EtSAP8 (Fig. 9N) and EtV5B (Fig. 9P) was not affected by the PEG treatment in both tissues. The fold increase in gene expression in response to PEG-induced osmotic stress was the highest for EtCIPK21 (Fig. 9H) followed by EtWOX9 (Fig. 9A) and EtPIP2-2 (Fig. 9L) (20-, 14- and eightfold, respectively) in roots and EtbZIP23 ((Fig. 9B) (20-fold) in shoots. Overall, the expression of the selected candidate genes showed varying degrees of response to PEG-induced osmotic stress in shoots and roots.
Analysis of drought-responsive genes expression using qRT-PCR in shoot (grey bar) and root (black bar) tissues of tef (Dabbie).CS, control shoot; PS, PEG treated shoot, CR, control root, and PR, PEG treated root. A EtWOX9, B EtbZIP23, C EtbZIP1, D EtNAC2, E EtDREB1, F EtDREB1C, G EtDREB2A;H EtCPK21, I EtMATE, J EtPIP1-1, K EtPIP1-2, L EtPIP1-3, M EtDRM3, N EtSAP8, O EtNRT1, and P EtV5B. ns, p > 0.05; *, p ≤ 0.05; **, p ≤ 0.01; ***, p ≤ 0.001)
Discussion
Tef is a C4 crop relatively tolerant to drought due to physiological and genetic mechanisms compared to C3 crops [82]. Therefore, identifying genes that regulate drought tolerance in tef is paramount important to improve drought-sensitive cultivars or crops and enhance crop yields under drought-prone conditions. In this study, we performed in silico analysis to retrieve 729 drought-responsive genes representing Arabidopsis, maize, sorghum, barley, wheat, and pearl and identified highly enriched candidate genes in tef. We used CoGEBlast to identify the collected drought-responsive genes in the tef genome and MEGA-11 to study the relationship of drought-responsive tef genes. Moreover, we used DAVID, ShinyGO, and BLAST2GO for gene orthology and enrichment analysis. To further confirm the pattern of gene expression in highly enriched genes, osmotic stress was induced by PEG and qRT-PCR was used using root and shoot samples and reported in terms of relative gene expression.
Using gene enrichment analysis, we categorized all the genes based on biological and molecular functions by utilizing statistical indices including p-value, FDR, and Kappa coefficient, and selected 20 genes that are predicted to have significant roles in drought tolerance and conducted genes and proteins interaction network analysis.
One of these genes is the WUSCHEL-Related Homeobox (WOX9)-like gene which we putatively named EtWOX9 for Eragrostis tef WOX9. The qRT-PCR relative gene expression analysis indicated that WOX9 was 13-fold upregulated in PEG-treated roots while it was downregulated in PEG-treated shoots (Fig. 9). The WOX9 gene may have a role in plants including the regulation of developmental processes. The WOX family is the homeobox transcription factor superfamily playing many functions in embryonic growth to organ formation in plants [83]. This gene family is implicated in developmental processes including cell division, development, stem cell repair, organ formation, seed formation, tissues, and organ regeneration [83,84,85,86]. Recent studies have indicated that WOX genes play a role in the regulation of abiotic stress resistance including drought [87]. In Glycine max, expression of the WOX gene family has been shown to be induced by drought, heat, cold, and salt stress in [84], and in Arabidopsis, WOX9 was reported to be expressed in the root tip meristem tissue and promote root cell multiplication which is a mechanism of drought tolerance [88, 89].
The other gene family that showed high-level GO classification was the basic leucine zipper (bZIP) transcriptional family genes. Two genes putatively named EtbZIP-1 and EtbZIP23 were identified in this study. The bZIP family proteins are the largest TFs family and the most diverse family that are implicated in various abiotic stress responses [90,91,92]. Our finding suggested that the two bZIP proteins play a role in the regulation of biological processes, cellular processes, stimulus and stress responses, biosynthetic processes, regulation of metabolic processes, and some signaling pathways (Table 4). Our analysis revealed that the bZIP23 may also play a role in mediating abscisic acid (ABA) signaling, regulating the expression of a wide range of stress-related genes in response to abiotic stresses through an ABA-dependent regulation pathway, conferring ABA-dependent drought and salinity tolerance. bZIP23 is known to bind specifically to the ABA-responsive elements (ABRE) of the promoter of target genes to mediate stress-responsive ABA signaling, and phytohormone signal transduction pathway and stomatal closure [92]. The qRT-PCR relative gene expression analysis indicated that bZIP23 was upregulated in both PEG-treated roots and shoots within 30 h of drought induction (Fig. 9). However, bZIP-1 was not found to be co-overexpressed with bZIP23 in both roots and shoots. A number of studies indicated that bZIP family proteins are strongly associated with drought tolerance in crops [93,94,95]. Though bZIP-1 and bZIP23 are highly enriched in the current analysis, there are about 93 bZIP family transcriptional factors (TF) so far reported in tef [96]. These TFs have not been isolated and characterized in tef. In Arabidopsis, the bZIP23 and bZIP19 were reported to be involved in Zn uptake and accumulation and were regarded as Zn sensors [97, 98]. Since tef is rich in Zn and Fe, bZIP TFs may play a role in Zn accumulation in tef; however, this needs further studies [31].
We also identified the EtNAC2 gene which is a member of the NAC TFs family which are involved in biotic and abiotic stress responses. Tef was reported to have 172 putative NAC transcriptional factors [96] from which NAC2 was found with high-level GO term. Expression analysis of the NAC2 showed that it is upregulated in PEG-treated shoots whilst it was downregulated in roots (Fig. 9). The NAC2 gene was predicted to play a role during dehydration and salt stress by binding specifically to the 5'-CATGTG-3' motif found in promoters of stress-responsive genes (Table 4). The transcriptional factor belonging to NAC (NAM, ATAF, and CUC) family has been widely recognized as plant biotic and abiotic stress-responsive factors [99, 100]. Some NAC TFs were implicated in the regulation of senescence and drought response [101, 102]. In Arabidopsis, overexpression of three NAC genes (ANAC019, ANAC055, and ANAC072) which were induced by drought stress improved stress tolerance in the transgenic lines compared to the wild type [103].
In this study, the expression of DRM3 gene was upregulated in both root and shoots though the level of expression is higher in shoots in response to drought stress (Fig. 9). The epigenetic role of the DRM3 gene in response to drought was previously reported from studies using whole genome bisulfite sequencing of mulberry (Morus alba) [94]. In Arabidopsis, DRM3 lacks catalytic activity and is reported to play a role in promoting DNA Pol V transcriptional elongation factor, controlling DNA methylation, and regulating RNA polymerase V transcript abundance [104]. Similarly, our analysis suggests that the mechanism by which this gene promotes drought tolerance could be through de novo DNA methylation and RNA-directed DNA methylation (RdDM) activities, suggesting that drought tolerance mechanism in tef could involve epigenetic mechanism.
Dehydration-responsive element-binding (DREB) proteins are members of TFs that are well-studied for their role in abiotic stress tolerance including drought, salt, and cold and contain conserved APETALA2/ethylene responsive factor (AP2/ERF) DNA binding domain [105, 106]. In this analysis, we identified three DREB TFs including EtDREB1A, EtDREB2A, and EtDREB1C with high-level GO terms. All three DREB families have shown different degrees of expression in shoots, though the DREB2A transcript was relatively higher. However, there was no change in the relative expression of DREB2A in roots while DREB1C and DREB1A transcripts are upregulated in PEG-treated shoots (Fig. 9). Overexpression of the Arabidopsis DREB1A gene under the regulation of stress-inducible rd29A promoter has been reported to improve drought and low-temperate tolerance in transgenic tobacco [107]. Similarly, co-overexpression of DREB2A and ascorbate peroxidase (APX) in Indica Rice (Oryza sativa L.) resulted in drought tolerance enhancement [108]. Furthermore, overexpression of full-length and partial DREB2A was reported to enhance soybean drought tolerance [109]. Recently, DREB1C has been shown to regulate nitrogen-use efficiency and flowering time in rice and help to boost grain yields and shorten the growth duration of rice [110]. In addition to shortening flowering time, DREB1C regulates the expression of several important growth-related genes including nitrate transporters and nitrate reductase, display a higher harvest index and increased remobilization of N and C to sink organs [110]. The biological role of tef DREB proteins remains to be characterized.
The Plasma Membrane Aquaporin (AQP) PIPI1-1, PIP1-3, and PIP2-2 are another group of genes detected by high-level GO term in this analysis. The PIP1-1 and PIP1-3 were up-regulation in both shoot and roots though PIP1-3 transcript was relatively higher in shoots. PIP2-2 was 13-fold selectively up-regulated in roots whilst it was down-regulated in shoots (Fig. 9). As members of major intrinsic proteins, AQPs facilitate the transport of water, glycerol, and small uncharged solutes through the cell membranes [111]. Our in-silico analysis predicted that these aquaporins function as water channels to facilitate the transport of water and small neutral solutes across the cell membrane. PIP2 aquaporins are implicated in water transport when expressed in Xenopus oocytes and yeast whereas most PIP1s do not have significant water channel activity, however, PIP1 type aquaporins have been shown to interact with PIP2 type channels to facilitate water transport [112,113,114,115]. Previously, Ren et al. [116] conducted a meta-analysis on the effect of the overexpression of the aquaporin gene family on drought stress response and reported that the PIP2 gene family has positive effects on drought tolerance in transgenic plants.
Our analysis also detected the EtCPP1 gene with highly enriched GO terms. It is predicted to play a role in the regulation of chlorophyll biosynthesis. The rice chaperone-like protein of protochlorophyllide oxidoreductase (POR), J-like protein has been reported to play a role in chlorophyll and tocopherol biosynthesis [117]. J-like proteins have been shown to modulate the functions of Hsp70, J-domain protein (JDP) systems in novel ways thereby regulating diverse plant processes [118].
The NRT1 protein was another drought-responsive gene that was detected in our analysis. However, its transcripts were downregulated in both PEG-treated shoots and roots (Fig. 9). The NRT1/NPR has been reported to facilitate carbohydrate and nitrogen accumulation in drought-stressed genotypes of grapevine [119]. Some NPF transporters can also transport different substrates, such as nitrate/auxin, nitrate/abscisic acid, nitrate/glucosinolate, or gibberellin/jasmonic acid [119,120,121]. In rice, OsNPF3.1 is a member of the NRT1/PTR genes that has been reported to affect plant height by increasing the nitrogen use efficiency [117]. Thus, downregulation of its expression might lead to lodging tolerance while decreasing nitrogen uptake.
Calcium-dependent protein kinases (CDPKs or CPKs) are key calcium-binding proteins that have pivotal role in abiotic stress tolerance through activation and regulation of several genes, transcription factors, enzymes, and ion channels by ABA-dependent manner [122]. Overexpression of OsCPK21 increases ABA levels and enhances salt tolerance by regulating and inducing the salt tolerance genes [123]. In our analysis, CPK21 was one of the genes identified in tef with high level GO term. The CPK21 functions in signal transduction pathways that positively regulate responses to abscisic acid (ABA) and salt stress. The qRT-PCR relative gene expression analysis revealed that CPK21 was up-regulated in both shoots and roots (Fig. 9). The relative up-regulation was higher in PEG-treated roots (24-fold) when compared with shoots.
Furthermore, EtMATE, GNT3, EtRING were detected with high GO terms in the tef genome. Our in-silico analysis showed that EtMATE plays a role in the protein detoxification process and multi-antimicrobial extrusion process. MATE (Multidrug and Toxic Compound Extrusion or Multi-Antimicrobial Extrusion) transporters comprise a universal gene family of membrane transporters that are present in all kingdoms of life. The EtMATE transcripts were upregulated in both roots and shoots though the level of expression was higher in roots (Fig. 9). MATE transporters have been implicated directly or indirectly in mechanisms of detoxification of noxious compounds or heavy metals, tolerance to aluminum toxicity, disease resistance, nutrient homeostasis, such as Fe3+ uptake, and the transport of diverse types of secondary metabolites, such as alkaloids, flavonoids, and anthocyanidins, as well as hormones, such as ABA, salicylic acid, and auxin [124].
To further understand the nature of functionally conserved domains among selected genes in C4 and C3, conserved domain identification was performed based on the full-length CDS (Fig. 8). The protein coded by DREB family genes contains a conserved AP2 domain, which consists of approximately 60 amino acids [125]. The AP2 subfamily binds to the GCAC(A/G)N(A/T)TCCC(A/G)ANG(C/T) element and has an important impact on reproductive organ development and meristem maintenance [126]. In addition to drought and other abiotic stresses, DREB1C has recently been implicated in increasing crop yield and nitrogen use efficiency [110]. In our conserved domain analysis, remarkable differences were detected among eight tef and rice homologs including DREB1C, bZIP-1, CPK21, PIP1-1, PIP2-2, NRT1, EtbZIP23, EtPIP1-3. For example, we observed variation in the size of AP2 domain between EtDREB1C and OsDREB1C. The AP2 domain in EtDREB1C is 30 amino acids compared to the AP2 domain of the rice DREB1C which is 61 amino acids long. As shown in the unscaled NJ phylogenetic tree, the tef DREB1C underwent a fast substantial evolutionary process when compared to rice (Supplementary Figure 3). With this variation, it is unclear whether the EtDREB1C has a conserved physiological function as its rice homolog and requires overexpression or knockout studies to validate its function.
The length of conserved domain of the TF bZIP-1 in tef was also shorter by six amino acids than the rice homolog (38 amino acids). Due to this difference, the rice bZIP-1 has an extra binding domain for protein Toc75. Toc75 at the outer envelope of chloroplasts initiates the import of nuclear-encoded proteins from the cytosol into the organelle [127]. In EtDREB1A, there are four copies of the AP2 conserved domain in the vicinity of one another within the same transcriptional machinery with a size of 60–68 amino acid while the OsDREB1A gene of rice has only one AP2 conserved domain. Our analysis also showed that the length of the substrate binding domain in EtCPK21 (279 amino acids) is twice that of rice. Unlike the rice homolog, CPK21 binding domain in tef has an extra binding domain (FRQ1) that was validated to help as Ca2+ binding protein. The FRQ1 gene is essential for the growth of budding yeast and a calcium-binding protein [128] but its function in plants is not well characterized. We also observed that the conserved site of PIP1-1 and PIP2-2 genes of tef is threefold longer than rice. As mentioned above, these are AQP genes that facilitate the transport of water, glycerol, and small uncharged solutes through the cell membranes [111]. The NRT1 gene in tef has three copies of the Major Facilitator Superfamily (MSF) protein with single transcriptional machinery, though only one is detected for rice NRT1. In addition to drought, NRT1 is responsible for nitrate uptake and transport, auxin transport, and mediates nitrate-modulated root development [129]. Overall, significant variations were observed in the number size of conserved domains among tef and rice drought-responsive genes, however, the implication of these variations in protein function remains to be understood. Taken together, we identified several candidate genes whose transcript levels respond to drought stress, suggesting their involvement in drought stress responses in tef, however, further research will validate their physiological functions.
Conclusion
In this study, we performed an in-silico analysis and identified 20 potential drought responsive genes in the tef genome that showed high homology with those previously reported in Arabidopsis, rice, maize, sorghum, barley, wheat and pearl millet. We used gene ontology functional enrichment analysis and KEGG pathway analysis to refine promising genes. Out of 253 genes and gene elements identified in the tef genome, we refined 20 top genes with highest enrichment score and statistical indices including kappa coefficient, FDR, and p-value. We also performed qPCR to validate the expression of the candidate drought-responsive genes. We found that 14 of 16 genes analyzed were differentially expressed in root, shoot or both tissues in response to drought stress, suggesting their potential role in drought stress responses. However, none of the tef genes analyzed in this study have been isolated and functionally characterized. Therefore, there is a need to functionally characterize these genes in model as well as crop plants. Genes that confer drought resistant under wet lab research are candidates for future drought mitigation programs through molecular breeding approaches.
Availability of data and materials
All data used in this manuscript are included in the main manuscript or included as supplementary data files.
Change history
15 November 2023
A Correction to this paper has been published: https://doi.org/10.1186/s12870-023-04601-4
References
Lesk C, Rowhani P, Ramankutty N. Influence of extreme weather disasters on global crop production. Nature. 2016;529(7584):84–7.
Lunt T, Jones AW, Mulhern WS, Lezaks DP, Jahn MM. Vulnerabilities to agricultural production shocks: an extreme, plausible scenario for assessment of risk for the insurance sector. Clim Risk Manag. 2016;13:1–9.
Ray DK, Mueller ND, West PC, Foley JA. Yield trends are insufficient to double global crop production by 2050. PLoS ONE. 2013;8(6):e66428.
Tilman D, Balzer C, Hill J, Befort BL. Global food demand and the sustainable intensification of agriculture. Proc Natl Acad Sci. 2011;108(50):20260–4.
Verslues PE, Agarwal M, Katiyar-Agarwal S, Zhu J, Zhu JK. Methods and concepts in quantifying resistance to drought, salt and freezing, abiotic stresses that affect plant water status. Plant J. 2006;45(4):523–39.
Wiltshire AJ, Kay G, Gornall JL, Betts RA. The impact of climate, CO2 and population on regional food and water resources in the 2050s. Sustainability. 2013;5(5):2129–51.
Hatfield JL, Prueger JH. Temperature extremes: effect on plant growth and development. Weather Clim Extrem. 2015;10:4–10.
Chaves MM, Costa JM, Saibo NJ. Recent advances in photosynthesis under drought and salinity. Adv Bot Res. 2011;57:49–104.
Daryanto S, Wang L, Jacinthe PA. Global synthesis of drought effects on cereal, legume, tuber and root crops production: a review. Agric Water Manag. 2017;179:18–33.
He M, Dijkstra FA. Drought effect on plant nitrogen and phosphorus: a meta-analysis. New Phytol. 2014;204(4):924–31.
Wilhite DA. Drought as a natural hazard: Conceptions and definitions. In: Wilhite DA. Chapter 1 Drought as a Natural Hazard: Concepts and Definitions. Routledge; 2000. p. 111–120.
Bartels D, Hussain SS. Current status and implications of engineering drought tolerance in plants using transgenic approaches. CABI Rev. 2008;17. cabidigitallibrary.org.
Greenhalgh E. State of the climate: drought. 2016. Accessed 1 Jul 2022.
Ehleringer JR, Monson RK. Evolutionary and ecological aspects of photosynthetic pathway variation. Annu Rev Ecol Syst. 1993;1:411–39.
Keeley JE, Rundel PW. Evolution of CAM and C4 carbon-concentrating mechanisms. Int J Plant Sci. 2003;164(S3):S55–77.
Hartzell S, Bartlett MS, Porporato A. Unified representation of the C3, C4, and CAM photosynthetic pathways with the Photo3 model. Ecol Model. 2018;384:173–87.
Hatfield JL, Dold C. Water-use efficiency: advances and challenges in a changing climate. Front Plant Sci. 2019;10:103.
Borland AM, Hartwell J, Weston DJ, Schlauch KA, Tschaplinski TJ, Tuskan GA, Yang X, Cushman JC. Engineering crassulacean acid metabolism to improve water-use efficiency. Trends Plant Sci. 2014;19(5):327–38.
Wang L, Czedik-Eysenberg A, Mertz RA, Si Y, Tohge T, Nunes-Nesi A, Arrivault S, Dedow LK, Bryant DW, Zhou W, Xu J. Comparative analyses of C4 and C3 photosynthesis in developing leaves of maize and rice. Nat Biotechnol. 2014;32(11):1158–65.
Rangan P, Furtado A, Henry RJ. New evidence for grain specific C4 photosynthesis in wheat. Sci Rep. 2016;6(1):31721.
Mabhaudhi T, Chimonyo VG, Hlahla S, Massawe F, Mayes S, Nhamo L, Modi AT. Prospects of orphan crops in climate change. Planta. 2019;250:695–708.
Seyfu K. Tef. Eragostis tef (Zucc.) Trotter. Promoting the conservation and use of underutilized crops. Rome: Institute Plant. Genetics and Crop Research; 1997.
Bultosa G. Tef: overview. In: Wrigley CW, Corke H, Seetharaman K, Faubion J, editors. Encyclopedia of food grains. 2nd ed. Kidlington, Oxford: Academic Press; 2016. p. 209.
Ingram AL, Doyle JJ. The origin and evolution of Eragrostistef (Poaceae) and related polyploids: evidence from nuclear waxy and plastid rps16. Am J Bot. 2003;90(1):116–22.
Gebru YA, Sbhatu DB, Kim KP. Nutritional composition and health benefits of teff (Eragrostistef(Zucc.)Trotter). J Food Qual. 2020;2020:1–6.
Tafes Desta B, Mekuria GF, Gezahegn AM. Exploiting the genetic potential of tef through improved agronomic practices: a review. Cogent Food Agric. 2022;8(1):2083539.
USDA. Global agricultural information network. Grain and Feed Annual, Report Number: ET2022–0014. 2022. https://apps.fas.usda.gov/newgainapi/api/Report/. Accessed 03 Jul 2022.
Abraham R. Achieving food security in Ethiopia by promoting productivity of future world food tef: a review. Adv Plants Agric Res. 2015;2(2):00045.
Ligaba-Osena A, Mengistu M, Beyene G, Cushman J, Glahn R, Pineros M. Grain mineral nutrient profiling and iron bioavailability of an ancient crop tef (‘Eragrostis tef’). Aust J Crop Sci. 2021;15(10):1314–24.
Zhu F. Chemical composition and food uses of tef (Eragrostis tef). Food Chem. 2016;239:402–15. https://doi.org/10.1016/j.foodchem.
Goersch MC, Schäfer L, Tonial M, de Oliveira VR, Ferraz ABF, Fachini J, da Silva JB, Niekraszewicz LAB, Rodrigues CE, Pasquali G, Dias JF, Kist TBL, Picada JN. Nutritional composition of Eragrostistef and its association with the observed antimutagenic effects. RSC Adv. 2019;9(7):3764–76. https://doi.org/10.1039/c8ra09733j.
Ereful NC, Jones H, Fradgley N, Boyd L, Cherie HA, Milner MJ. Nutritional and genetic variation in a core set of Ethiopian tef (Eragrostis tef) varieties. BMC Plant Biol. 2022;22(1):220. https://doi.org/10.1186/s12870-022-03595-9.
Girma D, Assefa K, Chanyalew S, Cannarozzi G, Kuhlemeier C, Tadele Z. The origins and progress of genomics research on tef (Eragrostis tef). Plant Biotechnol J. 2014;12(5):534–40.
Tesfay T, Gebresamuel G. Agronomic and economic evaluations of compound fertilizer applications under different planting methods and seed rates of tef [Eragrostistef(Zucc.) Trotter] in Northern Ethiopia. J Drylands. 2016;6(1):409–22.
Cannarozzi G, Plaza-Wüthrich S, Esfeld K, Larti S, Wilson YS, Girma D, de Castro E, Chanyalew S, Blösch R, Farinelli L, Lyons E. Genome and transcriptome sequencing identifies breeding targets in the orphan crop tef (Eragrostistef). BMC Genomics. 2014;15(1):1–21.
CSA. Central Statistical Agency, Agricultural Sample Survey 2018/2019. Volume I. Report on area and production of major crops (Private Peasant Holdings, Meher Season). Statistical Bulletin 589. 2019. Addis Ababa, Ethiopia.
Ayele M. Use of excised-leaf water content in breeding tef (Eragrostistef/Zucc./Trotter) for moisture stress areas. Acta Agron Hung. 1993;42:261–6.
Ferede B, Mekbib F, Assefa K, Chanyalew S, Abraha E, Tadele Z. In vitro evaluation of Tef [Eragrostistef(Zucc)Trotter] genotypes for drought tolerance. Ethiop J Agric Sci. 2019;29(3):73–88.
Ferede B, Mekbib F, Assefa K, Chanyalew S, Abraha E, Tadele Z. Evaluation of drought tolerance in tef [Eragrostistef(Zucc.)trotter] genotypes using drought tolerance indices. J Crop Sci Biotechnol. 2020;23:107–15.
Abraha MT, Shimelis HA, Laing MD, Assefa K. Selection of drought-tolerant tef (Eragrostistef) genotypes using drought tolerance indices. S Afr J Plant Soil. 2017;34(4):291–300.
Admas S, Belay G. Drought-resistance traits variability in EragrostistefXEragrostispilosa recombinant inbred lines. Afr J Agric Res. 2011;6(16):3755–61.
Mariani L, Ferrante A. Agronomic management for enhancing plant tolerance to abiotic stresses—drought, salinity, hypoxia, and lodging. Horticulturae. 2017;3(4):52.
Martinelli F, Cannarozzi G, Balan B, Siegrist F, Weichert A, Blösch R, Tadele Z. Identification of miRNAs linked with the drought response of tef [Eragrostistef(Zucc.)Trotter]. J Plant Physiol. 2018;224:163–72.
Blösch R, Rindisbacher A, Plaza-Wüthrich S, Röckel N, Weichert A, Cannarozzi G, Tadele Z. Identification of drought tolerant mutant lines of tef [Eragrostis tef (Zucc.) Trotter]. Afr Focus. 2019;32(2):25–38.
Sun Y, Shang L, Zhu QH, Fan L, Guo L. Twenty years of plant genome sequencing: achievements and challenges. Trends Plant Sci. 2022;27(4):391–401.
Cohen SP, Leach JE. Abiotic and biotic stresses induce a core transcriptome response in rice. Sci Rep. 2019;9(1):6273. https://doi.org/10.1038/s41598-019-42731-8.
Benny J, Pisciotta A, Caruso T, Martinelli F. Identification of key genes and its chromosome regions linked to drought responses in leaves across different crops through meta-analysis of RNA-Seq data. BMC Plant Biol. 2019;19(1):1–8.
Yasin JK, Mishra BK, Pillai MA, Verma N, Wani SH, Elansary HO, El-Ansary DO, Pandey PS, Chinnusamy V. Genome wide in-silico miRNA and target network prediction from stress responsive Horsegram (Macrotyloma uniflorum) accessions. Sci Rep. 2020;10(1):17203.
Mulat MW, Sinha VB. First report for availability of HRT-like genes in Eragrostis tef and in silico analysis for elucidating their potential functions. Plant Gene. 2020;23:100230.
Mulat MW, Sinha VB. VOZS identification from tef [Eragrostistef(Zucc.)Trotter] using in silico tools decipher their involvement in abiotic stress. Mater Today. 2022;49:3357–64.
Mulat MW, Sinha VB. Identification and characterization of Dof in tef [Eragrostis tef (Zucc.) Trotter] using in silico approaches. Gene Rep. 2020;19:100590.
Mulat MW, Sinha VB. In silico approach for unraveling the structural and functional roles of NF-X1-like proteins in underutilized cereal Eragrostis tef. Biol Bull. 2021;48:251–62.
Mulat MW, Sinha VB. Distribution and abundance of CREs in the promoters depicts crosstalk by WRKYs in tef [Eragrostistef(Zucc.)Troetter]. Gene Rep. 2021;1(23):101043.
Mulat MW, Sinha VB. Comparative in silico analysis of Eragrostis tef (Zucc.) Trotter with other species for elucidating presence of growth regulating factors (GRFs). Genet Resour Crop Evol. 2021;68:499–512.
Billah SA, Khan NZ, Ali W, Aasim M, Usman M, Alezzawi MA, Ullah H. Genome-wide in silico identification and characterization of the stress associated protein (SAP) gene family encoding A20/AN1 zinc-finger proteins in potato (Solanum tuberosum L.). PLoS ONE. 2022;17(8):e0273416.
Sadat MA, Ullah MW, Hossain MS, Ahmed B, Bashar KK. Genome-wide in silico identification of phospholipase D (PLD) gene family from Corchorus capsularis and Corchorus olitorius: reveals their responses to plant stress. J Genet Eng Biotechnol. 2022;20(1):28.
Chakraborty A, Viswanath A, Malipatil R, Semalaiyappan J, Shah P, Ronanki S, Rathore A, Singh SP, Govindaraj M, Tonapi VA, Thirunavukkarasu N. Identification of candidate genes regulating drought tolerance in pearl millet. Int J Mol Sci. 2022;23(13):6907.
Ahmar S, Gruszka D. In-silico study of brassinosteroid signaling genes in rice provides insight into mechanisms which regulate their expression. Front Genet. 2022;13:953458.
Khare T, Joshi S, Kaur K, Srivastav A, Shriram V, Srivastava AK, Suprasanna P, Kumar V. Genome-wide in silico identification and characterization of sodium-proton (Na+/H+) antiporters in Indica rice. Plant Gene. 2021;26:100280.
Mahammed F, Babu H, Fakrudin B, Lakshmana D, Rakshith M. In Silico Identification and Annotation of Drought Responsive Candidate Genes in Solanaceous Plants. Int J Creat Res Thoughts (IJCRT). 2021;9(1):3910–23. Available at SSRN: https://ssrn.com/abstract=3777445.
Chen L, Zhou Y, Lai W, Hu L, Jiang L, Liu S. In silico identification and expression analysis of nuclear factor Y (NF-Y) transcription factors in cucumber. Agronomy. 2020;10(2):236.
Lohani N, Babaei S, Singh MB, Bhalla PL. Genome-wide in silico identification and comparative analysis of Dof gene family in Brassica napus. Plants. 2021;10(4):709.
Kumar S, Bhati J, Saha A, Lal SB, Pandey PK, Mishra DC, Farooqi MS, Kumar A, Chaturvedi KK, Rai A. CerealESTDb: a comprehensive resource for abiotic stress-responsive annotated ESTs with predicted genes, gene ontology, and metabolic pathways in major cereal crops. Front Genet. 2022;13:842868.
Zhao K, Ren R, Ma X, Zhao K, Qu C, Cao D, Ma Q, Ma Y, Gong F, Li Z, Zhang X. Genome-wide investigation of defensin genes in peanut (Arachis hypogaea L.) reveals AhDef2. 2 conferring resistance to bacterial wilt. Crop J. 2022;10(3):809–19.
Alter S, Bader KC, Spannagl M, Wang Y, Bauer E, Schön CC, Mayer KFX. DroughtDB: an expert-curated compilation of plant drought stress genes and their homologs in nine species. Database. 2015;2015:bav046. https://doi.org/10.1093/database/bav046.
Degu HD. Analysis of differentially expressed genes induced by drought stress in tef (Eragrostistef) root. Nig J Biotechnol. 2019;36(2):167–87.
Meena RP, Vishwakarma H, Ghosh G, Gaikwad K, Chellapilla TS, Singh MP, Padaria JC. Novel ASR isolated from drought stress responsive SSH library in pearl millet confers multiple abiotic stress tolerance in PgASR3 transgenic Arabidopsis. Plant Physiol Biochem. 2020;156:7–19.
VanBuren R, Man Wai C, Wang X, Pardo J, Yocca AE, Wang H, Chaluvadi SR, Han G, Bryant D, Edger PP, Messing J. Exceptional subgenome stability and functional divergence in the allotetraploid Ethiopian cereal teff. Nat Commun. 2020;11(1):884.
Tamura K, Stecher G, Kumar S. MEGA11: Molecular evolutionary genetics analysis version 11. Mol Biol Evol. 2021;38(7):3022–7.
Letunic I, Bork P. Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 2021;49(W1):W293–6.
Dennis G, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, Lempicki RA. DAVID: database for annotation, visualization, and integrated discovery. Genome Biol. 2003;4(9):1–1.
Ge SX, Jung D, Yao R. ShinyGO: a graphical gene-set enrichment tool for animals and plants. Bioinformatics. 2020;36(8):2628–9.
Conesa A, Götz S, García-Gómez JM, Terol J, Talón M, Robles M. Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics. 2005;21(18):3674–6.
Lu S, Wang J, Chitsaz F, Derbyshire MK, Geer RC, Gonzales NR, Gwadz M, Hurwitz DI, Marchler GH, Song JS, Thanki N. CDD/SPARCLE: The conserved domain database in 2020. Nucleic Acids Res. 2020;48(D1):D265–8.
Jiang Q, Wang F, Li MY, Ma J, Tan GF, Xiong AS. Selection of suitable reference genes for qPCR normalization under abiotic stresses in Oenanthe javanica (BI.) DC. PLoS ONE. 2014;9(3):e92262.
GraphPad Prism version 8.0.1 for Windows, GraphPad Software, San Diego, California USA, www.graphpad.com.
Lv Y, Xu L, Dossa K, Zhou K, Zhu M, Xie H, Tang S, Yu Y, Guo X, Zhou B. Identification of putative drought-responsive genes in rice using gene co-expression analysis. Bioinformation. 2019;15(7):480.
Kumar M, Kumar Patel M, Kumar N, Bajpai AB, Siddique KH. Metabolomics and molecular approaches reveal drought stress tolerance in plants. Int J Mol Sci. 2021;22(17):9108.
Lozano-Elena F, Fàbregas N, Coleto-Alcudia V, Caño-Delgado AI. Analysis of metabolic dynamics during drought stress in Arabidopsis plants. Scientific Data. 2022;9(1):90.
Bebek G. Identifying gene interaction networks. In: Statistical human genetics: methods and protocols. 2012. p. 483–94.
Gonzalez MW, Kann MG. Chapter 4: Protein interactions and disease. PLoS Comput Biol. 2012;8(12):e1002819.
Pardo J, VanBuren R. Evolutionary innovations driving abiotic stress tolerance in C4 grasses and cereals. Plant Cell. 2021;33(11):3391–401.
Akbulut SE, Okay A, Aksoy T, Aras ES, Büyük İ. The genome-wide characterization of WOX gene family in Phaseolus vulgaris L. during salt stress. Physiol Mol Biol Plants. 2022;28(6):1297–309.
Hao Q, Zhang L, Yang Y, Shan Z, Zhou XA. Genome-wide analysis of the WOX gene family and function exploration of GmWOX18 in soybean. Plants. 2019;8(7):215.
Han N, Tang R, Chen X, Xu Z, Ren Z, Wang L. Genome-wide identification and characterization of WOX genes in Cucumis sativus. Genome. 2021;64(8):761–76.
Li Y, Jin C, Liu Y, Wang L, Li F, Wang B, Liu G, Jiang J, Li H. Global analysis of the WOX transcription factor gene family in populus× xiaohei TS Hwang et Liang reveals their stress− responsive patterns. Forests. 2022;13(1):122.
Yang T, Gao T, Wang C, Wang X, Chen C, Tian M, Yang W. In silico genome wide identification and expression analysis of the WUSCHEL-related homeobox gene family in Medicago sativa. Genomics Inform. 2022;20(2):e19.
Wu X, Dabi T, Weigel D. Requirement of homeobox gene STIMPY/WOX9 for Arabidopsis meristem growth and maintenance. Curr Biol. 2005;15(5):436–40.
Wu X, Chory J, Weigel D. Combinations of WOX activities regulate tissue proliferation during Arabidopsis embryonic development. Dev Biol. 2007;309(2):306–16.
Luang S, Sornaraj P, Bazanova N, Jia W, Eini O, Hussain SS, Kovalchuk N, Agarwal PK, Hrmova M, Lopato S. The wheat TabZIP2 transcription factor is activated by the nutrient starvation-responsive SnRK3/CIPK protein kinase. Plant Mol Biol. 2018;96:543–61.
Agarwal P, Baranwal VK, Khurana P. Genome-wide analysis of bZIP transcription factors in wheat and functional characterization of a TabZIP under abiotic stress. Sci Rep. 2019;9(1):4608.
Li H, Li L, ShangGuan G, Jia C, Deng S, Noman M, Liu Y, Guo Y, Han L, Zhang X, Dong Y. Genome-wide identification and expression analysis of bZIP gene family in Carthamus tinctorius L. Sci Rep. 2020;10(1):15521.
Wang P, Yang C, Chen H, Luo L, Leng Q, Li S, Han Z, Li X, Song C, Zhang X, Wang D. Exploring transcription factors reveals crucial members and regulatory networks involved in different abiotic stresses in Brassica napus L. BMC Plant Biol. 2018;18(1):1–21.
Li R, Hu F, Li B, Zhang Y, Chen M, Fan T, Wang T. Whole genome bisulfite sequencing methylome analysis of mulberry (Morus alba) reveals epigenome modifications in response to drought stress. Sci Rep. 2020;10(1):8013.
Niu X, Zhai N, Yang X, Su M, Liu C, Wang L, Qu P, Liu W, Yuan Q, Pei X. Identification of drought-resistant genes in Shanlan upland rice. Agriculture. 2022;12(2):150.
Tian F, Yang DC, Meng YQ, Jin J, Gao G. PlantRegMap: charting functional regulatory maps in plants. Nucleic Acids Res. 2020;48(D1):D1104–13.
Lilay GH, Castro PH, Campilho A, Assunção AG. The Arabidopsis bZIP19 and bZIP23 activity requires zinc deficiency–insight on regulation from complementation lines. Front Plant Sci. 2019;9:1955.
Lilay GH, Persson DP, Castro PH, Liao F, Alexander RD, Aarts MG, Assunção AG. Arabidopsis bZIP19 and bZIP23 act as zinc sensors to control plant zinc status. Nature Plants. 2021;7(2):137–43.
Shao H, Wang H, Tang X. NAC transcription factors in plant multiple abiotic stress responses: progress and prospects. Front Plant Sci. 2015;6:902.
McGrann GR, Steed A, Burt C, Goddard R, Lachaux C, Bansal A, Corbitt M, Gorniak K, Nicholson P, Brown JK. Contribution of the drought tolerance-related Stress-responsive NAC 1 transcription factor to resistance of barley to R amularia leaf spot. Mol Plant Pathol. 2015;16(2):201–9.
Cao Y, Zhai J, Wang Q, Yuan H, Huang X. Function of Hevea brasiliensis NAC1 in dehydration-induced laticifer differentiation and latex biosynthesis. Planta. 2017;245:31–44.
Meraj TA, Fu J, Raza MA, Zhu C, Shen Q, Xu D, Wang Q. Transcriptional factors regulate plant stress responses through mediating secondary metabolism. Genes. 2020;11(4):346.
Tran LS, Nakashima K, Sakuma Y, Simpson SD, Fujita Y, Maruyama K, Fujita M, Seki M, Shinozaki K, Yamaguchi-Shinozaki K. Isolation and functional analysis of Arabidopsis stress-inducible NAC transcription factors that bind to a drought-responsive cis-element in the early responsive to dehydration stress 1 promoter. Plant Cell. 2004;16(9):2481–98.
Zhong X, Hale CJ, Nguyen M, Ausin I, Groth M, Hetzel J, Vashisht AA, Henderson IR, Wohlschlegel JA, Jacobsen SE. Domains rearranged methyltransferase3 controls DNA methylation and regulates RNA polymerase V transcript abundance in Arabidopsis. Proc Natl Acad Sci. 2015;112(3):911–6.
Najafi S, Sorkheh K, Nasernakhaei F. Characterization of the APETALA2/Ethylene-responsive factor (AP2/ERF) transcription factor family in sunflower. Sci Rep. 2018;8(1):11576.
Zhao Q, Hu RS, Liu D, Liu X, Wang J, Xiang XH, Li YY. The AP2 transcription factor NtERF172 confers drought resistance by modifying NtCAT. Plant Biotechnol J. 2020;18(12):2444–55.
Kasuga M, Miura S, Shinozaki K, Yamaguchi-Shinozaki K. A combination of the Arabidopsis DREB1A gene and stress-inducible rd29A promoter improved drought-and low-temperature stress tolerance in tobacco by gene transfer. Plant Cell Physiol. 2004;45(3):346–50.
Sandhya J, Ashwini T, Manisha R, Vinodha M, Srinivas A. Drought tolerance enhancement with co-Overexpression of DREB2A and APX in indica rice (Oryza sativa L.). Am J Plant Sci. 2021;12(2):234–58.
Marinho JP, Pagliarini RF, Molinari MD, Marcolino-Gomes J, Caranhoto AL, Marin SR, Oliveira MC, Foloni JS, Melo CL, Kidokoro S, Mizoi J. Overexpression of full-length and partial DREB2A enhances soybean drought tolerance. Agron Sci Biotechnol. 2022;8:1–21.
Wei S, Li X, Lu Z, Zhang H, Ye X, Zhou Y, Li J, Yan Y, Pei H, Duan F, Wang D. A transcriptional regulator that boosts grain yields and shortens the growth duration of rice. Science. 2022;377(6604):eabi8455.
Ahmed S, Kouser S, Asgher M, Gandhi SG. Plant aquaporins: a frontward to make crop plants drought resistant. Physiol Plant. 2021;172(2):1089–105.
Fetter K, Van Wilder V, Moshelion M, Chaumont F. Interactions between plasma membrane aquaporins modulate their water channel activity. Plant Cell. 2004;16(1):215–28.
Sakurai J, Ishikawa F, Yamaguchi T, Uemura M, Maeshima M. Identification of 33 rice aquaporin genes and analysis of their expression and function. Plant Cell Physiol. 2005;46(9):1568–77.
Suga S, Maeshima M. Water channel activity of radish plasma membrane aquaporins heterologously expressed in yeast and their modification by site-directed mutagenesis. Plant Cell Physiol. 2004;45(7):823–30.
Bienert MD, Diehn TA, Richet N, Chaumont F, Bienert GP. Heterotetramerization of plant PIP1 and PIP2 aquaporins is an evolutionary ancient feature to guide PIP1 plasma membrane localization and function. Front Plant Sci. 2018;9:382.
Ren J, Yang X, Ma C, Wang Y, Zhao J, Kang L. Meta-analysis of the effect of the overexpression of aquaporin family genes on the drought stress response. Plant Biotechnol Rep. 2021;15:139–50.
Yang X, Nong B, Chen C, Wang J, Xia X, Zhang Z, Wei Y, Zeng Y, Feng R, Wu Y, Guo H. OsNPF3. 1, a member of the NRT1/PTR family, increases nitrogen use efficiency and biomass production in rice. Crop J. 2023;11(1):108–18.
Tamadaddi C, Verma AK, Zambare V, Vairagkar A, Diwan D, Sahi C. J-like protein family of Arabidopsis thaliana: the enigmatic cousins of J-domain proteins. Plant Cell Rep. 2022;41(6):1343–55.
Yıldırım K, Yağcı A, Sucu S, Tunç S. Responses of grapevine rootstocks to drought through altered root system architecture and root transcriptomic regulations. Plant Physiol Biochem. 2018;127:256–68.
Kumar A, Sandhu N, Kumar P, Pruthi G, Singh J, Kaur S, Chhuneja P. Genome-wide identification and in silico analysis of NPF, NRT2, CLC and SLAC1/SLAH nitrate transporters in hexaploid wheat (Triticum aestivum). Sci Rep. 2022;12(1):11227.
Corratgé-Faillie C, Lacombe B. Substrate (un) specificity of Arabidopsis NRT1/PTR FAMILY (NPF) proteins. J Exp Bot. 2017;68(12):3107–13.
Atif RM, Shahid L, Waqas M, Ali B, Rashid MA, Azeem F, Nawaz MA, Wani SH, Chung G. Insights on calcium-dependent protein kinases (CPKs) signaling for abiotic stress tolerance in plants. Int J Mol Sci. 2019;20(21):5298.
Asano T, Hakata M, Nakamura H, Aoki N, Komatsu S, Ichikawa H, Hirochika H, Ohsugi R. Functional characterisation of OsCPK21, a calcium-dependent protein kinase that confers salt tolerance in rice. Plant Mol Biol. 2011;75:179–91.
Santos AL, Chaves-Silva S, Yang L, et al. Global analysis of the MATE gene family of metabolite transporters in tomato. BMC Plant Biol. 2017;17:185.
Liang X, Luo G, Li W, Yao A, Liu W, Xie L, Han M, Li X, Han D. Overexpression of a Malus baccata CBF transcription factor gene, MbCBF1, Increases cold and salinity tolerance in Arabidopsis thaliana. Plant Physiol Biochem. 2022;192:230–42.
Feng K, Hou XL, Xing GM, Liu JX, Duan AQ, Xu ZS, Li MY, Zhuang J, Xiong AS. Advances in AP2/ERF super-family transcription factors in plant. Crit Rev Biotechnol. 2020;40(6):750–76.
Gross LE, Spies N, Simm S, Schleiff E. Toc75-V/OEP80 is processed during translocation into chloroplasts, and the membrane-embedded form exposes its POTRA domain to the intermembrane space. FEBS Open Bio. 2020;10(3):444–54.
Tong SM, Gao BJ, Peng H, Feng MG. Essential roles of two FRQ proteins (frq1 and frq2) in beauveria bassiana’s virulence, infection cycle, and calcofluor-specific signaling. Appl Environ Microbiol. 2021;87(6):e02545–e2620.
Wang W, Hu B, Li A, Chu C. NRT1. 1s in plants: functions beyond nitrate transport. J Exp Bot. 2020;71(15):4373–9.
Acknowledgements
Authors thank Dr. Eric Josephs for editing the manuscript.
Funding
This work was supported by the University of North Carolina at Greensboro (Grant # 133504 to AL-O) and NIH grant #1SC2GM144193-01 to A L-O.
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A.B-A collected the resources, analyzed the data and wrote the first draft and edited the final version. A. L-O conceived the study, edited the draft, and incorporated extra resources. Both authors read and agreed on the final manuscript.
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Supplementary Information
Additional file 1: Supplementary Figure 1.
Mapping pattern of 253 orthologous genes and gene elements on the tef genome (colors represent genes and same color on different chromosome represent existence of gene copy. A gene has copy element on several chromosomes (1A/1B, 2A/2B, 3A/3B, 4A/4B, 5A/5B, 6A/6B, 7A/7B, 8A/8B, 9A/9B, 10A/10B and contings_123, 124, 251, 471 and 765) with uneven distribution). Supplementary Figure 2. GO analysis of functionally annotated ESTSs in biological process (Fig. 2A) and molecular function (Fig. 2B) using Blast2GO. Bars represent the number of genes in each functional category. Supplementary Figure 3. Scaled NJ tree of 20 CDS of genes with high-level GO term compared across four crop plants. The multiple sequence alignment was conducted by CLUSTALW and the Phylogenetic tree was constructed by the Neighbor Joining (NJ) algorithm with default parameters and 1000 bootstrap replication. Genes were grouped into three distinct clusters (I, II, III). Et, Os, Si, and Ec indicates Eragrostis tef, Oryza sativa, Seteria italica and Eleusine coracana respectively.
Additional file 2: Supplementary Table 1.
Lists of 505 ESTs mapped on teff and submitted to Blast2GO. Supplementary Table 2. Lists of 224 genes submitted to DAVID for GO analysis. Supplementary Table 3. Lists of 29 ESTs annotated using Blast2GO. Supplementary Table 4. 160 genes with Gene ontology term for molecular function. Supplementary Table 5. 130 genes with Gene ontology term for biological process. Supplementary Table 6. 87 Genes with known GO terms enriched in different biological processes using UP_KW_BIOLOGICAL_PROCESS. Supplementary Table 7. 102 Genes with known GO terms enriched in different molecular function using UP_KW_MOLECULAR_FUNCTION. Supplementary Table 8. UP_tissue report of gene expression analysis. Supportive Table 9. List of genes highly enrich in biological process using ShniyGO. Supplementary Table 10. Genes significantly enriched in different molecular Function using shinyGO and using Arabidopsis model. Supplementary Table 11. Cluster of genes using gene set enrichment score in several molecular function. Supplementary Table 12. Cluster of genes having critical function in several molecular function using Kappa score. Supplementary Table 13 A. Genes involved in several biological pathways using KEGG pathway enrichment analysis. Supplementary Table 13 B. Pathway analysis using KEGG with ShinyGO. Supplementary Table 14. Genes grouped by functional categories defined by high-level GO terms.
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Bekele-Alemu, A., Ligaba-Osena, A. Comprehensive in silico analysis of the underutilized crop tef (Eragrostis tef (Zucc.) Trotter) genome reveals drought tolerance signatures. BMC Plant Biol 23, 506 (2023). https://doi.org/10.1186/s12870-023-04515-1
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DOI: https://doi.org/10.1186/s12870-023-04515-1