Genome-wide Identification, characterization and evolutionary analysis of SNF1-related kinase 1 (SnRK1) and late embryogenesis abundant (LEA) proteins gene family in Hordeum vulgare under drought stress

doi:10.21203/rs.3.rs-3237348/v1

The SNF1-related kinase 1 (SnRK1) and late embryogenesis abundant (LEA) proteins were identified, characterized and analyzed in H. vulgare. Results of qRT-PCR indicates the HvSnRK1 and HvLEA were up regulated with fold change 6.12 and 7.58, respectively under drought stress. Genome-wide analysis, were identified 13 SnRK1 and 7 LEA proteins in H. vulgare. Domain analysis confirmed the presence of the Protein kinase domain and LEA_4 on HvSnRK1 and HvLEA proteins, respectively. Motifs and genes structure analysis indicates, that genes with closer phylogenetic relationships exhibited more similar genetic structures. The most HvSnRK1 proteins were located in the cytoplasm whereas HvLEA proteins were located in the nucleus and mitochondria. 16 microRNAs were predicted against 7 HvSnRK1 genes whereas 1 microRNAs were predicted against 1 HvLEA gene. Predicted SnRK1 and LEA proteins models have a C-score range from − 0.75 and − 1.48 to -0.69 and − 0.20, respectively, which suggesting the structures of SnRK1 and LEA proteins are constructed with high accuracy. SnRK1 genes were found on chromosome 1, 2, 3, 4 and 5. LEA genes were found on chromosome 1, 3 and 4. Ka/Ks ratio were indicated that the SnRK1 and LEA genes were primarily influenced by purifying selection. Phylogenetic analysis were classified SnRK1 and LEA proteins into three clades for each one. Synteny analysis of SnRK1 and LEA proteins were have collinearity orthologous relationship in Z. mays. The gene ontology enrichment analysis were confirmed the functional role of SnRK1 and LEA as a stress responsive.

SNF1-related kinase 1 (SnRK1) protein

late embryogenesis abundant (LEA) protein

Genome-wide identification

evolutionary analysis

drought stress

Drought is becoming more extreme and longer around the world. The right amount of soaking rains can break a drought, it consider best solution to break a drought, but it need multiple soaking rains. Water stress could be reduced agricultural production to 50 percent in Africa (https://www.usgs.gov). During drought the water levels in the well can be lowered (Gómez-Gómez et al. 2014; Moreland 1993). Researchers is currently focused on the enzyme-catalyzed process to improve our knowledge of the effects of genetic expression under drought stress. Understanding of drought-plant tolerant can be used for producing varieties that are drought tolerant with high grain quality (Gous et al. 2015). Drought effects on grain starch biosynthesis (Gous et al. 2013). Plants respond to abiotic stresses such as salinity, drought and low temperature by activation of complex intracellular signaling pathways that regulate acclimation to environmental stress to survive (Diédhiou et al. 2008). Negative effects of climate change cause reduction plant production in the coming years. To face these negative effects, breeding of abiotic tolerant plants via classic breeding technique and genetic engineering is necessary. Stress tolerance genes such as the late embryogenesis abundant (LEA) genes that can confer resistance to diverse abiotic stresses into plants by means of genetic engineering. The results showed that The CmLEA-S gene from the melon plant enhance and improvement of salinity and drought stress tolerance to transgenic tobacco (Poku et al. 2020). Late embryogenesis abundant (LEA) proteins have important role in abiotic stresses tolerance in plants. In cotton, the identified LEA genes were classified into 8 groups based on phylogenetic tree analysis for their conserved domain. Almost all the LEA genes in their promoters contained ABRE, MBS, W-Box and TAC-elements, which known to be involved in drought stress and other stress responses. LEA genes have fewer number of introns and are distributed in all chromosomes with some genes clustering. Most of the duplicated LEA genes were segmental. Syntenic analysis showed that greater percentages of LEA genes are conserved. Segmental gene duplication played a key role in the expansion of LEA genes. Gene ontology analysis revealed that LEA genes are involved in desiccation and defense responses. Majority of the LEA genes were involved in secretory pathways. Expression profile analysis indicated that most of the LEA genes were highly expressed in drought tolerant (Magwanga et al. 2018). LEA proteins have first been discovered about 25 years ago as accumulating late in plant seed development. LEA proteins related to desiccation tolerance and abiotic stress tolerance. In silico analyses in Arabidopsis were identified 12 homeologous pairs and 9 tandem repeats among the 51 LEA genes were identified into 9 groups (Hundertmark and Hincha 2008). Sorghum bicolor, LEA proteins play an important role during abiotic stress and plant development. The systematic genome-wide analysis identify a total of 68 LEA genes, which are classified into 8 families and distributed on all the chromosomes. Segmental duplication played an important role in in their expansion. The transcriptional profiling under environmental stress indicates they might play an essential role in tolerance (Nagaraju et al. 2019). In maize, are only very limited reports of LEA proteins. A total of 83 LEA proteins were classified into nine groups which including 32 putative proteins in maize were identified in this study. Motif compositions of the LEA protiens are highly conserved in each groups. Some abiotic stress-responsive cis-elements were also found in the promoters of ZmLEA genes. Some proteins of ZmLEAs were regulated under some environmental stresses (Li and Cao 2016). RT-qPCR analysis showed that IpLEA was widely expressed in different organs of the I. pes-caprae plants, and the expression levels increased following salt, cold, osmotic, oxidative, and abscisic acid stress. Analysis of the promoter of IpLEA identified distinct cis-acting regulatory elements involved in environmental stress. Overexpressing of IpLEA in transgenic Arabidopsis plants were enhanced of salinity and drought tolerance (Zheng et al. 2019). SNF1-related protein kinase 2 (SnRK2) mediates signaling of A phytohormone abscisic acid (ABA) which has a major role in abiotic stress responses in plants (Kamiyama et al. 2021). Function of SNF1-related kinase (SnRK1) is a key regulator of plant response to abiotic stress, biotic stress, development stages, control of cell cycle, physiological processes and regulation of metabolism (Shen et al. 2012; Guidetti et al. 2007; Mohannath et al. 2014). Information about the role of SnRK1α in the biotic stress tolerance is little, especially in wheat. TaSnRK1αs are expressed in all developmental stages and in all organ (Perochon et al. 2019). In Arabidopsis thaliana, a SNF1-related protein kinase 2 (SnRK2) can significantly effect of drought response (Umezawa et al. 2004). In strawberry, The SNF1 (sucrose non-fermenting 1)-related protein kinases 1 (SnRKs1) are key role in the in the regulation of strawberry development and responses to abiotic stress. FvSnRK family genes were distributed in 7 chromosomes. The FvSnRK1.1 had 10 exons, Most of FvSnRK2s had 9 exons or 8 introns. FvSnRK3 members were grouped into intron-free and intron-harboring members which include ranged from 11 to 15 introns. Conserved motif analysis showed that FvSnRK sequences that belonged to the same subgroup contained their own specific motifs. Cis-element in promoter and expression pattern analyses of FvSnRK1.1 (Zhang et al. 2020).

Databases screening and identification of SNF1-related kinase 1 (SnRK1) and late embryogenesis abundant (LEA) proteins

The genome of Hordeum vulgare, Zea mays, Solanum lycopersicum and Arabidopsis thaliana were downloaded from the Phytozome database (https://phytozome.jgi.doe.gov). Gene and protein sequences were obtained from the NCBI (https://www.ncbi.nlm.nih.gov/gene/) and used as queries to search in the Phytozome database (https://phytozome.jgi.doe.gov) against the Hordeum vulgare proteins with an E-value: ≤1e^− 30.

Characterization of the SNF1-related kinase 1 (SnRK1) and late embryogenesis abundant (LEA) proteins

Circoletto (http://tools.bat.infspire.org/circoletto/) were visualized sequence identity of SnRK1 and LEA proteins. SnRK1 and LEA proteins physical and chemical properties, including each protein’s molecular weight, isoelectric point, total number of negatively charged residue, total number of positively charged residue, total number of atoms, Instability and grand average of hydropathicity (GRAVY), were computed using the Expasy ProtParam Tool (Gasteiger et al. 2005) (https://web.expasy.org/protparam/). The NCBI conserved domain tool (https://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi) against pfam v34.0–19178 PSSMs database for SnRK1 proteins and CDD v3.20–59693 PSSMs database for LEA proteins (Lu et al. 2020) and the InterPro tool (https://www.ebi.ac.uk/interpro/) (Paysan-lafosse et al. 2022) were used to analyze the domains of the SnRK1 and LEA proteins. MEME 5.5.2 (https://meme-suite.org/meme/tools/meme) (Bailey et al. 2015) were used to compute the conserved motifs of the SnRK1 and LEA proteins. Pfam (http://pfam-legacy.xfam.org/) were used for motif description (Mistry et al. 2020). WoLF PSORT Protein Subcellular Localization Prediction (https://wolfpsort.hgc.jp/) (Horton et al. 2007) were predicted the Protein subcellular localization, TBtools were visualized results (Chen et al. 2020). NLSDB (https://rostlab.org/services/nlsdb/) were used to search for nuclear localization signal potential (Nair et al. 2003). TMHMM server version 2.0 (https://services.healthtech.dtu.dk/services/TMHMM-2.0/) were confirm the transmembrane helical domains (TMs) in SnRK1 and LEA proteins (Krogh et al. 2001). NetPhos 3.1 server (https://services.healthtech.dtu.dk/service.php?NetPhos-3.1) were predicted Phosphorylation sites of SnRK1 and LEA proteins (Blom et al. 1999). I-TASSER program (https://zhanglab.ccmb.med.umich.edu/I-TASSER/) (Zheng et al. 2021) were predicted the three-dimensional (3-D) structure of SnRK1 and LEA proteins. STRING database (https://string-db.org/) _{(12 b)} were determined the physical interaction Network analysis between the proteins. psRNATarget database (https://www.zhaolab.org/psRNATarget/) (Dai et al. 2018) and miRBase (https://www.mirbase.org/) (Kozomara and Griffiths-Jones 2014) were predicted miRNAs. RactIP (http://ws.sato-lab.org/rtips/ractip/) were predicted joint structures formed from miRNA and SnRK1 and LEA proteins (Kato et al. 2017). IPknot (http://ws.sato-lab.org/rtips/ipknot++/) were used to predict of RNA secondary structures with pseudoknots for SnRK1 and LEA proteins (Sato and Kato 2022).

Promoter, gene structure, Chromosomal distribution and evolutionary analysis of HvSNF1-related kinase 1 ( SnRK1 ) and late embryogenesis abundant ( LEA ) genes

The promoter sequences of the SnRK1 and LEA genes in Hordeum vulgare 1500 bp upstream of the transcription start site TSS of each SnRK1 and LEA gene were retrieved from the Hordeum vulgare genome sequence file were downloaded from the Phytozome database. Plant CARE (http://bioinformatics.psb.ugent.be/webtools/plantcare/html/) were analyzed the Cis-regulatory elements (CRE) (Rombatus et al 1999). The graphical representation of CRE elements present in the promoter region of the gene were visualizing through TBTool (Chen et al. 2020). The gene structures were obtained from the GFF file were downloaded from phytozome of Hordeum vulgare genome and then were illustrated using theTBtools (Chen et al. 2020). According to the position information of the SnRK1 and LEA gene on the chromosome, the karyotype map of SnRK1 and LEA genes were drawn using TBtools (Chen et al. 2020). The output image were used to mention all SnRK1 and LEA genes on the chromosome. Rate of synonymous (Ks), non-synonymous (Ka) substitution were calculated by TBtools (Chen et al. 2020) to investigate selective pressure. Divergence time of the gene pairs was estimated using synonymous mutation rate of substitutions per synonymous site per million years ago (Mya), as follows: “T = Ks/2λ”, with λ value of 6.05 × 10^− 9(Singh et al. 2017). The duplicated genes were identified as paralogous gene pairs if the alignment cover ≥ 70% of the longer gene, the identity of the aligned region ≥ 70% (Gu et al. 2002) and were identified by MEGA-11 gene duplication wizard (Tamura et al. 2021). Paralogous gene pairs (tandem and segmental genes) collinearity analysis were visualized as a Circos plot through TBTool (Chen et al. 2020).

Phylogenetic and synteny analysis of HvSNF1-related kinase 1 (SnRK1) and late embryogenesis abundant (LEA) proteins family

Multiple sequence alignment of 77 SnRK1 and LEA proteins sequences from H. vulgare were alignment by the MUSCLE method. Molecular evolutionary genetic analysis (MEGA-11) were conducted a phylogenetic tree with the Maximum likelihood with 1000 bootstrap replicates based on the WAG with Freqs. (+ F) model (Tamura et al. 2021). The Itools online website (https://itol.embl.de/) were used to modify and visualizing tree (Letunic and Bork 2019). TBtools (Chen et al. 2020) were used to draw the syntenic relationship of the SnRK1 and LEA genes in H. vulgare against Zea mays, Solanum lycopersicum and Arabidopsis thaliana).

Gene ontology enrichment and functional relationship analysis of HvSNF1-related kinase 1 ( SnRK1 ) and late embryogenesis abundant ( LEA ) genes

ShinyGO 0.77 (http://bioinformatics.sdstate.edu/go/) were used for Gene Ontology Enrichment Analysis ₍₂₃₎. We performed the gene ontology (GO) annotation analysis by submitting all SnRK1 and LEA genes sequences to the eggNOG database (http://eggnog-mapper.embl.de/) (Cantalapiedra et al. 2021) and Phytozome database (https://phytozome.jgi.doe.gov). The GO annotation data was handled in SRPLOT (http://www.bioinformatics.com.cn/login_en/) to draw the genes ontology Chord for functional relationship SnRK1 and LEA genes ₍₂₅₎.

Barley plant growth and drought treatment

This experiments were conducted in department of botany and microbiology, faculty of science at Cairo University. Drought treatment were conducted to 2 groups the first group is a control plants whereas the other for stressed plants. 30 seeds were planted in small pots, in growth room in small pots for 14 days. Second group of Stressed plants were treated by continuous water withholding for 8 hours, while first group were treated with Hoagland’s solution as controls. The plants were harvested after 12 h of salt stress. Three control plants and Three stressed plants were used for RNA extraction and sequencing.

RNA isolation, qRT-PCR expression analysis and sequencing

The total RNA was isolated from leaf of 15-day seedlings of barley using GeneTireX kit. The residual DNA was removed using the Recombinant DNase I, RNase free (thermo scientific, Litwania). The first-strand cDNA was synthesized in a 20µL reaction solution using a Grisp reverse transcription kit (ww.grisp.pt) with approximately two micrograms of DNA-free total RNA from each sample. qRT-PCR was performed to relatively quantify the transcription levels of SnRK1 and LEA genes expressed in Leaves. The qPCR was performed with CFX connect Real-Time PCR System (Bio-Rad, Singapore) and under the following conditions: 94°C for 5 min, followed by 40 cycles of 94°C for 10 s, 58°C for 20 s, 72°C for 30 s and plate read, melt curve 65–95°C with increment 0.5°C for 10 s and plate read, then sequencing PCR results. The Ct (cycle threshold) value was used as a measure for the starting copy numbers of the target gene (Chen et al. 2015). The relative gene expression level was calculated using the 2^{− ΔΔCT} method (Livak and Schmittgen 2001). Glyceraldehyde-3-phosphate dehydrogenase gene (GAPDH, GeneBank accession no. AJ010224) were used as an internal reference gene.

Identification of SNF1-related kinase 1 (SnRK1) and late embryogenesis abundant (LEA) proteins

This study were identified SnRK1 (left primer: CCAGGCAGTATCTTCCAGGA and right primer: CAAAGCTGTGGTTGGCATCT) and LEA genes (left primer: CACCGAGGCCAAGGACAA and right primer: GGACTCCTTGGTGTACTGCG). We used Hordeum vulgare HvSnRK1alpha3 (SnRK1) mRNA (GenBank: AB910930.1) and Hordeum vulgare subsp. vulgare cultivar Ximala 10 late embryogenesis abundant protein (LEA3) mRNA (GenBank: FJ026804.1) as query genes, which have highest similarity with our targeted genes. SnRK1 alpha subunit (GenBank: BAO51907.1) and LEA3 (GenBank: ACH89913.1) were used as query proteins to screen SnRK1 and LEA proteins members in the genomes of Hordeum vulgare.

Characterization of the SNF1-related kinase 1 (SnRK1) and late embryogenesis abundant (LEA) proteins

A total of 13 HvSnRK1 and 7 HvLEA candidate genes were retrieved from the H. vulgare genome and were named according to their positions on chromosomes from HvPK-like_1 to HvPK-like_13 and HvLEA_4_1 to HvLEA_4_7 for HvSnRK1 and 7 HvLEA respectively (Online Resource S1). Sequence identity of 13 HvSnRK1 and 7 HvLEA proteins were showed by colour by E-value ratio (blue if ≤ 60%, green if ≤ 80%, orange if ≤ 90%) as shown in Fig. 1. Analysis of protein physical and chemical properties showed that the length of the HvSnRK1 family amino acid in H. vulgare ranged from 146 (HvPK-like_9) to 513 (HvPK-like_7) whereas HvLEA proteins ranged from 174 (HvLEA_4_1) to 225 (HvLEA_4_5). The molecular weight for HvSnRK1 and HvLEA ranged from 16886.69 (HvPK-like_9) and 17528.93 (HvLEA_4_1) to 58815.75 (HvPK-like_7) and 22937.04 (HvLEA_4_2), respectively. The PI ranged from 5.78 (HvPK-like_13) and 4.82 (HvLEA_4_7) to 9.15 (HvPK-like_4) and 9.06 (HvPK-like_2), respectively. The total number of atoms ranged from 2395 (HvPK-like_9) and 2418 (HvLEA_4_1) to 8272 (HvPK-like_7) and 3178 (HvLEA_4_2), respectively. The grad average of hydropathicity value (GRAVY) ranged from 0.476 (HvPK-like_10) and − 1.036 (HvLEA_4_4) to 0.079 (HvPK-like_13) and 0.492 (HvLEA_4_7), respectively (Table 1).

Gene name	Gene rename	number of amino acid	Molecular weight	Theoretical pI	Total number of negatively charged residue (Asp + Glu)	Total number of positively charged residue (Arg + Lys)	Total number of atoms	Instability	Grad average of hydropathicity (GRAVY)	Chromosome Name	Gene Start (bp)	Gene End (bp)
HORVU.MOREX.r3.1HG0082030	HvPK-like_1	500	56983.99	8.77	61	68	8071	unstable	-0.276	chr1H	489721090	489726039
HORVU.MOREX.r3.2HG0110800	HvPK-like_2	304	34185.21	8.45	39	42	4829	stable	-0.282	chr2H	33611366	33613270
HORVU.MOREX.r3.2HG0121170	HvPK-like_3	451	51126.76	7.95	57	59	7174	stable	-0.416	chr2H	75272812	75277875
HORVU.MOREX.r3.2HG0153230	HvPK-like_4	461	51770.31	9.15	57	67	7306	stable	-0.43	chr2H	355338435	355342439
HORVU.MOREX.r3.3HG0286490	HvPK-like_5	502	57379.3	8.44	61	65	8112	unstable	-0.365	chr3H	471947561	471952534
HORVU.MOREX.r3.3HG0320770	HvPK-like_6	392	44883.42	6.74	50	48	6298	unstable	-0.389	chr3H	597798700	597802231
HORVU.MOREX.r3.3HG0320810	HvPK-like_7	513	58815.75	6.58	65	62	8272	unstable	-0.294	chr3H	597849858	597855987
HORVU.MOREX.r3.3HG0320820	HvPK-like_8	346	39484.37	7.06	39	39	5529	unstable	-0.273	chr3H	597872534	597875669
HORVU.MOREX.r3.3HG0320830	HvPK-like_9	146	16886.69	8.87	18	21	2395	stable	-0.277	chr3H	597875730	597878020
HORVU.MOREX.r3.4HG0356420	HvPK-like_10	439	50278.72	7.18	67	67	7114	stable	-0.476	chr4H	146938826	146943753
HORVU.MOREX.r3.4HG0380570	HvPK-like_11	449	50975.63	8.27	63	66	7212	stable	-0.39	chr4H	413715267	413720462
HORVU.MOREX.r3.4HG0385110	HvPK-like_12	509	58211.08	8.58	59	65	8183	unstable	-0.345	chr4H	453580908	453587253
HORVU.MOREX.r3.5HG0479090	HvPK-like_13	223	24699.46	5.78	26	21	3471	stable	0.079	chr5H	423997969	423999249
HORVU.MOREX.r3.1HG0058690	HvLEA_4_1	174	17528.93	8.63	24	26	2418	stable	-0.949	chr1H	391030788	391031839
HORVU.MOREX.r3.1HG0079980	HvLEA_4_2	224	22937.04	9.06	30	34	3178	stable	-0.866	chr1H	485217521	485218281
HORVU.MOREX.r3.1HG0080000	HvLEA_4_3	195	19961.69	8.87	27	30	2768	stable	-1.025	chr1H	485251521	485252201
HORVU.MOREX.r3.1HG0080020	HvLEA_4_4	213	21819.79	9.02	28	32	3032	stable	-1.036	chr1H	485270377	485271473
HORVU.MOREX.r3.3HG0283640	HvLEA_4_5	225	22780.88	8.81	29	32	3142	stable	-0.808	chr3H	455071199	455072394
HORVU.MOREX.r3.4HG0408930	HvLEA_4_6	177	18366.21	4.85	30	22	2548	stable	-0.702	chr4H	584568406	584569347
HORVU.MOREX.r3.4HG0408940	HvLEA_4_7	176	17776.73	4.82	27	21	2449	stable	-0.492	chr4H	584570603	584571523

(Table 1) 13 HvSnRK1 and 7 HvLEA candidate proteins were retrieved from the H. vulgare genome showed that the molecular weight range, the PI and the grad average of hydropathicity value (GRAVY).

Domain analysis were carried out for all 13 HvSnRK1 and 7 HvLEA proteins using an NCBI domain search (pfam v34.0–19178 PSSMs database for HvSnRK1 proteins and CDD v3.20–59693 PSSMs database for HvLEA proteins). Domain analysis confirmed the presence of the Protein kinase domain and LEA_4 on HvSnRK1 and HvLEA proteins, respectively (Fig. 2 and Fig. 3). Motif analysis were indicated that the phylogenetic relationships were similar to the conserved motifs distributions within clade. For instance, the motif distributions of the HvSnRK1 and HvLEA proteins were carried similar motifs within the clade, with few differences. The motif distributions for HvPK-like_7, 6, 9, 8, and 12 proteins were carried conserved motifs number 1, 2, 4, 5, 7, 8, 9 and 10. HvPK-like_1 and 5 proteins were carried conserved motifs number 1, 2, 3, 4, 5, 6, 7, 8, 9 and 10. HvPK-like_3, 4, 10, 11, 2, 13 proteins were carried conserved motifs number 1, 2, 3, 4, 8 and 9 (Fig. 2). The motif distributions for HvLEA_4_2, 5 and 1 proteins were carried conserved motifs number 1, 2, 3, 4, 6 and10. HvLEA_4_3 and 4 proteins were carried conserved motifs number 1, 2, 3 and 4. HvLEA_4_6 and 7 proteins were carried conserved motifs number 1, 2, 3 and 5 (Fig. 3). Subcellular localization predicted that the most HvSnRK1 proteins were located in the cytoplasm whereas HvLEA proteins were located in the nucleus and mitochondria. The heatmap depicting the protein subcellular localization prediction of the HvSnRK1 and HvLEA genes (Fig. 4). 1 putative nuclear localization signal (NLS) were predicted against HvSnRK1 proteins (Table 2).

Table 2

Nuclear localization signal (NLS) were predicted against HvSnRK1 proteins.
Gene rename	Signal	SignalType	Start	End	AnnotationType	Origin
HvPK-like_1	KKIKGG	NLS	222	227	Potential	In Silico Mutagenesis
HvPK-like_5	KKIKGG	NLS	222	227	Potential	In Silico Mutagenesis
HvPK-like_8	KKIKGG	NLS	61	66	Potential	In Silico Mutagenesis
HvPK-like_12	KKIKGG	NLS	225	230	Potential	In Silico Mutagenesis

TMHMM result were predicted the transmembrane helical in HvPK-like_10 protein (Fig. 5a). HvPK-like_9 and HvLEA_4_7 proteins were have been found to be more phosphorylated with serine, threonine and tyrosine (Fig. 5b and 5c). Phosphorylation sites prediction of HvPK-like_9 and HvLEA_4_7 proteins for kinases (ATM, CKI, CKII, CaM-II, DNAPK, EGFR, GSK3, INSR, PKA, PKB, PKC, PKG, RSK, SRC, cdc2, cdk5 and p38MAPK (online resource S1).

(Fig. 2) (A) The rectangular phylogenetic tree of HvSnRK1 proteins was constructed using MEGA-11 software based on the maximum likelihood method, with a bootstrap value of 1000 replicates. (B) Conserved motifs of HvSnRK1 were predicted using the MEME (Motifs are shown with its number). (C) HvSnRK1 family protein domains. (D) HvSnRK1 gene structure.

(Fig. 3) (A) The rectangular phylogenetic tree of HvLEA proteins was constructed using MEGA-11 software based on the maximum likelihood method, with a bootstrap value of 1000 replicates. (B) Conserved motifs of HvLEA were predicted using the MEME (Motifs are shown with its number). (C) HvLEA family protein domains. (D) HvLEA gene structure.

(Fig. 4) Heatmap showing HvSnRK1 and HvLEA genes localization prediction in deferent organelle.

(Fig. 5) a. the transmembrane helical in HvPK-like_10 protein. b. HvPK-like_9 protein putative phosphorylation with serine, threonine and tyrosine. c. HvLEA_4_7 protein putative phosphorylation with serine, threonine and tyrosine.

To study the putative function of SnRK1 and LEA proteins in H. vulgare, HvPK-like_3, HvPK-like_5, HvPK-like_12, HvLEA_4_1, HvLEA_4_3 and HvLEA_4_7 proteins are modeled by I-TASSER software to predict of 3-D structures. The 3-D structures were construed according to the similar structural templates and crystal structures obtained from Protein Data Bank (Fig. 6). C-score were used to estimate the confidence of the constructed protein model for HvPK-like_3, HvPK-like_5, HvPK-like_12, HvLEA_4_1, HvLEA_4_3 and HvLEA_4_7 proteins. Predicted SnRK1 and LEA proteins models have a C-score range from − 0.75 and − 1.48 to -0.69 and − 0.20, Respectively (Table 3), which suggesting the structures of SnRK1 and LEA proteins are constructed with high accuracy.

Table 3

Structural dependent modeling parameters for the SnRK1 and LEA proteins.

Protein

C-Score

TM-Score

RMSD (Å)

Best Identified Structural Analogs in PDB

PDB Hit

TM-Score ^a

RMSD ^a

IDEN ^a

Cov

HvPK-like_3

-0.75

0.62 ± 0.14

8.8 ± 4.6

7myjA

0.880

1.40

0.355

0.902

HvPK-like_5

-0.52

0.65 ± 0.13

8.5 ± 4.5

7myjA

0.839

1.09

0.512

0.853

HvPK-like_12

-0.69

0.63 ± 0.14

8.9 ± 4.6

7myjA

0.824

1.30

0.502

0.841

HvLEA_4_1

-0.20

0.69 ± 0.12

5.5 ± 3.5

4tqlA

0.839

2.45

0.115

1.000

HvLEA_4_3

-1.48

0.53 ± 0.15

8.5 ± 4.5

4tqlA

0.827

2.62

0.119

0.964

HvLEA_4_7

-0.82

0.61 ± 0.14

6.8 ± 4.1

4tqlA

0.844

2.64

0.097

1.000

To further explore the potential function of HvLEAs during the possible protein-protein interaction (PPI) with other proteins, the PPI network is constructed by STRING database. Late embryogenesis abundant protein B19.4 (MOLC 6278.1) is hypothesized to interact with other putative proteins, such as Endonuclease iii homolog (MLOC_61349.1, NTH1), Alpha-amylase/trypsin inhibitor (MLOC_34493.1), TF-B3 domain-containing protein (MLOC_69727.2), Bzip domain-containing protein (MLOC_34679.1), Glycine cleavage system p protein (MLOC_36667.1), Dirigent proteins impart stereoselectivity (MLOC_17554.1), Rhodanese domain-containing protein(MLOC_17072.2) and Trypsin inhibitor CMe (MLOC_15578.1) as shown in Fig. 7.

16 microRNAs were predicted against 7 HvSnRK1 genes whereas 1 microRNAs were predicted against 1 HvLEA gene, microRNA targeting relationship for SnRK1 and LEA genes show in Table 4. Hvu-miRN5813, Hvu-miRN5815, Hvu-miRN5816, Hvu-miRN5823, Hvu-miRN5824 and Hvu-miRN5828 microRNAs were targeted HvPK-like_2, HvPK-like_11, and HvPK-like_13 proteins. Hvu-miR1130a and Hvu-miR1130b microRNAs were targeted HvPK-like_6 and HvPK-like_7 proteins. Hvu-miR164a, Hvu-miR164b, Hvu-miR164c, Hvu-miR164d and Hvu-miR164e microRNAs were targeted HvPK-like_1 protein. Hvu-miR168a microRNA were targeted HvPK-like_11 protein. Hvu-miRN5777 and Hvu-miRN5789 microRNAs were targeted HvPK-like_3 protein. Hvu-miRN5819 microRNA were targeted HvPK-like_2 and HvPK-like_13 proteins. Hvu-miR408a microRNA were targeted HvLEA_4_1 protein (Table 4). Prediction of RNA secondary structures with pseudoknots for HvSnRK1 (HvPK-like_3, HvPK-like_5 and HvPK-like_12) and HvLEA (HvLEA3_1, HvLEA3_3 and HvLEA3_7) genes (online resource S2).

Table 4

microRNA targeting relationship for SnRK1 and LEA genes.
miRNA_Acc.	Target_Acc.	miRNA_start	miRNA_end	Target_start	Target_end	miRNA_aligned_fragment	Target_aligned_fragment
Hvu-miRN5813	HvPK-like_13	1	21	67	87	AUAUUUCAGAACGGAGGGAGU	AUUUUCAUCGUUCUGGAGUAU
Hvu-miRN5815	HvPK-like_13	1	21	67	87	AUAUUUCAGAACGGAGGGAGU	AUUUUCAUCGUUCUGGAGUAU
Hvu-miRN5816	HvPK-like_13	1	21	67	87	AUAUUUCAGAACGGAGGGAGU	AUUUUCAUCGUUCUGGAGUAU
Hvu-miRN5823	HvPK-like_13	1	21	67	87	AUAUUUCAGAACGGAGGGAGU	AUUUUCAUCGUUCUGGAGUAU
Hvu-miRN5824	HvPK-like_13	1	21	67	87	AUAUUUCAGAACGGAGGGAGU	AUUUUCAUCGUUCUGGAGUAU
Hvu-miRN5828	HvPK-like_13	1	21	67	87	AUAUUUCAGAACGGAGGGAGU	AUUUUCAUCGUUCUGGAGUAU
Hvu-miRN5813	HvPK-like_11	1	21	277	297	AUAUUUCAGAACGGAGGGAGU	AUUUUCAUUGUUCUGGAAUAC
Hvu-miRN5813	HvPK-like_2	1	21	274	294	AUAUUUCAGAACGGAGGGAGU	AUCUUCAUCGUUCUGGAGUAU
Hvu-miRN5815	HvPK-like_11	1	21	277	297	AUAUUUCAGAACGGAGGGAGU	AUUUUCAUUGUUCUGGAAUAC
Hvu-miRN5815	HvPK-like_2	1	21	274	294	AUAUUUCAGAACGGAGGGAGU	AUCUUCAUCGUUCUGGAGUAU
Hvu-miRN5816	HvPK-like_11	1	21	277	297	AUAUUUCAGAACGGAGGGAGU	AUUUUCAUUGUUCUGGAAUAC
Hvu-miRN5816	HvPK-like_2	1	21	274	294	AUAUUUCAGAACGGAGGGAGU	AUCUUCAUCGUUCUGGAGUAU
Hvu-miRN5823	HvPK-like_11	1	21	277	297	AUAUUUCAGAACGGAGGGAGU	AUUUUCAUUGUUCUGGAAUAC
Hvu-miRN5823	HvPK-like_2	1	21	274	294	AUAUUUCAGAACGGAGGGAGU	AUCUUCAUCGUUCUGGAGUAU
Hvu-miRN5824	HvPK-like_11	1	21	277	297	AUAUUUCAGAACGGAGGGAGU	AUUUUCAUUGUUCUGGAAUAC
Hvu-miRN5824	HvPK-like_2	1	21	274	294	AUAUUUCAGAACGGAGGGAGU	AUCUUCAUCGUUCUGGAGUAU
Hvu-miRN5828	HvPK-like_11	1	21	277	297	AUAUUUCAGAACGGAGGGAGU	AUUUUCAUUGUUCUGGAAUAC
Hvu-miRN5828	HvPK-like_2	1	21	274	294	AUAUUUCAGAACGGAGGGAGU	AUCUUCAUCGUUCUGGAGUAU
Hvu-miR1130a	HvPK-like_7	1	21	800	820	UCUUAUAUUAUGGGUCGGAGG	GAGUCCACCCAUGGUUUAAGA
Hvu-miR1130a	HvPK-like_6	1	21	800	820	UCUUAUAUUAUGGGUCGGAGG	GAGUCCACCCAUGGUUUAAGA
Hvu-miR1130b	HvPK-like_7	1	21	800	820	UCUUAUAUUAUGGGUCGGAGG	GAGUCCACCCAUGGUUUAAGA
Hvu-miR1130b	HvPK-like_6	1	21	800	820	UCUUAUAUUAUGGGUCGGAGG	GAGUCCACCCAUGGUUUAAGA
Hvu-miR164a	HvPK-like_1	1	21	1462	1482	UGGAGAAGCAGGGCACGUGCA	GACCUGUGUUCUGCCUUUCUA
Hvu-miR164b	HvPK-like_1	1	21	1462	1482	UGGAGAAGCAGGGCACGUGCA	GACCUGUGUUCUGCCUUUCUA
Hvu-miR164c	HvPK-like_1	1	21	1462	1482	UGGAGAAGCAGGGCACGUGCA	GACCUGUGUUCUGCCUUUCUA
Hvu-miR164d	HvPK-like_1	1	21	1462	1482	UGGAGAAGCAGGGCACGUGCA	GACCUGUGUUCUGCCUUUCUA
Hvu-miR164e	HvPK-like_1	1	21	1462	1482	UGGAGAAGCAGGGCACGUGCU	GACCUGUGUUCUGCCUUUCUA
Hvu-miR168a	HvPK-like_11	1	21	240	259	UCGCUUGGUGCAGAUCGGGAC	UGUCCGA-CUGCACGAGGUGA
Hvu-miRN5777	HvPK-like_3	1	21	42	62	UCUCUCGGCCUCUGCCUCUUC	GAUGGGGCGGACGCUGGGGGA
Hvu-miRN5789	HvPK-like_3	1	21	613	633	GUUUCCUGCAAGCACUUCAUA	UAUGUGAUGCUUACAGGGAAU
Hvu-miRN5819	HvPK-like_2	1	22	623	644	GGGAGUAUUAUGAAAGGAGGUU	UAAUUCUUUUUGUGAUGCUUGC
Hvu-miRN5819	HvPK-like_13	1	22	416	437	GGGAGUAUUAUGAAAGGAGGUU	UAAUUCUUUUUGUGAUGCUUGC
Hvu-miR408a	HvLEA_4_1	1	20	144	163	UGCACUGCCUCUUCCCUGGC	GACGGGGAAGGGGCAGGGCG

Promoter, gene structure, Chromosomal distribution and evolutionary analysis of HvSNF1-related kinase 1 (SnRK1) and late embryogenesis abundant (LEA) genes

SnRK1 and LEA genes sequences (1500 bp upstream of start codon) (online resource S1) were selected for cis element analysis using the PlantCARE web tool to identify the biological functions (stress response, growth and development). The promoter region of SnRK1 and LEA genes in H, vulgare contained a large number of plant hormone response elements. Most SnRK1 and LEA proteins contained the defense and stress responsiveness element, the abscisic acid responsive, the methyl jasmonate (MeJA) responsive element and salisylic acid (Fig. 8).

Genes are response to stress loss intron during evolution to fast transcription for survival of environmental stresses. Exon-intron structure is important sources of plant biodiversity and gene family evolution. Gene structure result were founded all SnRK1 and LEA genes have intron (Fig. 2 and Fig. 3). Based on the information available at the Phytozome-13 website SnRK1 and LEA genes were physically drawn on the chromosomes in H. vulgare genome. SnRK1 genes were found on chromosome 1, 2, 3, 4 and 5. LEA genes were found on chromosome 1, 3 and 4 (Fig. 9).

Selective pressure on SnRK1 and LEA genes were investigate by the calculation of the ratio of non-synonymous/synonymous (Ka/Ks). A Ka/Ks ratio > 1 suggests positive selection, Ka/Ks ratio = 1 shows neutral selection, while ratio of Ka/Ks < 1 suggests purifying selection (Anton et al. 2002). In the present study, Ka/Ks ratio of all paralogous pairs were less than 1, which indicating that the SnRK1 and LEA genes were primarily influenced by purifying selection (positive Darwinian selection) and SnRK1 and LEA genes received strong environmental pressure during evolution (Table 5). The duplication time of the SnRK1 and LEA paralogous gene pairs in H. vulgare were ranged approximately from 3.381 and 17.944 to 79.43012 and 44.212 Mya, respectively (Fig. 10).

Table 5

Paralogous pairs genes of the *SnRK1* and *LEA* genes and Ka/Ks ratio.
locus 1	locus 2	Ka	Ks	Ka/Ks	Time
HvPK-like_5	HvPK-like_1	0.098392	1.042123	0.094415	79.43012
HvPK-like_6	HvPK-like_7	0.017247	0.044372	0.388693	3.381987
HvPK-like_13	HvPK-like_2	0.081679	0.169905	0.480737	12.95006
HvLEA_4_4	HvLEA_4_3	0.116358	0.235435	0.494226	17.94473
HvLEA_4_7	HvLEA_4_6	0.180798	0.359337	0.503143	27.38847
HvLEA_4_5	HvLEA_4_2	0.335583	0.580074	0.578518	44.21299

The phylogenetic tree were built using the Maximum likelihood with 1000 bootstrap replicates, SnRK1 and LEA protein sequences were used to analysis the possible evolutionary history in H. vulgare. In the resulting phylogenetic tree, the SnRK1 and LEA proteins were classified into three clades (Fig. 11).

SnRK1 and LEA genes were analyzed for interspecies collinearity to trace orthologous relationship in H. vulgare against Zea mays, Solanum lycopersicum and Arabidopsis thaliana. Collinearity analysis discovered robust orthologous of the SnRK1 and LEA genes among H. vulgare against the other three plant species (Table 6 and Fig. 12).

Table 6

Collinearity orthologous relationship in H. vulgare against Zea mays.
H. vulgare	Z. mays	S. lycopersicum	A. thaliana
HvPK-like_1	Zm00001eb293240_T002	-	-
HvPK-like_2	Zm00001eb015210_T004, Zm00001eb331220_T002	-	-
HvPK-like_3	Zm00001eb110020_T001, Zm00001eb328710_T002	-	-
HvPK-like_5	Zm00001eb293240_T002	-	-
HvPK-like_10	Zm00001eb196030_T005	-	-
HvPK-like_11	Zm00001eb015210_T004, Zm00001eb331220_T002	-	-
HvPK-like_12	Zm00001eb013270_T002	-	-
HvLEA_4_2	Zm00001eb155430_T002, Zm00001eb294480_T001	-	-
HvLEA_4_5	Zm00001eb155430_T002, Zm00001eb294480_T001	-	-

Gene ontology enrichment and functional relationship analysis

To further recognize the SnRK1 and LEA genes functions, we performed enrichment analysis and gene ontology analysis based on biological process and molecular function, GO terms help us understand the function of genes at a molecular level (Fig. 13). GO terms for SnRK1 and LEA genes were confirmed the functional role of SnRK1 and LEA as a stress responsive (Fig. 14).

In the current study, the SNF1-related kinase 1 (SnRK1) and late embryogenesis abundant (LEA) genes were identified, characterized and analyzed in H. vulgare. SnRK1 and LEA proteins were further described by conserved domains, motifs, gene structure, cis-regulatory element, 3-D protein structures, protein-protein interactions, protein subcellular localization, transmembrane helical, protein phosphorylation site, miRNA interaction, chromosomal localization, evolutionary analysis, phylogenetic relationship, synteny analysis and gene ontology analysis.

SnRK1: SNF1-related kinase 1 protein

LEA: late embryogenesis abundant protein

Ka/Ks: ratio of non-synonymous/synonymous

RMSD: Root Mean Square Deviation

Ethical approval:

The authors declare that the experimental research work involving the growth of plants in this study, was conducted in compliance with relevant institutional, national, and international guidelines and legislation.

Funding:

No funding received.

Contributions:

Mohamed AlZalaty devised the study and collecting the data, Amaal Maghraby conducted most of the analysis and prepared most of the tables and figures. Both authors discussed the data. Mohamed AlZalaty wrote the first draft of the manuscript. Both authors wrote, read, and approved the final version of the manuscript.

Data availability:

All data generated or analyzed during this study are included in this published article and its supplementary information files.

Conflicts of Interest:

The authors declare that they have no conflict of interest.

Anton N, Makova KD, Li WH (2002) The Ka/Ks ratio test for assessing the protein-coding potential of genomic regions: An empirical and simulation study. Genome Res. 12:198–202.
Bailey TL, Johnson J, Grant Ch E, Noble WS (2015) "The MEME Suite". Nucleic Acids Research 43(W1):W39-W49. doi: 10.1093/nar/gkv416.
Blom N, Gammeltoft S, Brunak S (1999) Sequence and Structure-based Prediction of Eukaryotic Protein Phosphorylation Sites. Journal of Molecular Biology 294(5): 1351-1362.
Cantalapiedra CP, Hernandez-Plaza A, Letunic I, Bork P, Huerta-Cepas J (2021) eggNOG-mapper v2: functional annotation, orthology assignments, and domain prediction at the metagenomic scale. Molecular Biology and Evolution 38(12):5825–5829. https://doi.org/10.1093/molbev/msab293
Chen C, Chen H, Zhang Y, Thomas HR, Frank MH, He Y, Xia R (2020) TBtools: An integrative toolkit developed for interactive analyses of big biological data. Mol. Plant 13:1194–1202.
Chen Y, Hu BY, Tan ZQ, Liu J, Yang ZM, Li ZH, Huang B (2015) Selection of reference genes for quantitative real-time PCR normalization in creeping bentgrass involved in four abiotic stresses. Plant Cell Rep. 34:1825–1834. https://doi.org/10.1007/s00299-015-1830-9
Dai X, Zhuang Z, Zhao PX (2018) psRNATarget: a plant small RNA target analysis server (2017 release). Nucleic Acids Research 46(W1):W49-W54. https://doi.org/10.1093/nar/gky316
Darzentas N. Circoletto: visualizing sequence similarity with Circos. Bioinformatics. 2010 Oct 15;26(20):2620-1. doi: 10.1093/bioinformatics/btq484. Epub 2010 Aug 24. PMID: 20736339. http://tools.bat.infspire.org/circoletto/
Diédhiou CJ, Popova OV, Dietz KJ, Golldack D (2008) The SNF1-type serine-threonine protein kinase SAPK4regulates stress-responsive gene expression in rice. BMC Plant Biol 8:49. https://doi.org/10.1186/1471-2229-8-49
Gasteiger E, Hoogland C, Gattiker A, Duvaud S, Wilkins MR, Appel RD, Bairoch A (2005) Protein Identification and Analysis Tools on the Expasy Server. (In) John M. Walker (ed): The Proteomics Protocols Handbook, Humana Press (2005). pp. 571-607.
Gómez-Gómez F, Rodríguez-Martínez J, Santiago M (2014) Hydrogeology of Puerto Rico and the Outlying Islands of Vieques, Culebra, and Mona. Scientific Investigations Map 3296.http://dx.doi.org/10.3133/sim3296
Goodstein DM, Shu Sh, Howson R, Neupane R, Hayes RD, Fazo J, Mitros Th, Dirks W, Hellsten U, Putnam N, Rokhsar DS (2012) Phytozome: a comparative platform for green plant genomics. Nucleic Acids Res. 40 (Database issue): D1178-D1186. https://phytozome.jgi.doe.gov
Gous PW, Gilbert R, Fox GP (2015) Drought-proofing barley (Hordeum vulgare) and its impact on grain quality: A review. J. Inst. Brew. 121:19– 27. https://doi.org/10.1002/jib.187
Gous PW, Hasjim J, Franckowiak J, Fox GP, Gilbert RG (2013) Barley genotype expressing “stay-green”-like characteristics maintains starch quality of the grain during water stress condition. Journal of Cereal Science 58(3):414-419. ISSN 0733-5210, https://doi.org/10.1016/j.jcs.2013.08.002.
Gu Z, Cavalcanti A, Chen FC, Bouman P, Li WH (2002) Extent of gene duplication in the genomes of Drosophila, nematode, and yeast. Mol Biol Evol. 19(3):256–62.
Guidetti S, Ragassi CF and Carrer H (2007) Identification of protein kinase SNF1 in CitEST. Genetics and Molecular Biology [online]. 2007, v. 30, n. 3 suppl [Accessed 9 October 2021], pp. 866-871. Available from: <https://doi.org/10.1590/S1415-47572007000500015>. Epub 06 Nov 2007. ISSN 1678-4685. https://doi.org/10.1590/S1415-47572007000500015.
Horton P, Park KJ, Obayashi T, Fujita N, Harada H, Adams-Collier CJ, Nakai K (2007) WoLF PSORT: protein localization predictor. Nucleic Acids Res. 35(Web Server issue):W585-7. doi: 10.1093/nar/gkm259. Epub 2007 May 21. PMID: 17517783; PMCID: PMC1933216.
Hundertmark M, Hincha, DK (2008) LEA (Late Embryogenesis Abundant) proteins and their encoding genes in Arabidopsis thaliana. BMC Genomics 9: 118. https://doi.org/10.1186/1471-2164-9-118
Kamiyama Y, Hirotani M, Ishikawa s, Umezawa T (2021) “Arabidopsis group C Raf-like protein kinases negatively regulate abscisic acid signaling and are direct substrates of SnRK2.” Proceedings of the National Academy of Sciences of the United States of America 118 (30) e2100073118. https://doi.org/10.1073/pnas.2100073118
Kato Y, Mori T, Sato K, Maegawa Sh, Hosokawa H, Akutsu T (2017) An accessibility-incorporated method for accurate prediction of RNA-RNA interactions from sequence data. Bioinformatics 33(2): 202–209.
Kozomara A, Griffiths-Jones S (2014) miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res. 42(Database issue):D68–73.
Krogh A, Larsson B, Von Heijne G, Sonnhammer EL (2001) Predicting Transmembrane Protein Topology with a Hidden Markov Model: Application to Complete Genomes. J. Mol. Biol. 305:567-580.
Letunic I, Bork P (2019) Interactive tree of life (iTOL) v4: Recent updates and new developments. Nucleic Acids Res. 47: W256–W259.
Li X, Cao J (2016) Late Embryogenesis Abundant (LEA) Gene Family in Maize: Identification, Evolution, and Expression Profiles. Plant Mol Biol Rep 34: 15–28. https://doi.org/10.1007/s11105-015-0901-y
Livak KJ, Schmittgen TD (2001) Analysis of relative gene expression data using real-time quantitative PCR and the 2^-ΔΔCT Method.Methods 25:402–408.
Lu S, Wang J, Chitsaz F, Derbyshire MK, Geer RC, Gonzales NR, Gwadz M, Hurwitz DI, Marchler GH, Song JS, Thanki N, Yamashita RA, Yang M, Zhang D, Zheng C, Lanczycki CJ, Marchler-Bauer A (2020) CDD/SPARCLE: the conserved domain database in 2020. Nucleic Acids Res. 48(D1):D265-D268. doi: 10.1093/nar/gkz991. PMID: 31777944; PMCID: PMC6943070.
Magwanga RO, Lu P, Kirungu JN, Lu H, Wang X, Cai X, Zhou Z, Zhang Z, Salih H, Wang K, Liu F. (2018) Characterization of the late embryogenesis abundant (LEA) proteins family and their role in drought stress tolerance in upland cotton. BMC Genet 19(1): 6.https://doi.org/10.1186/s12863-017-0596-1
Members from top institutes/universities from Chinese Academy of Sciences, Shanghai Jiaotong University, Xiangya School of Medicine.; http://www.bioinformatics.com.cn/login_en/
Mistry J, Chuguransky S, Williams L, Qureshi M, Salazar GA, Sonnhammer ELL, Tosatto SCE, Paladin L, Raj S, Richardson LJ, Finn RD, Bateman A (2020) Nucleic Acids Research (2020). doi: 10.1093/nar/gkaa913.
Mohannath G, Jackel JN, Lee YH, Buchmann RC, Wang H, Patil V, Adams AK, Bisaro DM (2014) A Complex Containing SNF1-Related Kinase (SnRK1) and Adenosine Kinase in Arabidopsis. PLoS ONE 9(1): e87592. doi:10.1371/journal.pone.0087592
Moreland JA (1993) Drought: U.S. Geological Survey Water Fact Sheet, Open-File Report 93-642, 2p.
Nagaraju M, Kumar SA, Reddy PS, Kumar A, Rao DM, Kavi Kishor PB (2019) Genome-scale identification, classification, and tissue specific expression analysis of late embryogenesis abundant (LEA) genes under abiotic stress conditions in Sorghum bicolor L. PLoS One. 2019 Jan 16;14(1):e0209980. doi: 10.1371/journal.pone.0209980. PMID: 30650107; PMCID: PMC6335061.
Nair R, Carter P, Rost B (2003) NLSdb: database of nuclear localization signals. Nucleic Acids Res. 31(1):397-9. doi: 10.1093/nar/gkg001. PMID: 12520032; PMCID: PMC165448.
Paysan-Lafosse T, Blum M, Chuguransky S, Grego T, Pinto BL, Salazar GA, Bileschi ML, Bork P, Bridge A, Colwell L, Gough J, Haft DH, Letunić I, Marchler-Bauer A, Mi H, Natale DA, Orengo CA, Pandurangan AP, Rivoire C, Sigrist CJA, Sillitoe I, Thanki N, Thomas PD, Tosatto SCE, Wu CH, Bateman A (2022) InterPro in 2022. Nucleic Acids Research, Nov 2022. (doi: 10.1093/nar/gkac993)
Perochon A, Vary Z, Malla KB, Halford NG, Paul MJ ,Doohan FM (2019) The wheat SnRK1α family and its contribution to Fusarium toxin tolerance. Plant Science. 288: 110217. https://doi.org/10.1016/j.plantsci.2019.110217
Poku SA, Chukwurah PN, Aung HH, Nakamura I (2020) Over-Expression of a Melon Y3SK2-Type LEA Gene Confers Drought and Salt Tolerance in Transgenic Tobacco Plants. Plants 9(12):1749. https://doi.org/10.3390/plants9121749
Resource Coordinators NCBI , Agarwala R, Barrett T, Beck J, Benson DA, Bollin C, Bolton E, Bourexis D, Brister JR, Bryant SH et al. (2018) Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 46:D8 –D13. https://www.ncbi.nlm.nih.gov/gene/
Rombauts S, Déhais P, Van Montagu M, Rouzé P (1999) PlantCARE, a Plant Cis-Acting Regulatory Element Database. Nucleic Acids Res. 27: 295–296. https://doi.org/10.1093/nar/27.1.295
Sato K, Kato Y (2022) Prediction of RNA secondary structure including pseudoknots for long sequences. Briefings in Bioinformatics 23(1):bbab395. https://doi.org/10.1093/bib/bbab395
Shen Q, Bao M, Zhou X (2012) A plant kinase plays roles in defense response against geminivirus by phosphorylation of a viral pathogenesis protein. Plant Signaling & Behavior 7(7): 888-892. DOI: 10.4161/psb.20646
Singh VK, Rajkumar MS, Garg R, Jain M (2017) Genome-wide identification and co-expression network analysis provide insights into the roles of auxin response factor gene family in chickpea. Sci. Rep. 7:10895.
Szklarczyk, D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, Simonovic M, Doncheva NT, Morris JH, Bork P, Jensen LJ, Mering CV (2019) STRING v11: Protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucl. Acids Res. 47:(D1):D607-D613. doi: 10.1093/nar/gky1131.
Tamura K, Stecher G, Kumar S (2021) MEGA11: Molecular evolutionary genetics analysis version 11. Mol. Biol. Evol. 38: 3022–3027.
Umezawa T, Yoshida R, Maruyama K, Shinozaki K (2004) SRK2C, a SNF1-related protein kinase 2, improves drought tolerance by controlling stress-responsive gene expression in Arabidopsis thaliana. Proceedings of the National Academy of Sciences 101(49):17306-17311. DOI: 10.1073/pnas.0407758101
Zhang Y, Ye Y, Jiang L, Lin Y, Gu X, Chen Q, Sun B, Zhang Y, Luo Y, Wang Y, Wang X, Tang H (2020) Genome-Wide Characterization of Snf1-Related Protein Kinases (SnRKs) and Expression Analysis of SnRK1.1 in Strawberry. Genes (Basel) 11(4):427. doi:10.3390/genes11040427
Zheng J, Su H, Lin R, Zhang H, Xia K, Jian S, Zhang M (2019) Isolation and characterization of an atypical LEA gene (IpLEA) from Ipomoea pes-caprae conferring salt/drought and oxidative stress tolerance. Sci Rep 9:14838. https://doi.org/10.1038/s41598-019-50813-w
Zheng W, Zhang Ch, Li Y, Pearce R, Bell EW, Zhang Y (2021) Folding non-homologous proteins by coupling deep-learning contact maps with I-TASSER assembly simulations. Cell Reports Methods, 1: 100014.

No competing interests reported.

Genome-wide Identification, characterization and evolutionary analysis of SNF1-related kinase 1 (SnRK1) and late embryogenesis abundant (LEA) proteins gene family in Hordeum vulgare under drought stress

Status:

Version 1

Abstract

Figures

Introduction

MATERIALS AND METHODS

Databases screening and identification of SNF1-related kinase 1 (SnRK1) and late embryogenesis abundant (LEA) proteins

Characterization of the SNF1-related kinase 1 (SnRK1) and late embryogenesis abundant (LEA) proteins

Barley plant growth and drought treatment

RNA isolation, qRT-PCR expression analysis and sequencing

Results and Discussion

Identification of SNF1-related kinase 1 (SnRK1) and late embryogenesis abundant (LEA) proteins

Characterization of the SNF1-related kinase 1 (SnRK1) and late embryogenesis abundant (LEA) proteins

Gene ontology enrichment and functional relationship analysis

Abbreviations

Declarations

Ethical approval:

The authors declare that the experimental research work involving the growth of plants in this study, was conducted in compliance with relevant institutional, national, and international guidelines and legislation.

Funding:

No funding received.

Data availability:

References

Additional Declarations

Supplementary Files

Status:

Version 1