Genetic Map Construction and Functional Characterization of Genes within the Segregation Distortion Regions (SDRs) in the F2:3 Populations Derived from Wild Cotton Species of the D Genome

doi:10.21203/rs.3.rs-30612/v1

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Genetic Map Construction and Functional Characterization of Genes within the Segregation Distortion Regions (SDRs) in the F2:3 Populations Derived from Wild Cotton Species of the D Genome

https://doi.org/10.21203/rs.3.rs-30612/v1

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published 06 Dec, 2020

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Background

Segregation distortion (SD) is a phenomenon common among stable or segregating populations, and the principle behind it still puzzles many researchers. The F_2:3 progenies developed from the wild cotton species of the D genomes were used to investigate the possible plant transcription factors within the segregation distortion regions (SDRs). A consensus map was developed between two maps from the four D genome, map A derived from an F_2:3 progenies of Gossypium klotzschianum and G. davidsonii while Map B from G. thurberi and G. trilobum F_2:3 generations. In each map 188 individual plants were used.

Results

The consensus linkage map had 1,492 markers across the 13 linkage groups; with a map size of 1,467.445 cM and average marker distance of 1.0370 cM. Chromosome D₅02 had the highest percentage of SD with 58.621% followed by Chromosome D₅07 with 47.887%. Six thousand and thirty eight genes were mined within the SDRs on chromosome D₅02 and D₅07 of the consensus map. Within chromosome D₅02 and D₅07, 2,308 and 3,730 genes were mined, respectively and were found to belong to 1,117 domains out of which 622 domains were common across the two chromosomes. Moreover, the first 9 domains were members of the plant resistance genes (R genes), while Pkinase; Protein kinase domain (PF00069) was the dominant group with 188 genes. Further analysis on the dominant domains revealed that 287 miRNAs were found to target various genes, such as the gr-miR398, gra-miR5207, miR164a, miR164b, miR164c among others, which have been found to target top-ranked stress-responsive transcription factors such as NAC genes. Moreover, some of the stress responsive cis-regulatory elements were also detected. Furthermore, RNA profiling of the genes from the dominant family showed that higher numbers of genes were highly upregulated under salt and osmotic stress conditions in addition their higher expression at different stages fiber development.

Conclusion

The result obtained provided an indication of the important role of the SDRs in the evolution of significant genes in plants.

Segregation distortion (SD) is described as a deviation from the expected Mendelian ratio within a segregating population due to various segregating distorter’s (Anhalt et al. 2008). Some of the factors that may lead to SDs include gametic and zygotic selections, non-homologous chromosome recombination, gene transfer, environmental agents, mapping population, marker types and genetic transmission (Xian-Liang et al. 2006). During the construction of genetic maps, it has been observed that some alleles in chromosomal regions skew from the normal Mendelian ratio. Theses alleles tend to cluster at segments of the chromosome, these regions are referred to as the segregation distortion region (SDR) (Lu et al. 2002).

Research has shown that SD could bring errors in the marker order and map distances in the linkage map thus reduce the accuracy of the maps. (Xian-Liang et al. 2006). However genes of significance have been mined within the SDR regions, for instance, the gene for crown rot resistance in wheat was identified within the SDR (Bovill et al. 2006), while the gene responsible for stem rust tolerance, was detected in the SDR on chromosome 2B in wheat (Tsilo et al. 2008). Moreover, SD has been observed in a variety of populations of organisms including insects (Sandler and Golic 1985), plants (Xian-Liang et al. 2006), and mammals (Kumari et al. 1992).

Higher frequencies of occurrence of the SDR have been found in populations developed through interspecific as compared to intraspecific crosses (Dai et al. 2017), for example in rice more SDRs were detected in the double haploid compared to the F_2:3 populations developed from the same intraspecific cross (Xu et al. 1997; Wu et al. 2010), thirty six SDRs were detected on 20 chromosomes in recombinant inbred lines in tetraploid cotton (Jamshed et al. 2016). Further evidence points out that the genes associated with zygotic and gametic selection could be responsible for SD (Manrique-Carpintero et al. 2016).

The use of molecular markers is preferred in the genotyping of populations because they are less influenced by phenotype and are significant in the study of SD (Zhang et al. 2013). The most used molecular marker in the analysis of SD is the simple sequence repeat (SSR); it has been widely used in the study of SD in the majority of plants and animals (Froelicher et al. 2000). Several studies on SDs that have been conducted in several plants species, including rice (Reflinur et al. 2014; Yang et al. 2014), maize (Lu et al. 2002; Wang et al. 2012), Wheat (Kumar et al. 2007), Barley (Liu et al. 2011), soybean (Liu et al. 2000), rapeseed (Yang et al. 2006) and in cotton (Zhang et al. 2000a; Wu et al. 2003; Amudha et al. 2012), among other plants. In the analysis of SD in the F_2:3 population of Aegilops tauschii, it was observed that some regions had skewed ratios towards particular alleles in the chromosomes (Fans et al. 1998).

The studies conducted in cotton showed that the majority of the SDs were mainly skewed towards the male parent rather than the female population, as was observed on chromosome 18 (Dai et al. 2017). However, in all the studies conducted to unravel the mystery of SDs in cotton, no experiment has been conducted to explore the SDs in the F_2:3 population derived from the diploid wild cotton parental lines. The latest attempt to explore the SDs in the wild cotton progenitors involved a backcross population developed between G. hirsutum as the reccurent parent and G. mustelinum as the donor cultivar (Chandnani et al. 2017). And therefore, to explore the phenomena of the SDs in wild cotton progenitors, an interspecific population between G. klotzschianum and G. davidsonii, and between G. thurberi and G. trilobum were developed. The four parental lines were primarily selected because of their diverse genetic traits and broader ecological niches. The four parental lines used in the construction of the genetic maps are known to have traits for resistance to bacterial blight (G. davidsonii) (Zhang et al. 2016), sucking pests such as aphids (G klotzschianum) (Wei et al. 2017), Fusarium wilt, silver leaf whitefly and cotton bollworm resistance (G. thurberi) (Natwick 2006), Verticillium wilt (G. trilobum) (Dong et al. 2019). A total of 188 individuals were genotyped using SSR markers, primarily focusing on the exploitation of the genetic mechanism of the SD in severely distorted chromosome D₅02 and chromosome D₅07. The analysis of the SD from the genetic maps constructed from the diploid cotton of the D genome was conducted. The first map was then developed from two closely related parents; G. klotzschianum and G. davidsonii (Kirungu et al. 2018b) and the second map developed from G. thurberi and G. trilobum (Li et al. 2018), in either of the maps, the F_2:3 population used, the genotypic data from the two maps were combined to generate the consensus map, the consensus map was was limited to the two maps, being the only available maps generated from the population developed from the wild cotton species of the D genome. The focus was on chromosome D₅02 and chromosome D₅07 which showed severe distortions of markers from the two maps. Moreover,the marker segregation and genes within in the SDRs were mined and analysed. The genes mined within the SDR and understanding their roles will of be significant in elucidating the role played by segregation distortion, and will help in improving the elite cultivated cotton germplasms with ever shrinking genetic base and significantly lower adaptive mechanisms to various abiotic and biotic stress factors.

Parental Materials

The two genetic maps were generated from an interspecific population obtained from the four parental lines. The first genetic map (Map A) was constructed from the F_2:3population derived from self-pollinating F₁ population of; G. klotzschianum (female parent ) and G. davidsonii (male parent). Similarly, the second genetic map (Map B) was constructed from F_2:3populations derived from G. thurberi (female parent) and G. trilobum (male parent). A total of 188 progenies were used as the mapping population. The F_2:3progenies from the four parental lines were developed and grown in the wild cotton nurseries, managed by institute of cotton research (ICR), located in Sanya island, Hainan province, China. The development of the F_2:3progenies followed a similar pattern as described by Magwanga et al (Odongo et al. 2020) in the development of the backcross progenies between G. tomentosum (donor male parental line) and G. hirsutum(recurrent female parental line).

Molecular Markers Genotyping

Total DNA was extracted from the F_2:3progenies and their parental lines using the CTAB method (Zhang et al. 2000b). Polymerase chain reaction (PCR) was conducted and the amplified PCR products were electrophorised on non denaturing 10% polyacrylamide gel electrophoresis in the 1×TBE buffer, the gels were then visualized after silver staining (Kinter and Sherman 2000). The primers used were the SWU markers which were developed by SouthWest University in china hence the acronym SWU. In the construction of the genetic map A, a total of 12,560 SWU markers screened of which 1000 markers were found to be polymorphic. Out of the 1000 polymorphic markers,728 markers were mapped and genrated the 13 linckage groups, designated as chromosome D₅01 to D₅13. In the second genetic map, map B 12,560 SWU markers were screened of which 996 markers were polymorphic and only 849 polymorphic markers were mapped onto the 13 linkage groups. For the consensus map construction, 1,492 polymorphic markers were applied to generate the genetic map, this after removing the duplicated markers. The details of the markers and their sequences are shown in Supplementary Table S1

Linkage Map Construction and Determination of the Segregation Distortion of Molecular Markers

Markers with less than 5% missing data were used in the mapping of the linkage groups in the three maps (Coulton et al. 2020) The Joinmap 4.0 mapping tool was applied with a recombination frequency of 0.40 and a LOD score of 3.0, any LOD above 2.5 is known to be above the noise level (Faleiro et al. 2003). The Kosambi mapping function was used to convert the recombination frequencies to map distances. The linkage groups were then drawn using Mapchart 2.3 Software (Voorrips 2002). The consensus map was constructed by merging the two individual data sets. Maps were drawn using MapChart 2.2 (Voorrips 2002)

Segregation Distortion Analysis

Segregation distortion (SD) within the mapping population was determined when the genotypic ratios deviated significantly from the expected Mendelian expectation (Reflinur et al. 2014). A Chi-square (χ2) test was performed for each marker to assess whether it significantly deviated from Mendelian segregation ratios. The markers showing segregation distortion were indicated by asterisks. The level of distortion (*P < 0.05, **P < 0.01, ***P < 0.00, ****P < 0.001, *****P < 0.0005, ******P < 0.0001, *******P < 0.00005. The Chi-square test was used to calculate the distortion of each marker.

Annotation of Genes at The Segregation Distortion Regions (SDRs) and The Analysis of Phylogenetic Tree

Sequences corresponding to the SSR markers were identified by BLASTN to the cotton ESTs with an E ≤ 1 e-15 and were annotated using BLASTX (NCBI, Bethesda, MD, USA). The mined genes within this SDR that belonged to the two most abundant subfamilies, the probable protein types and the Serine/threonine-protein kinase were then analyzed for their properties and function. A phylogenetic tree was constructed and, the multiple sequence alignments of all the proteins were done by Clustal omega, MEGA 7.0 software (Kumar et al. 2016). The neighboring method (NJ) was used with a bootstrap value of 1000 replications, other parameters were applied as per the default set up, as previously used in the analysis of the phylogenetic relationships of the LEA proteins in cotton (Magwanga et al. 2018b). Transcriptional response elements of genes for the two major subfamilies were predicted using an online tool; the PLACE database (http://www.dna.affrc.go.jp/PLACE/ signals can.html) (Higo et al. 1999). In addition, the genes targeted by miRNAs were predicted by searching 5′ and 3´ untranslated regions (UTRs) and the coding sequences (CDS) of all the genes for their complementary sequences for the cotton miRNAs using the psRNATarget server (http://plantgrn.noble.org/psRNATarget /function)

Gene Ontology (GO) Annotation

Analysis of GO annotation was conducted using Blast2GO PRO software version 4.1.1 (https://www.blast2go.com). The GO annotations described the hierarchal roles of the genes and their products; it entailed three independent ontological terms, the molecular function (MF), biological process (BP), and cellular component (CC) (Langfelder and Horvath 2008; Magwanga et al. 2018c).The protein sequences of the dominant gene domains were obtained within the SDR regions and subsequently analyzed through Blast2GO as previously applied in the analysis of the LEA genes in cotton (Magwanga et al. 2018b).

Linkage Map Construction

The first map was developed from the F_2:3population between G. klotzschianum and G. davidsonii, a total of 728 polymorphic markers were used. The total map length was 1,480.23 cM with an average marker interval of 2.182 cM (Kirungu et al. 2018b). This map was designated as map A. The second map, designated as map B, was derived by genotyping the F_2:3population developed between G. thurberi and G. trilobum, 849 polymorphic markers were used in the linkage map construction. The map size was 1,012.46 cM with an average marker distance of 1.193 cM. In both maps, it was observed that chromosome number two also annotated as D₅02 had the least map size of 82.908 cM and 28.665 cM in map A and map B, respectively. Interestingly in both the maps chromosome, D₅02 had a smaller map size but with the highest percentage of SD (Table 1). Similar results have been observed in other linkage maps in cotton (Yu et al. 2011; Li et al. 2016).

The consensus map was constructed by merging two data sets from the two genetic maps, a total of 1,492 markers, were mapped onto the 13 linkage groups encompassing the 13 chromosomes, only 85 markers remained unlinked. The diploid cotton species has thirteen chromosomes while the tetraploid cotton species has 52 chromosomes (Mendoza et al. 2013; Magwanga et al. 2018a), this work was based on the diploid cotton species of the D genome. The consensus map size was 1,467.445 cM with an average marker distance of 1.037cM. Even though the map size was relatively smaller compared to map A, the marker interval was low, which improved the precision of the consensus map. From the consensus map we observed that Chromosome D₅02 had the highest percentage of SD with 58.621% followed by Chromosome D₅07 with 47.887%. Chromosome D₅01 had the highest number of markers with 143 markers while Chromosome D₅02 had the least number of markers of 58 (Table 1). Most of the markers mapped on the consensus map were found to be contributed by map B rather than map A. A total of 797 markers from map B were mapped on the consensus map accounting for 53.41% while only 695 markers (46.58%) were from map A. The chromosome with the highest number of markers was Chromosome D₅01 with 143 markers while the chromosome with the least number of markers was Chromosome D₅02 with only 58 markers (Fig. 1)

Segregation Distortion (SD) Analysis

In map A, out of the 728 markers mapped, 159 markers were distorted accounting for 22.2 %, the highest SD was observed in Chromosome D₅02 with 76.087 % followed by Chromosome D₅07 with 40.698 %. The SDRs were located on Chromosome D₅02, D₅05, D₅07, and D₅08. Chromosome D₅02 had the largest SDR while Chromosome D₅07 had the highest number of SDR.

It was observed that the alleles in the SDR region were skewed towards a particular parental line like in Chromosome D₅02 they were skewed towards the female parent (G. klotzschianum ), while Chromosome D₅07 was skewed towards the heterozygosity (Kirungu et al. 2018b). In the second genetic map B, there was a slightly lower number of distorted markers, only 135 of the total mapped markers were distorted accounting for 15.783%, the highest segregation distortions were observed in Chromosome D₅02 and Chromosome D₅07 with 42.857 % and 38.333%, respectively (Table 1). Chromosomes that had the SDRs were D₅01, D₅02, D₅06, D₅07, D₅09, D₅10, and D₅11. Moreover, the largest SDR was located on Chromosome D₅02 while Chromosome D₅07 had the highest number of SDR.

In the consensus map, the highest SDs were located on Chromosome D₅02 and D₅07, with distortion percentages of 58.621% and 47.887%, respectively, similarly, the two chromosomes had the largest SDRs as shown in (Fig. 2). The largest SDR was located Chromosome D₅02-2 and was skewed toward the female parents while SDR located on Chromosome D₅02-1 was skewed towards the heterozygous. Chromosome D₅07 had the highest number of SDRs with a total of five (5) SDRs, all the SDR were skewed towards the heterozygotes except for the SDR located on Chromosome D₅07-1, which was skewed towards the female parents. Majority of the SDRs were skewed towards the heterozygotes similar results were observed in the analysis of SDRs in tetraploid cotton, more specifically on the chromosome 18 (Dai et al. 2017), rice (Wu et al. 2010), and in wheat (Fans et al. 1998).

Annotation of Genes at SDR

We conducted a blast search, and a total of 6,038 genes were mined within the SDR region in Chromosome D₅02 and Chromosome D₅07 (Supplementary Table S2). The proportions of the genes between the two chromosomes were 2,308 genes in Chromosome D₅02 and 3,730 genes in Chromosome D₅07. These genes were further grouped according to their domain, in which a total of 1,117 domains were obtained. There 622 domains that were shared between Chromosome D₅02 and Chromosome D₅07 the largest domain being the PF00069 (Pkinase; Protein kinase domain) with a total of 188 genes followed by PF13855 (LRR_8; Leucine-rich repeat) with 132 genes and the third was PF07714 (Pkinase_Tyr; Protein tyrosine kinase) with a total of 108 genes. The genes in the three main domains were highly correlated to abiotic stress responsiveness. The genes located within the largest 12 domain were analyzed, out of the 12 domains 9 domains were found to contain genes which were members of the resistant genes (R group of genes), these domains include Protein kinase domain, LRR_8; Leucine-rich repeat, Protein tyrosine kinase domain, NB-ARC domain, LRRNT_2, Leucine-rich repeat N-terminal domain, Pentatricopeptide repeat (PPR, Pentatricopeptide repeat (PPR_2) repeat family, Cytochromes P450 (CYPs), Myb-like DNA-binding domain and RNA recognition motif. (a.k.a. RRM, RBD, or RNP domain) (Table 2 and Table 3).

Analysis of the Physiochemical Properties and Structures of the Genes Obtained from the Dominant Domain the(Protein kinase domain) Mined within the SDR in Chromosome D₅02 and Chromosome D₅07

The dominant domain was the Protein kinase domain (PF00069), it has been widely studied, for instance, it was found to be the dominant domain in the analysis of the genes conserved between the two upland cotton, G. hirsutum and its wild relative G. tomentosum (Magwanga et al. 2018d). We, therefore, explored the genes which belonged to this domain. The physiochemical properties of these genes showed significant variations the molecular weight ranged between 10.351 kDa and 134.232 kDa, charge was between -27 and 40, Isoelectric Point (pl) values were between 4.375 and 10.382, the GRAVY values ranged between -0.721 and 0 251 while their protein lengths ranged between 611 aa and 12,310 aa (Supplementary Table S3). The GRAVY values were all below zero indicating that these genes were mainly hydrophilic in nature. the Protein kinase domain contained 28 different subfamilies, the subfamily with the highest number of genes was Probable types with a total of 64 genes, which included members such as the Probable inactive receptor kinase (4 genes); Probable leucine-rich repeat receptor-like serine (21), Probable L-type lectin-domain containing receptor kinase (3 genes); Probable receptor-like protein kinase (25 genes) among others (Supplementary Table S4).

The two most abundant subfamilies, the probable protein types and the Serine/threonine-protein kinase were further analyzed, by looking into their classification based on the phylogenetic tree analysis. The genes were found to be grouped into five (5) clades, with clade 2 being the majority (Fig. 3). The most interesting concept is that the members within clade 3 had a percentage bootstrap similarity value of 100%. The majority of these genes have previously been found to be highly correlated to biotic stress tolerance, for instance, 11 genes such as Gorai.007G335000, Gorai.002G039900, Gorai.002G040100, Gorai.002G041100, Gorai.002G041200, Gorai.002G041800, Gorai.002G042100, Gorai.002G047500, Gorai.002G047900, Gorai.007G182500 and Gorai.007G334900 are homologous to an Arabidopsis gene, At5g39020, that has a functional role in leaf senescence during viral infection in Arabidopsis (Espinoza et al. 2007). Moreover, the remaining genes were homologous to an Arabidopsis gene, At1g67000, which plays a greater role in salt stress pathways, it was also was found to be highly upregulated in the roots under salt stress conditions (Ma et al. 2006).

Cis-regulatory Elements Analyses of the Major Two Subfamilies: The Probable Protein Types and the Serine/Threonine-Protein Kinase

We examined the two major subfamilies in order to determine if there could be any of the regulatory elements in relation to either abiotic or biotic stress factors. Cis-regulatory elements are known to enhance the functions of the genes (Tümpel et al. 2006). In the analysis of the cis-elements, all the genes were found to be associated with either abiotic or biotic stress-responsive cis-regulatory elements, for instance, ARFAT with the sequence “TGTCTC” was found to be associated with 87 genes and which functions as ABA and auxin responsiveness. ABA is a plant phytohormone that is vital for plants' response towards stress (Trivedi et al. 2016). Other cis-regulatory element predicted were CBFHV with a role in dehydration-responsive element / cold acclimation, DRECRTCOREAT functions as activators that function in drought-, high-salt- and cold-responsive gene, lastly ABRELATERD1 with a function in early responsive to dehydration, AGMOTIFNTMYB2 is induced by various stress such as wounding or elicitor treatment among others (Fig. 4 and Supplementary Table S5). The cis-regulatory elements detected such as ABRE has previously been found to associate with top-ranked plant stress-responsive transcription factors such as the NAC, MYB (Nakashima et al. 2009).

miRNA Prediction for the Major Two Subfamilies; The probable protein types and the Serine/threonine-protein kinase

In the prediction analysis of the miRNA targeting the various genes obtained for the two major subfamilies, a total of 287 miRNAs were found to target 91 genes (Supplementary Table 5). The high miRNA targets detected for these genes showed that the genes obtained from the SDR on chromosome D₅02 and chromosome D₅07 have a significant role in various biological processes within the plant. The highest level of miRNA target was observed for the following genes; Gorai.002G039900 (6 miRNAs), Gorai.002G041100 (9 miRNAs), Gorai.002G114100 (9 miRNAs), Gorai.002G133000 (7 miRNAs), Gorai.002G134400 (8 miRNAs), Gorai.007G244000 (9 miRNAs), Gorai.007G271300 (10 miRNAs) among the rest. The miRNA's targets were observed to be very high, with a single gene being targeted by a minimum of two to a maximum of 10 miRNAs. Some of the miRNAs detected were gra-miR172a and gra-miR172b all found to target Gorai.007G059900 which is a member of the serine/threonine-protein kinase. The SAPK2 mined within the SDR located on chromosome D₅07 has been found to have a function in fiber development in cotton (Abdurakhmonov et al. 2008). Moreover, miR398 has been extensively studied and found to have a role in enhancing abiotic stress tolerance in plants, for instance, gr-miR398 was found to be upregulated in plants exposed to water deficit conditions, and thus found to be responsible for enhancing tolerance towards oxidative stress, water deficit, salt stress, abscisic acid stress, ultraviolet stress, copper and phosphate deficiency, high sucrose and bacterial infection (Jia et al. 2009; Lu et al. 2010; Pashkovskii et al. 2010). The same miRNA was found to target Gorai.007G335000 a member of the probable receptor-like protein kinase mined within the SDR on chromosome D₅07.

GO Annotation of the Major Two Subfamilies; The probable protein types and the Serine/threonine-protein kinase of the Dominant Gene Domains

In the analysis of the GO terms, a total of 188 genes were found to have GO terms, in which high number of genes were found to be involved in biological process (BP), with functions such as regulation of biological process, response to stimulus, single-organism process, metabolic process, and cellular process, in relation to cellular component (CC), four major functions were detected, namely the cell, cell part, membrane part, and membrane while in molecular functions (MF), only two functions were observed, binding and catalytic activity (Fig. 5). A unique observations were made in some of the genes found within the SDR regions, for instance, Gorai.002G14960 (BRASSINOSTEROID INSENSITIVE 1-like) was found to have 20 GO functions, with 3 cellular component functions, namely endosome (C: GO:0005768), plasma membrane (C: GO:0005886) an integral component of membrane (C: GO:0016021). Five molecular functions which were; protein serine/threonine kinase activity (F: GO:0004674), steroid binding (F: GO:0005496), ATP binding (F: GO:0005524), protein homodimerization activity (F: GO:0042803) and protein heterodimerization activity (F: GO:0046982). Similar very high number of biological processes were observed microtubule bundle formation (P: GO:0001578), protein phosphorylation (P: GO:0006468), skotomorphogenesis (P: GO:0009647), detection of brassinosteroid stimulus (P: GO:0009729), brassinosteroid mediated signaling pathway (P: GO:0009742), positive regulation of flower development (P: GO:0009911), response to UV-B (P: GO:0010224), pollen exine formation (P: GO:0010584), leaf development (P: GO:0048366), anthers wall tapetum cell differentiation (P: GO:0048657), negative regulation of cell death (P: GO:0060548) and regulation of seedling development (P: GO:1900140). Other genes harbored a range of GO functions from three to 10 different functions (Fig. 6 and Supplementary Table S6)

RNA Sequence Data Analysis Profiled under Abiotic Stress Conditions and in Different Fiber Developmental Stages

By the fact that the two major subfamilies were found to be targeted by stress-specific miRNAs and even found to be associated with some known cis-regulatory elements, we undertook to investigate if the genes would have any varying expression under drought, salt and even different stages of fiber development. We, therefore, blasted each of the protein sequences into NCBI to obtain homologous genes from upland cotton, G. hirsutum, the homolog genes were used to mine the RNA expression profiling under drought, cold, salt and different stages of fiber development from the cotton functional genome database (https://cottonfgd.org/search/). The raw data for the RNA sequencing were transformed into log 2 and used in the construction of the heat map. The RNA expression analysis showed that the genes were categorized into three groups, with group 1 members exhibiting higher expression analysis under different fiber development stages (Fig. 7). Majority of the highly upregulated genes were those obtained from the SDR regions in chromosome D₅07, such as Gorai.007G283900 (Serine/threonine-protein kinase Nek2), Gorai.007G186000 (Probable inactive receptor kinase At1g48480), Gorai.007G053000 (Serine/threonine-protein kinase SRK2I), Gorai.007G285300 (Serine/threonine-protein kinase WNK1), Gorai.007G235600 (Genome polyprotein), Gorai.007G247600 (Serine/threonine-protein kinase ppk15) and Gorai.007G308900. it is interesting to note that the gene which was highly upregulated in various stages of fiber development, was also found to be targeted by gr-miR164a, the same miRNA has been found to target the NAC transcription factor family (Xie et al. 2000). Moreover, mutant Arabidopsis lacking ath-miR164c was found to exhibit a slight defect in carpel fusion (Baker et al. 2005). In addition, miR164a,b,c has been found to have a regulatory role in the expression of CUP-SHAPED COTYLEDON1 (CUC1) and CUC2, which encode key transcriptional regulators involved in organ boundary specification (Huang et al. 2012). These previous findings show that the gene found to be targeted by miR164a/b/c could be playing an important role in fiber development.

Under abiotic stress conditions, genes exhibited differential expression, with group 3 members exhibiting significantly higher expression under salt, cold and drought stress conditions. Some of the genes which were highly expressed include Gorai.007G167300 (Probable serine/threonine-protein kinase WNK11), Gorai.007G247600 (Serine/threonine-protein kinase ppk15), Gorai.007G186000 (Probable inactive receptor kinase At1g48480), Gorai.002G102000 (Serine/threonine-protein kinase D6PKL2), Gorai.002G115600 (Serine/threonine-protein kinase CDL1), Gorai.007G295100 (Serine/threonine-protein kinase CDL1), Gorai.007G157300 (Serine/threonine-protein kinase MHK), Gorai.007G287200 (Probable serine/threonine-protein kinase At1g54610), Gorai.007G322800 (Probable serine/threonine-protein kinase At1g09600), Gorai.007G078700 (Probable receptor-like protein kinase At5g15080) and Gorai.007G020100 (Serine/threonine-protein kinase fray2). Among the highly expressed genes, Gorai.007G167300 was targeted by gra-miR398. Gorai.007G247600 was found to be targeted by gra-miR5207; miR398 is the first plant miRNA reported miRNA to be down-regulated by oxidative stresses. it has been intensively studied and found to be important in the regulatory process of copper homeostasis, in response to abiotic stresses such as heavy metals toxicity, sucrose, and heat, in addition to having a role in biotic stresses through the down-regulation of the expression of Cu/Zn-superoxide dismutase (CSD) (Sunkar 2006; Lu et al. 2010; Pashkovskii et al. 2010). The result shows that the SDR regions could be vital in the evolution of some of the significant genes required for the survival of the plants.

RT-qPCR Validation of The Selected Genes within the SDR Regions of Chromosome D502 and D507 under Drought and Salt Stress Conditions

Thirty (30) genes were profiled on the leaf tissues of the four parental lines under drought and salt stress conditions. The genes exhibited three types of expressions across the four parental lines, however, more genes were found to highly upregulated in the leaves of G. klotzschianum and G. thurberi compared to G. davidsonii and G. trilobum (Fig. 8A-D). the results obtained were in agreement to previous findings which have shown that G. thurberi is more tolerant to both biotic stress condition, more so to Verticillium dahliae which is a fungal pathogen which causes Verticillium wilt, a terminal disease to various crops (Dong et al. 2019). Moreover, in the study carried by Cai et al (Cai et al. 2019) revealed that G. thurberi was highly tolerant to cold stress compared to G.trilobum. Furthermore, Kirungu et al (Kirungu et al. 2018a) found that G. klotzschianum harbored more beneficial traits compared to G. davidsonii.

Genetic maps have become significantly important in understanding markers, breeding, association genetics, map-assisted gene cloning, gene mining and mapping of quantitative trait loci (QTLs) (Golestan Hashemi et al. 2015). In our study we integrated two genetic maps from the D genome of the diploid cotton with mapping size of 188 F_2:3population, the first genetic map (Map A) was composed of a genetic cross between G. klotzschianum (female parent )and G. davidsonii (male parent) while the second genetic map (Map B) was developed from G. thurberi (female parent) and G. trilobum (male parent). Map B had a higher number of markers linked and a smaller average distance as compared to map A. map B had a smaller average distance between markers as compared to map A, this map could play a fundamental role in the analysis of QTLs. In the construction of the consensus map we observed more markers were contributed by map B as compared to map A. Inconsistencies of marker order including the translocation or inversions between individual markers in consensus maps were observed especially on markers that were closely linked together as observed in SDR region of Chromosome D₅02-2 similar results were observed in the consensus map of flax seed (Cloutier et al. 2012).

The segregation distortion between the three maps ranged from 15.783% to 22.2% with map B having the highest percentage and map A having the lowest percentage. Segregation distorted markers have previously been studied in various plants (Takumi et al. 2013), the study of segregation distortion is of significant because distorted markers may be linked to genes or traits of interest, these genes may be beneficial or lethal to the organism. Therefore it's important to include the segregation distortion markers in the construction of genetic maps since the exclusion of such markers could cause biasness of the data and result in the loss of significant genetic information. In our study we examined the trend of segregation distortion within Chromosome D₅02 and Chromosome D₅07, we observed that the two chromosomes had highest segregation distortion markers, Chromosome D₅02 had the least mapped markers but had the highest percentage of segregation distortion ranging between 42.857% and 76.087% in the three genetic maps similar results have been observed in cotton (Li et al. 2007; Khan et al. 2016; Shang et al. 2016). The two chromosomes also showed SDR which was skewed towards a specific allele. These SDRs may be due to, pre- or post-zygotic selection and chromosome loss or rearrangements

From the analysis of the genes located on the dominant domain which is the Protein kinase we observed that 29 genes were not disrupted by introns (intronless), intronless genes contain a single exon and do not contain introns from its beginning to the end neither in its UTR or CDS regions (Yan et al. 2016). The intronless genes are known to promote the efficiency of transcription initiation and elongation in spliced genes. (Sakharkar et al. 2006). Their Isoelectric Point (pl) values ranged from both acidic to basic proteins, the pl values are known to affect the solubility of protein molecules, hence proteins are less soluble when the pH of the solution is at its isoelectric point (Dawes et al. 1994). All of the proteins were observed to have a GRAVY value less than zero, indicating that they were hydrophilic in nature. Hydrophilic proteins have a high solubility, hence these proteins could be playing significant role in desiccation tolerance (Hundertmark and Hincha 2008), and also aid in enzymatic activities involved in the biochemical processes.

The analysis of the genes mined within the SDR of chromosome D₅02 and chromosome D₅07, revealed that the dominant domain was the Pkinase gene family, with Pfam number of PF00069. Since the genes within this domain were so many, it was technically impossible to analyze all of them, and thus, we determined the dominant subfamily, out of which two were further analyzed. The two major dominant subfamilies were the probable kinases and the serine/threonine kinases genes. These domains have been widely studied in both plants and animals (Jun et al. 2015). In the cotton plant, overexpression of GbRLK, a putative receptor-like kinase gene have been found to confer tolerance to Verticillium wilt, a plant disease which is known to cause massive losses in cotton production regions (Jun et al. 2015). Similarly, overexpression of GbRLK gene isolated from G. barbadense has been found to confer drought and salt stress tolerance in transgenic Arabidopsis plants (Zhao et al. 2013). The detection of these genes within the SDR regions demonstrates the significant role played by the SDRs in the evolution or synthesis of vital proteins with a profound role in enhancing tolerance levels of plants to various abiotic and biotic stress factors. The main genes found to be located within the SDR in the consensus map were the R genes. This group of genes are known to play an integral role in signaling during pathogen recognition, hence assist in the activation of plant defense mechanisms.

The R genes work in coordination with other domains to bring combinatorial variations in signal response specificity to pathogens. Moreoover, the R genes are mainly associated with proteins that identify specific pathogen effectors, known as avirulence proteins, they work in specific genes. These genes are known to have a gene-to-gene interaction between an organism and its pathogens (Rouxel and Balesdent 2010). These genes were segregating within the SDR in synchrony with the aim of helping in plant defense mechanisms, these mechanisms involve a series of enzymatic activities within the proteins. From the recent analysis, it has been observed that the proteins encoded by resistance genes (R genes) display modular domain structures and require several dynamic interactions between specific domains to perform their function (Wang et al. 2016), hence a very close interaction of these genes at SDR. The four parental lines used in the construction of the genetic map are known to contain traits for resistance to bacterial blight (G. davidsonii), sucking pests such as aphids (G. klotzschianum), Fusarium wilt, silver leaf whitefly and cotton bollworm resistance (G. thurberi), Verticillium wilt (G. trilobum). This explains the reasons for a large number of plant resistant genes (R genes) detected within the SDR regions in chromosome D₅02 and D₅07.

The carrying out insilico analysis of the genes obtained within the SDR regions, the cis- regulatory elements, miRNA and GO analysis showed that the R genes could be playing a significant role within the plant. Recent evidence indicates that plant miRNAs play a role in biotic and abiotic stress responses (Sunkar et al. 2007). The analysis of the genes obtained within the SDR regions, a number of miRNAs were found to target several genes, for instance, miR157a and miR157b were found to a single gene Gorai.007G063800, a member of the serine/threonine-protein kinase. The same miRNA family was found to be the most abundant, followed by miR156, miR166, and miR168, with variation within each family in Pomegranate. This fruit has enormous importance in human health mainly because of its antioxidant properties, it does accumulate high amount of anthocyanins in skin and arils (Saminathan et al. 2016). The antioxidant enzymes are important to plants in reducing the deleterious effects of reactive oxygen species (ROS). When plants are exposed to stress, the production and elimination of the ROS process becomes altered leading to excessive accumulation of ROS within the cell resulting in oxidative stress, the association of miR157 to induction of antioxidant enzymes, showed that these genes within the SDR are critical for plants

The various Cis- regulatory elements (CREs) targeting the genes within the SDRs, were found to perform a myriad of CREs with diverse functions. More specifically geared towards enhancing plants tolerance to various environmental stresses, for instance ABREATCONSENSUS has been found to be targeting not only the stress-responsive genes but also those involved in transportation such as the nitrate transporter (NRT) genes as evident in poplar plant (Aichi et al. 2006; Bai et al. 2013). The results obtained for the CREs were further augmented by GO annotation. The various genes obtained within the SDR regions were found to be playing integral in all the three GO functional annotations, in cellular component (CC), functions such as an integral component of membrane (GO:0016021), cortical microtubule (GO:0055028) among others were detected. The integrity of the cell membrane is important since the membrane is the communicating channel between intra and extracellular environments, any damage to the cell membrane affects various biological process such as osmosis, and thus cell water retention, the detection of these cellular component roles showed that the genes found at the SDR regions have a function in maintaining cell membrane stability, and therefore enhancing the delicate osmotic balance within the cell. Moreover, an integral component of the membrane was a function found to be unanimous with the LEA genes (Magwanga et al. 2018b).

Several gametophytic and zygotic barriers causing deviation of allele frequencies from Mendelian ratios have been reported in a number of plants such as rice (Wang et al. 2009), and therefore detection of SDRs in the two populations developed from the two wild parental lines is a common feature more so among the F_2:3population. It is assumed based on Mendelian law that there is an equal probability of transmission of alleles from either parent during sexual reproduction, but this has not been the case in several studies, being there tend to be phenomena referred to the preferential transmission of alleles or genotypes known as segregation distortion (SD) (Nadeau 2017). The evolution of segregation distortion may have profound evolutionary implications, from previous studies the bulk pollen sequencing indicated a rapid evolution of segregation distortion (Corbett-Detig et al. 2019). SD have been described as powerful evolutionary tools that could lead to speciation (Liberman and Feldman 1982) SDR has been observed not only among the controlled population but also among the natural population (McLaughlin and Malik 2017). The results from the two maps and their consensus shows that SDs are a common feature in segregating population and could be used to mine genes of significance that could be introgressed into the already cultivated species.

The use of genetic map analysis has become increasingly significant in understanding, markers assisted selection, gene mining and gene cloning, however, intensive analysis of gene located within the SDR has not been widely studied in our research we examined the only two interspecific maps developed in the D genome of the diploid cotton. We constructed a consensus map from the two genetic maps and noted that in all the three maps D₅02 and D₅07 had the highest of SD, and hence we mined the genes within the SDR of D₅02 and D₅07 to find out if there were genes of significance that could be segregating within this regions. A total of 2,308 genes in D₅02 and 3,730 genes in D₅07, were mined within the SDR, these genes were grouped into 1117 domains of which 622 domains shared between the two chromosomes. We further analyzed that the 12 largest domain had a significant role in plant defense mechanism of which 9 out of the 12 domains belonged to the resistant genes (R group of genes) the largest domain was PF00069 with a total of 188 genes, we analyzed for the properties of these genes, the largest subdomain being the Serine/threonine-protein kinase. The analysis of the genes within the SDR revealed that genes that performed similar role clustered together within the SDR these genes have similar feature such as they were all hydrophilic in nature the study of these genes will provide future researcher in understanding of significance of genes within the SDR and the role of the consensus map in mining these genes

SDR: segregation distortion region; GO: Gene ontology; NRT: nitrate transporter; ROS: reactive oxygen species; cM: centiMorgan; QTL: quantitative trait loci; CRE: Cis- regulatory elements; PPR: Pentatricopeptide repeat; CYPs: Cytochromes P450; CC: cellular component; MF: Molecular function; LEA: Late Embryogenesis Abundant proteins

Acknowledgments

We are indebted to the entire cotton biology research team for their immense support and technical assistance in the course of this research work

Availability of data:

All files supporting the findings are included within the manuscripts as figures, tables and supplementary files

Author Contributions

JNK, ROM, KW, and FL: Conceptualization of the concept, JNK, ROM, KW, MLS, TG, KW and FL Data curation, JNK, ROM, KW, and FL; Formal analysis, JNK, ROM, KW, and FL; Funding acquisition, JNK, ROM, KW, FL, ZZ, LP, YX, Y H, YZ, XC, SGA, KW and FL; Resources, JNK, ROM, KW, SGA and FL; Validation, JNK and ROM; Wrote the original draft, JNK and ROM; reviewed & edited the final manuscript, All authors approved the final manuscript.

Funding

This research program was financially sponsored by the National key research and development plan (2016YFD0100306) and the National Natural Science Foundation of China (31671745, 31530053).

Availability of data and materials:

All files supporting the findings are included within the manuscripts as figures, tables and supplementary files

Ethics approval and consent to participate

No ethical nor consent to participate in this research was sought.

Consent for publication

No consent to publish the work was sort

Competing interests

The authors declare no any form of competing interest

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Table 1: Mapping statistics for the two individual maps and the consensus genetic maps of diploid cotton in the D Genome

Chr	Marker Per Chr			Average Distance (cM)			Map Size (cM)			Number Of SD			Average % SD
Chr	Map A	Map B	Consensus Map	Map A	Map B	Consensus Map	Map A	Map B	Consensus Map	Map A	Map B	Consens Map	Map A	Map B	Consensus Map
D₅01	89	60	143	1.304	1.713	0.788	116.045	102.761	112.698	3	12	13	3.371	20	9.091
D₅02	44	21	58	1.884	1.365	1.943	82.908	28.665	112.698	35	10	34	76.087	42.857	58.621
D₅03	45	56	94	2.59	1.136	1.259	116.528	63.601	118.325	8	5	7	17.778	8.929	7.447
D₅04	56	70	123	1.997	0.846	0.767	111.846	59.229	94.288	2	6	7	3.571	8.571	5.691
D₅05	49	89	136	2.361	1.04	0.856	115.671	92.563	116.432	17	8	25	34.694	8.989	18.382
D₅06	58	73	125	2.001	0.88	1.082	116.045	64.213	135.273	5	8	9	8.621	10.959	7.200
D₅07	86	60	142	1.446	1.15	0.809	124.358	69.003	114.899	35	23	68	40.698	38.333	47.887
D₅08	49	64	99	2.492	1.251	0.81	122.13	80.053	80.156	25	5	27	51.02	7.813	27.273
D₅09	69	93	141	1.697	1.038	0.944	117.06	96.559	133.117	12	10	15	16.216	10.753	10.638
D₅10	34	58	84	2.998	1.786	1.238	101.93	103.563	103.973	2	12	11	5.882	20.69	13.095
D₅11	63	82	140	1.806	0.788	1.007	113.801	64.604	140.985	5	23	25	7.937	28.049	17.857
D₅12	49	41	100	2.301	2.153	1.007	112.739	88.288	100.72	6	2	6	12.245	4.878	6.000
D₅13	37	82	107	3.491	1.212	0.971	129.164	99.356	103.881	4	11	7	10.526	13.415	6.542
Totals	728	849	1492	2.182	1.193	1.037	1480.23	1012.458	1467.445	159	135	254	22.2	15.783	18.133

Table 2: Characteristics of the genes found within the two common markers; swu16562 and swu16586 between the three genetic maps

Gene ID	Gene Name	Description	Molecular Weight (kDa)	Charge	pI	GRAVY value	Domain List	domain
Gorai.007G355900	NA	NA	26.649	-12	4.563	-0.408	-
Gorai.007G356000	At4g27220	Probable disease resistance protein At4g27220	252.737	-10	6.175	-0.127	PF00931	NB-ARC domain
Gorai.007G347200	LHP1	Chromo domain-containing protein LHP1	48.046	-13.5	4.855	-1.049	PF00385	Chromatin organization modifier
Gorai.007G347300	SIGB	RNA polymerase sigma factor sigB	64.627	15.5	9.115	-0.54	PF00140	Sigma-70 factor, region 1.2
Gorai.007G347400	NA	NA	16.507	17.5	9.897	-0.956	-
Gorai.007G347500	NA	NA	47.568	14.5	9	-0.157	PF06219	Protein of unknown function (DUF1005)
Gorai.007G347600	TFCA	Tubulin-folding cofactor A	12.859	-5	4.781	-0.821	PF02970	Tubulin binding cofactor A
Gorai.007G347700	CYP89A2	Cytochrome P450 89A2	58.73	10	8.563	-0.074	PF00067	Cytochromes P450 (CYPs)
Gorai.007G347800	CYP89A2	Cytochrome P450 89A2	58.775	15.5	9.358	-0.151	PF00067	Cytochromes P450 (CYPs)

Table 3: Distribution of genes of the 12 largest domains within D₅02 and D₅07 in the consensus map

PF number	Domain	D₅02 genes	D₅07 genes	Total genes per Domain
PF00069	Protein kinase domain	71	117	188
PF13855	LRR_8; Leucine rich repeat	52	78	130
PF07714	Protein tyrosine kinase domain	55	53	108
PF00931	NB-ARC domain	15	82	97
PF08263	LRRNT_2; Leucine rich repeat N-terminal domain	31	58	89
PF00560	LRR_1; Leucine Rich Repeat	24	61	85
PF01535	Pentatricopeptide repeat (PPR)	26	51	77
PF13041	Pentatricopeptide repeat (PPR_2) repeat family	26	49	75
PF00067	Cytochromes P450 (CYPs)	29	32	61
PF00249	Myb-like DNA-binding domain	15	41	56
PF00076	RNA recognition motif. (a.k.a. RRM, RBD, or RNP domain)	25	29	54
PF13639	zf-RING_2; Ring finger domain	15	36	51
Total		384	687	1071

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Genetic Map Construction and Functional Characterization of Genes within the Segregation Distortion Regions (SDRs) in the F2:3 Populations Derived from Wild Cotton Species of the D Genome

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