Skip to main content

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

Abstract

Background

Segregation distortion (SD) is a common phenomenon among stable or segregating populations, and the principle behind it still puzzles many researchers. The F2:3 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 genomes, map A derived from F2:3 progenies of Gossypium klotzschianum and G. davidsonii while Map B from G. thurberi and G. trilobum F2:3 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 an average marker distance of 1.037 0 cM. Chromosome D502 had the highest percentage of SD with 58.6%, followed by Chromosome D507 with 47.9%. 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 gourp out of which 622 groups were common across the two chromosomes. Moreover, genes within the top 9 groups related to plant resistance genes (R genes), whereas 188 genes encoding protein kinase domain (PF00069) comprised the largest group. Further analysis of the dominant gene group revealed that 287 miRNAs were found to target various genes, such as the gra-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, and also they were highly expressed at different stages of fiber development.

Conclusion

The results indicated the critical role of the SDRs in the evolution of the key regulatory genes in plants.

Background

Segregation distortion (SD) is described as a deviation from the expected Mendelian ratio within a segregating population due to various segregating distorters (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 (Mello et al. 1991). During the construction of genetic maps, it has been observed that some alleles in chromosomal regions skew from the normal Mendelian ratio. These alleles tend to cluster at segments of the chromosome, and 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 and thus reduce the accuracy of the maps (Yuan et al. 2019). However genes of significance have been mined within the SDRs, 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 (Yuan et al. 2019), and mammals (Kumari et al. 1992).

Higher frequencies of occurrence of the SDR have been found in populations developed through interspecific as compared with intraspecific crosses (Dai et al. 2017), for example in rice more SDRs were detected in the double haploid compared to the F2:3 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 (Cheng et al. 2016; Wang et al. 2019). Several studies on SDs have been conducted in several plant 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), cotton (Wu et al. 2003; Amudha et al. 2012), and other plants. In the analysis of SD in the F2:3 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 undertaken to explore the SDs in the F2:3 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 recurrent 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 generated from two closely related parents, G. klotzschianum and G. davidsonii (Kirungu et al. 2018) and the second map developed from G. thurberi and G. trilobum (Li et al. 2018), in either of the maps, the F2:3 population used, the genotypic data from the two maps were combined to generate the consensus map, and the consensus map was generated by using the two maps. The only available maps 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 the SDRs were mined and analyzed. The genes mined within the SDR and understanding their roles will 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.

Materials & methods

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 F2:3 population derived from the self-pollinating F1 population of G. klotzschianum (female parent) and G. davidsonii (male parent). Similarly, the second genetic map (Map B) was constructed from F2:3 populations derived from G. thurberi (female parent) and G. trilobum (male parent). A total of 188 progenies were used as the mapping population. The F2:3 progenies from the four parental lines were developed and grown in the wild cotton nurseries, managed by the Institute of Cotton Research, Chinese Academy of Agricultural Sciences (ICR, CAAS), located in Sanya, Hainan province, China. The development of the F2:3 progenies followed a similar pattern as described by Magwanga 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 F2:3 progenies and their parental lines using the CTAB method (Zhang et al. 2000b). Polymerase chain reaction (PCR) was conducted. The amplified PCR products were electrophoresed on non-denaturing 10% polyacrylamide gel electrophoresis in the 1 × TBE buffer, and the gels were then visualized after silver staining (Huang et al. 2018). 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 were screened of which 1 000 markers were found to be polymorphic. Out of the 1 000 polymorphic markers, 728 markers were mapped and generated 13 linkage 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 construction of consensus map, 1 492 polymorphic markers were applied to generate the genetic map, 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 (logarithm of odds) score of 3.0, for 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 constructed 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 was determined as follows: *, P < 0.05; **, P < 0.01; ***, P < 0.00; ****, P < 0.001; *****, P < 0.0005; ******, P < 0.0001; *******, P < 0.00005; in which *******, P < 0.00005 denoted the highly distorted markers. 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 (Nucleotide  basic local alignment search tool) to the cotton ESTs (Expressed sequence tag) with an E ≤ 1 e-15 and were annotated using BLASTX (Translated nucleotide sequence searched against protein sequences) (NCBI, Bethesda, MD, USA). The four genotypes, G. klotzschianum, G. davidsonii, G. thurberi and G. trilobum, have not been sequenced, the D5, G. raimondii was used as the reference genome. A similar method has been used to explore the genetic variation among the BC2F2 genotypes developed from G. hirsutum as the recurrent parent and G. tomentosum as the donor parent (Magwanga et al. 2018b). 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 1 000 replications, and other parameters were applied as per the default setup, 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/signalscan.html) (Higo et al. 1999). 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 SDRs and subsequently analyzed through Blast2GO as previously applied in the analysis of the LEA genes in cotton (Magwanga et al. 2018b).

RNA and RT-qPCR validation of key genes harbored within the SDRs

Based on the previous work by our research team, G. raimondii (D5), G. thurberi, and G. trilobum were profiled under biotic stress conditions, in which the plants were exposed to Verticillium dahliae infection (Dong et al. 2019). The genes which were harbored within the SDR were also prominently expressed, and majorities were members of the Probable Protein Types and the Serine/Threonine-Protein Kinase. Moreover, the de novo sequencing of the G. klotzschianum and G. davidsonii revealed a similar pattern (the data yet to be published). The highly upregulated genes were further validated under abiotic stress conditions, in which the seedlings of G. klotzschianum, G. davidsonii, G. thurberi, and G. trilobum at the third-leaf stage were exposed to drought and salt stress by exposing the seedings to 15% Polyethylene glycol 6000 (PEG6000) and 250 mmol•L-1 NaCl, respectively. The leaf tissues were then harvested for RNA extraction at 0 h, 1 h, 3 h, 6 h and 12 h of post-stress exposure. RNA extraction, purification, and RT-qPCR analysis were carried out as described by Lu et al. (2018). Cotton GrActin was applied as the reference gene.

Results

Linkage map construction

The first map was developed from the F2:3 population 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. 2018). This map was designated as map A. The second map, designated as map B, was derived by genotyping the F2:3 population developed between G. thurberi and G. trilobum, and 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 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).

Table 1 Mapping statistics for the two individual maps and the consensus genetic maps of diploid cotton in the D Genome. Map A represents genotyping of G. klotzschianum and G. davidsonii, Map B represents genotyping of G. thurberi and G trilobum while Consens Map represents the combination of genotypic data of map A and B.

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, and only 85 markers remained unlinked. The diploid cotton species has 13 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 1.037 cM. Even though the map size was relatively smaller than 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 58.6%, followed by Chromosome D507 with 47.9%. Chromosome D501 had the highest number of markers (143), while Chromosome D502 had the least number of markers (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 53.4% while only 695 markers (46.6%) were from map A. The chromosome with the highest number of markers (143)was Chromosome D501 while the chromosome with the least number of markers (58) was Chromosome D502 (Fig. 1).

Fig. 1
figure1

Consensus genetic linkage map representing 13 linkage groups of the diploid cotton of D genome, developed from map A (G. klotzschianum and G. davidsonii) and map B (G. thurberi and G. trilobum). Markers in green font represent map B while markers in red font represent map A. The markers in black represent markers translocated from other chromosomes within the maps; the markers within the SDR are italicized and bold. a-m: represent the individual chromosomes, from chromosome 1 to chromosome 13

Segregation distortion (SD) analysis

In map A, out of the 728 markers mapped, 159 markers were distorted accounting for 22.2%, and the highest SD was observed in Chromosome D502 with 76.1% followed by Chromosome D507 with 40.7%. 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 SDR were skewed towards a particular parental line, like that in Chromosome D502 towards the female parent (G. klotzschianum), and in Chromosome D507 towards the heterozygosity (Kirungu et al. 2018). In the second genetic map B, there was a slightly lower number of distorted markers, with only 135 accounting for 15.8%, and the highest two segregation distortions were observed in Chromosome D502 and Chromosome D507 with 42.8% and 38.3%, 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 58.6% and 47.9%, respectively. Similarly, the two chromosomes had the largest SDRs (Fig. 2). The largest SDR was located on 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 SDRs, and all the SDR were skewed towards the heterozygotes except for the SDR located on Chromosome D507–1, which was skewed towards the female parents. The majority of the SDRs were skewed towards the heterozygotes. Similar results were observed in the analysis of SDRs in tetraploid cotton, more specifically on Chromosome 18 (Dai et al. 2017), and rice (Wu et al. 2010), wheat (Fans et al. 1998). Based on the individual maps, the SDs were skewed towards the female compared with the male parent, the results obtained were in agreement with the study conducted on an interspecific F2 population in which the segregated distorted markers were skewed towards the female parent (Li et al. 2007).

Fig. 2
figure2

Segregation distortion region (SDR) in Chr D502 and D507 in Map A, Consensus map and Map B; Markers in green font represent map B while markers in red font represent map A. The markers in black represent markers translocated from other chromosomes within the maps; the markers within the SDR are italicized and made bold

Annotation of genes at SDR

We conducted a BLAST search, and a total of 6 038 genes were mined within the SDR in Chromosome D502 and D507 (Supplementary Table S2), with 2 308 genes in Chromosome D502 and 3 730 genes in Chromosome D507. These genes were further divided into 1 117 groups were obtained. There are 622 groups which were shared between Chromosome D502 and Chromosome D507. The largest group consists of a total of 188 genes, among which the Pkinase (PF00069) was encoded; followed by a group with 132 genes, among which PF13855 (LRR_8, Leucine-rich repeat) is encoded. The third consists of 108 genes, among which PF07714 (Pkinase_Tyr, Protein tyrosine kinase) is encoded. The genes in the three main groups were highly correlated with abiotic stress response. The genes located within the largest 12 grous were analyzed. Out of the 12 groups, 9 were found containing ‘members of the resistant genes (R group of genes), including that encoded Protein kinase, LRR_8, Protein tyrosine kinase , NB-ARC, LRRNT_2, Leucine-rich repeat N-terminal, PPR, PPR_2 family, Cytochromes P450 (CYPs), Myb-like DNA-binding, and RNA recognition motif (RRM, RBD, or RNP) (Tables 2 and 3).

Table 2 Characteristics of the genes found within the two common markers, SWU16562 and SWU16586, across maps A, B and the consensus map
Table 3 Distribution of genes of the 12 largest domains within Chromosome D502 and Chr D507 with the highest segregation distortion regions (SDRs) in the consensus map combing the genotypic data of the G. klotzschianum and G. davidsonii (Map A) and G. thurberi and G. trilobum (Map B)

Analysis of the physiochemical properties and structures of the genes obtained from the abundant/enriched domain mined within the SDR in chromosome D502 and chromosome D507

The abundant/enriched 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. 2018b). 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, the charge was between − 27 and 39.5; Isoelectric point (pI) values were between 4.375 and 10.382; the GRAVY values ranged from − 0.721 to −0.251 while their protein lengths ranged from 611 aa to 12 310 aa (Supplementary Table S3). The GRAVY values were all below zero, indicating that these genes were mainly hydrophilic. The group that encoded 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 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, i.e., 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, which 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 more significant 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).

Fig. 3
figure3

Phylogenetic tree analysis of the most abundant genes subfamily of the dominant domain, Pkinases mined within the SDRs of chromosome D502 and chromosome D507

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 to determine if there could be any of the regulatory elements related 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 which function 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 elements predicted were CBFHV with a role in dehydration-responsive element / cold acclimation, DRECRTCOREAT functioning as activators that function in drought-, high-salt- and cold-responsive gene, lastly ABRELATERD1 with a function in early responsive to dehydration, AGMOTIFNTMYB2 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 have previously been found to associate with top-ranked plant stress-responsive transcription factors such as the NAC, MYB (Nakashima et al. 2009).

Fig. 4
figure4

The average number of the cis-regulatory elements ABREATCONSENSUS (YACGTGGC), CBFHV (RYCGAC), DRECRTCOREAT (RCCGAC), ARR1AT (NGATT) and others in the promoter region of G. raimondii genes from the two major subfamilies of the dominant gene group mined within the SDRs of Chromosome D502 and chromosome D507. The promoter regions were analyzed in the 1 kB up/down stream promoter region of translation start sites using the PLACE database

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 S5). The high number of 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. 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 miRNAs and the genes association revealed that higher level of miRNA targets , for instance, a single gene was targeted by 210 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, gra-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 groups

In the analysis of the GO terms, a total of 188 genes were found to have GO terms, in which a high number of genes were found to be involved in biological process (BP), with functions such as regulation of the 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), and only two functions were observed, binding and catalytic activity (Fig. 5). Some unique results were found in some of the genes within the SDRs; 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) and integral component of membrane (C: GO:0016021). Five molecular functions 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). A 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).

Fig. 5
figure5

Gene ontology (GO) annotation results for the genes obtained within the SDR of chromosome D502 and D507. GO analysis of the 186 protein sequences predicted for their involvement in biological processes (BP), molecular functions (MF) and cellular components (CC)

Fig. 6
figure6

Detailed gene ontology (GO) annotation, analysis of the significantly expressed genes within the SDRs of chromosome D502 and D507. GO analysis of the 186 protein sequences predicted for their involvement in biological processes (BP), molecular functions (MF) and cellular components (CC)

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. Genes and their RNA sequences were then obtained from the de novo sequenced data. The raw data for the RNA sequencing were transformed into log2 form and used in the construction of the heat map. The RNA expression analysis showed that the genes were categorized into three groups, with members in group 1 exhibiting higher expression levels at different fiber development stages (Fig. 7). The majority of the highly upregulated genes were obtained from the SDRs 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 gra-miR164a, and 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 showed that the gene found to be targeted by miR164a/b/c could play an essential role in fiber development.

Fig. 7
figure7

Differential expression of the two major subfamilies under drought, salt, cold and fiber development. The heat map was visualized using the MeV_4_ 9_0 program. Yellow and blue indicate high and low levels of expression, respectively. a Heat map showing gene expression under fiber development. b Heat map showing gene expression under salt, cold and drought conditions

Under abiotic stress conditions, genes exhibited differential expression, with group 3 members exhibiting significantly higher expression under salt, cold and drought stresses. Some of the genes 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 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 SDRs could be vital in the evolution of some of the key regulatory genes required for the survival of the plants.

RT-qPCR validation of the selected genes within the SDRs of chromosome D502 and D507 under drought and salt stress conditions

Thirty 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 be highly upregulated in the leaves of G. klotzschianum and G. thurberi compared with 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 conditions, more so to Verticillium dahliae which is a fungal pathogen causing Verticillium wilt, a terminal disease to various crops (Dong et al. 2019). Moreover, Cai et al. (2019) revealed that G. thurberi was highly tolerant to cold stress compared with G.trilobum. Furthermore, Kirungu et al. (2018) found that G. klotzschianum harbored more beneficial traits compared with G. davidsonii.

Fig. 8
figure8

RT-qPCR validation of the selected genes within the SDRs of chromosome D502 and D507 under drought and salt stress conditions. The heat map was visualized using the MeV_4_ 9_0 program. Red and blue indicate high and low levels of expression, respectively, while white indicates none expressed genes. a Heat map showing gene expression in the leaf tissue of G. klotzschianum. b Heat map showing gene expression in the leaf tissue of G. davidsonii, c Heat map showing gene expression in the leaf tissue of G. thurberi and d Heat map showing gene expression in the leaf tissue of G. trilobum. Drought and salt stress was imposed by supplementing the Hoagland nutrient solution with 17% PEG and 250 mL of NaCl solution, respectively

Discussion

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 a mapping size of 188 F2:3 population. 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 with map A. This map could play a fundamental role in the analysis of QTLs. In the construction of the consensus map, more markers were contributed by map B as compared with 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 in the SDR of Chromosome D502–2. Similar results were observed in the consensus map of flax seed (Cloutier et al. 2012).

The segregation distortion among the three maps ranged from 15.8% (map A) to 22.2% (map B). Segregation distorted markers have previously been studied in various plants (Takumi et al. 2013). The study of segregation distortion is significant because distorted markers may be linked to genes or traits of interest, these genes may be beneficial or lethal to organisms. Therefore, it’s important to include the segregation distortion markers in the construction of genetic maps since the exclusion of such markers could cause bias 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 D507. We observed that both chromosomes had higher segregation distorted markers. Chromosome D502 had the least mapped markers with a higher percentage of segregation distortion ranging from 42.9% to 76.1% 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.

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 (pI) values ranged from both acidic to basic proteins. The pI 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. Hydrophilic proteins have a high solubility, hence these proteins could be playing a 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 D507 revealed that the dominant froup was the Pkinase gene family, with a Pfam number of PF00069. There were so many genes within this group, it was technically impossible to analyze all of them, and thus, we determined the dominant subfamily, and further analyzed two of them. The two major dominant subfamilies were the probable kinases and the serine/threonine kinases genes. These groups 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, has been found to confer tolerance to Verticillium wilt, a plant disease that is known to cause massive losses in cotton production (Jun et al. 2015).

Similarly, overexpression of the 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 SDRs 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 is 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 groups to bring combinatorial variations in signal response specificity to pathogens. Moreover, the R genes are mainly associated with those encode proteins that identify specific pathogen effectors, known as avirulence proteins, which specific in terms of their activities. 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 intending to help in plant defense mechanisms, these mechanisms are involved in 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 and coordination in terms of the activities of the genes located within the SDR. In a study conducted on determining significant QTLs for drought stress tolerance, the majority of the marker loci co-localized with known QTLs for blast tolerance or NBS-LRR disease resistance genes were located within the regions of significantly distortion levels (Dixit et al. 2014). Similar result was found on Bangladeshi rice landrace Capsule in relation to salt stress tolerance (Rahman et al. 2019). 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 SDRs in chromosome D502 and D507.

The carrying out insilico analysis of the genes obtained within the SDRs, the cis-regulatory elements, miRNA and GO analysis showed that the R genes could play 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). In the analysis of the genes obtained within the SDRs, several miRNAs were found to target several genes; for instance, miR157a and miR157b were found to target 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 a 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 stresses, the production and elimination of the ROS process 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, it is geared towards enhancing plants tolerance to various environmental stresses; for instance, ABREATCONSENSUS targets 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 SDRs were found to play an 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 because the membrane is the communicating channel between intra and extracellular environments, and any damage to the cell membrane affects various biological processes such as osmosis, thus affecting cell water retention. The detection of these cellular component roles showed that the genes found in the SDRs 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 several plants such as rice (Wang et al. 2009). Therefore detection of SDRs in the two populations developed from the two wild parental lines is a common feature more so among the F2:3 populations. 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 as 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 has 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 showed 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.

Conclusions

The use of genetic map analysis has become increasingly significant in understanding markers-assisted selection, gene mining and cloning. However, intensive investigation of genes 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 region. A total of 2 308 genes in D502 and 3 730 genes in D507 were mined within the SDR, these genes were divided into 1 117 groups of which 622 groups were shared between the two chromosomes. We further observed that the 12 largest domains had a significant role in the plant defense mechanism of which 9 belonged to the resistance genes (R group of genes), with 188 genes and a pfam number of PF00069. We analyzed for the properties of these genes, the largest subgroup encode the serine/threonine-protein kinase. The genes that performed similar roles clustered together within the SDR. These genes have similar feature being hydrophilic. The study of these genes will provide an understanding of the significance of genes within the SDR and the role of the consensus map in mining these genes.

Availability of data and materials

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

Abbreviations

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

References

  1. Abdurakhmonov IY, Devor EJ, Buriev ZT, et al. Small RNA regulation of ovule development in the cotton plant, G hirsutum L. BMC Plant Biol. 2008;8:93. https://doi.org/10.1186/1471-2229-8-93 .

  2. Aichi M, Yoshihara S, Yamashita M, et al. Characterization of the nitrate-nitrite transporter of the major facilitator superfamily (the nrtP gene product) from the cyanobacterium Nostoc punctiforme strain ATCC 29133. Biosci Biotechnol Biochem. 2006;70:2682–9. https://doi.org/10.1271/bbb.60286 .

  3. Amudha J, Balasubramani G, Malathi VG, et al. Segregation pattern of gene expression in cotton leaf curl virus-resistant transgenics. Arch Phytopathol Plant Protect. 2012;45:487–98. https://doi.org/10.1080/03235408.2011.587987 .

  4. Anhalt UCM, Heslop-Harrison PJS, Byrne S, et al. Segregation distortion in Lolium: evidence for genetic effects. Theor Appl Genet. 2008;117:297–306. https://doi.org/10.1007/s00122-008-0774-7 .

  5. Bai H, Euring D, Volmer K, et al. The nitrate transporter (NRT) gene family in poplar. PLoS One. 2013;8:e72126. https://doi.org/10.1371/journal.pone.0072126 .

  6. Baker CC, Sieber P, Wellmer F, et al. The early extra petals1 mutant uncovers a role for microRNA miR164c in regulating petal number in Arabidopsis. Curr Biol. 2005;15:303–15. https://doi.org/10.1016/j.cub.2005.02.017 .

  7. Bovill WD, Ma W, Ritter K, et al. Identification of novel QTL for resistance to crown rot in the doubled haploid wheat population “W21MMT70” x “Mendos”. Plant Breed. 2006;125:538–43. https://doi.org/10.1111/j.1439-0523.2006.01251.x .

  8. Cai X, Magwanga RO, Xu Y, et al. Comparative transcriptome, physiological and biochemical analyses reveal response mechanism mediated by CBF4 and ICE2 in enhancing cold stress tolerance in Gossypium thurberi. AoB Plants. 2019; 11:1–17. https://doi.org/10.1093/aobpla/plz045 .

  9. Chandnani R, Wang B, Draye X, et al. Segregation distortion and genome-wide digenic interactions affect transmission of introgressed chromatin from wild cotton species. Theor Appl Genet. 2017;130:2219–30. https://doi.org/10.1007/s00122-017-2952-y .

  10. Cheng J, Zhao Z, Li B, et al. A comprehensive characterization of simple sequence repeats in pepper genomes provides valuable resources for marker development in capsicum. Sci Rep. 2016;6:1–12. https://doi.org/10.1038/srep18919 .

  11. Cloutier S, Ragupathy R, Miranda E, et al. Integrated consensus genetic and physical maps of flax (Linum usitatissimum L.). Theor Appl Genet. 2012;125:1783–95. https://doi.org/10.1007/s00122-012-1953-0 .

  12. Corbett-Detig R, Medina P, Frérot H, et al. Bulk pollen sequencing reveals rapid evolution of segregation distortion in the male germline of Arabidopsis hybrids. Evol Lett. 2019;3:93–103. https://doi.org/10.1002/evl3.96 .

  13. Coulton A, Przewieslik-Allen AM, Burridge AJ, et al. Segregation distortion: utilizing simulated genotyping data to evaluate statistical methods. PLoS One. 2020;15(2):e0228951. https://doi.org/10.1371/journal.pone.0228951 .

  14. Dai B, Guo H, Huang C, et al. Identification and characterization of segregation distortion loci on cotton chromosome 18. Front Plant Sci. 2017;7:2037. https://doi.org/10.3389/fpls.2016.02037 .

  15. Dawes H, Boyes S, Keene J, et al. Protein instability of wines: influence of protein isolelectric point. Am J Enol Vitic. 1994;45:319–26.

    CAS  Google Scholar 

  16. Dixit S, Huang BE, Sta Cruz MT, et al. QTLs for tolerance of drought and breeding for tolerance of abiotic and biotic stress: an integrated approach. PLoS ONE. 2014;9(10):e109574. https://doi.org/10.1371/journal.pone.0109574 .

  17. Dong Q, Magwanga R, Cai X, et al. RNA-sequencing, physiological and RNAi analyses provide insights into the response mechanism of the ABC-mediated resistance to Verticillium dahliae infection in cotton. Genes. 2019;10:110. https://doi.org/10.3390/genes10020110 .

  18. Espinoza C, Medina C, Somerville S, et al. Senescence-associated genes induced during compatible viral interactions with grapevine and Arabidopsis. J Exp Bot. 2007;58:3197–212. https://doi.org/10.1093/jxb/erm165 .

  19. Faleiro FG, Schuster I, Ragagnin VA, et al. Characterization of recombinant inbred lines and QTL mapping associated to the cycle and yield of common bean. Pesqui Agropecu Bras. 2003;38:1387–97.

    Article  Google Scholar 

  20. Fans JD, Laddomada B, Gill BS. Molecular mapping of segregation distortion loci in Aegilops tauschii. Genetics. 1998;149:319–27.

    Google Scholar 

  21. Golestan Hashemi FS, Rafii MY, Ismail MR, et al. The genetic and molecular origin of natural variation for the fragrance trait in an elite Malaysian aromatic rice through quantitative trait loci mapping using SSR and gene-based markers. Gene. 2015;555:101–7. https://doi.org/10.1016/j.gene.2014.10.048 .

  22. Higo K, Ugawa Y, Iwamoto M, et al. Plant cis-acting regulatory DNA elements (PLACE) database: 1999. Nucleic Acids Res. 1999;27:297–300.

  23. Huang L, Deng X, Li R, et al. A fast silver staining protocol enabling simple and efficient detection of SSR markers using a non-denaturing polyacrylamide gel. J Vis Exp. 2018; 134:e57192. https://doi.org/10.3791/57192 .

  24. Huang T, Lopez-Giraldez F, Townsend JP, et al. RBE controls microRNA164 expression to effect floral organogenesis. Development. 2012;139:2161–9. https://doi.org/10.1242/dev.075069 .

  25. Hundertmark M, Hincha DK. LEA (late embryogenesis abundant) proteins and their encoding genes in Arabidopsis thaliana. BMC Genomics. 2008;9:118. https://doi.org/10.1186/1471-2164-9-118 .

  26. Jamshed M, Jia F, Gong J, et al. Identification of stable quantitative trait loci (QTLs) for fiber quality traits across multiple environments in Gossypium hirsutum recombinant inbred line population. BMC Genomics. 2016;17:1–13. https://doi.org/10.1186/s12864-016-2560-2 .

  27. Jia X, Wang WX, Ren L, et al. Differential and dynamic regulation of miR398 in response to ABA and salt stress in Populus tremula and Arabidopsis thaliana. Plant Mol Biol. 2009;71:51–9. https://doi.org/10.1007/s11103-009-9508-8 .

  28. Jun Z, Zhang Z, Gao Y, et al. Overexpression of GbRLK, a putative receptor-like kinase gene, improved cotton tolerance to Verticillium wilt. Sci Rep. 2015;5:15048. https://doi.org/10.1038/srep15048 .

  29. Khan MKR, Chen H, Zhou Z, et al. Genome wide SSR high density genetic map construction from an interspecific cross of Gossypium hirsutum × Gossypium tomentosum. Front Plant Sci. 2016;7:436. https://doi.org/10.3389/fpls.2016.00436 .

  30. Kirungu JN, Deng Y, Cai X, et al. Simple sequence repeat (SSR) genetic linkage map of D genome diploid cotton derived from an interspecific cross between Gossypium davidsonii and Gossypium klotzschianum. Int J Mol Sci. 2018;19:204. https://doi.org/10.3390/ijms19010204 .

  31. Kumar S, Gill BS, Faris JD. Identification and characterization of segregation distortion loci along chromosome 5B in tetraploid wheat. Mol Gen Genomics. 2007;278:187–96. https://doi.org/10.1007/s00438-007-0248-7 .

  32. Kumar S, Stecher G, Tamura K. MEGA7: molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol Biol Evol. 2016;33:1870–4. https://doi.org/10.1093/molbev/msw054 .

  33. Kumari JR, Srikumari CR, Valenzuela CY. ABO segregation distortion in Visakhapatnam, India. Anthropol Anz. 1992;50:307–14.

    Article  CAS  Google Scholar 

  34. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9:559. https://doi.org/10.1186/1471-2105-9-559 .

  35. Li P, Kirungu JN, Lu H, et al. SSR-linkage map of interspecific populations derived from Gossypium trilobum and Gossypium thurberi and determination of genes harbored within the segregating distortion regions. PLoS One. 2018;13:e0207271. https://doi.org/10.1371/journal.pone.0207271 .

  36. Li W, Lin Z, Zhang X. A novel segregation distortion in intraspecific population of asian cotton (Gossypium arboretum L.) detected by molecular markers. J Genet Genomics. 2007;34:634–40. https://doi.org/10.1016/S1673-8527(07)60072-1 .

  37. Li X, Jin X, Wang H, et al. Structure, evolution, and comparative genomics of tetraploid cotton based on a high-density genetic linkage map. DNA Res. 2016;23:283–93. https://doi.org/10.1093/dnares/dsw016 .

  38. Liberman U, Feldman MW. On the evolution of fluctuating segregation distortion. Theor Popul Biol. 1982;21:301–17. https://doi.org/10.1016/0040-5809(82)90020-X .

  39. Liu F, Wu XL, Chen SY. Segregation distortion of molecular markers in recombinant inbred populations in soybean (G. max). Acta Genet Sin. 2000;27(10):883–7 (in Chinese with English abstract).

  40. Liu X, You J, Guo L, et al. Genetic analysis of segregation distortion of SSR markers in F2 population of barley. J Agric Sci. 2011;3:172–7. https://doi.org/10.5539/jas.v3n2p172 .

  41. Lu H, Romero-Severson J, Bernardo R. Chromosomal regions associated with segregation distortion in maize. Theor Appl Genet. 2002;105:622–8. https://doi.org/10.1007/s00122-002-0970-9 .

  42. Lu P, Magwanga RO, Lu H, et al. A novel G-protein-coupled receptors gene from upland cotton enhances salt stress tolerance in transgenic Arabidopsis. Genes (Basel). 2018;9(4):209. https://doi.org/10.3390/genes9040209 .

  43. Lu Y, Feng Z, Bian L, et al. miR398 regulation in rice of the responses to abiotic and biotic stresses depends on CSD1 and CSD2 expression. Funct Plant Biol. 2010;38:44–53. https://doi.org/10.1071/FP10178 .

  44. Ma S, Gong Q, Bohnert HJ. Dissecting salt stress pathways. J Exp Bot. 2006;57(5):1097–107. https://doi.org/10.1093/jxb/erj098 .

  45. Magwanga R, Lu P, Kirungu J, et al. GBS mapping and analysis of genes conserved between Gossypium tomentosum and Gossypium hirsutum cotton cultivars that respond to drought stress at the seedling stage of the BC2F2 generation. Int J Mol Sci. 2018a;19:1614. https://doi.org/10.3390/ijms19061614 .

  46. Magwanga RO, Lu P, Kirungu JN, et al. Identification of QTLs and candidate genes for physiological traits associated with drought tolerance in cotton. J Cotton Res. 2020;3:3. https://doi.org/10.1186/s42397-020-0043-0 .

  47. Magwanga RO, Lu P, Kirungu JN, et al. Characterization of the late embryogenesis abundant (LEA) proteins family and their role in drought stress tolerance in upland cotton. BMC Genet. 2018b;19:6. https://doi.org/10.1186/s12863-017-0596-1 .

  48. Magwanga RO, Lu P, Kirungu JN, et al. Identification of cotton cyclin dependent kinase (CDK) genes and overexpression of Gh_D12G2017 (CDKF4) confer drought and salt stress tolerance in transgenic Arabidopsis. Int J Mol Sci. 2018c;19(9):2625.

  49. Manrique-Carpintero NC, Coombs JJ, Veilleux RE, et al. Comparative analysis of regions with distorted segregation in three diploid populations of potato. G3: Genes Genomes Genetics. 2016;6:2617–28. https://doi.org/10.1534/g3.116.030031 .

  50. McLaughlin RN, Malik HS. Genetic conflicts: the usual suspects and beyond. J Exp Biol. 2017;220:6–17. https://doi.org/10.1242/jeb.148148 .

  51. Mello CC, Kramer JM, Stinchcomb D, et al. Efficient gene transfer in C.elegans: extrachromosomal maintenance and integration of transforming sequences. EMBO J. 1991;10(12):3959–70.

  52. Mendoza CP, Ulloa M, Abdurakhmonov IY, et al. Genetic diversity and population structure of cotton (Gossypium spp.) of the New World assessed by SSR markers. Botany. 2013;11:54. https://doi.org/10.1139/cjb-2012-0192 .

  53. Nadeau JH. Do gametes woo? Evidence for their nonrandom union at fertilization. Genetics. 2017;207:369–87. https://doi.org/10.1534/genetics.117.300109 .

  54. Nakashima K, Ito Y, Yamaguchi-Shinozaki K. Transcriptional regulatory networks in response to abiotic stresses in Arabidopsis and grasses. Plant Physiol. 2009;149:88–95. https://doi.org/10.1104/pp.108.129791 .

  55. Natwick E. Resistance to silverleaf whitefly, Bemisia argentifolii (Hem, Aleyrodidae), in Gossypium thurberi, a wild cotton species. J Appl Entomol. 2006. https://doi.org/10.1111/j.1439-0418.2006.01083.x .

  56. Pashkovskii PP, Ryazanskii SS, Radyukina NL, et al. MIR398 and expression regulation of the cytoplasmic cu/Zn-superoxide dismutase gene in Thellungiella halophila plants under stress conditions. Russ J Plant Physiol. 2010;57:707–14. https://doi.org/10.1134/S1021443710050146 .

  57. Rahman MA, Thomson MJ, De Ocampo M, et al. Assessing trait contribution and mapping novel QTL for salinity tolerance using the Bangladeshi rice landrace capsule. Rice. 2019;12:63. https://doi.org/10.1186/s12284-019-0319-5 .

  58. Reflinur, Kim B, Jang SM, et al. Analysis of segregation distortion and its relationship to hybrid barriers in rice. Rice. 2014;7:3. https://doi.org/10.1186/s12284-014-0003-8 .

  59. Rouxel T, Balesdent MH. Avirulence genes. In: Encyclopedia of life sciences. Chichester: John Wiley & Sons Ltd; 2010. https://doi.org/10.1002/9780470015902.a0021267 .

  60. Sakharkar KR, Sakharkar MK, Culiat CT, et al. Functional and evolutionary analyses on expressed intronless genes in the mouse genome. FEBS Lett. 2006;580:1472–8. https://doi.org/10.1016/j.febslet.2006.01.070 .

  61. Saminathan T, Bodunrin A, Singh NV, et al. Genome-wide identification of microRNAs in pomegranate (Punica granatum L.) by high-throughput sequencing. BMC Plant Biol. 2016;16(1):122. https://doi.org/10.1186/s12870-016-0807-3 .

  62. Sandler L, Golic K. Segregation distortion in drosophila. Trends Genet. 1985;1:181–5. https://doi.org/10.1016/0168-9525(85)90074-5 .

  63. Shang L, Wang Y, Wang X, et al. Genetic analysis and QTL detection on fiber traits using two recombinant inbred lines and their backcross populations in upland cotton. G3 (Bethesda). 2016;6:2717–24. https://doi.org/10.1534/g3.116.031302 .

  64. Sunkar R. Posttranscriptional induction of two Cu/Zn superoxide dismutase genes in Arabidopsis is mediated by downregulation of miR398 and important for oxidative stress tolerance. Plant Cell Online. 2006;18:2051–65. https://doi.org/10.1105/tpc.106.041673 .

  65. Sunkar R, Chinnusamy V, Zhu J, et al. Small RNAs as big players in plant abiotic stress responses and nutrient deprivation. Trends Plant Sci. 2007;12:301–9. https://doi.org/10.1016/j.tplants.2007.05.001 .

  66. Takumi S, Motomura Y, Iehisa JCM, et al. Segregation distortion caused by weak hybrid necrosis in recombinant inbred lines of common wheat. Genetica. 2013;141:463–70. https://doi.org/10.1007/s10709-013-9745-2 .

  67. Trivedi DK, Gill SS, Tuteja N. Abscisic acid (ABA): biosynthesis, regulation, and role in abiotic stress tolerance. In: Tuteja N, Gill SS, editors. Abiotic stress response in plants. Weinheim: Wiley-VCH Verlag GmbH & Co. KGaA; 2016. p. 315–26. https://doi.org/10.1002/9783527694570.ch15 .

  68. Tsilo TJ, Jin Y, Anderson JA. Diagnostic microsatellite markers for the detection of stem rust resistance gene Sr36 in diverse genetic backgrounds of wheat. Crop Sci. 2008;48:253–61. https://doi.org/10.2135/cropsci2007.04.0204 .

  69. Tümpel S, Cambronero F, Wiedemann LM, et al. Evolution of cis elements in the differential expression of two Hoxa2 coparalogous genes in pufferfish (Takifugu rubripes). Proc Natl Acad Sci U S A. 2006;103:5419–24. https://doi.org/10.1073/pnas.0600993103 .

  70. Voorrips RE. MapChart: software for the graphical presentation of linkage maps and QTLs. J Hered. 2002;93:77–8. https://doi.org/10.1093/jhered/93.1.77 .

  71. Wang G, He QQ, Xu ZK, et al. High segregation distortion in maize B73 x teosinte crosses. Genet Mol Res. 2012;11:693–706. https://doi.org/10.4238/2012.March.19.3 .

  72. Wang S, Tan Y, Tan X, et al. Segregation distortion detected in six rice F2 populations generated from reciprocal hybrids at three altitudes. Genet Res. 2009;91:345–53. https://doi.org/10.1017/S0016672309990176 .

  73. Wang X, Yang B, Li K, et al. A conserved Puccinia striiformis protein interacts with wheat NPR1 and reduces induction of pathogenesis-related genes in response to pathogens. Mol Plant-Microbe Interact. 2016;29:977–89. https://doi.org/10.1094/MPMI-10-16-0207-R .

  74. Wang X, Zhang Y, Qiao L, et al. Comparative analyses of simple sequence repeats ( SSRs) in 23 mosquito species genomes : identification, characterization and distribution (Diptera : Culicidae). J Insect Sci. 2019:607–19. https://doi.org/10.1111/1744-7917.12577 .

  75. Wei Y, Xu Y, Lu P, et al. Salt stress responsiveness of a wild cotton species (Gossypium klotzschianum) based on transcriptomic analysis. PLoS One. 2017;12(5):e0178313. https://doi.org/10.1371/journal.pone.0178313 .

  76. Wu JH, Zhang XL, Luo XL, et al. Inheritance and segregation of transformants in cotton with two types of insect-resistant genes. Acta Genet Sin. 2003;30:631–6. (in Chinese with English abstract).

  77. Wu YP, Ko PY, Lee WC, et al. Comparative analyses of linkage maps and segregation distortion of two F2 populations derived from japonica crossed with indica rice. Hereditas. 2010;147:225–36. https://doi.org/10.1111/j.1601-5223.2010.02120.x .

  78. Xie Q, Frugis G, Colgan D, et al. Arabidopsis NAC1 transduces auxin signal downstream of TIR1 to promote lateral root development. Genes Dev. 2000;14:3024–36. https://doi.org/10.1101/gad.852200 .

  79. Xu Y, Zhu L, Xiao J, et al. Chromosomal regions associated with segregation distortion of molecular markers in F2, backcross, doubled haploid, and recombinant inbred populations in rice (Oryza sativa L.). Mol Gen Genet. 1997;253:535–45. https://doi.org/10.1007/s004380050355 .

  80. Yan H, Dai X, Feng K, et al. IGDD: A database of intronless genes in dicots. BMC Bioinformatics. 2016;17:289. https://doi.org/10.1186/s12859-016-1148-9 .

  81. Yang C, Wang Z, Yang X, et al. Segregation distortion affected by transgenes in early generations of rice crop-weed hybrid progeny: implications for assessing potential evolutionary impacts from transgene flow into wild relatives. J Syst Evol. 2014;52:466–76. https://doi.org/10.1111/jse.12078 .

  82. Yang RC, Thiagarajah MR, Bansal VK, et al. Detecting and estimating segregation distortion and linkage between glufosinate tolerance and blackleg resistance in Brassica napus L. Euphytica. 2006;148:217–25. https://doi.org/10.1007/s10681-005-9003-5 .

  83. Yu Y, Yuan D, Liang S, et al. Genome structure of cotton revealed by a genome-wide SSR genetic map constructed from a BC1 population between Gossypium hirsutum and G. barbadense. BMC Genomics. 2011;12:15. https://doi.org/10.1186/1471-2164-12-15 .

  84. Yuan JZ, Peng N, Feng CJ, et al. Effect of marker segregation distortion on high density linkage map construction and QTL mapping in Soybean ( Glycine max L. ). Heredity. 2019;123:579–92. https://doi.org/10.1038/s41437-019-0238-7 .

  85. Zhang F, Zhu G, Du L, et al. Genetic regulation of salt stress tolerance revealed by RNA-Seq in cotton diploid wild species, Gossypium davidsonii. Sci Rep. 2016;6:20582. https://doi.org/10.1038/srep20582 .

  86. Zhang J, Stewart JM. Economical and rapid method for extracting cotton genomic DNA. J Cotton Sci. 2000;4:193–201.

  87. Zhang Y, Wang L, Xin H, et al. Construction of a high-density genetic map for sesame based on large scale marker development by specific length amplified fragment (SLAF) sequencing. BMC Plant Biol. 2013;13:1–12. https://doi.org/10.1186/1471-2229-13-141 .

  88. Zhao J, Gao Y, Zhang Z, et al. A receptor-like kinase gene (GbRLK) from Gossypium barbadense enhances salinity and drought-stress tolerance in Arabidopsis. BMC Plant Biol. 2013;13:110. https://doi.org/10.1186/1471-2229-13-110 .

Download references

Acknowledgements

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

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).

Author information

Affiliations

Authors

Contributions

Kirungu JN, Magwanga RO, Wang K, and Liu F conceptualized the concept; Kirungu JN, Magwanga RO, Wang K, Shiraku ML, Mehari TG, and Liu F performed data curation; Kirungu JN, Magwanga RO, Wang K, and Liu F performed formal analysis; Kirungu JN, Magwanga RO, Wang K, and Liu F are responsive to funding acquisition; Kirungu JN, Magwanga RO, Wang K, Liu F, Zhou Z, Pu L, Xu Y, Hou Y, Zhou Y, Cai X, Agong SG, Wang K and Liu F are responsive to plant resources; Kirungu JN, Magwanga RO, Wang K, Agong SG and Liu F alid the results; Kirungu JN and Magwanga RO wrote the original draft; Kirungu JN and Magwanga RO reviewed & edited the final manuscript. All authors approved the final manuscript.

Corresponding authors

Correspondence to CAI Xiaoyan, ZHOU Zhongli, WANG Kunbo or LIU Fang.

Ethics declarations

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 form of competing interest.

Supplementary Information

Additional file 1: Table S1

. Details of primers used in this research

Additional file 2: Table S2

. Details of primers used for the RT-qPCR validation of the 30 selected genes within the SDRs on chromosome D502 and D507

Additional file 3: Table S3

. Genes within the dominant group

Additional file 4: Table S4

. Genes mined within the SDR of chromosome D502 and D507

Additional file 5: Table S5.

Cis-regulatory promoter elements identified for the genes obtained within the SDRs

Additional file 6: Table S6.

miRNA targets prediction

Additional file 7: Table S7

. GO annotation for the genes obtained within the SDRs of chromosome D502 and D507.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

KIRUNGU, J.N., MAGWANGA, R.O., SHIRAKU, M.L. et al. 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. J Cotton Res 3, 32 (2020). https://doi.org/10.1186/s42397-020-00072-2

Download citation

Keywords

  • Genetic map
  • Segregation distortion region
  • Cis-regulatory elements
  • Genes
  • miRNA