The chromosomal location of QTL stm9 detected in both data sets was coincident despite the significant Genotype × Season interaction in the ANOVA. To examine the cause of the significant Genotype × Season interaction in more detail, we plotted recombinant sub-NIL stmscore means across the two seasons to create interaction plots . Inspection of the plots suggests that the changes in sub-NIL mean values across seasons primarily derived from greater chilling susceptibility of susceptible sub-NILs in the Spring than in the Fall . Magnitude differences would cause the size of the LOD peaks to differ among seasons, but not change the peak location, which is in agreement with our results . In addition to the increase in the magnitude of means for stmscore of the susceptible sub-NILs, two sub-NILs were classified as tolerant in the Spring dataset but as susceptible in the Fall dataset . Sub-NIL C4 had a mean of slightly over 1.0 in the Spring data set, and clearly grouped as susceptible in the Fall dataset . None of these lines contain the S. habrochaites introgression for high-resolution mapped stm9, but the introgressions do all flank stm9. These results suggest the possibility that there are environmentally sensitive genetic modifiers of the stmscore phenotype in this region of chromosome 9,vertical farm tower and that the interaction of these modifiers with the environment could be causing the significant Genotype × Season interaction.

The rank changes seen within the tolerant group may be due to differences in the genomic content of S. habrochaites sequence in the flanking regions of QTL stm9, and not a direct effect of the environment on the gene or polymorphisms controlling the tolerant stm9 phenotype. Previous work in tomato has shown that the stomatal response of a plant when subjected to root chilling conditions differs between susceptible and tolerant phenotypes . Stomatal control is regulated by multiple environmental factors including light, temperature, day length, humidity, and CO2 levels . The Spring experiments were conducted under longer day lengths, higher air temperatures, and lower humidity than the Fall experiments . These seasonal differences affect the conditions in the greenhouse and may have contributed to the significant Genotype × Season interaction, as well as differences in relative response among the sub-NILs in the Spring versus Fall data sets. In the context of phenotypic plasticity, seasonal effects on sub-NIL performance would account for the more gradual separation of means in the Spring dataset compared to the Fall .Low marker density and small population sizes in initial genome-wide QTL mapping studies may bias upwards the estimation of QTL effects due to the inability to resolve closely linked, smaller effect QTL . Consequently, single large effect QTL may resolve or fractionate into multiple, smaller effect QTL after fine- and high-resolution mapping . The original interspecific BC1 population used by Truco et al. to map QTL for shoot turgor maintenance under root chilling consisted of 196 individuals genotyped with 112 markers. Truco et al. mapped a major effect QTL to a 28-cM region on the short arm of chromosome 9 which accounted for 33 % of the phenotypic variation for shoot turgor maintenance under root chilling . Despite the large initial genetic size of the QTL stm9 region detected by Truco et al. , subsequent fine-mapping by Goodstal et al. and high-resolution mapping in our present study do not provide any evidence of multiple QTL or QTL fractionation.

The relatively small genetic size of high resolution mapped stm9 and the lack of QTL fractionation indicates that this level of resolution is suitable for the identification of candidate genes for stm9. There are numerous examples in the literature of environmentally stable, high resolution mapped QTL that have led to candidate gene identification and in some cases subsequent causal gene/ polymorphism determination. Several QTL for chilling tolerance in rice have been high-resolution mapped and candidate genes identified. These QTL include qCTS12 , qCtss11 , and qCTB7 . Tomato-specific QTL examples include fw2.2, a fruit weight QTL, and se2.1, a stigma exsertion QTL, both identified in progeny derived from S. pennellii, another wild tomato relative . The causal gene underlying QTL fw2.2 was identified by Frary et al. , who proposed that changes in the regulation of ORFX , not changes in the sequence or structure of the expressed protein, are responsible for changes in fruit size. Chen and Tanksley determined the casual mutation underlying se2.1 is a mutation in the Style2.1 promoter that results in a down-regulation of Style2.1 expression during flower development. Collectively, the results from these studies suggest that candidate gene identification and functional testing for QTL stm9 should focus on mutations in regulatory and promoter regions of candidate genes in addition to mutations that may affect the sequence or structure of expressed proteins.Many genes have been identified as being involved directly or indirectly in plant tolerance or resistance to abiotic stresses , including chilling/cold tolerance . Plant responses to abiotic stresses can include multiple pathways that involve a variety of gene products such as receptors, signaling molecules, transporters, transcription regulators, and transcription factors . Many of the identified stress response pathways have been associated with tolerance to a range of abiotic stresses . The plant’s response to abiotic stress may result in both reversible and irreversible activation of stress response pathways . Because of the complex nature of the pathways involved, the specific genotype of the plant also has a large influence on abiotic stress response . Plant responses to abiotic stressors are dependent on the interplay of abiotic stress, environment, and genotype .

Therefore, a particular abiotic stress applied in different environmental contexts may result in overlapping, but distinct responses from a single genotype . We analyzed the physical region in the cultivated tomato reference genome that is syntenic to the S. habrochaites QTL stm9 region because an assembled S. habrochaites whole genome sequence is not available. All of the protein products of the S. lycopersicum annotated genes located within 30 kb of the QTL stm9 peak marker have features that are shared with genes involved in responses to water stress and other abiotic stresses. In addition, the majority of the S. lycopersicum genes located within the syntenic high-resolution mapped stm9 region have been implicated in abiotic stress response pathways . It is possible that plant responses to root chilling stress may induce a more complex transcriptional response than other types of water stress such as those caused by salt or polyethylene-glycol , although overlap has been seen in the response to all three stresses . For example, in grape, under root chilling stress only transcripts for protein synthesis and the cell cycle were up-regulated to a lesser extent than under salt or PEG stress. The regulation of plant metabolism, protein metabolism, signal transduction, calcium signaling, stress hormone pathways, and transcription factors were all increased to a greater extent under root chilling in grape . These categories of genes account for the majority of genes located within the syntenic S. lycopersicum QTL stm9 region. While the total number of annotated genes within the S. lycopersicum reference genome region containing QTL stm9 is relatively small, there are no estimates available for S. habrochaites due to the unavailability of assembled whole genome sequence for this wild species. A comparison of the genetic and S. lycopersicum physical maps of the chromosome 9 region containing stm9 shows a variable rate of recombination across this region .It is possible that this variable rate of recombination is due to the presence of repetitive elements or other local structural polymorphisms affecting the synteny and colinearity of the S. lycopersicum and S. habrochaites genome sequences in this region. In addition, our flow cytometry results indicated that the genome size of S. habrochaites is 1.5 × that of S. lycopersicum . The larger genome size of S. habrochaites suggests the possibility that the putative loss of function of genes and/or genetic elements in S. lycopersicum may be due to deletions or non-functional null mutations. Matsuba et al. sequenced a functional gene cluster for terpene biosynthesis on chromosome 8 of S. habrochaites acc. 1778 and identified several rearrangements, deletions, and a novel gene when compared to the same gene cluster on chromosome 8 of the S. lycopersicum reference genome. Our prior research suggests that the inability of cultivated tomato to maintain shoot turgor under root chilling is the result of a loss of function in S. lycopersicum . Taken together,vertical plant tower the current evidence suggests that the S. habrochaites allele for high-resolution mapped QTL stm9 may not be completely syntenic to S. lycopersicum, and that it may not contain the same genic compliment as the S. lycopersicum allele for stm9. Therefore, although the S. lycopersicum genome sequence is helpful in identifying potential candidate genes for shoot turgor maintenance under root chilling, the genomic sequence of the stm9 region of S. habrochaites is necessary for accurate, well-informed candidate gene identification.Stability of QTL expression for tolerance to abiotic stresses is important for successful deployment of stress tolerance QTL in breeding crop plants. Although a significant Genotype × Season interaction was identified for QTL stm9, the potential causes of the interaction suggest that this region would likely be useful as a stable source of root chilling tolerance for breeding. A number of other QTL have been identified as targets for breeding despite a significant Genotype × Season interaction in several species, including barley, rice, and maize .

The phenotypic plasticity likely contributed by the stm9 flanking regions suggest that any future breeding strategies should be undertaken with the smallest introgression possible that still contains the entire high-resolution mapped QTL stm9. The S. habrochaites introgression in sub-NIL C7 contains only the high resolution QTL stm9 region . This sub-NIL was grouped as tolerant in both the Spring and Fall datasets, and gave a consistently low stmscore in both seasons , suggesting it may serve as a suitable potential donor parent source of tolerance to root chilling in breeding programs. Due to the complexity of the abiotic stress response pathway, it is unlikely that the S. habrochaites QTL stm9 allele contains only a single gene conferring shoot turgor maintenance under root chilling. Single causal genes have been identified for a number of major QTL,but other major QTL have been shown to be controlled by two or more causal genes or polymorphisms.Identification and testing of the causal gene or polymorphisms underlying QTL stm9 for tolerance to root chilling will be an important step in the identification of genetic targets for improving stress tolerance of plants exposed to root chilling and other types of water stress through markerassisted breeding. Determination of the gene/polymorphisms responsible for a quantitative trait phenotype is facilitated by genomic approaches . Once a target region is identified via high-resolution mapping, a combination of genomic sequencing, structural genomic analysis, and transcriptome profiling can be used to assist in the identification of candidate genes. Therefore a biologically informed ranking of candidate genes located within the QTL stm9 region will require a combination of S. habrochaites genome sequence for this region as well as transcription profiles for susceptible and tolerant subNILs exposed to root chilling. It is hoped that a better understanding of the underlying mechanism for tolerance to rapid-onset water stress in wild tomato S. habrochaites may aid in the identification of chilling tolerance genes in other species of tropical and sub-tropical origin.WRC project W-769 focused on obtaining data on the seasonal dynamics of growth, resource allocation, and pollutant allocation in selected wetland plant species grown under conditions of elevated pollution as compared to non-polluted habitats. Recent studies have indicated that pollution from urban runoff, especially storm water discharge, has caused major water quality problems in streams, lakes, and reservoirs, including nutrient enrichment, introduction of toxic materials, turbidity and heavy sediment deposition . Wetlands have the capacity to intercept storm runoff and store storm waters, simultaneously removing suspended solids, and some dissolved pollutants prior to discharge into waterways. Aquatic and semi-aquatic plants play an important role in promoting both nutrient transformation and nutrient removal in aquatic treatment systems.