Title: Predication of maize seed under normal and drought stress contributions of genomics approaches to understanding plant stress response

BY : Tahir Iqbal

Abstract

Drought stress is a key constraint in agricultural science system that results in seed yield losses in maize crop worldwide. Genetic variability in resistance to abiotic strain, a main seed harvest restraint to worldwide cultivation of corn, will be recognized phenotypically and genotypically. Maize is a self-pollinating crop.

For humans and animals, it has a great nutritional value. In this study, eight inbreed entries: four sensitive to drought (XYZ1, XYZ2, XYZ3 and XYZ4) and four resistance to drought (XYZ5, XYZ6, XYZ7 and XYZ8) will be collected from germplasm source of maize, Anhui University, China . These inbred entries will be sown in season of February to May 2021-2022 and interrelated crossed in form of dialed pattern to get fifty-six hybrids of maize (F₁) by single crossing will be assessed to determine phenotypic stability among various yield attributing traits by using triplicate RCBD statistical technique. Here, we will found a hybrids that will show fitness for seed yield under normal and drought stress conditions. Complete correlation and path coefficient analyses of yield contributing factors to seed yield over 4 growing seasons will be done after taking data on cobs plant^-1, 1000 weight of grain, no. of seeds row cob^-1, overall content of dry matter and diameter of cob will statistically analyzed.

Genetic analysis of maize hybrids will ascertain major loci and alleles that will help as a basis to create levels of resistance for abiotic stresses mainly water deficit conditions in arid and semiarid areas. After the identification of most favorable hybrids under water deficit condition and segmentation of constant factors/traits in successful genetic makeup, then we will be able to use it in breeding program by application of genomic approaches for seed development. With the help of genomic approaches we will detect alleles and locus position that control traits.

After genomic and genetic analysis of filling process in grain of maize, by using that lines which we will derived from the inbred lines of winner genotype in contrast dynamics of seed-filling. After pollination we will record dry weight of seed at 14 time points. Then, we will fit these recorded data into a logistic based model for estimation of twelve specific parameters for explaining the filling procedure of seed.

Quantitative trait locus mapping of these studied parameters will identify a alleles position and with the help of RNA-sequencing analysis, we will identify genes that will show patterns of differential expression of gene at various interval of time and will be involved in metabolism of sucrose and starch, and biogenesis of organic compounds mainly secondary metabolites that shows a dynamic role in filling of seed for seed development and crop improvement.

NEED FOR THR PROJECT/INTRODUCTION

The increase of global temperature and statistically decline in rainfall patterns is mainly due to changes in global climate systems, with negative effects, resulting in loss of yield. Drought stress is a key constraint in agricultural science system that results in seed yield losses in maize crop worldwide (Boyer, 1982). The availability of water has become a major issue worldwide, as a result, the development of cultivars of crop having less consumption of water is our key concern for us. Corn (Zea Mays L.) is an essential crop that is cultivated in more than 175 countries, with 1,147 million tones’ in 2018 (World Data Atlas, 2018).

The 1^st largest producer of maize is United States, contributes nearly for 40 percent of the world’s total production, 2^nd largest producer is China followed by Brazil. It is well known that maize was considered one of the 1^st crops domesticated by local farmers in the middle of 7000 and 10,000 years ago, with solid proof of maize as food coming from Mexico at different archaeological site.

Other philosophies say that maize as originating from the mountain range of Himalayas in Asia. Due to wide adaptability to various agro-climatic conditions it has diverse culinary applications all over the world in grains crops. It is ranked at 3^rd no. after wheat and rice all over the world. More than 85 percent of total world’s land is agricultural based (Berzsenyi et al., 2006), as a result, to emphasis on the enhancement of genetic makeup that are best adaptable in drought stress situation is our key concern.

Phenotypic stability of the factors/traits in hybrids of maize is the greatest way to examine the genetic variability, correlation coefficient and path coefficient analyses (Chavan et al., 2015). Therefore, multivariance analysis (Ashmawy, 2003; El-Badawy and Mehasen, 2011) exhibits a good idea of the fundamental factors and a coherence between genetic makeup of individual and variable. Apart from this, biometrical techniques, correlation among factors, genetic advance and heritability can help us in understanding the contribution of genetic variability in existing population for improved selection criteria.

The challenging task for plant breeder is to assess the total genetic variation added by each factor. Among various yield attributing traits association, the filling process of process is a significant factor that affects size of seed and the final seed harvest. The filling of seed is a complex factor/trait involving addition of carbohydrates, oils and protein and expansion of cell. Through genetic control, this grain filling developmental process is also extremely sensitive to changes in environment, mainly condition of water deficit.

Enhancing the duration and rate of filling of grain boost photosynthetic partitioning and carbon assimilation to the grain, overcome the adverse environmental condition in the late growing season, and therefore results in stable and high yield. Consequently, a detailed studies of the environmental and genetic factors controlling filling process of seed will support our efforts towards breeding strategies to develop varieties with high yield and improved resistance capacity over water deficit conditions. The vigorous feature of the factor/trait poses an essential challenge for genetic analysis of quantitative traits.

Furthermost the present research concentrate on phase of linear form of growth by examining the weight of dry seed weight over a time range of isometric position to examine the rate of filling. Even if this technique is used for estimation of rough average rate of filling over a time range of filling, it is unsuccessful to highlight the development of active features of the process of entire seed filling. The efficacy of this technique has been proven by the combination of model of eco-physiological and genetic analysis to check the response of growth of leaf over high temperature and abiotic stress conditions in maize (Raymond et al. 2003). As a result, above described method totally depends on segmented based model and had lost important information regarding the process of grain filling (Borras et al. 2009; Vega and Sadras 2003). In view of the above circumstances, to

know about the dynamic information of process of continuous filling process of seed there is require to use a nonlinear from of function.

Logistic based models approaches are being widely accepted for evaluating complex developing dynamic traits mainly; population growth of human, height of plant, and the biomass of leaf. In this study, with the help of genomic approaches we will detect alleles and locus position that control traits. After genomic and genetic analysis of filling process in grain of maize, by using that lines which we will derived from the inbred lines of winner genotype in contrast dynamics of seed-filling. After pollination we will record dry weight of seed at 14 time points. Then, we will fit these recorded data into a logistic based model for estimation of twelve specific parameters for explaining the filling procedure of seed. Quantitative trait locus mapping of these studied parameters will identify a alleles position and with the help of RNA-sequencing analysis, we will identify genes that will show patterns of differential expression of gene at various interval of time and will be involved in metabolism of sucrose and starch, and biogenesis of organic compounds mainly secondary metabolites that shows a dynamic role in filling of seed for seed development and crop improvement.

Objectives:

1. To evaluate hybrids of maize in four growing seasons of crop by assessing it genotypically and phenotypically

2. Evaluation of germplasm for genetic variation

3. Estimation of genetic relationship among various plant characters

4. Selection of better performing lines for seed

5. Development of selection criteria

6. To identify most favorable hybrids under water deficit condition and segmentation of established traits in successful genetic makeup, to use it in breeding strategies by application genomic approaches, transcriptomics and quantitative trait locus (QTL) mapping techniques

7. To develop an approach that is based on logistic model

8. To identify genetic alleles and loci position that regulate dynamics developing traits

9. To identify genes that are involved in metabolism of sucrose and starch, and biogenesis of organic compounds mainly secondary metabolites that shows a dynamic role in filling of seed for seed development and crop improvement

MATERIALS AND METHODS

Experiment I

Proper irrigation system (a well-organized path to establish the drought stress and enhanced-watered in condition in field for research trials)

Proper irrigation system; enhancement-watered and water deficit conditions will be considered as treatments in current research study, for assessing the performance of hybrids for seed yield.

Fertigation (for supply of nutrients to crop)

One month before, leaf litter will be added after preparing seedbed and by the help of plowing we will mixed it well at each site of experiment. We will add potassium 30 kg ha^-1, nitrogen 101 kg ha^-1, and phosphorous 31 kg ha^-1 at the time of sowing.

Germplasm source (experiment 1)

Eight inbred entries: four sensitive to drought (XYZ1, XYZ2, XYZ3 and XYZ4) and four drought tolerant (XYZ5, XYZ6, XYZ7 and XYZ8) will be selected from germplasm resource of maize, Anhui University, China. These entries will be sown from February to May 2021–2022 season and intercrossed in complete form of dialed pattern to get fifty-six hybrids of maize (F₁) by single crossing: H₁ (XYZ1 × XYZ2), H₂ (XYZ1 × XYZ3), H₃ (XYZ1 × XYZ4), H₄ (XYZ× XYZ5), H₅ (XYZ1 × XYZ6), H₆ (XYZ1 × XYZ7), H₇ (XYZ1× XYZ8), H₈ (XYZ2× XYZ1), H₉ (XYZ2× XYZ3), H₁₀ (XYZ2 × XYZ4), H₁₁ (XYZ2 × XYZ5), H₁₂ (XYZ2 × XYZ6), H₁₃(XYZ2 × XYZ7), H₁₄ (XYZ2 × XYZ8), H₁₅ (XYZ3 × XYZ1), H₁₆ (XYZ3× XYZ2), H₁₇ (XYZ3 × XYZ4), H₁₈ (XYZ3 × XYZ5), H₁₉ (XYZ3× XYZ6), H₂₀ (XYZ3× XYZ7), H₂₁ (XYZ3× XYZ8), H₂₂ (XYZ4 × XYZ1), H₂₃ (XYZ4 × XYZ2), H₂₄ (XYZ4 × XYZ3), H₂₅ (XYZ4 × XYZ5), H₂₆ (XYZ4 × XYZ6), H₂₇ (XYZ × XYZ7), H₂₈ (XYZ× XYZ8), H₂₉ (XYZ5 × XYZ1), H₃₀ (XYZ5 × XYZ2), H₃₁ (XYZ5× XYZ3), H₃₂ (XYZ5× XYZ4), H₃₃ (XYZ5× XYZ6), H₃₄ (XYZ5 × XYZ7), H₃₅ (XYZ5 × XYZ8), H₃₆ (XYZ6 × XYZ1), H₃₇ (XYZ6 × XYZ), H₃₈ (XYZ6 × XYZ3), H₃₉ (XYZ6 × XYZ4), H₄₀ (XYZ6× XYZ5), H₄₁ (XYZ6 × XYZ7), H₄₂ (XYZ6 × XYZ8), H₄₃ (XYZ7× XYZ1), H₄₄ (XYZ7× XYZ2), H₄₅ (XYZ7× XYZ3), H₄₆ (XYZ7 × XYZ4), H₄₇ (XYZ7 × XYZ5), H₄₈ (XYZ7 × XYZ6), H₄₉ (XYZ7 × XYZ8), H₅₀ (XYZ8 × XYZ1), H₅₁ (XYZ8 × XYZ2), H₅₂ (XYZ8× XYZ3), H₅₃ (XYZ8 × XYZ4),

H₅₄ (XYZ8 × XYZ5), H₅₅ (XYZ8× XYZ6) and H₅₆ (XYZ8× XYZ7). A randomized complete block design (RCBD) will be used. Row-to-row and plant-to-plant distances will be 75 and 15 cm, respectively, each row had 10 plants. By using dibbler, sowing of seed will be done. Three seeds hill^-1 of each parental line will be sown and after eighteen day of sowing we will thinned up the 2 healthy plant/hill. Weeding, mowing, irrigation and howling (all agronomic practices) will be done throughout the growing season of crop. We will do randomized sampling of 10 plants plot^-1 of every genotype to measure these traits:

1. Content of chlorophyll

2. Weight of cob

3. Length of cob

4. Diameter of cob

5. Cob plant^-1

6. Number of seed rows cob-1

7. 1000 weight of grain

8. Number of seeds cob^-1

9. Total content of dry matter

10. Oil content of seed

11. Protein content of seed

12. Weight of fresh stem

13. Weight of fresh leaf

14. Fresh leaf weight to stem weight ratio

15. Area of leaf

16. Number of leaves plant^-1

17. Height of plant

18. Diameter of stem

19. Seed harvest in kg per hectare.

Assessment of hybrids of F₁ over 4 growing seasons of crop for phenotypic stability of yield of seed and its contributing traits under water deficit conditions (Experiment 2, 3, 4, 5)

Fifty-six cross hybrids of maize will be accessed from February to May 2022 (experiment 2), July to October 2022 (experiment 3), February to May 2023 (experiment 4) and June to September 2023 (experiment 5) in experimental research area of Department of Botany, School of Life Sciences, Anhui University, China.

Phonological and growth parameters of crop [parental lines (2020) and hybrids of F₁ (2021- 2022) will be assessed under field conditions of drought stress in field conditions]

1. Seedling emergence

2. Slicking interval

3. Physiological maturity of plants

Biomass of plant

For estimation of density of plant in plot at plant physiological maturity; we will counted the number plants and divided the population of plants by area of soil.

Quality parameters, content of chlorophyll and seed yield parameters

1. Content of chlorophyll

2. The contents of protein of maize seeds (15 seeds/genotype) will be measured by absorbance assay (280 nm)

3. Total content of oil will be analyzed, by method introduced by Matthau’s and Bruhl (2001).

Detailed statistical analysis

Analysis of Variance, Duncan’s Multiple Range Test

Analysis of variance (ANOVA) two-way will be done using the info-Gestate (12th edition) application for each 4 growing season of crop. We will used ANOVA to assess the impact of water deficit conditions, environmental surrounding and their interface on key factors, mainly yield of grain and its contributing factors. (DMRT) will be used to correlate the variances genotypically under water deficit condition.

Genomic constituents and correlation

Genomic constituents (genetic advance, heritability, genotype variance, environmental variance and phenotype variance) will be assessed in experiment 2-5. We will do simple correlation analysis for experiment 2, 3, 4, and 5 to evaluate the extent of correlation among under studied factors.

Joint phenotypic and genotypic correlation

Combined phenotypic and genotypic correlations will be calculated as formula proposed by to Falconer and Mackay (1996).

Multi variance analysis

To assess the overall variation contributed by yield contributing traits in hybrids, we will performed principal component analyses (The Pros Mixed SAS version 9.1; SAS Institute, 2001) and will construct the bi-plot (combined data over four seasons used for each factor/trait).

Experiment II

After identification of most favorable hybrids under water deficit condition and segmentation of established traits in successful genetic makeup, to use it in breeding strategies by application genomic approaches, transcriptomics and quantitative trait locus (QTL) mapping

Germplasm source, Experimental research area, and phenotyping

The inbred entries of successful genetic makeup after identification of the favorable hybrid under water deficit condition and segmentation of established traits that differed significantly in the rate and time range of grain filling.

The experiment will be conducted at two different locations in Anhui University, China in 2023 and 2024. For each population consisting of inbred lines, a total of seventy eight plants will be sown in a soil bed containing six rows, with a row length of 3.0 m, a distance between rows of 0.5 m, and 13 plants per row.

The dates of pollination will be recorded for entries of individual inbred to regulate the time of sampling. Seeds will sampled at specific time interval days after pollination (DAP). Two ears with the synchronous developmental process will be selected after every time interval. 50 grains in the middle of each ear will be removed and dried to a constant weight at 70-80 °C after fixing at 105 °C for 1 hour. The dry weight of 50 grains will be measured and recorded.

Technique for fitting and analyzing growth patterns and characteristics parameter assessment

The link among the weight of dry seed (w), the number of days after pollination (t), instant proportion of grain filling, average proportion of filling (v¯v¯), dynamic filling time range (T) and the dimension of the grain-filling time range will be defined by a logistic based function as devised by (West et al., 2001). The accumulated weight of dry grain at each of the 3 filling intervals, represented as w₁, w₂, and w₃, separately, will also be calculated. The stage from fertilization to t₁ is described as the gradual growth interval and that between t₁ and t₂ is described as the fast growth interval.

In summary, a whole of twelve characteristic parameters related to grain-filling process will be measured. Left over parameters k, a, and b will be measured using a nonlinear least-squares method carried out in R (Bates and Watts 1988).

Detailed statistical analysis of the parameters

Calculating the detailed statistics of the parameters will be performed in R. Broad-sense heritability across multiple environments as proposed by (Knapp et al., 1985). Pearson correlation coefficients between characteristic parameters will be calculated using the software package proposed by (De Mendiburu 2014).

Determining differences in the genetic make-up of an individual and ultra-high density bin- map construction

DNA will be extracted from the mature healthy leaves of population consisting of inbred entries of successful genetic makeup. Differences in the genetic make-up of an individual will be performed with the methods using TaqMan® Assays, covering the complete genome of maize. Chi-square tests will be done for all SNPs to identify segregation distortion. To eliminate redundant markers, we used a sliding-window approach (Huang et al. 2009) to construct a bin map. The order of bin markers will then checked using package proposed by (Broman et al. 2003). Genetic distances among bin markers will be calculated using the function proposed by (Kosambi 1944).

Quantitative Trait Locus analysis

QTLs associated with the 12 characteristic parameters will mapped by identifying which molecular markers correlate with an observed trait.

Gene set enrichment analysis and pathway analysis

At thirty days post pollination, grains will be collected and mRNAs will then isolated from individual plants. 2 bulked mRNA samples will then create by pooling the mRNAs of all individuals in each pool. The pooled mRNA samples will be subjected to RNA-set analysis to identify genes that will differentially expressed between the two pools. To determine the putative biological functions of DEGs, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using the cluster Profiler package (Yu et al., 2012).

Recognition of the associated SNPs

To identify SNPs that could distinguish between the two bulks, a series of tests will be performed in R. Fisher’s exact tests will be first performed for each SNP followed by calculation of the SNP index of each bulk and G value of each SNP using the QTL seqr package (Mansfield and Grummets 2018).

Expression analysis of the associated genes

To further characterize the expression of the associated genes in the filling process, grains of parents and extreme inbred entries will be sampled at specific time interval days after pollination. At each time point, grains in the middle of two similar ears will be sampled as two biological repeats, followed by RNA isolation and RNA-set analysis. The reads per kilo base of exon per million mapped reads (FPKM) of individual genes at different time points will be calculated based on the dimension of the genes and read counts. In addition, the weight of dry 50 grains for each inbred entries at different time intervals will be measured to fit the growth curves of grains.

Conclusion

This study will be helpful in better for clear identification of the promising hybrids under water deficit condition and segmentation of constant traits in successful genetic makeup we will use it in breeding strategies by applying genomic approaches, quantitative trait locus mapping and transcriptomics techniques for development of grain.

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Predication of maize seed under normal and drought stress contributions of genomics approaches to understanding plant stress response