Transcriptome-based Gene Networks for Systems-level Analysis of Plant Gene Functions

Present day genomic technologies are evolving at an unprecedented rate, allowing interrogation of cellular activities with increasing breadth and depth. However, we know very little about how the genome functions and what the identified genes do. The lack of functional annotations of genes greatly limits the post-analytical interpretation of new high throughput genomic datasets. For plant biologists, the problem is much severe. Less than 50% of all the identified genes in the model plant Arabidopsis thaliana, and only about 20% of all genes in the crop model Oryza sativa have some aspects of their functions assigned. Therefore, there is an urgent need to develop innovative methods to predict and expand on the currently available functional annotations of plant genes. With open-access catching the ‘pulse’ of modern day molecular research, an integration of the copious amount of transcriptome datasets allows rapid prediction of gene functions in specific biological contexts, which provide added evidence over traditional homology-based functional inference. The main goal of this dissertation was to develop data analysis strategies and tools broadly applicable in systems biology research. Two user friendly interactive web applications are presented: The Rice Regulatory Network (RRN) captures an abiotic-stress conditioned gene regulatory network designed to facilitate the identification of transcription factor targets during induction of various environmental stresses. The Arabidopsis Seed Active Network (SANe) is a transcriptional regulatory network that encapsulates various aspects of seed formation, including embryogenesis, endosperm development and seed-coat formation. Further, an edge-set enrichment analysis algorithm is proposed that uses network density as a parameter to estimate the gain or loss in correlation of pathways between two conditionally independent coexpression networks

ddRAD sequencing-based genotyping for population structure analysis in cultivated tomato provides new insights into the genomic diversity of Mediterranean 'da serbo' type long shelf-life germplasm

[EN] Double digest restriction-site associated sequencing (ddRAD-seq) is a flexible and cost-effective strategy for providing in-depth insights into the genetic architecture of germplasm collections. Using this methodology, we investigated the genomic diversity of a panel of 288 diverse tomato (Solanum lycopersicum L.) accessions enriched in 'da serbo' (called 'de penjar' in Spain) long shelf life (LSL) materials (152 accessions) mostly originating from Italy and Spain. The rest of the materials originate from different countries and include landraces for fresh consumption, elite cultivars, heirlooms, and breeding lines. Apart from their LSL trait, 'da serbo' landraces are of remarkable interest for their resilience. We identified 32,799 high-quality SNPs, which were used for model ancestry population structure and non-parametric hierarchical clustering. Six genetic subgroups were revealed, clearly separating most 'da serbo' landraces, but also the Spanish germplasm, suggesting a subdivision of the population based on type and geographical provenance. Linkage disequilibrium (LD) in the collection decayed very rapidly within <5kb. We then investigated SNPs showing contrasted minor frequency allele (MAF) in 'da serbo' materials, resulting in the identification of high frequencies in this germplasm of several mutations in genes related to stress tolerance and fruit maturation such as CTR1 and JAR1. Finally, a mini-core collection of 58 accessions encompassing most of the diversity was selected for further exploitation of key traits. Our findings suggest the presence of a genetic footprint of the 'da serbo' germplasm selected in the Mediterranean basin. Moreover, we provide novel insights on LSL 'da serbo' germplasm as a promising source of alleles for tolerance to stresses.The authors thank the European Union Horizon 2020 Research and Innovation program for funding this research under grant agreement No 774244 (Breeding for Resilient, Efficient and Sustainable Organic Vegetable Production; BRESOV).Esposito, S.; Cardi, T.; Campanelli, G.; Sestili, S.; Díez Niclós, MJTDJ.; Soler Aleixandre, S.; Prohens Tomás, J.... (2020). ddRAD sequencing-based genotyping for population structure analysis in cultivated tomato provides new insights into the genomic diversity of Mediterranean 'da serbo' type long shelf-life germplasm. Horticulture Research. 7(1):1-14. https://doi.org/10.1038/s41438-020-00353-611471Faostat 2018 http://www.fao.org/Jenkins, J. A. The origin of the cultivated tomato. Econ. Bot. 2, 379–392 (1948).Blanca, J. et al. Variation revealed by SNP genotyping and morphology provides insight into the origin of the tomato. 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Dissecting heat and drought tolerance in wheat and maize using plant systems biology

Population growth and climate change pose serious threats to food security. Heat and drought are major abiotic constraints to crop production and their co-occurrence will increase during the cropping season in several regions. However, there is a lack of studies investigating their combined effect in crop physiological and biochemical processes. Aiming to close this gap, two of the main crops were investigated, wheat and maize, under these conditions. In the first results chapter, it is shown that these co-occurring stresses equally affect the photosynthetic efficiency of genotypes adapted to Mexico (Sokoll) and the UK (Paragon). However, Paragon recovered faster upon stress relief due to an increased PSII photoprotection and cytosolic Invertase activity, suggesting that optimal sucrose export/utilization and increased electron transport machinery photoprotection are essential to limit wheat yield fluctuations under these conditions. In the second results chapter, by studying maize genotypes with contrasting drought or heat tolerance, it was observed that limited transpiration under high temperature allowed water saving upon deficit without decreasing photosynthetic efficiency. This was sustained by higher phosphorylated PEPC and electron transport rate. Limited transpiration rate and synchronized regulation of the C4 carbon assimilation metabolism showed to be key traits for drought and heat tolerance in maize. In the third results chapter, by screening ten wheat genotypes with different tolerance to drought or heat, it was observed that leaf temperature and evapotranspiration expressed significant genotype-environment interactions. Low leaf number and transpiration efficiency were essential to balance water-saving strategies and biomass production. Changes in the carbohydrate (cytosolic Invertase, Hexokinase, Phosphofructokinase) and antioxidant metabolism (Peroxidases, phenolic compounds) were associated with tolerance mechanisms. Altogether, these results expand our knowledge about crops metabolic responses to high temperature and water deficit. These findings can be further explored in breeding programs to improve crop resilience to climate change and meet food security

Time-resolved metabolomics reveals metabolic modulation in rice foliage

<p>Abstract</p> <p>Background</p> <p>To elucidate the interaction of dynamics among modules that constitute biological systems, comprehensive datasets obtained from "omics" technologies have been used. In recent plant metabolomics approaches, the reconstruction of metabolic correlation networks has been attempted using statistical techniques. However, the results were unsatisfactory and effective data-mining techniques that apply appropriate comprehensive datasets are needed.</p> <p>Results</p> <p>Using capillary electrophoresis mass spectrometry (CE-MS) and capillary electrophoresis diode-array detection (CE-DAD), we analyzed the dynamic changes in the level of 56 basic metabolites in plant foliage (<it>Oryza sativa </it>L. ssp. <it>japonica</it>) at hourly intervals over a 24-hr period. Unsupervised clustering of comprehensive metabolic profiles using Kohonen's self-organizing map (SOM) allowed classification of the biochemical pathways activated by the light and dark cycle. The carbon and nitrogen (C/N) metabolism in both periods was also visualized as a phenotypic linkage map that connects network modules on the basis of traditional metabolic pathways rather than pairwise correlations among metabolites. The regulatory networks of C/N assimilation/dissimilation at each time point were consistent with previous works on plant metabolism. In response to environmental stress, glutathione and spermidine fluctuated synchronously with their regulatory targets. Adenine nucleosides and nicotinamide coenzymes were regulated by phosphorylation and dephosphorylation. We also demonstrated that SOM analysis was applicable to the estimation of unidentifiable metabolites in metabolome analysis. Hierarchical clustering of a correlation coefficient matrix could help identify the bottleneck enzymes that regulate metabolic networks.</p> <p>Conclusion</p> <p>Our results showed that our SOM analysis with appropriate metabolic time-courses effectively revealed the synchronous dynamics among metabolic modules and elucidated the underlying biochemical functions. The application of discrimination of unidentified metabolites and the identification of bottleneck enzymatic steps even to non-targeted comprehensive analysis promise to facilitate an understanding of large-scale interactions among components in biological systems.</p

A Canadian oat genomic selection study incorporating genetic and environmental information

Oat (Avena sativa L.) is an important crop in Canada that has been seeded on an average of 3.3 million acres over the past five years. It is considered a healthy cereal due to the presence of beta-glucan in the grain, which been shown to reduce the risk of heart disease, as well as being a good source of protein that is rich in globulins. Identifying new breeding strategies that can improve breeding efficiency in oat is important for future progress in this crop. To this end, genomic and environmental factors, along with their interactions, were examined to determine what contributed to variation in important oat traits. This information was then used to develop genomic selection (GS) models that can be used in oat breeding programs. In the first study, 305 elite oat breeding lines grown in the Western Cooperative Oat Registration Trial (WCORT) from 2002 to 2014 were used to investigate important factors for genomic selection model building. The influence of phenotypic data, genotyping platforms, statistical model, marker density, population structure, training population size and trait heritability were assessed. It was determined that the machine learning model Support Vector Machine and the additive linear model rr-BLUP offered the best overall prediction accuracies. Prediction accuracy increased when using the iSelect Oat 6K SNP chip, as the marker number increased, with larger training population size and with traits that were more heritable. In the second study, environmental and correlated agronomic variables, along with their inter-relationships, that contributed to variation in yield and grain β-glucan content in oat lines was investigated. A hypothesized structural equation model (SEM) that included variables related to environmental and phenotypic traits was created and tested against observed yield data. Significant paths were identified to explain yield variation (59%-76%) among the three oat varieties. A similar approach was taken for β-glucan in which significant paths were found which explained 16%-41% of the variation in β-glucan. Results from this study suggest that a longer period to heading and maturity, and a taller stature were the three phenotypic traits that most positively influence yield. Limited precipitation before maturity, high temperatures during heading and grain filling were the three environmental variables that contributed to decreased yield. Precipitation and July temperature were the two most important environmental variables that influenced β-glucan, while maturity was the most important trait affecting β-glucan, although the direction of effect for maturity varied by oat variety. In the third study, additional information was added into the previous GS models to determine if prediction could be improved. Genotype, environment and their interaction were used to conduct genomic selection for yield. Four mega-environments were identified from Ward’s hierarchical clustering using the significant environmental variables identified in the second study. It was found that using individual locations to represent environment provided more accuracy compared to using mega-environments. The reaction norm model was also tested which allowed significant environmental variables to be incorporated as a covariance matrix in the model. Including an environmental covariance matrix and interaction terms increased prediction accuracy compared to models with only genotype main effects. Multiple trait GS did not provide better prediction accuracy for most the traits. In the final study, GS was used to predict the GEBVs of two populations, a biparental derived population and a population consisting of elite breeding lines from several different breeding programs. Higher predication accuracy was found in the elite breeding line population which was likely due to the closer genetic relationship between it and the training population. Finally, random selection and genomic selection were compared in the two populations. Genomic selection out-performed random selection in the elite breeding population, but not in the bi-parental population. Again, the poor performance of GS in the bi-parental population was best explained by the unrelatedness between it and the training population. Taken together, these studies provided deeper insight into how GS could be applied in oat breeding programs

DISSECTION OF STRESS RESPONSE NETWORKS REGULATING MULTIPLE STRESSES IN RICE

Important food crops like rice are constantly exposed to various stresses that can have devastating effect on their survival and productivity. Being sessile, these highly evolved organisms have developed elaborate molecular machineries to sense a mixture of stress signals and elicit a precise response to minimize the damage. However, recent discoveries revealed that the interplay of these stress regulatory and signaling molecules is highly complex and remains largely unknown. In this work, we conducted large scale analysis of differential gene expression using advanced computational methods to dissect regulation of stress response which is at the heart of all molecular changes leading to the observed phenotypic susceptibility. One of the most important stress conditions in terms of loss of productivity is drought. We performed genomic and proteomic analysis of epigenetic and miRNA mechanisms in regulation of drought responsive genes in rice and found subsets of genes with striking properties. Overexpressed genesets included higher number of epigenetic marks, miRNA targets and transcription factors which regulate drought tolerance. On the other hand, underexpressed genesets were poor in above features but were rich in number of metabolic genes with multiple co-expression partners contributing majorly towards drought resistance. Identification and characterization of the patterns exhibited by differentially expressed genes hold key to uncover the synergistic and antagonistic components of the cross talk between stress response mechanisms. We performed meta-analysis on drought and bacterial stresses in rice and Arabidopsis, and identified hundreds of shared genes. We found high level of conservation of gene expression between these stresses. Weighted co-expression network analysis detected two tight clusters of genes made up of master transcription factors and signaling genes showing strikingly opposite expression status. To comprehensively identify the shared stress responsive genes between multiple abiotic and biotic stresses in rice, we performed meta-analyses of microarray studies from seven different abiotic and six biotic stresses separately and found more than thirteen hundred shared stress responsive genes. Various machine learning techniques utilizing these genes classified the stresses into two major classes\u27 namely abiotic and biotic stresses and multiple classes of individual stresses with high accuracy and identified the top genes showing distinct patterns of expression. Functional enrichment and co-expression network analysis revealed the different roles of plant hormones, transcription factors in conserved and non-conserved genesets in regulation of stress response

Characterisation of selected bread wheat (Triticum aestivum L.) genotypes for drought tolerance based on SSR markers, morpho-physiological traits and drought indices.

Master of Science in Plant Breeding. University of KwaZulu-Natal, Pietermaritzburg, 2018.Bread wheat (Triticum aestivum L.) and durum wheat (T. turgidum L. var. durum) are staple cereal food crops worldwide. In South Africa, bread wheat is the second most economically important cereal after maize. Drought stress associated with climate change is a major cause of the yield gap in wheat production in South Africa. Drought tolerant wheat cultivars are yet to be developed and released in the country. Wheat improvement for drought tolerance is one of the major breeding goals in South Africa. Integrative pre-breeding techniques involving genotypic and phenotypic characterisation ensure an accurate selection of potential drought tolerant parents for breeding. Therefore, the specific objectives of the current study were: 1) to determine the genetic diversity and population structure of forty-seven diverse bread wheat genotypes introduced from the International Maize and Wheat Improvement Center (CIMMYT) using ten selected polymorphic Simple Sequence Repeat (SSR) markers, 2) to characterise fifteen bread wheat genotypes introduced from CIMMYT using physiological and morphological traits, and 3) to assess drought tolerance amongst fifteen selected bread wheat genotypes using nine drought tolerance indices. Genetic diversity and population structure of 47 CIMMYT derived bread wheat genotypes were examined using 10 SSR molecular markers. All the SSR markers used in the study were highly polymorphic. The highest PIC values were recorded for XGWM 132, WMS 179 and WMS 30 with 0.93, 0.89 and 0.89, respectively. Cluster analysis detected 3 distinct clusters with Clusters A and C consisting of most diverse genotypes. Two distinct heterotic patterns were identified to select unique parents for crosses. Analysis of molecular variance (AMOVA) detected significant genetic diversity among populations, among individuals and within individuals with explained percentage variance of 3%, 37% and 60%, respectively. Genetic diversity and population stratification was mainly due to private alleles detected. Based on detected genetic variability, a total of 15 genotypes were selected and subjected for phenotypic characterisation. The selected genotypes included SYM2016-037, SYM2016-038, SYM2016-029, SYM2016-010 and SYM2016-012 from Cluster A, SYM2016-044, SYM2016-004, SYM2016-016, SYM2016-019, SYM2016-014, SYM2016-008, SYM2016-006 and SYM2016-047 from Cluster B and SYM2016-042 and SYM2016-027 from Cluster C. The above selected 15 bread wheat genotypes were evaluated under field and greenhouse conditions using a randomised complete block design with 3 replications. Drought stress was imposed as follows: 1 week before 50% heading (WBH) and 1 week after 50% heading (WAH). A fully-irrigated water regime (NS, non-stress) was used as a comparative control. Genotypes were evaluated using 2 physiological and 8 morphological traits. Significant differences (P < 0.05) were detected among genotypes and genotype x test environment interaction. Genotype effect was significant for days to flowering, days to maturity, plant height, number of productive tillers, number of spikelets per spike, grain number and 100 grain weight. Genotype x test environment interaction was significant for canopy temperature, days to flowering, days to maturity, plant height, number of spikelets per spike, grain number, 100 seed weight and the yield. Significant correlations were detected between yield and days to flowering, days to maturity, plant height, number of productive tillers, number of spikelets per spike, grain number and 100 seed weight under greenhouse condition. The number of productive tillers per plant and the number of spikelets per spike were positively associated with yield under field evaluation. Principal component analysis revealed PC1 to be consistently associated with yield, 100 seed weight and number of spikelets per spike. Days to flowering and maturing, plant height and canopy temperature were positively associated with either PC2 or PC3 under greenhouse and field conditions. A yield penalty was noted for early flowering and maturing genotypes such as SYM2016-014, SYM2016-027 and SYM2016-029 relative to late flowering and maturing genotypes SYM2016-016, SYM2016-037 and SYM2016-006. Crossing of these complementary lines and continuous selection of progenies is essential to develop early maturing genotypes with stable and high yield potential. In this study, days to flowering and maturity, plant height, canopy temperature and 100 seed weight were favourable traits to screen genotypes for drought tolerance. Screening for drought tolerance under greenhouse condition was more reliable than under field evaluation. The above 15 wheat genotypes were evaluated using 9 drought tolerance indices based on yield data. The drought indices used were drought resistance (DR), mean productivity (MP), harmonic mean of yield (HM), stress susceptibility index (SSI), stress tolerance index (STI), tolerance index (TOL), yield index (YI), yield reduction index (YR) and yield stability index (YSI). Analysis of variance detected significant differences among genotypes (P < 0.001) and genotype by water regime interaction (P < 0.01) affecting yield response. Significant differences were also recorded among genotypes (P < 0.05) for DR, HM, MP, STI, YI and YSI. Consistent mean genotype ranking was recorded for HM, MP, STI, SSI and YI enabling selection of genotypes SYM2016-006, SYM2016-016 and SYM2016-037. PC analysis detected high variation of 82.2% among genotypes, with percentage variation partitioned as follows: 42.64% for PC1, 22.37% for PC2 and 12.18% for PC3. Both PC and bi-plot analyses revealed strong associations between HM, MP, STI, YI and yield under drought stressed and non-stressed conditions. High yielding genotypes such as SYM2016-006, SYM2016-016 and SYM2016-037 scored higher values for HM, MP, STI, YI and yield under drought stressed and non-stressed conditions. DR was associated with early maturing genotypes such as SYM2016-014, SYM2016-029 and SYM2016-38. These genotypes were considered as potential parents for future wheat breeding programmes emphasizing drought tolerance