71 research outputs found
Metabolomic profiling of C4 grass responses to environmental stress
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Previous issue date: 2020-03-30Over half of the world’s grass species, including some of the most productive food and energy crops, use C4 photosynthesis. However, the responses of C4 crops to global atmospheric change, specifically rising carbon dioxide ([CO2]) and ozone ([O3]) concentrations, have been examined disproportionately less than responses of C3 crops. This thesis uses metabolomics approaches, along with integrated ‘omic’ technologies, to achieve a greater understanding of C4 grass responses to abiotic stresses associated with global atmospheric change.
Rising temperatures will have a significant effect on major food and forage crops across the globe. Although the impact is expected to be greatest in tropical regions, the impacts of climate change have been poorly studied in those regions. In Chapter 2, Guinea grass (Panicum maximum Jacq.) was exposed to the individual and combined effects of elevated [CO2] and temperature, and transcript and metabolite profiles were examined. Samples were collected at a combined Free Air CO2 Enrichment and Temperature Controlled Enhancement (Trop-T-FACE) facility in Sao Paulo, Brazil. Field transcriptomics and metabolomics revealed that elevated temperature and [CO2] altered transcript and metabolite profiles associated with an environmental response, secondary metabolism and stomatal function. These metabolic responses were consistent with greater growth and leaf area production under elevated temperature. The tropical C4 grass had an unpredicted response to global climate change, with canopy warming during a cool growing season, enhancing growth and alleviating stress.
Tropospheric O3 is the most damaging air pollutant to crops. Exposure to O3 causes oxidative stress to vegetative and reproductive tissues which can affect the abundance of transcripts, proteins and metabolites, leading to accelerated senescence and decreased yield. In Chapter 3, two diverse maize (Zea mays) inbred lines and the hybrid cross were exposed to season-long elevated [O3] (~100 ppb) in the field based FACE facility modified for O3. The metabolomic profile of leaves was sampled over the course of leaf senescence to achieve an understanding of the biochemical response during this developmental stage. The hybrid line, B73 x Mo17, showed an acceleration of chlorophyll loss under elevated [O3] accompanied by a significant change in the metabolite profile. In contrast, the metabolite profile of the two inbred lines (B73 and Mo17) was not different in ambient and elevated [O3] treatments. Levels of secondary metabolites were increased in B73 x Mo17 leaves as they aged and to a significantly greater degree in elevated [O3] stress. Untargeted metabolomic profiling revealed that inbred and hybrid lines of maize differ in key metabolic responses to elevated O3 pollution.
The specific reactive oxygen species initially formed after O3 exposure and the antioxidants involved in apoplastic detoxification vary among plant species and even genotypes within a species. Very little is known about how C4 grasses respond to acute O3 exposure. In Chapter 4, maize and foxtail millet (Setaria viridis) were exposed to an acute O3 dose (200 – 400 nL L-1) for 24 hours. Leaf material from the youngest fully expanded leaf was taken for nuclear magnetic resonance (NMR) for identification of antioxidants and additional metabolites, and electron paramagnetic resonance (EPR) for ROS identification. EPR results showed the concentration of hydroxyl and superoxide radicals in leaves were higher after acute O3 exposure. Untargeted metabolomics performed with 1H-NMR showed altered amino acid content following initial O3 exposure, especially isoleucine and alanine. These experiments established the potential for EPR analysis of O3 response in live tissues and lay the foundation for further work to identify the specific molecular signature of the initial O3 response.
This dissertation research provides insight into the metabolomic mechanisms behind the response of C4 grasses to elevated [CO2], temperature, and [O3]. Metabolomics approaches can be used for high-throughput phenotyping of diverse metabolites and their responses to stress. While field metabolomics can be challenging, the results identified novel metabolic pathways of response to global atmospheric change.Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2022-05-01The student, Jessica Wedow, accepted the attached license on 2020-03-23 at 09:26.The student, Jessica Wedow, submitted this Dissertation for approval on 2020-03-23 at 09:35.This Dissertation was approved for publication on 2020-03-30 at 14:02.DSpace SAF Submission Ingestion Package generated from Vireo submission #14911 on 2020-08-25 at 17:27:02Embargo set by: Seth Robbins for item 115704
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Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemEmbargo set by: Seth Robbins for item 115704
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Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemEmbargo set by: Seth Robbins for item 115704
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Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemEmbargo set by: Seth Robbins for item 115704
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Weight Identity Among Older Adults in the United States: Genetic and Environmental Influences
No full textGene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment
No full textWe conduct a large-scale genetic association analysis of educational attainment in a sample of ~1.1 million individuals and identify 1,271 independent genome-wide-significant SNPs. For the SNPs taken together, we found evidence of heterogeneous effects across environments. The SNPs implicate genes involved in brain-development processes and neuron-to-neuron communication. In a separate analysis of the X chromosome, we identify 10 independent genome-wide-significant SNPs and estimate a SNP heritability of ~0.3% in both men and women, consistent with partial dosage compensation. A joint (multi-phenotype) analysis of educational attainment and three related cognitive phenotypes generates polygenic scores that explain 11-13% of the variance in educational attainment and 7-10% of the variance in cognitive performance. This prediction accuracy substantially increases the utility of polygenic scores as tools in research
Gender and genetic contributions to weight identity among adolescents and young adults in the U.S.
No full textThe Relative Contributions of Socioeconomic and Genetic Factors to Variations in Body Mass Index Among Young Adults
No full textBody size reference norms and subjective weight status: A gender and life course approach
No full textEducation, Smoking, and Cohort Change: Forwarding a Multidimensional Theory of the Environmental Moderation of Genetic Effects
No full textWhat machine learning teaches us about depression prediction across the life course: An exploratory comparison of predictive models
No full textIdentifying individuals at risk for depression early is important for preventing long-term mental health issues. However, the variability in depression severity, duration, and triggers complicates predictions. This study explores whether machine learning models can outperform traditional methods, like Logistic Regression, in predicting self-reported depressive symptoms and clinical depression during adolescence and adulthood. We applied five machine learning models with varying complexity levels - Logistic Regression, Decision Tree, XGBoost, Support Vector Machine, and Neural Networks - using data from a nationally representative longitudinal study of the U.S., which tracked participants for 20 years. The models were trained with early-life predictors (ages 12-18) from Wave I, including environmental factors (family, school, health) and genetic predispositions (polygenic scores) from Wave IV. Models were evaluated on their ability to predict depressive symptoms and clinical diagnoses in both adolescence and adulthood. After evaluating the performance of all five models, XGBoost emerged as the most effective, with a 0.02 increase in ROC-AUC compared to the benchmark Logistic Regression model. While this is a slight performance improvement, overall, Logistic Regression performs about as well as many of our ML models. Early-life data showed strong predictive value for depressive symptoms and clinical diagnoses in adolescence and adulthood, highlighting adolescence as a critical period. Polygenic scores do not add predictive power when combined with environmental data. Feature importance analyses identified self-perception and physical health as key predictors of depressive symptoms, while trauma and life-changing events were more influential for clinical depression
Data Related to Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use
No full textFiles include summary statistics for associations with each phenotype: Drinks per week, Cigarettes per day, Smoking initiation, Smoking cessation, and Age of initiation. Details for each file can be found in the readme file or in the article's Supplementary Text.We conducted a meta-analysis of over 30 genome wide association studies (GWAS) in over 1.2 million participants with European ancestry on nicotine and substance use. Specifically, we targeted different stages and kinds of substance use from initiation (smoking initiation and age of regular smoking initiation) to regular use (drinks per week and cigarettes per day) to cessation (smoking cessation). The GWAS included have all been imputed to Haplotype Reference Consortium, 1000 Genomes or a combination including more specific reference panels. The studies are then meta-analyzed using sample size, allele frequencies and the imputation quality score as weight. Here we present the final set of filtered meta-analysis summary statistics as presented in the paper (https://doi.org/10.1038/s41588-018-0307-5) excluding 23andMe. As per requirement and to ease dissemination of our results for other scientific endeavors, we are sharing our results here to facilitate downloading.R01DA037904R01HG008983R21DA040177Liu, Mengzhen; Jiang, Yu; Wedow, Robbee; Li, Yue; Brazel, David M; Chen, Fang; Datta, Gargi; Davila-Velderrain, Jose; McGuire, Daniel; Tian, Chao; Zhan, Xiaowei; 23andMe Research Team; HUNT All-In Psychiatry; Choquet, Hélène; Docherty, Anna R; Faul, Jessica D; Foerster, Johanna R; Fritsche, Lars G; Gabrielsen, Maiken Elvestad; Gordon, Scott D; Haessler, Jeffrey; Hottenga, Jouke-Jan; Huang, Hongyan; Jang, Seon-Kyeong; Jansen, Philip R; Ling, Yueh; Mägi, Reedik; Matoba, Nana; McMahon, George; Mulas, Antonella; Orrù, Valeria; Palviainen, Teemu; Pandit, Anita; Reginsson, Gunnar W, Skogholt, Anne Heidi; Smith, Jennifer A; Taylor, Amy E; Turman, Constance; Willemsen, Gonneke; Young, Hannah; Young, Kendra A; Zajac, Gregory J M; Zhao, Wei; Zhou, Wei; Bjornsdottir, Gyda; Boardman, Jason D; Boehnke, Michael; Boomsma, Dorret I; Chen, Chu; Cucca, Francesco; Davies, Gareth E; Eaton, Charles B; Ehringer, Marissa A; Esko, Tõnu; Fiorillo, Edoardo; Gillespie, Nathan A; Gudbjartsson, Daniel F; Haller, Toomas; Harris, Kathleen Mullan; Heath, Andrew C; Hewitt, John K; Hickie, Ian B; Hokanson, John E; Hopfer, Christian J; Hunter, David J; Iacono, William G; Johnson, Eric O; Kamatani, Yoichiro; Kardia, Sharon L. R; Keller, Matthew C; Kellis, Manolis; Kooperberg, Charles; Kraft, Peter; Krauter, Kenneth S; Laakso, Markku; Lind, Penelope A; Loukola, Anu; Lutz, Sharon M; Madden, Pamela A F; Martin, Nicholas G; McGue, Matt; McQueen, Matthew B; Medland, Sarah E; Metspalu, Andres; Mohlke, Karen L; Nielsen, Jonas B; Okada, Yukinori; Peters, Ulrike; Polderman, Tinca J C; Posthuma, Danielle; Reiner, Alexander P; Rice, John P; Rimm, Eric; Rose, Richard J; Runarsdottir, Valgerdur; Stallings, Michael C; Stančáková, Alena; Stefansson, Hreinn; Thai, Khanh K; Tindle, Hilary A; Tyrfingsson, Thorarinn; Wall, Tamara L; Weir, David R; Weisner, Constance; Whitfield, John B; Winsvold, Bendik Slagsvold; Yin, Jie; Zuccolo, Luisa; Bierut, Laura J; Hveem, Kristian; Lee, James J; Munafò, Marcus R; Saccone, Nancy L; Willer, Cristen J; Cornelis, Marilyn C; David, Sean P; Hinds, David A; Jorgenson, Eric; Kaprio, Jaakko; Stitzel, Jerry A; Stefansson, Kari; Thorgeirsson, Thorgeir E; Abecasis, Gonçalo; Liu Dajiang J; Vrieze Scott. (2019). Data Related to Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Retrieved from the University Digital Conservancy, https://doi.org/10.13020/3b1n-ff32
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