ORCID Profile
0000-0002-0337-8450
Current Organisation
Colorado State University
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Publisher: eLife Sciences Publications, Ltd
Date: 12-07-2022
DOI: 10.7554/ELIFE.70056
Abstract: Environmental variability poses a major challenge to any field study. Researchers attempt to mitigate this challenge through replication. Thus, the ability to detect experimental signals is determined by the degree of replication and the amount of environmental variation, noise, within the experimental system. A major source of noise in field studies comes from the natural heterogeneity of soil properties which create microtreatments throughout the field. In addition, the variation within different soil properties is often nonrandomly distributed across a field. We explore this challenge through a sorghum field trial dataset with accompanying plant, microbiome, and soil property data. Diverse sorghum genotypes and two watering regimes were applied in a split-plot design. We describe a process of identifying, estimating, and controlling for the effects of spatially distributed soil properties on plant traits and microbial communities using minimal degrees of freedom. Importantly, this process provides a method with which sources of environmental variation in field data can be identified and adjusted, improving our ability to resolve effects of interest and to quantify subtle phenotypes.
Publisher: Public Library of Science (PLoS)
Date: 14-09-2017
Publisher: Cold Spring Harbor Laboratory
Date: 30-04-2021
DOI: 10.1101/2021.04.29.441834
Abstract: Environmental variability poses a major challenge to any field study. Researchers attempt to mitigate this challenge through replication. Thus, the ability to detect experimental signals is determined by the degree of replication and the amount of environmental variation, noise, within the experimental system. A major source of noise in field studies comes from the natural heterogeneity of soil properties which create micro-treatments throughout the field. To make matters worse, the variation within different soil properties is often non-randomly distributed across a field. We explore this challenge through a sorghum field trial dataset with accompanying plant, microbiome and soil property data. Diverse sorghum genotypes and two watering regimes were applied in a split-plot design. We describe a process of identifying, estimating, and controlling for the effects of spatially distributed soil properties on plant traits and microbial communities using minimal degrees of freedom. Importantly, this process provides a tool with which sources of environmental variation in field data can be identified and removed, improving our ability to resolve effects of interest and to quantify subtle phenotypes. Data from field experiments are notoriously noisy. Proper field designs with high replication aid in mitigating this challenge, yet true biological correlations are still often masked by environmental variability. This work identifies soil property composition as a spatially distributed source of variance to three types of characteristics: plant phenotype, microbiome composition, and leaf traits. We show that once identified, spatial principal component regression was able to account for these effects so that more precise estimates of experimental factors were obtained. This generalizable method is applicable to erse field experiments.
Publisher: Public Library of Science (PLoS)
Date: 21-10-2016
Publisher: Elsevier BV
Date: 02-2020
DOI: 10.1016/J.JSTROKECEREBROVASDIS.2019.104476
Abstract: To search for novel pathophysiological pathways related to ischemic stroke using a metabolomics approach. We identified 204 metabolites in plasma by liquid chromatography mass spectrometry in 3 independent population-based s les (TwinGene, Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS) and Uppsala Longitudinal Study of Adult Men). TwinGene was used for discovery and the other 2 s les were meta-analyzed as replication. In PIVUS, traditional cardiovascular (CV) risk factors, multiple markers of subclinical CV disease, markers of coagulation/fibrinolysis were measured and analyzed in relation to top metabolites. In TwinGene (177 incident cases, median follow-up 4.3 years), levels of 28 metabolites were associated with incident ischemic stroke at a false discover rate (FDR) of 5%. In the replication (together 194 incident cases, follow-up 10 and 12 years, respectively), only sphingomyelin (32:1) was significantly associated (HR .69 per SD change, 95% CI .57-0.83, P value = .00014 FDR <5%) when adjusted for systolic blood pressure, diabetes, smoking, low density lipoportein (LDL)- and high density lipoprotein (HDL), body mass index (BMI) and atrial fibrillation. In PIVUS, sphingomyelin (32:1) levels were significantly related to both LDL- and HDL-cholesterol in a positive fashion, and to serum triglycerides, BMI and diabetes in a negative fashion. Furthermore, sphingomyelin (32:1) levels were related to vasodilation in the forearm resistance vessels, and inversely to leukocyte count (P < .0069 and .0026, respectively). An inverse relationship between sphingomyelin (32:1) and incident ischemic stroke was identified, replicated, and characterized. A possible protective role for sphingomyelins in stroke development has to be further investigated in additional experimental and clinical studies.
Publisher: Elsevier BV
Date: 09-2015
Publisher: Public Library of Science (PLoS)
Date: 11-12-2014
Publisher: Cold Spring Harbor Laboratory
Date: 09-02-2023
DOI: 10.1101/2023.02.08.527764
Abstract: Development of cereal crops with high nitrogen-use efficiency (NUE) is a priority for worldwide agriculture. In addition to conventional plant breeding and genetic engineering, the use of the plant microbiome offers another approach to improve crop NUE. To gain insight into the bacterial communities associated with sorghum lines that differ in NUE, a field experiment was designed comparing 24 erse sorghum lines under sufficient and deficient nitrogen (N). Amplicon sequencing and untargeted gas chromatography-mass spectrometry (GC-MS) were used to characterize the bacterial communities and the root metabolome associated with sorghum genotypes varying in sensitivity to low N. We demonstrated that N stress and sorghum type (energy, sweet, and grain sorghum) significantly influenced the root-associated bacterial communities and root metabolite composition of sorghum. Sorghum NUE was positively correlated with the bacterial richness and ersity in the rhizosphere. The greater alpha ersity in high NUE lines was associated with the decreased abundance of a dominant bacterial taxa, Pseudomonas . Multiple strong correlations were detected between root metabolites and rhizosphere bacterial communities in response to N stress and indicate that the shift in the sorghum microbiome due to low-N is associated with the root metabolites of the host plant. Taken together, our study provides new insight into the links between host genetic regulation of root metabolites and root-associated microbiome of sorghum genotypes differing in NUE and tolerance to low-N stress.
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 19-01-2021
Abstract: The molecular mechanisms involved in atrial fibrillation are not well known. We used plasma metabolomics to investigate if we could identify novel biomarkers and pathophysiological pathways of incident atrial fibrillation. We identified 200 endogenous metabolites in plasma/serum by nontargeted ultra‐performance liquid chromatography coupled to time‐of‐flight mass spectrometry in 3 independent population‐based s les (TwinGene, n=1935, mean age 68, 43% females PIVUS [Prospective Investigation of the Vasculature in Uppsala Seniors], n=897, mean age 70, 51% females and ULSAM [Uppsala Longitudinal Study of Adult Men], n=1118, mean age 71, all males), with available data on incident atrial fibrillation during 10 to 12 years of follow‐up. A meta‐analysis of ULSAM and PIVUS was used as a discovery s le and TwinGene was used for validation. In PIVUS, we also investigated associations between metabolites of interest and echocardiographic indices of myocardial geometry and function. Genome‐wide association studies were performed in all 3 cohorts for metabolites of interest. In the meta‐analysis of PIVUS and ULSAM with 430 incident cases, 4 metabolites were associated with incident atrial fibrillation at a false discovery rate %. Of those, only 9‐decenoylcarnitine was associated with incident atrial fibrillation and replicated in the TwinGene s le (288 cases) following adjustment for traditional risk factors (hazard ratio, 1.24 per unit 95% CI, 1.06–1.45, P =0.0061). A meta‐analysis of all 3 cohorts disclosed another 4 significant metabolites. In PIVUS, 9‐decenoylcarnitine was related to left atrium size and left ventricular mass. A Mendelian randomization analysis did not suggest a causal role of 9‐decenoylcarnitine in atrial fibrillation. A nontargeted metabolomics analysis disclosed 1 novel replicated biomarker for atrial fibrillation, 9‐Decenoylcarnitine, but this acetylcarnitine is likely not causally related to atrial fibrillation.
Publisher: Wiley
Date: 30-05-2019
DOI: 10.1002/EHF2.12453
Publisher: Springer Science and Business Media LLC
Date: 06-06-2018
DOI: 10.1038/S41598-018-26701-0
Abstract: Insulin resistance (IR) predisposes to type 2 diabetes and cardiovascular disease but its causes are incompletely understood. Metabolic challenges like the oral glucose tolerance test (OGTT) can reveal pathogenic mechanisms. We aimed to discover associations of IR with metabolite trajectories during OGTT. In 470 non-diabetic men (age 70.6 ± 0.6 years), plasma s les obtained at 0, 30 and 120 minutes during an OGTT were analyzed by untargeted liquid chromatography-mass spectrometry metabolomics. IR was assessed with the hyperinsulinemic-euglycemic cl method. We applied age-adjusted linear regression to identify metabolites whose concentration change was related to IR. Nine trajectories, including monounsaturated fatty acids, lysophosphatidylethanolamines and a bile acid, were significantly associated with IR, with the strongest associations observed for medium-chain acylcarnitines C10 and C12, and no associations with L-carnitine or C2-, C8-, C14- or C16-carnitine. Concentrations of C10- and C12-carnitine decreased during OGTT with a blunted decline in participants with worse insulin resistance. Associations persisted after adjustment for obesity, fasting insulin and fasting glucose. In mouse 3T3-L1 adipocytes exposed to different acylcarnitines, we observed blunted insulin-stimulated glucose uptake after treatment with C10- or C12-carnitine. In conclusion, our results identify medium-chain acylcarnitines as possible contributors to IR.
Publisher: Springer Science and Business Media LLC
Date: 05-10-2020
DOI: 10.1038/S41598-020-72456-Y
Abstract: Better risk prediction and new molecular targets are key priorities in type 2 diabetes (T2D) research. Little is known about the role of the urine metabolome in predicting the risk of T2D. We aimed to use non-targeted urine metabolomics to discover biomarkers and improve risk prediction for T2D. Urine s les from two community cohorts of 1,424 adults were analyzed by ultra-performance liquid chromatography/mass spectrometry (UPLC-MS). In a discovery/replication design, three out of 62 annotated metabolites were associated with prevalent T2D, notably lower urine levels of 3-hydroxyundecanoyl-carnitine. In participants without diabetes at baseline, LASSO regression in the training set selected six metabolites that improved prediction of T2D beyond established risk factors risk over up to 12 years' follow-up in the test s le, from C-statistic 0.866 to 0.892. Our results in one of the largest non-targeted urinary metabolomics study to date demonstrate the role of the urine metabolome in identifying at-risk persons for T2D and suggest urine 3-hydroxyundecanoyl-carnitine as a biomarker candidate.
No related grants have been discovered for Jessica Prenni.