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18 Regional patterns of mitochondrial function using phosphorus magnetic resonance spectroscopy in older adults at-risk for Alzheimer’s disease.
- Francesca V Lopez, Andrew O’Shea, Stacey Alvarez-Alvarado, Adrianna Ratajska, Lauren Kenney, Rachel Schade, Katie Rodriguez, Alyssa Ray, Rebecca O’Connell, Lauren Santos, Emily Van Etten, Hyun Song, Emma Armstrong, Tiffany Gin, Zhiguang Huo, Gene Alexander, Adam J Woods, Dawn Bowers
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- Journal:
- Journal of the International Neuropsychological Society / Volume 29 / Issue s1 / November 2023
- Published online by Cambridge University Press:
- 21 December 2023, pp. 331-332
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- Article
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Objective:
The brain is reliant on mitochondria to carry out a host of vital cellular functions (e.g., energy metabolism, respiration, apoptosis) to maintain neuronal integrity. Clinically relevant, dysfunctional mitochondria have been implicated as central to the pathogenesis of Alzheimer’s disease (AD). Phosphorous magnetic resonance spectroscopy (31p MRS) is a non-invasive and powerful method for examining in vivo mitochondrial function via high energy phosphates and phospholipid metabolism ratios. At least one prior 31p MRS study found temporal-frontal differences for high energy phosphates in persons with mild AD. The goal of the current study was to examine regional (i.e., frontal, temporal) 31p MRS ratios of mitochondrial function in a sample of older adults at-risk for AD. Given the high energy consumption in temporal lobes (i.e., hippocampus) and preferential age-related changes in frontal structure-function, we predicted 31p MRS ratios of mitochondrial function would be greater in temporal as compared to frontal regions.
Participants and Methods:The current study leveraged baseline neuroimaging data from an ongoing multisite study at the University of Florida and University of Arizona. Participants were older adults with memory complaints and a first-degree family history of AD [N = 70; mean [M] age [years] = 70.9, standard deviation [SD] =5.1; M education [years] = 16.2, SD = 2.2; M MoCA = 26.5, SD = 2.4; 61.4% female; 91.5% non-latinx white]. To achieve optimal sensitivity, we used a single voxel method to examine 31p MRS ratios (bilateral prefrontal and left temporal). Mitochondrial function was estimated by computing 5 ratios for each voxel: summed adenosine triphosphate to total pooled phosphorous (ATP/TP; momentary energy), ATP to inorganic phosphate (ATP/Pi; energy consumption), phosphocreatine to ATP (PCr/ATP; energy reserve), phosphocreatine to inorganic phosphate (PCr/Pi; oxidative phosphorylation), and phosphomonoesters to phosphodiesters (PME/PDE; cellular membrane turnover rate). All ratios were corrected for voxel size and cerebrospinal fluid fraction. Separate repeated measures analyses of variance controlling for scanner site differences (RM ANCOVAs) were performed.
Results:31p MRS ratios were unrelated to demographic characteristics and were not included as additional covariates in analyses. Results of separate RM ANCOVAs revealed all 31p MRS ratios of mitochondrial function were greater in left temporal relative to bilateral prefrontal voxel: ATP/TP (p < .001), ATP/Pi (p = .001), PCr/ATP (p = .004), PCr/Pi (p = .004), and PME/PDE (p = .017). Effect sizes (partial eta squared) ranged from 0.6-.20.
Conclusions:Consistent and extending one prior study, all 31p MRS ratios of mitochondrial function were greater in temporal as compared to frontal regions in older adults at-risk for AD. This may in part be related to the intrinsically high metabolic rate of the temporal region and preferential age-related changes in frontal structure-function. Alternatively, findings may reflect the influence of unaccounted factors (e.g., hemodynamics, auditory stimulation). Longitudinal study designs may inform whether patterns of mitochondrial function across different brain regions are present early in development, occur across the lifespan, or some combination. In turn, this may inform future studies examining differences in mitochondrial function (as measured using 31p MRS) in AD.
2 - MetaOmics: Transcriptomic Meta-Analysis Methods for Biomarker Detection, Pathway Analysis and Other Exploratory Purposes
- from Part A - Horizontal Meta-Analysis
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- By Sunghwan Kim, University of Pittsburgh, Pittsburgh, PA, Zhiguang Huo, University of Pittsburgh, Pittsburgh, PA, Yongseok Park, University of Pittsburgh, Pittsburgh, PA, George C. Tseng, University of Pittsburgh, Pittsburgh, PA
- George Tseng, University of Pittsburgh, Debashis Ghosh, Pennsylvania State University, Xianghong Jasmine Zhou, University of Southern California
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- Book:
- Integrating Omics Data
- Published online:
- 05 September 2015
- Print publication:
- 23 September 2015, pp 39-67
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Summary
Abstract
In this chapter, we present a MetaOmics software suite to combine multiple transcriptomic studies for meta-analysis. MetaOmics contains more than a dozen in-housedeveloped methods and consists of seven subpackages for different data analysis and biological objectives: MetaQC for quality control assessment, MetaDE for differentially expressed gene detection, MetaPath for pathway enrichment analysis, MetaPCA for dimension reduction, MetaClust for clustering analysis, MetaNetwork for network analysis, and MetaPredict for prediction analysis.With the increasing number of experimental data accumulated in the public domain, application of related omics metaanalysis methods provides increased statistical power and validated conclusions to improve disease treatment and mechanism understanding.
Introduction
With the advances in high-throughput experimental technology in the past decades, the production of genomic data has become affordable and large genomic data are prevalent in recent biomedical research. Effective data management and analysis tools are essential to fully decipher the biological information inside the tremendous amount of experimental data. In the past decade, enormous bodies of transcriptomic data have been accumulated from microarray experiments, which resulted in several large public data depositories, such as Gene Expression Omnibus (GEO) and ArrayExpress. Recent development of next generation sequencing (NGS) technology accelerated the data accumulation in databases like Sequence Read Archive (SRA). In general, each individual study often has small or moderate sample size. As a result, the statistical power of candidate marker or pathway detection in each study is often limited, the reproducibility of the conclusions is relatively low, and the generalizability of the inferred information has been frequently criticized. Combining multiple studies has emerged as an appealing practice because of improved statistical power and estimation accuracy, while it may also provide validation about the final conclusion. Many “transcriptomic meta-analysis” methods have been developed and widely applied in the real data analysis. In the literature, however, most of the methods were proposed to identify candidate marker genes differentially expressed between two or multiple conditions. Similar “meta-analysis” ideas can be extended for enriched pathway detection, clustering analysis, dimension reduction, and network and disease classification analysis (see Ramasamy et al. (2008) and Tseng et al. (2012) for more details). In this chapter, we first introduce statistical methods in the “MetaOmics” software suite, including those still under development in our lab.