Biology subjects

Karu, N.

Publications and source records attributed to Karu, N..

4 recordsLinked to original sources

bioRxiv2025

APOE Genotype Influences on The Brain Metabolome of Aging Mice - Role for Mitochondrial Energetics in Mechanisms of Resilience in APOE2 Genotype.

Alzheimers disease (AD) risk and progression are significantly influenced by APOE genotype with APOE4 increasing and APOE2 decreasing susceptibility compared to APOE3. While the effect of those genotypes was extensively studied on blood metabolome, less is known about their impact in the brain. Here we investigated the impacts of APOE genotypes and aging on brain metabolic profiles across the lifespan, using human APOE-targeted replacement mice. Biocrates P180 targeted metabolomics platform was used to measure a broad range of metabolites probing various metabolic processes. In all genotypes investigated we report changes in acylcarnitines, biogenic amines, amino acids, phospholipids and sphingomyelins during aging. The decreased ratio of medium to long-chain acylcarnitine suggests a reduced level of fatty acid {beta}-oxidation and thus the possibility of mitochondrial dysfunction as these animals age. Additionally, aging APOE2/2 mice had altered branch-chain amino acids (BCAA) profile and increased their downstream metabolite C5 acylcarnitine, indicating increased branched-chain amino acid utilization in TCA cycle and better energetic profile endowed by this protective genotype. We compared these results with human dorsolateral prefrontal cortex metabolomic data from the Religious Orders Study/Memory and Aging Project, and we found that the carriers of APOE2/3 genotype had lower markers of impaired BCAA katabolism, including tiglyl carnitine, methylmalonate and 3-methylglutaconate. In summary, these results suggest a potential involvement of the APOE2 genotype in BCAA utilization in the TCA cycle and nominate these humanized APOE mouse models for further study of APOE in AD, brain aging, and brain BCAA utilization for energy. We have previously shown lower plasma BCAA to be associated with incident dementia, and their higher levels in brain with AD pathology and cognitive impairment. Those findings together with our current results could potentially explain the AD-protective effect of APOE2 genotype by enabling higher utilization of BCAA for energy during the decline of fatty acid {beta}-oxidation.

systems biology↗

bioRxiv2023

Comparative metabolomics and microbiome analysis of Ethanol vs. OMNImet/gene GUT fecal stabilization

Metabolites from feces provide important insights into the functionality of the gut microbiome. As immediate freezing is not always feasible in gut microbiome studies, there is a need for sampling protocols that provide stability of the fecal metabolome and microbiome at room temperature (RT). For this purpose, we investigated the stability of various metabolites and the microbiome (16S ribosomal RNA) in feces collected in 95% ethanol (EtOH) or OMNImet(R)*GUT/ OMNIgene(R)*GUT. To simulate in field-collection scenarios, the samples were stored at different temperatures at varying durations (24h +4{degrees}C, 24h RT, 36h RT, 48h RT, and 7 days RT), and compared to aliquots immediately frozen at -80{degrees}C. We applied several targeted and untargeted metabolomics platforms to measure lipids, polar untargeted metabolites, endocannabinoids, short chain fatty acids (SCFAs), and bile acids (BAs). We found that SCFAs in the non-stabilized samples increased over time, while a stable profile was recorded in sample aliquots stored in 95% EtOH and OMNImet(R)*GUT. When comparing the metabolite levels between fecal aliquots stored at room temperature and at +4{degrees}C, we detected several changes in microbial metabolites, including multiple BAs and SCFAs. Taken together, we found that storing fecal samples at room temperature and stabilizing them in 95% EtOH yielded metabolomic results comparable to flash freezing. We also found that overall composition of the gut microbiome did not vary significantly between different storage types. However, there were notable differences observed in alpha diversity. Taken together, the stability of the metabolome and microbiome in 95 % EtOH provided similar results as the validated commercial collection kits OMNImet(R)*GUT and OMNIgene(R)*GUT, respectively. IMPORTANCEThe analysis of the gut metabolome and microbiome requires the separate collection of fecal specimens using conventional methods or commercial kits. However, these approaches can potentially introduce sampling errors and biases. In addition, the logistical requirements of studying large human cohorts have driven the need for home collection and transport of human fecal specimens at room temperature. By adopting a unified sampling approach at room temperature, we can enhance sampling convenience and practicality, leading to a more precise and comprehensive understanding of gut microbial function. However, the development and applications of such unified sampling systems still face limitations. The results presented in this study aim to address this knowledge gap by investigating the stability of metabolites and the microbiome (16S ribosomal RNA) from fecal samples collected using 95% EtOH, in comparison to well-established commercial collection kits for fecal metabolome (OMNImet(R)*GUT) and microbiome (OMNIgene(R) *GUT) profiling. Additionally, we perform a comparative analysis of various platforms and metabolomic coverage using matrices containing ethanol, evaluating aspects of sensitivity, robustness, and throughput.

biochemistry↗

bioRxiv2023

A Multi-omics Data Analysis Workflow Packaged as a FAIR Digital Object

BackgroundApplying good data management and FAIR data principles (Findable, Accessible, Interoperable, and Reusable) in research projects can help disentangle knowledge discovery, study result reproducibility, and data reuse in future studies. Based on the concepts of the original FAIR principles for research data, FAIR principles for research software were recently proposed. FAIR Digital Objects enable discovery and reuse of Research Objects, including computational workflows for both humans and machines. Practical examples can help promote the adoption of FAIR practices for computational workflows in the research community. We developed a multi-omics data analysis workflow implementing FAIR practices to share it as a FAIR Digital Object. FindingsWe conducted a case study investigating shared patterns between multi-omics data and childhood externalizing behavior. The analysis workflow was implemented as a modular pipeline in the workflow manager Nextflow, including containers with software dependencies. We adhered to software development practices like version control, documentation, and licensing. Finally, the workflow was described with rich semantic metadata, packaged as a Research Object Crate, and shared via WorkflowHub. ConclusionsAlong with the packaged multi-omics data analysis workflow, we share our experiences adopting various FAIR practices and creating a FAIR Digital Object. We hope our experiences can help other researchers who develop omics data analysis workflows to turn FAIR principles into practice. O_TEXTBOXKey PointsO_LIThe FAIR4RS principles provide guidelines to enhance the discovery and reuse of research software. C_LIO_LIFAIR Digital Objects support Findability, Accessibility, Interoperability, and Reusability by both humans and machines. C_LIO_LIWe here demonstrate the implementation multi-omics data analysis workflow and share it as a FAIR Digital Object. C_LI C_TEXTBOX

bioinformatics↗

bioRxiv2022

Heritability of Urinary Amines, Organic Acids, and Steroid Hormones in Children

Variation in metabolite levels reflects individual differences in genetic and environmental factors. Here, we investigated the role of these factors in urinary metabolomics data in children. We examined the effects of sex and age on 86 metabolites, as measured on three metabolomics platforms that target amines, organic acids, and steroid hormones. Next, we estimated their heritability in a twin cohort of 1300 twins (age range: 5.7 - 12.9 years). We observed associations between age and 50 metabolites and between sex and 21 metabolites. The mean monozygotic (MZ) and dizygotic (DZ) correlations for urinary metabolites were 0.51 (range: 0.25-0.75) and 0.16 (range: 0.01-0.46) for the amines, 0.52 (range: 0.33-0.64) and 0.23 (range: 0.07-0.35) for the organic acids, and 0.61 (range: 0.43-0.81) and 0.25 (range: 0.11-0.44) for the steroids. Broad-sense heritability was 0.49 (range: 0.25-0.64), 0.50 (range: 0.33-0.62), and 0.64 (range: 0.43-0.81) for 50 amines, 13 organic acids, and 6 steroids, and narrow-sense heritability was 0.50 (range: 0.37-0.68), 0.50 (0.23-0.61), and 0.47 (range: 0.32-0.70) for 6 amines, 7 organic acids, and 4 steroids. We conclude that urinary metabolites in children have substantial heritability, with similar estimates for amines and organic acids, and higher estimates for steroid hormones.

genomics↗