PyMIDA: A Graphical User Interface for Mass Isotopomer Distribution Analysis.

Mass isotopomer distribution analysis (MIDA) is an analytical technique that measures the synthesis rate of biological polymers using combinatorial probabilities and stable isotope labeling. Over the past few decades, this method has been developed and applied to a wide range of uses that have increased our understanding of metabolism and the etiology and monitoring of disease. There is currently no publicly available piece of software for performing MIDA calculations in a targeted manner without its functionality being limited to a specific use case. We present a cross-platform Python graphical user interface implementation for research to obtain kinetic parameters easily from stable-isotope labeling studies and provide the code and user manual on GitHub.

Measurement of gluconeogenesis by 2H2O labeling and mass isotopomer distribution analysis.

The gluconeogenesis pathway, which converts nonsugar molecules into glucose, is critical for maintaining glucose homeostasis. Techniques that measure flux through this pathway are invaluable for studying metabolic diseases such as diabetes that are associated with dysregulation of this pathway. We introduce a new method that measures fractional gluconeogenesis by heavy water labeling and gas chromatographic-mass spectrometric analysis. This technique circumvents cumbersome benchwork or inference of positionality from mass spectra. The enrichment and pattern of deuterium label on glucose is quantified by use of mass isotopomer distribution analysis, which informs on how much of glucose-6-phosphate-derived glucose comes from the gluconeogenesis (GNG) pathway. We use an in vivo model of the GNG pathway that is based on previously published models but offers a new approach to calculating GNG pathway and subpathway contributions using combinatorial probabilities. We demonstrated that this method accurately quantifies fractional GNG through experiments that perturb flux through the pathway and by probing analytical sensitivity. While this method was developed in mice, the results suggest that it is translatable to humans in a clinical setting.

Aging alters the metabolic flux signature of the ER-unfolded protein response in vivo in mice.

Age is a risk factor for numerous diseases, including neurodegenerative diseases, cancers, and diabetes. Loss of protein homeostasis is a central hallmark of aging. Activation of the endoplasmic reticulum unfolded protein response (UPRER ) includes changes in protein translation and membrane lipid synthesis. Using stable isotope labeling, a flux "signature" of the UPRER in vivo in mouse liver was developed by inducing ER stress with tunicamycin and measuring rates of both proteome-wide translation and de novo lipogenesis. Several changes in protein synthesis across ontologies were noted with age, including a more dramatic suppression of translation under ER stress in aged mice as compared with young mice. Binding immunoglobulin protein (BiP) synthesis rates and mRNA levels were increased more in aged than young mice. De novo lipogenesis rates decreased under ER stress conditions in aged mice, including both triglyceride and phospholipid fractions. In young mice, a significant reduction was seen only in the triglyceride fraction. These data indicate that aged mice have an exaggerated metabolic flux response to ER stress, which may indicate that aging renders the UPRER less effective in resolving proteotoxic stress.

ER Unfolded Protein Response in Liver In Vivo Is Characterized by Reduced, Not Increased, De Novo Lipogenesis and Cholesterol Synthesis Rates with Uptake of Fatty Acids from Adipose Tissue: Integrated Gene Expression, Translation Rates and Metabolic Fluxes.

The unfolded protein response in the endoplasmic reticulum (UPRER) is involved in a number of metabolic diseases. Here, we characterize UPRER-induced metabolic changes in mouse livers in vivo through metabolic labeling and mass spectrometric analysis of lipid and proteome-wide fluxes. We induced UPRER by tunicamycin administration and measured synthesis rates of proteins, fatty acids and cholesterol, as well as RNA-seq. Contrary to reports in isolated cells, hepatic de novo lipogenesis and cholesterogenesis were markedly reduced, as were mRNA levels and synthesis rates of lipogenic proteins. H&E staining showed enrichment with lipid droplets while electron microscopy revealed ER morphological changes. Interestingly, the pre-labeling of adipose tissue prior to UPRER induction resulted in the redistribution of labeled fatty acids from adipose tissue to the liver, with replacement by unlabeled glycerol in the liver acylglycerides, indicating that the liver uptake was of free fatty acids, not whole glycerolipids. The redistribution of adipose fatty acids to the liver was not explicable by altered plasma insulin, increased fatty acid levels (lipolysis) or by reduced food intake. Synthesis of most liver proteins was suppressed under UPRER conditions, with the exception of BiP, other chaperones, protein disulfide isomerases, and proteins of ribosomal biogenesis. Protein synthesis rates generally, but not always, paralleled changes in mRNA. In summary, this combined approach, linking static changes with fluxes, revealed an integrated reduction of lipid and cholesterol synthesis pathways, from gene expression to translation and metabolic flux rates, under UPRER conditions. The reduced lipogenesis does not parallel human fatty liver disease. This approach provides powerful tools to characterize metabolic processes underlying hepatic UPRER in vivo.

An Integrated Systems Genetics and Omics Toolkit to Probe Gene Function.

Identifying genetic and environmental factors that impact complex traits and common diseases is a high biomedical priority. Here, we developed, validated, and implemented a series of multi-layered systems approaches, including (expression-based) phenome-wide association, transcriptome-/proteome-wide association, and (reverse-) mediation analysis, in an open-access web server (systems-genetics.org) to expedite the systems dissection of gene function. We applied these approaches to multi-omics datasets from the BXD mouse genetic reference population, and identified and validated associations between genes and clinical and molecular phenotypes, including previously unreported links between Rpl26 and body weight, and Cpt1a and lipid metabolism. Furthermore, through mediation and reverse-mediation analysis we established regulatory relations between genes, such as the co-regulation of BCKDHA and BCKDHB protein levels, and identified targets of transcription factors E2F6, ZFP277, and ZKSCAN1. Our multifaceted toolkit enabled the identification of gene-gene and gene-phenotype links that are robust and that translate well across populations and species, and can be universally applied to any populations with multi-omics datasets.