Metabolic consequences of mitochondrial coenzyme A deficiency in patients with PANK2 mutations.

Abstract link

Abstract


Pantothenate kinase-associated neurodegeneration (PKAN) is a rare, inborn error of metabolism characterized by iron accumulation in the basal ganglia and by the presence of dystonia, dysarthria, and retinal degeneration. Mutations in pantothenate kinase 2 (PANK2), the rate-limiting enzyme in mitochondrial coenzyme A biosynthesis, represent the most common genetic cause of this disorder. How mutations in this core metabolic enzyme give rise to such a broad clinical spectrum of pathology remains a mystery. To systematically explore its pathogenesis, we performed global metabolic profiling on plasma from a cohort of 14 genetically defined patients and 18 controls. Notably, lactate is elevated in PKAN patients, suggesting dysfunctional mitochondrial metabolism. As predicted, but never previously reported, pantothenate levels are higher in patients with premature stop mutations in PANK2. Global metabolic profiling and follow-up studies in patient-derived fibroblasts also reveal defects in bile acid conjugation and lipid metabolism, pathways that require coenzyme A. These findings raise a novel therapeutic hypothesis, namely, that dietary fats and bile acid supplements may hold potential as disease-modifying interventions. Our study illustrates the value of metabolic profiling as a tool for systematically exploring the biochemical basis of inherited metabolic diseases.

A MetaMapp  mapping and visualization of the 227 metabolites from this study. Node size reflects the fold change in patient/control comparison. Only the the nodes that have passed the p-value criteria of 0.05 are labelled. Red means up and blue means down regulated. Bottom figure is the volcano plot to show the changes. 

Mapping of metabolites detected with QTRAP MS

A positive/negative ion-switching, targeted mass spectrometry-based metabolomics platform for bodily fluids, cells, and fresh and fixed tissue. 

The revival of interest in cancer cell metabolism in recent years has prompted the need for quantitative analytical platforms for studying metabolites from in vivo sources. We implemented a quantitative polar metabolomics profiling platform using selected reaction monitoring with a 5500 QTRAP hybrid triple quadrupole mass spectrometer that covers all major metabolic pathways. The platform uses hydrophilic interaction liquid chromatography with positive/negative ion switching to analyze 258 metabolites (289 Q1/Q3 transitions) from a single 15-min liquid chromatography–mass spectrometry acquisition with a 3-ms dwell time and a 1.55-s duty cycle time. Previous platforms use more than one experiment to profile this number of metabolites from different ionization modes. The platform is compatible with polar metabolites from any biological source, including fresh tissues, cancer cells, bodily fluids and formalin-fixed paraffin-embedded tumor tissue. Relative quantification can be achieved without using internal standards, and integrated peak areas based on total ion current can be used for statistical analyses and pathway analyses across biological sample conditions. The procedure takes ~12 h from metabolite extraction to peak integration for a data set containing 15 total samples (~6 h for a single sample).

Where are the lipids?

mapping of circulating metabolome

Coordination of the transcriptome and metabolome by the circadian clock
PNAS 2012
The circadian clock governs a large array of physiological functions through the transcriptional control of a significant fraction of the genome. Disruption of the clock leads to metabolic disorders, including obesity and diabetes. As food is a potent zeitgeber (ZT) for peripheral clocks, metabolites are implicated as cellular transducers of circadian time for tissues such as the liver. From a comprehensive dataset of over 500 metabolites identified by mass spectrometry, we reveal the coordinate clock-controlled oscillation of many metabolites, including those within the amino acid and carbohydrate metabolic pathways as well as the lipid, nucleotide, and xenobiotic metabolic pathways. Using computational modeling, we present evidence of synergistic nodes between the circadian transcriptome and specific metabolic pathways. Validation of these nodes reveals that diverse metabolic pathways, including the uracil salvage pathway, oscillate in a circadian fashion and in a CLOCK-dependent manner. This integrated map illustrates the coherence within the circadian metabolome, transcriptome, and proteome and how these are connected through specific nodes that operate in concert to achieve metabolic homeostasis.