Mitochondrial chaotic dynamics: Redox-energetic behavior at the edge of stability

Mitochondria serve multiple key cellular functions, including energy generation, redox balance, and regulation of apoptotic cell death, thus making a major impact on healthy and diseased states. Increasingly recognized is that biological network stability/instability can play critical roles in determining health and disease. We report for the first-time mitochondrial chaotic dynamics, characterizing the conditions leading from stability to chaos in this organelle. Using an experimentally validated computational model of mitochondrial function, we show that complex oscillatory dynamics in key metabolic variables, arising at the “edge” between fully functional and pathological behavior, sets the stage for chaos. Under these conditions, a mild, regular sinusoidal redox forcing perturbation triggers chaotic dynamics with main signature traits such as sensitivity to initial conditions, positive Lyapunov exponents, and strange attractors. At the “edge” mitochondrial chaos is exquisitely sensitive to the antioxidant capacity of matrix Mn superoxide dismutase as well as to the amplitude and frequency of the redox perturbation. These results have potential implications both for mitochondrial signaling determining health maintenance, and pathological transformation, including abnormal cardiac rhythms.publishedVersionKembro, Jackelyn Melissa. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina.Kembro, Jackelyn Melissa. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Biológicas y Tecnológicas; Argentina.Cortassa, Sonia. National Institutes of Health. NIH · NIA Intramural Research Program; Estados Unidos.Lloyd, David. Cardiff University. School of Biosciences 1; Inglaterra.Sollot, Steven. Johns Hopkins University. Laboratory of Cardiovascular Science; Estados Unidos.Sollot, Steven. Johns Hopkins University. Laboratory of Cardiovascular Science; Estados Unidos

Volatile organic compounds in uremia.

BACKGROUND: Although "uremic fetor" has long been felt to be diagnostic of renal failure, the compounds exhaled in uremia remain largely unknown so far. The present work investigates whether breath analysis by ion mobility spectrometry can be used for the identification of volatile organic compounds retained in uremia. METHODS: Breath analysis was performed in 28 adults with an eGFR ≥ 60 ml/min per 1.73 m(2), 26 adults with chronic renal failure corresponding to an eGFR of 10-59 ml/min per 1.73 m(2), and 28 adults with end-stage renal disease (ESRD) before and after a hemodialysis session. Breath analysis was performed by ion mobility spectrometryafter gas-chromatographic preseparation. Identification of the compounds of interest was performed by thermal desorption gas chromatography/mass spectrometry. RESULTS: Breath analyses revealed significant differences in the spectra of patients with and without renal failure. Thirteen compounds were chosen for further evaluation. Some compounds including hydroxyacetone, 3-hydroxy-2-butanone and ammonia accumulated with decreasing renal function and were eliminated by dialysis. The concentrations of these compounds allowed a significant differentiation between healthy, chronic renal failure with an eGFR of 10-59 ml/min, and ESRD (p<0.05 each). Other compounds including 4-heptanal, 4-heptanone, and 2-heptanone preferentially or exclusively occurred in patients undergoing hemodialysis. CONCLUSION: Impairment of renal function induces a characteristic fingerprint of volatile compounds in the breath. The technique of ion mobility spectrometry can be used for the identification of lipophilic uremic retention molecules

Volatile Organic Compounds in Uremia

BACKGROUND: Although “uremic fetor” has long been felt to be diagnostic of renal failure, the compounds exhaled in uremia remain largely unknown so far. The present work investigates whether breath analysis by ion mobility spectrometry can be used for the identification of volatile organic compounds retained in uremia. METHODS: Breath analysis was performed in 28 adults with an eGFR ≥60 ml/min per 1.73 m(2), 26 adults with chronic renal failure corresponding to an eGFR of 10–59 ml/min per 1.73 m(2), and 28 adults with end-stage renal disease (ESRD) before and after a hemodialysis session. Breath analysis was performed by ion mobility spectrometryafter gas-chromatographic preseparation. Identification of the compounds of interest was performed by thermal desorption gas chromatography/mass spectrometry. RESULTS: Breath analyses revealed significant differences in the spectra of patients with and without renal failure. Thirteen compounds were chosen for further evaluation. Some compounds including hydroxyacetone, 3-hydroxy-2-butanone and ammonia accumulated with decreasing renal function and were eliminated by dialysis. The concentrations of these compounds allowed a significant differentiation between healthy, chronic renal failure with an eGFR of 10–59 ml/min, and ESRD (p<0.05 each). Other compounds including 4-heptanal, 4-heptanone, and 2-heptanone preferentially or exclusively occurred in patients undergoing hemodialysis. CONCLUSION: Impairment of renal function induces a characteristic fingerprint of volatile compounds in the breath. The technique of ion mobility spectrometry can be used for the identification of lipophilic uremic retention molecules

Receiver-operating-characteristic (ROC) curves to distinguish different stages of renal failure.

<p>ROC curves for the sum of the signal intensities of hydroxyacetone, hydroxy-2-butanone, ammonia, 0.5468–17.0, and 0.5985–55.6 in differentiating (A) healthy subjects and patients with chronic renal failure (CRF) corresponding to an eGFR of 10–59 ml/min per 1.73 m² (AUC 0.76), (B) healthy subjects and patients with endstage renal disease (ESRD, AUC 0.83), and (C) healthy subjects and all patients with impaired renal function (CRF and ESRD; AUC 0.80).</p

Signal intensities of exemplary analytes that accumulate with decreasing renal function and are eliminated by dialysis.

<p>Figures A–C present signal intensities of exemplary analytes P1–P3 and Figure D the sum of the signal intensities of the five analytes that accumulate with decreasing function and are eliminated by dialysis (P1–P5) in 28 healthy controls, 26 patients with chronic renal failure (CRF) stage 2–4 according to K/DOQI-criteria, 28 patients with end-stage renal disease (ESRD, CRF stage 5D) prior to and 22 after hemodialysis. Signal intensities were tested for statistical significance by two-tailed t-tests; *p<0.05, **p<0.01, ***p<0.001.</p

Representative multi-capillary column/ion mobility spectra (MCC/IMS) of breath samples.

<p>Breath sample of (A) a healthy adult, (B) an end-stage renal disease proband before and (C) after hemodialysis treatment. Areas of interest are marked and labeled by numbers. Substances corresponding to these numbers are given in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0046258#pone-0046258-t002" target="_blank">Table 2</a>. Signal intensity is coded by colours (yellow: very high; red high, blue: moderate, white: no signal).</p

Signal intensities of exemplary analytes that accumulate during hemodialysis.

<p>Signal intensities of analytes P9 and P11 in 28 healthy controls, 26 patients with chronic renal failure (CRF) stage 2–4 according to K/DOQI-criteria, 28 patients with end-stage renal disease (ESRD, CRF stage 5D) prior to and 22 after hemodialysis. Signal intensities were tested for statistical significance by two-tailed t-tests; *p<0.05, **p<0.01, ***p<0.001.</p

Scheme of an ion mobility spectrometer (MCC/IMS).

<p>The multi-capillary column (MCC) provides a preseparation of the molecules in the gas phase. In the ionization chamber proton transfer from the reactant ions to the analyte molecules takes place, thus forming protonated analyte ions. The drift time of the ions in the electric field depends on size and shape of the analytes. The retention time in the MCC and mobility in the IMS characterize the identity of the analyte. The intensity of the signal is a measure of the analyte's concentration.</p

Experimental parameters of the multi-capillary column ion mobility spectrometer (MCC/IMS) as used in the present study.

*<p>Multichrom, Novosibirsk, Russia.</p