Mining yeast in silico unearths a golden nugget for mitochondrial biology

Complex II ambiguities-FADH2 in the electron transfer system

The prevailing notion that reduced cofactors NADH and FADH2 transfer electrons from the tricarboxylic acid cycle to the mitochondrial electron transfer system creates ambiguities regarding respiratory Complex II (CII). CII is the only membrane-bound enzyme in the tricarboxylic acid cycle and is part of the electron transfer system of the mitochondrial inner membrane feeding electrons into the coenzyme Q-junction. The succinate dehydrogenase subunit SDHA of CII oxidizes succinate and reduces the covalently bound prosthetic group FAD to FADH2 in the canonical forward tricarboxylic acid cycle. However, several graphical representations of the electron transfer system depict FADH2 in the mitochondrial matrix as a substrate to be oxidized by CII. This leads to the false conclusion that FADH2 from the β-oxidation cycle in fatty acid oxidation feeds electrons into CII. In reality, dehydrogenases of fatty acid oxidation channel electrons to the Q-junction but not through CII. The ambiguities surrounding Complex II in the literature and educational resources call for quality control, to secure scientific standards in current communications of bioenergetics, and ultimately support adequate clinical applications. This review aims to raise awareness of the inherent ambiguity crisis, complementing efforts to address the well-acknowledged issues of credibility and reproducibility.

Mitochondrial Respiratory Defect Causes Dysfunctional Lactate Turnover via AMP-activated Protein Kinase Activation in Human-induced Pluripotent Stem Cell-derived Hepatocytes

A defective mitochondrial respiratory chain complex (DMRC) causes various metabolic disorders in humans. However, the pathophysiology of DMRC in the liver remains unclear. To understand DMRC pathophysiology in vitro, DMRC-induced pluripotent stem cells were generated from dermal fibroblasts of a DMRC patient who had a homoplasmic mutation (m.3398T→C) in the mitochondrion-encoded NADH dehydrogenase 1 (MTND1) gene and that differentiated into hepatocytes (DMRC hepatocytes) in vitro. DMRC hepatocytes showed abnormalities in mitochondrial characteristics, the NAD(+)/NADH ratio, the glycogen storage level, the lactate turnover rate, and AMPK activity. Intriguingly, low glycogen storage and transcription of lactate turnover-related genes in DMRC hepatocytes were recovered by inhibition of AMPK activity. Thus, AMPK activation led to metabolic changes in terms of glycogen storage and lactate turnover in DMRC hepatocytes. These data demonstrate for the first time that energy depletion may lead to lactic acidosis in the DMRC patient by reduction of lactate uptake via AMPK in liver.

Microarray data mining using landmark gene-guided clustering

Background

Clustering is a popular data exploration technique widely used in microarray data analysis. Most conventional clustering algorithms, however, generate only one set of clusters independent of the biological context of the analysis. This is often inadequate to explore data from different biological perspectives and gain new insights. We propose a new clustering model that can generate multiple versions of different clusters from a single dataset, each of which highlights a different aspect of the given dataset.

Results

By applying our SigCalc algorithm to three yeast Saccharomyces cerevisiae datasets we show two results. First, we show that different sets of clusters can be generated from the same dataset using different sets of landmark genes. Each set of clusters groups genes differently and reveals new biological associations between genes that were not apparent from clustering the original microarray expression data. Second, we show that many of these new found biological associations are common across datasets. These results also provide strong evidence of a link between the choice of landmark genes and the new biological associations found in gene clusters.

Conclusion

We have used the SigCalc algorithm to project the microarray data onto a completely new subspace whose co-ordinates are genes (called landmark genes), known to belong to a Biological Process. The projected space is not a true vector space in mathematical terms. However, we use the term subspace to refer to one of virtually infinite numbers of projected spaces that our proposed method can produce. By changing the biological process and thus the landmark genes, we can change this subspace. We have shown how clustering on this subspace reveals new, biologically meaningful clusters which were not evident in the clusters generated by conventional methods. The R scripts (source code) are freely available under the GPL license. The source code is available [see Additional File 1] as additional material, and the latest version can be obtained at . The code is under active development to incorporate new clustering methods and analysis.