The past 20 years have resulted in unprecedented progress in understanding brain energy metabolism and its role in health and disease. In this review, which was initiated at the 14th International Society for Neurochemistry Advanced School, we address the basic concepts of brain energy metabolism and approach the question of why the brain has high energy expenditure. Our review illustrates that the vertebrate brain has a high need for energy because of the high number of neurons and the need to maintain a delicate interplay between energy metabolism, neurotransmission, and plasticity. Disturbances to the energetic balance, to mitochondria quality control or to glia-neuron metabolic interaction may lead to brain circuit malfunction or even severe disorders of the CNS. We cover neuronal energy consumption in neural transmission and basic ('housekeeping') cellular processes. Additionally, we describe the most common (glucose) and alternative sources of energy namely glutamate, lactate, ketone bodies, and medium chain fatty acids. We discuss the multifaceted role of non-neuronal cells in the transport of energy substrates from circulation (pericytes and astrocytes) and in the supply (astrocytes and microglia) and usage of different energy fuels. Finally, we address pathological consequences of disrupted energy homeostasis in the CNS.
Many algorithms to detect copy number variations (CNVs) using exome sequencing (ES) data have been reported and evaluated on their sensitivity and specificity, reproducibility, and precision. However, operational optimization of such algorithms for a better performance has not been fully addressed. ES of 1199 samples including 763 patients with different disease profiles was performed. ES data were analyzed to detect CNVs by both the eXome Hidden Markov Model (XHMM) and modified Nord's method. To efficiently detect rare CNVs, we aimed to decrease sequencing biases by analyzing, at the same time, the data of all unrelated samples sequenced in the same flow cell as a batch, and to eliminate sex effects of X-linked CNVs by analyzing female and male sequences separately. We also applied several filtering steps for more efficient CNV selection. The average number of CNVs detected in one sample was <5. This optimization together with targeted CNV analysis by Nord's method identified pathogenic/likely pathogenic CNVs in 34 patients (4.5%, 34/763). In particular, among 142 patients with epilepsy, the current protocol detected clinically relevant CNVs in 19 (13.4%) patients, whereas the previous protocol identified them in only 14 (9.9%) patients. Thus, this batch-based XHMM analysis efficiently selected rare pathogenic CNVs in genetic diseases.