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Phenolic compounds derived from olive oil have beneficial health properties against cancer, neurodegenerative, and metabolic diseases. Therefore, there are discrepancies in their impact on mitochondrial function that result in changes in oxidative capacity, mitochondrial respiration, and energetic demands. This review focuses on the versatile role of oleuropein, a potent antioxidant that regulates the AMPK/SIRT1/mTOR pathway to modulate autophagy/mitophagy and maintain metabolic homeostasis.

Repeat expansion diseases are a group of neuromuscular and neurodegenerative disorders characterized by expansions of several successive repeated DNA sequences. Currently, more than 50 repeat expansion diseases have been described. These disorders involve diverse pathogenic mechanisms, including loss-of-function mechanisms, toxicity associated with repeat RNA, or repeat-associated non-ATG (RAN) products, resulting in impairments of cellular processes and damaged organelles. Mitochondria, double membrane organelles, play a crucial role in cell energy production, metabolic processes, calcium regulation, redox balance, and apoptosis regulation. Its dysfunction has been implicated in the pathogenesis of repeat expansion diseases. In this review, we provide an overview of the signaling pathways or proteins involved in mitochondrial functioning described in these disorders. The focus of this review will be on the analysis of published data related to three representative repeat expansion diseases: Huntington’s disease, C9orf72-frontotemporal dementia/amyotrophic lateral sclerosis, and myotonic dystrophy type 1. We will discuss the common effects observed in all three repeat expansion disorders and their differences. Additionally, we will address the current gaps in knowledge and propose possible new lines of research. Importantly, this group of disorders exhibit alterations in mitochondrial dynamics and biogenesis, with specific proteins involved in these processes having been identified. Understanding the underlying mechanisms of mitochondrial alterations in these disorders can potentially lead to the development of neuroprotective strategies.

The identification of Parkinson’s disease (PD) biomarkers has become a main goal for the diagnosis of this neurodegenerative disorder. PD has not only been intrinsically related to neurological problems, but also to a series of alterations in peripheral metabolism. The purpose of this study was to identify metabolic changes in the liver in mouse models of PD with the scope of finding new peripheral biomarkers for PD diagnosis. To achieve this goal, we used mass spectrometry technology to determine the complete metabolomic profile of liver and striatal tissue samples from WT mice, 6-hydroxydopamine-treated mice (idiopathic model) and mice affected by the G2019S-LRRK2 mutation in LRRK2/PARK8 gene (genetic model). This analysis revealed that the metabolism of carbohydrates, nucleotides and nucleosides was similarly altered in the liver from the two PD mouse models. However, long-chain fatty acids, phosphatidylcholine and other related lipid metabolites were only altered in hepatocytes from G2019S-LRRK2 mice. In summary, these results reveal specific differences, mainly in lipid metabolism, between idiopathic and genetic PD models in peripheral tissues and open up new possibilities to better understand the etiology of this neurological disorder.

Myotonic dystrophy type 1 (DM1) is an autosomal dominant disease caused by a CTG repeat expansion in the 3′ untranslated region of the dystrophia myotonica protein kinase gene. AKT dephosphorylation and autophagy are associated with DM1. Autophagy has been widely studied in DM1, although the endocytic pathway has not. AKT has a critical role in endocytosis, and its phosphorylation is mediated by the activation of tyrosine kinase receptors, such as epidermal growth factor receptor (EGFR). EGF-activated EGFR triggers the internalization and degradation of ligand–receptor complexes that serve as a PI3K/AKT signaling platform. Here, we used primary fibroblasts from healthy subjects and DM1 patients. DM1-derived fibroblasts showed increased autophagy flux, with enlarged endosomes and lysosomes. Thereafter, cells were stimulated with a high concentration of EGF to promote EGFR internalization and degradation. Interestingly, EGF binding to EGFR was reduced in DM1 cells and EGFR internalization was also slowed during the early steps of endocytosis. However, EGF-activated EGFR enhanced AKT and ERK1/2 phosphorylation levels in the DM1-derived fibroblasts. Therefore, there was a delay in EGF-stimulated EGFR endocytosis in DM1 cells; this alteration might be due to the decrease in the binding of EGF to EGFR, and not to a decrease in AKT phosphorylation.

KEAP1 is a cytoplasmic protein that functions as an adaptor for the Cullin-3-based ubiquitin E3 ligase system, which regulates the degradation of many proteins, including NFE2L2/NRF2 and p62/SQSTM1. Loss of KEAP1 leads to an accumulation of protein ubiquitin aggregates and defective autophagy. To better understand the role of KEAP1 in the degradation machinery, we investigated whether Keap1 deficiency affects the endosome-lysosomal pathway. We used KEAP1-deficient mouse embryonic fibroblasts (MEFs) and combined Western blot analysis and fluorescence microscopy with fluorometric and pulse chase assays to analyze the levels of lysosomal-endosomal proteins, lysosomal function, and autophagy activity. We found that the loss of keap1 downregulated the protein levels and activity of the cathepsin D enzyme. Moreover, KEAP1 deficiency caused lysosomal alterations accompanied by an accumulation of autophagosomes. Our study demonstrates that KEAP1 deficiency increases nondegradative lysosomes and identifies a new role for KEAP1 in lysosomal function that may have therapeutic implications.

Behavioral studies differentiate the rodent dorsal striatum (DS) into lateral and medial regions; however, anatomical evidence suggests that it is a unified structure. To understand striatal dynamics and basal ganglia functions, it is essential to clarify the circuitry that supports this behavioral-based segregation. Here, we show that the mouse DS is made of two non-overlapping functional circuits divided by a boundary. Combining in vivo optopatch-clamp and extracellular recordings of spontaneous and evoked sensory activity, we demonstrate different coupling of lateral and medial striatum to the cortex together with an independent integration of the spontaneous activity, due to particular corticostriatal connectivity and local attributes of each region. Additionally, we show differences in slow and fast oscillations and in the electrophysiological properties between striatonigral and striatopallidal neurons. In summary, these results demonstrate that the rodent DS is segregated in two neuronal circuits, in homology with the caudate and putamen nuclei of primates.

Parkinson’s disease (PD) is a neurodegenerative disorder that is characterized by the loss of dopaminergic neurons in the substantia nigra pars compacta. This neuronal loss, inherent to age, is related to exposure to environmental toxins and/or a genetic predisposition. PD-induced cell death has been studied thoroughly, but its characterization remains elusive. To date, several types of cell death, including apoptosis, autophagy-induced cell death, and necrosis, have been implicated in PD progression. In this study, we evaluated necroptosis, which is a programmed type of necrosis, in primary fibroblasts from PD patients with and without the G2019S leucine-rich repeat kinase 2 (LRRK2) mutation and in rotenone-treated cells (SH-SY5Y and fibroblasts). The results showed that programmed necrosis was not activated in the cells of PD patients, but it was activated in cells exposed to rotenone. Necrostatin-1 (Nec-1), an inhibitor of the necroptosis pathway, prevented rotenone-induced necroptosis in PD models. However, Nec-1 affected mitochondrial morphology and failed to protect mitochondria against rotenone toxicity. Therefore, despite the inhibition of rotenone-mediated necroptosis, PD models were susceptible to the effects of both Nec-1 and rotenone.

Although the visual cortex receives information at multiple temporal patterns, much of the research in the field has focused only on intervals shorter than 1 second. Consequently, there is almost no information on what happens at longer temporal intervals. We have tried to address this question recording neuronal populations of the primary visual cortex during visual stimulation with repetitive grating stimuli and intervals ranging from 1 to 7 seconds. Our results showed that firing rate and response stability were dependent of interval duration. In addition, there were collective oscillations with different properties in response to changes in intervals duration. These results suggest that visual cortex could encode visual information at several time scales using oscillations at multiple frequencies.

Machine learning and artificial intelligence have strong roots on principles of neural computation. Some examples are the structure of the first perceptron, inspired in the retina, neuroprosthetics based on ganglion cell recordings or Hopfield networks. In addition, machine learning provides a powerful set of tools to analyze neural data, which has already proved its efficacy in so distant fields of research as speech recognition, behavioral states classification, or LFP recordings. However, despite the huge technological advances in neural data reduction of dimensionality, pattern selection, and clustering during the last years, there has not been a proportional development of the analytical tools used for Time–Frequency (T–F) analysis in neuroscience. Bearing this in mind, we introduce the convenience of using non-linear, non-stationary tools, EMD algorithms in particular, for the transformation of the oscillatory neural data (EEG, EMG, spike oscillations…) into the T–F domain prior to its analysis with machine learning tools. We support that to achieve meaningful conclusions, the transformed data we analyze has to be as faithful as possible to the original recording, so that the transformations forced into the data due to restrictions in the T–F computation are not extended to the results of the machine learning analysis. Moreover, bioinspired computation such as brain–machine interface may be enriched from a more precise definition of neuronal coding where non-linearities of the neuronal dynamics are considered.