Palmitic Acid Upregulates Type I Interferon-Mediated Antiviral Response and Cholesterol Biosynthesis in Human Astrocytes.

Chronic intake of a high-fat diet increases saturated fatty acids in the brain causing the progression of neurodegenerative diseases. Palmitic acid is a free fatty acid abundant in the diet that at high concentrations may penetrate the blood-brain barrier and stimulate the production of pro-inflammatory cytokines, leading to inflammation in astrocytes. The use of the synthetic neurosteroid tibolone in protection against fatty acid toxicity is emerging, but its transcriptional effects on palmitic acid-induced lipotoxicity remain unclear. Herein, we performed a transcriptome profiling of normal human astrocytes to investigate the molecular mechanisms by which palmitic acid causes cellular damage to astrocytes, and whether tibolone could reverse its detrimental effects. Astrocytes undergo a profound transcriptional change at 2 mM palmitic acid, affecting the expression of 739 genes, 366 upregulated and 373 downregulated. However, tibolone at 10 nM does not entirely reverse palmitic acid effects. Additionally, the protein-protein interaction reveals two novel gene clustering modules. The first module involves astrocyte defense responses by upregulation of pathways associated with antiviral innate immunity, and the second is linked to lipid metabolism. Our data suggest that activation of viral response signaling pathways might be so far, the initial molecular mechanism of astrocytes in response to a lipotoxic insult by palmitic acid, triggered particularly upon increased expression levels of IFIT2, IRF1, and XAF1. Therefore, this novel approach using a global gene expression analysis may shed light on the pleiotropic effects of palmitic acid on astrocytes, and provide a basis for future studies addressed to elucidate these responses in neurodegenerative conditions, which is highly valuable for the design of therapeutic strategies.

Metabolomic Analysis of Human Astrocytes in Lipotoxic Condition: Potential Biomarker Identification by Machine Learning Modeling.

The association between neurodegenerative diseases (NDs) and obesity has been well studied in recent years. Obesity is a syndrome of multifactorial etiology characterized by an excessive accumulation and release of fatty acids (FA) in adipose and non-adipose tissue. An excess of FA generates a metabolic condition known as lipotoxicity, which triggers pathological cellular and molecular responses, causing dysregulation of homeostasis and a decrease in cell viability. This condition is a hallmark of NDs, and astrocytes are particularly sensitive to it, given their crucial role in energy production and oxidative stress management in the brain. However, analyzing cellular mechanisms associated with these conditions represents a challenge. In this regard, metabolomics is an approach that allows biochemical analysis from the comprehensive perspective of cell physiology. This technique allows cellular metabolic profiles to be determined in different biological contexts, such as those of NDs and specific metabolic insults, including lipotoxicity. Since data provided by metabolomics can be complex and difficult to interpret, alternative data analysis techniques such as machine learning (ML) have grown exponentially in areas related to omics data. Here, we developed an ML model yielding a 93% area under the receiving operating characteristic (ROC) curve, with sensibility and specificity values of 80% and 93%, respectively. This study aimed to analyze the metabolomic profiles of human astrocytes under lipotoxic conditions to provide powerful insights, such as potential biomarkers for scenarios of lipotoxicity induced by palmitic acid (PA). In this work, we propose that dysregulation in seleno-amino acid metabolism, urea cycle, and glutamate metabolism pathways are major triggers in astrocyte lipotoxic scenarios, while increased metabolites such as alanine, adenosine, and glutamate are suggested as potential biomarkers, which, to our knowledge, have not been identified in human astrocytes and are proposed as candidates for further research and validation.

Integrated Metabolomics and Lipidomics Reveal High Accumulation of Glycerophospholipids in Human Astrocytes under the Lipotoxic Effect of Palmitic Acid and Tibolone Protection.

Lipotoxicity is a metabolic condition resulting from the accumulation of free fatty acids in non-adipose tissues which involves a series of pathological responses triggered after chronic exposure to high levels of fatty acids, severely detrimental to cellular homeostasis and viability. In brain, lipotoxicity affects both neurons and other cell types, notably astrocytes, leading to neurodegenerative processes, such as Alzheimer (AD) and Parkinson diseases (PD). In this study, we performed for the first time, a whole lipidomic characterization of Normal Human Astrocytes cultures exposed to toxic concentrations of palmitic acid and the protective compound tibolone, to establish and identify the set of potential metabolites that are modulated under these experimental treatments. The study covered 3843 features involved in the exo- and endo-metabolome extracts obtained from astrocytes with the mentioned treatments. Through multivariate statistical analysis such as PCA (principal component analysis), partial least squares (PLS-DA), clustering analysis, and machine learning enrichment analysis, it was possible to determine the specific metabolites that were affected by palmitic acid insult, such as phosphoethanolamines, phosphoserines phosphocholines and glycerophosphocholines, with their respective metabolic pathways impact. Moreover, our results suggest the importance of tibolone in the generation of neuroprotective metabolites by astrocytes and may be relevant to the development of neurodegenerative processes.

The Emerging Role of Long Non-Coding RNAs and MicroRNAs in Neurodegenerative Diseases: A Perspective of Machine Learning.

Neurodegenerative diseases (NDs) are characterized by progressive neuronal dysfunction and death of brain cells population. As the early manifestations of NDs are similar, their symptoms are difficult to distinguish, making the timely detection and discrimination of each neurodegenerative disorder a priority. Several investigations have revealed the importance of microRNAs and long non-coding RNAs in neurodevelopment, brain function, maturation, and neuronal activity, as well as its dysregulation involved in many types of neurological diseases. Therefore, the expression pattern of these molecules in the different NDs have gained significant attention to improve the diagnostic and treatment at earlier stages. In this sense, we gather the different microRNAs and long non-coding RNAs that have been reported as dysregulated in each disorder. Since there are a vast number of non-coding RNAs altered in NDs, some sort of synthesis, filtering and organization method should be applied to extract the most relevant information. Hence, machine learning is considered as an important tool for this purpose since it can classify expression profiles of non-coding RNAs between healthy and sick people. Therefore, we deepen in this branch of computer science, its different methods, and its meaningful application in the diagnosis of NDs from the dysregulated non-coding RNAs. In addition, we demonstrate the relevance of machine learning in NDs from the description of different investigations that showed an accuracy between 85% to 95% in the detection of the disease with this tool. All of these denote that artificial intelligence could be an excellent alternative to help the clinical diagnosis and facilitate the identification diseases in early stages based on non-coding RNAs.

Brain lipidomics as a rising field in neurodegenerative contexts: Perspectives with Machine Learning approaches.

Lipids are essential for cellular functioning considering their role in membrane composition, signaling, and energy metabolism. The brain is the second most abundant organ in terms of lipid concentration and diversity only after adipose tissue. However, in the central system (CNS) lipid dysregulation has been linked to the etiology, progression, and severity of neurodegenerative diseases such as Alzheimers, Parkinson, and Multiple Sclerosis. Advances in the human genome and subsequent sequencing technologies allowed us the study of lipidomics as a promising approach to diagnosis and treatment of neurodegeneration. Lipidomics advances rapidly increased the amount and quality of data allowing the integration with other omic types as well as implementing novel bioinformatic and quantitative tools such as machine learning (ML). Integration of lipidomics data with ML, as a powerful quantitative predictive approach, led to improvements in diagnostic biomarker prediction, clinical data integration, network, and systems approaches for neural behavior, novel etiology markers for inflammation, and neurodegeneration progression and even Mass Spectrometry image analysis. In this sense, by exploiting lipidomics data with ML is possible to improve the identification of new biomarkers or unveil new molecular mechanisms associated with lipid impairment across neurodegeneration. In this review, we present the lipidomic neurobiology state-of-the-art highlighting its potential applications to study neurodegenerative conditions. Also, we present theoretical background, applications, and advances in the integration of lipidomics with ML. This review opens the door to new approaches in this rising field.

Advances in Astrocyte Computational Models: From Metabolic Reconstructions to Multi-omic Approaches.

The growing importance of astrocytes in the field of neuroscience has led to a greater number of computational models devoted to the study of astrocytic functions and their metabolic interactions with neurons. The modeling of these interactions demands a combined understanding of brain physiology and the development of computational frameworks based on genomic-scale reconstructions, system biology, and dynamic models. These computational approaches have helped to highlight the neuroprotective mechanisms triggered by astrocytes and other glial cells, both under normal conditions and during neurodegenerative processes. In the present review, we evaluate some of the most relevant models of astrocyte metabolism, including genome-scale reconstructions and astrocyte-neuron interactions developed in the last few years. Additionally, we discuss novel strategies from the multi-omics perspective and computational models of other glial cell types that will increase our knowledge in brain metabolism and its association with neurodegenerative diseases.

Tibolone Ameliorates the Lipotoxic Effect of Palmitic Acid in Normal Human Astrocytes.

Lipotoxicity is a pathological condition resulting from the excessive accumulation of fatty acids, like palmitic acid (PA), within the cell. This pathological phenomenon induces deleterious metabolic changes in cells and is associated with neurodegenerative diseases, dyslipidemia, and obesity. Recent evidence has demonstrated that tibolone, a synthetic steroid, protects cellular damage through various mechanisms; but its underlying actions upon lipotoxic damage are unknown. In this study, we assessed the effects of tibolone administration on normal human astrocytes subject to supraphysiological levels of palmitic acid as a model to induce cytotoxicity. Our results demonstrated that tibolone attenuated lipotoxic damage of PA in normal human astrocytes by reducing PI uptake in 53%, prevented cardiolipin loss by 17%, reduced fragmented/condensed nuclei by 50.81% and attenuated the production of superoxide ions by around 20%. In conclusion, these data suggest that protective effects of tibolone against lipotoxicity may be mediated, in part, through modulation of the different cellular mechanisms of astrocytes.

Astrocytes Mediate Protective Actions of Estrogenic Compounds after Traumatic Brain Injury.

Traumatic brain injury (TBI) is a serious public health problem. It may result in severe neurological disabilities and in a variety of cellular metabolic alterations for which available therapeutic strategies are limited. In the last decade, the use of estrogenic compounds, which activate protective mechanisms in astrocytes, has been explored as a potential experimental therapeutic approach. Previous works have suggested estradiol (E2) as a neuroprotective hormone that acts in the brain by binding to estrogen receptors (ERs). Several steroidal and nonsteroidal estrogenic compounds can imitate the effects of estradiol on ERs. These include hormonal estrogens, phytoestrogens and synthetic estrogens, such as selective ER modulators or tibolone. Current evidence of the role of astrocytes in mediating protective actions of estrogenic compounds after TBI is reviewed in this paper. We conclude that the use of estrogenic compounds to modulate astrocytic properties is a promising therapeutic approach for the treatment of TBI.

Astrocytes and endoplasmic reticulum stress: A bridge between obesity and neurodegenerative diseases.

Endoplasmic reticulum (ER) is a subcellular organelle involved in protein folding and processing. ER stress constitutes a cellular process characterized by accumulation of misfolded proteins, impaired lipid metabolism and induction of inflammatory responses. ER stress has been suggested to be involved in several human pathologies, including neurodegenerative diseases and obesity. Different studies have shown that both neurodegenerative diseases and obesity trigger similar cellular responses to ER stress. Moreover, both diseases are assessed in astrocytes as evidences suggest these cells as key regulators of brain homeostasis. However, the exact contributions to the effects of ER stress in astrocytes in the various neurodegenerative diseases and its relation with obesity are not well known. Here, we discuss recent advances in the understanding of molecular mechanisms that regulate ER stress-related disorders in astrocytes such as obesity and neurodegeneration. Moreover, we outline the correlation between the activated proteins of the unfolded protein response (UPR) in these pathological conditions in order to identify possible therapeutic targets for ER stress in astrocytes. We show that ER stress in astrocytes shares UPR activation pathways during both obesity and neurodegenerative diseases, demonstrating that UPR related proteins like ER chaperone GRP 78/Bip, PERK pathway and other exogenous molecules ameliorate UPR response and promote neuroprotection.

Genome-Scale Reconstruction of the Human Astrocyte Metabolic Network.

Astrocytes are the most abundant cells of the central nervous system; they have a predominant role in maintaining brain metabolism. In this sense, abnormal metabolic states have been found in different neuropathological diseases. Determination of metabolic states of astrocytes is difficult to model using current experimental approaches given the high number of reactions and metabolites present. Thus, genome-scale metabolic networks derived from transcriptomic data can be used as a framework to elucidate how astrocytes modulate human brain metabolic states during normal conditions and in neurodegenerative diseases. We performed a Genome-Scale Reconstruction of the Human Astrocyte Metabolic Network with the purpose of elucidating a significant portion of the metabolic map of the astrocyte. This is the first global high-quality, manually curated metabolic reconstruction network of a human astrocyte. It includes 5,007 metabolites and 5,659 reactions distributed among 8 cell compartments, (extracellular, cytoplasm, mitochondria, endoplasmic reticle, Golgi apparatus, lysosome, peroxisome and nucleus). Using the reconstructed network, the metabolic capabilities of human astrocytes were calculated and compared both in normal and ischemic conditions. We identified reactions activated in these two states, which can be useful for understanding the astrocytic pathways that are affected during brain disease. Additionally, we also showed that the obtained flux distributions in the model, are in accordance with literature-based findings. Up to date, this is the most complete representation of the human astrocyte in terms of inclusion of genes, proteins, reactions and metabolic pathways, being a useful guide for in-silico analysis of several metabolic behaviors of the astrocyte during normal and pathologic states.

Relationship Between Obesity, Alzheimer's Disease, and Parkinson's Disease: an Astrocentric View.

Obesity is considered one of the greatest risk to human health and is associated with several factors including genetic components, diet, and physical inactivity. Recently, the relationship between obesity and numerous progressive and aging-related neurodegenerative diseases such as Parkinson's disease (PD) and Alzheimer's disease (AD) have been observed. Thus, the involvement of the most abundant and heterogeneous group of glial cells in neurodegenerative diseases, the astrocytes, is caused by a combination of the failure on their normal homeostatic functions and the increase of toxic metabolites upon pathological event. Upon brain damage, molecular signals induce astrocyte activation and migration to the site of injury, entering in a highly active state, with the aim to contribute to ameliorating or worsening the pathology. In this regard, the aim of this review is to elucidate the relationship between obesity, Alzheimer's disease, and Parkinson's disease and highlight the role of astrocytes in these pathologies.