Experimental Computational and Structural Biochemistry

Through the integration of mathematical foundation with computational programming in Matlab, R and Python we have been developing algorithms to process and represent complex neural systems regarding metabolism and NGS. 

Our research involves the application of computational and mathematical techniques to problems in biomedicine. 

We integrate novel techniques in computer science and programming to address multidisciplinary fields and approaches. 

Our group is dedicated to the development of computational models for the understanding of complex neural systems including potential therapeutic molecules, neurobiochemistry, ion channels, neurosciences, and physiology and regulation systems.

We developed the free platform Ansep (Astrocyte-Neuron Simulation Enviroment), which is available for the scientific community for the model and analysis of metabolic Networks, offering tools that contribute to a better understanding of neurodegeneration processes. 

Recent Publications

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

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 Alzheimeŕs, 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.

Abstract

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.

Abstract

The central function of telomerase is maintaining the telomere length. However, several extra-telomeric roles have been identified for this protein complex. In this study, we evaluated the effect of the silencing of the catalytic subunit of telomerase (TERT) on the expression of candidate microRNAs, cell activation markers and glial-related genes in a glioblastoma cell line (T98G). The silencing was performed by a siRNA and the qPCR method was used to analyze the expression of TERT and downstream genes. Flow cytometry was used to quantify the TERT protein, and bioinformatics analysis was carried out to analyze the functions of microRNA target genes. Here, it was observed that after a 50% reduction of the TERT gene, the expression of ARG1 (Arginase 1) was upregulated, whereas NES (Nestin), GLUL (Glutamate-Ammonia Ligase), VIM (Vimentin) and the hsa-miR-29b-3p microRNA were downregulated (P-value <0.05). A bioinformatic analysis showed that target genes of hsa-miR-29b are associated with focal adhesion, the PI3K-Akt signaling pathway, among others. These results are important because they contribute to the knowledge of extratelomeric functions by providing relevant evidence about novel genes modulated by TERT. 

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

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.

Use of a neuron-glia genome-scale metabolic reconstruction to model the metabolic consequences of the Arylsulphatase a deficiency through a systems biology approach
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Abstract 

Metachromatic leukodystrophy (MLD) is a human neurodegenerative disorder characterized by progressive damage on the myelin band in the nervous system. MLD is caused by the impaired function of the lysosomal enzyme Arylsulphatase A (ARSA). The physiopathology mechanisms and the biochemical consequences in the brain of ARSA deficiency are not entirely understood. In recent years, the use of genome-scale metabolic (GEM) models has been explored as a tool for the study of the biochemical alterations in MLD. Previously, we modeled the metabolic consequences of different lysosomal storage diseases using single GEMs. In the case of MLD, using a glia GEM, we previously predicted that the metabolism of glycosphingolipids and neurotransmitters was altered. The results also suggested that mitochondrial metabolism and amino acid transport were the main reactions affected. In this study, we extended the modeling of the metabolic consequences of ARSA deficiency through the integration of neuron and glial cell metabolic models. Cell-specific models were generated from Recon2, and these were used to create a neuron-glial bi-cellular model. We propose a workflow for the integration of this type of model and its subsequent study. The results predicted the impairment pathways involved in the transport of amino acids, lipids metabolism, and catabolism of purines and pyrimidines. The use of this neuron-glial GEM metabolic reconstruction allowed to improve the prediction capacity of the metabolic consequences of ARSA deficiency, which might pave the way for the modeling of the biochemical alterations of other inborn errors of metabolism with central nervous system involvement.

Modulation of Small RNA Signatures by Astrocytes on Early Neurodegeneration Stages;Implications for Biomarker Discovery
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Abstract

Diagnosis of neurodegenerative disease (NDD) is complex, therefore simpler, less invasive, more accurate biomarkers are needed. small non-coding RNA (sncRNA) dysregulates in NDDs and sncRNA signatures have been explored for the diagnosis of NDDs, however, the performance of previous biomarkers is still better. Astrocyte dysfunction promotes neurodegeneration and thus derived scnRNA signatures could provide a more precise way to identify of changes related to NDD course and pathogenesis, and it could be useful for the dissection of mechanistic insights operating in NDD. Often sncRNA are transported outside the cell by the action of secreted particles such as extracellular vesicles (EV), which protect sncRNA from degradation. Furthermore, EV associated sncRNA can cross the BBB to be found in easier to obtain peripheral samples, EVs also inherit cell-specific surface markers that can be used for the identification of Astrocyte Derived Extracellular Vesicles (ADEVs) in a peripheral sample. By the study of the sncRNA transported in ADEVs it is possible to identify astrocyte specific sncRNA signatures that could show astrocyte dysfunction in a more simpler manner than previous methods. However, sncRNA signatures in ADEV are not a copy of intracellular transcriptome and methodological aspects such as the yield of sncRNA produced in ADEV or the variable amount of ADEV captured after separation protocols must be considered. Here we review the role as signaling molecules of ADEV derived sncRNA dysregulated in conditions associated with risk of neurodegeneration, providing an explanation of why to choose ADEV for the identification of astrocyte-specific transcriptome. Finally, we discuss possible limitations of this approach and the need to improve the detection limits of sncRNA for the use of ADEV derived sncRNA signatures.


In silico interactions of statins with cell death-inducing DNA fragmentation factor-like effector A (CIDEA)
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Abstract

Statins are the low-density lipoproteins (LDL)-cholesterol-lowering drugs of first choice and are used to prevent the increased risk of cardiovascular and cerebrovascular diseases. Although some of their effects are well known, little is known about their ability to regulate other lipid-related proteins which control apoptotic mechanisms. The aim of this study was to explore whether statins can bind to cell death-inducing DNA fragmentation factor-like effector A (CIDEA), which might be a possible pleiotropic mechanism of action of these drugs on the modulation of apoptosis and lipid metabolism. The structures of statins were subjected to molecular docking and dynamics with the human CIDEA protein to investigate the interaction pattern and identify which residues are important. The docking results indicated that atorvastatin and rosuvastatin showed the best interaction energy (−8.51 and −8.04 kcal/mol, respectively) followed by fluvastatin (−7.39), pitavastatin (−6.5), lovastatin (−6.23), pravastatin (−6.04) and simvastatin (−5.29). Atorvastatin and rosuvastatin were further subjected to molecular dynamics at 50 ns with CIDEA and the results suggested that rosuvastatin-CIDEA complex had lower root-mean square deviation and root-mean square fluctuation when compared with atorvastatin-CIDEA. Since two arginine residues -ARG19 and ARG22-were identified to be common for the interaction with CIDEA, a single-point mutation was induced in these residues to determine whether they are important for binding interaction. Mutation of these two residues seemed to affect mostly the interaction of atorvastatin with CIDEA, suggesting that they are important for the binding and therefore indicate another possible metabolic mechanism of the pleiotropic effects of this statin.

Recent Book Chapters

In Silico Identification of Novel Interactions for FABP5 (Fatty Acid-Binding Protein 5) with Nutraceuticals: Possible Repurposing Approach


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Cabezas, R., Sahebkar, A., Echeverria, V., González, J., Md Ashraf, G., & Barreto, G. E. (2021). In Silico Identification of Novel Interactions for FABP5 (Fatty Acid-Binding Protein 5) with Nutraceuticals: Possible Repurposing Approach. En Barreto, G. E., & Sahebkar, A. (Eds.), Pharmacological Properties of Plant-Derived Natural Products and Implications for Human Health (pp 589-599). Suiza: Springer Nature Switzerland AG.

 

Neuroprotective Role of Hypothermia in Hypoxic-ischemic Brain Injury: Combined Therapies using Estrogen


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Barreto, G. E., Saraceno, E., Gonzalez, J., Kolliker, R., Castilla, R., & Capani F. (2015). Chapter 8 - Neuroprotection with Estradiol in Experimental Perinatal Asphyxia: A New Approach. En Duncan, K. E. (Ed.), Estrogen Effects on Traumatic Brain Injury (pp. 113-124). United States of America: Elsevier.

 

Neuroprotection by Exogenous Estrogenic Compounds Following Traumatic Brain Injury


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Lopez-Rodriguez, A. B., Ávila-Rodriguez, M., Vega-Vela, N. E., Capani, F., Gonzalez, J., García-Segura, L. M., & Barreto G. E. (2015). Chapter 6 - Neuroprotection by Exogenous Estrogenic Compounds Following Traumatic Brain Injury. En Duncan, K. E. (Ed.), Estrogen Effects on Traumatic Brain Injury (pp. 73-90). United States of America: Elsevier.

 

Natural Antioxidants in Dementia: An Overview 


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Cabezas, R., Avila, M. F., Torrente, D., Gonzalez, J., Santos El-Bachá, R., Guedes, R., & Barreto, G. E. (2015). Chapter 76 - Natural Antioxidants in Dementia: An Overview. En Martin, C. R., & Preedy, V. R. (Eds.), Diet and Nutrition in Dementia and Cognitive Decline (pp. 827-836). United States of America: Elsevier.

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