Carrera de Biotecnología

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    Análisis de variantes de splicing y factores de transcripción (HSF1, CBP y Sp1) en la evolución de la enfermedad de Huntington
    (Universidad Técnica de Ambato. Facultad de Ciencia e Ingeniería en Alimentos y Biotecnología. Carrera de Biotecnología, 2025-02) Silva Gamboa, Christopher Joel; Galarza Galarza, Cristian Fernando
    Huntington's disease (HD) is a neurodegenerative disorder characterized by the expansion of CAG repeats in the HTT (Huntingtin) gene, leading to significant molecular dysfunctions. In the present study, the impact of splicing variants (SV) and transcription factors (TF) on disease progression was investigated using an in-silico approach with sequencing data based on RNA-seq technologies. Differential splicing variants were identified and their possible influence on the generation of aberrant HTT gene isoforms was analysed. Among these variants, the 109CAG condition showed a particularly severe impact on mRNA processing, primarily affecting huntingtin protein stability. In addition, the functional networks of the HSF1, CBP, and Sp1 factors were constructed and analysed. CBP and Sp1 emerged as central nodes, indicating that they play key roles in global epigenetic and genetic regulation, while HSF1 does not show significant connections with the others. These results suggest that interactions between SVs and TFs exacerbate cellular dysfunction, contributing to HD progression. Finally, the research concludes that both splicing variants and transcription factors represent critical components in HD pathogenesis. These findings provide a solid basis for exploring therapeutic interventions aimed at modulating these molecular processes.
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    Evaluación de un modelo de aprendizaje automático basado en redes neuronales para el diagnóstico temprano de la enfermedad de Alzheimer
    (Universidad Técnica de Ambato. Facultad de Ciencia e Ingeniería en Alimentos y Biotecnología. Carrera de Biotecnología, 2025-02) Sarabia Ortiz, Lilián Catalina; Galarza Galarza, Cristian Fernando
    Alzheimer's disease, as the leading cause of dementia, poses a critical challenge in its early detection, where timely interventions can delay its progression. This issue is addressed by evaluating deep learning models based on neural networks to classify magnetic resonance imaging (MRI) scans into different stages of the disease using data from the OASIS and ADNI databases. The importance lies in the need for precise and automated tools to improve early diagnosis, particularly in stages such as mild cognitive impairment. The methodology included constructing a dataset from preprocessed images and applying it to the EfficientNet B7 and ResNet50 architectures. These were trained with advanced techniques such as data augmentation and validated across scenarios involving controlled data, modified images, and new data. Performance metrics such as precision, recall, specificity, F1-score, and ROC-AUC curves were analyzed. The results showed that the EfficientNet B7 architecture outperformed ResNet50 in precision, sensitivity, and specificity, especially in classifying early stages of Alzheimer's disease. EfficientNet B7 demonstrated greater generalization ability, achieving high precision with preprocessed and new images, while ResNet50 showed limitations when working with heterogeneous data. This highlights the importance of modern architectures in solving complex problems like early Alzheimer’s detection, although it also evidences that relying solely on controlled datasets like OASIS and ADNI may limit applicability in real clinical scenarios.
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    Evaluación in silico de microbiomas intestinales para identificar biomarcadores específicos en enfermedades de Crohn y Celíaca usando técnicas de metagenómica comparativa
    (Universidad Técnica de Ambato. Facultad de Ciencia e Ingeniería en Alimentos y Biotecnología. Carrera de Biotecnología, 2025-02) Lucero Chisaguano, Israel Joel; Galarza Galarza, Cristian Fernando
    Understanding the intestinal microbiota is crucial for advancing the diagnosis and treatment of inflammatory diseases such as Crohn's disease (CDr) and Celiac disease (CD). This study employs comparative metagenomics techniques to identify specific biomarkers to differentiate these pathologies. The results highlight the relevance of microbial dysbiosis as a common factor, but also highlight significant differences in the composition and functions of the intestinal microbiota in each disease, which will advance the understanding of these pathologies, and create opportunities for the development of diagnostic tools and innovative therapies. The analysis was performed using 16S rRNA gene samples, processed with the QIIME2 tool under strict sample quality criteria. In parallel, the impact of sex was evaluated by analyzing significant variations in beta microbial diversity in patients with CDr. Functional analysis performed with PICRUSt revealed specific metabolic pathways related to inflammation in CD and oxidative stress in rCD. Microbial differences observed showed an increase in Proteobacteria, along with reductions in Firmicutes and Actinobacteria. In addition, biomarkers were identified in common such as, Faecalibacterium and Lachnospiraceae, and specific ones such as Alistipes in CDr and Lachnoanaerobaculum in CD. These results validate metagenomics as a tool to explore the microbiome-disease relationship, facilitating early diagnosis and the design of personalized therapies aimed at restoring microbial balance, marking a significant milestone in the development of precision medicine for inflammatory bowel diseases.
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    Evaluación in silico de microbiomas intestinales para identificar biomarcadores específicos en enfermedades de Crohn y Celíaca usando técnicas de metagenómica comparativa
    (Universidad Técnica de Ambato. Facultad de Ciencia e Ingeniería en Alimentos y Biotecnología. Carrera de Biotecnología, 2025-02) López Herrera, Alan Joshua; Galarza Galarza, Cristian Fernando
    Understanding the intestinal microbiota is crucial for advancing the diagnosis and treatment of inflammatory diseases such as Crohn's disease (CDr) and Celiac disease (CD). This study employs comparative metagenomics techniques to identify specific biomarkers to differentiate these pathologies. The results highlight the relevance of microbial dysbiosis as a common factor, but also highlight significant differences in the composition and functions of the intestinal microbiota in each disease, which will advance the understanding of these pathologies, and create opportunities for the development of diagnostic tools and innovative therapies. The analysis was performed using 16S rRNA gene samples, processed with the QIIME2 tool under strict sample quality criteria. In parallel, the impact of sex was evaluated by analyzing significant variations in beta microbial diversity in patients with CDr. Functional analysis performed with PICRUSt revealed specific metabolic pathways related to inflammation in CD and oxidative stress in rCD. Microbial differences observed showed an increase in Proteobacteria, along with reductions in Firmicutes and Actinobacteria. In addition, biomarkers were identified in common such as, Faecalibacterium and Lachnospiraceae, and specific ones such as Alistipes in CDr and Lachnoanaerobaculum in CD. These results validate metagenomics as a tool to explore the microbiome-disease relationship, facilitating early diagnosis and the design of personalized therapies aimed at restoring microbial balance, marking a significant milestone in the development of precision medicine for inflammatory bowel diseases.
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    Análisis in silico de la influencia de las redes y vías regulatorias en las funciones biológicas de genes afectados en la Púrpura trombocitopénica inmune (ITP)
    (Universidad Técnica de Ambato. Facultad de Ciencia e Ingeniería en Alimentos y Biotecnología. Carrera de Biotecnología, 2025-02) Jumbo Criollo, Paola Monserrath; Galarza Galarza, Cristian Fernando
    This work highlights the importance of in silico analysis to understand the regulatory networks and pathways associated with Immune Thrombocytopenic Purpura (ITP), an autoimmune disease characterized by a marked decrease in platelet count. Key genes such as HSPA5, CRACD, RELB, HBM, and JUND were identified, which play fundamental roles in processes like apoptosis, inflammation, and platelet homeostasis. These findings provide a solid foundation for the development of targeted and personalized therapies. The methodology employed included bioinformatics tools such as Reactome, Cytoscape, and KEGG, complemented by differential expression analysis (DEG) and functional enrichment. These tools enabled the mapping of genes within regulatory networks and critical metabolic pathways, such as UPR, MAPK, and IL-17. Furthermore, gene and pathway interactions were evaluated, confirming their statistical relevance, and regulatory models were constructed linking these genes to essential functions in the pathogenesis of ITP. Functional analysis suggests innovative therapeutic strategies, such as modulating endoplasmic reticulum stress through HSPA5 and regulating inflammation mediated by RELB and JUND. This comprehensive approach expands the understanding of the molecular mechanisms underlying ITP, proposing alternative pathways that could serve as a basis for developing personalized therapies aimed at improving patients' quality of life.
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    Análisis de la rizósfera de cultivos de fresa selectos de la Unión De Productores Agroecológicos De Tungurahua aplicando técnicas de meta-taxonomía 16s Rrna. Articulado al Proyecto de Investigación aprobado mediante Resolución Nro. UTA-CONIN-2023-0294-R
    (Universidad Técnica de Ambato. Facultad de Ciencia e Ingeniería en Alimentos y Biotecnología. Carrera de Biotecnología, 2025-02) Rojas Fernández, Juliana Antonella; Calero Cáceres, William Ricardo
    cultivated in Tungurahua, Ecuador, highlighting its critical role in soil health and agricultural sustainability. Understanding the composition and functionality of microbial communities in the rhizosphere is essential for developing efficient agricultural practices and enhancing crop resilience to environmental stressors. This research provides foundational insights for future studies exploring the potential of these microbial communities to improve soil fertility and agricultural productivity under specific agroecological conditions. Samples were collected from six representative localities and analyzed using bioinformatics tools such as QIIME 2, EzBiocloud, and PICRUSt to assess microbial diversity and predict functional profiles. Proteobacteria was identified as the dominant phylum, followed by Actinobacteria and Acidobacteria. The highest microbial richness was observed in La Florida and Chiquicha Chico, with Chao1 values surpassing four thousand, whereas Yacupamba exhibited lower diversity due to acidic soils and low organic matter levels. Key metabolic pathways such as nitrogen fixation and antimicrobial compound synthesis were identified, with notable microorganisms including A. xylosoxidans and A. calcoaceticus. Although statistical differences were not significant, the results are highly relevant for generating hypotheses about the relationship between microbial composition and soil health. Expanding the scope of this study with larger sample sizes and integrating advanced techniques like functional metagenomics and metabolomics could facilitate the development of specific biofertilizers and sustainable agricultural strategies, ultimately enhancing crop resilience and productivity under changing environmental conditions.
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    Análisis de la rizósfera de cultivos de fresa selectos de la Unión de Productores Agroecológicos de Tungurahua aplicando técnicas de meta-taxonomía 16s Rrna. Articulado al Proyecto de Investigación aprobado mediante Resolución Nro. UTA-CONIN-2023-0294-R
    (Universidad Técnica de Ambato. Facultad de Ciencia e Ingeniería en Alimentos y Biotecnología. Carrera de Biotecnología, 2025-02) Cajas Corrales, Kathelyn Noemi; Calero Cáceres, William Ricardo
    This study analyzes the rhizosphere microbiota of F. x ananassa Albión Californiana cultivated in Tungurahua, Ecuador, highlighting its critical role in soil health and agricultural sustainability. Understanding the composition and functionality of microbial communities in the rhizosphere is essential for developing efficient agricultural practices and enhancing crop resilience to environmental stressors. This research provides foundational insights for future studies exploring the potential of these microbial communities to improve soil fertility and agricultural productivity under specific agroecological conditions. Samples were collected from six representative localities and analyzed using bioinformatics tools such as QIIME 2, EzBiocloud, and PICRUSt to assess microbial diversity and predict functional profiles. Proteobacteria was identified as the dominant phylum, followed by Actinobacteria and Acidobacteria. The highest microbial richness was observed in La Florida and Chiquicha Chico, with Chao1 values surpassing four thousand, whereas Yacupamba exhibited lower diversity due to acidic soils and low organic matter levels. Key metabolic pathways such as nitrogen fixation and antimicrobial compound synthesis were identified, with notable microorganisms including A. xylosoxidans and A. calcoaceticus. Although statistical differences were not significant, the results are highly relevant for generating hypotheses about the relationship between microbial composition and soil health. Expanding the scope of this study with larger sample sizes and integrating advanced techniques like functional metagenomics and metabolomics could facilitate the development of specific biofertilizers and sustainable agricultural strategies, ultimately enhancing crop resilience and productivity under changing environmental conditions.
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    Evaluación in silico de la influencia de la heterogeneidad tumoral en la evolución clonal del cáncer de mama
    (Universidad Técnica de Ambato. Facultad de Ciencia e Ingeniería en Alimentos y Biotecnología. Carrera de Biotecnología, 2025-02) Caicedo Albán, Samantha Lizbeth; Galarza Galarza, Cristian Fernando
    The present study seeks to understand the influence of tumor heterogeneity on cancer progression and metastasis development, proposing a starting point for personalized treatment. Data from breast cancer were used, with sequences from samples belonging to primary tumors and tumors in the lymph nodes. Bioinformatics techniques were applied for processing, and differential expression analysis was used to identify genes that were expressed in each of the experimental conditions. Interaction networks were represented to identify the different interactions of elite genes with other genes. The functionality of these genes was validated using the functional enrichment technique to identify the role that these genes play in cell integrity and homeostasis. A search for variants was performed with the selected genes to identify the influence that these genes have on the development and progression of metastasis. Tumor heterogeneity on clonal evolution was evaluated by analyzing the influence of the variants of each type of tumor, observing that GL has a higher incidence on metastasis compared to TP.
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    Análisis de variantes genéticas en la Esclerosis Lateral Amiotrófica (ELA) y diseño de ARN guías (sgARN)
    (Universidad Técnica de Ambato. Facultad de Ciencia e Ingeniería en Alimentos y Biotecnología. Carrera de Biotecnología, 2024-08) Villafuerte Guastay, Mery Carolina; Galarza Galarza, Cristian Fernando
    The present research focuses on Amyotrophic Lateral Sclerosis (ALS), a neurodegenerative disease characterised by wide genetic variability. More than 40 ALS related genes have been identified, with C9orf72 being the most common, associated in approximately 40 percent of cases. This gene and others such as CBS, FIG4, FUS, OPTN, SETX, SOD1, TARDBP, UBQLN2 and VAPB play a role in the pathogenesis of the disease through mechanisms such as protein aggregate formation and vesicular trafficking dysfunction. Using differential expression analysis with the DataSet GSE833, differentially expressed genes were identified between sporadic ALS, familial ALS and control samples. Results showed greater variability in sporadic ALS samples, suggesting a combination of genetic and environmental factors. Genes such as UBE3A, ARHGAP25, GDF10, SELL, FPR1, SPP2, PTGDR, FURIN, ELN, ITGB2 and BAK1 were highlighted by under- or over-expression, implying alterations in biological processes such as calcium ion regulation, phagocytosis and apoptosis. As an alternative to address these genetic variants, guide RNA (sgRNA) sequences were designed using tools such as CHOPCHOP, CRISPOR and CCTop. These sgRNAs are specific and targeted to the most significant variants in the BAK1, GDF10 and SPP2 genes; GC content, self-complementarity, efficiency and specificity are assessed. The optimal sequence for each variant was selected for future proofreading trials, with the aim of improving the understanding and potential treatment of ALS.
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    Propuesta de una quimioteca de compuestos relacionados estructuralmente y diseñados para el tratamiento la enfermedad de Huntington
    (Universidad Técnica de Ambato. Facultad de Ciencia e Ingeniería en Alimentos y Biotecnología. Carrera de Biotecnología, 2024-08) Sanchez Guayta, Kevin Joel; Galarza Galarza, Cristian Fernando
    Huntington's disease (HD) is an inherited neurodegenerative disorder that affects movement, cognition and behaviour. It is characterised by uncontrollable chorea movements, difficulty speaking and swallowing, memory and thinking problems, and mood swings. HD progresses over time and usually results in death 15-20 years after diagnosis; treatment focuses on controlling symptoms. Medications can help relieve chorea movements, muscle stiffness and psychiatric problems. Physiotherapy and speech therapy may also be helpful. Research into HD is ongoing, with the goal of finding new therapies and, ultimately, a cure. Scientists are using various methods, such as bioinformatics and computer science, to identify genes and proteins that may be involved in the disease. In this study, these methods were used to identify 20 elite genes potentially implicated in HD. The researchers also identified several promising therapeutic targets, including DRD2, PDE10A and CNR1. These findings provide new insights into the molecular basis of HD and may lead to the development of new treatments. HD research is encouraging and there is steady progress towards understanding and treating this devastating disease. Scientists are working hard to find a cure, and with continued support, it is hoped that one day this goal will be achieved.