Priorización de genes y búsqueda de fármacos por medio de herramientas informáticas y técnicas de aprendizaje de máquinas en osteosarcoma

Programa Oficial de Doutoramento en Tecnoloxías da Información e as Comunicacións. 5032V01Tese por compendio de publicacións[Resumen] El osteosarcoma es el subtipo más común de cáncer de hueso primario y afecta principalmente a adolescentes. En los últimos años, varios estudios se han centrado en dilucidar los mecanismos moleculares de este sarcoma; sin embargo, su etiología molecular aún no se ha determinado con precisión. Por otro lado, su diagnóstico clínico es generalista y sus terapias no han cambiado en las últimas décadas. Aunque hoy en día las tasas de supervivencia a 5 años pueden alcanzar hasta el 60-70%, las complicaciones agudas y los efectos tardíos del tratamiento del osteosarcoma son dos de los factores limitantes de los tratamientos. Así, el objetivo de esta tesis doctoral es desarrollar una estrategia de priorización que permita la identificación de genes asociados con la patogenicidad del osteosarcoma y explicar de forma más completa la etiología de esta enfermedad. Por otro lado, se busca desarrollar algoritmos de predicción de fármacos basados en aprendizaje de máquinas que permitan proponer nuevos agentes terapéuticos para el tratamiento de esta enfermedad. Todos los resultados obtenidos se publicaron en revistas científicas internacionales con importante factor de impacto JCR.[Abstract] Osteosarcoma is the most common subtype of primary bone cancer, affecting mainly adolescents. In recent years, several studies have focused on elucidating the molecular mechanisms of this sarcoma; however, its molecular etiology has not yet been accurately determined. On the other hand, the clinical diagnosis is generalist and therapies have not changed in recent decades. Although nowadays 5-year survival rates can reach up to 60-70%, acute complications and late effects of osteosarcoma therapy are two of the limiting factors in treatments. Thus, the objective of this doctoral thesis is to develop a prioritization strategy that allows the identification of genes associated with the pathogenicity of osteosarcoma, and to explain more fully the etiology of this disease. On the other hand, it seeks to develop drug prediction algorithms based on machine learning techniques that allow proposing new therapeutic agents for the treatment of this disease. All the results obtained in this research were published in international scientific journals with an important JCR impact factor.[Resumo] O osteosarcoma é o subtipo máis común de cancro óseo primario, que afecta principalmente a adolescentes. Nos últimos anos, varios estudos centráronse en dilucidar os mecanismos moleculares deste sarcoma; con todo, a súa etioloxía molecular aínda non foi determinada con precisión. Por outra banda, o seu diagnóstico clínico é xeralista e as súas terapias non cambiaron nas últimas décadas. Aínda que hoxe as taxas de supervivencia a 5 anos poden chegar ata o 60- 70%, as complicacións agudas e os efectos tardíos do tratamento con osteosarcoma son dous dos factores limitantes dos tratamentos. Deste xeito, o obxectivo desta tese de doutoramento é desenvolver unha estratexia de priorización que permita a identificación de xenes asociados á patoxenicidade do osteosarcoma e explicar máis plenamente a etioloxía desta enfermidade. Por outra banda, buscamos desenvolver algoritmos de predición de medicamentos baseados na aprendizaxe automática que permitan propoñer novos axentes terapéuticos para o tratamento desta enfermidade. Todos os resultados obtidos publicáronse en revistas científicas internacionais cun importante factor de impacto JCR

Estudio de los polimorfismos -56C/T del gen IFNGRI y -336A/G en CD209 en población infectada con Helicobacter pylori

Helicobacter pylori es una bacteria Gram negativa, reconocida como patógeno carcinógeno tipo I según la Agencia Internacional de Investigaciones en el Cáncer y relacionada con gastritis y úlceras. El sistema inmune ayuda en la erradicación de patógenos que desarrollan enfermedades e interfieren con procesos celulares normales. CD209 es un receptor de membrana que reconoce moléculas con carbohidratos; en este caso, lipopolisacáridos presentes en la pared de péptidoglicano de Helicobacter pylori, y ayuda en la activación de la respuesta inmune adaptativa. IFN-γ es una citocina que estimula a macrófagos induciendo mecanismos directos antimicrobiales y antitumorigénicos, aumentando la acción de los linfocitos Th1 en la infección por Helicobacter pylori

Drugs Repurposing Using QSAR, Docking and Molecular Dynamics for Possible Inhibitors of the SARS-CoV-2 Mpro Protease

[Abstract] Wuhan, China was the epicenter of the first zoonotic transmission of the severe acute respiratory syndrome coronavirus clade 2 (SARS-CoV-2) in December 2019 and it is the causative agent of the novel human coronavirus disease 2019 (COVID-19). Almost from the beginning of the COVID-19 outbreak several attempts were made to predict possible drugs capable of inhibiting the virus replication. In the present work a drug repurposing study is performed to identify potential SARS-CoV-2 protease inhibitors. We created a Quantitative Structure–Activity Relationship (QSAR) model based on a machine learning strategy using hundreds of inhibitor molecules of the main protease (Mpro) of the SARS-CoV coronavirus. The QSAR model was used for virtual screening of a large list of drugs from the DrugBank database. The best 20 candidates were then evaluated in-silico against the Mpro of SARS-CoV-2 by using docking and molecular dynamics analyses. Docking was done by using the Gold software, and the free energies of binding were predicted with the MM-PBSA method as implemented in AMBER. Our results indicate that levothyroxine, amobarbital and ABP-700 are the best potential inhibitors of the SARS-CoV-2 virus through their binding to the Mpro enzyme. Five other compounds showed also a negative but small free energy of binding: nikethamide, nifurtimox, rebimastat, apomine and rebastinib.Universidad de Las Américas (Quito, Ecuador); BIO.TPA.20.03Instituto de Salud Carlos III; PI17/01826Xunta de Galicia; ED431C 2018/4

Perturbation-Theory Machine Learning (PTML) Multilabel Model of the ChEMBL Dataset of Preclinical Assays for Antisarcoma Compounds

[Abstract] Sarcomas are a group of malignant neoplasms of connective tissue with a different etiology than carcinomas. The efforts to discover new drugs with antisarcoma activity have generated large datasets of multiple preclinical assays with different experimental conditions. For instance, the ChEMBL database contains outcomes of 37,919 different antisarcoma assays with 34,955 different chemical compounds. Furthermore, the experimental conditions reported in this dataset include 157 types of biological activity parameters, 36 drug targets, 43 cell lines, and 17 assay organisms. Considering this information, we propose combining perturbation theory (PT) principles with machine learning (ML) to develop a PTML model to predict antisarcoma compounds. PTML models use one function of reference that measures the probability of a drug being active under certain conditions (protein, cell line, organism, etc.). In this paper, we used a linear discriminant analysis and neural network to train and compare PT and non-PT models. All the explored models have an accuracy of 89.19–95.25% for training and 89.22–95.46% in validation sets. PTML-based strategies have similar accuracy but generate simplest models. Therefore, they may become a versatile tool for predicting antisarcoma compounds.Ministerio de Economía y Competitividad; CTQ2016-74881-PMinisterio de Economía y Competitividad; UNLC08-1E-002Ministerio de Economía y Competitividad; UNLC13-13-3503Xunta de Galicia; ED431C 2018/49Xunta de Galicia; ED431D 2017/16Xunta de Galicia; ED431G/01Xunta de Galicia; ED431D 2017/23Gobierno Vasco; IT1045-16Instituto de Salud Carlos III; PI17/0182

A Multi-Objective Approach for Anti-Osteosarcoma Cancer Agents Discovery through Drug Repurposing

[Abstract] Osteosarcoma is the most common type of primary malignant bone tumor. Although nowadays 5-year survival rates can reach up to 60–70%, acute complications and late effects of osteosarcoma therapy are two of the limiting factors in treatments. We developed a multi-objective algorithm for the repurposing of new anti-osteosarcoma drugs, based on the modeling of molecules with described activity for HOS, MG63, SAOS2, and U2OS cell lines in the ChEMBL database. Several predictive models were obtained for each cell line and those with accuracy greater than 0.8 were integrated into a desirability function for the final multi-objective model. An exhaustive exploration of model combinations was carried out to obtain the best multi-objective model in virtual screening. For the top 1% of the screened list, the final model showed a BEDROC = 0.562, EF = 27.6, and AUC = 0.653. The repositioning was performed on 2218 molecules described in DrugBank. Within the top-ranked drugs, we found: temsirolimus, paclitaxel, sirolimus, everolimus, and cabazitaxel, which are antineoplastic drugs described in clinical trials for cancer in general. Interestingly, we found several broad-spectrum antibiotics and antiretroviral agents. This powerful model predicts several drugs that should be studied in depth to find new chemotherapy regimens and to propose new strategies for osteosarcoma treatment.Universidad de Las Américas (Quito, Ecuador); ENF.RCA.18.01Gobierno Vasco; IT1045-16)-2016–202

Gene Prioritization through Consensus Strategy, Enrichment Methodologies Analysis, and Networking for Osteosarcoma Pathogenesis

[Abstract] Osteosarcoma is the most common subtype of primary bone cancer, affecting mostly adolescents. In recent years, several studies have focused on elucidating the molecular mechanisms of this sarcoma; however, its molecular etiology has still not been determined with precision. Therefore, we applied a consensus strategy with the use of several bioinformatics tools to prioritize genes involved in its pathogenesis. Subsequently, we assessed the physical interactions of the previously selected genes and applied a communality analysis to this protein–protein interaction network. The consensus strategy prioritized a total list of 553 genes. Our enrichment analysis validates several studies that describe the signaling pathways PI3K/AKT and MAPK/ERK as pathogenic. The gene ontology described TP53 as a principal signal transducer that chiefly mediates processes associated with cell cycle and DNA damage response It is interesting to note that the communality analysis clusters several members involved in metastasis events, such as MMP2 and MMP9, and genes associated with DNA repair complexes, like ATM, ATR, CHEK1, and RAD51. In this study, we have identified well-known pathogenic genes for osteosarcoma and prioritized genes that need to be further explored.Instituto Carlos III; PI17/01826Xunta de Galicia; ED431C 2018/49Xunta de Galicia; ED431G/0

OncoOmics approaches to reveal essential genes in breast cancer: a panoramic view from pathogenesis to precision medicine

[Abstract] Breast cancer (BC) is the leading cause of cancer-related death among women and the most commonly diagnosed cancer worldwide. Although in recent years large-scale efforts have focused on identifying new therapeutic targets, a better understanding of BC molecular processes is required. Here we focused on elucidating the molecular hallmarks of BC heterogeneity and the oncogenic mutations involved in precision medicine that remains poorly defined. To fill this gap, we established an OncoOmics strategy that consists of analyzing genomic alterations, signaling pathways, protein-protein interactome network, protein expression, dependency maps in cell lines and patient-derived xenografts in 230 previously prioritized genes to reveal essential genes in breast cancer. As results, the OncoOmics BC essential genes were rationally filtered to 140. mRNA up-regulation was the most prevalent genomic alteration. The most altered signaling pathways were associated with basal-like and Her2-enriched molecular subtypes. RAC1, AKT1, CCND1, PIK3CA, ERBB2, CDH1, MAPK14, TP53, MAPK1, SRC, RAC3, BCL2, CTNNB1, EGFR, CDK2, GRB2, MED1 and GATA3 were essential genes in at least three OncoOmics approaches. Drugs with the highest amount of clinical trials in phases 3 and 4 were paclitaxel, docetaxel, trastuzumab, tamoxifen and doxorubicin. Lastly, we collected ~3,500 somatic and germline oncogenic variants associated with 50 essential genes, which in turn had therapeutic connectivity with 73 drugs. In conclusion, the OncoOmics strategy reveals essential genes capable of accelerating the development of targeted therapies for precision oncology.Instituto de Salud Carlos III; PI17/0182

Prediction of Breast Cancer Proteins Involved in Immunotherapy, Metastasis, and RNA-Binding Using Molecular Descriptors and Artifcial Neural Networks

[Abstract] Breast cancer (BC) is a heterogeneous disease where genomic alterations, protein expression deregulation, signaling pathway alterations, hormone disruption, ethnicity and environmental determinants are involved. Due to the complexity of BC, the prediction of proteins involved in this disease is a trending topic in drug design. This work is proposing accurate prediction classifer for BC proteins using six sets of protein sequence descriptors and 13 machine-learning methods. After using a univariate feature selection for the mix of fve descriptor families, the best classifer was obtained using multilayer perceptron method (artifcial neural network) and 300 features. The performance of the model is demonstrated by the area under the receiver operating characteristics (AUROC) of 0.980±0.0037, and accuracy of 0.936±0.0056 (3-fold cross-validation). Regarding the prediction of 4,504 cancer-associated proteins using this model, the best ranked cancer immunotherapy proteins related to BC were RPS27, SUPT4H1, CLPSL2, POLR2K, RPL38, AKT3, CDK3, RPS20, RASL11A and UBTD1; the best ranked metastasis driver proteins related to BC were S100A9, DDA1, TXN, PRNP, RPS27, S100A14, S100A7, MAPK1, AGR3 and NDUFA13; and the best ranked RNA-binding proteins related to BC were S100A9, TXN, RPS27L, RPS27, RPS27A, RPL38, MRPL54, PPAN, RPS20 and CSRP1. This powerful model predicts several BC-related proteins that should be deeply studied to fnd new biomarkers and better therapeutic targets. Scripts can be downloaded at https://github.com/muntisa/ neural-networks-for-breast-cancer-proteins.This work was supported by a) Universidad UTE (Ecuador), b) the Collaborative Project in Genomic Data Integration (CICLOGEN) PI17/01826 funded by the Carlos III Health Institute from the Spanish National plan for Scientific and Technical Research and Innovation 2013-2016 and the European Regional Development Funds (FEDER) - “A way to build Europe”; c) the General Directorate of Culture, Education and University Management of Xunta de Galicia ED431D 2017/16 and “Drug Discovery Galician Network” Ref. ED431G/01 and the “Galician Network for Colorectal Cancer Research” (Ref. ED431D 2017/23); d) the Spanish Ministry of Economy and Competitiveness for its support through the funding of the unique installation BIOCAI (UNLC08-1E-002, UNLC13-13-3503) and the European Regional Development Funds (FEDER) by the European Union; e) the Consolidation and Structuring of Competitive Research Units - Competitive Reference Groups (ED431C 2018/49), funded by the Ministry of Education, University and Vocational Training of the Xunta de Galicia endowed with EU FEDER funds; f) research grants from Ministry of Economy and Competitiveness, MINECO, Spain (FEDER CTQ2016-74881-P), Basque government (IT1045-16), and kind support of Ikerbasque, Basque Foundation for Science; and, g) Sociedad Latinoamericana de Farmacogenómica y Medicina Personalizada (SOLFAGEM)Xunta de Galicia; ED431D 2017/16Xunta de Galicia; ED431G/01Xunta de Galicia; ED431D 2017/23Xunta de Galicia; ED431C 2018/49Gobierno Vasco; IT1045-1

Lymphocyte Profile and Immune Checkpoint Expression in Drug-Induced Liver Injury: An Immunophenotyping Study

The identification of specific HLA risk alleles in drug-induced liver injury (DILI) points toward an important role of the adaptive immune system in DILI development. In this study, we aimed to corroborate the role of an adaptive immune response in DILI through immunophenotyping of leukocyte populations and immune checkpoint expressions. Blood samples were collected from adjudicated DILI (n = 12), acute viral hepatitis (VH; n = 13), acute autoimmune hepatitis (AIH; n = 9), and acute liver injury of unknown etiology (n = 15) at day 1 (recognition), day 7, and day >30. Blood samples from patients with nonalcoholic fatty liver disease (NAFLD; n = 20) and healthy liver controls (HLCs; n = 54) were extracted at one time point. Leukocyte populations and immune checkpoint expressions were determined based on cell surface receptors, except for CTLA-4 that was determined intracellularly, using flow cytometry. At recognition, DILI demonstrated significantly higher levels of activated helper T-cell (P < 0.0001), activated cytotoxic T-cells (P = 0.0003), Th1 (P = 0.0358), intracellular CTLA-4 level in helper T-cells (P = 0.0192), and PD-L1 presenting monocytes (P = 0.0452) than HLC. These levels approached those of HLC over time. No significant differences were found between DILI and VH. However, DILI presented higher level of activated helper T-cells and CTLA-4 than NAFLD and lower PD-L1 level than AIH. Our findings suggest that an adaptive immune response is involved in DILI in which activated CD4+ and CD8+ play an important role. Increased expression of negative immune checkpoints is likely the effect of peripheral tolerance regulation.The present study has been supported by grants of Instituto de Salud Carlos III cofounded by Fondo Europeo de Desarrollo Regional – FEDER (contract numbers: PI19/00883, PI16/01748, P18-RT-3364-2020, and PT20/000127). CIBERehd and Plataforma ISCiii Ensayos Clínicos are funded by Instituto de Salud Carlos III. Funding for open access charge: Universidad de Málaga/CBUA. The funding sources had no involvement in the study design; in the collection, analysis, and interpretation of data; in the writing of the report, or in the decision to submit the manuscript for publication

Microbiota diversity in nonalcoholic fatty liver disease and in drug-induced liver injury

The gut microbiota could play a significant role in the progression of nonalcoholic fatty liver disease (NAFLD); however, its relevance in drug-induced liver injury (DILI) remains unexplored. Since the two hepatic disorders may share damage pathways, we analysed the metagenomic profile of the gut microbiota in NAFLD, with or without significant liver fibrosis, and in DILI, and we identified the main associated bacterial metabolic pathways. In the NAFLD group, we found a decrease in Alistipes, Barnesiella, Eisenbergiella, Flavonifractor, Fusicatenibacter, Gemminger, Intestinimonas, Oscillibacter, Parasutterella, Saccharoferementans and Subdoligranulum abundances compared with those in both the DILI and control groups. Additionally, we detected an increase in Enterobacter, Klebsiella, Sarcina and Turicibacter abundances in NAFLD, with significant liver fibrosis, compared with those in NAFLD with no/mild liver fibrosis. The DILI group exhibited a lower microbial bacterial richness than the control group, and lower abundances of Acetobacteroides, Blautia, Caloramator, Coprococcus, Flavobacterium, Lachnospira, Natronincola, Oscillospira, Pseudobutyrivibrio, Shuttleworthia, Themicanus and Turicibacter compared with those in the NAFLD and control groups. We found seven bacterial metabolic pathways that were impaired only in DILI, most of which were associated with metabolic biosynthesis. In the NAFLD group, most of the differences in the bacterial metabolic pathways found in relation to those in the DILI and control groups were related to fatty acid and lipid biosynthesis. In conclusion, we identified a distinct bacterial profile with specific bacterial metabolic pathways for each type of liver disorder studied. These differences can provide further insight into the physiopathology and development of NAFLD and DILI.This work was supported in part by a grant from the Instituto de Salud Carlos III (Spain) (PI18/01804, PI19/00883, PI21/01248), from the Consejería de Economía, Conocimiento, Empresas y Universidad (Junta de Andalucía, Spain) (PI18–RT‐3364, UMA18-FEDERJA-194), and from the Consejería de Salud (Junta de Andalucía, Spain) (PI-0285–2016). This study has been co-funded by FEDER funds (“A way to make Europe”) (“Andalucía se mueve con Europa”). CRD is supported by a grant from the Consejería de Transformación Económica, Industria, Conocimiento y Universidades de Junta de Andalucía (Spain) (DOC_01610). FMR is supported by a grant from the ISCIII (Spain) (FI19/00189). AC is supported by a grant from the ISCIII (Spain) (IFI18/00047). EGF is supported by the Nicolas Monardes program from the Consejería de Salud de Andalucía (Spain) (C-0031–2016). Funding for open access charge: Universidad de Málaga / CBUA (Spain)