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Article: Editorial: Critical assessment of massive data analysis (CAMDA) annual conference 2021.

Łabaj, Paweł P / Dopazo, Joaquin / Xiao, Wenzhong / Kreil, David P

2023 Volume 14, Page(s) 1154398

Language	English
Publishing date	2023-02-16
Publishing country	Switzerland
Document type	Editorial
ZDB-ID	2606823-0
ISSN	1664-8021
ISSN	1664-8021
DOI	10.3389/fgene.2023.1154398
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Article ; Online: Crosstalk between Metabolite Production and Signaling Activity in Breast Cancer.

Çubuk, Cankut / Loucera, Carlos / Peña-Chilet, María / Dopazo, Joaquin

International journal of molecular sciences

2023 Volume 24, Issue 8

Abstract: The reprogramming of metabolism is a recognized cancer hallmark. It is well known that different signaling pathways regulate and orchestrate this reprogramming that contributes to cancer initiation and development. However, recent evidence is ... ...

Abstract	The reprogramming of metabolism is a recognized cancer hallmark. It is well known that different signaling pathways regulate and orchestrate this reprogramming that contributes to cancer initiation and development. However, recent evidence is accumulating, suggesting that several metabolites could play a relevant role in regulating signaling pathways. To assess the potential role of metabolites in the regulation of signaling pathways, both metabolic and signaling pathway activities of Breast invasive Carcinoma (BRCA) have been modeled using mechanistic models. Gaussian Processes, powerful machine learning methods, were used in combination with SHapley Additive exPlanations (SHAP), a recent methodology that conveys causality, to obtain potential causal relationships between the production of metabolites and the regulation of signaling pathways. A total of 317 metabolites were found to have a strong impact on signaling circuits. The results presented here point to the existence of a complex crosstalk between signaling and metabolic pathways more complex than previously was thought.
MeSH term(s)	Humans ; Female ; Breast Neoplasms/metabolism ; Signal Transduction ; Machine Learning ; Metabolic Networks and Pathways
Language	English
Publishing date	2023-04-18
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2019364-6
ISSN	1422-0067 ; 1422-0067 ; 1661-6596
ISSN (online)	1422-0067
ISSN	1422-0067 ; 1661-6596
DOI	10.3390/ijms24087450
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Article: drexml: A command line tool and Python package for drug repurposing.

Esteban-Medina, Marina / de la Oliva Roque, Víctor Manuel / Herráiz-Gil, Sara / Peña-Chilet, María / Dopazo, Joaquín / Loucera, Carlos

Computational and structural biotechnology journal

2024 Volume 23, Page(s) 1129–1143

Abstract: We introduce drexml, a command line tool and Python package for rational data-driven drug repurposing. The package employs machine learning and mechanistic signal transduction modeling to identify drug targets capable of regulating a particular disease. ... ...

Abstract	We introduce drexml, a command line tool and Python package for rational data-driven drug repurposing. The package employs machine learning and mechanistic signal transduction modeling to identify drug targets capable of regulating a particular disease. In addition, it employs explainability tools to contextualize potential drug targets within the functional landscape of the disease. The methodology is validated in Fanconi Anemia and Familial Melanoma, two distinct rare diseases where there is a pressing need for solutions. In the Fanconi Anemia case, the model successfully predicts previously validated repurposed drugs, while in the Familial Melanoma case, it identifies a promising set of drugs for further investigation.
Language	English
Publishing date	2024-03-01
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	2694435-2
ISSN	2001-0370
ISSN	2001-0370
DOI	10.1016/j.csbj.2024.02.027
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Article ; Online: Functional Profiling of Soft Tissue Sarcoma Using Mechanistic Models.

Payá-Milans, Miriam / Peña-Chilet, María / Loucera, Carlos / Esteban-Medina, Marina / Dopazo, Joaquín

International journal of molecular sciences

2023 Volume 24, Issue 19

Abstract: Soft tissue sarcoma is an umbrella term for a group of rare cancers that are difficult to treat. In addition to surgery, neoadjuvant chemotherapy has shown the potential to downstage tumors and prevent micrometastases. However, finding effective ... ...

Abstract	Soft tissue sarcoma is an umbrella term for a group of rare cancers that are difficult to treat. In addition to surgery, neoadjuvant chemotherapy has shown the potential to downstage tumors and prevent micrometastases. However, finding effective therapeutic targets remains a research challenge. Here, a previously developed computational approach called mechanistic models of signaling pathways has been employed to unravel the impact of observed changes at the gene expression level on the ultimate functional behavior of cells. In the context of such a mechanistic model, RNA-Seq counts sourced from the Recount3 resource, from The Cancer Genome Atlas (TCGA) Sarcoma project, and non-diseased sarcomagenic tissues from the Genotype-Tissue Expression (GTEx) project were utilized to investigate signal transduction activity through signaling pathways. This approach provides a precise view of the relationship between sarcoma patient survival and the signaling landscape in tumors and their environment. Despite the distinct regulatory alterations observed in each sarcoma subtype, this study identified 13 signaling circuits, or elementary sub-pathways triggering specific cell functions, present across all subtypes, belonging to eight signaling pathways, which served as predictors for patient survival. Additionally, nine signaling circuits from five signaling pathways that highlighted the modifications tumor samples underwent in comparison to normal tissues were found. These results describe the protective role of the immune system, suggesting an anti-tumorigenic effect in the tumor microenvironment, in the process of tumor cell detachment and migration, or the dysregulation of ion homeostasis. Also, the analysis of signaling circuit intermediary proteins suggests multiple strategies for therapy.
MeSH term(s)	Humans ; Sarcoma/pathology ; Soft Tissue Neoplasms ; RNA-Seq ; Gene Expression Profiling ; Tumor Microenvironment/genetics
Language	English
Publishing date	2023-09-29
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2019364-6
ISSN	1422-0067 ; 1422-0067 ; 1661-6596
ISSN (online)	1422-0067
ISSN	1422-0067 ; 1661-6596
DOI	10.3390/ijms241914732
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Article ; Online: Discovering potential interactions between rare diseases and COVID-19 by combining mechanistic models of viral infection with statistical modeling.

López-Sánchez, Macarena / Loucera, Carlos / Peña-Chilet, María / Dopazo, Joaquín

Human molecular genetics

2022 Volume 31, Issue 12, Page(s) 2078–2089

Abstract: Recent studies have demonstrated a relevant role of the host genetics in the coronavirus disease 2019 (COVID-19) prognosis. Most of the 7000 rare diseases described to date have a genetic component, typically highly penetrant. However, this vast spectrum ...

Abstract	Recent studies have demonstrated a relevant role of the host genetics in the coronavirus disease 2019 (COVID-19) prognosis. Most of the 7000 rare diseases described to date have a genetic component, typically highly penetrant. However, this vast spectrum of genetic variability remains yet unexplored with respect to possible interactions with COVID-19. Here, a mathematical mechanistic model of the COVID-19 molecular disease mechanism has been used to detect potential interactions between rare disease genes and the COVID-19 infection process and downstream consequences. Out of the 2518 disease genes analyzed, causative of 3854 rare diseases, a total of 254 genes have a direct effect on the COVID-19 molecular disease mechanism and 207 have an indirect effect revealed by a significant strong correlation. This remarkable potential of interaction occurs for >300 rare diseases. Mechanistic modeling of COVID-19 disease map has allowed a holistic systematic analysis of the potential interactions between the loss of function in known rare disease genes and the pathological consequences of COVID-19 infection. The results identify links between disease genes and COVID-19 hallmarks and demonstrate the usefulness of the proposed approach for future preventive measures in some rare diseases.
MeSH term(s)	COVID-19/genetics ; Humans ; Models, Statistical ; Rare Diseases/genetics ; Virus Diseases
Language	English
Publishing date	2022-01-12
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1108742-0
ISSN	1460-2083 ; 0964-6906
ISSN (online)	1460-2083
ISSN	0964-6906
DOI	10.1093/hmg/ddac007
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Article ; Online: Genomics and transcriptomics in drug discovery.

Dopazo, Joaquin

Drug discovery today

2014 Volume 19, Issue 2, Page(s) 126–132

Abstract: The popularization of genomic high-throughput technologies is causing a revolution in biomedical research and, particularly, is transforming the field of drug discovery. Systems biology offers a framework to understand the extensive human genetic ... ...

Abstract	The popularization of genomic high-throughput technologies is causing a revolution in biomedical research and, particularly, is transforming the field of drug discovery. Systems biology offers a framework to understand the extensive human genetic heterogeneity revealed by genomic sequencing in the context of the network of functional, regulatory and physical protein-drug interactions. Thus, approaches to find biomarkers and therapeutic targets will have to take into account the complex system nature of the relationships of the proteins with the disease. Pharmaceutical companies will have to reorient their drug discovery strategies considering the human genetic heterogeneity. Consequently, modeling and computational data analysis will have an increasingly important role in drug discovery.
MeSH term(s)	Biomarkers/metabolism ; Biomedical Research/methods ; Drug Discovery/methods ; Drug Industry/methods ; Genomics/methods ; High-Throughput Nucleotide Sequencing ; Humans ; Molecular Targeted Therapy ; Proteins/metabolism ; Systems Biology/methods ; Transcriptome
Chemical Substances	Biomarkers ; Proteins
Language	English
Publishing date	2014-02
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Review
ZDB-ID	1324988-5
ISSN	1878-5832 ; 1359-6446
ISSN (online)	1878-5832
ISSN	1359-6446
DOI	10.1016/j.drudis.2013.06.003
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Article ; Online: The mechanistic functional landscape of retinitis pigmentosa: a machine learning-driven approach to therapeutic target discovery.

Esteban-Medina, Marina / Loucera, Carlos / Rian, Kinza / Velasco, Sheyla / Olivares-González, Lorena / Rodrigo, Regina / Dopazo, Joaquin / Peña-Chilet, Maria

Journal of translational medicine

2024 Volume 22, Issue 1, Page(s) 139

Abstract: Background: Retinitis pigmentosa is the prevailing genetic cause of blindness in developed nations with no effective treatments. In the pursuit of unraveling the intricate dynamics underlying this complex disease, mechanistic models emerge as a tool of ... ...

Abstract	Background: Retinitis pigmentosa is the prevailing genetic cause of blindness in developed nations with no effective treatments. In the pursuit of unraveling the intricate dynamics underlying this complex disease, mechanistic models emerge as a tool of proven efficiency rooted in systems biology, to elucidate the interplay between RP genes and their mechanisms. The integration of mechanistic models and drug-target interactions under the umbrella of machine learning methodologies provides a multifaceted approach that can boost the discovery of novel therapeutic targets, facilitating further drug repurposing in RP. Methods: By mapping Retinitis Pigmentosa-related genes (obtained from Orphanet, OMIM and HPO databases) onto KEGG signaling pathways, a collection of signaling functional circuits encompassing Retinitis Pigmentosa molecular mechanisms was defined. Next, a mechanistic model of the so-defined disease map, where the effects of interventions can be simulated, was built. Then, an explainable multi-output random forest regressor was trained using normal tissue transcriptomic data to learn causal connections between targets of approved drugs from DrugBank and the functional circuits of the mechanistic disease map. Selected target genes involvement were validated on rd10 mice, a murine model of Retinitis Pigmentosa. Results: A mechanistic functional map of Retinitis Pigmentosa was constructed resulting in 226 functional circuits belonging to 40 KEGG signaling pathways. The method predicted 109 targets of approved drugs in use with a potential effect over circuits corresponding to nine hallmarks identified. Five of those targets were selected and experimentally validated in rd10 mice: Gabre, Gabra1 (GABARα1 protein), Slc12a5 (KCC2 protein), Grin1 (NR1 protein) and Glr2a. As a result, we provide a resource to evaluate the potential impact of drug target genes in Retinitis Pigmentosa. Conclusions: The possibility of building actionable disease models in combination with machine learning algorithms to learn causal drug-disease interactions opens new avenues for boosting drug discovery. Such mechanistically-based hypotheses can guide and accelerate the experimental validations prioritizing drug target candidates. In this work, a mechanistic model describing the functional disease map of Retinitis Pigmentosa was developed, identifying five promising therapeutic candidates targeted by approved drug. Further experimental validation will demonstrate the efficiency of this approach for a systematic application to other rare diseases.
MeSH term(s)	Mice ; Animals ; Retinitis Pigmentosa/drug therapy ; Retinitis Pigmentosa/genetics ; Retinitis Pigmentosa/metabolism ; Signal Transduction
Language	English
Publishing date	2024-02-06
Publishing country	England
Document type	Journal Article
ZDB-ID	2118570-0
ISSN	1479-5876 ; 1479-5876
ISSN (online)	1479-5876
ISSN	1479-5876
DOI	10.1186/s12967-024-04911-7
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Article ; Online: SigPrimedNet: A Signaling-Informed Neural Network for scRNA-seq Annotation of Known and Unknown Cell Types

Gundogdu, Pelin / Alamo, Inmaculada / Nepomuceno-Chamorro, Isabel A. / Dopazo, Joaquín / Loucera, Carlos

Biology (Basel). 2023 Apr. 10, v. 12, no. 4

2023

Abstract: Single-cell RNA sequencing is increasing our understanding of the behavior of complex tissues or organs, by providing unprecedented details on the complex cell type landscape at the level of individual cells. Cell type definition and functional ... ...

Abstract	Single-cell RNA sequencing is increasing our understanding of the behavior of complex tissues or organs, by providing unprecedented details on the complex cell type landscape at the level of individual cells. Cell type definition and functional annotation are key steps to understanding the molecular processes behind the underlying cellular communication machinery. However, the exponential growth of scRNA-seq data has made the task of manually annotating cells unfeasible, due not only to an unparalleled resolution of the technology but to an ever-increasing heterogeneity of the data. Many supervised and unsupervised methods have been proposed to automatically annotate cells. Supervised approaches for cell-type annotation outperform unsupervised methods except when new (unknown) cell types are present. Here, we introduce SigPrimedNet an artificial neural network approach that leverages (i) efficient training by means of a sparsity-inducing signaling circuits-informed layer, (ii) feature representation learning through supervised training, and (iii) unknown cell-type identification by fitting an anomaly detection method on the learned representation. We show that SigPrimedNet can efficiently annotate known cell types while keeping a low false-positive rate for unseen cells across a set of publicly available datasets. In addition, the learned representation acts as a proxy for signaling circuit activity measurements, which provide useful estimations of the cell functionalities.
Keywords	RNA ; data collection ; landscapes ; neural networks
Language	English
Dates of publication	2023-0410
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article ; Online
ZDB-ID	2661517-4
ISSN	2079-7737
ISSN	2079-7737
DOI	10.3390/biology12040579
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Article: SigPrimedNet: A Signaling-Informed Neural Network for scRNA-seq Annotation of Known and Unknown Cell Types.

Gundogdu, Pelin / Alamo, Inmaculada / Nepomuceno-Chamorro, Isabel A / Dopazo, Joaquin / Loucera, Carlos

Biology

2023 Volume 12, Issue 4

Abstract	Single-cell RNA sequencing is increasing our understanding of the behavior of complex tissues or organs, by providing unprecedented details on the complex cell type landscape at the level of individual cells. Cell type definition and functional annotation are key steps to understanding the molecular processes behind the underlying cellular communication machinery. However, the exponential growth of scRNA-seq data has made the task of manually annotating cells unfeasible, due not only to an unparalleled resolution of the technology but to an ever-increasing heterogeneity of the data. Many supervised and unsupervised methods have been proposed to automatically annotate cells. Supervised approaches for cell-type annotation outperform unsupervised methods except when new (unknown) cell types are present. Here, we introduce SigPrimedNet an artificial neural network approach that leverages (i) efficient training by means of a sparsity-inducing signaling circuits-informed layer, (ii) feature representation learning through supervised training, and (iii) unknown cell-type identification by fitting an anomaly detection method on the learned representation. We show that SigPrimedNet can efficiently annotate known cell types while keeping a low false-positive rate for unseen cells across a set of publicly available datasets. In addition, the learned representation acts as a proxy for signaling circuit activity measurements, which provide useful estimations of the cell functionalities.
Language	English
Publishing date	2023-04-10
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2661517-4
ISSN	2079-7737
ISSN	2079-7737
DOI	10.3390/biology12040579
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Article ; Online: Polystyrene nanoplastics affect transcriptomic and epigenomic signatures of human fibroblasts and derived induced pluripotent stem cells: Implications for human health

Stojkovic, Miodrag / Ortuño, Francisco / Han, Dongjun / Stojkovic, Petra / Dopazo, Joaquín / Stankovic, Konstantina M.

Environmental Pollution. 2023 Mar., v. 320 p.120849-

2023

Abstract: Plastic pollution is increasing at an alarming rate yet the impact of this pollution on human health is poorly understood. Because human induced pluripotent stem cells (hiPSC) are frequently derived from dermal fibroblasts, these cells offer a powerful ... ...

Abstract	Plastic pollution is increasing at an alarming rate yet the impact of this pollution on human health is poorly understood. Because human induced pluripotent stem cells (hiPSC) are frequently derived from dermal fibroblasts, these cells offer a powerful platform for the identification of molecular biomarkers of environmental pollution in human cells. Here, we describe a novel proof-of-concept for deriving hiPSC from human dermal fibroblasts deliberately exposed to polystyrene (PS) nanoplastic particles; unexposed hiPSC served as controls. In parallel, unexposed hiPSC were exposed to low and high concentrations of PS nanoparticles. Transcriptomic and epigenomic signatures of all fibroblasts and hiPSCs were defined using RNA-seq and whole genome methyl-seq, respectively. Both PS-treated fibroblasts and derived hiPSC showed alterations in expression of ESRRB and HNF1A genes and circuits involved in the pluripotency of stem cells, as well as in pathways involved in cancer, inflammatory disorders, gluconeogenesis, carbohydrate metabolism, innate immunity, and dopaminergic synapse. Similarly, the expression levels of identified key transcriptional and DNA methylation changes (DNMT3A, ESSRB, FAM133CP, HNF1A, SEPTIN7P8, and TTC34) were significantly affected in both PS-exposed fibroblasts and hiPSC. This study illustrates the power of human cellular models of environmental pollution to narrow down and prioritize the list of candidate molecular biomarkers of environmental pollution. This knowledge will facilitate the deciphering of the origins of environmental diseases.
Keywords	DNA methylation ; biomarkers ; epigenome ; fibroblasts ; gluconeogenesis ; human health ; humans ; innate immunity ; nanoplastics ; pollution ; polystyrenes ; sequence analysis ; synapse ; transcription (genetics) ; transcriptomics ; Human induced pluripotent stem cells ; RNA sequencing ; Human diseases ; BP ; CC ; DEG ; DMR ; FC ; FDR ; Fibs50T ; Fibs50NT ; GO ; GSEA ; hiPSC50T ; hiPSC50NT ; hiPSC50NTp ; hiPSC ; HCS ; IS ; MF ; NT ; PCA ; PS ; TEM ; VD
Language	English
Dates of publication	2023-03
Publishing place	Elsevier Ltd
Document type	Article ; Online
ZDB-ID	280652-6
ISSN	1873-6424 ; 0013-9327 ; 0269-7491
ISSN (online)	1873-6424
ISSN	0013-9327 ; 0269-7491
DOI	10.1016/j.envpol.2022.120849
Database	NAL-Catalogue (AGRICOLA)

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