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Book ; Online: Systems Biology and the Challenge of Deciphering the Metabolic Mechanisms Underlying Cancer

Diener, Christian / Resendis-Antonio, Osbaldo

2017

Abstract: Since the discovery of the Warburg effect in the 1920s cancer has been tightly associated with the genetic and metabolic state of the cell. One of the hallmarks of cancer is the alteration of the cellular metabolism in order to promote proliferation and ... ...

Abstract	Since the discovery of the Warburg effect in the 1920s cancer has been tightly associated with the genetic and metabolic state of the cell. One of the hallmarks of cancer is the alteration of the cellular metabolism in order to promote proliferation and undermine cellular defense mechanisms such as apoptosis or detection by the immune system. However, the strategies by which this is achieved in different cancers and sometimes even in different patients of the same cancer is very heterogeneous, which hinders the design of general treatment options.Recently, there has been an ongoing effort to study this phenomenon on a genomic scale in order to understand the causality underlying the disease. Hence, current "omics" technologies have contributed to identify and monitor different biological pieces at different biological levels, such as genes, proteins or metabolites. These technological capacities have provided us with vast amounts of clinical data where a single patient may often give rise to various tissue samples, each of them being characterized in detail by genomescale data on the sequence, expression, proteome and metabolome level. Data with such detail poses the imminent problem of extracting meaningful interpretations and translating them into specific treatment options. To this purpose, Systems Biology provides a set of promising computational tools in order to decipher the mechanisms driving a healthy cell's metabolism into a cancerous one. However, this enterprise requires bridging the gap between large data resources, mathematical analysis and modeling specifically designed to work with the available data. This is by no means trivial and requires high levels of communication and adaptation between the experimental and theoretical side of research
Keywords	Science (General) ; Physiology ; Biology (General)
Size	1 electronic resource (142 p.)
Publisher	Frontiers Media SA
Document type	Book ; Online
Note	English ; Open Access
HBZ-ID	HT020099567
ISBN	9782889453337 ; 2889453332
Database	ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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Article ; Online: Macrophage Boolean networks in the time of SARS-CoV-2.

de León, Ugo Avila-Ponce / Resendis-Antonio, Osbaldo

Frontiers in immunology

2022 Volume 13, Page(s) 997434

MeSH term(s)	Humans ; SARS-CoV-2 ; COVID-19 ; Gene Regulatory Networks ; Models, Genetic ; Macrophages
Language	English
Publishing date	2022-10-17
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2606827-8
ISSN	1664-3224 ; 1664-3224
ISSN (online)	1664-3224
ISSN	1664-3224
DOI	10.3389/fimmu.2022.997434
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Uncoding the interdependency of tumor microenvironment and macrophage polarization: insights from a continuous network approach.

Avila-Ponce de León, Ugo / Vázquez-Jiménez, Aarón / Padilla-Longoria, Pablo / Resendis-Antonio, Osbaldo

Frontiers in immunology

2023 Volume 14, Page(s) 1150890

Abstract: The balance between pro- and anti-inflammatory immune system responses is crucial to preventing complex diseases like cancer. Macrophages are essential immune cells that contribute to this balance constrained by the local signaling profile of the tumor ... ...

Abstract	The balance between pro- and anti-inflammatory immune system responses is crucial to preventing complex diseases like cancer. Macrophages are essential immune cells that contribute to this balance constrained by the local signaling profile of the tumor microenvironment. To understand how pro- and anti-inflammatory unbalance emerges in cancer, we developed a theoretical analysis of macrophage differentiation that is derived from activated monocytes circulating in the blood. Once recruited to the site of inflammation, monocytes can be polarized based on the specific interleukins and chemokines in the microenvironment. To quantify this process, we used a previous regulatory network reconstructed by our group and transformed Boolean Network attractors of macrophage polarization to an ODE scheme, it enables us to quantify the activation of their genes in a continuous fashion. The transformation was developed using the interaction rules with a fuzzy logic approach. By implementing this approach, we analyzed different aspects that cannot be visualized in the Boolean setting. For example, this approach allows us to explore the dynamic behavior at different concentrations of cytokines and transcription factors in the microenvironment. One important aspect to assess is the evaluation of the transitions between phenotypes, some of them characterized by an abrupt or a gradual transition depending on specific concentrations of exogenous cytokines in the tumor microenvironment. For instance, IL-10 can induce a hybrid state that transits between an M2c and an M2b macrophage. Interferon-
MeSH term(s)	Humans ; Tumor Microenvironment ; Cell Differentiation ; Cytokines/metabolism ; Macrophages ; Neoplasms/metabolism ; Anti-Inflammatory Agents/pharmacology
Chemical Substances	Cytokines ; Anti-Inflammatory Agents
Language	English
Publishing date	2023-05-22
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2606827-8
ISSN	1664-3224 ; 1664-3224
ISSN (online)	1664-3224
ISSN	1664-3224
DOI	10.3389/fimmu.2023.1150890
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: MICOM: Metagenome-Scale Modeling To Infer Metabolic Interactions in the Gut Microbiota.

Diener, Christian / Gibbons, Sean M / Resendis-Antonio, Osbaldo

mSystems

2020 Volume 5, Issue 1

Abstract: Compositional changes in the gut microbiota have been associated with a variety of medical conditions such as obesity, Crohn's disease, and diabetes. However, connecting microbial community composition to ecosystem function remains a challenge. Here, we ... ...

Abstract	Compositional changes in the gut microbiota have been associated with a variety of medical conditions such as obesity, Crohn's disease, and diabetes. However, connecting microbial community composition to ecosystem function remains a challenge. Here, we introduce MICOM, a customizable metabolic model of the human gut microbiome. By using a heuristic optimization approach based on L2 regularization, we were able to obtain a unique set of realistic growth rates that corresponded well with observed replication rates. We integrated adjustable dietary and taxon abundance constraints to generate personalized metabolic models for individual metagenomic samples. We applied MICOM to a balanced cohort of metagenomes from 186 people, including a metabolically healthy population and individuals with type 1 and type 2 diabetes. Model results showed that individual bacterial genera maintained conserved niche structures across humans, while the community-level production of short-chain fatty acids (SCFAs) was heterogeneous and highly individual specific. Model output revealed complex cross-feeding interactions that would be difficult to measure
Language	English
Publishing date	2020-01-21
Publishing country	United States
Document type	Journal Article
ISSN	2379-5077
ISSN	2379-5077
DOI	10.1128/mSystems.00606-19
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Boolean modeling reveals that cyclic attractors in macrophage polarization serve as reservoirs of states to balance external perturbations from the tumor microenvironment.

de León, Ugo Avila-Ponce / Vázquez-Jiménez, Aarón / Matadamas-Guzmán, Meztli / Resendis-Antonio, Osbaldo

Frontiers in immunology

2022 Volume 13, Page(s) 1012730

Abstract: Cyclic attractors generated from Boolean models may explain the adaptability of a cell in response to a dynamical complex tumor microenvironment. In contrast to this idea, we postulate that cyclic attractors in certain cases could be a systemic mechanism ...

Abstract	Cyclic attractors generated from Boolean models may explain the adaptability of a cell in response to a dynamical complex tumor microenvironment. In contrast to this idea, we postulate that cyclic attractors in certain cases could be a systemic mechanism to face the perturbations coming from the environment. To justify our conjecture, we present a dynamic analysis of a highly curated transcriptional regulatory network of macrophages constrained into a cancer microenvironment. We observed that when M1-associated transcription factors (STAT1 or NF-κB) are perturbed and the microenvironment balances to a hyper-inflammation condition, cycle attractors activate genes whose signals counteract this effect implicated in tissue damage. The same behavior happens when the M2-associated transcription factors are disturbed (STAT3 or STAT6); cycle attractors will prevent a hyper-regulation scenario implicated in providing a suitable environment for tumor growth. Therefore, here we propose that cyclic macrophage phenotypes can serve as a reservoir for balancing the phenotypes when a specific phenotype-based transcription factor is perturbed in the regulatory network of macrophages. We consider that cyclic attractors should not be simply ignored, but it is necessary to carefully evaluate their biological importance. In this work, we suggest one conjecture: the cyclic attractors can serve as a reservoir to balance the inflammatory/regulatory response of the network under external perturbations.
MeSH term(s)	Algorithms ; Tumor Microenvironment ; Gene Regulatory Networks ; Macrophages ; Transcription Factors/genetics
Chemical Substances	Transcription Factors
Language	English
Publishing date	2022-12-05
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2606827-8
ISSN	1664-3224 ; 1664-3224
ISSN (online)	1664-3224
ISSN	1664-3224
DOI	10.3389/fimmu.2022.1012730
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Editorial: Systems Biology and the Challenge of Deciphering the Metabolic Mechanisms Underlying Cancer.

Resendis-Antonio, Osbaldo / Diener, Christian

Frontiers in physiology

2017 Volume 8, Page(s) 537

Language	English
Publishing date	2017-07-28
Publishing country	Switzerland
Document type	Editorial
ZDB-ID	2564217-0
ISSN	1664-042X
ISSN	1664-042X
DOI	10.3389/fphys.2017.00537
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Machine Learning and COVID-19: Lessons from SARS-CoV-2.

Avila-Ponce de León, Ugo / Vazquez-Jimenez, Aarón / Cervera, Alejandra / Resendis-González, Galilea / Neri-Rosario, Daniel / Resendis-Antonio, Osbaldo

Advances in experimental medicine and biology

2023 Volume 1412, Page(s) 311–335

Abstract: Currently, methods in machine learning have opened a significant number of applications to construct classifiers with capacities to recognize, identify, and interpret patterns hidden in massive amounts of data. This technology has been used to solve a ... ...

Abstract	Currently, methods in machine learning have opened a significant number of applications to construct classifiers with capacities to recognize, identify, and interpret patterns hidden in massive amounts of data. This technology has been used to solve a variety of social and health issues against coronavirus disease 2019 (COVID-19). In this chapter, we present some supervised and unsupervised machine learning techniques that have contributed in three aspects to supplying information to health authorities and diminishing the deadly effects of the current worldwide outbreak on the population. First is the identification and construction of powerful classifiers capable of predicting severe, moderate, or asymptomatic responses in COVID-19 patients starting from clinical or high-throughput technologies. Second is the identification of groups of patients with similar physiological responses to improve the triage classification and inform treatments. The final aspect is the combination of machine learning methods and schemes from systems biology to link associative studies with mechanistic frameworks. This chapter aims to discuss some practical applications in the use of machine learning techniques to handle data coming from social behavior and high-throughput technologies, associated with COVID-19 evolution.
MeSH term(s)	Humans ; COVID-19 ; SARS-CoV-2 ; Machine Learning ; COVID-19 Testing ; Systems Biology
Language	English
Publishing date	2023-06-28
Publishing country	United States
Document type	Journal Article
ISSN	2214-8019 ; 0065-2598
ISSN (online)	2214-8019
ISSN	0065-2598
DOI	10.1007/978-3-031-28012-2_17
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A New Approach to Personalized Nutrition: Postprandial Glycemic Response and its Relationship to Gut Microbiota.

Guizar-Heredia, Rocio / Noriega, Lilia G / Rivera, Ana Leonor / Resendis-Antonio, Osbaldo / Guevara-Cruz, Martha / Torres, Nimbe / Tovar, Armando R

Archives of medical research

2023 Volume 54, Issue 3, Page(s) 176–188

Abstract: A prolonged and elevated postprandial glucose response (PPGR) is now considered a main factor contributing for the development of metabolic syndrome and type 2 diabetes, which could be prevented by dietary interventions. However, dietary recommendations ... ...

Abstract	A prolonged and elevated postprandial glucose response (PPGR) is now considered a main factor contributing for the development of metabolic syndrome and type 2 diabetes, which could be prevented by dietary interventions. However, dietary recommendations to prevent alterations in PPGR have not always been successful. New evidence has supported that PPGR is not only dependent of dietary factors like the content of carbohydrates, or the glycemic index of the foods, but is also dependent on genetics, body composition, gut microbiota, among others. In recent years, continuous glucose monitoring has made it possible to establish predictions on the effect of different dietary foods on PPGRs through machine learning methods, which use algorithms that integrate genetic, biochemical, physiological and gut microbiota variables for identifying associations between them and clinical variables with aim of personalize dietary recommendations. This has allowed to improve the concept of personalized nutrition, since it is now possible to recommend through these predictions specific dietary foods to prevent elevated PPGRs that are highly variable among individuals. Additional components that can enrich the predictive algorithms are findings of nutrigenomics, nutrigenetics and metabolomics. Thus, this review aims to summarize the evidence of the components that integrate personalized nutrition focused on the prevention of PPGRs, and to show the future of personalized nutrition by laying the groundwork for the development of individualized dietary management and its impact on the improvement of metabolic diseases.
MeSH term(s)	Humans ; Gastrointestinal Microbiome ; Diabetes Mellitus, Type 2/prevention & control ; Blood Glucose Self-Monitoring ; Blood Glucose ; Glucose
Chemical Substances	Blood Glucose ; Glucose (IY9XDZ35W2)
Language	English
Publishing date	2023-03-27
Publishing country	United States
Document type	Journal Article ; Review ; Research Support, Non-U.S. Gov't
ZDB-ID	1156844-6
ISSN	1873-5487 ; 0188-4409 ; 0188-0128
ISSN (online)	1873-5487
ISSN	0188-4409 ; 0188-0128
DOI	10.1016/j.arcmed.2023.02.007
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Exploring Metabolic Anomalies in COVID-19 and Post-COVID-19: A Machine Learning Approach with Explainable Artificial Intelligence

Oropeza-Valdez, Juan José / Padron-Manrique, Cristian / Vázquez-Jiménez, Aarón / Soberon, Xavier / Resendis-Antonio, Osbaldo

bioRxiv

Abstract: The COVID-19 pandemic, caused by SARS-CoV-2, has led to significant challenges worldwide, including diverse clinical outcomes and prolonged post-recovery symptoms known as Long COVID or Post-COVID-19 syndrome. Emerging evidence suggests a crucial role of ...

Abstract	The COVID-19 pandemic, caused by SARS-CoV-2, has led to significant challenges worldwide, including diverse clinical outcomes and prolonged post-recovery symptoms known as Long COVID or Post-COVID-19 syndrome. Emerging evidence suggests a crucial role of metabolic reprogramming in the infection9s long-term consequences. This study employs a novel approach utilizing machine learning (ML) and explainable artificial intelligence (XAI) to analyze metabolic alterations in COVID-19 and Post-COVID-19 patients. By integrating ML with SHAP (SHapley Additive exPlanations) values, we aimed to uncover metabolomic signatures and identify potential biomarkers for these conditions. Our analysis included a cohort of 142 COVID-19, 48 Post-COVID-19 samples and 38 CONTROL patients, with 111 identified metabolites. Traditional analysis methods like PCA and PLS-DA were compared with advanced ML techniques to discern metabolic changes. Notably, XGBoost models, enhanced by SHAP for explainability, outperformed traditional methods, demonstrating superior predictive performance and providing different insights into the metabolic basis of the disease9s progression and its aftermath, the analysis revealed several metabolomic subgroups within the COVID-19 and Post-COVID-19 conditions, suggesting heterogeneous metabolic responses to the infection and its long-term impacts. This study highlights the potential of integrating ML and XAI in metabolomics research.
Keywords	covid19
Language	English
Publishing date	2024-04-17
Publisher	Cold Spring Harbor Laboratory
Document type	Article ; Online
DOI	10.1101/2024.04.15.589583
Database	COVID19

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Article ; Online: Exploring Metabolic Anomalies in COVID-19 and Post-COVID-19: A Machine Learning Approach with Explainable Artificial Intelligence

Oropeza-Valdez, Juan José / Padron-Manrique, Cristian / Vazquez-Jimenez, Aaron / Soberon-Mainero, Xavier / Resendis-Antonio, Osbaldo

bioRxiv

Abstract	The COVID-19 pandemic, caused by SARS-CoV-2, has led to significant challenges worldwide, including diverse clinical outcomes and prolonged post-recovery symptoms known as Long COVID or Post-COVID-19 syndrome. Emerging evidence suggests a crucial role of metabolic reprogramming in the infection9s long-term consequences. This study employs a novel approach utilizing machine learning (ML) and explainable artificial intelligence (XAI) to analyze metabolic alterations in COVID-19 and Post-COVID-19 patients. By integrating ML with SHAP (SHapley Additive exPlanations) values, we aimed to uncover metabolomic signatures and identify potential biomarkers for these conditions. Our analysis included a cohort of 142 COVID-19, 48 Post-COVID-19 samples and 38 CONTROL patients, with 111 identified metabolites. Traditional analysis methods like PCA and PLS-DA were compared with advanced ML techniques to discern metabolic changes. Notably, XGBoost models, enhanced by SHAP for explainability, outperformed traditional methods, demonstrating superior predictive performance and providing different insights into the metabolic basis of the disease9s progression and its aftermath, the analysis revealed several metabolomic subgroups within the COVID-19 and Post-COVID-19 conditions, suggesting heterogeneous metabolic responses to the infection and its long-term impacts. This study highlights the potential of integrating ML and XAI in metabolomics research.
Keywords	covid19
Language	English
Publishing date	2024-04-17
Publisher	Cold Spring Harbor Laboratory
Document type	Article ; Online
DOI	10.1101/2024.04.15.589583
Database	COVID19

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