Exploring metabolic anomalies in COVID-19 and post-COVID-19: a machine learning approach with explainable artificial intelligence

Frontiers in Molecular Biosciences 2024

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

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 infection’s 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. Samples were taken from a cohort of 142 COVID-19, 48 Post-COVID-19, and 38 control patients, comprising 111 identified metabolites. Traditional analysis methods, like PCA and PLS-DA, were compared with ML techniques, particularly eXtreme Gradient Boosting (XGBoost) enhanced by SHAP (SHapley Additive exPlanations) values for explainability. XGBoost, combined with SHAP, outperformed traditional methods, demonstrating superior predictive performance and providing new insights into the metabolic basis of the disease’s progression and aftermath. The analysis revealed metabolomic subgroups within the COVID-19 and Post-COVID-19 conditions, suggesting heterogeneous metabolic responses to the infection and its long-term impacts. Key metabolic signatures in Post-COVID-19 include taurine, glutamine, alpha-Ketoglutaric acid, and LysoPC a C16:0. This study highlights the potential of integrating ML and XAI for a fine-grained description in metabolomics research, offering a more detailed understanding of metabolic anomalies in COVID-19 and Post-COVID-19 conditions.

Diffusion on PCA-UMAP Manifold: The Impact of Data Structure Preservation to Denoise High-Dimensional Single-Cell RNA Sequencing Data

Biology (Basel) 2024

Cristian Padron-Manrique, Aarón Vázquez-Jiménez, Yoscelina Estrella Martínez-López, Daniel Neri-Rosario, Diego Armando Esquivel-Hernández, Jean Paul Sánchez-Castañeda, David Girón-Villalobos, Edgar Mixcoha and Osbaldo Resendis-Antonio

Single-cell transcriptomics (scRNA-seq) is revolutionizing biological research, yet it faces challenges such as inefficient transcript capture and noise. To address these challenges, methods like neighbor averaging or graph diffusion are used. These methods often rely on k-nearest neighbor graphs from low-dimensional manifolds. However, scRNA-seq data suffer from the ‘curse of dimensionality’, leading to the over-smoothing of data when using imputation methods. To overcome this, sc-PHENIX employs a PCA-UMAP diffusion method, which enhances the preservation of data structures and allows for a refined use of PCA dimensions and diffusion parameters (e.g., k-nearest neighbors, exponentiation of the Markov matrix) to minimize noise introduction. This approach enables a more accurate construction of the exponentiated Markov matrix (cell neighborhood graph), surpassing methods like MAGIC. sc-PHENIX significantly mitigates over-smoothing, as validated through various scRNA-seq datasets, demonstrating improved cell phenotype representation. Applied to a multicellular tumor spheroid dataset, sc-PHENIX identified known extreme phenotype states, showcasing its effectiveness. sc-PHENIX is open-source and available for use and modification.

Effect of metformin and metformin/linagliptin on gut microbiota in patients with prediabetes

Scientific Reports 2024

Yoscelina Estrella Martínez-López, Daniel Neri-Rosario, Diego Armando Esquivel-Hernández, Cristian Padron-Manrique, Aarón Vázquez-Jiménez, Jean Paul Sánchez-Castañeda, David Girón-Villalobos, Cristian Mendoza-Ortiz, María de Lourdes Reyes-Escogido, Maria Lola Evia-Viscarra, Alberto Aguilar-Garcia, Osbaldo Resendis-Antonio and Rodolfo Guardado-Mendoza

Lifestyle modifications, metformin, and linagliptin reduce the incidence of type 2 diabetes (T2D) in people with prediabetes. The gut microbiota (GM) may enhance such interventions' efficacy. We determined the effect of linagliptin/metformin (LM) vs metformin (M) on GM composition and its relationship to insulin sensitivity (IS) and pancreatic β-cell function (Pβf) in patients with prediabetes. A cross-sectional study was conducted at different times: basal, six, and twelve months in 167 Mexican adults with prediabetes. These treatments increased the abundance of GM SCFA-producing bacteria M (Fusicatenibacter and Blautia) and LM (Roseburia, Bifidobacterium, and [Eubacterium] hallii group). We performed a mediation analysis with structural equation models (SEM). In conclusion, M and LM therapies improve insulin sensitivity and Pβf in prediabetics. GM is partially associated with these improvements since the SEM models suggest a weak association between specific bacterial genera and improvements in IS and Pβf.

mb-PHENIX: Diffusion and Supervised Uniform Manifold Approximation for denoising microbiota data

Bioinformatics 2023

Cristian Padron-Manrique, Aaron Vazquez-Jimenez, Diego A Esquivel-Hernandez, Yoscelina Estrella Martinez-Lopez, Daniel Neri-Rosario, Jean Paul Sanchez, David Giron-Villalobos and Osbaldo Resendis-Antonio

Motivation Microbiota data encounters challenges arising from technical noise and the curse of dimensionality, which affect the reliability of scientific findings. Furthermore, abundance matrices exhibit a zero-inflated distribution due to biological and technical influences. Consequently, there is a growing demand for advanced algorithms that can effectively recover missing taxa while also considering the preservation of data structure. Results We present mb-PHENIX, an open-source algorithm developed in Python that recovers taxa abundances from the noisy and sparse microbiota data. Our method infers the missing information of count matrix (in 16S microbiota and shotgun studies) by applying imputation via diffusion with supervised Uniform Manifold Approximation Projection (sUMAP) space as initialization. Our hybrid machine learning approach allows to denoise microbiota data, revealing differential abundance microbes among study groups where traditional abundance analysis fails. Availability and implementation The mb-PHENIX algorithm is available at https://github.com/resendislab/mb-PHENIX. An easy-to-use implementation is available on Google Colab (see GitHub).

Dysbiosis signatures of gut microbiota and the progression of type 2 diabetes: a machine learning approach in a Mexican cohort

Front. Endocrinol. 2023

Daniel Neri-Rosario, Yoscelina E. Martínez-López, Diego A. Esquivel-Hernández, Jean Paul Sánchez-Castañeda, Crístian Padron-Manrique, Aarón Vázquez-Jiménez, David Giron-Villalobos and Osbaldo Resendis-Antonio

Introduction: The gut microbiota (GM) dysbiosis is one of the causal factors for the progression of different chronic metabolic diseases, including type 2 diabetes mellitus (T2D). Understanding the basis that laid this association may lead to developing new therapeutic strategies for preventing and treating T2D, such as probiotics, prebiotics, and fecal microbiota transplants. It may also help identify potential early detection biomarkers and develop personalized interventions based on an individual’s gut microbiota profile. Here, we explore how supervised Machine Learning (ML) methods help to distinguish taxa for individuals with prediabetes (prediabetes) or T2D.

Methods: To this aim, we analyzed the GM profile (16s rRNA gene sequencing) in a cohort of 410 Mexican naïve patients stratified into normoglycemic, prediabetes, and T2D individuals. Then, we compared six different ML algorithms and found that Random Forest had the highest predictive performance in classifying T2D and prediabetes patients versus controls.

Results: We identified a set of taxa for predicting patients with T2D compared to normoglycemic individuals, including Allisonella, Slackia, Ruminococus_2, Megaspgaera, Escherichia/Shigella, and Prevotella, among them. Besides, we concluded that Anaerostipes, Intestinibacter, Prevotella_9, Blautia, Granulicatella, and Veillonella were the relevant genus in patients with prediabetes compared to normoglycemic subjects.

Discussion: These findings allow us to postulate that GM is a distinctive signature in prediabetes and T2D patients during the development and progression of the disease. Our study highlights the role of GM and opens a window toward the rational design of new preventive and personalized strategies against the control of this disease.

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

Front Immunol 2023

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

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- γ can induce a hybrid between M1 and M1a macrophage. We further demonstrated the plasticity of macrophages based on a combination of cytokines and the existence of hybrid phenotypes or partial polarization. This mathematical model allows us to unravel the patterns of macrophage differentiation based on the competition of expression of transcriptional factors. Finally, we survey how macrophages may respond to a continuously changing immunological response in a tumor microenvironment.

A network perspective on the ecology of gut microbiota and progression of type 2 diabetes: Linkages to keystone taxa in a Mexican cohort

Front. Endocrinol 2023

Diego A. Esquivel-Hernández, Yoscelina Estrella Martínez-López, Jean Paul Sánchez-Castañeda, Daniel Neri-Rosario, Crístian Padrón-Manrique, David Giron-Villalobos, Crístian Mendoza-Ortíz and Osbaldo Resendis-Antonio

Introduction: The human gut microbiota (GM) is a dynamic system which ecological interactions among the community members affect the host metabolism. Understanding the principles that rule the bidirectional communication between GM and its host, is one of the most valuable enterprise for uncovering how bacterial ecology influences the clinical variables in the host.

Methods: Here, we used SparCC to infer association networks in 16S rRNA gene amplicon data from the GM of a cohort of Mexican patients with type 2 diabetes (T2D) in different stages: NG (normoglycemic), IFG (impaired fasting glucose), IGT (impaired glucose tolerance), IFG + IGT (impaired fasting glucose plus impaired glucose tolerance), T2D and T2D treated (T2D with a 5-year ongoing treatment).

Results: By exploring the network topology from the different stages of T2D, we observed that, as the disease progress, the networks lose the association between bacteria. It suggests that the microbial community becomes highly sensitive to perturbations in individuals with T2D. With the purpose to identify those genera that guide this transition, we computationally found keystone taxa (driver nodes) and core genera for a Mexican T2D cohort. Altogether, we suggest a set of genera driving the progress of the T2D in a Mexican cohort, among them Ruminococcaceae NK4A214 group, Ruminococcaceae UCG-010, Ruminococcaceae UCG-002, Ruminococcaceae UCG-005, Alistipes, Anaerostipes, and Terrisporobacter.

Discussion: Based on a network approach, this study suggests a set of genera that can serve as a potential biomarker to distinguish the distinct degree of advances in T2D for a Mexican cohort of patients. Beyond limiting our conclusion to one population, we present a computational pipeline to link ecological networks and clinical stages in T2D, and desirable aim to advance in the field of precision medicine.

Spermiogenesis alterations in the absence of CTCF revealed by single cell RNA sequencing

Front. Cell Dev. Biol. 2023

Ulises Torres-Flores, Fernanda Díaz-Espinosa, Taydé López-Santaella, Rosa Rebollar-Vega, Aarón Vázquez-Jiménez, Ian J. Taylor, Rosario Ortiz-Hernández, Olga M. Echeverría, Gerardo H. Vázquez-Nin, María Concepción Gutierrez-Ruiz, Inti Alberto De la Rosa-Velázquez, Osbaldo Resendis-Antonio and Abrahan Hernández-Hernandez

CTCF is an architectonic protein that organizes the genome inside the nucleus in almost all eukaryotic cells. There is evidence that CTCF plays a critical role during spermatogenesis as its depletion produces abnormal sperm and infertility. However, defects produced by its depletion throughout spermatogenesis have not been fully characterized. In this work, we performed single cell RNA sequencing in spermatogenic cells with and without CTCF. We uncovered defects in transcriptional programs that explain the severity of the damage in the produced sperm. In the early stages of spermatogenesis, transcriptional alterations are mild. As germ cells go through the specialization stage or spermiogenesis, transcriptional profiles become more altered. We found morphology defects in spermatids that support the alterations in their transcriptional profiles. Altogether, our study sheds light on the contribution of CTCF to the phenotype of male gametes and provides a fundamental description of its role at different stages of spermiogenesis.

A New Approach to Personalized Nutrition: Postprandial Glycemic Response and its Relationship to Gut Microbiota

Archives of Medical Research 2023

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

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.

Chronic Comorbidities in Middle Aged Patients Contribute to Ineffective Emergency Hematopoiesis in Covid-19 Fatal Outcomes

Archives of Medical Research 2023

Rubí Romo-Rodríguez, Karla Gutiérrez-de Anda, Jebea A López-Blanco, Gabriela Zamora-Herrera, Paulina Cortés-Hernández, Gerardo Santos-López, Luis Márquez-Domínguez, Armando Vilchis-Ordoñez, Dalia Ramírez-Ramírez, Juan Carlos Balandrán, Israel Parra-Ortega, Osbaldo Resendis-Antonio, Lenin Domínguez-Ramírez, Constantino López-Macías, Laura C Bonifaz, Lourdes A Arriaga-Pizano, Arturo Cérbulo-Vázquez, Eduardo Ferat-Osorio, Antonieta Chavez-González, Samuel Treviño, Eduardo Brambila, Miguel Ángel Ramos-Sánchez, Ricardo Toledo-Tapia, Fabiola Domínguez, Jorge Bayrán-Flores, Alejandro Cruz-Oseguera, Julio Roberto Reyes-Leyva, Socorro Méndez-Martínez, Jorge Ayón-Aguilar, Aurora Treviño-García, Eduardo Monjaraz and Rosana Pelayo

Background and Aims Mexico is among the countries with the highest estimated excess mortality rates due to the COVID–19 pandemic, with more than half of reported deaths occurring in adults younger than 65 years old. Although this behavior is presumably influenced by the young demographics and the high prevalence of metabolic diseases, the underlying mechanisms have not been determined. Methods The age–stratified case fatality rate (CFR) was estimated in a prospective cohort with 245 hospitalized COVID–19 cases, followed through time, for the period October 2020–September 2021. Cellular and inflammatory parameters were exhaustively investigated in blood samples by laboratory test, multiparametric flow cytometry and multiplex immunoassays. Results The CFR was 35.51%, with 55.2% of deaths recorded in middle–aged adults. On admission, hematological cell differentiation, physiological stress and inflammation parameters, showed distinctive profiles of potential prognostic value in patients under 65 at 7 days follow–up. Pre–existing metabolic conditions were identified as risk factors of poor outcomes. Chronic kidney disease (CKD), as single comorbidity or in combination with diabetes, had the highest risk for COVID–19 fatality. Of note, fatal outcomes in middle–aged patients were marked from admission by an inflammatory landscape and emergency myeloid hematopoiesis at the expense of functional lymphoid innate cells for antiviral immunosurveillance, including NK and dendritic cell subsets. Conclusions Comorbidities increased the development of imbalanced myeloid phenotype, rendering middle–aged individuals unable to effectively control SARS–CoV–2. A predictive signature of high–risk outcomes at day 7 of disease evolution as a tool for their early stratification in vulnerable populations is proposed. Keywords: Chronic comorbidities; Emergency hematopoiesis; COVID–19; Middle adulthood; Inflammation; Chronic kidney disease

Macrophage Boolean networks in the time of SARS-CoV-2

Frontiers in Immunology 2022

Ugo Avila-Ponce de León and Osbaldo Resendis-Antonio

The post-pandemic period of the current coronavirus disease (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has lasted longer than expected despite the huge impact of the world-wide vaccination campaign in the past years. Since the pandemic began, endless mathematical models have been published to describe the viral outbreak at a population level. However, the molecular mechanism that drives the pathogenesis of the virus in the human microenvironment has been scarce. The existing mechanistic models deal to answer how SARS-CoV-2 invades the lung microenvironment and shapes the composition of the immune system in favor of virus duplication. From the clinical point of view, almost all patients that develop severe COVID-19 lead from a life-threatening acute respiratory distress syndrome (ARDS), which is associated with a hyper-inflammatory microenvironment and injury in the alveolar and lung epithelium. One of the cells associated with this syndrome is an uncontrolled hyper-activated macrophage-associated syndrome (MAS), which promotes a systemic inflammatory response that exacerbates the progress of the cytokine storm in the host. Furthermore, single-cell high-throughput technologies applied in COVID-19 infections have shown that macrophages and monocytes are more abundant over other immune cells in bronchoalveolar lavage fluid (BALF) and peripheral blood mononuclear cells (PBMC). In addition, the cytokines produced by monocytes and macrophages modulate the response of the immune system in patients with COVID-19. For instance, there is evidence supporting that monocytes in severe patients have a lower expression of the human leukocyte antigen HLA-DRB1, which represses the activation of the immune response through the low production of foreign peptides. Besides, monocytes have a lower expression of interferon-stimulated genes in severe COVID-19 patients, resulting in the delay of the interferon response against SARS-CoV-2. Furthermore, macrophages in severe COVID-19 patients are associated with overexpression of pro-inflammatory genes when compared with moderate COVID-19 patients. Altogether, these and other findings highlight the remarkable role that monocytes and macrophage polarization have in the progression of the disease.

Type 2 diabetes, gut microbiome, and systems biology: A novel perspective for a new era

Gut Microbes 2022

Yoscelina Estrella Martínez-López, Diego A Esquivel-Hernández, Jean Paul Sánchez-Castañeda, Daniel Neri-Rosario, Rodolfo Guardado-Mendoza and Osbaldo Resendis-Antonio

The association between the physio-pathological variables of type 2 diabetes (T2D) and gut microbiota composition suggests a new avenue to track the disease and improve the outcomes of pharmacological and non-pharmacological treatments. This enterprise requires new strategies to elucidate the metabolic disturbances occurring in the gut microbiome as the disease progresses. To this end, physiological knowledge and systems biology pave the way for characterizing microbiota and identifying strategies in a move toward healthy compositions. Here, we dissect the recent associations between gut microbiota and T2D. In addition, we discuss recent advances in how drugs, diet, and exercise modulate the microbiome to favor healthy stages. Finally, we present computational approaches for disentangling the metabolic activity underlying host-microbiota codependence. Altogether, we envision that the combination of physiology and computational modeling of microbiota metabolism will drive us to optimize the diagnosis and treatment of T2D patients in a personalized way.

prePrint: A network perspective on the ecology of gut microbiota and progression of Type 2 Diabetes: linkages to keystone taxa in a Mexican cohort

Research Square 2022

Diego Armando Esquivel-Hernández, Yoscelina Estrella Martínez-López, Jean Paul Sánchez-Castañeda, Daniel Neri-Rosario, Cristian Padrón-Manrique, David Girón-Villalobos, Cristian Mendoza-Ortíz and Osbaldo Resendis-Antonio

Background

The human gut microbiota (GM) is a dynamic system which ecological interactions among the community members affect the host metabolism. Understanding the principles that rule the bidirectional communication between GM and its host, is one of the most valuable enterprise for uncovering how bacterial ecology influences the clinical variables in the host. Results

Here, we used SparCC to infer association networks in 16S rRNA gene amplicon data from the GM of a cohort of Mexican patients with type 2 diabetes (T2D) in different stages: NG (normoglycemic), IFG (impaired fasting glucose), IGT (impaired glucose tolerance), IFG + IGT (impaired fasting glucose plus impaired glucose tolerance), T2D and T2D treated (T2D with a 5-year ongoing treatment). By exploring the network topology from the different stages of T2D, we observed that, as the disease progress, the networks lose the association between bacteria. It suggests that the microbial community becomes highly sensitive to perturbations in individuals with T2D. With the purpose to identify those genera that guide this transition, we computationally found keystone taxa (driver nodes) and core genera for a Mexican T2D cohort. Altogether, we suggest a set of genera driving the progress of the T2D in a Mexican cohort, among them Ruminococcaceae NK4A214 group, Ruminococcaceae UCG-010, Ruminococcaceae UCG-002, Ruminococcaceae UCG-005, Alistipes, Anaerostipes, and Terrisporobacter. Conclusions

Based on a network approach, this study suggests a set of genera that can serve as a potential biomarker to distinguish the distinct degree of advances in T2D for a Mexican cohort of patients. Beyond limiting our conclusion to one population, we present a computational pipeline to link ecological networks and clinical stages in T2D, and desirable aim to advance in the field of precision medicine.

Comparative subcellular localization of NRF2 and KEAP1 during the hepatocellular carcinoma development in vivo

Biochimica Et Biophysica Acta (BBA)-Molecular Cell Research 2022

Dafne Guerrero-Escalera, Brisa Rodope Alarcón-Sánchez, Jaime Arellanes-Robledo, Armando Cruz-Rangel, Luis del Pozo-Yauner, Victoria Chagoya de Sánchez, Osbaldo Resendis-Antonio, Saul Villa-Treviño, Julia Esperanza Torres-Mena and Julio Isael Pérez-Carreón

The activation of Nuclear Factor, Erythroid 2 Like 2 – Kelch Like ECH Associated Protein 1 (NRF2-KEAP1) signaling pathway plays a critical dual role by either protecting or promoting the carcinogenesis process. However, its activation or nuclear translocation during hepatocellular carcinoma (HCC) progression has not been addressed yet. This study characterizes the subcellular localization of both NRF2 and KEAP1 during diethylnitrosamine-induced hepatocarcinogenesis in the rat. NRF2-KEAP1 pathway was continuously activated along with the increased expression of its target genes, namely Nqo1, Hmox1, Gclc, and Ptgr1. Similarly, the nuclear translocation of NRF2, MAF, and KEAP1 increased in HCC cells from weeks 12 to 22 during HCC progression. Likewise, colocalization of NRF2 with KEAP1 was higher in the cell nuclei of HCC neoplastic nodules than in surrounding cells. Moreover, immunofluorescence analyses revealed that the interaction of KEAP1 with filamentous Actin was disrupted in HCC cells. This disruption may be contributing to the release and nuclear translocation of NRF2 since the cortical actin cytoskeleton serves as anchoring of KEAP1. In conclusion, this evidence indicates that NRF2 is progressively activated and promotes the progression of experimental HCC.

Physiological Network Is Disrupted in Severe COVID-19

Frontiers in Physiology 2022

Antonio Barajas-Martínez, Roopa Mehta, Elizabeth Ibarra-Coronado, Ruben Fossion, Vania J Martínez Garcés, Monserrat Ramírez Arellano, Ibar A González Alvarez, Yamilet Viana Moncada Bautista, Omar Y Bello-Chavolla, Natalia Ramírez Pedraza, Bethsabel Rodríguez Encinas, Carolina Isabel Pérez Carrión, María Isabel Jasso Ávila, Jorge Carlos Valladares-García, Pablo Esteban Vanegas-Cedillo, Diana Hernández Juárez, Arsenio Vargas-Vázquez, Neftali Eduardo Antonio-Villa, Paloma Almeda-Valdes, Osbaldo Resendis-Antonio, Marcia Hiriart, Alejandro Frank, Carlos A Aguilar-Salinas and Ana Leonor Rivera

The human body is a complex system maintained in homeostasis thanks to the interactions between multiple physiological regulation systems. When faced with physical or biological perturbations, this system must react by keeping a balance between adaptability and robustness. The SARS-COV-2 virus infection poses an immune system challenge that tests the organism’s homeostatic response. Notably, the elderly and men are particularly vulnerable to severe disease, poor outcomes, and death. Mexico seems to have more infected young men than anywhere else. The goal of this study is to determine the differences in the relationships that link physiological variables that characterize the elderly and men, and those that characterize fatal outcomes in young men. To accomplish this, we examined a database of patients with moderate to severe COVID-19 (471 men and 277 women) registered at the “Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán” in March 2020. The sample was stratified by outcome, age, and sex. Physiological networks were built using 67 physiological variables (vital signs, anthropometric, hematic, biochemical, and tomographic variables) recorded upon hospital admission. Individual variables and system behavior were examined by descriptive statistics, differences between groups, principal component analysis, and network analysis. We show how topological network properties, particularly clustering coefficient, become disrupted in disease. Finally, anthropometric, metabolic, inflammatory, and pulmonary cluster interaction characterize the deceased young male group.

Stochastic Analysis of the RT-PCR Process in Single-Cell RNA-Seq

Mathematics 2021

Aarón Vázquez-Jiménez and Osbaldo Resendis-Antonio

The single-cell RNA-seq allows exploring the transcriptome for one cell at a time. By doing so, cellular regulation is pictured. One limitation is the dropout events phenomenon, where a gene is observed at a low or moderate expression level in one cell but not detected in another. Dropouts obscure legitimate biological heterogeneity leading to the description of a small fraction of the meaningful relations. We used a stochastic approach to model the Reverse Transcription Polymerase Chain Reaction (RT-PCR) kinetic, in which we contemplated the temperature profile, RT-PCR duration, and reaction rates. By studying the underlying biochemical processes of RT-PCR, using a computational and analytical framework, we show a minimal amount of RNA to avoid dropout events. We further use this fact to characterize the limits in the dispersion reduction. Dispersion asymptotically decreases as the RNA initial value increases. Despite always being a basal dispersion, their decreasing speed is modulated mainly by the degradation rates, particularly for the RNA. We concluded that the critical step into the RT-PCR is the RT phase due to the fragile nature of the RNA. We propose that limiting RNA degradation might ensure that the portraited transcriptional landscape is unbiased by technical error.

On Deep Landscape Exploration of COVID-19 Patients Cells and Severity Markers

Frontiers in Immunology 2021

Aarón Vázquez-Jiménez, Ugo Avila-Ponce De León, Meztli Matadamas-Guzman, Erick Andrés Muciño-Olmos, Estrella Martínez-López, Thelma Escobedo-Tapia and Osbaldo Resendis-Antonio

COVID-19 is a disease with a spectrum of clinical responses ranging from moderate to critical. To study and control its effects, a large number of researchers are focused on two substantial aims. On the one hand, the discovery of diverse biomarkers to classify and potentially anticipate the disease severity of patients. These biomarkers could serve as a medical criterion to prioritize attention to those patients with higher prone to severe responses. On the other hand, understanding how the immune system orchestrates its responses in this spectrum of disease severities is a fundamental issue required to design new and optimized therapeutic strategies. In this work, using single-cell RNAseq of bronchoalveolar lavage fluid of nine patients with COVID-19 and three healthy controls, we contribute to both aspects. First, we presented computational supervised machine-learning models with high accuracy in classifying the disease severity (moderate and severe) in patients with COVID-19 starting from single-cell data from bronchoalveolar lavage fluid. Second, we identified regulatory mechanisms from the heterogeneous cell populations in the lungs microenvironment that correlated with different clinical responses. Given the results, patients with moderate COVID-19 symptoms showed an activation/inactivation profile for their analyzed cells leading to a sequential and innocuous immune response. In comparison, severe patients might be promoting cytotoxic and pro-inflammatory responses in a systemic fashion involving epithelial and immune cells without the possibility to develop viral clearance and immune memory. Consequently, we present an in-depth landscape analysis of how transcriptional factors and pathways from these heterogeneous populations can regulate their expression to promote or restrain an effective immune response directly linked to the patients prognosis.

Transcriptional and Microenvironmental Landscape of Macrophage Transition in Cancer: A Boolean Analysis

Frontiers in Immunology 2021

Ugo Avila-Ponce De León, Aarón Vázquez-Jiménez, Meztli Matadamas-Guzman, Rosana Pelayo and Osbaldo Resendis-Antonio

The balance between pro- and anti-inflammatory immune system responses is crucial to face and counteract complex diseases such as cancer. Macrophages are an essential population that contributes to this balance in collusion with the local tumor microenvironment. Cancer cells evade the attack of macrophages by liberating cytokines and enhancing the transition to the M2 phenotype with pro-tumoral functions. Despite this pernicious effect on immune systems, the M1 phenotype still exists in the environment and can eliminate tumor cells by liberating cytokines that recruit and activate the cytotoxic actions of TH1 effector cells. Here, we used a Boolean modeling approach to understand how the tumor microenvironment shapes macrophage behavior to enhance pro-tumoral functions. Our network reconstruction integrates experimental data and public information that let us study the polarization from monocytes to M1, M2a, M2b, M2c, and M2d subphenotypes. To analyze the dynamics of our model, we modeled macrophage polarization in different conditions and perturbations. Notably, our study identified new hybrid cell populations, undescribed before. Based on the in vivo macrophage behavior, we explained the hybrid macrophages’ role in the tumor microenvironment. The in silico model allowed us to postulate transcriptional factors that maintain the balance between macrophages with anti- and pro-tumoral functions. In our pursuit to maintain the balance of macrophage phenotypes to eliminate malignant tumor cells, we emulated a theoretical genetically modified macrophage by modifying the activation of NFκB and a loss of function in HIF1-α and discussed their phenotype implications. Overall, our theoretical approach is as a guide to design new experiments for unraveling the principles of the dual host-protective or -harmful antagonistic roles of transitional macrophages in tumor immunoediting and cancer cell fate decisions.

MicroRNAs Regulate Metabolic Phenotypes During Multicellular Tumor Spheroids Progression

Frontiers in Oncology 2020

Erick Andrés Muciño-Olmos, Aarón Vázquez-Jiménez, Diana Elena López-Esparza, Vilma Maldonado, Mahara Valverde and Osbaldo Resendis-Antonio

During tumor progression, cancer cells ire their metabolism to face their bioenergetic demands. In recent years, microRNAs (miRNAs) have emerged as regulatory elements that inhibit the translation and stability of crucial mRNAs, some of them causing direct metabolic alterations in cancer. In this study, we investigated the relationship between miRNAs and their targets mRNAs that control metabolism, and how this fine-tuned regulation is diversified depending on the tumor stage. To do so, we implemented a paired analysis of RNA-seq and small RNA-seq in a breast cancer cell line (MCF7). The cell line was cultured in multicellular tumor spheroid (MCTS) and monoculture conditions. For MCTS, we selected two-time points during their development to recapitulate a proliferative and quiescent stage and contrast their miRNA and mRNA expression patterns associated with metabolism. As a result, we identified a set of new direct putative regulatory interactions between miRNAs and metabolic mRNAs representative for proliferative and quiescent stages. Notably, our study allows us to suggest that miR-3143 regulates the carbon metabolism by targeting hexokinase-2. Also, we found that the overexpression of several miRNAs could directly overturn the expression of mRNAs that control glycerophospholipid and N-Glycan metabolism. While this set of miRNAs downregulates their expression in the quiescent stage, the same set is upregulated in proliferative stages. This last finding suggests an additional metabolic switch of the above mentioned metabolic pathways between the quiescent and proliferative stages. Our results contribute to a better understanding of how miRNAs modulate the metabolic landscape in breast cancer MCTS, which eventually will help to design new strategies to mitigate cancer phenotype.

Analysis of Epithelial-Mesenchymal Transition Metabolism Identifies Possible Cancer Biomarkers Useful in Diverse Genetic Backgrounds

Frontiers in Onology 2020

Meztli Matadamas-Guzman, Cecilia Zazueta, Emilio Rojas and Osbaldo Resendis-Antonio

Epithelial-to-mesenchymal transition (EMT) relates to many molecular and cellular alterations that occur when epithelial cells undergo a switch in differentiation generating mesenchymal-like cells with newly acquired migratory and invasive properties. In cancer cells, EMT leads to drug resistance and metastasis. Moreover, differences in genetic backgrounds, even between patients with the same type of cancer, also determine resistance to some treatments. Metabolic rewiring is essential to induce EMT, hence it is important to identify key metabolic elements for this process, which can be later used to treat cancer cells with different genetic backgrounds. Here we used a mathematical modeling approach to determine which are the metabolic reactions altered after induction of EMT, based on metabolomic and transcriptional data of three non-small cell lung cancer (NSCLC) cell lines. The model suggested that the most affected pathways were the Krebs cycle, amino acid metabolism, and glutathione metabolism. However, glutathione metabolism had many alterations either on the metabolic reactions or at the transcriptional level in the three cell lines. We identified Glutamate-cysteine ligase (GCL), a key enzyme of glutathione synthesis, as an important common feature that is dysregulated after EMT. Analyzing survival data of men with lung cancer, we observed that patients with mutations in GCL catalytic subunit (GCLC) or Glutathione peroxidase 1 (GPX1) genes survived less time than people without mutations on these genes. Besides, patients with low expression of ANPEP, GPX3 and GLS genes also survived less time than those with high expression. Hence, we propose that glutathione metabolism and glutathione itself could be good targets to delay or potentially prevent EMT induction in NSCLC cell lines.

Unveiling functional heterogeneity in breast cancer multicellular tumor spheroids through single-cell RNA-seq

Scientific Reports 2020

Erick Andrés Muciño-Olmos, Aarón Vázquez-Jiménez, Ugo Avila-Ponce de León, Meztli Matadamas-Guzman, Vilma Maldonado, Tayde López-Santaella, Abrahan Hernández-Hernández and Osbaldo Resendis-Antonio

Heterogeneity is an intrinsic characteristic of cancer. Even in isogenic tumors, cell populations exhibit differential cellular programs that overall supply malignancy and decrease treatment efficiency. In this study, we investigated the functional relationship among cell subtypes and how this interdependency can promote tumor development in a cancer cell line. To do so, we performed single-cell RNA-seq of MCF7 Multicellular Tumor Spheroids as a tumor model. Analysis of single-cell transcriptomes at two-time points of the spheroid growth, allowed us to dissect their functional relationship. As a result, three major robust cellular clusters, with a non-redundant complementary composition, were found. Meanwhile, one cluster promotes proliferation, others mainly activate mechanisms to invade other tissues and serve as a reservoir population conserved over time. Our results provide evidence to see cancer as a systemic unit that has cell populations with task stratification with the ultimate goal of preserving the hallmarks in tumors.

Memote: A community driven effort towards a standardized genome-scale metabolic model test suite

Nature Biotechnology 2020

Christian Lieven, Moritz E. Beber, Brett G. Olivier, Frank T. Bergmann, Meric Ataman, Parizad Babaei, Jennifer A. Bartell, Lars M. Blank, Siddharth Chauhan, Kevin Correia, Christian Diener, Andreas Dräger, Birgitta E. Ebert, Janaka N. Edirisinghe, Jose P. Faria, Adam Feist, Georgios Fengos, Ronan M. T. Fleming, Beatriz García-Jiménez, Vassily Hatzimanikatis, Wout van Helvoirt, Christopher S. Henry, Henning Hermjakob, Markus J. Herrgård, Hyun Uk Kim, Zachary King, Jasper J. Koehorst, Steffen Klamt, Edda Klipp, Meiyappan Lakshmanan, Nicolas Le Novère, Dong-Yup Lee, Sang Yup Lee, Sunjae Lee, Nathan E. Lewis, Hongwu Ma, Daniel Machado, Radhakrishnan Mahadevan, Paulo Maia, Adil Mardinoglu, Gregory L. Medlock, Jonathan M. Monk, Jens Nielsen, Lars Keld Nielsen, Juan Nogales, Intawat Nookaew, Osbaldo Resendis-Antonio, Bernhard O. Palsson, Jason A. Papin, Kiran R. Patil, Mark Poolman, Nathan D. Price, Anne Richelle, Isabel Rocha, Benjamin J. Sanchez, Peter J. Schaap, Rahuman S. Malik Sheriff, Saeed Shoaie, Nikolaus Sonnenschein, Bas Teusink, Paulo Vilaça, Jon Olav Vik, Judith A. Wodke, Joana C. Xavier, Qianqian Yuan, Maksim Zakhartsev and Cheng Zhang

Several studies have shown that neither the formal representation nor the functional requirements of genome-scale metabolic models (GEMs) are precisely defined. Without a consistent standard, comparability, reproducibility, and interoperability of models across groups and software tools cannot be guaranteed. Here, we present memote (https://github.com/opencobra/memote) an open-source software containing a community-maintained, standardized set of metabolic model tests. The tests cover a range of aspects from annotations to conceptual integrity and can be extended to include experimental datasets for automatic model validation. In addition to testing a model once, memote can be configured to do so automatically, i.e., while building a GEM. A comprehensive report displays the model’s performance parameters, which supports informed model development and facilitates error detection. Memote provides a measure for model quality that is consistent across reconstruction platforms and analysis software and simplifies collaboration within the community by establishing workflows for publicly hosted and version controlled models.

Micom: metagenome-scale modeling to infer metabolic interactions in the microbiota.

Msystems 2020

Christian Diener and Osbaldo Resendis-Antonio

Alterations in the gut microbiota have been associated with a variety of medical conditions such as obesity, Crohn’s disease and diabetes. However, establishing the causality between the microbial composition and disease remains a challenge. We introduce a strategy based on metabolic models of complete microbial gut communities in order to derive the particular metabolic consequences of the microbial composition for the diabetic gut in a balanced cohort of 186 individuals. By using a heuristic optimization approach based on L2 regularization we were able to obtain a unique set of realistic growth rates that allows growth for the majority of observed taxa in a sample. We also integrated various additional constraints such as diet and the measured abundances of microbial species to derive the resulting metabolic alterations for individual metagenomic samples. In particular, we show that growth rates vary greatly across samples and that there exists a network of bacteria implicated in health and disease that mutually influence each others growth rates. Studying individual exchange fluxes between the microbiota and the gut lumen we observed that consumption of metabolites by the microbiota follows a niche structure whereas production of short chain fatty acids by the microbiota was highly sample-specific and was altered in type 2 diabetes and restored after metformin treatment in samples from danish individuals. Additionally, we found that production of butyrate could not be easily influenced by single-target interventions.

Distinct microbes, metabolites, and ecologies define the microbiome in deficient and proficient mismatch repair colorectal cancers.

Genome Medicine 2018

Vanessa L. Hale, Patricio Jeraldo, Jun Chen, Michael Mundy, Janet Yao, Sambhawa Priya, Gary Keeney, Kelly Lyke, Jason Ridlon, Bryan A. White, Amy J. French, Stephen N. Thibodeau, Christian Diener, Osbaldo Resendis-Antonio, Jaime Gransee, Tumpa Dutta, Xuan-Mai Petterson, Jaeyun Sung, Ran Blekhman, Lisa Boardman, David Larson, Heidi Nelson and Nicholas Chia

Links between colorectal cancer (CRC) and the gut microbiome have been established, but the specific microbial species and their role in carcinogenesis remain an active area of inquiry. Our understanding would be enhanced by better accounting for tumor subtype, microbial community interactions, metabolism, and ecology. We collected paired colon tumor and normal-adjacent tissue and mucosa samples from 83 individuals who underwent partial or total colectomies for CRC. Mismatch repair (MMR) status was determined in each tumor sample and classified as either deficient MMR (dMMR) or proficient MMR (pMMR) tumor subtypes. Samples underwent 16S rRNA gene sequencing and a subset of samples from 50 individuals were submitted for targeted metabolomic analysis to quantify amino acids and short-chain fatty acids. A PERMANOVA was used to identify the biological variables that explained variance within the microbial communities. dMMR and pMMR microbial communities were then analyzed separately using a generalized linear mixed effects model that accounted for MMR status, sample location, intra-subject variability, and read depth. Genome-scale metabolic models were then used to generate microbial interaction networks for dMMR and pMMR microbial communities. We assessed global network properties as well as the metabolic influence of each microbe within the dMMR and pMMR networks.We demonstrate distinct roles for microbes in dMMR and pMMR CRC. Bacteroides fragilis and sulfidogenic Fusobacterium nucleatum were significantly enriched in dMMR CRC, but not pMMR CRC. These findings were further supported by metabolic modeling and metabolomics indicating suppression of B. fragilis in pMMR CRC and increased production of amino acid proxies for hydrogen sulfide in dMMR CRC.Integrating tumor biology and microbial ecology highlighted distinct microbial, metabolic, and ecological properties unique to dMMR and pMMR CRC. This approach could critically improve our ability to define, predict, prevent, and treat colorectal cancers.

Synthesis of multi-omic data and community metabolic models reveals insights into the role of hydrogen sulfide in colon cancer.

Methods (San Diego, Calif.) 2018

Vanessa L Hale, Patricio Jeraldo, Michael Mundy, Janet Yao, Gary Keeney, Nancy Scott, E Heidi Cheek, Jennifer Davidson, Megan Green, Christine Martinez, John Lehman, Chandra Pettry, Erica Reed, Kelly Lyke, Bryan A White, Christian Diener, Osbaldo Resendis-Antonio, Jaime Gransee, Tumpa Dutta, Xuan-Mai Petterson, Lisa Boardman, David Larson, Heidi Nelson and Nicholas Chia

Multi-omic data and genome-scale microbial metabolic models have allowed us to examine microbial communities, community function, and interactions in ways that were not available to us historically. Now, one of our biggest challenges is determining how to integrate data and maximize data potential. Our study demonstrates one way in which to test a hypothesis by combining multi-omic data and community metabolic models. Specifically, we assess hydrogen sulfide production in colorectal cancer based on stool, mucosa, and tissue samples collected on and off the tumor site within the same individuals. 16S rRNA microbial community and abundance data were used to select and inform the metabolic models. We then used MICOM, an open source platform, to track the metabolic flux of hydrogen sulfide through a defined microbial community that either represented on-tumor or off-tumor sample communities. We also performed targeted and untargeted metabolomics, and used the former to quantitatively evaluate our model predictions. A deeper look at the models identified several unexpected but feasible reactions, microbes, and microbial interactions involved in hydrogen sulfide production for which our 16S and metabolomic data could not account. These results will guide future in vitro, in vivo, and in silico tests to establish why hydrogen sulfide production is increased in tumor tissue.

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

Frontiers in Physiology 2017

Osbaldo Resendis-Antonio and Christian Diener

This is the Editorial for our Frontiers Research Topic “Systems Biology and the challenge of deciphering the metabolic mechanisms underlying cancer”.

The corresponding E-Book will be available soon.

"Gestaltomics": Systems Biology Schemes for the Study of Neuropsychiatric Diseases.

Frontiers in Physiology 2017

Nora A Gutierrez Najera, Osbaldo Resendis-Antonio and Humberto Nicolini

Keywords: diagnosis, lung cancer, omics, psychiatry, systems biology

The integration of different sources of biological information about what defines a behavioral phenotype is difficult to unify in an entity that reflects the arithmetic sum of its individual parts. In this sense, the challenge of Systems Biology for understanding the “psychiatric phenotype” is to provide an improved vision of the shape of the phenotype as it is visualized by “Gestalt” psychology, whose fundamental axiom is that the observed phenotype (behavior or mental disorder) will be the result of the integrative composition of every part. Therefore, we propose the term “Gestaltomics” as a term from Systems Biology to integrate data coming from different sources of information (such as the genome, transcriptome, proteome, epigenome, metabolome, phenome, and microbiome). In addition to this biological complexity, the mind is integrated through multiple brain functions that receive and process complex information through channels and perception networks (i.e., sight, ear, smell, memory, and attention) that in turn are programmed by genes and influenced by environmental processes (epigenetic). Today, the approach of medical research in human diseases is to isolate one disease for study; however, the presence of an additional disease (co-morbidity) or more than one disease (multimorbidity) adds complexity to the study of these conditions. This review will present the challenge of integrating psychiatric disorders at different levels of information (Gestaltomics). The implications of increasing the level of complexity, for example, studying the co-morbidity with another disease such as cancer, will also be discussed.

Natural selection drove metabolic specialization of the chromatophore in Paulinella chromatophora.

BMC Evolutionary Biology 2017

Cecilio Valadez-Cano, Roberto Olivares-Hernández, Osbaldo Resendis-Antonio, Alexander DeLuna and Luis Delaye

Keywords: Adaptation, Endosymbiont, Metabolic evolution, Metabolic integration

Genome degradation of host-restricted mutualistic endosymbionts has been attributed to inactivating mutations and genetic drift while genes coding for host-relevant functions are conserved by purifying selection. Unlike their free-living relatives, the metabolism of mutualistic endosymbionts and endosymbiont-originated organelles is specialized in the production of metabolites which are released to the host. This specialization suggests that natural selection crafted these metabolic adaptations. In this work, we analyzed the evolution of the metabolism of the chromatophore of Paulinella chromatophora by in silico modeling. We asked whether genome reduction is driven by metabolic engineering strategies resulted from the interaction with the host. As its widely known, the loss of enzyme coding genes leads to metabolic network restructuring sometimes improving the production rates. In this case, the production rate of reduced-carbon in the metabolism of the chromatophore.

Personalized Prediction of Proliferation Rates and Metabolic Liabilities in Cancer Biopsies.

Frontiers in Physiology 2017

Christian Diener and Osbaldo Resendis-Antonio

Keywords: NCI60, TCGA, flux balance analysis, personalized medicine, proliferation, systems biology

Cancer is a heterogeneous disease and its genetic and metabolic mechanism may manifest differently in each patient. This creates a demand for studies that can characterize phenotypic traits of cancer on a per-sample basis. Combining two large data sets, the NCI60 cancer cell line panel, and The Cancer Genome Atlas, we used a linear interaction model to predict proliferation rates for more than 12,000 cancer samples across 33 different cancers from The Cancer Genome Atlas. The predicted proliferation rates are associated with patient survival and cancer stage and show a strong heterogeneity in proliferative capacity within and across different cancer panels. We also show how the obtained proliferation rates can be incorporated into genome-scale metabolic reconstructions to obtain the metabolic fluxes for more than 3000 cancer samples that identified specific metabolic liabilities for nine cancer panels. Here we found that affected pathways coincided with the literature, with pentose phosphate pathway, retinol, and branched-chain amino acid metabolism being the most panel-specific alterations and fatty acid metabolism and ROS detoxification showing homogeneous metabolic activities across all cancer panels. The presented strategy has potential applications in personalized medicine since it can leverage gene expression signatures for cell line based prediction of additional metabolic properties which might help in constraining personalized metabolic models and improve the identification of metabolic alterations in cancer for individual patients.

Host-Microbiome Interaction and Cancer: Potential Application in Precision Medicine.

Frontiers in Physiology 2016

Alejandra V Contreras, Benjamin Cocom-Chan, Georgina Hernandez-Montes, Tobias Portillo-Bobadilla and Osbaldo Resendis-Antonio

Keywords: cancer metabolism, metabolome, microbiome, next generation sequencing (NGS), precision medicine, systems integration

It has been experimentally shown that host-microbial interaction plays a major role in shaping the wellness or disease of the human body. Microorganisms coexisting in human tissues provide a variety of benefits that contribute to proper functional activity in the host through the modulation of fundamental processes such as signal transduction, immunity and metabolism. The unbalance of this microbial profile, or dysbiosis, has been correlated with the genesis and evolution of complex diseases such as cancer. Although this latter disease has been thoroughly studied using different high-throughput (HT) technologies, its heterogeneous nature makes its understanding and proper treatment in patients a remaining challenge in clinical settings. Notably, given the outstanding role of host-microbiome interactions, the ecological interactions with microorganisms have become a new significant aspect in the systems that can contribute to the diagnosis and potential treatment of solid cancers. As a part of expanding precision medicine in the area of cancer research, efforts aimed at effective treatments for various kinds of cancer based on the knowledge of genetics, biology of the disease and host-microbiome interactions might improve the prediction of disease risk and implement potential microbiota-directed therapeutics. In this review, we present the state of the art of sequencing and metabolome technologies, computational methods and schemes in systems biology that have addressed recent breakthroughs of uncovering relationships or associations between microorganisms and cancer. Together, microbiome studies extend the horizon of new personalized treatments against cancer from the perspective of precision medicine through a synergistic strategy integrating clinical knowledge, HT data, bioinformatics, and systems biology.

Evolution of Centrality Measurements for the Detection of Essential Proteins in Biological Networks.

Frontiers in Physiology 2016

Mahdi Jalili, Ali Salehzadeh-Yazdi, Shailendra Gupta, Olaf Wolkenhauer, Marjan Yaghmaie, Osbaldo Resendis-Antonio and Kamran Alimoghaddam

Keywords: biological centrality, biological network, centrality, essentiality, topological network analysis

The space of enzyme regulation in HeLa cells can be inferred from its intracellular metabolome.

Scientific Reports 2016

Christian Diener, Felipe Muñoz-Gonzalez, Sergio Encarnación and Osbaldo Resendis-Antonio

During the transition from a healthy state to a cancerous one, cells alter their metabolism to increase proliferation. The underlying metabolic alterations may be caused by a variety of different regulatory events on the transcriptional or post-transcriptional level whose identification contributes to the rational design of therapeutic targets. We present a mechanistic strategy capable of inferring enzymatic regulation from intracellular metabolome measurements that is independent of the actual mechanism of regulation. Here, enzyme activities are expressed by the space of all feasible kinetic constants (k-cone) such that the alteration between two phenotypes is given by their corresponding kinetic spaces. Deriving an expression for the transformation of the healthy to the cancer k-cone we identified putative regulated enzymes between the HeLa and HaCaT cell lines. We show that only a few enzymatic activities change between those two cell lines and that this regulation does not depend on gene transcription but is instead post-transcriptional. Here, we identify phosphofructokinase as the major driver of proliferation in HeLa cells and suggest an optional regulatory program, associated with oxidative stress, that affects the activity of the pentose phosphate pathway.

Modeling metabolism: a window toward a comprehensive interpretation of networks in cancer.

Seminars in Cancer Biology 2014

Osbaldo Resendis-Antonio, Carolina González-Torres, Gustavo Jaime-Muñoz, Claudia Erika Hernandez-Patiño and Carlos Felipe Salgado-Muñoz

Keywords: Cancer metabolism, Mathematical models, P4 medicine, Systems biology: Constraint-based modeling

Given the multi-factorial nature of cancer, uncovering its metabolic alterations and evaluating their implications is a major challenge in biomedical sciences that will help in the optimal design of personalized treatments. The advance of high-throughput technologies opens an invaluable opportunity to monitor the activity at diverse biological levels and elucidate how cancer originates, evolves and responds under drug treatments. To this end, researchers are confronted with two fundamental questions: how to interpret high-throughput data and how this information can contribute to the development of personalized treatment in patients. A variety of schemes in systems biology have been suggested to characterize the phenotypic states associated with cancer by utilizing computational modeling and high-throughput data. These theoretical schemes are distinguished by the level of complexity of the biological mechanisms that they represent and by the computational approaches used to simulate them. Notably, these theoretical approaches have provided a proper framework to explore some distinctive metabolic mechanisms observed in cancer cells such as the Warburg effect. In this review, we focus on presenting a general view of some of these approaches whose application and integration will be crucial in the transition from local to global conclusions in cancer studies. We are convinced that multidisciplinary approaches are required to construct the bases of an integrative and personalized medicine, which has been and remains a fundamental task in the medicine of this century.

Systems biology of cancer: moving toward the integrative study of the metabolic alterations in cancer cells.

Frontiers in Physiology 2013

Claudia E Hernández Patiño, Gustavo Jaime-Muñoz and Osbaldo Resendis-Antonio

Keywords: cancer metabolic phenotype, computational modeling of metabolism, constraint-based modeling, genome scale metabolic reconstruction, high throughput biology

One of the main objectives in systems biology is to understand the biological mechanisms that give rise to the phenotype of a microorganism by using high-throughput technologies (HTs) and genome-scale mathematical modeling. The computational modeling of genome-scale metabolic reconstructions is one systemic and quantitative strategy for characterizing the metabolic phenotype associated with human diseases and potentially for designing drugs with optimal clinical effects. The purpose of this short review is to describe how computational modeling, including the specific case of constraint-based modeling, can be used to explore, characterize, and predict the metabolic capacities that distinguish the metabolic phenotype of cancer cell lines. As we show herein, this computational framework is far from a pure theoretical description, and to ensure proper biological interpretation, it is necessary to integrate high-throughput data and generate predictions for later experimental assessment. Hence, genome-scale modeling serves as a platform for the following: (1) the integration of data from HTs, (2) the assessment of how metabolic activity is related to phenotype in cancer cell lines, and (3) the design of new experiments to evaluate the outcomes of the in silico analysis. By combining the functions described above, we show that computational modeling is a useful methodology to construct an integrative, systemic, and quantitative scheme for understanding the metabolic profiles of cancer cell lines, a first step to determine the metabolic mechanism by which cancer cells maintain and support their malignant phenotype in human tissues.

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

Frontiers in Immunology 2012

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

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.

Functional modules, structural topology, and optimal activity in metabolic networks.

PLoS Computational Biology 2012

Osbaldo Resendis-Antonio, Magdalena Hernández, Yolanda Mora and Sergio Encarnación

Modular organization in biological networks has been suggested as a natural mechanism by which a cell coordinates its metabolic strategies for evolving and responding to environmental perturbations. To understand how this occurs, there is a need for developing computational schemes that contribute to integration of genomic-scale information and assist investigators in formulating biological hypotheses in a quantitative and systematic fashion. In this work, we combined metabolome data and constraint-based modeling to elucidate the relationships among structural modules, functional organization, and the optimal metabolic phenotype of Rhizobium etli, a bacterium that fixes nitrogen in symbiosis with Phaseolus vulgaris. To experimentally characterize the metabolic phenotype of this microorganism, we obtained the metabolic profile of 220 metabolites at two physiological stages: under free-living conditions, and during nitrogen fixation with P. vulgaris. By integrating these data into a constraint-based model, we built a refined computational platform with the capability to survey the metabolic activity underlying nitrogen fixation in R. etli. Topological analysis of the metabolic reconstruction led us to identify modular structures with functional activities. Consistent with modular activity in metabolism, we found that most of the metabolites experimentally detected in each module simultaneously increased their relative abundances during nitrogen fixation. In this work, we explore the relationships among topology, biological function, and optimal activity in the metabolism of R. etli through an integrative analysis based on modeling and metabolome data. Our findings suggest that the metabolic activity during nitrogen fixation is supported by interacting structural modules that correlate with three functional classifications: nucleic acids, peptides, and lipids. More fundamentally, we supply evidence that such modular organization during functional nitrogen fixation is a robust property under different environmental conditions.

Systems biology of bacterial nitrogen fixation: high-throughput technology and its integrative description with constraint-based modeling.

BMC Systems Biology 2011

Osbaldo Resendis-Antonio, Magdalena Hernández, Emmanuel Salazar, Sandra Contreras, Gabriel Martínez Batallar, Yolanda Mora and Sergio Encarnación

Bacterial nitrogen fixation is the biological process by which atmospheric nitrogen is uptaken by bacteroids located in plant root nodules and converted into ammonium through the enzymatic activity of nitrogenase. In practice, this biological process serves as a natural form of fertilization and its optimization has significant implications in sustainable agricultural programs. Currently, the advent of high-throughput technology supplies with valuable data that contribute to understanding the metabolic activity during bacterial nitrogen fixation. This undertaking is not trivial, and the development of computational methods useful in accomplishing an integrative, descriptive and predictive framework is a crucial issue to decoding the principles that regulated the metabolic activity of this biological process.

Proteomic patterns of cervical cancer cell lines, a network perspective.

BMC Systems Biology 2011

Juan Carlos Higareda-Almaraz, María del Rocío Enríquez-Gasca, Magdalena Hernández-Ortiz, Osbaldo Resendis-Antonio and Sergio Encarnación-Guevara

Cervical cancer is a major mortality factor in the female population. This neoplastic is an excellent model for studying the mechanisms involved in cancer maintenance, because the Human Papilloma Virus (HPV) is the etiology factor in most cases. With the purpose of characterizing the effects of malignant transformation in cellular activity, proteomic studies constitute a reliable way to monitor the biological alterations induced by this disease. In this contextual scheme, a systemic description that enables the identification of the common events between cell lines of different origins, is required to distinguish the essence of carcinogenesis.

Modeling core metabolism in cancer cells: surveying the topology underlying the Warburg effect.

PloS One 2010

Osbaldo Resendis-Antonio, Alberto Checa and Sergio Encarnación

Alterations on glucose consumption and biosynthetic activity of amino acids, lipids and nucleotides are metabolic changes for sustaining cell proliferation in cancer cells. Irrevocable evidence of this fact is the Warburg effect which establishes that cancer cells prefers glycolysis over oxidative phosphorylation to generate ATP. Regulatory action over metabolic enzymes has opened a new window for designing more effective anti-cancer treatments. This enterprise is not trivial and the development of computational models that contribute to identifying potential enzymes for breaking the robustness of cancer cells is a priority.

Filling kinetic gaps: dynamic modeling of metabolism where detailed kinetic information is lacking.

PloS One 2009

Osbaldo Resendis-Antonio

Integrative analysis between dynamical modeling of metabolic networks and data obtained from high throughput technology represents a worthy effort toward a holistic understanding of the link among phenotype and dynamical response. Even though the theoretical foundation for modeling metabolic network has been extensively treated elsewhere, the lack of kinetic information has limited the analysis in most of the cases. To overcome this constraint, we present and illustrate a new statistical approach that has two purposes: integrate high throughput data and survey the general dynamical mechanisms emerging for a slightly perturbed metabolic network.

Regulation by transcription factors in bacteria: beyond description.

FEMS Microbiology Reviews 2008

Enrique Balleza, Lucia N López-Bojorquez, Agustino Martínez-Antonio, Osbaldo Resendis-Antonio, Irma Lozada-Chávez, Yalbi I Balderas-Martínez, Sergio Encarnación and Julio Collado-Vides

Transcription is an essential step in gene expression and its understanding has been one of the major interests in molecular and cellular biology. By precisely tuning gene expression, transcriptional regulation determines the molecular machinery for developmental plasticity, homeostasis and adaptation. In this review, we transmit the main ideas or concepts behind regulation by transcription factors and give just enough examples to sustain these main ideas, thus avoiding a classical ennumeration of facts. We review recent concepts and developments: cis elements and trans regulatory factors, chromosome organization and structure, transcriptional regulatory networks (TRNs) and transcriptomics. We also summarize new important discoveries that will probably affect the direction of research in gene regulation: epigenetics and stochasticity in transcriptional regulation, synthetic circuits and plasticity and evolution of TRNs. Many of the new discoveries in gene regulation are not extensively tested with wetlab approaches. Consequently, we review this broad area in Inference of TRNs and Dynamical Models of TRNs. Finally, we have stepped backwards to trace the origins of these modern concepts, synthesizing their history in a timeline schema.

Metabolic reconstruction and modeling of nitrogen fixation in Rhizobium etli.

PLoS Computational Biology 2007

Osbaldo Resendis-Antonio, Jennifer L Reed, Sergio Encarnación, Julio Collado-Vides and Bernhard Ø Palsson

Rhizobiaceas are bacteria that fix nitrogen during symbiosis with plants. This symbiotic relationship is crucial for the nitrogen cycle, and understanding symbiotic mechanisms is a scientific challenge with direct applications in agronomy and plant development. Rhizobium etli is a bacteria which provides legumes with ammonia (among other chemical compounds), thereby stimulating plant growth. A genome-scale approach, integrating the biochemical information available for R. etli, constitutes an important step toward understanding the symbiotic relationship and its possible improvement. In this work we present a genome-scale metabolic reconstruction (iOR363) for R. etli CFN42, which includes 387 metabolic and transport reactions across 26 metabolic pathways. This model was used to analyze the physiological capabilities of R. etli during stages of nitrogen fixation. To study the physiological capacities in silico, an objective function was formulated to simulate symbiotic nitrogen fixation. Flux balance analysis (FBA) was performed, and the predicted active metabolic pathways agreed qualitatively with experimental observations. In addition, predictions for the effects of gene deletions during nitrogen fixation in Rhizobia in silico also agreed with reported experimental data. Overall, we present some evidence supporting that FBA of the reconstructed metabolic network for R. etli provides results that are in agreement with physiological observations. Thus, as for other organisms, the reconstructed genome-scale metabolic network provides an important framework which allows us to compare model predictions with experimental measurements and eventually generate hypotheses on ways to improve nitrogen fixation.

Identification of regulatory network topological units coordinating the genome-wide transcriptional response to glucose in Escherichia coli.

BMC Microbiology 2007

Rosa María Gutierrez-Ríos, Julio A Freyre-Gonzalez, Osbaldo Resendis, Julio Collado-Vides, Milton Saier and Guillermo Gosset

Glucose is the preferred carbon and energy source for Escherichia coli. A complex regulatory network coordinates gene expression, transport and enzyme activities in response to the presence of this sugar. To determine the extent of the cellular response to glucose, we applied an approach combining global transcriptome and regulatory network analyses.

Robustness and evolvability in genetic regulatory networks.

Journal of Theoretical Biology 2006

Maximino Aldana, Enrique Balleza, Stuart Kauffman and Osbaldo Resendiz

Living organisms are robust to a great variety of genetic changes. Gene regulation networks and metabolic pathways self-organize and reaccommodate to make the organism perform with stability and reliability under many point mutations, gene duplications and gene deletions. At the same time, living organisms are evolvable, which means that these kind of genetic perturbations can eventually make the organism acquire new functions and adapt to new environments. It is still an open problem to determine how robustness and evolvability blend together at the genetic level to produce stable organisms that yet can change and evolve. Here we address this problem by studying the robustness and evolvability of the attractor landscape of genetic regulatory network models under the process of gene duplication followed by divergence. We show that an intrinsic property of this kind of networks is that, after the divergence of the parent and duplicate genes, with a high probability the previous phenotypes, encoded in the attractor landscape of the network, are preserved and new ones might appear. The above is true in a variety of network topologies and even for the case of extreme divergence in which the duplicate gene bears almost no relation with its parent. Our results indicate that networks operating close to the so-called “critical regime” exhibit the maximum robustness and evolvability simultaneously.

Modular analysis of the transcriptional regulatory network of E. coli.

Trends in Genetics : TIG 2005

Osbaldo Resendis-Antonio, Julio A Freyre-González, Ricardo Menchaca-Méndez, Rosa M Gutiérrez-Ríos, Agustino Martínez-Antonio, Cristhian Avila-Sánchez and Julio Collado-Vides

The transcriptional network of Escherichia coli is currently the best-understood regulatory network of a single cell. Motivated by statistical evidence, suggesting a hierarchical modular architecture in this network, we identified eight modules with well-defined physiological functions. These modules were identified by a clustering approach, using the shortest path to trace regulatory relationships across genes in the network. We report the type (feed forward and bifan) and distribution of motifs between and within modules. Feed-forward motifs tend to be embedded within modules, whereas bi-fan motifs tend to link modules, supporting the notion of a hierarchical network with defined functional modules.