Epigenetic reprogramming of H3K4me3 in adipose-derived stem cells by HFS diet consumption leads to a disturbed transcriptomic profile in adipocytes.

Adipose tissue metabolism is actively involved in the regulation of energy balance. Adipose-derived stem cells (ASCs) play a critical role in maintaining adipose tissue function through their differentiation into mature adipocytes. This study aimed to investigate the impact of an obesogenic environment on the epigenetic landscape of ASCs and its impact on adipocyte differentiation and its metabolic consequences. Our results showed that ASCs from rats on a high-fat sucrose (HFS) diet displayed reduced adipogenic capacity, increased fat accumulation, and formed larger adipocytes compared to the control (C) group. Mitochondrial analysis revealed heightened activity in undifferentiated ASC-HFS, but decreased respiratory and glycolytic capacity in mature adipocytes. The HFS diet significantly altered the H3K4me3 profile in ASCs on genes related to adipogenesis, mitochondrial function, inflammation, and immunomodulation. After differentiation, adipocytes retained H3K4me3 alterations, confirming upregulation of genes associated with inflammatory and immunomodulatory pathways. RNA-seq confirmed the upregulation of genes associated with inflammatory and immunomodulatory pathways in adipocytes. Overall, the HFS diet induced significant epigenetic and transcriptomic changes in ASCs, impairing differentiation and causing dysfunctional adipocyte formation.

Nucleoredoxin Redox Interactions Are Sensitized by Aging and Potentiated by Chronic Alcohol Consumption in the Mouse Liver.

Aging is characterized by increased reactive species, leading to redox imbalance, oxidative damage, and senescence. The adverse effects of alcohol consumption potentiate aging-associated alterations, promoting several diseases, including liver diseases. Nucleoredoxin (NXN) is a redox-sensitive enzyme that targets reactive oxygen species and regulates key cellular processes through redox protein-protein interactions. Here, we determine the effect of chronic alcohol consumption on NXN-dependent redox interactions in the liver of aged mice. We found that chronic alcohol consumption preferentially promotes the localization of NXN either into or alongside senescent cells, declines its interacting capability, and worsens the altered interaction ratio of NXN with FLII, MYD88, CAMK2A, and PFK1 proteins induced by aging. In addition, carbonylated protein and cell proliferation increased, and the ratios of collagen I and collagen III were inverted. Thus, we demonstrate an emerging phenomenon associated with altered redox homeostasis during aging, as shown by the declining capability of NXN to interact with partner proteins, which is enhanced by chronic alcohol consumption in the mouse liver. This evidence opens an attractive window to elucidate the consequences of both aging and chronic alcohol consumption on the downstream signaling pathways regulated by NXN-dependent redox-sensitive interactions.

mb-PHENIX: diffusion and supervised uniform manifold approximation for denoizing microbiota data.

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.

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.

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.

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

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.

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

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.

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.

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

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.

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

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.

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

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.

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

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.

Physiological Network Is Disrupted in Severe COVID-19.

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.

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

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.

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

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.

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.

Sex Differences in the Physiological Network of Healthy Young Subjects.

Within human physiology, systemic interactions couple physiological variables to maintain homeostasis. These interactions change according to health status and are modified by factors such as age and sex. For several physiological processes, sex-based distinctions in normal physiology are present and defined in isolation. However, new methodologies are indispensable to analyze system-wide properties and interactions with the objective of exploring differences between sexes. Here we propose a new method to construct complex inferential networks from a normalization using the clinical criteria for health of physiological variables, and the correlations between anthropometric and blood tests biomarkers of 198 healthy young participants (117 women, 81 men, from 18 to 27 years old). Physiological networks of men have less correlations, displayed higher modularity, higher small-world index, but were more vulnerable to directed attacks, whereas networks of women were more resilient. The networks of both men and women displayed sex-specific connections that are consistent with the literature. Additionally, we carried out a time-series study on heart rate variability (HRV) using Physionet's Fantasia database. Autocorrelation of HRV, variance, and Poincare's plots, as a measure of variability, are statistically significant higher in young men and statistically significant different from young women. These differences are attenuated in older men and women, that have similar HRV distributions. The network approach revealed differences in the association of variables related to glucose homeostasis, nitrogen balance, kidney function, and fat depots. The clusters of physiological variables and their roles within the network remained similar regardless of sex. Both methodologies show a higher number of associations between variables in the physiological system of women, implying redundant mechanisms of control and simultaneously showing that these systems display less variability in time than those of men, constituting a more resilient system.

Quantitative proteomic analysis of extracellular vesicle subgroups isolated by an optimized method combining polymer-based precipitation and size exclusion chromatography.

The molecular characterization of extracellular vesicles (EVs) has revealed a great heterogeneity in their composition at a cellular and tissue level. Current isolation methods fail to efficiently separate EV subtypes for proteomic and functional analysis. The aim of this study was to develop a reproducible and scalable isolation workflow to increase the yield and purity of EV preparations. Through a combination of polymer-based precipitation and size exclusion chromatography (Pre-SEC), we analyzed two subsets of EVs based on their CD9, CD63 and CD81 content and elution time. EVs were characterized using transmission electron microscopy, nanoparticle tracking analysis, and Western blot assays. To evaluate differences in protein composition between the early- and late-eluting EV fractions, we performed a quantitative proteomic analysis of MDA-MB-468-derived EVs. We identified 286 exclusive proteins in early-eluting fractions and 148 proteins with a differential concentration between early- and late-eluting fractions. A density gradient analysis further revealed EV heterogeneity within each analyzed subgroup. Through a systems biology approach, we found significant interactions among proteins contained in the EVs which suggest the existence of functional clusters related to specific biological processes. The workflow presented here allows the study of EV subtypes within a single cell type and contributes to standardizing the EV isolation for functional studies.

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

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.