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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 (Ad). 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 than 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 the 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.NEW & NOTEWORTHY Obesity is associated with the development of chronic diseases like metabolic syndrome and type 2 diabetes, and adipose tissue plays a crucial role. In a rat model, our study reveals how an obesogenic environment primes adipocyte precursor cells, leading to epigenetic changes that affect inflammation, adipogenesis, and mitochondrial activity after differentiation. We highlight the importance of histone modifications, especially the trimethylation of histone H3 to lysine 4 (H3K4me3), showing its influence on adipocyte expression profiles.
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Adipócitos , Adipogenia , Tecido Adiposo , Dieta Hiperlipídica , Epigênese Genética , Histonas , Transcriptoma , Animais , Ratos , Adipócitos/metabolismo , Dieta Hiperlipídica/efeitos adversos , Histonas/metabolismo , Masculino , Adipogenia/genética , Adipogenia/fisiologia , Tecido Adiposo/metabolismo , Diferenciação Celular/genética , Células-Tronco/metabolismo , Obesidade/metabolismo , Obesidade/genética , Reprogramação Celular/fisiologia , Células Cultivadas , Ratos Wistar , Ratos Sprague-DawleyRESUMO
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 ß-cell function (Pßf) 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 Pßf 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 Pßf.
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Microbioma Gastrointestinal , Linagliptina , Metformina , Estado Pré-Diabético , Humanos , Metformina/farmacologia , Metformina/uso terapêutico , Microbioma Gastrointestinal/efeitos dos fármacos , Estado Pré-Diabético/tratamento farmacológico , Estado Pré-Diabético/microbiologia , Masculino , Feminino , Pessoa de Meia-Idade , Estudos Transversais , Linagliptina/uso terapêutico , Linagliptina/farmacologia , Hipoglicemiantes/farmacologia , Hipoglicemiantes/uso terapêutico , Diabetes Mellitus Tipo 2/tratamento farmacológico , Diabetes Mellitus Tipo 2/microbiologia , Diabetes Mellitus Tipo 2/metabolismo , Resistência à Insulina , Adulto , Células Secretoras de Insulina/efeitos dos fármacos , Células Secretoras de Insulina/metabolismo , IdosoRESUMO
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.
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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).
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Microbiota , Reprodutibilidade dos Testes , Algoritmos , Aprendizado de Máquina , DifusãoRESUMO
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.
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Diabetes Mellitus Tipo 2 , Microbioma Gastrointestinal , Estado Pré-Diabético , Humanos , Diabetes Mellitus Tipo 2/diagnóstico , Estado Pré-Diabético/diagnóstico , Disbiose , RNA Ribossômico 16S/genética , Aprendizado de MáquinaRESUMO
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.
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COVID-19 , Humanos , SARS-CoV-2 , Aprendizado de Máquina , Teste para COVID-19 , Biologia de SistemasRESUMO
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.
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Diabetes Mellitus Tipo 2 , Microbioma Gastrointestinal , Intolerância à Glucose , Humanos , Diabetes Mellitus Tipo 2/epidemiologia , Intolerância à Glucose/epidemiologia , Microbioma Gastrointestinal/genética , RNA Ribossômico 16S/genética , GlucoseRESUMO
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.
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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.
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Algoritmos , Microambiente Tumoral , Redes Reguladoras de Genes , Macrófagos , Fatores de Transcrição/genéticaAssuntos
COVID-19 , SARS-CoV-2 , Humanos , Redes Reguladoras de Genes , Modelos Genéticos , MacrófagosRESUMO
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.
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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.
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Carcinoma Hepatocelular/patologia , Proteína 1 Associada a ECH Semelhante a Kelch/metabolismo , Neoplasias Hepáticas/patologia , Fator 2 Relacionado a NF-E2/metabolismo , Citoesqueleto de Actina/metabolismo , Animais , Carcinoma Hepatocelular/induzido quimicamente , Carcinoma Hepatocelular/veterinária , Núcleo Celular/metabolismo , Ciclo-Oxigenase 1/genética , Ciclo-Oxigenase 1/metabolismo , Dietilnitrosamina/toxicidade , Progressão da Doença , Proteína 1 Associada a ECH Semelhante a Kelch/genética , Neoplasias Hepáticas/induzido quimicamente , Neoplasias Hepáticas/veterinária , Proteínas de Membrana/genética , Proteínas de Membrana/metabolismo , Fator 2 Relacionado a NF-E2/genética , Proteínas Proto-Oncogênicas c-maf/genética , Proteínas Proto-Oncogênicas c-maf/metabolismo , Ratos , Ratos Endogâmicos F344RESUMO
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.
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Líquido da Lavagem Broncoalveolar/citologia , Líquido da Lavagem Broncoalveolar/imunologia , COVID-19/patologia , Pulmão/citologia , SARS-CoV-2/imunologia , Linfócitos B/imunologia , Biomarcadores , Líquido da Lavagem Broncoalveolar/química , Células Dendríticas/imunologia , Células Epiteliais/citologia , Células Epiteliais/virologia , Humanos , Células Matadoras Naturais/imunologia , Pulmão/química , Aprendizado de Máquina , Macrófagos/imunologia , Monócitos/imunologia , Neutrófilos/imunologia , RNA Viral/genética , Análise de Sequência de RNA , Índice de Gravidade de Doença , Análise de Célula Única , Linfócitos T/imunologiaRESUMO
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.
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Macrófagos/fisiologia , Neoplasias/imunologia , Transcrição Gênica , Microambiente Tumoral , Polaridade Celular , Redes Reguladoras de Genes , Humanos , Subunidade alfa do Fator 1 Induzível por Hipóxia/fisiologia , Modelos Teóricos , NF-kappa B/fisiologiaRESUMO
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.
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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.
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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.
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Neoplasias da Mama/genética , Sequenciamento do Exoma/métodos , Análise de Célula Única/métodos , Esferoides Celulares/citologia , Feminino , Redes Reguladoras de Genes , Heterogeneidade Genética , Humanos , Células MCF-7 , Análise de Sequência de RNA , Células Tumorais CultivadasRESUMO
During tumor progression, cancer cells rewire 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.
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Type 2 diabetes (T2D) is a global epidemic that affects more than 8% of the world's population and is a leading cause of death in Mexico. Diet and lifestyle are known to contribute to the onset of T2D. However, the role of the gut microbiome in T2D progression remains uncertain. Associations between microbiome composition and diabetes are confounded by medication use, diet, and obesity. Here we present data on a treatment-naive cohort of 405 Mexican individuals across varying stages of T2D severity. Associations between gut bacteria and more than 200 clinical variables revealed a defined set of bacterial genera that were consistent biomarkers of T2D prevalence and risk. Specifically, gradual increases in blood glucose levels, beta cell dysfunction, and the accumulation of measured T2D risk factors were correlated with the relative abundances of four bacterial genera. In a cohort of 25 individuals, T2D treatment-predominantly metformin-reliably returned the microbiome to the normoglycemic community state. Deep clinical characterization allowed us to broadly control for confounding variables, indicating that these microbiome patterns were independent of common T2D comorbidities, like obesity or cardiovascular disease. Our work provides the first solid evidence for a direct link between the gut microbiome and T2D in a critically high-risk population. In particular, we show that increased T2D risk is reflected in gradual changes in the gut microbiome. Whether or not these T2D-associated changes in the gut contribute to the etiology of T2D or its comorbidities remains to be seen.
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Bactérias/classificação , Fezes/microbiologia , Microbioma Gastrointestinal , Estado Pré-Diabético/patologia , Bactérias/efeitos dos fármacos , Bactérias/isolamento & purificação , Estudos de Casos e Controles , Estudos de Coortes , Diabetes Mellitus Tipo 2 , Humanos , Hipoglicemiantes/uso terapêutico , Estilo de Vida , Metformina/uso terapêutico , México/epidemiologia , Estado Pré-Diabético/tratamento farmacológico , Estado Pré-Diabético/epidemiologia , Estado Pré-Diabético/microbiologia , Fatores de RiscoRESUMO
Transcription factors OCT4, SOX2, KLF4, C-MYC, and NANOG (OSKM-N) regulate pluripotency and stemness, and their ectopic expression reprograms human and murine fibroblasts that constitute the key of regenerative medicine. To determine their contribution to cell transformation, we analyzed the gene expression profiles of these transcription factors in cervical cancer samples and found that they are preferentially expressed in the tumor component. Also, cancer stem cell-enriched cultures grown as sphere cultures showed overexpression of OSKM-N genes. Importantly, we observed that lentiviral-mediated transduction of these factors confers, to a nontumorigenic immortalized human cell line, properties of cancer stem cells as the ability to form tumors in a mouse model. When we performed a meta-analysis using microarray data from cervical cancer biopsies and normal tissues, we found that the expression of OSKM-N and some target genes allowed separating tumor and normal tissues between samples, which enhanced the importance of OSKM-N in the tumorigenesis. Finally, we analyzed and compared both transcript and protein expression profiles of these factors within a cohort of patients with cervical cancer. To our knowledge, this is the first time that the expression of OSKM-N is described to induce one of the main characteristics of the cancer stem cell, the tumorigenicity. And, more importantly, its exogenous expression in a nontumorigenic cell line is sufficient to induce a tumorigenic phenotype; furthermore, the differential expression of this transcription factor distinguishes tumor tissue and normal tissue in cervical samples.