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1.
Am J Physiol Endocrinol Metab ; 327(1): E13-E26, 2024 Jul 01.
Artigo em Inglês | MEDLINE | ID: mdl-38717362

RESUMO

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.


Assuntos
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-Dawley
2.
Bioinformatics ; 39(12)2023 12 01.
Artigo em Inglês | MEDLINE | ID: mdl-38015858

RESUMO

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).


Assuntos
Microbiota , Reprodutibilidade dos Testes , Algoritmos , Aprendizado de Máquina , Difusão
3.
Int J Mol Sci ; 25(20)2024 Oct 10.
Artigo em Inglês | MEDLINE | ID: mdl-39456679

RESUMO

Today, the intratumoral composition is a relevant factor associated with the progression and aggression of cancer. Although it suggests a metabolic interdependence among the subpopulations inside the tumor, a detailed map of how this interdependence contributes to the malignant phenotype is still lacking. To address this issue, we developed a systems biology approach integrating single-cell RNASeq and genome-scale metabolic reconstruction to map the metabolic cross-feeding among the subpopulations previously identified in the spheroids of MCF7 breast cancer. By calibrating our model with expression profiles and the experimental growth rate, we concluded that the reverse Warburg effect emerges as a mechanism to optimize community growth. Furthermore, through an in silico analysis, we identified lactate, alpha-ketoglutarate, and some amino acids as key metabolites whose disponibility alters the growth rate of the spheroid. Altogether, this work provides a strategy for assessing how space and intratumoral heterogeneity influence the metabolic robustness of cancer, issues suggesting that computational strategies should move toward the design of optimized treatments.


Assuntos
Neoplasias da Mama , Simulação por Computador , Esferoides Celulares , Humanos , Neoplasias da Mama/metabolismo , Neoplasias da Mama/patologia , Neoplasias da Mama/genética , Feminino , Esferoides Celulares/metabolismo , Células MCF-7 , Efeito Warburg em Oncologia , Biologia de Sistemas/métodos
4.
Adv Exp Med Biol ; 1412: 311-335, 2023.
Artigo em Inglês | MEDLINE | ID: mdl-37378775

RESUMO

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.


Assuntos
COVID-19 , Humanos , SARS-CoV-2 , Aprendizado de Máquina , Teste para COVID-19 , Biologia de Sistemas
5.
Methods ; 149: 59-68, 2018 10 01.
Artigo em Inglês | MEDLINE | ID: mdl-29704665

RESUMO

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.


Assuntos
Neoplasias Colorretais/metabolismo , Sulfeto de Hidrogênio/metabolismo , Mucosa Intestinal/metabolismo , Metabolômica/métodos , Microbiota/fisiologia , Modelos Biológicos , Adulto , Idoso , Idoso de 80 Anos ou mais , Clostridium perfringens/metabolismo , Neoplasias Colorretais/microbiologia , Feminino , Fusobacterium nucleatum/metabolismo , Humanos , Mucosa Intestinal/microbiologia , Masculino , Pessoa de Meia-Idade , Adulto Jovem
6.
BMC Evol Biol ; 17(1): 99, 2017 04 14.
Artigo em Inglês | MEDLINE | ID: mdl-28410570

RESUMO

BACKGROUND: 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. RESULTS: We reconstructed the metabolic networks of the chromatophore of P. chromatophora CCAC 0185 and a close free-living relative, the cyanobacterium Synechococcus sp. WH 5701. We found that the evolution of free-living to host-restricted lifestyle rendered a fragile metabolic network where >80% of genes in the chromatophore are essential for metabolic functionality. Despite the lack of experimental information, the metabolic reconstruction of the chromatophore suggests that the host provides several metabolites to the endosymbiont. By using these metabolites as intracellular conditions, in silico simulations of genome evolution by gene lose recover with 77% accuracy the actual metabolic gene content of the chromatophore. Also, the metabolic model of the chromatophore allowed us to predict by flux balance analysis a maximum rate of reduced-carbon released by the endosymbiont to the host. By inspecting the central metabolism of the chromatophore and the free-living cyanobacteria we found that by improvements in the gluconeogenic pathway the metabolism of the endosymbiont uses more efficiently the carbon source for reduced-carbon production. In addition, our in silico simulations of the evolutionary process leading to the reduced metabolic network of the chromatophore showed that the predicted rate of released reduced-carbon is obtained in less than 5% of the times under a process guided by random gene deletion and genetic drift. We interpret previous findings as evidence that natural selection at holobiont level shaped the rate at which reduced-carbon is exported to the host. Finally, our model also predicts that the ABC phosphate transporter (pstSACB) which is conserved in the genome of the chromatophore of P. chromatophora strain CCAC 0185 is a necessary component to release reduced-carbon molecules to the host. CONCLUSION: Our evolutionary analysis suggests that in the case of Paulinella chromatophora natural selection at the holobiont level played a prominent role in shaping the metabolic specialization of the chromatophore. We propose that natural selection acted as a "metabolic engineer" by favoring metabolic restructurings that led to an increased release of reduced-carbon to the host.


Assuntos
Cercozoários/citologia , Cercozoários/fisiologia , Cianobactérias/fisiologia , Evolução Biológica , Cercozoários/genética , Simulação por Computador , Cianobactérias/genética , Hexoses/metabolismo , Seleção Genética , Simbiose , Synechococcus/citologia , Synechococcus/metabolismo
7.
Semin Cancer Biol ; 30: 79-87, 2015 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-24747697

RESUMO

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.


Assuntos
Modelos Biológicos , Neoplasias/metabolismo , Medicina de Precisão , Biologia de Sistemas , Humanos
8.
Front Mol Biosci ; 11: 1429281, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-39314212

RESUMO

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.

9.
Sci Rep ; 14(1): 9678, 2024 04 27.
Artigo em Inglês | MEDLINE | ID: mdl-38678119

RESUMO

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.


Assuntos
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 , Idoso
10.
Antioxidants (Basel) ; 13(3)2024 Feb 20.
Artigo em Inglês | MEDLINE | ID: mdl-38539791

RESUMO

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.

11.
PLoS Comput Biol ; 8(10): e1002720, 2012.
Artigo em Inglês | MEDLINE | ID: mdl-23071431

RESUMO

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.


Assuntos
Redes e Vias Metabólicas/fisiologia , Metaboloma/fisiologia , Metabolômica/métodos , Rhizobium etli/metabolismo , Carbono/metabolismo , Modelos Biológicos , Fixação de Nitrogênio/fisiologia , Phaseolus/microbiologia , Fenótipo , Proteoma , Rhizobium etli/genética , Simbiose/fisiologia , Biologia de Sistemas/métodos
12.
Front Immunol ; 14: 1150890, 2023.
Artigo em Inglês | MEDLINE | ID: mdl-37283734

RESUMO

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.


Assuntos
Neoplasias , Microambiente Tumoral , Humanos , Diferenciação Celular , Citocinas/metabolismo , Macrófagos , Neoplasias/metabolismo , Anti-Inflamatórios/farmacologia
13.
Arch Med Res ; 54(3): 176-188, 2023 04.
Artigo em Inglês | MEDLINE | ID: mdl-36990891

RESUMO

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.


Assuntos
Diabetes Mellitus Tipo 2 , Microbioma Gastrointestinal , Humanos , Diabetes Mellitus Tipo 2/prevenção & controle , Automonitorização da Glicemia , Glicemia , Glucose
14.
Front Endocrinol (Lausanne) ; 14: 1170459, 2023.
Artigo em Inglês | MEDLINE | ID: mdl-37441494

RESUMO

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.


Assuntos
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 Máquina
15.
Front Endocrinol (Lausanne) ; 14: 1128767, 2023.
Artigo em Inglês | MEDLINE | ID: mdl-37124757

RESUMO

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.


Assuntos
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 , Glucose
16.
Front Cell Dev Biol ; 11: 1119514, 2023.
Artigo em Inglês | MEDLINE | ID: mdl-37065848

RESUMO

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.

17.
Arch Med Res ; 54(3): 197-210, 2023 04.
Artigo em Inglês | MEDLINE | ID: mdl-36990888

RESUMO

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.


Assuntos
COVID-19 , Humanos , SARS-CoV-2 , Pandemias , Estudos Prospectivos , Comorbidade , Hematopoese
18.
Front Immunol ; 13: 1012730, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-36544764

RESUMO

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.


Assuntos
Algoritmos , Microambiente Tumoral , Redes Reguladoras de Genes , Macrófagos , Fatores de Transcrição/genética
19.
Gut Microbes ; 14(1): 2111952, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-36004400

RESUMO

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.


Assuntos
Diabetes Mellitus Tipo 2 , Microbioma Gastrointestinal , Microbiota , Diabetes Mellitus Tipo 2/terapia , Dieta , Microbioma Gastrointestinal/fisiologia , Humanos , Biologia de Sistemas
20.
Biochim Biophys Acta Mol Cell Res ; 1869(5): 119222, 2022 05.
Artigo em Inglês | MEDLINE | ID: mdl-35093454

RESUMO

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.


Assuntos
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 F344
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