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1.
Mol Genet Genomics ; 299(1): 60, 2024 May 27.
Artigo em Inglês | MEDLINE | ID: mdl-38801463

RESUMO

Type 2 diabetes (DM2) is an increasingly prevalent disease that challenges tuberculosis (TB) control strategies worldwide. It is significant that DM2 patients with poor glycemic control (PDM2) are prone to developing tuberculosis. Furthermore, elucidating the molecular mechanisms that govern this susceptibility is imperative to address this problem. Therefore, a pilot transcriptomic study was performed. Human blood samples from healthy controls (CTRL, HbA1c < 6.5%), tuberculosis (TB), comorbidity TB-DM2, DM2 (HbA1c 6.5-8.9%), and PDM2 (HbA1c > 10%) groups (n = 4 each) were analyzed by differential expression using microarrays. We use a network strategy to identify potential molecular patterns linking the differentially expressed genes (DEGs) specific for TB-DM2 and PDM2 (p-value < 0.05, fold change > 2). We define OSM, PRKCD, and SOCS3 as key regulatory genes (KRGs) that modulate the immune system and related pathways. RT-qPCR assays confirmed upregulation of OSM, PRKCD, and SOCS3 genes (p < 0.05) in TB-DM2 patients (n = 18) compared to CTRL, DM2, PDM2, or TB groups (n = 17, 19, 15, and 9, respectively). Furthermore, OSM, PRKCD, and SOCS3 were associated with PDM2 susceptibility pathways toward TB-DM2 and formed a putative protein-protein interaction confirmed in STRING. Our results reveal potential molecular patterns where OSM, PRKCD, and SOCS3 are KRGs underlying the compromised immune response and susceptibility of patients with PDM2 to develop tuberculosis. Therefore, this work paved the way for fundamental research of new molecular targets in TB-DM2. Addressing their cellular implications, and the impact on the diagnosis, treatment, and clinical management of TB-DM2 could help improve the strategy to end tuberculosis for this vulnerable population.


Assuntos
Diabetes Mellitus Tipo 2 , Proteína 3 Supressora da Sinalização de Citocinas , Tuberculose , Humanos , Diabetes Mellitus Tipo 2/genética , Diabetes Mellitus Tipo 2/sangue , Diabetes Mellitus Tipo 2/complicações , Projetos Piloto , Tuberculose/genética , Tuberculose/sangue , Masculino , Feminino , Pessoa de Meia-Idade , Proteína 3 Supressora da Sinalização de Citocinas/genética , Proteína 3 Supressora da Sinalização de Citocinas/metabolismo , Controle Glicêmico , Perfilação da Expressão Gênica , Idoso , Adulto , Redes Reguladoras de Genes , Estudos de Casos e Controles , Transcriptoma/genética , Suscetibilidade a Doenças
2.
Front Mol Biosci ; 10: 1100486, 2023.
Artigo em Inglês | MEDLINE | ID: mdl-36936993

RESUMO

Introduction: Similar to what it has been reported with preceding viral epidemics (such as MERS, SARS, or influenza), SARS-CoV-2 infection is also affecting the human immunometabolism with long-term consequences. Even with underreporting, an accumulated of almost 650 million people have been infected and 620 million recovered since the start of the pandemic; therefore, the impact of these long-term consequences in the world population could be significant. Recently, the World Health Organization recognized the post-COVID syndrome as a new entity, and guidelines are being established to manage and treat this new condition. However, there is still uncertainty about the molecular mechanisms behind the large number of symptoms reported worldwide. Aims and Methods: In this study we aimed to evaluate the clinical and lipidomic profiles (using non-targeted lipidomics) of recovered patients who had a mild and severe COVID-19 infection (acute phase, first epidemic wave); the assessment was made two years after the initial infection. Results: Fatigue (59%) and musculoskeletal (50%) symptoms as the most relevant and persistent. Functional analyses revealed that sterols, bile acids, isoprenoids, and fatty esters were the predicted metabolic pathways affected in both COVID-19 and post-COVID-19 patients. Principal Component Analysis showed differences between study groups. Several species of phosphatidylcholines and sphingomyelins were identified and expressed in higher levels in post-COVID-19 patients compared to controls. The paired analysis (comparing patients with an active infection and 2 years after recovery) show 170 dysregulated features. The relationship of such metabolic dysregulations with the clinical symptoms, point to the importance of developing diagnostic and therapeuthic markers based on cell signaling pathways.

3.
Arch Med Res ; 54(1): 17-26, 2023 01.
Artigo em Inglês | MEDLINE | ID: mdl-36564298

RESUMO

BACKGROUND: The early diagnosis of diabetic nephropathy (DN) is essential for improving the prognosis and effectively manage patients affected with this disease. The standard biomarkers, including albuminuria and glomerular filtration rate, are not very precise. New molecular biomarkers are needed to more accurately identify DN and better predict disease progression. Characteristic DN biomarkers can be identified using transcriptomic analysis. AIM OF THE STUDY: To evaluate the transcriptomic profile of controls (CTRLs, n = 15), patients with prediabetes (PREDM, n = 15), patients with type-2 diabetes mellitus (DM2, n = 15), and patients with DN (n = 15) by microarray analysis to find new biomarkers. RT-PCR was then used to confirm gene biomarkers specific for DN. MATERIALS AND METHODS: Blood samples were used to isolate RNA for microarray expression analysis. 26,803 unique gene sequences and 30,606 LncRNA sequences were evaluated-Selected gene biomarkers for DN were validated using qPCR assays. Sensitivity, specificity, and area under the curve (AUC) were calculated as measures of diagnostic accuracy. RESULTS: The DN transcriptome was composed of 300 induced genes, compared to CTRLs, PREDM, and DM-2 groups. RT-qPCR assays validated that METLL22, PFKL, CCNB1 and CASP2 genes were induced in the DN group compared to CTRLs, PREDM, and DM-2 groups. The ROC analysis for these four genes showed 0.9719, 0.8853, 0.8533 and 0.7748 AUC values, respectively. CONCLUSION: Among induced genes in the DN group, we found that CASP2, PFKL and CCNB1 may potentially be used as biomarkers to diagnose DN. Of these, METLL22 had the highest AUC score, at 0.9719.


Assuntos
Diabetes Mellitus Tipo 2 , Nefropatias Diabéticas , Humanos , Nefropatias Diabéticas/diagnóstico , Nefropatias Diabéticas/genética , Diabetes Mellitus Tipo 2/complicações , Diabetes Mellitus Tipo 2/genética , Perfilação da Expressão Gênica , Biomarcadores , Transcriptoma
4.
Diagnostics (Basel) ; 13(1)2022 Dec 30.
Artigo em Inglês | MEDLINE | ID: mdl-36611425

RESUMO

COVID-19 infection triggered a global public health crisis during the 2020-2022 period, and it is still evolving. This highly transmissible respiratory disease can cause mild symptoms up to severe pneumonia with potentially fatal respiratory failure. In this cross-sectional study, 41 PCR-positive patients for SARS-CoV-2 and 42 healthy controls were recruited during the first wave of the pandemic in Mexico. The plasmatic expression of five circulating miRNAs involved in inflammatory and pathological host immune responses was assessed using RT-qPCR (Reverse Transcription quantitative Polymerase Chain Reaction). Compared with controls, a significant upregulation of miR-146a, miR-155, and miR-221 was observed; miR-146a had a positive correlation with absolute neutrophil count and levels of brain natriuretic propeptide (proBNP), and miR-221 had a positive correlation with ferritin and a negative correlation with total cholesterol. We found here that CDKN1B gen is a shared target of miR-146a, miR-221-3p, and miR-155-5p, paving the way for therapeutic interventions in severe COVID-19 patients. The ROC curve built with adjusted variables (miR-146a, miR-221-3p, miR-155-5p, age, and male sex) to differentiate individuals with severe COVID-19 showed an AUC of 0.95. The dysregulation of circulating miRNAs provides new insights into the underlying immunological mechanisms, and their possible use as biomarkers to discriminate against patients with severe COVID-19. Functional analysis showed that most enriched pathways were significantly associated with processes related to cell proliferation and immune responses (innate and adaptive). Twelve of the predicted gene targets have been validated in plasma/serum, reflecting their potential use as predictive prognosis biomarkers.

5.
Diagnostics (Basel) ; 12(11)2022 Nov 15.
Artigo em Inglês | MEDLINE | ID: mdl-36428864

RESUMO

According to the World Health Organization (WHO), type 2 diabetes mellitus (T2DM) is a result of the inefficient use of insulin by the body. More than 95% of people with diabetes have T2DM, which is largely due to excess weight and physical inactivity. This study proposes an intelligent feature selection of metabolites related to different stages of diabetes, with the use of genetic algorithms (GA) and the implementation of support vector machines (SVMs), K-Nearest Neighbors (KNNs) and Nearest Centroid (NEARCENT) and with a dataset obtained from the Instituto Mexicano del Seguro Social with the protocol name of the following: "Análisis metabolómico y transcriptómico diferencial en orina y suero de pacientes pre diabéticos, diabéticos y con nefropatía diabética para identificar potenciales biomarcadores pronósticos de daño renal" (differential metabolomic and transcriptomic analyses in the urine and serum of pre-diabetic, diabetic and diabetic nephropathy patients to identify potential prognostic biomarkers of kidney damage). In order to analyze which machine learning (ML) model is the most optimal for classifying patients with some stage of T2DM, the novelty of this work is to provide a genetic algorithm approach that detects significant metabolites in each stage of progression. More than 100 metabolites were identified as significant between all stages; with the data analyzed, the average accuracies obtained in each of the five most-accurate implementations of genetic algorithms were in the range of 0.8214-0.9893 with respect to average accuracy, providing a precise tool to use in detections and backing up a diagnosis constructed entirely with metabolomics. By providing five potential biomarkers for progression, these extremely significant metabolites are as follows: "Cer(d18:1/24:1) i2", "PC(20:3-OH/P-18:1)", "Ganoderic acid C2", "TG(16:0/17:1/18:1)" and "GPEtn(18:0/20:4)".

6.
Metabolites ; 11(11)2021 10 22.
Artigo em Inglês | MEDLINE | ID: mdl-34822382

RESUMO

Gestational diabetes mellitus (GDM) is one of the most frequent pregnancy complications with potential adverse outcomes for mothers and newborns. Its effects on the newborn appear during the neonatal period or early childhood. Therefore, an early diagnosis is crucial to prevent the development of chronic diseases later in adult life. In this study, the urinary metabolome of babies born to GDM mothers was characterized. In total, 144 neonatal and maternal (second and third trimesters of pregnancy) urinary samples were analyzed using targeted metabolomics, combining liquid chromatographic mass spectrometry (LC-MS/MS) and flow injection analysis mass spectrometry (FIA-MS/MS) techniques. We provide here the neonatal urinary concentration values of 101 metabolites for 26 newborns born to GDM mothers and 22 newborns born to healthy mothers. The univariate analysis of these metabolites revealed statistical differences in 11 metabolites. Multivariate analyses revealed a differential metabolic profile in newborns of GDM mothers characterized by dysregulation of acylcarnitines, amino acids, and polyamine metabolism. Levels of hexadecenoylcarnitine (C16:1) and spermine were also higher in newborns of GDM mothers. The maternal urinary metabolome revealed significant differences in butyric, isobutyric, and uric acid in the second and third trimesters of pregnancy. These metabolic alterations point to the impact of GDM in the neonatal period.

7.
PLoS One ; 16(8): e0256784, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34460840

RESUMO

Viral sepsis has been proposed as an accurate term to describe all multisystemic dysregulations and clinical findings in severe and critically ill COVID-19 patients. The adoption of this term may help the implementation of more accurate strategies of early diagnosis, prognosis, and in-hospital treatment. We accurately quantified 110 metabolites using targeted metabolomics, and 13 cytokines/chemokines in plasma samples of 121 COVID-19 patients with different levels of severity, and 37 non-COVID-19 individuals. Analyses revealed an integrated host-dependent dysregulation of inflammatory cytokines, neutrophil activation chemokines, glycolysis, mitochondrial metabolism, amino acid metabolism, polyamine synthesis, and lipid metabolism typical of sepsis processes distinctive of a mild disease. Dysregulated metabolites and cytokines/chemokines showed differential correlation patterns in mild and critically ill patients, indicating a crosstalk between metabolism and hyperinflammation. Using multivariate analysis, powerful models for diagnosis and prognosis of COVID-19 induced sepsis were generated, as well as for mortality prediction among septic patients. A metabolite panel made of kynurenine/tryptophan ratio, IL-6, LysoPC a C18:2, and phenylalanine discriminated non-COVID-19 from sepsis patients with an area under the curve (AUC (95%CI)) of 0.991 (0.986-0.995), with sensitivity of 0.978 (0.963-0.992) and specificity of 0.920 (0.890-0.949). The panel that included C10:2, IL-6, NLR, and C5 discriminated mild patients from sepsis patients with an AUC (95%CI) of 0.965 (0.952-0.977), with sensitivity of 0.993(0.984-1.000) and specificity of 0.851 (0.815-0.887). The panel with citric acid, LysoPC a C28:1, neutrophil-lymphocyte ratio (NLR) and kynurenine/tryptophan ratio discriminated severe patients from sepsis patients with an AUC (95%CI) of 0.829 (0.800-0.858), with sensitivity of 0.738 (0.695-0.781) and specificity of 0.781 (0.735-0.827). Septic patients who survived were different from those that did not survive with a model consisting of hippuric acid, along with the presence of Type II diabetes, with an AUC (95%CI) of 0.831 (0.788-0.874), with sensitivity of 0.765 (0.697-0.832) and specificity of 0.817 (0.770-0.865).


Assuntos
COVID-19/patologia , Metabolômica , Sepse/diagnóstico , Adulto , Área Sob a Curva , COVID-19/complicações , COVID-19/virologia , Quimiocinas/sangue , Citocinas/sangue , Feminino , Humanos , Cinurenina/sangue , Linfócitos/citologia , Masculino , Pessoa de Meia-Idade , Neutrófilos/citologia , Curva ROC , Estudos Retrospectivos , Fatores de Risco , SARS-CoV-2/isolamento & purificação , Sepse/etiologia , Índice de Gravidade de Doença , Triptofano/sangue
8.
Diagnostics (Basel) ; 11(12)2021 Nov 25.
Artigo em Inglês | MEDLINE | ID: mdl-34943434

RESUMO

Differences in clinical manifestations, immune response, metabolic alterations, and outcomes (including disease severity and mortality) between men and women with COVID-19 have been reported since the pandemic outbreak, making it necessary to implement sex-specific biomarkers for disease diagnosis and treatment. This study aimed to identify sex-associated differences in COVID-19 patients by means of a genetic algorithm (GALGO) and machine learning, employing support vector machine (SVM) and logistic regression (LR) for the data analysis. Both algorithms identified kynurenine and hemoglobin as the most important variables to distinguish between men and women with COVID-19. LR and SVM identified C10:1, cough, and lysoPC a 14:0 to discriminate between men with COVID-19 from men without, with LR being the best model. In the case of women with COVID-19 vs. women without, SVM had a higher performance, and both models identified a higher number of variables, including 10:2, lysoPC a C26:0, lysoPC a C28:0, alpha-ketoglutaric acid, lactic acid, cough, fever, anosmia, and dysgeusia. Our results demonstrate that differences in sexes have implications in the diagnosis and outcome of the disease. Further, genetic and machine learning algorithms are useful tools to predict sex-associated differences in COVID-19.

9.
Metabolites ; 10(4)2020 Apr 23.
Artigo em Inglês | MEDLINE | ID: mdl-32340350

RESUMO

The knowledge of normal metabolite values for neonates is key to establishing robust cut-off values to diagnose diseases, to predict the occurrence of new diseases, to monitor a neonate's metabolism, or to assess their general health status. For full term-newborns, many reference biochemical values are available for blood, serum, plasma and cerebrospinal fluid. However, there is a surprising lack of information about normal urine concentration values for a large number of important metabolites in neonates. In the present work, we used targeted tandem mass spectrometry (MS/MS)-based metabolomic assays to identify and quantify 136 metabolites of biomedical interest in the urine from 48 healthy, full-term term neonates, collected in the first 24 h of life. In addition to this experimental study, we performed a literature review (covering the past eight years and over 500 papers) to update the references values in the Human Metabolome Database/Urine Metabolome Database (HMDB/UMDB). Notably, 86 of the experimentally measured urinary metabolites are being reported in neonates/infants for the first time and another 20 metabolites are being reported in human urine for the first time ever. Sex differences were found for 15 metabolites. The literature review allowed us to identify another 78 urinary metabolites with concentration data. As a result, reference concentration values and ranges for 378 neonatal urinary metabolites are now publicly accessible via the HMDB.

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