Frontiers in molecular medicine最新文献

Aortic valve stenosis (AS) is a major health problem. Extensive myocardial remodeling increases operative risk and might lead to incomplete reverse remodeling with persistent symptoms after aortic valve replacement (AVR); this makes the optimal timing of AVR a clinical challenge. The pathogenesis behind incomplete reverse remodeling is unclear. Central among signaling pathways in the remodeling heart is the pro-hypertrophic Ca²⁺-activated calcineurin and its downstream nuclear factor of activated T-cell (NFATc1-c4) transcription factors. We investigated calcineurin-NFATc dynamics in patient and mouse hearts during remodeling and reverse remodeling. Myocardial biopsies were obtained from AS patients during AVR and left ventricles harvested from mice subjected to aortic banding (AB) and debanding (DB). The transcript and protein of the NFATc-responsive gene regulator of calcineurin 1-4 (RCAN1-4) and luciferase activity in NFAT-luciferase mice were used as read-outs for calcineurin-NFATc activity. Calcineurin-NFATc activation was sustained through AB 24 h to 18 weeks and elevated in AS patients. All four NFATc isoforms were elevated in AS, while NFATc4 was persistently elevated during chronic remodeling after AB in mice. NFAT activation remained reversible when 1 week's AB was followed by 1 week's DB and accompanied functional improvement. However, when DB for 1 week followed AB for 4 weeks, NFAT activation was not reversed. In conclusion, calcineurin-NFAT dynamics correspond with cardiac remodeling and reverse remodeling during experimental AB and DB. Our data suggest that calcineurin-NFATc attenuation is important for reverse remodeling and outcomes after AVR for AS.

Parkinson's Disease (PD) is a neurodegenerative disorder with highly heterogeneous phenotypes. Accordingly, it has been challenging to robustly identify genetic factors associated with disease risk, prognosis and therapy response via genome-wide association studies (GWAS). In this review we first provide an overview of existing statistical methods to detect associations between genetic variants and the disease phenotypes in existing PD GWAS. Secondly, we discuss the potential of machine learning approaches to better quantify disease phenotypes and to move beyond disease understanding towards a better-personalized treatment of the disease.

Endoplasmic reticulum (ER) function is vital for protein homeostasis ("proteostasis"). Protein misfolding in the ER of podocytes (glomerular visceral epithelial cells) is an important contributor to the pathogenesis of human glomerular diseases. ER protein misfolding causes ER stress and activates a compensatory signaling network called the unfolded protein response (UPR). Disruption of the UPR, in particular deletion of the UPR transducer, inositol-requiring enzyme 1α (IRE1α) in mouse podocytes leads to podocyte injury and albuminuria in aging, and exacerbates injury in glomerulonephritis. The UPR may interact in a coordinated manner with autophagy to relieve protein misfolding and its consequences. Recent studies have identified novel downstream targets of IRE1α, which provide new mechanistic insights into proteostatic pathways. Novel pathways of IRE1α signaling involve reticulophagy, mitochondria, metabolism, vesicular trafficking, microRNAs, and others. Mechanism-based therapies for glomerulopathies are limited, and development of non-invasive ER stress biomarkers, as well as targeting ER stress with pharmacological compounds may represent a therapeutic opportunity for preventing or attenuating progression of chronic kidney disease.

Indole and p-cresol are precursors of the most important uremic toxins, generated from the fermentation of amino acids tryptophan and tyrosine by the proteolytic community of intestinal bacteria. The present study focused on the relationship between the microbiome composition, the fecal levels of indole and p-cresol, and their kinetics of generation/degradation in fecal cultures. The concentration of indole and p-cresol, the volatilome, the dry weight, and the amount of ammonium and carbohydrates were analyzed in the feces of 10 healthy adults. Indole and p-cresol widely differed among samples, laying in the range of 1.0-19.5 μg/g and 1.2-173.4 μg/g, respectively. Higher fecal levels of indole and p-cresol were associated with lower carbohydrates and higher ammonium levels, that are markers of a more pronounced intestinal proteolytic metabolism. Positive relationship was observed also with the dry/wet weight ratio, indicator of prolonged intestinal retention of feces. p-cresol and indole presented a statistically significant negative correlation with OTUs of uncultured Bacteroidetes and Firmicutes, the former belonging to Bacteroides and the latter to the families Butyricicoccaceae (genus Butyricicoccus), Monoglobaceae (genus Monoglobus), Lachnospiraceae (genera Faecalibacterium, Roseburia, and Eubacterium ventriosum group). The kinetics of formation and/or degradation of indole and p-cresol was investigated in fecal slurries, supplemented with the precursor amino acids tryptophan and tyrosine in strict anaerobiosis. The presence of the precursors bursted indole production but had a lower effect on the rate of p-cresol formation. On the other hand, supplementation with indole reduced the net rate of formation. The taxa that positively correlated with fecal levels of uremic toxins presented a positive correlation also with p-cresol generation rate in biotransformation experiments. Moreover other bacterial groups were positively correlated with generation rate of p-cresol and indole, further expanding the range of taxa associated to production of p-cresol (Bacteroides, Alistipes, Eubacterium xylanophylum, and Barnesiella) and indole (e.g., Bacteroides, Ruminococcus torques, Balutia, Dialister, Butyricicoccus). The information herein presented contributes to disclose the relationships between microbiota composition and the production of uremic toxins, that could provide the basis for probiotic intervention on the gut microbiota, aimed to prevent the onset, hamper the progression, and alleviate the impact of nephropaties.

Oncogenes or tumor suppressor genes are rarely mutated in several pediatric tumors and some early stage adult cancers. This suggests that an aberrant epigenetic reprogramming may crucially affect the tumorigenesis of these tumors. Compelling evidence support the hypothesis that cancer stem cells (CSCs), a cell subpopulation within the tumor bulk characterized by self-renewal capacity, metastatic potential and chemo-resistance, may derive from normal stem cells (NSCs) upon an epigenetic deregulation. Thus, a better understanding of the specific epigenetic alterations driving the transformation from NSCs into CSCs may help to identify efficacious treatments to target this aggressive subpopulation. Moreover, deepening the knowledge about these alterations may represent the framework to design novel therapeutic approaches also in the field of regenerative medicine in which bioengineering of NSCs has been evaluated. Here, we provide a broad overview about: 1) the role of aberrant epigenetic modifications contributing to CSC initiation, formation and maintenance, 2) the epigenetic inhibitors in clinical trial able to specifically target the CSC subpopulation, and 3) epigenetic drugs and stem cells used in regenerative medicine for cancer and diseases.

Giant cell arteritis (GCA) is an inflammatory chronic disease mainly occurring in elderly individuals. The pathogenesis of GCA is still far from being completely elucidated. However, in susceptible arteries, an aberrant immune system activation drives the occurrence of vascular remodeling which is mainly characterized by intimal hyperplasia and luminal obstruction. Vascular damage leads to ischemic manifestations involving extra-cranial branches of carotid arteries, mostly temporal arteries, and aorta. Classically, GCA was considered a pathological process resulting from the interaction between an unknown environmental trigger, such as an infectious agent, with local dendritic cells (DCs), activated CD4 T cells and effector macrophages. In the last years, the complexity of GCA has been underlined by robust evidence suggesting that several cell subsets belonging to the innate immunity can contribute to disease development and progression. Specifically, a role in driving tissue damage and adaptive immunity activation was described for dendritic cells (DCs), monocytes and macrophages, mast cells, neutrophils and wall components, such as endothelial cells (ECs) and vascular smooth muscle cells (VSMCs). In this regard, molecular pathways related to cytokines, chemokines, growth factors, vasoactive molecules and reactive oxygen species may contribute to the inflammatory process underlying GCA. Altogether, innate cellular and molecular pathways may clarify many pathogenetic aspects of the disease, paving the way for the identification of new biomarkers and for the development of new treatment targets for GCA. This review aims to deeply dissect past and new evidence on the innate immunological disruption behind GCA providing a comprehensive description of disease development from the innate perspective.

Background: Heat shock protein 60 (HSP60), a potentially homeostatic antigen, is involved in physiological and non-physiological conditions. Experimental data support the role of HSP60 in placental and mitochondrial steroidogenesis. Furthermore, HSP60 is translocated into the endothelial-cell plasma membrane and the extracellular space under stress conditions, promoting the atherosclerotic process. Therefore, we investigated the association between HSP60 and endothelial function in postmenopausal women, considering the possible atherogenic effect of androgenic hormones. Methods: This study included 123 healthy postmenopausal women. Exclusion criteria were treated hypertension or dyslipidaemia, menopause hormone therapy during the last 6 months, and previously diagnosed peripheral vascular disease or cardiovascular disease. Fasting venous blood samples were obtained for biochemical and hormonal assessment and evaluation of HSP60. Sonographic assessment of flow-mediated dilation (FMD) occurred immediately after that in one session. Results: Univariate analysis showed that women with FMD values below median 5.12% had lower logHSP60 values (low vs. high FMD, HSP60 values: 2.01 ± 1.16 ng/ml vs. 3.22 ± 1.17 ng/ml, p-value = 0.031). Multivariable analysis showed that logHSP60 was associated with FMD (b-coefficient = 0.171, p-value = 0.046), adjusting for traditional cardiovascular risk factors (TRFs) and insulin levels. Further adjustment for testosterone and DHEAS rendered the result non-significant. In the multivariable analysis, FMD was associated with insulin (b-coefficient = -0.166, p-value = 0.034), testosterone (b-coefficient = -0.165, p-value = 0.034), DHEAS (b-coefficient = -0.187, p-value = 0.017), adjusting for TRFs. Discussion: The results of this study indicate that the association between androgens and endothelial function is possibly mediated by HSP60 molecules, in women with low insulin resistance and androgenicity. Further prospective studies are needed to explore the significance of our findings.

Autoimmune cholestatic liver disease includes both Primary Biliary Cholangitis (PBC) and Primary Sclerosing Cholangitis (PSC). Both conditions result in impairment of hepatic bile flow ultimately leading to chronic liver injury, liver fibrosis and eventually end stage cirrhosis. Early and accurate diagnosis are important for the risk stratification, follow up and management of these patients. The underlying pathogenesis of these conditions have not been completely resolved and poses a barrier for the development of new diagnostic and prognostics tools. Current research work suggests that the pathogenesis of autoimmune cholestatic liver disease results from environmental, genetic, and a large component of underlying immune dysfunction. While the current available serum biomarkers and imaging modalities showcases progression in precision medicine for the management of autoimmune cholestatic liver disease, development of new biomarkers are still an area of need in this field. In this review, we will discuss the current and emerging biomarkers in patients with PBC, PSC, and a special population that exhibit overlap syndrome with autoimmune hepatitis (AIH). The use of these biomarkers for diagnosis and prognosis of these patients will be reviewed through the lens of the current understanding of the complex immune pathophysiology of these conditions.

Background: Pregnancy triggers longitudinal metabolic alterations in women to allow precisely-programmed fetal growth. Comprehensive characterization of such a "metabolic clock" of pregnancy may provide a molecular reference in relation to studies of adverse pregnancy outcomes. However, a high-resolution temporal profile of metabolites along a healthy pregnancy remains to be defined. Methods: Two independent, normal pregnancy cohorts with high-density weekly urine sampling (discovery: 478 samples from 19 subjects at California; validation: 171 samples from 10 subjects at Alabama) were studied. Urine samples were profiled by liquid chromatography-mass spectrometry (LC-MS) for untargeted metabolomics, which was applied for gestational age dating and prediction of time to delivery. Results: 5,473 urinary metabolic features were identified. Partial least-squares discriminant analysis on features with robust signals (n = 1,716) revealed that the samples were distributed on the basis of the first two principal components according to their gestational age. Pathways of bile secretion, steroid hormone biosynthesis, pantohenate, and CoA biosynthesis, benzoate degradation, and phenylpropanoid biosynthesis were significantly regulated, which was collectively applied to discover and validate a predictive model that accurately captures the chronology of pregnancy. With six urine metabolites (acetylcholine, estriol-3-glucuronide, dehydroepiandrosterone sulfate, α-lactose, hydroxyexanoy-carnitine, and l-carnitine), models were constructed based on gradient-boosting decision trees to date gestational age in high accordance with ultrasound results, and to accurately predict time to delivery. Conclusion: Our study characterizes the weekly baseline profile of the human pregnancy metabolome, which provides a high-resolution molecular reference for future studies of adverse pregnancy outcomes.

The applicability of multivariate approaches for the joint analysis of genomics and phenomics information is currently limited by the lack of scalability, and by the difficulty of interpreting the related findings from a biological perspective. To tackle these limitations, we present Bayesian Genome-to-Phenome Sparse Regression (G2PSR), a novel multivariate regression method based on sparse SNP-gene constraints. The statistical framework of G2PSR is based on a Bayesian neural network, were constraints on SNPs-genes associations are integrated by incorporating a priori knowledge linking variants to their respective genes, to then reconstruct the phenotypic data in the output layer. Interpretability is promoted by inducing sparsity on the genes through variational dropout, allowing to estimate the uncertainty associated with each gene, and related SNPs, in the reconstruction task. Ultimately, G2PSR is conceived to prevent multiple testing correction and to assess the combined effect of SNPs, thus increasing the statistical power in detecting genome-to-phenome associations. The effectiveness of G2PSR was demonstrated on synthetic and real data, with respect to state-of-the-art methods based on group-wise sparsity constraints. The application on real data consisted in an imaging-genetics analysis on the Alzheimer's Disease Neuroimaging Initiative data, relating SNPs from more than 3,500 genes to clinical and multi-variate brain volumetric information. The experimental results show that our method can provide accurate selection of relevant genes in dataset with large SNPs-to-samples ratio, thus overcoming the main limitations of current genome-to-phenome association methods.