The variants at FLNA and FLNB contribute to the susceptibility of hypertension and stroke with differentially expressed mRNA

Effect of disulfidptosis-related genes SLC3A2, SLC7A11 and FLNB polymorphisms on risk of autoimmune thyroiditis in a Chinese population

PURPOSE

This study aimed to evaluate the associations between disulfidptosis related genes-SLC3A2, SLC7A11 and FLNB polymorphisms and risk of autoimmune thyroiditis (AIT).

METHODS

Six SNPs in the SLC3A2, SLC7A11 and FLNB were genotyped in 650 AIT cases and 650 controls using a MassARRAY platform.

RESULTS

Minor alleles of SLC3A2-rs12794763, rs1059292 and FLNB-rs839240 might lead to a higher risk of AIT (p < 0.001), while SLC7A11-rs969319-C allele tends to decrease the risk of the disease (p = 0.006). Genetic model analysis showed that SLC3A2-rs12794763, SLC3A2-rs1059292 and FLNB-rs839240 polymorphisms were risk factors for AIT (p < 0.001); while SLC7A11-rs969319 showed a protective role for the disease in all genetic models (p < 0.005). Stratification analysis showed that SLC3A2-rs1059292 and rs12794763 were correlated with higher risk of AIT regardless of sex (p < 0.05). Moreover, FLNB-rs839240 exhibited higher risk of disease only in females (p < 0.05). By contrast, SLC7A11-rs969319 showed a protective role only in females (p < 0.05).

CONCLUSION

Our results shed new light on the association between disulfidptosis-related genes and AIT risk.

Untargeted metabolomic, and proteomic analysis identifies metabolic biomarkers and pathway alterations in individuals with 22q11.2 deletion syndrome

INTRODUCTION

The chromosome 22q11.2 deletion syndrome (22q11.2DS) is characterized by a well-defined microdeletion and is associated with a wide range of brain-related phenotypes including schizophrenia spectrum disorders (SCZ), autism spectrum disorders (ASD), anxiety disorders and attention deficit disorders (ADHD). The typically deleted region in 22q11.2DS contains multiple genes which haploinsufficiency has the potential of altering the protein and the metabolic profiles.

OBJECTIVES

Alteration in metabolic processes and downstream protein pathways during the early brain development may help to explain the increased prevalence of the observed neurodevelopmental phenotypes in 22q11.2DS. However, relatively little is known about the correlation of dysregulated protein/metabolite expression and neurobehavioral impairments in individuals who developed them over time.

METHODS

In this study, we performed untargeted metabolic and proteomic analysis in plasma samples derived from 30 subjects including 16 participants with 22q11.2DS and 14 healthy controls (TD) enrolled in a longitudinal study, aiming to identify a metabolic and protein signature informing about the underlying mechanisms involved in disease development and progression. The metabolic and proteomic profiles were also compared between the participants with 22q11.2DS with and without various comorbidities, such as medical involvement, psychiatric conditions, and autism spectrum disorder (ASD) to detect potential changes among multiple specimens, collected overtime, with the aim to understand the basic underlying mechanisms involved in disease development and progression.

RESULTS

We observed a large number of statistically significant differences in metabolites between the two groups. Among them, the levels of taurine and arachidonic acid were significantly lower in 22q11.2DS compared to the TD group. In addition, we identified 16 proteins that showed significant changes in expression levels (adjusted P < 0.05) in 22q11.2DS as compared to TD, including those involved in 70 pathways such as gene expression, the PI3K-Akt signaling pathway and the complement system. Within participants with 22q11.2DS, no significant changes in those with and without medical or psychiatric conditions were observed.

CONCLUSION

To our knowledge, this is the first report on plasma metabolic and proteomic profiling and on the identification of unique biomarkers in 22q11.2DS. These findings may suggest the potential role of the identified metabolites and proteins as biomarkers for the onset of comorbid conditions in 22q11.2DS. Ultimately, the altered protein pathways in 22q11.2DS may provide insights of the biological mechanisms underlying the neurodevelopmental phenotype and may provide missing molecular outcome measures in future clinical trials to assess early-diagnosis treatment and the efficacy of response to targeted treatment.

mRNA Metabolism and Hypertension

Hypertension is the most frequent cardiovascular risk factor all over the world. It remains a leading contributor to the risk of cardiovascular events and death. In the year 2015, about 1.5 billion of adult people worldwide had hypertension (as defined by office systolic blood pressure ≥ 140 mmHg or office diastolic blood pressure ≥ 90 mmHg). Moreover, the number of hypertensive patients with age ranging from 30 to 79 years doubled in the last 30 years (from 317 million men and 331 million women in the year 1990 to 652 million men and 626 million women in 2019) despite stable age-standardized prevalence worldwide. Despite such impressive growth, the proportion of controlled hypertension is very low. A better understanding of the pathogenesis of hypertension may contribute to the development of innovative therapeutic strategies. In this context, alterations of the messenger RNA metabolism have been recently evaluated as contributors to the pathogenesis of hypertension, and pharmacological modulation of RNA metabolism is under investigation as potential and novel therapeutic armamentarium in hypertension.

Prediction of Differentially Expressed Genes and a Diagnostic Signature of Preeclampsia via Integrated Bioinformatics Analysis

Background

Preeclampsia (PE), which has a high incidence rate worldwide, is a potentially dangerous syndrome to pregnant women and newborns. However, the exact mechanism of its pathogenesis is still unclear. In this study, we used bioinformatics analysis to identify hub genes, establish a logistic model, and study immune cell infiltration to clarify the physiopathogenesis of PE.

Methods

We downloaded the GSE75010 and GSE10588 datasets from the GEO database and performed weighted gene coexpression network analysis (WGCNA) as well as Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The online search tool for the retrieval of interacting genes and Cytoscape software were used to identify hub genes, which were then used to establish a logistic model. We also analyzed immune cell infiltration. Finally, we verified the expression of the genes included in the predictive model via RT-PCR.

Results

A total of 100 and 212 differently expressed genes were identified in the GSE75010 and GSE10588 datasets, respectively, and after overlapping with WGCNA results, 17 genes were identified. KEGG and GO analyses further indicated the involvement of these genes in bioprocesses, such as gonadotropin secretion, immune cell infiltration, and the SMAD and MAPK pathways. Additionally, protein-protein interaction network analysis identified 10 hub genes, six (FLT1, FLNB, FSTL3, INHA, TREM1, and SLCO4A1) of which were used to establish a logistic model for PE. RT-PCR analysis also confirmed that, except FSTL3, these genes were upregulated in PE. Our results also indicated that macrophages played the most important role in immune cell infiltration in PE.

Conclusion

This study identified 10 hub genes in PE and used 6 of them to establish a logistic model and also analyzed immune cell infiltration. These findings may enhance the understanding of PE and enable the identification of potential therapeutic targets for PE.