Serum concentrations of TNF-α and its soluble receptors in Graves' disease

Processing stimulus dynamics by the NF-κB network in single cells

Cells at the site of an infection experience numerous biochemical signals that vary in amplitude, space, and time. Despite the diversity of dynamic signals produced by pathogens and sentinel cells, information-processing pathways converge on a limited number of central signaling nodes to ultimately control cellular responses. In particular, the NF-κB pathway responds to dozens of signals from pathogens and self, and plays a vital role in processing proinflammatory inputs. Studies addressing the influence of stimulus dynamics on NF-κB signaling are rare due to technical limitations with live-cell measurements. However, recent advances in microfluidics, automation, and image analysis have enabled investigations that yield high temporal resolution at the single-cell level. Here, we summarize the recent research which measures and models the NF-κB response to pulsatile and fluctuating stimulus concentrations, as well as different combinations and sequences of signaling molecules. Collectively, these studies show that the NF-κB network integrates external inflammatory signals and translates these into downstream transcriptional responses.

Possible molecular exploration of herbal pair Haizao-Kunbu in the treatment of Graves' disease by network pharmacology, molecular docking, and molecular dynamic analysis

Objective

To promote the development and therapeutic application of new medications, it is crucial to conduct a thorough investigation into the mechanism by which the traditional Chinese herb pair of Haizao-Kunbu (HK) treats Graves' disease (GD).

Materials and methods

Chemical ingredients of HK, putative target genes, and GD-associated genes were retrieved from online public databases. Using Cytoscape 3.9.1, a compound-gene target network was established to explore the association between prosperous ingredients and targets. STRING, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes pathway analyses visualized core targets and disease pathways. Additionally, we conducted a refined analysis of the binding interactions between active ingredients and their respective targets. To visualize these findings, we employed precise molecular docking techniques. Furthermore, we carried out molecular dynamics simulations to gain insights into the formation of more tightly bound complexes.

Results

We found that there were nine key active ingredients in HK, which mainly acted on 21 targets. These targets primarily regulated several biological processes such as cell population proliferation, protein phosphorylation, and regulation of kinase activity, and acted on PI3K-AKT and MAPK pathways to treat GD. Analysis of the molecular interaction simulation under computer technology revealed that the key targets exhibited strong binding activity to active ingredients, and Fucosterol-AKT1 and Isofucosterol-AKT1 complexes were highly stable in humans.

Conclusion

This study demonstrates that HK exerts therapeutic effects on GD in a multi-component, multi-target, and multi-pathway manner by regulating cell proliferation, differentiation, inflammation, and immunomodulatory-related targets. This study provides a theoretical foundation for further investigation into GD.

DNA Methylation in Autoimmune Thyroid Disease

Graves disease and Hashimoto disease form part of the spectrum of autoimmune thyroid disease (AITD), to which genetic and environmental factors are recognized contributors. Epigenetics provides a potential link between environmental influences, gene expression, and thyroid autoimmunity. DNA methylation (DNAm) is the best studied epigenetic process, and global hypomethylation of leukocyte DNA is reported in several autoimmune disorders. This review summarizes the current understanding of DNAm in AITD. Targeted DNAm studies of blood samples from AITD patients have reported differential DNAm in the promoter regions of several genes implicated in AITD, including TNF, IFNG, IL2RA, IL6, ICAM1, and PTPN22. In many cases, however, the findings await replication and are unsupported by functional studies to support causal roles in AITD pathogenesis. Furthermore, thyroid hormones affect DNAm, and in many studies confounding by reverse causation has not been considered. Recent studies have shown that DNAm patterns in candidate genes including ITGA6, PRKAA2, and DAPK1 differ between AITD patients from regions with different iodine status, providing a potential mechanism for associations between iodine and AITD. Research focus in the field is moving from candidate gene studies to an epigenome-wide approach. Genome-wide methylation studies of AITD patients have demonstrated multiple differentially methylated positions, including some in immunoregulatory genes such as NOTCH1, HLA-DRB1, TNF, and ICAM1. Large, epigenome-wide studies are required to elucidate the pathophysiological role of DNAm in AITD, with the potential to provide novel diagnostic and prognostic biomarkers as well as therapeutic targets.

Study of Correlation between Serum Osteoprotegerin, TNF-Alfa, and Biomarkers of Bone Metabolism in Patients with Treatment-Naive Graves' Disease-A Cross-Sectional Study

Objectives

Primary - a study of the correlation between serum osteoprotegerin (OPG), and biomarkers of bone metabolism in patients with treatment-naive Graves' disease (GD). Secondary - serum level of OPG, TNF-alfa, and biomarkers of bone metabolism in patients three months after treatment of GD with methimazole (MMI).

Materials and Methods

A total of thirty-five treatment-naive newly diagnosed GDs were recruited for the study, most of them female. All patients started with MMI for treatment and various blood parameters were measured at baseline and three months after treatment. Measurements: Serum calcium (Ca), phosphorus (P), bone-specific alkaline phosphatase (B-ALP), OPG, TNF-alfa, and urine deoxypyridinoline (U-DPD) along with serum-free T3 and T4, thyroid-stimulating hormone (TSH) and thyroid receptor antibody (TR-ab) were analysed at baseline and three months after MMI treatment. All the patients had euthyroid at three months of MMI treatment.

Results

Mean serum OPG (0.94 ± 1.39 vs. 0.63 ± 0.27 ng/ml; P = 0.262) level at baseline and after treatment with MMI did not show any significant change. Mean TSH level (0.207 ± 0.59 vs. 1.00 ± 1.95, P = 0.025) was significantly low at baseline than after treatment; FT4 (5.9 ± 5.22 v 1.77 ± 1.89 ng/dl; P < 0.001), FT3 (12.19 ± 6.91 vs. 4.99 ± 3.55 pg/ml; P < 0.001), and TNF-alfa values decreased significantly after treatment, however, PTH (58.09 ± 28.75 vs. 75.57 ± 41.50; P < 0.026) increased significantly after treatment.

Discussion

There is no correlation of OPG with thyroid hormone profile, TSH, thyroid receptor antibody (TR-ab), and bone metabolic parameters such as serum Ca, P, B-ALP, TNF-alfa, and U-DPD in our study. Mean TNF-alfa decreased significantly (393.43 ± 270.473 vs. 139.34 ± 101.264 pg/ml; P = 0.001) level after treatment with MMI. TNF-alfa was positively correlated with TR-ab (r = 0.374; P = 0.027) and B-ALP (r = 0.388; P = 0.021).

Conclusion

The bone turnover marker in GD seems to be mediated other than OPG. We observed an increase in circulating TNF-alfa in GD with a significant decrease after treatment. TNF-alfa could be a marker of GD activity as evidenced by a close positive correlation with TR-ab, a sensitive marker of GD autoimmunity. TNF-alfa could be a factor associated with bone turnover markers in GD despite its euthyroid state.

Association of Polygenetic Risk Scores Related to Immunity and Inflammation with Hyperthyroidism Risk and Interactions between the Polygenetic Scores and Dietary Factors in a Large Cohort

Graves's disease and thyroiditis induce hyperthyroidism, the causes of which remain unclear, although they are involved with genetic and environmental factors. We aimed to evaluate polygenetic variants for hyperthyroidism risk and their interaction with metabolic parameters and nutritional intakes in an urban hospital-based cohort. A genome-wide association study (GWAS) of participants with (cases; n = 842) and without (controls, n = 38,799) hyperthyroidism was used to identify and select genetic variants. In clinical and lifestyle interaction with PRS, 312 participants cured of hyperthyroidism were excluded. Single nucleotide polymorphisms (SNPs) associated with gene-gene interactions were selected by hyperthyroidism generalized multifactor dimensionality reduction. Polygenic risk scores (PRSs) were generated by summing the numbers of selected SNP risk alleles. The best gene-gene interaction model included tumor-necrosis factor (TNF)_rs1800610, mucin 22 (MUC22)_rs1304322089, tribbles pseudokinase 2 (TRIB2)_rs1881145, cytotoxic T-lymphocyte-associated antigen 4 (CTLA4)_rs231775, lipoma-preferred partner (LPP)_rs6780858, and human leukocyte antigen (HLA)-J_ rs767861647. The PRS of the best model was positively associated with hyperthyroidism risk by 1.939-fold (1.317-2.854) after adjusting for covariates. PRSs interacted with age, metabolic syndrome, and dietary inflammatory index (DII), while hyperthyroidism risk interacted with energy, calcium, seaweed, milk, and coffee intake (P < 0.05). The PRS impact on hyperthyroidism risk was observed in younger (<55 years) participants and adults without metabolic syndrome. PRSs were positively associated with hyperthyroidism risk in participants with low dietary intakes of energy (OR = 2.74), calcium (OR = 2.84), seaweed (OR = 3.43), milk (OR = 2.91), coffee (OR = 2.44), and DII (OR = 3.45). In conclusion, adults with high PRS involved in inflammation and immunity had a high hyperthyroidism risk exacerbated under low intakes of energy, calcium, seaweed, milk, or coffee. These results can be applied to personalized nutrition in a clinical setting.

The complement pathway is activated in people with HIV and is associated with non-AIDS comorbidities

Unbiased plasma proteomics in a matched case-control study of treated people with HIV (PWH) revealed the complement cascade as being among the top pathways enriched in PWH. Specific complement components, namely C5, associated significantly with non-AIDS comorbidity prevalence, and did so more strongly than previously established predictive biomarkers.