Effect of iodine on early stage thyroid autonomy

AbstractThyroid autonomy is a frequent cause of thyrotoxicosis in regions with iodine deficiency. Epidemiological data suggest that iodide may influence the course of pre-existing thyroid autonomy.Making use of FRTL-5 cells stably expressing a constitutively activating TSH receptor mutation as an in vitro model of thyroid autonomy, we investigated the impact of iodide on proliferation, function and changes in global gene expression.We demonstrate that iodine inhibits growth in TSHR WT and L629F mutant FRTL-5 cells and downregulates e.g. protocadherin cluster (Pcdha1–13) and thyroid responsive element (Thrsp). In addition functional genes e.g. iodotyrosine deiodinase (iyd) and oncogen junB are upregulated, while sodium-iodide-symporter (Nis) and thyroid peroxidase (Tpo) are downregulated by iodide.Iodide tunes down the biological activity of autonomous thyrocytes and may thus be of therapeutic benefit not only to prevent the occurrence of somatic TSHR mutations, causing thyroid autonomy, but also to slow down the development of clinically relevant disease

Sepsis Diagnostics: Intensive Care Scoring Systems Superior to MicroRNA Biomarker Testing

Sepsis represents a serious medical problem accounting for numerous deaths of critically ill patients in intensive care units (ICUs). An early, sensitive, and specific diagnosis is considered a key element for improving the outcome of sepsis patients. In addition to classical laboratory markers, ICU scoring systems and serum miRNAs are discussed as potential sepsis biomarkers. In the present prospective observational study, the suitability of miRNAs in sepsis diagnosis was tested based on proper validated and normalized data (i.e., absolute quantification by means of Droplet Digital PCR (ddPCR)) in direct comparison to classical sepsis markers and ICU scores within the same patient cohort. Therefore, blood samples of septic intensive care patients (n = 12) taken at day of admission at ICU were compared to non-septic intensive care patients (n = 12) and a healthy control group (n = 12). Our analysis indicates that all tested biomarkers have only a moderate informative power and do not allow an unequivocal differentiation between septic and non-septic ICU patients. In conclusion, there is no standalone laboratory parameter that enables a reliable diagnosis of sepsis. miRNAs are not superior to classical parameters in this respect. It seems recommendable to measure multiple parameters and scores and to interpret them with regard to the clinical presentation

Adhesion GPCR GPR56 Expression Profiling in Human Tissues

Despite the immense functional relevance of GPR56 (gene ADGRG1) in highly diverse (patho)physiological processes such as tumorigenesis, immune regulation, and brain development, little is known about its exact tissue localization. Here, we validated antibodies for GPR56-specific binding using cells with tagged GPR56 or eliminated ADGRG1 in immunotechniques. Using the most suitable antibody, we then established the human GPR56 tissue expression profile. Overall, ADGRG1 RNA-sequencing data of human tissues and GPR56 protein expression correlate very well. In the adult brain especially, microglia are GPR56-positive. Outside the central nervous system, GPR56 is frequently expressed in cuboidal or highly prismatic secreting epithelia. High ADGRG1 mRNA, present in the thyroid, kidney, and placenta is related to elevated GPR56 in thyrocytes, kidney tubules, and the syncytiotrophoblast, respectively. GPR56 often appears in association with secreted proteins such as pepsinogen A in gastric chief cells and insulin in islet β-cells. In summary, GPR56 shows a broad, not cell-type restricted expression in humans

A multi-gene approach to differentiate papillary thyroid carcinoma from benign lesions: gene selection using support vector machines with bootstrapping

Selection of novel molecular markers is an important goal of cancer genomics studies. The aim of our analysis was to apply the multivariate bioinformatical tools to rank the genes – potential markers of papillary thyroid cancer (PTC) according to their diagnostic usefulness. We also assessed the accuracy of benign/malignant classification, based on gene expression profiling, for PTC. We analyzed a 180-array dataset (90 HG-U95A and 90 HG-U133A oligonucleotide arrays), which included a collection of 57 PTCs, 61 benign thyroid tumors, and 62 apparently normal tissues. Gene selection was carried out by the support vector machines method with bootstrapping, which allowed us 1) ranking the genes that were most important for classification quality and appeared most frequently in the classifiers (bootstrap-based feature ranking, BBFR); 2) ranking the samples, and thus detecting cases that were most difficult to classify (bootstrap-based outlier detection). The accuracy of PTC diagnosis was 98.5% for a 20-gene classifier, its 95% confidence interval (CI) was 95.9–100%, with the lower limit of CI exceeding 95% already for five genes. Only 5 of 180 samples (2.8%) were misclassified in more than 10% of bootstrap iterations. We specified 43 genes which are most suitable as molecular markers of PTC, among them some well-known PTC markers (MET, fibronectin 1, dipeptidylpeptidase 4, or adenosine A1 receptor) and potential new ones (UDP-galactose-4-epimerase, cadherin 16, gap junction protein 3, sushi, nidogen, and EGF-like domains 1, inhibitor of DNA binding 3, RUNX1, leiomodin 1, F-box protein 9, and tripartite motif-containing 58). The highest ranking gene, metallophosphoesterase domain-containing protein 2, achieved 96.7% of the maximum BBFR score

The Human Blood Transcriptome in a Large Population Cohort and Its Relation to Aging and Health

Background: The blood transcriptome is expected to provide a detailed picture of an organism’s physiological state with potential outcomes for applications in medical diagnostics and molecular and epidemiological research.We here present the analysis of blood specimens of 3,388 adult individuals, together with phenotype characteristics such as disease history, medication status, lifestyle factors, and body mass index (BMI). The size and heterogeneity of this data challenges analytics in terms of dimension reduction, knowledge mining, feature extraction, and data integration. Methods: Self-organizing maps (SOM)-machine learning was applied to study transcriptional states on a population-wide scale. This method permits a detailed description and visualization of the molecular heterogeneity of transcriptomes and of their association with different phenotypic features. Results: The diversity of transcriptomes is described by personalized SOM-portraits, which specify the samples in terms of modules of co-expressed genes of different functional context. We identified two major blood transcriptome types where type 1 was found more in men, the elderly, and overweight people and it upregulated genes associated with inflammation and increased heme metabolism, while type 2 was predominantly found in women, younger, and normal weight participants and it was associated with activated immune responses, transcriptional, ribosomal, mitochondrial, and telomere-maintenance cell-functions. We find a striking overlap of signatures shared by multiple diseases, aging, and obesity driven by an underlying common pattern, which was associated with the immune response and the increase of inflammatory processes. Conclusions: Machine learning applications for large and heterogeneous omics data provide a holistic view on the diversity of the human blood transcriptome. It provides a tool for comparative analyses of transcriptional signatures and of associated phenotypes in population studies and medical applications

Extensive weight loss reveals distinct gene expression changes in human subcutaneous and visceral adipose tissue

Weight loss has been shown to significantly improve Adipose tissue (AT) function, however changes in AT gene expression profiles particularly in visceral AT (VAT) have not been systematically studied. Here, we tested the hypothesis that extensive weight loss in response to bariatric surgery (BS) causes AT gene expression changes, which may affect energy and lipid metabolism, inflammation and secretory function of AT. We assessed gene expression changes by whole genome expression chips in AT samples obtained from six morbidly obese individuals, who underwent a two step BS strategy with sleeve gastrectomy as initial and a Roux-en-Y gastric bypass as second step surgery after 12 ± 2 months. Global gene expression differences in VAT and subcutaneous (S)AT were analyzed through the use of genome-scale metabolic model (GEM) for adipocytes. Significantly altered gene expressions were PCR-validated in 16 individuals, which also underwent a two-step surgery intervention. We found increased expression of cell death-inducing DFFA-like effector a (CIDEA), involved in formation of lipid droplets in both fat depots in response to significant weight loss. We observed that expression of the genes associated with metabolic reactions involved in NAD+, glutathione and branched chain amino acid metabolism are significantly increased in AT depots after surgery-induced weight loss

Extensive weight loss reveals distinct gene expression changes in human subcutaneous and visceral adipose tissue

Weight loss has been shown to significantly improve Adipose tissue (AT) function, however changes in AT gene expression profiles particularly in visceral AT (VAT) have not been systematically studied. Here, we tested the hypothesis that extensive weight loss in response to bariatric surgery (BS) causes AT gene expression changes, which may affect energy and lipid metabolism, inflammation and secretory function of AT. We assessed gene expression changes by whole genome expression chips in AT samples obtained from six morbidly obese individuals, who underwent a two step BS strategy with sleeve gastrectomy as initial and a Roux-en-Y gastric bypass as second step surgery after 12 ± 2 months. Global gene expression differences in VAT and subcutaneous (S)AT were analyzed through the use of genome-scale metabolic model (GEM) for adipocytes. Significantly altered gene expressions were PCR-validated in 16 individuals, which also underwent a two-step surgery intervention. We found increased expression of cell death-inducing DFFA-like effector a (CIDEA), involved in formation of lipid droplets in both fat depots in response to significant weight loss. We observed that expression of the genes associated with metabolic reactions involved in NAD+, glutathione and branched chain amino acid metabolism are significantly increased in AT depots after surgery-induced weight loss

Integration of Genome-Wide SNP Data and Gene-Expression Profiles Reveals Six Novel Loci and Regulatory Mechanisms for Amino Acids and Acylcarnitines in Whole Blood

Profiling amino acids and acylcarnitines in whole blood spots is a powerful tool in the laboratory diagnosis of several inborn errors of metabolism. Emerging data suggests that altered blood levels of amino acids and acylcarnitines are also associated with common metabolic diseases in adults. Thus, the identification of common genetic determinants for blood metabolites might shed light on pathways contributing to human physiology and common diseases. We applied a targeted mass-spectrometry-based method to analyze whole blood concentrations of 96 amino acids, acylcarnitines and pathway associated metabolite ratios in a Central European cohort of 2, 107 adults and performed genome-wide association (GWA) to identify genetic modifiers of metabolite concentrations. We discovered and replicated six novel loci associated with blood levels of total acylcarnitine, arginine (both on chromosome 6;rs12210538, rs17657775),propionylcarnitine (chromosome 10;rs12779637),2-hydroxyisovalerylcarnitine (chromosome 21;rs1571700),stearoylcarnitine (chromosome 1;rs3811444),and aspartic acid traits (chromosome 8;rs750472). Based on an integrative analysis of expression quantitative trait loci in blood mononuclear cells and correlations between gene expressions and metabolite levels, we provide evidence for putative causative genes: SLC22A16 for total acylcarnitines, ARG1 for arginine, HLCS for 2-hydroxyisovalerylcarnitine, JAM3 for stearoylcarnitine via a trans-effect at chromosome 1, and PPP1R16A for aspartic acid traits. Further, we report replication and provide additional functional evidence for ten loci that have previously been published for metabolites measured in plasma, serum or urine. In conclusion, our integrative analysis of SNP, gene-expression and metabolite data points to novel genetic factors that may be involved in the regulation of human metabolism. At several loci, we provide evidence for metabolite regulation via gene-expression and observed overlaps with GWAS loci for common diseases. These results form a strong rationale for subsequent functional and disease-related studies

Washing scaling of GeneChip microarray expression

BACKGROUND Post-hybridization washing is an essential part of microarray experiments. Both the quality of the experimental washing protocol and adequate consideration of washing in intensity calibration ultimately affect the quality of the expression estimates extracted from the microarray intensities. RESULTS We conducted experiments on GeneChip microarrays with altered protocols for washing, scanning and staining to study the probe-level intensity changes as a function of the number of washing cycles. For calibration and analysis of the intensity data we make use of the 'hook' method which allows intensity contributions due to non-specific and specific hybridization of perfect match (PM) and mismatch (MM) probes to be disentangled in a sequence specific manner. On average, washing according to the standard protocol removes about 90% of the non-specific background and about 30-50% and less than 10% of the specific targets from the MM and PM, respectively. Analysis of the washing kinetics shows that the signal-to-noise ratio doubles roughly every ten stringent washing cycles. Washing can be characterized by time-dependent rate constants which reflect the heterogeneous character of target binding to microarray probes. We propose an empirical washing function which estimates the survival of probe bound targets. It depends on the intensity contribution due to specific and non-specific hybridization per probe which can be estimated for each probe using existing methods. The washing function allows probe intensities to be calibrated for the effect of washing. On a relative scale, proper calibration for washing markedly increases expression measures, especially in the limit of small and large values. CONCLUSIONS Washing is among the factors which potentially distort expression measures. The proposed first-order correction method allows direct implementation in existing calibration algorithms for microarray data. We provide an experimental 'washing data set' which might be used by the community for developing amendments of the washing correction.This publication is supported by the Leipzig Interdisciplinary Research Cluster of Genetic Factors, Clinical Phenotypes and Environment (LIFE Center, Universität Leipzig) and an Australian Academy of Science Visits to Europe grant. LIFE is funded by means of the European Union, by the European Regional Development Fund (ERFD) and by means of the Free State of Saxony within the framework of the excellence initiative