GWAS of thyroid stimulating hormone highlights pleiotropic effects and inverse association with thyroid cancer

Correction: Volume12, Issue1 Article Number7354 DOI10.1038/s41467-021-27675-w PublishedDEC 16 2021Thyroid stimulating hormone (TSH) is critical for normal development and metabolism. To better understand the genetic contribution to TSH levels, we conduct a GWAS meta-analysis at 22.4 million genetic markers in up to 119,715 individuals and identify 74 genome-wide significant loci for TSH, of which 28 are previously unreported. Functional experiments show that the thyroglobulin protein-altering variants P118L and G67S impact thyroglobulin secretion. Phenome-wide association analysis in the UK Biobank demonstrates the pleiotropic effects of TSH-associated variants and a polygenic score for higher TSH levels is associated with a reduced risk of thyroid cancer in the UK Biobank and three other independent studies. Two-sample Mendelian randomization using TSH index variants as instrumental variables suggests a protective effect of higher TSH levels (indicating lower thyroid function) on risk of thyroid cancer and goiter. Our findings highlight the pleiotropic effects of TSH-associated variants on thyroid function and growth of malignant and benign thyroid tumors. Thyroid stimulating hormone (TSH) is critical for normal development and metabolism. Here, the authors conduct a GWAS and suggest protective effect of higher TSH on risk of thyroid cancer and goitre.Peer reviewe

Author Correction:GWAS of thyroid stimulating hormone highlights the pleiotropic effects and inverse association with thyroid cancer

The original version of this article contained an error in the results, in the second paragraph of the subsection entitled “Fine-mapping for potentially causal variants among TSH loci”, in which effect sizes for two variants were incorrectly reported

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

GWAS of thyroid stimulating hormone highlights pleiotropic effects and inverse association with thyroid cancer

Thyroid stimulating hormone (TSH) is critical for normal development and metabolism. To better understand the genetic contribution to TSH levels, we conduct a GWAS meta-analysis at 22.4 million genetic markers in up to 119,715 individuals and identify 74 genome-wide significant loci for TSH, of which 28 are previously unreported. Functional experiments show that the thyroglobulin protein-altering variants P118L and G67S impact thyroglobulin secretion. Phenome-wide association analysis in the UK Biobank demonstrates the pleiotropic effects of TSH-associated variants and a polygenic score for higher TSH levels is associated with a reduced risk of thyroid cancer in the UK Biobank and three other independent studies. Two-sample Mendelian randomization using TSH index variants as instrumental variables suggests a protective effect of higher TSH levels (indicating lower thyroid function) on risk of thyroid cancer and goiter. Our findings highlight the pleiotropic effects of TSH-associated variants on thyroid function and growth of malignant and benign thyroid tumors

Multiorgan MRI findings after hospitalisation with COVID-19 in the UK (C-MORE): a prospective, multicentre, observational cohort study

Introduction: The multiorgan impact of moderate to severe coronavirus infections in the post-acute phase is still poorly understood. We aimed to evaluate the excess burden of multiorgan abnormalities after hospitalisation with COVID-19, evaluate their determinants, and explore associations with patient-related outcome measures. Methods: In a prospective, UK-wide, multicentre MRI follow-up study (C-MORE), adults (aged ≥18 years) discharged from hospital following COVID-19 who were included in Tier 2 of the Post-hospitalisation COVID-19 study (PHOSP-COVID) and contemporary controls with no evidence of previous COVID-19 (SARS-CoV-2 nucleocapsid antibody negative) underwent multiorgan MRI (lungs, heart, brain, liver, and kidneys) with quantitative and qualitative assessment of images and clinical adjudication when relevant. Individuals with end-stage renal failure or contraindications to MRI were excluded. Participants also underwent detailed recording of symptoms, and physiological and biochemical tests. The primary outcome was the excess burden of multiorgan abnormalities (two or more organs) relative to controls, with further adjustments for potential confounders. The C-MORE study is ongoing and is registered with ClinicalTrials.gov, NCT04510025. Findings: Of 2710 participants in Tier 2 of PHOSP-COVID, 531 were recruited across 13 UK-wide C-MORE sites. After exclusions, 259 C-MORE patients (mean age 57 years [SD 12]; 158 [61%] male and 101 [39%] female) who were discharged from hospital with PCR-confirmed or clinically diagnosed COVID-19 between March 1, 2020, and Nov 1, 2021, and 52 non-COVID-19 controls from the community (mean age 49 years [SD 14]; 30 [58%] male and 22 [42%] female) were included in the analysis. Patients were assessed at a median of 5·0 months (IQR 4·2–6·3) after hospital discharge. Compared with non-COVID-19 controls, patients were older, living with more obesity, and had more comorbidities. Multiorgan abnormalities on MRI were more frequent in patients than in controls (157 [61%] of 259 vs 14 [27%] of 52; p<0·0001) and independently associated with COVID-19 status (odds ratio [OR] 2·9 [95% CI 1·5–5·8]; padjusted=0·0023) after adjusting for relevant confounders. Compared with controls, patients were more likely to have MRI evidence of lung abnormalities (p=0·0001; parenchymal abnormalities), brain abnormalities (p<0·0001; more white matter hyperintensities and regional brain volume reduction), and kidney abnormalities (p=0·014; lower medullary T1 and loss of corticomedullary differentiation), whereas cardiac and liver MRI abnormalities were similar between patients and controls. Patients with multiorgan abnormalities were older (difference in mean age 7 years [95% CI 4–10]; mean age of 59·8 years [SD 11·7] with multiorgan abnormalities vs mean age of 52·8 years [11·9] without multiorgan abnormalities; p<0·0001), more likely to have three or more comorbidities (OR 2·47 [1·32–4·82]; padjusted=0·0059), and more likely to have a more severe acute infection (acute CRP >5mg/L, OR 3·55 [1·23–11·88]; padjusted=0·025) than those without multiorgan abnormalities. Presence of lung MRI abnormalities was associated with a two-fold higher risk of chest tightness, and multiorgan MRI abnormalities were associated with severe and very severe persistent physical and mental health impairment (PHOSP-COVID symptom clusters) after hospitalisation. Interpretation: After hospitalisation for COVID-19, people are at risk of multiorgan abnormalities in the medium term. Our findings emphasise the need for proactive multidisciplinary care pathways, with the potential for imaging to guide surveillance frequency and therapeutic stratification

Association and Regulation of Protein Factors of Field Effect in Prostate Tissues

Field effect or field cancerization denotes the presence of molecular aberrations in structurally intact cells residing in histologically normal tissues adjacent to solid tumors. Currently, the etiology of prostate field‑effect formation is unknown and there is a prominent lack of knowledge of the underlying cellular and molecular pathways. We have previously identified an upregulated expression of several protein factors representative of prostate field effect, i.e., early growth response‑1 (EGR‑1), platelet‑derived growth factor‑A (PDGF‑A), macrophage inhibitory cytokine‑1 (MIC‑1), and fatty acid synthase (FASN) in tissues at a distance of 1 cm from the visible margin of intracapsule prostate adenocarcinomas. We have hypothesized that the transcription factor EGR‑1 could be a key regulator of prostate field‑effect formation by controlling the expression of PDGF‑A, MIC‑1, and FASN. Taking advantage of our extensive quantitative immunofluorescence data specific for EGR‑1, PDGF‑A, MIC‑1, and FASN generated in disease‑free, tumor‑adjacent, and cancerous human prostate tissues, we chose comprehensive correlation as our major approach to test this hypothesis. Despite the static nature and sample heterogeneity of association studies, we show here that sophisticated data generation, such as by spectral image acquisition, linear unmixing, and digital quantitative imaging, can provide meaningful indications of molecular regulations in a physiologically relevant in situ environment. Our data suggest that EGR‑1 acts as a key regulator of prostate field effect through induction of pro‑proliferative (PDGF‑A and FASN), and suppression of pro‑apoptotic (MIC‑1) factors. These findings were corroborated by computational promoter analyses and cell transfection experiments in non‑cancerous prostate epithelial cells with ectopically induced and suppressed EGR‑1 expression. Among several clinical applications, a detailed knowledge of pathways of field effect may lead to the development of targeted intervention strategies preventing progression from pre‑malignancy to cancer

Prostate field cancerization: deregulated expression of macrophage inhibitory cytokine 1 (MIC-1) and platelet derived growth factor A (PDGF-A) in tumor adjacent tissue.

Prostate field cancerization denotes molecular alterations in histologically normal tissues adjacent to tumors. Such alterations include deregulated protein expression, as we have previously shown for the key transcription factor early growth response 1 (EGR-1) and the lipogenic enzyme fatty acid synthase (FAS). Here we add the two secreted factors macrophage inhibitory cytokine 1 (MIC-1) and platelet derived growth factor A (PDGF-A) to the growing list of protein markers of prostate field cancerization. Expression of MIC-1 and PDGF-A was measured quantitatively by immunofluorescence and comprehensively analyzed using two methods of signal capture and several groupings of data generated in human cancerous (n = 25), histologically normal adjacent (n = 22), and disease-free (n = 6) prostate tissues. A total of 208 digitized images were analyzed. MIC-1 and PDGF-A expression in tumor tissues were elevated 7.1x to 23.4x and 1.7x to 3.7x compared to disease-free tissues, respectively (p<0.0001 to p = 0.08 and p<0.01 to p = 0.23, respectively). In support of field cancerization, MIC-1 and PDGF-A expression in adjacent tissues were elevated 7.4x to 38.4x and 1.4x to 2.7x, respectively (p<0.0001 to p<0.05 and p<0.05 to p = 0.51, respectively). Also, MIC-1 and PDGF-A expression were similar in tumor and adjacent tissues (0.3x to 1.0x; p<0.001 to p = 0.98 for MIC-1; 0.9x to 2.6x; p<0.01 to p = 1.00 for PDGF-A). All analyses indicated a high level of inter- and intra-tissue heterogeneity across all types of tissues (mean coefficient of variation of 86.0%). Our data shows that MIC-1 and PDGF-A expression is elevated in both prostate tumors and structurally intact adjacent tissues when compared to disease-free specimens, defining field cancerization. These secreted factors could promote tumorigenesis in histologically normal tissues and lead to tumor multifocality. Among several clinical applications, they could also be exploited as indicators of disease in false negative biopsies, identify areas of repeat biopsy, and add molecular information to surgical margins

MIC-1 detection and quantitation in human prostate tissues (commercial tissue microarray).

<p>(A-B) Immunofluorescence with anti-MIC-1 antibody in a representative prostate tumor (A) and tumor adjacent tissue (B); pictures represent overlays of nuclear staining by DAPI (blue) and Alexa Fluor 488 immunostaining (yellow/white); the insets are Alexa Fluor 488 immunostaining only; white bars represent 10 micrometers. The diamond, closed arrow, and open arrow in B denote a typical lumen, epithelial cell compartment, and stromal cell compartment, respectively. (C-D) MIC-1 expression levels (indicated as signal intensities [pixel count]) in matched tumor adjacent and tumor tissues; the types of analysis were the following (as per <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0119314#sec002" target="_blank">Materials and Methods</a>): (C) Whole slide analysis (WSA), (D) region of interest (ROI) analysis. Individual data points are shown as small black squares (partially overlapping); the boxes represent group medians (line across middle) and quartiles (25th and 75th percentiles) at its ends; lines above and below boxes indicate 10th and 90th percentiles, respectively. For each analysis, the number of images and cases is indicated; p values above the panels denote the level of statistical significance for the differences between groups, as calculated by the student’s t-test (p(t)) and by the Wilcoxon rank sums test (p(WRS)).</p

Quantitative immunofluorescence of MIC-1 in human prostate tissues.

<p>(A-D) MIC-1 expression levels (indicated as signal intensities [pixel count]) in disease-free, tumor adjacent, and tumor tissues; the types of analysis were the following (as per <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0119314#sec002" target="_blank">Materials and Methods</a>): (A and B) Whole slide analysis (WSA) for all (A) and non-matched (B) cases in the UNMH/CHTN cohort; (C and D) region of interest (ROI) analysis for all (C) and non-matched (D) cases in the UNMH/CHTN cohort. Individual data points are shown as small black squares (partially overlapping); the boxes represent group medians (line across middle) and quartiles (25th and 75th percentiles) at its ends; lines above and below boxes indicate 10th and 90th percentiles, respectively. For each analysis, the number of images and cases is indicated; p values above the panels denote the level of statistical significance for the differences between groups, as calculated by the student’s t-test (p(t)) and by the Wilcoxon rank sums test (p(WRS)).</p