Whole blood transcriptomic signature of Cushing's syndrome.

OBJECTIVE: Cushing's syndrome is characterized by high morbidity and mortality with high interindividual variability. Easily measurable biomarkers, in addition to the hormone assays currently used for diagnosis, could reflect the individual biological impact of glucocorticoids. The aim of this study is to identify such biomarkers through the analysis of whole blood transcriptome. DESIGN: Whole blood transcriptome was evaluated in 57 samples from patients with overt Cushing's syndrome, mild Cushing's syndrome, eucortisolism, and adrenal insufficiency. Samples were randomly split into a training cohort to set up a Cushing's transcriptomic signature and a validation cohort to assess this signature. METHODS: Total RNA was obtained from whole blood samples and sequenced on a NovaSeq 6000 System (Illumina). Both unsupervised (principal component analysis) and supervised (Limma) methods were used to explore the transcriptome profile. Ridge regression was used to build a Cushing's transcriptome predictor. RESULTS: The transcriptomic profile discriminated samples with overt Cushing's syndrome. Genes mostly associated with overt Cushing's syndrome were enriched in pathways related to immunity, particularly neutrophil activation. A prediction model of 1500 genes built on the training cohort demonstrated its discriminating value in the validation cohort (accuracy .82) and remained significant in a multivariate model including the neutrophil proportion (P = .002). Expression of FKBP5, a single gene both overexpressed in Cushing's syndrome and implied in the glucocorticoid receptor signaling, could also predict Cushing's syndrome (accuracy .76). CONCLUSIONS: Whole blood transcriptome reflects the circulating levels of glucocorticoids. FKBP5 expression could be a nonhormonal marker of Cushing's syndrome.

Macrophages preserve endothelial cell specialization in the adrenal gland to modulate aldosterone secretion and blood pressure.

Macrophages play crucial roles in organ-specific functions and homeostasis. In the adrenal gland, macrophages closely associate with sinusoidal capillaries in the aldosterone-producing zona glomerulosa. We demonstrate that macrophages preserve capillary specialization and modulate aldosterone secretion. Using macrophage-specific deletion of VEGF-A, single-cell transcriptomics, and functional phenotyping, we found that the loss of VEGF-A depletes PLVAP+ fenestrated endothelial cells in the zona glomerulosa, leading to increased basement membrane collagen IV deposition and subendothelial fibrosis. This results in increased aldosterone secretion, called "haptosecretagogue" signaling. Human aldosterone-producing adenomas also show capillary rarefaction and basement membrane thickening. Mice with myeloid cell-specific VEGF-A deletion exhibit elevated serum aldosterone, hypokalemia, and hypertension, mimicking primary aldosteronism. These findings underscore macrophage-to-endothelial cell signaling as essential for endothelial cell specialization, adrenal gland function, and blood pressure regulation, with broader implications for other endocrine organs.

Association of adrenal steroids with metabolomic profiles in patients with primary and endocrine hypertension.

Introduction: Endocrine hypertension (EHT) due to pheochromocytoma/paraganglioma (PPGL), Cushing's syndrome (CS), or primary aldosteronism (PA) is linked to a variety of metabolic alterations and comorbidities. Accordingly, patients with EHT and primary hypertension (PHT) are characterized by distinct metabolic profiles. However, it remains unclear whether the metabolomic differences relate solely to the disease-defining hormonal parameters. Therefore, our objective was to study the association of disease defining hormonal excess and concomitant adrenal steroids with metabolomic alterations in patients with EHT. Methods: Retrospective European multicenter study of 263 patients (mean age 49 years, 50% females; 58 PHT, 69 PPGL, 37 CS, 99 PA) in whom targeted metabolomic and adrenal steroid profiling was available. The association of 13 adrenal steroids with differences in 79 metabolites between PPGL, CS, PA and PHT was examined after correction for age, sex, BMI, and presence of diabetes mellitus. Results: After adjustment for BMI and diabetes mellitus significant association between adrenal steroids and metabolites - 18 in PPGL, 15 in CS, and 23 in PA - were revealed. In PPGL, the majority of metabolite associations were linked to catecholamine excess, whereas in PA, only one metabolite was associated with aldosterone. In contrast, cortisone (16 metabolites), cortisol (6 metabolites), and DHEA (8 metabolites) had the highest number of associated metabolites in PA. In CS, 18-hydroxycortisol significantly influenced 5 metabolites, cortisol affected 4, and cortisone, 11-deoxycortisol, and DHEA each were linked to 3 metabolites. Discussions: Our study indicates cortisol, cortisone, and catecholamine excess are significantly associated with metabolomic variances in EHT versus PHT patients. Notably, catecholamine excess is key to PPGL's metabolomic changes, whereas in PA, other non-defining adrenal steroids mainly account for metabolomic differences. In CS, cortisol, alongside other non-defining adrenal hormones, contributes to these differences, suggesting that metabolic disorders and cardiovascular morbidity in these conditions could also be affected by various adrenal steroids.

MicroRNAs in aldosterone production and action.

Aldosterone is a cardiovascular hormone with a key role in blood pressure regulation, among other processes, mediated through its targeting of the mineralocorticoid receptor in the renal tubule and selected other tissues. Its secretion from the adrenal gland is a highly controlled process subject to regulatory influence from the renin-angiotensin system and the hypothalamic-pituitary-adrenal axis. MicroRNAs are small endogenous non-coding RNA molecules capable of regulating gene expression post-transcriptionally through stimulation of mRNA degradation or suppression of translation. Several studies have now identified that microRNA levels are changed in cases of aldosterone dysregulation and that microRNAs are capable of regulating the expression of various genes involved in aldosterone production and action. In this article we summarise the major studies concerning this topic. We also discuss the potential role for circulating microRNAs as diagnostic biomarkers for primary aldosteronism, a highly treatable form of secondary hypertension, which would be highly desirable given the current underdiagnosis of this condition.

Somatic mutations of CADM1 in aldosterone-producing adenomas and gap junction-dependent regulation of aldosterone production.

Aldosterone-producing adenomas (APAs) are the commonest curable cause of hypertension. Most have gain-of-function somatic mutations of ion channels or transporters. Herein we report the discovery, replication and phenotype of mutations in the neuronal cell adhesion gene CADM1. Independent whole exome sequencing of 40 and 81 APAs found intramembranous p.Val380Asp or p.Gly379Asp variants in two patients whose hypertension and periodic primary aldosteronism were cured by adrenalectomy. Replication identified two more APAs with each variant (total, n = 6). The most upregulated gene (10- to 25-fold) in human adrenocortical H295R cells transduced with the mutations (compared to wildtype) was CYP11B2 (aldosterone synthase), and biological rhythms were the most differentially expressed process. CADM1 knockdown or mutation inhibited gap junction (GJ)-permeable dye transfer. GJ blockade by Gap27 increased CYP11B2 similarly to CADM1 mutation. Human adrenal zona glomerulosa (ZG) expression of GJA1 (the main GJ protein) was patchy, and annular GJs (sequelae of GJ communication) were less prominent in CYP11B2-positive micronodules than adjacent ZG. Somatic mutations of CADM1 cause reversible hypertension and reveal a role for GJ communication in suppressing physiological aldosterone production.

Cluster analysis of angiotensin biomarkers to identify antihypertensive drug treatment in population studies.

BACKGROUND: The recent progress in molecular biology generates an increasing interest in investigating molecular biomarkers as markers of response to treatments. The present work is motivated by a study, where the objective was to explore the potential of the molecular biomarkers of renin-angiotensin-aldosterone system (RAAS) to identify the undertaken antihypertensive treatments in the general population. Population-based studies offer an opportunity to assess the effectiveness of treatments in real-world scenarios. However, lack of quality documentation, especially when electronic health record linkage is unavailable, leads to inaccurate reporting and classification bias. METHOD: We present a machine learning clustering technique to determine the potential of measured RAAS biomarkers for the identification of undertaken treatments in the general population. The biomarkers were simultaneously determined through a novel mass-spectrometry analysis in 800 participants of the Cooperative Health Research In South Tyrol (CHRIS) study with documented antihypertensive treatments. We assessed the agreement, sensitivity and specificity of the resulting clusters against known treatment types. Through the lasso penalized regression, we identified clinical characteristics associated with the biomarkers, accounting for the effects of cluster and treatment classifications. RESULTS: We identified three well-separated clusters: cluster 1 (n = 444) preferentially including individuals not receiving RAAS-targeting drugs; cluster 2 (n = 235) identifying angiotensin type 1 receptor blockers (ARB) users (weighted kappa κw = 74%; sensitivity = 73%; specificity = 83%); and cluster 3 (n = 121) well discriminating angiotensin-converting enzyme inhibitors (ACEi) users (κw = 81%; sensitivity = 55%; specificity = 90%). Individuals in clusters 2 and 3 had higher frequency of diabetes as well as higher fasting glucose and BMI levels. Age, sex and kidney function were strong predictors of the RAAS biomarkers independently of the cluster structure. CONCLUSIONS: Unsupervised clustering of angiotensin-based biomarkers is a viable technique to identify individuals on specific antihypertensive treatments, pointing to a potential application of the biomarkers as useful clinical diagnostic tools even outside of a controlled clinical setting.

New advances in endocrine hypertension: from genes to biomarkers.

Hypertension (HT) is a major cardiovascular risk factor that affects 10% to 40% of the general population in an age-dependent manner. Detection of secondary forms of HT is particularly important because it allows the targeted management of the underlying disease. Among hypertensive patients, the prevalence of endocrine HT reaches up to 10%. Adrenal diseases are the most frequent cause of endocrine HT and are associated with excess production of mineralocorticoids (mainly primary aldosteronism), glucocorticoids (Cushing syndrome), and catecholamines (pheochromocytoma). In addition, a few rare diseases directly affecting the action of mineralocorticoids and glucocorticoids in the kidney also lead to endocrine HT. Over the past years, genomic and genetic studies have allowed improving our knowledge on the molecular mechanisms of endocrine HT. Those discoveries have opened new opportunities to transfer knowledge to clinical practice for better diagnosis and specific treatment of affected subjects. In this review, we describe the physiology of adrenal hormone biosynthesis and action, the clinical and biochemical characteristics of different forms of endocrine HT, and their underlying genetic defects. We discuss the impact of these discoveries on diagnosis and management of patients, as well as new perspectives related to the use of new biomarkers for improved patient care.

Mechanism by Which Inflammation and Oxidative Stress Induce Mineralocorticoid Receptor Gene Expression in Aging Vascular Smooth Muscle Cells.

BACKGROUND: Vascular MR (mineralocorticoid receptor) expression increases with age driving aging-associated vascular stiffness and hypertension. MR has two isoforms (1α and 1ß) with distinct 5'-untranslated and promoter sequences (P1 and P2), but the gene regulatory mechanisms remain unknown. We investigated mechanisms driving MR gene transcriptional regulation in aging human smooth muscle cells (SMC). METHODS: MR was quantified in aortic tissue and primary human aortic SMC (HASMC) comparing adult and aged donors and adult HASMC treated with H2O2, to induce aging. Predicted transcription factor (TF) binding sites in the MR gene were validated using chromatin immunoprecipitations and reporter assays. The impact of TF inhibitors on MR isoforms and fibrosis target gene expression was examined. RESULTS: Expression of both MR mRNA isoforms increased with donor age or H2O2 treatment in HASMCs. HIF1α (hypoxia-inducible factor) and the inflammatory TF NFκB (nuclear factor kappa B) both increased with age in HASMCs and are predicted to bind MR promoters. H2O2 induced HIF1α and NFκB expression and DNA binding of HIF1α to the MR P1 promoter and of NFκB to both MR promoters in HASMCs. HIF1α inhibition decreased MR-1α isoform expression while NFκB inhibition decreased both MR isoforms. HIF1α, NFκB, and MR inhibition decreased the expression of a SMC-MR target gene implicated in vascular fibrosis. In human aortic tissues, expression of HIF1α and NFκB each positively correlated with donor age and MR expression (P<0.0001). CONCLUSIONS: These data implicate the inflammatory TF, NFκB, and oxidative stress-induced TF, HIF1α, in regulating SMC MR transcription in aging HASMCs, which drives aging-related vascular stiffness and cardiovascular disease.

Vascular and hormonal interactions in the adrenal gland.

Primary aldosteronism is the most common form of secondary arterial hypertension, due to excessive aldosterone production from the adrenal gland. Although somatic mutations have been identified in aldosterone producing adenoma, the exact mechanisms leading to increased cell proliferation and nodule formation remain to be established. One hypothesis is that changes in vascular supply to the adrenal cortex, due to phenomena of atherosclerosis or high blood pressure, may influence the morphology of the adrenal cortex, resulting in a compensatory growth and nodule formation in response to local hypoxia. In this review, we will summarize our knowledge on the mechanisms regulating adrenal cortex development and function, describe adrenal vascularization in normal and pathological conditions and address the mechanisms allowing the cross-talk between the hormonal and vascular components to allow the extreme tissue plasticity of the adrenal cortex in response to endogenous and exogenous stimuli. We will then address recent evidence suggesting a role for alterations in the vascular compartment that could eventually be involved in nodule formation and the development of primary aldosteronism.

Whole blood methylome-derived features to discriminate endocrine hypertension.

BACKGROUND: Arterial hypertension represents a worldwide health burden and a major risk factor for cardiovascular morbidity and mortality. Hypertension can be primary (primary hypertension, PHT), or secondary to endocrine disorders (endocrine hypertension, EHT), such as Cushing's syndrome (CS), primary aldosteronism (PA), and pheochromocytoma/paraganglioma (PPGL). Diagnosis of EHT is currently based on hormone assays. Efficient detection remains challenging, but is crucial to properly orientate patients for diagnostic confirmation and specific treatment. More accurate biomarkers would help in the diagnostic pathway. We hypothesized that each type of endocrine hypertension could be associated with a specific blood DNA methylation signature, which could be used for disease discrimination. To identify such markers, we aimed at exploring the methylome profiles in a cohort of 255 patients with hypertension, either PHT (n = 42) or EHT (n = 213), and at identifying specific discriminating signatures using machine learning approaches. RESULTS: Unsupervised classification of samples showed discrimination of PHT from EHT. CS patients clustered separately from all other patients, whereas PA and PPGL showed an overall overlap. Global methylation was decreased in the CS group compared to PHT. Supervised comparison with PHT identified differentially methylated CpG sites for each type of endocrine hypertension, showing a diffuse genomic location. Among the most differentially methylated genes, FKBP5 was identified in the CS group. Using four different machine learning methods-Lasso (Least Absolute Shrinkage and Selection Operator), Logistic Regression, Random Forest, and Support Vector Machine-predictive models for each type of endocrine hypertension were built on training cohorts (80% of samples for each hypertension type) and estimated on validation cohorts (20% of samples for each hypertension type). Balanced accuracies ranged from 0.55 to 0.74 for predicting EHT, 0.85 to 0.95 for predicting CS, 0.66 to 0.88 for predicting PA, and 0.70 to 0.83 for predicting PPGL. CONCLUSIONS: The blood DNA methylome can discriminate endocrine hypertension, with methylation signatures for each type of endocrine disorder.

Identification of risk loci for primary aldosteronism in genome-wide association studies.

Primary aldosteronism affects up to 10% of hypertensive patients and is responsible for treatment resistance and increased cardiovascular risk. Here we perform a genome-wide association study in a discovery cohort of 562 cases and 950 controls and identify three main loci on chromosomes 1, 13 and X; associations on chromosome 1 and 13 are replicated in a second cohort and confirmed by a meta-analysis involving 1162 cases and 3296 controls. The association on chromosome 13 is specific to men and stronger in bilateral adrenal hyperplasia than aldosterone producing adenoma. Candidate genes located within the two loci, CASZ1 and RXFP2, are expressed in human and mouse adrenals in different cell clusters. Their overexpression in adrenocortical cells suppresses mineralocorticoid output under basal and stimulated conditions, without affecting cortisol biosynthesis. Our study identifies the first risk loci for primary aldosteronism and highlights new mechanisms for the development of aldosterone excess.

Machine learning for classification of hypertension subtypes using multi-omics: A multi-centre, retrospective, data-driven study.

BACKGROUND: Arterial hypertension is a major cardiovascular risk factor. Identification of secondary hypertension in its various forms is key to preventing and targeting treatment of cardiovascular complications. Simplified diagnostic tests are urgently required to distinguish primary and secondary hypertension to address the current underdiagnosis of the latter. METHODS: This study uses Machine Learning (ML) to classify subtypes of endocrine hypertension (EHT) in a large cohort of hypertensive patients using multidimensional omics analysis of plasma and urine samples. We measured 409 multi-omics (MOmics) features including plasma miRNAs (PmiRNA: 173), plasma catechol O-methylated metabolites (PMetas: 4), plasma steroids (PSteroids: 16), urinary steroid metabolites (USteroids: 27), and plasma small metabolites (PSmallMB: 189) in primary hypertension (PHT) patients, EHT patients with either primary aldosteronism (PA), pheochromocytoma/functional paraganglioma (PPGL) or Cushing syndrome (CS) and normotensive volunteers (NV). Biomarker discovery involved selection of disease combination, outlier handling, feature reduction, 8 ML classifiers, class balancing and consideration of different age- and sex-based scenarios. Classifications were evaluated using balanced accuracy, sensitivity, specificity, AUC, F1, and Kappa score. FINDINGS: Complete clinical and biological datasets were generated from 307 subjects (PA=113, PPGL=88, CS=41 and PHT=112). The random forest classifier provided ∼92% balanced accuracy (∼11% improvement on the best mono-omics classifier), with 96% specificity and 0.95 AUC to distinguish one of the four conditions in multi-class ALL-ALL comparisons (PPGL vs PA vs CS vs PHT) on an unseen test set, using 57 MOmics features. For discrimination of EHT (PA + PPGL + CS) vs PHT, the simple logistic classifier achieved 0.96 AUC with 90% sensitivity, and ∼86% specificity, using 37 MOmics features. One PmiRNA (hsa-miR-15a-5p) and two PSmallMB (C9 and PC ae C38:1) features were found to be most discriminating for all disease combinations. Overall, the MOmics-based classifiers were able to provide better classification performance in comparison to mono-omics classifiers. INTERPRETATION: We have developed a ML pipeline to distinguish different EHT subtypes from PHT using multi-omics data. This innovative approach to stratification is an advancement towards the development of a diagnostic tool for EHT patients, significantly increasing testing throughput and accelerating administration of appropriate treatment. FUNDING: European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 633983, Clinical Research Priority Program of the University of Zurich for the CRPP HYRENE (to Z.E. and F.B.), and Deutsche Forschungsgemeinschaft (CRC/Transregio 205/1).

Predicting Hypertension Subtypes with Machine Learning Using Targeted Metabolites and Their Ratios.

Hypertension is a major global health problem with high prevalence and complex associated health risks. Primary hypertension (PHT) is most common and the reasons behind primary hypertension are largely unknown. Endocrine hypertension (EHT) is another complex form of hypertension with an estimated prevalence varying from 3 to 20% depending on the population studied. It occurs due to underlying conditions associated with hormonal excess mainly related to adrenal tumours and sub-categorised: primary aldosteronism (PA), Cushing's syndrome (CS), pheochromocytoma or functional paraganglioma (PPGL). Endocrine hypertension is often misdiagnosed as primary hypertension, causing delays in treatment for the underlying condition, reduced quality of life, and costly antihypertensive treatment that is often ineffective. This study systematically used targeted metabolomics and high-throughput machine learning methods to predict the key biomarkers in classifying and distinguishing the various subtypes of endocrine and primary hypertension. The trained models successfully classified CS from PHT and EHT from PHT with 92% specificity on the test set. The most prominent targeted metabolites and metabolite ratios for hypertension identification for different disease comparisons were C18:1, C18:2, and Orn/Arg. Sex was identified as an important feature in CS vs. PHT classification.

Preanalytical Pitfalls in Untargeted Plasma Nuclear Magnetic Resonance Metabolomics of Endocrine Hypertension.

Despite considerable morbidity and mortality, numerous cases of endocrine hypertension (EHT) forms, including primary aldosteronism (PA), pheochromocytoma and functional paraganglioma (PPGL), and Cushing's syndrome (CS), remain undetected. We aimed to establish signatures for the different forms of EHT, investigate potentially confounding effects and establish unbiased disease biomarkers. Plasma samples were obtained from 13 biobanks across seven countries and analyzed using untargeted NMR metabolomics. We compared unstratified samples of 106 PHT patients to 231 EHT patients, including 104 PA, 94 PPGL and 33 CS patients. Spectra were subjected to a multivariate statistical comparison of PHT to EHT forms and the associated signatures were obtained. Three approaches were applied to investigate and correct confounding effects. Though we found signatures that could separate PHT from EHT forms, there were also key similarities with the signatures of sample center of origin and sample age. The study design restricted the applicability of the corrections employed. With the samples that were available, no biomarkers for PHT vs. EHT could be identified. The complexity of the confounding effects, evidenced by their robustness to correction approaches, highlighted the need for a consensus on how to deal with variabilities probably attributed to preanalytical factors in retrospective, multicenter metabolomics studies.

Preanalytical Considerations and Outpatient Versus Inpatient Tests of Plasma Metanephrines to Diagnose Pheochromocytoma.

CONTEXT: Sampling of blood in the supine position for diagnosis of pheochromocytoma and paraganglioma (PPGL) results in lower rates of false positives for plasma normetanephrine than seated sampling. It is unclear how inpatient vs outpatient testing and other preanalytical factors impact false positives. OBJECTIVE: We aimed to identify preanalytical precautions to minimize false-positive results for plasma metanephrines. METHODS: Impacts of different blood sampling conditions on plasma metanephrines were evaluated, including outpatient vs inpatient testing, sampling of blood in semi- vs fully recumbent positions, use of cannulae vs direct venipuncture, and differences in outside temperature. A total of 3147 patients at 10 tertiary referral centers were tested for PPGL, including 278 with and 2869 without tumors. Rates of false-positive results were analyzed. RESULTS: Outpatient rather than inpatient sampling resulted in 44% higher plasma concentrations and a 3.4-fold increase in false-positive results for normetanephrine. Low temperature, a semi-recumbent position, and direct venipuncture also resulted in significantly higher plasma concentrations and rates of false-positive results for plasma normetanephrine than alternative sampling conditions, although with less impact than outpatient sampling. Higher concentrations and rates of false-positive results for plasma normetanephrine with low compared with warm temperatures were only apparent for outpatient sampling. Preanalytical factors were without impact on plasma metanephrines in patients with PPGL. CONCLUSION: Although inpatient blood sampling is largely impractical for screening patients with suspected PPGL, other preanalytical precautions (eg, cannulae, warm testing conditions) may be useful. Inpatient sampling may be reserved for follow-up of patients with difficult to distinguish true- from false-positive results.

Identification of glucocorticoid-related molecular signature by whole blood methylome analysis.

OBJECTIVE: Cushing's syndrome represents a state of excessive glucocorticoids related to glucocorticoid treatments or to endogenous hypercortisolism. Cushing's syndrome is associated with high morbidity, with significant inter-individual variability. Likewise, adrenal insufficiency is a life-threatening condition of cortisol deprivation. Currently, hormone assays contribute to identify Cushing's syndrome or adrenal insufficiency. However, no biomarker directly quantifies the biological glucocorticoid action. The aim of this study was to identify such markers. DESIGN: We evaluated whole blood DNA methylome in 94 samples obtained from patients with different glucocorticoid states (Cushing's syndrome, eucortisolism, adrenal insufficiency). We used an independent cohort of 91 samples for validation. METHODS: Leukocyte DNA was obtained from whole blood samples. Methylome was determined using the Illumina methylation chip array (~850 000 CpG sites). Both unsupervised (principal component analysis) and supervised (Limma) methods were used to explore methylome profiles. A Lasso-penalized regression was used to select optimal discriminating features. RESULTS: Whole blood methylation profile was able to discriminate samples by their glucocorticoid status: glucocorticoid excess was associated with DNA hypomethylation, recovering within months after Cushing's syndrome correction. In Cushing's syndrome, an enrichment in hypomethylated CpG sites was observed in the region of FKBP5 gene locus. A methylation predictor of glucocorticoid excess was built on a training cohort and validated on two independent cohorts. Potential CpG sites associated with the risk for specific complications, such as glucocorticoid-related hypertension or osteoporosis, were identified, needing now to be confirmed on independent cohorts. CONCLUSIONS: Whole blood DNA methylome is dynamically impacted by glucocorticoids. This biomarker could contribute to better assessment of glucocorticoid action beyond hormone assays.

Colocalization of Wnt/ß-Catenin and ACTH Signaling Pathways and Paracrine Regulation in Aldosterone-producing Adenoma.

CONTEXT: Aldosterone-producing adenomas (APAs) are a common cause of primary aldosteronism (PA). Despite the discovery of somatic mutations in APA and the characterization of multiple factors regulating adrenal differentiation and function, the sequence of events leading to APA formation remains to be determined. OBJECTIVE: We investigated the role of Wnt/ß-catenin and adrenocorticotropin signaling, as well as elements of paracrine regulation of aldosterone biosynthesis in adrenals with APA and their relationship to intratumoral heterogeneity and mutational status. METHODS: We analyzed the expression of aldosterone-synthase (CYP11B2), CYP17A1, ß-catenin, melanocortin type 2 receptor (MC2R), phosphorlyated cAMP response element-binding protein (pCREB), tryptase, S100, CD34 by multiplex immunofluorescence, and immunohistochemistry-guided reverse transcription-quantitative polymerase chain reaction. Eleven adrenals with APA and 1 with micronodular hyperplasia from patients with PA were analyzed. Main outcome measures included localization of CYP11B2, CYP17A1, ß-catenin, MC2R, pCREB, tryptase, S100, CD34 in APA and aldosterone-producing cell clusters (APCCs). RESULTS: Immunofluorescence revealed abundant mast cells and a dense vascular network in APA, independent of mutational status. Within APA, mast cells were localized in areas expressing CYP11B2 and were rarely colocalized with nerve fibers, suggesting that their degranulation is not controlled by innervation. In these same areas, ß-catenin was activated, suggesting a zona glomerulosa cell identity. In heterogeneous APA with KCNJ5 mutations, MC2R and vascular endothelial growth factor A expression was higher in areas expressing CYP11B2. A similar pattern was observed in APCC, with high expression of CYP11B2, activated ß-catenin, and numerous mast cells. CONCLUSION: Our results suggest that aldosterone-producing structures in adrenals with APA share common molecular characteristics and cellular environment, despite different mutation status, suggesting common developmental mechanisms.

Circulating microRNAs as Diagnostic Markers in Primary Aldosteronism.

Primary aldosteronism (PA) is a common and highly treatable condition, usually resulting from adrenocortical tumorous growth or hyperplasia. PA is currently underdiagnosed owing to its complex and protracted diagnostic procedures. A simplified biomarker-based test would be highly valuable in reducing cardiovascular morbidity and mortality. Circulating microRNAs are emerging as potential biomarkers for a number of conditions due to their stability and accessibility. PA is known to alter microRNA expression in adrenocortical tissue; if these changes or their effects are mirrored in the circulating miRNA profile, then this could be exploited by a diagnostic test. However, the reproducibility of studies to identify biomarker-circulating microRNAs has proved difficult for other conditions due to a series of technical challenges. Therefore, any studies seeking to definitively identify circulating microRNA biomarkers of PA must address this in their design. To this end, we are currently conducting the circulating microRNA arm of the ongoing ENS@T-HT study. In this review article, we present evidence to support the utility of circulating microRNAs as PA biomarkers, describe the practical challenges to this approach and, using ENS@T-HT as an example, discuss how these might be overcome.

Somatic mutations of GNA11 and GNAQ in CTNNB1-mutant aldosterone-producing adenomas presenting in puberty, pregnancy or menopause.

Most aldosterone-producing adenomas (APAs) have gain-of-function somatic mutations of ion channels or transporters. However, their frequency in aldosterone-producing cell clusters of normal adrenal gland suggests a requirement for codriver mutations in APAs. Here we identified gain-of-function mutations in both CTNNB1 and GNA11 by whole-exome sequencing of 3/41 APAs. Further sequencing of known CTNNB1-mutant APAs led to a total of 16 of 27 (59%) with a somatic p.Gln209His, p.Gln209Pro or p.Gln209Leu mutation of GNA11 or GNAQ. Solitary GNA11 mutations were found in hyperplastic zona glomerulosa adjacent to double-mutant APAs. Nine of ten patients in our UK/Irish cohort presented in puberty, pregnancy or menopause. Among multiple transcripts upregulated more than tenfold in double-mutant APAs was LHCGR, the receptor for luteinizing or pregnancy hormone (human chorionic gonadotropin). Transfections of adrenocortical cells demonstrated additive effects of GNA11 and CTNNB1 mutations on aldosterone secretion and expression of genes upregulated in double-mutant APAs. In adrenal cortex, GNA11/Q mutations appear clinically silent without a codriver mutation of CTNNB1.

Somatic mutations in adrenals from patients with primary aldosteronism not cured after adrenalectomy suggest common pathogenic mechanisms between unilateral and bilateral disease.

OBJECTIVE: Primary aldosteronism (PA) is the most common form of secondary and curable hypertension. Different germline and somatic mutations are found in aldosterone-producing adenoma (APA) and familial forms of the disease, while the causes of bilateral adrenal hyperplasia (BAH) remain largely unknown. Adrenalectomy is the recommended treatment for patients with APA; however, 6% of patients are not cured and show persistent PA after surgery suggesting BAH. The objective of this study was to analyze clinical data of patients with APA without biochemical success after adrenalectomy as well as the histological and genetic characteristics of their adrenal glands. DESIGN AND METHODS: Clinical data of 12 patients with partial and absent biochemical cure were compared to those from 39 PA patients with hormonal cure after surgery. Histological, morphological, and genetic characterization of the adrenals was carried out by CYP11B2 and CYP11B1 immunostaining and by CYP11B2-guided NGS. RESULTS: Patients with absent hormonal cure displayed a longer duration of arterial hypertension and lower lateralization index of aldosterone production. In ten patients, APAs expressing CYP11B2 were identified. No difference in histological and morphological characteristics was observed between patients with or without a hormonal cure. Somatic mutations in APA driver genes were identified in all CYP11B2 positive APAs; CACNA1D mutations were the most frequent genetic abnormality. CONCLUSIONS: Patients with partial and absent biochemical cure were diagnosed later and exhibited a lower lateralization index of aldosterone production, suggesting asymmetric aldosterone production in the context of BAH. Somatic mutations in adrenal glands from those patients indicate common mechanisms underlying BAH and APA.