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

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

Decreased α-cell mass and early structural alterations of the exocrine pancreas in patients with type 1 diabetes: An analysis based on the nPOD repository

<div><p>Background and aims</p><p>Abnormal glucagon secretion and functional alterations of the exocrine pancreas have been described in patients with type 1 diabetes (T1D), but their respective anatomical substrata have seldom been investigated. Our aim was to develop an automated morphometric analysis process to characterize the anatomy of α-cell and exocrine pancreas in patients with T1D, using the publicly available slides of the Network for Pancreatic Organ Donors (nPOD).</p><p>Materials and methods</p><p>The ratio of β- and α-cell area to total tissue area were quantified in 75 patients with T1D (thereafter patients) and 66 control subjects (thereafter controls), on 2 insulin-stained and 4 glucagon-stained slides from both the head and the tail of the pancreas. The β- and α-cell masses were calculated in the 66 patients and the 50 controls for which the pancreas weight was available. Non-exocrine-non-endocrine tissue area (i.e. non-acinar, non-insular tissue) to total tissue area ratio was evaluated on both insulin- and glucagon-stained slides. Results were expressed as mean ±SD.</p><p>Results</p><p>An automated quantification method was set up using the R software and was validated by quantification of β-cell mass, a well characterized parameter. β-cell mass was 29.6±112 mg in patients and 628 ±717 mg in controls (p<0.0001). α-cell mass was 181±176 mg in patients and 349 ±241mg in controls (p<0.0001). Non-exocrine-non-endocrine area to total tissue area ratio was 39±9% in patients and 29± 10% in controls (p<0.0001) and increased with age in both groups, with no correlation with diabetes duration in patients.</p><p>Conclusion</p><p>The absolute α-cell mass was lower in patients compared to controls, in proportion to the decrease in pancreas weight observed in patients. Non-exocrine-non-endocrine area to total tissue area ratio increased with age in both groups but was higher in patients at all ages.</p></div

Representative example of the different steps used for selecting Fast Red-stained areas.

<p>A: Original image (slide 3160). B: Preselection of high-level red pixels. C: Definition of the regions of interest by circles of 50-pixel radius around each preselected pixel. D: The final output, as obtained by further selecting low-level red pixels in the regions of interest.</p

Example on the slide 14262.

<p>Selected Fast Red-stained pixels and subsequent results. Given that these 3 different areas were evaluated on pictures of different sizes (full-resolution picture for Fast Red-stained area versus resized pictures for total and exocrine+endocrine pancreatic tissue areas), all the results were normalized by the whole slide surface to allow further calculation of the parameters of interest, i.e. ratio of non-exocrine-non-endocrine pancreatic tissue (total pancreatic tissue minus exocrine+endocrine pancreatic tissue) to total pancreatic tissue area and ratio of either α-or β-cell area to total pancreatic tissue area, further converted into mass by multiplication by pancreas weight when this data was available. To take into account the difference in tissue area between slides, the average calculated for each patient, on respectively 6 slides for the ratio of non-exocrine-non-endocrine pancreatic to total pancreatic tissue, 4 slides for the ratio of α- cell area to total pancreatic tissue ratio and 2 slides for the ratio of β-cell area to total pancreatic tissue ratio, was weighted according to the total pancreatic tissue area of each slide.</p

Characteristics of study subjects and pancreata.

<p>Characteristics of study subjects and pancreata.</p

β- and α-cell to total tissue area ratio and β- and α-cell mass in patients with T1D and control subjects.

<p>A: β<b>-</b>cell area/total tissue area ratio. B: β<b>-</b>cell mass. T1D patients are symbolized by grey circles and controls by black triangles. Bars represent mean with SD and data were analyzed using the Mann-Whitney test. ****p<0.0001.</p

Example on the slide 14262.

<p>The second R script, used for the selection of the pixels belonging to functional pancreatic tissue, generated 16 different propositions. The choice of the investigator is framed in red and the subsequent results indicated under the picture.</p

Example on the slide 14262.

<p>The first R script, used for the selection of the pixels belonging to total pancreatic tissue, generated 22 different propositions. The choice of the investigator is framed in red and the subsequent result indicated under the picture. An example of background pixel has been also pointed out on the picture miniature.</p

α-cell to total tissue area ratio in the head and tail of the pancreas of patients with T1D and control subjects.

<p>Data representation is the same as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0191528#pone.0191528.g005" target="_blank">Fig 5</a>.</p