Pan-Genomic Regulation of Gene Expression in Normal and Pathological Human Placentas.

In this study, we attempted to find genetic variants affecting gene expression (eQTL = expression Quantitative Trait Loci) in the human placenta in normal and pathological situations. The analysis of gene expression in placental diseases (Pre-eclampsia and Intra-Uterine Growth Restriction) is hindered by the fact that diseased placental tissue samples are generally taken at earlier gestations compared to control samples. The difference in gestational age is considered a major confounding factor in the transcriptome regulation of the placenta. To alleviate this significant problem, we propose here a novel approach to pinpoint disease-specific cis-eQTLs. By statistical correction for gestational age at sampling as well as other confounding/surrogate variables systematically searched and identified, we found 43 e-genes for which proximal SNPs influence expression level. Then, we performed the analysis again, removing the disease status from the covariates, and we identified 54 e-genes, 16 of which are identified de novo and, thus, possibly related to placental disease. We found a highly significant overlap with previous studies for the list of 43 e-genes, validating our methodology and findings. Among the 16 disease-specific e-genes, several are intrinsic to trophoblast biology and, therefore, constitute novel targets of interest to better characterize placental pathology and its varied clinical consequences. The approach that we used may also be applied to the study of other human diseases where confounding factors have hampered a better understanding of the pathology.

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.

Immune Microenvironment and Lineage Tracing Help to Decipher Rosette-Forming Glioneuronal Tumors: A Multi-Omics Analysis.

Rosette-forming glioneuronal tumors (RGNT) are rare low-grade primary central nervous system (CNS) tumors. The methylation class (MC) RGNT (MC-RGNT) delineates RGNT from other neurocytic CNS tumors with similar histological features. We performed a comprehensive molecular analysis including whole-exome sequencing, RNAseq, and methylome on 9 tumors with similar histology, focusing on the immune microenvironment and cell of origin of RGNT. Three RGNT in this cohort were plotted within the MC-RGNT and characterized by FGFR1 mutation plus PIK3CA or NF1 mutations. RNAseq analysis, validated by immunohistochemistry, identified 2 transcriptomic groups with distinct immune microenvironments. The "cold" group was distinguishable by a low immune infiltration and included the 3 MC-RGNT and 1 MC-pilocytic astrocytoma; the "hot" group included other tumors with a rich immune infiltration. Gene set enrichment analysis showed that the "cold" group had upregulated NOTCH pathway and mainly oligodendrocyte precursor cell and neuronal phenotypes, while the "hot" group exhibited predominantly astrocytic and neural stem cell phenotypes. In silico deconvolution identified the cerebellar granule cell lineage as a putative cell of origin of RGNT. Our study identified distinct tumor biology and immune microenvironments as key features relevant to the pathogenesis and management of RGNT.

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.

Alternative splicing in normal and pathological human placentas is correlated to genetic variants.

Two major obstetric diseases, preeclampsia (PE), a pregnancy-induced endothelial dysfunction leading to hypertension and proteinuria, and intra-uterine growth-restriction (IUGR), a failure of the fetus to acquire its normal growth, are generally triggered by placental dysfunction. Many studies have evaluated gene expression deregulations in these diseases, but none has tackled systematically the role of alternative splicing. In the present study, we show that alternative splicing is an essential feature of placental diseases, affecting 1060 and 1409 genes in PE vs controls and IUGR vs controls, respectively, many of those involved in placental function. While in IUGR placentas, alternative splicing affects genes specifically related to pregnancy, in preeclamptic placentas, it impacts a mix of genes related to pregnancy and brain diseases. Also, alternative splicing variations can be detected at the individual level as sharp splicing differences between different placentas. We correlate these variations with genetic variants to define splicing Quantitative Trait Loci (sQTL) in the subset of the 48 genes the most strongly alternatively spliced in placental diseases. We show that alternative splicing is at least partly piloted by genetic variants located either in cis (52 QTL identified) or in trans (52 QTL identified). In particular, we found four chromosomal regions that impact the splicing of genes in the placenta. The present work provides a new vision of placental gene expression regulation that warrants further studies.

Pangenomic Classification of Pituitary Neuroendocrine Tumors.

Pituitary neuroendocrine tumors (PitNETs) are common, with five main histological subtypes: lactotroph, somatotroph, and thyrotroph (POU1F1/PIT1 lineage); corticotroph (TBX19/TPIT lineage); and gonadotroph (NR5A1/SF1 lineage). We report a comprehensive pangenomic classification of PitNETs. PitNETs from POU1F1/PIT1 lineage showed an epigenetic signature of diffuse DNA hypomethylation, with transposable elements expression and chromosomal instability (except for GNAS-mutated somatotrophs). In TPIT lineage, corticotrophs were divided into three classes: the USP8-mutated with overt secretion, the USP8-wild-type with increased invasiveness and increased epithelial-mesenchymal transition, and the large silent tumors with gonadotroph transdifferentiation. Unexpected expression of gonadotroph markers was also found in GNAS-wild-type somatotrophs (SF1 expression), challenging the current definition of SF1/gonadotroph lineage. This classification improves our understanding and affects the clinical stratification of patients with PitNETs.