Physiological responses to parathyroid hormone depend on the subcellular location of receptor activity.

Significance: Blood Ca ²⁺ homeostasis is maintained by the actions of the parathyroid hormone (PTH) on its cognate receptor PTHR. PTH binding to PTHR mediates prolonged adenosine 3′,5′-cyclic monophosphate (cAMP) responses in cells after receptor internalization. Here, we show that extracellular Ca ²⁺ prolongs the residence time of ligands on the receptor, consequently, increasing both the duration of receptor activation and cAMP signaling. This positive Ca ²⁺ allostery is lost for the PTH mutant R25C, recently identified as a new cause of hypocalcemia in humans. Using mass spectrometry approaches, we identify acidic clusters within the first extracellular loop of PTHR as determinants of Ca ²⁺ allostery and endosomal cAMP signaling. These findings provide insight into the molecular etiology of hypocalcemia and disease relevance of endosomal cAMP signaling.

BRD4 is a well-recognized transcriptional activator, but how it regulates gene transcriptional repression in a cell type-specific manner has remained elusive. In this study, we report that BRD4 works with Polycomb repressive complex 2 (PRC2) to repress transcriptional expression of the T-helper 2 (Th2)-negative regulators Foxp3 and E3-ubiqutin ligase Fbxw7 during lineage-specific differentiation of Th2 cells from mouse primary naïve CD4⁺ T cells. Brd4 binds to the lysine-acetylated-EED subunit of the PRC2 complex via its second bromodomain (BD2) to facilitate histone H3 lysine 27 trimethylation (H3K27me3) at target gene loci and thereby transcriptional repression. We found that Foxp3 represses transcription of Th2-specific transcription factor Gata3, while Fbxw7 promotes its ubiquitination-directed protein degradation. BRD4-mediated repression of Foxp3 and Fbxw7 in turn promotes BRD4- and Gata3-mediated transcriptional activation of Th2 cytokines including Il4, Il5, and Il13. Chemical inhibition of the BRD4 BD2 induces transcriptional de-repression of Foxp3 and Fbxw7, and thus transcriptional downregulation of Il4, Il5, and Il13, resulting in inhibition of Th2 cell lineage differentiation. Our study presents a previously unappreciated mechanism of BRD4's role in orchestrating a Th2-specific transcriptional program that coordinates gene repression and activation, and safeguards cell lineage differentiation.

Background: Biopsy-based diagnosis is essential for maintaining kidney allograft longevity by ensuring prompt treatment for graft complications. Although histologic assessment remains the gold standard, it carries significant limitations such as subjective interpretation, suboptimal reproducibility, and imprecise quantitation of disease burden. It is hoped that molecular diagnostics could enhance the efficiency, accuracy, and reproducibility of traditional histologic methods.Methods: Quantitative label-free mass spectrometry analysis was performed on a set of formalin-fixed, paraffin-embedded (FFPE) biopsies from kidney transplant patients, including five samples each with diagnosis of T-cell-mediated rejection (TCMR), polyomavirus BK nephropathy (BKPyVN), and stable (STA) kidney function control tissue. Using the differential protein expression result as a classifier, three different machine learning algorithms were tested to build a molecular diagnostic model for TCMR.Results: The label-free proteomics method yielded 800-1350 proteins that could be quantified with high confidence per sample by single-shot measurements. Among these candidate proteins, 329 and 467 proteins were defined as differentially expressed proteins (DEPs) for TCMR in comparison with STA and BKPyVN, respectively. Comparing the FFPE quantitative proteomics data set obtained in this study using label-free method with a data set we previously reported using isobaric labeling technology, a classifier pool comprised of features from DEPs commonly quantified in both data sets, was generated for TCMR prediction. Leave-one-out cross-validation result demonstrated that the random forest (RF)-based model achieved the best predictive power. In a follow-up blind test using an independent sample set, the RF-based model yields 80% accuracy for TCMR and 100% for STA. When applying the established RF-based model to two public transcriptome datasets, 78.1%-82.9% sensitivity and 58.7%-64.4% specificity was achieved respectively.Conclusions: This proof-of-principle study demonstrates the clinical feasibility of proteomics profiling for FFPE biopsies using an accurate, efficient, and cost-effective platform integrated of quantitative label-free mass spectrometry analysis with a machine learning-based diagnostic model. It costs less than 10 dollars per test.