Cancer-associated polybromo-1 bromodomain 4 missense variants variably impact bromodomain ligand binding and cell growth suppression.

The polybromo, brahma-related gene 1-associated factors (PBAF) chromatin remodeling complex subunit polybromo-1 (PBRM1) contains six bromodomains that recognize and bind acetylated lysine residues on histone tails and other nuclear proteins. PBRM1 bromodomains thus provide a link between epigenetic posttranslational modifications and PBAF modulation of chromatin accessibility and transcription. As a putative tumor suppressor in several cancers, PBRM1 protein expression is often abrogated by truncations and deletions. However, ∼33% of PBRM1 mutations in cancer are missense and cluster within its bromodomains. Such mutations may generate full-length PBRM1 variant proteins with undetermined structural and functional characteristics. Here, we employed computational, biophysical, and cellular assays to interrogate the effects of PBRM1 bromodomain missense variants on bromodomain stability and function. Since mutations in the fourth bromodomain of PBRM1 (PBRM1-BD4) comprise nearly 20% of all cancer-associated PBRM1 missense mutations, we focused our analysis on PBRM1-BD4 missense protein variants. Selecting 16 potentially deleterious PBRM1-BD4 missense protein variants for further study based on high residue mutational frequency and/or conservation, we show that cancer-associated PBRM1-BD4 missense variants exhibit varied bromodomain stability and ability to bind acetylated histones. Our results demonstrate the effectiveness of identifying the unique impacts of individual PBRM1-BD4 missense variants on protein structure and function, based on affected residue location within the bromodomain. This knowledge provides a foundation for drawing correlations between specific cancer-associated PBRM1 missense variants and distinct alterations in PBRM1 function, informing future cancer personalized medicine approaches.

EHMT2 Inactivation in Pancreatic Epithelial Cells Shapes the Transcriptional Landscape and Inflammation Response of the Whole Pancreas.

The Euchromatic Histone Methyl Transferase Protein 2 (EHMT2), also known as G9a, deposits transcriptionally repressive chromatin marks that play pivotal roles in the maturation and homeostasis of multiple organs. Recently, we have shown that EHMT2 inactivation alters growth and immune gene expression networks, antagonizing KRAS-mediated pancreatic cancer initiation and promotion. Here, we elucidate the essential role of EHMT2 in maintaining a transcriptional landscape that protects organs from inflammation. Comparative RNA-seq studies between normal postnatal and young adult pancreatic tissue from EHMT2 conditional knockout animals ( EHMT2 fl/fl ) targeted to the exocrine pancreatic epithelial cells ( Pdx1-Cre and P48 Cre/+ ), reveal alterations in gene expression networks in the whole organ related to injury-inflammation-repair, suggesting an increased predisposition to damage. Thus, we induced an inflammation repair response in the EHMT2 fl/fl pancreas and used a data science-based approach to integrate RNA-seq-derived pathways and networks, deconvolution digital cytology, and spatial transcriptomics. We also analyzed the tissue response to damage at the morphological, biochemical, and molecular pathology levels. The EHMT2 fl/fl pancreas displays an enhanced injury-inflammation-repair response, offering insights into fundamental molecular and cellular mechanisms involved in this process. More importantly, these data show that conditional EHMT2 inactivation in exocrine cells reprograms the local environment to recruit mesenchymal and immunological cells needed to mount an increased inflammatory response. Mechanistically, this response is an enhanced injury-inflammation-repair reaction with a small contribution of specific EHMT2-regulated transcripts. Thus, this new knowledge extends the mechanisms underlying the role of the EHMT2-mediated pathway in suppressing pancreatic cancer initiation and modulating inflammatory pancreatic diseases.

Structural and Dynamic Analyses of Pathogenic Variants in PIK3R1 Reveal a Shared Mechanism Associated among Cancer, Undergrowth, and Overgrowth Syndromes.

The PI3K enzymes modify phospholipids to regulate cell growth and differentiation. Somatic variants in PI3K are recurrent in cancer and drive a proliferative phenotype. Somatic mosaicism of PIK3R1 and PIK3CA are associated with vascular anomalies and overgrowth syndromes. Germline PIK3R1 variants are associated with varying phenotypes, including immunodeficiency or facial dysmorphism with growth delay, lipoatrophy, and insulin resistance associated with SHORT syndrome. There has been limited study of the molecular mechanism to unify our understanding of how variants in PIK3R1 drive both undergrowth and overgrowth phenotypes. Thus, we compiled genomic variants from cancer and rare vascular anomalies and sought to interpret their effects using an unbiased physics-based simulation approach for the protein complex. We applied molecular dynamics simulations to mechanistically understand how genetic variants affect PIK3R1 and its interactions with PIK3CA. Notably, iSH2 genetic variants associated with undergrowth destabilize molecular interactions with the PIK3CA receptor binding domain in simulations, which is expected to decrease activity. On the other hand, overgrowth and cancer variants lead to loss of inhibitory interactions in simulations, which is expected to increase activity. We find that all disease variants display dysfunctions on either structural characteristics or intermolecular interaction energy. Thus, this comprehensive characterization of novel mosaic somatic variants associated with two opposing phenotypes has mechanistic importance and biomedical relevance and may aid in future therapeutic developments.

Deep computational phenotyping of genomic variants impacting the SET domain of KMT2C reveal molecular mechanisms for their dysfunction.

Introduction: Kleefstra Syndrome type 2 (KLEFS-2) is a genetic, neurodevelopmental disorder characterized by intellectual disability, infantile hypotonia, severe expressive language delay, and characteristic facial appearance, with a spectrum of other distinct clinical manifestations. Pathogenic mutations in the epigenetic modifier type 2 lysine methyltransferase KMT2C have been identified to be causative in KLEFS-2 individuals. Methods: This work reports a translational genomic study that applies a multidimensional computational approach for deep variant phenotyping, combining conventional genomic analyses, advanced protein bioinformatics, computational biophysics, biochemistry, and biostatistics-based modeling. We use standard variant annotation, paralog annotation analyses, molecular mechanics, and molecular dynamics simulations to evaluate damaging scores and provide potential mechanisms underlying KMT2C variant dysfunction. Results: We integrated data derived from the structure and dynamics of KMT2C to classify variants into SV (Structural Variant), DV (Dynamic Variant), SDV (Structural and Dynamic Variant), and VUS (Variant of Uncertain Significance). When compared with controls, these variants show values reflecting alterations in molecular fitness in both structure and dynamics. Discussion: We demonstrate that our 3D models for KMT2C variants suggest distinct mechanisms that lead to their imbalance and are not predictable from sequence alone. Thus, the missense variants studied here cause destabilizing effects on KMT2C function by different biophysical and biochemical mechanisms which we adeptly describe. This new knowledge extends our understanding of how variations in the KMT2C gene cause the dysfunction of its methyltransferase enzyme product, thereby bearing significant biomedical relevance for carriers of KLEFS2-associated genomic mutations.

A multi-layered computational structural genomics approach enhances domain-specific interpretation of Kleefstra syndrome variants in EHMT1.

This study investigates the functional significance of assorted variants of uncertain significance (VUS) in euchromatic histone lysine methyltransferase 1 (EHMT1), which is critical for early development and normal physiology. EHMT1 mutations cause Kleefstra syndrome and are linked to various human cancers. However, accurate functional interpretations of these variants are yet to be made, limiting diagnoses and future research. To overcome this, we integrate conventional tools for variant calling with computational biophysics and biochemistry to conduct multi-layered mechanistic analyses of the SET catalytic domain of EHMT1, which is critical for this protein function. We use molecular mechanics and molecular dynamics (MD)-based metrics to analyze the SET domain structure and functional motions resulting from 97 Kleefstra syndrome missense variants within the domain. Our approach allows us to classify the variants in a mechanistic manner into SV (Structural Variant), DV (Dynamic Variant), SDV (Structural and Dynamic Variant), and VUS (Variant of Uncertain Significance). Our findings reveal that the damaging variants are mostly mapped around the active site, substrate binding site, and pre-SET regions. Overall, we report an improvement for this method over conventional tools for variant interpretation and simultaneously provide a molecular mechanism for variant dysfunction.

Writers and readers of H3K9me2 form distinct protein networks during the cell cycle that include candidates for H3K9 mimicry.

Histone H3 lysine 9 methylation (H3K9me), which is written by the Euchromatic Histone Lysine Methyltransferases EHMT1 and EHMT2 and read by the heterochromatin protein 1 (HP1) chromobox (CBX) protein family, is dysregulated in many types of cancers. Approaches to inhibit regulators of this pathway are currently being evaluated for therapeutic purposes. Thus, knowledge of the complexes supporting the function of these writers and readers during the process of cell proliferation is critical for our understanding of their role in carcinogenesis. Here, we immunopurified each of these proteins and used mass spectrometry to define their associated non-histone proteins, individually and at two different phases of the cell cycle, namely G1/S and G2/M. Our findings identify novel binding proteins for these writers and readers, as well as corroborate known interactors, to show the formation of distinct protein complex networks in a cell cycle phase-specific manner. Furthermore, there is an organizational switch between cell cycle phases for interactions among specific writer-reader pairs. Through a multi-tiered bioinformatics-based approach, we reveal that many interacting proteins exhibit histone mimicry, based on an H3K9-like linear motif. Gene ontology analyses, pathway enrichment, and network reconstruction inferred that these comprehensive EHMT and CBX-associated interacting protein networks participate in various functions, including transcription, DNA repair, splicing, and membrane disassembly. Combined, our data reveals novel complexes that provide insight into key functions of cell cycle-associated epigenomic processes that are highly relevant for better understanding these chromatin-modifying proteins during cell cycle and carcinogenesis.

A Multi-Layered Computational Structural Genomics Approach Enhances Domain-Specific Interpretation of Kleefstra Syndrome Variants in EHMT1.

This study investigates the functional significance of assorted variants of uncertain significance (VUS) in euchromatic histone lysine methyltransferase 1 (EHMT1), which is critical for early development and normal physiology. EHMT1 mutations cause Kleefstra syndrome and are linked to various human cancers. However, accurate functional interpretation of these variants are yet to be made, limiting diagnoses and future research. To overcome this, we integrate conventional tools for variant calling with computational biophysics and biochemistry to conduct multi-layered mechanistic analyses of the SET catalytic domain of EHMT1, which is critical for this protein function. We use molecular mechanics and molecular dynamics (MD)-based metrics to analyze the SET domain structure and functional motions resulting from 97 Kleefstra syndrome missense variants within this domain. Our approach allows us to classify the variants in a mechanistic manner into SV (Structural Variant), DV (Dynamic Variant), SDV (Structural and Dynamic Variant), and VUS (Variant of Uncertain Significance). Our findings reveal that the damaging variants are mostly mapped around the active site, substrate binding site, and pre-SET regions. Overall, we report an improvement for this method over conventional tools for variant interpretation and simultaneously provide a molecular mechanism of variant dysfunction.

Beyond structural bioinformatics for genomics with dynamics characterization of an expanded KRAS mutational landscape.

Current capabilities in genomic sequencing outpace functional interpretations. Our previous work showed that 3D protein structure calculations enhance mechanistic understanding of genetic variation in sequenced tumors and patients with rare diseases. The KRAS GTPase is among the critical genetic factors driving cancer and germline conditions. Because KRAS-altered tumors frequently harbor one of three classic hotspot mutations, nearly all studies have focused on these mutations, leaving significant functional ambiguity across the broader KRAS genomic landscape observed in cancer and non-cancer diseases. Herein, we extend structural bioinformatics with molecular simulations to study an expanded landscape of 86 KRAS mutations. We identify multiple coordinated changes strongly associated with experimentally established KRAS biophysical and biochemical properties. The patterns we observe span hotspot and non-hotspot alterations, which can all dysregulate Switch regions, producing mutation-restricted conformations with different effector binding propensities. We experimentally measured mutation thermostability and identified shared and distinct patterns with simulations. Our results indicate mutation-specific conformations, which show potential for future research into how these alterations reverberate into different molecular and cellular functions. The data we present is not predictable using current genomic tools, demonstrating the added functional information derived from molecular simulations for interpreting human genetic variation.

RAG genomic variation causes autoimmune diseases through specific structure-based mechanisms of enzyme dysregulation.

Interpreting genetic changes observed in individual patients is a critical challenge. The array of immune deficiency syndromes is typically caused by genetic variation unique to individuals. Therefore, new approaches are needed to interpret functional variation and accelerate genomics interpretation. We constructed the first full-length structural model of human RAG recombinase across four functional states of the recombination process. We functionally tested 182 clinically observed RAG missense mutations. These experiments revealed dysfunction due to recombinase dysfunction and altered chromatin interactions. Structural modeling identified mechanical and energetic roles for each mutation. We built regression models for RAG1 (R2 = 0.91) and RAG2 (R2 = 0.97) to predict RAG activity changes. We applied our model to 711 additional RAG variants observed in population studies and identified a subset that may impair RAG function. Thus, we demonstrated a fundamental advance in the mechanistic interpretation of human genetic variations spanning from rare and undiagnosed diseases to population health.

Advanced computational analysis of CD40LG variants in atypical X-linked hyper-IgM syndrome.

X-Linked Hyper-IgM Syndrome is caused by pathogenic variants in CD40LG. Three patients with atypical clinical and immunological features were identified with variants in CD40LG requiring further characterization. Flow cytometry was used to evaluate CD40L protein expression and binding capacity to a surrogate receptor, CD40-muIg. Though functional anomalies were observed, there was still a lack of clarity regarding the underlying mechanism. We developed structural models for wild-type and the three variants of CD40L protein observed in these patients (p. Lys143Asn, Leu225Ser and Met36Arg) to evaluate structural alterations by molecular mechanic calculations, and assess protein movement by molecular dynamic simulations. These studies demonstrate that functional analysis of variants of unknown significance in CD40LG can be supplemented by advanced computational analysis in atypical clinical contexts. These studies in combination identify the deleterious effects of these variants and potential mechanisms for protein dysfunction.

IL6Myc mouse is an immunocompetent model for the development of aggressive multiple myeloma.

Multiple Myeloma (MM) is a plasma cell neoplasm originating in the bone marrow and is the second most common blood cancer in the United States. One challenge in understanding the pathogenesis of MM and improving treatment is a lack of immunocompetent mouse models. We previously developed the IL6Myc mouse that generates plasmacytomas at 100% penetrance that phenotypically resemble aggressive MM. Using comprehensive genomic analysis, we found that the IL6Myc tumors resemble aggressive MM by RNA and protein expression. We also found that IL6Myc tumors accumulated fusions and missense mutations in genes that overlap significantly with human myeloma, indicating that the mouse is good model for studying disease etiology. Lastly, we derived cell lines from IL6Myc tumors that express cell surface markers typical of MM and readily engraft into mice, home to the bone marrow, and induce osteolytic disease. The cell lines may be useful in developing immunotherapies directed against BAFF-R and TACI, though not BCMA, and may also be a good model for studying dexamethasone resistance. These data indicate that the IL6Myc model is useful for studying development of aggressive MM and for developing new treatments against such forms of the disease.

Priming therapy by targeting enhancer-initiated pathways in patient-derived pancreatic cancer cells.

BACKGROUND: Systems biology leveraging multi-OMICs technologies, is rapidly advancing development of precision therapies and matching patients to targeted therapies, leading to improved responses. A new pillar of precision oncology lies in the power of chemogenomics to discover drugs that sensitizes malignant cells to other therapies. Here, we test a chemogenomic approach using epigenomic inhibitors (epidrugs) to reset patterns of gene expression driving the malignant behavior of pancreatic tumors. METHODS: We tested a targeted library of ten epidrugs targeting regulators of enhancers and super-enhancers on reprogramming gene expression networks in seventeen patient-derived primary pancreatic cancer cell cultures (PDPCCs), of both basal and classical subtypes. We subsequently evaluated the ability of these epidrugs to sensitize pancreatic cancer cells to five chemotherapeutic drugs that are clinically used for this malignancy. FINDINGS: To comprehend the impact of epidrug priming at the molecular level, we evaluated the effect of each epidrugs at the transcriptomic level of PDPCCs. The activating epidrugs showed a higher number of upregulated genes than the repressive epidrugs (χ2 test p-value <0.01). Furthermore, we developed a classifier using the baseline transcriptome of epidrug-primed-chemosensitized PDPCCs to predict the best epidrug-priming regime to a given chemotherapy. Six signatures with a significant association with the chemosensitization centroid (R ≤ -0.80; p-value < 0.01) were identified and validated in a subset of PDPCCs. INTERPRETATION: We conclude that targeting enhancer-initiated pathways in patient-derived primary cells, represents a promising approach for developing new therapies for human pancreatic cancer. FUNDING: This work was supported by INCa (Grants number 2018-078 to ND and 2018- 079 to JI), Canceropole PACA (ND), Amidex Foundation (ND), and INSERM (JI).

Bi-allelic variants in HMGCR cause an autosomal-recessive progressive limb-girdle muscular dystrophy.

Statins are a mainstay intervention for cardiovascular disease prevention, yet their use can cause rare severe myopathy. HMG-CoA reductase, an essential enzyme in the mevalonate pathway, is the target of statins. We identified nine individuals from five unrelated families with unexplained limb-girdle like muscular dystrophy and bi-allelic variants in HMGCR via clinical and research exome sequencing. The clinical features resembled other genetic causes of muscular dystrophy with incidental high CPK levels (>1,000 U/L), proximal muscle weakness, variable age of onset, and progression leading to impaired ambulation. Muscle biopsies in most affected individuals showed non-specific dystrophic changes with non-diagnostic immunohistochemistry. Molecular modeling analyses revealed variants to be destabilizing and affecting protein oligomerization. Protein activity studies using three variants (p.Asp623Asn, p.Tyr792Cys, and p.Arg443Gln) identified in affected individuals confirmed decreased enzymatic activity and reduced protein stability. In summary, we showed that individuals with bi-allelic amorphic (i.e., null and/or hypomorphic) variants in HMGCR display phenotypes that resemble non-genetic causes of myopathy involving this reductase. This study expands our knowledge regarding the mechanisms leading to muscular dystrophy through dysregulation of the mevalonate pathway, autoimmune myopathy, and statin-induced myopathy.

Beyond Structural Bioinformatics for Genomics with Dynamics Characterization of an Expanded KRAS Mutational Landscape.

Current capabilities in genomic sequencing outpace functional interpretations. Our previous work showed that 3D protein structure calculations enhance mechanistic understanding of genetic variation in sequenced tumors and patients with rare diseases. The KRAS GTPase is among the critical genetic factors driving cancer and germline conditions. Because KRAS-altered tumors frequently harbor one of three classic hotspot mutations, nearly all studies have focused on these mutations, leaving significant functional ambiguity across the broader KRAS genomic landscape observed in cancer and non-cancer diseases. Herein, we extend structural bioinformatics with molecular simulations to study an expanded landscape of 86 KRAS mutations. We identify multiple coordinated changes strongly associated with experimentally established KRAS biophysical and biochemical properties. The patterns we observe span hotspot and non-hotspot alterations, which can all dysregulate Switch regions, producing mutation-restricted conformations with different effector binding propensities. We experimentally measured mutation thermostability and identified shared and distinct patterns with simulations. Our results indicate mutation-specific conformations which show potential for future research into how these alterations reverberate into different molecular and cellular functions. The data we present is not predictable using current genomic tools, demonstrating the added functional information derived from molecular simulations for interpreting human genetic variation.

The splanchnic mesenchyme is the tissue of origin for pancreatic fibroblasts during homeostasis and tumorigenesis.

Pancreatic cancer is characterized by abundant desmoplasia, a dense stroma composed of extra-cellular and cellular components, with cancer associated fibroblasts (CAFs) being the major cellular component. However, the tissue(s) of origin for CAFs remains controversial. Here we determine the tissue origin of pancreatic CAFs through comprehensive lineage tracing studies in mice. We find that the splanchnic mesenchyme, the fetal cell layer surrounding the endoderm from which the pancreatic epithelium originates, gives rise to the majority of resident fibroblasts in the normal pancreas. In a genetic mouse model of pancreatic cancer, resident fibroblasts expand and constitute the bulk of CAFs. Single cell RNA profiling identifies gene expression signatures that are shared among the fetal splanchnic mesenchyme, adult fibroblasts and CAFs, suggesting a persistent transcriptional program underlies splanchnic lineage differentiation. Together, this study defines the phylogeny of the mesenchymal component of the pancreas and provides insights into pancreatic morphogenesis and tumorigenesis.

Structural bioinformatics enhances the interpretation of somatic mutations in KDM6A found in human cancers.

The histone demethylase KDM6A has recently elicited significant attention because its mutations are associated with a rare congenital disorder (Kabuki syndrome) and various types of human cancers. However, distinguishing KDM6A mutations that are deleterious to the enzyme and their underlying mechanisms of dysfunction remain to be fully understood. Here, we report the results from a multi-tiered approach evaluating the impact of 197 KDM6A somatic mutations using information derived from combining conventional genomics data with computational biophysics. This comprehensive approach incorporates multiple scores derived from alterations in protein sequence, structure, and molecular dynamics. Using this method, we classify the KDM6A mutations into 136 damaging variants (69.0%), 32 tolerated variants (16.2%), and 29 variants of uncertain significance (VUS, 14.7%), which is a significant improvement from the previous classification based on the conventional tools (over 40% VUS). We further classify the damaging variants into 15 structural variants (SV), 88 dynamic variants (DV), and 33 structural and dynamic variants (SDV). Comparison with variant scoring methods used in current clinical diagnosis guidelines demonstrates that our approach provides a more comprehensive evaluation of damaging potential and reveals mechanisms of dysfunction. Thus, these results should be taken into consideration for clinical assessment of the damaging potential of each mutation, as they provide hypotheses for experimental validation and critical information for the development of mutant-specific drugs to fight diseases caused by KDM6A dysfunctions.

Molecular Modeling is an Enabling Approach to Complement and Enhance Channelopathy Research.

Hundreds of human membrane proteins form channels that transport necessary ions and compounds, including drugs and metabolites, yet details of their normal function or how function is altered by genetic variants to cause diseases are often unknown. Without this knowledge, researchers are less equipped to develop approaches to diagnose and treat channelopathies. High-resolution computational approaches such as molecular modeling enable researchers to investigate channelopathy protein function, facilitate detailed hypothesis generation, and produce data that is difficult to gather experimentally. Molecular modeling can be tailored to each physiologic context that a protein may act within, some of which may currently be difficult or impossible to assay experimentally. Because many genomic variants are observed in channelopathy proteins from high-throughput sequencing studies, methods with mechanistic value are needed to interpret their effects. The eminent field of structural bioinformatics integrates techniques from multiple disciplines including molecular modeling, computational chemistry, biophysics, and biochemistry, to develop mechanistic hypotheses and enhance the information available for understanding function. Molecular modeling and simulation access 3D and time-dependent information, not currently predictable from sequence. Thus, molecular modeling is valuable for increasing the resolution with which the natural function of protein channels can be investigated, and for interpreting how genomic variants alter them to produce physiologic changes that manifest as channelopathies. © 2022 American Physiological Society. Compr Physiol 12:3141-3166, 2022.

NADPH oxidase 4 contributes to TRPV4-mediated endothelium-dependent vasodilation in human arterioles by regulating protein phosphorylation of TRPV4 channels.

Impaired endothelium-dependent vasodilation has been suggested to be a key component of coronary microvascular dysfunction (CMD). A better understanding of endothelial pathways involved in vasodilation in human arterioles may provide new insight into the mechanisms of CMD. The goal of this study is to investigate the role of TRPV4, NOX4, and their interaction in human arterioles and examine the underlying mechanisms. Arterioles were freshly isolated from adipose and heart tissues obtained from 71 patients without coronary artery disease, and vascular reactivity was studied by videomicroscopy. In human adipose arterioles (HAA), ACh-induced dilation was significantly reduced by TRPV4 inhibitor HC067047 and by NOX 1/4 inhibitor GKT137831, but GKT137831 did not further affect the dilation in the presence of TRPV4 inhibitors. GKT137831 also inhibited TRPV4 agonist GSK1016790A-induced dilation in HAA and human coronary arterioles (HCA). NOX4 transcripts and proteins were detected in endothelial cells of HAA and HCA. Using fura-2 imaging, GKT137831 significantly reduced GSK1016790A-induced Ca2+ influx in the primary culture of endothelial cells and TRPV4-WT-overexpressing human coronary artery endothelial cells (HCAEC). However, GKT137831 did not affect TRPV4-mediated Ca2+ influx in non-phosphorylatable TRPV4-S823A/S824A-overexpressing HCAEC. In addition, treatment of HCAEC with GKT137831 decreased the phosphorylation level of Ser824 in TRPV4. Finally, proximity ligation assay (PLA) revealed co-localization of NOX4 and TRPV4 proteins. In conclusion, both TRPV4 and NOX4 contribute to ACh-induced dilation in human arterioles from patients without coronary artery disease. NOX4 increases TRPV4 phosphorylation in endothelial cells, which in turn enhances TRPV4-mediated Ca2+ entry and subsequent endothelium-dependent dilation in human arterioles.