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Category Archives: Genetic Medicine

Biomarkers and Candidate Therapeutic Drugs in Heart Failure | IJGM – Dove Medical Press

Yang Guo,1 4 Bobin Ning,5 Qunhui Zhang,1 4 Jing Ma,4 Linlin Zhao,1 4 QiQin Lu,1 4 Dejun Zhang1,4

1Research Center for High Altitude Medicine, Medical College of Qinghai University, Xining, 810001, Peoples Republic of China; 2Key Laboratory of Application and Foundation for High Altitude Medicine Research in Qinghai Province, Medical College of Qinghai University, Xining, 810001, Peoples Republic of China; 3Qinghai-Utah Joint Research Key Lab for High Altitude Medicine, Medical College of Qinghai University, Xining, 810001, Peoples Republic of China; 4Department of Eco-Environmental Engineering, Qinghai University, Xining, 810016, Peoples Republic of China; 5Department of Medicine, The General Hospital of the Peoples Liberation Army, Beijing, 100038, Peoples Republic of China

Correspondence: Dejun ZhangDepartment of Eco-Environmental Engineering, Qinghai University, Qinghai, 810016, Peoples Republic of ChinaEmail [emailprotected]

Purpose: The objective of this study was to identify the potential regulatory mechanisms, diagnostic biomarkers, and therapeutic drugs for heart failure (HF).Methods: Differentially expressed genes (DEGs) between HF and non-failing donors were screened from the GSE57345, GSE5406, and GSE3586 datasets. Database for Annotation Visualization and Integrated Discovery and Metascape were used for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses respectively. The GSE57345 dataset was used for weighted gene co-expression network analysis (WGCNA). The intersecting hub genes from the DEGs and WGCNA were identified and verified with the GSE5406 and GSE3586 datasets. The diagnostic value of the hub genes was calculated through receiver operating characteristic analysis and net reclassification index (NRI). Gene set enrichment analysis (GSEA) was used to filter out the signaling pathways associated with the hub genes. SYBYL 2.1 was used for molecular docking of hub targets and potential HF drugs obtained from the connection map.Results: Functional annotation of the DEGs showed enrichment of negative regulation of angiogenesis, endoplasmic reticulum stress response, and heart development. PTN, LUM, ISLR, and ASPN were identified as the hub genes of HF. GSEA showed that the key genes were related to the transforming growth factor- (TGF-) and Wnt signaling pathways. Sirolimus, LY-294002, and wortmannin have been confirmed as potential drugs for HF.Conclusion: We identified new hub genes and candidate therapeutic drugs for HF, which are potential diagnostic, therapeutic and prognostic targets and warrant further investigation.

Keywords: differentially expressed genes, weighted gene co-expression network analysis, diagnostic biomarkers, therapeutic drugs, heart failure

HF is a clinical syndrome characterized by congestion of the lungs and vena cava, leading to abnormal heart structure or function, which is the final stage of the development of heart disease.1 Approximately 40 million people worldwide suffer from HF, and the incidence rates are steadily rising.2 Despite significant progress in the HF management in recent decades, the treatment options are mainly palliative rather than curative.3 Given the complex pathogenesis of HF, it is essential to elucidate the underlying molecular mechanisms in order to identify potential therapeutic targets and prognostic markers.

Bioinformatics is a high-throughput technique that can screen multiple databases to identify the potential pathological biomarkers of various diseases.4 Weighted gene co-expression network analysis (WGCNA) is a systems biology application that mines the genetic interaction networks to construct highly coordinated gene modules.4 WGCNA has been widely used for detecting disease biomarkers, and elucidating biological mechanisms and drug interactions.57 Although biomarkers of HF have been identified, but due to heterogeneity of HF and its complicated pathophysiological manifestations, a single gene cannot accurately predict the characteristics of HF.8,9

In this study, the differentially expressed genes (DEGs) between HF patients and non-failing donors (NFD) were screened from multiple GEO datasets and functionally annotated. The hub genes were then screened through the degree of connectivity in the PPI network, and used to build a co-expression network with WGCNA. The intersecting hub genes between DEGs and WGCNA were identified and validated in other human HF datasets. Gene set enrichment analysis (GSEA) was used to discover the signaling pathways associated with these hub genes. Finally, the potential HF drugs were predicted through the Connectivity Map (cMap) database, and molecular docking between the drug candidates and hub genes was simulated using SYBYL 2.1 software.

We conducted a bioinformatics analysis using DEGS and WGCNA to further investigate the occurrence and development of HF and identify potential therapeutic drugs for biomarkers of HF.

The study design is outlined in Figure 1. We searched the GEO database and included data on human heart tissue samples in this study. The mRNA expression profiles from HF and NFD samples were downloaded from the GSE57345, GSE5406 and GSE3586 datasets of the GEO database (http://www.ncbi.nlm.nih.gov/geo/). The subjects included in our study suffered from heart failure with reduced ejection fraction (HFrEF) and heart failure with preserved ejection fraction (HFpEF). Among them were 96 patients with ischemic heart disease, 84 patients with dilated cardiomyopathy (CMP), and 139 non-heart failure samples from GSE57345.10 The GSE5406 dataset included 16 samples without heart failure, 86 patients with dilated cardiomyopathy (CMP), and 108 patients with ischemic heart disease.11 The GSE3586 dataset included 13 patients with dilated cardiomyopathy and 15 patients without heart failure.12 The gene annotation files of GSE57345, GSE5406 and GSE3586 were GPL11532, GPL96, and GPL3050 respectively. GEO2R online tool was used for screening DEGs between HF and NFD with p < 0.05 (calculated by t-test) as the threshold.

The overlapping DEGs were uploaded to the Database for Annotation, Visualization, and Integrated Discovery (DAVID, https://david.ncifcrf.gov/) and Metascape (http://metascape.org/) databases for Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses respectively. P<0.05 was considered statistically significant.

The protein-protein interaction (PPI) network was constructed using the STRING database, and visualized with the Cytoscape software (3.7.2). DEGs with connectivity 5 in the PPI network were considered to be the hub genes.

The R package WGCNA was used to construct the weight co-expression network of the GSE57345 dataset. The GSE57345 dataset was used in the WGCNA analysis because it contained the largest sample size. The weighing coefficient was first calculated based on R2> 0.9 of the scale-free real biological network. After determining the adjacency function parameter , a hierarchical clustering tree of different gene modules was constructed. The Pearson correlation coefficient was then used to transform the correlation matrix into an adjacency matrix and subsequently to a topological overlap matrix (TOM).

The correlation among the genes in the aforementioned modules was analyzed and a heatmap was constructed. The module most closely related to the HF state was considered the key module of HF. To verify specific modulus-character associations, the correlation between GS and MM was investigated. Genes with |MM| > 0.8 were subsequently screened out as hub genes in the key module. The intersecting hub genes between the key module and the DEGs were finally defined as the hub genes of HF.

We used the GSE57345, GSE5406, and GSE3586 datasets to confirm the identity of the hub genes that may be associated with HF. Then, we used the t-test to determine the significance of the correlation between the expression of hub genes and HF. Receiver operating characteristic (ROC) curves were drawn for the core genes in the three datasets, and the area under the curve (AUC) was calculated. Specificity, sensitivity, and net reclassification index (NRI) were calculated to evaluate the value of the genetic diagnosis.

GSEA was performed to clarify the biological functions of the hub genes using the KEGG gene set (c2.cp. kegg. v7.2.) as the default and p < 0.05, as the threshold. The HF samples of the GSE57345 data set were divided into high and low expression groups of each hub gene. The enrichment graph was plotted using the clusterProfiler package of the R language and GSEA function.

The CMap database includes 1309 compounds and expression data of > 7000 human genes. The DEGs intersecting GSE57345, GSE5406, and GSE3585 datasets were uploaded to CMap. The negatively correlated small molecules were screened out using p < 0.0001 and mean < 0.4 as the criteria. ChemBioDrawUltta 17.0 software (http://www.chemdraw.com.cn) was used to draw the 3D structural formulas of potential therapeutic drugs and save them in mol2 format as small molecule compounds. The 3D crystal structures of the core targets were downloaded from the UniProt database (https://www.uniprot.org/). The Surflex-Dock module of SYBYL2.1 software was used for molecular docking, with total score >4 as the threshold for binding ability. The docking results were visualized using the Pymol software.

A total of 583 DEGs were identified between the HF and NFD samples across three GEO datasets (Figure 2AD). The most significantly enriched GO terms pertaining to biological process (BP) were negative regulation of angiogenesis (p = 1.55E-04), response to endoplasmic reticulum stress (p = 6.63E-04), heart development (p = 0.002463), regulation of ventricular cardiac muscle cell action potential (p = 0.004523), MAPK cascade (p = 0.005025), and blood vessel development (p =0.007107) (Figure 3A and Table S1). The cellular components (CC) terms including extracellular exosome (p =2.72E-08), cytoplasm (p =9.57E-06), actin cytoskeleton p (p =4.94E-05), mitochondrion (p =8.84E-05), nucleoplasm (p =1.03E-04), Golgi apparatus (p =1.21E-04) and lysosome (p =6.84E-04) were significantly enriched (Figure 3B and Table S1). The top enriched molecular function (MF) terms were activating transcription factor binding (p = 0.003852), actin filament binding (p =0.006599), cadherin binding involved in cell-cell adhesion (p = 0.00701), transcription coactivator activity (p =0.018332) and collagen binding (p =0.034108) (Figure 3C and Table S1). In addition, KEGG analysis revealed that the Ras signaling pathway (p =5.15548E-07), Focal adhesion (p =1.11814E-05), Lysosome (p = 2.43906E-05), MAPK signaling pathway (p = 4.23641E-05), PI3K-Akt signaling (p = 4.85396E-05), Protein processing in endoplasmic reticulum (p = 5.21303E-05) and Hippo signaling pathway (p =7.03525E-05) were significantly enriched among the DEGs (Figure 3D and Table S1).

Figure 2 The DEGs between HF and NFD. The volcano plots of DEGs in (A) GSE57345, (B) GSE5406 and (C) GSE3586. (D) Venn diagrams of DEGs in three data sets.

Figure 3 GO and KEGG pathway enrichment analysis. Significantly enriched GO terms for (A) Biological processes, (B) Cellular component, (C) Molecular function. (D) KEGG pathway Molecular function p < 0.05 is considered statistically significant.

A total of 1589 genes were screened from the GSE57345 dataset for WGCNA (p < 0.05, |Log2FoldChange| > 0.5). Sample clustering showed no significant differences in the WGCNA (Figure 4A). At = 4, the scale-free network fitting index R2 was 0.9, and the average connectivity approached 0, indicating that this value could obtain a scale-free network that met all requirements. Thus, = 4 was selected to construct a scale-free network (Figure 4BC). A dynamic shearing algorithm was used to cluster the genes and module divisions.

Figure 4 Determination of soft-threshold power . (A) Clustering dendrogram of 319 samples. (B) Scale-free topology fit index as a function of the soft-threshold power. The red line indicates that R2 is equal to 0.9. (C) Mean connectivity as a function of the soft-threshold power.

Five gene co-expression modules were finally obtained by calculating the module feature vector of each and merging similar modules (Figure 5A). The genes were clustered in the black, blue, yellow and green-yellow modules, and those that could not be clustered into any module were specified to the gray module. The yellow module with 73 genes showed the strongest correlation with HF (r = 0.77, p = 1e-61) (Figure 5B), as well as with the clinical phenotype as per GS and MM analyses (cor = 0.96, p = 5.5e-41; Figure 5CG). The genes distributed in the upper right corner were closely related to HF pathogenesis, and are likely the key disease genes. Twenty-one genes in the yellow module were confirmed as hub genes.

Figure 5 Identification of key HF gene modules. (A) Clustering dendrograms of genes and module detecting. (B) Heat map of the correlation between HF modules. (CG) Correlation of GS and MM in the HF-related module. p < 0.05 is considered statistically significant.

From the 583 overlapping DEGs, 322 hub genes were selected for subsequent analysis (Figure 6A). The key intersecting genes between the 322 hub DEGs and 21 hub genes of the yellow module included PTN, LUM, ISLR and ASPN. The expression levels of these potentially key genes were analyzed in the HF and NFD samples of the GSE57345, GSE5406 and GSE3586 datasets. We further visualized their expression levels in the GSE57345 data set, and found that all four genes were overexpressed in HF samples compared to the NFD group (p < 0.05, Figure 6B). Afterwards, the genes were verified in GSE5406 and GSE3586, which verified higher expression levels in the HF group (p < 0.05, Figure 6C and D).

Figure 6 Analysis of key genes. (A) The Venn diagram of hub genes in the yellow module and hub genes in DEGs. Expression of PTN, LUM, ISLR and ASPN in the (B) GSE57345, (C) GSE5406 and (D) GSE3586 datasets. **p < 0.01 and ***p < 0.001 are considered statistically significant.

The potential diagnostic value of PTN, LUM, ISLR and ASPN was ascertained by plotting the ROC curve based on the expression data in GSE57345, GSE5406 and GSE3586. As shown in Figure 7AC, the AUC of all genes in all datasets exceeded 0.9, except for 0.785 calculated for ISLR in GSE3586. We uploaded AUC and Standard Error data into MedCalc software, and applied the Z test to compare the expression of PTN, LUM, ISLR and APSN between the datasets. The results showed that there was no statistical difference (p >0.05). We used the NRI to analyze differences in the expression levels of the four predicted HF hub genes in the GSE57345, GSE5406 and GSE3586 datasets. The PTN and ISLR of the GSE5406 dataset showed significant differences in predicting HF (NRI [95% CI]: 0.3228 [0.03610.6095], p: 0.027); the prediction effects of the PTN and ISLR genes were significantly different, NRI [95% CI]: 0.3905 [0.00730.7736], p: 0.045). The prediction effects of ISLR and LUM gene in the GSE5406 dataset were significantly different (NRI [95% CI]: 0.5077 [0.93610.0793], p: 0.020). Subsequently, ROC analysis was performed to determine the diagnostic value of the four key genes, and the results suggested that these four hub genes can diagnose HF with high sensitivity and specificity (Table 1).

Table 1 ROC Curve Analysis of Hub Genes

Figure 7 The ROC curve of hub genes (A) GSE57345. (B) GSE5406. (C) GSE3586. The x-axis shows specificity, and the y-axis shows sensitivity.

Abbreviations: ROC, receiver operating characteristic; AUC: area under the ROC curve.

GSEA of PTN, LUM, ISLR and ASPN revealed direct involvement in the pathogenesis of HF. As shown in Figure 8AD, all genes were enriched in arrhythmogenic right ventricular cardiomyopathy (ARVC), dilated cardiomyopathy, ecm receptor interaction, focal adhesion, gap junction, hypertrophic cardiomyopathy (HCM), regulation of actin cytoskeleton and TGF- signaling pathway. In addition, PTN, LUM and ASPN were enriched in the WNT signal pathway, LUM in the calcium signaling pathway, and ASPN is likely involved in seleno-amino acid metabolism.

Figure 8 Results of GSEA. (A) PTN. (B) LUM. (C) ISLR. (D) ASPN.

There were 264 upregulated and 499 down-regulated genes intersecting across the three datasets. The genes were uploaded to the cMap database to filter out negatively related small molecule compounds (p < 0.0001 and mean < 0.4). Sirolimus, LY-294002, and wortmannin were identified as potential drugs of HF (Figure 9AC). Molecular docking showed that sirolimus had good affinity for PTN, ISLR, LUM, and ASPN, and wortmannin for PTN and LUM (Figure 9D). The molecular docking diagrams of potential compounds and hub targets are shown in Figure 9EJ.

Figure 9 The potential therapeutic drugs of HF. (AC) The 2D structure of Sirolimus, LY-294002 and Wortmannin. (D) Heat map of the docking score between potential drugs and hub targets. The intensity of red color indicates binding ability. (EJ) Molecular docking diagram of certain core compounds and hub targets.

In this study, we combined WGCNA and DEGs to screen for genes associated with HF and found that the expression levels of the PTN, ISLR, LUM, and ASPN genes were all upregulated in HF. Further analysis using the ROC curve showed that these four genes may be potential biomarkers of HF. At present, PTN and ISLR have not been reported to be associated with HF, but there is evidence that they may be potentially associated with HF. Single-gene GSEA showed that the hub genes of HF are related to arrhythmic right ventricular cardiomyopathy, dilated cardiomyopathy and hypertrophic cardiomyopathy, which is consistent with reports demonstrating that these cardiovascular disorders precede the final HF stage.1315 Single-gene GSEA showed that the hub genes of HF are related to arrhythmic right.

GO analysis of the DEGs showed that endoplasmic reticulum stress (ERS) is closely related to HF pathogenesis, which is consistent with previous reports.1618 The risk factors of HF can induce ERS in myocardial cells, which culminates in apoptosis and cardiovascular dysfunction. In addition, the DEGs were enriched in ferroptosis, MAPK signaling pathway, PI3K-Akt signaling pathway, and the Hippo signaling pathway, all of which are involved in HF. Liu et al19 detected a high level of ferroptosis in the cardiomyocytes of a rat model of pressure overload-induced HF. Exogenous expression of ferritin FTH1 and GPX4 and reduction in ROS levels through NOX4 knockdown inhibited ferroptosis in cardiomyocytes and improved cardiac function. Fang and Koleini et al20,21 found that doxorubicin induced the accumulation of oxidized phospholipids in undifferentiated cardiomyocytes and up-regulated heme oxygenase1 (HMOX1), resulting in heme degradation, free iron overload, and ferroptosis, which eventually leads to HF. The loss of myeloid differentiation protein 1 (MD1) activates ROS and exacerbates autophagy induced by the MAPK signaling pathway. Therefore, the MD1-ROS-MAPK axis is a novel therapeutic target for HF that can preserve the ejection fraction.22 Mao Liu et al23 showed that paeoniflorin reduced myocardial fibrosis and improved cardiac function in rats with chronic HF by regulating the p38/MAPK signaling pathway. Apelin-13 can slow down oxidative stress by inhibiting the PI3K/Akt signaling pathway in the rat HF model and ameliorate angiotensin IIinduced cardiac insufficiency, impaired cardiac hemodynamics, and fibroblast fibrosis.24 Hou and Li et al25,26 showed that YAP/TAZ can initiate the transcription of connective tissue growth factor by interacting with the TEAD domain family, increase the expression of extracellular matrix genes, promote cardiac remodeling and fibrosis, and thus delay the progression of HF. Leach et al27 found that knocking out the SALV gene increased the number of left ventricular myocardial cells in mice with myocardial infarction, which reduced ventricular fibrosis and increased the number of new capillaries around the injured myocardium, indicating that the Hippo signaling pathway can enhance heart function.

PTN is a highly conserved proto-oncogene closely related to tumor angiogenesis and metastasis.28 It is highly expressed in various malignant tumors, such as breast cancer, prostate cancer and rectal cancer,29,30 and promotes the proliferation, mitosis, differentiation, and migration of vascular endothelial cells.31 Overexpression of PTN gene can promote bone formation, whereas PTN gene knockout mice have dysfunctional bone growth and remodeling.32 LUM is a member of the SLRP family of leucine-rich proteins that are secreted by the extracellular matrix. It is widely distributed in various tissues, and shows aberrant expression levels in pancreatic cancer, colorectal cancer, breast cancer and cervical cancer.33 LUM has both oncogenic and tumor-suppressive functions depending on the cancer type. For instance, LUM facilitated the metastasis of colon cancer cells by reconstructing the actin cytoskeleton, but inhibited the adhesion of osteosarcoma cells via the TGF-2 signaling pathway.34,35 ISLR is a conserved immune-related protein that is mainly expressed in stromal cells.36 Xu et al37 showed that ISLR can inhibit Hippo signal transduction during intestinal regeneration and tumorigenesis and activate YAP factor in epithelial cells. Knocking out ISLR in mouse stromal cells significantly affected intestinal regeneration and inhibited colorectal tumorigenesis. Zhang and Hara et al38,39 further showed that the ISLR can promote muscle regeneration and improve myocardial tissue repair. However, little is known regarding the correlation between ISLR and HF. ASPN is an extracellular matrix protein and a member of the leucine-rich small proteoglycan family.40 Sasaki et al41 showed that ASPN protected gastric tumor cells against oxidative stress by up-regulating HIF1 and reducing the levels of mitochondrial ROS. It also increased the expression of CD44 to accelerate the migration and infiltration of gastric cancer cells. However, other reports indicate an anti-tumorigenic role of ASPN in breast cancer.42,43 Studies also show that ASPN is up-regulated during aortic stenosis or coronary artery ligation in ischemic cardiomyopathy patients and animal models.44 ASPN may also increase the apoptosis and fibrosis of H9C2 cardiomyocytes.45 However, the exact role of ASPN in HF pathogenesis needs further investigation.

Lu A et al46 found that Wnt3a binds to FZD and LRP5/6 receptors, thereby activating the classic Wnt-Dvl--catenin signaling pathway and promoting myocardial hypertrophy. Wnt signaling can inhibit Na+ channels by directly or indirectly inhibiting the expression of Scn5a. Thus, blocking these intracellular cascades is a rational therapeutic strategy against HF.46 He et al47 found that Wnt3a and Wnt5a ligand were up-regulated in a mouse model of cardiac hypertrophy, underscoring the role of the Wnt signaling pathway in HF pathogenesis. TGF- is an important factor regulating myocardial fibrosis, which gradually worsens during HF and alters cardiac function from the compensatory phase to the decompensated phase.48 Kakhi et al49 found that sirolimus, an mTOR inhibitor, reversed new HF after kidney transplantation in mammals. Gao et al50 also showed that rapamycin (sirolimus) can reduce cardiomyocyte apoptosis and promote autophagy by regulating mTOR and ERS, thus preventing myocardial damage caused by chronic HF. LY294002 and wortmannin are protein kinase inhibitors that block the PI3K signaling pathway. Melatonin alleviates cardiac hypertrophy by inhibiting the Akt/mTOR pathway and reducing Atg5-dependent autophagy, which can be reversed by LY294002.51 Studies show that apelin may reduce the myocardial damage caused by acute HF by regulating the APJ/Akt/ERS signaling pathway. However, wortmannin and LY294002 can reverse the cardioprotective effects of apelin.52 The PI3K-Akt signaling pathway was also enriched among the HF-related DEGs, indicating a vital mechanistic role in its pathological progression. Molecular docking showed that sirolimus and wortmannin had a high affinity to the hub targets, LY-294002 bound weakly and may therefore have other targets. Nevertheless, all three drugs could be potentially effective for treating HF.

There are several limitations in this study. First, the data used in this study was obtained from the GEO database, which lacks clinical, in vivo, and in vitro experimental research certifications for pivotal genes and HF-associated genes. Second, the datasets used in this study were relatively small, and a larger sample size is needed to verify our results. However, our findings provide new insights into the underlying molecular mechanisms of HF, along with potential diagnostic biomarkers and candidate therapeutic drugs, which will help provide new clues for HF research, diagnosis and treatment, and target selection.

PTN, LUM, ISLR, and ASPN are overexpressed in HF patients compared to NFD, and are mainly related to the TGF- and Wnt signaling pathways. Sirolimus, LY-294002, and wortmannin are potential drug candidates for HF treatment. The in silico data will need to be verified by functional and clinical studies.

All datasets generated and analyzed during the current study were uploaded with the manuscript as additional files.

The ethics committee of the Qinghai University has waived the need for ethical approval for the reasons that the present study used public database, so it did not involve ethics.

This research was supported by the Technology Department project of Qinghai Science (No. 2020-ZJ-922).

The authors report no conflicts of interest in this work.

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35. Nikitovic D, Chalkiadaki G, Berdiaki A, et al. Lumican regulates osteosarcoma cell adhesion by modulating TGF2 activity. Int J Biochem Cell Biol. 2011;43(6):928935. doi:10.1016/j.biocel.2011.03.008

36. Nagasawa A, Kubota R, Imamura Y, et al. Cloning of the cDNA for a new member of the immunoglobulin superfamily (ISLR) containing leucine-rich repeat (LRR). Genomics. 1997;44(3):273279. doi:10.1006/geno.1997.4889

37. Xu J, Tang Y, Sheng X, et al. Secreted stromal protein ISLR promotes intestinal regeneration by suppressing epithelial Hippo signaling. EMBO J. 2020;39(7):e103255. doi:10.15252/embj.2019103255

38. Zhang K, Zhang Y, Gu L, et al. Islr regulates canonical Wnt signaling-mediated skeletal muscle regeneration by stabilizing Dishevelled-2 and preventing autophagy. Nat Commun. 2018;9(1):5129. doi:10.1038/s41467-018-07638-4

39. Hara A, Kobayashi H, Asai N, et al. Roles of the mesenchymal stromal/stem cell marker meflin in cardiac tissue repair and the development of diastolic dysfunction. Circ Res. 2019;125(4):414430. doi:10.1161/CIRCRESAHA.119.314806

40. Polley A, Khanam R, Sengupta A, et al. Asporin reduces adult aortic valve interstitial cell mineralization induced by osteogenic media and wnt signaling manipulation in vitro. Int J Cell Biol. 2020;2020:2045969. doi:10.1155/2020/2045969

41. Sasaki Y, Takagane K, Konno T, et al. Expression of asporin reprograms cancer cells to acquire resistance to oxidative stress. Cancer Sci. 2021;112(3):12511261. doi:10.1111/cas.14794

42. Castellana B, Escuin D, Peir G, et al. ASPN and GJB2 are implicated in the mechanisms of invasion of ductal breast carcinomas. J Cancer. 2012;3:175183. doi:10.7150/jca.4120

43. Simkova D, Kharaishvili G, Korinkova G, et al. The dual role of asporin in breast cancer progression. Oncotarget. 2016;7(32):5204552060. doi:10.18632/oncotarget.10471

44. Wang HB, Huang R, Yang K, et al. Identification of differentially expressed genes and preliminary validations in cardiac pathological remodeling induced by transverse aortic constriction. Int J Mol Med. 2019;44(4):14471461. doi:10.3892/ijmm.2019.4291

45. Li XL, Yu F, Li BY, et al. The protective effects of grape seed procyanidin B2 against asporin mediates glycated low-density lipoprotein induced-cardiomyocyte apoptosis and fibrosis. Cell Biol Int. 2019. doi:10.1002/cbin.11229

46. Lu A, Kamkar M, Chu C, et al. Direct and indirect suppression of Scn5a gene expression mediates cardiac Na+ channel inhibition by Wnt signalling. Can J Cardiol. 2020;36(4):564576. doi:10.1016/j.cjca.2019.09.019

47. He J, Cai Y, Luo LM, et al. Expression of Wnt and NCX1 and its correlation with cardiomyocyte apoptosis in mouse with myocardial hypertrophy. Asian Pac J Trop Med. 2015;8(11):930936. doi:10.1016/j.apjtm.2015.10.002

48. Yatabe J, Sanada H, Yatabe MS, et al. Angiotensin II type 1 receptor blocker attenuates the activation of ERK and NADPH oxidase by mechanical strain in mesangial cells in the absence of angiotensin II. Am J Physiol Renal Physiol. 2009;296(5):F10521060. doi:10.1152/ajprenal.00580.2007

49. Kakhi S, Phanish MK, Anderson L. Dilated cardiomyopathy in an adult renal transplant recipient: recovery upon tacrolimus to sirolimus switch: a case report. Transplant Proc. 2020;52(9):27582761. doi:10.1016/j.transproceed.2020.06.011

50. Gao G, Chen W, Yan M, et al. Rapamycin regulates the balance between cardiomyocyte apoptosis and autophagy in chronic heart failure by inhibiting mTOR signaling. Int J Mol Med. 2020;45(1):195209. doi:10.3892/ijmm.2019.4407

51. Xu CN, Kong LH, Ding P, et al. Melatonin ameliorates pressure overload-induced cardiac hypertrophy by attenuating Atg5-dependent autophagy and activating the Akt/mTOR pathway. Biochim Biophys Acta Mol Basis Dis. 2020;1866(10):165848. doi:10.1016/j.bbadis.2020.165848

52. Li Y, Lu H, Xu W, et al. Apelin ameliorated acute heart failure via inhibiting endoplasmic reticulum stress in rabbits. Amino Acids. 2021;53(3):417427. doi:10.1007/s00726-021-02955-3

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A Novel Mutation in the TRPM4 Gene | RRCC – Dove Medical Press

Introduction

Long QT syndrome (LQTS) is defined by a prolonged QT interval accompanied by morphological abnormalities in the T and/or U waves on the electrocardiograph (ECG).1 The primary clinical symptom of LQTS is syncope produced by ventricular arrhythmias.24 The clinical diagnosis of LQTS is based on a combination of the patients medical and family history, as well as the 12-lead ECG.5 According to the guidelines, LQTS diagnosis can be made in case the QTc is more than 460ms, and the patient presents some antecedents, most notably a family history of SCD and unexplained syncope.6

LQTS can be classified into two types based on its etiology: congenital LQTS (cLQTS) and acquired LQTS (aLQTS). While the former is a relatively rare genetic cardiovascular disease with a low incidence rate (1/2000-1/3000),7 the latter is frequently subsequent to electrolyte disorders, cardiomyopathy, cerebrovascular accidents, and autonomic dysfunction.

The pathogenesis of cLQTS is related to the mutation of genes encoding for ion channels, such as KCNH2,3,8 KCNQ1,2,9 TRPM4,1012 and so on, and causing ion channel dysfunction with reduced repolarization ion flow and/or increased delocalization ion flow, which in turn leads to prolonged repolarization. Among ion channel genes, mutations in KCNQ1 and KCNH2, which encode voltage-gated K+ channels involved in cardiac action potential (AP) repolarization are most common,10 followed by mutations in SCN5A which encode voltage-gated Na (1.78%), while mutations in other genes including TRPM4 are rare (below 1% of LQTS).11 Dr. Hof and colleagues were the first to hypothesize that TRPM4 mutations cause long QT syndrome, and they detected four TRPM4 variants, including c.1321 G >A, c.1495 C >T, c.1496 G >C, and c.2531 G >A, with no changes in the key LQTS genes.11

Herein, we reported a Chinese proband with cLQTS with a new mutation (NM_017636: exon4: c.434delC, p. Ala145ValfsTer133) in the TRPM4 with the hope that this report may be helpful in future genetic studies and prospective, genetically informed research.

A 75-year-old male was implanted with a permanent pacemaker 18 years ago due to a local diagnosis of bradycardia characterized by recurrent syncope since the age of 20, yet postoperative syncope continued to occur. Syncope occurred again a day before admission, and then he was taken to our hospital. Electrocardiography (ECG) at disease onset indicated sinus bradycardia, anterior wall T wave changes with visible u waves (Figure 1).

Figure 1 The admission ECG showed sinus bradycardia with QTc interval 432ms and U wave.

On admission, the following vital signs were recorded: blood pressure of 135/88mmHg, pulse rate of 59 beats per minute, the body temperature of 36.4C, and breathing rate of 18 beats per minute. Physical examination revealed no evidence of heart failure or pathological nervous system features.

After admission, repeated electrocardiograms suggested prolonged QT intervals, sinus bradycardia, and T wave changes (Figure 2). Ambulatory ECG also showed sinus bradycardia (mean heart rate 59 beats), prolonged QT interval (540ms), and torsade de pointes (Figure 3). Whats more, the electrodes on the patients pacemaker were discovered to be depleted for nearly five years. Laboratory data showed a slightly elevated level of troponin, as well as N-terminal-pro-brain natriuretic peptide, while other laboratory indexes including hepatic and renal function, electrolytes, coagulation, and inflammatory indexes were normal. We also performed a cranial MRI on this patient, and no neurological lesion was found that could cause syncope. Echocardiography indicated no abnormalities in cardiac structure, and the left ventricular ejection fraction was 61%. Moreover, selective coronary angiography was performed, indicating that the patient has no apparent pathological stenosis in the coronary arteries.

Figure 2 (AC) During the hospitalization, the ECG showed the dynamic changes of T wave; the longest QTc interval was 540ms.

Figure 3 Electrocardiogram monitoring shows torsion de pointes tachycardia.

According to the above results and the diagnostic criteria of LQTS, a highly suspected diagnosis of LQTS was finally made (Rating 6.5 out of 5).1,13,14

Then the etiology of LQTS was further explored. For no acquired LQTS associated risk factors such as electrolyte disorders, cardiomyopathy, cerebrovascular accidents, and autonomic dysfunction were found in the patients previous medical history and related examinations after admission, we are suspicious of the patients Genetics of LQTS.

After obtaining the informed consent, we conducted whole-exome sequencing (WES) on the patient and his family to confirm our diagnosis. Gene testing revealed that the patient carried a TRPM4 heterozygous shift mutation (NM_017636: exon4: c.434delC, p. Ala145ValfsTer133). Moreover, WES analysis of his family members revealed that his sister carried the same TRPM4 mutation as the patient (Figure 4), but his two brothers and son did not. Regrettably, the probands parents have all died, and hence their genes have not been obtained.

Figure 4 The results of genetic testing showed the proband and his sister carried a TRPM4 heterozygous shift mutation (NM_017636: exon4: c.434delC, p. Ala145ValfsTer133) (A), while his two brothers and son did not (B).

Because of the high risk of sudden cardiac death, we recommend implanting a cardioverter defibrillator (ICD) for the patient. Due to economic reasons, the patient refused. Due to the patients strong preference for cautious treatment, we administered Shengsong Yangxin Capsule as a placebo.

cLQTS is a rare cardiac disorder inherited in an autosomal trait, with an estimated incidence of 1:20001:3000.7 It is accepted that cLQTS is a rare ion channelopathy, and a host of genes were described to be responsible for LQTS. So far, 15 genes with more than 7000 mutations have been considered to be associated with cLQTS.15 Among the six genes encode for a pore-forming ion channel, while others encode for regulatory subunits or proteins. Mutations in KCNQ1 (3035%) and KCNH2 (2530%) encoding voltage-gated K+ channels involved in cardiac action potential (AP) repolarization are the most common among ion channel genes,10,14 followed by mutations in SCN5A, which encode voltage-gated Na+ (1.78%).11,14 In comparison, mutations in other genes, including TRPM4 are rare (below 1% of LQTS).11,12,14

As far as the pathology of LQTS, it is generally known that QT duration depends on both ventricular AP duration and AP propagation within the ventricle and ion channel dysfunction with reduced repolarization ion flow and/or increased delocalization ion flow leads to prolonged repolarization. According to a sack of animal experiments on TRPM4, TRPM4 affects cardiac electrophysiological activity through nonselective cation permeability, which leads to cLQTS.11 Unfortunately, additional research is required to decipher the biological mechanism underlying TRPM4-induced loss of function of nonselective cation permeability.

Above all, gene test counts for cLQTS. The importance of gene detection for cLQTS lies in exploring its pathogenic mechanism and its treatment, for the drugs targeted specific ion channels can be used with exerting maximal effects.

In our case, a new mutation site on TRPM4 (NM_017636: exon4: c.434delC, p. Ala145ValfsTer133) was discovered through whole-exon detection, which can provide a brand-new direction for gene screening of patients with cLQTS and further complements its diagnostic criteria. As for the detail of gene tests, we used PolyPhen2 to predict whether a new mutation is damaging to the resultant protein function. And then, according to the relevant literature, we did consider that TRPM4 is as same amino acid change as a previously established pathogenic variant regardless of nucleotide change after searching the OMIM database. But the absence of the literature for molecular pathology makes us failure to achieve the information of damaged protein. At last, combined clinical history, ECG, and the results of gene test, it was suspected that TRPM4 mutation (NM_017636: exon4: c.434delC, p. Ala145ValfsTer133) was the pathogenic variant.

In the treatment of cLQTS, beta-blockers effectively prevent cardiovascular disease and ventricular arrhythmia, and ICD implantation is regarded as the ultimate therapy.16 Because of the high risk of sudden cardiac death, we recommend implanting a cardioverter defibrillator (ICD) for the patient. Due to economic reasons, the patient refused, and we administered a placebo.

The incidence of cLQTS is very low, with the incidence of LQTS caused by TRPM4 being even lower, leading to less research on the gene TRPM4. Therefore, we reported a new mutation in TRPM4 (NM_017636: exon4: c.434delC, p. Ala145ValfsTer133) to provide more evidence for gene screening, to improve the detection rate of healthy gene carriers or patients in the early incubation stage, thereby providing further complements to the clinical data of the study about TRPM4. Notwithstanding its limitation such as lack of this patients past clinical data that can help to compare the symptom before and after the permanent pacemaker implantation, detailed information of the pedigree of this patients parents and so on, this report does hopefully serve as useful feedback information for genetic pathogenesis of cLQTS caused by TRPM4 variants.

cLQTS, congenital long QT syndrome; LQTS, long QT syndrome; ECG, electrocardiograph; AP, action potential; ICD, implanting cardioverter defibrillator; WES, whole-exome sequencing; TRPM4, transient receptor potential melastatin 4; aLQTS, acquired LQTS.

All relevant data supporting the conclusions of this article are included within the article.

The need for institutional ethics approval for this case report was waived. Written informed consent was obtained from the patient for publication of this case report and accompanying images.

The patient has provided informed consent for the publication of the case. A copy of the written consent is available for review by the Editor-in-Chief of this journal.

Dr. Rui Huang and Dr. Yinhua Luo are co-first authors for this study.

All authors made substantial contributions to conception and design, acquisition of data, or analysis and interpretation of data; took part in drafting the article or revising it critically for important intellectual content; agreed to submit to the current journal; gave final approval of the version to be published; and agree to be accountable for all aspects of the work.

This work was supported in part by the National Natural Science Foundation of China (82160072) and the Science and Technology Support Project of Enshi Science and Technology Bureau (D20210024).

The authors declare that they have no conflicts of interest.

1. Vohra J. The long QT syndrome. Heart Lung Circ. 2007;16(Suppl 3):S5S12. doi:10.1016/j.hlc.2007.05.008

2. Beiyin G, Tingliang L, Lei Y, et al. Head-up tilt test induces T-wave alternans in long QT syndrome with KCNQ1 gene mutation: case report CARE-compliant article. Medicine. 2020;99(20):e19818.

3. Henk-Jan B, Lucia B. Orgasm induced torsades de pointes in a patient with a novel mutation with long-QT syndrome type 2: a case report. Eur Heart J Case Rep. 2018;2(2):yty062.

4. Joel G, Kinsley H, Amanda W, et al. Recurrent torsades with refractory QT prolongation in a 54-year-old man. Am J Case Rep. 2018;19:1515.

5. Priori SG, Wilde AA, Horie M, et al. HRS/EHRA/APHRS expert consensus statement on the diagnosis and management of patients with inherited primary arrhythmia syndromes: document endorsed by HRS, EHRA, and APHRS in May 2013 and by ACCF, AHA, PACES, and AEPC in June 2013. Heart Rhythm. 2013;10(12):19321963. doi:10.1016/j.hrthm.2013.05.014

6. Priori SG, Blomstrm-Lundqvist C, Mazzanti A, et al. [2015 ESC guidelines for the management of patients with ventricular arrhythmias and the prevention of sudden cardiac death]. Kardiol Pol. 2015;73(10):795900. Croatian. doi:10.5603/KP.2015.0190

7. Zumhagen S, Stallmeyer B, Friedrich C, et al. Inherited long QT syndrome: clinical manifestation, genetic diagnostics, and therapy. Herzschrittmacherther Elektrophysiol. 2012;23(3):211219. doi:10.1007/s00399-012-0232-8

8. Du F, Wang G, Wang D, et al. Targeted next generation sequencing revealed a novel deletion-frameshift mutation of KCNH2 gene in a Chinese Han family with long QT syndrome: a case report and review of Chinese cases. Medicine. 2020;99(16):e19749. doi:10.1097/MD.0000000000019749

9. Motoi N, Marehiko U, Ryota E, et al. A novel KCNQ1 nonsense variant in the isoform-specific first exon causes both jervell and Lange-Nielsen syndrome 1 and long QT syndrome 1: a case report. BMC Med Genet. 2017;18(1):16.

10. Amin AS, Pinto YM, Wilde AA. Long QT syndrome: beyond the causal mutation. J Physiol. 2013;591(17):41254139. doi:10.1113/jphysiol.2013.254920

11. Hof T, Liu H, Sall L, et al. TRPM4 non-selective cation channel variants in long QT syndrome. BMC Med Genet. 2017;18(1):31. doi:10.1186/s12881-017-0397-4

12. Guinamard R, Bouvagnet P, Hof T, et al. TRPM4 in cardiac electrical activity. Cardiovasc Res. 2015;108(1):2130. doi:10.1093/cvr/cvv213

13. Hayashi K, Konno T, Fujino N, et al. Impact of updated diagnostic criteria for long QT syndrome on clinical detection of diseased patients: results from a study of patients carrying gene mutations. JACC Clin Electrophysiol. 2016;2(3):279287. doi:10.1016/j.jacep.2016.01.003

14. Neira V, Enriquez A, Simpson C, et al. Update on long QT syndrome. J Cardiovasc Electrophysiol. 2019;30(12):30683078. doi:10.1111/jce.14227

15. Tester DJ, Ackerman MJ. Genetics of long QT syndrome. Methodist Debakey Cardiovasc J. 2014;10(1):2933. doi:10.14797/mdcj-10-1-29

16. Betge S, Schulze-Bahr E, Fitzek C, et al. [Long QT syndrome causing grand mal epilepsy: case report, pedigree, therapeutic options, and review of the literature]. Nervenarzt. 2006;77(10):12101217. German. doi:10.1007/s00115-006-2118-7

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Origin of Rare Disease FOP Rooted in Muscle Regeneration Dysfunction – Newswise

Newswise PHILADELPHIA Fibrodysplasia ossificans progressiva (FOP) is a rare disease characterized by extensive bone growth outside of the normal skeleton that pre-empts the bodys normal responses to even minor injuries. It results in what some term a second skeleton, which locks up joint movement and could make it hard to breathe. However, new research in mice by a team at the Perelman School of Medicine at the University of Pennsylvania shows that forming extra-skeletal bone might not be the only driver of the disease. Impaired and inefficient muscle tissue regeneration appears to open the door for unwanted bone to form in areas where new muscle should occur after injuries. This discovery opens up the possibility of pursuing new therapies for FOP and was published today in NPJ Regenerative Medicine.

While we have made great strides toward better understanding this disease, this work shows how basic biology can provide great insights into appropriate regenerative medicine therapies, said the studys lead author, Foteini Mourkioti, PhD, an assistant professor of Orthopaedic Surgery and Cell and Developmental Biology, as well as the co-director of the Penn Institute for Regenerative Medicine, Musculoskeletal Program. From the lab, were now able to show that there is potential for a whole new realm of therapies for patients with this devastating condition.

About 15 years ago, researchers at Penn including this studys co-author, Eileen Shore, PhD, a professor in Orthopaedic Surgery and Genetics and the co-director of the Center for Research in FOP and Related Disorders discovered that a mutation in the ACVR1 gene was responsible for FOP. In that study, the team found that the mutation changed cells within muscles and connective tissues, misdirecting cells within the tissue to behave like bone cells, resulting in new and unnecessary extra-skeletal bone within the body.

However, while investigations of how the FOP mutation alters the regulation of cell fate decisions have been extensively pursued in recent years, little attention has been paid to the effects of the genetic mutation on muscle and its impact on the cells that repair muscle injuries, Shore said. We were convinced that pursuing research in this area could provide clues not only for preventing extra bone formation but also for improving muscle function and regeneration, bringing new clarity to FOP as a whole.

The researchers studied muscle from mice with the same mutation in the ACVR1 gene that people with FOP have. They focused on two specific types of muscle tissue stem cells: fibro-adipogenetic progenitors (FAPs) and muscle stem cells (MuSCs). Typically, muscle injury repair requires a careful balance of these two cell types. Injured tissue responds by an expansion of FAP cells, which are assigned to recruit muscle stem cells that will regenerate the damaged muscle tissue. After about three days, FAPs die off, their job done. At the same time, MuSCs transition toward a more mature, differentiated state, called muscle fiber, essential to organized movement of our muscles.

In the mice with the ACVR1 mutation that Mourkioti, Shore, and their co-authors studied, apoptosis the process through which FAP cells die as a part of proper muscle regeneration had slowed significantly, leading to a high presence of FAPs past their usual lifespan. This altered their balance with the MuSCs. The injured tissue also showed a diminished capacity for muscle stem cell maturation and, as a result, muscle fibers were considerably smaller in mice carrying the ACVR1 mutation compared to muscle fibers in mice without the mutation.

The prolonged persistence of diseased FAPs within the regenerating muscle contributes to the altered muscle environment in FOP, which reduces muscle regeneration and allows the over-abundant FAPs to contribute to the formation of extra-skeletal bone, Mourkioti said. This provides a completely new perspective on how excess extra-skeletal bone is formed and how it could be prevented.

The current targets for treating FOP focus on slowing extra-skeletal bone growth. This research may provide a pivotal new direction. We propose that therapeutic interventions should consider promoting the regenerating potential of muscles together with the reduction of ectopic bone formation, Shore and Mourkioti wrote. By addressing both stem cell populations and their roles in the origin of FOP, there is the possibility of greatly enhanced therapies.

This study was supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases (R01AR04191615S1, F31-AR069982), the National Institutes of Health (R01AR071399, NIH P30-AR069619), and the International Fibrodysplasia Ossificans Progressiva Association (IFOPA).

Other authors in the study include Alexandra Stanley, Elisia Tichy, Jacob Kocan and Douglas Roberts.

###

Penn Medicine is one of the worlds leading academic medical centers, dedicated to the related missions of medical education, biomedical research, and excellence in patient care. Penn Medicine consists of the Raymond and Ruth Perelman School of Medicine at the University of Pennsylvania (founded in 1765 as the nations first medical school) and the University of Pennsylvania Health System, which together form a $8.9 billion enterprise.

The Perelman School of Medicine has been ranked among the top medical schools in the United States for more than 20 years, according to U.S. News & World Report's survey of research-oriented medical schools. The School is consistently among the nation's top recipients of funding from the National Institutes of Health, with $496 million awarded in the 2020 fiscal year.

The University of Pennsylvania Health Systems patient care facilities include: the Hospital of the University of Pennsylvania and Penn Presbyterian Medical Centerwhich are recognized as one of the nations top Honor Roll hospitals by U.S. News & World ReportChester County Hospital; Lancaster General Health; Penn Medicine Princeton Health; and Pennsylvania Hospital, the nations first hospital, founded in 1751. Additional facilities and enterprises include Good Shepherd Penn Partners, Penn Medicine at Home, Lancaster Behavioral Health Hospital, and Princeton House Behavioral Health, among others.

Penn Medicine is powered by a talented and dedicated workforce of more than 44,000 people. The organization also has alliances with top community health systems across both Southeastern Pennsylvania and Southern New Jersey, creating more options for patients no matter where they live.

Penn Medicine is committed to improving lives and health through a variety of community-based programs and activities. In fiscal year 2020, Penn Medicine provided more than $563 million to benefit our community.

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Oxytocin and autism: Study on a receptor variant provides new insights into the cellular origin of the disorder – EurekAlert

image:Dr. Mercedes Alfonso-Prieto from the Institute for Computational Biomedicine at Forschungszentrum Jlich view more

Credit: Forschungszentrum Jlich / Ralf-Uwe Limbach

Oxytocin is known as the "love hormone". It strengthens social bonding and promotes trust and empathy. These behavioral traits are caused by the binding of the hormone to the oxytocin receptor in the brain. Researchers at the University of Regensburg and Forschungszentrum Jlich have now demonstrated, with the help of cell culture experiments and computer simulations, how genetic variations of the receptor affect the hormone signaling inside brain cells. Their findings provide a better understanding why nasal sprays with oxytocin are not always helpful to treat autistic patients and they point to alternative strategies that could lead to new therapies in the long term.

Changes in the finely tuned mechanism of oxytocin interacting with its receptor might trigger psychosocial disorders. Researchers have been assuming this for a long time. In fact, genetic variants of the oxytocin receptor have already been associated with autism spectrum disorder. However, the causal mechanisms linking these mutations to the onset of autism are still unknown.

There are some indications that there is more than one single cause. As a consequence, the simple use of nasal sprays with oxytocin helps only in certain cases to improve social interaction in children and adolescents with autism spectrum disorder. Various studies on therapeutic effects, conducted for example by researchers at Forschungszentrum Jlich ( DOI: 10.1038/s41386-018-0258-7) or recently published by US researchers in the New England Journal of Medicine in October (DOI: 10.1056/NEJMoa2103583), obtained quite different results, with the treatment efficacy being highly variable among patients. But without the knowledge of the molecular mechanism that causes oxytocin nasal sprays to work only in some patients, an effective, standardized treatment is unattainable.

In a recent work, together with partners from the University of Regensburg, researchers at the Jlich Institute for Neuroscience and Medicine (INM-9/IAS-5) have now described in detail how a specific variant in the DNA sequence of the oxytocin receptor affect the hormone-triggered response of the neurons.

"The devil really is in the detail here," explains Dr. Mercedes Alfonso-Prieto. "The oxytocin receptor consists of 389 amino acids. The variant we studied deviates from the normal type in only one - a rather subtle change, but one that is amplified inside the cell as if by a kind of domino effect."

Together with colleagues at her institute, led by Prof. Paolo Carloni, she modelled and simulated the effect of the mutation. "We focused on understanding the mechanism of how the structure of the oxytocin receptor is changed and how this affects the subsequent cellular response," explains Mercedes Alfonso-Prieto.

The surprising result: the mutated variant is more active and stable than the normal receptor, contrary to what might intuitively be expected.

Despite its connection to autism, we were puzzled that the receptor variant had been previously classified as non-pathogenic, say Dr. Magdalena Meyer and Dr. Benjamin Jurek from the University of Regensburg, who studied the cell response to oxytocin experimentally in the laboratory. The Regensburg working group, led by Prof. Inga Neumann, has been researching the neurobiological effects of oxytocin for many years. The current work in the prestigious journal Molecular Psychiatry now provides new starting points for developing new therapies in the long term.

"Since the mutant variant reacts excessively to oxytocin, increasing the oxytocin level by means of a nasal spray is probably not the best therapeutic strategy for treating autistic patients with this mutation," explains Magdalena Meyer. More success is promised by drug development approaches that aim to find molecules that can restore the normal response levels of the receptor.

Molecular Psychiatry

Structure-function relationships of the disease-linked A218T oxytocin receptor variant

4-Jan-2022

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Researchers find new potential targets for skin-cancer treatment | Penn Today – Penn Today

Mutations of the gene MLL4 in epithelial skin cells can inhibit healthy cell turnover and may lead to keratinocyte cancers, which collectively outnumber all other human cancers. Targeting pathways altered by MLL4 mutations to induce proper cell turnover and death offers an approach to suppress tumor growth, according to a new study from the Perelman School of Medicine. The study is published in the journal Science Advances.

The gene MLL4 is one of the most commonly mutated genes across all of human cancers. Previous work has shown that MLL4 is linked to tumor suppression in a variety of cancers, but its role in skin cancer was unknown. In order to uncover how MLL4 influences cancer growth, researchers knocked out only this gene in the epithelial tissues in an animal model and found that skin cells proliferated, the skin thickened, and a form of normal skin cell turnover and death, called ferroptosis, was inhibited.

Your skin cells need to be able to undergo cell death, says Brian Capell, an assistant professor of dermatology and genetics and lead author of the study. If cells dont die and turn over, they stick around, continue to grow, and become cancerous. We saw that a loss of MLL4 impaired ferroptosis by activating genes that encourage tumor growth while simultaneously suppressing genes that prevent cancerous proliferation.

To combat cancer-cell development in individuals with MLL4 genetic mutations, the researchers believe that specific medications that can promote ferroptosis may be a future way to inhibit cancer growth and encourage healthy cell turnover.

Read more at Penn Medicine News.

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Researchers zero in on therapeutic target for aggressive uterine cancer – Michigan Medicine

A team of scientists led by the University of Michigan Health Rogel Cancer Center has found that a class of United States Food and Drug Administration-approved drugs can effectively stop a highly aggressive type of uterine cancer in its tracks, paving a quick path toward new treatment strategies for a deadly cancer with limited therapeutic options.

Collaborating with researchers from Case Western Reserve University and Memorial Sloan Kettering Cancer Center, the team showed that ribonucleotide reductase, or RNR, inhibitors target two mutations in the gene that encodes the tumor suppressor PP2A, present in up to 40% of uterine serous carcinomas.

The team reported its findings in Cancer Research.

Uterine cancer is the most common gynecologic cancer with more than 60,000 cases diagnosed each year in the U.S. The endometrioid subtype is most common and in general responds well to targeted immunotherapies. By contrast, the uterine serous subtype has few genetic mutations that would make it a candidate for targeted therapies, and patients face rapid disease progression and a dire prognosis.

While uterine serous carcinoma represents only 10% of uterine cancers, it accounts for the majority of deaths. We showed that the PP2A mutation is common in uterine serous carcinoma, and we found a potential new treatment option for these patients, said first author Caitlin OConnor, Ph.D., a research fellow in the Division of Genetic Medicine at Michigan Medicine. We can rapidly translate this bench work to patients.

PP2A is a tumor suppressor, stopping cancer growth much like the brakes on a car. In previous studies, the team showed that about a third of uterine serous carcinomas harbor two mutations that disable the brake, said senior author Goutham Narla, M.D., Ph.D., chief of genetic medicine at Michigan Medicine. We asked ourselves, how can we take advantage of these mutations?

To start, the researchers ran a high-throughput screen of 3,200 drug compounds against uterine serous cell samples from patients with recurrent cancer. The results showed that a family of anti-cancer drugs called ribonucleic reductase inhibitors killed cancer cells that harbored the mutations.

Researchers then narrowed their focus to one of the drug screen hits, the RNR inhibitor clofarabine, and tested it in a mouse model of uterine serous carcinoma. RNR inhibitors interfere with the growth of tumor cells by blocking the formation of DNA. Consistent with the cell-based data from the drug screen, clofarabine shrank the tumors in mice.

To further explore RNR inhibition as a potential therapeutic strategy for uterine serous carcinoma, the team did a retrospective analysis of patients treated with the RNR inhibitor gemcitabine as a later-line therapy for this subtype, compared to patients with the endometrioid subtype. We found that the uterine serous carcinoma-type patients actually did better than the endometrioid patients, OConnor said.

There is currently only one first-line chemotherapy for uterine serous carcinoma: carboplatin, Narla noted. This type of uterine cancer has a short progression, and its a particularly lethal form, so we want to find a drug that will work earlier on in disease progression and find a molecular way to target the cancer. We believe we may have that here, he said.

The research team is now planning to begin a clinical trial of gemcitabine in patients with uterine serous carcinoma. They also plan to extend this work to other cancers that harbor the PP2A mutations, including lung, colon and ovarian cancer.

Additional authors include Sarah E. Taylor of Case Western Reserve University; Kathryn M. Miller and Dmitriy Zamarin of Memorial Sloan Kettering Cancer Center; Fallon K. Noto of Hera BioLabs, Inc.; Lauren Hurst, Terrance J. Haanen, Tahra K. Suhan, Kaitlin P. Zawacki, Jonida Trako, Arathi Mohan, Jaya Sangodkhar and Analisa DiFeo of the University of Michigan.

The research was supported by grants from the National Institutes of Health (R01 CA-181654, R01 CA-240993, T32 CA-009676), and funding provided by the Rogel Cancer Center.

OConnor and Narla are named inventors on a U.S. provisional patent application concerning compositions and methods for treating high grade subtypes of uterine cancer. OConnor, Suhan, Zawacki and Sangodkar are consultants for RAPPTA Therapeutics. Narla is chief scientific officer of, reports receiving commercial research support from and has ownership interest in RAPPTA Therapeutics and is an adviser to Hera BioLabs. Zamarin reports research support to his institution from Astra Zeneca, Plexxikon, and Genentech, and personal/consultancy fees from Synlogic Therapeutics, GSK, Genentech, Xencor, Memgen, Immunos, Celldex, Calidi, and Agenus, and is an investor on a patent related to use of oncolytic Newcastle Disease Virus for cancer therapy.

Paper cited: Targeting ribonucleotide reductase induces synthetic lethality in PP2A-deficient uterine serous carcinoma, Cancer Research. DOI: 10.1158/0008-5472.CAN-21-1987

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Researchers zero in on therapeutic target for aggressive uterine cancer - Michigan Medicine

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