Elevated Hedgehog-Interacting Protein Levels in Subjects with Prediabetes and Type 2 Diabetes

Elevated circulating HHIP levels in patients with metabolic syndrome

Abnormal fat accumulation can lead to metabolic syndrome (MetS), increasing the risk of diabetes and cardiovascular disease in MetS patients. Early identification of MetS risk is essential for effective disease prevention. Using bioinformatics methods, we sought biomarkers for MetS. After analyzing the GSE9624 and GSE15524 datasets, we identified three commonly differentially expressed genes: COX7A1, PRR12, and HHIP. Subsequently, we validated the expression of these DEGs using the GSE65540 dataset. Quantitative PCR and immunoblotting confirmed significantly elevated HHIP expression in the adipose tissue of HFD-fed and ob/ob mice. Furthermore, a population-based cohort study demonstrated that serum HHIP levels were significantly greater in MetS patients than in healthy controls and were correlated with all MetS components. Receiver operating characteristic (ROC) curve analysis confirmed the robust predictive capacity of HHIP levels for metabolic syndrome, with an area under the curve (AUC) of 0.72 (95 % confidence interval: 0.68-0.78, P < 0.001). Binary logistic regression showed that the serum HHIP concentration was significantly associated with MetS even after adjusting for anthropometric and lipid profile variables. In conclusion, our findings demonstrate that changes in HHIP expression are significantly associated with adverse MetS indicators, indicating that HHIP can serve as a new biomarker for the diagnosis of MetS.

The identification of key genes and pathways in polycystic ovary syndrome by bioinformatics analysis of next-generation sequencing data

Background

Polycystic ovary syndrome (PCOS) is a reproductive endocrine disorder. The specific molecular mechanism of PCOS remains unclear. The aim of this study was to apply a bioinformatics approach to reveal related pathways or genes involved in the development of PCOS.

Methods

The next-generation sequencing (NGS) dataset GSE199225 was downloaded from the gene expression omnibus (GEO) database and NGS dataset analyzed is obtained from in vitro culture of PCOS patients’ muscle cells and muscle cells of healthy lean control women. Differentially expressed gene (DEG) analysis was performed using DESeq2. The g:Profiler was utilized to analyze the gene ontology (GO) and REACTOME pathways of the differentially expressed genes. A protein–protein interaction (PPI) network was constructed and module analysis was performed using HiPPIE and cytoscape. The miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed. The hub genes were validated by using receiver operating characteristic (ROC) curve analysis.

Results

We have identified 957 DEG in total, including 478 upregulated genes and 479 downregulated gene. GO terms and REACTOME pathways illustrated that DEG were significantly enriched in regulation of molecular function, developmental process, interferon signaling and platelet activation, signaling, and aggregation. The top 5 upregulated hub genes including HSPA5, PLK1, RIN3, DBN1, and CCDC85B and top 5 downregulated hub genes including DISC1, AR, MTUS2, LYN, and TCF4 might be associated with PCOS. The hub gens of HSPA5 and KMT2A, together with corresponding predicted miRNAs (e.g., hsa-mir-34b-5p and hsa-mir-378a-5p), and HSPA5 and TCF4 together with corresponding predicted TF (e.g., RCOR3 and TEAD4) were found to be significantly correlated with PCOS.

Conclusions

These study uses of bioinformatics analysis of NGS data to obtain hub genes and key signaling pathways related to PCOS and its associated complications. Also provides novel ideas for finding biomarkers and treatment methods for PCOS and its associated complications.

Study on Potential Differentially Expressed Genes in Idiopathic Pulmonary Fibrosis by Bioinformatics and Next-Generation Sequencing Data Analysis

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive lung disease with reduced quality of life and earlier mortality, but its pathogenesis and key genes are still unclear. In this investigation, bioinformatics was used to deeply analyze the pathogenesis of IPF and related key genes, so as to investigate the potential molecular pathogenesis of IPF and provide guidance for clinical treatment. Next-generation sequencing dataset GSE213001 was obtained from Gene Expression Omnibus (GEO), and the differentially expressed genes (DEGs) were identified between IPF and normal control group. The DEGs between IPF and normal control group were screened with the DESeq2 package of R language. The Gene Ontology (GO) and REACTOME pathway enrichment analyses of the DEGs were performed. Using the g:Profiler, the function and pathway enrichment analyses of DEGs were performed. Then, a protein-protein interaction (PPI) network was constructed via the Integrated Interactions Database (IID) database. Cytoscape with Network Analyzer was used to identify the hub genes. miRNet and NetworkAnalyst databaseswereused to construct the targeted microRNAs (miRNAs), transcription factors (TFs), and small drug molecules. Finally, receiver operating characteristic (ROC) curve analysis was used to validate the hub genes. A total of 958 DEGs were screened out in this study, including 479 up regulated genes and 479 down regulated genes. Most of the DEGs were significantly enriched in response to stimulus, GPCR ligand binding, microtubule-based process, and defective GALNT3 causes HFTC. In combination with the results of the PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network, hub genes including LRRK2, BMI1, EBP, MNDA, KBTBD7, KRT15, OTX1, TEKT4, SPAG8, and EFHC2 were selected. Cyclothiazide and rotigotinethe are predicted small drug molecules for IPF treatment. Our findings will contribute to identification of potential biomarkers and novel strategies for the treatment of IPF, and provide a novel strategy for clinical therapy.

Plasma proteins and onset of type 2 diabetes and diabetic complications: Proteome-wide Mendelian randomization and colocalization analyses

We conduct proteome-wide Mendelian randomization and colocalization analyses to decipher the associations of blood proteins with the risk of type 2 diabetes and diabetic complications. Genetic data on plasma proteome are obtained from 54,306 UK Biobank participants and 35,559 Icelanders. Summary-level data on type 2 diabetes are obtained from the DIAGRAM (DIAbetes Genetics Replication And Meta-analysis consortium) consortium (74,124 cases) and FinnGen study (33,043 cases). Data on 10 diabetic complications are obtained from FinnGen and corresponding studies. Among 1,886 proteins, genetically predicted levels of 47 plasma proteins are associated with type 2 diabetes. Eleven of these proteins have strong support of colocalization. Seventeen proteins are associated with at least one diabetic complication, although a few have colocalization support. HLA-DRA, AGER, HSPA1A, and HSPA1B are associated with most microvascular complications. This study reveals causal proteins for the onset of type 2 diabetes and diabetic complications, which enhances the understanding of molecular etiology and development of therapeutics.

Hedgehog/GLI and gastric cancer: Research progress and current status

Hedgehog/GLI (Hh/GLI) is an important signaling pathway. It has been confirmed in various cancer studies that mutated or dysregulated Hh signals may be the behavioral phenotype of tumors, leading to the occurrence of various cancers. The abnormally activated Hh pathway endows tumor cells with a tendency to occur, proliferate, and migrate. In recent years, studies have found that the Hh signaling pathway induces gastric cancer (GC) invasion and epithelial mesenchymal transition. This article reviews the research progress and current status of Hh/GLI related to GC. Unveiling the new veil of GC occurrence will open a new approach for targeted therapy of this malignancy.

Circulating HHIP Levels in Women with Insulin Resistance and PCOS: Effects of Physical Activity, Cold Stimulation and Anti-Diabetic Drug Therapy

Serum human hedgehog-interacting protein (HHIP) concentration is associated with diabetes. However, the relationship between HHIP and polycystic ovary syndrome (PCOS) or abnormal sex hormones remains unknown. This study was an observational cross-sectional study, with additional short-term intervention studies and follow-up studies. Bioinformatics analysis was performed to explore the association of PCOS with metabolic-related genes and signaling pathways. OGTT and EHC were performed on all participants. Lipid infusion, cold exposure, and 45-min treadmill test were performed on all healthy women. A total of 137 women with PCOS were treated with metformin, GLP-1RA, or TZDs for 24 weeks. Serum HHIP levels were higher in insulin resistance (IR) and PCOS women. Circulating HHIP levels were significantly correlated with adiponectin (Adipoq) levels, obesity, IR, and metabolic indicators. A correlation presented between HHIP and DHEA-S, FAI, SHBG, and FSH. Serum HHIP levels were significantly elevated by oral glucose challenge in healthy women, but not affected by EHC. Lipid infusion decreased serum HHIP levels, while cold exposure increased HHIP levels in healthy women. GLP-1RA and TZD treatment reduced serum HHIP levels in PCOS women, while metformin treatment did not affect HHIP levels. HHIP may be a useful biomarker and novel drug target for PCOS and IR individuals.

Circulating HHIP Levels in Women with Insulin Resistance and PCOS: Effects of Physical Activity, Cold Stimulation and Anti-Diabetic Drug Therapy

Serum human hedgehog-interacting protein (HHIP) concentration is associated with diabetes. However, the relationship between HHIP and polycystic ovary syndrome (PCOS) or abnormal sex hormones remains unknown. This study was an observational cross-sectional study, with additional short-term intervention studies and follow-up studies. Bioinformatics analysis was performed to explore the association of PCOS with metabolic-related genes and signaling pathways. OGTT and EHC were performed on all participants. Lipid infusion, cold exposure, and 45-min treadmill test were performed on all healthy women. A total of 137 women with PCOS were treated with metformin, GLP-1RA, or TZDs for 24 weeks. Serum HHIP levels were higher in insulin resistance (IR) and PCOS women. Circulating HHIP levels were significantly correlated with adiponectin (Adipoq) levels, obesity, IR, and metabolic indicators. A correlation presented between HHIP and DHEA-S, FAI, SHBG, and FSH. Serum HHIP levels were significantly elevated by oral glucose challenge in healthy women, but not affected by EHC. Lipid infusion decreased serum HHIP levels, while cold exposure increased HHIP levels in healthy women. GLP-1RA and TZD treatment reduced serum HHIP levels in PCOS women, while metformin treatment did not affect HHIP levels. HHIP may be a useful biomarker and novel drug target for PCOS and IR individuals.

Noncoding RNAs related to the hedgehog pathway in cancer: clinical implications and future perspectives

Cancer is a type of malignant affliction threatening human health worldwide; however, the molecular mechanism of cancer pathogenesis remains to be elusive. The oncogenic hedgehog (Hh) pathway is a highly evolutionarily conserved signaling pathway in which the hedgehog-Patched complex is internalized to cellular lysosomes for degradation, resulting in the release of Smoothened inhibition and producing downstream intracellular signals. Noncoding RNAs (ncRNAs) with diversified regulatory functions have the potency of controlling cellular processes. Compelling evidence reveals that Hh pathway, ncRNAs, or their crosstalk play complicated roles in the initiation, metastasis, apoptosis and drug resistance of cancer, allowing ncRNAs related to the Hh pathway to serve as clinical biomarkers for targeted cancer therapy. In this review, we attempt to depict the multiple patterns of ncRNAs in the progression of malignant tumors via interactions with the Hh crucial elements in order to better understand the complex regulatory mechanism, and focus on Hh associated ncRNA therapeutics aimed at boosting their application in the clinical setting.

Distinct peripheral blood monocyte and neutrophil transcriptional programs following intracerebral hemorrhage and different etiologies of ischemic stroke

Understanding cell-specific transcriptome responses following intracerebral hemorrhage (ICH) and ischemic stroke (IS) will improve knowledge of the immune response to brain injury. Transcriptomic profiles of 141 samples from 48 subjects with ICH, different IS etiologies, and vascular risk factor controls were characterized using RNA-seq in isolated neutrophils, monocytes and whole blood. In both IS and ICH, monocyte genes were down-regulated, whereas neutrophil gene expression changes were generally up-regulated. The monocyte down-regulated response to ICH included innate, adaptive immune, dendritic, NK cell and atherosclerosis signaling. Neutrophil responses to ICH included tRNA charging, mitochondrial dysfunction, and ER stress pathways. Common monocyte and neutrophil responses to ICH included interferon signaling, neuroinflammation, death receptor signaling, and NFAT pathways. Suppressed monocyte responses to IS included interferon and dendritic cell maturation signaling, phagosome formation, and IL-15 signaling. Activated neutrophil responses to IS included oxidative phosphorylation, mTOR, BMP, growth factor signaling, and calpain proteases-mediated blood-brain barrier (BBB) dysfunction. Common monocyte and neutrophil responses to IS included JAK1, JAK3, STAT3, and thrombopoietin signaling. Cell-type and cause-specific approaches will assist the search for future IS and ICH biomarkers and treatments.

The Serum Concentrations of Hedgehog-Interacting Protein, a Novel Biomarker, Were Decreased in Overweight or Obese Subjects

Although it was known that obesity is an independent risk factor for metabolic disorders including diabetes, the factors that link these diseases were obscure. The Hedgehog-interacting protein (Hhip) is a negative regulator in tissue remodeling, and inhibits the proliferation of adipocytes, and promotes their differentiation. In addition, Hhip was positively associated with diabetes. However, the relationship between Hhip and obesity in the human body remains unclear. An analysis of the relationship between Hhip and normal weight, overweight, and obesity levels. Participants receiving a physical checkup were recruited. Anthropometric and biochemical data were collected. Serum Hhip levels were determined by enzyme-linked immunosorbent assay (ELISA). Subjects were classified into normal-weight, overweight, and obese groups based on their body mass index (BMI). The association between Hhip and obesity was examined by multivariate linear regression analysis. In total, 294 subjects who were either of a normal weight (n = 166), overweight (n = 90), or obese (n = 38) were enrolled. Hhip concentrations were 6.51 ± 4.86 ng/mL, 5.79 ± 4.33 ng/mL, and 3.97 ± 3.4 ng/mL in normal-weight, overweight, and obese groups, respectively (p for trend = 0.032). Moreover, the regression analysis showed that BMI (β = −0.144, 95% confidence interval (CI) = −0.397−0.046, p = 0.013) was negatively associated with Hhip concentrations after adjusting for sex and age. Being overweight (β = −0.181, 95% CI = −3.311−0.400, p = 0.013) and obese (β = −0.311, 95% CI = −6.393−2.384, p < 0.001) were independently associated with Hhip concentrations after adjusting for sex, age, fasting plasma glucose, the insulin level, and other cardiometabolic risk factors. Our results showed that overweight and obese subjects had lower Hhip concentrations than those of normal weight. Being overweight and obese were negatively associated with Hhip concentrations. Hhip might be a link between obesity and diabetes.