Weighted Gene Coexpression Network Analysis Identified MicroRNA Coexpression Modules and Related Pathways in Type 2 Diabetes Mellitus

The TGFβ Induced MicroRNAome of the Trabecular Meshwork

Primary open-angle glaucoma (POAG) is a progressive optic neuropathy with a complex, multifactorial aetiology. Raised intraocular pressure (IOP) is the most important clinically modifiable risk factor for POAG. All current pharmacological agents target aqueous humour dynamics to lower IOP. Newer therapeutic agents are required as some patients with POAG show a limited therapeutic response or develop ocular and systemic side effects to topical medication. Elevated IOP in POAG results from cellular and molecular changes in the trabecular meshwork driven by increased levels of transforming growth factor β (TGFβ) in the anterior segment of the eye. Understanding how TGFβ affects both the structural and functional changes in the outflow pathway and IOP is required to develop new glaucoma therapies that target the molecular pathology in the trabecular meshwork. In this study, we evaluated the effects of TGF-β1 and -β2 treatment on miRNA expression in cultured human primary trabecular meshwork cells. Our findings are presented in terms of specific miRNAs (miRNA-centric), but given miRNAs work in networks to control cellular pathways and processes, a pathway-centric view of miRNA action is also reported. Evaluating TGFβ-responsive miRNA expression in trabecular meshwork cells will further our understanding of the important pathways and changes involved in the pathogenesis of glaucoma and could lead to the development of miRNAs as new therapeutic modalities in glaucoma.

The role of exosome derived miRNAs in inter-cell crosstalk among insulin-related organs in type 2 diabetes mellitus

Exosomes are small extracellular vesicles secreted by almost all cell types, and carry diverse cargo including RNA, and other substances. Recent studies have focused exosomal microRNAs (miRNAs) on various human diseases, including type 2 diabetes mellitus (T2DM) and metabolic syndrome (METS) which accompany the occurrence of insulin resistance. The regulation of insulin signaling has connected with some miRNA expression which play a significant regulatory character in insulin targeted cells or organs, such as fat, muscle, and liver. The miRNAs carried by exosomes, through the circulation in the body fluids, mediate all kinds of physiological and pathological process involved in the human body. Studies have found that exosome derived miRNAs are abnormally expressed and cross-talked with insulin targeted cells or organs to affect insulin pathways. Further investigations of the mechanisms of exosomal miRNAs in T2DM will be valuable for the diagnostic biomarkers and therapeutic targets of T2DM. This review will summarize the molecular mechanism of action of the miRNAs carried by exosomes which are secreted from insulin signaling related cells, and elucidate the pathogenesis of insulin resistance to provide a new strategy for the potential diagnostic biomarkers and therapeutic targets for the type 2 diabetes.

SIRT1 and ZNF350 as novel biomarkers for osteoporosis: a bioinformatics analysis and experimental validation

BACKGROUND

Osteoporosis (OP) is characterized by bone mass decrease and bone tissue microarchitectural deterioration in bone tissue. This study identified potential biomarkers for early diagnosis of OP and elucidated the mechanism of OP.

METHODS

Gene expression profiles were downloaded from Gene Expression Omnibus (GEO) for the GSE56814 dataset. A gene co-expression network was constructed using weighted gene co-expression network analysis (WGCNA) to identify key modules associated with healthy and OP samples. Functional enrichment analysis was conducted using the R clusterProfiler package for modules to construct the transcriptional regulatory factor networks. We used the "ggpubr" package in R to screen for differentially expressed genes between the two samples. Gene set variation analysis (GSVA) was employed to further validate hub gene expression levels between normal and OP samples using RT-PCR and immunofluorescence to evaluate the potential biological changes in various samples.

RESULTS

There was a distinction between the normal and OP conditions based on the preserved significant module. A total of 100 genes with the highest MM scores were considered key genes. Functional enrichment analysis suggested that the top 10 biological processes, cellular component and molecular functions were enriched. The Toll-like receptor signaling pathway, TNF signaling pathway, PI3K-Akt signaling pathway, osteoclast differentiation, JAK-STAT signaling pathway, and chemokine signaling pathway were identified by Kyoto Encyclopedia of Genes and Genomes pathway analysis. SIRT1 and ZNF350 were identified by Wilcoxon algorithm as hub differentially expressed transcriptional regulatory factors that promote OP progression by affecting oxidative phosphorylation, apoptosis, PI3K-Akt-mTOR signaling, and p53 pathway. According to RT-PCR and immunostaining results, SIRT1 and ZNF350 levels were significantly higher in OP samples than in normal samples.

CONCLUSION

SIRT1 and ZNF350 are important transcriptional regulatory factors for the pathogenesis of OP and may be novel biomarkers for OP treatment.

MicroRNAs Associated with Keloids Identified by Microarray Analysis and In Vitro Experiments

MicroRNAs (miRNAs) play a crucial role in gene regulation and the development of keloid. This research aimed to identify and verify miRNAs associated with keloids by microarray analysis and in vitro experiments, shedding light on seeking for potential therapeutic molecular targets. In this study, the weighted gene co-expression network analysis was performed based on the GSE113620. The key miRNA module most relevant to the keloid was further screened to identify hub miRNAs, and then hub miRNAs was verified by the microarray analysis and qRT-PCR experiments. Additionally, targeted genes of hub miRNAs were predicted and verified. Gene ontology (GO) analysis and KEGG enrichment analysis were also conducted. Five miRNA modules were divided, and the blue module exhibited the highest correlation with keloids. Then, hsa-miR-127-3p, hsa-miR-214-3p, hsa-miR-155-5p, hsa-miR-409-5p, and hsa-miR-542-5p were identified as the hub miRNAs. Subsequently, the microarray analysis and qRT-PCR results demonstrated that the expression of five miRNAs were upregulated in keloid tissues. The GO analysis revealed that the target genes of these miRNAs were mainly enriched in biological processes including gene transcription, protein phosphorylation and the MAPK (mitogen-activated protein kinase) cascade, and the KEGG pathway enrichment analysis showed that the PI3K-AKT signaling pathway were significantly enriched. In conclusion, these five miRNAs (hsa-miR-127-3p, hsa-miR-155-5p, hsa-miR-214-3p, hsa-miR-409-5p, and hsa-miR-542-5p) play vital roles in the pathogenesis of keloid and might be potential therapeutic targets. These miRNAs might regulate genes enriched in gene transcription, protein phosphorylation, the MAPK cascade, and the PI3K-Akt signaling pathway.

Circulating miR-107 as a diagnostic biomarker of osteoporotic vertebral compression fracture increases bone formation in vitro and in vivo

AIMS

This study aimed to examine the key circulating miRNAs in the plasma of patients with osteoporotic vertebral compression fracture and assess their potential role as diagnostic biomarkers and explore their function in vitro and in vivo.

METHODS

Weighted gene co-expression network analysis (WGCNA) was applied to identify hub miRNAs for subsequent analysis. The candidate miRNAs were tested using plasma from 144 patients and the results were applied to construct receiver operating characteristic (ROC) curves to assess their diagnostic value. In addition, the function of the target microRNA was validated in MC3T3-E1 cells, human bone marrow-derived mesenchymal stromal cells (BMSCs), and an ovariectomized (OVX) mouse model.

KEY FINDINGS

Seven modules were obtained by WGCNA analysis. The expression levels of circulating miR-107 in the red module were significantly lower in osteoporotic patients than in healthy controls. In addition, miR-107 provided discrimination with an AUC > 85 % by ROC analyses to differentiate women osteoporosis patients from healthy controls and differentiate women osteoporotic patients with vertebral compression fractures from osteoporotic patients without vertebral compression fractures. In vitro experiments revealed that miR-107 levels were increased in osteogenically induced MC3T3-E1 cells and BMSCs and transfection with synthetic miR-107 could promote bone formation. Lastly, the bone parameters were improved by miR-107 upregulation in OVX mice.

SIGNIFICANCE

Our findings show that circulating miR-107 plays an essential role in facilitating osteogenesis and may be a useful diagnostic biomarker and therapeutic target in osteoporosis.

Multifaceted functions of RPS27a: An unconventional ribosomal protein

The ribosomal protein S27a (RPS27a) is cleaved from the fusion protein ubiquitin-RPS27a (Ub-RPS27a). Generally, Ub and RPS27a are coexpressed as a fusion protein but function independently after Ub is cleaved from RPS27a by a deubiquitinating enzyme. As an RP, RPS27a assembles into ribosomes, but it also functions independently of ribosomes. RPS27a is involved in the development and poor prognosis of various cancers, such as colorectal cancer, liver cancer, chronic myeloid leukemia, and renal carcinoma, and is associated with poor prognosis. Notably, the murine double minute 2/P53 axis is a major pathway through which RPS27a regulates cancer development. Moreover, RPS27a maintains sperm motility, regulates winged aphid indirect flight muscle degeneration, and facilitates plant growth. Additionally, RPS27a is a metalloprotein and mercury (Hg) biomarker. In the present review, we described the origin, structure, and biological functions of RPS27a.

Analysis of the different growth years accumulation of flavonoids in Dendrobium moniliforme (L.) Sw. by the integration of metabolomic and transcriptomic approaches

Dendrobium moniliforme (L.) Sw. is a valuable herbal crop, and flavonoids are primarily distributed as active ingredients in the stem, but the composition and synthesis mechanisms of flavonoids in different growth years are not clear. The accumulation of flavonoids in D. moniliforme from four different years was investigated, using a combined metabolomics and transcriptomics approach in this study. The phenylpropanoid and flavonoid biosynthetic pathways were significantly enriched in the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of differentially expressed genes (DEGs) and differentially accumulated metabolites (DAMs). The widely targeted metabolomics technique revealed a total of 173 kinds of flavonoid metabolites. The metabolomics data confirmed the trend of total flavonoids (TF) content in stems of D. moniliforme, with chalcone, naringenin, eriodictyol, dihydroquercetin, and other flavonoids considerably up-accumulating in the third year. Twenty DEGs were detected that regulate flavonoid synthesis and the expression of these genes in different growth years was verified using real-time quantitative PCR (qRT-PCR). Furthermore, a comprehensive regulatory network was built for flavonoid biosynthesis and it was discovered that there is one FLS gene, one CCR gene and two MYB transcription factors (TFs) with a high connection with flavonoid biosynthesis by weighted gene co-expression network analysis (WGCNA). In this study, the correlation between genes involved in flavonoid biosynthesis and metabolites was revealed, and a new regulatory mechanism related to flavonoid biosynthesis in D. moniliforme was proposed. These results provide an important reference for the farmers involved in the cultivation of D. moniliforme.

Cardiovascular Disease-Associated MicroRNAs as Novel Biomarkers of First-Trimester Screening for Gestational Diabetes Mellitus in the Absence of Other Pregnancy-Related Complications

We assessed the diagnostic potential of cardiovascular disease-associated microRNAs for the early prediction of gestational diabetes mellitus (GDM) in singleton pregnancies of Caucasian descent in the absence of other pregnancy-related complications. Whole peripheral venous blood samples were collected within 10 to 13 weeks of gestation. This retrospective study involved all pregnancies diagnosed with only GDM (n = 121) and 80 normal term pregnancies selected with regard to equality of sample storage time. Gene expression of 29 microRNAs was assessed using real-time RT-PCR. Upregulation of 11 microRNAs (miR-1-3p, miR-20a-5p, miR-20b-5p, miR-23a-3p, miR-100-5p, miR-125b-5p, miR-126-3p, miR-181a-5p, miR-195-5p, miR-499a-5p, and miR-574-3p) was observed in pregnancies destinated to develop GDM. Combined screening of all 11 dysregulated microRNAs showed the highest accuracy for the early identification of pregnancies destinated to develop GDM. This screening identified 47.93% of GDM pregnancies at a 10.0% false positive rate (FPR). The predictive model for GDM based on aberrant microRNA expression profile was further improved via the implementation of clinical characteristics (maternal age and BMI at early stages of gestation and an infertility treatment by assisted reproductive technology). Following this, 69.17% of GDM pregnancies were identified at a 10.0% FPR. The effective prediction model specifically for severe GDM requiring administration of therapy involved using a combination of these three clinical characteristics and three microRNA biomarkers (miR-20a-5p, miR-20b-5p, and miR-195-5p). This model identified 78.95% of cases at a 10.0% FPR. The effective prediction model for GDM managed by diet only required the involvement of these three clinical characteristics and eight microRNA biomarkers (miR-1-3p, miR-20a-5p, miR-20b-5p, miR-100-5p, miR-125b-5p, miR-195-5p, miR-499a-5p, and miR-574-3p). With this, the model identified 50.50% of GDM pregnancies managed by diet only at a 10.0% FPR. When other clinical variables such as history of miscarriage, the presence of trombophilic gene mutations, positive first-trimester screening for preeclampsia and/or fetal growth restriction by the Fetal Medicine Foundation algorithm, and family history of diabetes mellitus in first-degree relatives were included in the GDM prediction model, the predictive power was further increased at a 10.0% FPR (72.50% GDM in total, 89.47% GDM requiring therapy, and 56.44% GDM managed by diet only). Cardiovascular disease-associated microRNAs represent promising early biomarkers to be implemented into routine first-trimester screening programs with a very good predictive potential for GDM.

HAMP as a Prognostic Biomarker for Colorectal Cancer Based on Tumor Microenvironment Analysis

Colorectal cancer (CRC) is the most common digestive tumor in the world and has a high mortality rate. The development and treatment of CRC are related to the immune microenvironment, but immune response-related prognostic biomarkers are lacking. In this study, we used The Cancer Genome Atlas (TCGA) to explore the tumor microenvironment (TME) and weighted gene coexpression network analysis (WGCNA) to identify significant prognostic genes. We also identified differentially expressed genes in the TCGA data and explored immune-related genes and transcription factors (TFs). Then, we built a TF regulatory network and performed a comprehensive prognostic analysis of an lncRNA-associated competitive endogenous RNA network (ceRNA network) to build a prognostic model. CCR8 and HAMP were identified both in the WGCNA key module and as immune-related genes. HAMP had good prognostic value for CRC and was highly expressed in CRC tissues and had a negative correlation with CD4⁺ T cells and M0 macrophages based on immunohistochemistry and immunofluorescence staining of clinical specimens.We found that HAMP had high prognostic and therapeutic target value for CRC and was associated with liver metastasis. These analysis results revealed that HAMP may be a candidate immune-related prognostic biomarker for CRC.

Integrated Analysis of Gene Co-Expression Network and Prediction Model Indicates Immune-Related Roles of the Identified Biomarkers in Sepsis and Sepsis-Induced Acute Respiratory Distress Syndrome

Sepsis is a series of clinical syndromes caused by immunological response to severe infection. As the most important and common complication of sepsis, acute respiratory distress syndrome (ARDS) is associated with poor outcomes and high medical expenses. However, well-described studies of analysis-based researches, especially related bioinformatics analysis on revealing specific targets and underlying molecular mechanisms of sepsis and sepsis-induced ARDS (sepsis/se-ARDS), still remain limited and delayed despite the era of data-driven medicine. In this report, weight gene co-expression network based on data from a public database was constructed to identify the key modules and screen the hub genes. Functional annotation by enrichment analysis of the modular genes also demonstrated the key biological processes and signaling pathway; among which, extensive immune-involved enrichment was remarkably associated with sepsis/se-ARDS. Based on the differential expression analysis, least absolute shrink and selection operator, and multivariable logistic regression analysis of the screened hub genes, SIGLEC9, TSPO, CKS1B and PTTG3P were identified as the candidate biomarkers for the further analysis. Accordingly, a four-gene-based model for diagnostic prediction assessment was established and then developed by sepsis/se-ARDS risk nomogram, whose efficiency was verified by calibration curves and decision curve analyses. In addition, various machine learning algorithms were also applied to develop extra models based on the four genes. Receiver operating characteristic curve analysis proved the great diagnostic and predictive performance of these models, and the multivariable logistic regression of the model was still found to be the best as further verified again by the internal test, training, and external validation cohorts. During the development of sepsis/se-ARDS, the expressions of the identified biomarkers including SIGLEC9, TSPO, CKS1B and PTTG3P were all regulated remarkably and generally exhibited notable correlations with the stages of sepsis/se-ARDS. Moreover, the expression levels of these four genes were substantially correlated during sepsis/se-ARDS. Analysis of immune infiltration showed that multiple immune cells, neutrophils and monocytes in particular, might be closely involved in the process of sepsis/se-ARDS. Besides, SIGLEC9, TSPO, CKS1B and PTTG3P were considerably correlated with the infiltration of various immune cells including neutrophils and monocytes during sepsis/se-ARDS. The discovery of relevant gene co-expression network and immune signatures might provide novel insights into the pathophysiology of sepsis/se-ARDS.

Comprehensive bioinformatics analysis identifies LAPTM5 as a potential blood biomarker for hypertensive patients with left ventricular hypertrophy

Left ventricular hypertrophy (LVH) is a pivotal manifestation of hypertensive organ damage associated with an increased cardiovascular risk. However, early diagnostic biomarkers for assessing LVH in patients with hypertension (HT) remain indefinite. Here, multiple bioinformatics tools combined with an experimental verification strategy were used to identify blood biomarkers for hypertensive LVH. GSE74144 mRNA expression profiles were downloaded from the Gene Expression Omnibus (GEO) database to screen candidate biomarkers, which were used to perform weighted gene co-expression network analysis (WGCNA) and establish the least absolute shrinkage and selection operator (LASSO) regression model, combined with support vector machine-recursive feature elimination (SVM-RFE) algorithms. Finally, the potential blood biomarkers were verified in an animal model. A total of 142 hub genes in peripheral blood leukocytes were identified between HT with LVH and HT without LVH, which were mainly involved in the ATP metabolic process, oxidative phosphorylation, and mitochondrial structure and function. Notably, lysosomal associated transmembrane protein 5 (LAPTM5) was identified as the potential diagnostic marker of hypertensive LVH, which showed strong correlations with diverse marker sets of reactive oxygen species (ROS) and autophagy. RT-PCR validation of blood samples and cardiac magnetic resonance imaging (CMRI) showed that the expression of LAPTM5 was significantly higher in the HT with LVH model than in normal controls, LAPTM5 demonstrated a positive association with the left ventricle wall thickness as well as electrocardiogram (ECG) parameters widths of the QRS complex and QTc interval. In conclusion, LAPTM5 may be a potential biomarker for the diagnosis of LVH in patients with HT, and it can provide new insights for future studies on the occurrence and the molecular mechanisms of hypertensive LVH.

Identification of Potential Biomarkers of Type 2 Diabetes Mellitus-Related Immune Infiltration Using Weighted Gene Coexpression Network Analysis

Background

Type 2 diabetes mellitus (T2DM) is characterized by chronic low-grade inflammation, showing an increasing trend. The infiltration of immune cells into adipose tissue has been shown to be an important pathogenic cause of T2DM. The purpose of this study is to use the relevant database to identify some abnormally expressed or dysfunctional genes related to diabetes from the perspective of immune infiltration.

Methods

Weighted gene coexpression network analysis (WGCNA) was employed to systematically identify the coexpressed gene modules and hub genes associated with T2DM development based on a microarray dataset (GSE23561) from the Gene Expression Omnibus (GEO) database. The key genes in modules highly related to clinical features were calculated and screened by using R software, and their participation in T2DM was determined by gene enrichment analysis. The mRNA levels of CSF1R, H2AFV, LCK, and TLR9 in pre-T2DM mice and normal wild-type mice were detected by WGCNA screening and real-time quantitative reverse transcriptase polymerase chain reaction (qRT-PCR).

Results

We constructed 14 coexpressed gene modules, and the brown module was shown to be significantly related to T2DM. Through verification of the protein-protein interaction (PPI) network, four upregulated hub genes, CSF1R, H2AFV, LCK, and TLR9, were screened from the brown module and successfully distinguishedT2DM patients from healthy people. These hub genes may be used as biomarkers and important indicators for patient diagnosis. Enrichment analysis showed that these hub genes were highly associated with IL-6-related inflammatory metabolism, immune regulation, and immune cell infiltration. Finally, we verified the hub genes CSF1R, LCK, and TLR9 in a T2DM animal model and found that their mRNA levels were significantly higher in animals with T2DM than in control group mice (NC).

Conclusions

In summary, our results suggest that these hub genes (CSF1R, LCK, and TLR9) can serve as biomarkers and immunotherapeutic targets for T2DM.

Identification of Key LncRNAs and Pathways in Prediabetes and Type 2 Diabetes Mellitus for Hypertriglyceridemia Patients Based on Weighted Gene Co-Expression Network Analysis

Aims

Prevalence of prediabetes and type 2 diabetes mellitus(T2DM) are increasing worldwide. Key lncRNAs were detected to provide a reference for searching potential biomarkers of prediabetes and T2DM in hypertriglyceridemia patients.

Methods

The study included 18 hypertriglyceridemia patients: 6 newly diagnosed type 2 diabetes patients, 6 samples with prediabetes and 6 samples with normal blood glucose. Weighted gene co-expression network analysis (WGCNA) was conducted to construct co-expression network and obtain modules related to blood glucose, thus detecting key lncRNAs.

Results

The green, yellow and yellow module was significantly related to blood glucose in T2DM versus normal controls, T2DM versus prediabetes, prediabetes versus normal controls, respectively. ENST00000503273, ENST00000462720, ENST00000480633 and ENST00000485392 were detected as key lncRNAs for the above three groups, respectively.

Conclusions

For hypertriglyceridemia patients with different blood glucose levels, ENST00000503273, ENST00000462720 and ENST00000480633 could be potential biomarkers of T2DM.

Circulating Nucleic Acid-Based Biomarkers of Type 2 Diabetes

Type 2 diabetes (T2D) is a deficiency in how the body regulates glucose. Uncontrolled T2D will result in chronic high blood sugar levels, eventually resulting in T2D complications. These complications, such as kidney, eye, and nerve damage, are even harder to treat. Identifying individuals at high risk of developing T2D and its complications is essential for early prevention and treatment. Numerous studies have been done to identify biomarkers for T2D diagnosis and prognosis. This review focuses on recent T2D biomarker studies based on circulating nucleic acids using different omics technologies: genomics, transcriptomics, and epigenomics. Omics studies have profiled biomarker candidates from blood, urine, and other non-invasive samples. Despite methodological differences, several candidate biomarkers were reported for the risk and diagnosis of T2D, the prognosis of T2D complications, and pharmacodynamics of T2D treatments. Future studies should be done to validate the findings in larger samples and blood-based biomarkers in non-invasive samples to support the realization of precision medicine for T2D.

MicroRNA Sequences Modulated by Beta Cell Lipid Metabolism: Implications for Type 2 Diabetes Mellitus

Alterations in lipid metabolism within beta cells and islets contributes to dysfunction and apoptosis of beta cells, leading to loss of insulin secretion and the onset of type 2 diabetes. Over the last decade, there has been an explosion of interest in understanding the landscape of gene expression which influences beta cell function, including the importance of small non-coding microRNA sequences in this context. This review sought to identify the microRNA sequences regulated by metabolic challenges in beta cells and islets, their targets, highlight their function and assess their possible relevance as biomarkers of disease progression in diabetic individuals. Predictive analysis was used to explore networks of genes targeted by these microRNA sequences, which may offer new therapeutic strategies to protect beta cell function and delay the onset of type 2 diabetes.

Bioinformatics and machine learning approach identifies potential drug targets and pathways in COVID-19

Current coronavirus disease-2019 (COVID-19) pandemic has caused massive loss of lives. Clinical trials of vaccines and drugs are currently being conducted around the world; however, till now no effective drug is available for COVID-19. Identification of key genes and perturbed pathways in COVID-19 may uncover potential drug targets and biomarkers. We aimed to identify key gene modules and hub targets involved in COVID-19. We have analyzed SARS-CoV-2 infected peripheral blood mononuclear cell (PBMC) transcriptomic data through gene coexpression analysis. We identified 1520 and 1733 differentially expressed genes (DEGs) from the GSE152418 and CRA002390 PBMC datasets, respectively (FDR < 0.05). We found four key gene modules and hub gene signature based on module membership (MMhub) statistics and protein-protein interaction (PPI) networks (PPIhub). Functional annotation by enrichment analysis of the genes of these modules demonstrated immune and inflammatory response biological processes enriched by the DEGs. The pathway analysis revealed the hub genes were enriched with the IL-17 signaling pathway, cytokine-cytokine receptor interaction pathways. Then, we demonstrated the classification performance of hub genes (PLK1, AURKB, AURKA, CDK1, CDC20, KIF11, CCNB1, KIF2C, DTL and CDC6) with accuracy >0.90 suggesting the biomarker potential of the hub genes. The regulatory network analysis showed transcription factors and microRNAs that target these hub genes. Finally, drug-gene interactions analysis suggests amsacrine, BRD-K68548958, naproxol, palbociclib and teniposide as the top-scored repurposed drugs. The identified biomarkers and pathways might be therapeutic targets to the COVID-19.

HPLC-MS and Network Pharmacology Analysis to Reveal Quality Markers of Huo-Xue-Jiang-Tang Yin, a Chinese Herbal Medicine for Type 2 Diabetes Mellitus

Huo-Xue-Jiang-Tang Yin (HXJTY) is a Chinese medicine formulation, which has been widely used for the treatment of various lipometabolism- and glycometabolism-related diseases in clinics. Currently, HXJTY is mainly prescribed to treat patients with type 2 diabetes mellitus (T2DM), yet its chemical and pharmacologic profiles remain to be elucidated. Here, the potential bioactive compound and action mechanism were investigated using chemical and network pharmacology analysis. A rapid HPLC-MS was employed to identify and quantify the component of HXJTY. On the basis of the identified chemical markers from HXJTY, a network pharmacology study, including target gene prediction and functional enrichment, was applied to screen out the main quality markers of HXJTY and explore its potential mechanism for the treatment of T2DM. The results showed that a total of 22 components were identified and quantified from HXJTY by HPLC-MS. Furthermore, 12 active components such as astragaloside IV, calycosin-7-O-β-D-glucoside, hydroxysafflor yellow A, and others were proposed as quality markers of HXJTY for treating T2DM based on network pharmacology analysis. In addition, 125 corresponding possible therapeutic target genes of T2DM were obtained. These target genes are mainly related to peptidase activity, hydrolase activity, phosphatase activity, and cofactor binding, suggesting the involvement of PI3K-Akt, MAPK, AGE-RAGE, and Rap1 signaling pathways in HXJTY-treated T2DM. Our results may provide a useful approach to identify potential quality markers and molecular mechanism of HXJTY for treating T2DM.

Clinical epigenetics settings for cancer and cardiovascular diseases: real-life applications of network medicine at the bedside

Despite impressive efforts invested in epigenetic research in the last 50 years, clinical applications are still lacking. Only a few university hospital centers currently use epigenetic biomarkers at the bedside. Moreover, the overall concept of precision medicine is not widely recognized in routine medical practice and the reductionist approach remains predominant in treating patients affected by major diseases such as cancer and cardiovascular diseases. By its' very nature, epigenetics is integrative of genetic networks. The study of epigenetic biomarkers has led to the identification of numerous drugs with an increasingly significant role in clinical therapy especially of cancer patients. Here, we provide an overview of clinical epigenetics within the context of network analysis. We illustrate achievements to date and discuss how we can move from traditional medicine into the era of network medicine (NM), where pathway-informed molecular diagnostics will allow treatment selection following the paradigm of precision medicine.

Causal associations of waist circumference and waist-to-hip ratio with type II diabetes mellitus: new evidence from Mendelian randomization

Type II diabetes mellitus (T2DM) is a metabolic disease with high incidence, which has seriously affected human life and health. The associations among waist circumference (WC), waist-to-hip ratio (WHR), and T2DM were discovered in observational studies. However, the causality of these associations still remains unknown. The present study aims to apply two-sample Mendelian randomization (TSMR) using genetic variants as instrumental variables (IVs) to evaluate the causality among WC, WHR, and T2DM. The participants were from three independent studies in genome-wide association studies (GWAS) datasets, which included 127,997 Europeans for WC, 73,137 Europeans for WHR and 659,316 Europeans for T2DM. Furthermore, 16 were associated WC SNPs and eight were associated WHR SNPs as instrument variables were selected for TSMR using P < 5 × 10^-8 standard. The pooled odd ratios (ORs) for the assessment of higher WC and WHR on the risk of T2DM for these SNPs were calculated using inverse variance weighted (IVW) method, and validated through extensively complementary analyses. The OR for T2DM per SD (cm) higher WC was 2.623 (95% CI 2.286-3.010, P = 5.000E-43), and the OR for T2DM per SD (cm) higher WHR was 1.751 (95% CI 1.122-2.733, P = 0.014). Consistent results for other methods were obtained. Furthermore, the range of OR fluctuation between WC and T2DM was from 2.623 to 2.986, while that between WHR and T2DM was from 0.990 to 2.931. Overall, these present results provide genetic support that suggests that the use of TSMR, and higher WC and WHR increased the T2DM risk among the European population.

Serum microRNA Levels in Diabetes Mellitus

The aim of our study is to evaluate the serum circulating levels of some miRNA, such as hsa-let-7b-5p, hsa-let-7a-5p, hsa-miR-320b, hsa-miR-23a-3p, hsa-miR-27a-3p, hsa-miR-15a-5p, and hsa-miR-495-3, in diabetic patients without diabetic retinopathy (DR), diabetic patients with DR, and, healthy subjects in order to find reliable and reproducible biomarkers for DR. A total of 45 subjects underwent serum sampling for miRNAs evaluation and a complete ophthalmologic examination, including microperimetry and widefield swept source optical coherence tomography angiography (OCTA). Total circulating RNA was isolated from patients using the miRNeasy Serum/Plasma Kit. Serum miRNA expression levels were significantly different in the three groups. In detail, circulating hsa-miR-15a-5p levels were significantly reduced in both diabetic patients without DR and diabetic patients with DR (p = 0.027). Serum hsa-miR-495-3p was lower in diabetic patients with DR and diabetic patients without DR (p = 0.049). Hsa-miR-23a-3p serum expression levels were significantly lower in diabetic patients with DR and diabetic patients without DR (p = 0.013). Significant associations of miRNAs with anatomical/perfusion parameters and functional parameters were observed in the diabetic groups. We find evidence of damage in progression biomarkers in DR that are evidently early in patients with diabetes without DR. Serum miRNAs levels are considered to have strong potential as a novel biomarker for the early detection of DR in subjects suffering from diabetes and could represent noninvasive target therapies to block the progression of the disease at the early stages.

Identification of Infertility-Associated Topologically Important Genes Using Weighted Co-expression Network Analysis

Endometriosis has been associated with a high risk of infertility. However, the underlying molecular mechanism of infertility in endometriosis remains poorly understood. In our study, we aimed to discover topologically important genes related to infertility in endometriosis, based on the structure network mining. We used microarray data from the Gene Expression Omnibus (GEO) database to construct a weighted gene co-expression network for fertile and infertile women with endometriosis and to identify gene modules highly correlated with clinical features of infertility in endometriosis. Additionally, the protein–protein interaction network analysis was used to identify the potential 20 hub messenger RNAs (mRNAs) while the network topological analysis was used to identify nine candidate long non-coding RNAs (lncRNAs). Functional annotations of clinically significant modules and lncRNAs revealed that hub genes might be involved in infertility in endometriosis by regulating G protein-coupled receptor signaling (GPCR) activity. Gene Set Enrichment Analysis showed that the phospholipase C-activating GPCR signaling pathway is correlated with infertility in patients with endometriosis. Taken together, our analysis has identified 29 hub genes which might lead to infertility in endometriosis through the regulation of the GPCR network.

An Integrative Pharmacology-Based Approach for Evaluating the Potential Effects of Purslane Seed in Diabetes Mellitus Treatment Using UHPLC-LTQ-Orbitrap and TCMIP V2.0

Portulaca oleracea L., known as the “vegetable for long life,” is an annual succulent herb that is widely distributed worldwide. Many clinical and experimental studies have demonstrated that purslane seed (MCXZ) can be used as an adjunctive and alternative therapy for the treatment of diabetes mellitus (DM). However, the underlying active constituents and pharmacological mechanisms through which MCXZ exerts effects in DM remain unclear. In the present study, we confirmed that MCXZ treatment resulted in hypoglycemic activity, lowering the fasting blood glucose and glycated hemoglobin levels in streptozotocin-induced diabetic mice. Then, ultra-high-pressure liquid chromatography coupled with linear ion trap-Orbitrap tandem mass spectrometry was used to systematically analyze the chemical profile of MCXZ, resulting in the identification of 84 constituents, including 31 organic acids and nine flavonoids. Finally, the Integrative Pharmacology-based Research Platform of Traditional Chinese Medicine was employed to analyze the key active components of MCXZ and the molecular mechanisms through which these components acted in DM. Ten key active compounds were identified based on the topological importance of their corresponding putative targets within the known DM-associated therapeutic target network of known MCXZ putative targets. Functionally, these candidate targets play critical anti-hyperlipidemia, anti-hyperglycemia, immunity regulation, and inflammatory roles involving DM-related pathways, such as the vascular endothelial growth factor (VEGF) signaling pathway and Fc gamma R-mediated phagocytosis, which indicated that MCXZ exhibited anti-diabetic activity through multi-faced actions.

Lactation Associated Genes Revealed in Holstein Dairy Cows by Weighted Gene Co-Expression Network Analysis (WGCNA)

Simple Summary

Weighted gene coexpression network analysis (WGCNA) is a novel approach that can quickly analyze the relationships between genes and traits. In the past few years, studies on the gene expression changes of dairy cow mammary glands were only based on transcriptome comparisons between two lactation stages. Few studies focused on the relationships between gene expression of the dairy mammary gland and lactation stage or milk composition in a lactation cycle. In this study, we detected milk yield and composition in a lactation cycle. For the first time, we constructed a gene coexpression network using WGCNA on the basis of 18 gene expression profiles during six stages of a lactation cycle by transcriptome sequencing, generating 10 specific modules. Genes in each module were performed with gene ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Module–trait relationship analysis showed a series of potential candidates related to milk yield and composition. The current study provides an important theoretical basis for the further molecular breeding of dairy cows.

Abstract

Weighted gene coexpression network analysis (WGCNA) is a novel approach that can quickly analyze the relationships between genes and traits. In this study, the milk yield, lactose, fat, and protein of Holstein dairy cows were detected in a lactation cycle. Meanwhile, a total of 18 gene expression profiles were detected using mammary glands from six lactation stages (day 7 to calving, −7 d; day 30 post-calving, 30 d; day 90 post-calving, 90 d; day 180 post-calving, 180 d; day 270 post-calving, 270 d; day 315 post-calving, 315 d). On the basis of the 18 profiles, WGCNA identified for the first time 10 significant modules that may be related to lactation stage, milk yield, and the main milk composition content. Genes in the 10 significant modules were examined with gene ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The results revealed that the galactose metabolism pathway was a potential candidate for milk yield and milk lactose synthesis. In −7 d, ion transportation was more frequent and cell proliferation related terms became active. In late lactation, the suppressor of cytokine signaling 3 (SOCS3) might play a role in apoptosis. The sphingolipid signaling pathway was a potential candidate for milk fat synthesis. Dairy cows at 315 d were in a period of cell proliferation. Another notable phenomenon was that nonlactating dairy cows had a more regular circadian rhythm after a cycle of lactation. The results provide an important theoretical basis for the further molecular breeding of dairy cows.

Hub gene identification and prognostic model construction for isocitrate dehydrogenase mutation in glioma

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We identified ten hub genes which were driving IDH status in GBM and LGG.

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We constructed a prognostic model for IDH-mutant patients.

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Our findings have important clinical implications for accurate treatment in glioma.

Our study attempted to identify hub genes related to isocitrate dehydrogenase (IDH) mutation in glioma and develop a prognostic model for IDH-mutant glioma patients. In a first step, ten hub genes significantly associated with the IDH status were identified by weighted gene coexpression analysis (WGCNA). The functional enrichment analysis demonstrated that the most enriched terms of these hub genes were cadherin binding and glutathione metabolism. Three of these hub genes were significantly linked with the survival of glioma patients. 328 samples of IDH-mutant glioma were separated into two datasets: a training set (N = 228) and a test set (N = 100). Based on the training set, we identified two IDH-mutant subtypes with significantly different pathological features by using consensus clustering. A 31 gene-signature was identified by the least absolute shrinkage and selection operator (LASSO) algorithm and used for establishing a differential prognostic model for IDH-mutant patients. In addition, the test set was employed for validating the prognostic model, and the model was proven to be of high value in classifying prognostic information of samples. The functional annotation revealed that the genes related to the model were mainly enriched in nuclear division, DNA replication, and cell cycle. Collectively, this study provided novel insights into the molecular mechanism of IDH mutation in glioma, and constructed a prognostic model which can be effective for predicting prognosis of glioma patients with IDH-mutation, which might promote the development of IDH target agents in glioma therapies and contribute to accurate prognostication and management in IDH-mutant glioma patients.

Weighted gene co-expression network analysis identifies FCER1G as a key gene associated with diabetic kidney disease

Background

Diabetic kidney disease (DKD) is the primary cause of end-stage renal disease. However, the pathogenesis of DKD remains unclarified, and there is an urgent need for improved treatments. Recently, many crucial genes closely linked to the molecular mechanism underlying various diseases were discovered using weighted gene co-expression network analysis.

Methods

We used a gene expression omnibus series dataset GSE104948 with 12 renal glomerular DKD tissue samples and 18 control samples obtained from the gene expression omnibus database and performed weighted gene co-expression network analysis. After obtaining the trait-related modules, gene ontology and Kyoto encyclopedia of genes and genomes enrichment analyses of the modules were conducted and the key gene associated with DKD was selected from the top two most significant gene ontology terms using the maximal clique centrality method. Finally, we verified the key gene using protein-protein interaction analysis, additional datasets, and explored the relationship between the key gene and DKD renal function using the Nephroseq v5 online database.

Results

Among the 10 gene co-expression modules identified, the darkorange2 and red modules were highly related to DKD and the normal biological process, respectively. Majority of the genes in the darkorange2 module were related to immune and inflammatory responses, and potentially related to the progression of DKD due to their abnormal up-regulation. After performing sub-network analysis of the genes extracted from the top two most significant gene ontology terms and calculating the maximal clique centrality values of each gene, FCER1G, located at the center of the protein-protein interaction network, was identified as a key gene related to DKD. Furthermore, gene expression omnibus validation in additional datasets also showed that FCER1G was overexpressed in DKD compared with normal tissues. Finally, Pearson’s correlation analysis between the expression of FCER1G and DKD renal function revealed that the abnormal upregulation of FCER1G was related to diabetic glomerular lesions.

Conclusions

Our study demonstrated for the first time that FCER1G is a crucial gene associated with the pathogenesis of DKD.

Substantially Altered Expression Profile of Diabetes/Cardiovascular/Cerebrovascular Disease Associated microRNAs in Children Descending from Pregnancy Complicated by Gestational Diabetes Mellitus-One of Several Possible Reasons for an Increased Cardiovascular Risk

Gestational diabetes mellitus (GDM), one of the major pregnancy-related complications, characterized as a transitory form of diabetes induced by insulin resistance accompanied by a low/absent pancreatic beta-cell compensatory adaptation to the increased insulin demand, causes the acute, long-term, and transgenerational health complications. The aim of the study was to assess if alterations in gene expression of microRNAs associated with diabetes/cardiovascular/cerebrovascular diseases are present in whole peripheral blood of children aged 3-11 years descending from GDM complicated pregnancies. A substantially altered microRNA expression profile was found in children descending from GDM complicated pregnancies. Almost all microRNAs with the exception of miR-92a-3p, miR-155-5p, and miR-210-3p were upregulated. The microRNA expression profile also differed between children after normal and GDM complicated pregnancies in relation to the presence of overweight/obesity, prehypertension/hypertension, and/or valve problems and heart defects. Always, screening based on the combination of microRNAs was superior over using individual microRNAs, since at 10.0% false positive rate it was able to identify a large proportion of children with an aberrant microRNA expression profile (88.14% regardless of clinical findings, 75.41% with normal clinical findings, and 96.49% with abnormal clinical findings). In addition, the higher incidence of valve problems and heart defects was found in children with a prior exposure to GDM. The extensive file of predicted targets of all microRNAs aberrantly expressed in children descending from GDM complicated pregnancies indicates that a large group of these genes is involved in ontologies of diabetes/cardiovascular/cerebrovascular diseases. In general, children with a prior exposure to GDM are at higher risk of later development of diabetes mellitus and cardiovascular/cerebrovascular diseases, and would benefit from dispensarisation as well as implementation of primary prevention strategies.

Diabetes Mellitus and Cardiovascular Risk Assessment in Mothers with a History of Gestational Diabetes Mellitus Based on Postpartal Expression Profile of MicroRNAs Associated with Diabetes Mellitus and Cardiovascular and Cerebrovascular Diseases

Mothers with a history of gestational diabetes mellitus (GDM) have an increased risk of developing diabetes in the future and a lifelong cardiovascular risk. Postpartal expression profile of cardiovascular/cerebrovascular disease associated microRNAs was assessed 3–11 years after the delivery in whole peripheral blood of young and middle-aged mothers with a prior exposure to GDM with the aim to identify a high-risk group of mothers at risk of later development of diabetes mellitus and cardiovascular/cerebrovascular diseases who would benefit from implementation of early primary prevention strategies and long-term follow-up. The hypothesis of the assessment of cardiovascular risk in women was based on the knowledge that a series of microRNAs play a role in the pathogenesis of diabetes mellitus and cardiovascular/cerebrovascular diseases. Abnormal expression profile of multiple microRNAs was found in women with a prior exposure to GDM (miR-1-3p, miR-16-5p, miR-17-5p, miR-20a-5p, miR-20b-5p, miR-21-5p, miR-23a-3p, miR-24-3p, miR-26a-5p, miR-29a-3p, miR-100-5p, miR-103a-3p, miR-125b-5p, miR-126-3p, miR-130b-3p, miR-133a-3p, miR-143-3p, miR-145-5p, miR-146a-5p, miR-181a-5p, miR-195-5p, miR-199a-5p, miR-221-3p, miR-342-3p, miR-499a-5p, and-miR-574-3p). Postpartal combined screening of miR-1-3p, miR-16-5p, miR-17-5p, miR-20b-5p, miR-21-5p, miR-23a-3p, miR-26a-5p, miR-29a-3p, miR-103a-3p, miR-133a-3p, miR-146a-5p, miR-181a-5p, miR-195-5p, miR-199a-5p, miR-221-3p, and miR-499a-5p showed the highest accuracy for the identification of mothers with a prior exposure to GDM at a higher risk of later development of cardiovascular/cerebrovascular diseases (AUC 0.900, p < 0.001, sensitivity 77.48%, specificity 93.26%, cut off >0.611270413). It was able to identify 77.48% mothers with an increased cardiovascular risk at 10.0% FPR. Any of changes in epigenome (upregulation of miR-16-5p, miR-17-5p, miR-29a-3p, and miR-195-5p) that were induced by GDM-complicated pregnancy are long-acting and may predispose mothers affected with GDM to later development of diabetes mellitus and cardiovascular/cerebrovascular diseases. In addition, novel epigenetic changes (upregulation of serious of microRNAs) appeared in a proportion of women that were exposed to GDM throughout the postpartal life. Likewise, a previous occurrence of either GH, PE, and/or FGR, as well as a previous occurrence of GDM, is associated with the upregulation of miR-1-3p, miR-17-5p, miR-20a-5p, miR-20b-5p, miR-29a-3p, miR-100-5p, miR-125b-5p, miR-126-3p, miR-130b-3p, miR-133a-3p, miR-143-3p, miR-145-5p, miR-146a-5p, miR-181a-5p, miR-199a-5p, miR-221-3p, and miR-499a-5p. On the other hand, upregulation of miR-16-5p, miR-21-5p, miR-23a-3p, miR-24-3p, miR-26a-5p, miR-103a-3p, miR-195-5p, miR-342-3p, and miR-574-3p represents a unique feature of aberrant expression profile of women with a prior exposure to GDM. Screening of particular microRNAs may stratify a high-risk group of mothers with a history of GDM who might benefit from implementation of early primary prevention strategies.