Hereditas最新文献

Background: Preeclampsia, a multisystem disorder of unknown etiology, is one of the leading causes of maternal and perinatal morbidity and mortality. Identifying sensitive, noninvasive markers can aid its prevention and improve prognosis. microRNAs (miRs), which function as negative regulators of gene expression, are closely related to preeclampsia occurrence and development. Herein we investigated the relationship between the DLK1-Dio3 imprinted miR cluster derived from placental and peripheral blood exosomes of pregnant women with preeclampsia and routine clinical diagnostic indicators, and also determined its potential as a noninvasive diagnostic marker.

Methods: Exosomes were extracted from the placenta and peripheral blood of pregnant women with preeclampsia.

Results: qPCR data indicated that the expression level of miRs, such as miR-134, miR-31-5p, miR-655, miR-412, miR-539, miR-409, and miR-496, in pregnant women with preeclampsia was significantly lower than that in healthy controls; miR-31-5p expression was the most different. Gene ontology analysis predicted that genes negatively regulated by miR-31-5p were mainly enriched in cellular entity, cellular process, and binding; moreover, Kyoto Encyclopedia of Genes and Genomes pathway analyses indicated that genes were involved in gonadotropin-releasing hormone receptor pathway and other signaling pathways. Correlation analysis revealed that miR-31-5p was significantly negatively correlated with clinical indicators of preeclampsia, such as systolic and diastolic pressure, lactate dehydrogenase, and proteinuria.

Conclusion: We believe that exosome-derived miR-31-5p can serve as an effective and sensitive biomarker to determine the course of preeclampsia in pregnant women.

Background: Childhood systemic lupus erythematosus (cSLE) is a multisystemic, life-threatening autoimmune disease. Compared to adults, SLE in childhood is more active, can cause multisystem involvement including renal, neurological and hematological, and can cause cumulative damage across systems more rapidly. Autophagy, one of the core functions of cells, is involved in almost every process of the immune response and has been shown to be associated with many autoimmune diseases, being a key factor in the interplay between innate and adaptive immunity. Autophagy influences the onset, progression and severity of SLE. This paper identifies new biomarkers for the diagnosis and treatment of childhood SLE based on an artificial neural network of autophagy-related genes.

Methods: We downloaded dataset GSE100163 from the Gene Expression Omnibus database and used Protein-protein Interaction Network (PPI) and Least Absolute Shrinkage and Selection Operator (LASSO) to screen the signature genes of autophagy-related genes in cSLE. A new artificial neural network model for cSLE diagnosis was constructed using the signature genes. The predictive efficiency of the model was also validated using the dataset GSE65391. Finally, "CIBERSORT" was used to calculate the infiltration of immune cells in cSLE and to analyze the relationship between the signature genes and the infiltration of immune cells.

Results: We identified 37 autophagy-related genes that differed in cSLE and normal samples, and finally obtained the seven most relevant signature genes for cSLE (DDIT3, GNB2L1, CTSD, HSPA8, ULK1, DNAJB1, CANX) by PPI and LASOO regression screening, and constructed an artificial neural network diagnostic model for cSLE. Using this model, we plotted the ROC curves for the training and validation group diagnoses with the area under the curve of 0.976 and 0.783, respectively. Finally, we performed immunoassays on cSLE samples, and the results showed that Plasma cells, Macrophages M0, Dendritic cells activated and Neutrophils were significantly infiltrated in cSLE.

Conclusion: We constructed an artificial neural network diagnostic model of seven autophagy-related genes that can be used for the diagnosis of cSLE. Meanwhile, the characteristic genes affect the immune infiltration of cSLE, which may provide new perspectives for the exploration of cSLE treatment and related mechanisms.

Background: Giant congenital melanocytic nevus (GCMN) is the benign nevomelanocytic proliferation. Mutations in NRAS have been previously detected in GCMN, but mutations in BRAF are generally lacking in the Chinese population. Mutated genes in this disease can estimate the risk of malignant transformation in GCMN. Therefore, it is worth investigating the genetic information of GCMN.

Methods: Here, we presented two cases of GCMN of the upper extremities. The clinical and histological data were analyzed. The whole exome sequencing (WES) was performed to investigate the mutational profile of peripheral venous blood (PB), normal skin (NS), small melanocytic nevus (SMN), deep penetrating and non-penetrating GCMN (dPGCMN and nPGCMN).

Results: We showed a reduction in the circumference of involved upper extremities in both patients. The clinical and histopathological data indicated the reduction of adipose tissue associated with the invasion of GCMN. The WES data revealed that MUC16, MAP3K15 and ABCA1 were novel potential candidate genes for the disease as well as biomarkers for predicting malignant transformation.

Conclusion: The MUC16, MAP3K15 and ABCA1 may serve as novel biomarkers for predicting malignant transformation and targets for the diagnoses and therapy for the GCMN.

Background: Inflammation and long noncoding RNAs (lncRNAs) are gradually becoming important in the development of bladder cancer (BC). Nevertheless, the potential of inflammatory response-related lncRNAs (IRRlncRNAs) as a prognostic signature remains unexplored in BC.

Methods: The Cancer Genome Atlas (TCGA) provided RNA expression profiles and clinical information of BC samples, and GSEA Molecular Signatures database provided 1171 inflammation-related genes. IRRlncRNAs were identified using Pearson correlation analysis. After that, consensus clustering was performed to form molecular subtypes. After performing least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses, a risk model constructed based on the prognostic IRRlncRNAs was validated in an independent cohort. Kaplan-Meier (KM) analysis, univariate and multivariate Cox regression, clinical stratification analysis, and time-dependent receiver operating characteristic (ROC) curves were utilized to assess clinical effectiveness and accuracy of the risk model. In clusters and risk model, functional enrichment was investigated using GSEA and GSVA, and immune cell infiltration analysis was demonstrated by ESTIMATE and CIBERSORT analysis.

Results: A total of 174 prognostic IRRlncRNAs were confirmed, and 406 samples were divided into 2 clusters, with cluster 2 having a significantly inferior prognosis. Moreover, cluster 2 exhibited a higher ESTIMATE score, immune infiltration, and PD-L1 expression, with close relationships with the inflammatory response. Further, 12 IRRlncRNAs were identified and applied to construct the risk model and divide BC samples into low-risk and high-risk groups successfully. KM, ROC, and clinical stratification analysis demonstrated that the risk model performed well in predicting prognosis. The risk score was identified as an independently significant indicator, enriched in immune, cell cycle, and apoptosis-related pathways, and correlated with 9 immune cells.

Conclusion: We developed an inflammatory response-related subtypes and steady prognostic risk model based on 12 IRRlncRNAs, which was valuable for individual prognostic prediction and stratification and outfitted new insight into inflammatory response in BC.

Background: Apple production in Sweden and elsewhere is being threatened by the fungus, Neonectria ditissima, which causes a disease known as European canker. The disease can cause extensive damage and the removal of diseased wood and heavily infected trees can be laborious and expensive. Currently, there is no way to eradicate the fungus from infected trees and our knowledge of the infection process is limited. Thus, to target and modify genes efficiently, the genetic transformation technique developed for N. ditissima back in 2003 was modified.

Results: The original protocol from 2003 was upgraded to use enzymes currently available in the market for making protoplasts. The protoplasts were viable, able to uptake foreign DNA, and able to regenerate back into a mycelial colony, either as targeted gene-disruption mutants or as ectopic mutants expressing the green fluorescent protein (GFP).

Conclusions: A new genetic transformation protocol has been established and the inclusion of hydroxyurea in the buffer during the protoplast-generation step greatly increased the creation of knockout mutants via homologous recombination. Pathogenicity assays using the GFP-mutants showed that the mutants were able to infect the host and cause disease.

Gout is a chronic metabolic disease that seriously affects human health. It is also a major challenge facing the world, which has brought a heavy burden to patients and society. Hyperuricemia (HUA) is the most important risk factor for gout. In recent years, with the improvement of living standards and the change of dietary habits, the incidence of gout in the world has increased dramatically, and gradually tends to be younger. An increasing number of studies have shown that gene mutations may play an important role in the development of HUA and gout. Therefore, we reviewed the existing literature and summarized the susceptibility genes and research status of HUA and gout, in order to provide reference for the early diagnosis, individualized treatment and the development of new targeted drugs of HUA and gout.

Background: Arecoline is a well-known risk factor for oral submucosal fibrosis and cancer. However, the mechanistic correlation between arecoline and hepatocellular cancer remains elusive. Here, we investigated the effect of arecoline on the proliferation and migration of human HepG2 hepatoma cells and its potential oncogenic mechanisms.

Methods: Bioinformatic technologies were used to identify the deferentially expressed miRNAs (DE-miRNAs) and hub target genes of arecoline-induced cancers. These DE-miRNAs, hub genes and pathway were proved in arecoline-treated HepG2 cells.

Results: A total of 86 DE-miRNAs and 460 target genes were identified. These target genes are associated with DNA-templated regulation of transcription and other biological processes. Significant molecular functions were protein binding, calcium ion binding, and enrichment in the nucleus and cytoplasm. These genes are involved in the PI3K-AKT pathway. CDK1, CCND1, RAF1, CDKN1B and BTRC were defined as the top 5 hub target genes, and patients with high expression of CDK1 showed poor prognosis. Compared with control group, 2.5 µM arecoline treatment increased the proliferation and migration ability of the HepG2 cells. Treatment with 2.5 µM arecoline increased the levels of miR-21-3p, miR-21-5p and miR-1267, upregulated the expression of PI3K-AKT pathway factors, CDK1, CCND1 but decreased RAF1 expression.

Conclusion: A low concentration arecoline can induce the proliferation and migration of HepG2 cells, with the potential mechanism of action linked to high levels of exosomal miR-21 and miR-1267, activation of the PI3K-AKT pathway, upregulation of CDK1 and CCND1, and downregulation of RAF1.

Background: Hereditary angioedema (HAE) is a rare disease characterized by recurrent attacks of severe swellings of the skin and submucosa. More than 900 variants of the SERPING1 gene associated with HAE have been identified. However, only approximately 50 variants have been identified in the Chinese population. This study aimed to update the mutational spectrum in Chinese HAE patients and provide evidence for the accurate diagnosis of HAE.

Methods: A total of 97 unrelated HAE patients were enrolled in the study. Sanger sequencing and multiple ligation-dependent probe amplification analysis were used to identify the variants in the SERPING1 gene. The variants were reviewed in a number of databases, including the Human Gene Mutation Database (HGMD) ( http://www.hgmd.cf.ac.uk/ ) and the Leiden Open Variation Database (LOVD, https://databases.lovd.nl/shared/variants/SERPING1 ). The American College of Medical Genetics and Genomics-Association for Molecular Pathology (ACMG-AMP) criteria was used to determine the pathogenicity of the variants.

Results: Of the 97 patients, 76 different variants were identified in 90 of them and no disease-causing variants were identified in the remaining 7 patients. Among the 76 variants, 35 variants were novel and submitted to ClinVar. Missense and in-frame variants were the most common variants (36.8%), followed by frameshift (28.9%), nonsense (14.5%), splice site (13.2%) variants, and gross deletions/duplications (6.6%).

Conclusions: Our findings broaden the mutational spectrum of SERPING1 and provide evidence for accurate diagnosis and predictive genetic counseling.

Background: Sepsis is a life-threatening multi-organ dysfunction caused by the dysregulated host response to infection. Sepsis remains a major global concern with high mortality and morbidity, while management of sepsis patients relies heavily on early recognition and rapid stratification. This study aims to identify the crucial genes and biomarkers for sepsis which could guide clinicians to make rapid diagnosis and prognostication.

Methods: Preliminary analysis of multiple global datasets, including 170 samples from patients with sepsis and 110 healthy control samples, revealed common differentially expressed genes (DEGs) in peripheral blood of patients with sepsis. After Gene Oncology (GO) and pathway analysis, the Weighted Gene Correlation Network Analysis (WGCNA) was used to screen for genes most related with clinical diagnosis. Also, the Protein-Protein Interaction Network (PPI Network) was constructed based on the DEGs and the hub genes were found. The results of WGCNA and PPI network were compared and one shared gene was discovered. Then more datasets of 728 experimental samples and 355 control samples were used to prove the diagnostic and prognostic value of this gene. Last, we used real-time PCR to confirm the bioinformatic results.

Results: Four hundred forty-four common differentially expressed genes in the blood of sepsis patients from different ethnicities were identified. Fifteen genes most related with clinical diagnosis were found by WGCNA, and 24 hub genes with most node degrees were identified by PPI network. ARG1 turned out to be the unique overlapped gene. Further analysis using more datasets showed that ARG1 was not only sharply up-regulated in sepsis than in healthy controls, but also significantly high-expressed in septic shock than in non-septic shock, significantly high-expressed in severe or lethal sepsis than in uncomplicated sepsis, and significantly high-expressed in non-responders than in responders upon early treatment. These all demonstrate the performance of ARG1 as a key biomarker. Last, the up-regulation of ARG1 in the blood was confirmed experimentally.

Conclusions: We identified crucial genes that may play significant roles in sepsis by WGCNA and PPI network. ARG1 was the only overlapped gene in both results and could be used to make an accurate diagnosis, discriminate the severity and predict the treatment response of sepsis.

Background: Psoriasis is a condition in which skin cells build up and form itchy scales and dry patches. It is also considered a common lifelong disease with an unclear pathogenesis. Furthermore, an effective cure for psoriasis is still unavailable. Reductive apoptosis of keratinocytes and immune infiltration are common in psoriasis. This study aimed to explore underlying functions of key apoptosis-related genes and the characteristics of immune infiltration in psoriasis. We used GSE13355 and GSE30999 to screen differentially expressed apoptosis related genes (DEARGs) in our study. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and gene set enrichment analysis (GSEA) were performed using clusterProfiler package. Protein-protein interaction (PPI) network was constructed to acquire key DEARGs. Transcription factor (TF)-target and miRNA-mRNA network analyses, drug sensitivity prediction, and immune infiltration were applied. Key DEARGs were validated using real-time quantitative PCR (RT-qPCR).

Results: We identified 482 and 32 DEARGs from GSE13355 and GSE30999, respectively. GO analysis showed that DEARGs were commonly enriched in cell chemotaxis, receptor ligand activity, and signaling receptor activator activity. KEGG pathway analysis indicated that viral protein interaction with cytokine and cytokine receptor was maximally enriched pathway. The GSEA analysis of GSE13355 and GSE30999 demonstrated a high consistency degree of enriched pathways. Thirteen key DEARGs with upregulation were obtained in the PPI network. Eleven key DEARGs were confirmed using RT-qPCR. Additionally, 5 TFs and 553 miRNAs were acquired, and three novel drugs were predicted. Moreover, Dendritic.cells.activated exhibited high levels of immune infiltration while Mast.cells.resting showed low levels of immune infiltration in psoriasis groups.

Conclusion: Results of this study may reveal some insights into the underlying molecular mechanism of psoriasis and provide novel targeted drugs.