The frequency of the known mitochondrial variants associated with drug-induced toxicity in a Korean population

Background Few studies have annotated the whole mitochondrial DNA (mtDNA) genome associated with drug responses in Asian populations. This study aimed to characterize mtDNA genetic profiles, especially the distribution and frequency of well-known genetic biomarkers associated with diseases and drug-induced toxicity in a Korean population. Method Whole mitochondrial genome was sequenced for 118 Korean subjects by using a next-generation sequencing approach. The bioinformatic pipeline was constructed for variant calling, haplogroup classification and annotation of mitochondrial mutation. Results A total of 681 variants was identified among all subjects. The MT-TRNP gene and displacement loop showed the highest numbers of variants (113 and 74 variants, respectively). The m.16189T > C allele, which is known to reduce the mtDNA copy number in human cells was detected in 25.4% of subjects. The variants (m.2706A > G, m.3010A > G, and m.1095T > C), which are associated with drug-induced toxicity, were observed with the frequency of 99.15%, 30.51%, and 0.08%, respectively. The m.2150T > A, a genotype associated with highly disruptive effects on mitochondrial ribosomes, was identified in five subjects. The D and M groups were the most dominant groups with the frequency of 34.74% and 16.1%, respectively. Conclusions Our finding was consistent with Korean Genome Project and well reflected the unique profile of mitochondrial haplogroup distribution. It was the first study to annotate the whole mitochondrial genome with drug-induced toxicity to predict the ADRs event in clinical implementation for Korean subjects. This approach could be extended for further study for validation of the potential ethnic-specific mitochondrial genetic biomarkers in the Korean population.

Identification of Potential Genetic Biomarkers and Target Genes of Peri-Implantitis Using Bioinformatics Tools

Objectives. To investigate potential genetic biomarkers of peri-implantitis and target genes for the therapy of peri-implantitis by bioinformatics analysis of publicly available data. Methods. The GSE33774 microarray dataset was downloaded from the Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) between peri-implantitis and healthy gingival tissues were identified using the GEO2R tool. GO enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed using the DAVID database and the Metascape tool, and the results were expressed as a bubble diagram. The protein-protein interaction network of DEGs was constructed using the Search Tool for the Retrieval of Interacting Genes (STRING) and visualized using Cytoscape. The hub genes were screened by the cytoHubba plugin of Cytoscape. The potential target genes associated with peri-implantitis were obtained from the DisGeNET database and the Open Targets Platform. The intersecting genes were identified using the Venn diagram web tool. Results. Between the peri-implantitis group and the healthy group, 205 DEGs were investigated including 140 upregulated genes and 65 downregulated genes. These DEGs were mainly enriched in functions such as the immune response, inflammatory response, cell adhesion, receptor activity, and protease binding. The results of KEGG pathway enrichment analysis revealed that DEGs were mainly involved in the cytokine-cytokine receptor interaction, pathways in cancer, and the PI3K-Akt signaling pathway. The intersecting genes, including IL6, TLR4, FN1, IL1β, CXCL8, MMP9, and SPP1, were revealed as potential genetic biomarkers and target genes of peri-implantitis. Conclusions. This study provides supportive evidence that IL6, TLR4, FN1, IL1β, CXCL8, MMP9, and SPP1 might be used as potential target biomarkers for peri-implantitis which may provide further therapeutic potentials for peri-implantitis.

Next Generation Sequencing-Based Mitochondrial Genome Analysis In Relation To The Drug- Induced Toxicity In Korean Subjects

Background: Mitochondrial variants have been investigated to be associated with many diseases, which was reported largely from European populations. Few studies, however, have annotated the whole mitochondrial DNA (mtDNA) genome associated with drug responses including adverse drug reactions (ADRs), especially in Asian populations. This study was performed to characterize mtDNA genetic profiles, especially the distribution and frequency of well-known genetic biomarkers associated with diseases and drug-induced toxicity, in the Korean population by using high throughput next-generation sequencing.Results: A total of 681 variants was identified among all 118 Korean subjects. The MT-TRNP and displacement loop (D-loop) showed the highest numbers of variants (113 and 74 variants, respectively). In the D-loop, 25.4% of the subjects were identified to have m.16189T>C allele, which was known to reduce the mtDNA copy number variation in human cells. The variant m.1095T>C, annotated to aminoglycoside-induced ototoxicity, was found from only one subject in this study, while the frequency of m.2706A>G and m.3010A>G, which are associated with antibiotic-induced toxicity, were 99.15% and 30.51%, respectively. The 5 subjects were identified to have the variant m.2150T>TA, a genotype associated with highly disruptive effects on mitochondrial ribosomes. In addition, the mitochondrial haplogroups of 118 Korean subjects were found that D and M groups were the most dominant groups with the frequency of 34.74% and 16.1%, respectively. Conclusions: Our finding was constant with Korean 1K project and well reflected the unique profile of mitochondrial haplogroup distribution. It was the first study to annotate the whole mitochondrial genome with drug-induced toxicity to predict the ADRs event in clinical implementation for Korean population. This approach could be extended for further study for validation the potential ethnic specific mitochondrial genetic biomarkers in Korea population.

Molecular and Genetic Immune Biomarkers of Primary and Immune-Therapy Induced Hypophysitis: From Laboratories to the Clinical Practice

Hypophysitis is a rare and potentially life-threatening disease, characterized by an elevated risk of complications, such as the occurrence of acute central hypoadrenalism, persistent hypopituitarism, or the extension of the inflammatory process to the neighboring neurological structures. In recent years, a large number of cases has been described. The diagnosis of hypophysitis is complex because it is based on clinical and radiological criteria. Due to this, the integration of molecular and genetic biomarkers can help physicians in the diagnosis of hypophysitis and play a role in predicting disease outcome. In this paper, we review current knowledge about molecular and genetic biomarkers of hypophysitis with the aim of suggesting a possible integration of these biomarkers in clinical practice.

Analysis of potential genetic biomarkers and molecular mechanism of smoking-related postmenopausal osteoporosis using weighted gene co-expression network analysis and machine learning

Objectives Smoking is a significant independent risk factor for postmenopausal osteoporosis, leading to genome variations in postmenopausal smokers. This study investigates potential biomarkers and molecular mechanisms of smoking-related postmenopausal osteoporosis (SRPO). Materials and methods The GSE13850 microarray dataset was downloaded from Gene Expression Omnibus (GEO). Gene modules associated with SRPO were identified using weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) analysis, and pathway and functional enrichment analyses. Feature genes were selected using two machine learning methods: support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF). The diagnostic efficiency of the selected genes was assessed by gene expression analysis and receiver operating characteristic curve. Results Eight highly conserved modules were detected in the WGCNA network, and the genes in the module that was strongly correlated with SRPO were used for constructing the PPI network. A total of 113 hub genes were identified in the core network using topological network analysis. Enrichment analysis results showed that hub genes were closely associated with the regulation of RNA transcription and translation, ATPase activity, and immune-related signaling. Six genes (HNRNPC, PFDN2, PSMC5, RPS16, TCEB2, and UBE2V2) were selected as genetic biomarkers for SRPO by integrating the feature selection of SVM-RFE and RF. Conclusion The present study identified potential genetic biomarkers and provided a novel insight into the underlying molecular mechanism of SRPO.

Reverse Engineering of the Pediatric Sepsis Regulatory Network and Identification of Master Regulators

Sepsis remains a leading cause of death in ICUs all over the world, with pediatric sepsis accounting for a high percentage of mortality in pediatric ICUs. Its complexity makes it difficult to establish a consensus on genetic biomarkers and therapeutic targets. A promising strategy is to investigate the regulatory mechanisms involved in sepsis progression, but there are few studies regarding gene regulation in sepsis. This work aimed to reconstruct the sepsis regulatory network and identify transcription factors (TFs) driving transcriptional states, which we refer to here as master regulators. We used public gene expression datasets to infer the co-expression network associated with sepsis in a retrospective study. We identified a set of 15 TFs as potential master regulators of pediatric sepsis, which were divided into two main clusters. The first cluster corresponded to TFs with decreased activity in pediatric sepsis, and GATA3 and RORA, as well as other TFs previously implicated in the context of inflammatory response. The second cluster corresponded to TFs with increased activity in pediatric sepsis and was composed of TRIM25, RFX2, and MEF2A, genes not previously described as acting in a coordinated way in pediatric sepsis. Altogether, these results show how a subset of master regulators TF can drive pathological transcriptional states, with implications for sepsis biology and treatment.