Idiopathic generalized epilepsy and hypokalemic periodic paralysis in a family of South Indian descent

Quantitative mapping of genetic similarity in human heritable diseases by shared mutations

Many genetic diseases exhibit considerable epidemiological comorbidity and common symptoms, which provokes debate about the extent of their etiological overlap. The rapid growth in the number of known disease-causing mutations in the Human Gene Mutation Database (HGMD) has allowed us to characterize genetic similarities between diseases by ascertaining the extent to which identical genetic mutations are shared between diseases. Using this approach, we show that 41.6% of disease pairs in all possible pairs (42, 083) exhibit a significant sharing of mutations (P value < 0.05). These mutation-related disease pairs are in agreement with heritability-based disease-disease relations in 48 neurological and psychiatric disease pairs (Spearman's correlation coefficient = 0.50; P value = 3.4 × 10^-5 ), and share over-expressed genes significantly more often than unrelated disease pairs (1.5-1.8-fold higher; P value ≤ 1.6 × 10^-4 ). The usefulness of mutation-related disease pairs was further demonstrated for predicting novel mutations and identifying individuals susceptible to Crohn disease. Moreover, the mutation-based disease network concurs closely with that based on phenotypes.

Identification of Transcription Factor-Gene Regulatory Network in Acute Myocardial Infarction

BACKGROUND

Acute myocardial infarction (AMI) is a common disease with serious mortality and morbidity, worldwide. The present study aimed to identify differentially expressed genes (DEGs) and construct a transcription factor-gene regulatory network to further study the early diagnosis of AMI.

METHODS

The integrated analysis of publicly available Gene Expression Omnibus datasets of AMI was performed. Differentially expressed genes were identified between AMI and normal blood samples. Gene Ontology enrichment analyses, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and the transcription factor-gene regulatory network were used to obtain insights into the functions of DEGs. Quantitative real-time polymerase chain reactions (qRT-PCR) were performed to validate the expression level of DEGs.

RESULTS

A total of 2,502 DEGs, including 917 up-regulated genes and 1,585 down-regulated genes, were identified between AMI and normal blood samples by integrating four expression profiles of AMI. Differentially expressed genes were significantly enriched in pathways including complement and coagulation cascades, Staphylococcus aureus infection, and cell adhesion molecules. Transcription factors were screened and performed to construct the regulatory network. The transcription factor-gene regulatory network consisted of 871 interactions between 80 transcription factors and 716 DEGs. ETS homologous factor (EHF) was one of transcription factors that had high connectivity with DEGs and regulated CACNB4 in the network. Verification by qRT-PCR revealed that EHF, KRT6A and DSG3 were significantly up-regulated, while CACNG4 was significantly down-regulated in AMI. Furthermore, CACNG6, CACNB4 and CLDN18 had a tendency to be down-regulated, and CALML3 had a tendency to be up-regulated in AMI.

CONCLUSIONS

The identification of important differentially expressed transcription factors and genes in the development of AMI would be the groundwork for the early diagnosis and early intervention of AMI.