Using the BITOLA system to identify candidate molecules in the interaction between oral lichen planus and depression

Depressive Disorder and Dermatological Autoimmune Diseases

Depressive disorders are a growing problem worldwide. They are also characterized by high comorbidity, including from the circle of dermatological diseases. Autoimmune diseases seem to be particularly correlated with depressive comorbidity, raising the question of their possible common pathomechanism. The PubMed database was searched, focusing on results published after 2016. A particular reciprocal correlation of depressive disorders with psoriasis, atopic dermatitis, alopecia areata, impetigo, lupus and systemic scleroderma was found. One possible explanation for the co-occurrence of the above diseases is that the inflammatory theory may be applicable to depression, the various elements of which also apply to autoimmune diseases.

Identification of biological pathways and genes associated with neurogenic heterotopic ossification by text mining

Background

Neurogenic heterotopic ossification is a disorder of aberrant bone formation affecting one in five patients sustaining a spinal cord injury or traumatic brain injury (SCI-TBI-HO). However, the underlying mechanisms of SCI-TBI-HO have proven difficult to elucidate. The aim of the present study is to identify the most promising candidate genes and biological pathways for SCI-TBI-HO.

Methods

In this study, we used text mining to generate potential explanations for SCI-TBI-HO. Moreover, we employed several additional datasets, including gene expression profile data, drug data and tissue-specific gene expression data, to explore promising genes that associated with SCI-TBI-HO.

Results

We identified four SCI-TBI-HO-associated genes, including GDF15, LDLR, CCL2, and CLU. Finally, using enrichment analysis, we identified several pathways, including integrin signaling, insulin pathway, internalization of ErbB1, urokinase-type plasminogen activator and uPAR-mediated signaling, PDGFR-beta signaling pathway, EGF receptor (ErbB1) signaling pathway, and class I PI3K signaling events, which may be associated with SCI-TBI-HO.

Conclusions

These results enhance our understanding of the molecular mechanisms of SCI-TBI-HO and offer new leads for researchers and innovative therapeutic strategies.

Using literature-based discovery to identify candidate genes for the interaction between myocardial infarction and depression

Background

A multidirectional relationship has been demonstrated between myocardial infarction (MI) and depression. However, the causal genetic factors and molecular mechanisms underlying this interaction remain unclear. The main purpose of this study was to identify potential candidate genes for the interaction between the two diseases.

Methods

Using a bioinformatics approach and existing gene expression data in the biomedical discovery support system (BITOLA), we defined the starting concept X as “Myocardial Infarction” and end concept Z as “Major Depressive Disorder” or “Depressive disorder”. All intermediate concepts relevant to the “Gene or Gene Product” for MI and depression were searched. Gene expression data and tissue-specific expression of potential candidate genes were evaluated using the Human eFP (electronic Fluorescent Pictograph) Browser, and intermediate concepts were filtered by manual inspection.

Results

Our analysis identified 128 genes common to both the “MI” and “depression” text mining concepts. Twenty-three of the 128 genes were selected as intermediates for this study, 9 of which passed the manual filtering step. Among the 9 genes, LCAT, CD4, SERPINA1, IL6, and PPBP failed to pass the follow-up filter in the Human eFP Browser, due to their low levels in the heart tissue. Finally, four genes (GNB3, CNR1, MTHFR, and NCAM1) remained.

Conclusions

GNB3, CNR1, MTHFR, and NCAM1 are putative new candidate genes that may influence the interactions between MI and depression, and may represent potential targets for therapeutic intervention.