What do the genetic association data say about the high risk of suicide in people with depression? A novel network-based approach to find common molecular basis for depression and suicidal behavior and related therapeutic targets

Background Available sources indicate that the risk of suicide in people with major depression is higher than other psychiatric disorders. Although it seems that these two conditions may have a shared cause in some cases, no studies have been conducted to identify a common basis for them. Methods In this study, following an extensive review of literature, we found almost all the genes that are involved in major depression and suicidal behavior, and we isolated genes shared between the two conditions. Then, we found all physical or functional interactions within three mentioned gene sets and reconstructed three genetic interactive networks. All networks were analyzed topologically and enriched functionally. Finally, using a drug repurposing approach, we found the main available drugs that interacted with the most central genes shared between suicidal behavior and depression. Results The results demonstrated that BDNF, SLC6A4, CREB1, and TNF are the most fundamental shared genes; and generally, disordered dopaminergic, serotonergic, and immunologic pathways in neuronal projections are the main shared deficient pathways. In addition, we found two genes, SLC6A4 and SLC6A2, to be the main therapeutic targets, and Serotonin-Norepinephrine Reuptake Inhibitors (SNRI) and Tricyclic Antidepressants (TCA) to be the most effective drugs for individuals with depression at risk for suicide. Conclusions Our results, in addition to shedding light on the integrated molecular basis of depression-suicide, offer new therapeutic targets for individuals with depression at high risk for suicide and could pave the way for future preclinical and clinical studies. However, integrative systems biology-based studies highly depend on existing data and related databases, as well as the arrival of new experimental data sources in the future, possibly affecting the current results. © 2018 Elsevier B.V

What do the genetic association data say about the high risk of suicide in people with depression? A novel network-based approach to find common molecular basis for depression and suicidal behavior and related therapeutic targets

Background Available sources indicate that the risk of suicide in people with major depression is higher than other psychiatric disorders. Although it seems that these two conditions may have a shared cause in some cases, no studies have been conducted to identify a common basis for them. Methods In this study, following an extensive review of literature, we found almost all the genes that are involved in major depression and suicidal behavior, and we isolated genes shared between the two conditions. Then, we found all physical or functional interactions within three mentioned gene sets and reconstructed three genetic interactive networks. All networks were analyzed topologically and enriched functionally. Finally, using a drug repurposing approach, we found the main available drugs that interacted with the most central genes shared between suicidal behavior and depression. Results The results demonstrated that BDNF, SLC6A4, CREB1, and TNF are the most fundamental shared genes; and generally, disordered dopaminergic, serotonergic, and immunologic pathways in neuronal projections are the main shared deficient pathways. In addition, we found two genes, SLC6A4 and SLC6A2, to be the main therapeutic targets, and Serotonin-Norepinephrine Reuptake Inhibitors (SNRI) and Tricyclic Antidepressants (TCA) to be the most effective drugs for individuals with depression at risk for suicide. Conclusions Our results, in addition to shedding light on the integrated molecular basis of depression-suicide, offer new therapeutic targets for individuals with depression at high risk for suicide and could pave the way for future preclinical and clinical studies. However, integrative systems biology-based studies highly depend on existing data and related databases, as well as the arrival of new experimental data sources in the future, possibly affecting the current results. © 2018 Elsevier B.V