The basolateral amygdala-anterior cingulate pathway contributes to depression-like behaviors and comorbidity with chronic pain behaviors in male mice.

While depression and chronic pain are frequently comorbid, underlying neuronal circuits and their psychopathological relevance remain poorly defined. Here we show in mice that hyperactivity of the neuronal pathway linking the basolateral amygdala to the anterior cingulate cortex is essential for chronic pain-induced depression. Moreover, activation of this pathway in naive male mice, in the absence of on-going pain, is sufficient to trigger depressive-like behaviors, as well as transcriptomic alterations that recapitulate core molecular features of depression in the human brain. These alterations notably impact gene modules related to myelination and the oligodendrocyte lineage. Among these, we show that Sema4a, which was significantly upregulated in both male mice and humans in the context of altered mood, is necessary for the emergence of emotional dysfunction. Overall, these results place the amygdalo-cingulate pathway at the core of pain and depression comorbidity, and unravel the role of Sema4a and impaired myelination in mood control.

Identification of Systems Level Molecular Signatures from Glioblastoma Multiforme Derived Extracellular Vesicles.

Glioblastoma multiforme (GBM) is one of the most lethal malignancies of the central nervous system characterized by high mortality rate. The complexity of GBM pathogenesis, progression, and prognosis is not fully understood yet. GBM-derived extracellular vesicles (EVs) carry several oncogenic elements that facilitate GBM progression. The purpose of this study was to identify systems level molecular signatures from GBM-derived EVs using integrative analysis of publicly available transcriptomic data generated from plasma and serum samples. The dataset contained 19 samples in total, of which 15 samples were from plasma (11 GBM patients and 4 healthy samples) and 4 samples were from serum (2 GBM and 2 healthy samples). We carried out statistical analysis to identify differentially expressed genes (DEGs), functional enrichment analysis of the DEGs, protein-protein interaction networks, module analysis, transcription factors and target gene regulatory networks analysis, and identification of hub genes. The differential expression of the identified hub genes were validated with the independent TCGA-GBM dataset. We have identified a few crucial genes and pathways associated with GBM prognosis and therapy resistance. The DEGs identified from plasma were associated with inflammatory processes and viral infection. On the other hand, the hub genes identified from the serum samples were significantly associated with protein ubiquitinylation processes and cytokine signaling regulation. The findings indicate that GBM-derived plasma and serum DEGs may be associated with distinct cellular processes and pathways which facilitate GBM progression. The findings will provide better understanding of the molecular mechanisms of GBM pathogenesis and progression. These results can further be utilized for developing and validating minimally invasive diagnostic and therapeutic molecular biomarkers for GBM.

Integrative network analysis identifies differential regulation of neuroimmune system in Schizophrenia and Bipolar disorder.

Background: Neuropsychiatric disorders such as Schizophrenia (SCZ) and Bipolar disorder (BPD) pose a broad range of problems with different symptoms mainly characterized by some combination of abnormal thoughts, emotions, behaviour, etc. However, in depth molecular and pathophysiological mechanisms among different neuropsychiatric disorders have not been clearly understood yet. We have used RNA-seq data to investigate unique and overlapping molecular signatures between SCZ and BPD using an integrative network biology approach. Methods: RNA-seq count data were collected from NCBI-GEO database generated on post-mortem brain tissues of controls (n = 24) and patients of BPD (n = 24) and SCZ (n = 24). Differentially expressed genes (DEGs) were identified using the consensus of DESeq2 and edgeR tools and used for downstream analysis. Weighted gene correlation networks were constructed to find non-preserved (NP) modules for SCZ, BPD and control conditions. Topological analysis and functional enrichment analysis were performed on NP modules to identify unique and overlapping expression signatures during SCZ and BPD conditions. Results: We have identified four NP modules from the DEGs of BPD and SCZ. Eleven overlapping genes have been identified between SCZ and BPD networks, and they were found to be highly enriched in inflammatory responses. Among these eleven genes, TNIP2, TNFRSF1A and AC005840.1 had higher sum of connectivity exclusively in BPD network. In addition, we observed that top five genes of NP module from SCZ network were downregulated which may be a key factor for SCZ disorder. Conclusions: Differential activation of the immune system components and pathways may drive the common and unique pathogenesis of the BPD and SCZ.