RESUMO
BACKGROUND: Current treatments for Multiple Sclerosis (MS) poorly address chronic innate neuroinflammation nor do they offer effective remyelination. The vagus nerve has a strong regulatory role in inflammation and Vagus Nerve Stimulation (VNS) has potential to affect both neuroinflammation and remyelination in MS. OBJECTIVE: This study investigated the effects of VNS on demyelination and innate neuroinflammation in a validated MS rodent model. METHODS: Lysolecithin (LPC) was injected in the corpus callosum (CC) of 46 Lewis rats, inducing a demyelinated lesion. 33/46 rats received continuously-cycled VNS (cVNS) or one-minute per day VNS (1minVNS) or sham VNS from 2 days before LPC-injection until perfusion at 3 days post-injection (dpi) (corresponding with a demyelinated lesion with peak inflammation). 13/46 rats received cVNS or sham from 2 days before LPC-injection until perfusion at 11 dpi (corresponding with a partial remyelinated lesion). Immunohistochemistry and proteomics analyses were performed to investigate the extend of demyelination and inflammation. RESULTS: Immunohistochemistry showed that cVNS significantly reduced microglial and astrocytic activation in the lesion and lesion border, and significantly reduced the Olig2+ cell count at 3 dpi. Furthermore, cVNS significantly improved remyelination with 57.4 % versus sham at 11 dpi. Proteomic gene set enrichment analyses showed increased activation of (glutamatergic) synapse pathways in cVNS versus sham, most pronounced at 3 dpi. CONCLUSION: cVNS improved remyelination of an LPC-induced lesion. Possible mechanisms might include modulation of microglia and astrocyte activity, increased (glutamatergic) synapses and enhanced oligodendrocyte clearance after initial injury.
RESUMO
A major challenge in precision oncology is to detect targetable cancer vulnerabilities in individual patients. Modeling high-throughput omics data in biological networks allows identifying key molecules and processes of tumorigenesis. Traditionally, network inference methods rely on many samples to contain sufficient information for learning, resulting in aggregate networks. However, to implement patient-tailored approaches in precision oncology, we need to interpret omics data at the level of individual patients. Several single-sample network inference methods have been developed that infer biological networks for an individual sample from bulk RNA-seq data. However, only a limited comparison of these methods has been made and many methods rely on 'normal tissue' samples as reference, which are not always available. Here, we conducted an evaluation of the single-sample network inference methods SSN, LIONESS, SWEET, iENA, CSN and SSPGI using transcriptomic profiles of lung and brain cancer cell lines from the CCLE database. The methods constructed functional gene networks with distinct network characteristics. Hub gene analyses revealed different degrees of subtype-specificity across methods. Single-sample networks were able to distinguish between tumor subtypes, as exemplified by node strength clustering, enrichment of known subtype-specific driver genes among hubs and differential node strength. We also showed that single-sample networks correlated better to other omics data from the same cell line as compared to aggregate networks. We conclude that single-sample network inference methods can reflect sample-specific biology when 'normal tissue' samples are absent and we point out peculiarities of each method.
