G-Protein-Coupled Receptor Expression and Purification

Silencing of FTO inhibits oxidative stress to relieve neuropathic pain by m6A modification of GPR177

BACKGROUND

Neuropathic pain (NP) is a challenging health condition owing to its complex nature and associated multiple etiologies. The occurrence of NP involves the abnormal activity of neurons mediated by oxidative stress (OS). Previous research has demonstrated that m6A methylation plays a role in the regulatory pathway of NP. This study aimed to investigate the specific molecular pathways through which m6A methylation modifiers alleviate NP.

METHODS

For this purpose, an NO rat model was developed via spared nerve injury (SNI), followed by quantifying the animal's pain assessment via paw withdrawal threshold (PWT) and paw withdrawal latency (PWL). The OS in SNI rats was evaluated by measuring reactive oxygen species, superoxide dismutase, and catalase (CAT) in spinal cord tissues. Moreover, quantitative-real-time polymerase chain reaction and western blot analysis were employed for detecting fat mass and obesity-associated (FTO) and GPR177 levels, while m6A levels of GPR117 were analyzed via MeRIP.

RESULTS

The results indicated an enhanced OS with highly expressed FTO in spinal cord tissue samples, where knocking down Fto effectively relieved NP and OS in SNI rats. Mechanistic investigations revealed that Fto-mediated reduction of Grp177 m6A modification was involved in the WNT5a/TRPV1 axis-mediated OS remission of NP. Moreover, in vitro experiment results indicated that YTHDF2 was an important m6A methylated reading protein for this process.

CONCLUSIONS

Fto silencing leads to increased m6A methylation of Grp177 through a YTHDF2-dependent mechanism, resulting in decreased Grp177 stability and ultimately reducing NP in rats by OS suppression.

Systematic assessment of pathway databases, based on a diverse collection of user-submitted experiments

A knowledge-based grouping of genes into pathways or functional units is essential for describing and understanding cellular complexity. However, it is not always clear a priori how and at what level of specificity functionally interconnected genes should be partitioned into pathways, for a given application. Here, we assess and compare nine existing and two conceptually novel functional classification systems, with respect to their discovery power and generality in gene set enrichment testing. We base our assessment on a collection of nearly 2000 functional genomics datasets provided by users of the STRING database. With these real-life and diverse queries, we assess which systems typically provide the most specific and complete enrichment results. We find many structural and performance differences between classification systems. Overall, the well-established, hierarchically organized pathway annotation systems yield the best enrichment performance, despite covering substantial parts of the human genome in general terms only. On the other hand, the more recent unsupervised annotation systems perform strongest in understudied areas and organisms, and in detecting more specific pathways, albeit with less informative labels.