Naringenin relieves paclitaxel-induced pain by suppressing calcitonin gene-related peptide signalling and enhances the anti-tumour action of paclitaxel.

BACKGROUND AND PURPOSE: Chemotherapy-induced peripheral neuropathy (CIPN) commonly causes neuropathic pain, but its pathogenesis remains unclear, and effective therapies are lacking. Naringenin, a natural dihydroflavonoid compound, has anti-inflammatory, anti-nociceptive and anti-tumour activities. However, the effects of naringenin on chemotherapy-induced pain and chemotherapy effectiveness remain unexplored. EXPERIMENTAL APPROACH: Female and male mouse models of chemotherapy-induced pain were established using paclitaxel. Effects of naringenin were assessed on pain induced by paclitaxel or calcitonin gene-related peptide (CGRP) and on CGRP expression in dorsal root ganglia (DRG) and spinal cord tissue. Additionally, we examined peripheral macrophage infiltration, glial activation, c-fos expression, DRG neuron excitability, microglial M1/M2 polarization, and phosphorylation of spinal NF-κB. Furthermore, we investigated the synergic effect and related mechanisms of naringenin and paclitaxel on cell survival of cancer cells in vitro. KEY RESULTS: Systemic administration of naringenin attenuated paclitaxel-induced pain in both sexes. Naringenin reduced paclitaxel-enhanced CGRP expression in DRGs and the spinal cord, and alleviated CGRP-induced pain in naïve mice of both sexes. Naringenin mitigated macrophage infiltration and reversed paclitaxel-elevated c-fos expression and DRG neuron excitability. Naringenin decreased spinal glial activation and NF-κB phosphorylation in both sexes but influenced microglial M1/M2 polarization only in females. Co-administration of naringenin with paclitaxel enhanced paclitaxel's anti-tumour effect, impeded by an apoptosis inhibitor. CONCLUSION AND IMPLICATIONS: Naringenin's anti-nociceptive mechanism involves CGRP signalling and neuroimmunoregulation. Furthermore, naringenin facilitates paclitaxel's anti-tumour action, possibly involving apoptosis. This study demonstrates naringenin's potential as a supplementary treatment in cancer therapy by mitigating side effects and potentiating efficacy of chemotherapy.

Trimethoxyflavanone relieves Paclitaxel-induced neuropathic pain via inhibiting expression and activation of P2X7 and production of CGRP in mice.

Paclitaxel (PTX) is an anticancer drug used to treat solid tumors, but one of its common adverse effects is chemotherapy-induced peripheral neuropathy (CIPN). Currently, there is limited understanding of neuropathic pain associated with CIPN and effective treatment strategies are inadequate. Previous studies report the analgesic actions of Naringenin, a dihydroflavonoid compound, in pain. Here we observed that the anti-nociceptive action of a Naringenin derivative, Trimethoxyflavanone (Y3), was superior to Naringenin in PTX-induced pain (PIP). An intrathecal injection of Y3 (1 µg) reversed the mechanical and thermal thresholds of PIP and suppressed the PTX-induced hyper-excitability of dorsal root ganglion (DRG) neurons. PTX enhanced the expression of ionotropic purinergic receptor P2X7 (P2X7) in satellite glial cells (SGCs) and neurons in DRGs. The molecular docking simulation predicts possible interactions between Y3 and P2X7. Y3 reduced the PTX-enhanced P2X7 expression in DRGs. Electrophysiological recordings revealed that Y3 directly inhibited P2X7-mediated currents in DRG neurons of PTX-treated mice, suggesting that Y3 suppressed both expression and function of P2X7 in DRGs post-PTX administration. Y3 also reduced the production of calcitonin gene-related peptide (CGRP) in DRGs and at the spinal dorsal horn. Additionally, Y3 suppressed the PTX-enhanced infiltration of Iba1-positive macrophage-like cells in DRGs and overactivation of spinal astrocytes and microglia. Therefore, our results indicate that Y3 attenuates PIP via inhibiting P2X7 function, CGRP production, DRG neuron sensitization, and abnormal spinal glial activation. Our study implies that Y3 could be a promising drug candidate against CIPN-associated pain and neurotoxicity.

The contribution of neuro-immune crosstalk to pain in the peripheral nervous system and the spinal cord.

Pain is an unpleasant sensation associated with injury, inflammation, and infection. It has been demonstrated that communication between immune cells and neurons plays a vital role in pain and pain-related diseases (e.g. multiple sclerosis, osteoarthritis, irritable bowel syndrome). Growing data from preclinical and clinical studies have established that the bilateral regulations between peripheral immune cells and nociceptive neurons could be beneficial or detrimental for the development of pain and immune defense. We here review the mechanisms underlying neuroimmune crosstalk between circulating immune cells (e.g. macrophages, T cells, mast cells, neutrophils, monocytes) and nociceptors in the peripheral nervous system and the spinal cord. Deciphering the mechanisms by which neuroimmune interaction integrates neuronal inputs and immune responses helps to understand the pathogenesis of pain-related diseases and develop effective medications.

Imbalance Data Processing Strategy for Protein Interaction Sites Prediction.

Protein-protein interactions play essential roles in various biological progresses. Identifying protein interaction sites can facilitate researchers to understand life activities and therefore will be helpful for drug design. However, the number of experimental determined protein interaction sites is far less than that of protein sites in protein-protein interaction or protein complexes. Therefore, the negative and positive samples are usually imbalanced, which is common but bring result bias on the prediction of protein interaction sites by computational approaches. In this work, we presented three imbalance data processing strategies to reconstruct the original dataset, and then extracted protein features from the evolutionary conservation of amino acids to build a predictor for identification of protein interaction sites. On a dataset with 10,430 surface residues but only 2,299 interface residues, the imbalance dataset processing strategies can obviously reduce the prediction bias, and therefore improve the prediction performance of protein interaction sites. The experimental results show that our prediction models can achieve a better prediction performance, such as a prediction accuracy of 0.758, or a high F-measure of 0.737, which demonstrated the effectiveness of our method.

Semi-supervised prediction of protein interaction sites from unlabeled sample information.

BACKGROUND: The recognition of protein interaction sites is of great significance in many biological processes, signaling pathways and drug designs. However, most sites on protein sequences cannot be defined as interface or non-interface sites because only a small part of protein interactions had been identified, which will cause the lack of prediction accuracy and generalization ability of predictors in protein interaction sites prediction. Therefore, it is necessary to effectively improve prediction performance of protein interaction sites using large amounts of unlabeled data together with small amounts of labeled data and background knowledge today. RESULTS: In this work, three semi-supervised support vector machine-based methods are proposed to improve the performance in the protein interaction sites prediction, in which the information of unlabeled protein sites can be involved. Herein, five features related with the evolutionary conservation of amino acids are extracted from HSSP database and Consurf Sever, i.e., residue spatial sequence spectrum, residue sequence information entropy and relative entropy, residue sequence conserved weight and residual Base evolution rate, to represent the residues within the protein sequence. Then three predictors are built for identifying the interface residues from protein surface using three types of semi-supervised support vector machine algorithms. CONCLUSION: The experimental results demonstrated that the semi-supervised approaches can effectively improve prediction performance of protein interaction sites when unlabeled information is involved into the predictors and one of them can achieve the best prediction performance, i.e., the accuracy of 70.7%, the sensitivity of 62.67% and the specificity of 78.72%, respectively. With comparison to the existing studies, the semi-supervised models show the improvement of the predication performance.