Citations

Neuropathic pain is a debilitating chronic condition that lacks effective treatment. The role of cytokine- and chemokine-mediated neuroinflammation in its pathogenesis has been well documented. Follistatin (FST) is a secreted protein known to antagonize the biological activity of cytokines in the transforming growth factor–β (TGF-β) superfamily. The involvement of FST in neuropathic pain and the underlying mechanism remain largely unknown. Here, we report that FST was up-regulated in A-fiber sensory neurons after spinal nerve ligation (SNL) in mice. Inhibition or deletion of FST alleviated neuropathic pain and reduced the nociceptive neuron hyperexcitability induced by SNL. Conversely, intrathecal or intraplantar injection of recombinant FST, or overexpression of FST in the dorsal root ganglion (DRG) neurons, induced pain hypersensitivity. Furthermore, exogenous FST increased neuronal excitability in nociceptive neurons. The biolayer interferometry (BLI) assay and coimmunoprecipitation (co-IP) demonstrated direct binding of FST to the insulin-like growth factor–1 receptor (IGF1R), and IGF1R inhibition reduced FST-induced activation of extracellular signal–regulated kinase (ERK) and protein kinase B (AKT), as well as neuronal hyperexcitability. Further co-IP analysis revealed that the N-terminal domain of FST exhibits the highest affinity for IGF1R, and blocking this interaction with a peptide derived from FST attenuated Nav1.7-mediated neuronal hyperexcitability and neuropathic pain after SNL. In addition, FST enhanced neuronal excitability in human DRG neurons through IGF1R. Collectively, our findings suggest that FST, released from A-fiber neurons, enhances Nav1.7-mediated hyperexcitability of nociceptive neurons by binding to IGF1R, making it a potential target for neuropathic pain treatment.

Malignancy has a crucial impact on long-term survival after liver transplantation. There have been enhanced early detection rates with refined cancer screening and improved prognosis for many cancer diagnoses in the general population with the advent of targeted anticancer therapies. Similar advancements have not occurred in the transplant population over this same timeframe. Individualized strategies to reduce the risk of cancer are needed in this high-risk population. Strict adherence to screening and surveillance specific to the transplant population is required. Lifestyle modifications and medication management (both immunosuppressive and non-immunosuppressive) that may impact cancer risk and outcome are highlighted here. As more effective anticancer therapies evolve, transplant recipients’ access to these agents is paramount to truly impact cancer-related outcomes in this population. With adequate immunosuppression, rejection rates with immunotherapy are lower than previously purported. Prospective studies of immunosuppression modifications needed to minimize rejection and maximize cancer response are ongoing and will reduce the fear from oncology and transplant providers alike, allowing utilization of the most optimal therapy available to the individual. This review aims to assess current data to aid in clinical management and identify the need to facilitate further progress in this field.

Abstract Single-cell technologies enable researchers to investigate cell functions at an individual cell level and study cellular processes with higher resolution. Several multi-omics single-cell sequencing techniques have been developed to explore various aspects of cellular behavior. Using NEAT-seq as an example, this method simultaneously obtains three kinds of omics data for each cell: gene expression, chromatin accessibility, and protein expression of transcription factors (TFs). Consequently, NEAT-seq offers a more comprehensive understanding of cellular activities in multiple modalities. However, there is a lack of tools available for effectively integrating the three types of omics data. To address this gap, we propose a novel pipeline called MultiSC for the analysis of MULTIomic Single-Cell data. Our pipeline leverages a multimodal constraint autoencoder (single-cell hierarchical constraint autoencoder) to integrate the multi-omics data during the clustering process and a matrix factorization–based model (scMF) to predict target genes regulated by a TF. Moreover, we utilize multivariate linear regression models to predict gene regulatory networks from the multi-omics data. Additional functionalities, including differential expression, mediation analysis, and causal inference, are also incorporated into the MultiSC pipeline. Extensive experiments were conducted to evaluate the performance of MultiSC. The results demonstrate that our pipeline enables researchers to gain a comprehensive view of cell activities and gene regulatory networks by fully leveraging the potential of multiomics single-cell data. By employing MultiSC, researchers can effectively integrate and analyze diverse omics data types, enhancing their understanding of cellular processes.