Patients with chronic pain usually suffer from working memory deficits, which may decrease their intellectual ability significantly. Despite intensive clinical studies, the mechanism underlying this form of memory impairment remains elusive. In this study, we investigated this issue in the spared nerve injury (SNI) model of neuropathic pain, a most common form of chronic pain. We found that SNI impaired working memory and short-term memory in rats and mice. To explore the potential mechanisms, we studied synaptic transmission/plasticity in hippocampus, a brain region critically involved in memory function. We found that frequency facilitation, a presynaptic form of short-term plasticity, and long-term potentiation at CA3-CA1 synapses were impaired after SNI. Structurally, density of presynaptic boutons in hippocampal CA1 synapses was reduced significantly. At the molecular level, we found that tumor necrosis factor-α (TNF-α) increased in cerebrospinal fluid, in hippocampal tissue and in plasma after SNI. Intracerebroventricular or intrahippocampal injection of recombinant rat TNF mimicked the effects of SNI in naive rats, whereas inhibition of TNF-α or genetic deletion of TNF receptor 1 prevented both memory deficits and synaptic dysfunction induced by SNI. As TNF-α is critical for development of neuropathic pain, we suggested that the over-production of TNF-α following peripheral nerve injury might lead to neuropathic pain and memory deficits, simultaneously.

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Understanding the nature of the genetic regulation of gene expression promises to advance our understanding of the genetic basis of disease. However, the methodological impact of the use of local ancestry on high-dimensional omics analyses, including, most prominently, expression quantitative trait loci (eQTL) mapping and trait heritability estimation, in admixed populations remains critically underexplored. Here, we develop a statistical framework that characterizes the relationships among the determinants of the genetic architecture of an important class of molecular traits. We provide a computationally efficient approach to local ancestry analysis in eQTL mapping while increasing control of type I and type II error over traditional approaches. Applying our method to National Institute of General Medical Sciences (NIGMS) and Genotype-Tissue Expression (GTEx) datasets, we show that the use of local ancestry can improve eQTL mapping in admixed and multiethnic populations, respectively. We estimate the trait variance explained by ancestry by using local admixture relatedness between individuals. By using simulations of diverse genetic architectures and degrees of confounding, we show improved accuracy in estimating heritability when accounting for local ancestry similarity. Furthermore, we characterize the sparse versus polygenic components of gene expression in admixed individuals. Our study has important methodological implications for genetic analysis of omics traits across a range of genomic contexts, from a single variant to a prioritized region to the entire genome. Our findings highlight the importance of using local ancestry to better characterize the heritability of complex traits and to more accurately map genetic associations.

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