Identification of novel common variants associated with chronic pain using conditional false discovery rate analysis with major depressive disorder and assessment of pleiotropic effects of LRFN5

Chronic pain is a complex trait that is moderately heritable and genetically, as well as phenotypically, correlated with major depressive disorder (MDD). Use of the conditional false discovery rate (cFDR) approach, which leverages pleiotropy identified from existing GWAS outputs, has been successful in discovering novel associated variants in related phenotypes. Here, genome-wide association study outputs for both von Korff chronic pain grade and for MDD were used to identify variants meeting a cFDR threshold for each outcome phenotype separately, as well as a conjunctional cFDR (ccFDR) threshold for both phenotypes together. Using a moderately conservative threshold, we identified a total of 11 novel single nucleotide polymorphisms (SNPs), six of which were associated with chronic pain grade and nine of which were associated with MDD. Four SNPs on chromosome 14 were associated with both chronic pain grade and MDD. SNPs associated only with chronic pain grade were located within SLC16A7 on chromosome 12. SNPs associated only with MDD were located either in a gene-dense region on chromosome 1 harbouring LINC01360, LRRIQ3, FPGT and FPGT-TNNI3K, or within/close to LRFN5 on chromosome 14. The SNPs associated with both outcomes were also located within LRFN5. Several of the SNPs on chromosomes 1 and 14 were identified as being associated with expression levels of nearby genes in the brain and central nervous system. Overall, using the cFDR approach, we identified several novel genetic loci associated with chronic pain and we describe likely pleiotropic effects of a recently identified MDD locus on chronic pain

A machine-learned analysis of human gene polymorphisms modulating persisting pain points to major roles of neuroimmune processes

Background Human genetic research has implicated functional variants of more than one hundred genes in the modulation of persisting pain. Artificial intelligence and machine-learning techniques may combine this knowledge with results of genetic research gathered in any context, which permits the identification of the key biological processes involved in chronic sensitization to pain. MethodsResultsBased on published evidence, a set of 110 genes carrying variants reported to be associated with modulation of the clinical phenotype of persisting pain in eight different clinical settings was submitted to unsupervised machine-learning aimed at functional clustering. Subsequently, a mathematically supported subset of genes, comprising those most consistently involved in persisting pain, was analysed by means of computational functional genomics in the Gene Ontology knowledgebase. Clustering of genes with evidence for a modulation of persisting pain elucidated a functionally heterogeneous set. The situation cleared when the focus was narrowed to a genetic modulation consistently observed throughout several clinical settings. On this basis, two groups of biological processes, the immune system and nitric oxide signalling, emerged as major players in sensitization to persisting pain, which is biologically highly plausible and in agreement with other lines of pain research. ConclusionsSignificanceThe present computational functional genomics-based approach provided a computational systems-biology perspective on chronic sensitization to pain. Human genetic control of persisting pain points to the immune system as a source of potential future targets for drugs directed against persisting pain. Contemporary machine-learned methods provide innovative approaches to knowledge discovery from previous evidence. We show that knowledge discovery in genetic databases and contemporary machine-learned techniques can identify relevant biological processes involved in Persitent pain.Peer reviewe

Exploitation of Conformational Dynamics in Imparting Selective Inhibition for Related Matrix Metalloproteinases

Matrix metalloproteinases (MMPs) have numerous physiological functions and share a highly similar catalytic domain. Differential dynamical information on the closely related human MMP-8, -13, and -14 was integrated onto the benzoxazinone molecular template. An <i>in silico</i> library of 28,099 benzoxazinones was generated and evaluated in the context of the molecular-dynamics information. This led to experimental evaluation of 19 synthesized compounds and identification of selective inhibitors, which have potential utility in delineating the physiological functions of MMPs. Moreover, the approach serves as an example of how dynamics of closely related active sites may be exploited to achieve selective inhibition by small molecules and should find applications in other enzyme families with similar active sites

Exploitation of Conformational Dynamics in Imparting Selective Inhibition for Related Matrix Metalloproteinases

Matrix metalloproteinases (MMPs) have numerous physiological functions and share a highly similar catalytic domain. Differential dynamical information on the closely related human MMP-8, -13, and -14 was integrated onto the benzoxazinone molecular template. An <i>in silico</i> library of 28,099 benzoxazinones was generated and evaluated in the context of the molecular-dynamics information. This led to experimental evaluation of 19 synthesized compounds and identification of selective inhibitors, which have potential utility in delineating the physiological functions of MMPs. Moreover, the approach serves as an example of how dynamics of closely related active sites may be exploited to achieve selective inhibition by small molecules and should find applications in other enzyme families with similar active sites