Transcriptome Analysis of the Human Tibial Nerve Identifies Sexually Dimorphic Expression of Genes Involved in Pain, Inflammation, and Neuro-Immunity

Sex differences in gene expression are important contributors to normal physiology and mechanisms of disease. This is increasingly apparent in understanding and potentially treating chronic pain where molecular mechanisms driving sex differences in neuronal plasticity are giving new insight into why certain chronic pain disorders preferentially affect women vs. men. Large transcriptomic resources are now available and can be used to mine for sex differences to gather insight from molecular profiles using donor cohorts. We performed in-depth analysis of 248 human tibial nerve (hTN) transcriptomes from the GTEx Consortium project to gain insight into sex-dependent gene expression in the peripheral nervous system (PNS). We discover 149 genes with sex differential gene expression. Many of the more abundant genes in men are associated with inflammation and appear to be primarily expressed by glia or immune cells, with some genes downstream of Notch signaling. In women, we find the differentially expressed transcription factor SP4 that is known to drive a regulatory program, and may impact sex differences in PNS physiology. Many of these 149 differentially expressed (DE) genes have some previous association with chronic pain but few of them have been explored thoroughly. Additionally, using clinical data in the GTEx database, we identify a subset of DE, sexually dimorphic genes in diseases associated with chronic pain: arthritis and Type II diabetes. Our work creates a unique resource that identifies sexually dimorphic gene expression in the human PNS with implications for discovery of sex-specific pain mechanisms

Sex-Stratified Genome-Wide Association Study of Multisite Chronic Pain in UK Biobank

Chronic pain is highly prevalent worldwide and imparts a significant socioeconomic and public health burden. Factors influencing susceptibility to, and mechanisms of, chronic pain development, are not fully understood, but sex is thought to play a significant role, and chronic pain is more prevalent in women than in men. To investigate sex differences in chronic pain, we carried out a sex-stratified genome-wide association study of Multisite Chronic Pain (MCP), a derived chronic pain phenotype, in UK Biobank on 178,556 men and 209,093 women, as well as investigating sex-specific genetic correlations with a range of psychiatric, autoimmune and anthropometric phenotypes and the relationship between sex-specific polygenic risk scores for MCP and chronic widespread pain. We also assessed whether MCP-associated genes showed expression pattern enrichment across tissues. A total of 123 SNPs at five independent loci were significantly associated with MCP in men. In women, a total of 286 genome-wide significant SNPs at ten independent loci were discovered. Meta-analysis of sex-stratified GWAS outputs revealed a further 87 independent associated SNPs. Gene-level analyses revealed sex-specific MCP associations, with 31 genes significantly associated in females, 37 genes associated in males, and a single gene, DCC, associated in both sexes. We found evidence for sex-specific pleiotropy and risk for MCP was found to be associated with chronic widespread pain in a sex-differential manner. Male and female MCP were highly genetically correlated, but at an rg of significantly less than 1 (0.92). All 37 male MCP-associated genes and all but one of 31 female MCP-associated genes were found to be expressed in the dorsal root ganglion, and there was a degree of enrichment for expression in sex-specific tissues. Overall, the findings indicate that sex differences in chronic pain exist at the SNP, gene and transcript abundance level, and highlight possible sex-specific pleiotropy for MCP. Results support the proposition of a strong central nervous-system component to chronic pain in both sexes, additionally highlighting a potential role for the DRG and nociception

CSMET: Comparative Genomic Motif Detection via Multi-Resolution Phylogenetic Shadowing

Functional turnover of transcription factor binding sites (TFBSs), such as whole-motif loss or gain, are common events during genome evolution. Conventional probabilistic phylogenetic shadowing methods model the evolution of genomes only at nucleotide level, and lack the ability to capture the evolutionary dynamics of functional turnover of aligned sequence entities. As a result, comparative genomic search of non-conserved motifs across evolutionarily related taxa remains a difficult challenge, especially in higher eukaryotes, where the cis-regulatory regions containing motifs can be long and divergent; existing methods rely heavily on specialized pattern-driven heuristic search or sampling algorithms, which can be difficult to generalize and hard to interpret based on phylogenetic principles. We propose a new method: Conditional Shadowing via Multi-resolution Evolutionary Trees, or CSMET, which uses a context-dependent probabilistic graphical model that allows aligned sites from different taxa in a multiple alignment to be modeled by either a background or an appropriate motif phylogeny conditioning on the functional specifications of each taxon. The functional specifications themselves are the output of a phylogeny which models the evolution not of individual nucleotides, but of the overall functionality (e.g., functional retention or loss) of the aligned sequence segments over lineages. Combining this method with a hidden Markov model that autocorrelates evolutionary rates on successive sites in the genome, CSMET offers a principled way to take into consideration lineage-specific evolution of TFBSs during motif detection, and a readily computable analytical form of the posterior distribution of motifs under TFBS turnover. On both simulated and real Drosophila cis-regulatory modules, CSMET outperforms other state-of-the-art comparative genomic motif finders

Limb Body Wall Complex with Sacrococcygeal Mass and Agenesis of External Genitalia

Limb body wall complex (LBWC) is a rare clinicopathological entity, characterized by the presence of an abdominal wall defect associated with variable spectrum of limb and visceral anomalies. A stillborn baby of LBWC with placentoabdominal phenotype is reported here. Kyphoscoliosis, sacrococcygeal mass and agenesis of external genitalia are the associated features

AUTOMATING THE PROCESS OF GAZE TRACKING DATA USING SOFT CLUS TERING

The aim of the paper is to automate the processing of gaze tracking data through soft clustering techniques. Standard analysis software for eye gaze tracking data requires users to define areas of interest, which may not be best option for exploratory analysis, where users may want to analyze eye gaze tracking data to know the area of interest. We have presented results on using Fuzzy c-means and Expectation Maximization algorithms on gaze tracking data and using an entropy based cluster validation index, we tried to automate identification of areas of interest. In our study, data from search task in digitally rendered 2D architectural plans have been explored and results indicated that irrespective of clustering technique, users fixated attention only 2 or 3 times for individual image. We have also presented GUI of a tool that can automatically identify areas of interest for any gaze tracking data sample using FCM or EM Algorithms

AUTOMATING THE PROCESS OF GAZE TRACKING DATA USING SOFT CLUS TERING

The aim of the paper is to automate the processing of gaze tracking data through soft clustering techniques. Standard analysis software for eye gaze tracking data requires users to define areas of interest, which may not be best option for exploratory analysis, where users may want to analyze eye gaze tracking data to know the area of interest. We have presented results on using Fuzzy c-means and Expectation Maximization algorithms on gaze tracking data and using an entropy based cluster validation index, we tried to automate identification of areas of interest. In our study, data from search task in digitally rendered 2D architectural plans have been explored and results indicated that irrespective of clustering technique, users fixated attention only 2 or 3 times for individual image. We have also presented GUI of a tool that can automatically identify areas of interest for any gaze tracking data sample using FCM or EM Algorithms