Dario Kringel

A data science approach to candidate gene selection of pain regarded as a process of learning and neural plasticity.

Abstract The increasing availability of “big data” enables novel research approaches to chronic pain while also requiring novel techniques for data mining and knowledge discovery. We used machine learning to combine the knowledge about n = 535 genes identified empirically as relevant to pain with the knowledge about the functions of thousands of genes. Starting from an accepted description of chronic pain as displaying systemic features described by the terms “learning” and “neuronal plasticity,” a functional genomics analysis proposed that among the functions of the 535 “pain genes,” the biological processes “learning or memory” (P = 8.6 × 10−64) and “nervous system development” (P = 2.4 × 10−40) are statistically significantly overrepresented as compared with the annotations to these processes expected by chance. After establishing that the hypothesized biological processes were among important functional genomics features of pain, a subset of n = 34 pain genes were found to be annotated with both Gene Ontology terms. Published empirical evidence supporting their involvement in chronic pain was identified for almost all these genes, including 1 gene identified in March 2016 as being involved in pain. By contrast, such evidence was virtually absent in a randomly selected set of 34 other human genes. Hence, the present computational functional genomics–based method can be used for candidate gene selection, providing an alternative to established methods.

Emergent biomarker derived from next-generation sequencing to identify pain patients requiring uncommonly high opioid doses

Next-generation sequencing (NGS) provides unrestricted access to the genome, but it produces ‘big data’ exceeding in amount and complexity the classical analytical approaches. We introduce a bioinformatics-based classifying biomarker that uses emergent properties in genetics to separate pain patients requiring extremely high opioid doses from controls. Following precisely calculated selection of the 34 most informative markers in the OPRM1, OPRK1, OPRD1 and SIGMAR1 genes, pattern of genotypes belonging to either patient group could be derived using a k-nearest neighbor (kNN) classifier that provided a diagnostic accuracy of 80.6±4%. This outperformed alternative classifiers such as reportedly functional opioid receptor gene variants or complex biomarkers obtained via multiple regression or decision tree analysis. The accumulation of several genetic variants with only minor functional influences may result in a qualitative consequence affecting complex phenotypes, pointing at emergent properties in genetics.

Pain research funding by the European Union Seventh Framework Programme

It is well known to the regular readers of this Journal that chronic pain is a major health care problem as defined by the World Health Organization. A recent literature review showed a 1-month prevalence of moderate to severe non-cancer pain in European adults of 19% and of subjects older than 70 years even one in three is affected (Elliott et al., 1999; Breivik et al., 2006). With an EU population of half of a billion in 2010, this impacts on 80 million Europeans suffering from chronic pain (http:// euobserver.com/social/30551) and projects with high socio-economic costs due to medical expenses and lost working days (Hill and Schug, 2009; Reid et al., 2011). The high prevalence of chronic pain clearly points at insufficient treatment options. Indeed, evidence from Cochrane systematic reviews shows that the available analgesic drugs provide efficacious pain relief, defined as a decrease in the intensity of pain by 50% lasting for 12 weeks, only in a minority of patients (Moore et al., 2009, 2012; Derry et al., 2012). Hence, analgesics with novel mechanisms of action are required that need to be developed on the basis of a better understanding of the complex pathophysiology (Julius and Basbaum, 2001) and clinical representation of pain. It is increasingly recognized that pain is not a mere symptom which disappears together with the causing condition, hence the often perceived under-representation of pain in the financial volume of earlier funding directs investments towards the underlying diseases, but pain is a disease that requires specific treatment. Among typical examples, the post-zoster neuropathic pain that may persist long after the acute virus-caused inflammation has been resolved and that would not benefit from continued antiinfective therapy, but clearly requires specific pain treatment. Similarly, the treatment of migraine requires specific therapeutics and cannot be addressed by solving some underlying disease as the pain condition itself is the disease. Moreover, neuropathic phantom pain clearly requires analgesic treatment considering the evident lack of alternative options to repair its reason. This is reflected in the present funding policy of the European Union that in its currently active Seventh Framework Programme (FP7) supports pain research with a total of more than 46 million Euros provided to eight groups (Table 1) of collaborating clinical and basic scientists at locations spanning almost the whole geographical extension of our home continent (Fig. 1 and Table 2). The funding policy accommodates (1) the increasing awareness of the importance of the treatment of pain as a human right (International Covenant on Economic, Social and Cultural Rights, 1966) and (2) the recognition of chronic pain conditions as diseases rather than symptoms of another disease. Understanding the pathophysiological mechanisms of pain is the logical scientific path to effective and safe novel personalized therapy options. After the first year of the funding, all projects have been established and can be acknowledged in the official journal of the European Pain Federation EFIC where many European pain scientists are united via national societies. One research focus of the funded projects comprises chronic pain conditions, e.g. low back pain, migraine or neuropathic pain. Most projects combine preclinical with clinical research in both, a translational concept or its opposite, i.e. in a bedside-to-bench direction that obtains its principal information from human data and uses laboratory experiments not to generate new hypotheses but to verify those obtained by clinical observation (L€ otsch and Geisslinger, 2010). The eight projects (Table 1 and Fig. 1) will be summarized in the following paragraph in the succession of their appearance at the FP7 website at http://ec.europa. eu/research/health/medical-research/brain-research/l_ coor_en.htm and based on the websites of single projects or personal communications from project speakers.

Integrated Computational Analysis of Genes Associated with Human Hereditary Insensitivity to Pain. A Drug Repurposing Perspective

Genes causally involved in human insensitivity to pain provide a unique molecular source of studying the pathophysiology of pain and the development of novel analgesic drugs. The increasing availability of “big data” enables novel research approaches to chronic pain while also requiring novel techniques for data mining and knowledge discovery. We used machine learning to combine the knowledge about n = 20 genes causally involved in human hereditary insensitivity to pain with the knowledge about the functions of thousands of genes. An integrated computational analysis proposed that among the functions of this set of genes, the processes related to nervous system development and to ceramide and sphingosine signaling pathways are particularly important. This is in line with earlier suggestions to use these pathways as therapeutic target in pain. Following identification of the biological processes characterizing hereditary insensitivity to pain, the biological processes were used for a similarity analysis with the functions of n = 4,834 database-queried drugs. Using emergent self-organizing maps, a cluster of n = 22 drugs was identified sharing important functional features with hereditary insensitivity to pain. Several members of this cluster had been implicated in pain in preclinical experiments. Thus, the present concept of machine-learned knowledge discovery for pain research provides biologically plausible results and seems to be suitable for drug discovery by identifying a narrow choice of repurposing candidates, demonstrating that contemporary machine-learned methods offer innovative approaches to knowledge discovery from available evidence.

Next-generation sequencing of human opioid receptor genes based on a custom AmpliSeq™ library and ion torrent personal genome machine.

Background The opioid system is involved in the control of pain, reward, addictive behaviors and vegetative effects. Opioids exert their pharmacological actions through the agonistic binding at opioid receptors and variation in the coding genes has been found to modulate opioid receptor expression or signaling. However, a limited selection of functional opioid receptor variants is perceived as insufficient in providing a genetic diagnosis of clinical phenotypes and therefore, unrestricted access to opioid receptor genetics is required. Methods Next-generation sequencing (NGS) workflow was based on a custom AmpliSeq™ panel and designed for sequencing of human genes related to the opioid receptor group (OPRM1, OPRD1, OPRK1, SIGMA1, OPRL1) on an Ion PGM™ Sequencer. A cohort of 79 previously studied chronic pain patients was screened to evaluate and validate the detection of exomic sequences of the coding genes with 25 base pair exon padding. In-silico analysis was performed using SNP and Variation Suite® software. Results The amplicons covered approximately 90% of the target sequence. A median of 2.54 × 106 reads per run was obtained generating a total of 35,447 nucleotide reads from each DNA sample. This identified approximately 100 chromosome loci where nucleotides deviated from the reference sequence GRCh37 hg19, including functional variants such as the OPRM1 rs1799971 SNP (118 A > G) as the most scientifically regarded variant or rs563649 SNP coding for μ-opioid receptor splice variants. Correspondence between NGS and Sanger derived nucleotide sequences was 100%. Conclusion Results suggested that the NGS approach based on AmpliSeq™ libraries and Ion PGM sequencing is a highly efficient mutation detection method. It is suitable for large-scale sequencing of opioid receptor genes. The method includes the variants studied so far for functional associations and adds a large amount of genetic information as a basis for complete analysis of human opioid receptor genetics and its functional consequences.

Machine-learned analysis of the association of next-generation sequencing-based human TRPV1 and TRPA1 genotypes with the sensitivity to heat stimuli and topically applied capsaicin.

Abstract Heat pain and its modulation by capsaicin varies among subjects in experimental and clinical settings. A plausible cause is a genetic component, of which TRPV1 ion channels, by their response to both heat and capsaicin, are primary candidates. However, TRPA1 channels can heterodimerize with TRPV1 channels and carry genetic variants reported to modulate heat pain sensitivity. To address the role of these candidate genes in capsaicin-induced hypersensitization to heat, pain thresholds acquired before and after topical application of capsaicin and TRPA1/TRPV1 exomic sequences derived by next-generation sequencing were assessed in n = 75 healthy volunteers and the genetic information comprised 278 loci. Gaussian mixture modeling indicated 2 phenotype groups with high or low capsaicin-induced hypersensitization to heat. Unsupervised machine learning implemented as swarm-based clustering hinted at differences in the genetic pattern between these phenotype groups. Several methods of supervised machine learning implemented as random forests, adaptive boosting, k-nearest neighbors, naive Bayes, support vector machines, and for comparison, binary logistic regression predicted the phenotype group association consistently better when based on the observed genotypes than when using a random permutation of the exomic sequences. Of note, TRPA1 variants were more important for correct phenotype group association than TRPV1 variants. This indicates a role of the TRPA1 and TRPV1 next-generation sequencing–based genetic pattern in the modulation of the individual response to heat-related pain phenotypes. When considering earlier evidence that topical capsaicin can induce neuropathy-like quantitative sensory testing patterns in healthy subjects, implications for future analgesic treatments with transient receptor potential inhibitors arise.

Use of Computational Functional Genomics in Drug Discovery and Repurposing for Analgesic Indications

The novel research area of functional genomics investigates biochemical, cellular, or physiological properties of gene products with the goal of understanding the relationship between the genome and the phenotype. These developments have made analgesic drug research a data‐rich discipline mastered only by making use of parallel developments in computer science, including the establishment of knowledge bases, mining methods for big data, machine‐learning, and artificial intelligence, (Table ) which will be exemplarily introduced in the following.

Next-generation sequencing of the human TRPV1 gene and the regulating co-players LTB4R and LTB4R2 based on a custom AmpliSeq™ panel

Background Transient receptor potential cation channel subfamily V member 1 (TRPV1) are sensitive to heat, capsaicin, pungent chemicals and other noxious stimuli. They play important roles in the pain pathway where in concert with proinflammatory factors such as leukotrienes they mediate sensitization and hyperalgesia. TRPV1 is the target of several novel analgesics drugs under development and therefore, TRPV1 genetic variants might represent promising candidates for pharmacogenetic modulators of drug effects. Methods A next-generation sequencing (NGS) panel was created for the human TRPV1 gene and in addition, for the leukotriene receptors BLT1 and BLT2 recently described to modulate TRPV1 mediated sensitisation processes rendering the coding genes LTB4R and LTB4R2 important co-players in pharmacogenetic approaches involving TRPV1. The NGS workflow was based on a custom AmpliSeq™ panel and designed for sequencing of human genes on an Ion PGM™ Sequencer. A cohort of 80 healthy subjects of Western European descent was screened to evaluate and validate the detection of exomic sequences of the coding genes with 25 base pair exon padding. Results The amplicons covered approximately 97% of the target sequence. A median of 2.81 x 106 reads per run was obtained. This identified approximately 140 chromosome loci where nucleotides deviated from the reference sequence GRCh37 hg19 comprising the three genes TRPV1, LTB4R and LTB4R2. Correspondence between NGS and Sanger derived nucleotide sequences was 100%. Conclusions Results suggested that the NGS approach based on AmpliSeq™ libraries and Ion Personal Genome Machine (PGM) sequencing is a highly efficient mutation detection method. It is suitable for large-scale sequencing of TRPV1 and functionally related genes. The method adds a large amount of genetic information as a basis for complete analysis of TRPV1 ion channel genetics and its functional consequences.

Development of an AmpliSeqTM panel for next-generation sequencing of a set of genetic predictors of persisting pain

Background: Many gene variants modulate the individual perception of pain and possibly also its persistence. The limited selection of single functional variants is increasingly being replaced by analyses of the full coding and regulatory sequences of pain-relevant genes accessible by means of next generation sequencing (NGS). Methods: An NGS panel was created for a set of 77 human genes selected following different lines of evidence supporting their role in persisting pain. To address the role of these candidate genes, we established a sequencing assay based on a custom AmpliSeqTM panel to assess the exomic sequences in 72 subjects of Caucasian ethnicity. To identify the systems biology of the genes, the biological functions associated with these genes were assessed by means of a computational over-representation analysis. Results: Sequencing generated a median of 2.85 ⋅ 106 reads per run with a mean depth close to 200 reads, mean read length of 205 called bases and an average chip loading of 71%. A total of 3,185 genetic variants were called. A computational functional genomics analysis indicated that the proposed NGS gene panel covers biological processes identified previously as characterizing the functional genomics of persisting pain. Conclusion: Results of the NGS assay suggested that the produced nucleotide sequences are comparable to those earned with the classical Sanger sequencing technique. The assay is applicable for small to large-scale experimental setups to target the accessing of information about any nucleotide within the addressed genes in a study cohort.

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

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.