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. METHODS: Based 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. RESULTS: 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. CONCLUSIONS: The 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. SIGNIFICANCE: We show that knowledge discovery in genetic databases and contemporary machine-learned techniques can identify relevant biological processes involved in Persitent pain.

Prediction of persistent post-surgery pain by preoperative cold pain sensitivity: biomarker development with machine-learning-derived analysis.

BACKGROUND: To prevent persistent post-surgery pain, early identification of patients at high risk is a clinical need. Supervised machine-learning techniques were used to test how accurately the patients' performance in a preoperatively performed tonic cold pain test could predict persistent post-surgery pain. METHODS: We analysed 763 patients from a cohort of 900 women who were treated for breast cancer, of whom 61 patients had developed signs of persistent pain during three yr of follow-up. Preoperatively, all patients underwent a cold pain test (immersion of the hand into a water bath at 2-4 °C). The patients rated the pain intensity using a numerical ratings scale (NRS) from 0 to 10. Supervised machine-learning techniques were used to construct a classifier that could predict patients at risk of persistent pain. RESULTS: Whether or not a patient rated the pain intensity at NRS=10 within less than 45 s during the cold water immersion test provided a negative predictive value of 94.4% to assign a patient to the "persistent pain" group. If NRS=10 was never reached during the cold test, the predictive value for not developing persistent pain was almost 97%. However, a low negative predictive value of 10% implied a high false positive rate. CONCLUSIONS: Results provide a robust exclusion of persistent pain in women with an accuracy of 94.4%. Moreover, results provide further support for the hypothesis that the endogenous pain inhibitory system may play an important role in the process of pain becoming persistent.

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.

A small yet comprehensive subset of human experimental pain models emerging from correlation analysis with a clinical quantitative sensory testing protocol in healthy subjects.

BACKGROUND: Picturing the complexity of pain in human experimental settings has increased the predictivity for clinical pain but requires increasingly complex test batteries. This raises problems in studies in which time is objectively limited, for example by the course of action of an analgesic drug. We addressed the selection of a small yet comprehensive set of pain tests for the use in such a situation. METHOD: Nineteen different pain measures from 'classical' pain models (n = 9) and a clinically established QST-pain test battery (n = 10), were obtained from 72 healthy volunteers (34 men). The nonparametric correlation structure among the various pain measures was analysed using Ward clustering. RESULTS: Four clusters emerged, each consisting of highly correlated pain measures. The pain model groups emerged comprised (I) pain thresholds and tolerances to blunt pressure or electrical pain; (II) pain thresholds to thermal stimuli; (III) pain measures obtained following application of punctate mechanical, intranasal CO2 chemical or cutaneous laser heat stimuli; and (IV) detection thresholds to thermal stimuli. The first three clusters agreed with an immediate mechanistic interpretation as reflecting C-fibre mediated pain, thermal pain and Aδ-fibre mediated pain, respectively, whereas the last cluster contained non-painful measures and was disregarded. CONCLUSIONS: When basing a selection of a small comprehensive set of pain models on the assumption that highly correlated pain measures account for redundant results and therefore, one member of each group suffices an economic yet comprehensive pain study, results suggest inclusion of established C-fibre, Aδ-fibre mediated and thermal pain measures.

A brain-lesion pattern based algorithm for the diagnosis of posttraumatic olfactory loss.

BACKGROUND: Brain areas processing olfactory information exhibit functionally relevant morphological dynamics. This suggests the exploitation of anatomical information in the diagnosis of an olfactory dysfunction. Following previous identifications of olfactory eloquent areas such as the olfactory bulbs and tracts, we focused at a brain-morphology based algorithm for establishing the diagnosis of olfactory loss following brain injury. METHODOLOGY: Forty-one patients with a history of head trauma dated back 40 ± 39 months, and additional 23 patients without head trauma, were assessed for damages in 11 olfaction-relevant brain areas using magnetic resonance imaging (MRI). Olfactory function was derived from the use of a standardized, reliable and validated olfactory test. An olfactory diagnostic algorithm was derived following classification and regression tree analysis of the brain lesion pattern. RESULTS: Subjects were assigned to olfactory diagnoses of anosmia, hyposmia or normosmia. These diagnoses were predictable at an accuracy of 62.3 % from the degree of damage in the olfactory bulb and in the left temporal lobe pole. The main diagnosis algorithm addressed the presence of anosmia, which could be predicted from the degree of damage in these brain areas at an accuracy of 81.3 %. CONCLUSIONS: We independently reproduced previously identified brain regions in which morphological damage is associated with olfactory loss. Based on this reproduction, an algorithm was developed for the diagnosis of anosmia from central-nervous damage. Thus, we introduce a morphological component to the olfactory diagnosis that specifically addresses clinical cases of olfactory loss following head trauma.

Targeting lipid metabolism by the lipoprotein lipase inhibitor orlistat results in apoptosis of B-cell chronic lymphocytic leukemia cells.

Constitutively activated pathways contribute to apoptosis resistance in chronic lymphocytic leukemia (CLL). Little is known about the metabolism of lipids and function of lipases in CLL cells. Performing gene expression profiling including B-cell receptor (BCR) stimulation of CLL cells in comparison to healthy donor CD5+ B cells, we found significant overexpression of lipases and phospholipases in CLL cells. In addition, we observed that the recently defined prognostic factor lipoprotein lipase (LPL) is induced by stimulation of BCR in CLL cells but not in CD5+ normal B cells. CLL cellular lysates exhibited significantly higher lipase activity compared to healthy donor controls. Incubation of primary CLL cells (n=26) with the lipase inhibitor orlistat resulted in induction of apoptosis, with a half-maximal dose (IC(50)) of 2.35 microM. In healthy B cells a significantly higher mean IC(50) of 148.5 microM of orlistat was observed, while no apoptosis was induced in healthy peripheral blood mononuclear cells (PBMCs; P<0.001). Orlistat-mediated cytotoxicity was decreased by BCR stimulation. Finally, the cytotoxic effects of orlistat on primary CLL cells were enhanced by the simultaneous incubation with fludarabine (P=0.003). In summary, alterations of lipid metabolism are involved in CLL pathogenesis and might represent a novel therapeutic target in CLL.

A method for automated temporal knowledge acquisition applied to sleep-related breathing disorders.

This paper presents a method for the discovery of temporal patterns in multivariate time series and their conversion into a linguistic knowledge representation applied to sleep-related breathing disorders. The main idea lies in introducing several abstraction levels that allow a step-wise identification of temporal patterns. Self-organizing neural networks are used to discover elementary patterns in the time series. Machine learning (ML) algorithms use the results of the neural networks to automatically generate a rule-based description. At the next levels, temporal grammatical rules are inferred. This method covers one of the main "bottlenecks" in the design of knowledge-based systems, namely, the knowledge acquisition problem. An evaluation of the rules lead to an overall sensitivity of 0.762, and a specificity of 0.758.

Isolation and sequence analysis of the chicken GABAA receptor alpha 1-subunit gene promoter.

A genomic clone containing the 5'-flanking sequence of the chicken GABAA receptor alpha 1-subunit-encoding gene (GabR alpha 1) was isolated and characterized. An intron was found to interrupt the 5'-untranslated region. The transcription start point (tsp) was determined by primer extension, RNase protection and the amplification of chick brain first-strand cDNA. DNA sequence analysis revealed a number of putative transcriptional regulatory motifs, including a TATA box 30 nucleotides upstream from the tsp, and that this region is a CpG island. While there is conservation between the chicken and human GabR alpha 1 sequences, the chicken GabR alpha 1 promoter has a different structure to those reported for the GABAA receptor beta 3- and delta-subunit-encoding genes.

Glutamate receptors of Drosophila melanogaster. Primary structure of a putative NMDA receptor protein expressed in the head of the adult fly.

The NMDA subtype of ionotropic glutamate receptors has been implicated in the activity-dependent modification of synaptic efficacy in the mammalian brain. Here we describe a cDNA isolated from Drosophila melanogaster which encodes a putative invertebrate NMDA receptor protein (DNMDAR-I). The deduced amino acid sequence of DNMDAR-I displays 46% amino acid identity to the rat NMDAR1 polypeptide and shows significant homology (16-23%) to other vertebrate and invertebrate glutamate receptor proteins. The DNMDAR-I gene maps to position 83AB of chromosome 3R and is highly expressed in the head of adult flies. Our data indicate that the NMDA subtype of glutamate receptors evolved early during phylogeny and suggest the existence of activity-dependent synaptic plasticity in the insect brain.

Molecular analysis of Drosophila glutamate receptors.

Insects and other invertebrates use L-glutamate as a neurotransmitter in the central nervous system and at the neuromuscular junction. In contrast to the well-studied effects of L-glutamate on invertebrate muscle cells, relatively little is known about the physiological role of glutamate receptors (GluRs) in the invertebrate central nervous system. We have applied a molecular cloning approach to elucidate the molecular structure of neuronal and muscle-specific Drosophila glutamate receptor subunits (DGluRs). Several domains conserved between rat GluR subunits and DGluRs indicate regions of high functional significance. Drosophila genetics may now be used as a valuable experimental tool to gain further insight into the role of DGluRs in development, synaptic plasticity and control of gene expression.

Glutamate receptors of Drosophila melanogaster: cloning of a kainate-selective subunit expressed in the central nervous system.

We report the isolation and functional characterization of cDNAs encoding a Drosophila kainate-selective glutamate receptor. The deduced mature 964-residue protein (DGluR-I) is 108,482 Da and exhibits significant homology to mammalian glutamate receptor subunits. Injection of DGluR-I cRNA into Xenopus oocytes generated kainate-operated ion channels which were blocked by the selective non-N-methyl-D-aspartate receptor antagonist 6-cyano-7-nitro-quinoxaline-2,3-dione and philanthotoxin. DGluR-I transcripts are differentially expressed during Drosophila development and, in late embryogenesis, accumulate in the central nervous system.

Molecular cloning of an invertebrate glutamate receptor subunit expressed in Drosophila muscle.

Insects and other invertebrates use glutamate as a neurotransmitter in the central nervous system and at the neuromuscular junction. A complementary DNA from Drosophila melanogaster, designated DGluR-II, has been isolated that encodes a distant homolog of the cloned mammalian ionotropic glutamate receptor family and is expressed in somatic muscle tissue of Drosophila embryos. Electrophysiological recordings made in Xenopus oocytes that express DGluR-II revealed depolarizing responses to L-glutamate and L-aspartate but low sensitivity to quisqualate, alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionate (AMPA), and kainate. The DGluR-II protein may represent a distinct glutamate receptor subtype, which shares its structural design with other members of the ionotropic glutamate receptor family.