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

Genomic strategies for drug repurposing

Functional genomics, a multidisciplinary subject, investigates the functions of genes and their products in biological systems to better understand diseases and find new drugs. Drug repurposing is an economically efficient approach that entails discovering novel therapeutic applications for already-available medications. Genomics enables the identification of illness and therapeutic molecular characteristics and interactions, which in turn facilitates the process of drug repurposing. Techniques like gene expression profiling and Mendelian randomization are helpful in identifying possible medication candidates. Progress in computer science allows for the investigation and modeling of gene expression networks that involve large amounts of data. The amalgamation of data concerning DNA, RNA, and protein functions bears similarity to pharmacogenomics, a crucial aspect in crafting cancer therapeutics. Functional genomics in drug discovery, particularly for cancer, is still not thoroughly investigated, despite the existence of a significant amount of literature on the subject. Next-generation sequencing and proteomics present highly intriguing opportunities. Publicly available databases and mining techniques facilitate the development of cancer treatments based on functional genomics. Broadening the exploration and utilization of functional genomics holds significant potential for advancing drug discovery and repurposing, particularly within the realm of oncology.

Cerebrospinal Fluid Proteome Map Reveals Molecular Signatures of Reversible Cerebral Vasoconstriction Syndrome

Reversible cerebral vasoconstriction syndrome (RCVS) is a complex neurovascular disorder characterized by repetitive thunderclap headaches and reversible cerebral vasoconstriction. The pathophysiological mechanism of this mysterious syndrome remains underexplored and there is no clinically available molecular biomarker. To provide insight into the pathogenesis of RCVS, this study reported the first landscape of dysregulated proteome of cerebrospinal fluid (CSF) in patients with RCVS (n  =  21) compared to the age- and sex-matched controls (n  =  20) using data-independent acquisition mass spectrometry. Protein-protein interaction and functional enrichment analysis were employed to construct functional protein networks using the RCVS proteome. An RCVS-CSF proteome library resource of 1054 proteins was established, which illuminated large groups of upregulated proteins enriched in the brain and blood-brain barrier (BBB). Personalized RCVS-CSF proteomic profiles from 17 RCVS patients and 20 controls reveal proteomic changes involving the complement system, adhesion molecules, and extracellular matrix, which may contribute to the disruption of BBB and dysregulation of neurovascular units. Moreover, an additional validation cohort validated a panel of biomarker candidates and a two-protein signature predicted by machine learning model to discriminate RCVS patients from controls with an area under the curve of 0.997. This study reveals the first RCVS proteome and a potential pathogenetic mechanism of BBB and neurovascular unit dysfunction. It also nominates potential biomarker candidates that are mechanistically plausible for RCVS, which may offer potential diagnostic and therapeutic opportunities beyond the clinical manifestations.

The evolution of Big Data in neuroscience and neurology

Neurological diseases are on the rise worldwide, leading to increased healthcare costs and diminished quality of life in patients. In recent years, Big Data has started to transform the fields of Neuroscience and Neurology. Scientists and clinicians are collaborating in global alliances, combining diverse datasets on a massive scale, and solving complex computational problems that demand the utilization of increasingly powerful computational resources. This Big Data revolution is opening new avenues for developing innovative treatments for neurological diseases. Our paper surveys Big Data's impact on neurological patient care, as exemplified through work done in a comprehensive selection of areas, including Connectomics, Alzheimer's Disease, Stroke, Depression, Parkinson's Disease, Pain, and Addiction (e.g., Opioid Use Disorder). We present an overview of research and the methodologies utilizing Big Data in each area, as well as their current limitations and technical challenges. Despite the potential benefits, the full potential of Big Data in these fields currently remains unrealized. We close with recommendations for future research aimed at optimizing the use of Big Data in Neuroscience and Neurology for improved patient outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40537-023-00751-2.

Machine-Learning Analysis of Serum Proteomics in Neuropathic Pain after Nerve Injury in Breast Cancer Surgery Points at Chemokine Signaling via SIRT2 Regulation

BACKGROUND

Persistent postsurgical neuropathic pain (PPSNP) can occur after intraoperative damage to somatosensory nerves, with a prevalence of 29-57% in breast cancer surgery. Proteomics is an active research field in neuropathic pain and the first results support its utility for establishing diagnoses or finding therapy strategies.

METHODS

57 women (30 non-PPSNP/27 PPSNP) who had experienced a surgeon-verified intercostobrachial nerve injury during breast cancer surgery, were examined for patterns in 74 serum proteomic markers that allowed discrimination between subgroups with or without PPSNP. Serum samples were obtained both before and after surgery.

RESULTS

Unsupervised data analyses, including principal component analysis and self-organizing maps of artificial neurons, revealed patterns that supported a data structure consistent with pain-related subgroup (non-PPSPN vs. PPSNP) separation. Subsequent supervised machine learning-based analyses revealed 19 proteins (CD244, SIRT2, CCL28, CXCL9, CCL20, CCL3, IL.10RA, MCP.1, TRAIL, CCL25, IL10, uPA, CCL4, DNER, STAMPB, CCL23, CST5, CCL11, FGF.23) that were informative for subgroup separation. In cross-validated training and testing of six different machine-learned algorithms, subgroup assignment was significantly better than chance, whereas this was not possible when training the algorithms with randomly permuted data or with the protein markers not selected. In particular, sirtuin 2 emerged as a key protein, presenting both before and after breast cancer treatments in the PPSNP compared with the non-PPSNP subgroup.

CONCLUSIONS

The identified proteins play important roles in immune processes such as cell migration, chemotaxis, and cytokine-signaling. They also have considerable overlap with currently known targets of approved or investigational drugs. Taken together, several lines of unsupervised and supervised analyses pointed to structures in serum proteomics data, obtained before and after breast cancer surgery, that relate to neuroinflammatory processes associated with the development of neuropathic pain after an intraoperative nerve lesion.

Proteomics based markers of clinical pain severity in juvenile idiopathic arthritis

INTRODUCTION

Juvenile idiopathic arthritis (JIA) is a cluster of autoimmune rheumatic diseases occurring in children 16 years of age or less. While it is well-known that pain may be experienced during inflammatory and non-inflammatory states, much remains ambiguous regarding the molecular mechanisms that may drive JIA pain. Thus, in this pilot study, we explored the variability of the serum proteomes in relation to pain severity in a cohort of JIA patients.

METHODS

Serum samples from 15 JIA patients (male and female, 12.7 ± 2.8 years of age) were assessed using liquid chromatography/mass spectrometry (LC/MS). Correlation analyses were performed to determine the relationships among protein levels and self-reported clinical pain severity. Additionally, how the expression of pain-associated proteins related to markers of inflammation (Erythrocyte Sedimentation Rate (ESR)) or morphological properties of the central nervous system (subcortical volume and cortical thickness) implicated in JIA were also evaluated.

RESULTS

306 proteins were identified in the JIA cohort of which 14 were significantly (p < 0.05) associated with clinical pain severity. Functional properties of the identified pain-associated proteins included but were not limited to humoral immunity (IGLV3.9), inflammatory response (PRG4) and angiogenesis (ANG). Associations among pain-associated proteins and ESR (IGHV3.9, PRG4, CST3, VWF, ALB), as well as caudate nucleus volume (BTD, AGT, IGHV3.74) and insular cortex thickness (BTD, LGALS3BP) were also observed.

CONCLUSIONS

The current proteomic findings suggest both inflammatory- and non-inflammatory mediated mechanisms as potential factors associated with JIA pain. Validation of these preliminary observations using larger patient cohorts and a longitudinal study design may further point to novel serologic markers of pain in JIA.

Epigenetic signature of chronic low back pain in human T cells

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This study reveals sex-specific DNA methylation signatures in human T cells that discriminate chronic low back pain participants from healthy controls.

Objective:

Determine if chronic low back pain (LBP) is associated with DNA methylation signatures in human T cells that will reveal novel mechanisms and potential therapeutic targets and explore the feasibility of epigenetic diagnostic markers for pain-related pathophysiology.

Methods:

Genome-wide DNA methylation analysis of 850,000 CpG sites in women and men with chronic LBP and pain-free controls was performed. T cells were isolated (discovery cohort, n = 32) and used to identify differentially methylated CpG sites, and gene ontologies and molecular pathways were identified. A polygenic DNA methylation score for LBP was generated in both women and men. Validation was performed in an independent cohort (validation cohort, n = 63) of chronic LBP and healthy controls.

Results:

Analysis with the discovery cohort revealed a total of 2,496 and 419 differentially methylated CpGs in women and men, respectively. In women, most of these sites were hypomethylated and enriched in genes with functions in the extracellular matrix, in the immune system (ie, cytokines), or in epigenetic processes. In men, a unique chronic LBP DNA methylation signature was identified characterized by significant enrichment for genes from the major histocompatibility complex. Sex-specific polygenic DNA methylation scores were generated to estimate the pain status of each individual and confirmed in the validation cohort using pyrosequencing.

Conclusion:

This study reveals sex-specific DNA methylation signatures in human T cells that discriminates chronic LBP participants from healthy controls.

Correlation Between Gut Microbiome Composition and Acute Pain Perception in Young Healthy Male Subjects

OBJECTIVE

Recently, there has been growing interest in the gut-brain axis because it is emerging as a player influencing the health status of the host human. It is a known fact that the gut microbiome (GM) through the gut-brain axis has been implicated in numerous diseases. We previously reported that stool condition was associated with pain perception. Stool consistency and constipation are known to be associated with GM composition. Thus, we imagine that GM composition could influence pain perception. The aim of this study was to investigate the correlations between GM composition and pain perception and psychological states in young healthy male subjects.

SUBJECTS

A total of 42 healthy young male volunteers completed the present study.

METHODS

The volunteers' pain perceptions were assessed by pressure pain threshold, current perception threshold, temporal summation of pain, and conditioned pain modulation, and a questionnaire on psychological state was obtained. During the current perception threshold examination, we used 5, 250, and 2,000 Hz to stimulate C, Aδ, and Aβ fibers. In addition, GM composition was evaluated by using 16S rRNA analysis.

RESULTS

Pressure pain threshold showed a significant and negative correlation with Bacteroidetes phylum, in contrast to a significant and positive correlation with Firmicutes phylum. Current perception threshold of Aδ and Firmicutes phylum showed a significant correlation. There was a negative correlation between anxiety state and Bifidobacterium genus. In contrast, there was no significant correlation between psychological states and pain perceptions.

CONCLUSION

The present study showed that acute pain perception was associated with GM composition in young healthy males.

The methyl donor S-adenosyl methionine reverses the DNA methylation signature of chronic neuropathic pain in mouse frontal cortex

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Chronic administration of S-adenosylmethionine reverses neuropathic pain–induced changes in DNA methylation in the mouse frontal cortex.

Chronic pain is associated with persistent but reversible structural and functional changes in the prefrontal cortex (PFC). This stable yet malleable plasticity implicates epigenetic mechanisms, including DNA methylation, as a potential mediator of chronic pain–induced cortical pathology. We previously demonstrated that chronic oral administration of the methyl donor S-adenosyl methionine (SAM) attenuates long-term peripheral neuropathic pain and alters global frontal cortical DNA methylation. However, the specific genes and pathways associated with the resolution of chronic pain by SAM remain unexplored.

Prioritizing Pain-Associated Targets with Machine Learning

While hundreds of genes have been associated with pain, much of the molecular mechanisms of pain remain unknown. As a result, current analgesics are limited to few clinically validated targets. Here, we trained a machine learning (ML) ensemble model to predict new targets for 17 categories of pain. The model utilizes features from transcriptomics, proteomics, and gene ontology to prioritize targets for modulating pain. We focused on identifying novel G-protein-coupled receptors (GPCRs), ion channels, and protein kinases because these proteins represent the most successful drug target families. The performance of the model to predict novel pain targets is 0.839 on average based on AUROC, while the predictions for arthritis had the highest accuracy (AUROC = 0.929). The model predicts hundreds of novel targets for pain; for example, GPR132 and GPR109B are highly ranked GPCRs for rheumatoid arthritis. Overall, gene-pain association predictions cluster into three groups that are enriched for cytokine, calcium, and GABA-related cell signaling pathways. These predictions can serve as a foundation for future experimental exploration to advance the development of safer and more effective analgesics.

Machine Learning in Chronic Pain Research: A Scoping Review

Given the high prevalence and associated cost of chronic pain, it has a significant impact on individuals and society. Improvements in the treatment and management of chronic pain may increase patients’ quality of life and reduce societal costs. In this paper, we evaluate state-of-the-art machine learning approaches in chronic pain research. A literature search was conducted using the PubMed, IEEE Xplore, and the Association of Computing Machinery (ACM) Digital Library databases. Relevant studies were identified by screening titles and abstracts for keywords related to chronic pain and machine learning, followed by analysing full texts. Two hundred and eighty-seven publications were identified in the literature search. In total, fifty-three papers on chronic pain research and machine learning were reviewed. The review showed that while many studies have emphasised machine learning-based classification for the diagnosis of chronic pain, far less attention has been paid to the treatment and management of chronic pain. More research is needed on machine learning approaches to the treatment, rehabilitation, and self-management of chronic pain. As with other chronic conditions, patient involvement and self-management are crucial. In order to achieve this, patients with chronic pain need digital tools that can help them make decisions about their own treatment and care.

The transition from acute to chronic pain: dynamic epigenetic reprogramming of the mouse prefrontal cortex up to 1 year after nerve injury

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DNA methylation undergoes rapid and large changes in mouse prefrontal cortex at multiple time points postinjury, implicating hundreds of genes in a time-dependent manner.

Chronic pain is associated with persistent structural and functional changes throughout the neuroaxis, including in the prefrontal cortex (PFC). The PFC is important in the integration of sensory, cognitive, and emotional information and in conditioned pain modulation. We previously reported widespread epigenetic reprogramming in the PFC many months after nerve injury in rodents. Epigenetic modifications, including DNA methylation, can drive changes in gene expression without modifying DNA sequences. To date, little is known about epigenetic dysregulation at the onset of acute pain or how it progresses as pain transitions from acute to chronic. We hypothesize that acute pain after injury results in rapid and persistent epigenetic remodelling in the PFC that evolves as pain becomes chronic. We further propose that understanding epigenetic remodelling will provide insights into the mechanisms driving pain-related changes in the brain. Epigenome-wide analysis was performed in the mouse PFC 1 day, 2 weeks, 6 months, and 1 year after peripheral injury using the spared nerve injury in mice. Spared nerve injury resulted in rapid and persistent changes in DNA methylation, with robust differential methylation observed between spared nerve injury and sham-operated control mice at all time points. Hundreds of differentially methylated genes were identified, including many with known function in pain. Pathway analysis revealed enrichment in genes related to stimulus response at early time points, immune function at later time points, and actin and cytoskeletal regulation throughout the time course. These results emphasize the importance of considering pain chronicity in both pain research and in treatment optimization.

Computational functional genomics-based reduction of disease-related gene sets to their key components

MOTIVATION

The genetic architecture of diseases becomes increasingly known. This raises difficulties in picking suitable targets for further research among an increasing number of candidates. Although expression based methods of gene set reduction are applied to laboratory-derived genetic data, the analysis of topical sets of genes gathered from knowledge bases requires a modified approach as no quantitative information about gene expression is available.

RESULTS

We propose a computational functional genomics-based approach at reducing sets of genes to the most relevant items based on the importance of the gene within the polyhierarchy of biological processes characterizing the disease. Knowledge bases about the biological roles of genes can provide a valid description of traits or diseases represented as a directed acyclic graph (DAG) picturing the polyhierarchy of disease relevant biological processes. The proposed method uses a gene importance score derived from the location of the gene-related biological processes in the DAG. It attempts to recreate the DAG and thereby, the roles of the original gene set, with the least number of genes in descending order of importance. This obtained precision and recall of over 70% to recreate the components of the DAG charactering the biological functions of n=540 genes relevant to pain with a subset of only the k=29 best-scoring genes.

CONCLUSIONS

A new method for reduction of gene sets is shown that is able to reproduce the biological processes in which the full gene set is involved by over 70%; however, by using only ∼5% of the original genes.

AVAILABILITY AND IMPLEMENTATION

The necessary numerical parameters for the calculation of gene importance are implemented in the R package dbtORA at https://github.com/IME-TMP-FFM/dbtORA.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Using Deep Learning and Resting-State fMRI to Classify Chronic Pain Conditions

Chronic pain is known as a complex disease due to its comorbidities with other symptoms and the lack of effective treatments. As a consequence, chronic pain seems to be under-diagnosed in more than 75% of patients. At the same time, the advance in brain imaging, the popularization of machine learning techniques and the development of new diagnostic tools based on these technologies have shown that these tools could be an option in supporting decision-making of healthcare professionals. In this study, we computed functional brain connectivity using resting-state fMRI data from one hundred and fifty participants to assess the performance of different machine learning models, including deep learning (DL) neural networks in classifying chronic pain patients and pain-free controls. The best result was obtained by training a convolutional neural network fed with data preprocessed using the MSDL probabilistic atlas and using the dynamic time warping (DTW) as connectivity measure. DL models had a better performance compared to other less costly models such as support vector machine (SVM) and RFC, with balanced accuracy ranged from 69 to 86%, while the area under the curve (ROC) ranged from 0.84 to 0.93. Also, DTW overperformed correlation as connectivity measure. These findings support the notion that resting-state fMRI data could be used as a potential biomarker of chronic pain conditions.

Machine-learned analysis of global and glial/opioid intersection-related DNA methylation in patients with persistent pain after breast cancer surgery

BACKGROUND

Glial cells in the central nervous system play a key role in neuroinflammation and subsequent central sensitization to pain. They are therefore involved in the development of persistent pain. One of the main sites of interaction of the immune system with persistent pain has been identified as neuro-immune crosstalk at the glial-opioid interface. The present study examined a potential association between the DNA methylation of two key players of glial/opioid intersection and persistent postoperative pain.

METHODS

In a cohort of 140 women who had undergone breast cancer surgery, and were assigned based on a 3-year follow-up to either a persistent or non-persistent pain phenotype, the role of epigenetic regulation of key players in the glial-opioid interface was assessed. The methylation of genes coding for the Toll-like receptor 4 (TLR4) as a major mediator of glial contributions to persistent pain or for the μ-opioid receptor (OPRM1) was analyzed and its association with the pain phenotype was compared with that conferred by global genome-wide DNA methylation assessed via quantification of the methylation in the retrotransposon LINE1.

RESULTS

Training of machine learning algorithms indicated that the global DNA methylation provided a similar diagnostic accuracy for persistent pain as previously established non-genetic predictors. However, the diagnosis can be based on a single DNA based marker. By contrast, the methylation of TLR4 or OPRM1 genes could not contribute further to the allocation of the patients to the pain-related phenotype groups.

CONCLUSIONS

While clearly supporting a predictive utility of epigenetic testing, the present analysis cannot provide support for specific epigenetic modulation of persistent postoperative pain via methylation of two key genes of the glial-opioid interface.

A Network Pharmacology Approach for the Identification of Common Mechanisms of Drug-Induced Peripheral Neuropathy

Drug-induced peripheral neuropathy is a side effect of a variety of therapeutic agents that can affect therapeutic adherence and lead to regimen modifications, impacting patient quality of life. The molecular mechanisms involved in the development of this condition have yet to be completely described in the literature. We used a computational network pharmacology approach to explore the Connectivity Map, a large collection of transcriptional profiles from drug perturbation experiments to identify common genes affected by peripheral neuropathy-inducing drugs. Consensus profiles for 98 of these drugs were used to construct a drug-gene perturbation network. We identified 27 genes significantly associated with neuropathy-inducing drugs. These genes may have a potential role in the action of neuropathy-inducing drugs. Our results suggest that molecular mechanisms, including alterations in mitochondrial function, microtubule and cytoskeleton function, ion channels, transcriptional regulation including epigenetic mechanisms, signal transduction, and wound healing, may play a critical role in drug-induced peripheral neuropathy.

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

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.

Sex-Dependent Sensory Phenotypes and Related Transcriptomic Expression Profiles Are Differentially Affected by Angelman Syndrome

Angelman syndrome (AS) is a genetic disorder which entails autism, intellectual disability, lack of speech, motor deficits, and seizure susceptibility. It is caused by the lack of UBE3A protein expression, which is an E3-ubiquitin ligase. Despite AS equal prevalence in males and females, not much data on how sex affects the syndrome was reported. In the herein study, we thoroughly characterized many behavioral phenotypes of AS mice. The behavioral data acquired was analyzed with respect to sex. In addition, we generated a new mRNA sequencing dataset. We analyzed the coding transcriptome expression profiles with respect to the effects of genotype and sex observed in the behavioral phenotypes. We identified several neurobehavioral aspects, especially sensory perception, where AS mice either lack the male-to-female differences observed in wild-type littermates or even show opposed differences. However, motor phenotypes did not show male-to-female variation between wild-type (WT) and AS mice. In addition, by utilizing the mRNA sequencing, we identified genes and isoforms with expression profiles that mirror the sensory perception results. These genes are differentially regulated in the two sexes with inverse expression profiles in AS mice compared to WT littermates. Some of these are known pain-related and estrogen-dependent genes. The observed differences in sex-dependent neurobehavioral phenotypes and the differential transcriptome expression profiles in AS mice strengthen the evidence for molecular cross talk between Ube3a protein and sex hormone receptors or their elicited pathways. These interactions are essential for understanding Ube3a deletion effects, beyond its E3-ligase activity.

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 AmpliSeq^TM 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.

The Association Between Constipation or Stool Consistency and Pain Severity in Patients With Chronic Pain

Background

Bacteria can influence a variety of gut functions. Some studies showed that stool consistency and constipation were associated with gut microbiome (GM) composition, and enterotype, dysbiosis. Growing evidence indicates the significant role of GM in the homeostatic function of the host body. The GM may regulate multiple neurochemical and neurometabolic pathways. Chronicity of the pain is actively modulated at the molecular to the network level by means of several neurotransmitters. The GM to some extent can affect pain perception.

Objectives

The current study aimed at investigating the relationship between constipation state or usual stool form and pain severity of patients with chronic pain.

Methods

The current study was conducted on 365 patients with chronic pain. The participants were evaluated on their stool form (the Bristol stool form scale; BSFS), constipation state (the Cleveland clinic constipation score; CCCS), body mass index (BMI), and usual pain severity (numerical rating scale; NRS). In addition, the participants were assigned into five groups according to the pain region (i e, low back and/or lower limb, whole body, neck and/or upper back and/or upper limb, head and/or face, chest and/or abdominal).

Results

The CCS showed a significant and positive association with the pain severity of the total patients and patients with low back and/or lower limb pain. Simultaneous multiple linear regression analyses revealed that a predictor of the pain severity was the CCS for the total patients and patients with low back and/or lower limb, whole body pain.

Conclusions

Constipation displayed a significant and positive association with the pain severity of the total patients and patients with low back and/or lower limb pain, whole body.

CRISPR/Cas9 editing of Nf1 gene identifies CRMP2 as a therapeutic target in neurofibromatosis type 1-related pain that is reversed by (S)-Lacosamide

Neurofibromatosis type 1 (NF1) is a rare autosomal dominant disease linked to mutations of the Nf1 gene. Patients with NF1 commonly experience severe pain. Studies on mice with Nf1 haploinsufficiency have been instructive in identifying sensitization of ion channels as a possible cause underlying the heightened pain suffered by patients with NF1. However, behavioral assessments of Nf1 mice have led to uncertain conclusions about the potential causal role of Nf1 in pain. We used the clustered regularly interspaced short palindromic repeats (CRISPR)-associated 9 (CRISPR/Cas9) genome editing system to create and mechanistically characterize a novel rat model of NF1-related pain. Targeted intrathecal delivery of guide RNA/Cas9 nuclease plasmid in combination with a cationic polymer was used to generate allele-specific C-terminal truncation of neurofibromin, the protein encoded by the Nf1 gene. Rats with truncation of neurofibromin, showed increases in voltage-gated calcium (specifically N-type or CaV2.2) and voltage-gated sodium (particularly tetrodotoxin-sensitive) currents in dorsal root ganglion neurons. These gains-of-function resulted in increased nociceptor excitability and behavioral hyperalgesia. The cytosolic regulatory protein collapsin response mediator protein 2 (CRMP2) regulates activity of these channels, and also binds to the targeted C-terminus of neurofibromin in a tripartite complex, suggesting a possible mechanism underlying NF1 pain. Prevention of CRMP2 phosphorylation with (S)-lacosamide resulted in normalization of channel current densities, excitability, as well as of hyperalgesia following CRISPR/Cas9 truncation of neurofibromin. These studies reveal the protein partners that drive NF1 pain and suggest that CRMP2 is a key target for therapeutic intervention.

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.

Computational functional genomics-based approaches in analgesic drug discovery and repurposing

Persistent pain is a major healthcare problem affecting a fifth of adults worldwide with still limited treatment options. The search for new analgesics increasingly includes the novel research area of functional genomics, which combines data derived from various processes related to DNA sequence, gene expression or protein function and uses advanced methods of data mining and knowledge discovery with the goal of understanding the relationship between the genome and the phenotype. Its use in drug discovery and repurposing for analgesic indications has so far been performed using knowledge discovery in gene function and drug target-related databases; next-generation sequencing; and functional proteomics-based approaches. Here, we discuss recent efforts in functional genomics-based approaches to analgesic drug discovery and repurposing and highlight the potential of computational functional genomics in this field including a demonstration of the workflow using a novel R library ‘dbtORA’.

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 1) which will be exemplarily introduced in the following.

Stool consistency is significantly associated with pain perception

Background

Commensal as well as pathogenic bacteria can influence a variety of gut functions, thereby leading to constipation and diarrhea in severe cases. In fact, several researchers have reported evidence supporting the association between stool consistency or constipation and the Gut microbiome (GM) composition and dysbiosis. GM influences the human health and disease via the gut-brain axis. We thus hypothesized that the pathogenic bacteria increases pain perception to some extent, which means that there could be an association between stool consistency or constipation and pain perception of healthy subjects.

Design

Observational study.

Objectives

The aim of the present study was to investigate the association between stool consistency or constipation and pain perception of healthy subjects.

Methods

Thirty-eight healthy subjects participated in this study. The participants were assessed on their usual stool form (the Bristol Stool Form Scale: BSFS), constipation (the Cleveland Clinic Constipation score: CCS), degree of obesity, pain perception by mechanical stimulus, cold pain threshold, and a questionnaire on psychological state.

Results

The BSFS was significantly and positively associated with pain perception, and showed a significant association with anxiety states. Furthermore, pain perception was significantly associated with anxiety states. However, there were no significant associations between the CCS and any independent variables. In addition, we found that a significant predictor to the pain perception was BSFS. Moreover, there were significant relationships among the psychological states, BSFS and obesity.

Conclusion

These results suggest that the stool form is associated with pain perception and anxiety status.

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.