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

Artificial intelligence and machine learning in pain research: a data scientometric analysis

The collection of increasing amounts of data in health care has become relevant for pain therapy and research. This poses problems for analyses with classical approaches, which is why artificial intelligence (AI) and machine learning (ML) methods are being included into pain research. The current literature on AI and ML in the context of pain research was automatically searched and manually curated. Common machine learning methods and pain settings covered were evaluated. Further focus was on the origin of the publication and technical details, such as the included sample sizes of the studies analyzed with ML. Machine learning was identified in 475 publications from 18 countries, with 79% of the studies published since 2019. Most addressed pain conditions included low back pain, musculoskeletal disorders, osteoarthritis, neuropathic pain, and inflammatory pain. Most used ML algorithms included random forests and support vector machines; however, deep learning was used when medical images were involved in the diagnosis of painful conditions. Cohort sizes ranged from 11 to 2,164,872, with a mode at n = 100; however, deep learning required larger data sets often only available from medical images. Artificial intelligence and ML, in particular, are increasingly being applied to pain-related data. This report presents application examples and highlights advantages and limitations, such as the ability to process complex data, sometimes, but not always, at the cost of big data requirements or black-box decisions.

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.

Regulatory T-cells inhibit microglia-induced pain hypersensitivity in female mice

Peripheral nerve injury-induced neuropathic pain is a chronic and debilitating condition characterized by mechanical hypersensitivity. We previously identified microglial activation via release of colony-stimulating factor 1 (CSF1) from injured sensory neurons as a mechanism contributing to nerve injury-induced pain. Here, we show that intrathecal administration of CSF1, even in the absence of injury, is sufficient to induce pain behavior, but only in male mice. Transcriptional profiling and morphologic analyses after intrathecal CSF1 showed robust immune activation in male but not female microglia. CSF1 also induced marked expansion of lymphocytes within the spinal cord meninges, with preferential expansion of regulatory T-cells (Tregs) in female mice. Consistent with the hypothesis that Tregs actively suppress microglial activation in females, Treg deficient (Foxp3^DTR) female mice showed increased CSF1-induced microglial activation and pain hypersensitivity equivalent to males. We conclude that sexual dimorphism in the contribution of microglia to pain results from Treg-mediated suppression of microglial activation and pain hypersensitivity in female mice.

Spared Nerve Injury Causes Sexually Dimorphic Mechanical Allodynia and Differential Gene Expression in Spinal Cords and Dorsal Root Ganglia in Rats

Neuropathic pain is more prevalent in women. However, females are under-represented in animal experiments, and the mechanisms of sex differences remain inadequately understood. We used the spared nerve injury (SNI) model in rats to characterize sex differences in pain behaviour, unbiased RNA-Seq and proteomics to study the mechanisms. Male and female rats were subjected to SNI- and sham-surgery. Mechanical and cold allodynia were assessed. Ipsilateral lumbar dorsal root ganglia (DRG) and spinal cord (SC) segments were collected for RNA-seq analysis with DESeq2 on Day 7. Cerebrospinal fluid (CSF) samples for proteomic analysis and DRGs and SCs for analysis of IB-4 and CGRP, and IBA1 and GFAP, respectively, were collected on Day 21. Females developed stronger mechanical allodynia. There were no differences between the sexes in CGRP and IB-4 in the DRG or glial cell markers in the SC. No CSF protein showed change following SNI. DRG and SC showed abundant changes in gene expression. Sexually dimorphic responses were found in genes related to T-cells (cd28, ctla4, cd274, cd4, prf1), other immunological responses (dpp4, c5a, cxcr2 and il1b), neuronal transmission (hrh3, thbs4, chrna4 and pdyn), plasticity (atf3, c1qc and reg3b), and others (bhlhe22, mcpt1l, trpv6). We observed significantly stronger mechanical allodynia in females and numerous sexually dimorphic changes in gene expression following SNI in rats. Several genes have previously been linked to NP, while some are novel. Our results suggest gene targets for further studies in the development of new, possibly sex-specific, therapies for NP.

A comprehensive review on biomarkers associated with painful temporomandibular disorders

Pain of the orofacial region is the primary complaint for which patients seek treatment. Of all the orofacial pain conditions, one condition that possess a significant global health problem is temporomandibular disorder (TMD). Patients with TMD typically frequently complaints of pain as a symptom. TMD can occur due to complex interplay between peripheral and central sensitization, endogenous modulatory pathways, and cortical processing. For diagnosis of TMD pain a descriptive history, clinical assessment, and imaging is needed. However, due to the complex nature of pain an additional step is needed to render a definitive TMD diagnosis. In this review we explicate the role of different biomarkers involved in painful TMD. In painful TMD conditions, the role of biomarkers is still elusive. We believe that the identification of biomarkers associated with painful TMD may stimulate researchers and clinician to understand the mechanism underlying the pathogenesis of TMD and help them in developing newer methods for the diagnosis and management of TMD. Therefore, to understand the potential relationship of biomarkers, and painful TMD we categorize the biomarkers as molecular biomarkers, neuroimaging biomarkers and sensory biomarkers. In addition, we will briefly discuss pain genetics and the role of potential microRNA (miRNA) involved in TMD pain.

Sources of Individual Differences in Pain

Pain is an immense clinical and societal challenge, and the key to understanding and treating it is variability. Robust interindividual differences are consistently observed in pain sensitivity, susceptibility to developing painful disorders, and response to analgesic manipulations. This review examines the causes of this variability, including both organismic and environmental sources. Chronic pain development is a textbook example of a gene-environment interaction, requiring both chance initiating events (e.g., trauma, infection) and more immutable risk factors. The focus is on genetic factors, since twin studies have determined that a plurality of the variance likely derives from inherited genetic variants, but sex, age, ethnicity, personality variables, and environmental factors are also considered.

Computational Functional Genomics-Based AmpliSeq™ Panel for Next-Generation Sequencing of Key Genes of Pain

The genetic background of pain is becoming increasingly well understood, which opens up possibilities for predicting the individual risk of persistent pain and the use of tailored therapies adapted to the variant pattern of the patient's pain-relevant genes. The individual variant pattern of pain-relevant genes is accessible via next-generation sequencing, although the analysis of all "pain genes" would be expensive. Here, we report on the development of a cost-effective next generation sequencing-based pain-genotyping assay comprising the development of a customized AmpliSeq™ panel and bioinformatics approaches that condensate the genetic information of pain by identifying the most representative genes. The panel includes 29 key genes that have been shown to cover 70% of the biological functions exerted by a list of 540 so-called "pain genes" derived from transgenic mice experiments. These were supplemented by 43 additional genes that had been independently proposed as relevant for persistent pain. The functional genomics covered by the resulting 72 genes is particularly represented by mitogen-activated protein kinase of extracellular signal-regulated kinase and cytokine production and secretion. The present genotyping assay was established in 61 subjects of Caucasian ethnicity and investigates the functional role of the selected genes in the context of the known genetic architecture of pain without seeking functional associations for pain. The assay identified a total of 691 genetic variants, of which many have reports for a clinical relevance for pain or in another context. The assay is applicable for small to large-scale experimental setups at contemporary genotyping costs.

Systems biology-based approaches to summarize and identify novel genes and pathways associated with acute and chronic postsurgical pain

STUDY OBJECTIVE

To employ systems biology-based machine learning to identify biologic processes over-represented with genetic variants (gene enrichment) implicated in post-surgical pain.

DESIGN

Informed systems biology based integrative computational analyses.

SETTING

Pediatric research and teaching institution.

INTERVENTIONS

Pubmed search (01/01/2001-10/31/2017) was performed to identify "training" genes associated with postoperative pain in humans. Candidate genes were identified and prioritized using Toppgene suite, based on functional enrichment using several gene ontology annotations, and curated gene sets associated with mouse phenotype-knockout studies.

MEASUREMENTS

Computationally top-ranked candidate genes and literature-curated genes were included in pathway enrichment analyses. Hierarchical clustering was used to visualize select functional enrichment results between the two phenotypes.

MAIN RESULTS

Literature review identified 38 training genes associated with postoperative pain and 31 with CPSP. We identified 2610 prioritized novel candidate genes likely associated with acute and chronic postsurgical pain, the top 10th percentile jointly enriched (p 0.05; Benjamini-Hochberg correction) several pathways, topmost being cAMP response element-binding protein and ion channel pathways. Heat maps demonstrated enrichment of inflammatory/drug metabolism processes in acute postoperative pain and immune mechanisms in CPSP.

CONCLUSION

High interindividual variability in pain responses immediately after surgery and risk for CPSP suggests genetic susceptibility. Lack of large homogenous sample sizes have led to underpowered genetic association studies. Systems biology can be leveraged to integrate genetic-level data with biologic processes to generate prioritized candidate gene lists and understand novel biological pathways involved in acute postoperative pain and CPSP. Such data would be key to informing future polygenic studies with targeted genome wide profiling. This study demonstrates the utility of functional annotation - based prioritization and enrichment approaches and identifies novel genes and unique/shared biological processes involved in acute and chronic postoperative pain. Results provide framework for future targeted genetic profiling of CPSP risk, to enable preventive and therapeutic approaches.

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.

Potential biomarkers for persistent and neuropathic pain therapy

Persistent, in particular neuropathic pain affects millions of people worldwide. However, the response rate of patients to existing analgesic drugs is less than 50%. There are several possibilities to increase this response rate, such as optimization of the pharmacokinetic and pharmacodynamic properties of analgesics. Another promising approach is to use prognostic biomarkers in patients to determine the optimal pharmacological therapy for each individual. Here, we discuss recent efforts to identify plasma and CSF biomarkers, as well as genetic biomarkers and sensory testing, and how these readouts could be exploited for the prediction of a suitable pharmacological treatment. Collectively, the information on single biomarkers may be stored in knowledge bases and processed by machine-learning and related artificial intelligence techniques, resulting in the optimal pharmacological treatment for individual pain patients. We highlight the potential for biomarker-based individualized pain therapies and discuss biomarker reliability and their utility in clinical practice, as well as limitations of this approach.