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

Genetics Affecting the Prognosis of Dental Treatments

Genetics plays a significant role in determining an individual's susceptibility to dental diseases, the response to dental treatments, and the overall prognosis of dental interventions. Here, the authors explore the various genetic factors affecting the prognosis of dental treatments focusing on dental caries, orthodontic treatment, oral cancer, prosthodontic treatment, periodontal disease, developmental disorders, pharmacogenetics, and genetic predisposition to faster wound healing. Understanding the genetic underpinnings of dental health can help personalize treatment plans, predict outcomes, and improve the overall quality of dental care.

Chronic Postsurgical Pain in Children and Adolescents: A Call for Action

Chronic postsurgical pain (CPSP) affects a significant proportion of children and adolescents after major surgery and is a detriment to both short- and long-term recovery outcomes. While clinical characteristics and psychosocial risk factors for developing CPSP in children and adults are well established in the literature, there has been little progress on the prevention and management of CPSP after pediatric surgery. Limited evidence to support current pharmacologic approaches suggests a fundamentally new paradigm must be considered by clinicians to both conceptualize and address this adverse complication. This narrative review provides a comprehensive evaluation of both the known and emerging mechanisms that support our current understanding of CPSP. Additionally, we discuss the importance of optimizing perioperative analgesic strategies to mitigate CPSP based on individual patient risks. We highlight the importance of postoperative pain trajectories to identify those most at risk for developing CPSP, the early referral to multi-disciplinary pain clinics for comprehensive evaluation and treatment of CPSP, and additional work needed to differentiate CPSP characteristics from other chronic pain syndromes in children. Finally, we recognize ongoing challenges associated with the universal implementation of available knowledge about pediatric CPSP into practically useful care plans for clinicians.

Pharmacogenetic landscape of pain management variants among Mediterranean populations

Background

Chronic pain is a major socioeconomic burden in the Mediterranean region. However, we noticed an under-representation of these populations in the pharmacogenetics of pain management studies. In this context, we aimed 1) to decipher the pharmacogenetic variant landscape among Mediterranean populations compared to worldwide populations in order to identify therapeutic biomarkers for personalized pain management and 2) to better understand the biological process of pain management through in silico investigation of pharmacogenes pathways.

Materials and Methods

We collected genes and variants implicated in pain response using the Prisma guidelines from literature and PharmGK database. Next, we extracted these genes from genotyping data of 829 individuals. Then, we determined the variant distribution among the studied populations using multivariate (MDS) and admixture analysis with R and STRUCTURE software. We conducted a Chi2 test to compare the interethnic frequencies of the identified variants. We used SNPinfo web server, miRdSNP database to identify miRNA-binding sites. In addition, we investigated the functions of the identified genes and variants using pathway enrichment analysis and annotation tools. Finally, we performed docking analysis to assess the impact of variations on drug interactions.

Results

We identified 63 variants implicated in pain management. MDS analysis revealed that Mediterranean populations are genetically similar to Mexican populations and divergent from other populations. STRUCTURE analysis showed that Mediterranean populations are mainly composed of European ancestry. We highlighted differences in the minor allele frequencies of three variants (rs633, rs4680, and rs165728) located in the COMT gene. Moreover, variant annotation revealed ten variants with potential miRNA-binding sites. Finally, protein structure and docking analysis revealed that two missense variants (rs4680 and rs6267) induced a decrease in COMT protein activity and affinity for dopamine.

Conclusion

Our findings revealed that Mediterranean populations diverge from other ethnic groups. Furthermore, we emphasize the importance of pain-related pathways and miRNAs to better implement these markers as predictors of analgesic responses in the Mediterranean region.

Targeting ion channels with ultra-large library screening for hit discovery

Ion channels play a crucial role in a variety of physiological and pathological processes, making them attractive targets for drug development in diseases such as diabetes, epilepsy, hypertension, cancer, and chronic pain. Despite the importance of ion channels in drug discovery, the vastness of chemical space and the complexity of ion channels pose significant challenges for identifying drug candidates. The use of in silico methods in drug discovery has dramatically reduced the time and cost of drug development and has the potential to revolutionize the field of medicine. Recent advances in computer hardware and software have enabled the screening of ultra-large compound libraries. Integration of different methods at various scales and dimensions is becoming an inevitable trend in drug development. In this review, we provide an overview of current state-of-the-art computational chemistry methodologies for ultra-large compound library screening and their application to ion channel drug discovery research. We discuss the advantages and limitations of various in silico techniques, including virtual screening, molecular mechanics/dynamics simulations, and machine learning-based approaches. We also highlight several successful applications of computational chemistry methodologies in ion channel drug discovery and provide insights into future directions and challenges in this field.

Current Evidence for Biological Biomarkers and Mechanisms Underlying Acute to Chronic Pain Transition across the Pediatric Age Spectrum

Chronic pain is highly prevalent in the pediatric population. Many factors are involved in the transition from acute to chronic pain. Currently, there are conceptual models proposed, but they lack a mechanistically sound integrated theory considering the stages of child development. Objective biomarkers are critically needed for the diagnosis, risk stratification, and prognosis of the pathological stages of pain chronification. In this article, we summarize the current evidence on mechanisms and biomarkers of acute to chronic pain transitions in infants and children through the developmental lens. The goal is to identify gaps and outline future directions for basic and clinical research toward a developmentally informed theory of pain chronification in the pediatric population. At the outset, the importance of objective biomarkers for chronification of pain in children is outlined, followed by a summary of the current evidence on the mechanisms of acute to chronic pain transition in adults, in order to contrast with the developmental mechanisms of pain chronification in the pediatric population. Evidence is presented to show that chronic pain may have its origin from insults early in life, which prime the child for the development of chronic pain in later life. Furthermore, available genetic, epigenetic, psychophysical, electrophysiological, neuroimaging, neuroimmune, and sex mechanisms are described in infants and older children. In conclusion, future directions are discussed with a focus on research gaps, translational and clinical implications. Utilization of developmental mechanisms framework to inform clinical decision-making and strategies for prevention and management of acute to chronic pain transitions in children, is highlighted.

Genetic and epigenetic mechanisms influencing acute to chronic postsurgical pain transitions in pediatrics: Preclinical to clinical evidence

Background

Chronic postsurgical pain (CPSP) in children remains an important problem with no effective preventive or therapeutic strategies. Recently, genomic underpinnings explaining additional interindividual risk beyond psychological factors have been proposed.

Aims

We present a comprehensive review of current preclinical and clinical evidence for genetic and epigenetic mechanisms relevant to pediatric CPSP.

Methods

Narrative review.

Results

Animal models are relevant to translational research for unraveling genomic mechanisms. For example, Cacng2, p2rx7, and bdnf mutant mice show altered mechanical hypersensitivity to injury, and variants of the same genes have been associated with CPSP susceptibility in humans; similarly, differential DNA methylation (H1SP) and miRNAs (miR-96/7a) have shown translational implications. Animal studies also suggest that crosstalk between neurons and immune cells may be involved in nociceptive priming observed in neonates. In children, differential DNA methylation in regulatory genomic regions enriching GABAergic, dopaminergic, and immune pathways, as well as polygenic risk scores for enhanced prediction of CPSP, have been described. Genome-wide studies in pediatric CPSP are scarce, but pathways identified by adult gene association studies point to potential common mechanisms.

Conclusions

Bench-to-bedside genomics research in pediatric CPSP is currently limited. Reverse translational approaches, use of other -omics, and inclusion of pediatric/CPSP endophenotypes in large-scale biobanks may be potential solutions. Time of developmental vulnerability and longitudinal genomic changes after surgery warrant further investigation. Emergence of promising precision pain management strategies based on gene editing and epigenetic programing emphasize need for further research in pediatric CPSP-related genomics.

Preventing pediatric chronic postsurgical pain: Time for increased rigor

Chronic postsurgical pain (CPSP) results from a cascade of events in the peripheral and central nervous systems following surgery. Several clinical predictors, including the prior pain state, premorbid psychological state (e.g., anxiety, catastrophizing), intraoperative surgical load (establishment of peripheral and central sensitization), and acute postoperative pain management, may contribute to the patient's risk of developing CPSP. However, research on the neurobiological and biobehavioral mechanisms contributing to pediatric CPSP and effective preemptive/treatment strategies are still lacking. Here we evaluate the perisurgical process by identifying key problems and propose potential solutions for the pre-, intra-, and postoperative pain states to both prevent and manage the transition of acute to chronic pain. We propose an eight-step process involving preemptive and preventative analgesia, behavioral interventions, and the use of biomarkers (brain-based, inflammatory, or genetic) to facilitate timely evaluation and treatment of premorbid psychological factors, ongoing surgical pain, and postoperative pain to provide an overall improved outcome. By achieving this, we can begin to establish personalized precision medicine for children and adolescents presenting to surgery and subsequent treatment selection.

Identification of a Pain-Specific Gene Expression Profile for Pediatric Recurrent Abdominal Pain

Objectives: Functional Abdominal Pain (FAP) and Irritable Bowel Syndrome (IBS) are common recurrent abdominal pain diagnoses with the hallmark, lack of inflammation. To identify a biological signature for IBS/FAP in the colon, this study used genetic profiling to uncover gene expression changes associated with IBS/FAP and abdominal pain. Methods: Patients (8 to 17 years) newly diagnosed with IBS or FAP were enrolled in the study. At diagnostic colonoscopy, three rectal biopsies were collected, and gene expression analysis was performed using a Qiagen PCR Array. Relative fold difference in gene expression for 84 pain-associated genes was calculated using the 2-ΔΔ Cq method compared with pain-free controls. Factors affecting pain burden (Pain Burden Interview; PBI) were analyzed, including age, sex, rectal inflammation, and gene expression. Data were analyzed using multiple stepwise linear regression and 2-tailed t tests (P ≤ 0.05). Results: Of the 22 total patients in the study, 19 were diagnosed with either IBS-Constipation (frequency of 5.26%), IBS-Diarrhea (47.37%), IBS-Mixed (10.53%), or FAP (36.84%). IBS/FAP patients reported significantly higher pain burden at the time of diagnosis compared to pain-free controls (p < 0.001), as well as significantly higher abdominal pain (p = 0.01). Of the 84 genes, expression of GRIN1 (p = 0.02), MAPK3 (p = 0.04), P2X4 (p = 0.04), and PTGES3 (p = 0.02) were all significantly associated with PBI score. Discussion: Abdominal pain associated with IBS/FAP in pediatric patients may be linked to the expression of GRIN1, MAPK3, P2X4, and PTGES3, pointing to potential novel therapeutic targets for management of recurring abdominal pain.

Brain injury and inflammation genes common to a number of neurological diseases and the genes involved in the genesis of GABAnergic neurons are altered in monoamine oxidase B knockout mice

Monoamine oxidase B (MAO B) oxidizes trace amine phenylethylamine (PEA), and neurotransmitters serotonin and dopamine in the brain. We reported previously that PEA levels increased significantly in all brain regions, but serotonin and dopamine levels were unchanged in MAO B knockout (KO) mice. PEA and dopamine are both synthesized from phenylalanine by aromatic L-amino acid decarboxylase in dopaminergic neurons in the striatum. A high concentration of PEA in the striatum may cause dopaminergic neuronal death in the absence of MAO B. We isolated the RNA from brain tissue of MAO B KO mice (2-month old) and age-matched wild type (WT) male mice and analyzed the altered genes by Affymetrix microarray. Differentially expressed genes (DEGs) in MAO B KO compared to WT mice were analyzed by Partek Genomics Suite, followed by Ingenuity Pathway Analysis (IPA) to assess their functional relationships. DEGs in MAO B KO mice are involved in brain inflammation and the genesis of GABAnergic neurons. The significant DEGs include four brain injury or inflammation genes (upregulated: Ido1, TSPO, AVP, Tdo2), five gamma-aminobutyric acid (GABA) receptors (down-regulated: GABRA2, GABRA3, GABRB1, GABRB3, GABRG3), five transcription factors related to adult neurogenesis (upregulated: Wnt7b, Hes5; down-regulated: Pax6, Tcf4, Dtna). Altered brain injury and inflammation genes in MAO B knockout mice are involved in various neurological disorders: attention deficit hyperactive disorder, panic disorder, obsessive compulsive disorder, autism, amyotrophic lateral sclerosis, Parkinson's diseases, Alzheimer's disease, bipolar affective disorder. Many were commonly involved in these disorders, indicating that there are overlapping molecular pathways.

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.

Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions

Neurological disorders significantly outnumber diseases in other therapeutic areas. However, developing drugs for central nervous system (CNS) disorders remains the most challenging area in drug discovery, accompanied with the long timelines and high attrition rates. With the rapid growth of biomedical data enabled by advanced experimental technologies, artificial intelligence (AI) and machine learning (ML) have emerged as an indispensable tool to draw meaningful insights and improve decision making in drug discovery. Thanks to the advancements in AI and ML algorithms, now the AI/ML-driven solutions have an unprecedented potential to accelerate the process of CNS drug discovery with better success rate. In this review, we comprehensively summarize AI/ML-powered pharmaceutical discovery efforts and their implementations in the CNS area. After introducing the AI/ML models as well as the conceptualization and data preparation, we outline the applications of AI/ML technologies to several key procedures in drug discovery, including target identification, compound screening, hit/lead generation and optimization, drug response and synergy prediction, de novo drug design, and drug repurposing. We review the current state-of-the-art of AI/ML-guided CNS drug discovery, focusing on blood-brain barrier permeability prediction and implementation into therapeutic discovery for neurological diseases. Finally, we discuss the major challenges and limitations of current approaches and possible future directions that may provide resolutions to these difficulties.

Methylation quantitative trait locus analysis of chronic postsurgical pain uncovers epigenetic mediators of genetic risk

Background: Overlap of pathways enriched by single nucleotide polymorphisms and DNA-methylation underlying chronic postsurgical pain (CPSP), prompted pilot study of CPSP-associated methylation quantitative trait loci (meQTL). Materials & methods: Children undergoing spine-fusion were recruited prospectively. Logistic-regression for genome- and epigenome-wide CPSP association and DNA-methylation-single nucleotide polymorphism association/mediation analyses to identify meQTLs were followed by functional genomics analyses. Results: CPSP (n = 20/58) and non-CPSP groups differed in pain-measures. Of 2753 meQTLs, DNA-methylation at 127 cytosine-guanine dinucleotides mediated association of 470 meQTLs with CPSP (p < 0.05). At PARK16 locus, CPSP risk meQTLs were associated with decreased DNA-methylation at RAB7L1 and increased DNA-methylation at PM20D1. Corresponding RAB7L1/PM20D1 blood eQTLs (GTEx) and cytosine-guanine dinucleotide-loci enrichment for histone marks, transcription factor binding sites and ATAC-seq peaks suggest altered transcription factor-binding. Conclusion: CPSP-associated meQTLs indicate epigenetic mechanisms mediate genetic risk. Clinical trial registration: NCT01839461, NCT01731873 (ClinicalTrials.gov).

Systems Biology Guided Gene Enrichment Approaches Improve Prediction of Chronic Post-surgical Pain After Spine Fusion

Objectives

Incorporation of genetic factors in psychosocial/perioperative models for predicting chronic postsurgical pain (CPSP) is key for personalization of analgesia. However, single variant associations with CPSP have small effect sizes, making polygenic risk assessment important. Unfortunately, pediatric CPSP studies are not sufficiently powered for unbiased genome wide association (GWAS). We previously leveraged systems biology to identify candidate genes associated with CPSP. The goal of this study was to use systems biology prioritized gene enrichment to generate polygenic risk scores (PRS) for improved prediction of CPSP in a prospectively enrolled clinical cohort.

Methods

In a prospectively recruited cohort of 171 adolescents (14.5 ± 1.8 years, 75.4% female) undergoing spine fusion, we collected data about anesthesia/surgical factors, childhood anxiety sensitivity (CASI), acute pain/opioid use, pain outcomes 6-12 months post-surgery and blood (for DNA extraction/genotyping). We previously prioritized candidate genes using computational approaches based on similarity for functional annotations with a literature-derived "training set." In this study, we tested ranked deciles of 1336 prioritized genes for increased representation of variants associated with CPSP, compared to 10,000 randomly selected control sets. Penalized regression (LASSO) was used to select final variants from enriched variant sets for calculation of PRS. PRS incorporated regression models were compared with previously published non-genetic models for predictive accuracy.

Results

Incidence of CPSP in the prospective cohort was 40.4%. 33,104 case and 252,590 control variants were included for association analyses. The smallest gene set enriched for CPSP had 80/1010 variants associated with CPSP (p < 0.05), significantly higher than in 10,000 randomly selected control sets (p = 0.0004). LASSO selected 20 variants for calculating weighted PRS. Model adjusted for covariates including PRS had AUROC of 0.96 (95% CI: 0.92-0.99) for CPSP prediction, compared to 0.70 (95% CI: 0.59-0.82) for non-genetic model (p < 0.001). Odds ratios and positive regression coefficients for the final model were internally validated using bootstrapping: PRS [OR 1.98 (95% CI: 1.21-3.22); β 0.68 (95% CI: 0.19-0.74)] and CASI [OR 1.33 (95% CI: 1.03-1.72); β 0.29 (0.03-0.38)].

Discussion

Systems biology guided PRS improved predictive accuracy of CPSP risk in a pediatric cohort. They have potential to serve as biomarkers to guide risk stratification and tailored prevention. Findings highlight systems biology approaches for deriving PRS for phenotypes in cohorts less amenable to large scale GWAS.