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

Chidambaran, Vidya; Ashton, Maria E.; Martin, Lisa J.; Jegga, Anil G.

doi:10.1016/j.jclinane.2020.109738

Cited by 16 publications

(12 citation statements)

References 57 publications

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“…For the present study, we analyzed the expression of 84 genes implicated in the transduction, maintenance, and modulation of pain, and the expression of four genes, GRIN1, MAPK3, P2X4, and PTGES3, was associated with pain burden in pediatric RAP patients. This mirrored the results of a recent systems biology-based study where this same set of genes, among others, was found to be enriched in patients with both acute and persistent post-surgical pain (36), adding further support to their potential role in pain susceptibility.…”

Section: Discussionsupporting

confidence: 77%

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

et al. 2021

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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.

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Section: Discussionsupporting

confidence: 77%

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

et al. 2021

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“…Using the literature derived genes ( N = 31) as “training genes,” we previously identified novel candidate genes based on their similarity scores (“guilt by association”) to the curated training genes using ToppFun application of the Transcriptome Ontology Pathway PubMed based prioritization of genes (ToppGene) Suite, a one-stop portal of computational software tools for gene enrichment ( Chen et al, 2009 ). Pathways based on training and top 10% candidate genes associated with CPSP are described in detail elsewhere ( Chidambaran et al, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

“…Given the lack of GWAS based data and the likelihood of small effect sizes, additional approaches to deriving PRS are required for pediatric CPSP. We recently described a systems-biology approach to identify genes and genetic pathways involved in CPSP ( Chidambaran et al, 2020 ). This approach allows prioritization of functionally associated genes, hence substantially decreases the burden of statistical power for gene association testing and overcomes sample size limitations.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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ObjectivesIncorporation 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.MethodsIn 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.ResultsIncidence 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)].DiscussionSystems 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.

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“…The other clinical applications of AI systems in pain management include prediction of pain severity/modality and analgesic requirements, 441–443 individualized medicine decision support in analgesic treatment, 444,445 prediction of the effectiveness of the analgesics, 446,447 and prediction of medication overuse 448–450 . Besides the clinical applications, researchers have employed ML methods at the early stages of analgesic discovery, such as identifying novel genes and pathways associated with acute and chronic pain 451 and predicting inhibitors of a drug target for pain (i.e., NaV1.7 sodium channel) 452 . To facilitate the prediction of novel multi‐target analgesics or drug combinations for pain treatment, researchers have established a comprehensive pain‐domain‐specific chemogenomics knowledgebase that includes the analgesics in current use, pain‐related targets with all available 3D structures, and the compounds reported for these target proteins 453 …”

Section: Ai/ml Applications In Cns Drug Discoverymentioning

confidence: 99%

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

Vatansever

Schlessinger

Wacker

et al. 2020

Medicinal Research Reviews

218

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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.

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

Cited by 16 publications

References 57 publications

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

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

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

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

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