Identifying genetic determinants of inflammatory pain in mice using a large-scale gene-targeted screen

Wotton, Janine M.; Peterson, Emma; Flenniken, Ann M.; Bains, Rasneer S.; Veeraragavan, Surabi; Bower, Lynette; Bubier, Jason A; Parisien, Marc; Bezginov, Alexandr; Haselimashhadi, Hamed; Mason, Jeremy; Moore, Michayla A.; Stewart, Michelle; Clary, Dave; Delbarre, Daniel J.; Anderson, Laura; D’Souza, Abigail; Goodwin, Leslie O.; Harrison, Mark E.; Huang, Ziyue; McKay, Matthew J.; Qu, Dawei; Srinivasan, Subhiksha; Urban, Rachel; Vukobradovic, Igor; Ward, Christopher; Willett, Amelia M.; Braun, Robert E.; Brown, Steve; Dickinson, Mary E.; Heaney, Jason D.; Kumar, Vivek; Lloyd, Kent; Mallon, Ann‐Marie; McKerlie, Colin; Murray, Stephen A.; Nutter, Lauryl M. J.; Parkinson, Helen; Seavitt, John R.; Wells, Sara; Samaco, Rodney C.; Chesler, Elissa J.; Smedley, Damian; Diatchenko, Luda; Baumbauer, Kyle M.; Young, Erin E.; Bonin, Robert P.; Mandillo, Silvia; White, Jacqueline K.

doi:10.1097/j.pain.0000000000002481

Cited by 8 publications

(2 citation statements)

References 86 publications

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“…Time sampling while scoring has been used to reduce labor, but are still not high-throughput enough to advance pain and analgesic research [30]. To address these limitations, we previously automated the measurement of licking behavior with a bottom-up camera [32, 33]. However, this specialized arena is small and limits the amount of features, particularly gait, which can be extracted, limiting its sensitivity to detect nociception.…”

Section: Introductionmentioning

confidence: 99%

A high-throughput machine vision-based univariate scale for pain and analgesia in mice

Sabnis

Hession

Kim

et al. 2022

Preprint

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Treatment of acute and chronic pain represent a widespread clinical challenge with poor therapeutic options. While rodents are an invaluable model to study pain, scoring nociceptive responses in clinically relevant paradigms and at high-throughput remains an unmet challenge. Therefore, there is a need for automated, high-throughput methods that sensitively and accurately assess pain and analgesia. Such objective and scalable technologies will enable the discovery of novel analgesics and yield mechanistic insights into the neural and genetic mechanisms of pain. Here, we adopt the open field arena to build a univariate scale for the formalin injection model of inflammatory pain by using a machine learning approach that incorporates 82 behavioral features. This tool outperforms traditional measures of licking and shaking in detection of formalin dose, and was validated using 4 diverse mouse strains. We also detected previously unreported differences in formalin induced nocifensive behaviors that were strain and sex specific. This model also reliably identifies morphine induced antinociception. This novel, sensitive, and inexpensive tool provides a method for quantifying voluntary nociceptive responses to facilitate genetic mapping and analgesic compound screening in a high throughput manner.

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Section: Introductionmentioning

confidence: 99%

A high-throughput machine vision-based univariate scale for pain and analgesia in mice

Sabnis

Hession

Kim

et al. 2022

Preprint

Self Cite

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“…Biological connections toward the suggested genes, which are genome-wide signi cant in the GxE analysis, and the ve diseases with the most increased and decreased prevalence within each cluster plus effect direction for depression Wotton et al, 2022),. three high prevalence diseases in Cluster 5(Juhasz et al, 2023).…”

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confidence: 98%

Impact of gene-by-trauma interaction in MDD-related multimorbidity clusters

Bonk,

Eszlari,

Kirchner

et al. 2023

Preprint

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Background: Major depressive disorder (MDD) is considerably heterogeneous in terms of comorbidities, which may hamper the disentanglement of its biological mechanism. In a previous study, we classified the lifetime trajectories of MDD-related multimorbidities into seven distinct clusters, each characterized by unique genetic and environmental risk-factor profiles. The current objective was to investigate genome-wide gene-by-environment (G×E) interactions with childhood trauma burden, within the context of these clusters. Methods: We analyzed 76,856 participants and 3,875,386 single-nucleotide polymorphisms (SNPs) of the UK Biobank database. Childhood trauma burden was assessed using the Childhood Trauma Screener (CTS). For each cluster, Plink 2.0 was used to calculate SNP×CTS interaction effects on the participants’ cluster membership probabilities. We especially focused on the effects of 31 candidate genes and associated SNPs selected from previous G×E studies for childhood maltreatment’s association with depression. Results: At SNP-level, only the high-multimorbidity Cluster 6 revealed a genome-wide significant SNP rs145772219. At gene-level, LDLRAD4 was genome-wide significant for the low-multimorbidity Cluster 1 and C6orf89and TAAR2 for the high-multimorbidity Cluster 7. Regarding candidate SNPs for G×E interactions, individual SNP results could be replicated for specific clusters. The candidate genes DRD2 (Cluster 1), and DBH and MTHFR (both Cluster 5), and TPH1(Cluster 6) survived multiple testing correction. Limitations: CTS is a short retrospective self-reported measurement. Clusters could be influenced by genetics of individual disorders. Conclusions: The first G×E GWAS for MDD-related multimorbidity trajectories successfully replicated findings from previous G×E studies related to depression, and revealed risk clusters for the contribution of childhood trauma.

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Commentary: The International Mouse Phenotyping Consortium: high-throughput in vivo functional annotation of the mammalian genome

Lloyd

2024

Mamm Genome

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The International Mouse Phenotyping Consortium (IMPC) is a worldwide effort producing and phenotyping knockout mouse lines to expose the pathophysiological roles of all genes in human diseases and make mice and data available and accessible to the global research community. It has created new knowledge on the function of thousands of genes for which little to anything was known. This new knowledge has informed the genetic basis of rare diseases, posited gene product influences on common diseases, influenced research on targeted therapies, revealed functional pleiotropy, essentiality, and sexual dimorphism, and many more insights into the role of genes in health and disease. Its scientific contributions have been many and widespread, however there remain thousands of “dark” genes yet to be illuminated. Nearing the end of its current funding cycle, IMPC is at a crossroads. The vision forward is clear, the path to proceed less so.

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Identifying genetic determinants of inflammatory pain in mice using a large-scale gene-targeted screen

Abstract: A reverse genetics screen of 110 single-gene knockout mouse strains identified 13 genes affecting nociception, including 10 that have no previous phenotypic associations with pain.

Cited by 8 publications

References 86 publications

A high-throughput machine vision-based univariate scale for pain and analgesia in mice

A high-throughput machine vision-based univariate scale for pain and analgesia in mice

Impact of gene-by-trauma interaction in MDD-related multimorbidity clusters

Commentary: The International Mouse Phenotyping Consortium: high-throughput in vivo functional annotation of the mammalian genome

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