Back

Subtype-Resolved Pain-Signaling Architectures Reveal Conserved Drug-Target Interaction Networks in DRG Nociceptors

do Nascimento, A. M.; Vieceli, F. M.; Yan, C. Y. I.; Reis, E. M.; Schechtman, D.

2026-04-15 cell biology
10.64898/2026.04.14.718550 bioRxiv
Show abstract

Pain management has been challenging and a major obstacle lies in the limited translational success between preclinical studies, often based on rodent models and evoked nociception behavioral assays, whose validity is often questioned. The dorsal root ganglia (DRG) contains diverse nociceptor subtypes that serve as the primary afferent pathways for detecting painful stimuli and analgesics often target proteins expressed in nociceptors. This makes the distinct protein repertoires and molecular interactors within nociceptor subtypes a key focus for understanding which molecular players drive pain processing and how they may be therapeutically targeted. The confirmation of cross-species conservation of pain-related signaling pathways, mediated by nociceptors, could help to elucidate the molecular mechanisms by which the drugs act across species. In this context, we constructed and compared experimentally-validated protein-protein interaction (PPI) networks based on drug targets and their direct binding partners for nociceptor subtypes supported by single-nuclei transcriptome data from mouse and human DRGs. We found that overall gene expression is more conserved across mice than in human nociceptor subtypes, indicating a higher degree of molecular specialization of human nociceptors. Overall signaling network analyses revealed subtype- and species-specific conservation related to pain signaling, with some particularities, in which key drug targets mediate broader cellular processes beyond pain signaling and neuronal depolarization. Altogether, this resource may help to further understand the molecular mechanisms of specific drug targeting, and the proposed workflow can be used to identify and prioritize pain-related pathways in the DRG, advancing target identification and translational medicine.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
Advanced Science
249 papers in training set
Top 0.2%
22.7%
2
eLife
5422 papers in training set
Top 4%
12.4%
3
Scientific Reports
3102 papers in training set
Top 17%
6.4%
4
Pain
70 papers in training set
Top 0.3%
6.4%
5
Cell Discovery
54 papers in training set
Top 1%
4.3%
50% of probability mass above
6
Cell Reports
1338 papers in training set
Top 11%
4.3%
7
iScience
1063 papers in training set
Top 6%
3.1%
8
PLOS Biology
408 papers in training set
Top 5%
2.9%
9
Genomics, Proteomics & Bioinformatics
171 papers in training set
Top 2%
2.6%
10
Nature Communications
4913 papers in training set
Top 48%
1.9%
11
British Journal of Pharmacology
34 papers in training set
Top 0.2%
1.9%
12
Journal of Investigative Dermatology
42 papers in training set
Top 0.3%
1.7%
13
Communications Biology
886 papers in training set
Top 8%
1.7%
14
Glia
74 papers in training set
Top 0.3%
1.3%
15
International Journal of Molecular Sciences
453 papers in training set
Top 12%
1.0%
16
Science Advances
1098 papers in training set
Top 25%
1.0%
17
Cells
232 papers in training set
Top 4%
1.0%
18
Nature Machine Intelligence
61 papers in training set
Top 3%
0.9%
19
Frontiers in Cell and Developmental Biology
218 papers in training set
Top 7%
0.9%
20
Frontiers in Pharmacology
100 papers in training set
Top 4%
0.8%
21
Cell Reports Methods
141 papers in training set
Top 5%
0.8%
22
Progress in Neurobiology
41 papers in training set
Top 2%
0.8%
23
Journal of Clinical Investigation
164 papers in training set
Top 6%
0.8%
24
Cell Communication and Signaling
35 papers in training set
Top 1%
0.8%
25
Patterns
70 papers in training set
Top 3%
0.7%
26
Cell Death & Disease
126 papers in training set
Top 3%
0.7%
27
Neuroscience Bulletin
11 papers in training set
Top 0.7%
0.7%
28
PLOS Computational Biology
1633 papers in training set
Top 27%
0.6%
29
International Journal of Biological Macromolecules
65 papers in training set
Top 4%
0.6%
30
Frontiers in Genetics
197 papers in training set
Top 11%
0.6%