Summary: Researchers have developed a powerful new method that reveals the full network of RNA-protein interactions inside human cells, providing detailed insight into molecular communication that underlies health and disease. The approach freezes contacts between RNAs and proteins, tags each partner, and converts those contacts into unique DNA barcodes that are read by sequencing.
Applied to human cell lines, the technique detected more than 350,000 RNA-protein associations, including previously unknown links to pathways involved in Alzheimer’s disease and cancer. By exposing these hidden molecular interactions, the technology creates a roadmap for identifying precise therapeutic targets and designing next-generation treatments.
Key Facts:
- Comprehensive network: Over 350,000 RNA-protein interactions mapped in human cells.
- Disease relevance: New associations connected to Alzheimer’s and cancer pathways.
- Therapeutic focus: Precise binding sites and sequence preferences identified, enabling targeted drug strategies.
Source: UCSD
Bioengineers at the University of California San Diego have introduced a novel, scalable technology that maps RNA-protein associations across the human cellular landscape — an advance with direct implications for cancer, neurodegenerative disease, and other conditions.
Interactions between RNA molecules and proteins control gene expression, stress responses, cell survival, and many other central processes. Traditional methods captured only limited subsets of these contacts, leaving the broader interaction network largely uncharted. This new approach fills that gap by recording interactions comprehensively and without bias.
“Think of it as a wiring diagram for a cell’s molecular conversations,” said Sheng Zhong, professor in the Shu Chien–Gene Lay Department of Bioengineering at the UC San Diego Jacobs School of Engineering and lead author of the study published in Nature Biotechnology.
The method captures physical contacts between RNAs and proteins inside living cells by chemically fixing those encounters, tagging the protein, and linking it to the bound RNA. Each RNA-protein pair is then transformed into a distinct chimeric DNA barcode through proximity ligation, and those barcodes are decoded by standard high-throughput sequencing. The output is a single-experiment, genome-wide catalog of RNA-protein associations.
When applied to two human cell lines, the system reconstructed a large human RNA-protein association network (HuRPA) that includes more than 350,000 associations involving roughly 7,000 RNAs and about 11,000 proteins. The dataset confirmed known RNA-binding proteins and uncovered hundreds of unexpected RNA-associated proteins, substantially expanding the set of candidate regulators for follow-up study.
Among the notable findings, the metabolic enzyme phosphoglycerate dehydrogenase (PHGDH) — previously linked by the team to Alzheimer’s risk and proposed as an early blood biomarker — was observed binding messenger RNAs tied to cell survival and neuronal growth. These interactions suggest additional ways PHGDH could influence brain function.
The long noncoding RNA LINC00339, which is elevated in several cancers, was found interacting with 15 membrane proteins. Those contacts provide plausible mechanisms by which this RNA might influence tumor growth and metastasis and point to specific interfaces that could be targeted therapeutically.
Crucially, the technique maps not only which RNA and protein pair but also the protein regions involved and the sequence motifs preferred by a given protein. That higher resolution information narrows the search for effective interventions — whether the goal is to block harmful interactions, stabilize protective ones, or design small molecules or oligonucleotides that disrupt a disease-driving contact surface.
“Interactions that act as control knobs for disease are now visible and can be prioritized as drug targets,” said co-first author Shuanghong Xue, a postdoctoral scholar in Zhong’s laboratory. “Depending on their role, an RNA-protein interaction could be blocked, preserved, or enhanced to achieve a therapeutic effect.”
The authors emphasize that mapping is the first essential step. Most newly discovered associations still require functional validation to determine their biological roles and relevance to disease. The major contribution of this work is an unbiased, genome-scale atlas that enables researchers to focus resources on the most promising candidate interactions.
Zhong’s group is already extending the approach to disease-relevant models, including Alzheimer’s and Parkinson’s, to identify misfiring RNA-protein interactions that may drive pathology and that could serve as the basis for novel therapeutics.
Full study: “Genome-wide mapping of RNA-protein associations through sequencing.” Co-first authors Zhijie Qi and Shuanghong Xue contributed innovations in bioinformatics, AI-assisted analysis, and molecular methods.
Funding: Supported by the National Institutes of Health (grants R01GM138852, DP1DK126138, UH3CA256960, and R01HD107206).
Key Questions Answered:
A: A sequencing-based technology that maps the full network of RNA-protein interactions in human cells.
A: RNA-protein interactions govern key cellular processes; when they malfunction, they can drive diseases such as cancer and neurodegeneration.
A: Proteins are chemically linked to RNAs they contact, converted into chimeric DNA barcodes through proximity ligation, and decoded by sequencing to identify interacting partners and contact sites.
A: New drug targets and therapeutic strategies that either block disease-promoting RNA-protein interactions or preserve protective ones.
About this genetics and neurotech research news
Author: Liezel Labios
Source: UCSD
Contact: Liezel Labios – UCSD
Image: The image is credited to Neuroscience News
Original Research: Closed access. “Genome-wide mapping of RNA-protein associations through sequencing” by Sheng Zhong et al., published in Nature Biotechnology.
Abstract
Genome-wide mapping of RNA-protein associations through sequencing
RNA-protein interactions critically regulate gene expression and cellular behavior, but their structural and functional diversity has made comprehensive mapping difficult. The authors introduce PRIM-seq (protein-RNA interaction mapping by sequencing), a method for de novo identification of RNA-binding proteins and their RNA partners in a single, unbiased assay.
PRIM-seq produces unique chimeric DNA sequences by proximity ligation of RNAs to protein-linked DNA barcodes, which are subsequently decoded by high-throughput sequencing. Applied to two human cell lines, PRIM-seq generated a human RNA-protein association network (HuRPA) containing over 350,000 associations involving approximately 7,000 RNAs and 11,000 proteins, including thousands of proteins that engage multiple distinct RNAs.
The study experimentally validates several findings, including the protein-association status of the lincRNA LINC00339, RNA-associating behavior of chromatin-conformation regulators SMC1A, SMC3 and RAD21, and RNA binding by the metabolic enzyme PHGDH. PRIM-seq enables systematic discovery and prioritization of RNA-binding proteins and their targets without reliance on gene- or protein-specific reagents.