Phase separation of α-crystallin-GFP protein and its implication in cataract disease is a research paper published in Scientific Reports (2023). On theSindex it has a DataRank of 0.555. It has been cited 14 times, with 14 citing works in its 1-hop citation network.
AbstractCataract, the leading cause of blindness worldwide, is caused by crystallin protein aggregation within the protected lens environment. Phase separation has been implicated as an important mechanism of protein aggregation diseases, such as neurodegeneration. Similarly, cataract has been proposed to be a protein condensation disease in the last century. However, whether crystallin proteins aggregate via a phase separation mechanism and which crystallin protein initiates the aggregation remain unclear. Here, we showed that all types of crystallin-GFP proteins remain soluble under physiological conditions, including protein concentrations, ion strength, and crowding environments. However, in age or disease-induced aberrant conditions, α-crystallin-GFP, including αA- and αB-crystallin-GFP, but not other crystallin-GFP proteins, undergo phase separation in vivo and in vitro. We found that aging-related changes, including higher crystallin concentrations, increased Na+, and decreased K+ concentrations, induced the aggregation of α-crystallin-GFP. Furthermore, H2O2, glucose, and sorbitol, the well-known risk factors for cataract, significantly enhanced the aggregation of αB-crystallin-GFP. Taken together, our results revealed that α-crystallin-GFP forms aggregates via a phase transition process, which may play roles in cataract disease. Opposite to the previously reported function of enhancing the solubility of other crystallin, α-crystallin may be the major aggregated crystallin in the lens of cataract patients.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.406
From this paper's citation signal
Citation Network Contribution
0.149
From 9 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 73% comes from its base citations and 27% from the citation network (9 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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