ArticlePloS one2026
System biology and network-based approach to identify the therapeutic signatures and potential inhibitors against polycystic lipomembranous osteodysplasia with Sclerosing Leukoencephalopathy.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Polycystic Lipomembranous Osteodysplasia with Sclerosing Leukoencephalopathy (PLOSL), often recognized as Nasu-Hakola disease, is an uncommon autosomal recessive systemic condition described by numerous bone lesions that resemble cysts and progressive early-onset dementia. One novel way to find possible drug targets is to use Network Analyst tools for network-based gene expression profiling. Identifying the target hub genes, essential for the initiation and progression of PLOSL, is validated by the significance level (p-score) attained using Cytoscape in the survival analysis of the major central genes. The X2K online tool also examined the regulatory kinases that formed the interaction between protein molecule networks. Out of the 53 genes obtained to be differentially expressed, PPARG and AIF1 had the greatest degree score, followed by C1QA with 5 degrees and SIGLEC1 and MSR1 with 4 degrees. Furthermore, Molecular docking of target PPARG gene with AMG-131 and Elafibranor drugs, having the chemical formulas 2-[2,6-dimethyl-4-[(E)-3-(4-methylsulfanylphenyl)-3-oxoprop-1-enyl]phenoxy], and 3,5-dichloro-4-quinolin-3-yloxyphenyl) benzenesulfonamide, along control (Rosiglitazone (S) shows binding affinities of -7.2 kcal/mol, -7.4 kcal/mol, and -7.6 kcal/mol respectively. The enzyme remained extremely stable in the complex throughout 200 ns, with a mean Root Mean Square Deviation (RMSD) of 2.95 Å for the AMG-131 complex system and 2.93 Å against the Elafibranor complex system. Root Mean Square Fluctuation (RMSF) anticipated steady behavior with average RMSD for the active site residues in the docked system. Arg76 and Leu28leu Leu118 were shown to be essential enzyme residues for binding, anchoring, and bridging strong hydrogen and hydrophobic interactions between the enzyme and the inhibitor, according to the Radial Distribution Function (RDF). These results broadened our knowledge of putative biomarkers for PLOSL diseases, and an experimental strategy will improve our results even more in the future.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.