Bioinformatics and computational analyses of kidney stone modulatory proteins lead to solid experimental evidence and therapeutic potential

Resveratrol inhibits calcium oxalate crystal growth, reduces adhesion to renal cells and induces crystal internalization into the cells, but promotes crystal aggregation

“…These stone development steps include the formation of newly generated crystals (or neocrystals) as a consequence of supersaturation of crystalline ions (i.e., calcium and oxalate), increase in size or growth of the already-formed crystals, clumping of the crystals, crystal retention on the surface of renal epithelial cells, and engulfment of crystals by the cells to be eliminated by endolysosomes ( Chaiyarit et al, 2016 ; Chaiyarit and Thongboonkerd, 2020 ). Each of these stone development steps can be influenced by a variety of factors, such as urinary supersaturation of ions ( Ibis et al, 2021 ), urine pH ( Manissorn et al, 2017 ), and the presence of stone modulatory molecules ( Peerapen and Thongboonkerd, 2021 ; Thongboonkerd, 2019 ; Vinaiphat and Thongboonkerd, 2017 ; Yoodee and Thongboonkerd, 2023 ).…”

“…Numerous lines of evidence have documented various kinds of stone modulatory molecules that can either inhibit or promote each stone development step. The endorsed stone modulatory molecules include proteins ( Peerapen and Thongboonkerd, 2021 ; Sassanarakkit et al, 2020 ; Thongboonkerd, 2019 ; Vinaiphat and Thongboonkerd, 2017 ; Yoodee and Thongboonkerd, 2023 ), glycosaminoglycans ( Chanthick and Thongboonkerd, 2022 ), plant extracts ( Khan et al, 2021 ) and bioactive compounds ( Chaiyarit et al, 2024 ; Kanlaya et al, 2023 ; Khamchun et al, 2021 ; Peerapen et al, 2022 ; Peerapen and Thongboonkerd, 2016 ). Some bioactive compounds, such as diosmin and quercetin, exhibited dual modulatory activities ( Chaiyarit et al, 2024 ; Khamchun et al, 2021 ).…”

Identifying molecular subgroups of patients with preeclampsia through bioinformatics

“…Bioinformatics analysis is an important tool for analyzing and evaluating datasets in modern biomedical research. It can be used to predict protein structure, sequence alignment, protein-protein interaction (PPI), and other characteristics, and it is particularly useful for revealing the biochemical and functional roles of proteins (9). This study aimed to use unsupervised computational learning, weighted gene co-expression network analysis (WGCNA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses to explore the entry points of the transcriptome dataset of patients with PE in the Gene Expression Omnibus (GEO) database, as well as to conduct bioinformatics analysis on these datasets to construct a molecular subgroup of these patients, providing new insights into the molecular mechanisms of PE.…”