Identification of PIK3CG as a hub in septic myocardial injury using network pharmacology and weighted gene co‐expression network analysis

Abstract

Sepsis causes multiple organ injuries, among which the heart is one most severely damaged organ. Melatonin (MEL) alleviates septic myocardial injury, although a systematic and comprehensive approach is still lacking to understand the precise protective machinery of MEL. This study aimed to examine the underlying mechanisms of MEL on improvement of septic myocardial injury at a systematic level. This study integrated three analytic modalities including database investigations, RNA‐seq analysis, and weighted gene co‐expression network analysis (WCGNA), in order to acquire a set of genes associated with the pathogenesis of sepsis. The Drugbank database was employed to predict genes that may serve as pharmacological targets for MEL‐elicited benefits, if any. A pharmacological protein–protein interaction network was subsequently constructed, and 66 hub genes were captured which were enriched in a variety of immune response pathways. Notably, PIK3CG, one of the hub genes, displayed high topological characteristic values, strongly suggesting its promise as a novel target for MEL‐evoked treatment of septic myocardial injury. Importantly, molecular docking simulation experiments as well as in vitro and in vivo studies supported an essential role for PIK3CG in MEL‐elicited effect on septic myocardial injury. This study systematically clarified the mechanisms of MEL intervention in septic myocardial injury involved multiple targets and multiple pathways. Moreover, PIK3CG‐governed signaling cascade plays an important role in the etiology of sepsis and septic myocardial injury. Findings from our study provide valuable information on novel intervention targets for the management of septic myocardial injury. More importantly, this study has indicated the utility of combining a series of techniques for disease target discovery and exploration of possible drug targets, which should shed some light on elucidation of experimental and clinical drug action mechanisms systematically.

Screening and identification of key biomarkers associated with endometriosis using bioinformatics and next generation sequencing data analysis

“…ROBO2 [760], VCAM1 [761], GRP (gastrin releasing peptide) [762], FABP4 [763], ANO1 [764], SOX6 [765], TFAP2C [766], RAMP3 [767], PLA2G7 [768], MMP12 [769], FAIM2 [770], APOD (apolipoprotein D) [771], LAG3 [772], SOX18 [773], F2RL2 [774], CCR1 [775], FLT1 [776], FABP5 [629], TRPC3 [777]. THSD7A [778], DKK2 [779], PRKCB (protein kinase C beta) [780], DHCR24 [781], PDE3B [782], BMP4 [783], IL1RL1 [784], MYPN (myopalladin) [785], PLCG2 [786], PRL (prolactin) [787], WNT5A [788], MEOX1 [789], TIMP3 [790], FRZB (frizzled related protein) [791], CPE (carboxypeptidase E) [792], ADAMTS9 [793], NDNF (neuron derived neurotrophic factor) [794], PDGFB (platelet derived growth factor subunit B) [795], PIK3CG [796], LDLR (low density lipoprotein receptor) [797], CD4 [798], TRPA1 [799], F2RL3 [800], C1QL1 [801], ADAMTS5 [802], PDE4B [803], NES (nestin) [804], TH (tyrosine hydroxylase) [805], MMP8 [806], KDR (kinase insert domain receptor) [807], ADRB2 [808], ACKR3 [809], PTPRC (protein tyrosine phosphatase receptor type C) [810], KL (klotho) [811], KL (klotho) [812], PLAU (plasminogen activator, urokinase) [813], CCND2 [814], PTGS1 [815], INSIG1 [816], IRX2 [817], SIGLEC1 [818], UCN2 [819], CYP2J2 [820], CYP1A1 [821], ASTN2 [822], NTN1 [823], PDGFD (platelet derived growth factor D) [824], MSTN (myostatin) [663], LEPR (leptin receptor) [664], IL15 [825], CACNA1H [826], BMP2 [827], SYT7 [<...…”

Exploring the potential mechanism of WuFuYin against hypertrophic scar using network pharmacology and molecular docking

“…PIK3CG , alternatively known as PI3Kγ/p110γ, is a subtype of PI3K and a key regulatory molecule during inflammation[ 23 ]. A study found that PIK3CG affected inflammation through mediating TLR9, and that the inhibition of PIK3CG reduced phosphorylated Akt.…”

Celastrol attenuates streptozotocin-induced diabetic cardiomyopathy in mice by inhibiting the ACE / Ang II / AGTR1 signaling pathway

“…Network pharmacology approaches provide a fundamental strategy for uncovering the mechanisms of action of multi-target drugs [ 16 ], during which the data obtained from multiple databases are analyzed and multiple interaction networks are constructed. Computational molecular docking analysis plays an essential role in evaluating the potential of drug binding to its target and deciphering the underlying mechanisms [ 17 ]. In this work, we performed animal studies, computational molecular docking analysis, and network pharmacology analysis to examine the impact of celastrol on streptozotocin (STZ)-induced DCM and the molecular mechanisms involved.…”