Midkine: Utility as a Predictor of Early Diabetic Nephropathy in Children with Type 1 Diabetes Mellitus

Metwalley, Kotb Abbass; Farghaly, Hekma Saad; Gabri, Magda Farghali; Abdel-Aziz, Safwat M.; Ismail, Asmaa Mohammed; Raafat, Duaa Mohamed; Elnakeeb, Islam

doi:10.4274/jcrpe.galenos.2021.2020.0303

Cited by 9 publications

(5 citation statements)

References 28 publications

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“…[697], IL18 [698], CYGB (cytoglobin) [636], EGF (epidermal growth factor) [699], TRPM2 [700], BANK1 [701], SORT1 [643], HSPG2 [645], NOX4 [702], MYH9 [703], TRPV4 [704], B4GALNT1 [705], LAG3 [706], CACNA1C [654], LRP5 [707], PIM1 [658], MDK (midkine) [708], CA2 [709], TRPC6 [710], CYP2D6 [711] and STAT4 [712] might be a biological target of type 1 diabetes mellitus. CCK (cholecystokinin) [713], IL33 [714], IRF4 [715], CXCL8 [716], MMP3 [717], DDIT4L [718], KL (klotho) [719], CXCL6 [87], CXCL1 [720], STC1 [721], PDK4 [722], RAB27B [723], CXCL5 [724], GPRC5A [725], TXNIP (thioredoxin interacting protein) [726], RAB3B [727], HSPA5 [728], DKK1 [93], NAMPT (nicotinamidephosphoribosyltransferase) [729], TNFAIP3 [730], GHR (growth hormone receptor) [731], ANXA1 [732], CD40 [733], KITLG (KIT ligand) [734], SMURF2 [735], NRG1 [684], GCLC (glutamate-cysteine ligase catalytic subunit) [685], NPHP1 [736], EPOR (erythropoietin receptor) [737], ADK (adenosine kinase) [738], SAA1…”

Section: Discussionmentioning

confidence: 99%

Screening and identification of key biomarkers in diabetic kidney disease and its complications: Evidence from bioinformatics and next generation sequencing data analysis

Vastrad¹,

Vastrad²

2023

Preprint

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Diabetic patients are prone to diabetic kidney disease (DKD), which may cause cardiovascular damage, hypertension and obesity, and reduce quality of life. As a result, the life quality of patients was seriously reduced. However, the pathogenesis of diabetic kidney disease (DKD) has not been fully elucidated, and current treatments remain inadequate. Therefore, it is essential to explore the molecular mechanism of DKD and its complications. Next Generation Sequancing (GSE217709) dataset was obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were picked out by R software. Then Gene ontology (GO) and REACTOME pathway enrichment analysis were performed by g:Profiler database, protein-protein interaction (PPI) of DEGs was constructed by Human Integrated Protein-Protein Interaction rEference (HIPPIE) database . Module analysis was carried out by Cytoscape plug-in PEWCC. Subsequently, miRNA-hub gene regulatory network and TF- hub gene regulatory network were performed by miRNet database and NetworkAnalyst database. Finally, validation of hub genes was performed by receiver operating characteristic (ROC) curve analysis to predict the diagnostic effectiveness of the hub genes. In total, 958 DEGs, including 479 up regulated and 479 down regulated genes, were identified. The GO and pathway enrichment changes of DEGs were mainly enriched in biological regulation, multicellular organismal process, signaling by GPCR and extracellular matrix organization. Ten hub genes (HSPA8, HSP90AA1, HSPA5, SDCBP, HSP90B1, VCAM1, MYH9, FLNA, MDFI and PML) associated with DKD and its complications were identified. Bioinformatics analysis is a useful tool to explore the molecular mechanism and pathogenesis of DKD and its complications. The identified hub genes may participate in the onset and development of DKD and its complications and serve as therapeutic targets.

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Section: Discussionmentioning

confidence: 99%

Screening and identification of key biomarkers in diabetic kidney disease and its complications: Evidence from bioinformatics and next generation sequencing data analysis

Vastrad¹,

Vastrad²

2023

Preprint

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“…Immune cell infiltration and smooth muscle cell migration produce Neointima development (Said et al, 2022). MK is involved in diabetic nephropathy, and the data show that MK speeds up the intracellular signaling network triggered by hyperglycemia in diabetic nephropathy and is involved in tubulointerstitial inflammation (Metwalley et al, 2021)…”

Section: -Kidneymentioning

confidence: 92%

A Review Of Midkine As Biomarker Protein For Diagnostic Several Dieases

Jawad

Hussein

2022

Med.Sci.Jour.Adv.Res

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Background: This review deals with a collection of information about a protein marker that is very important and alternates presence in many cases of normal and pathological growth, as it can be observed in high concentrations in the stages of fetal development and can be expressed significantly in many cases and disorders and midkine is considered a diagnostic marker for them as in types of cancers. It has a protein structure of 121 amino acids and its expression is located on the eleventh chromosome and has several functions, especially its expression in nervous tissues during the mid-gestation. Conclusion: Midkine plays an important role in the inflammatory process in terms of its importance in the infiltration of immune cells into the peripheral organs and targeting midkine may be a viable new therapeutic option for chronic infections, especially infections of the kidneys, joints, nervous system and colon.

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“…Further, MK is closely related to a variety of kidney diseases (21). It was reported that MK is a biomarker for early detection of diabetic nephropathy in children with type 1 diabetes mellitus (T1DM) (22). In Mdk −/− mice, nephropathy was strikingly milder than Mdk +/+ mice after Streptozotocin injection (23).…”

Section: Original Articlementioning

confidence: 99%

Midkine promotes kidney injury in diabetic kidney disease by increasing neutrophil extracellular traps formation

Liu¹,

Ren²,

You-song³

et al. 2022

Ann Transl Med

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Background: We sought to investigate the role of midkine (MK) on neutrophil extracellular trap formation (NETosis) and diabetic kidney disease (DKD) progression. Methods: The expression of MK and NETosis in the renal tissue of DKD patients was examined by immunohistochemistry and immunofluorescence, respectively. Neutrophils extracted from mouse bone marrow by gradient centrifugation were treated with MK for this in-vitro study. A mouse diabetes model was induced by a high-fat diet combined with an intraperitoneal injection of streptozocin (STZ). Antisense oligodeoxynucleotide (ODN) for MK inhibition was administered via tail vein injection. Results: We found that the expression of MK was increased in the kidney tissue of DKD patients. Additionally, a greater number of neutrophils were primed toward NETosis in the kidney tissue of DKD patients, which was manifested by the increased expression of NETosis biomarkers citrullinated histone H3 (H3Cit) and myeloperoxidase (MPO). In vitro, MK treatment concentration-dependently increased neutrophil proliferation (cell counting kit-8). Further, western blot and enzyme-linked immunosorbent assays showed that MK (100 ng/mL) significantly promoted NETosis and the expression of inflammatory factors interleukin (IL)-1 and IL-6 secretion in high-glucose treated neutrophils. In the mouse diabetes model, MK promoted the pathological damage and fibrosis of kidney tissue, as demonstrated by the reversion of the pathological damage and fibrosis by the MK antisense ODN [diabetes mellitus (DM) + MK -ODN] treatment. Additionally, the inhibition of MK reduced the formation of NETs. Conclusions: MK promotes DKD progression by increasing NETosis.

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Midkine: Utility as a Predictor of Early Diabetic Nephropathy in Children with Type 1 Diabetes Mellitus

Cited by 9 publications

References 28 publications

Screening and identification of key biomarkers in diabetic kidney disease and its complications: Evidence from bioinformatics and next generation sequencing data analysis

Screening and identification of key biomarkers in diabetic kidney disease and its complications: Evidence from bioinformatics and next generation sequencing data analysis

A Review Of Midkine As Biomarker Protein For Diagnostic Several Dieases

Midkine promotes kidney injury in diabetic kidney disease by increasing neutrophil extracellular traps formation

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