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Background: Colostrum is well known to have excellent nutritional value for newborns. The aim of this study was to investigate the dynamic expression pattern of microRNA in human colostrum and mature milk. Furthermore, we identified the specific microRNA in human colostrum and analyzed the regulatory function of human colostrum.Methods: We collected breast milk samples from 18 lactating volunteers. The expression of microRNA in breast milk was detected by microarray analysis. The expression differences were characterized by log2FC (|log2fold change| >1.58) and associated P values (P<0.05). Furthermore, the prediction of microRNA targets, bioinformatics analysis and network generation were carried out using network database.Results: Our results showed that during the human lactation process, the composition of microRNAs in human milk changes dynamically. Compared to the microRNA expression profile in human mature milk, the expression levels of 49 microRNAs were significantly different and 67 microRNAs were specifically expressed in human colostrum. Based on the results of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, the predicted target mRNAs of the identified colostrum-specific microRNAs were involved in the regulation of distinct biological processes, such as signal transduction, positive regulation of GTPase activity, and protein phosphorylation. Moreover, the predicted mRNA targets were from large spectrums of signaling pathways, such as the MAPK, Ras, Hippo, Wnt, and mTOR signaling pathways, as well as the longevity regulating pathway.Conclusions: Our study illuminates the landscape of microRNA expressions in human colostrum and mature milk, and emphasizes the value of microRNAs as nutritional additives in milk-related commercial products.

BackgroundAging and immune infiltration have essential role in the physiopathological mechanisms of diabetic nephropathy (DN), but their relationship has not been systematically elucidated. We identified aging-related characteristic genes in DN and explored their immune landscape.MethodsFour datasets from the Gene Expression Omnibus (GEO) database were screened for exploration and validation. Functional and pathway analysis was performed using Gene Set Enrichment Analysis (GSEA). Characteristic genes were obtained using a combination of Random Forest (RF) and Support Vector Machine Recursive Feature Elimination (SVM-RFE) algorithm. We evaluated and validated the diagnostic performance of the characteristic genes using receiver operating characteristic (ROC) curve, and the expression pattern of the characteristic genes was evaluated and validated. Single-Sample Gene Set Enrichment Analysis (ssGSEA) was adopted to assess immune cell infiltration in samples. Based on the TarBase database and the JASPAR repository, potential microRNAs and transcription factors were predicted to further elucidate the molecular regulatory mechanisms of the characteristic genes.ResultsA total of 14 differentially expressed genes related to aging were obtained, of which 10 were up-regulated and 4 were down-regulated. Models were constructed by the RF and SVM-RFE algorithms, contracted to three signature genes: EGF-containing fibulin-like extracellular matrix (EFEMP1), Growth hormone receptor (GHR), and Vascular endothelial growth factor A (VEGFA). The three genes showed good efficacy in three tested cohorts and consistent expression patterns in the glomerular test cohorts. Most immune cells were more infiltrated in the DN samples compared to the controls, and there was a negative correlation between the characteristic genes and most immune cell infiltration. 24 microRNAs were involved in the transcriptional regulation of multiple genes simultaneously, and Endothelial transcription factor GATA-2 (GATA2) had a potential regulatory effect on both GHR and VEGFA.ConclusionWe identified a novel aging-related signature allowing assessment of diagnosis for DN patients, and further can be used to predict immune infiltration sensitivity.

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