Gene co-expression network for analysis of plasma exosomal miRNAs in the elderly as markers of aging and cognitive decline

Identification of critical lncRNAs for milk fat metabolism in dairy cows using WGCNA and the construction of a ceRNAs network

As key regulators, long non-coding RNAs (lncRNAs) play a crucial role in the ruminant mammary gland. However, the function of lncRNAs in milk fat synthesis from dairy cows is largely unknown. In this study, we used the weighted gene co-expression network analysis (WGCNA) to comprehensive analyze the expression profile data of lncRNAs from the group's previous Illumina PE150 sequencing results based on bovine mammary epithelial cells from high- and low-milk-fat-percentage (MFP) cows, and identify core_lncRNAs significantly associated with MFP by module membership (MM) and gene significance (GS). Functional enrichment analysis (Gene Ontology, Kyoto Encyclopedia of Genes and Genomes) of core_lncRNA target genes (co-localization and co-expression) was performed to screen potential lncRNAs regulating milk fat metabolism and further construct an interactive regulatory network of lipid metabolism-related competing endogenous RNAs (ceRNAs). A total of 4876 lncRNAs were used to construct the WGCNA. The MEdarkturquoise module among the 19 modules obtained was significantly associated with MFP (r = 0.78, p-value <0.05) and contained 64 core_lncRNAs (MM > 0.8, GS > 0.4). Twenty-four lipid metabolism-related lncRNAs were identified by core_lncRNA target gene enrichment analysis. TCONS_00054233, TCONS_00152292, TCONS_00048619, TCONS_00033839, TCONS_00153791 and TCONS_00074642 were key candidate lncRNAs for regulating milk fat synthesis. The 22 ceRNAs most likely to be involved in milk fat metabolism were constructed by interaction network analysis, and TCONS_00133813 and bta-miR-2454-5p were located at the network's core. TCONS_00133813_bta-miR-2454-5p_TNFAIP3, TCONS_00133813_bta-miR-2454-5p_ARRB1 and TCONS_00133813_bta-miR-2454-5p_PIK3R1 are key candidate ceRNAs associated with milk fat metabolism. This study provides a framework for the co-expression module of MFP-related lncRNAs in ruminants, identifies several major lncRNAs and ceRNAs that influence milk fat synthesis, and provides a new understanding of the complex biology of milk fat synthesis in dairy cows.

Identifying key genes in milk fat metabolism by weighted gene co-expression network analysis

Milk fat is the most important and energy-rich substance in milk, and its content and composition are important reference elements in the evaluation of milk quality. However, the current identification of valuable candidate genes affecting milk fat is limited. IlluminaPE150 was used to sequence bovine mammary epithelial cells (BMECs) with high and low milk fat rates (MFP), the weighted gene co-expression network (WGCNA) was used to analyze mRNA expression profile data in this study. As a result, a total of 10,310 genes were used to construct WGCNA, and the genes were classified into 18 modules. Among them, violet (r = 0.74), yellow (r = 0.75) and darkolivegreen (r =  − 0.79) modules were significantly associated with MFP, and 39, 181, 75 hub genes were identified, respectively. Combining enrichment analysis and differential genes (DEs), we screened five key candidate DEs related to lipid metabolism, namely PI4K2A, SLC16A1, ATP8A2, VEGFD and ID1, respectively. Relative to the small intestine, liver, kidney, heart, ovary and uterus, the gene expression of PI4K2A is the highest in mammary gland, and is significantly enriched in GO terms and pathways related to milk fat metabolism, such as monocarboxylic acid transport, phospholipid transport, phosphatidylinositol signaling system, inositol phosphate metabolism and MAPK signaling pathway. This study uses WGCNA to form an overall view of MFP, providing a theoretical basis for identifying potential pathways and hub genes that may be involved in milk fat synthesis.

Exosomes in Age-Related Cognitive Decline: Mechanistic Insights and Improving Outcomes

Aging is the most prominent risk factor for cognitive decline, yet behavioral symptomology and underlying neurobiology can vary between individuals. Certain individuals exhibit significant age-related cognitive impairments, while others maintain intact cognitive functioning with only minimal decline. Recent developments in genomic, proteomic, and functional imaging approaches have provided insights into the molecular and cellular substrates of cognitive decline in age-related neuropathologies. Despite the emergence of novel tools, accurately and reliably predicting longitudinal cognitive trajectories and improving functional outcomes for the elderly remains a major challenge. One promising approach has been the use of exosomes, a subgroup of extracellular vesicles that regulate intercellular communication and are easily accessible compared to other approaches. In the current review, we highlight recent findings which illustrate how the analysis of exosomes can improve our understanding of the underlying neurobiological mechanisms that contribute to cognitive variation in aging. Specifically, we focus on exosome-mediated regulation of miRNAs, neuroinflammation, and aggregate-prone proteins. In addition, we discuss how exosomes might be used to enhance individual patient outcomes by serving as reliable biomarkers of cognitive decline and as nanocarriers to deliver therapeutic agents to the brain in neurodegenerative conditions.

Overexpressed Tumor Suppressor Exosomal miR-15a-5p in Cancer Cells Inhibits PD1 Expression in CD8+T Cells and Suppresses the Hepatocellular Carcinoma Progression

BACKGROUND

Hepatocellular carcinoma (HCC) is the most common primary liver tumor, and the main reason is the unclear pathogenesis of HCC, which leads to a high fatality rate of HCC. Therefore, it is of great clinical significance to explore the molecular mechanism of HCC and find a targeted therapeutic approach from the molecular level.

MATERIALS AND METHODS

MicroRNA-15a-5p (miR-15a-5p) expression level was measured by bioinformatics and qRT-PCR. Luciferase assay and RIP assays were used to verify the relationship between programmed cell death protein 1 (PD1) PD 1 with miR-15a-5p. Exosomes were identified using TEM, Zetasizer Nano ZS, and western blot. Edu, Transwell, and scratch assay were performed to explore the role of miR-15a-5p or exo-miR-15a-5p on HepG2 cells progression.

RESULTS

MicroRNA-15a-5p (miR-15a-5p) was decreased in HCC tissues and cell lines, which indicated a poor prognosis. Overexpression of miR-15a-5p inhibited viability, proliferation, migration and invasion of HepG2 cells. Then, we isolated exosomes from cancer cells, and found that miR-15a-5p was packaged into exosomes from cancer cells. Furthermore, exo-miR-15a-5p was secreted into CD8+ T cells, then directly inhibited PD1 expression via targeted binding. Then, we co-cultured CD8+ T cells transfected with PD1 with HepG2 transfected with miR-15a-5p, PD1 remitted the inhibitory role of miR-15a-5p on HCC progression.

CONCLUSION

Together, present study revealed exo-miR-15a-5p from cancer cells inhibited PD1 expression in CD8+ T cells, which suppressed the development of HCC.

Lactation Associated Genes Revealed in Holstein Dairy Cows by Weighted Gene Co-Expression Network Analysis (WGCNA)

Simple Summary

Weighted gene coexpression network analysis (WGCNA) is a novel approach that can quickly analyze the relationships between genes and traits. In the past few years, studies on the gene expression changes of dairy cow mammary glands were only based on transcriptome comparisons between two lactation stages. Few studies focused on the relationships between gene expression of the dairy mammary gland and lactation stage or milk composition in a lactation cycle. In this study, we detected milk yield and composition in a lactation cycle. For the first time, we constructed a gene coexpression network using WGCNA on the basis of 18 gene expression profiles during six stages of a lactation cycle by transcriptome sequencing, generating 10 specific modules. Genes in each module were performed with gene ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Module–trait relationship analysis showed a series of potential candidates related to milk yield and composition. The current study provides an important theoretical basis for the further molecular breeding of dairy cows.

Abstract

Weighted gene coexpression network analysis (WGCNA) is a novel approach that can quickly analyze the relationships between genes and traits. In this study, the milk yield, lactose, fat, and protein of Holstein dairy cows were detected in a lactation cycle. Meanwhile, a total of 18 gene expression profiles were detected using mammary glands from six lactation stages (day 7 to calving, −7 d; day 30 post-calving, 30 d; day 90 post-calving, 90 d; day 180 post-calving, 180 d; day 270 post-calving, 270 d; day 315 post-calving, 315 d). On the basis of the 18 profiles, WGCNA identified for the first time 10 significant modules that may be related to lactation stage, milk yield, and the main milk composition content. Genes in the 10 significant modules were examined with gene ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The results revealed that the galactose metabolism pathway was a potential candidate for milk yield and milk lactose synthesis. In −7 d, ion transportation was more frequent and cell proliferation related terms became active. In late lactation, the suppressor of cytokine signaling 3 (SOCS3) might play a role in apoptosis. The sphingolipid signaling pathway was a potential candidate for milk fat synthesis. Dairy cows at 315 d were in a period of cell proliferation. Another notable phenomenon was that nonlactating dairy cows had a more regular circadian rhythm after a cycle of lactation. The results provide an important theoretical basis for the further molecular breeding of dairy cows.

Expression of miRNAs in plasma exosomes derived from patients with atrial fibrillation

BACKGROUND

Studies have revealed the association between exosomes and cardiovascular diseases. However, the typical changes of plasma miRNAs in patients with atrial fibrillation (AF) are still controversial, the use of exosomal miRNAs to diagnose and predict the prognosis of AF has not been described.

HYPOTHESIS

We hypothesized that there were differences in the exosomal miRNAs between AF and normal sinus rhythm (SR) patients, which might be used as the novel biomarkers to reflect the progression of AF.

METHODS

miRNAs were isolated from the plasma of patients, and the target genes of differential miRNAs via enrichment analysis to discover potential pathogenesis related to AF. Combined with high-throughput sequencing results, real-time PCR was used to verify the relative expression of target miRNAs in patients.

RESULTS

This study confirmed that the expression of plasma-derived exosomal miRNAs between patients with AF and SR were different. Target gene enrichment analysis suggested that the target genes of 20 miRNAs, which were significantly upregulated were mainly enriched in biological processes such as gene expression process, inflammation response, enzyme modification, etc. Meanwhile, mitogen-activated protein kinase (MAPK), mammalian target of rapamycin (mTOR), and other pathways were highly enriched. The expressions of miR-92b-3p, miR-1306-5p, and miR-let-7b-3p had differences between patients with AF and SR.

CONCLUSION

These miRNAs and target genes were involved in the process of AF through affecting biological processes such as energy metabolism, lipid metabolism, inflammation, and enzyme activity. It suggested that the exosomal miRNAs might be used as the novel biomarkers to reflect the progression of AF.

Differential Expression and miRNA-Gene Interactions in Early and Late Mild Cognitive Impairment

Mild cognitive impairment (MCI) and Alzheimer's Disease (AD) are complex diseases with their molecular architecture not elucidated. APOE, Amyloid Beta Precursor Protein (APP), and Presenilin-1 (PSEN1) are well-known genes associated with both MCI and AD. Recently, epigenetic alterations and dysregulated regulatory elements, such as microRNAs (miRNAs), have been reported associated with neurodegeneration. In this study, differential expression analysis (DEA) was performed for genes and miRNAs based on microarray and RNA-Seq data. Global gene profile of healthy individuals, early and late mild cognitive impairment (EMCI and LMCI, respectively), and AD was obtained from ADNI Cohort. miRNA global profile of healthy individuals and AD patients was extracted from public RNA-Seq data. DEA performed with limma package on ADNI Cohort data highlighted eight differential expressed (DE) genes (AGER, LINC00483, MMP19, CATSPER1, ARFGAP1, GPER1, PHLPP2, TRPM2) (false discovery rate (FDR) p-value < 0.05) between EMCI and LMCI patients. Previous molecular studies showed associations between these genes with dementia and neurological-related pathways. Five dysregulated miRNAs were identified by DEA performed with RNA-Seq data and edgeR (FDR p-value < 0.002). All reported miRNAs in AD interact with the aforementioned genes. Our integrative transcriptomic analysis was able to identify a set of miRNA-gene interactions that may be involved in cognitive and neurodegeneration processes.