CircRNF111 Contributes to Adipocyte Differentiation by Elevating PPARγ Expression via miR-27a-3p

Non-Coding RNAs in Regulating Fat Deposition in Farm Animals

Fat deposition represents a crucial feature in the expenditure of physical energy and affects the meat quality of farm animals. It is regulated by multiple genes and regulators. Of them, non-coding RNAs (ncRNAs) play a critical role in modulating the fat deposition process. As well as being an important protein source, farm animals can be used as medical models, so many researchers worldwide have explored their mechanism of fat deposition. This article summarizes the transcription factors, regulatory genes, and signaling pathways involved in the molecular regulation process of fat deposition; outlines the progress of researching the roles of microRNAs (miRNAs), long ncRNAs (lncRNAs), and circular RNAs (circRNAs) in fat deposition in common farm animals including pigs, cattle, sheep, ducks, and chickens; and identifies scientific problems in the field that must be further investigated. It has been demonstrated that ncRNAs play a critical role in regulating the fat deposition process and have great potential in improving meat quality traits.

Recent advances in investigation of circRNA/lncRNA-miRNA-mRNA networks through RNA sequencing data analysis

Non-coding RNAs (ncRNAs) are RNA molecules that are transcribed from DNA but are not translated into proteins. Studies over the past decades have revealed that ncRNAs can be classified into small RNAs, long non-coding RNAs and circular RNAs by genomic size and structure. Accumulated evidences have eludicated the critical roles of these non-coding transcripts in regulating gene expression through transcription and translation, thereby shaping cellular function and disease pathogenesis. Notably, recent studies have investigated the function of ncRNAs as competitive endogenous RNAs (ceRNAs) that sequester miRNAs and modulate mRNAs expression. The ceRNAs network emerges as a pivotal regulatory function, with significant implications in various diseases such as cancer and neurodegenerative disease. Therefore, we highlighted multiple bioinformatics tools and databases that aim to predict ceRNAs interaction. Furthermore, we discussed limitations of using current technologies and potential improvement for ceRNAs network detection. Understanding of the dynamic interplay within ceRNAs may advance the biological comprehension, as well as providing potential targets for therapeutic intervention.

DEPDC1B, CDCA2, APOBEC3B, and TYMS are potential hub genes and therapeutic targets for diagnosing dialysis patients with heart failure

Introduction

Heart failure (HF) has a very high prevalence in patients with maintenance hemodialysis (MHD). However, there is still a lack of effective and reliable HF diagnostic markers and therapeutic targets for patients with MHD.

Methods

In this study, we analyzed transcriptome profiles of 30 patients with MHD by high-throughput sequencing. Firstly, the differential genes between HF group and control group of patients with MHD were screened. Secondly, HF-related genes were screened by WGCNA, and finally the genes intersecting the two were selected as candidate genes. Machine learning was used to identify hub gene and construct a nomogram model, which was verified by ROC curve and RT-qPCR. In addition, we further explored potential mechanism and function of hub genes in HF of patients with MHD through GSEA, immune cell infiltration analysis, drug analysis and establishment of molecular regulatory network.

Results

Totally 23 candidate genes were screened out by overlapping 673 differentially expressed genes (DEGs) and 147 key module genes, of which four hub genes (DEPDC1B, CDCA2, APOBEC3B and TYMS) were obtained by two machine learning algorithms. Through GSEA analysis, it was found that the four genes were closely related to ribosome, cell cycle, ubiquitin-mediated proteolysis. We constructed a ceRNA regulatory network, and found that 4 hub genes (TYMS, CDCA2 and DEPDC1B) might be regulated by 4 miRNAs (hsa-miR-1297, hsa-miR-4465, hsa-miR-27a-3p, hsa-miR-129-5p) and 21 lncRNAs (such as HCP5, CAS5, MEG3, HCG18). 24 small molecule drugs were predicted based on TYMS through DrugBank website. Finally, qRT-PCR experiments showed that the expression trend of biomarkers was consistent with the results of transcriptome sequencing.

Discussion

Overall, our results reveal the molecular mechanism of HF in patients with MHD and provide insights into potential diagnostic markers and therapeutic targets.

Identification and Validation of Hub Genes and Construction of miRNA-Gene and Transcription Factor-Gene Networks in Adipogenesis of Mesenchymal Stem Cells

Background

Adipogenic differentiation stands as a crucial pathway in the range of differentiation options for mesenchymal stem cells (MSCs), carrying significant importance in the fields of regenerative medicine and the treatment of conditions such as obesity and osteoporosis. However, the exact mechanisms that control the adipogenic differentiation of MSCs are not yet fully understood.

Materials and Methods

We procured datasets, namely GSE36923, GSE80614, GSE107789, and GSE113253, from the Gene Expression Omnibus database. These datasets enabled us to perform a systematic analysis, including the identification of differentially expressed genes (DEGs) pre- and postadipogenic differentiation in MSCs. Subsequently, we conducted an exhaustive analysis of DEGs common to all four datasets. To gain further insights, we subjected these overlapped DEGs to comprehensive gene ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analyses. Following the construction of protein-protein interaction (PPI) networks, we meticulously identified a cohort of hub genes pivotal to the adipogenic differentiation process and validated them using real-time quantitative polymerase chain reaction. Subsequently, we ventured into the construction of miRNA-gene and TF-gene interaction networks.

Results

Our rigorous analysis revealed a total of 18 upregulated DEGs and 12 downregulated DEGs that consistently appeared across all four datasets. Notably, the peroxisome proliferator-activated receptor signaling pathway, regulation of lipolysis in adipocytes, and the adipocytokine signaling pathway emerged as the top-ranking pathways significantly implicated in the regulation of these DEGs. Subsequent to the construction of the PPI network, we identified and validated 10 key node genes, namely IL6, FABP4, ADIPOQ, LPL, PLIN1, RBP4, ACACB, NT5E, KRT19, and G0S2. Our endeavor to construct miRNA-gene interaction networks led to the discovery of the top 10 pivotal miRNAs, including hsa-mir-27a-3p, hsa-let-7b-5p, hsa-mir-1-3p, hsa-mir-124-3p, hsa-mir-155-5p, hsa-mir-16-5p, hsa-mir-101-3p, hsa-mir-21-3p, hsa-mir-146a-5p, and hsa-mir-148b-3p. Furthermore, the construction of TF-gene interaction networks revealed the top 10 critical TFs: ZNF501, ZNF512, YY1, EZH2, ZFP37, ZNF2, SOX13, MXD3, ELF3, and TFDP1.

Conclusions

In summary, our comprehensive study has successfully unraveled the pivotal hub genes that govern the adipogenesis of MSCs. Moreover, the meticulously constructed miRNA-gene and TF-gene interaction networks are poised to significantly augment our comprehension of the intricacies underlying MSC adipogenic differentiation, thus providing a robust foundation for future advances in regenerative biology.

LncRNA BlncAD1 Modulates Bovine Adipogenesis by Binding to MYH10, PI3K/Akt Signaling Pathway, and miR-27a-5p/CDK6 Axis

Research on adipogenesis will help to improve the meat quality of livestock. Long noncoding RNAs (lncRNAs) are involved in mammalian adipogenesis as epigenetic modulators. In this study, we analyzed lncRNA expression during bovine adipogenesis and detected 195 differentially expressed lncRNAs, including lncRNA BlncAD1, which was significantly upregulated in mature bovine adipocytes. Gain- and loss-of-function experiments confirmed that BlncAD1 promoted the proliferation, apoptosis, and differentiation of bovine preadipocytes. RNA pull-down revealed that the nonmuscle myosin 10 (MYH10) is a potential binding protein of BlncAD1. Then, we elucidated that loss of BlncAD1 caused increased ubiquitination of MYH10, which confirmed that BlncAD1 regulates adipogenesis by enhancing the stability of the MYH10 protein. Western blotting was used to demonstrate that BlncAD1 activated the PI3K/Akt signaling pathway. Bioinformatic analysis and dual-luciferase reporter assays indicated that BlncAD1 competitively absorbed miR-27a-5p. The overexpression and interference of miR-27a-5p in bovine preadipocytes displayed that miR-27a-5p inhibited proliferation, apoptosis, and differentiation. Further results suggested that miR-27a-5p targeted the CDK6 gene and that BlncAD1 controlled the proliferation of bovine preadipocytes by modulating the miR-27a-5p/CDK6 axis. This study revealed the complex mechanisms of BlncAD1 underlying bovine adipogenesis for the first time, which would provide useful information for genetics and breeding improvement of Chinese beef cattle.

A review of the role of transcription factors in regulating adipogenesis and lipogenesis in beef cattle

In the past few decades, genomic selection and other refined strategies have been used to increase the growth rate and lean meat production of beef cattle. Nevertheless, the fast growth rates of cattle breeds are often accompanied by a reduction in intramuscular fat (IMF) deposition, impairing meat quality. Transcription factors play vital roles in regulating adipogenesis and lipogenesis in beef cattle. Meanwhile, understanding the role of transcription factors in regulating adipogenesis and lipogenesis in beef cattle has gained significant attention to increase IMF deposition and meat quality. Therefore, the aim of this paper was to provide a comprehensive summary and valuable insight into the complex role of transcription factors in adipogenesis and lipogenesis in beef cattle. This review summarizes the contemporary studies in transcription factors in adipogenesis and lipogenesis, genome-wide analysis of transcription factors, epigenetic regulation of transcription factors, nutritional regulation of transcription factors, metabolic signalling pathways, functional genomics methods, transcriptomic profiling of adipose tissues, transcription factors and meat quality and comparative genomics with other livestock species. In conclusion, transcription factors play a crucial role in promoting adipocyte development and fatty acid biosynthesis in beef cattle. They control adipose tissue formation and metabolism, thereby improving meat quality and maintaining metabolic balance. Understanding the processes by which these transcription factors regulate adipose tissue deposition and lipid metabolism will simplify the development of marbling or IMF composition in beef cattle.

Identification and screening of circular RNAs during adipogenic differentiation of ovine preadipocyte by RNA-seq

Circular RNAs (circRNAs) are a class of non-coding RNAs that play important roles in preadipocyte differentiation and adipogenesis. However, little is known about genome-wide identification, expression profile, and function of circRNAs in sheep. To investigate the role of circRNAs during ovine adipogenic differentiation, the subcutaneous adipose tissue of Tibetan rams was collected in June 2022. Subsequently, the preadipocytes were immediately isolated from collected adipose tissue and then induced to begin differentiation. The adipocytes samples cultured on days 0, 2, and 8 of preadipocytes differentiation were used to perform RNA sequencing (RNA-seq) analysis to construct the expression profiles of circRNAs. Subsequently, the function of differentially expressed circRNAs was investigated by performing the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of their parent genes. Finally, a circRNAs-miRNAs-mRNAs network involved in adipogenic differentiation was been analyzed. As a result, a total of 6,449 candidate circRNAs were identified in ovine preadipocytes. Of these circRNAs identified, 63 candidate circRNAs were differentially expressed among the three differentiation stages and their parent genes were mainly enriched in acetyl-CoA metabolic process, positive regulation of lipid biosynthetic process, positive regulation of steroid biosynthetic process, and focal adhesion pathway (P < 0.05). Based on a circRNAs-miRNAs-mRNAs regulatory network constructed, circ_004977, circ_006132 and circ_003788 were found to function as competing endogenous RNAs (ceRNAs) to regulate ovine preadipocyte differentiation and lipid metabolism. The results provide an improved understanding of functions and molecular mechanisms of circRNAs underlying ovine adipogenesis in sheep.

The Function and Regulation Mechanism of Non-Coding RNAs in Muscle Development

Animal skeletal muscle growth is regulated by a complex molecular network including some non-coding RNAs (ncRNAs). In this paper, we review the non-coding RNAs related to the growth and development of common animal skeletal muscles, aiming to provide a reference for the in-depth study of the role of ncRNAs in the development of animal skeletal muscles, and to provide new ideas for the improvement of animal production performance.

Non-Coding RNAs and Adipogenesis

Adipogenesis is regarded as an intricate network in which multiple transcription factors and signal pathways are involved. Recently, big efforts have focused on understanding the epigenetic mechanisms and their involvement in the regulation of adipocyte development. Multiple studies investigating the regulatory role of non-coding RNAs (ncRNAs) in adipogenesis have been reported so far, especially lncRNA, miRNA, and circRNA. They regulate gene expression at multiple levels through interactions with proteins, DNA, and RNA. Exploring the mechanism of adipogenesis and developments in the field of non-coding RNA may provide a new insight to identify therapeutic targets for obesity and related diseases. Therefore, this article outlines the process of adipogenesis, and discusses updated roles and mechanisms of ncRNAs in the development of adipocytes.

Nanoparticles Mediated circROBO1 Silencing to Inhibit Hepatocellular Carcinoma Progression by Modulating miR-130a-5p/CCNT2 Axis

BACKGROUND

Circular RNAs (circRNAs) are becoming vital biomarkers and therapeutic targets for malignant tumors due to their high stability and specificity in tissues. However, biological functions of circRNAs in hepatocellular carcinoma (HCC) are still not well studied.

METHODS

Gene Expression Omnibus (GEO) database and qRT-PCR were used to evaluate expression of circROBO1 (hsa_circ_0066568) in HCC tissues and cell lines. CCK-8, colony formation, EdU staining, flow cytometry for cell cycle analysis, and xenograft model assays were performed to detect the circROBO1 function in vitro and in vivo. RNA pull-down, RNA immunoprecipitation (RIP), and Luciferase reporter assays were used to investigate the relationship among circROBO1, miR-130a-5p, and CCNT2. More importantly, we developed nanoparticles made from poly lactic-co-glycolic acid (PLGA) and polyethylene glycol (PEG) chains as the delivery system of si-circROBO1 and then applied them to HCC in vitro and in mice.

RESULTS

circROBO1 was obviously upregulated in HCC tissues and cell lines, and elevated circROBO1 was closely correlated with worse prognosis for HCC patients. Functionally, knocking down circROBO1 significantly suppressed HCC cells growth in vitro and in mice. Mechanistically, circROBO1 acted as a competing endogenous RNA to downregulate miR-130a-5p, leading to CCNT2 expression upregulation. Furthermore, miR-130a-5p mimic or CCNT2 knockdown reversed the role of circROBO1 overexpression on HCC cells, which demonstrated that circROBO1 promoted HCC development via miR-130a-5p/CCNT2 axis. In addition, we developed nanoparticles loaded with si-circROBO1, named as PLGA-PEG (si-circROBO1) NPs, which significantly prevented the proliferation of HCC cells, and did not exhibit apparent toxicity to major organs in vivo.

CONCLUSION

Our findings firstly demonstrate that circROBO1 overexpression promotes HCC progression by regulating miR-130a-5p/CCNT2 axis, which may serve as an effective nanotherapeutic target for HCC treatment.

Screening for genes, miRNAs and transcription factors of adipogenic differentiation and dedifferentiation of mesenchymal stem cells

BACKGROUND

The purpose of present study was to reveal the molecular mechanisms responsible for both adipogenic differentiation and dedifferentiation of mesenchymal stem cells (MSCs).

METHODS

Microarray data GSE36923 were obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between adipogenically differentiated cells vs undifferentiated bone marrow-derived MSCs, adipogenically differentiated cells vs dedifferentiated cells samples at day 7 and adipogenically differentiated cells vs dedifferentiated cells samples at day 35 were screened, and overlapped DEGs across the three groups were analyzed. The underlying functions of the upregulated and downregulated DEGs were investigated by Gene ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analysis. The protein-protein interaction network was constructed, and hub genes were obtained subsequently. Hub genes were verified with GSE113253 dataset, and then miRNA-gene network and TF-gene network were constructed.

RESULTS

A total of 284 upregulated DEGs and 376 downregulated DEGs overlapped across the three groups. PPAR signaling pathway, AMPK signaling pathway, insulin signaling pathway, carbon metabolism, pyruvate metabolism, fatty acid metabolism, regulation of lipolysis in adipocytes, biosynthesis of amino acids, citrate cycle (TCA cycle) and 2-Oxocarboxylic acid metabolism were the top 10 pathways involving in the upregulated DEGs, and graft-versus-host disease, allograft rejection, viral myocarditis, cell adhesion molecules, phagosome, type I diabetes mellitus, antigen processing and presentation, autoimmune thyroid disease, intestinal immune network for IgA production and rheumatoid arthritis were the top 10 pathways in downregulated DEGs. After validation, the 8 hub genes were IL6, PPARG, CCL2, FASN, CEBPA, ADIPOQ, FABP4 and LIPE. Ten key miRNAs were hsa-mir-27a-3p, hsa-mir-182-5p, hsa-mir-7-5p, hsa-mir-16-5p, hsa-mir-1-3p, hsa-mir-155-5p, hsa-mir-21-3p, hsa-mir-34a-5p, hsa-mir-27a-5p and hsa-mir-30c-5p, and 10 key TFs were TFDP1, GTF2A2, ZNF584, NRF1, ZNF512, NFRKB, CEBPG, KLF16, GLIS2 and MXD4.

CONCLUSION

Our study constructed miRNA-gene network and TF-gene network involved in both adipogenic differentiation and dedifferentiation of MSCs, contributing to enhancing the efficiency of MSCs transplantation in soft tissue defect repair and developing more potent remedies for adipogenesis-related skeletal disorders.