hTFtarget: A Comprehensive Database for Regulations of Human Transcription Factors and Their Targets

Senescence in human mesenchymal stem cells (MSCs) not only contributes to organism aging and the development of a variety of diseases but also severely impairs their therapeutic properties as a promising cell therapy. Studies searching for efficient biomarkers that represent cellular senescence have attracted much attention; however, no single marker currently provides an accurate cell-free representation of cellular senescence. Here, we studied characteristics of MSC-derived microvesicles (MSC-MVs) that may reflect the senescence in their parental MSCs. We found that senescent late passage (LP) MSCs secreted higher levels of MSC-MVs with smaller size than did early passage (EP) MSCs, and the level of CD105+ MSC-MVs decreased with senescence in the parental MSCs. Also, a substantially weaker ability to promote osteogenesis in MSCs was observed in LP than EP MSC-MVs. Comparative analysis of RNA sequencing showed the same trend of decreasing number of highly-expressed miRNAs with increasing number of passages in both MSCs and MSC-MVs. Most of the highly-expressed genes in LP MSCs and the corresponding MSC-MVs were involved in the regulation of senescence-related diseases, such as Alzheimer's disease. Furthermore, based on the miRNA profiling, transcription factors (TF) and genes regulatory networks of MSC senescence, and the datasets from GEO database, we confirmed that expression of miR-146a-5p in MSC-MVs resembled the senescent state of their parental MSCs. Our findings provide evidence that MSC-MVs are a key factor in the senescence-associated secretory phenotype of MSCs and demonstrate that their integrated characteristics can dynamically reflect the senescence state of MSCs representing a potential biomarker for monitoring MSC senescence.

Microvesicles as Potential Biomarkers for the Identification of Senescence in Human Mesenchymal Stem Cells.

The PI3K/AKT-signaling pathway is one of the most frequently activated signal-transduction pathways in cancer. We examined how dysregulated gene expression is associated with miRNA expression in this pathway in colorectal cancer (CRC).

We used data from 217 CRC cases to evaluate differential pathway gene expression between paired carcinoma and normal mucosa and identify miRNAs that are associated with these genes. Gene expression data from RNA-Seq and miRNA expression data from Agilent Human miRNA Microarray V19.0 were analyzed. We focused on genes most associated with CRC (fold change (FC) of >1.5 or <0.67) that were statistically significant after adjustment for multiple comparisons.

Of the 304 genes evaluated, 76 had a FC of 1.50; 47 of these genes were associated with miRNA differential expression. There were 145 mRNA:miRNA seed-region matches of which 26 were inversely associated suggesting a greater likelihood of a direct association. Most miRNA:mRNA associations were with factors that stimulated the pathway. For instance, both IL6R and PDGFRA had inverse seed-region matches with seven miRNAs, suggesting that these miRNAs have a direct effect on these genes and may be key elements in activation of the pathway. Other miRNA:mRNA associations with similar impact on the pathway were miR-203a with ITGA4, miR-6071 with ITGAV, and miR-375 with THBS2, all genes involved in extracellular matrix function that activate PI3Ks.

Gene expression in the PI3K/Akt-signaling pathway is dysregulated in CRC. MiRNAs were associated with many of these dysregulated genes either directly or in an indirect manner. This article is protected by copyright. All rights reserved

https://onlinelibrary.wiley.com/doi/pdf/10.1002/mc.22752

The PI3K/AKT signaling pathway: Associations of miRNAs with dysregulated gene expression in colorectal cancer

Tumor suppressor genes (TSGs) and oncogenes (OG) are involved in carcinogenesis. MiRNAs also contribute to cellular pathways leading to cancer. We use data from 217 colorectal cancer (CRC) cases to evaluate differences in TSGs and OGs expression between paired CRC and normal mucosa and evaluate how TSGs and OGs are associated with miRNAs. Gene expression data from RNA-Seq and miRNA expression data from Agilent Human miRNA Microarray V19.0 were used. We focus on genes most strongly associated with CRC (fold change (FC) of ≥1.5 or ≤0.67) that were statistically significant after adjustment for multiple comparisons. Of the 74 TSGs evaluated, 22 were associated with carcinoma/normal mucosa differential expression. Ten TSGs were up-regulated (FAM123B, RB1, TP53, RUNX1, MSH2, BRCA1, BRCA2, SOX9, NPM1, and RNF43); six TSGs were down-regulated (PAX5, IZKF1, GATA3, PRDM1, TET2, and CYLD); four were associated with MSI tumors (MLH1, PTCH1, and CEBPA down-regulated and MSH6 up-regulated); and two were associated with MSS tumors (PHF6 and ASXL1 up-regulated). Thirteen of these TSGs were associated with 44 miRNAs. Twenty-seven of the 59 OGs evaluated were dysregulated: 14 down-regulated (KLF4, BCL2, SSETBP1, FGFR2, TSHR, MPL, KIT, PDGFRA, GNA11, GATA2, FGFR3, AR, CSF1R, and JAK3), seven up-regulated (DNMT1, EZH2, PTPN11, SKP2, CCND1, MET, and MYC); three down-regulated for MSI (FLT3, CARD11, and ALK); two up-regulated for MSI (IDH2 and HRAS); and one up-regulated with MSS tumors (CTNNB1). These findings suggest possible co-regulatory function between TSGs, OGs, and miRNAs, involving both direct and indirect associations that operate through feedback and feedforward loops.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/gcc.22481

The co-regulatory networks of tumor suppressor genes, oncogenes, and miRNAs in colorectal cancer.

Apoptosis is genetically regulated and involves intrinsic and extrinsic pathways. We examined 133 genes within these pathways to identify whether they are expressed differently in colorectal carcinoma (CRC) and normal tissue (N = 217) and if they are associated with similar differential miRNA expression. Gene expression data (RNA-Seq) and miRNA expression data (Agilent Human miRNA Microarray V19.0) were generated. We focused on dysregulated genes with a fold change (FC) of > 1.50 or  1.50). Of these 41 genes, 11 were significantly associated with miRNA differential expression. BIRC5 had the greatest number of miRNA associations (14) and the most miRNAs with a seed-region match (10). Four of these matches, miR-145-5p, miR-150-5p, miR-195-5p, and miR-650, had a negative beta coefficient. CSF2RB was associated with ten total miRNAs (five with a seed-region match, and one miRNA, miR-92a-3p, with a negative beta coefficient). Of the three miRNAs associated with CTSS, miR-20b-5p, and miR-501-3p, had a seed-region match and a negative beta coefficient between miRNA:mRNA pairs. Several miRNAs that were associated with dysregulated gene expression, seed-region matches, and negative beta coefficients also were associated with CRC-specific survival. Our data suggest that miRNAs could influence several apoptosis-related genes. BIRC5, CTSS, and CSF2R all had seed-region matches with miRNAs that would favor apoptosis. Our study identifies several miRNA associated with apoptosis-related genes, that if validated, could be important therapeutic targets.

/pdf/dysregulated-genes-and-mirnas-in-the-apoptosis-pathway-in-1oboj3rh3i.pdf

Dysregulated genes and miRNAs in the apoptosis pathway in colorectal cancer patients

The maturation process of lymphocyte was related to many blood diseases, such as lymphoma and lymphoid leukemia. Many TFs and miRNAs were separately studied in the development of B and T cells. In this study, we aim to discover the TF and miRNA co-regulation and identify key regulators in the B and T cells maturation. We obtained the candidate genes, miRNAs and TFs for each stage of their maturation, then constructed the TF-miRNA-gene feed-forward loops (FFLs) for each stage by our previous methods. Statistical test for FFLs indicated their enrichment and significance. TF-miRNA co-regulatory networks for each stage were constructed by combining their FFLs. Hub analysis revealed the key regulators in each stage, for example, MYC, STAT5A, PAX5 and miR-17 ~ 92 in the transition of pro-B cells into pre-B cells. We also identified a few common regulators and modules in two stages of B cell maturation (e.g. miR-146a/NFKB1/BCL11A) and two stages of T cell maturation (e.g. miR-20/CCND2/SORL1), as well as some shared regulators in the early stages of both B and T cell development. Our network will help to increase understanding of mature process of B and T cell, as well as the related blood diseases.

/pdf/transcription-factor-and-mirna-co-regulatory-network-reveals-6c46ofgk7q.pdf

Transcription factor and miRNA co-regulatory network reveals shared and specific regulators in the development of B cell and T cell.

Scoring the correlation between two genes by their shared properties is a common and basic work in biological study. A prospective way to score this correlation is to quantify the overlap between the two sets of homogeneous properties of the two genes. However the proper model has not been decided, here we focused on studying the quantification of overlap and proposed a more effective model after theoretically compared 7 existing models. We defined three characteristic parameters (d, R, r) of an overlap, which highlight essential differences among the 7 models and grouped them into two classes. Then the pros and cons of the two groups of model were fully examined by their solution space in the (d, R, r) coordinate system. Finally we proposed a new model called OScal (Overlap Score calculator), which was modified on Poisson distribution (one of 7 models) to avoid its disadvantages. Tested in assessing gene relation using different data, OScal performs better than existing models. In addition, OScal is a basic mathematic model, with very low computation cost and few restrictive conditions, so it can be used in a wide-range of research areas to measure the overlap or similarity of two entities.

/pdf/scoring-the-correlation-of-genes-by-their-shared-properties-3tdhj1enls.pdf

Scoring the correlation of genes by their shared properties using OScal, an improved overlap quantification model.

Myocardial infarction (MI) is a leading cause of death in the world and many genes are involved in it. Transcription factor (TFs) and microRNAs (miRNAs) are key regulators of gene expression. We hypothesized that miRNAs and TFs might play combinatory regulatory roles in MI. After collecting MI candidate genes and miRNAs from various resources, we constructed a comprehensive MI-specific miRNA-TF co-regulatory network by integrating predicted and experimentally validated TF and miRNA targets. We found some hub nodes (e.g. miR-16 and miR-26) in this network are important regulators, and the network can be severed as a bridge to interpret the associations of previous results, which is shown by the case of miR-29 in this study. We also constructed a regulatory network for MI recurrence and found several important genes (e.g. DAB2, BMP6, miR-320 and miR-103), the abnormal expressions of which may be potential regulatory mechanisms and markers of MI recurrence. At last we proposed a cellular model to discuss major TF and miRNA regulators with signaling pathways in MI. This study provides more details on gene expression regulation and regulators involved in MI progression and recurrence. It also linked up and interpreted many previous results.

/pdf/mirna-and-tf-co-regulatory-network-analysis-for-the-3lwt4hprsu.pdf

MiRNA and TF co-regulatory network analysis for the pathology and recurrence of myocardial infarction

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Transcription factor and miRNA co-regulatory network reveals shared and specific regulators in the development of B cell and T cell.

Scoring the correlation of genes by their shared properties using OScal, an improved overlap quantification model.

MiRNA and TF co-regulatory network analysis for the pathology and recurrence of myocardial infarction