Unveiling cell-type-specific microRNA networks through alternative polyadenylation in glioblastoma

BACKGROUND

Glioblastoma multiforme (GBM) is characterized by its cellular complexity, with a microenvironment consisting of diverse cell types, including oligodendrocyte precursor cells (OPCs) and neoplastic CD133 + radial glia-like cells. This study focuses on exploring the distinct cellular transitions in GBM, emphasizing the role of alternative polyadenylation (APA) in modulating microRNA-binding and post-transcriptional regulation.

RESULTS

Our research identified unique APA profiles that signify the transitional phases between neoplastic cells and OPCs, underscoring the importance of APA in cellular identity and transformation in GBM. A significant finding was the disconnection between differential APA events and gene expression alterations, indicating that APA operates as an independent regulatory mechanism. We also highlighted the specific genes in neoplastic cells and OPCs that lose microRNA-binding sites due to APA, which are crucial for maintaining stem cell characteristics and DNA repair, respectively. The constructed networks of microRNA-transcription factor-target genes provide insights into the cellular mechanisms influencing cancer cell survival and therapeutic resistance.

CONCLUSIONS

This study elucidates the APA-driven regulatory framework within GBM, spotlighting its influence on cell state transitions and microRNA network dynamics. Our comprehensive analysis using single-cell RNA sequencing data to investigate the microRNA-binding sites altered by APA profiles offers a robust foundation for future research, presenting a novel approach to understanding and potentially targeting the complex molecular interplay in GBM.

Investigating gene expression datasets of hippocampus tissue to discover Alzheimer's disease-associated molecular markers

BACKGROUND

Alzheimer's disease (AD) is an advancing neurodegenerative disorder distinguished by the formation of amyloid plaques and neurofibrillary tangles in the human brain. Nevertheless, the lack of peripheral biomarkers that can detect the development of AD remains a significant limitation.

OBJECTIVE

The main aim of this work was to discover the molecular markers associated with AD.

METHODS

We conducted a comprehensive microarray analysis of gene expression data from hippocampus tissue in AD patients and control samples using three microarray datasets (GSE1297, GSE28146, and GSE29378) collected from Gene Expression Omnibus (GEO). The datasets were pre-processed and normalized, revealing 346 significant genes, 103 of which were upregulated and 243 downregulated. The PPI network of significant genes was constructed to detect the top 50 hub genes, which were then further analyzed using Gene Ontology (GO) terms, Kyoto Encyclopedia of Genes and Genomes pathway (KEGG), and GSEA, revealing 47 key genes involved in AD-related pathways. These key genes were then subjected to feed forward loop (FFL) motif analysis for the prediction of transcriptional factors (TFs) and microRNAs (miRNAs) mediated gene regulatory networks.

RESULTS

The interaction of AD-associated TFs HNF4A, SPI1, EGR1, STAT3, and MYC and miRNAs hsa-miR-155-5p and hsa-miR-16-5p in the transcriptional and post-transcriptional events of 3 upregulated and 10 downregulated genes: H2AFZ, MCM3, MYO1C, AXIN1, CCND1, ETS2, MYH9, RELA, RHEB, SOCS3, TBL1X, TBP, TXNIP, and YWHAZ, respectively, has been identified. The miRNA/TF-mediated three types of the FFL motifs, i.e., miRNA-FFL, TF-FFL, and composite-FFL, were constructed, and seven common genes among these FFL were identified: CCND1, MYH9, SOCS3, RHEB, MYO1C, TXNIP, AXIN1, and TXNIP.

CONCLUSIONS

These findings may provide insights into the development of potential molecular markers for therapeutic management of AD.

Circulating hsa-miR-320a and its regulatory network in type 1 diabetes mellitus

Introduction

Increasing evidence from human and animal model studies indicates the significant role of microRNAs (miRNAs) in pancreatic beta cell function, insulin signaling, immune responses, and pathogenesis of type 1 diabetes (T1D).

Methods

We aimed, using next-generation sequencing, to screen miRNAs from peripheral blood mononuclear cells of eight independent Kuwaiti-Arab families with T1D affected siblings, consisting of 18 T1D patients and 18 unaffected members, characterized by no parent-to-child inheritance pattern.

Results

Our analysis revealed 20 miRNAs that are differentially expressed in T1D patients compared with healthy controls. Module-based weighted gene co-expression network analysis prioritized key consensus miRNAs in T1D pathogenesis. These included hsa-miR-320a-3p, hsa-miR-139-3p, hsa-miR-200-3p, hsa-miR-99b-5p and hsa-miR-6808-3p. Functional enrichment analysis of differentially expressed miRNAs indicated that PI3K-AKT is one of the key pathways perturbed in T1D. Gene ontology analysis of hub miRNAs also implicated PI3K-AKT, along with mTOR, MAPK, and interleukin signaling pathways, in T1D. Using quantitative RT-PCR, we validated one of the key predicted miRNA-target gene-transcription factor networks in an extended cohort of children with new-onset T1D positive for islet autoantibodies. Our analysis revealed that hsa-miR-320a-3p and its key targets, including PTEN, AKT1, BCL2, FOXO1 and MYC, are dysregulated in T1D, along with their interacting partners namely BLIMP3, GSK3B, CAV1, CXCL3, TGFB, and IL10. Receiver Operating Characteristic analysis highlighted the diagnostic potential of hsa-miR-320a-3p, CAV1, GSK3B and MYC for T1D.

Discussion

Our study presents a novel link between hsa-miR-320a-3p and T1D, and highlights its key regulatory role in the network of mRNA markers and transcription factors involved in T1D pathogenesis.

Codon Bias of the DDR1 Gene and Transcription Factor EHF in Multiple Species

Milk production is an essential economic trait in cattle, and understanding the genetic regulation of this trait can enhance breeding strategies. The discoidin domain receptor 1 (DDR1) gene has been identified as a key candidate gene that influences milk production, and ETS homologous factor (EHF) is recognized as a critical transcription factor that regulates DDR1 expression. Codon usage bias, which affects gene expression and protein function, has not been fully explored in cattle. This study aims to examine the codon usage bias of DDR1 and EHF transcription factors to understand their roles in dairy production traits. Data from 24 species revealed that both DDR1 and EHF predominantly used G/C-ending codons, with the GC3 content averaging 75.49% for DDR1 and 61.72% for EHF. Synonymous codon usage analysis identified high-frequency codons for both DDR1 and EHF, with 17 codons common to both genes. Correlation analysis indicated a negative relationship between the effective number of codons and codon adaptation index for both DDR1 and EHF. Phylogenetic and clustering analyses revealed similar codon usage patterns among closely related species. These findings suggest that EHF plays a crucial role in regulating DDR1 expression, offering new insights into genetically regulating milk production in cattle.

Bioinformatics tools and resources for cancer and application

ABSTRACT

Tumor bioinformatics plays an important role in cancer research and precision medicine. The primary focus of traditional cancer research has been molecular and clinical studies of a number of fundamental pathways and genes. In recent years, driven by breakthroughs in high-throughput technologies, large-scale cancer omics data have accumulated rapidly. How to effectively utilize and share these data is particularly important. To address this crucial task, many computational tools and databases have been developed over the past few years. To help researchers quickly learn and understand the functions of these tools, in this review, we summarize publicly available bioinformatics tools and resources for pan-cancer multi-omics analysis, regulatory analysis of tumorigenesis, tumor treatment and prognosis, immune infiltration analysis, immune repertoire analysis, cancer driver gene and driver mutation analysis, and cancer single-cell analysis, which may further help researchers find more suitable tools for their research.

Unraveling the molecular pathogenesis of Type 2 Diabetes and its impact on female infertility: A bioinformatics and systems biology approach

Type 2 diabetes mellitus (T2D) has been linked with female infertility (FI). Nevertheless, our understanding of the molecular hallmarks and underlying mechanisms remains elusive. This research article aimed to find the hub genes, pathways, transcription factors, and miRNA involved. For this study, softwares like cytoscape, string, Enrichr, FFL loop, etc., were utilized. This research article employed differentially expressed genes (DEGs) to identify multiple biological targets to understand the association between T2D and female infertility (FI). Between T2D and FI, we found 3869 differentially expressed genes. We have also analyzed different pathways like thyroid hormone signaling pathways, AGE-RAGE signaling pathways in diabetic complications and ubiquitin-mediated proteolysis through pathway analysis. Moreover, hub genes MED17, PRKCG, THRA, FOXO1, NCOA2, PLCG2, COL1A1, CXCL8, PRPF19, ANAPC5, UBE2I, XIAP and KEAP1 have been identified. Additionally, these hub genes were subjected to identify the miRNA-mRNA regulation network specific to T2D-associated female infertility. In the FFL study (Feed Forward Loop), transcription factor (SP1, NFKB1, RELA and FOX01), miRNA (has-mir-7-5p, has-let-7a-5p, hsa-mir-16-5p, hsa-mir-155-5p, has-mir-122-5p, has-let-7b-5p, has-mir-124-3p, has-mir-34a-5p, has-mir-130a-3p, has-let-7i-5p, and hsa-mir-27a-3p) and six genes (XIAP, THRA, NCOA2, MED17, FOXO1, and COL1A1) among the thirteen key genes were recognized as regulator and inhibitor. Our analysis reveals that these genes can serve as a significant biomarker for female infertility linked with Type 2 Diabetes, through the prioritization of candidate genes. This study gives us insight into the molecular and cellular mechanism of T2D-associated FI. This finding helps in developing novel therapeutic approaches and will improve efficacy and reduce side effects of the treatment. This research requires further experimental investigation of the principal targets.

Predicting the Effect of miRNA on Gene Regulation to Foster Translational Multi-Omics Research-A Review on the Role of Super-Enhancers

Gene regulation is crucial for cellular function and homeostasis. It involves diverse mechanisms controlling the production of specific gene products and contributing to tissue-specific variations in gene expression. The dysregulation of genes leads to disease, emphasizing the need to understand these mechanisms. Computational methods have jointly studied transcription factors (TFs), microRNA (miRNA), and messenger RNA (mRNA) to investigate gene regulatory networks. However, there remains a knowledge gap in comprehending gene regulatory networks. On the other hand, super-enhancers (SEs) have been implicated in miRNA biogenesis and function in recent experimental studies, in addition to their pivotal roles in cell identity and disease progression. However, statistical/computational methodologies harnessing the potential of SEs in deciphering gene regulation networks remain notably absent. However, to understand the effect of miRNA on mRNA, existing statistical/computational methods could be updated, or novel methods could be developed by accounting for SEs in the model. In this review, we categorize existing computational methods that utilize TF and miRNA data to understand gene regulatory networks into three broad areas and explore the challenges of integrating enhancers/SEs. The three areas include unraveling indirect regulatory networks, identifying network motifs, and enriching pathway identification by dissecting gene regulators. We hypothesize that addressing these challenges will enhance our understanding of gene regulation, aiding in the identification of therapeutic targets and disease biomarkers. We believe that constructing statistical/computational models that dissect the role of SEs in predicting the effect of miRNA on gene regulation is crucial for tackling these challenges.

Potential therapeutic targets for COVID-19 complicated with pulmonary hypertension: a bioinformatics and early validation study

Coronavirus disease (COVID-19) and pulmonary hypertension (PH) are closely correlated. However, the mechanism is still poorly understood. In this article, we analyzed the molecular action network driving the emergence of this event. Two datasets (GSE113439 and GSE147507) from the GEO database were used for the identification of differentially expressed genes (DEGs).Common DEGs were selected by VennDiagram and their enrichment in biological pathways was analyzed. Candidate gene biomarkers were selected using three different machine-learning algorithms (SVM-RFE, LASSO, RF).The diagnostic efficacy of these foundational genes was validated using independent datasets. Eventually, we validated molecular docking and medication prediction. We found 62 common DEGs, including several ones that could be enriched for Immune Response and Inflammation. Two DEGs (SELE and CCL20) could be identified by machine-learning algorithms. They performed well in diagnostic tests on independent datasets. In particular, we observed an upregulation of functions associated with the adaptive immune response, the leukocyte-lymphocyte-driven immunological response, and the proinflammatory response. Moreover, by ssGSEA, natural killer T cells, activated dendritic cells, activated CD4 T cells, neutrophils, and plasmacytoid dendritic cells were correlated with COVID-19 and PH, with SELE and CCL20 showing the strongest correlation with dendritic cells. Potential therapeutic compounds like FENRETI-NIDE, AFLATOXIN B1 and 1-nitropyrene were predicted. Further molecular docking and molecular dynamics simulations showed that 1-nitropyrene had the most stable binding with SELE and CCL20.The findings indicated that SELE and CCL20 were identified as novel diagnostic biomarkers for COVID-19 complicated with PH, and the target of these two key genes, FENRETI-NIDE and 1-nitropyrene, was predicted to be a potential therapeutic target, thus providing new insights into the prediction and treatment of COVID-19 complicated with PH in clinical practice.

SP1-stimulated miR-208a-5p aggravates sepsis-induced myocardial injury via targeting XIAP

The development of sepsis can lead to many organ dysfunction and even death. Myocardial injury is one of the serious complications of sepsis leading to death. New evidence suggests that microRNAs (miRNAs) play a critical role in infection myocardial injury. However, the mechanism which miR-208a-5p regulates sepsis-induced myocardial injury remains unclear. To mimic sepsis-induced myocardial injury in vitro, rat primary cardiomyocytes were treated with LPS. Cell viability and apoptosis were tested by CCK-8 and flow cytometry, respectively. The secretion of inflammatory factors was analyzed by ELISA. mRNA and protein levels were detected by RT-qPCR and Western blotting. The interaction among SP1, XIAP and miR-208a-5p was detected using dual luciferase report assay. Ultrasonic analysis and HE staining was performed to observe the effect of miR-208a-5p in sepsis-induced rats. Our findings indicated that miR-208a-5p expression in primary rat cardiomyocytes was increased by LPS. MiR-208a-5p inhibitor reversed LPS-induced cardiomyocytes injury through inhibiting the apoptosis. Furthermore, the inflammatory injury in cardiomyocytes was induced by LPS, which was rescued by miR-208a-5p inhibitor. In addition, downregulation of miR-208a-5p improved LPS-induced sepsis myocardial injury in vivo. Mechanistically, XIAP might be a target gene of miR-208a-5p. SP1 promoted transcription of miR-208a by binding to the miR-208a promoter region. Moreover, silencing of XIAP reversed the regulatory of miR-208a-5p inhibitor on cardiomyocytes injury. To sum up, those findings revealed silencing of miR-208a-5p could alleviate sepsis-induced myocardial injury, which would grant a new process for the treatment of sepsis.

Integrated modeling framework reveals co-regulation of transcription factors, miRNAs and lncRNAs on cardiac developmental dynamics

AIMS

Dissecting complex interactions among transcription factors (TFs), microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) are central for understanding heart development and function. Although computational approaches and platforms have been described to infer relationships among regulatory factors and genes, current approaches do not adequately account for how highly diverse, interacting regulators that include noncoding RNAs (ncRNAs) control cardiac gene expression dynamics over time.

METHODS

To overcome this limitation, we devised an integrated framework, cardiac gene regulatory modeling (CGRM) that integrates LogicTRN and regulatory component analysis bioinformatics modeling platforms to infer complex regulatory mechanisms. We then used CGRM to identify and compare the TF-ncRNA gene regulatory networks that govern early- and late-stage cardiomyocytes (CMs) generated by in vitro differentiation of human pluripotent stem cells (hPSC) and ventricular and atrial CMs isolated during in vivo human cardiac development.

RESULTS

Comparisons of in vitro versus in vivo derived CMs revealed conserved regulatory networks among TFs and ncRNAs in early cells that significantly diverged in late staged cells. We report that cardiac genes ("heart targets") expressed in early-stage hPSC-CMs are primarily regulated by MESP1, miR-1, miR-23, lncRNAs NEAT1 and MALAT1, while GATA6, HAND2, miR-200c, NEAT1 and MALAT1 are critical for late hPSC-CMs. The inferred TF-miRNA-lncRNA networks regulating heart development and contraction were similar among early-stage CMs, among individual hPSC-CM datasets and between in vitro and in vivo samples. However, genes related to apoptosis, cell cycle and proliferation, and transmembrane transport showed a high degree of divergence between in vitro and in vivo derived late-stage CMs. Overall, late-, but not early-stage CMs diverged greatly in the expression of "heart target" transcripts and their regulatory mechanisms.

CONCLUSIONS

In conclusion, we find that hPSC-CMs are regulated in a cell autonomous manner during early development that diverges significantly as a function of time when compared to in vivo derived CMs. These findings demonstrate the feasibility of using CGRM to reveal dynamic and complex transcriptional and posttranscriptional regulatory interactions that underlie cell directed versus environment-dependent CM development. These results with in vitro versus in vivo derived CMs thus establish this approach for detailed analyses of heart disease and for the analysis of cell regulatory systems in other biomedical fields.

A Data-Mining Approach to Identify NF-kB-Responsive microRNAs in Tissues Involved in Inflammatory Processes: Potential Relevance in Age-Related Diseases

The nuclear factor NF-kB is the master transcription factor in the inflammatory process by modulating the expression of pro-inflammatory genes. However, an additional level of complexity is the ability to promote the transcriptional activation of post-transcriptional modulators of gene expression as non-coding RNA (i.e., miRNAs). While NF-kB’s role in inflammation-associated gene expression has been extensively investigated, the interplay between NF-kB and genes coding for miRNAs still deserves investigation. To identify miRNAs with potential NF-kB binding sites in their transcription start site, we predicted miRNA promoters by an in silico analysis using the PROmiRNA software, which allowed us to score the genomic region’s propensity to be miRNA cis-regulatory elements. A list of 722 human miRNAs was generated, of which 399 were expressed in at least one tissue involved in the inflammatory processes. The selection of “high-confidence” hairpins in miRbase identified 68 mature miRNAs, most of them previously identified as inflammamiRs. The identification of targeted pathways/diseases highlighted their involvement in the most common age-related diseases. Overall, our results reinforce the hypothesis that persistent activation of NF-kB could unbalance the transcription of specific inflammamiRNAs. The identification of such miRNAs could be of diagnostic/prognostic/therapeutic relevance for the most common inflammatory-related and age-related diseases.

Identification of Driver Genes and miRNAs in Ovarian Cancer through an Integrated In-Silico Approach

Ovarian cancer is the eighth-most common cancer in women and has the highest rate of death among all gynecological malignancies in the Western world. Increasing evidence shows that miRNAs are connected to the progression of ovarian cancer. In the current study, we focus on the identification of miRNA and its associated genes that are responsible for the early prognosis of patients with ovarian cancer. The microarray dataset GSE119055 used in this study was retrieved via the publicly available GEO database by NCBI for the analysis of DEGs. The miRNA GSE119055 dataset includes six ovarian carcinoma samples along with three healthy/primary samples. In our study, DEM analysis of ovarian carcinoma and healthy subjects was performed using R Software to transform and normalize all transcriptomic data along with packages from Bioconductor. Results: We identified miRNA and its associated hub genes from the samples of ovarian cancer. We discovered the top five upregulated miRNAs (hsa-miR-130b-3p, hsa-miR-18a-5p, hsa-miR-182-5p, hsa-miR-187-3p, and hsa-miR-378a-3p) and the top five downregulated miRNAs (hsa-miR-501-3p, hsa-miR-4324, hsa-miR-500a-3p, hsa-miR-1271-5p, and hsa-miR-660-5p) from the network and their associated genes, which include seven common genes (SCN2A, BCL2, MAF, ZNF532, CADM1, ELAVL2, and ESRRG) that were considered hub genes for the downregulated network. Similarly, for upregulated miRNAs we found two hub genes (PRKACB and TAOK1).

Updated review of advances in microRNAs and complex diseases: experimental results, databases, webservers and data fusion

MicroRNAs (miRNAs) are gene regulators involved in the pathogenesis of complex diseases such as cancers, and thus serve as potential diagnostic markers and therapeutic targets. The prerequisite for designing effective miRNA therapies is accurate discovery of miRNA-disease associations (MDAs), which has attracted substantial research interests during the last 15 years, as reflected by more than 55 000 related entries available on PubMed. Abundant experimental data gathered from the wealth of literature could effectively support the development of computational models for predicting novel associations. In 2017, Chen et al. published the first-ever comprehensive review on MDA prediction, presenting various relevant databases, 20 representative computational models, and suggestions for building more powerful ones. In the current review, as the continuation of the previous study, we revisit miRNA biogenesis, detection techniques and functions; summarize recent experimental findings related to common miRNA-associated diseases; introduce recent updates of miRNA-relevant databases and novel database releases since 2017, present mainstream webservers and new webserver releases since 2017 and finally elaborate on how fusion of diverse data sources has contributed to accurate MDA prediction.

Multi-omics characterization of autophagy-related molecular features for therapeutic targeting of autophagy

Autophagy is a major contributor to anti-cancer therapy resistance. Many efforts have been made to understand and overcome autophagy-mediated therapy resistance, but these efforts have been unsuccessful in clinical applications. In this study, we establish an autophagy signature to estimate tumor autophagy status. We then classify approximately 10,000 tumor samples across 33 cancer types from The Cancer Genome Atlas into autophagy score-high and autophagy score-low groups. We characterize the associations between multi-dimensional molecular features and tumor autophagy, and further analyse the effects of autophagy status on drug response. In contrast to the conventional view that the induction of autophagy serves as a key resistance mechanism during cancer therapy, our analysis reveals that autophagy induction may also sensitize cancer cells to anti-cancer drugs. We further experimentally validate this phenomenon for several anti-cancer drugs in vitro and in vivo, and reveal that autophagy inducers potentially sensitizes tumor cells to etoposide through downregulating the expression level of DDIT4. Our study provides a comprehensive landscape of molecular alterations associated with tumor autophagy and highlights an opportunity to leverage multi-omics analysis to utilize multiple drug sensitivity induced by autophagy.

AnimalTFDB 4.0: a comprehensive animal transcription factor database updated with variation and expression annotations

Transcription factors (TFs) are proteins that interact with specific DNA sequences to regulate gene expression and play crucial roles in all kinds of biological processes. To keep up with new data and provide a more comprehensive resource for TF research, we updated the Animal Transcription Factor Database (AnimalTFDB) to version 4.0 (http://bioinfo.life.hust.edu.cn/AnimalTFDB4/) with up-to-date data and functions. We refined the TF family rules and prediction pipeline to predict TFs in genome-wide protein sequences from Ensembl. As a result, we predicted 274 633 TF genes and 150 726 transcription cofactor genes in AnimalTFDB 4.0 in 183 animal genomes, which are 86 more species than AnimalTFDB 3.0. Besides double data volume, we also added the following new annotations and functions to the database: (i) variations (including mutations) on TF genes in various human cancers and other diseases; (ii) predicted post-translational modification sites (including phosphorylation, acetylation, methylation and ubiquitination sites) on TFs in 8 species; (iii) TF regulation in autophagy; (iv) comprehensive TF expression annotation for 38 species; (v) exact and batch search functions allow users to search AnimalTFDB flexibly. AnimalTFDB 4.0 is a useful resource for studying TF and transcription regulation, which contains comprehensive annotation and classification of TFs and transcription cofactors.

The Analysis of SNPs' Function in miR-21 and miR146a/b in Multiple Sclerosis and Active Lesions: An In Silico Study

Multiple sclerosis (MS) is a central nervous disorder caused by several factors. Studies have recently shown that non-coding RNA such as miRNA could participate in MS initiation, progression, and active lesion. This study aims to theoretically analyze the potential impact of single-nucleotide polymorphisms (SNPs) on mir-21 and mir-146a/b, which has been previously demonstrated as MS microRNA signature. To fulfill this purpose, the SNPs were investigated for functionality through several online tools, including miRNA-SNP, SNP2-TFBS, RBP-Var, and RNAfold. Furthermore, SNPs of miR-21 and miR-146a/b that exist in pre-miRNA, mature miRNA, and promoter area were extracted; moreover, miRNA and RNA-binding protein interactions were analyzed. This article presented a list of validated SNPs that could affect the expression or function of miR-21 and miR-146a/b for the future practical study of MS and active lesions.

A miR-9-5p/FOXO1/CPEB3 Feed-Forward Loop Drives the Progression of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide, but its regulatory mechanism remains unclear and potential clinical biomarkers are still lacking. Co-regulation of TFs and miRNAs in HCC and FFL module studies may help to identify more precise and critical driver modules in HCC development. Here, we performed a comprehensive gene expression and regulation analysis for HCC in vitro and in vivo. Transcription factor and miRNA co-regulatory networks for differentially expressed genes between tumors and adjacent tissues revealed the critical feed-forward loop (FFL) regulatory module miR-9-5p/FOXO1/CPEB3 in HCC. Gain- and loss-of-function studies demonstrated that miR-9-5p promotes HCC tumor proliferation, while FOXO1 and CPEB3 inhibit hepatocarcinoma growth. Furthermore, by luciferase reporter assay and ChIP-Seq data, CPEB3 was for the first time identified as a direct downstream target of FOXO1, negatively regulated by miR-9-5p. The miR-9-5p/FOXO1/CPEB3 FFL was associated with poor prognosis, and promoted cell growth and tumor progression of HCC in vitro and in vivo. Our study identified for the first time the existence of miR-9-5p/FOXO1/CPEB3 FFL and revealed its regulatory role in HCC progression, which may represent a new potential target for cancer therapy.

Identification of Potential miRNA-mRNA Regulatory Network in Denervated Muscular Atrophy by Bioinformatic Analysis

Muscle atrophy caused by long-term denervation leads to the loss of skeletal muscle mass and strength, resulting in a poor recovery of functional muscles and decreasing quality of life. Increasing differentially expressed microRNAs (DEMs) have been reported to be involved in the pathogenesis of denervated muscle atrophy. However, there is still insufficient evidence to explain the role of miRNAs and their target genes in skeletal muscle atrophy. Therefore, an integrative exploration of the miRNA-mRNA regulatory network in denervated muscle atrophy is necessary. A total of 21 (16 upregulated and 5 downregulated) DEMs were screened out in the GSE81914 dataset. Med1, Myod1, Nfkb1, Rela, and Camta1 were predicted and verified to be significantly upregulated in denervated muscle atrophy, from which 6 key TF-miRNA relationship pairs, including Med1-mir-1949, Med1-mir-146b, Myod1-mir-29b, Nfkb1-mir-21, Rela-mir-21, and Camta1-mir-132, were obtained. 60 target genes were then predicted by submitting candidate DEMs to the miRNet database. GO and KEGG pathway enrichment analysis showed that target genes of DEMs were mainly enriched in the apoptotic process and PI3K/Akt signaling pathway. Through the PPI network construction, key modules and hub genes were obtained and potentially modulated by mir-29b, mir-132, and mir-133a. According to the qRT-PCR results, the expression of COL1A1 and Ctgf is opposite to their related miRNAs in denervated muscle atrophy. In the study, a potential miRNA-mRNA regulatory network was firstly constructed in denervated muscle atrophy, in which the mir-29b-COL1A1 and mir-133a-Ctgf pathways may provide new insights into the pathogenesis and treatment.

Tfcancer: a manually curated database of transcription factors associated with human cancers

SUMMARY

Transcription factors (TFs) are critical regulation elements and its dysregulation can lead to a variety of cancers. However, currently, there are no such online resources for large-scale collection, storage and analysis of TF-cancer associations in those cancers. To fill this gap, we present a database called TFcancer (http://lcbb.swjtu.edu.cn/tfcancer/), which contains 3136 experimentally supported associations between 364 TFs and 33 TCGA cancers by manually curating more than 1800 literature. TFcancer mainly concentrates on four aspects: TF expression, molecular alteration, regulatory relationships between TFs and target genes, and biological processes and signaling pathways of TFs in cancers. TFcancer not only provides a user-friendly interface for browsing and searching but also allows flexible data downloading and user data submitting. It is believed that TFcancer is a helpful and valuable resource for researchers who seek to understand the functions and molecular mechanisms of TFs involved in human cancers.

AVAILABILITY AND IMPLEMENTATION

The TFcancer are freely available at http://lcbb.swjtu.edu.cn/tfcancer/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

EVAtlas: a comprehensive database for ncRNA expression in human extracellular vesicles

Extracellular vesicles (EVs) packing various molecules play vital roles in intercellular communication. Non-coding RNAs (ncRNAs) are important functional molecules and biomarkers in EVs. A comprehensive investigation of ncRNAs expression in EVs under different conditions is a fundamental step for functional discovery and application of EVs. Here, we curated 2030 small RNA-seq datasets for human EVs (1506 sEV and 524 lEV) in 24 conditions and over 40 diseases. We performed a unified reads dynamic assignment algorithm (RDAA) considering mismatch and multi-mapping reads to quantify the expression profiles of seven ncRNA types (miRNA, snoRNA, piRNA, snRNA, rRNA, tRNA and Y RNA). We constructed EVAtlas (http://bioinfo.life.hust.edu.cn/EVAtlas), a comprehensive database for ncRNA expression in EVs with four functional modules: (i) browse and compare the distribution of ncRNAs in EVs from 24 conditions and eight sources (plasma, serum, saliva, urine, sperm, breast milk, primary cell and cell line); (ii) prioritize candidate ncRNAs in condition related tissues based on their expression; (iii) explore the specifically expressed ncRNAs in EVs from 24 conditions; (iv) investigate ncRNA functions, related drugs, target genes and EVs isolation methods. EVAtlas contains the most comprehensive ncRNA expression in EVs and will be a key resource in this field.

miRTargetLink 2.0-interactive miRNA target gene and target pathway networks

Which genes, gene sets or pathways are regulated by certain miRNAs? Which miRNAs regulate a particular target gene or target pathway in a certain physiological context? Answering such common research questions can be time consuming and labor intensive. Especially for researchers without computational experience, the integration of different data sources, selection of the right parameters and concise visualization can be demanding. A comprehensive analysis should be central to present adequate answers to complex biological questions. With miRTargetLink 2.0, we develop an all-in-one solution for human, mouse and rat miRNA networks. Users input in the unidirectional search mode either a single gene, gene set or gene pathway, alternatively a single miRNA, a set of miRNAs or an miRNA pathway. Moreover, genes and miRNAs can jointly be provided to the tool in the bidirectional search mode. For the selected entities, interaction graphs are generated from different data sources and dynamically presented. Connected application programming interfaces (APIs) to the tailored enrichment tools miEAA and GeneTrail facilitate downstream analysis of pathways and context-annotated categories of network nodes. MiRTargetLink 2.0 is freely accessible at https://www.ccb.uni-saarland.de/mirtargetlink2.

EnhFFL: A database of enhancer mediated feed-forward loops for human and mouse

Feed-forward loops (FFLs) are thought to be one of the most common and important classes of transcriptional network motifs involved in various diseases. Enhancers are cis-regulatory elements that positively regulate protein-coding genes or microRNAs (miRNAs) by recruiting DNA-binding transcription factors (TFs). However, a comprehensive resource to identify, store, and analyze the FFLs of typical enhancer and super-enhancer FFLs is not currently available. Here, we present EnhFFL, an online database to provide a data resource for users to browse and search typical enhancer and super-enhancer FFLs. The current database covers 46 280/7000 TF-enhancer-miRNA FFLs, 9997/236 enhancer-miRNA-gene FFLs, 3 561 164/3 193 182 TF-enhancer-gene FFLs, and 1259/235 TF-enhancer feed-back loops (FBLs) across 91 tissues/cell lines of human and mouse, respectively. Users can browse loops by selecting species, types of tissue/cell line, and types of FFLs. EnhFFL supports searching elements including name/ID, genomic location, and the conservation of miRNA target genes. We also developed tools for users to screen customized FFLs using the threshold of q value as well as the confidence score of miRNA target genes. Disease and functional enrichment analysis showed that master miRNAs that are widely engaged in FFLs including TF-enhancer-miRNAs and enhancer-miRNA-genes are significantly involved in tumorigenesis. Database URL:http://lcbb.swjtu.edu.cn/EnhFFL/.

In silico Analysis of Polymorphisms in microRNAs Deregulated in Alzheimer Disease

BACKGROUND

Alzheimer's disease (AD) is a degenerative condition characterized by progressive cognitive impairment and dementia. Findings have revolutionized current knowledge of miRNA in the neurological conditions. Two regulatory mechanisms determine the level of mature miRNA expression; one is miRNA precursor processing, and the other is gene expression regulation by transcription factors. This study is allocated to the in-silico investigation of miRNA's SNPs and their effect on other cell mechanisms.

METHODS

We used databases which annotate the functional effect of SNPs on mRNA-miRNA and miRNA-RBP interaction. Also, we investigated SNPs which are located on the promoter or UTR region.

RESULTS

miRNA SNP3.0 database indicated several SNPs in miR-339 and miR-34a in the upstream and downstream of pre-miRNA and mature miRNAs. While, for some miRNAs miR-124, and miR-125, no polymorphism was observed, and also miR-101 with ΔG -3.1 and mir-328 with ΔG 5.8 had the highest and lowest potencies to produce mature microRNA. SNP2TFBS web-server presented several SNPs which altered the Transcription Factor Binding Sites (TFBS) or generated novel TFBS in the promoter regions of related miRNA. At last, RBP-Var database provided a list of SNPs which alter miRNA-RBP interaction pattern and can also influence other miRNAs' expression.

DISCUSSION

The results indicated that SNPs microRNA affects both miRNA function and miRNA expression. Our study expands molecular insight into how SNPs in different parts of miRNA, including the regulatory (promoter), the precursor (pre-miRNA), functional regions (seed region of mature miRNA), and RBP-binding motifs, which theoretically may be correlated to the Alzheimer's disease.

The expression and regulation of HOX genes and membrane proteins among different cytogenetic groups of acute myeloid leukemia

BACKGROUND

The cytogenetic aberrations were considered as markers for diagnosis and prognosis in acute myeloid leukemia (AML), while the expression and regulation under different cytogenetic groups remain to be fully elucidated.

METHODS

In this paper, for favorable, poor, and cytogenetically normal groups of AML patients, we performed comprehensive bioinformatics analyses including identifying differentially expressed genes (DEGs) and microRNAs (miRNAs) among them, functional enrichment and regulatory networks.

RESULTS

We found that DEGs were enriched in membrane-related processes. Eleven genes and two miRNAs were significantly differentially expressed among these three AML groups. In survival analysis, membrane-related genes and several miRNAs were significant on prognostic outcome. Notably, six HOXA and three HOXB genes were significantly in low expression and high methylation in AML with favorable cytogenetics. Meanwhile, the miRNA-HOX gene co-regulatory networks revealed that HOXA5 was a hub node and regulated an AML oncogene SPARC.

CONCLUSION

Our work may provide novel insights to the molecular characteristics and classification between AML with different cytogenetics.