Analysis of miRNA, mRNA, and TF interactions through network-based methods

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.

Systems biology based miRNA-mRNA expression pattern analysis of Emodin in breast cancer cell lines

Breast cancer has been among the most prominent cancers with high mortality. Currently most of the offered therapeutics are toxic; hence, less toxic therapeutic intervention is required. Here, we studied the molecular mechanisms of the effect of a phytoestrogen Emodin on estrogen receptor positive MCF-7 and negative MDA-MB-231 cells by carrying out a comprehensive network assessment. Differentially expressed microRNAs along with their previously identified differentially expressed mRNAs were analyzed through microarrays by using integrative systems biology approach. For each cell line miRNA-target gene networks were built, gene ontology and pathway enrichment analyses were performed, enrichment maps were constructed and the potential key genes, miRNAs and miRNA-gene interactions were studied.

Molecular Biomarker Identification Using a Network-Based Bioinformatics Approach That Links COVID-19 With Smoking

The COVID-19 coronavirus, which primarily affects the lungs, is the source of the disease known as SARS-CoV-2. According to "Smoking and COVID-19: a scoping review," about 32% of smokers had a severe case of COVID-19 pneumonia at their admission time and 15% of non-smokers had this case of COVID-19 pneumonia. We were able to determine which genes were expressed differently in each group by comparing the expression of gene transcriptomic datasets of COVID-19 patients, smokers, and healthy controls. In all, 37 dysregulated genes are common in COVID-19 patients and smokers, according to our analysis. We have applied all important methods namely protein-protein interaction, hub-protein interaction, drug-protein interaction, tf-gene interaction, and gene-MiRNA interaction of bioinformatics to analyze to understand deeply the connection between both smoking and COVID-19 severity. We have also analyzed Pathways and Gene Ontology where 5 significant signaling pathways were validated with previous literature. Also, we verified 7 hub-proteins, and finally, we validated a total of 7 drugs with the previous study.

miRNAs and lncRNAs as Novel Therapeutic Targets to Improve Cancer Immunotherapy

Simple Summary

Cancer onset and progression are promoted by high deregulation of the immune system. Recently, major advances in molecular and clinical cancer immunology have been achieved, offering new agents for the treatment of common tumors, often with astonishing benefits in terms of prolonged survival and even cure. Unfortunately, most tumors are still resistant to current immune therapy approaches, and basic knowledge of the resistance mechanisms is eagerly awaited. We focused our attention on noncoding RNAs, a class of RNA that regulates many biological processes by targeting selectively crucial molecular pathways and that, recently, had their role in cancer cell immune escape and modulation of the tumor microenvironment identified, suggesting their function as promising immunotherapeutic targets. In this scenario, we point out that noncoding RNAs are progressively emerging as immunoregulators, and we depict the current information on the complex network involving the immune system and noncoding RNAs and the promising therapeutic options under investigation. Novel opportunities are emerging from noncoding-RNAs for the treatment of immune-refractory tumors.

Abstract

Immunotherapy is presently one of the most promising areas of investigation and development for the treatment of cancer. While immune checkpoint-blocking monoclonal antibodies and chimeric antigen receptor (CAR) T-cell-based therapy have recently provided in some cases valuable therapeutic options, the goal of cure has not yet been achieved for most malignancies and more efforts are urgently needed. Noncoding RNAs (ncRNA), including microRNAs (miRNAs) and long noncoding RNAs (lncRNAs), regulate several biological processes via selective targeting of crucial molecular signaling pathways. Recently, the key roles of miRNA and lncRNAs as regulators of the immune-response in cancer have progressively emerged, since they may act (i) by shaping the intrinsic tumor cell and microenvironment (TME) properties; (ii) by regulating angiogenesis, immune-escape, epithelial-to-mesenchymal transition, invasion, and drug resistance; and (iii) by acting as potential biomarkers for prognostic assessment and prediction of response to immunotherapy. In this review, we provide an overview on the role of ncRNAs in modulating the immune response and the TME. We discuss the potential use of ncRNAs as potential biomarkers or as targets for development or clinical translation of new therapeutics. Finally, we discuss the potential combinatory approaches based on ncRNA targeting agents and tumor immune-checkpoint inhibitor antibodies or CAR-T for the experimental treatment of human cancer.

Distinct Expression of miR-378 in Nonsyndromic Cleft Lip and/or Cleft Palate: A Cogitation of Skewed Sex Ratio in Prevalence

OBJECTIVE

Nonsyndromic cleft lip and/or cleft palate (NSCL/P) is an isolated phenotype of orofacial clefts with skewed sex ratio in prevalence. This study aims to identify differentially expressed genes (DEGs) and microRNAs (DEMs) of NSCL/P by integrated bioinformatics analysis, revealing mechanisms for sexual dimorphism in prevalence.

MATERIALS AND METHODS

First, we downloaded the expression profile data from Gene Expression Omnibus database to identify DEGs and DEMs. Second, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses performed DEGs' functions. Then, clustered DEGs were identified through protein-protein interaction networks. Combining clustered DEGs with key genes searched in GeneCards enlarged NSCL/P-related genes. Moreover, the genes were linked by transcription factors (TFs). Subsequently, connected by the above TFs, DEMs and genes were used to establish the miRNA-TF-messenger RNA (mRNA) regulatory networks.

RESULTS

The DEGs in sex-ignored group, female-only group, and male-only group were obtained, respectively. Among the DEMs, miR-378 was downregulated in females but upregulated in males. In female-only group, the miRNA-TF-mRNA regulatory networks showed miR-378-SP1-POLE2/CDK6/EZR regulatory axis was found to be key candidates of NSCL/P.

CONCLUSIONS

Our findings suggest that different expression of miR-378 is consistent with the skewed sex ratio in the prevalence of NSCL/P.

TFmiR2: constructing and analyzing disease-, tissue- and process-specific transcription factor and microRNA co-regulatory networks

SUMMARY

TFmiR2 is a freely available web server for constructing and analyzing integrated transcription factor (TF) and microRNA (miRNA) co-regulatory networks for human and mouse. TFmiR2 generates tissue- and biological process-specific networks for the set of deregulated genes and miRNAs provided by the user. Furthermore, the service can now identify key driver genes and miRNAs in the constructed networks by utilizing the graph theoretical concept of a minimum connected dominating set. These putative key players as well as the newly implemented four-node TF-miRNA motifs yield novel insights that may assist in developing new therapeutic approaches.

AVAILABILITY AND IMPLEMENTATION

The TFmiR2 web server is available at http://service.bioinformatik.uni-saarland.de/tfmir2.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

L-HetNetAligner: A novel algorithm for Local Alignment of Heterogeneous Biological Networks

Networks are largely used for modelling and analysing a wide range of biological data. As a consequence, many different research efforts have resulted in the introduction of a large number of algorithms for analysis and comparison of networks. Many of these algorithms can deal with networks with a single class of nodes and edges, also referred to as homogeneous networks. Recently, many different approaches tried to integrate into a single model the interplay of different molecules. A possible formalism to model such a scenario comes from node/edge coloured networks (also known as heterogeneous networks) implemented as node/ edge-coloured graphs. Therefore, the need for the introduction of algorithms able to compare heterogeneous networks arises. We here focus on the local comparison of heterogeneous networks, and we formulate it as a network alignment problem. To the best of our knowledge, the local alignment of heterogeneous networks has not been explored in the past. We here propose L-HetNetAligner a novel algorithm that receives as input two heterogeneous networks (node-coloured graphs) and builds a local alignment of them. We also implemented and tested our algorithm. Our results confirm that our method builds high-quality alignments. The following website *contains Supplementary File 1 material and the code.

Integrative Transcriptomics Reveals Sexually Dimorphic Control of the Cholinergic/Neurokine Interface in Schizophrenia and Bipolar Disorder

RNA sequencing analyses are often limited to identifying lowest p value transcripts, which does not address polygenic phenomena. To overcome this limitation, we developed an integrative approach that combines large-scale transcriptomic meta-analysis of patient brain tissues with single-cell sequencing data of CNS neurons, short RNA sequencing of human male- and female-originating cell lines, and connectomics of transcription factor and microRNA interactions with perturbed transcripts. We used this pipeline to analyze cortical transcripts of schizophrenia and bipolar disorder patients. Although these pathologies show massive transcriptional parallels, their clinically well-known sexual dimorphisms remain unexplained. Our method reveals the differences between afflicted men and women and identifies disease-affected pathways of cholinergic transmission and gp130-family neurokine controllers of immune function interlinked by microRNAs. This approach may open additional perspectives for seeking biomarkers and therapeutic targets in other transmitter systems and diseases.

Gene Expression Profiles and microRNA Regulation Networks in Tiller Primordia, Stem Tips, and Young Spikes of Wheat Guomai 301

Tillering and spike differentiation are two key events for wheat (Triticum aestivum L.). A study on the transcriptomes and microRNA group profiles of wheat at the two key developmental stages will bring insight into the molecular regulation mechanisms. Guomai 301 is a representative excellent new high yield wheat cultivar in the Henan province in China. The transcriptomes and microRNA (miRNA) groups of tiller primordia (TPs), stem tips (STs), and young spikes (YSs) in Guomai 301 were compared to each other. A total of 1741 tillering specifically expressed and 281 early spikes differentiating specifically expressed differentially expressed genes (DEGs) were identified. Six major expression profile clusters of tissue-specific DEGs for the three tissues were classified by gene co-expression analysis using K-means cluster. The ribosome (ko03010), photosynthesis-antenna proteins (ko00196), and plant hormone signal transduction (ko04075) were the main metabolic pathways in TPs, STs, and YSs, respectively. Similarly, 67 TP specifically expressed and 19 YS specifically expressed differentially expressed miRNAs were identified, 65 of them were novel. The roles of 3 well known miRNAs, tae-miR156, tae-miR164, and tae-miR167a, in post-transcriptional regulation were similar to that of other researches. There were 651 significant negative miRNA-mRNA interaction pairs in TPs and YSs, involving 63 differentially expressed miRNAs (fold change > 4) and 416 differentially expressed mRNAs. Among them 12 key known miRNAs and 16 novel miRNAs were further analyzed, and miRNA-mRNA regulatory networks during tillering and early spike differentiating were established.

From Single Level Analysis to Multi-Omics Integrative Approaches: A Powerful Strategy towards the Precision Oncology

Integration of multi-omics data from different molecular levels with clinical data, as well as epidemiologic risk factors, represents an accurate and promising methodology to understand the complexity of biological systems of human diseases, including cancer. By the extensive use of novel technologic platforms, a large number of multidimensional data can be derived from analysis of health and disease systems. Comprehensive analysis of multi-omics data in an integrated framework, which includes cumulative effects in the context of biological pathways, is therefore eagerly awaited. This strategy could allow the identification of pathway-addiction of cancer cells that may be amenable to therapeutic intervention. However, translation into clinical settings requires an optimized integration of omics data with clinical vision to fully exploit precision cancer medicine. We will discuss the available technical approach and more recent developments in the specific field.

TLHNMDA: Triple Layer Heterogeneous Network Based Inference for MiRNA-Disease Association Prediction

In recent years, microRNAs (miRNAs) have been confirmed to be involved in many important biological processes and associated with various kinds of human complex diseases. Therefore, predicting potential associations between miRNAs and diseases with the huge number of verified heterogeneous biological datasets will provide a new perspective for disease therapy. In this article, we developed a novel computational model of Triple Layer Heterogeneous Network based inference for MiRNA-Disease Association prediction (TLHNMDA) by using the experimentally verified miRNA-disease associations, miRNA-long noncoding RNA (lncRNA) interactions, miRNA function similarity information, disease semantic similarity information and Gaussian interaction profile kernel similarity for lncRNAs into an triple layer heterogeneous network to predict new miRNA-disease associations. As a result, the AUCs of TLHNMDA are 0.8795 and 0.8795 ± 0.0010 based on leave-one-out cross validation (LOOCV) and 5-fold cross validation, respectively. Furthermore, TLHNMDA was implemented on three complex human diseases to evaluate predictive ability. As a result, 84% (kidney neoplasms), 78% (lymphoma) and 76% (prostate neoplasms) of top 50 predicted miRNAs for the three complex diseases can be verified by biological experiments. In addition, based on the HMDD v1.0 database, 98% of top 50 potential esophageal neoplasms-associated miRNAs were confirmed by experimental reports. It is expected that TLHNMDA could be a useful model to predict potential miRNA-disease associations with high prediction accuracy and stability.

A causal mediation model of ischemia reperfusion injury in the retina

The goal of this study is to develop a model that explains the relationship between microRNAs, transcription factors, and their co-target genes. This relationship was previously reported in gene regulatory loops associated with 24 hour (24h) and 7 day (7d) time periods following ischemia-reperfusion injury in a rat's retina. Using a model system of retinal ischemia-reperfusion injury, we propose that microRNAs first influence transcription factors, which in turn act as mediators to influence transcription of genes via triadic regulatory loops. Analysis of the relative contributions of direct and indirect regulatory influences on genes revealed that a substantial fraction of the regulatory loops (69% for 24 hours and 77% for 7 days) could be explained by causal mediation. Over 40% of the mediated loops in both time points were regulated by transcription factors only, while about 20% of the loops were regulated entirely by microRNAs. The remaining fractions of the mediated regulatory loops were cooperatively mediated by both microRNAs and transcription factors. The results from these analyses were supported by the patterns of expression of the genes, transcription factors, and microRNAs involved in the mediated loops in both post-ischemic time points. Additionally, network motif detection for the mediated loops showed a handful of time specific motifs related to ischemia-reperfusion injury in a rat's retina. In summary, the effects of microRNAs on genes are mediated, in large part, via transcription factors.

Systems biology study of transcriptional and post-transcriptional co-regulatory network sheds light on key regulators involved in important biological processes in Citrus sinensis

Transcriptional and post-transcriptional regulators including transcription regulator, transcription factor and miRNA are the main endogenous molecular elements which control complex cellular mechanisms such as development, growth and response to biotic and abiotic stresses in a coordinated manner in plants. Utilizing the most recent information on such relationships in a plant species, obtained from high-throughput experimental technologies and advanced computational tools, we can reconstruct its co-regulatory network which consequently sheds light on key regulators involved in its important biological processes. In this article, combined systems biology approaches such as mining the literatures, various databases and network reconstruction, analysis, and visualization tools were employed to infer and visualize the coregulatory relationships between miRNAs and transcriptional regulators in Citrus sinensis. Using computationally and experimentally verified miRNA-target interactions and constructed co-expression networks on array-based data, 10 coregulatory networks and 10 corresponding subgraphs include FFL motifs were obtained for 10 distinct tissues/conditions. Then PPI subnetworks were extracted for transcripts/genes included in mentioned subgraphs in order to the functional analysis of extracted coregulatory circuits. These proposed coregulatory connections shed light on precisely identifying C. sinensis metabolic pathways key switches, which are demanded for ultimate goals such as genome editing.

[Molecular biological research progress of non-coding RNAs modulating osteoarthritis]

Objective

To summarize the molecular biological research progress of non-coding RNAs modulating osteoarthritis (OA), and provide a reference basis for biological study and clinical treatment of OA.

Methods

Recent domestic and foreign related literature about the regulation of OA pathological process by non-coding RNAs was widely reviewed.

Results

Non-coding RNAs can be divided into three types based on the length of RNA. A lot of non-coding RNAs participating in OA pathological process are screened out by high throughput sequencing technology and microarray technology, and it is verified that these non-coding RNAs involve in the regulation of OA by RT-PCR. The mechanism of OA mediated target is clarified by knocking-down and overexpressing of the most prominent expressed non-coding RNAs in OA. There are the complicated gene expressed network topology in non-coding RNAs, and between non-coding RNAs and coding RNAs. It provides a basis for clearing the effect of gene structure and function, and finding the definite therapeutic target of OA.

Conclusion

There is preliminary study on molecular biological mechanism of non-coding RNAs mediating OA, but the key structure or sequence of non-coding RNAs, formation and interaction of effecting composite structure about mediating OA are unknown, and it needs further study.