miRsig: a consensus-based network inference methodology to identify pan-cancer miRNA-miRNA interaction signatures

GramSeq-DTA: A Grammar-Based Drug-Target Affinity Prediction Approach Fusing Gene Expression Information

Drug-target affinity (DTA) prediction is a critical aspect of drug discovery. The meaningful representation of drugs and targets is crucial for accurate prediction. Using 1D string-based representations for drugs and targets is a common approach that has demonstrated good results in drug-target affinity prediction. However, these approach lacks information on the relative position of the atoms and bonds. To address this limitation, graph-based representations have been used to some extent. However, solely considering the structural aspect of drugs and targets may be insufficient for accurate DTA prediction. Integrating the functional aspect of these drugs at the genetic level can enhance the prediction capability of the models. To fill this gap, we propose GramSeq-DTA, which integrates chemical perturbation information with the structural information of drugs and targets. We applied a Grammar Variational Autoencoder (GVAE) for drug feature extraction and utilized two different approaches for protein feature extraction as follows: a Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN). The chemical perturbation data are obtained from the L1000 project, which provides information on the up-regulation and down-regulation of genes caused by selected drugs. This chemical perturbation information is processed, and a compact dataset is prepared, serving as the functional feature set of the drugs. By integrating the drug, gene, and target features in the model, our approach outperforms the current state-of-the-art DTA prediction models when validated on widely used DTA datasets (BindingDB, Davis, and KIBA). This work provides a novel and practical approach to DTA prediction by merging the structural and functional aspects of biological entities, and it encourages further research in multi-modal DTA prediction.

Heterogeneous Clustering of Multiomics Data for Breast Cancer Subgroup Classification and Detection

The rapid growth of diverse -omics datasets has made multiomics data integration crucial in cancer research. This study adapts the expectation-maximization routine for the joint latent variable modeling of multiomics patient profiles. By combining this approach with traditional biological feature selection methods, this study optimizes latent distribution, enabling efficient patient clustering from well-studied cancer types with reduced computational expense. The proposed optimization subroutines enhance survival analysis and improve runtime performance. This article presents a framework for distinguishing cancer subtypes and identifying potential biomarkers for breast cancer. Key insights into individual subtype expression and function were obtained through differentially expressed gene analysis and pathway enrichment for BRCA patients. The analysis compared 302 tumor samples to 113 normal samples across 60,660 genes. The highly upregulated gene COL10A1, promoting breast cancer progression and poor prognosis, and the consistently downregulated gene CDG300LG, linked to brain metastatic cancer, were identified. Pathway enrichment analysis revealed similarities in cellular matrix organization pathways across subtypes, with notable differences in functions like cell proliferation regulation and endocytosis by host cells. GO Semantic Similarity analysis quantified gene relationships in each subtype, identifying potential biomarkers like MATN2, similar to COL10A1. These insights suggest deeper relationships within clusters and highlight personalized treatment potential based on subtypes.

CORTADO: Hill Climbing Optimization for Cell-Type Specific Marker Gene Discovery

The advent of single-cell RNA sequencing (scRNA-seq) has greatly enhanced our ability to explore cellular heterogeneity with high resolution. Identifying subpopulations of cells and their associated molecular markers is crucial in understanding their distinct roles in tissues. To address the challenges in marker gene selection, we introduce CORTADO, a computational framework based on hill-climbing optimization for the efficient discovery of cell-type-specific markers. CORTADO optimizes three critical properties: differential expression in the clusters of interest, distinctiveness in gene expression profiles to minimize redundancy, and sparseness to ensure a concise and biologically meaningful marker set. Unlike traditional methods that rely on ranking genes by p-values, CORTADO incorporates both differential expression metrics and penalties for overlapping expression profiles, ensuring that each selected marker uniquely represents its cluster while maintaining biological relevance. Its flexibility supports both constrained and unconstrained marker selection, allowing users to specify the number of markers to identify, making it adaptable to diverse analytical needs and scalable to datasets with varying complexities. To validate its performance, we apply CORTADO to several datasets, including the DLPFC 151507 dataset, the Zeisel mouse brain dataset, and a peripheral blood mononuclear cell dataset. Through enrichment analysis and examination of spatial localization-based expression, we demonstrate the robustness of CORTADO in identifying biologically relevant and non-redundant markers in complex datasets. CORTADO provides an efficient and scalable solution for cell-type marker discovery, offering improved sensitivity and specificity compared to existing methods.

COFFEE: consensus single cell-type specific inference for gene regulatory networks

The inference of gene regulatory networks (GRNs) is crucial to understanding the regulatory mechanisms that govern biological processes. GRNs may be represented as edges in a graph, and hence, it have been inferred computationally for scRNA-seq data. A wisdom of crowds approach to integrate edges from several GRNs to create one composite GRN has demonstrated improved performance when compared with individual algorithm implementations on bulk RNA-seq and microarray data. In an effort to extend this approach to scRNA-seq data, we present COFFEE (COnsensus single cell-type speciFic inFerence for gEnE regulatory networks), a Borda voting-based consensus algorithm that integrates information from 10 established GRN inference methods. We conclude that COFFEE has improved performance across synthetic, curated, and experimental datasets when compared with baseline methods. Additionally, we show that a modified version of COFFEE can be leveraged to improve performance on newer cell-type specific GRN inference methods. Overall, our results demonstrate that consensus-based methods with pertinent modifications continue to be valuable for GRN inference at the single cell level. While COFFEE is benchmarked on 10 algorithms, it is a flexible strategy that can incorporate any set of GRN inference algorithms according to user preference. A Python implementation of COFFEE may be found on GitHub: https://github.com/lodimk2/coffee.

CHAI: consensus clustering through similarity matrix integration for cell-type identification

Several methods have been developed to computationally predict cell-types for single cell RNA sequencing (scRNAseq) data. As methods are developed, a common problem for investigators has been identifying the best method they should apply to their specific use-case. To address this challenge, we present CHAI (consensus Clustering tHrough similArIty matrix integratIon for single cell-type identification), a wisdom of crowds approach for scRNAseq clustering. CHAI presents two competing methods which aggregate the clustering results from seven state-of-the-art clustering methods: CHAI-AvgSim and CHAI-SNF. CHAI-AvgSim and CHAI-SNF demonstrate superior performance across several benchmarking datasets. Furthermore, both CHAI methods outperform the most recent consensus clustering method, SAME-clustering. We demonstrate CHAI's practical use case by identifying a leader tumor cell cluster enriched with CDH3. CHAI provides a platform for multiomic integration, and we demonstrate CHAI-SNF to have improved performance when including spatial transcriptomics data. CHAI overcomes previous limitations by incorporating the most recent and top performing scRNAseq clustering algorithms into the aggregation framework. It is also an intuitive and easily customizable R package where users may add their own clustering methods to the pipeline, or down-select just the ones they want to use for the clustering aggregation. This ensures that as more advanced clustering algorithms are developed, CHAI will remain useful to the community as a generalized framework. CHAI is available as an open source R package on GitHub: https://github.com/lodimk2/chai.

Neuron enriched extracellular vesicles' MicroRNA expression profiles as a marker of early life alcohol consumption

Alcohol consumption may impact and shape brain development through perturbed biological pathways and impaired molecular functions. We investigated the relationship between alcohol consumption rates and neuron-enriched extracellular vesicles' (EVs') microRNA (miRNA) expression to better understand the impact of alcohol use on early life brain biology. Neuron-enriched EVs' miRNA expression was measured from plasma samples collected from young people using a commercially available microarray platform while alcohol consumption was measured using the Alcohol Use Disorders Identification Test. Linear regression and network analyses were used to identify significantly differentially expressed miRNAs and to characterize the implicated biological pathways, respectively. Compared to alcohol naïve controls, young people reporting high alcohol consumption exhibited significantly higher expression of three neuron-enriched EVs' miRNAs including miR-30a-5p, miR-194-5p, and miR-339-3p, although only miR-30a-5p and miR-194-5p survived multiple test correction. The miRNA-miRNA interaction network inferred by a network inference algorithm did not detect any differentially expressed miRNAs with a high cutoff on edge scores. However, when the cutoff of the algorithm was reduced, five miRNAs were identified as interacting with miR-194-5p and miR-30a-5p. These seven miRNAs were associated with 25 biological functions; miR-194-5p was the most highly connected node and was highly correlated with the other miRNAs in this cluster. Our observed association between neuron-enriched EVs' miRNAs and alcohol consumption concurs with results from experimental animal models of alcohol use and suggests that high rates of alcohol consumption during the adolescent/young adult years may impact brain functioning and development by modulating miRNA expression.

CHAI: Consensus Clustering Through Similarity Matrix Integration for Cell-Type Identification

Several methods have been developed to computationally predict cell-types for single cell RNA sequencing (scRNAseq) data. As methods are developed, a common problem for investigators has been identifying the best method they should apply to their specific use-case. To address this challenge, we present CHAI (consensus Clustering tHrough similArIty matrix integratIon for single cell type identification), a wisdom of crowds approach for scRNAseq clustering. CHAI presents two competing methods which aggregate the clustering results from seven state of the art clustering methods: CHAI-AvgSim and CHAI-SNF. Both methods demonstrate improved performance on a diverse selection of benchmarking datasets, besides also outperforming a previous consensus clustering method. We demonstrate CHAI's practical use case by identifying a leader tumor cell cluster enriched with CDH3. CHAI provides a platform for multiomic integration, and we demonstrate CHAI-SNF to have improved performance when including spatial transcriptomics data. CHAI is intuitive and easily customizable; it provides a way for users to add their own clustering methods to the pipeline, or down-select just the ones they want to use for the clustering aggregation. CHAI is available as an open source R package on GitHub: https://github.com/lodimk2/chai.

COFFEE: Consensus Single Cell-Type Specific Inference for Gene Regulatory Networks

The inference of gene regulatory networks (GRNs) is crucial to understanding the regulatory mechanisms that govern biological processes. GRNs may be represented as edges in a graph, and hence have been inferred computationally for scRNA-seq data. A wisdom of crowds approach to integrate edges from several GRNs to create one composite GRN has demonstrated improved performance when compared to individual algorithm implementations on bulk RNA-seq and microarray data. In an effort to extend this approach to scRNA-seq data, we present COFFEE (COnsensus single cell-type speciFic inFerence for gEnE regulatory networks), a Borda voting based consensus algorithm that integrates information from 10 established GRN inference methods. We conclude that COFFEE has improved performance across synthetic, curated and experimental datasets when compared to baseline methods. Additionally, we show that a modified version of COFFEE can be leveraged to improve performance on newer cell-type specific GRN inference methods. Overall, our results demonstrate that consensus based methods with pertinent modifications continue to be valuable for GRN inference at the single cell level.

Examining indicators of complex network vulnerability across diverse attack scenarios

Complex networks capture the structure, dynamics, and relationships among entities in real-world networked systems, encompassing domains like communications, society, chemistry, biology, ecology, politics, etc. Analysis of complex networks lends insight into the critical nodes, key pathways, and potential points of failure that may impact the connectivity and operational integrity of the underlying system. In this work, we investigate the topological properties or indicators, such as shortest path length, modularity, efficiency, graph density, diameter, assortativity, and clustering coefficient, that determine the vulnerability to (or robustness against) diverse attack scenarios. Specifically, we examine how node- and link-based network growth or depletion based on specific attack criteria affect their robustness gauged in terms of the largest connected component (LCC) size and diameter. We employ partial least squares discriminant analysis to quantify the individual contribution of the indicators on LCC preservation while accounting for the collinearity stemming from the possible correlation between indicators. Our analysis of 14 complex network datasets and 5 attack models invariably reveals high modularity and disassortativity to be prime indicators of vulnerability, corroborating prior works that report disassortative modular networks to be particularly susceptible to targeted attacks. We conclude with a discussion as well as an illustrative example of the application of this work in fending off strategic attacks on critical infrastructures through models that adaptively and distributively achieve network robustness.

Network pharmacology-based strategy to investigate the effect and mechanism of α-solanine against glioma

BACKGROUND

An anti-tumour activity has been demonstrated for α-solanine, a bioactive compound extracted from the traditional Chinese herb Solanum nigrum L. However, its efficacy in the treatment of gliomas and the underlying mechanisms remain unclear. The aim of this study was to investigate the inhibitory effects of α-solanine on glioma and elucidate its mechanisms and targets using network pharmacology, molecular docking, and molecular biology experiments.

METHODS

Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) was utilized to predict the potential targets of α-solanine. GeneCards was used to gather glioma-related targets, and the STRING online database was used to analyze protein-protein interaction (PPI) networks for the shared targets. Hub genes were identified from the resulting PPI network and further investigated using Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Additionally, prognostic and gene set enrichment analyses (GSEA) were carried out to identify potential therapeutic targets and their underlying mechanisms of action in relation to the prognosis of gliomas. In vitro experiments were conducted to verify the findings from the network pharmacology analysis.

RESULTS

A total of 289 α-solanine targets and 1149 glioma-related targets were screened, of which 78 were common targets. 11 hub genes were obtained, including SRC, HRAS, HSP90AA1, IGF1, MAPK1, MAPK14, KDR, STAT1, JAK2, MAP2K1, and IGF1R. The GO and KEGG pathway analyses unveiled that α-solanine was strongly associated with several signaling pathways, including positive regulation of MAP kinase activity and PI3K-Akt. Moreover, α-solanine (10 µM and 15 µM) inhibited the proliferation and migration but promoted the apoptosis of glioma cells. Finally, STAT1 was identified as a potential mediator of the effect of α-solanine on glioma prognosis.

CONCLUSION

α-Solanine can inhibit the proliferation and migration of gliomas by regulating multiple targets and signalling pathways. These findings lay the foundation for the creation of innovative clinical anti-glioma agents.

Neuron Enriched Exosomal MicroRNA Expression Profiles as a Marker of Early Life Alcohol Consumption

Background

Alcohol consumption may impact and shape brain development through perturbed biological pathways and impaired molecular functions. We investigated the relationship between alcohol consumption rates and neuron-enriched exosomal microRNA (miRNA) expression to better understand the impact of alcohol use on early life brain biology.

Methods

Neuron-enriched exosomal miRNA expression was measured from plasma samples collected from young people using a commercially available microarray platform while alcohol consumption was measured using the Alcohol Use Disorders Identification Test. Linear regression and network analyses were used to identify significantly differentially expressed miRNAs and to characterize the implicated biological pathways, respectively.

Results

Compared to alcohol naïve controls, young people reporting high alcohol consumption exhibited significantly higher expression of four neuron-enriched exosomal miRNAs including miR-30a-5p, miR-194-5p, and miR-339-3p, although only miR-30a-5p and miR-194-5p survived multiple test correction. The miRNA-miRNA interaction network inferred by a network inference algorithm did not detect any differentially expressed miRNAs with a high cutoff on edge scores. However, when the cutoff of the algorithm was reduced, five miRNAs were identified as interacting with miR-194-5p and miR-30a-5p. These seven miRNAs were associated with 25 biological functions; miR-194-5p was the most highly connected node and was highly correlated with the other miRNAs in this cluster.

Conclusions

Our observed association between neuron-enriched exosomal miRNAs and alcohol consumption concurs with results from experimental animal models of alcohol use and suggests that high rates of alcohol consumption during the adolescent/young adult years may impact brain functioning and development by modulating miRNA expression.

Non-coding RNAs in human health and disease: potential function as biomarkers and therapeutic targets

Human diseases have been a critical threat from the beginning of human history. Knowing the origin, course of action and treatment of any disease state is essential. A microscopic approach to the molecular field is a more coherent and accurate way to explore the mechanism, progression, and therapy with the introduction and evolution of technology than a macroscopic approach. Non-coding RNAs (ncRNAs) play increasingly important roles in detecting, developing, and treating all abnormalities related to physiology, pathology, genetics, epigenetics, cancer, and developmental diseases. Noncoding RNAs are becoming increasingly crucial as powerful, multipurpose regulators of all biological processes. Parallel to this, a rising amount of scientific information has revealed links between abnormal noncoding RNA expression and human disorders. Numerous non-coding transcripts with unknown functions have been found in addition to advancements in RNA-sequencing methods. Non-coding linear RNAs come in a variety of forms, including circular RNAs with a continuous closed loop (circRNA), long non-coding RNAs (lncRNA), and microRNAs (miRNA). This comprises specific information on their biogenesis, mode of action, physiological function, and significance concerning disease (such as cancer or cardiovascular diseases and others). This study review focuses on non-coding RNA as specific biomarkers and novel therapeutic targets.

pPe Op inhibits HGC-27 cell proliferation, migration and invasion by upregulating miR-30b-5p and down-regulating the Rac1/Cdc42 pathway

Gastric cancer is the fifth most frequently occurring and the fourth most lethal malignant cancer worldwide. A bioactive protein (pPe Op) from Omphalia lapidescens exhibits significant inhibitory effects on gastric cancer cells. miRNA deep sequencing analysis shows that miR-30b-5p is significantly upregulated in HGC-27 cells treated with pPe Op. Verification results show that the expression level of miR-30b-5p is significantly increased in HGC-27 cells after pPe Op treatment. Additionally, miR-30b-5p is significantly downregulated in clinical gastric cancer tissues compared to that in adjacent normal tissues. Following pPe Op treatment and/or transfection with miR-30b-5p mimic, the proliferation, migration, and invasion of HGC-27 cells are significantly impaired. Immunofluorescence microscopy shows that pPe Op and/or miR-30b-5p destroy(s) microfilaments and microstructures and inhibit(s) the formation of pseudopodia. Bioinformatics analysis, dual-luciferase reporter assay, and western blot analysis confirm that miR-30b-5p downregulates Rac1/Cdc42 expression and activation by targeting RAB22A. Available data indicate that miR-30b-5p plays an anti-gastric cancer role in mediating pPe Op. pPe Op upregulates miR-30b-5p expression, which in turn inhibits RAB22A expression, resulting in a reduction in the expression and activation of Rac1 and Cdc42 and their downstream targets, thus destroying the cytoskeletal structure and inhibiting the proliferation, migration, and invasion of cancer cells.

Identification of miRNA-mRNA-TFs regulatory network and crucial pathways involved in asthma through advanced systems biology approaches

Asthma is a life-threatening and chronic inflammatory lung disease that is posing a true global health challenge. The genetic basis of the disease is fairly well examined. However, the molecular crosstalk between microRNAs (miRNAs), target genes, and transcription factors (TFs) networks and their contribution to disease pathogenesis and progression is not well explored. Therefore, this study was aimed at dissecting the molecular network between mRNAs, miRNAs, and TFs using robust computational biology approaches. The transcriptomic data of bronchial epithelial cells of severe asthma patients and healthy controls was studied by different systems biology approaches like differentially expressed gene detection, functional enrichment, miRNA-target gene pairing, and mRNA-miRNA-TF molecular networking. We detected the differential expression of 1703 (673 up-and 1030 down-regulated) genes and 71 (41 up-and 30 down-regulated) miRNAs in the bronchial epithelial cells of asthma patients. The DEGs were found to be enriched in key pathways like IL-17 signaling (KEGG: 04657), Th1 and Th2 cell differentiation (KEGG: 04658), and the Th17 cell differentiation (KEGG: 04659) (p-values = 0.001). The results from miRNAs-target gene pairs-transcription factors (TFs) have detected the key roles of 3 miRs (miR-181a-2-3p; miR-203a-3p; miR-335-5p), 6 TFs (TFAM, FOXO1, GFI1, IRF2, SOX9, and HLF) and 32 miRNA target genes in eliciting autoimmune reactions in bronchial epithelial cells of the respiratory tract. Through systemic implementation of comprehensive system biology tools, this study has identified key miRNAs, TFs, and miRNA target gene pairs as potential tissue-based asthma biomarkers.

Identification of neoplasm-specific signatures of miRNA interactions by employing a systems biology approach

MicroRNAs represent major regulatory components of the disease epigenome and they constitute powerful biomarkers for the accurate diagnosis and prognosis of various diseases, including cancers. The advent of high-throughput technologies facilitated the generation of a vast amount of miRNA-cancer association data. Computational approaches have been utilized widely to effectively analyze and interpret these data towards the identification of miRNA signatures for diverse types of cancers. Herein, a novel computational workflow was applied to discover core sets of miRNA interactions for the major groups of neoplastic diseases by employing network-based methods. To this end, miRNA-cancer association data from four comprehensive publicly available resources were utilized for constructing miRNA-centered networks for each major group of neoplasms. The corresponding miRNA-miRNA interactions were inferred based on shared functionally related target genes. The topological attributes of the generated networks were investigated in order to detect clusters of highly interconnected miRNAs that form core modules in each network. Those modules that exhibited the highest degree of mutual exclusivity were selected from each graph. In this way, neoplasm-specific miRNA modules were identified that could represent potential signatures for the corresponding diseases.

DisiMiR: Predicting Pathogenic miRNAs Using Network Influence and miRNA Conservation

MiRNAs have been shown to play a powerful regulatory role in the progression of serious diseases, including cancer, Alzheimer's, and others, raising the possibility of new miRNA-based therapies for these conditions. Current experimental methods, such as differential expression analysis, can discover disease-associated miRNAs, yet many of these miRNAs play no functional role in disease progression. Interventional experiments used to discover disease causal miRNAs can be time consuming and costly. We present DisiMiR: a novel computational method that predicts pathogenic miRNAs by inferring biological characteristics of pathogenicity, including network influence and evolutionary conservation. DisiMiR separates disease causal miRNAs from merely disease-associated miRNAs, and was accurate in four diseases: breast cancer (0.826 AUC), Alzheimer's (0.794 AUC), gastric cancer (0.853 AUC), and hepatocellular cancer (0.957 AUC). Additionally, DisiMiR can generate hypotheses effectively: 78.4% of its false positives that are mentioned in the literature have been confirmed to be causal through recently published research. In this work, we show that DisiMiR is a powerful tool that can be used to efficiently and flexibly to predict pathogenic miRNAs in an expression dataset, for the further elucidation of disease mechanisms, and the potential identification of novel drug targets.

Human microRNA similarity in breast cancer

MicroRNAs (miRNAs) play important roles in a variety of human diseases, including breast cancer. A number of miRNAs are up- and down-regulated in breast cancer. However, little is known about miRNA similarity and similarity network in breast cancer. Here, a collection of 272 breast cancer-associated miRNA precursors (pre-miRNAs) were utilized to calculate similarities of sequences, target genes, pathways and functions and construct a combined similarity network. Well-characterized miRNAs and their similarity network were highlighted. Interestingly, miRNA sequence-dependent similarity networks were not identified in spite of sequence–target gene association. Similarity networks with minimum and maximum number of miRNAs originate from pathway and mature sequence, respectively. The breast cancer-associated miRNAs were divided into seven functional classes (classes I–VII) followed by disease enrichment analysis and novel miRNA-based disease similarities were found. The finding would provide insight into miRNA similarity, similarity network and disease heterogeneity in breast cancer.

Relevant and Non-Redundant Feature Selection for Cancer Classification and Subtype Detection

Simple Summary

Here we introduce a new feature selection algorithm DTA, which selects important, non-redundant, and relevant features from diverse omics data. DTA selects non-redundant features by maximizing the similarity between each patient pair by an approximate k-cover algorithm. We successfully applied this algorithm to three different biological problems: (a) disease to healthy sample classification, (b) multiclass classification of different disease samples, and (c) disease subtypes detection. DTA outperformed other feature selection techniques in the binary classification of healthy and disease samples and multiclass classification of various diseases. It also improved the performance of a subtype detection algorithm by selecting the important features for few cancer types.

Abstract

Biologists seek to identify a small number of significant features that are important, non-redundant, and relevant from diverse omics data. For example, statistical methods such as LIMMA and DEseq distinguish differentially expressed genes between a case and control group from the transcript profile. Researchers also apply various column subset selection algorithms on genomics datasets for a similar purpose. Unfortunately, genes selected by such statistical or machine learning methods are often highly co-regulated, making their performance inconsistent. Here, we introduce a novel feature selection algorithm that selects highly disease-related and non-redundant features from a diverse set of omics datasets. We successfully applied this algorithm to three different biological problems: (a) disease-to-normal sample classification; (b) multiclass classification of different disease samples; and (c) disease subtypes detection. Considering the classification of ROC-AUC, false-positive, and false-negative rates, our algorithm outperformed other gene selection and differential expression (DE) methods for all six types of cancer datasets from TCGA considered here for binary and multiclass classification problems. Moreover, genes picked by our algorithm improved the disease subtyping accuracy for four different cancer types over state-of-the-art methods. Hence, we posit that our proposed feature reduction method can support the community to solve various problems, including the selection of disease-specific biomarkers, precision medicine design, and disease sub-type detection.

Integrated bioinformatic analysis of miR-15a/16-1 cluster network in cervical cancer

The miR-15a/16-1 cluster is abnormally expressed in cervical cancer (CC) tissues and plays a vital role in cervical carcinogenesis. We aimed to evaluate the miR-15a/16-1 expression in healthy and cancerous cervical tissues, identify the associated networks, and to test its prognostic significance. miR-15a/16-1-MC expressions were analyzed in TCGA-CESC datasets by UALCAN, GEPIA2, and Datasetviewer. miR-15a/16-1 validated targets were extracted from mirTarBase and in silico functional analysis of the target genes were performed using WebGestalt. The interaction networks were constructed by the miRNet, STRING, and NetworkAnalyst tools. The prognostic significance and metastatic potential of the target genes were predicted using UALCAN and HCMDB. The FDA approved drugs to target miR-15a/16-1 and target gene network in CC were performed using DGIdb, STITCH and PanDrugs. TCGA-CESC and GEO data analysis suggested significant overexpression of miR-15a/16-1 in CC samples. The Kaplan-Meier survival analysis showed that miR-15a and its four target genes (BCL2, CCNE1, NUP50, and RBPJ) influence the overall survival of CC patients. Among the 66 differentially expressed target genes, 12 of them are linked to head, neck, or lung metastasis. Functional enrichment analysis predicted the association of this cluster with p53 signaling, human papillomavirus infection, PI3-AKT signaling pathway, and pathways in cancer. Drug-gene interaction analysis showed 52 potential FDA approved drugs to interact with the miR-15a/16-1 target genes. Nine of the 52 drugs are currently used as a chemotherapeutic agent for the treatment of CC patients. The present study shows that miR-15a/16-1 expression can be used as a clinical marker and target for therapy in CC.

The Transcriptional Regulatory Network of Corynebacterium pseudotuberculosis

Corynebacterium pseudotuberculosis is a Gram-positive, facultative intracellular, pathogenic bacterium that infects several different hosts, yielding serious economic losses in livestock farming. It causes several diseases including oedematous skin disease (OSD) in buffaloes, ulcerative lymphangitis (UL) in horses, and caseous lymphadenitis (CLA) in sheep, goats and humans. Despite its economic and medical-veterinary importance, our understanding concerning this organism’s transcriptional regulatory mechanisms is still limited. Here, we review the state of the art knowledge on transcriptional regulatory mechanisms of this pathogenic species, covering regulatory interactions mediated by two-component systems, transcription factors and sigma factors. Key transcriptional regulatory players involved in virulence and pathogenicity of C. pseudotuberculosis, such as the PhoPR system and DtxR, are in the focus of this review, as these regulators are promising targets for future vaccine design and drug development. We conclude that more experimental studies are needed to further understand the regulatory repertoire of this important zoonotic pathogen, and that regulators are promising targets for future vaccine design and drug development.

Application of microRNA Database Mining in Biomarker Discovery and Identification of Therapeutic Targets for Complex Disease

Over the past two decades, it has become increasingly evident that microRNAs (miRNA) play a major role in human diseases such as cancer and cardiovascular diseases. Moreover, their easy detection in circulation has made them a tantalizing target for biomarkers of disease. This surge in interest has led to the accumulation of a vast amount of miRNA expression data, prediction tools, and repositories. We used the Human microRNA Disease Database (HMDD) to discover miRNAs which shared expression patterns in the related diseases of ischemia/reperfusion injury, coronary artery disease, stroke, and obesity as a model to identify miRNA candidates for biomarker and/or therapeutic intervention in complex human diseases. Our analysis identified a single miRNA, hsa-miR-21, which was casually linked to all four pathologies, and numerous others which have been detected in the circulation in more than one of the diseases. Target analysis revealed that hsa-miR-21 can regulate a number of genes related to inflammation and cell growth/death which are major underlying mechanisms of these related diseases. Our study demonstrates a model for researchers to use HMDD in combination with gene analysis tools to identify miRNAs which could serve as biomarkers and/or therapeutic targets of complex human diseases.

Co-Expression Networks for Causal Gene Identification Based on RNA-Seq Data of Corynebacterium pseudotuberculosis

Corynebacterium pseudotuberculosis is a Gram-positive bacterium that causes caseous lymphadenitis, a disease that predominantly affects sheep, goat, cattle, buffalo, and horses, but has also been recognized in other animals. This bacterium generates a severe economic impact on countries producing meat. Gene expression studies using RNA-Seq are one of the most commonly used techniques to perform transcriptional experiments. Computational analysis of such data through reverse-engineering algorithms leads to a better understanding of the genome-wide complexity of gene interactomes, enabling the identification of genes having the most significant functions inferred by the activated stress response pathways. In this study, we identified the influential or causal genes from four RNA-Seq datasets from different stress conditions (high iron, low iron, acid, osmosis, and PH) in C. pseudotuberculosis, using a consensus-based network inference algorithm called miRsigand next identified the causal genes in the network using the miRinfluence tool, which is based on the influence diffusion model. We found that over 50% of the genes identified as influential had some essential cellular functions in the genomes. In the strains analyzed, most of the causal genes had crucial roles or participated in processes associated with the response to extracellular stresses, pathogenicity, membrane components, and essential genes. This research brings new insight into the understanding of virulence and infection by C. pseudotuberculosis.

Trends in the development of miRNA bioinformatics tools

MicroRNAs (miRNAs) are small noncoding RNAs that regulate gene expression via recognition of cognate sequences and interference of transcriptional, translational or epigenetic processes. Bioinformatics tools developed for miRNA study include those for miRNA prediction and discovery, structure, analysis and target prediction. We manually curated 95 review papers and ∼1000 miRNA bioinformatics tools published since 2003. We classified and ranked them based on citation number or PageRank score, and then performed network analysis and text mining (TM) to study the miRNA tools development trends. Five key trends were observed: (1) miRNA identification and target prediction have been hot spots in the past decade; (2) manual curation and TM are the main methods for collecting miRNA knowledge from literature; (3) most early tools are well maintained and widely used; (4) classic machine learning methods retain their utility; however, novel ones have begun to emerge; (5) disease-associated miRNA tools are emerging. Our analysis yields significant insight into the past development and future directions of miRNA tools.

Micro-RNA 205-5p is Involved in the Progression of Gastric Cancer and Targets Phosphatase and Tensin Homolog (PTEN) in SGC-7901 Human Gastric Cancer Cells

Background

This study aimed to investigate the role of micro-RNA 205-5p (miR-205-5p) in the progression of gastric cancer, and the target of miR-205-5p in human gastric cancer cells in vitro.

Material/Methods

Expression of miR-205-5p and PTEN in gastric cancer tissue samples and adjacent normal gastric tissue from 35 patients was studied using immunohistochemistry and in situ hybridization. SGC-7901 human gastric cancer cells included a normal control (NC) group, a group transfected with empty vector (Vector), a group treated with miR-205-5p inhibitor (miR-inhibitor), and a group treated with miR-205-5p inhibitor and small interfering PTEN mRNA (miR-inhibitor+si-PTEN). Quantitative reverse transcription polymerase chain reaction (qRT-PCR) measured miR-205-5p expression, cell proliferation was measured by MTT assay, cell apoptosis by flow cytometry, transwell and wound healing assays measured cell migration, and transmission electron microscopy (TEM) showed ultrastructural changes in SGC-7901 cells. PTEN, AKT and p-AKT protein expression were measured using Western blot. The correlation between miR-205-5p and PTEN was analyzed using a dual-luciferase reporter assay.

Results

Increased expression of miR-205-5p and PTEN in gastric cancer tissues were correlated with tumor stage. In SGC-7901 cells, miR-205-5p mRNA expression in the miR-inhibitor and miR-inhibitor+si-PTEN groups was significantly lower than that in the NC group (P<0.001). In the miR-inhibitor group, cell proliferation was significantly decreased, and apoptosis was significantly increased (P<0.001).

Conclusions

In gastric cancer, increased expression of miR-205-5p was associated with tumor stage, and in SGC-7901 cells PTEN was a target gene for miR-205-5p.

Evaluation of the Common Molecular Basis in Alzheimer's and Parkinson's Diseases

Alzheimer's disease (AD) and Parkinson's disease (PD) are the most common neurodegenerative disorders related to aging. Though several risk factors are shared between these two diseases, the exact relationship between them is still unknown. In this paper, we analyzed how these two diseases relate to each other from the genomic, epigenomic, and transcriptomic viewpoints. Using an extensive literature mining, we first accumulated the list of genes from major genome-wide association (GWAS) studies. Based on these GWAS studies, we observed that only one gene (HLA-DRB5) was shared between AD and PD. A subsequent literature search identified a few other genes involved in these two diseases, among which SIRT1 seemed to be the most prominent one. While we listed all the miRNAs that have been previously reported for AD and PD separately, we found only 15 different miRNAs that were reported in both diseases. In order to get better insights, we predicted the gene co-expression network for both AD and PD using network analysis algorithms applied to two GEO datasets. The network analysis revealed six clusters of genes related to AD and four clusters of genes related to PD; however, there was very low functional similarity between these clusters, pointing to insignificant similarity between AD and PD even at the level of affected biological processes. Finally, we postulated the putative epigenetic regulator modules that are common to AD and PD.

Clustered miRNAs and their role in biological functions and diseases

MicroRNAs (miRNAs) are endogenous, small non-coding RNAs known to regulate expression of protein-coding genes. A large proportion of miRNAs are highly conserved, localized as clusters in the genome, transcribed together from physically adjacent miRNAs and show similar expression profiles. Since a single miRNA can target multiple genes and miRNA clusters contain multiple miRNAs, it is important to understand their regulation, effects and various biological functions. Like protein-coding genes, miRNA clusters are also regulated by genetic and epigenetic events. These clusters can potentially regulate every aspect of cellular function including growth, proliferation, differentiation, development, metabolism, infection, immunity, cell death, organellar biogenesis, messenger signalling, DNA repair and self-renewal, among others. Dysregulation of miRNA clusters leading to altered biological functions is key to the pathogenesis of many diseases including carcinogenesis. Here, we review recent advances in miRNA cluster research and discuss their regulation and biological functions in pathological conditions.

MicroRNA-188-3p is involved in sevoflurane anesthesia-induced neuroapoptosis by targeting MDM2

Sevoflurane is a commonly used inhalation anesthetic. Sevoflurane-induced neuroapoptosis and cognitive impairments in animals are widely reported, however, the underlying molecular mechanisms remain largely unknown. The results of the present study demonstrated that sevoflurane anesthesia induced spatial memory impairments in rats, as determined by the Morris water maze test. Mechanistically, the current study demonstrated that sevoflurane administration significantly enhanced the expression of microRNA (miR)-188-3p. Furthermore, inhibition of miR-188-3p using lentiviral miR-188-3p inhibitors attenuated sevoflurane-induced cognitive impairments in rats. The present study also demonstrated that miR-188-3p targeted MDM2 proto-oncogene (MDM2) and negatively regulated the expression of MDM2, as determined by luciferase assays, reverse transcription-quantitative polymerase chain reaction and western blot analysis. Furthermore, decreased abundance of MDM2 following transfection with miR-188-3p mimics was associated with increased stability of p53 protein. Suppression of p53 activity using the specific p53 inhibitor pifithrin-α alleviated sevoflurane-induced neuroapoptosis. These results indicate that the miR-188-3p-MDM2-p53 axis may have a critical role in sevoflurane-induced cognitive dysfunction. Therefore, miR-188-3p may be a potential target for the treatment of sevoflurane-induced cognitive impairment.

Determining causal miRNAs and their signaling cascade in diseases using an influence diffusion model

In recent studies, miRNAs have been found to be extremely influential in many of the essential biological processes. They exhibit a self-regulatory mechanism through which they act as positive/negative regulators of expression of genes and other miRNAs. This has direct implications in the regulation of various pathophysiological conditions, signaling pathways and different types of cancers. Studying miRNA-disease associations has been an extensive area of research; however deciphering miRNA-miRNA network regulatory patterns in several diseases remains a challenge. In this study, we use information diffusion theory to quantify the influence diffusion in a miRNA-miRNA regulation network across multiple disease categories. Our proposed methodology determines the critical disease specific miRNAs which play a causal role in their signaling cascade and hence may regulate disease progression. We extensively validate our framework using existing computational tools from the literature. Furthermore, we implement our framework on a comprehensive miRNA expression data set for alcohol dependence and identify the causal miRNAs for alcohol-dependency in patients which were validated by the phase-shift in their expression scores towards the early stages of the disease. Finally, our computational framework for identifying causal miRNAs implicated in diseases is available as a free online tool for the greater scientific community.