Dynamical network analysis reveals key microRNAs in progressive stages of lung cancer

Non-coding RNAs identification and regulatory networks in pathogen-host interaction in the microsporidia congenital infection

BACKGROUND

The interaction networks between coding and non-coding RNAs (ncRNAs) including long non-coding RNA (lncRNA), covalently closed circular RNA (circRNA) and miRNA are significant to elucidate molecular processes of biological activities and interactions between host and pathogen. Congenital infection caused by vertical transmission of microsporidia N. bombycis can result in severe economic losses in the silkworm-feeding industry. However, little is known about ncRNAs that take place in the microsporidia congenital infection. Here we conducted whole-transcriptome RNA-Seq analyses to identify ncRNAs and regulatory networks for both N. bombycis and host including silkworm embryos and larvae during the microsporidia congenital infection.

RESULTS

A total of 4,171 mRNAs, 403 lncRNA, 62 circRNAs, and 284 miRNAs encoded by N. bombycis were identified, among which some differentially expressed genes formed cross-talk and are involved in N. bombycis proliferation and infection. For instance, a lncRNA/circRNA competing endogenous RNA (ceRNA) network including 18 lncRNAs, one circRNA, and 20 miRNAs was constructed to describe 14 key parasites genes regulation, such as polar tube protein 3 (PTP3), ricin-B-lectin, spore wall protein 4 (SWP4), and heat shock protein 90 (HSP90). Regarding host silkworm upon N. bombycis congenital infection, a total of 14,889 mRNAs, 3,038 lncRNAs, 19,039 circRNAs, and 3,413 miRNAs were predicted based on silkworm genome with many differentially expressed coding and non-coding genes during distinct developmental stages. Different species of RNAs form interacting network to modulate silkworm biological processes, such as growth, metamorphosis and immune responses. Furthermore, a lncRNA/circRNA ceRNA network consisting of 140 lncRNAs, five circRNA, and seven miRNAs are constructed hypothetically to describe eight key host genes regulation, such as Toll-6, Serpin-6, inducible nitric oxide synthase (iNOS) and Caspase-8. Notably, cross-species analyses indicate that parasite and host miRNAs play a vital role in pathogen-host interaction in the microsporidia congenital infection.

CONCLUSION

This is the first comprehensive pan-transcriptome study inclusive of both N. bombycis and its host silkworm with a specific focus on the microsporidia congenital infection, and show that ncRNA-mediated regulation plays a vital role in the microsporidia congenital infection, which provides a new insight into understanding the basic biology of microsporidia and pathogen-host interaction.

T2-DAG: a powerful test for differentially expressed gene pathways via graph-informed structural equation modeling

MOTIVATION

A major task in genetic studies is to identify genes related to human diseases and traits to understand functional characteristics of genetic mutations and enhance patient diagnosis. Compared with marginal analyses of individual genes, identification of gene pathways, i.e. a set of genes with known interactions that collectively contribute to specific biological functions, can provide more biologically meaningful results. Such gene pathway analysis can be formulated into a high-dimensional two-sample testing problem. Given the typically limited sample size of gene expression datasets, most existing two-sample tests tend to have compromised powers because they ignore or only inefficiently incorporate the auxiliary pathway information on gene interactions.

RESULTS

We propose T2-DAG, a Hotelling's T2-type test for detecting differentially expressed gene pathways, which efficiently leverages the auxiliary pathway information on gene interactions from existing pathway databases through a linear structural equation model. We further establish its asymptotic distribution under pertinent assumptions. Simulation studies under various scenarios show that T2-DAG outperforms several representative existing methods with well-controlled type-I error rates and substantially improved powers, even with incomplete or inaccurate pathway information or unadjusted confounding effects. We also illustrate the performance of T2-DAG in an application to detect differentially expressed KEGG pathways between different stages of lung cancer.

AVAILABILITY AND IMPLEMENTATION

The R (R Development Core Team, 2021) package T2DAG which implements the proposed T2-DAG test is available on Github at https://github.com/Jin93/T2DAG.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

MicroRNA Interactome Multiomics Characterization for Cancer Research and Personalized Medicine: An Expert Review

MicroRNAs (miRNAs) that are mutually modulated by their interacting partners (interactome) are being increasingly noted for their significant role in pathogenesis and treatment of various human cancers. Recently, miRNA interactome dissected with multiomics approaches has been the subject of focus since individual tools or methods failed to provide the necessary comprehensive clues on the complete interactome. Even though single-omics technologies such as proteomics can uncover part of the interactome, the biological and clinical understanding still remain incomplete. In this study, we present an expert review of studies involving multiomics approaches to identification of miRNA interactome and its application in mechanistic characterization, classification, and therapeutic target identification in a variety of cancers, and with a focus on proteomics. We also discuss individual or multiple miRNA-based interactome identification in various pathological conditions of relevance to clinical medicine. Various new single-omics methods that can be integrated into multiomics cancer research and the computational approaches to analyze and predict miRNA interactome are also highlighted in this review. In all, we contextulize the power of multiomics approaches and the importance of the miRNA interactome to achieve the vision and practice of predictive, preventive, and personalized medicine in cancer research and clinical oncology.