Role of microRNA modulation in the interferon-α/ribavirin suppression of HIV-1 in vivo

Expression Analyses of MicroRNAs in Hamster Lung Tissues Infected by SARS-CoV-2

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is an infectious disease with multiple severe symptoms, such as fever over 37.5°C, cough, dyspnea, and pneumonia. In our research, microRNAs (miRNAs) binding to the genome sequences of severe acute respiratory syndrome coronavirus (SARS-CoV), Middle East respiratory-related coronavirus (MERS-CoV), and SARS-CoV-2 were identified by bioinformatic tools. Five miRNAs (hsa-miR-15a-5p, hsa-miR-15b-5p, hsa-miR-195-5p, hsa-miR-16-5p, and hsa-miR-196a-1-3p) were found to commonly bind to SARS-CoV, MERS-CoV, and SARS-CoV-2. We also identified miRNAs that bind to receptor proteins, such as ACE2, ADAM17, and TMPRSS2, which are important for understanding the infection mechanism of SARS-CoV-2. The expression patterns of those miRNAs were examined in hamster lung samples infected by SARS-CoV-2. Five miRNAs (hsa-miR-15b-5p, hsa-miR-195-5p, hsa-miR-221-3p, hsa-miR-140-3p, and hsa-miR-422a) showed differential expression patterns in lung tissues before and after infection. Especially, hsa-miR-15b-5p and hsa-miR-195-5p showed a large difference in expression, indicating that they may potentially be diagnostic biomarkers for SARS-CoV-2 infection.

Identifying differentially coexpressed module during HIV disease progression: A multiobjective approach

Microarray analysis based on gene coexpression is widely used to investigate the coregulation pattern of a group (or cluster) of genes in a specific phenotype condition. Recent approaches go one step beyond and look for differential coexpression pattern, wherein there exists a significant difference in coexpression pattern between two phenotype conditions. These changes of coexpression patterns generally arise due to significant change in regulatory mechanism across different conditions governed by natural progression of diseases. Here we develop a novel multiobjective framework DiffCoMO, to identify differentially coexpressed modules that capture altered coexpression in gene modules across different stages of HIV-1 progression. The objectives are built to emphasize the distance between coexpression pattern of two phenotype stages. The proposed method is assessed by comparing with some state-of-the-art techniques. We show that DiffCoMO outperforms the state-of-the-art for detecting differential coexpressed modules. Moreover, we have compared the performance of all the methods using simulated data. The biological significance of the discovered modules is also investigated using GO and pathway enrichment analysis. Additionally, miRNA enrichment analysis is carried out to identify TF to miRNA and miRNA to TF connections. The gene modules discovered by DiffCoMO manifest regulation by miRNA-28, miRNA-29 and miRNA-125 families.

Interferons Induce Expression of SAMHD1 in Monocytes through Down-regulation of miR-181a and miR-30a

SAMHD1 is a phosphohydrolase maintaining cellular dNTP homeostasis but also acts as a critical regulator in innate immune responses due to its antiviral activity and association with autoimmune disease, leading to aberrant activation of interferon. SAMHD1 expression is differentially regulated by interferon in certain primary cells, but the underlying mechanism is not understood. Here, we report a detailed characterization of the promotor region, the 5'- and 3'-untranslated region (UTR) of SAMHD1, and the mechanism responsible for the cell type-dependent up-regulation of SAMHD1 protein by interferon. We demonstrate that induction of SAMHD1 by type I and II interferons depends on 3'-UTR post-transcriptional regulation, whereas the promoter drives basal expression levels. We reveal novel functional target sites for the microRNAs miR-181a, miR-30a, and miR-155 in the SAMHD1 3'-UTR. Furthermore, we demonstrate that down-regulation of endogenous miR-181a and miR-30a levels inversely correlates with SAMHD1 protein up-regulation upon type I and II interferon stimulation in primary human monocytes. These miRNAs are not modulated by interferon in macrophages or dendritic cells, and consequently protein levels of SAMHD1 remain unchanged. These results suggest that SAMHD1 is a non-classical interferon-stimulated gene regulated through cell type-dependent down-regulation of miR-181a and miR-30a in innate sentinel cells.

miR-422a is an independent prognostic factor and functions as a potential tumor suppressor in colorectal cancer

AIM: To determine the expression of miR-422a in colorectal cancer (CRC) tissues and to further explore the prognostic value and function of miR-422a in CRC carcinogenesis.

METHODS: miR-422a expression was analyzed in 102 CRC tissues and paired normal mucosa adjacent to carcinoma by quantitative real-time PCR. The relationship of miR-422a expression with clinicopathological parameters was also analyzed. Kaplan-Meier analysis and Cox multivariate analysis were performed to estimate the potential role of miR-422a. Cell proliferation, migration, and invasion were used for in vitro functional analysis of miR-422a.

RESULTS: The levels of miR-422a were dramatically reduced in CRC tissues compared with normal mucosa (P < 0.05), and significantly correlated with local invasion (P = 0.004) and lymph node metastasis (P < 0.001). Kaplan-Meier survival and Cox regression multivariate analyses revealed that miR-422a expression (HR = 0.568, P = 0.015) and clinical TNM stage (HR = 2.942, P = 0.003) were independent prognostic factors for overall survival in CRC patients. Furthermore, in vitro experiments showed that overexpression of miR-422a inhibited the proliferation, migration, and invasion of SW480 and HT-29 cells.

CONCLUSION: Down-regulation of miR-422a may serve as an independent prognosis factor in CRC. MiR-422a functions as a tumor suppressor and regulates progression of CRC.

Constructing an integrated genetic and epigenetic cellular network for whole cellular mechanism using high-throughput next-generation sequencing data

Background

Epigenetics has been investigated in cancer initiation, and development, especially, since the appearance of epigenomics. Epigenetics may be defined as the mechanisms that lead to heritable changes in gene function and without affecting the sequence of genome. These mechanisms explain how individuals with the same genotype produce phenotypic differences in response to environmental stimuli. Recently, with the accumulation of high-throughput next-generation sequencing (NGS) data, a key goal of systems biology is to construct networks for different cellular levels to explore whole cellular mechanisms. At present, there is no satisfactory method to construct an integrated genetic and epigenetic cellular network (IGECN), which combines NGS omics data with gene regulatory networks (GRNs), microRNAs (miRNAs) regulatory networks, protein-protein interaction networks (PPINs), and epigenetic regulatory networks of methylation using high-throughput NGS data.

Results

We investigated different kinds of NGS omics data to develop a systems biology method to construct an integrated cellular network based on three coupling models that describe genetic regulatory networks, protein–protein interaction networks, microRNA (miRNA) regulatory networks, and methylation regulation. The proposed method was applied to construct IGECNs of gastric cancer and the human immune response to human immunodeficiency virus (HIV) infection, to elucidate human defense response mechanisms. We successfully constructed an IGECN and validated it by using evidence from literature search. The integration of NGS omics data related to transcription regulation, protein-protein interactions, and miRNA and methylation regulation has more predictive power than independent datasets. We found that dysregulation of MIR7 contributes to the initiation and progression of inflammation-induced gastric cancer; dysregulation of MIR9 contributes to HIV-1 infection to hijack CD4+ T cells through dysfunction of the immune and hormone pathways; dysregulation of MIR139-5p, MIRLET7i, and MIR10a contributes to the HIV-1 integration/replication stage; dysregulation of MIR101, MIR141, and MIR152 contributes to the HIV-1 virus assembly and budding mechanisms; dysregulation of MIR302a contributes to not only microvesicle-mediated transfer of miRNAs but also dysfunction of NF-κB signaling pathway in hepatocarcinogenesis.

Conclusion

The coupling dynamic systems of the whole IGECN can allow us to investigate genetic and epigenetic cellular mechanisms via omics data and big database mining, and are useful for further experiments in the field of systems and synthetic biology.