Sequence diversity in the 3’ untranslated region of alphavirus modulates IFIT2-dependent restriction in a cell type-dependent manner

ABSTRACTAlphaviruses (family Togaviridae) are a diverse group of positive-sense RNA (+ssRNA) viruses that are transmitted by arthropods and are the causative agent of several significant human and veterinary diseases. Interferon (IFN)-induced proteins with tetratricopeptide repeats (IFITs) are a family of RNA-binding IFN stimulated genes (ISGs) that are highly upregulated following viral infection, and have been identified as potential restrictors of alphaviruses. The mechanism by which IFIT1 restricts RNA viruses is dependent on self and non-self-discrimination of RNA, and alphaviruses evade this recognition via their 5’UTR. However, the role of IFIT2 during alphavirus replication and the mechanism of viral replication inhibition is unclear. In this study, we identify IFIT2 as a restriction factor for Venezuelan equine encephalitis virus (VEEV) and show that IFIT2 binds the 3’ untranslated region (3’UTR) of the virus. We investigated the potential role of variability in the 3’UTR of the virus affecting IFIT2 antiviral activity by studying infection with VEEV. Comparison of recombinant VEEV clones containing 3’UTR sequences derived from epizootic and enzootic isolates exhibited differential sensitivity to IFIT2 restriction in vitro infection studies, suggesting that the alphavirus 3’UTR sequence may function in part to evade IFIT2 restriction. In vitro binding assays demonstrate that IFIT2 binds to the VEEV 3’UTR, however in contrast to previous studies VEEV restriction did not appear to be dependent on the ability of IFIT2 to inhibit translation of viral RNA, suggesting a novel mechanism of IFIT2 restriction. Our study demonstrates that IFIT2 is a restriction factor for alphaviruses and variability in the 3’UTR of VEEV can modulate viral restriction by IFIT2. Ongoing studies are exploring the biological consequences of IFIT2-VEEV RNA interaction in viral pathogenesis and defining sequence and structural features of RNAs that regulate IFIT2 recognition.

Bioinformatics Analysis for Identifying Micro-RNAs, Long Noncoding RNAs, Transcription Factors, Immune Genes Regulatory Networks, and Potential Therapeutic Agents for Diabetic Cardiomyopathy Using an Integrated Bioinformatics Analysis

Objectives We identified functional genes and studied the underlying molecular mechanisms of diabetic cardiomyopathy (DCM) using bioinformatics tools. Methods Original gene expression profiles were obtained from the GSE21610 and GSE112556 datasets. We used GEO2R to screen the differentially expressed genes (DEGs). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed on DEGs. Protein–protein interaction (PPI) networks of DEGs were constructed using STRING and hub genes of signaling pathways were identified using Cytoscape. Aberrant hub gene expression was verified using The Cancer Genome Atlas dataset. Connectivity Map was used to predict the drugs that could treat DCM. Results The DEGs in DCM were mainly enriched in the nuclei and cytoplasm and involved in DCM- and chemokine-related signaling pathways. In the PPI network, 32 nodes were chosen as hub nodes and an RNA interaction network was constructed with 517 interactions. The expression of key genes (JPIK3R1, CCR9, XIST, WDFY3.AS2, hsa-miR-144-5p, and hsa-miR-146b-5p) was significantly different between DCM and normal tissues. Danazol, ikarugamycin, and semustine were identified as therapeutic agents against DCM using CMAP. Conclusion The identified hub genes could be associated with DCM pathogenesis and the above drugs could be used for treating DCM.

Long non-coding RNA-associated competing endogenous RNA axes in the olfactory epithelium in schizophrenia: a bioinformatics analysis

The etiology of schizophrenia (SCZ), as a serious mental illness, is unknown. The significance of genetics in SCZ pathophysiology is yet unknown, and newly identified mechanisms involved in the regulation of gene transcription may be helpful in determining how these changes affect SCZ development and progression. In the current work, we used a bioinformatics approach to describe the role of long non-coding RNA (lncRNA)-associated competing endogenous RNAs (ceRNAs) in the olfactory epithelium (OE) samples in order to better understand the molecular regulatory processes implicated in SCZ disorders in living individuals. The Gene Expression Omnibus database was used to obtain the OE microarray dataset (GSE73129) from SCZ sufferers and control subjects, which contained information about both lncRNAs and mRNAs. The limma package of R software was used to identify the differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs). RNA interaction pairs were discovered using the Human MicroRNA Disease Database, DIANA-LncBase, and miRTarBase databases. In this study, the Pearson correlation coefficient was utilized to find positive correlations between DEmRNAs and DElncRNAs in the ceRNA network. Eventually, lncRNA-associated ceRNA axes were developed based on co-expression relations and DElncRNA-miRNA-DEmRNA interactions. This work found six potential DElncRNA-miRNA-DEmRNA loops in SCZ pathogenesis, including, SNTG2-AS1/hsa-miR-7-5p/SLC7A5, FLG-AS1/hsa-miR-34a-5p/FOSL1, LINC00960/hsa-miR-34a-5p/FOSL1, AQP4-AS1/hsa-miR-335-5p/FMN2, SOX2-OT/hsa-miR-24-3p/NOS3, and CASC2/hsa-miR-24-3p/NOS3. According to the findings, ceRNAs in OE might be promising research targets for studying SCZ molecular mechanisms. This could be a great opportunity to examine different aspects of neurodevelopment that may have been hampered early in SCZ patients.

Multi-omics analysis of an immune-based prognostic predictor in non-small cell lung cancer

Background Inhibitors targeting immune checkpoints, such as PD-1/PD-L1 and CTLA-4, have prolonged survival in small groups of non-small cell lung cancer (NSCLC) patients, but biomarkers predictive of the response to the immune checkpoint inhibitors (ICIs) remain rare. Methods The nonnegative matrix factorization (NMF) was performed for TCGA-NSCLC tumor samples based on the LM22 immune signature to construct subgroups. Characterization of NMF subgroups involved the single sample gene set variation analysis (ssGSVA), and mutation/copy number alteration and methylation analyses. Construction of RNA interaction network was based on the identification of differentially expressed RNAs (DERs). The prognostic predictor was constructed by a LASSO-Cox regression model. Four GEO datasets were used for the validation analysis. Results Four immune based NMF subgroups among NSCLC patients were identified. Genetic and epigenetic analyses between subgroups revealed an important role of somatic copy number alterations in determining the immune checkpoint expression on specific immune cells. Seven hub genes were recognized in the regulatory network closely related to the immune phenotype, and a three-gene prognosis predictor was constructed. Conclusions Our study established an immune-based prognosis predictor, which might have the potential to select subgroups benefiting from the ICI treatment, for NSCLC patients using publicly available databases.

Protein-RNA interactome analysis reveals wide association of KSHV ORF57 with host non-coding RNAs and polysomes

Kaposi’s sarcoma-associated herpesvirus (KSHV) ORF57 is an RNA-binding post-transcriptional regulator. We recently applied an affinity-purified anti-ORF57 antibody to conduct ORF57-CLIP (Cross-linking Immunoprecipitation) in combination with RNA-sequencing (CLIP-seq) and analyzed the genome-wide host RNA transcripts in association with ORF57 in BCBL-1 cells with lytic KSHV infection. Mapping of the CLIPed RNA reads to the human genome (GRCh37) revealed that most of the ORF57-associated RNA reads were from rRNAs. The remaining RNA reads mapped to several classes of host non-coding and protein-coding mRNAs. We found ORF57 binds and regulates expression of a subset of host lncRNAs, including LINC00324, LINC00355, and LINC00839 which are involved in cell growth. ORF57 binds snoRNAs responsible for 18S and 28S rRNA modifications, but does not interact with fibrillarin and NOP58. We validated ORF57 interactions with 67 snoRNAs by ORF57-RNA immunoprecipitation (RIP)-snoRNA-array assays. Most of the identified ORF57 rRNA binding sites (BS) overlap with the sites binding snoRNAs. We confirmed ORF57-snoRA71B RNA interaction in BCBL-1 cells by ORF57-RIP and Northern blot analyses using a 32 P-labeled oligo probe from the 18S rRNA region complementary to snoRA71B. Using RNA oligos from the rRNA regions that ORF57 binds for oligo pulldown-Western blot assays, we selectively verified ORF57 interactions with 5.8S and 18S rRNAs. Polysome profiling revealed that ORF57 associates with both monosomes and polysomes and its association with polysomes increases PABPC1 binding to, but prevent Ago2 from polysomes. Our data indicate a functional correlation with ORF57 binding and suppression of Ago2 activities for ORF57 promotion of gene expression. Significance As an RNA-binding protein, KSHV ORF57 regulates RNA splicing, stability, and translation and inhibits host innate immunity by blocking the formation of RNA granules in virus infected cells. In this report, ORF57 was found to interact many host non-coding RNAs, including lncRNAs, snoRNAs and ribosomal RNAs to carry out additional unknown functions. ORF57 binds a group of lncRNAs via the identified RNA motifs by ORF57 CLIP-seq to regulate their expression. ORF57 associates with snoRNAs independently of fibrillarin and NOP58 proteins, and with ribosomal RNA in the regions that commonly bind snoRNAs. Knockdown of fibrillarin expression decreases the expression of snoRNAs and CDK4, but not affect viral gene expression. More importantly, we found that ORF57 binds translationally active polysomes and enhances PABPC-1 but prevents Ago2 association with polysomes. Data provide a compelling evidence on how ORF57 in KSHV infected cells might regulate protein synthesis by blocking Ago2’s hostile activities on translation.

Evidence for a long-r ange RNA-RNA interaction between ORF8 and the downstream region of the Spike polybasic insertion of SARS-CoV-2

SARS-CoV-2 has affected people all over the world as the causative agent of COVID-19. The virus is related to the highly lethal SARS-CoV responsible for the 2002-2003 SARS outbreak in Asia. Intense research is ongoing to understand why both viruses have different spreading capacities and mortality rates. Similar to other betacoronaviruses, long-range RNA-RNA interactions occur between different parts of the viral genomic RNA, resulting in discontinuous transcription and production of various sub-genomic RNAs. These sub-genomic RNAs are then translated into different viral proteins. An important difference between both viruses is a polybasic insertion in the Spike region of SARS-CoV-2, absent in SARS-CoV. Here we show that a 26-base-pair long-range RNA-RNA interaction occurs between the genomic region downstream of the Spike insertion and ORF8 in SARS-CoV-2. Predictions suggest that the corresponding ORF8 region forms the most energetically favorable interaction with that of Spike region from amongst all possible candidate regions within SARS-CoV-2 genomic RNA. We also found signs of sequence covariation in the predicted interaction using a large dataset with 27,592 full-length SARS-CoV-2 genomes. In particular, a synonymous mutation in ORF8 accommodated for base pairing with Spike [G23675 C28045U], and a non-synonymous mutation in Spike accommodated for base pairing with ORF8 [C23679U G28042] both of which were in close proximity of one another. The predicted interactions can potentially be related to regulation of sub-genomic RNA production rates.

A sense-antisense RNA interaction promotes breast cancer metastasis via regulation of NQO1 expression

Antisense RNAs are ubiquitous in human cells, yet the role that they play in healthy and diseased states remains largely unexplored. Here, we developed a computational framework to catalog and profile antisense RNAs and applied it to poorly and highly metastatic breast cancer cell lines. We identified one antisense RNA that plays a functional role in driving breast cancer progression by upregulating the redox enzyme NQO1, and hence named NQO1-antisense RNA or NQO1-AS. This upregulation occurs via a stabilizing interaction between NQO1-AS and its complementary region in the 3’UTR of NQO1 mRNA. By increasing expression of NQO1 protein, breast cancer cells are able to tolerate higher levels of oxidative stress, enabling them to colonize the lung. During this process the cancer cells become dependent on NQO1 to protect them from ferroptosis. We have shown that this dependence can be exploited therapeutically in xenograft models of metastasis. Together, our findings establish a previously unknown role for NQO1-AS in the progression of breast cancer by serving as a post-transcriptional regulator of RNA processing and decay for its sense mRNA.