Mouse mammary tumor virus (MMTV) is a betaretrovirus that causes breast cancer in mice. The mouse mammary epithelial cells are the most permissive cells for MMTV, expressing the highest levels of virus upon infection and being the ones later transformed by the virus due to repeated rounds of infection/superinfection and integration, leading eventually to mammary tumors. The aim of this study was to identify genes and molecular pathways dysregulated by MMTV expression in mammary epithelial cells. Towards this end, mRNAseq was performed on normal mouse mammary epithelial cells stably expressing MMTV, and expression of host genes was analyzed compared with cells in its absence. The identified differentially expressed genes (DEGs) were grouped on the basis of gene ontology and relevant molecular pathways. Bioinformatics analysis identified 12 hub genes, of which 4 were up-regulated (Angp2, Ccl2, Icam, and Myc) and 8 were down-regulated (Acta2, Cd34, Col1a1, Col1a2, Cxcl12, Eln, Igf1, and Itgam) upon MMTV expression. Further screening of these DEGs showed their involvement in many diseases, especially in breast cancer progression when compared with available data. Gene Set Enrichment Analysis (GSEA) identified 31 molecular pathways dysregulated upon MMTV expression, amongst which the PI3-AKT-mTOR was observed to be the central pathway down-regulated by MMTV. Many of the DEGs and 6 of the 12 hub genes identified in this study showed expression profile similar to that observed in the PyMT mouse model of breast cancer, especially during tumor progression. Interestingly, a global down-regulation of gene expression was observed, where nearly 74% of the DEGs in HC11 cells were repressed by MMTV expression, an observation similar to what was observed in the PyMT mouse model during tumor progression, from hyperplasia to adenoma to early and late carcinomas. Comparison of our results with the Wnt1 mouse model revealed further insights into how MMTV expression could lead to activation of the Wnt1 pathway independent of insertional mutagenesis. Thus, the key pathways, DEGs, and hub genes identified in this study can provide important clues to elucidate the molecular mechanisms involved in MMTV replication, escape from cellular anti-viral response, and potential to cause cell transformation. These data also validate the use of the MMTV-infected HC11 cells as an important model to study early transcriptional changes that could lead to mammary cell transformation.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is responsible for coronavirus disease of 2019 that infected more than 760 million people worldwide with over 6.8 million deaths to date. COVID-19 is one of the most challenging diseases of our times due to the nature of its spread, its effect on multiple organs, and an inability to predict disease prognosis, ranging from being completely asymptomatic to death. Upon infection, SARS-CoV-2 alters the host immune response by changing host-transcriptional machinery. MicroRNAs (miRNAs) are regarded as post-transcriptional regulators of gene expression that can be perturbed by invading viruses. Several in vitro and in vivo studies have reported such dysregulation of host miRNA expression upon SARS-CoV-2 infection. Some of this could occur as an anti-viral response of the host to the viral infection. Viruses themselves can counteract that response by mounting their own pro-viral response that facilitates virus infection, an aspect which may cause pathogenesis. Thus, miRNAs could serve as possible disease biomarkers in infected people. In the current review, we have summarised and analysed the existing data about miRNA dysregulation in patients infected with SARS-CoV-2 to determine their concordance between studies, and identified those that could serve as potential biomarkers during infection, disease progression, and death, even in people with other comorbidities. Having such biomarkers can be vital in not only predicting prognosis, but also the development of novel miRNA-based anti-virals and Abbreviations: 3 0 UTR, 3 0 untranslated regions; 5 0 UTR, 5 0

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