- Browse by Author
Browsing by Author "Manzourolajdad, Amirhossein"
Now showing 1 - 4 of 4
Results Per Page
Sort Options
Item A Putative long-range RNA-RNA interaction between ORF8 and Spike of SARS-CoV-2(Public Library of Science, 2022-09-01) Omoru, Okiemute Beatrice; Pereira, Filipe; Janga, Sarath Chandra; Manzourolajdad, Amirhossein; BioHealth Informatics, School of Informatics and ComputingSARS-CoV-2 has affected people worldwide as the causative agent of COVID-19. The virus is related to the highly lethal SARS-CoV-1 responsible for the 2002-2003 SARS outbreak in Asia. Research is ongoing to understand why both viruses have different spreading capacities and mortality rates. Like other beta coronaviruses, 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 other viral proteins. In this work, we performed a comparative analysis for novel long-range RNA-RNA interactions that may involve the Spike region. Comparing in-silico fragment-based predictions between reference sequences of SARS-CoV-1 and SARS-CoV-2 revealed several predictions amongst which a thermodynamically stable long-range RNA-RNA interaction between (23660-23703 Spike) and (28025-28060 ORF8) unique to SARS-CoV-2 was observed. The patterns of sequence variation using data gathered worldwide further supported the predicted stability of the sub-interacting region (23679-23690 Spike) and (28031-28042 ORF8). Such RNA-RNA interactions can potentially impact viral life cycle including sub-genomic RNA production rates.Item Experimental and computational methods for studying the dynamics of RNA-RNA interactions in SARS-COV2 genomes(Oxford University Press, 2024) Srivastava, Mansi; Dukeshire, Matthew R.; Mir, Quoseena; Omoru, Okiemute Beatrice; Manzourolajdad, Amirhossein; Janga, Sarath Chandra; BioHealth Informatics, School of Informatics and ComputingLong-range ribonucleic acid (RNA)–RNA interactions (RRI) are prevalent in positive-strand RNA viruses, including Beta-coronaviruses, and these take part in regulatory roles, including the regulation of sub-genomic RNA production rates. Crosslinking of interacting RNAs and short read-based deep sequencing of resulting RNA–RNA hybrids have shown that these long-range structures exist in severe acute respiratory syndrome coronavirus (SARS-CoV)-2 on both genomic and sub-genomic levels and in dynamic topologies. Furthermore, co-evolution of coronaviruses with their hosts is navigated by genetic variations made possible by its large genome, high recombination frequency and a high mutation rate. SARS-CoV-2’s mutations are known to occur spontaneously during replication, and thousands of aggregate mutations have been reported since the emergence of the virus. Although many long-range RRIs have been experimentally identified using high-throughput methods for the wild-type SARS-CoV-2 strain, evolutionary trajectory of these RRIs across variants, impact of mutations on RRIs and interaction of SARS-CoV-2 RNAs with the host have been largely open questions in the field. In this review, we summarize recent computational tools and experimental methods that have been enabling the mapping of RRIs in viral genomes, with a specific focus on SARS-CoV-2. We also present available informatics resources to navigate the RRI maps and shed light on the impact of mutations on the RRI space in viral genomes. Investigating the evolution of long-range RNA interactions and that of virus–host interactions can contribute to the understanding of new and emerging variants as well as aid in developing improved RNA therapeutics critical for combating future outbreaks.Item HNRNPK is retained in the cytoplasm by Keratin 19 to stabilize target mRNAs(2022) Fallatah, Arwa; Anastasakis, Dimitrios G.; Manzourolajdad, Amirhossein; Sharma, Pooja; Wang, Xiantao; Jacob, Alexis; Alsharif, Sarah; Elgerbi, Ahmed; Coulombe, Pierre A.; Hafner, Markus; Chung, Byung Min; BioHealth Informatics, School of Informatics and ComputingHeterogeneous nuclear ribonucleoprotein K (HNRNPK) regulates pre-mRNA processing and long non-coding RNA localization in the nucleus. It was previously shown that shuttling of HNRNPK to the cytoplasm promotes cell proliferation and cancer metastasis. However, the mechanism of HNRNPK cytoplasmic localization, its cytoplasmic RNA ligands, and impact on posttranscriptional gene regulation remain uncharacterized. Here we show that the intermediate filament protein Keratin 19 (K19) directly interacts with HNRNPK and sequesters it in the cytoplasm. Correspondingly, in K19 knockout breast cancer cells, HNRNPK does not localize in the cytoplasm, resulting in reduced cell proliferation. We mapped cytoplasmic HNRNPK target mRNAs using PAR-CLIP where transcriptome data to show that, in the cytoplasm, HNRNPK stabilizes target mRNAs bound to the 3’ untranslated region at the expected C-rich sequence elements. Furthermore, these mRNAs are typically involved in cancer progression and include the p53 signaling pathway that is dysregulated upon HNRNPK knockdown or K19 knockout. This study identifies how a cytoskeletal protein can directly regulate gene expression by controlling subcellular localization of RNA binding proteins to support pathways involved in cancer progression.Item Variant-Specific Analysis Reveals a Novel Long-Range RNA-RNA Interaction in SARS-CoV-2 Orf1a(MDPI, 2022-09) Dukeshire, Matthew; Schaeper, David; Venkatesan, Pravina; Manzourolajdad, Amirhossein; BioHealth Informatics, School of Informatics and ComputingSince the start of the COVID-19 pandemic, understanding the pathology of the SARS-CoV-2 RNA virus and its life cycle has been the priority of many researchers. Currently, new variants of the virus have emerged with various levels of pathogenicity and abundance within the human-host population. Although much of viral pathogenicity is attributed to the viral Spike protein’s binding affinity to human lung cells’ ACE2 receptor, comprehensive knowledge on the distinctive features of viral variants that might affect their life cycle and pathogenicity is yet to be attained. Recent in vivo studies into the RNA structure of the SARS-CoV-2 genome have revealed certain long-range RNA-RNA interactions. Using in silico predictions and a large population of SARS-CoV-2 sequences, we observed variant-specific evolutionary changes for certain long-range RRIs. We also found statistical evidence for the existence of one of the thermodynamic-based RRI predictions, namely Comp1, in the Beta variant sequences. A similar test that disregarded sequence variant information did not, however, lead to significant results. When performing population-based analyses, aggregate tests may fail to identify novel interactions due to variant-specific changes. Variant-specific analyses can result in de novo RRI identification.