microRNA-122 stimulates translation of hepatitis C virus RNA

Hepatitis C virus (HCV) is a positive strand RNA virus that propagates primarily in the liver. We show here that the liver-specific microRNA-122 (miR-122), a member of a class of small cellular RNAs that mediate post-transcriptional gene regulation usually by repressing the translation of mRNAs through interaction with their 3′-untranslated regions (UTRs), stimulates the translation of HCV. Sequestration of miR-122 in liver cell lines strongly reduces HCV translation, whereas addition of miR-122 stimulates HCV translation in liver cell lines as well as in the non-liver HeLa cells and in rabbit reticulocyte lysate. The stimulation is conferred by direct interaction of miR-122 with two target sites in the 5′-UTR of the HCV genome. With a replication-defective NS5B polymerase mutant genome, we show that the translation stimulation is independent of viral RNA synthesis. miR-122 stimulates HCV translation by enhancing the association of ribosomes with the viral RNA at an early initiation stage. In conclusion, the liver-specific miR-122 may contribute to HCV liver tropism at the level of translation

The Hepatitis C Virus RNA 3′-Untranslated Region Strongly Enhances Translation Directed by the Internal Ribosome Entry Site

The positive-strand RNA genome of the hepatitis C virus (HCV) is flanked by 5′- and 3′-untranslated regions (UTRs). Translation of the viral RNA is directed by the internal ribosome entry site (IRES) in the 5′-UTR, and subsequent viral RNA replication requires sequences in the 3′-UTR and in the 5′-UTR. Addressing previous conflicting reports on a possible function of the 3′-UTR for RNA translation in this study, we found that reporter construct design is an important parameter in experiments testing 3′-UTR function. A translation enhancer function of the HCV 3′-UTR was detected only after transfection of monocistronic reporter RNAs or complete RNA genomes having a 3′-UTR with a precise 3′ terminus. The 3′-UTR strongly stimulates HCV IRES-dependent translation in human hepatoma cell lines but only weakly in nonliver cell lines. The variable region, the poly(U · C) tract, and the most 3′ terminal stem-loop 1 of the highly conserved 3′ X region contribute significantly to translation enhancement, whereas stem-loops 2 and 3 of the 3′ X region are involved only to a minor extent. Thus, the signals for translation enhancement and for the initiation of RNA minus-strand synthesis in the HCV 3′-UTR partially overlap, supporting the idea that these sequences along with viral and possibly also cellular factors may be involved in an RNA 3′-5′ end interaction and a switch between translation and RNA replication

Multiple Occurrences of a 168-Nucleotide Deletion in SARS-CoV-2 ORF8, Unnoticed by Standard Amplicon Sequencing and Variant Calling Pipelines

Brandt D, Simunovic M, Busche T, et al. Multiple Occurrences of a 168-Nucleotide Deletion in SARS-CoV-2 ORF8, Unnoticed by Standard Amplicon Sequencing and Variant Calling Pipelines. Viruses. 2021;13(9): 1870.Genomic surveillance of the SARS-CoV-2 pandemic is crucial and mainly achieved by amplicon sequencing protocols. Overlapping tiled-amplicons are generated to establish contiguous SARS-CoV-2 genome sequences, which enable the precise resolution of infection chains and outbreaks. We investigated a SARS-CoV-2 outbreak in a local hospital and used nanopore sequencing with a modified ARTIC protocol employing 1200 bp long amplicons. We detected a long deletion of 168 nucleotides in the ORF8 gene in 76 samples from the hospital outbreak. This deletion is difficult to identify with the classical amplicon sequencing procedures since it removes two amplicon primer-binding sites. We analyzed public SARS-CoV-2 sequences and sequencing read data from ENA and identified the same deletion in over 100 genomes belonging to different lineages of SARS-CoV-2, pointing to a mutation hotspot or to positive selection. In almost all cases, the deletion was not represented in the virus genome sequence after consensus building. Additionally, further database searches point to other deletions in the ORF8 coding region that have never been reported by the standard data analysis pipelines. These findings and the fact that ORF8 is especially prone to deletions, make a clear case for the urgent necessity of public availability of the raw data for this and other large deletions that might change the physiology of the virus towards endemism

Multiple Occurrences of a 168-Nucleotide Deletion in SARS-CoV-2 ORF8, Unnoticed by Standard Amplicon Sequencing and Variant Calling Pipelines

Genomic surveillance of the SARS-CoV-2 pandemic is crucial and mainly achieved by amplicon sequencing protocols. Overlapping tiled-amplicons are generated to establish contiguous SARS-CoV-2 genome sequences, which enable the precise resolution of infection chains and outbreaks. We investigated a SARS-CoV-2 outbreak in a local hospital and used nanopore sequencing with a modified ARTIC protocol employing 1200 bp long amplicons. We detected a long deletion of 168 nucleotides in the ORF8 gene in 76 samples from the hospital outbreak. This deletion is difficult to identify with the classical amplicon sequencing procedures since it removes two amplicon primer-binding sites. We analyzed public SARS-CoV-2 sequences and sequencing read data from ENA and identified the same deletion in over 100 genomes belonging to different lineages of SARS-CoV-2, pointing to a mutation hotspot or to positive selection. In almost all cases, the deletion was not represented in the virus genome sequence after consensus building. Additionally, further database searches point to other deletions in the ORF8 coding region that have never been reported by the standard data analysis pipelines. These findings and the fact that ORF8 is especially prone to deletions, make a clear case for the urgent necessity of public availability of the raw data for this and other large deletions that might change the physiology of the virus towards endemism

EMMA 2-A MAGE-compliant system for the collaborative analysis and integration of microarray data

Dondrup M, Albaum S, Griebel T, et al. EMMA 2-A MAGE-compliant system for the collaborative analysis and integration of microarray data. BMC Bioinformatics. 2009;10(1): 50.Background: Understanding transcriptional regulation by genome-wide microarray studies can contribute to unravel complex relationships between genes. Attempts to standardize the annotation of microarray data include the Minimum Information About a Microarray Experiment (MIAME) recommendations, the MAGE-ML format for data interchange, and the use of controlled vocabularies or ontologies. The existing software systems for microarray data analysis implement the mentioned standards only partially and are often hard to use and extend. Integration of genomic annotation data and other sources of external knowledge using open standards is therefore a key requirement for future integrated analysis systems. Results: The EMMA 2 software has been designed to resolve shortcomings with respect to full MAGE-ML and ontology support and makes use of modern data integration techniques. We present a software system that features comprehensive data analysis functions for spotted arrays, and for the most common synthesized oligo arrays such as Agilent, Affymetrix and NimbleGen. The system is based on the full MAGE object model. Analysis functionality is based on R and Bioconductor packages and can make use of a compute cluster for distributed services. Conclusion: Our model-driven approach for automatically implementing a full MAGE object model provides high flexibility and compatibility. Data integration via SOAP-based web-services is advantageous in a distributed client-server environment as the collaborative analysis of microarray data is gaining more and more relevance in international research consortia. The adequacy of the EMMA 2 software design and implementation has been proven by its application in many distributed functional genomics projects. Its scalability makes the current architecture suited for extensions towards future transcriptomics methods based on high-throughput sequencing approaches which have much higher computational requirements than microarrays