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Cenik, C.

Publications and source records attributed to Cenik, C..

4 recordsLinked to original sources

Aberrant BCMA Signaling Promotes Tumor Growth by Altering Protein Translation Machinery, a Therapeutic Target for the Treatment of Relapse/Refractory Multiple Myeloma

Recent T Cell therapies have been effective in the treatment of hematological cancers. However, immunotoxicity and treatment relapse pose significant clinical challenges. Here, we revealed distinctive requirement for neutralizing TNF receptor ligands APRIL and BAFF in MM and DLBCL. Furthermore, we investigated the use of BCMA decoy receptor (sBCMA-Fc) as a therapeutic inhibitor of ARPIL and BAFF. While wild-type sBCMA-Fc successfully blocked APRIL signaling with picomolar binding affinity, inhibiting tumor growth in MM models, it lacked efficacy in inhibiting DLBCL progression due to its weak binding for BAFF. To expand the therapeutic utility of sBCMA-Fc, using a protein engineering approach, we generated an affinity enhanced mutant sBCMA-Fc fusion molecule (sBCMA-Fc V3) with 4-folds and 500-folds enhancement in binding to APRIL and BAFF respectively. The sBCMA-Fc V3 clone significantly enhanced antitumor activity against both MM and DLBCL. Importantly, sBCMA-Fc V3 was proven to be a viable clinical candidate by showing adequate toxicity profile and on-target mechanism of action in nonhuman primates. SUMMARYThis study demonstrates the dichotomous function of APRIL and BAFF in MM and DLBCL, that can be safely targeted by an engineered fusion protein designed to trap APRIL and BAFF with ultra-high binding affinity.

cancer biology

Loss of coordinated expression between ribosomal and mitochondrial genes revealed by comprehensive characterization of a large family with a rare mendelian disorder

Non-canonical intronic variants are a poorly characterized yet highly prevalent class of alterations associated with Mendelian disorders. Here, we report the first RNA expression and splicing analysis from a family whose members carry a non-canonical splice variant in an intron of RPL11 (c.396+3A>G). This mutation is causative for Diamond Blackfan Anemia (DBA) in this family despite incomplete penetrance and variable expressivity. Our analyses revealed a complex pattern of disruptions with many novel junctions of RPL11. These include an RPL11 transcript that is translated with a late stop codon in the 3 untranslated region (3UTR) of the main isoform. We observed that RPL11 transcript abundance is comparable among carriers regardless of symptom severity. Interestingly, both the small and large ribosomal subunit transcripts were significantly overexpressed in individuals with a history of anemia in addition to congenital abnormalities. Finally, we discovered that coordinated expression between mitochondrial components and RPL11 was lost in all carriers, which may lead to variable expressivity. Overall, this study highlights the importance of RNA splicing and expression analyses in families for molecular characterization of Mendelian diseases.

genomics

Genes with 5' terminal oligopyrimidine tracts preferentially escape global suppression of translation by the SARS-CoV-2 NSP1 protein

Viruses rely on the host translation machinery to synthesize their own proteins. Consequently, they have evolved varied mechanisms to co-opt host translation for their survival. SARS-CoV-2 relies on a non-structural protein, Nsp1, for shutting down host translation. However, it is currently unknown how viral proteins and host factors critical for viral replication can escape a global shutdown of host translation. Here, using a novel FACS-based assay called MeTAFlow, we report a dose-dependent reduction in both nascent protein synthesis and mRNA abundance in cells expressing Nsp1. We perform RNA-Seq and matched ribosome profiling experiments to identify gene-specific changes both at the mRNA expression and translation level. We discover a functionally-coherent subset of human genes are preferentially translated in the context of Nsp1 expression. These genes include the translation machinery components, RNA binding proteins, and others important for viral pathogenicity. Importantly, we uncovered a remarkable enrichment of 5' terminal oligo-pyrimidine (TOP) tracts among preferentially translated genes. Using reporter assays, we validated that 5 UTRs from TOP transcripts can drive preferential expression in the presence of NSP1. Finally, we found that LARP1, a key effector protein in the mTOR pathway may contribute to preferential translation of TOP transcripts in response to Nsp1 expression. Collectively, our study suggests fine tuning of host gene expression and translation by Nsp1 despite its global repressive effect on host protein synthesis.

molecular biology

RiboFlow, RiboR and RiboPy: An ecosystem for analyzing ribosome profiling data at read length resolution

SummaryRibosome occupancy measurements enable protein abundance estimation and infer mechanisms of translation. Recent studies have revealed that sequence read lengths in ribosome profiling data are highly variable and carry critical information. Consequently, data analyses require the computation and storage of multiple metrics for a wide range of ribosome footprint lengths. We developed a software ecosystem including a new efficient binary file format named ribo. Ribo files store all essential data grouped by ribosome footprint lengths. Users can assemble ribo files using our RiboFlow pipeline that processes raw ribosomal profiling sequencing data. RiboFlow is highly portable and customizable across a large number of computational environments with built-in capabilities for parallelization. We also developed interfaces for writing and reading ribo files in the R (RiboR) and Python (RiboPy) environments. Using RiboR and RiboPy, users can efficiently access ribosome profiling quality control metrics, generate essential plots, and carry out analyses. Altogether, these components create a complete software ecosystem for researchers to study translation through ribosome profiling. Availability and ImplementationFor a quickstart, please see https://ribosomeprofiling.github.io. Source code, installation instructions and links to documentation are available on GitHub: https://github.com/ribosomeprofiling

bioinformatics