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Siddiqa, A.

Publications and source records attributed to Siddiqa, A..

2 recordsLinked to original sources

HPV16 Utilizes Phospholipase C(s) for its Genome Egress

Nuclear delivery of human papillomavirus (HPV) requires infected cells to undergo mitosis. Incoming HPV DNA is protected by a transport vesicle structure, which becomes unstable post-mitosis. We have previously shown that HPV genome egress takes on average 4 hours post-mitosis completion; thus, we propose that an enzymatic process is involved in degrading this transport vesicle to allow genome egress. Several phospholipase C (PLC) isoforms, including PLC{delta}3, PLC{zeta}1, and PLCL1, have been previously implicated in HPV infection in large-scale siRNA screens, and the parvovirus VP1 capsid protein has phospholipase enzymatic activity. Therefore, we hypothesized that cellular phospholipases residing in the nucleus mediate egress of the HPV genome from transport vesicles. We utilized siRNA-mediated knockdown and CRISPR/Cas9 knockout techniques to target specific PLC isoforms in both HeLa and HaCaT cell lines. We found that targeting PLC isoforms PLC{beta}1, {beta}4, {delta}3, {delta}4, and {gamma}1 significantly decreased HPV infection in both HeLa and HaCaT cells, as measured using a luciferase-based reporter assay. We also discovered a novel interaction between HPV16 minor protein L2 and PLCs {beta}1, {beta}4, {delta}1, {delta}3, and {delta}4. Furthermore, we observed that knockout of PLC{beta}4 and {delta}4 delayed egress of the HPV genome from nuclear membrane-bound vesicles after infection of HeLa cells. We propose that HPV utilizes phospholipase Cs to achieve genome egress from its protective vesicular structure after nuclear delivery.

microbiology↗

Trackable and scalable LC-MS metabolomics data processing using asari

Significant challenges still exist in the computational processing of data from LC-MS metabolomic experiments into metabolite features. In this study, we examine the issues of provenance and reproducibility in the current software tools. The inconsistency among these tools is attributed to the deficiencies of mass alignment and controls of feature quality. To address these issues, we have developed a new open-source software tool, asari, for LC-MS metabolomics data processing. Asari is designed with a set of new algorithmic framework and data structures, and all steps are explicitly trackable. Asari compares favorably to other tools in feature detection and quantification. It offers substantial improvement of computational performance over current tools, and is highly scalable.

biochemistry↗