Biology subjects

Wozniak, J.

Publications and source records attributed to Wozniak, J..

6 recordsLinked to original sources

bioRxiv2023

Catalytic bias and redox-driven inactivation of ancestral FeFe hydrogenases from group B

The biodiversity of hydrogenases, the enzymes that oxidize and produce H2, is only just beginning to be explored. Here we use direct electrochemistry to characterize two enzymes from a subgroup of ancestral FeFe hydrogenases, defined by the presence of three adjacent cysteine residues near the active site: the third FeFe hydrogenase from Clostridium pasteurianum (CpIII) and the second from Megasphaera elsdenii (MeII). To examine the functional role of the unusual TSCCCP motif, which defines the group B2 and is replaced with TSCCP in group A hydrogenases, we also produced a CpIII variant where the supernumerary cysteine is deleted. CpIII and MeII inactivate under oxidative conditions in a manner that is distinct from all other previously characterized hydrogenases from group A. Our results suggest that the supernumerary cysteine allows the previously observed sulfide-independent formation of the Hinact state in these enzymes. We also evidence a second reversible, oxidative inactivation process. Because of their inactivation under oxidative conditions, these enzymes are inefficient H2 oxidation catalysts, but their active site itself is not tuned to make them more active in one particular direction.

biochemistry↗

bioRxiv2021

Targeted Protein Acetylation in Cells Using Heterobifunctional Molecules

Protein acetylation is a central event in orchestrating diverse cellular processes. However, current strategies to investigate protein acetylation in cells are often non-specific or lack temporal and magnitude control. Here, we developed an acetylation tagging system, AceTAG, to induce acetylation of targeted proteins. The AceTAG system utilizes bifunctional molecules to direct the lysine acetyltransferase p300/CBP to proteins fused with the small protein tag FKBP12F36V, resulting in their induced acetylation. Using AceTAG, we induced targeted acetylation of a diverse array of proteins in cells, specifically histone H3.3, the NF-{kappa}B subunit p65/RelA, and the tumor suppressor p53. We demonstrate that targeted acetylation with the AceTAG system is rapid, selective, reversible, and can be controlled in a dose-dependent fashion. AceTAG represents a useful strategy to modulate protein acetylation and will enable the exploration of targeted acetylation in basic biological and therapeutic contexts. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=67 SRC="FIGDIR/small/454011v1_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@e7eeddorg.highwire.dtl.DTLVardef@f2fbf1org.highwire.dtl.DTLVardef@7a9d23org.highwire.dtl.DTLVardef@1e2a5b4_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2021

Proteomic Organellar Mapping Reveals Modulation of Peroxisomes by HIV-1

As systems biology approaches to virology have become more tractable, highly studied viruses such as HIV can now be analyzed in new, unbiased ways, including spatial proteomics. We employed here a differential centrifugation protocol to fractionate Jurkat T cells for proteomic analysis by mass spectrometry; these cells contain inducible HIV-1 genomes, enabling us to look for changes in the spatial proteome induced by viral gene expression. Using these proteomics data, we evaluated the merits of several reported machine learning pipelines for classification of the spatial proteome and identification of protein translocations. From these analyses we found that classifier performance in this system was organelle-dependent, with Bayesian t-augmented Gaussian mixture modeling outperforming support vector machine (SVM) learning for mitochondrial and ER proteins, but underperforming on cytosolic, nuclear, and plasma membrane proteins by QSep analysis. We also observed a generally higher performance for protein translocation identification using a Bayesian model, BANDLE, on SVM-classified data. Comparative BANDLE analysis of cells induced to express the wild-type viral genome vs. cells induced to express a genome unable to express the accessory protein Nef identified known Nef-dependent interactors such as TCR signaling components and coatomer complex. Lastly, we found that SVM classification showed higher consistency and was less sensitive to HIV-dependent noise. These findings illustrate important considerations for studies of the spatial proteome following viral infection or viral gene expression and provide a reference for future studies of HIV-gene-dropout viruses.

cell biology↗

bioRxiv2021

A combined EM and proteomic analysis places HIV-1 Vpu at the crossroads of retromer and ESCRT complexes: PTPN23 is a Vpu-cofactor

The HIV-1 accessory protein Vpu modulates membrane protein trafficking and degradation to provide evasion of immune surveillance. Targets of Vpu include CD4, HLAs, and BST-2. Several cellular pathways co-opted by Vpu have been identified, but the picture of Vpus itinerary and activities within membrane systems remains incomplete. Here, we used fusion proteins of Vpu and the enzyme ascorbate peroxidase (APEX2) to compare the ultrastructural locations and the proximal proteomes of wild type Vpu and Vpu-mutants. The proximity-omes of the proteins correlated with their ultrastructural locations and placed wild type Vpu near both retromer and ESCRT-0 complexes. Hierarchical clustering of protein abundances across the mutants was essential to interpreting the data and identified Vpu degradation-targets including CD4, HLA-C, and SEC12 as well as Vpu-cofactors including HGS, STAM, clathrin, and PTPN23, an ALIX-like protein. The Vpu-directed degradation of BST-2 required PTPN23 but not the retromer subunits. These data suggest that Vpu directs targets from sorting endosomes to degradation at multi-vesicular bodies via ESCRT-0 and PTPN23. Author SummaryVpu triggers the degradation or mis-localization of proteins important to the hosts immune response. Vpu acts as an adaptor, linking cellular protein targets to the ubiquitination and membrane trafficking machinery. Vpu has been localized to various cellular membrane systems. By fusing wild type Vpu and Vpu-mutants to the enzyme ascorbate peroxidase, we defined the cellular proteome in proximity to Vpu and correlated this with the proteins location. We found that wild type Vpu is proximal to ESCRT proteins, retromer complexes, and sorting and late endosomal proteins. Functionally, we found that the Vpu-mediated degradation of the innate defense protein BST-2 required PTPN23, an ALIX-like protein, consistent with our observation of Vpus presence at the limiting membranes of multi-vesicular bodies.

microbiology↗

bioRxiv2019

Managing genomic variant calling workflows with Swift/T

Genomic variant discovery is frequently performed using the GATK Best Practices variant calling pipeline, a complex workflow with multiple steps, fans/merges, and conditionals. This complexity makes management of the workflow difficult on a computer cluster, especially when running in parallel on large batches of data: hundreds or thousands of samples at a time. Here we describe a wrapper for the GATK-based variant calling workflow using the Swift/T parallel scripting language. Standard built-in features include the flexibility to split by chromosome before variant calling, optionally permitting the analysis to continue when faulty samples are detected, and allowing users to analyze multiple samples in parallel within each cluster node. The use of Swift/T conveys two key advantages: (1) Thanks to the embedded ability of Swift/T to transparently operate in multiple cluster scheduling environments (PBS Torque, SLURM, Cray aprun environment, etc.,) a single workflow is trivially portable across numerous clusters; (2) The leaf functions of Swift/T permit developers to easily swap executables in and out of the workflow, conditional on the analysts choice, which makes the workflow easy to maintain. This modular design permits separation of the workflow into multiple stages and the request of resources optimal for each stage of the pipeline. While Swift/Ts implicit data-level parallelism eliminates the need for the developer to code parallel analysis of multiple samples, it does make debugging of the workflow a bit more difficult, as is the case with any implicitly parallel code. With the above features, users have a powerful and portable way to scale up their variant calling analysis to run in many traditional computer cluster architectures.\n\nhttps://github.com/ncsa/Swift-T-Variant-Calling\n\nhttp://swift-t-variant-calling.readthedocs.io/en/latest/

bioinformatics↗

bioRxiv2017

High-throughput cancer hypothesis testing with an integrated PhysiCell-EMEWS workflow

BackgroundCancer is a complex, multiscale dynamical system, with interactions between tumor cells and non-cancerous host systems. Therapies act on this combined cancer-host system, sometimes with unexpected results. Systematic investigation of mechanistic computational models can augment traditional laboratory and clinical studies, helping identify the factors driving a treatments success or failure. However, given the uncertainties regarding the underlying biology, these multiscale computational models can take many potential forms, in addition to encompassing high-dimensional parameter spaces. Therefore, the exploration of these models is computationally challenging. We propose that integrating two existing technologies--one to aid the construction of multiscale agent-based models, the other developed to enhance model exploration and optimization--can provide a computational means for high-throughput hypothesis testing, and eventually, optimization.\n\nResultsIn this paper, we introduce a high throughput computing (HTC) framework that integrates a mechanistic 3-D multicellular simulator (PhysiCell) with an extreme-scale model exploration platform (EMEWS) to investigate high-dimensional parameter spaces. We show early results in applying PhysiCell-EMEWS to 3-D cancer immunotherapy and show insights on therapeutic failure. We describe a generalized PhysiCell-EMEWS workflow for high-throughput cancer hypothesis testing, where hundreds or thousands of mechanistic simulations are compared against data-driven error metrics to perform hypothesis optimization.\n\nConclusionsWhile key notational and computational challenges remain, mechanistic agent-based models and high-throughput model exploration environments can be combined to systematically and rapidly explore key problems in cancer. These high-throughput computational experiments can improve our understanding of the underlying biology, drive future experiments, and ultimately inform clinical practice.

systems biology↗