Biology subjects

Markmiller, S.

Publications and source records attributed to Markmiller, S..

5 recordsLinked to original sources

bioRxiv2022

VPS13A and VPS13C influence lipid droplet abundance

Lipid transfer proteins mediate the exchange of lipids between closely apposed membranes at organelle contact sites and play key roles in lipid metabolism, membrane homeostasis, and cellular signaling. A recently discovered novel family of lipid transfer proteins, which includes the VPS13 proteins (VPS13A-D), adopt a rod-like bridge conformation with an extended hydrophobic groove that enables the bulk transfer of membrane lipids for membrane growth. Loss of function mutations in VPS13A and VPS13C cause chorea acanthocytosis and Parkinsons disease, respectively. VPS13A and VPS13C localize to multiple organelle contact sites, including endoplasmic reticulum (ER) - lipid droplet (LD) contact sites, but the functional roles of these proteins in LD regulation remains mostly unexplored. Here, we employ CRISPR-Cas9 genome editing to generate VPS13A and VPS13C knockout cell lines in U-2 OS cells via deletion of exon 2 and introduction of an early frameshift. Analysis of LD content in these cell lines revealed that loss of either VPS13A or VPS13C results in reduced LD abundance under oleate-stimulated conditions. These data implicate VPS13A and VPS13C in LD regulation and raise the intriguing possibility that VPS13A and VPS13C-mediated lipid transfer facilitates LD biogenesis.

cell biology↗

bioRxiv2019

Glioblastomas derived from genetically modified pluripotent stem cells recapitulate pathobiology

Many current cellular models aimed at elucidating cancer biology do not recapitulate pathobiology including tumor heterogeneity, an inherent feature of cancer that underlies treatment resistance. Here we introduce a new cancer modeling paradigm using genetically engineered human pluripotent stem cells (hiPSCs) that capture authentic cancer pathobiology. Orthotopic engraftment of neural progenitor cells derived from hiPSCs that have been genome-edited to contain tumor-associated genetic driver mutations revealed by The Cancer Genome Atlas project for glioblastoma (GBM) result in formation of high-grade gliomas. As observed in GBM patient samples, these models harbor inter-tumor heterogeneity resembling different GBM molecular subtypes, and intra-tumor heterogeneity. Further, re-engraftment of primary tumor neurospheres generates secondary tumors with features characteristic of patient samples and present mutation-dependent patterns of tumor evolution. Thus, these cancer avatar models provide a platform for a comprehensive longitudinal assessment of human tumor development as governed by molecular subtype mutations and lineage-restricted differentiation.

cancer biology↗

bioRxiv2018

Modulation of RNA-dependent interactions in stress granules prevents persistent TDP-43 accumulation in ALS/FTD

Human genetic variants are usually represented by four values with variable length: chromosome, position, reference and alternate alleles. Thereis no guarantee that these components are represented in a consistent way across different data sources, and processing variant-based data can be inefficient because four different comparison operations are needed for each variant, three of which are string comparisons. Working with strings, in contrast to numbers, poses extra challenges on computer memory allocation and data-representation. Existing variant identifiers do not typicallyrepresent every possible variant we may be interested in, nor they are directly reversible. To overcome these limitations, VariantKey, a novel reversible numerical encoding schema for human genetic variants, is presented here alongside a multi-language open-source software implementation (http://github.com/genomicspls/variantkey). VariantKey represents variants as single 64 bit numeric entities, while preserving the ability to be searched and sorted by chromosome and position. The individual components of short variants can be directly read back from the VariantKey, while long variants are supported with a fast lookup table.\n\nHighlightsO_LI~100 compounds identified by high-content screen inhibit SGs in HEK293, NPCs and iPS-MNs.\nC_LIO_LIALS-associated RBPs are recruited to SGs in an RNA-dependent manner\nC_LIO_LIMolecules with planar moieties prevent recruitment of ALS-associated RBPs to SGs\nC_LIO_LICompounds inhibit TDP-43 accumulation in SGs and in TARDBP mutant iPS-MNs.\nC_LI

neuroscience↗

bioRxiv2018

Active protein neddylation or ubiquitylation is dispensable for stress granule dynamics

Many protein homeostasis stressors induce the formation of membraneless cytoplasmic stress granules (SGs) that contain large assemblies of repressed mRNAs and associated RNA binding proteins. Similar stressors have been shown to globally alter the function of the ubiquitin proteasome system (UPS) resulting in the accumulation of ubiquitylated proteins. Previous studies have demonstrated that ubiquitin and specific UPS components co-localize with SGs and that reducing the abundance or activity of ubiquitin pathway proteins can inhibit SG formation. These studies suggest that SG dynamics and composition may be regulated by ubiquitylation of SG resident proteins. Using ubiquitin-specific proteomic approaches, we demonstrate that many proteins, including some SG proteins are dynamically ubiquitylated upon SG-inducing sodium arsenite treatment. We utilized potent and selective inhibitors of the ubiquitin activating enzyme (UAE) or the NEDD8 activating enzyme (NAE) to directly test if active protein ubiquitylation or neddylation was required for SG dynamics. Using ubiquitin-site specific proteomics, we establish that UAE inhibition results in the rapid loss of nearly all protein ubiquitylation regardless of ubiquitin chain type. Addition of UAE or NAE inhibitors to cells did not alter arsenite-induced SG formation or dissolution. While we confirmed that ubiquitin co-localizes with both sodium arsenite and thapsigargin-induced SGs, antibodies that recognize all forms of ubiquitin more strongly co-localize with SGs compared to antibodies that preferentially recognize polyubiquitin or specific polyubiquitin-linkages. Interestingly, ubiquitin itself co-localizes with SGs in a UAE independent manner suggesting that the ubiquitin present within SGs is likely unconjugated ubiquitin. Our findings clearly demonstrate that active protein ubiquitylation or neddylation is not required for SG dynamics. These results suggest that ubiquitin-binding SG proteins may recruit free ubiquitin into SGs to modulate SG protein interactions.

cell biology↗

bioRxiv2017

Proximity labeling reveals an extensive steady-state stress granule interactome and insights to neurodegeneration

Stress granules (SGs) are transient ribonucleoprotein (RNP) aggregates that form in response to proteotoxic stress. Although SGs are distinct from aggregates observed in neurodegenerative disorders, they share protein components. We used APEX-mediated proximity labeling combined with quantitative mass spectrometry and high-throughput imaging to identify >100 previously unknown SG proteins in human cells, about 10% of which localize to SGs in a cell type- or stress type-dependent manner. Supporting a link between SG proteins and neurodegeneration, we demonstrate aberrant SG composition and subcellular distribution in iPSC-derived motor neurons from ALS patients, and identify several known and previously unidentified SG proteins that modify toxicity of mutant FUS and TDP-43 overexpression in Drosophila. We show that even in an unstressed steady-state, SG proteins form a densely-connected protein interaction network (PIN) and propose a model in which existing RNPs coalesce rapidly into microscopically visible granules that can act as gateways to pathological protein aggregation.\n\nHighlights O_LIAPEX proximity labeling of dynamic RNP granules identifies over 100 novel SG proteins\nC_LIO_LISG proteins form a densely-connected protein interaction network in unstressed cells\nC_LIO_LISystematic immunofluorescence analysis reveals stress- and cell type-specific SG composition\nC_LIO_LIALS motor neurons contain SGs with distinct content and subcellular distribution\nC_LI

cell biology↗