Biology subjects

Samavarchi-Tehrani, P.

Publications and source records attributed to Samavarchi-Tehrani, P..

6 recordsLinked to original sources

bioRxiv2026

Spatiotemporal remodeling of cytoskeletal and junction networks during somatic cell reprogramming

Summary/AbstractReprogramming somatic cells into induced pluripotent stem cells involves a dramatic reorganization of the cytoskeleton and junctions during the critical mesenchymal-to-epithelial transition stage. While protein abundance changes have been profiled, the spatiotemporal dynamics of protein-protein associations involving these structural components remain poorly resolved. Here, we present a time-resolved proximity proteomics resource that maps cytoskeletal and junctional remodeling across 27 baits during the early stages of reprogramming. We identified over 1100 high-confidence interactions, including many not previously reported, capturing the dynamic reorganization of cell architecture. By integrating proximity-dependent biotinylation with quantitative proteomics, we distinguished spatial relocalizations from abundance-driven effects. Dynamic redistributions of actin regulators and desmosomal proteins were observed, and a targeted short interfering RNA screen uncovered early acting structural proteins essential for colony formation. Our findings reveal adhesion and cytoskeletal maturation as structural bottlenecks in reprogramming and provide a broadly applicable framework for mapping subcellular remodeling during dynamic cell fate transitions.

systems biology↗

bioRxiv2023

Dynamic extracellular proximal interaction profiling reveals Low-Density Lipoprotein Receptor as a new Epidermal Growth Factor signaling pathway component

Plasma membrane proteins are critical mediators of cell-cell and cell-environment interactions, pivotal in intracellular signal transmission vital for cellular functionality. Proximity-dependent biotinylation approaches such as BioID combined with mass spectrometry have begun illuminating the landscape of proximal protein interactions within intracellular compartments. However, their deployment in studies of the extracellular environment remains scarce. Here, we present extracellular TurboID (ecTurboID), a method designed to profile cell surface interactions in living cells on short timescales. We first report on the careful optimization of experimental and data analysis strategies that enable the capture of extracellular protein interaction information. Leveraging the ecTurboID technique, we unveiled the proximal interactome of multiple plasma membrane proteins, notably the epidermal growth factor receptor (EGFR). This led to identifying the low-density lipoprotein receptor (LDLR) as a newfound extracellular protein associating with EGFR, contingent upon the presence of the EGF ligand. We showed that 15 minutes of EGF stimulation induced LDLR localization to the plasma membrane to associate with proteins involved in EGFR regulation. This modified proximity labelling methodology allows us to dynamically study the associations between plasma membrane proteins in the extracellular environment. One Sentence SummaryWe developed extracellular TurboID (ecTurboID) as a new proximity dependent biotinylation approach that can capture dynamic interactions at the cell surface, identifying Low-Density Lipoprotein Receptor as a new ligand-dependent extracellular partner of Epidermal Growth Factor Receptor.

molecular biology↗

bioRxiv2020

A SARS-CoV-2 - host proximity interactome

Viral replication is dependent on interactions between viral polypeptides and host proteins. Identifying virus-host protein interactions can thus uncover unique opportunities for interfering with the virus life cycle via novel drug compounds or drug repurposing. Importantly, many viral-host protein interactions take place at intracellular membranes and poorly soluble organelles, which are difficult to profile using classical biochemical purification approaches. Applying proximity-dependent biotinylation (BioID) with the fast-acting miniTurbo enzyme to 27 SARS-CoV-2 proteins in a lung adenocarcinoma cell line (A549), we detected 7810 proximity interactions (7382 of which are new for SARS-CoV-2) with 2242 host proteins (results available at covid19interactome.org). These results complement and dramatically expand upon recent affinity purification-based studies identifying stable host-virus protein complexes, and offer an unparalleled view of membrane-associated processes critical for viral production. Host cell organellar markers were also subjected to BioID in parallel, allowing us to propose modes of action for several viral proteins in the context of host proteome remodelling. In summary, our dataset identifies numerous high confidence proximity partners for SARS-CoV-2 viral proteins, and describes potential mechanisms for their effects on specific host cell functions.

systems biology↗

bioRxiv2020

A SARS-CoV-2 BioID-based virus-host membrane protein interactome and virus peptide compendium: new proteomics resources for COVID-19 research

Key steps of viral replication take place at host cell membranes, but the detection of membrane-associated protein-protein interactions using standard affinity-based approaches (e.g. immunoprecipitation coupled with mass spectrometry, IP-MS) is challenging. To learn more about SARS-CoV-2 - host protein interactions that take place at membranes, we utilized a complementary technique, proximity-dependent biotin labeling (BioID). This approach uncovered a virus-host topology network comprising 3566 proximity interactions amongst 1010 host proteins, highlighting extensive virus protein crosstalk with: (i) host protein folding and modification machinery; (ii) membrane-bound vesicles and organelles, and; (iii) lipid trafficking pathways and ER-organelle membrane contact sites. The design and implementation of sensitive mass spectrometric approaches for the analysis of complex biological samples is also important for both clinical and basic research proteomics focused on the study of COVID-19. To this end, we conducted a mass spectrometry-based characterization of the SARS-CoV-2 virion and infected cell lysates, identifying 189 unique high-confidence virus tryptic peptides derived from 17 different virus proteins, to create a high quality resource for use in targeted proteomics approaches. Together, these datasets comprise a valuable resource for MS-based SARS-CoV-2 research, and identify novel virus-host protein interactions that could be targeted in COVID-19 therapeutics.

cell biology↗

bioRxiv2019

A proximity biotinylation map of a human cell

Compartmentalization is a defining characteristic of eukaryotic cells, partitioning cellular processes into discrete subcellular locations. High throughput microscopy1 and biochemical fractionation coupled with mass spectrometry2-6 have helped to define the proteomes of a variety of organelles and macromolecular structures. However, many other intracellular compartments have remained refractory to such approaches, due for example to difficulty in purifying non-membrane bound structures. Proximity-dependent biotinylation techniques such as BioID provide an alternative approach for defining the composition of cellular compartments in living cells7-10. Here we present a BioID-based map of a human cell based on 192 markers from 32 different subcellular compartments, comprising 35,902 high confidence proximity interactions, and defining the intracellular locations of 4,145 unique proteins in HEK 293 cells. Our localization predictions meet or exceed previous-approaches, with higher specificity, and enabled the discovery of proteins at the mitochondrial outer membrane-endoplasmic reticulum (ER) interface that are critical for mitochondrial homeostasis. Based on this dataset, we have established humancellmap.org as a community resource that provides online tools for localization analysis of user BioID data, and demonstrate how this resource can be used to better understand BioID datasets.

molecular biology↗

bioRxiv2019

Gene Info: Easy retrieval of gene product information on any website

Viewing information about gene products is a constant part of the molecular biologists life. While there are many high quality and well-designed resources to fulfill this need, they require the user to navigate to these resources, execute a search, select the desired result and then view its information. This can be a repetitive, time-consuming and even disruptive process, for example when exploring the results of large scale genomics or proteomics screens or reading an online article.

bioinformatics↗