Biology subjects

Nabi, I. R.

Publications and source records attributed to Nabi, I. R..

14 recordsLinked to original sources

bioRxiv2026

The Gp78 ubiquitin E3 ligase drives mitophagy by regulating omegasome formation

Autophagy is initiated at endoplasmic reticulum (ER)-associated omegasomes, yet how the ER contributes to mitochondrial cargo degradation during early autophagosome formation remains unclear. Gp78 is an ER-resident RING E3 ubiquitin ligase involved in ER-associated degradation (ERAD) that also promotes Parkin-independent mitophagy. However, the spatial relationship between Gp78 ubiquitin ligase activity, omegasome formation, and mitochondrial degradation has yet to be defined. Combining targeted mitochondria labeling and super-resolution STED microscopy, we show that the Gp78 ubiquitin ligase is localized to and regulates DFCP1-positive omegasome formation, where it drives mitochondrial protein breakdown. Using fluorescent mitophagy reporters and acute ivermectin-induced mitophagy, generation of mitophagy intermediates lacking mitochondrial outer membrane (TOMM20) was found to be dependent on the Gp78 RING finger as well as the BAG6-UBL4A-USP13 ubiquitin-ligase module, and independent of PINK1 and Parkin. Object-based analysis of 3D STED images further showed that both DFCP1- and LC3-positive structures associate primarily with intact mitochondrial fragments which show progressive degradation of mitochondrial content. Gp78 localizes to and promotes, via BAG6, the formation of DFCP1-positive omegasomes and mitochondrial protein degradation in these early autophagic structures. ER-derived omegasome biogenesis is therefore coupled to Gp78-dependent ubiquitin ligase activity and local degradation of mitochondrial cargo during mitophagy.

cell biology↗

bioRxiv2025

HDCluster: High-Degree Graph Clustering for Robust Analysis of Single Molecule Localization Microscopy

Clustering is a fundamental task in data analysis: grouping similar objects together into distinguishable subsets. Here, we introduce HDCluster, a novel high-degree graph-based clustering algorithm designed to effectively and rapidly handle various real-world clustering applications, particularly in the context of super-resolution single molecule localization microscopy (SMLM). HDCluster efficiently handles datasets with large and variable numbers of clusters, without requiring prior knowledge of the cluster count, relying on only one parameter. The high speed and efficiency of HDCluster allow it to handle large SMLM datasets with millions of localizations. A comprehensive quantitative comparison against state-of-the-art clustering methods using simulated, public, and real-world datasets demonstrates that HDCluster outperforms other clustering algorithms in terms of time efficiency and clustering performance measures, such as ARI and AMI. HDCluster is particularly robust to noise, making it a promising and effective tool for various clustering tasks in big-data settings, such as SMLM.

bioinformatics↗

bioRxiv2025

Adversarial erasing enhanced multiple instance learning (siMILe): Discriminative identification of oligomeric protein structures in single molecule localization microscopy

Single-molecule localization microscopy (SMLM) achieves nanoscale imaging of complex protein structures in the cell. However, the ability to capture structural variability across cell conditions (cell lines, gene expression, treatment) from 3D point cloud SMLM data remains limited. We present siMILe, a weakly-supervised multiple instance learning (MIL) machine learning method to close this gap in interpretable subcellular discovery. siMILe identifies condition-specific changes in protein assemblies by leveraging their shape and network features, without requiring structure-level supervision. siMILe improves structure classification by extending embedded instance selection (MILES) through adversarial erasing and a symmetric classifier. We validated siMILe by detecting caveolae from caveolin-1 (Cav1) labeled PC3 prostate cancer cells differentially expressing cavin-1. In PC3-CAVIN1 cells, cavin-1 closely associates with siMILe-identified caveolae, to a lesser extent with higher-order non-caveolar Cav1 scaffolds, but not small Cav1 oligomers corresponding to 8S complexes, supporting a role for progressive cavin-1 interaction in 8S complex oligomerization. We also validated siMILe on simulated SMLM data and in detecting inhibitor-induced structural variations within clathrin-coated pit data. These results highlight siMILes potential to identify differential molecular structures in distinct cell conditions. siMILe extends the SuperResNET SMLM software platform with the ability to detect interpretable structural differences across conditions.

bioinformatics↗

bioRxiv2024

SuperResNET single molecule localization microscopy model-free network analysis of Nup96 achieves molecular resolution

SuperResNET is an integrated machine learning-based analysis software for visualizing and quantifying 3D point cloud data acquired by single molecule localization microscopy (SMLM). The computational modules of SuperResNET include correction for multiple blinking of a single fluorophore, denoising, segmentation (clustering), and feature extraction, which are then used for cluster group identification, modularity analysis, blob retrieval and visualization in 2D and 3D. Using publicly available dSTORM data, we apply a graphical user interface (GUI) version of SuperResNET to nucleoporin Nup96 structures, that present a highly organized octagon structure comprised of eight corners. SuperResNET GUI effectively segments nuclear pores and Nup96 corners based on differential proximity threshold analysis. SuperResNET GUI quantitatively analyzes features from segmented nuclear pore structures, including complete structures with 8-fold symmetry, and from segmented corners. SuperResNET GUI modularity analysis of segmented corners distinguishes two modules at 11.1 nm distance, corresponding to two individual Nup96 molecules. SuperResNET GUI is therefore a model-free tool that can reconstruct network architecture and molecular distribution of subcellular structures without the bias of a specified prior model, attaining molecular resolution from dSTORM data. SuperResNET GUI provides flexibility to report on structural diversity in situ within the cell without model-fitting, providing opportunities for biological discovery.

cell biology↗

bioRxiv2024

Super-resolution single molecule network analysis (SuperResNET) detects changes to clathrin structure by small molecule inhibitors

Specificity of small molecules for their target molecule in the cell is critical to determine their effective use as biologics and therapeutics. Small molecule inhibitors of clathrin endocytosis, Pitstop 2, and the dynamin inhibitor Dynasore, have off-target effects and their specificity has been challenged. Here, we used SuperResNET to apply network analysis to 20 nm resolution dSTORM single-molecule localization microscopy (SMLM) to test whether Pitstop 2 and Dynasore alter the morphology of clathrin coated pits in intact cells. SuperResNET analysis of dSTORM data from HeLa and Cos7 cells identifies three classes of clathrin structures: small oligomers (Class I); pits and vesicles (Class II); and larger clusters corresponding to fused clathrin pits and clathrin plaques (Class III). SuperResNET analysis of high resolution MinFlux imaging identifies Class 1 oligomers as well as Class 2 structures including morphologically identifiable clathrin pits and vesicles. SuperResNET feature analysis of dSTORM data shows that Pitstop 2 and Dynasore induce the formation of distinct homogenous populations of clathrin structures in HeLa cells. Pitstop 2 blobs are smaller and more elongated than those induced by Dynasore, indicating that these two clathrin inhibitors arrest clathrin endocytosis at distinct stages. Pitstop 2 and Dynasore are not impacting clathrin structure via actin depolymerization as the actin depolymerizing agent latrunculin A (LatA) induced larger heterogeneous clathrin structures. Ternary analysis of SuperResNET shape features presents a distinct profile for Pitstop 2 Class II structures. The most representative Pitstop blobs align with and resemble MinFlux clathrin pits while control structures resemble Minflux clathrin vesicles. SuperResNET analysis of SMLM data is therefore a highly sensitive approach to detect the effect of small molecules on target molecule structure in situ in the cell.

cell biology↗

bioRxiv2024

nERdy: network analysis of endoplasmic reticulum dynamics

The endoplasmic reticulum (ER) comprises smooth tubules, ribosome-studded sheets, and peripheral sheets that can present as tubular matrices. ER shaping proteins determine ER morphology, however, understanding their role in tubular matrix formation requires reconstructing the dynamic, convoluted ER network. Existing reconstruction methods are sensitive to parameters or require extensive annotation and training for deep learning. We introduce nERdy, an image processing based approach, and nERdy+, a D4-equivariant neural network, for accurate extraction and representation of ER networks and junction dynamics, outperforming previous methods. Comparison of stable and dynamic representations of the extracted ER structure reports on tripartite junction movement and distinguishes tubular matrices from peripheral ER networks. Analysis of live cell confocal and STED time series data shows that Atlastin and Reticulon 4 promote dynamic tubular matrix formation and enhance junction dynamics, identifying novel roles for these ER shaping proteins in regulating ER structure and dynamics.

cell biology↗

bioRxiv2022

Automatic sub-precision membrane contact site detection identifies convoluted tubular riboMERCs

Identification and morphological analysis of mitochondria-ER contacts (MERCs) by fluorescent microscopy is limited by sub-pixel resolution inter-organelle distances. Application of a Membrane Contact Site (MCS) detection algorithm, MCS-DETECT, to 3D STED super-resolution image volumes reconstructs sub-resolution MERCs. MCS-DETECT shows that elongated ribosome-studded riboMERCs, present in HT-1080 but not COS-7 cells, are morphologically distinct from smaller smooth contacts and larger contacts induced by mitochondria-ER linker expression in COS-7 cells. riboMERC expression is reduced in Gp78 knockout HT-1080 cells and induced by Gp78 ubiquitin ligase activity in COS-7 cells. Knockdown of the riboMERC tether RRBP1 eliminates riboMERCs in both wild-type and Gp78 knockout HT-1080 cells. By MCS-DETECT, Gp78-dependent riboMERCs present complex tubular shapes that intercalate between and contact multiple mitochondria, that are lost upon RRBP1 knockdown. MCS-DETECT of 3D whole cell super-resolution image volumes therefore identifies a novel dual regulatory mechanism for tubular riboMERCs, whose formation is dependent on RRBP1 and size modulated by Gp78 E3 ubiquitin ligase activity. eTOC SummaryApplication of the sub-pixel resolution Membrane Contact Site (MCS) detection algorithm, MCS-DETECT, to 3D STED super-resolution image volumes identifies a novel dual regulatory mechanism for tubular riboMERCs, whose formation is dependent on RRBP1 and size modulated by Gp78 E3 ubiquitin ligase activity.

cell biology↗

bioRxiv2021

Gp78-mediated basal mitophagy promotes mitochondrial health and limits mitochondrial ROS production

Mitochondria are major sources of cytotoxic reactive oxygen species (ROS) that contribute to cancer progression. Mitophagy, the selective elimination of mitochondria by autophagy, monitors and maintains mitochondrial health and integrity, eliminating ROS-producing mitochondria. However, mechanisms underlying mitophagic control of mitochondrial homeostasis under basal conditions remain poorly understood. Gp78 E3 ubiquitin ligase is an endoplasmic reticulum membrane protein that induces mitochondrial fission and mitophagy of depolarized mitochondria. Here, we report that CRISPR/Cas9 knockout of Gp78 in HT-1080 fibrosarcoma cells increased mitochondrial volume and rendered cells resistant to carbonyl cyanide m-chlorophenyl hydrazone (CCCP)-induced mitophagy. These effects were phenocopied by knockdown of the essential autophagy protein ATG5 in wild-type HT-1080 cells. Use of the mito-Keima mitophagy probe confirmed that Gp78 promoted both basal and damage-induced mitophagy. Application of a spot detection algorithm (SPECHT) to GFP-mRFP tandem fluorescent-tagged LC3 (tfLC3)-positive autophagosomes reported elevated autophagosomal maturation in wild-type HT-1080 cells relative to Gp78 knockout cells, predominantly in proximity to mitochondria. Mitophagy inhibition by either Gp78 knockout or ATG5 knockdown reduced mitochondrial potential and increased mitochondrial ROS. Live cell analysis of tfLC3 in HT-1080 cells showed the preferential association of autophagosomes with mitochondria of reduced potential. Basal Gp78-dependent mitophagic flux is therefore selectively associated with reduced potential mitochondria promoting maintenance of a healthy mitochondrial population and limiting ROS production.

cell biology↗

bioRxiv2021

A novel highly potent inhibitor of TMPRSS2-like proteases blocks SARS-CoV-2 variants of concern and is broadly protective against infection and mortality in mice

The COVID-19 pandemic caused by the SARS-CoV-2 virus remains a global public health crisis. Although widespread vaccination campaigns are underway, their efficacy is reduced against emerging variants of concern (VOCs) 1,2. Development of host-directed therapeutics and prophylactics could limit such resistance and offer urgently needed protection against VOCs 3,4. Attractive pharmacological targets to impede viral entry include type-II transmembrane serine proteases (TTSPs), such as TMPRSS2, whose essential role in the virus lifecycle is responsible for the cleavage and priming of the viral spike protein 5-7. Here, we identify and characterize a small-molecule compound, N-0385, as the most potent inhibitor of TMPRSS2 reported to date. N-0385 exhibited low nanomolar potency and a selectivity index of >106 at inhibiting SARS-CoV-2 infection in human lung cells and in donor-derived colonoids 8. Importantly, N-0385 acted as a broad-spectrum coronavirus inhibitor of two SARS-CoV-2 VOCs, B.1.1.7 and B.1.351. Strikingly, single daily intranasal administration of N-0385 early in infection significantly improved weight loss and clinical outcomes, and yielded 100% survival in the severe K18-human ACE2 transgenic mouse model of SARS-CoV-2 disease. This demonstrates that TTSP-mediated proteolytic maturation of spike is critical for SARS-CoV-2 infection in vivo and suggests that N-0385 provides a novel effective early treatment option against COVID-19 and emerging SARS-CoV-2 VOCs.

microbiology↗

bioRxiv2020

SUPER RESOLUTION MICROSCOPY AND DEEP LEARNING IDENTIFY ZIKA VIRUS REORGANIZATION OF THE ENDOPLASMIC RETICULUM

The endoplasmic reticulum (ER) is a complex subcellular organelle composed of diverse structures such as tubules, sheets and tubular matrices. Flaviviruses such as Zika virus (ZIKV) induce reorganization of endoplasmic reticulum (ER) membranes to facilitate viral replication. Here, using 3D super resolution microscopy, ZIKV infection is shown to induce the formation of dense tubular matrices associated with viral replication in the central ER. Viral non-structural proteins NS4B and NS2B associate with replication complexes within the ZIKV-induced tubular matrix and exhibit distinct ER distributions outside this central ER region. Deep neural networks trained to identify ZIKV-infected versus mock-infected cells successfully identified ZIKV-induced central ER tubular matrices as a determinant of viral infection. Super resolution microscopy and deep learning are therefore able to identify and localize morphological features of the ER and may be of use to screen for inhibitors of infection by ER-reorganizing viruses.

cell biology↗

bioRxiv2019

Reticulon and CLIMP-63 control nanodomain organization of peripheral ER tubules

The endoplasmic reticulum (ER) is an expansive, membrane-enclosed organelle composed of smooth peripheral tubules and rough, ribosome-studded central ER sheets whose morphology is determined, in part, by the ER-shaping proteins, reticulon and CLIMP-63, respectively. Here, STimulated Emission Depletion (STED) super-resolution microscopy shows that reticulon and CLIMP-63 also control the organization and dynamics of peripheral ER tubule nanodomains. STED imaging shows that lumenal ERmoxGFP, membrane Sec61{beta}GFP, knock-in calreticulin-GFP and antibody-labeled ER resident proteins calnexin and derlin-1 are all localized to periodic puncta along the length of peripheral ER tubules that are not readily observable by diffraction limited confocal microscopy. Reticulon segregates away from and restricts lumenal blob length while CLIMP-63 associates with and increases lumenal blob length. Reticulon and CLIMP-63 also regulate the nanodomain distribution of ER resident proteins, being required for the preferential segregation of calnexin and derlin-1 puncta away from lumenal ERmoxGFP blobs. High-speed (40 ms/frame) live cell STED imaging shows that reticulon and CLIMP-63 control nanoscale compartmentalization of lumenal flow in peripheral ER tubules. Reticulon enhances and CLIMP-63 disrupts the local accumulation of lumenal ERmoxGFP at spatially defined sites along ER tubules. The ER shaping proteins reticulon and CLIMP-63 therefore control lumenal ER nanodomain dynamics, heterogeneity and interaction with ER resident proteins in peripheral ER tubules.

cell biology↗

bioRxiv2019

Caveolae and scaffold detection from single molecule localization microscopy data using deep learning

Caveolae are plasma membrane invaginations whose formation requires caveolin-1 (Cav1), the adaptor protein polymerase I, and the transcript release factor (PTRF or CAVIN1). Caveolae have an important role in cell functioning, signaling, and disease. In the absence of CAVIN1/PTRF, Cav1 forms non-caveolar membrane domains called scaffolds. In this work, we train machine learning models to automatically distinguish between caveolae and scaffolds from single molecule localization microscopy (SMLM) data. We apply machine learning algorithms to discriminate biological structures from SMLM data. Our work is the first that is leveraging machine learning approaches (including deep learning models) to automatically identifying biological structures from SMLM data. In particular, we develop and compare three binary classification methods to identify whether or not a given 3D cluster of Cav1 proteins is a caveolae. The first uses a random forest classifier applied to 28 hand-crafted/designed features, the second uses a convolutional neural net (CNN) applied to a projection of the point clouds onto three planes, and the third uses a PointNet model, a recent development that can directly take point clouds as its input. We validate our methods on a dataset of super-resolution microscopy images of PC3 prostate cancer cells labeled for Cav1. Specifically, we have images from two cell populations: 10 PC3 and 10 CAVIN1/PTRF-transfected PC3 cells (PC3-PTRF cells) that form caveolae. We obtained a balanced set of 1714 different cellular structures. Our results show that both the random forest on hand-designed features and the deep learning approach achieve high accuracy in distinguishing the intrinsic features of the caveolae and non-caveolae biological structures. More specifically, both random forest and deep CNN classifiers achieve classification accuracy reaching 94% on our test set, while the PointNet model only reached 83% accuracy. We also discuss the pros and cons of the different approaches.

molecular biology↗

bioRxiv2018

Super-resolution modularity analysis shows that polyhedral caveolin-1 oligomers combine to form scaffolds and caveolae

Caveolin-1 (Cav1), the coat protein for caveolae, also forms non-caveolar Cav1 scaffolds. Single molecule Cav1 super-resolution microscopy analysis previously identified caveolae and three distinct scaffold domains: smaller S1A and S2B scaffolds and larger hemispherical S2 scaffolds. Application here of network modularity analysis of SMLM data for endogenous Cav1 labeling in HeLa cells shows that small scaffolds combine to form larger scaffolds and caveolae. We find modules within Cav1 blobs by maximizing the intra-connectivity between Cav1 molecules within a module and minimizing the inter-connectivity between Cav1 molecules across modules, which is achieved via spectral decomposition of the localizations adjacency matrix. Features of modules are then matched with intact blobs to find the similarity between the module-blob pairs of group centers. Our results show that smaller S1A and S1B scaffolds are made up of small polygons, that S1B scaffolds correspond to S1A scaffold dimers and that caveolae and hemi-spherical S2 scaffolds are complex, modular structures formed from S1B and S1A scaffolds, respectively. Polyhedral interactions of Cav1 oligomers therefore leads progressively to the formation of larger and more complex scaffold domains and the biogenesis of caveolae.

cell biology↗

bioRxiv2018

Gp78 E3 ubiquitin ligase mediates both basal and damage-induced mitophagy

Mitophagy, the elimination of mitochondria by the autophagy machinery, evolved to monitor mitochondrial health and maintain mitochondrial integrity. PINK1 is a sensor of mitochondrial health that recruits Parkin and other mitophagy-inducing ubiquitin ligases to depolarized mitochondria. However, mechanisms underlying mitophagic control of mitochondrial homeostasis, basal mitophagy, remain poorly understood. The Gp78 E3 ubiquitin ligase, an endoplasmic reticulum membrane protein, induces mitochondrial fission, endoplasmic reticulum-mitochondria contacts and mitophagy of depolarized mitochondria. CRISPR/Cas9 knockout of Gp78 in HT-1080 fibrosarcoma cells results in reduced ER-mitochondria contacts, increased mitochondrial volume and resistance to CCCP-induced mitophagy. Knockdown (KD) of the essential autophagy protein ATG5 increased mitochondrial volume of wild-type cells but did not impact mitochondrial volume of Gp78 knockout cells. This suggests that endogenous Gp78 actively eliminates mitochondria by autophagy in wild-type HT-1080 cells. Damage-induced mitophagy of depolarized mitochondria, in the presence of CCCP, but not basal mitophagy was prevented by knockdown of PINK1. This suggests that endogenous Gp78 plays dual roles in mitophagy induction: 1) control of mitochondrial homeostasis through mitophagy of undamaged mitochondria; and 2) elimination of damaged mitochondria through PINK1.

cell biology↗