Biology subjects

Hakozaki, H.

Publications and source records attributed to Hakozaki, H..

5 recordsLinked to original sources

bioRxiv2024

Phospho-KNL-1 recognition by a TPR domain targets the BUB-1/BUB-3 complex to C. elegans kinetochores

During mitosis, the Bub1-Bub3 complex concentrates at kinetochores, the microtubule-coupling interfaces on chromosomes, where it contributes to spindle checkpoint activation, kinetochore-spindle microtubule interactions, and protection of centromeric cohesion. Bub1 has a conserved N-terminal tetratricopeptide (TPR) domain followed by a binding motif for its conserved interactor Bub3. The current model for Bub1-Bub3 localization to kinetochores is that Bub3, along with its bound motif from Bub1, recognizes phosphorylated "MELT" motifs in the kinetochore scaffold protein Knl1. Motivated by the greater phenotypic severity of BUB-1 versus BUB-3 loss in C. elegans, we show that the BUB-1 TPR domain directly recognizes a distinct class of phosphorylated motifs in KNL-1 and that this interaction is essential for BUB-1-BUB-3 localization and function. BUB-3 recognition of phospho-MELT motifs additively contributes to drive super-stoichiometric accumulation of BUB-1-BUB-3 on its KNL-1 scaffold during mitotic entry. Bub1s TPR domain interacts with Knl1 in other species, suggesting that collaboration of TPR-dependent and Bub3-dependent interfaces in Bub1-Bub3 localization and functions may be conserved.

cell biology↗

bioRxiv2024

Far-red chemigenetic biosensors for multi-dimensional and super-resolved kinase activity imaging

Fluorescent biosensors revolutionized biomedical science by enabling the direct measurement of signaling activities in living cells, yet the current technology is limited in resolution and dimensionality. Here, we introduce highly sensitive chemigenetic kinase activity biosensors that combine the genetically encodable self-labeling protein tag HaloTag7 with bright far-red-emitting synthetic fluorophores. This technology enables five-color biosensor multiplexing, 4D activity imaging, and functional super-resolution imaging via stimulated emission depletion (STED) microscopy.

bioengineering↗

bioRxiv2023

Self-supervised deep learning uncovers the semantic landscape of drug-induced latent mitochondrial phenotypes

Imaging-based high-content screening aims to identify substances that modulate cellular phenotypes. Traditional approaches screen compounds for their ability to shift disease phenotypes toward healthy phenotypes, but these end point-based screens lack an atlas-like mapping between phenotype and cell state that covers the full spectrum of possible phenotypic responses. In this study, we present MitoSpace: a novel mitochondrial phenotypic atlas that leverages self-supervised deep learning to create a semantically meaningful latent space from images without relying on any data labels for training. Our approach employs a dataset of [~]100,000 microscopy images of Cal27 and HeLa cells treated with 25 drugs affecting mitochondria, but can be generalized to any cell type, cell organelle, or drug library with no changes to the methodology. We demonstrate how MitoSpace enhances our understanding of the range of mitochondrial phenotypes induced by pharmacological interventions. We find that i) self-supervised learning can automatically uncover the semantic landscape of drug induced latent mitochondrial phenotypes and can map individual cells to the correct functional area of the drug they are treated with, ii) the traditional classification of mitochondrial morphology along a fragmented to fused axis is more complex than previously thought, with additional axes being identified, and iii) latent spaces trained in a self-supervised manner are superior to those trained with supervised models, and generalize to other cell types and drug conditions without explicit training on those cell types or drug conditions. Future applications of MitoSpace include creating mitochondrial biomarkers for drug discovery and determining the effects of unknown drugs and diseases for diagnostic purposes.

pharmacology and toxicology↗

bioRxiv2022

Rapid iPSC inclusionopathy models shed light on formation, consequence and molecular subtype of alpha-synuclein inclusions

Intracellular inclusions accompanying neurodegeneration are histopathologically and ultrastructurally heterogeneous but the significance of this heterogeneity is unclear. iPSC models, while promising for disease modeling, do not form inclusions in a reasonable timeframe and suffer from limited tractability. Here, we developed an iPSC toolbox utilizing piggyBac-based or targeted transgenes to rapidly induce CNS cells with concomitant expression of aggregation-prone proteins. This system is amenable to screening and longitudinal tracking at single-cell and single-inclusion resolution. For proof-of-principle, cortical neuron -synuclein "inclusionopathy" models were engineered to form inclusions through exogenous seeding or -synuclein mutation. These models recapitulated known fibril- and lipid-rich inclusion subtypes, uncovering dynamic interactions between them, and refined the classification of inclusions in postmortem brain. Genetic-modifier and protein-interaction screens pinpointed proteins like RhoA whose sequestration into specific inclusion subtypes is likely to be toxic. This iPSC platform should enhance our understanding of proteinaceous pathologies in neurodegeneration and facilitate therapeutics development.

neuroscience↗

bioRxiv2022

MitoTNT: Mitochondrial Temporal Network Tracking for 4D live-cell fluorescence microscopy data

Mitochondria form a network in the cell that rapidly changes through fission, fusion, and motility. This four-dimensional (4D, x,y,z,time) temporal network has only recently been made accessible through advanced imaging methods such as lattice light-sheet microscopy. Quantitative analysis tools for the resulting datasets however have been lacking. Here we present MitoTNT, the first-in-class software for Mitochondrial Temporal Network Tracking in 4D live-cell fluorescence microscopy data. MitoTNT uses spatial proximity and network topology to compute an optimal tracking. Tracking is >90% accurate in dynamic spatial mitochondria simulations and are in agreement with published motility results in vitro. Using MitoTNT, we reveal correlated mitochondrial movement patterns, local fission and fusion fingerprints, asymmetric fission and fusion dynamics, cross-network transport patterns, and network-level responses to pharmacological manipulations. MitoTNT is implemented in python with a JupyterLab interface. The extendable and user-friendly design aims at making temporal network tracking accessible to the wider mitochondria community.

cell biology↗