Hierarchical integration of mitochondrial and nuclear positioning pathways by the Num1 EF hand

2022 ◽  
Author(s):  
Heidi L. Anderson ◽  
Jason C. Casler ◽  
Laura L. Lackner
Keyword(s):  
Cell Cycle ◽  
Structure Function ◽  
Budding Yeast ◽  
Function Approach ◽  
Cell Cortex ◽  
Mitochondrial Inheritance ◽  
Nuclear Positioning ◽  
Spindle Positioning ◽  
Ef Hand ◽  
The Right

Positioning organelles at the right place and time is critical for their function and inheritance. In budding yeast, mitochondrial and nuclear positioning require the anchoring of mitochondria and dynein to the cell cortex by clusters of Num1. We have previously shown that mitochondria drive the assembly of cortical Num1 clusters, which then serve as anchoring sites for mitochondria and dynein. When mitochondrial inheritance is inhibited, mitochondrial-driven assembly of Num1 in buds is disrupted and defects in dynein-mediated spindle positioning are observed. Using a structure-function approach to dissect the mechanism of mitochondria-dependent dynein anchoring, we found the EF hand-like motif (EFLM) of Num1 and its ability to bind calcium are required to bias dynein anchoring on mitochondria-associated Num1 clusters. Consistently, when the EFLM is disrupted, we no longer observe defects in dynein activity following inhibition of mitochondrial inheritance. Thus, the Num1 EFLM functions to bias dynein anchoring and activity in nuclear inheritance subsequent to mitochondrial inheritance. We hypothesize that this hierarchical integration of organelle positioning pathways by the Num1 EFLM contributes to the regulated order of organelle inheritance during the cell cycle.

scholarly journals Mitochondria-driven assembly of a cortical anchor for mitochondria and dynein

2017 ◽  
Vol 216 (10) ◽  
pp. 3061-3071 ◽  
Author(s):  
Lauren M. Kraft ◽  
Laura L. Lackner
Keyword(s):  
Plasma Membrane ◽  
Mitotic Spindle ◽  
Cell Cortex ◽  
Dual Function ◽  
Mitochondrial Inheritance ◽  
Cellular Functions ◽  
Core Component ◽  
Spindle Positioning ◽  
The Core ◽  
Contact Sites

Interorganelle contacts facilitate communication between organelles and impact fundamental cellular functions. In this study, we examine the assembly of the MECA (mitochondria–endoplasmic reticulum [ER]–cortex anchor), which tethers mitochondria to the ER and plasma membrane. We find that the assembly of Num1, the core component of MECA, requires mitochondria. Once assembled, Num1 clusters persistently anchor mitochondria to the cell cortex. Num1 clusters also function to anchor dynein to the plasma membrane, where dynein captures and walks along astral microtubules to help orient the mitotic spindle. We find that dynein is anchored by Num1 clusters that have been assembled by mitochondria. When mitochondrial inheritance is inhibited, Num1 clusters are not assembled in the bud, and defects in dynein-mediated spindle positioning are observed. The mitochondria-dependent assembly of a dual-function cortical anchor provides a mechanism to integrate the positioning and inheritance of the two essential organelles and expands the function of organelle contact sites.

scholarly journals Overexpression of Mdm36 reveals Num1 foci that mediate dynein-dependent microtubule sliding in budding yeast

2020 ◽  
Author(s):  
Safia Omer ◽  
Katia Brock ◽  
John Beckford ◽  
Wei-Lih Lee
Keyword(s):  
Budding Yeast ◽  
Current Model ◽  
Cytoplasmic Dynein ◽  
Cell Cortex ◽  
Direct Imaging ◽  
Spindle Positioning ◽  
Sliding Mechanism ◽  
Pulling Forces ◽  
Pulling Force

ABSTRACTCurrent model for spindle positioning requires attachment of the microtubule (MT) motor cytoplasmic dynein to the cell cortex, where it generates pulling force on astral MTs to effect spindle displacement. How dynein is anchored by cortical attachment machinery to generate large spindle-pulling forces remains unclear. Here, we show that cortical clustering of Num1, the yeast dynein attachment molecule, is limited by Mdm36. Overexpression of Mdm36 results in an overall enhancement of Num1 clustering but reveals a population of dim Num1 clusters that mediate dynein-anchoring at the cell cortex. Direct imaging shows that bud-localized, dim Num1 clusters containing only ∼6 copies of Num1 molecules mediate dynein-dependent spindle pulling via lateral MT sliding mechanism. Mutations affecting Num1 clustering interfere with mitochondrial tethering but not dynein-based spindle-pulling function of Num1. We propose that formation of small ensembles of attachment molecules is sufficient for dynein anchorage and cortical generation of large spindle-pulling force.

scholarly journals Overexpression of Mdm36 reveals Num1 foci that mediate dynein-dependent microtubule sliding in budding yeast

2020 ◽  
Vol 133 (20) ◽  
pp. jcs246363 ◽  
Author(s):  
Safia Omer ◽  
Katia Brock ◽  
John Beckford ◽  
Wei-Lih Lee
Keyword(s):  
Budding Yeast ◽  
Current Model ◽  
Cytoplasmic Dynein ◽  
Cell Cortex ◽  
Direct Imaging ◽  
Spindle Positioning ◽  
Sliding Mechanism ◽  
Pulling Forces ◽  
Assembly Factor ◽  
Pulling Force

ABSTRACTThe current model for spindle positioning requires attachment of the microtubule (MT) motor cytoplasmic dynein to the cell cortex, where it generates pulling force on astral MTs to effect spindle displacement. How dynein is anchored by cortical attachment machinery to generate large spindle-pulling forces remains unclear. Here, we show that cortical clustering of Num1, the yeast dynein attachment molecule, is limited by its assembly factor Mdm36. Overexpression of Mdm36 results in an overall enhancement of Num1 clustering but reveals a population of dim Num1 clusters that mediate dynein anchoring at the cell cortex. Direct imaging shows that bud-localized, dim Num1 clusters containing around only six Num1 molecules mediate dynein-dependent spindle pulling via a lateral MT sliding mechanism. Mutations affecting Num1 clustering interfere with mitochondrial tethering but do not interfere with the dynein-based spindle-pulling function of Num1. We propose that formation of small ensembles of attachment molecules is sufficient for dynein anchorage and cortical generation of large spindle-pulling forces.This article has an associated First Person interview with the first author of the paper.

scholarly journals Cell cycle–regulated cortical dynein/dynactin promotes symmetric cell division by differential pole motion in anaphase

2012 ◽  
Vol 23 (17) ◽  
pp. 3380-3390 ◽  
Author(s):  
Elizabeth S. Collins ◽  
Sai Keshavan Balchand ◽  
Jessica L. Faraci ◽  
Patricia Wadsworth ◽  
Wei-Lih Lee
Keyword(s):  
Cell Cycle ◽  
Mammalian Cells ◽  
Affinity Purification ◽  
Spindle Pole ◽  
Cell Cortex ◽  
Spindle Positioning ◽  
Artificial Chromosomes ◽  
Pole Motion ◽  
Mammalian Cell Lines ◽  
In Cells

In cultured mammalian cells, how dynein/dynactin contributes to spindle positioning is poorly understood. To assess the role of cortical dynein/dynactin in this process, we generated mammalian cell lines expressing localization and affinity purification (LAP)–tagged dynein/dynactin subunits from bacterial artificial chromosomes and observed asymmetric cortical localization of dynein and dynactin during mitosis. In cells with asymmetrically positioned spindles, dynein and dynactin were both enriched at the cortex distal to the spindle. NuMA, an upstream targeting factor, localized asymmetrically along the cell cortex in a manner similar to dynein and dynactin. During spindle motion toward the distal cortex, dynein and dynactin were locally diminished and subsequently enriched at the new distal cortex. At anaphase onset, we observed a transient increase in cortical dynein, followed by a reduction in telophase. Spindle motion frequently resulted in cells entering anaphase with an asymmetrically positioned spindle. These cells gave rise to symmetric daughter cells by dynein-dependent differential spindle pole motion in anaphase. Our results demonstrate that cortical dynein and dynactin dynamically associate with the cell cortex in a cell cycle–regulated manner and are required to correct spindle mispositioning in LLC-Pk1 epithelial cells.

scholarly journals Mechanisms of MTOC clustering and spindle positioning in the budding yeast Cryptococcus neoformans: a microtubule grow-and-catch model

2019 ◽  
Author(s):  
Saptarshi Chatterjee ◽  
Subhendu Som ◽  
Neha Varshney ◽  
Kaustuv Sanyal ◽  
Raja Paul
Keyword(s):  
Nuclear Envelope ◽  
Cryptococcus Neoformans ◽  
Mitotic Spindle ◽  
Computational Models ◽  
Budding Yeast ◽  
Cell Cortex ◽  
Critical Determinant ◽  
Spindle Formation ◽  
Spindle Positioning ◽  
Septin Ring

AbstractMitotic spindle formation in the pathogenic budding yeast, Cryptococcus neoformans, depends on multitudes of inter-dependent interactions involving kinetochores (KTs), microtubules (MTs), spindle pole bodies (SPBs), and molecular motors. Before the formation of the mitotic spindle, multiple visible microtubule organizing centers (MTOCs), coalesce into a single focus to serve as an SPB. We propose a ‘grow-and-catch’ model, in which cytoplasmic MTs (cMTs) nucleated by MTOCs grow and catch each other to promote MTOC clustering. Our quantitative modeling identifies multiple redundant mechanisms mediated by a combination of cMT-cell cortex interactions and inter-cMT coupling to facilitate MTOC clustering within the physiological time limit as determined by time-lapse live-cell microscopy. Besides, we screened various possible mechanisms by computational modeling and propose optimal conditions that favor proper spindle positioning - a critical determinant for timely chromosome segregation. These analyses also reveal that a combined effect of MT buckling, dynein pull, and cortical push maintain spatiotemporal spindle localization.Author summaryCells actively self-assemble a bipolar spindle to facilitate chromosomal segregation. Multiple MTOCs, on the outer nuclear envelope, cluster into a single SPB before spindle formation during semi-open mitosis of the budding yeast Cryptococcus neoformans. Eventually, the SPB duplicates and organizes the spindle to position it within the daughter bud near the septin ring during anaphase. In this work, we tested various computational models to match physiological phenomena in an attempt to find plausible mechanisms of MTOC clustering and spindle positioning in C. neoformans. Notably, we propose an MT ‘grow-and-catch’ model that relies on possible redundant mechanisms for timely MTOC clustering mediated by (a) minus end-directed motors that crosslink and slide anti-parallel cMTs from different MTOCs on the nuclear envelope and (b) a Bim1 mediated biased sliding of cMTs along the cell cortex toward the septin ring that pulls MTOCs in the presence of suppressed dynein activity. By combining an analytical model and stochastic MT dynamics simulations, we screened various MT-based forces to detect steady spindle positioning. By screening the outputs of various models, it is revealed that proper spindle positioning near the septin ring requires MT buckling from the cell cortex.

scholarly journals Budding yeast relies on G1 cyclin specificity to couple cell cycle progression with morphogenetic development

2021 ◽  
Vol 7 (23) ◽  
pp. eabg0007
Author(s):  
Deniz Pirincci Ercan ◽  
Florine Chrétien ◽  
Probir Chakravarty ◽  
Helen R. Flynn ◽  
Ambrosius P. Snijders ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Cell Cycle Progression ◽  
Budding Yeast ◽  
Quantitative Model ◽  
Cyclin Dependent Kinase ◽  
Cycle Progression ◽  
Model Cell ◽  
Mitotic Cyclin ◽  
Substrate Phosphorylation ◽  
The Cost

Two models have been put forward for cyclin-dependent kinase (Cdk) control of the cell cycle. In the qualitative model, cell cycle events are ordered by distinct substrate specificities of successive cyclin waves. Alternatively, in the quantitative model, the gradual rise of Cdk activity from G1 phase to mitosis leads to ordered substrate phosphorylation at sequential thresholds. Here, we study the relative contributions of qualitative and quantitative Cdk control in Saccharomyces cerevisiae. All S phase and mitotic cyclins can be replaced by a single mitotic cyclin, albeit at the cost of reduced fitness. A single cyclin can also replace all G1 cyclins to support ordered cell cycle progression, fulfilling key predictions of the quantitative model. However, single-cyclin cells fail to polarize or grow buds and thus cannot survive. Our results suggest that budding yeast has become dependent on G1 cyclin specificity to couple cell cycle progression to essential morphogenetic events.

scholarly journals Self-Organized Nuclear Positioning Synchronizes the Cell Cycle in Drosophila Embryos

Cell ◽  
2019 ◽  
Vol 177 (4) ◽  
pp. 925-941.e17 ◽  
Author(s):  
Victoria E. Deneke ◽  
Alberto Puliafito ◽  
Daniel Krueger ◽  
Avaneesh V. Narla ◽  
Alessandro De Simone ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Nuclear Positioning ◽  
Self Organized ◽  
Drosophila Embryos

scholarly journals Systems Level Modeling of the Cell Cycle Using Budding Yeast

2007 ◽  
Vol 3 ◽  
pp. 117693510700300 ◽  
Author(s):  
B.P. Ingalls ◽  
B.P. Duncker ◽  
D.R. Kim ◽  
B.J. McConkey
Keyword(s):  
Cell Cycle ◽  
Budding Yeast ◽  
Human Cancer ◽  
Quality Data ◽  
Cell Cycle Gene ◽  
Mathematical Framework ◽  
Gene Transcript ◽  
Proteomics Data ◽  
Yeast Saccharomyces Cerevisiae ◽  
Protein Levels

Proteins involved in the regulation of the cell cycle are highly conserved across all eukaryotes, and so a relatively simple eukaryote such as yeast can provide insight into a variety of cell cycle perturbations including those that occur in human cancer. To date, the budding yeast Saccharomyces cerevisiae has provided the largest amount of experimental and modeling data on the progression of the cell cycle, making it a logical choice for in-depth studies of this process. Moreover, the advent of methods for collection of high-throughput genome, transcriptome, and proteome data has provided a means to collect and precisely quantify simultaneous cell cycle gene transcript and protein levels, permitting modeling of the cell cycle on the systems level. With the appropriate mathematical framework and sufficient and accurate data on cell cycle components, it should be possible to create a model of the cell cycle that not only effectively describes its operation, but can also predict responses to perturbations such as variation in protein levels and responses to external stimuli including targeted inhibition by drugs. In this review, we summarize existing data on the yeast cell cycle, proteomics technologies for quantifying cell cycle proteins, and the mathematical frameworks that can integrate this data into representative and effective models. Systems level modeling of the cell cycle will require the integration of high-quality data with the appropriate mathematical framework, which can currently be attained through the combination of dynamic modeling based on proteomics data and using yeast as a model organism.

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