A Biophysical Analysis of Mitochondrial Movement: Differences Between Transport in Neuronal Cell Bodies Versus Processes

Narayanareddy, Babu Reddy Janakaloti; Vartiainen, Suvi; Hariri, Neema; O’Dowd, Diane K.; Gross, Steven P.

doi:10.1111/tra.12171

Cited by 47 publications

(45 citation statements)

References 29 publications

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“…Actin filament depolarization was shown to influence mitochondrial motility either negatively (Muller et al, 2005; Quintero et al, 2006) or positively (Ligon and Steward, 2000; Morris and Hollenbeck, 1995), partly depending on the cell type analyzed (Kandel et al, 2015). Interestingly, depolymerizing the actin cytoskeleton was recently suggested to blunt mitochondrial motility only in neuronal processes, and not in the cell body (Narayanareddy et al, 2014), providing further support to the idea that distinct control mechanisms may operate in different spatially-restricted cytoplasmic regions.…”

Section: Descriptions Of Mitochondrial Morphologymentioning

confidence: 80%

“…These features are often interdependent. For example, mitochondrial size will increase with mitochondrial perimeter (the number of pixels forming the boundary of a mitochondrial object) (Wiemerslage and Lee, 2016) and mitochondrial movement is a decreasing function of mitochondrial size (with small mitochondria moving more rapidly than large mitochondrial sub-networks) (Caino et al, 2016; Narayanareddy et al, 2014). Mitochondrial movement also varies in relation to the position inside the cell (with peripheral mitochondria moving faster than mitochondria located in the more densely populated perinuclear region) (Kiryu-Seo et al, 2010; Watanabe et al, 2007).…”

Section: Descriptions Of Mitochondrial Morphologymentioning

confidence: 99%

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Connecting mitochondrial dynamics and life-or-death events via Bcl-2 family proteins

Aouacheria

Baghdiguian

Lamb

et al. 2017

Neurochemistry International

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The morphology of a population of mitochondria is the result of several interacting dynamical phenomena, including fission, fusion, movement, elimination and biogenesis. Each of these phenomena is controlled by underlying molecular machinery, and when defective can cause disease. New understanding of the relationships between form and function of mitochondria in health and disease is beginning to be unraveled on several fronts. Studies in mammals and model organisms have revealed that mitochondrial morphology, dynamics and function appear to be subject to regulation by the same proteins that regulate apoptotic cell death. One protein family that influences mitochondrial dynamics in both healthy and dying cells is the Bcl-2 protein family. Connecting mitochondrial dynamics with life-death pathway forks may arise from the intersection of Bcl-2 family proteins with the proteins and lipids that determine mitochondrial shape and function. Bcl-2 family proteins also have multifaceted influences on cells and mitochondria, including calcium handling, autophagy and energetics, as well as the subcellular localization of mitochondrial organelles to neuronal synapses. The remarkable range of physical or functional interactions by Bcl-2 family proteins is challenging to assimilate into a cohesive understanding. Most of their effects may be distinct from their direct roles in apoptotic cell death and are particularly apparent in the nervous system. Dual roles in mitochondrial dynamics and cell death extend beyond BCL-2 family proteins. In this review, we discuss many processes that govern mitochondrial structure and function in health and disease, and how Bcl-2 family proteins integrate into some of these processes.

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Section: Descriptions Of Mitochondrial Morphologymentioning

confidence: 80%

Section: Descriptions Of Mitochondrial Morphologymentioning

confidence: 99%

Connecting mitochondrial dynamics and life-or-death events via Bcl-2 family proteins

Aouacheria

Baghdiguian

Lamb

et al. 2017

Neurochemistry International

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“…The second region corresponds to the bulk cytosolic area, where mitochondria were assigned high velocity magnitudes (v h ) in the interval 0 ≤ v h ≤ 1 μms -1 . Finally, since small mitochondria move faster than large mitochondria [1, 39], we assumed that mitochondrial velocities are inversely proportional to the mass, i.e., v = v*/m, where v* assumes a value of v l or v h according to the intracellular position.…”

Section: Resultsmentioning

confidence: 99%

Agent-Based Modeling of Mitochondria Links Sub-Cellular Dynamics to Cellular Homeostasis and Heterogeneity

et al. 2017

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Mitochondria are semi-autonomous organelles that supply energy for cellular biochemistry through oxidative phosphorylation. Within a cell, hundreds of mobile mitochondria undergo fusion and fission events to form a dynamic network. These morphological and mobility dynamics are essential for maintaining mitochondrial functional homeostasis, and alterations both impact and reflect cellular stress states. Mitochondrial homeostasis is further dependent on production (biogenesis) and the removal of damaged mitochondria by selective autophagy (mitophagy). While mitochondrial function, dynamics, biogenesis and mitophagy are highly-integrated processes, it is not fully understood how systemic control in the cell is established to maintain homeostasis, or respond to bioenergetic demands. Here we used agent-based modeling (ABM) to integrate molecular and imaging knowledge sets, and simulate population dynamics of mitochondria and their response to environmental energy demand. Using high-dimensional parameter searches we integrated experimentally-measured rates of mitochondrial biogenesis and mitophagy, and using sensitivity analysis we identified parameter influences on population homeostasis. By studying the dynamics of cellular subpopulations with distinct mitochondrial masses, our approach uncovered system properties of mitochondrial populations: (1) mitochondrial fusion and fission activities rapidly establish mitochondrial sub-population homeostasis, and total cellular levels of mitochondria alter fusion and fission activities and subpopulation distributions; (2) restricting the directionality of mitochondrial mobility does not alter morphology subpopulation distributions, but increases network transmission dynamics; and (3) maintaining mitochondrial mass homeostasis and responding to bioenergetic stress requires the integration of mitochondrial dynamics with the cellular bioenergetic state. Finally, (4) our model suggests sources of, and stress conditions amplifying, cell-to-cell variability of mitochondrial morphology and energetic stress states. Overall, our modeling approach integrates biochemical and imaging knowledge, and presents a novel open-modeling approach to investigate how spatial and temporal mitochondrial dynamics contribute to functional homeostasis, and how subcellular organelle heterogeneity contributes to the emergence of cell heterogeneity.

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“…First, the force hindering the motor movement varies with cargo size and subcellular location; the load or viscoelastic drag exerted against motors inside the cell varies dynamically [48,49]. Yet, actual forces opposing the cargo movement in cytosolic environment are 1 pN [46,47].…”

Section: Discussionmentioning

confidence: 99%

Energetic costs, precision, and efficiency of a biological motor in cargo transport

Hwang

Hyeon

2017

Preprint

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Molecular motors play key roles in organizing the interior of cells. An efficient motor in cargo transport would travel with a high speed and a minimal error in transport time (or distance) while consuming minimal amount of energy. The travel distance and its variance of motor are, however, physically constrained by energy consumption, the principle of which has recently been formulated into the thermodynamic uncertainty relation. Here, we reinterpret the uncertainty measure (Q) defined in the thermodynamic uncertainty relation such that a motor efficient in cargo transport is characterized with a small Q. Analyses on the motility data from several types of molecular motors show that Q is a nonmonotic function of ATP concentration and load (f ). For kinesin-1, Q is locally minimized at [ATP] ≈ 200 µM and f ≈ 4 pN. Remarkably, for the mutant with a longer neck-linker this local minimum vanishes, and the energetic cost to achieve the same precision as the wild-type increases significantly, which underscores the importance of molecular structure in transport properties. For the biological motors studied here, their value of Q is semi-optimized under the cellular condition ([ATP] ≈ 1 mM, f = 0 − 1 pN). We find that among the motors, kinesin-1 at single molecule level is the most efficient in cargo transport.Biological systems are in nonequilibrium steady states (NESS) in which the energy and material currents flow constantly in and out of the system. Subjected to incessant thermal and nonequilibrium fluctuations, cellular processes are inherently stochastic and error-prone. To minimize detrimentaion effects, cells are equipped with a plethora of energy-consuming error-correction mechanisms. Trade-off relations between the energetic cost and information processing are ubiquitous in cellular processes, and have been a recurring theme in biology for many decades [1? ? -4].A recent study by Barato and Seifert [5] has formulated a concise inequality known as the thermodynamic uncertainty relation, the trade-off between energy consumption and precision of an observable from dissipative processes in NESS. To be more explicit, the uncertainty measure Q, defined as a product between the energy consumption (heat dissipation, Q(t)) from an energy-driven process in the steady state and the squared relative error of an output observable X(t), 2 X (t) = δX 2 / X 2 , is constant. It has been further conjectured that for an arbitrary chemical network formulated by Markov jump processes Q cannot be smaller than 2k B T ,The measure Q quantifies the uncertainty of a dynamic process. The smaller the value of Q, the more regular and predictable is the trajectory generated from the process, rendering the output observable more precise. The proof and physical significance of this inequality have been discussed [5][6][7][8][9][10]. In the presence of large fluctuations inherent to cellular processes, harnessing energy into precise motion is critical for accuracy in cellular computation.The uncertainty measure Q can be used to assess the e...

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A Biophysical Analysis of Mitochondrial Movement: Differences Between Transport in Neuronal Cell Bodies Versus Processes

Cited by 47 publications

References 29 publications

Connecting mitochondrial dynamics and life-or-death events via Bcl-2 family proteins

Connecting mitochondrial dynamics and life-or-death events via Bcl-2 family proteins

Agent-Based Modeling of Mitochondria Links Sub-Cellular Dynamics to Cellular Homeostasis and Heterogeneity

Energetic costs, precision, and efficiency of a biological motor in cargo transport

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