Читать книгу Modern Trends in Structural and Solid Mechanics 3 - Группа авторов - Страница 16

Mitochondria exist in varying numbers, dependent on cell type, and sometimes form intracellular networks of interconnecting organelles called a reticulum, extending throughout the cytosol and in close contact with the nucleus, the endoplasmic reticulum, the Golgi network and the cytoskeleton (Benard et al. 2007). The cytosol is the intracellular fluid that surrounds all the organelles and other components of the cell. The endoplasmic reticulum, an organelle, has protein-related functions, and works with the Golgi network to deliver proteins to where they are needed within the cell.

The mitochondrial network is a net-like formation. “Mitochondrion” traces back to the Greek word meaning “thread grain”. Mitochondria may exist as small isolated particles, or as extended filaments, networks or clusters connected via intermitochondrial junctions. Serial-section three-dimensional images showed filamentous mitochondria frequently linked into networks (Skulachev 2001).

Extended mitochondria, and electrically coupled mitochondrial clusters, can transmit power in the form of membrane potential between remote parts of the cell. The coupled clusters are switched on and off as needed, in order to avoid local damage due to a simultaneous discharge of too many of the organelles at once. As energy demand increases, isolated mitochondria unite into extended mitochondrial systems. In tissues composed of large cells with high energy demands, such as the brain, heart and kidneys, the so-called mitochondrial reticulum occupies much of the cell volume.

Certain physiological and pathological conditions lead to the decoupling of mitochondrial filaments and networks into single mitochondria. Extended mitochondrial systems of various topologies exist only when their energy coupling and transmitting machineries are functioning normally (Skulachev 2001). Muscle fibers require a specialized spatial organization of the mitochondrial network (Vinogradskaya et al. 2014).

Mitochondrial fission, fusion, motility and tethering, the four conserved and interdependent mitochondrial activities, alter the mitochondrial network shape and its distribution in the cell. A dysfunction of one activity can have consequences on another. For example, it is observed that the attenuation of fission disrupts the transport of mitochondria to neuronal synapses, resulting in detrimental effects on cell function. Tethering defects can reduce fission rates (Lackner 2014). Relative rates of mitochondrial fission and fusion govern the connectivity of the network, where energetic needs coordinate the two processes. Feedforward and feedback mechanisms coordinate the complex relationships between energy supply and demand. We anticipate that these activities operate within an optimal domain.

In complex polarized cells such as neurons, mitochondria must be actively transported and tethered to, and maintained in, active synaptic regions. Tethers are important for positioning mitochondria within the overall cell structure and also relative to other organelles.

Mitochondrial morphology (network organization) and bioenergetic functions are coupled bidirectionally (Benard et al. 2007). The metabolic needs of the cell optimize the organelle’s bioenergetic capacity using frequent cycles of fusion and fission to adapt the morphology of the mitochondrial compartment to current supply and demand, as well as other required functions, of which there are many (Pagliuso et al. 2018). A disruption of these, for example, unopposed fission or fusion, adversely impacts cellular and organismal metabolism, leading to potentially devastating dysfunction (Wai and Langer 2016).

Fusion engages the entire mitochondrial compartment in respiratory active cells, maximizing ATP synthesis by mixing the matrix and the inner membrane, allowing close cooperation within the respiratory machinery. Metabolites, enzymes and mitochondrial gene products are spread throughout the mitochondrial compartment. Extensive adaptations of mitochondria to bioenergetic conditions occur at the level of the inner membrane ultrastructure and the remodeling of mitochondria cristae. A sudden need for metabolic energy results in cell stress and can lead to the formation of hyperfused mitochondrial networks. Such short-term stress exposure in starvation results in fusion that optimizes mitochondrial function and plays a beneficial role for the long-term maintenance of bioenergetic capacity. In a complementary way, irreversibly damaged mitochondria are eliminated by fission (autophagy), contributing to the maintenance of bioenergetic capacity (Westermann 2012). It is interesting, as a practical matter, that stresses to the cells benefit mitochondria in the same way as exercising in the gym benefits our muscles. Whether keeping the body at a proper range of temperatures when it is too cold, keeping up energy production during reduced caloric intake or matching energy needs during strenuous activity, the mitochondria comes out stronger and healthier, with larger numbers. Stress is an optimization mechanism.

Extensive disturbances to the dynamic balance between fission and fusion are linked to neurodegenerative and metabolic diseases (Chauhan et al. 2014). One purpose of this cycle between fission and fusion is to minimize the accumulation of reactive oxygen species (ROS). ROS are formed as a natural byproduct of the normal metabolism of oxygen and have important roles in cell signaling and homeostasis. However, during times of stress, ROS levels can increase dramatically, resulting in significant damage to cell structures. Cumulatively, this is known as oxidative stress.

Mitochondrial performance can be estimated by its bioenergetic capacity (ATP generation), metabolic capacity (mTOR activity) and damage accumulation (ROS production and/or mutation accumulation in mtDNA). Several nutrient sensing pathways link glucose metabolism to mitochondrial ATP, mTOR and ROS levels, which, in turn, directly or indirectly control proteins of fission–fusion machinery, like the fission proteins Drp1 and Fis1. The system flow chart is shown in Figure 1.2.

Figure 1.2. Derivation of mitochondrial performance phenomenologically (Chauhan et al. (2014), with permission)

The ATP module in the network above can, for example, be modeled by the following three first-order ordinary differential rate equations (Chauhan et al. 2014):

[1.1]

The quantities in the square brackets represent concentrations. JGlyc, JResp and Jcons are the respective fluxes of glycolysis, mitochondrial respiration and ATP consumption in other cellular processes, derived phenomenologically. q₁ is the fission rate, k₁ and k₂ are the respective synthesis rates, and d₁ and d₂ are the respective degradation rates. p₁ to p₁₂ are the rate constants. Even at the micro-level of the elements shown in Figure 1.2, optimal performance requires a balance between numerous processes governed by coupled equations of the form of equation [1.1].

System biology modeling approaches address such complex interactions between components of the mitochondria, leading to the fission–fusion cycles, as well as oscillations in concentrations of ATP and Ca²⁺. They also address how small perturbations in biochemical concentrations result in very different fates, implying that bistability exists. Such bistability indicates “choices” in the biological processes. Feedback and feedforward loops exist to control and optimally balance the fission–fusion cycles, as well as the ATP production machinery.

Furthermore, the key proteins involved in mitochondrial dynamics are regulated in direct response to the bioenergetic state of the mitochondria. Three members of the dynamin family, which are GTPase enzymes, critical for many cell functions, are key components of the fission and fusion machineries (van der Bliek et al. 2013). We still have only a limited knowledge of the mitochondrial proteome, its entire set of proteins, but expect it to be customized and optimized to the location in the body.

Mitochondrial health (Patel et al. 2013) has been linked to its membrane potential, making it useful as an equivalent measure of health, an indicator of the effectiveness of the electron transport chain, the number of ATPases using the membrane potential, the proton leaks across the inner mitochondrial membrane and other geometric factors. Optimal mitochondrial health is linked to autophagy and fusion, the balance between fission and fusion, and the density of mitochondria in the cell.

Existing limited models of abnormal mitochondrial dynamics are insufficient to explain phenotypic variability – the aggregate of an organism’s observable characteristics – in symptoms. The mechanisms of mitochondrial functions across multiple levels of organization – molecular and organelle levels – are needed (Eisner 2018). The current, mostly descriptive representations cannot accurately model multivariate dynamics since physiological and pathological processes result from biochemical, morphological and mechanical dynamics at more than one scale, and we do not fully understand these.

The primary sites of neuronal energy consumption are at the synapses (pre and post), where mitochondria need to congregate and adapt to local energy needs. They do this via feedforward and feedback regulatory mechanisms known as mitochondrial plasticity, where adaptations to neuronal energy states occur via changes in morphology, function and position (Rossi and Pekkurnaz 2019). Mitochondrial distribution and dynamics are regulated at the molecular level by mitochondrial and axonal cytoskeleton tracks. A motor–adaptor complex exists on the mitochondrial surface that contains the transport motor proteins kinesin and dynein. Proteins Miro and Milton mostly govern mitochondrial movement (Schwarz 2013), and multiple signaling pathways converge to tailor mitochondrial positioning (Rossi and Pekkurnaz 2019). A constrained optimization framework may be used to model mitochondrial movement via an evolutionarily refined weighting mechanism. Similarly, immediate energy needs at synapses result in mitochondrial plasticity, in a mechanism for the constrained optimization of energy availability and use, where it is most needed.

The matching of energy supply to demand is evolutionarily conserved, where the organelle and the cell must optimize energy use locally and globally to ensure that balance. Where there are shortfalls in energy, the “optimal” choice may become a dysfunctional pathway, resulting in pathologies and neurodegenerative diseases. Mathematical models that incorporate data can represent the optimal choices and be powerful tools for a systematic understanding.