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Degradation of mitochondria through mitophagy contributes to the maintenance of mitochondrial function. In this study, we identified that Atg43, a mitochondrial outer membrane protein, serves as a mitophagy receptor in the model organism Schizosaccharomyces pombe to promote the selective degradation of mitochondria. Atg43 contains an Atg8-family-interacting motif essential for mitophagy. Forced recruitment of Atg8 to mitochondria restores mitophagy in Atg43-deficient cells, suggesting that Atg43 tethers expanding isolation membranes to mitochondria. We found that the mitochondrial import factors, including the Mim1–Mim2 complex and Tom70, are crucial for mitophagy. Artificial mitochondrial loading of Atg43 bypasses the requirement of the import factors, suggesting that they contribute to mitophagy through Atg43. Atg43 not only maintains growth ability during starvation but also facilitates vegetative growth through its mitophagy-independent function. Thus, Atg43 is a useful model to study the mechanism and physiological roles, as well as the origin and evolution, of mitophagy in eukaryotes.

Transport networks are ubiquitous in both social and biological systems. Robust network performance involves a complex trade-off involving cost, transport efficiency, and fault tolerance. Biological networks have been honed by many cycles of evolutionary selection pressure and are likely to yield reasonable solutions to such combinatorial optimization problems. Furthermore, they develop without centralized control and may represent a readily scalable solution for growing networks in general. We show that the slime mold Physarum polycephalum forms networks with comparable efficiency, fault tolerance, and cost to those of real-world infrastructure networks--in this case, the Tokyo rail system. The core mechanisms needed for adaptive network formation can be captured in a biologically inspired mathematical model that may be useful to guide network construction in other domains.

Mitophagy is a process that selectively degrades mitochondria. When mitophagy is induced in yeast, the mitochondrial outer membrane protein Atg32 is phosphorylated, interacts with the adaptor protein Atg11 and is recruited into the vacuole with mitochondria. We screened kinase‐deleted yeast strains and found that CK2 is essential for Atg32 phosphorylation, Atg32–Atg11 interaction and mitophagy. Inhibition of CK2 specifically blocks mitophagy, but not macroautophagy, pexophagy or the Cvt pathway. In vitro , CK2 phosphorylates Atg32 at serine 114 and serine 119. We conclude that CK2 regulates mitophagy by directly phosphorylating Atg32. Atg32 is the yeast mitophagy receptor for mitophagy, a process that selectively degrades mitochondria. Here, CK2 is shown to phosphorylate Atg32 and be essential for Atg32‐Atg11 interaction and ensuing mitophagy.

Mitophagy plays an important role in mitochondrial homeostasis. In yeast, the phosphorylation of the mitophagy receptor Atg32 by casein kinase 2 is essential for mitophagy. This phosphorylation is counteracted by the yeast equivalent of the STRIPAK complex consisting of the PP2A-like protein phosphatase Ppg1 and Far3-7-8-9-10-11 (Far complex), but the underlying mechanism remains elusive. Here we show that two subpopulations of the Far complex reside in the mitochondria and endoplasmic reticulum, respectively, and play distinct roles; the former inhibits mitophagy via Atg32 dephosphorylation, and the latter regulates TORC2 signaling. Ppg1 and Far11 form a subcomplex, and Ppg1 activity is required for the assembling integrity of Ppg1-Far11-Far8. The Far complex preferentially interacts with phosphorylated Atg32, and this interaction is weakened by mitophagy induction. Furthermore, the artificial tethering of Far8 to Atg32 prevents mitophagy. Taken together, the Ppg1-mediated Far complex formation and its dissociation from Atg32 are crucial for mitophagy regulation.

Understanding how biological systems solve problems could aid the design of novel computational methods. Information processing in unicellular eukaryotes is of particular interest, as these organisms have survived for more than a billion years using a simple system. The large amoeboid plasmodium of Physarum is able to solve a maze and to connect multiple food locations via a smart network. This study examined how Physarum amoebae compute these solutions. The mechanism involves the adaptation of the tubular body, which appears to be similar to a network, based on cell dynamics. Our model describes how the network of tubes expands and contracts depending on the flux of protoplasmic streaming, and reproduces experimental observations of the behavior of the organism. The proposed algorithm based on Physarum is simple and powerful.

An increase in the water temperature of a lake or reservoir is the most significant factor affecting the ecological balance of the system. Besides increasing the growth rate and standing crops of algae, a higher water temperature will expand the thermal stratification period and deepen the thermocline, depleting the DO in the hypolimnion. A newly developed ecological model of deep lakes and reservoirs is described, which is used in conjunction with a water-temperature model to simulate the effects of global warming on a lake ecosystem. The water-temperature model calculates seasonal changes in water temperature versus depth in deep lakes and reservoirs, while the ecological model calculates changes in the composition of phytoplankton species, their respective concentrations, and nutrient concentrations. Using data from Lake Yunoko, Japan, both the water-temperature and the ecological models are shown to be correlated with observed values. Results of the combined model indicate that an increased atmospheric temperature of 24 degree C will increase thermal stratification in the summer, increase nutrient concentrations in bottom water, increase phytoplankton concentrations at the beginning of the autumn circulation period, and change the phytoplankton species composition.

This paper describes a newly developed combined water temperature-ecological (WT-ECO) model which is employed to simulate the effects of global warming on lake and reservoir ecosystems. The WT model includes (i) variations in the eddy diffusion coefficient based on the degree of thermal stratification and the velocity of wind, and (ii) a sub-model for simulating the freezing and thawing processes of surface water, water temperatures, and the mixing rates between two adjacent layers of water. The ECO model then uses these results to calculate the resultant effect on a lake's ecological dynamics, e.g., composition of phytoplankton species, their respective concentrations, and nutrient concentrations. When the model was benchmarked against Lake Yunoko, a dimictic lake, fairly good agreement was obtained over a 4-yr period; thereby indicating it is suitably calibrated. In addition, to assess the effects of global warming on a lake ecosystem, changes in Lake Yunoko's water temperature/quality were simulated in response to an increase in air temperature of 2 - 4°C. Results indicate that such an increase will (i) increase thermal stratification in summer, which increases the nutrient concentrations in bottom water due to nutrient release from bottom sediment, (ii) increase the concentration of phytoplankton at the beginning of the autumn circulation period, and (iii) change the composition of phytoplankton species.

This paper describes a newly developed combined water temperature-ecological (WT-ECO) model which is employed to simulate the effects of global warming on lake and reservoir ecosystems. The WT model includes (i) variations in the eddy diffusion coefficient based on the degree of thermal stratification and the velocity of wind, and (ii) a sub-model for simulating the freezing and thawing processes of surface water, water temperatures, and the mixing rates between two adjacent layers of water. The ECO model then uses these results to calculate the resultant effect on a lake's ecological dynamics, e.g., composition of phytoplankton species, their respective concentrations, and nutrient concentrations. When the model was benchmarked against Lake Yunoko, a dimictic lake, fairly good agreement was obtained over a 4-yr period; thereby indicating it is suitably calibrated. In addition, to assess the effects of global warming on a lake ecosystem, changes in Lake Yunoko's water temperature/quality were simulated in response to an increase in air temperature of 2 - 4 °C. Results indicate that such an increase will (i) increase thermal stratification in summer, which increases the nutrient concentrations in bottom water due to nutrient release from bottom sediment, (ii) increase the concentration of phytoplankton at the beginning of the autumn circulation period, and (iii) change the composition of phytoplankton species.