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Queuosine (Q) is a conserved hypermodification of the wobble base of tRNA containing GUN anticodons but the physiological consequences of Q deficiency are poorly understood in bacteria. This work combines transcriptomic, proteomic and physiological studies to characterize a Q-deficient Escherichia coli K12 MG1655 mutant. The absence of Q led to an increased resistance to nickel and cobalt, and to an increased sensitivity to cadmium, compared to the wild-type (WT) strain. Transcriptomic analysis of the WT and Q-deficient strains, grown in the presence and absence of nickel, revealed that the nickel transporter genes (nikABCDE) are downregulated in the Q– mutant, even when nickel is not added. This mutant is therefore primed to resist to high nickel levels. Downstream analysis of the transcriptomic data suggested that the absence of Q triggers an atypical oxidative stress response, confirmed by the detection of slightly elevated reactive oxygen species (ROS) levels in the mutant, increased sensitivity to hydrogen peroxide and paraquat, and a subtle growth phenotype in a strain prone to accumulation of ROS.

The cell’s dense 3D actin filament network presents numerous challenges to vesicular transport by teams of myosin Va (MyoVa) molecular motors. These teams must navigate their cargo through diverse actin structures ranging from Arp2/3-branched lamellipodial networks to the dense, unbranched cortical networks. To define how actin filament network organization affects MyoVa cargo transport, we created two different 3D actin networks in vitro. One network was comprised of randomly oriented, unbranched actin filaments; the other was comprised of Arp2/3-branched actin filaments, which effectively polarized the network by aligning the actin filament plus-ends. Within both networks, we defined each actin filament’s 3D spatial position using superresolution stochastic optical reconstruction microscopy (STORM) and its polarity by observing the movement of single fluorescent reporter MyoVa. We then characterized the 3D trajectories of fluorescent, 350-nm fluid-like liposomes transported by MyoVa teams (∼10 motors) moving within each of the two networks. Compared with the unbranched network, we observed more liposomes with directed and fewer with stationary motion on the Arp2/3-branched network. This suggests that the modes of liposome transport by MyoVa motors are influenced by changes in the local actin filament polarity alignment within the network. This mechanism was supported by an in silico 3D modelmore »that provides a broader platform to understand how cellular regulation of the actin cytoskeletal architecture may fine tune MyoVa-based intracellular cargo transport.

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Fires in boreal forests of Alaska are changing, threatening human health and ecosystems. Given expected increases in fire activity with climate warming, insight into the controls on fire size from the time of ignition is necessary. Such insight may be increasingly useful for fire management, especially in cases where many ignitions occur in a short time period. Here we investigated the controls and predictability of final fire size at the time of ignition. Using decision trees, we show that ignitions can be classified as leading to small, medium or large fires with 50.4±5.2% accuracy. This was accomplished using two variables: vapour pressure deficit and the fraction of spruce cover near the ignition point. The model predicted that 40% of ignitions would lead to large fires, and those ultimately accounted for 75% of the total burned area. Other machine learning classification algorithms, including random forests and multi-layer perceptrons, were tested but did not outperform the simpler decision tree model. Applying the model to areas with intensive human management resulted in overprediction of large fires, as expected. This type of simple classification system could offer insight into optimal resource allocation, helping to maintain a historical fire regime and protect Alaskan ecosystems.