RCC1L (WBSCR16) isoforms coordinate mitochondrial ribosome assembly through their interaction with GTPases

by Aurelio Reyes, Paola Favia, Sara Vidoni, Vittoria Petruzzella, Massimo Zeviani

Mitochondrial translation defects can be due to mutations affecting mitochondrial- or nuclear-encoded components. The number of known nuclear genes involved in mitochondrial translation has significantly increased in the past years. RCC1L (WBSCR16), a putative GDP/GTP exchange factor, has recently been described to interact with the mitochondrial large ribosomal subunit. In humans, three different RCC1L isoforms have been identified that originate from alternative splicing but share the same N-terminus, RCC1LV1, RCC1LV2 and RCC1LV3. All three isoforms were exclusively localized to mitochondria, interacted with its inner membrane and could associate with homopolymeric oligos to different extent. Mitochondrial immunoprecipitation experiments showed that RCC1LV1 and RCC1LV3 associated with the mitochondrial large and small ribosomal subunit, respectively, while no significant association was observed for RCC1LV2. Overexpression and silencing of RCC1LV1 or RCC1LV3 led to mitoribosome biogenesis defects that resulted in decreased translation. Indeed, significant changes in steady-state levels and distribution on isokinetic sucrose gradients were detected not only for mitoribosome proteins but also for GTPases, (GTPBP10, ERAL1 and C4orf14), and pseudouridylation proteins, (TRUB2, RPUSD3 and RPUSD4). All in all, our data suggest that RCC1L is essential for mitochondrial function and that the coordination of at least two isoforms is essential for proper ribosomal assembly.

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Acoustic tweezers move objects in the body remotely

Researchers at the University of Washington and Moscow State University demonstrated the technique by trapping 3-mm glass beads in vortex-shaped beams of ultrasound. By steering the beams electronically, or by simply moving the ultrasound transducer, they directed the beads along complex 3D paths within a water tank and in the bladders of live pigs.

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New England’s trees capturing more carbon, says 25-year study

Climate change has increased the productivity of forests, according to a new study that synthesizes hundreds of thousands of carbon observations collected over the last quarter century at the Harvard Forest Long-Term Ecological Research site, one of the most intensively studied forests in the world.

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Predicting precision grip grasp locations on three-dimensional objects

by Lina K. Klein, Guido Maiello, Vivian C. Paulun, Roland W. Fleming

We rarely experience difficulty picking up objects, yet of all potential contact points on the surface, only a small proportion yield effective grasps. Here, we present extensive behavioral data alongside a normative model that correctly predicts human precision grasping of unfamiliar 3D objects. We tracked participants’ forefinger and thumb as they picked up objects of 10 wood and brass cubes configured to tease apart effects of shape, weight, orientation, and mass distribution. Grasps were highly systematic and consistent across repetitions and participants. We employed these data to construct a model which combines five cost functions related to force closure, torque, natural grasp axis, grasp aperture, and visibility. Even without free parameters, the model predicts individual grasps almost as well as different individuals predict one another’s, but fitting weights reveals the relative importance of the different constraints. The model also accurately predicts human grasps on novel 3D-printed objects with more naturalistic geometries and is robust to perturbations in its key parameters. Together, the findings provide a unified account of how we successfully grasp objects of different 3D shape, orientation, mass, and mass distribution.

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Flexible neural connectivity under constraints on total connection strength

by Gabriel Koch Ocker, Michael A. Buice

Neural computation is determined by neurons’ dynamics and circuit connectivity. Uncertain and dynamic environments may require neural hardware to adapt to different computational tasks, each requiring different connectivity configurations. At the same time, connectivity is subject to a variety of constraints, placing limits on the possible computations a given neural circuit can perform. Here we examine the hypothesis that the organization of neural circuitry favors computational flexibility: that it makes many computational solutions available, given physiological constraints. From this hypothesis, we develop models of connectivity degree distributions based on constraints on a neuron’s total synaptic weight. To test these models, we examine reconstructions of the mushroom bodies from the first instar larva and adult Drosophila melanogaster. We perform a Bayesian model comparison for two constraint models and a random wiring null model. Overall, we find that flexibility under a homeostatically fixed total synaptic weight describes Kenyon cell connectivity better than other models, suggesting a principle shaping the apparently random structure of Kenyon cell wiring. Furthermore, we find evidence that larval Kenyon cells are more flexible earlier in development, suggesting a mechanism whereby neural circuits begin as flexible systems that develop into specialized computational circuits.

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Multiplexing information flow through dynamic signalling systems

by Giorgos Minas, Dan J. Woodcock, Louise Ashall, Claire V. Harper, Michael R. H. White, David A. Rand

We consider how a signalling system can act as an information hub by multiplexing information arising from multiple signals. We formally define multiplexing, mathematically characterise which systems can multiplex and how well they can do it. While the results of this paper are theoretical, to motivate the idea of multiplexing, we provide experimental evidence that tentatively suggests that the NF-κB transcription factor can multiplex information about changes in multiple signals. We believe that our theoretical results may resolve the apparent paradox of how a system like NF-κB that regulates cell fate and inflammatory signalling in response to diverse stimuli can appear to have the low information carrying capacity suggested by recent studies on scalar signals. In carrying out our study, we introduce new methods for the analysis of large, nonlinear stochastic dynamic models, and develop computational algorithms that facilitate the calculation of fundamental constructs of information theory such as Kullback–Leibler divergences and sensitivity matrices, and link these methods to new theory about multiplexing information. We show that many current models such as those of the NF-κB system cannot multiplex effectively and provide models that overcome this limitation using post-transcriptional modifications.

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Combining hypoxia-activated prodrugs and radiotherapy in silico: Impact of treatment scheduling and the intra-tumoural oxygen landscape

by Sara Hamis, Mohammad Kohandel, Ludwig J. Dubois, Ala Yaromina, Philippe Lambin, Gibin G. Powathil

Hypoxia-activated prodrugs (HAPs) present a conceptually elegant approach to not only overcome, but better yet, exploit intra-tumoural hypoxia. Despite being successful in vitro and in vivo, HAPs are yet to achieve successful results in clinical settings. It has been hypothesised that this lack of clinical success can, in part, be explained by the insufficiently stringent clinical screening selection of determining which tumours are suitable for HAP treatments. Taking a mathematical modelling approach, we investigate how tumour properties and HAP-radiation scheduling influence treatment outcomes in simulated tumours. The following key results are demonstrated in silico: (i) HAP and ionising radiation (IR) monotherapies may attack tumours in dissimilar, and complementary, ways. (ii) HAP-IR scheduling may impact treatment efficacy. (iii) HAPs may function as IR treatment intensifiers. (iv) The spatio-temporal intra-tumoural oxygen landscape may impact HAP efficacy. Our in silico framework is based on an on-lattice, hybrid, multiscale cellular automaton spanning three spatial dimensions. The mathematical model for tumour spheroid growth is parameterised by multicellular tumour spheroid (MCTS) data.

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Optimising efficacy of antibiotics against systemic infection by varying dosage quantities and times

by Andy Hoyle, David Cairns, Iona Paterson, Stuart McMillan, Gabriela Ochoa, Andrew P. Desbois

Mass production and use of antibiotics has led to the rise of resistant bacteria, a problem possibly exacerbated by inappropriate and non-optimal application. Antibiotic treatment often follows fixed-dose regimens, with a standard dose of antibiotic administered equally spaced in time. But are such fixed-dose regimens optimal or can alternative regimens be designed to increase efficacy? Yet, few mathematical models have aimed to identify optimal treatments based on biological data of infections inside a living host. In addition, assumptions to make the mathematical models analytically tractable limit the search space of possible treatment regimens (e.g. to fixed-dose treatments). Here, we aimed to address these limitations by using experiments in a Galleria mellonella (insect) model of bacterial infection, to create a fully parametrised mathematical model of a systemic Vibrio infection. We successfully validated this model with biological experiments, including treatments unseen by the mathematical model. Then, by applying artificial intelligence, this model was used to determine optimal antibiotic dosage regimens to treat the host to maximise survival while minimising total antibiotic used. As expected, host survival increased as total quantity of antibiotic applied during the course of treatment increased. However, many of the optimal regimens tended to follow a large initial ‘loading’ dose followed by doses of incremental reductions in antibiotic quantity (dose ‘tapering’). Moreover, application of the entire antibiotic in a single dose at the start of treatment was never optimal, except when the total quantity of antibiotic was very low. Importantly, the range of optimal regimens identified was broad enough to allow the antibiotic prescriber to choose a regimen based on additional criteria or preferences. Our findings demonstrate the utility of an insect host to model antibiotic therapies in vivo and the approach lays a foundation for future regimen optimisation for patient and societal benefits.

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