Healthy mitochondria are critical for reproduction. During aging, both reproductive fitness and mitochondrial homeostasis decline. Mitochondrial metabolism and dynamics are key factors in supporting mitochondrial homeostasis. However, how they are coupled to control reproductive health remains unclear. We report that mitochondrial GTP (mtGTP) metabolism acts through mitochondrial dynamics factors to regulate reproductive aging. We discovered that germline-only inactivation of GTP- but not ATP-specific succinyl-CoA synthetase (SCS) promotes reproductive longevity in Caenorhabditis elegans. We further identified an age-associated increase in mitochondrial clustering surrounding oocyte nuclei, which is attenuated by GTP-specific SCS inactivation. Germline-only induction of mitochondrial fission factors sufficiently promotes mitochondrial dispersion and reproductive longevity. Moreover, we discovered that bacterial inputs affect mtGTP levels and dynamics factors to modulate reproductive aging. These results demonstrate the significance of mtGTP metabolism in regulating oocyte mitochondrial homeostasis and reproductive longevity and identify mitochondrial fission induction as an effective strategy to improve reproductive health.

Healthy mitochondria are critical for reproduction. During aging, both reproductive fitness and mitochondrial homeostasis decline. Mitochondrial metabolism and dynamics are key factors in supporting mitochondrial homeostasis. However, how they are coupled to control reproductive health remains unclear. We report that mitochondrial GTP (mtGTP) metabolism acts through mitochondrial dynamics factors to regulate reproductive aging. We discovered that germline-only inactivation of GTP- but not ATP-specific succinyl-CoA synthetase (SCS) promotes reproductive longevity in Caenorhabditis elegans. We further identified an age-associated increase in mitochondrial clustering surrounding oocyte nuclei, which is attenuated by GTP-specific SCS inactivation. Germline-only induction of mitochondrial fission factors sufficiently promotes mitochondrial dispersion and reproductive longevity. Moreover, we discovered that bacterial inputs affect mtGTP levels and dynamics factors to modulate reproductive aging. These results demonstrate the significance of mtGTP metabolism in regulating oocyte mitochondrial homeostasis and reproductive longevity and identify mitochondrial fission induction as an effective strategy to improve reproductive health.

Healthy mitochondria are critical for reproduction. During aging, both reproductive fitness and mitochondrial homeostasis decline. Mitochondrial metabolism and dynamics are key factors in supporting mitochondrial homeostasis. However, how they are coupled to control reproductive health remains unclear. We report that mitochondrial GTP metabolism acts through mitochondrial dynamics factors to regulate reproductive aging. We discovered that germline-only inactivation of GTP- but not ATP-specific succinyl-CoA synthetase (SCS), promotes reproductive longevity in Caenorhabditis elegans. We further revealed an age-associated increase in mitochondrial clustering surrounding oocyte nuclei, which is attenuated by the GTP-specific SCS inactivation. Germline-only induction of mitochondrial fission factors sufficiently promotes mitochondrial dispersion and reproductive longevity. Moreover, we discovered that bacterial inputs affect mitochondrial GTP and dynamics factors to modulate reproductive aging. These results demonstrate the significance of mitochondrial GTP metabolism in regulating oocyte mitochondrial homeostasis and reproductive longevity and reveal mitochondrial fission induction as an effective strategy to improve reproductive health.

Mitochondrial DNA (mtDNA) is maintained within nucleoprotein complexes known as nucleoids. These structures are highly condensed by the DNA packaging protein, mitochondrial Transcription Factor A (TFAM). Nucleoids also include RNA, RNA:DNA hybrids, and are associated with proteins involved with RNA processing and mitochondrial ribosome biogenesis. Here we characterize the ability of TFAM to bind various RNA containing substrates in order to determine their role in TFAM distribution and function within the nucleoid. We find that TFAM binds to RNA-containing 4-way junctions but does not bind appreciably to RNA hairpins, internal loops, or linear RNA:DNA hybrids. Therefore the RNA within nucleoids largely excludes TFAM, and its distribution is not grossly altered with removal of RNA. Within the cell, TFAM binds to mitochondrial tRNAs, consistent with our RNA 4-way junction data. Kinetic binding assays and RNase-insensitive TFAM distribution indicate that DNA remains the preferred substrate within the nucleoid. However, TFAM binds to tRNA with nanomolar affinity and these complexes are not rare. TFAM-immunoprecipitated tRNAs have processed ends, suggesting that binding is not specific to RNA precursors. The amount of each immunoprecipitated tRNA is not well correlated with tRNA celluar abundance, indicating unequal TFAM binding preferences. TFAM-mt-tRNA interaction suggests potentially new functions for this protein.

First identified in dividing cells as revolving clusters of actin filaments, these are now understood as mitochondrially-associated actin waves that are active throughout the cell cycle. These waves are formed from the polymerization of actin onto a subset of mitochondria. Within minutes, this F-actin depolymerizes while newly formed actin filaments assemble onto neighboring mitochondria. In interphase, actin waves locally fragment the mitochondrial network, enhancing mitochondrial content mixing to maintain organelle homeostasis. In dividing cells actin waves spatially mix mitochondria in the mother cell to ensure equitable partitioning of these organelles between daughter cells. Progress has been made in understanding the consequences of actin cycling as well as the underlying molecular mechanisms, but many questions remain, and here we review these elements. Also, we draw parallels between mitochondrially-associated actin cycling and cortical actin waves. These dynamic systems highlight the remarkable plasticity of the actin cytoskeleton.

Spiking neural networks (SNNs) offer a promising paradigm for modeling brain dynamics and developing neuromorphic intelligence, yet an online learning system capable of training rich spiking dynamics over long horizons with low memory footprints has been missing. Existing online approaches either incur quadratic memory growth, sacrifice biological fidelity through oversimplified models, or lack end-to-end automated tooling. Here, we introduce BrainTrace, a model-agnostic, linear-memory, and automated online learning system for spiking neural networks. BrainTrace standardizes model specification to encompass diverse neuronal and synaptic dynamics; implements a linear-memory online learning rule by exploiting intrinsic properties of spiking dynamics; and provides a compiler that automatically generates optimized online-learning code for arbitrary user-defined models. Across diverse dynamics and tasks, BrainTrace achieves strong learning performance with a low memory footprint and high computational throughput. Critically, these properties enable online fitting of a whole-brain-scale Drosophila SNN that recapitulates region-level functional activity. By reconciling generality, efficiency, and usability, BrainTrace establishes a foundation for spiking network modeling at scale.

Population activity measurement by calcium imaging can be combined with cellular resolution optogenetic activity perturbations to enable the mapping of neural connectivity in vivo. This requires accurate inference of perturbed and unperturbed neural activity from calcium imaging measurements, which are noisy and indirect, and can also be contaminated by photostimulation artifacts. We have developed a new fully Bayesian approach to jointly inferring spiking activity and neural connectivity from in vivo all-optical perturbation experiments. In contrast to standard approaches that perform spike inference and analysis in two separate maximum-likelihood phases, our joint model is able to propagate uncertainty in spike inference to the inference of connectivity and vice versa. We use the framework of variational autoencoders to model spiking activity using discrete latent variables, low-dimensional latent common input, and sparse spike-and-slab generalized linear coupling between neurons. Additionally, we model two properties of the optogenetic perturbation: off-target photostimulation and photostimulation transients. Our joint model includes at least two sets of discrete random variables; to avoid the dramatic slowdown typically caused by being unable to differentiate such variables, we introduce two strategies that have not, to our knowledge, been used with variational autoencoders. Using this model, we were able to fit models on 30 minutes of data in just 10 minutes. We performed an all-optical circuit mapping experiment in primary visual cortex of the awake mouse, and use our approach to predict neural connectivity between excitatory neurons in layer 2/3. Predicted connectivity is sparse and consistent with known correlations with stimulus tuning, spontaneous correlation and distance.

Understanding learning through synaptic plasticity rules in the brain is a grand challenge for neuroscience. Here we introduce a novel computational framework for inferring plasticity rules from experimental data on neural activity trajectories and behavioral learning dynamics. Our methodology parameterizes the plasticity function to provide theoretical interpretability and facilitate gradient-based optimization. For instance, we use Taylor series expansions or multilayer perceptrons to approximate plasticity rules, and we adjust their parameters via gradient descent over entire trajectories to closely match observed neural activity and behavioral data. Notably, our approach can learn intricate rules that induce long nonlinear time-dependencies, such as those incorporating postsynaptic activity and current synaptic weights. We validate our method through simulations, accurately recovering established rules, like Oja’s, as well as more complex hypothetical rules incorporating reward-modulated terms. We assess the resilience of our technique to noise and, as a tangible application, apply it to behavioral data from Drosophila during a probabilistic reward-learning experiment. Remarkably, we identify an active forgetting component of reward learning in flies that enhances the predictive accuracy of previous models. Overall, our modeling framework provides an exciting new avenue to elucidate the computational principles governing synaptic plasticity and learning in the brain.