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.

The regulation of mitochondrial DNA (mtDNA) expression is crucial for mitochondrial biogenesis during development and differentiation. We have disrupted the mouse gene for mitochondrial transcription factor A (Tfam; formerly known as m-mtTFA) by gene targetting of loxP-sites followed by cre-mediated excision in vivo. Heterozygous knockout mice exhibit reduced mtDNA copy number and respiratory chain deficiency in heart. Homozygous knockout embryos exhibit a severe mtDNA depletion with abolished oxidative phosphorylation. Mutant embryos proceed through implantation and gastrulation, but die prior to embryonic day (E)10.5. Thus, Tfam is the first mammalian protein demonstrated to regulate mtDNA copy number in vivo and is essential for mitochondrial biogenesis and embryonic development.

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.

A formalism is given in which the optical field generated by a near-field optical aperture is described as an analytic expansion over a complete set of optical modes. This vectoral solution preserves the divergent behavior of the near field and the dipolar nature of the far field. Numerical calculation of the fields requires only evaluation of a well behaved, one-dimensional integral. The formalism is directly applicable to experiments in near-field scanning optical microscopy when relatively flat samples are evaluated.

The Honeybee Brain Atlas serves as 3D database and communicative platform to accumulate structural data, i.e. reconstructed neurons, derived from confocal scans (Brandt et al., 2005) (www.neurobiologie.fu-berlin.de/beebrain/) (1). Transforming neurons into the atlas requires manual segmentation of neuropils within confocal images, a time-consuming task requiring expertise in identifying biological structures which can result in different outcomes from various segmenters.

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.