Generating microislands of culture substrate on coverslips by spray application of poly-d lysine is a commonly used method for culturing isolated neurons that form self (autaptic) synapses. This preparation has multiple advantages for studying synaptic transmission in isolation; however, generating microislands by spraying produces islands of non-uniform size and thus cultures vary widely in the number of islands containing single neurons. To address these problems, we developed a high-throughput method for reliably generating uniformly shaped microislands of culture substrate. Stamp molds formed of poly(dimethylsiloxane) (PDMS) were fabricated with arrays of circles and used to generate stamps made of 9.2% agarose. The agarose stamps were capable of loading sufficient poly D-lysine and collagen dissolved in acetic acid to rapidly generate coverslips containing at least 64 microislands per coverslip. When hippocampal neurons were cultured on these coverslips, there were significantly more single-neuron islands per coverslip. We noted that single neurons tended to form one of three distinct neurite-arbor morphologies, which varied with island size and the location of the cell body on the island. To our surprise, the number of synapses per autaptic neuron did not correlate with arbor shape or island size, suggesting that other factors regulate the number of synapses formed by isolated neurons. The stamping method we report can be used to increase the number of single-neuron islands per culture and aid in the rapid visualization of microislands.

Although most experimentally characterized proteins with similar sequences assume the same folds and perform similar functions, an increasing number of exceptions is emerging. One class of exceptions comprises sequence-similar fold switchers, whose secondary structures shift from α-helix <-> β-sheet through a small number of mutations, a sequence insertion, or a deletion. Predictive methods for identifying sequence-similar fold switchers are desirable because some are associated with disease and/or can perform different functions in cells. Here, we use homology-based secondary structure predictions to identify sequence-similar fold switchers from their amino acid sequences alone. To do this, we predicted the secondary structures of sequence-similar fold switchers using three different homology-based secondary structure predictors: PSIPRED, JPred4, and SPIDER3. We found that α-helix <-> β-strand prediction discrepancies from JPred4 discriminated between the different conformations of sequence-similar fold switchers with high statistical significance (P < 1.8*10 ). Thus, we used these discrepancies as a classifier and found that they can often robustly discriminate between sequence-similar fold switchers and sequence-similar proteins that maintain the same folds (Matthews Correlation Coefficient of 0.82). We found that JPred4 is a more robust predictor of sequence-similar fold switchers because of (a) the curated sequence database it uses to produce multiple sequence alignments and (b) its use of sequence profiles based on Hidden Markov Models. Our results indicate that inconsistencies between JPred4 secondary structure predictions can be used to identify some sequence-similar fold switchers from their sequences alone. Thus, the negative information from inconsistent secondary structure predictions can potentially be leveraged to identify sequence-similar fold switchers from the broad base of genomic sequences.

Animal sensory systems are optimally adapted to those features typically encountered in natural surrounds, thus allowing neurons with limited bandwidth to encode challengingly large input ranges. Natural scenes are not random, and peripheral visual systems in vertebrates and insects have evolved to respond efficiently to their typical spatial statistics. The mammalian visual cortex is also tuned to natural spatial statistics, but less is known about coding in higher order neurons in insects. To redress this we here record intracellularly from a higher order visual neuron in the hoverfly. We show that the cSIFE neuron, which is inhibited by stationary images, is maximally inhibited when the slope constant of the amplitude spectrum is close to the mean in natural scenes. The behavioural optomotor response is also strongest to images with naturalistic image statistics. Our results thus reveal a close coupling between the inherent statistics of natural scenes and higher order visual processing in insects.

A derivative of rhodamine 110 has been designed and assessed as a probe for cytochrome P450 activity. This probe is the first to utilize a ’trimethyl lock’ that is triggered by cleavage of an ether bond. In vitro, fluorescence was manifested by the CYP1A1 isozyme with k(cat)/K(M)=8.8x10(3)M(-1)s(-1) and K(M)=0.09microM. In cellulo, the probe revealed the induction of cytochrome P450 activity by the carcinogen 2,3,7,8-tetrachlorodibenzo-p-dioxin, and its repression by the chemoprotectant resveratrol.

Human mitochondrial transcription factor A is a 25-kDa protein that binds immediately upstream of the two major mitochondrial promoters, thereby leading to correct and efficient initiation of transcription. Although the nature of yeast mitochondrial promoters is significantly different from that of human promoters, a potential functional homolog of the human transcriptional activator protein has been previously identified in yeast mitochondria. The importance of the yeast protein in yeast mitochondrial DNA function has been shown by inactivation of its nuclear gene (ABF2) in Saccharomyces cerevisiae cells resulting in loss of mitochondrial DNA. We report here that the nuclear gene for human mitochondrial transcription factor A can be stably expressed in yeast cells devoid of the yeast homolog protein. The human protein is imported efficiently into yeast mitochondria, is processed correctly, and rescues the loss-of-mitochondrial DNA phenotype in a yeast abf2 strain, thus functionally substituting for the yeast protein. Both human and yeast proteins affect yeast mitochondrial transcription initiation in vitro, suggesting that the two proteins may have a common role in this fundamental process.

Imaging is used to map activity across populations of neurons. Microscopes with cellular resolution have small (<1 millimeter) fields of view and cannot simultaneously image activity distributed across multiple brain areas. Typical large field of view microscopes do not resolve single cells, especially in the axial dimension. We developed a 2-photon random access mesoscope (2p-RAM) that allows high-resolution imaging anywhere within a volume spanning multiple brain areas (∅ 5 mm x 1 mm cylinder). 2p-RAM resolution is near diffraction limited (lateral, 0.66 μm, axial 4.09 μm at the center; excitation wavelength = 970 nm; numerical aperture = 0.6) over a large range of excitation wavelengths. A fast three-dimensional scanning system allows efficient sampling of neural activity in arbitrary regions of interest across the entire imaging volume. We illustrate the use of the 2p-RAM by imaging neural activity in multiple, non-contiguous brain areas in transgenic mice expressing protein calcium sensors.

Myelin is best known for its role in increasing the conduction velocity and metabolic efficiency of long-range excitatory axons. Accordingly, the myelin observed in neocortical gray matter is thought to mostly ensheath excitatory axons connecting to subcortical regions and distant cortical areas. Using independent analyses of light and electron microscopy data from mouse neocortex, we show that a surprisingly large fraction of cortical myelin (half the myelin in layer 2/3 and a quarter in layer 4) ensheathes axons of inhibitory neurons, specifically of parvalbumin-positive basket cells. This myelin differs significantly from that of excitatory axons in distribution and protein composition. Myelin on inhibitory axons is unlikely to meaningfully hasten the arrival of spikes at their pre-synaptic terminals, due to the patchy distribution and short path-lengths observed. Our results thus highlight the need for exploring alternative roles for myelin in neocortical circuits.

Wing dimorphisms have long served as models for examining the ecological and evolutionary tradeoffs associated with alternative phenotypes. Here, we investigated the genetic cause of the pea aphid () male wing dimorphism, wherein males exhibit one of two morphologies that differ in correlated traits that include the presence or absence of wings. We mapped this trait difference to a single genomic region and, using third generation, long-read sequencing, we identified a 120 kb insertion in the wingless allele. This insertion includes a duplicated gene, which is a strong candidate gene in the minimal mapped interval to cause the dimorphism. We found that both alleles were present prior to pea aphid biotype lineage diversification, we estimated that the insertion occurred millions of years ago, and we propose that both alleles have been maintained in the species, likely due to balancing selection.

Early stages of visual processing are thought to decorrelate, or whiten, the incoming temporally varying signals. Because the typical correlation time of natural stimuli, as well as the extent of temporal receptive fields of lateral geniculate nucleus (LGN) neurons, is much greater than neuronal time constants, such decorrelation must be done in stages combining contributions of multiple neurons. We propose to model temporal decorrelation in the visual pathway with the lattice filter, a signal processing device for stage-wise decorrelation of temporal signals. The stage-wise architecture of the lattice filter maps naturally onto the visual pathway (photoreceptors -> bipolar cells -> retinal ganglion cells -> LGN) and its filter weights can be learned using Hebbian rules in a stage-wise sequential manner. Moreover, predictions of neural activity from the lattice filter model are consistent with physiological measurements in LGN neurons and fruit fly second-order visual neurons. Therefore, the lattice filter model is a useful abstraction that may help unravel visual system function.

The forthcoming assembly of the adult Drosophila melanogaster central brain connectome, containing over 125,000 neurons and 50 million synaptic connections, provides a template for examining sensory processing throughout the brain. Here, we create a leaky integrate-and-fire computational model of the entire Drosophila brain, based on neural connectivity and neurotransmitter identity, to study circuit properties of feeding and grooming behaviors. We show that activation of sugar-sensing or water-sensing gustatory neurons in the computational model accurately predicts neurons that respond to tastes and are required for feeding initiation. Computational activation of neurons in the feeding region of the Drosophila brain predicts those that elicit motor neuron firing, a testable hypothesis that we validate by optogenetic activation and behavioral studies. Moreover, computational activation of different classes of gustatory neurons makes accurate predictions of how multiple taste modalities interact, providing circuit-level insight into aversive and appetitive taste processing. Our computational model predicts that the sugar and water pathways form a partially shared appetitive feeding initiation pathway, which our calcium imaging and behavioral experiments confirm. Additionally, we applied this model to mechanosensory circuits and found that computational activation of mechanosensory neurons predicts activation of a small set of neurons comprising the antennal grooming circuit that do not overlap with gustatory circuits, and accurately describes the circuit response upon activation of different mechanosensory subtypes. Our results demonstrate that modeling brain circuits purely from connectivity and predicted neurotransmitter identity generates experimentally testable hypotheses and can accurately describe complete sensorimotor transformations.