Pheromones, chemical signals that convey social information, mediate many insect social behaviors, including navigation and aggregation. Several studies have suggested that behavior during the immature larval stages of Drosophila development is influenced by pheromones, but none of these compounds or the pheromone-receptor neurons that sense them have been identified. Here we report a larval pheromone-signaling pathway. We found that larvae produce two novel long-chain fatty acids that are attractive to other larvae. We identified a single larval chemosensory neuron that detects these molecules. Two members of the pickpocket family of DEG/ENaC channel subunits (ppk23 and ppk29) are required to respond to these pheromones. This pheromone system is evolving quickly, since the larval exudates of D. simulans, the sister species of D. melanogaster, are not attractive to other larvae. Our results define a new pheromone signaling system in Drosophila that shares characteristics with pheromone systems in a wide diversity of insects.

Biological systems display extraordinary robustness. Robustness of transcriptional enhancers results mainly from clusters of binding sites for the same transcription factor, and it is not clear how robust enhancers can evolve loss of expression through point mutations. Here, we report the high-resolution functional dissection of a robust enhancer of the shavenbaby gene that has contributed to morphological evolution. We found that robustness is encoded by many binding sites for the transcriptional activator Arrowhead and that, during evolution, some of these activator sites were lost, weakening enhancer activity. Complete silencing of enhancer function, however, required evolution of a binding site for the spatially restricted potent repressor Abrupt. These findings illustrate that recruitment of repressor binding sites can overcome enhancer robustness and may minimize pleiotropic consequences of enhancer evolution. Recruitment of repression may be a general mode of evolution to break robust regulatory linkages.

Novel body structures are often generated by the redeployment of ancestral components of the genome. In this issue of Developmental Cell, Glassford et al. (2015) present a thorough analysis of the co-option of a gene regulatory network in the origin of an evolutionary novelty.

Postimplantation mammalian embryo culture methods have been generally inefficient and limited to brief periods after dissection out of the uterus. Platforms have been recently developed for highly robust and prolonged ex utero culture of mouse embryos from egg-cylinder stages until advanced organogenesis. These platforms enable appropriate and faithful development of pregastrulating embryos (E5.5) until the hind limb formation stage (E11). Late gastrulating embryos (E7.5) are grown in rotating bottles in these settings, while extended culture from pregastrulation stages (E5.5 or E6.5) requires a combination of static and rotating bottle cultures. In addition, sensitive regulation of O2 and CO2 concentration, gas pressure, glucose levels, and the use of a specific ex utero culture medium are critical for proper embryo development. Here, a detailed step-by-step protocol for extended ex utero mouse embryo culture is provided. The ability to grow normal mouse embryos ex utero from gastrulation to organogenesis represents a valuable tool for characterizing the effect of different experimental perturbations during embryonic development.

The mammalian body plan is established shortly after the embryo implants into the maternal uterus, and our understanding of post-implantation developmental processes remains limited. Although pre- and peri-implantation mouse embryos are routinely cultured in vitro, approaches for the robust culture of post-implantation embryos from egg cylinder stages until advanced organogenesis remain to be established. Here we present highly effective platforms for the ex utero culture of post-implantation mouse embryos, which enable the appropriate development of embryos from before gastrulation (embryonic day (E) 5.5) until the hindlimb formation stage (E11). Late gastrulating embryos (E7.5) are grown in three-dimensional rotating bottles, whereas extended culture from pre-gastrulation stages (E5.5 or E6.5) requires a combination of static and rotating bottle culture platforms. Histological, molecular and single-cell RNA sequencing analyses confirm that the ex utero cultured embryos recapitulate in utero development precisely. This culture system is amenable to the introduction of a variety of embryonic perturbations and micro-manipulations, the results of which can be followed ex utero for up to six days. The establishment of a system for robustly growing normal mouse embryos ex utero from pre-gastrulation to advanced organogenesis represents a valuable tool for investigating embryogenesis, as it eliminates the uterine barrier and allows researchers to mechanistically interrogate post-implantation morphogenesis and artificial embryogenesis in mammals.

Learning in deep neural networks is known to depend critically on the knowledge embedded in the initial network weights. However, few theoretical results have precisely linked prior knowledge to learning dynamics. Here we derive exact solutions to the dynamics of learning with rich prior knowledge in deep linear networks by generalising Fukumizu's matrix Riccati solution \citep{fukumizu1998effect}. We obtain explicit expressions for the evolving network function, hidden representational similarity, and neural tangent kernel over training for a broad class of initialisations and tasks. The expressions reveal a class of task-independent initialisations that radically alter learning dynamics from slow non-linear dynamics to fast exponential trajectories while converging to a global optimum with identical representational similarity, dissociating learning trajectories from the structure of initial internal representations. We characterise how network weights dynamically align with task structure, rigorously justifying why previous solutions successfully described learning from small initial weights without incorporating their fine-scale structure. Finally, we discuss the implications of these findings for continual learning, reversal learning and learning of structured knowledge. Taken together, our results provide a mathematical toolkit for understanding the impact of prior knowledge on deep learning.

Evolution perfected brain design by maximizing its functionality while minimizing costs associated with building and maintaining it. Assumption that brain functionality is specified by neuronal connectivity, implemented by costly biological wiring, leads to the following optimal design problem. For a given neuronal connectivity, find a spatial layout of neurons that minimizes the wiring cost. Unfortunately, this problem is difficult to solve because the number of possible layouts is often astronomically large. We argue that the wiring cost may scale as wire length squared, reducing the optimal layout problem to a constrained minimization of a quadratic form. For biologically plausible constraints, this problem has exact analytical solutions, which give reasonable approximations to actual layouts in the brain. These solutions make the inverse problem of inferring neuronal connectivity from neuronal layout more tractable.

During embryogenesis, limb-innervating lateral motor column (LMC) spinal motor neurons (MN) are generated in excess and subsequently nearly half of them die. Many motor neuron survival factors (MnSFs) have been shown to suppress this default programmed cell death (PCD) program through their receptors (MnSFRs), raising the possibility that they are involved in matching specific MNs with their target muscles. Published observations suggest a combinatorial model of MnSF/Rs function, which assumes that during the PCD phase, MNs are expressing combinations of MnSFRs, whereas the limb muscles innervated by these MNs express cognate combinations of MnSFs. We tested this model by expression profiling of MnSFs and their receptors in the avian lumbosacral spinal cord and limb muscles during the peak PCD period. Our findings highlight the complexity of MnSF/Rs function in the control of LMC motor neuron survival.

Two-photon probe excitation data are commonly presented as absorption cross section or molecular brightness (the detected fluorescence rate per molecule). We report two-photon molecular brightness spectra for a diverse set of organic and genetically encoded probes with an automated spectroscopic system based on fluorescence correlation spectroscopy. The two-photon action cross section can be extracted from molecular brightness measurements at low excitation intensities, while peak molecular brightness (the maximum molecular brightness with increasing excitation intensity) is measured at higher intensities at which probe photophysical effects become significant. The spectral shape of these two parameters was similar across all dye families tested. Peak molecular brightness spectra, which can be obtained rapidly and with reduced experimental complexity, can thus serve as a first-order approximation to cross-section spectra in determining optimal wavelengths for two-photon excitation, while providing additional information pertaining to probe photostability. The data shown should assist in probe choice and experimental design for multiphoton microscopy studies. Further, we show that, by the addition of a passive pulse splitter, nonlinear bleaching can be reduced-resulting in an enhancement of the fluorescence signal in fluorescence correlation spectroscopy by a factor of two. This increase in fluorescence signal, together with the observed resemblance of action cross section and peak brightness spectra, suggests higher-order photobleaching pathways for two-photon excitation.

Direction selectivity represents a fundamental visual computation. In mammalian retina, On-Off direction-selective ganglion cells (DSGCs) respond strongly to motion in a preferred direction and weakly to motion in the opposite, null direction. Electrical recordings suggested three direction-selective (DS) synaptic mechanisms: DS GABA release during null-direction motion from starburst amacrine cells (SACs) and DS acetylcholine and glutamate release during preferred direction motion from SACs and bipolar cells. However, evidence for DS acetylcholine and glutamate release has been inconsistent and at least one bipolar cell type that contacts another DSGC (On-type) lacks DS release. Here, whole-cell recordings in mouse retina showed that cholinergic input to On-Off DSGCs lacked DS, whereas the remaining (glutamatergic) input showed apparent DS. Fluorescence measurements with the glutamate biosensor intensity-based glutamate-sensing fluorescent reporter (iGluSnFR) conditionally expressed in On-Off DSGCs showed that glutamate release in both On- and Off-layer dendrites lacked DS, whereas simultaneously recorded excitatory currents showed apparent DS. With GABA-A receptors blocked, both iGluSnFR signals and excitatory currents lacked DS. Our measurements rule out DS release from bipolar cells onto On-Off DSGCs and support a theoretical model suggesting that apparent DS excitation in voltage-clamp recordings results from inadequate voltage control of DSGC dendrites during null-direction inhibition. SAC GABA release is the apparent sole source of DS input onto On-Off DSGCs.