Protein signaling and morphological development of the tail fluke in the embryonic beluga whale (Delphinapterus leucas)

BACKGROUND

During the land-to-sea transition of cetaceans (whales, dolphins, and porpoises), the hindlimbs were lost and replaced by an elaborate tail fluke that evolved 32 Ma. All modern cetaceans utilize flukes for lift-based propulsion, and nothing is known of this organ's molecular origins during embryonic development. This study utilizes immunohistochemistry to identify the spatiotemporal location of protein signals known to drive appendage outgrowth in other vertebrates (e.g., Sonic Hedgehog [SHH], GREMLIN [GREM], wingless-type family member 7a [WNT], and fibroblast growth factors [FGFs]) and to test the hypothesis that signals associated with outgrowth and patterning of the tail fluke are similar to a tetrapod limb. Specifically, this study utilizes an embryo of a beluga whale (Delphinapterus leucas) as a case study.

RESULTS

Results showed epidermal signals of WNT and FGFs, and mesenchymal/epidermal signals of SHH and GREM. These patterns are most consistent with vertebrate limb development. Overall, these data are most consistent with the hypothesis that outgrowth of tail flukes in cetaceans employs a signaling pattern that suggests genes essential for limb outgrowth and patterning shape this evolutionarily novel appendage.

CONCLUSIONS

While these data add insights into the molecular signals potentially driving the evolution and development of tail flukes in cetaceans, further exploration of the molecular drivers of fluke development is required.

Universal DNA methylation age across mammalian tissues

Aging, often considered a result of random cellular damage, can be accurately estimated using DNA methylation profiles, the foundation of pan-tissue epigenetic clocks. Here, we demonstrate the development of universal pan-mammalian clocks, using 11,754 methylation arrays from our Mammalian Methylation Consortium, which encompass 59 tissue types across 185 mammalian species. These predictive models estimate mammalian tissue age with high accuracy (r > 0.96). Age deviations correlate with human mortality risk, mouse somatotropic axis mutations and caloric restriction. We identified specific cytosines with methylation levels that change with age across numerous species. These sites, highly enriched in polycomb repressive complex 2-binding locations, are near genes implicated in mammalian development, cancer, obesity and longevity. Our findings offer new evidence suggesting that aging is evolutionarily conserved and intertwined with developmental processes across all mammals.

Targeting c-Myc-activated genes with a correlation method: detection of global changes in large gene expression network dynamics

This work studies the dynamics of a gene expression time series network. The network, which is obtained from the correlation of gene expressions, exhibits global dynamic properties that emerge after a cell state perturbation. The main features of this network appear to be more robust when compared with those obtained with a network obtained from a linear Markov model. In particular, the network properties strongly depend on the exact time sequence relationships between genes and are destroyed by random temporal data shuffling. We discuss in detail the problem of finding targets of the c-myc protooncogene, which encodes a transcriptional regulator whose inappropriate expression has been correlated with a wide array of malignancies. The data used for network construction are a time series of gene expression, collected by microarray analysis of a rat fibroblast cell line expressing a conditional Myc-estrogen receptor oncoprotein. We show that the correlation-based model can establish a clear relationship between network structure and the cascade of c-myc-activated genes.

A biophysical model of bidirectional synaptic plasticity: dependence on AMPA and NMDA receptors

In many regions of the brain, including the mammalian cortex, the magnitude and direction of activity-dependent changes in synaptic strength depend on the frequency of presynaptic stimulation (synaptic plasticity), as well as the history of activity at those synapses (metaplasticity). We present a model of a molecular mechanism of bidirectional synaptic plasticity based on the observation that long-term synaptic potentiation (LTP) and long-term synaptic depression (LTD) correlate with the phosphorylation/dephosphorylation of sites on the alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor subunit protein GluR1. The primary assumption of the model, for which there is wide experimental support, is that postsynaptic calcium concentration and consequent activation of calcium-dependent protein kinases and phosphatases are the triggers for the induction of LTP/LTD. As calcium influx through the n-methyl-d-aspartate (NMDA) receptor plays a fundamental role in the induction of LTP/LTD, changes in the properties of NMDA receptor-mediated calcium influx will dramatically affect activity-dependent synaptic plasticity (metaplasticity). We demonstrate that experimentally observed metaplasticity can be accounted for by activity-dependent regulation of NMDA receptor subunit composition and function. Our model produces a frequency-dependent LTP/LTD curve with a sliding synaptic modification threshold similar to what has been proposed theoretically by Bienenstock, Cooper, and Munro and observed experimentally.

Statistics of lateral geniculate nucleus (LGN) activity determine the segregation of ON/OFF subfields for simple cells in visual cortex

The receptive fields for simple cells in visual cortex show a strong preference for edges of a particular orientation and display adjacent excitatory and inhibitory subfields. These subfields are projections from ON-center and OFF-center lateral geniculate nucleus cells, respectively. Here we present a single-cell model using ON and OFF channels, a natural scene environment, and synaptic modification according to the Bienenstock, Cooper, and Munro (BCM) theory. Our results indicate that lateral geniculate nucleus cells must act predominantly in the linear region around the level of spontaneous activity, to lead to the observed segregation of ON/OFF subfields.

Formation of direction selectivity in natural scene environments

Most simple and complex cells in the cat striate cortex are both orientation and direction selective. In this article we use single-cell learning rules to develop both orientation and direction selectivity in a natural scene environment. We show that a simple principal component analysis rule is inadequate for developing direction selectivity, but that the BCM rule as well as similar higher-order rules can. We also demonstrate that the convergence of lagged and nonlagged cells depends on the velocity of motion in the environment, and that strobe rearing disrupts this convergence, resulting in a loss of direction selectivity.

Structured long-range connections can provide a scaffold for orientation maps

In the visual cortex of the cat and ferret, it is established that maturation of orientation selectivity is shaped by experience-dependent plasticity. However, recent experiments indicate that orientation maps are remarkably stable and experience-independent. We present a model to account for these seemingly paradoxical results. In this model, a scaffold consisting of non-isotropic lateral connections is laid down in horizontal circuitry before visual experience. These lateral connections provide an experience-independent framework for the developing orientation maps by inducing a broad orientation tuning bias in the model neurons. Experience-dependent plasticity of the thalamocortical connections sharpens the tuning while the preferred orientation of the neurons remains unchanged. This model is verified by computer simulations in which the scaffolds are generated both artificially and inferred from experimental optical imaging data. The plasticity is modeled by the BCM synaptic plasticity rule, and the input environment consists of natural images. We use this model to provide a possible explanation of the recent observation in which two eyes without common visual experience develop similar orientation maps. Finally, we propose an experiment involving the disruption of lateral connections to distinguish this model from models proposed by others.

Solutions of the BCM learning rule in a network of lateral interacting nonlinear neurons

We introduce a new method for obtaining the fixed points for neurons that follow the BCM learning rule. The new formalism, which is based on the objective function formulation, permits analysis of a laterally connected network of nonlinear neurons and allows explicit calculation of the fixed points under various network conditions. We show that the stable fixed points, in terms of the postsynaptic activity, are not altered by the lateral connectivity or nonlinearity. We show that the lateral connectivity alters the probability of attaining different states in a network of interacting neurons. We further show the exact alteration in presynaptic weights as a result of the neuronal nonlinearity.

The role of presynaptic activity in monocular deprivation: comparison of homosynaptic and heterosynaptic mechanisms

Although investigations in computational neuroscience have been extensive, the opportunity (that has made such a marked difference in physical sciences) to test detailed and subtle quantitative consequences of a theory against experimental results is rare. In this paper, we outline a testable consequence of two contrasting theories of synaptic plasticity applied to the disconnection in visual cortex of the closed eye in monocular deprivation. This disconnection is sometimes thought to be the consequence of a process that stems from a competition of inputs for a limited resource such as neurotrophin. Such a process leads to what we call spatial competition, or heterosynaptic synaptic modification. A contrasting view-exemplified by the Bienenstock, Cooper, and Munro (BCM) theory-is that patterns of input activity compete in the temporal domain. This temporal competition is homosynaptic and does not require a conserved resource. The two mechanisms, homosynaptic and heterosynaptic, are the distinguishing characteristics of two general classes of learning rules we explore by using a realistic environment composed of natural scenes. These alternative views lead to opposite dependence on the level of presynaptic activity of the rate of disconnection of the closed eye in monocular deprivation. This strong and testable consequence sets the stage for a critical distinguishing experiment. This experiment has been done and supports the second view. These results have important implications for the processes of learning and memory storage in neocortex.

The effect of dynamic synapses on spatiotemporal receptive fields in visual cortex

Temporal dynamics are a general feature of synaptic transmission. Recently, novel aspects of temporal dynamics of synaptic transmission have been reported in the neocortex. Here, we examine the possible effects of these dynamics on the spatiotemporal receptive fields of simple cells in V1. We do this by examining a simple model of a cortical neuron that displays stimulus orientation selectivity as a consequence of the pattern of thalamocortical synaptic weights. In our model, the receptive field structure is encoded functionally in either presynaptic probability of release or postsynaptic efficacy. We show that these different assumptions about the origin of receptive field structure lead to very different spatiotemporal dynamics in the case of flashed-bar stimulus. In addition, the results of the reverse correlation study suggest a possible test for differentiating between models. We also show that the temporal code induced by dynamic synapses can be used to distinguish between different inputs that induce the same average firing rate.

Receptive Field Formation in Natural Scene Environments. Comparison of Single-Cell Learning Rules

We study several statistically and biologically motivated learning rules using the same visual environment: one made up of natural scenes and the same single-cell neuronal architecture. This allows us to concentrate on the feature extraction and neuronal coding properties of these rules. Included in these rules are kurtosis and skewness maximization, the quadratic form of the Bienenstock-Cooper-Munro (BCM) learning rule, and single-cell independent component analysis. Using a structure removal method, we demonstrate that receptive fields developed using these rules depend on a small portion of the distribution. We find that the quadratic form of the BCM rule behaves in a manner similar to a kurtosis maximization rule when the distribution contains kurtotic directions, although the BCM modification equations are computationally simpler.

BCM network develops orientation selectivity and ocular dominance in natural scene environment

A two-eye visual environment is used in training a network of BCM neurons. We study the effect of misalignment between the synaptic density functions from the two eyes, on the formation of orientation selectivity and ocular dominance in a lateral inhibition network. The visual environment we use is composed of natural images. We show that for the BCM rule a natural image environment with binocular cortical misalignment is sufficient for producing networks with orientation-selective cells and ocular dominance columns. This work is an extension of our previous single cell misalignment model Shouval et al., 1996.

Temporally graded retrograde amnesia following separate and combined lesions of the perirhinal cortex and fornix in the rat

The involvement of the perirhinal cortex and the fornix in retrograde and anterograde amnesia in the rat was investigated in this experiment. Male Sprague-Dawley rats were trained on a series of five visual discrimination problems at distinct time intervals prior to receiving bilateral, electrolytic lesions of the perirhinal cortex or the fornix, combined lesions of both these structures, or sham operations. Following recovery from surgery, rats were retested on the preoperatively learned discrimination problems, as well as learning a new discrimination and discrimination reversal. Results indicated that all animals with lesions exhibited temporally graded retrograde amnesia, whereby memories acquired in the recent past (1-3 weeks) were impaired, and memories acquired in the remote past (6-8 weeks) were spared. There was no difference in the magnitude of retrograde amnesia between the three lesion groups. Animals in the perirhinal, fornix, and combined lesion groups were able to learn a new discrimination problem at a rate comparable to control rats; however, the animals with lesions were impaired at learning the discrimination reversal. The perirhinal, fornix, and combined lesion animals also exhibited a significantly faster forgetting rate over a 2-week retention interval than control rats. These results suggest that medial temporal structures including the perirhinal cortex and the fornix are involved in the consolidation of mnemonic information and that their involvement in this process occurs over a discrete period of time.

Effect of binocular cortical misalignment on ocular dominance and orientation selectivity

We model a two-eye visual environment composed of natural images and study its effect on single cell synaptic modification. In particular, we study the effect of binocular cortical misalignment on receptive field formation after eye opening. We show that binocular misalignment affects principal component analysis (PCA) and Bienenstock, Cooper, and Munro (BCM) learning in different ways. For the BCM learning rule this misalignment is sufficient to produce varying degrees of ocular dominance, whereas for PCA learning binocular neurons emerge in every case.

Organization of receptive fields in networks with Hebbian learning: the connection between synaptic and phenomenological models

In this paper we address the question of how interactions affect the formation and organization of receptive fields in a network composed of interacting neurons with Hebbian-type learning. We show how to partially decouple single cell effects from network effects, and how some phenomenological models can be seen as approximations to these learning networks. We show that the interaction affects the structure of receptive fields. We also demonstrate how the organization of different receptive fields across the cortex is influenced by the interaction term, and that the type of singularities depends on the symmetries of the receptive fields.

A model of prenatal acquisition of speech parameters

An unsupervised neural network model inductively acquires the ability to distinguish categorically the stop consonants of English, in a manner consistent with prenatal and early postnatal auditory experience, and without reference to any specialized knowledge of linguistic structure or the properties of speech. This argues against the common assumption that linguistic knowledge, and speech perception in particular, cannot be learned and must therefore be innately specified.

Formation of receptive fields in realistic visual environments according to the Bienenstock, Cooper, and Munro (BCM) theory

The Bienenstock, Cooper, and Munro (BCM) theory of synaptic plasticity has successfully reproduced the development of orientation selectivity and ocular dominance in kitten visual cortex in normal, as well as deprived, visual environments. To better compare the consequences of this theory with experiment, previous abstractions of the visual environment are replaced in this work by real visual images with retinal processing. The visual environment is represented by 24 gray-scale natural images that are shifted across retinal fields. In this environment, the BCM neuron develops receptive fields similar to the fields of simple cells found in kitten striate cortex. These fields display adjacent excitatory and inhibitory bands when tested with spot stimuli, orientation selectivity when tested with bar stimuli, and spatial-frequency selectivity when tested with sinusoidal gratings. In addition, their development in various deprived visual environments agrees with experimental results.

Synaptic plasticity in visual cortex: comparison of theory with experiment

1. The aim of this work was to assess whether a form of synaptic modification based on the theory of Bienenstock, Cooper, and Munro (BCM) can, with a fixed set of parameters, reproduce both the kinetics and equilibrium states of experience-dependent modifications cortex. 2. According to the BCM theory, the connection strength of excitatory geniculocortical synapses varies as the product of a measure of input activity (d) and a function (phi) of the summed postsynaptic response. For all postsynaptic responses greater than spontaneous but less than a critical value called the "modification threshold" (theta), phi has a negative value. For all postsynaptic responses greater than theta, phi has a positive value. A novel feature of the BCM theory is that the value of theta is not fixed, but rather "slides" as a nonlinear function of the average postsynaptic response. 3. This theory permits precise specification of theoretical equivalents of experimental situations, allowing detailed, quantitative comparisons of theory with experiment. Such comparisons were carried out here in a series of computer simulations. 4. Simulations are performed by presenting input to a model cortical neuron, calculating the summed postsynaptic response, and then changing the synaptic weights according to the BCM theory. This process is repeated until the synaptic weights reach an equilibrium state. 5. Two types of geniculocortical input are simulated: "pattern" and "noise." Pattern input is assumed to correspond to the type of input that arises when a visual contour of a particular orientation is presented to the retina. This type of input is said to be "correlated" when the two sets of geniculocortical fibers relaying information from the two eyes convey the same patterns at the same time. Noise input is assumed to correspond to the type of input that arises in the absence of visual contours and, by definition, is uncorrelated. 6. By varying the types of input available to the two sets of geniculocortical synapses, we simulate the following types of visual experience: 1) normal binocular contour vision, 2) monocular deprivation, 3) reverse suture, 4) strabismus, 5) binocular deprivation, and 6) normal contour vision after a period of monocular deprivation. 7. The constraints placed on the set of parameters by each type of simulated visual environment, and the effects that such constraints have on the evolution of the synaptic weights, are investigated in detail.(ABSTRACT TRUNCATED AT 400 WORDS)

Mean-field theory of a neural network

A single-cell theory for the development of selectivity and ocular dominance in visual cortex has been generalized to incorporate more realistic neural networks that approximate the actual anatomy of small regions of cortex. In particular, we have analyzed a network consisting of excitatory and inhibitory cells, both of which may receive information from the lateral geniculate nucleus (LGN) and then interact through cortico-cortical synapses in a mean-field approximation. Our investigation of the evolution of a cell in this mean-field network indicates that many of the results on existence and stability of fixed points that have been obtained previously in the single-cell theory can be successfully generalized here. We can, in addition, make explicit further statements concerning the independent effects of excitatory and inhibitory neurons on selectivity and ocular dominance. For example, shutting off inhibitory cells lessens selectivity and alters ocular dominance (masked synapses). These inhibitory cells may be selective, but there is no theoretical necessity that they be so. Further, the intercortical inhibitory synapses do not have to be very responsive to visual experience. Most of the learning process can occur among the excitatory LGN-cortical synapses.

A relaxation model for memory with high storage density

We present a relaxation model for memory based on a generalized coulomb potential. The model has arbitrarily large storage capacity and, in addition, well-defined basins of attraction about stored memory states. The model is compared with the Hopfield relaxation model.

A physiological basis for a theory of synapse modification

The functional organization of the cerebral cortex is modified dramatically by sensory experience during early postnatal life. The basis for these modifications is a type of synaptic plasticity that may also contribute to some forms of adult learning. The question of how synapses modify according to experience has been approached by determining theoretically what is required of a modification mechanism to account for the available experimental data in the developing visual cortex. The resulting theory states precisely how certain variables might influence synaptic modifications. This insight has led to the development of a biologically plausible molecular model for synapse modification in the cerebral cortex.

Theory of an immune system retrovirus

Human immunodeficiency virus (HIV; formerly known as human T-cell lymphotropic virus type III/lymphadenopathy-associated virus, HTLV-III/LAV), the retrovirus that infects T4-positive (helper) T cells of the immune system, has been implicated as the agent responsible for the acquired immune deficiency syndrome. In this paper, I contrast the growth of a "normal" virus with what I call an immune system retrovirus: a retrovirus that attacks the T4-positive T cells of the immune system. I show that remarkable interactions with other infections as well as strong virus concentration dependence are general properties of immune system retroviruses. Some of the consequences of these ideas are compared with observations.

Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex

The development of stimulus selectivity in the primary sensory cortex of higher vertebrates is considered in a general mathematical framework. A synaptic evolution scheme of a new kind is proposed in which incoming patterns rather than converging afferents compete. The change in the efficacy of a given synapse depends not only on instantaneous pre- and postsynaptic activities but also on a slowly varying time-averaged value of the postsynaptic activity. Assuming an appropriate nonlinear form for this dependence, development of selectivity is obtained under quite general conditions on the sensory environment. One does not require nonlinearity of the neuron's integrative power nor does one need to assume any particular form for intracortical circuitry. This is first illustrated in simple cases, e.g., when the environment consists of only two different stimuli presented alternately in a random manner. The following formal statement then holds: the state of the system converges with probability 1 to points of maximum selectivity in the state space. We next consider the problem of early development of orientation selectivity and binocular interaction in primary visual cortex. Giving the environment an appropriate form, we obtain orientation tuning curves and ocular dominance comparable to what is observed in normally reared adult cats or monkeys. Simulations with binocular input and various types of normal or altered environments show good agreement with the relevant experimental data. Experiments are suggested that could test our theory further.

A neural model for category learning

We present a general neural model for supervised learning of pattern categories which can resolve pattern classes separated by nonlinear, essentially arbitrary boundaries. The concept of a pattern class develops from storing in memory a limited number of class elements (prototypes). Associated with each prototype is a modifiable scalar weighting factor (lambda) which effectively defines the threshold for categorization of an input with the class of the given prototype. Learning involves (1) commitment of prototypes to memory and (2) adjustment of the various lambda factors to eliminate classification errors. In tests, the model ably defined classification boundaries that largely separated complicated pattern regions. We discuss the role which divisive inhibition might play in a possible implementation of the model by a network of neurons.

A theory for the acquisition and loss of neuron specificity in visual cortex

We assume that between lateral geniculate and visual cortical cells there exist labile synapses that modify themselves in a new fashion called threshold passive modification and in addition, non-labile synapses that contain permanent information. In the theory which results there is an increase in the specificity of response of a cortical cell when it is exposed to stimuli due to normal patterned visual experience. Non-patterned input, such as might be expected when an animal is dark-reared or raised with eyelids sutured, results in a loss of specificity, with details depending on whether noise to labile and non-labile junctions is correlated. Specificity can sometimes be regained, however, with a return of input due to patterned vision. We propose that this provides a possible explanation of experimental results obtained by Imbert and Buisseret (1975); Blakemore and Van Sluyters (1975); Buisseret and Imbert (1976); and Frégnac and Imbert (1977, 1978).