State-dependent modulation of neuronal circuits in C. elegans sleep

C. elegans is a compact system of 302 neurons with identifiable and mapped connections that makes it ideal for systems analysis. This work is a demonstration of what I have been able to learn about the nature of state-specific modulation and reversibility during a state called lethargus, a sleep-like state in the worm. I begin with description about the nervous system of the worm, the nature of sleep in the worm, the questions about behavior and its apparent circuit properties, the tools available and used to manipulate the nervous system, and what I have been able to learn from these studies. I end with clues that the physiology helps to teach us about the dynamics of state specific modulation, what makes sleep so different from other states, and how we can use these measurements to understand which modulators, neurotransmitters, and channels can be used to create different dynamics in a simple model system.

Studying conscious and unconscious vision with functional Magnetic Resonance Imaging : the BOLD promise

Waking up from a dreamless sleep, I open my eyes, recognize my wife’s face and am filled with joy. In this thesis, I used functional Magnetic Resonance Imaging (fMRI) to gain insights into the mechanisms involved in this seemingly simple daily occurrence, which poses at least three great challenges to neuroscience: how does conscious experience arise from the activity of the brain? How does the brain process visual input to the point of recognizing individual faces? How does the brain store semantic knowledge about people that we know? To start tackling the first question, I studied the neural correlates of unconscious processing of invisible faces. I was unable to image significant activations related to the processing of completely invisible faces, despite existing reports in the literature. I thus moved on to the next question and studied how recognition of a familiar person was achieved in the brain; I focused on finding invariant representations of person identity – representations that would be activated any time we think of a familiar person, read their name, see their picture, hear them talk, etc. There again, I could not find significant evidence for such representations with fMRI, even in regions where they had previously been found with single unit recordings in human patients (the Jennifer Aniston neurons). Faced with these null outcomes, the scope of my investigations eventually turned back towards the technique that I had been using, fMRI, and the recently praised analytical tools that I had been trusting, Multivariate Pattern Analysis. After a mostly disappointing attempt at replicating a strong single unit finding of a categorical response to animals in the right human amygdala with fMRI, I put fMRI decoding to an ultimate test with a unique dataset acquired in the macaque monkey. There I showed a dissociation between the ability of fMRI to pick up face viewpoint information and its inability to pick up face identity information, which I mostly traced back to the poor clustering of identity selective units. Though fMRI decoding is a powerful new analytical tool, it does not rid fMRI of its inherent limitations as a hemodynamics-based measure.

Pattern formation during Caenorhabditis Elegans vulval development

Pattern formation during animal development involves at least three processes: establishment of the competence of precursor cells to respond to intercellular signals, formation of a pattern of different cell fates adopted by precursor cells, and execution of the cell fate by generating a pattern of distinct descendants from precursor cells. I have analyzed the fundamental mechanisms of pattern formation by studying the development of Caenorhabditis elegans vulva.

In C. elegans, six multipotential vulval precursor cells (VPCs) are competent to respond to an inductive signal LIN-3 (EGF) mediated by LET- 23 (RTK) and a lateral signal via LIN-12 (Notch) to form a fixed pattern of 3°-3°-2°-1°-2°-3°. Results from expressing LIN-3 as a function of time in animals lacking endogenous LIN-3 indicate that both VPCs and VPC daughters are competent to respond to LIN-3. Although the daughters of VPCs specified to be 2° or 3° can be redirected to adopt the 1°fate, the decision to adopt the 1° fate is irreversible. Coupling of VPC competence to cell cycle progression reveals that VPC competence may be periodic during each cell cycle and involve LIN-39 (HOM-C). These mechanisms are essential to ensure a bias towards the 1° fate, while preventing an excessive response.

After adopting the 1° fate, the VPC executes its fate by dividing three rounds to form a fixed pattern of four inner vulF and four outer vulE descendants. These two types of descendants can be distinguished by a molecular marker zmp-1::GFP. A short-range signal from the anchor cell (AC), along with signaling between the inner and outer 1° VPC descendants and intrinsic polarity of 1° VPC daughters, patterns the 1° lineage. The Ras and the Wnt signaling pathways may be involved in these mechanisms.

The temporal expression pattern of egl-17::GFP, another marker ofthe 1° fate, correlates with three different steps of 1° fate execution: the commitment to the 1° fate, as well as later steps before and after establishment of the uterine-vulval connection. Six transcription factors, including LIN-1(ETS), LIN-39 (HOM-C), LIN-11(LIM), LIN-29 (zinc finger), COG-1 (homeobox) and EGL-38 (PAX2/5/8), are involved in different steps during 1° fate execution.

Experimental and theoretical studies of Notch signaling-mediated spatial pattern

Notch signaling acts in many diverse developmental spatial patterning processes. To better understand why this particular pathway is employed where it is and how downstream feedbacks interact with the signaling system to drive patterning, we have pursued three aims: (i) to quantitatively measure the Notch system's signal input/output (I/O) relationship in cell culture, (ii) to use the quantitative I/O relationship to computationally predict patterning outcomes of downstream feedbacks, and (iii) to reconstitute a Notch-mediated lateral induction feedback (in which Notch signaling upregulates the expression of Delta) in cell culture. The quantitative Notch I/O relationship revealed that in addition to the trans-activation between Notch and Delta on neighboring cells there is also a strong, mutual cis-inactivation between Notch and Delta on the same cell. This feature tends to amplify small differences between cells. Incorporating our improved understanding of the signaling system into simulations of different types of downstream feedbacks and boundary conditions lent us several insights into their function. The Notch system converts a shallow gradient of Delta expression into a sharp band of Notch signaling without any sort of feedback at all, in a system motivated by the Drosophila wing vein. It also improves the robustness of lateral inhibition patterning, where signal downregulates ligand expression, by removing the requirement for explicit cooperativity in the feedback and permitting an exceptionally simple mechanism for the pattern. When coupled to a downstream lateral induction feedback, the Notch system supports the propagation of a signaling front across a tissue to convert a large area from one state to another with only a local source of initial stimulation. It is also capable of converting a slowly-varying gradient in parameters into a sharp delineation between high- and low-ligand populations of cells, a pattern reminiscent of smooth muscle specification around artery walls. Finally, by implementing a version of the lateral induction feedback architecture modified with the addition of an autoregulatory positive feedback loop, we were able to generate cells that produce enough cis ligand when stimulated by trans ligand to themselves transmit signal to neighboring cells, which is the hallmark of lateral induction.

The C. elegans ALA neuron : its transcriptome and role in inducing sleep

A long-standing yet to be accomplished task in understanding behavior is to dissect the function of each gene involved in the development and function of a neuron. The C. elegans ALA neuron was chosen in this study for its known function in sleep, an ancient but less understood animal behavior. Single-cell transcriptome profiling identified 8,133 protein-coding genes in the ALA neuron, of which 57 are neuropeptide-coding genes. The most enriched genes are also neuropeptides. In combination with gain-of-function and loss-of-function assays, here I showed that the ALA-enriched FMRFamide neuropeptides, FLP-7, FLP-13, and FLP-24, are sufficient and necessary for inducing C. elegans sleep. These neuropeptides act as neuromodulators through GPCRs, NPR-7, and NPR-22. Further investigation in zebrafish indicates that FMRFamide neuropeptides are sleep-promoting molecules in animals. To correlate the behavioral outputs with genomic context, I constructed a gene regulatory network of the relevant genes controlling C. elegans sleep behavior through EGFR signaling in the ALA neuron. First, I identified an ALA cell-specific motif to conduct a genome-wide search for possible ALA-expressed genes. I then filtered out non ALA-expressed genes by comparing the motif-search genes with ALA transcriptomes from single-cell profiling. In corroborating with ChIP-seq data from modENCODE, I sorted out direct interaction of ALA-expressed transcription factors and differentiation genes in the EGFR sleep regulation pathway. This approach provides a network reference for the molecular regulation of C. elegans sleep behavior, and serves as an entry point for the understanding of functional genomics in animal behaviors.

The Regulation of Sleep and Circadian Rhythms: The Role of Melatonin and Adenosine in Zebrafish

Sleep is a highly conserved behavioral state whose regulation is still unclear. In this thesis I initially briefly introduce the known sleep circuitry and regulation in vertebrates, and why zebrafish is seen as a good model to study sleep-regulation. I describe the existing two-process model of sleep regulation, which posits that the two processes C (circadian) and S (homeostatic) control timing of sleep-wake behavior. I then study the role melatonin plays in the circadian regulation of sleep using zebrafish. Firstly, we find that the absence of melatonin results in a reduction of sleep at night, establishing that endogenous melatonin is required for sleep at night. Secondly, melatonin mutants show a reduction in sleep in animals with no functional behavioral rhythms suggesting that melatonin does not require intact circadian rhythms for its effect on sleep. Thirdly, melatonin mutants do not exhibit any changes in circadian rhythms, suggesting that the circadian clock does not require melatonin for its function. Fourthly, we find that in the absence of melatonin, there is no rhythmic expression of sleep, suggesting that melatonin is the output molecule of process C. Lastly, we describe a connection between adenosine signaling (output molecules of process S), and melatonin. Following this we proceed to study the role adenosine signaling plays in sleep-wake behavior. We find that firstly, adenosine receptor A1 and A2 are involved in sleep- wake behavior in zebrafish, based on agonist/antagonist behavioral results. Secondly, we find that several brain regions such as PACAP cells in the rostral midbrain, GABAergic cells in the forebrain and hindbrain, Dopamine and serotonin cells in the caudal hypothalamus and sox2 cells lining the hindbrain ventricle are activated in response to the A1 antagonist and VMAT positive cells are activated in response to the A2A agonist, suggesting these areas are involved in adenosine signaling in zebrafish. Thirdly, we find that knocking out the zebrafish adenosine receptors has no effect on sleep architecture. Lastly, we find that while the A1 agonist phenotype requires the zfAdora1a receptor, the antagonist and the A2A agonist behavioral phenotypes are not mediated by the zfAdora1a, zfAdora1b and zfAdoraA2Aa, zfAdora2Ab receptors respectively.

Firing Patterns of Cerebellar Purkinje Cells During Locomotion and Sleep

The cerebellum is a major supraspinal center involved in the coordination of movement. The principal neurons of the cerebellar cortex, Purkinje cells, receive excitatory synaptic input from two sources: the parallel and climbing fibers. These pathways have markedly different effects: the parallel fibers control the rate of simple sodium spikes, while the climbing fibers induce characteristic complex spike bursts, which are accompanied by dendritic calcium transients and play a key role in regulating synaptic plasticity. While many studies using a variety of species, behaviors, and cerebellar regions have documented modulation in Purkinje cell activity during movement, few have attempted to record from these neurons in unrestrained rodents. In this dissertation, we use chronic, multi-tetrode recording in freely-behaving rats to study simple and complex spike firing patterns during locomotion and sleep. Purkinje cells discharge rhythmically during stepping, but this activity is highly variable across steps. We show that behavioral variables systematically influence the step-locked firing rate in a step-phase-dependent way, revealing a functional clustering of Purkinje cells. Furthermore, we find a pronounced disassociation between patterns of variability driven by the parallel and climbing fibers, as well as functional differences between cerebellar lobules. These results suggest that Purkinje cell activity not only represents step phase within each cycle, but is also shaped by behavior across steps, facilitating control of movement under dynamic conditions. During sleep, we observe an attenuation of both simple and complex spiking, relative to awake behavior. Although firing rates during slow wave sleep (SWS) and rapid eye movement sleep (REM) are similar, simple spike activity is highly regular in SWS, while REM is characterized by phasic increases and pauses in simple spiking. This phasic activity in REM is associated with pontine waves, which propagate into the cerebellar cortex and modulate both simple and complex spiking. Such a temporal coincidence between parallel and climbing fiber activity is known to drive plasticity at parallel fiber synapses; consequently, pontocerebellar waves may provide a mechanism for tuning synaptic weights in the cerebellum during active sleep.

Steady surface wave pattern in a shear flow

The subject under investigation concerns the steady surface wave patterns created by small concentrated disturbances acting on a non-uniform flow of a heavy fluid. The initial value problem of a point disturbance in a primary flow having an arbitrary velocity distribution (U(y), 0, 0) in a direction parallel to the undisturbed free surface is formulated. A geometric optics method and the classical integral transformation method are employed as two different methods of solution for this problem. Whenever necessary, the special case of linear shear (i.e. U(y) = 1+ϵy)) is chosen for the purpose of facilitating the final integration of the solution.

The asymptotic form of the solution obtained by the method of integral transforms agrees with the leading terms of the solution obtained by geometric optics when the latter is expanded in powers of small ϵ r.

The overall effect of the shear is to confine the wave field on the downstream side of the disturbance to a region which is smaller than the wave region in the case of uniform flows. If U(y) vanishes, and changes sign at a critical plane y = y_cr (e.g. ϵy_cr = -1 for the case of linear shear), then the boundary of this asymmetric wave field approaches this critical vertical plane. On this boundary the wave crests are all perpendicular to the x-axis, indicating that waves are reflected at this boundary.

Inside the wave field, as in the case of a point disturbance in a uniform primary flow, there exist two wave systems. The loci of constant phases (such as the crests or troughs) of these wave systems are not symmetric with respect to the x-axis. The geometric optics method and the integral transform method yield the same result of these loci for the special case of U(y) = U_o(1 + ϵy) and for large Kr (ϵr ˂˂ 1 ˂˂ Kr).

An expression for the variation of the amplitude of the waves in the wave field is obtained by the integral transform method. This is in the form of an expansion in small ϵr. The zeroth order is identical to the expression for the uniform stream case and is thus not applicable near the boundary of the wave region because it becomes infinite in that neighborhood. Throughout this investigation the viscous terms in the equations of motion are neglected, a reasonable assumption which can be justified when the wavelengths of the resulting waves are sufficiently large.