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Reverse Engineering of the Lordosis Behavior Neuronal Circuit

Reverse Engineering of the Lordosis Behavior Neuronal Circuit. Donald W. Pfaff Anna Lee, Nandini Vasudevan, Lee-Ming Kow Laboratory of Neurobiology and Behavior Rockefeller University New York. Endocrine, neural, genetic mechanisms for the simple sex behavior, lordosis.

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Reverse Engineering of the Lordosis Behavior Neuronal Circuit

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  1. Reverse Engineering of the Lordosis Behavior Neuronal Circuit Donald W. Pfaff Anna Lee, Nandini Vasudevan, Lee-Ming Kow Laboratory of Neurobiology and Behavior Rockefeller University New York

  2. Endocrine, neural, genetic mechanisms for the simple sex behavior, lordosis. • Reverse engineering the circuit, module by module • Negative and positive feedback • Hormone actions with different time constants • Mechanisms for fundamental CNS arousal

  3. The Receptors(Pfaff, Science 1968; J. Comp. Neurology, 1973)

  4. Neural circuit. Neural circuit for lordosis behavior.From Estrogens & Brain Function.(Springer-Verlag).

  5. Genes. Genes controlling lordosis behavior. From Drive. (M.I.T. Press).

  6. The Behavior(‘Estrogens and Brain Function’) Steroid hormones coordinate brain function with rest of body to ensure reproduction appropriate to environment. Mechanisms understood from receptor chemistry (Angstroms) through seasonality (light years).

  7. Conclusion Specific biochemical reactions in specific nerve cell groups of the mammalian brain determine a biologically crucial behavior.

  8. adds endocrine dependence. coordinates brain with body translates slow neuroendocrine to fast CNS signaling coordinates responses across all the spinal segments “local business”: segmental stretch and flexion reflexes in response to local stimuli

  9. III. Questions about feedback • Given the cutaneous stimulus, lordosis behavior looks ballistic; no feedback. • The negative feedbacks in these systems are endocrine: steroid hormones from peripheral glands turning off release of the neuropeptides that led to their synthesis. • Time-limited positive feedbacks do exist in my systems: voltage-dependent conductances causing action potentials; & estradiol + progesterone causing the ovulatory surge of LH. • CNS mechanisms for reproduction need not be homeostatic.

  10. Feed-forward,pre-lordosis behaviors • Fast forward, sudden stop, functions to: • F braked, prepared to support wt. of M. • F muscularly tense; facilitates lordosis. • Leaves M in correct mounting position. • “Paces” mating; facilitates fertilization.

  11. Hormone-dependent behavioral funnel M F O L F A C T I O N IFF Estradiol: Scent marks IFF Testosterone: Flank gland, Scent marks (long time, wide space) U L T R A S O U N D up the pheromone gradient M F (limited t,s) CUTANEOUS STIMULI M F (lordosis) (TIME) ( S P A C E )

  12. IV. Hormone actions with different time constants In a complex non-linear system with feedbacks, do mechanisms with different time constants offer any advantages? Do they encourage stable performance?

  13. Seeding Experimental Design hER (from Dr. P. Chambon) bgal(pSV-galactosidase) 48 hours 3xERE-Luc (three-tandem ERE linked to luciferase, from Dr. D. McDonnell) SK-N-BE2C (Human neuroblastoma cells) 24 hours Pulse 1 Pulse 2 2-pulse paradigm 4 hrs 2h 2h 24 hrs Luc & gal enzyme assays, results expressed as Luc/bgal to control for transfection efficiency. Cell lysis

  14. Rapid and slow/genomic hormone actions foster lordosis behavior. Agonist Transmitter receptor Ion chnl E2 Excitation mER Genomic actions of estrogen (PKA) PKC (PKA) Rapid membrane actions { } PKA PKC (In parallel) In VMN Neuroblastoma cells; MCF-7 cells; VMN neurons Lordosis

  15. For the future, get data relevant to equations of this sort: δ(t) = F [t, t2, δERn, g(t2, t1, δERm)] Where δERm is the change in output at time t1 that is a consequence of estrogen action at the membrane in the first pulse. The effect of this change on the system at the later time t2 is given by some function g which depends on the signal δERm and evolves between the times t1 and t2. It may be interpreted as the change in the basal state at time t2 in response to the signal at time t1. The second pulse, given by δERn, is the change due to the estrogen action in the cell nucleus. The final response of the system is thus given by the function F, which depends on the times involved, the second pulse δERn and on g. Equations conceived and written in collaboration with Professor Parameswaran Nair, Department of Physics, CCNY.

  16. V. Mechanisms for fundamental CNS arousal • The “structure” of CNS arousal mechanisms is amenable to investigation. • We are working toward a formal, quantitative description of these mechanisms.

  17. Literature Review:

  18. COGNITIVE FUNCTION EMOTIONAL FUNCTION DECISION MAKING FEELINGS (minutes) SUSTAINED ATTENTION MOODS(hours) ATTENTION TEMPERAMENT(lifetime) ALERTNESS AROUSAL AROUSAL

  19. Fundamental Arousal of Brain and Behavior: Applications • Stupor, vegetative, coma • Aging • Alzheimer’s • ADHD • Autism • Anesthesia • Sleep Disorders • Mood Disorders (Depression, Bipolar Disorders) • Vigilance/Military • Vigilance/Shift Work • Vigilance/Dangerous Occupations • Toxicology (e.g., Lead in water) • Fatigue states (CFIDS, FMS, Gulf War)

  20. ‘High’ throughput assay of all three components of CNS arousal

  21. Descending Arousal-controlling Systems

  22. δA = Fg(Ag) · F1(As1) + F2(As2)...+ Fn (Asn) Where A is the state of arousal of the CNS at any moment. Ag= generalized arousal. Asn, a specific form of arousal.

  23. Idea: Information theory maths shed light on CNS arousal mechanisms • Arousal-related neurons respond best to high-information (salient, surprising, unpredictable) stimuli (Harvard U. Press, 2005) • Claude Shannon devised an intuitively pleasing, mathematically precise definition of information as follows: Where H is the total amount of “Shannon” information and p(x) is the probability of event x.

  24. Arousal / Information theorythinking naturally yields a universal phenomenon: HABITUATION.

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