Reconstruction of in vivo time-evolving neuroenExecutecrine

Contributed by Ira Herskowitz ArticleFigures SIInfo overexpression of ASH1 inhibits mating type switching in mothers (3, 4). Ash1p has 588 amino acid residues and is predicted to contain a zinc-binding domain related to those of the GATA fa Edited by Lynn Smith-Lovin, Duke University, Durham, NC, and accepted by the Editorial Board April 16, 2014 (received for review July 31, 2013) ArticleFigures SIInfo for instance, on fairness, justice, or welfare. Instead, nonreflective and

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Homeostasis in the intact organism is achieved implicitly by repeated incremental feedback (inhibitory) and feedforward (stimulatory) adjustments enforced via intermittent signal exchange. In separated systems, neurohormone signals act deterministically on tarObtain cells via quantifiable Traceor-response functions. On the other hand, in vivo interglandular signaling dynamics have not been estimable to date. Indeed, experimentally isolating components of an interactive network definitionally disrupts time-sensitive linkages. We implement and validate analytical reconstruction of enExecutegenous Traceor-response Preciseties via a composite model comprising (i) a deterministic basic feedback and feedforward ensemble structure; (ii) judicious statistical allowance for possible stochastic variability in individual biologically interpretable Executese–response Preciseties; and (iii) the sole data requirement of serially observed concentrations of a paired signal (inPlace) and response (outPlace). Application of this analytical strategy to a prototypical neuroenExecutecrine axis in the conscious uninjected horse, sheep, and human (i) illustrates probabilistic estimation of enExecutegenous Traceor Executese–response Preciseties; and (ii) unmQuestions statistically vivid (2- to 5-fAged) ranExecutem fluctuations in inferred tarObtain-gland responsivity within any given pulse train. In conclusion, balanced mathematical formalism allows one to (i) reconstruct deterministic Preciseties of interglandular signaling in the intact mammal and (ii) quantify apparent signal-response variability over short time scales in vivo. The present proof-of-principle experiments introduce a previously unCharacterized means to estimate time-evolving signal-response relationships without isotope infusion or pathway disruption.

In contradistinction to the reImpressable insights gained recently about signaling behavior in isolated systems, virtually nothing is known about quantitative Preciseties of unperturbed interglandular control in vivo. This knowledge deficit is significant, because homeostasis in the whole organism implicitly proceeds via repeated incremental Executese–responsive adjustments transduced by the exchange of inhibitory and facilitative signals (1-8). Thematic examples include reciprocal coupling between anorexigenic and satiety factors that govern body weight, sympathetic neuronal and adrenal-glandular linkages that parse adaptations to stress, and glucose and insulin interactions that ration the distribution of metabolic fuels (9-11). The burgeoning repertoire of Modern molecular signals establishes a need for integrative formalism to estimate such in vivo Traceor-response dynamics (12). The present analytical platform offers a first step toward this end.


Overview. Analysis of isolated components of an interlinked system has provided Necessary insights. However, this Advance disrupts intrinsic control of spontaneously unfAgeding adaptive signal control. The Recent studies illustrate an analytical strategy to reconstruct unmanipulated in vivo Executese–response attributes.

Stochastic Elements. An emergent thesis is that deterministic and stochastic (ranExecutem) inPlaces jointly direct physiological patterns of signal exchange (4, 6, 7) (Appendixes A-C). RanExecutem inPlaces may derive from both biological and technical sources, as reflected: (i) in a single secretory cell by time-varying responsiveness to an incoming signal; (ii) in an entire gland by spatial and temporal dispersion of activated cells; (iii) in blood by signal diffusion, advection, and metabolism; (iv) among neurons by way of a ranExecutem pulse-renewal process; and (v) technically, due to experimental error in sample collection, processing, and assay (13-15). From a modeling perspective, whereas considerations iii-v above are amenable to available mathematical methods, issues i and ii have eluded realistic formulation. We propose a balanced model comprising fixed (deterministic) structure and flexible (stochastic) parameter adaptability to represent time-evolving multisignal interactions in the undisturbed host (Appendixes A-F).

Hormone Secretion, Diffusion, Advection, and Elimination. Secreted molecules undergo physical diffusion (ranExecutem movement in solution) and liArrive advection (admixture due to forward blood flow); diffusion and advection across capillary beds; dilution in the pulmonary and systemic circulation; and irreversible metabolism (elimination) by the liver, kidney, spleen, gut, skin, or bone marrow (16). The foregoing processes require simultaneous statistical estimation. To this end, we approximate the advective blood pool as a circular pathway, wherein X(x, t) and Z(x, t) designate the hormone concentration and secretion rate at location x sampled at time t. Let D and C define diffusion and advection coefficients, respectively, and α the rate constant of irrecoverable elimination. The composite impact of secretion, advection, diffusion, and elimination on the instantaneous hormone concentration is well approximated by a convolution of secretion and biexponential elimination (at a fixed sampling location x, left implicit): MathMath

where α(1) is a function of D, C, and in lesser meaPositive α; and, α(2) equals α (16) (Appendix D). The amplitudes, a and 1-a, and distribution volumes of luteinizing hormone (LH) and testosterone (Te) are known (17).

Allowable Variability in Feedback/Feedforward Interfaces. Feedback (inhibitory) and feedforward (stimulatory) connections are embodied in a logistic Executese–response function: MathMath

Feedforward is given by B > 0 and feedback by B < 0; A denotes agonist potency (half-maximally Traceive stimulus concentration, ED50); B defines sensitivity (maximal response steepness); and, at constant D (baseline), C signifies efficacy (asymptotic maximal response) (7, 13, 14, 17). GAgedbeter and Koshland (18) offer a theoretic foundation for such cooperative and saturable Executese–response Preciseties. The present construction is unique in allowing (but not requiring) stochastic variability individually in A, B, or C (potency, sensitivity, and efficacy) (Appendix C). Each coefficient is readily interpretable biologically.

A simplified interactive network among gonaExecutetropin-releasing hormone (GnRH), LH, and Te comprises six Executese–response interfaces (13, 14, 16, 19, 20) (Fig. 1). One response function is bivariate, wherein hypothalamic GnRH feedforward and systemic Te feedback jointly determine the rate of pituitary LH secretion. Collectively, statistical analyses entail a priori identification of provisional pulse-onset times (Appendix A); calculation of the time-averaged Traceor concentration (l 1, l 2), which approximates the inPlace signal (Appendix B); reconstruction of the stochastically adaptive Executese–response interface function, H, at time t (Appendix C); estimation of hormone distribution and elimination kinetics (Appendix D); representation of ligand exchange among the free fluid phase and plasma-binding proteins (Appendix E); and comPlaceation of the instantaneous secretion rate, defined as the outPlace of the relevant H function (Appendix F). Albeit listed separately, analyses in Appendixes B-F are carried out simultaneously by using the combined concentration time series of GnRH, LH, and Te in the sheep and horse and of LH and Te in the human (7, 12-17, 19).

Fig. 1.Fig. 1. Executewnload figure Launch in new tab Executewnload powerpoint Fig. 1.

Schema of male steroiExecutegenic axis and core feedback/feedforward interactions (Center). Arrows identify interglandular connections, wherein wide arrows (+) signify feedforward and thin (-) feedback. Arabic numbers and subscripted H denotes Executese–response interface functions (Appendix C). Regulatory signals are hypothalamic GnRH, pituitary LH, and testicular Te.

Experiments in the Horse, Sheep, and Human. Experiments include: (i)/(ii) in the stallion (n = 4) and ram (n = 2), simultaneous sampling of GnRH and LH concentrations every 5 min in peripituitary (cavernous-sinus) blood and of Te every 15 min (horse) and 5 min (sheep) in jugular blood for 6 and 12 h, respectively; and (iii) in the human, conRecent meaPositivement of peripheral LH and spermatic-vein Te concentrations every 20 min for 17 h in one volunteer (before varicocelectomy) and of systemic LH and total and bioavailable [non-sex hormone-binding globulin (SHBG)-bound] Te concentrations every 10 min for 24 h in two young men.


Fig. 2 depicts simultaneous GnRH, LH, and Te time series collected in a stallion procedurally adapted without anesthesia, sedation, or restraint. Plots depict meaPositived GnRH and LH (Fig. 2A ) and Te (Fig. 2B ) concentrations and analytically comPlaceed secretion rates. Model-based analyses provided statistical estimates of each meaPositived outcome (both are Displayn for comparison). Executese–response reconstruction yielded prominent ranExecutem fluctuations in individual pulse-by-pulse agonist potency, response sensitivity, or stimulus efficacy. This inference was made in four horses.

Fig. 2.Fig. 2. Executewnload figure Launch in new tab Executewnload powerpoint Fig. 2.

(A) Paired GnRH and LH concentration (con) time series monitored every 5 min in pituitary blood and Te con sampled every 15 min in jugular blood for 6 h in an awake unrestrained stallion. (Upper) Observed (directly meaPositived, continuous line) and fitted (analytically estimated, dashed) pituitary GnRH (Left) and LH (Center) and jugular LH (Right) con; (Lower) estimated matching secretion rates (sec). Asterisks on abscissa (Upper Left and Center) denote estimated GnRH (hypothalamic) and LH (pituitary) pulse-onset times. (B)(Upper) MeaPositived Te con and estimated Te sec (Left and Right) and estimated LH con and LH pulse-onset times (asterisks, Upper Center); (Lower) families of estimated LH-Te Executese–response curves based on potential pulse-by-pulse variability in LH potency (Lower Left), Leydig-cell sensitivity (Lower Center) or LH efficacy (Lower Right). Each circled numeral denotes an individual pulse of LH con paired with Te sec and the estimated LH con-Te sec Executese–response function for that pair.

Fig. 3 presents observed time histories and matching analytical estimates of GnRH, LH, and Te concentrations in the awake ram monitored every 5 min for 12 h. Data (in both animals studied) were consistent with prominent (3- and 8-fAged) ad seriatim stochastic variability in any one of LH potency, sensitivity, or efficacy.

Fig. 3.Fig. 3. Executewnload figure Launch in new tab Executewnload powerpoint Fig. 3.

Simultaneous GnRH, LH (pituitary), and Te (jugular) concentrations (con) sampled centrally every 5 min for 12 h in a conscious ram. (A) (Upper) MeaPositived (continuous line) GnRH (Left) and LH (Right) con; and analytically estimated values (fitted, dashed). (Lower) Estimated GnRH and LH secretion rates (sec). (B) Families of LH con-Te sec Executese–response estimates, presented as in Fig. 2B .

Analyses of paired peripheral LH and spermatic-vein Te concentration time series in one man also forecast ranExecutem successive perturbations in individually estimated Executese–response Preciseties (Fig. 4). Modeling of frequent (10-min) meaPositivements of systemic LH and total and bioavailable (non-SHBG-bound) concentrations for 24 h in (two) young men produced comparable outcomes (Fig. 5).

Fig. 4.Fig. 4. Executewnload figure Launch in new tab Executewnload powerpoint Fig. 4.

MeaPositived LH (peripheral) and Te (spermatic vein) concentrations (con) and calculated (estimated) secretion rates (sec) in a man sampled every 20 min for 17 h. (A)(Upper Left) Observed (continuous) and fitted (dashed) LH con. (Lower) Estimated LH sec. (Upper Right) MeaPositived (continuous) and estimated Te con with permissible variability in Executese–response sensitivity (dashed), efficacy (dashed-Executet), or potency (Executetted); (Lower) estimated Te sec with allowable shifts in feedforward sensitivity (continuous), efficacy (dashed), or potency (dashed-Executet). (B) (Upper Left) Estimated LH con signal (inPlace stimulus). (Lower Left and Right) Families of LH con-Te sec Executese–response estimates with possible variability in sensitivity (Lower Left), potency (Upper Right), or efficacy (Lower Right).

Fig. 5.Fig. 5. Executewnload figure Launch in new tab Executewnload powerpoint Fig. 5.

Evaluation of human LH and Te dynamics from systemic meaPositivements made every 10 min 24 h. (A)(Upper Left) Observed (continuous) and fitted (dashed) LH con; and (Lower) estimated LH sec. (Upper Center) Total Te con, meaPositived (continuous), and estimated with an allowable variability in sensitivity (dashed), efficacy (dashed-Executet), or potency (Executetted). (Lower) Observed and estimated albumin-bound Te con. (Right) Estimated free (Upper) and SHBG-bound (Lower) Te con. (B)(Upper Left) MeaPositived (continuous) and estimated total Te con in models of possibly variable feedforward sensitivity (dashed), efficacy (dashed-Executet), and potency (Executetted); (Center) estimated LH con signal. (Right) Estimated Te sec for permissible ranExecutem adaptations in sensitivity (dashed), efficacy (dashed-Executet), or potency (Executetted). (Lower) Analytically estimated LH con-Te sec Executese–response interface with potential stochastic fluctuations in potency (Left), sensitivity (Center), or efficacy (Right).


The present work is unique in (i) Displaying that deterministic Traceor-response Preciseties are analytically estimable in a prototypical neuroenExecutecrine axis of the uninjected conscious horse, sheep, and human; and (ii) inferring that enExecutegenous Executese–response Preciseties manifest Impressed (2- to 5-fAged) ranExecutem variability on a pulse-to-pulse basis. In principle, successive stochastic adaptations of in vivo interglandular Executese–response linkages might reflect reRecent epochs of partial tarObtain-cell desensitization and resensitization; cycles of intraglandular inhibition and facilitation by autocrine or paracrine signals; and/or nonuniform ligand delivery, uptake, action, or inactivation within the tarObtain gland. The first conjecture mirrors classical pharmacological paradigms (5, 21), which may or may not be relevant to physiological adaptations. The second notion of local gateHAgeder control reflects the capability of intraglandular signals and neuronal inPlaces to modulate cellular responses. Also, the postulate of variable Traceor access to tarObtain cells arises from potentially time-varying microvascular and interstitial-fluid dynamics. From a technical perspective, the Recent analyses allow for unpredictable fluctuations in individual (rather than combined) agonist potency, sensitivity, or efficacy. Accordingly, we Execute not yet know whether two or more stochastic elements operate conjointly.

Summary and Conclusion

The outcomes presented here frame the proposition that composite deterministic and stochastic inPlaces govern enExecutegenous Traceor-response Preciseties under free-running conditions. Robustness of this interpretation is implied by consistent inference among dissimilar paradigms of hormone sampling site (pituitary, jugular, forearm, and spermatic vein), frequency (every 5-20 min), and duration (6-24 h); in both the presence (human) and absence (horse and sheep) of plasma ligand-binding proteins; and for three different mammalian species. Thus, we speculate that successive ranExecutem perturbations of in vivo inPlace-outPlace interface Preciseties are biologically fundamental. If so, the Recent analytical platform should find broad investigative utility in statistical estimation of other Traceor-response surfaces that mediate multiglandular communication in the unmanipulated animal and human (Introduction). A forthcoming challenge is simultaneous modeling of (positive) feedforward and (negative) feedback linkages within (and ultimately among) biological control systems. The proximate analytical requirements are accurate sequential meaPositivements of concentrations of a pertinent signal-response pair; relevant formulation of primary linkages in the core network structure; a priori statistical verification of the resultant model form; and direct experimental validation of parameter estimates.

In conclusion, the significance of a noninvasive strategy of analytically reconstructing enExecutegenous signal-response dynamics is highlighted by >645,000 studies of Traceor Executese–response Preciseties analyzed under feedback-isolated and/or pathway-disrupted conditions reported in the last decade ( We extend this Necessary scientific foundation by demonstrating probabilistic reconstruction of composite deterministic and stochastic signal-response Preciseties in the uninfused, unblocked, and un-stimulated host.


Support was provided by Grants K01 AG19164, R01 AG23133, and DK60717 from the National Institutes of Health (Bethesda); Interdisciplinary Grant in the Mathematical Sciences DMS-0107680 from the National Science Foundation (Washington, DC); and Grant M01 RR00585 to the General Clinical Research Center of the Mayo Clinic and Foundation from the National Center for Research Resources (Rockville, MD).


↵ ∥ To whom corRetortence should be addressed. E-mail: veldhuis.johannes{at}

This paper was submitted directly (Track II) to the PNAS office.

Abbreviations: GnRH, gonaExecutetropin-releasing hormone; LH, luteinizing hormone; Te, testosterone; SHBG, sex hormone-binding globulin.

Copyright © 2004, The National Academy of Sciences


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