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Endocrinology 144(3):823– 831
Copyright © 2003 by The Endocrine Society
doi: 10.1210/en.2002-220585
Gonadotropin-Releasing Hormone Neurons Generate
Interacting Rhythms in Multiple Time Domains
CRAIG S. NUNEMAKER, MARTIN STRAUME, R. ANTHONY DEFAZIO,
AND
SUZANNE M. MOENTER
Departments of Internal Medicine and Cell Biology, National Science Foundation Center for Biological Timing (C.S.N.,
M.S., R.A.D.F., S.M.M.), and the Center for Biomathematical Technology (M.S.), University of Virginia, Charlottesville,
Virginia 22908
Pulsatile release of GnRH is prerequisite for fertility. The
possibility that multiple rhythms interact to generate GnRH
pulses was raised by observations of changes in action potential firing and intracellular calcium levels occurring much
more frequently than hormone pulses. To examine this further, we analyzed firing patterns from targeted extracellular
recordings of green fluorescent protein-expressing GnRH
neurons in acute brain slices prepared from adult ovariectomized and ovariectomized ⴙestradiol mice. Fourier spectral
analysis identified rhythms in multiple time domains, which
we grouped into bursts (a period of <100 sec), clusters (100 –
1000 sec), or episodes (>1000 sec). Bursts were the fundamental unit of activity and consisted of trains of action currents
(the currents during action potentials). Episodes and clusters
were lower frequency changes in firing rate resulting from
alterations in the time between bursts. Specifically, mean interburst interval during episode peaks was less than during
nadirs. In contrast, neither burst duration nor action currents/burst differed between peaks and nadirs. Estradiol increased episode period by changing the patterning of bursts,
not burst duration or action currents/burst. We propose a low
frequency rhythm that is subject to external influences alters
the patterning of a fundamental unit of activity to change
ultimately GnRH pulse frequency. (Endocrinology 144:
823– 831, 2003)
R
HYTHMIC ACTIVITY PROVIDES the foundation of
many endocrine systems. The rhythmic activity of individual cells, however, can occur on a time scale that differs
from the integrated rhythmic secretion of the system, as is the
case with ␤-cells of the pancreas (1) and oxytocin neurons (2).
In the central control of fertility, GnRH secretion occurs at
intervals from minutes to hours, varying with respect to
stage of the reproductive cycle, season, steroid milieu, and
other factors (3–7). Cultures of immortalized GnRH neurons
(GT1 cells) spontaneously generate secretory pulses (8 –10),
episodes of electrical activity (11), and synchronized exocytosis (12) at 20- to 35-min intervals, demonstrating integrated
activity.
At the level of individual cells, higher-frequency rhythms
have been observed. For example, GT1 cells display intracellular calcium oscillations and action potential bursts at
intervals of 3– 60 sec (13, 14). Two recent reports using murine green fluorescent protein (GFP)-expressing GnRH neurons in dissociated (15) or slice preparations (16) described
burst firing on the order of seconds. Rhythms at an intermediate interval (mean ⬃8 min) have also been observed in
intracellular calcium oscillations of embryonic GnRH neurons derived from rhesus monkey olfactory placodes (6).
We have begun to address the issue of multiple rhythmic
mechanisms within the GnRH system by characterizing firing patterns using Fourier spectral analysis, an established
means of extracting information about underlying rhythms
in time series data (17, 18). We analyzed action current patterns recorded extracellularly from GFP-expressing GnRH
neurons in acute brain slices prepared from adult mice (16).
To assess the effects of steroids on rhythmic activity in these
various time domains, patterns were compared between recordings of GnRH neurons from ovariectomized (OVX) mice
and mice that were ovariectomized and implanted with a
physiological level of estradiol (OVX⫹E). Using spectral
analysis on firing patterns from these two animal models, we
were able to identify rhythms in multiple time domains,
explore possible interactions among these rhythms, and
characterize their sensitivity to estradiol.
Materials and Methods
Animal model and slice recordings
The analyses in the present report were made on data presented in
part in a previous report; analysis in that report was restricted to Cluster7 analysis (19) of long-term firing patterns. Details of experimental
methods can be found in that report (16). Briefly, GnRH neurons were
recorded from adult female, GnRH-GFP mice (20). Mice were anesthetized, bilaterally ovariectomized, and either implanted with a Silastic
capsule containing 0.625 ␮g estradiol (OVX⫹E, n ⫽ 13 mice) or not
treated further (OVX, n ⫽ 15 mice) 5–9 d before recording to avoid acute
effects of steroid manipulation. Because of the small blood volume of
mice, it was not possible to measure serum estradiol in addition to the
LH values previously reported (16). To estimate estradiol levels, serum
from animals treated identically were assayed for estradiol as were pools
of remaining serum from the two treatment groups. These data have
been previously reported (21). Estradiol in OVX⫹E mice was 30.8 ⫾ 6.1
pg/ml. This is similar to values reported during the estrous and
diestrous days of the cycle of young adult mice (22). The Animal Care
and Use Committee of the University of Virginia approved all procedures used in these experiments.
GFP-expressing GnRH neurons in 200-␮m coronal brain slices were
targeted by fluorescence for long-term (20 –220 min) extracellular recordings. Recordings were made in voltage-clamp mode with a holding
potential of 0 mV, filtering at 10 kHz, and digitized with an ITC-18
acquisition interface (Instrutech, Port Washington, NY). Action currents
(events), the membrane currents associated with action potential firing,
Abbreviations: GFP, Green fluorescent protein; OVX, ovariectomized; OVX⫹E, ovariectomized and implanted with estradiol.
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Endocrinology, March 2003, 144(3):823– 831
were detected using Pulse Control Event Tracker software (Instrutech).
Events were binned at 1-sec intervals for Fourier spectral analysis.
Data analysis
Fourier spectral analysis was performed (Monte Carlo-Fast Fourier
Transform, MC-FFT, Marty Straume, Center for Biomathematical Technology, University of Virginia) on electrical activity time series data after
converting experimentally acquired events to binned activity records at
1-sec resolution. Time series were linear-regression detrended (i.e. drift
in baseline during the recording eliminated and mean adjusted to zero),
and a string of zeros was added to either end to at least two times the
original data series length. These procedures minimize zero/lowfrequency power contributions and eliminate circular correlation effects,
respectively, in derived Fourier power/frequency spectra. To evaluate
statistical significance at 95% probability, empirical resampling was
used. Specifically, 1000 temporally shuffled, randomized surrogates of
each original time series (by resampling without replacement) were
analyzed to produce 1000 corresponding surrogate Fourier spectra to
provide a noise floor upon which significance can be determined. The
mean power and associated sd were calculated at each assessed frequency from these 1000 surrogate spectra and compared with the power
of the original time series at each corresponding frequency by way of a
multiple-measures corrected one-sided Z-score (in which the multiplemeasures correction was based on the number of data points comprising
the original time series being analyzed). Frequency components exhibiting greater than 95% significance probability by this criterion were
considered significant (i.e. for something to be significant the log 10 of
the [95% PowerRatio] must be greater than zero).
Results of MC-FFT analysis of each time series were summarized with
respect to three period ranges: 0 –100 sec (bursts), 100-1000 sec (clusters),
and greater than 1000 sec (episodes). Normalized power was employed
as the basis for frequency weighting within these three period ranges.
Only those frequency components for which the MC-FFT was significant
(as defined above) were considered. Weighted average period estimates
were calculated as the inverse of the weighted-average mean frequency.
Estimated period confidence limits were derived from weighted-average frequency dispersion calculations (in terms of variably weighted
root mean squared deviations of frequency) (17). Also reported for each
period range was the corresponding fraction of significant power in the
specified period range relative to the total significant power represented
in the spectrum as a whole. For burst and cluster patterns, comparisons
between OVX and OVX⫹E treatment groups were made using a twotailed t test assuming unequal variance (mean ⫾ sem).
For the episode time domain (⬎1000 sec), it was often difficult to
distinguish between spectral peaks derived from repetitive patterns and
trends in which patterns were inferred from a single event. For example,
if one episode of increased firing occurs at any point during a 90-min
recording, spectral analysis would generate a spectral peak corresponding to a 90-min interval, even though only one episode was observed,
a situation that precludes measurement of a true interevent interval. This
makes spectral analysis less than ideal for identifying rhythms in this
time domain. To minimize these trending effects, no spectral peak with
corresponding interval greater than half the total recording time was
included for further analysis, unless two or more episodes were clearly
visible from the firing rate plot (e.g. see Fig. 4A). Using these criteria,
spectral peaks greater than 1000 sec were identified in 10 of 28 recordings. Comparisons between OVX (n ⫽ 5) and OVX⫹E (n ⫽ 5) treatment
groups were made using a two-tailed t test assuming unequal variance
(mean ⫾ sem).
There are additional limitations to spectral analysis that should be
noted. For example, very strong spectral peaks could display smaller
amplitude harmonics at specific fractions of the fundamental peak. This
can hamper interpretation of intervals within a time domain. Harmonics
would not interfere, however, with identification of phenomena in
clearly distinct time domains, such as between bursts and episodes. Also,
although spectral analysis can clearly identify repetitive patterns, it
cannot determine when they occur in the time series, making it difficult
to identify changes in burst characteristics (e.g. in peak vs. nadir phase
of longer-period rhythms).
Segments of episodic peaks and nadirs were thus chosen based on
previous Cluster7 analysis (11) to compare burst characteristics at different points in time across a single recording. Five-minute segments
Nunemaker et al. • Multiple Rhythms in GnRH Firing Patterns
during an episode peak (increased firing rate) and an episode nadir
(decreased firing rate) were analyzed. Each 5-min segment was binned
at 1-sec intervals to produce three measures of burst characteristics: the
number of action currents per burst, burst duration (the number of
consecutive bins with one or more events), and burst interval (the number of bins between the start of one burst and the start of a second burst).
A burst started with the first bin containing a nonzero integer and ended
with the next zero. The mean value for each characteristic was determined from the total number of bursts identified in the 5-min segment
for each recording. Comparisons between peak and nadir segments were
made for 5 OVX recordings and 5 OVX⫹E recordings using a one-tailed
t test assuming unequal variance (mean ⫾ sem). A one-tailed test was
assumed because the change in characteristic was hypothesized to result
in a decrease in firing rate from peak to nadir phase.
To quantify possible differences in burst characteristics between OVX
and OVX⫹E treatment groups, the means for each burst characteristic
described above were determined for the duration of each recording
(n ⫽ 5, OVX, OVX⫹E) and compared using a two-tailed t test assuming
unequal variance. The data stream containing burst interval information
was further analyzed with the Cluster7 pulse detection algorithm to
identify long-term patterns in burst firing interval. Cluster7 compares
clusters of points by pooled t test to look for peaks and nadirs over time
(19). Using peak and nadir settings of 10 points each, Cluster7 identified
when burst interval increased (representative of a nadir in firing rate) in
both OVX and OVX⫹E recordings. The interval between increases in
burst interval was calculated, and treatment groups were compared
with a one-tailed t test based on the hypothesis that fewer episodes
would be observed in OVX⫹E as predicated by our previous study (16).
Results
Rhythmic firing patterns identified in multiple time
domains by spectral analysis
In previous work (16), we noted that GnRH neurons displayed burst firing patterns that occur at much shorter intervals than the secretory pulses that are critical to reproductive function. To further explore how burst firing might
relate to rhythms at secretory intervals, action currents recorded from GnRH neurons were binned at 1-sec intervals
for the duration of the recording (20 –220 min) for use in
spectral analysis, a means of identifying significant patterns
of activity in multiple time domains (17, 18). An example of
a binned data stream is shown in Fig. 1A. These data streams
were analyzed for spectral peaks representing patterns in
action current firing frequency that conformed to a sinusoidal waveform. The spectral peaks from this analysis (Fig. 1B)
reveal patterns of action current firing frequency at both
30-sec and at approximately 500-sec (8.5 min) intervals.
These are illustrated by the sine waves in Fig. 1A. For purposes of further analysis, these patterns were arbitrarily classified as bursts (⬍100 sec), clusters (100 –1000 sec), and episodes (⬎1000 sec, not present in the example in Fig. 1).
Bursts
Burst firing in GnRH neurons was defined as repetitive
trains of two or more action currents. Patterns differed from
cell to cell in characteristics such as burst interval, burst
duration, and number of action currents per burst. Figure 2
displays several examples to illustrate the variability in burst
firing patterns displayed by GnRH neurons recorded from
both OVX and OVX⫹E mice. In a majority of cells (60%), the
average burst interval was under 20 sec (Fig. 2, A–D), with
burst duration and number of action currents per burst differing from cell to cell. In other cells (33%), burst firing
Nunemaker et al. • Multiple Rhythms in GnRH Firing Patterns
Endocrinology, March 2003, 144(3):823– 831 825
1600 sec (⬃27 min) and burst intervals less than 10 sec (Fig.
3A). These patterns and the raw action currents underlying
them are illustrated in Fig. 3B. Note the peaks of increased
firing rate throughout the recording separated by approximately 400 sec, representing cluster patterns (solid sine
wave) and the phases of increased firing rate at the beginning
and end of the recording (dotted sine wave). Examination of
the underlying change in burst firing pattern suggests a
mechanism for generating these phases of increased and
decreased firing rate, specifically changes in the interval between bursts (Fig. 3C). To illustrate this, examples of 1-min
segments of burst firing (Fig. 3C) during phases of decreased
firing (nadir, 1) and increased firing (peak, 2) are shown for
the time points indicated in Fig. 3B. Bursts were typically
pairs of action potentials in this example and appeared similar at peak and nadir phases (Fig. 3C). The spacing between
bursts (burst interval), however, was much greater on average during the nadir phase, resulting in a reduced overall
firing rate in comparison with the peak phase.
Modulation of burst interval underlies long-term
pattern changes
FIG. 1. Illustration of Fourier spectral analysis. Input files for spectral analysis were firing activity binned at 1-sec intervals. A, Input
file (events/second) for a representative recording made from a GnRH
neuron in an OVX⫹E mouse. Sine waves with the most significant
periods (spectral peaks determined by the spectral analysis output
shown in B) are drawn above traces. B, Fourier spectral analysis
identifies patterns within a data stream that conform to a sinusoidal
waveform. Note the pattern does not need to persist throughout the
entire data stream to be identified. The periods of those sine waves
that are significant with respect to a randomized noise floor (i.e. above
0 on the y-axis) are plotted. In this example, rhythms at intervals of
30 sec (burst) and approximately 500 sec (cluster) were most significant (rhythm interval corresponds to the period of the sine wave).
duration and interval were longer (Fig. 2, E and F). A few
cells (7%) did not display burst firing at all but rather displayed irregular, tonic firing of single action currents (Fig. 2,
G and H). This inherent variability in high frequency firing
patterns generated by individual GnRH neurons may indicate differences in burst-generating mechanisms or inputs to
subpopulations of these cells.
Clusters and episodes
To examine whether burst firing relates to the much lower
frequency rhythm of GnRH secretory pulses, firing patterns
were evaluated in additional time domains. In addition to
bursts, spectral analysis detected many rhythms with intervals on the order of minutes. These patterns were subdivided
into two categories: 1) episode patterns defined at more than
1000 sec based on previously observed intervals determined
by the Cluster7 pulse detection algorithm (11, 16, 19), and 2)
cluster patterns between 100 and 1000 sec. Clusters and episodes both consisted of multiple bursts in rapid succession,
resulting in phases of increased firing rate.
An example of these patterns is shown in Fig. 3. Spectral
analysis identified cluster patterns at approximately 350- to
450-sec intervals (Fig. 3A), between the episodic interval at
To further explore the notion that changes in burst firing
contribute to the formation of long-term firing patterns similar to those of GnRH pulses, we investigated three specific
characteristics of burst firing that may be modulated: 1) number of action currents per burst, 2) burst duration, and
3) burst interval because different characteristics could be
indicative of different underlying ion channels. A subset of
recordings from OVX and OVX⫹E mice (n ⫽ 5 each) that
displayed distinct episodes of increased firing were analyzed
for these burst characteristics at both episodic peaks of increased firing rate and adjacent nadirs of decreased firing
rate. During either peak and nadir phases, there was no
statistical difference in findings between OVX and OVX⫹E
groups (P ⬎ 0.12), so values were pooled for additional
analysis and presentation (peak OVX vs. OVX⫹E: action
currents per burst 4.9 ⫾ 0.6 vs. 5.0 ⫾ 1.1 events, burst duration
2.6 ⫾ 0.3 vs. 2.2 ⫾ 0.2 sec, burst interval 6.6 ⫾ 1.4 vs. 6.2 ⫾
1.5 sec; nadir OVX vs. OVX⫹E: action currents per burst 4.1 ⫾
0.7 vs. 2.9 ⫾ 1.2 events, burst duration 2.0 ⫾ 0.4 vs. 1.7 ⫾ 0.4
sec, burst interval 24.4 ⫾ 5.9 vs. 33.6 ⫾ 10.5 sec). A representative example of a GnRH neuron recorded from an
OVX⫹E mouse is shown in Fig. 4. Two episode peaks (asterisks) are shown flanking an intervening nadir, with the
corresponding burst firing patterns over 1 min displayed in
insets at each phase (Fig. 4A). The distribution of burst intervals plotted against burst duration (Fig. 4, B–D) indicates
that burst interval is increased during the nadir phase (Fig.
4C), compared with the peak phases (Fig. 4, B and D). Among
the 10 recordings analyzed, a significant increase in burst
interval (P ⬍ 0.0025) occurred during nadirs, whereas burst
duration and events per burst were not significantly altered
(Fig. 5). In other words, the time between bursts was altered
within each recording but not the composition of the burst
itself, suggesting burst firing may represent a quantal unit of
rhythmic activity in GnRH neurons. Modulating the interval
between bursts is one possible mechanism GnRH neurons
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Endocrinology, March 2003, 144(3):823– 831
Nunemaker et al. • Multiple Rhythms in GnRH Firing Patterns
FIG. 2. Representative burst-firing patterns of GnRH neurons. Action current bursts of varying duration, number of events per burst, and
interval are shown for GnRH neurons recorded from OVX (A, C, E, G) and OVX⫹E (B, D, F, H) mice. A 1-min segment is shown for each example.
Note the wide variability in firing pattern among cells. Scale bars are shown to the right of each panel. Horizontal dotted lines represent baseline
trace for each panel.
use to produce changes in long-term firing rate underlying
secretory pulses.
Estradiol effects on firing patterns
We have previously reported that estradiol increases the
interval between episodes of increased firing rate (16). To
investigate estradiol effects on rhythmic activity of GnRH
neurons in all time domains, recordings were compared between OVX and OVX⫹E mice. Dispersion (i.e. data spread)
and fraction of significant power were unaffected by estradiol in all time domains (data not shown). The mean period
of rhythms in the burst time and cluster time domain were
not altered by estradiol (Fig 6, A and B). Note, however, that
over the duration of each recording, the interval between
bursts still underwent changes between peaks and nadirs of
low-frequency rhythms (see Figs. 3 and 4). Although these
same changes occurred in the presence and absence of estradiol (see Fig. 5), estradiol appeared to affect how often the
intervals between bursts were modified to produce the lowfrequency rhythms. Specifically, in the presence of estradiol,
the reduction in burst interval that produces an episode peak
occurred less frequently. As a result, estradiol caused a significant increase in period in the episode time domain (Fig.
6C). Although the numerical values for episode interval ob-
tained from the spectral analysis in the present study and the
Cluster7 analysis in the previous report differed, the increase
in episode interval was consistent with our previous analysis
(16). These data suggest estradiol-sensitive elements may be
confined to the expression of low-frequency rhythms.
To confirm that estradiol changed the long-term patterning of bursts, the Cluster7 pulse detection algorithm (19) was
used to analyze burst intervals sequentially in a subset of
longer recordings that displayed long-term episodic firing
patterns (n ⫽ 5 each, OVX, OVX⫹E) to determine whether
phases of increased and decreased burst interval occurred. In
this analysis, several long burst intervals located near one
another temporally in a recording indicate a nadir in the
firing pattern of that cell. Representative plots of burst firing
intervals are shown for one OVX (Fig. 7A) and one OVX⫹E
recording (Fig. 7B). Values near zero on the y-axis in Fig. 7,
A and B, indicate more intense firing with very short burst
intervals. Peaks in these plots, conversely, indicate decreased
firing rate with longer intervals between bursts. Cluster7
identified when burst intervals were increased, thus indicating nadirs in the overall firing rate. Estradiol increased the
time from one nadir to the next (OVX, 16.8 ⫾ 2.0 min;
OVX⫹E, 29.0 ⫾ 4.4 min, P ⬍ 0.02). The mean burst interval
of the entire recording did not differ between treatment
Nunemaker et al. • Multiple Rhythms in GnRH Firing Patterns
Endocrinology, March 2003, 144(3):823– 831 827
FIG. 3. Representative example of significant
repetitive firing patterns in three time domains in a GnRH neuron from an OVX mouse.
A, Spectral output with respect to a randomized noise floor (0 on the y-axis) of the firing
pattern in B. Arrows point to significant
rhythms which were termed bursts (⬍10 sec
in this example), clusters (⬃350 – 450 sec in
this example), and episodes (⬃1600 sec in this
example). B, Firing rate of this neuron displayed at 1-min intervals. Vertical lines at the
top of the graph indicate each action current
composing the firing rate plot. Sine waves illustrate the dominant spectral peaks for clusters (solid) and episodes (dotted). 1 and 2 mark
the origin of the burst firing patterns displayed in C. C, One-minute segments of burst
firing expanded from B, as indicated by 1 and
2. Location 1 illustrates a nadir in firing rate;
location 2 illustrates a peak in firing rate.
Scale bars are shown in the lower right.
FIG. 4. Burst interval is altered to produce
peaks and nadir in firing rate. A, Firing rate
of a GnRH neuron from an OVX⫹E mouse
displayed at 1-min intervals. Three 1-min segments are expanded from sections of this recording labeled peak 1, nadir, and peak 2. Asterisks indicate episode peaks detected by
Cluster7. Horizontal bars indicate 5-min segments of the recording that were analyzed for
several burst-firing characteristics. B–D,
Scatter plots of burst interval (y-axis) vs. burst
duration (x-axis) for the 5-min segments specified in A. Nearly all burst intervals are less
than 10 sec during both peak phases (B, D). In
contrast, several burst intervals are observed
at 20 –25 sec and approximately 40 –50 sec
during the nadir phase (C). Burst duration is
less than 10 sec in all instances and does not
appear to differ between peak and nadir
phases (B–D).
groups (OVX, 18.5 ⫾ 5.6 sec; OVX⫹E, 16.5 ⫾ 3.2 sec), nor did
number of events per burst (OVX, 4.0 ⫾ 0.3 events; OVX⫹E,
4.0 ⫾ 0.8 events) or burst duration (OVX, 1.9 ⫾ 0.1 sec;
OVX⫹E 2.1 ⫾ 0.2 sec), confirming the analysis of these characteristics in peak and nadir segments of these same recordings. These results indicate estradiol effects were confined to
the long-term patterning of bursts but not the short-term
burst firing phenomenon itself.
Discussion
The cellular mechanisms underlying episodic GnRH secretion are not well understood. In the present study, spectral
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Endocrinology, March 2003, 144(3):823– 831
Nunemaker et al. • Multiple Rhythms in GnRH Firing Patterns
FIG. 5. Comparison of burst-firing characteristics between peak and
nadir phases of episodic activity. Segments of 5-min duration from
peaks and nadirs in firing rate were analyzed for differences in number of events (action currents) per burst, burst duration, and burst
interval. A significant increase in burst interval was observed during
the nadir phase in recordings from OVX and OVX⫹E mice (*, P ⬍
0.0025, n ⫽ 10 each). Because there was no difference between OVX
and OVX⫹E groups for any characteristic, data were pooled for analysis.
analysis of firing patterns demonstrated that GnRH neurons
in acutely prepared brain slices display rhythmic activity in
multiple time domains ranging from burst firing on the order
of seconds to episodes of increased firing rate that occur on
the order of many minutes. Further analysis of burst-firing
characteristics revealed a relationship between the highfrequency rhythms (bursts) and the low-frequency rhythms
(clusters and episodes). Specifically, peaks and nadirs in
firing rate representing a low-frequency rhythm similar to
that established for GnRH release were due to changes in the
interval between bursts of action currents representing a
high-frequency rhythm. These findings suggest a working
model, referred to throughout this discussion, in which distinct rhythm generators in GnRH neurons interact to produce
secretion at intervals relevant to reproductive function
(Fig. 8).
This model consists of two putative rhythms intrinsic to
GnRH neurons. The first is burst firing (Fig. 8A, highfrequency rhythm). Burst firing has been observed previously in both GT1 cells (13, 14) and more recently in GFPGnRH neurons (15, 16, 23). Burst firing has also been
observed in acutely isolated GnRH neurons, indicating this
mode of firing is intrinsic (Ref. 15 and Nunemaker, C. S., and
S. M. Moenter, unpublished observations). Furthermore, in
other neuroendocrine systems, burst firing has been positively correlated with hormone release (24, 25). During each
recording, cell-specific burst-firing characteristics were
maintained (i.e. burst duration and action currents per burst
did not change). A burst can thus be considered the fundamental unit of activity of a GnRH neuron. In contrast to the
characteristics of bursts, the interval between these fundamental activity units was repeatedly increased and decreased within each recording. Multiple bursts in rapid succession produced peaks in firing rate; these peaks were
separated by periods of relative quiescence in which the
interval between bursts was longer. A low-frequency rhythm
is thus elaborated from changes in the timing of the highfrequency rhythm (Fig. 8B, low-frequency rhythm, clusters,
and episodes).
In the present study, low-frequency rhythms were arbi-
FIG. 6. Estradiol effects on mean spectral periods are confined to the
episode time domain. Mean (⫾ SEM) interval identified by spectral
analysis in firing patterns of GnRH neurons recorded from OVX and
OVX⫹E mice in the range of bursts (0 –100 sec) (A), clusters (100 –
1000 sec) (B), and episodes (⬎1000 sec) (C) over the entire recording
period. Estradiol increased the interval between episodes by 50% (*,
P ⬍ 0.05) but did not affect mean burst or cluster intervals. The
number of cells exhibiting patterns in each time domain is indicated
above the respective column in each graph. Note this analysis of
overall period does not reveal the changes in period that occur within
recordings for bursts as illustrated in Figs. 3–5.
trarily divided based on period into clusters (100 –1000 sec)
and episodes (⬎1000 sec) because distinct rhythms in these
two time domains were often observed in the same cell (e.g.
Fig. 3). Whether these represent functionally different
Nunemaker et al. • Multiple Rhythms in GnRH Firing Patterns
rhythms is an exciting question for further study. Clusters are
similar in period to both the firing patterns of acutely isolated
GnRH neurons (15) and intracellular calcium oscillations in
cultures of embryonic GnRH neurons (6). The former suggests this rhythm is also endogenous to GnRH neurons. An
intriguing speculation is that rhythms with periods in the
cluster time domain represent the upper frequency of pulsatile GnRH release. Of interest in this regard, clusters had
a mean period near the upper limit of that measured for
GnRH pulse interval (6 –12 min in sheep) (26 –28). For the
remainder of this discussion, however, we will combine clusters and episodes under the moniker “low-frequency
rhythm” for clarity.
FIG. 7. Estradiol changes burst interval pattern. Representative examples of burst interval patterns are shown for GnRH neurons from
an OVX (A) and OVX⫹E (B) mouse. Burst interval (y-axis, seconds)
is plotted vs. time of occurrence (x-axis, minutes). Significant shifts
between higher and lower burst interval detected by Cluster7 detected are indicated by the lines at the top of each graph.
Endocrinology, March 2003, 144(3):823– 831 829
At this point, we can only speculate about the mechanisms
underlying an intrinsic low-frequency rhythm because our
observations in this study are limited to changes in action
current firing rate. This rhythm could be produced as a result
of cyclic changes in the activity of kinases, phosphatases, or
other posttranslational modifications. Another possibility is
that substances produced by GnRH neurons complete autocrine feedback loops. For example, GnRH agonists have
been shown to reduce the frequency of pulsatile GnRH release from GT1 cells (29). In addition, ␥-aminobutyric acid
release has been observed from GT1 cells (30), and GT1 cells
and GnRH neurons express functional ␥-aminobutyric acid
receptors (31–34). It is also possible that the low-frequency
rhythm is governed by cycles of transcription and translation. There is mounting evidence in GT1 cells against this
hypothesis, however. Specifically, blockade of transcription
and translation does not alter episodic release of GnRH (10)
or exocytosis measured by FM1-43 incorporation (35).
Regardless of the mechanism, the low-frequency rhythm
in firing rate is associated with modulation of burst interval
(Fig. 8C). This observation begs two questions: How is burst
interval modulated? And how might the low frequency
rhythm participate in this modulation? With regard to the
former, burst interval in other neural systems is modulated
by various ion channels, including hyperpolarization-activated channels (nonspecific cation channels), low-voltage
activated calcium channels, calcium-activated potassium
channels (36 –39), or channels involved in setting resting
membrane potential and/or firing threshold (40). The lowfrequency rhythm may thus participate in setting burst interval by altering ion channels. For example, a change in
phosphorylation state or intracellular messenger could affect
the current through these channels and thereby the response
of the cell. Intracellular mediators can also change ion channel function. For example, gating of cyclic nucleotide-gated
channels by cAMP increases the frequency of calcium oscillations in GT1 cells (41). These calcium oscillations occurred
in the same time domain as bursts in the present study. A
low-frequency rhythm of cAMP levels may act through cyclic nucleotide-gated channels to modulate the interval between bursts as observed in the present study.
The intrinsic rhythms of individual GnRH neurons are
FIG. 8. Model of interacting rhythms in GnRH neurons. A, A high-frequency burst rhythm is depicted by repetitive action currents (vertical
lines) along a thin dotted horizontal line. An arrow points to the first illustrated burst. B, A low-frequency rhythm with a 20-min peak-to-peak
interval is depicted by the thick line. C, Burst interval is decreased during peaks in the low-frequency rhythm and increased during nadirs.
D, External influences modulate the low-frequency rhythm, illustrated by dotted lines stretching the interval between peaks, to produce different
intervals between peaks for different stages of the reproductive cycle. Although an increase in interval is shown, modulation of the low-frequency
rhythm could also result in a decreased interval (not shown).
830 Endocrinology, March 2003, 144(3):823– 831
likely modulated by external influences to produce hormone
release at the intervals observed during different phases of
the normal reproductive cycle (Fig. 8D). Steroids, for example, are particularly important in regulating the frequency of
GnRH release in vivo (7, 42, 43). In the present study, estradiol
increased the interval between episodes of increased firing
rate. Recall that interburst interval is the only characteristic
of burst firing that is changed between episode peaks and
nadirs in both OVX and OVX⫹E groups (Figs. 3–5). Together
these observations indicate that estradiol produces the increase in episode interval by reducing the frequency with
which interburst interval is altered. Although the overall
spectral analysis of burst data showed no difference in the
mean burst period (interburst interval) because of estradiol
(Fig. 6), the mean does not reflect the patterning of burst
intervals. These data are consistent with the hypothesis that
estradiol alters the low-frequency rhythm, which in turn
affects patterning but not the other characteristics of the
high-frequency rhythm (Fig. 7). Such a differential sensitivity
to estradiol further supports the notion that multiple distinct
rhythm generators exist in GnRH neurons. Steroids could
influence the low-frequency rhythm through posttranslational modification of ion channels as proposed above. In this
regard, estradiol and other steroid hormones have been
shown to activate membrane-associated kinase cascades (44 –
46). In GnRH neurons, estradiol alters potassium currents at
least in part through changing phosphorylation state of the
channels or closely associated proteins (21).
In addition to influences arising external to the GnRH
neurosecretory network such as steroids, rhythms intrinsic to
individual GnRH neurons could also be modulated by interactions among GnRH neurons. In this regard, rhythms
emerging from networked cells have been shown to differ
from those of isolated component cells. For example, dissociated pancreatic ␤-cells display rhythmic burst firing at 5- to
10-sec intervals, whereas burst firing from these cells within
the islets of Langerhans occurs at intervals greater than 60 sec
(1), and insulin pulses from the whole pancreas occur even
less frequently (5–10 min) (47). Synchronizing interactions
have been demonstrated in GT1 cells (11, 12, 14). Furthermore, in cultured rhesus monkey embryonic GnRH neurons,
calcium oscillations ranging from 1 to 35 min in individual
cells were observed to synchronize at approximately 60-min
intervals, an appropriate interval for GnRH secretion in this
species (6). Although synchronization among GnRH neurons
is likely necessary to produce hormone pulses sufficient to
stimulate pituitary response, it was not possible to assess this
or other network effects in the present study for two reasons.
First, only one cell was targeted for each recording, precluding simultaneous comparison between cells. Second, because
it is not known at what anatomical level GnRH neurons are
integrated into a network, it is difficult to determine the
extent of network disruption that occurs in preparation of
coronal brain slices. Nonetheless, the likelihood of network
and other external influences suggest additional layers of
complexity in the rhythmic output of GnRH neurons.
In summary, we propose a model in which burst firing
represents the fundamental unit used to construct pulsatile
secretory patterns in GnRH neurons. A second, lowerfrequency rhythm in individual neurons is sensitive to mod-
Nunemaker et al. • Multiple Rhythms in GnRH Firing Patterns
ulation from both outside and within the GnRH network (e.g.
steroid hormones, GnRH-GnRH interactions) and in turn
affects the high-frequency rhythm. These components interact to produce firing patterns in individual neurons that are
physiologically relevant to reproductive function.
Acknowledgments
We thank Xu-Zhi Xu for excellent technical assistance, Dr. Glenn
Harris, Dr. Fred Karsch, and Shannon Sullivan for editorial comments.
Received June 5, 2002. Accepted November 26, 2002.
Address all correspondence and requests for reprints to: Suzanne M.
Moenter, Department of Internal Medicine, P.O. Box 800578, Jefferson
Park Avenue, University of Virginia, Charlottesville, Virginia 22908.
E-mail: [email protected].
This work was supported by NIH Grants HD-34860 and HD-41469 (to
S.M.M.), the National Institute of Child Health and Human Development/NIH through cooperative agreement U54-HD-28934 as part of the
Specialized Cooperative Centers Program in Reproductive Research,
and the National Science Foundation Center for Biological Timing.
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