Olfactory transduction is the creation of an electrical signal in the membrane of a sensory cell in response to the arrival of an odorant molecule. The nomenclature varies in the literature, so that transduction may refer to an initial graded change in membrane conductance or to the complete process leading to action potentials in the axon. Nevertheless, the crucial initial step in olfactory transduction is always binding of the odor to an olfactory receptor (OR).
In vertebrates and the nematode Caenorhabditis elegans, binding of an appropriate ligand to a G-protein-coupled
receptor changes the conformation of the receptor and leads to activation of G-proteins and generation of second messengers (Ha and Smith 2008). In insects the mechanisms of signal transduction are under debate. There are only moderate sequence similarities between the fruit fly’s ORs and other G-protein-coupled receptors.
However, mutations affecting different second messenger pathways (cAMP, IP3) lead to a change in olfactory perception (Wicher et al. 2008, 2009; Kain et al.
2008). In addition the presence of different G proteins or G protein subunits were shown (Boto et al. 2010; Chatterjee et al. 2009; Laue et al 1997; Schmidt et al.1998; Talluri et al. 1995; Tanoue et al.
2008)
It is thought that OR83b combines with other ORs to function as heteromeric complexes, but it remains unclear whether these heteromers operate independently as direct ligand gated ion channels, or if they are also G-protein activated (Fig. 3).
Figure 3: Models of olfactory transduction. (a) Direct ligand‐gated model (Sato et al. 2008). Direct ligand gated nonselective cation channels are activated rapidly by stimulation with very high concentrations of odor in the absence of G‐protein signaling. (b) G‐protein‐coupled model (Wicher et al. 2008). In this model ORx is a G‐
protein‐coupled receptor and OR83b is a cyclic nucleotide gated ion channel. Odor activation leads to a faster ionotropic pathway and a slow pathway that involves G‐protein‐coupling of ORx, leading to the production of intracellular cAMP, which activates OR83b. Abbreviations: AC, adenylate cyclase; Gs, stimulatory G‐protein;
cAMP, cyclic adenosine monophosphate; OR, olfactory receptor (Nakagawa 2009, modified)
pathway has the advantage of a very fast reaction, while a second messenger dependent (metabotropic) pathway provides a higher sensitivity to the system.
Another important feature of odorant receptors are a set of proteins in the receptor lymph that are essential for odorant-receptor binding. Relatively little is known about the detailed functional properties of these odorant binding proteins in OSNs that detect fruit odors.
They are probably essential for transferring odorants across the hydrophobic to hydrophilic interface and have been hypothesized to play important roles in olfactory neuron dynamics (Kaissling 2009).
In OSNs that detect pheromone (11-cis vaccenyl acetate) the neurons are not directly activated by the volatile ligand, but instead are activated by the pheromone- pheromone binding protein complex. A mutation in the lush gene that leads to a lack of the odorant binding protein LUSH the flies are behaviorally and electrically unresponsive to the pheromone (Xu et al. 2005).
Odor plumes
An odor is a chemical compound that animals can perceive with their olfactory
Chemical molecules become airborne as they are released from their solid or liquid structures. In a stable atmosphere without turbulence an odor concentration gradient forms around the source and could lead animals to the odor source by simply following the gradient. In the natural environment structurally complex odor plumes are created as the wind takes volatile odor molecules away from their sources (Murlis et al. 1992). These time dependent properties are crucial for insects in finding odor sources.
Figure 4: Paired laser -induced fluorescence of an odor jet. Image is false colored so that concentration increases from blue (lowest) to red (highest) (Weissburg 2000, modified).
Inside an odor plume, filaments of low and high concentrations, and all ranges in between, intersperse with areas in the plume where odor is totally absent (Vetter et al. 2006) (Fig. 4).
In addition to these biophysical properties described above, the insect itself influences the spatiotemporal sequences
of odors that are detected by the OSNs. In moth it was found that the wing beat frequencies are also playing a role in odor detection. Each down stroke of the wings accelerates the airflow over the antennae and therefore the sensilla as well (Tripathy et al. 2010).
Hence, in order to find odor sources animals need to be able follow these spatiotemporal sequences of low and high odor concentration.
Odor stimulation
Some insects are known to rely on the time dependent properties of odor plumes to find the odor source. For example, male moths following a pheromone trace leading to a female will only follow a plume that contains rapid concentration changes (Justus et al. 2005; Vickers et al.
2001; Vickers 2006). Other examples are blood feeding insects that follow CO2
plumes to find mammalian hosts. The structure of an odor plume influences the up wind flight behavior of mosquitoes, and some haematophagous insects only followed an odor plume when the CO2
pulsations were similar to those of the human breathing range (Geier et al. 1999;
Barrozo and Lazzari 2006).
Creating such complex spatiotemporal stimuli is so sufficiently difficult that many
physiological experiments ignore this aspect of the odor plume and only characterize the responses as functions of odor identity and concentration. A commonly used approach to stimulating olfactory receptors is to deliver short odor pulses and observe the responses during the stimulus and for a short time afterwards. Because of the difference between this type of stimulus and a natural odor stimulus, which may include rich spatiotemporal information, it is very likely that such experiments do not reveal the whole dynamic repertoire of OSN responses.
The difficulty of measuring odor concentration has meant that even those studies that attempt to include dynamic information have usually relied on brief pulsatile stimuli. Time duration of those stimuli ranged from 100ms to 5 sec (Hallem and Carlson 2006). However, recent advances in gas phase measurements have shown that the resultant concentration changes at the animal may be very different from those that were anticipated (Vetter et al. 2006).
In this work, recent advances in gas phase instrumentation (photo ionization detector: PID) were used to stimulate
measured, odor concentrations to changing, but as accurately as possible olfactory receptive organs with rapidly
strongly time dependent properties.
Stimuli measured by the PID ranged between 0 and 330 Hz.
Analysis methodology
A technique called linear system analysis (LSA) provides an established general method of measuring the dynamic properties of input–output systems (Bendat and Piersol 1980). LSA has been used in previous studies to investigate dynamic properties of different types of receptors e.g. mechano-, photo- and chemoreceptors (Juusola and French 1997; Justus et al. 2005). In this work LSA was used to examine the dynamic responses of Drosophila melanogaster antenna and olfactory sensilla, respectively.
When a linear system receives a sinusoidal input it produces a sinusoidal output of the same frequency, but possibly differing in amplitude (Gain) and time shift (Phase).
Any linear dynamic system can be completely characterized by the measurement of these amplitude and phase changes, known as its frequency response function. Frequency response analysis (FRA) is a technique for characterizing the dynamic properties of
yellow highlight in Fig. 5).
Figure 5: Visualization of steps necessary to calculate frequency response and view them in a bode plot.
The exact physical nature of the system is not important, only that its input and output signals can be represented as single-valued functions of time. Examples would be the height of water in a bathtub (the output signal) versus the rate of water flowing into the bathtub (the input signal), or the electrical charge on a capacitor (output signal) as a function of net current flowing into and out of it in an electronic circuit (the input signal). In a
manner that depends not only on the input (which can also vary with time) but on the properties of the physical system (e.g. shape of the bathtub) and the memory of the system (e.g. how fast water was flowing into the bathtub at all times in the past). The simplest and most common type of frequency response analysis assumes that the system is linear and stationary. Linearity means that doubling the input signal will double the output signal. Stationary means that the properties of the system (e.g. shape of the bath) do not change with time.
Almost any single valued function of time (such as the height of water in the bath versus time) can be represented by the sum of a set of sinusoidal functions of time (the Fourier theory). A sinusoid in the input signal will always produce a sinusoid of the same frequency in the output, although the amplitude of the output sinusoid and the times of its peaks and troughs (the phase) relative to the input will usually be different. Since any signal can be considered to be a sum of different sinusoids, and any sinusoidal input produces a sinusoidal output, it follows that measuring how each sinusoidal frequency is affected in amplitude and phase will allow to predict the output signal for any input signal.
Measuring the frequency response of an
unknown system can simply be done by subjecting it to a series of sinusoidal inputs at different frequencies, measuring the resultant outputs and calculating the change in amplitude and time shift versus frequency. Stimulating an unknown system with “white noise” (a random unpredictable signal containing sinusoids of all possible frequencies within a certain frequency band) allows the entire frequency response to be measured from a single experiment. The different sinusoidal frequencies in the input and output signals are then separated by the Fourier transform and the changes from input to output compared as if they had each been used separately.
An example for a low-pass frequency response would be a bath, where rapid sinusoidal fluctuations of the water flowing into the bath (e.g. 100 cycles per second) would make very small changes in the height of the bath water versus time. Slow fluctuations (e.g. 0.001 cycles per second) would produce large changes in the height versus time. The bath frequency response giving a smaller output as the frequency rises. The frequency response is a representation of the system's response to a sinusoidal input at different frequencies. The output of the system to the input is a sinusoid of the same frequency but possibly with a
to view the frequency response of a system is via a Bode plot (Fig. 5).
In the present work the unknown system was the fly. How does the antenna or single sensilla respond to odor stimulation at different frequencies?
To investigate this question random noise was used as the input signal and the recorded biological signal (EAGs or action potentials, respectively) as the output signal. To avoid aliasing the action potentials were digitally filtered by convolution with a sinc function (sin(x)/x) (French and Holden 1971). To limit the bandwidth, the signal was down sampled to 100 Hz. The sampled data (tracer gas concentration as input and action potentials as output) were transferred from the time domain into the frequency domain via the Fast Fourier transform (FFT) (Cooley and Tukey 1965) into segments of sample pairs. Response functions between the PID voltage and action potentials were calculated by direct spectral estimation and plotted as Bode plots of phase and log gain versus log frequency (Bendat and Piersol 1980).
The upper part of the Bode plot shows the relation of the output to the input signal.
It shows how strong the response is at different frequencies. A response that is fitted by a low pass filter function indicates that its power remains the same
becomes less sensitive at higher frequencies. A band pass filter response indicates that the sensitivity of the system peaks at certain frequencies and is lower at lower and higher frequencies. The filter function time constants indicate at which frequencies the changes in the rising or falling of the response occurs. The lower part of the Bode plot shows the phase shift (time shift) between input (stimulus) and the output (action potential) signal.
Aims of the thesis
It is clear that temporal changes in odor concentration are vitally important to many animals, but this raises many questions. How are such temporal changes detected? How does temporal sensitivity vary amongst the large number of odors that an animal may detect?
Which stages of the odor detection cascade are time-dependent? Which stages limit the temporal changes that can be detected? How are dynamic signals coded into action potentials and processed in higher centers?
Answering these questions requires accurate methods of delivering dynamically changing spatiotemporal odor stimuli and quantitatively characterizing the resultant neural responses. My thesis describes how an accurate dynamic
odorant stimulation and analysis system was developed and tested in the fruit fly.
Then the system was used and to provide an initial characterization of primary odorant receptor cells of D. melanogaster at the single neuron level. These are early, but crucial, steps in answering some of the important questions about kinetics in insect odor detection posed above.
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