A humanized version of Foxp2 does not affect ultrasonic vocalization in adult mice

The transcription factor FOXP2 has been linked to severe speech and language impairments in humans. An analysis of the evolution of the FOXP2 gene has identified two amino acid substitutions that became fixed after the split of the human and chimpanzee lineages. Studying the functional consequences of these two substitutions in the endogenous Foxp2 gene of mice showed alterations in dopamine levels, striatal synaptic plasticity, neuronal morphology and cortico‐striatal‐dependent learning. In addition, ultrasonic vocalizations (USVs) of pups had a significantly lower average pitch than control littermates. To which degree adult USVs would be affected in mice carrying the ‘humanized’ Foxp2 variant remained unclear. In this study, we analyzed USVs of 68 adult male mice uttered during repeated courtship encounters with different females. Mice carrying the Foxp2hum/hum allele did not differ significantly in the number of call elements, their element structure or in their element composition from control littermates. We conclude that neither the structure nor the usage of USVs in adult mice is affected by the two amino acid substitutions that occurred in FOXP2 during human evolution. The reported effect for pup vocalization thus appears to be transient. These results are in line with accumulating evidence that mouse USVs are hardly influenced by vocal learning. Hence, the function and evolution of genes that are necessary, but not sufficient for vocal learning in humans, must be either studied at a different phenotypic level in mice or in other organisms.

Humans are the only primates that have developed volitional control over their sound production. Monkeys and apes lack the ability to imitate sounds vocally. Further, nonhuman primates do not need auditory input to develop their species-specific vocalization structure (Ackermann et al. 2014;Egnor & Hauser 2004;Jürgens 2009). Except for some few anecdotal findings, which suggest that captive apes may show some limited capacity for vocal production learning (Wich et al. 2012), the majority of studies showed that vocal learning does not play a role in the normal development of nonhuman primate vocalizations (Arriaga et al. 2012;Fitch 2010;. In terrestrial mammals and humans, the basic neurological and muscular systems necessary for vocalizations overlap to a large degree (Ackermann et al. 2014;Jürgens 2002). The present view is that a direct projection from the motor cortex to the laryngeal motor neurons, similar to the direct forebrain projection to vocal motor neurons in birds, is necessary for the ability to gain control over vocal production. Monkeys and apes lack such a direct projection (Arriaga et al. 2012;Hage et al. 2013;Simonyan 2014;Simonyan & Jürgens 2003).
One of the pressing questions is therefore to understand how the neurological basis for volitional vocal control emerged during evolution. Potentially, the tuning of cortico-striatal circuits, similar to the anterior forebrain pathway in birds with its specialized striatal region Area X, might have paved the way for recruiting neural circuits for vocal learning (Enard 2011;Murugan et al. 2013;Pfenning et al. 2014;Simonyan et al. 2012). In the search for the genetic basis that gave rise to such adaptations, FOXP2 is a strong candidate gene. Humans that are heterozygous for a non-functional FOXP2 allele suffer from an impairment that especially affects their speech and language development (Lai et al. 2001;Macdermot et al. 2005;Vargha-Khadem et al. 2005;Watkins 2011). Analyses of the evolution of the FOXP2 gene showed two amino acid substitutions, which became fixed in the human lineage after its separation from the chimpanzee. It was suggested that these two substitutions underwent positive selection potentially due to their effect on speech and language (Enard et al. 2002;Zhang et al. 2002).
To test the functional consequences of these two substitutions, Enard and colleagues established a mouse model, which carries these substitutions in the endogenous mouse Foxp2 gene (Enard et al. 2009). Mice homozygous for this humanized Foxp2 allele (Foxp2 hum/hum ) show specific alterations in the cortico-basal ganglia circuits, including changes to tissue dopamine levels, striatal synaptic plasticity, dendrite morphology and gene expression (Enard 2011;Enard et al. 2009;Reimers-Kipping et al. 2011;Schreiweis et al. 2014). Strikingly, pups carrying the humanized Foxp2 gene differed in their ultrasonic vocalizations (USVs). Although both Foxp hum/hum pups and their wild-type littermates were able to produce isolation calls within the normal strain-specific range and did not differ in their calling activity, the isolation calls themselves showed significant differences in the level of peak frequency (main energy), i.e. in the pitch of their USVs (Enard et al. 2009).
Importantly, the available evidence suggests that the structure of mouse vocalizations is largely innate and that auditory learning is not a prerequisite for the production of species (or strain) typical vocalizations (Ehret 2005;Hammerschmidt et al. 2012bHammerschmidt et al. , 2015. Nevertheless, it might be that the humanized version of Foxp2 affects the motor patterns sub-serving vocal production. If this is the case, the differences between adult mice carrying the humanized Foxp2 version and their wild-type littermates should be more pronounced because adult mice produce far more complex USVs than pups do (Fischer & Hammerschmidt 2011;Holy & Guo 2005). To explore this possibility, we studied the courtship vocalization of adult males by exposing resident males of different Foxp2 genotypes to encounters with females ( Fig. 1a). We compared the structure of the emitted USVs among genotypes and specifically predicted more elaborate temporal and/or spectral patterns in Foxp2 hum/hum male mice.

Mice and recording setup
We used mice carrying the humanized Foxp2 allele (Foxp2 hum ) before the neomycin cassette was removed [derived from the Bl6 ES cell 5H10, as described in the study of Enard et al. (2009)] and mice carrying the humanized Foxp2 allele after the neomycin cassette had been removed [Foxp2 humΔneo ; 5H10 line as described in the study of Enard et al. (2009)]. We used these two lines as the presence of a neomycin cassette can in principle have effects in addition to the two amino acid changes and to make maximal use of the available mice in the colony. A total of 144 virgin mice, aged 9-16 weeks, were grouped into 24 analysis units of six mice each (13 units/line Foxp2 hum and 11 units/line Foxp2 humΔneo ). Each unit was composed of one homozygous, one heterozygous and one wild-type male and female mice. These were matched as much as possible for age and litter among the genotypes and housed in groups of two to five individuals (for number of males per genotype and line, see Table 1). Approximately 48 h prior to testing, the three females of one unit were group-housed in a clean cage and received dirty bedding from cages containing the males of the unit. Approximately 24 h prior to testing, males were single-housed. Vocalization recordings took place in a separate testing room after a short habituation period between 0800 and 1700 h. The lid, food grid and water bottle of the home cage containing the single-housed male were removed and the cage was put into a sound-proof plexiglass chamber (custom-made), which was cleaned with an anti-odorant disinfectant (Decosept, Borer Chemie, Zuchwil, Switzerland). To obtain a more consistent call quality, we reduced the home cage surface by inserting a plexiglass separator into the male home cage and positioned the ultrasound microphone (CM16) centrally above the remaining home cage surface. Ultrasonic vocalizations have a highly confined active space and the plexiglass separator limited the distance between the male and the microphone. After 120 seconds of habituation, the male was recorded for 30 seconds (control recording) before a female was added to the cage, the chamber was closed and ultrasonic courtship songs were recorded for 2 min. We decided to use this time frame because preliminary experience (pre-study) had indicated that subjects frequently fall silent after the first 2 min. The order of male/female encounters of each unit were pseudo-randomly assigned so that each male (i.e. of each genotype) encountered each female (i.e. of each genotype) once and no animal was tested twice consecutively (Fig. 1a). Units of each line were recorded on separate days, except for the last recording day, where two units of the Foxp2 humΔneo line and one unit of the Foxp2 hum line were recorded. For the recordings, the microphone was connected to a preamplifier (UltraSoundGate 116, hardware and software: Avisoft Bioacoustics, Glienicke, Germany), which was connected to a computer using the recording software AVISOFT SASLAB PRO v4.33 with a sampling frequency of 300 kHz (hardware and software: Avisoft Bioacoustics, Glienicke, Germany). Mice were housed at a 10 h/14 h dark/light cycle under standard conditions in isolated ventilated racks (lights on at 0700 h). All animal work was performed in accordance with guidelines of the Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany, and federal regulations of Saxony, Germany.

Analysis of element structure
The sampling frequency of 300 kHz resulted in a frequency range of 150 kHz. To reduce background noise at lower frequencies, which could negatively influence the estimation of whistle tracking algorithm, we used a high-pass FIR filter of 35 kHz. We used the whistle tracking algorithm of AVISOFT SAS LAB PRO 5.2 (Avisoft Bioacoustics) with the following settings: monotonic, maximum change per step 8 pix = 4.7 kHz, minimum continuity = 8 milliseconds, hold time = 15 milliseconds. These criteria were compared with previous analyses of male mouse vocalizations (Fischer & Hammerschmidt 2011;Hammerschmidt et al. 2012a;Jamain et al. 2008). Based on these settings, we calculated the number of given call elements, inter-element interval (ICI: from the end of an element to the start of the next element) and element duration.
For the analysis of call structure, we used the AVISOFT RECORDER 4.2 (Avisoft Bioacoustics) to store the single elements as separated wav files. We used the same whistle tracking algorithm with the same settings as described above. From the stored elements, we calculated spectrograms (frequency range: 150 kHz, frequency resolution: 293 Hz, time resolution: 0.21 milliseconds). The spectrograms were submitted to the custom software program LMA 2012 [developed by K. Hammerschmidt, see Fischer et al. (2013)] to estimate characteristic acoustic parameters (Fig. 1b,c). As mice typically concentrate the energy of their calls into one small frequency band, so-called whistles or pure tone-like sounds (see Figs. 1c, S1), we focused on the peak frequency, i.e. the loudest frequency in the spectrum, which corresponds in most cases to the fundamental frequency (F0). Mice often produce soft sounds, and minor head movements as well as close body contact can lead to strong amplitude fluctuations in USVs. Therefore, we used LMA to control the estimated peak frequency visually and excluded incorrectly estimated elements from the analysis.
For each call element, we determined the duration and the duration of amplitude gaps. We defined the start of an element when the sound energy of a time segment exceeded 10% of the mean maximum amplitude of the respective element. We used the same criterion to determine the end of an element. 'Duration' was calculated as the difference between these two measurements in milliseconds. An amplitude gap within an element is defined if the sound energy of a time segment falls below 10% of the mean maximum amplitude. The parameter 'amplitude gap' represents the sum of all time segments below the 10% threshold multiplied by the time resolution in milliseconds. Furthermore, we determined the start (PF start), maximum peak frequency (PF max), as well as the greatest difference in peak frequency between two consecutive 0.21-millisecond bins (so-called frequency jumps; PF jump). In addition, we calculated the location of the maximum frequency measured in relation to element duration ((1/duration) × location). To describe the element modulation, we calculated the slope of a linear trend ('element slope') through the peak frequencies of consecutive 0.21-millisecond bins.
To achieve a balanced distribution of elements per subject, we selected 150 elements per subject of the 120-second recordings (50 elements within every 40-second interval). In total, we analyzed 24 661 out of 85 635 recorded elements from 68 males. To measure structural differences between genotypes, we first compared the acoustic parameters across the entire vocal repertoire, i.e. irrespective of the specific element type ('unpartitioned vocal repertoire'). Next, we conducted the analysis at the level of different element types. We used a two-step cluster analysis (IBM SPSS STATISTICS 21, Armonk, New York, USA) with the log-likelihood distance measure and the Schwarz-Bayesian information criterion (BIC) to find the cluster solution with the best fit. First, we tested a three-cluster solution, which was the solution with the best BIC value, like in former mouse studies (Hammerschmidt et al. 2012a(Hammerschmidt et al. ,2012b. In addition, we chose the six-cluster solution, which turned out to be the solution with the highest number of cluster by same silhouette coefficient (S C = 0.3). Cluster solutions with a higher number of clusters dropped clearly under S C of 0.3. Silhouette values ranged between −1.0 and 1.0 and values exceeding 0.5 are usually considered as solid solutions, silhouette values below 0.3 are seen as insufficient solutions (Rousseeuw 1987).

Element composition
To test whether mice carrying the humanized Foxp2 differ in the structure of the calls, we analyzed the sequential composition. We did this at an even finer grained level of analysis, using a visual classification. We did this for two reasons: firstly, the automated characterization is only suited for calls with a high signal-to-noise ratio. The exclusion of all call elements that were disturbed by background noise, for instance, would have hampered the sequential analysis. Secondly, these finer categories made a type II error less likely. The visual classification was similar to previous descriptions of male mouse courtship vocalizations (Ey et al. 2012;Scattoni et al. 2008). We classified the elements of a call into 19 element types. In addition, we grouped these element types into three general element categories (Fig. S1). We calculated the percentage of given element types  (element type and general element category), transition rates of both categorizations, as well as chain lengths per subject and encounter.

Further statistical procedures
All data were analyzed using an LMM (IBM SPSS STATISTICS 21, USA), including genotype and line (Foxp2 hum and Foxp2 humΔneo ) as fixed factors, and the interaction between genotype and line. For the description of general vocal behavior across all call types (unpartitioned repertoire), we included encounter order as a fixed factor. Whenever we used repeated recordings per subject, we included subject as random factor. We corrected all P-values for multiple testing using Simes correction.
To exclude the possibility that acoustic differences would be restricted to elements with a short duration, as in the case of pup isolation calls (Enard et al. 2009), we conducted an additional analysis, focusing on short elements   (<50 milliseconds) only. The analysis did not show any significant differences in the structure of the calls between wild-type males (wt/wt) and males carrying the humanized version of the Foxp2 gene (hum/hum, Table 2) either.

Difference in element structure
The analysis of the unpartitioned vocal repertoire showed no significant differences in relation to genotype, line or genotype/line interaction (Fig. 2, Table 3). We only found significant differences in relation to repetitive encounters. Dependent on whether the males had the first, second or third encounter with females, we found an increase in element duration, and a decrease in peak frequency (Fig. 3, Table 3).
The two-step cluster analysis identified a three-cluster solution as the best solution. The first cluster comprised long elements with major frequency jumps. The second cluster consisted of elements with medium duration and a general descending frequency course. The third element cluster contained short elements without frequency jumps. The analysis showed no significant differences in relation to genotypes or line in any of the different call types (Table 4).
In addition, we tested a similarly good cluster solution with six clusters to compare the obtained results with a more differentiated data set. We found significant differences between genotypes only in one cluster (CT5), in two of the acoustic parameters: PF jump [wt/wt = 30.1 ± 0.79 kHz (mean ± SEM); hum/wt = 32.9 ± 0.67 kHz; hum/hum = 33.9 ± 0.68 kHz], PF max loc (wt/wt = 0.55 ± 0.018; hum/wt = 0.51 ± 0.14; hum/hum = 0.48 ± 0.014). This suggests that males homozygous for humanized Foxp2 produced call elements with slightly more pronounced frequency jumps and a slightly earlier frequency maximum (Table S1).

Differences in element composition
We tested 65 males, for which we had a sufficient number of elements to conduct a meaningful sequence analysis. The  We tested the chain length, how many times successive elements fell into the same element type and the transition rate, i.e. how often a subject changed from one element type to another. We did these comparisons at two levels: at the level of the 19 single element types and with the three general element categories to compare changes and transition of short, medium and long elements. We found no significant differences in any of these measurements between mice with the humanized Foxp2 gene, heterozygous or wild types (Table 5). We also found no significant differences in element type usage (Table S2).

Discussion
We did not find a significant influence of the humanized Foxp2 allele on USVs of adult mice. This result was independent from the way we classified the elements, i.e. whether we tested the unpartitioned data set containing all elements or focused on single element types. We also found no differences in our sequential analysis. Wild-type mice and mice carrying the humanized Foxp2 gene did not differ in the percentage of element types or in their sequential order. In all of these comparisons, we found only one significant difference in one element type (ET5) that was identified via a cluster solution with a total of six clusters. In this cluster, we found a minor difference in the amount of frequency jump and the location of the maximum peak. Given the number of statistical tests carried out in this study, it appears possible that this difference constitutes a false positive. Whether or not this change is of biological significance remains unclear in any case. The obvious problem with interpreting negative results is that we cannot exclude whether an alternative method would have picked up some differences that our method may have missed. In a pilot study, we had determined many more acoustic variables, but these were highly correlating with the variables in the present analysis. We therefore decided to use the present reduced set of acoustic variables, to reduce the likelihood of type 2 error in the subsequent statistical tests. This set of parameters has proved to be sensitive in other studies (Enard et al. 2009;Fischer & Hammerschmidt 2011;Hammerschmidt et al. 2012a), and they picked up differences in relation to the number of encounters. We are therefore relatively confident that the lack of a significant difference between genotypes is not simply due to an inefficient method. In addition, mice have a very limited frequency resolution in the frequency range of their courtship vocalizations (Portfors et al. 2011); therefore, it is unlikely that further acoustic analysis or playback experiments could show a positive result. Ultimately, this would be an empirical question.
For now, our present results strongly suggest that the two amino acid substitutions in Foxp2 do not have a substantial effect on adult mouse vocalization. Yet, we found that repeated encounters had a significant influence on song structure. This variation in vocal output, specifically the decreased number of call elements, could reflect motivational changes. Behavioral observations would be needed to clarify whether the mice explored less, for instance, and therefore produced not only fewer, but also longer vocalizations.
Apparently, the two amino acid substitutions are not sufficient to affect adult mouse vocalization patterns. This may be due to the fact that mice are not obligate vocal learners. The finding that male courtship songs of mice have a high structural complexity (Holy & Guo 2005) and that the structure of calls in pups change with age and social context (Grimsley et al. 2011) suggested that mice have a considerable amount of vocal flexibility. However, to what extent these variations in vocal repertoires are influenced by cortical control, motivational status and/or maturation processes is not yet clear.
Interestingly, Arriaga et al. (2012) identified a weak direct cortical projection to brainstem vocal motor neurons suggesting that mice should be able to have a limited degree of volitional control. However, several other studies show that deaf mice or cross-fostered mice still develop their strain-specific vocalizations (Hammerschmidt et al. 2012b;Kikusui et al. 2011;Mahrt et al. 2013). Hence, auditory feedback -a key feature for vocal learning -does not appear to be required for mouse vocalizations. In a recent study, Emx1-CRE;Esco2 fl/fl male mice lacking the hippocampus and large parts of the cortex show no differences in structure and complexity of courtship vocalization in comparison to control males (Hammerschmidt et al. 2015). This shows that cortical areas are not necessary for the production of USVs in mice irrespective of the putative direct cortical projection to brainstem vocal motor neurons (Arriaga et al. 2012). Hence, mouse vocalizations appear to depend much less on cortical influence and structures than human speech. Thus, if the humanized Foxp2 affects cortico-basal ganglia circuits as suggested (Enard et al. 2009), they may indeed have no effects on mouse adult vocalizations.
As the adult vocalizations appear to be unaffected by the human-specific amino acid substitutions, their previously described effects on element pitch and peak frequency modulation of pup isolation calls (Enard et al. 2009) are presumably transient. Both studies have the same methodological background and the same recording time. In both cases, we controlled for multiple testing. An interesting question is how such small differences (e.g. mean peak frequency of 77 ± 0.7 and 73.7 ± 0.7 kHz for wt/wt and hum/hum littermates, respectively) may be caused. In principle, a large range of even small developmental effects of humanized Foxp2 on neural and non-neural structures affecting pup vocalizations could be responsible. In our view, such effects would be very difficult to discover and study. Thus, we do not currently view the pup vocalization phenotype as a promising route to further study the effects of humanized Foxp2.
A more promising approach may be to study the genetic and neural basis of human speech in other model organisms that do show vocal learning such as song birds (Wohlgemuth et al. 2014) or develop new model species such as bats (Morell 2014). Nevertheless, the practical and scientific advantages of studying mice are huge and there is accumulating evidence that they have validity for studying at least some aspects of the genetic basis of human evolution (Enard 2014). If the cortical and subcortical effects on vocalizations are difficult or even impossible to study in mice, one may be able to study them indirectly by measuring other correlated phenotypes or endophenotypes, i.e. phenotypes which are more directly related to the genotype than vocalization. More work needs to be carried out to identify such phenotypes and clarify if they have validity for human speech. Motor learning paradigms have been suggested to be valid to decipher the effects of heterozygous loss-of-function mutations in Foxp2 French et al. 2012;Groszer et al. 2008). Some aspects of motor learning are indeed affected in mice carrying humanized Foxp2 (Schreiweis et al. 2014) as these mice learn stimulus-response associations faster than wild-type littermates potentially because the balance of activity between striatal regions is altered as indicated by measuring dopamine levels, gene expression patterns and an NMDA (N-methyl-D-aspartate) receptor-dependent form of long-term depression (Schreiweis et al. 2014). The study of such phenotypes may hopefully be used to better understand the human-specific function of FOXP2.

Supporting Information
Additional supporting information may be found in the online version of this article at the publisher's web-site: Figure S1: Examples of calls describing the categories used for visual classification.
Table S1: Significant differences between genotypes' six-cluster solution. Results are given as P-values and are corrected for multiple testing (Simes). Significant differences are marked in bold. Table S2: Differences in element type usage. ET, element type; GEC, general element category; P-values show differences in relation to genotype and line and are corrected for multiple testing (Simes). Significant differences are marked in bold.