Cortical circuits for silent speechreading in deaf and hearing people

This fMRI study explored the functional neural organisation of seen speech in congenitally deaf native signers and hearing non-signers. Both groups showed extensive activation in perisylvian regions for speechreading words compared to viewing the model at rest. In contrast to earlier findings, activation in left middle and posterior portions of superior temporal cortex, including regions within the lateral sulcus and the superior and middle temporal gyri, was greater for deaf than hearing participants. This activation pattern survived covarying for speechreading skill, which was better in deaf than hearing participants. Furthermore, correlational analysis showed that regions of activation related to speechreading skill varied with the hearing status of the observers. Deaf participants showed a positive correlation between speechreading skill and activation in the middle/posterior superior temporal cortex. In hearing participants, however, more posterior and inferior temporal activation (including fusiform and lingual gyri) was positively correlated with speechreading skill. Together, these findings indicate that activation in the left superior temporal regions for silent speechreading can be modulated by both hearing status and speechreading skill.
1
Introduction
Auditory speech processing reliably engages perisylvian regions, particularly in the left hemisphere (e.g., Scott & Johnsrude, 2003). In hearing people, perisylvian regions are also recruited for silent speechreading. In particular, silent speechreading elicits activation in superior temporal regions, including middle and posterior portions of the superior temporal gyrus, its dorsal and ventral surfaces (i.e., lateral sulcus and superior temporal sulcus or STS, respectively) and the middle temporal gyrus (Bernstein et al., 2002; Calvert et al., 1997; Calvert et al., 1999; Calvert, Campbell, & Brammer, 2000; Ludman et al., 2000; MacSweeney et al., 2000; Paulesu et al., 2003; Pekkola et al., 2005; Ruytjens, Albers, van Dijk, Wit, & Willemsen, 2006), and inferior frontal regions (Buccino et al., 2004; Campbell et al., 2001; Nishitani & Hari, 2002; Paulesu et al., 2003; Watkins, Strafella, & Paus, 2003). Generally, seen speech appears to engage similar circuits to those activated when speech is heard. This includes portions of the superior temporal cortex reliably involved in processing auditory information. Activation in this region also appears to be modulated by speechreading skill. Hall, Fussell, and Summerfield (2005) did not find marked activation in the superior temporal gyrus at the group level when hearing adults observed silently spoken sentences, as compared to viewing facial gurning. However, their participants varied greatly in their ability to speechread, and a positive correlation was found between activation in the left posterior superior temporal gyrus and speechreading skill.
Deaf people can outperform hearing people in comprehending seen speech (Bernstein, Demorest, & Tucker, 2000; Mohammed, Campbell, MacSweeney, Barry, & Coleman, 2006). Nevertheless, earlier reports suggested that superior temporal activation for speechreading was less reliably observed in deaf than in hearing people (MacSweeney et al., 2001; MacSweeney et al., 2002). However, the group size for these studies was small (n = 6), and so there may not have been sufficient statistical power to detect activation in this region. Furthermore, while the speechreading task in MacSweeney et al.'s (2002) study was easy (identify spoken numbers between 1 and 9), it was compared with a relatively high-level task—counting numbers of meaningless mouth actions. In contrast, a separate study by Sadato et al. (2005), reported activation in superior temporal regions in both hearing and deaf participants viewing speech-like actions. Here, the stimulus was a cartoon avatar opening and closing its mouth to form different vowel-like patterns, which participants may have interpreted as phonological gestures.
The present study is the first to examine patterns of activation in deaf people who are proficient speechreaders while they searched for a speechread target embedded in lists of unrelated words. We anticipated that both hearing status and speechreading ability, measured outside the scanner, may determine the extent of activation in perisylvian regions. This was explored in two complementary ways. First, the group comparison between deaf and hearing activation patterns was assessed with speechreading skill entered into the analysis as a covariate. Speechreading skill was assessed using the Test of Adult Speechreading (TAS, Mohammed et al., 2006). By ‘partialling out’ individual differences in speechreading ability, we hoped to establish whether activation in brain regions could be modulated as a function of hearing status, irrespective of speechreading skill. Second, we used correlational analysis to establish, for each group in turn, which regions were sensitive to variations in speechreading skill.
To summarise, this study examines cortical correlates for the perception of lists of speechread words under lexical target detection conditions. We aimed to identify regions that may be activated during observation of silently spoken lexical items that are not drawn from a closed set, and when the contrast (baseline) condition was a speaker at rest. The questions posed were: (1) To what extent do prelingually deaf people who are proficient signers and speechreaders show activation in superior temporal regions, including auditory cortical processing regions? (2) Are the patterns of activation different in deaf and hearing people? (3) In which regions is speechreading ability positively correlated with activation?
2
Method
2.1
Participants
Thirteen (six female; mean age: 27.4; age range: 18–49) deaf adults were tested. All were congenitally (severely or profoundly) deaf (81 dB mean loss or greater in the better ear over four octaves, spanning 500–4000 Hz). Across the group, the mean hearing loss in the better ear was 103 dB. They were all native signers, having acquired British Sign Language (BSL) from their deaf signing parents. Thirteen (six female; mean age: 29.4; age range: 18–43) hearing, monolingual speakers of English were also tested. All participants were right-handed with no known neurological or behavioural abnormalities. Non-verbal IQ was measured using the Block Design subtest of the WAIS-R. Speechreading was measured using the Test of Adult Speechreading (TAS). The TAS comprises three subtests of silent speechreading in English: word identification, sentence identification, and short story identification (Mohammed et al., 2006). Independent-samples t-tests showed that deaf and hearing participants did not differ on non-verbal IQ (p > 0.1). However, deaf participants scored significantly higher than hearing non-signers on the TAS (t (24) = 4.779, p < 0.001), confirming earlier findings (Mohammed et al., 2006) with an independent sample of participants. Participant characteristics are summarised in Table 1. Standard scores (TAS z-scores) were derived from the populations reported in Mohammed et al.'s (2006) study, together with those for the present study. These scores were calculated separately for deaf and for hearing groups. Standard scores were used in order to correct for the differences in statistical distribution of scores within the deaf and the hearing groups, and were used in the correlational analyses (see Table 1).
All participants gave written informed consent to participate in the study according to the Declaration of Helsinki (BMJ 1991; 302: 1194) and the study was approved by the Institute of Psychiatry/South London and Maudsley NHS Trust Research Ethics Committee.
2.2
Stimuli
Stimuli were full-colour motion video of silently mouthed English words. Stimuli were modelled by a deaf native signer of BSL, who spoke English fluently (i.e., a BSL-English bilingual). The model was viewed full-face and torso. The words to be speechread were piloted on adult hearing volunteers who were not scanned. The final stimuli comprised only those words that were speechreadable by the hearing pilots. Stimuli consisted of both content words (nouns) and descriptive terms (both adjectival and adverbial).
2.3
fMRI experimental design and task
The speechreading task was one of four conditions presented to participants. The other three conditions comprised signed language (BSL) material (not reported here). The speech stimuli were presented in blocks, alternating with blocks of the other three experimental conditions (30-s blocks for each condition), and with a 15-s baseline condition. The total run duration for all four conditions and baseline was 15 min. Both deaf and hearing participants were given the same target-detection task and instructions. During the speechreading condition, participants were instructed to watch the speech patterns produced by the model and to try to understand them. They were required to make a push-button response whenever the model was seen to be saying ‘yes’. This relatively passive task was chosen in preference to a ‘deeper’ processing task (such as semantic classification) for several reasons. First, it allowed for relatively automatic processing of non-target items to occur (as confirmed in post-scan tests). Second, it ensured similar difficulty of the task across stimulus conditions. As hearing non-signers would not be able to perform a semantic task on the sign stimuli, using a sparse target detection task enabled all participants to perform the same task during all experimental conditions. Over the course of the experiment, participants viewed 96 stimulus items, 24 in each of the four experimental conditions. Items were not repeated within the same block and were pseudorandomised to ensure that repeats were not clustered at the end of the experiment. Each participant saw five blocks of the speechreading condition.
The baseline condition comprised video of the model at rest. The model's face and torso were shown, as in the experimental conditions. During the baseline condition, participants were directed to press a button when a grey fixation cross, digitally superimposed on the face region of the resting model, turned red. To maintain vigilance, targets in both the experimental and baseline conditions occurred randomly at a rate of one per block. Prior to the scan, participants practiced the tasks and were shown examples of the ‘yes’ targets outside the scanner using video of a model and words that were similar but not identical to those used in the experiment. Following the experiment, a sample of the hearing participants (8 of 13) and all of the deaf participants were asked to identify the items they had seen.
Stimuli in the experimental conditions appeared at a rate of 15 items per block. The rate of articulation across all experimental conditions, including the speechreading blocks, was approximately one item every 2 s. All stimuli were projected onto a screen located at the base of the scanner table via a Sanyo XU40 LCD projector and then projected to a mirror angled above the participant's head.
2.4
Imaging parameters
Gradient echoplanar MRI data were acquired with a 1.5-T General Electric Signa Excite (Milwaukee, WI, USA) with TwinSpeed gradients and fitted with an 8-channel quadrature head coil. Three hundred -weighted images depicting BOLD contrast were acquired at each of the 40 near-axial 3 mm thick planes parallel to the intercommissural (AC-PC) line (0.3 mm interslice gap; TR = 3 s, TE = 40 ms, flip angle = 90°). The field of view for the fMRI runs was 240 mm, and the matrix size was 64 × 64, with a resultant in-plane voxel size of 3.75 mm. High-resolution EPI scans were acquired to facilitate registration of individual fMRI datasets to Talairach space (Talairach & Tournoux, 1988). These comprised 40 near-axial 3 mm slices (0.3 mm gap), which were acquired parallel to the AC-PC line. The field of view for these scans was matched to that of the fMRI scans, but the matrix size was increased to 128 × 128, resulting in an in-plane voxel size of 1.875 mm. Other scan parameters (TR = 3 s, TE = 40 ms, flip angle = 90°) were, where possible, matched to those of the main EPI run, resulting in similar image contrast.
2.5
Data analysis
The fMRI data were first corrected for motion artefact, then smoothed using a Gaussian filter (FWHM 7.2 mm) to improve the signal to noise ratio over each voxel and its immediate neighbours prior to data analysis. In addition, low frequency trends were removed by a wavelet-based procedure in which the time series at each voxel was first transformed into the wavelet domain and the wavelet coefficients of the three levels corresponding to the lowest temporal frequencies of the data were set to zero. The wavelet transform was then inverted to give the detrended time series. The least-squares fit was computed between the observed time series at each voxel and the convolutions of two gamma variate functions (peak responses at 4 and 8 s) with the experimental design (Friston, Josephs, Rees, & Turner, 1998). The best fit between the weighted sum of these convolutions and the time series at each voxel was computed using the constrained BOLD effect model suggested by Friman, Borga, Lundberg, and Knutsson (2003) in order to constrain the range of fits to those that reflect the physiological features of the BOLD response1.
Following computation of the model fit, a goodness of fit statistic was derived by calculating the ratio between the sum of squares due to the model fit and the residual sum of squares (SSQ ratio) at each voxel. Permutation testing, as well as its freedom from many of the distributional assumptions of parametric tests, also offers the possibility of testing a number of statistics that are not easily testable parametrically. The SSQ ratio is such a statistic and is a simplified substitute for the F statistic suggested by Edgington (1995) that avoids the necessity of calculating the residual degrees of freedom of the time series following model fitting.
The data were permuted by the wavelet-based method described by Bullmore et al. (2001) with the exception that, prior to permutation, any wavelet coefficients exceeding the calculated threshold (as described by Donoho and Johnstone (1994)) were removed. These were replaced by the threshold value. This step reduces the likelihood of refitting large, experimentally unrelated components of the signal following permutation.
Significant values of the SSQ were identified by comparing this statistic with the null distribution, determined by repeating the fitting procedure 20 times at each voxel and combining data over all intracerebral voxels. This procedure preserves the noise characteristics of the time series during the permutation process, and the global assessment of the null distribution performed in this way provides good control of Type I error rates (Bullmore et al., 2001). The voxel-wise SSQ ratios were calculated for each subject from the observed data and, following time series permutation, were transformed into standard space (Talairach & Tournoux, 1988) as described previously (Brammer et al., 1997; Bullmore et al., 1996). The Talairach transformation stage was performed in two parts. First, each participant's fMRI data was realigned with their own high resolution -weighted images using a rigid body transformation. Second, an affine transformation to the Talairach template was computed. The cost function for both transformations was the maximization of the correlation between the images. Voxel size in Talairach space was 3 mm × 3 mm × 3 mm.
2.6
Group analysis
Identification of active 3-D clusters was performed by first thresholding the median voxel-level SSQ ratio maps at the false positive probability of 0.05. The activated voxels were assembled into 3-D connected clusters and the sum of the SSQ ratios (statistical cluster mass) was determined for each cluster. This procedure was repeated for the median SSQ ratio maps obtained from the wavelet-permuted data to compute the null distribution of statistical cluster masses under the null hypothesis. The cluster-wise false positive threshold was then set using this distribution to give an expected false positive rate of <1 cluster per brain (Bullmore et al., 1999).
2.7
ANOVA
Differences between the groups were calculated by fitting the data at each voxel in which all participants had non-zero data using the following linear model, Y = a + bX + e, where Y is the vector of BOLD effect sizes for each individual, X is the contrast matrix for the particular inter-group contrast required, a is the mean effect across all individuals in the groups, b is the computed group difference and e is a vector of residual errors. The model is fitted by minimising the sum of absolute deviations rather than the sums of squares to reduce outlier effects. The null distribution of b is computed by permuting data between groups (assuming the null hypothesis of no effect of group) and refitting the above model. This permutation method thus gives an exact test (for this set of data) of the probability of the value of b in the unpermuted data under the null hypothesis. The permutation process permits estimation of the distribution of b under the null hypothesis of no mean difference. Identification of significantly activated clusters was performed by using the cluster-wise false positive threshold that yielded an expected false positive rate of <1 cluster per brain (Bullmore et al., 1999).
2.8
ANCOVA
Analysis of covariance was used to address behavioural differences between the deaf and hearing participants in relation to the patterns of activation for the speechreading condition (see Table 1). Differences in responses (R) were inferred at each voxel using the linear model, R = a0 + a1H + a2X + e, where H codes the contrast(s) of interest between groups, X is a covariate and e is the residual error. Maps of the standardized coefficient (size of group difference) (a1), were tested for significance against the null distribution of a1 (no effect of group membership) generated by repeatedly refitting the above model at each voxel following randomization of group membership (H).
2.9
Correlational analysis
In order to examine the relationship between brain activation and speechreading skill, correlational analysis was performed between the BOLD effect data for each individual and the Test of Adult Speechreading (TAS) z-score. These were calculated separately for each group. Pearson product–moment correlation coefficients were calculated between the observed behavioural and BOLD effect data. The null distribution of correlation coefficients was then computed by permuting the BOLD data 100 times per voxel and then combining the data over all voxels. Median voxel-level maps were computed at the false probability of 0.05 and cluster-level maps, where r was significant, were computed such that the expected false positive rate was <1 cluster per brain.
3
Results
3.1
Behavioural data
All participants completed the behavioural (target detection) task in the scanner reasonably accurately. Deaf participants identified the speechreading targets more accurately than hearing participants (mean accuracy (max = 5), deaf = 4.69, hearing = 3.85, t(24) = 2.99, p = 0.007). Speechreading target identification was slower in deaf than hearing participants (mean RT, deaf = 1192.63 ms, hearing = 920.08 ms, t(24) = 4.15, p < 0.001). Following scanning, participants were presented with the experimental stimuli. The deaf participants identified more words than the hearing participants (mean percent correct identification, deaf = 69%, hearing = 46%), t(19) = 4.11, p = 0.001). The behavioural data suggest that deaf participants’ greater accuracy in identification of non-target items (as indicated by the post-scan test) may have interfered with their processing of the target (as indicated by the relatively slow reaction times to targets in the scanner).
3.2
fMRI data
3.2.1
Speechreading vs. baseline
In both deaf and hearing groups, extensive activation was observed in fronto-temporal cortices, bilaterally (Table 2, Fig. 1). In deaf participants, activation in the left superior temporal cortex was focused at the border between the posterior superior temporal gyrus and the transverse temporal gyrus (BA 42/41) and extended to the middle (BA 21) and inferior (BAs 37, 19) temporal gyri and the supramarginal gyrus (BA 40). This cluster of activation also extended to inferior (BAs 44, 45) and middle (BAs 6, 9) frontal gyri and precentral gyrus (BA 4). In the right hemisphere, a cluster of activation focused in the superior/middle temporal gyri (BA 22/21) extended to BAs 42 and 41 and posterior inferior temporal gyrus (BAs 37, 19). Activation in the right frontal cortex was focused in the precentral gyrus (BA 6) and extended to the inferior (BAs 44, 45) and middle (BAs 46, 9) frontal gyri. Additional activation was observed at the border of the medial frontal gyrus and the anterior cingulate gyrus (BA 6/32).
In hearing participants, we observed activation focused in the left middle temporo-occipital junction (BA 37) and in the right superior/middle temporal gyrus (BA 22/21). These clusters of activation extended to include the superior and transverse temporal gyri (BAs 22, 42, 41), the postcentral gyri (BA 43) and the middle and inferior temporal (BAs 21, 37, 19, 20) and cerebellar gyri. In the left hemisphere, this cluster also extended to the supramarginal gyrus (BA 40). In both hemispheres, clusters in the inferior parietal cortex were focused in the supramarginal gyrus (BA 40). These clusters extended to angular (BA 39) and middle occipital (BA 19) gyri. The cluster in the right hemisphere extended medially to the border of the dorsal posterior cingulate gyrus (BA 31). Activation in frontal cortices was focused in the precentral gyrus (BA 4/6) of the left hemisphere and in the inferior frontal gyrus (BA 44) of the right hemisphere. In both hemispheres, frontal activation included the inferior (BAs 44, 45 47) middle (BA 46) and superior (BA 9) frontal gyri and the precentral gyrus (BAs 4, 6). In the right hemisphere, the frontal cluster extended anteriorly to the border of the frontal pole (BA 10). Additional activation was observed in the right medial frontal gyrus (BA 6), extending to medial BA 8 and anterior cingulate gyrus (BAs 24 and 32).2
3.2.2
Deaf vs. hearing
Deaf native signers displayed significantly greater activation in left and right superior temporal cortices than hearing non-signers. In the left hemisphere, the cluster of activation (116 voxels) was focused at the border between the posterior superior temporal gyrus (i.e., planum temporale) and the transverse temporal (i.e., Heschl's) gyrus (BA 42/41; x = −54, y = −22, z = 10). In the right hemisphere, the cluster (61 voxels) was focused at the border between the superior and middle temporal gyri (BA 22/21; x = 51 y = −7 z = −3). Hearing non-signers showed greater activation than deaf signers in the right prefrontal cortex (128 voxels, focused in BA 44; x = 40, y = 11, z = 26).
When speechreading performance, as indicated by individual TAS z-score, was entered as a covariate into this analysis, deaf participants displayed greater activation than hearing participants in the left temporal cortex. The cluster of activation (120 voxels) was focused at the border between the posterior superior temporal gyrus (i.e., planum temporale) and the transverse temporal (i.e., Heschl's) gyrus (BA 42/41; x = −54, y = −22, z = 10). The focus of this cluster was verified using probabilistic maps provided by Penhune, Zatorre, MacDonald, and Evans (1996) (25–50% probability of Heschl's gyrus) and Westbury, Zatorre, and Evans (1999) (26–45% probability of planum temporale). Based on these probability maps, 15 voxels within this cluster, displayed ≥50% probability of being located in Heschl's gyrus, and five voxels showed ≥46% probability of being in planum temporale. This cluster also extended into the posterior lateral portion of the superior temporal gyrus (BA 22) and the middle and posterior portions of the superior temporal sulcus and middle temporal gyrus (BA 21; see Fig. 2). No brain regions were significantly more active in hearing than deaf participants when speechreading was a covariate in the analysis.
3.2.3
Cortical activation for speechreading: correlations with speechreading skill
Speechreading skill, as measured by performance on the Test of Adult Speechreading (TAS), varied considerably across participants (Table 1). Several brain regions were significantly positively associated with TAS z-scores in both deaf and hearing groups.
3.2.4
Deaf group
In the deaf group, ten clusters of activation (≥5 voxels) were positively associated with speechreading skill (see Table 3). In the temporal lobe, clusters in the superior temporal cortex were focused in the lateral portion of the transverse temporal gyrus (BA 41) in the right hemisphere, and in the superior temporal gyrus (BA 42) in the left hemisphere. However, although the Talairach and Tournoux (1988) atlas suggests that this cluster incorporates the transverse temporal gyrus, the probability map of this region provided by Penhune et al. (1996) suggests otherwise. In fact, only one voxel (in the left hemisphere cluster) displayed a ≥50% probability of being located in this region (Penhune et al., 1996). Both clusters extended to include the posterior superior temporal gyrus (BAs 42, 22). Additional areas showing significant correlation included the middle portion of the right middle temporal gyrus (BA 21). In the frontal cortex, correlations were observed in the middle frontal gyri of both hemispheres (BA 6). In the right hemisphere, correlations were also observed in the dorsolateral prefrontal cortex (BA 46), precentral gyrus (BA 6/4) and in the anterior insula. Additional correlations were observed in the anterior cingulate gyrus (BA 32/24) and the cerebellum.
3.2.5
Hearing group
In the hearing group, clusters of activation that were positively correlated with TAS z-scores included the fusiform (BA 37) and lingual (BA 18) gyri of the right hemisphere and the right postcentral gyrus (BA 4). Additional positive correlations were observed in the posterior cingulate gyrus (BA 23).
4
Discussion
Deaf participants were better speechreaders than hearing participants, both in terms of their TAS performance (Table 1) and, when tested post-scan at identifying the words presented in the scanner. The finding that deaf people can be better speechreaders than hearing individuals is not new (Bernstein et al., 2000; Mohammed et al., 2006). Deaf people, including deaf people who use a signed language, rely on speechreading, whether hearing-aid supported or un-aided, to communicate in the wider hearing community. In contrast, in hearing people, where the auditory channel dominates for speech identification, reliance on silent seen speech is generally unfamiliar and unpractised. In the present study most participants, whether deaf or hearing, could speechread much of the spoken material, and it can be assumed, therefore, that some of what they were shown in the scanner was lexically processed—albeit more in deaf than in hearing participants. Interpretation of the imaging data must bear these considerations in mind. Covariance and correlational analyses allow the behavioural and neuroimaging results to be aligned.
The group-level analyses, conducted separately for the deaf and hearing groups, contrasted silent speechreading with a low-level target detection task. As such, these analyses cannot allow unambiguous interpretation of the specificity of such activation in relation to speechreading alone, but they do suggest a general pattern against which the group differences can be explored. In hearing people, the pattern of activation replicates that which has been observed in many previous studies, showing extensive activation across the temporal cortex. While some of this activation must relate to visual movement detection and to the perception of biological motion, especially in posterior and inferior regions (see, for example, Zeki et al., 1991), it is likely that much of the activation in superior temporal regions relates to speechreading, since several studies contrasting speechreading with a higher-level baseline, such as observing non-speech-like mouth movements, report enhanced activation in this region (e.g., Calvert et al., 1997; Paulesu et al., 2003). The present study found that, in both hearing and deaf participants, activation associated with speechreading words included the dorsal surface of the superior temporal cortex including the junction of the superior temporal gyrus and the lateral portion of the transverse temporal (Heschl's) gyrus (BA 42/41). Spatial smoothing intrinsic to transforming data into standard brain space may limit the spatial resolution in this study. Thus the finding that activation for silent speechreading included the lateral portion of Heschl's gyrus must be interpreted with caution. Nevertheless, this finding is consistent with previous neuroimaging research that delineated this region on individual brains (Pekkola et al., 2005). In addition, left inferior frontal regions were activated when observing speech silently. This has also been observed where the contrasts were with higher-level conditions such as watching non-vocal mouth actions (Buccino et al., 2004; Campbell et al., 2001; Paulesu et al., 2003; Watkins et al., 2003) and may reflect the operation of mirror neuron systems in the observation of speech actions.
The finding of superior temporal activation for speechreading in deaf people extends earlier studies exploring the neural organisation of processing a variety of oral gestures in hearing people. This pattern of superior temporal activation found in the present study is consistent with the findings recently reported by Sadato et al. (2005), who presented deaf participants with simple segmental utterances including vowel-like lip shapes. At first sight, the present results do not fit with those we have previously reported using a closed stimulus set, covert articulation and a gurning control condition conducted with a small group of deaf people (MacSweeney et al., 2001; MacSweeney et al., 2002). However, we did report activation within right superior temporal regions, when analysis combining the data from two experiments allowed for an increase in power (MacSweeney et al., 2002). A further study involving a larger group of deaf participants, and manipulating task, baseline condition and stimuli, will help establish whether our previous studies simply lacked power or whether task and stimulus factors systematically affect the extent to which superior temporal regions are recruited during silent speechreading in those born profoundly deaf.
4.1
Deaf vs. hearing
When hearing non-signers were compared with deaf signers, and speechreading skill (which differed between the groups) was entered as a covariate (Fig. 2) greater activation was observed for the deaf than hearing group in left middle-posterior superior temporal regions. This cluster of activation was focused at the border between the posterior and transverse temporal gyri (BA 42/41) and extended to the middle and posterior portions of the superior temporal gyrus and sulcus, and middle temporal gyrus. No regions showed greater activation in hearing than deaf participants. In hearing people, the role of the posterior superior temporal sulcus (p-STS) has been proposed as a key ‘binding site’, responsible for cross- and supra-modal processing of co-incident auditory and visual streams in audiovisual speech processing (Calvert et al., 1999; Calvert et al., 2000). However, in deaf people, p-STS cannot play this role, since the association between seen and heard speech in deaf people is variable and relatively unsystematic. In the present study, not only was activation in this region observed in the absence of audition; it was greater in deaf than hearing people. One possibility is that activation by seen speech in p-STS is sensitive to the dominant speech modality within this multimodal region. That is, activation by silent speech in this region may be greater in deaf people because the region has developed to be sensitive to visual speech, while for hearing people it has developed to be sensitive to auditory speech characteristics, with visual speech as a secondary function. Structural imaging of the connections between p-STS and visual and auditory cortices in deaf and hearing individuals could be employed to test this hypothesis.
A non-mutually exclusive possibility is that greater activation in superior temporal regions for deaf than hearing individuals reflects a more general plasticity of these regions in deaf people. Several studies suggest that brain regions considered specialised for audition can be recruited for processing stimuli from other modalities in deaf people (e.g., Fine, Finney, Boynton, & Dobkins, 2005; Finney, Fine, & Dobkins, 2001; Sadato et al., 2005). While the extent and specificity of primary auditory cortex recruitment by visual events remains unclear (Bavelier, Dye, & Hauser, 2006), some studies (e.g., MacSweeney et al., 2004) suggest that perception of signed language, and even of non-linguistic biological movement, can recruit regions within superior temporal cortex to a greater extent in deaf native signers than in hearing people exposed to a signed language from birth (hearing native signers).
4.2
Correlations of activation with individual differences in speechreading skill
TAS speechreading scores and post-scan speechreading of the items seen in the scanner were positively correlated (deaf: r = 0.476, p(1-tailed) = 0.05; hearing: r = 0.673, p(1-tailed) = 0.034); thus we can infer that the higher the TAS score, the more likely it is that participants would have processed the speechread material lexically. However, TAS scores were not normally distributed across the two groups. For this reason, standard scores (TAS-z) derived for each group formed the basis for exploring the relationship between speechreading skill and cortical activation. Within each group, different patterns of association were observed. In deaf participants, the correlational analyses showed that activation in the posterior portion of the superior temporal gyri (as well as middle temporal and middle frontal gyri) was positively associated with speechreading.
In the hearing participants, who were less able and more varied speechreaders than the deaf participants, speechreading skill was positively associated with activation in the right lingual and posterior cingulate gyri, which is consistent with findings from Hall et al. (2005). Additional activations displaying a positive correlation with speechreading skill included the right postcentral and inferior temporal (fusiform) gyri, perhaps suggesting relatively greater involvement of articulatory skill and face processing in hearing individuals’ speechreading, respectively.
Taken together, these data show that hearing status is an important determinant of activation in left superior temporal regions when words are speechread. In particular, silent speechreading elicits greater activation in the left middle and posterior portions of the superior temporal cortex, including the superior and middle temporal gyri and the lateral portion of the transverse temporal gyrus in deaf than hearing people, even when speechreading skill is held constant. However, speechreading skill can moderate this activation, showing a positive relationship in deaf but not hearing participants. The relatively small group sizes used in the correlational analysis (n = 13 in each group), however, require that this interpretation should be provisional. Hall et al. (2005) did not find reliable activation in superior temporal gyrus for silent speechreading in contrast to viewing facial gurning in a group of 33 hearing participants, who also varied widely in speechreading skill. However, they did report a reliable positive correlation between speechreading skill and activation in this region. The inference from that study together with the present one must be that, when speechread material is linguistically processed, superior temporal regions within the left hemisphere are likely to be recruited. Additionally, the present study shows that it was deaf rather than hearing people who showed this relationship most clearly, and where individual differences in speechreading skill made an additional impact, despite the range of speechreading skill being larger in the hearing than the deaf group.
We have shown that, when auditory regions are not activated by acoustic stimulation, they can nevertheless be activated by silent speech in the form of speechreading. This finding may have some practical as well as theoretical significance. Current practice in relation to speech training for prelingually deaf children preparing for cochlear implantation emphasises acoustic processing. In auditory-verbal training, the speaking model is required to hide her or his lips with the aim of training the child's acoustic skills (e.g., Chan, Chan, Kwok, & Yu, 2000; Rhoades & Chisholm, 2000). Thus, a neurological hypothesis is being advanced which suggests that the deaf child should not watch spoken (or signed) language since this may adversely affect the sensitivity of auditory brain regions to acoustic activation following cochlear implantation. Such advice may not be warranted if speechreading activates auditory regions in both deaf and hearing individuals.
Speechreading gives access to spoken language structure by eye. It therefore has the potential to impact positively on the development of auditory speech processing following cochlear implantation. While there are few consistent correlates of improved post-implant speech processing in prelingually deaf cochlear implantees, efficiency in speechreading is implicated. For example, pre-implant silent speechreading skills are positively associated with general speech and language outcomes (Bergeson, Pisoni, & Davis, 2005). The possibility that superior temporal regions in deaf individuals, once tuned to visible speech, may then more readily adapt to perceiving speech multimodally should be seriously considered when recommendations concerning pediatric cochlear implantation procedures are being developed.

speechreading
deafness
fmri
brain
sign language
language


Preconception Care and Treatment with Assisted Reproductive Technologies

Couples with fertility problems seeking treatment with assisted reproductive technologies (ART) such as in vitro fertilization should receive preconception counseling on all factors that are provided when counseling patients without fertility problems. Additional counseling should address success rates and possible risks from ART therapies. Success rates from ART are improving, with the highest live birth rates averaging about 40% per cycle among women less than 35 years old. A woman’s age lowers the chance of achieving a live birth, as do smoking, obesity, and infertility diagnoses such as hydrosalpinx, uterine leiomyoma, or male factor infertility. Singletons conceived with ART may have lower birth weights. Animal studies suggest that genetic imprinting disorders may be induced by certain embryo culture conditions. The major risk from ovarian stimulation is multiple gestation. About one-third of live-birth deliveries from ART have more than one infant, and twins represent 85% of these multiple-birth children. There are more complications in multiple gestation pregnancies, infants are more likely to be born preterm and with other health problems, and families caring for multiples experience more stress. Transferring fewer embryos per cycle reduces the multiple birth rate from ART, but the patient may have to pay for additional cycles of ART because of a lower likelihood of pregnancy.
Current knowledge
Fertility problems affect about 10% of couples in developed countries [1–3]. These couples should receive preconception counseling on all factors that are discussed during preconception counseling for patients without fertility problems. Additional counseling should address success rates and possible risks from the specific fertility treatments being considered because of the couple’s diagnoses and preferences. To provide an example of an approach to preconception counseling for the infertile couple, treatment with assisted reproductive technologies (ART) such as in vitro fertilization (IVF) is reviewed here.
About 1% of births in the United States are the result of ART [4]. ART care represents a unique opportunity for preconception counseling because the moment of conception is easily discernable. Counseling should address expected success rates from ART based on the woman and the man’s risk profile. Older maternal age is the strongest risk factor for not achieving a successful outcome from ART. Live birth rates average about 40% per cycle among women less than 35 years old, and gradually decrease to about 6% per cycle among women over age 42 who use their own oocytes [4]. Older women who accept donor oocytes increase their chance of a live birth to approximately the rate seen in the age group of the donor woman [5]. Cumulative live birth rates account for the fact that many couples return for further treatment if the first cycle of ART is unsuccessful. For couples who complete two to three ART cycles, cumulative live birth rates are about 50–60% for women 35 years old or younger, declining to about 30% by age 40 [6]. As men’s age increases, the time required for a couple to conceive lengthens, even after controlling for the woman’s age and other risk factors for reduced fertility [7].
The chance of having a live birth from ART therapy is influenced by the health habits and the infertility diagnoses of the couple. Current tobacco smoking by women decreases ovarian function and is manifested by increased basal levels of follicle stimulating hormone. Such women produce fewer oocytes during ART and have lower pregnancy rates [8, 9]. Current smoking by the male partner also decreases pregnancy rates through direct effects on sperm and by exposing the woman partner to side-stream smoke. Increasing body mass index (BMI) is associated with decreasing ovarian response to follicle stimulating hormone, resulting in fewer oocytes collected in a given IVF cycle, as well as increasing rates of early pregnancy loss [10]. Although no strong evidence associates underweight with adverse ART outcomes, maternal underweight (BMI < 18.5 kg/m2) is risk factor for low birth weight in the general population.
As the number of specific infertility diagnoses increases for a couple, pregnancy and live birth rates from ART decrease. Moreover, certain infertility diagnoses are somewhat more difficult to overcome than others during treatment. Live birth rates after ART tend to be slightly higher if the only diagnosis is endometriosis, and slightly lower if the only diagnosis is uterine leiomyoma or hydrosalpinx [4, 6, 11]. Leiomyoma may decrease uterine receptivity, mainly when an intramural or submucosal fibroid distorts the endometrial cavity. The mechanism by which hydrosalpinx worsens the prognosis in ART is not understood, but tubal fluid in hydrosalpinx has embryotoxic properties and salpingectomy improves live birth rates [12]. Male factor infertility is present in about 40% of couples presenting for ART, and may be a barrier to achieving pregnancy unless intracytoplasmic sperm injection (ICSI) is used [4, 6, 11].
The most common adverse outcome from infertility treatment is multiple gestation. ART produced 11,544 multiple birth deliveries in the United States during 2002 [4]. Of all the ART infants born that year, 45.7% were twins and 7.6% were triplets or higher order multiples. Although health outcomes are good to excellent in many twin pregnancies, mothers of twins experience more pregnancy complications, lasting infant neurologic impairment may occur from very preterm birth, and the neonatal death rate of twins is 5–10 times greater than of singletons. For triplets, 90% of births are preterm and the relative risk of death in the first month of life is increased 20-fold [13].
To place the risk of multiple gestation from ART in perspective, estimates are available on the proportion of multiple births from unassisted conception and from treatment with ovulation medications without ART. These estimates have been made by subtracting the rate of multiple births during the era before ovulation-inducing drugs (unassisted conception) and the rate of multiple births resulting from ART from the total rate of multiple births, adjusted for trends in maternal age [4, 14]. Such an analysis shows that about 65% of twins and 15–18% of triplets are from natural conception each year. About 20% of twins and 35–40% of triplets are probably from ovulation induction without ART, and about 15% of twins and 40–45% of triplet births result from ART [14].
In general, women with the best prognosis for achieving a live birth from ART are younger than 35 years, use freshly fertilized embryos from their own eggs, and have good quality embryos—a marker of the latter is having more embryos available than are transferred. For this group, recent surveillance statistics showed that transferring one, two, three or four embryos resulted in live birth rates of 47.4, 51.8, 49.6 and 45.5%, respectively; the singleton rates among these births were 100.0, 60.4, 52.8 and 40.7%, respectively [4]. By age 38–40 years the prognosis is poorer among all women who use freshly fertilized embryos from their own eggs. Transferring one, two, three or four embryos in this group resulted in live birth rates of 10.4, 23.3, 28.9 and 29.9% respectively; the singleton rates among these births were 97.4, 81.2, 79.2 and 70.4%, respectively. Thus, for women with the best prognosis, transferring more embryos provides little improvement in live birth rates with an increasing risk of multiples. For women with a poorer prognosis, additional embryos may improve live birth rates up to a point, and the risk for multiples may increase less dramatically [4].
During ART, ovarian hyperstimulation syndrome is associated with production of a large number of follicles. Usually the only symptoms are modest abdominal pain. More severely affected women, however, experience increased capillary permeability that leads to ascites and in rare cases, death. Symptoms requiring hospitalization occur in 0–3% of IVF cycles [15]. Ovarian cancer risk from medications used to stimulate folliculogenesis has been studied because of concerns that this treatment may trigger growth of ovarian cortical cells through the process of healing at the sites of ovulation. Although studies show that women treated with ART have about a 50% increase in ovarian cancer risk compared to the general population, when compared to control women with infertility who did not receive ART, ovarian cancer risk is not elevated [16]. Thus it appears that infertility itself may be the risk factor for ovarian cancer, rather than ART.
Singletons conceived with ART may be at greater risk of being born prematurely, or to be small for gestational age [17]. Available studies have been limited by lack of data on key confounders. When matching is used to control for the most important confounders, the risk difference between spontaneously conceived singletons and singletons conceived with ART remains, but appears to be relatively small. The rates of fetal growth restriction differ by about 3.7%, with an overall rate of growth restriction of 8.6% among spontaneous singletons compared to a rate of 12.3% among singletons conceived with ART. The rates of very preterm birth differ by about 1.3%, with an overall rate of very preterm birth of 0.7% for spontaneous singletons compared to 2.0% for singletons conceived with ART [17].
In some studies, an increased risk of birth defects has been noted among IVF children compared to the general population, but in other studies the difference tends to disappear when risk factors for both infertility and birth defects such as older parental age and multiple gestation are controlled [18]. Infertility is sometimes caused by sex chromosome aberrations, such as Y chromosome deletions that lead to reduced sperm counts in men [18, 19]. Sperm of men with reduced fertility has more aneuploidy [20]. Whether treatment of male factor infertility by ICSI will cause fertility problems in male offspring cannot be known until long-term follow-up is available.
Adverse embryo culture conditions can cause imprinting disorders in mice during a procedure similar to ART. This lends biologic plausibility to imprinting disorders as a possible adverse effect of IVF [18, 19, 21]. Imprinting is the mechanism by which congenital disorders such as Beckwith-Wiedemann syndrome, Angelman syndrome and Prader-Willi syndrome occur. These syndromes have been reported in children conceived with ART, although the rarity of the syndromes and problems generalizing from animal experiments to humans makes a causal relationship difficult to establish. In an Australian study that used data from a national genetics testing laboratory, 37 cases of Beckwith-Wiedemann syndrome occurred in 1.3 million live births, a rate of one in every 35,580 births [22]. Four of the cases were associated with IVF compared to a matched control group in which one mother had IVF (P < 0.05). In a cohort study from the Danish national registries for IVF and for all births, however, no increase in imprinting diseases was linked to ART [23].
Recommendations for action
Reproductive endocrinologists use a combination of clinical factors to advise couples of the likelihood of success from infertility treatment, and they use clinical tests such basal follicle stimulating hormone levels to further characterize prognosis. Professional practice guidelines are available that recommend transferring fewer embryos among the couples with greatest risk for multiple gestation [24].
Professional guidelines note the elements to be considered when educating patients during the informed consent process for ART [25]. Reproductive endocrinologists routinely counsel patients about the risks from ART, including multiple gestation, ovarian hyperstimulation syndrome, physical injury during procedures, and success rates [26]. It may be prudent to counsel patients about emerging scientific issues as new research is published, such as concerns about embryo culture and ICSI. This would be similar to the approach used for other potential concerns. That is, even though a causal relationship between ART treatment methods and development of ovarian cancer has not been substantiated, the majority of reproductive endocrinologists inform patients of the issue [26].
Effectiveness of interventions
Little research exists on comprehensive preconception counseling for infertility patients. Studies that have examined counseling about the most common adverse outcome, multiple births, have found that many infertility patients prefer to have twins and sometimes triplets rather than a single infant [27–29]. A randomized trial of a new educational intervention was ineffective at reducing this preference, although other research has found that raising awareness of the risks of multiple births reduces patient preference for multiples [27, 28].
Integration with other preconception services
Routine preconception counseling for women and men should include information on the many well-documented risk factors for experiencing infertility later in life. When patients who have trouble conceiving present for ART, reinforcing these concepts and combining preconception care at the key decision-making points should help maximize health outcomes for parents and their future children.

assisted reproductive technologies
in vitro fertilization
preconception counseling
infertility
multiple
pregnancy
pregnancy complications


Caloric restriction counteracts age-dependent changes in prolyl-4-hydroxylase domain (PHD) 3 expression

Caloric restriction remains the most reproducible measure known to extend life span or diminish age-associated changes. Previously, we have described an elevated expression of the prolyl-4-hydroxylase domain (PHD) 3 with increasing age in mouse and human heart. PHDs modulate the cellular response towards hypoxia by regulating the stability of the α-subunit of the transcriptional activator hypoxia inducible factor (HIF). In the present study we demonstrate that elevated PHD3, but not PHD1 or PHD2, expression is not restricted to the heart but does also occur in rat skeletal muscle and liver. Elevated expression of PHD3 is counteracted by a decrease in caloric intake (40% caloric restriction applied for 6 months) in all three tissues. Age-associated changes in PHD3 expression inversely correlated with the expression of the HIF-target gene macrophage migration inhibitory factor (MIF), which has been previously described to be involved in cellular HIF-mediated anti-ageing effects. These data give insight into the molecular consequences of caloric restriction, which influences hypoxia-mediated gene expression via PHD3.
Introduction
One of the hallmarks of ageing is the decline of physiological functions. This is accompanied with progressive organ failure and an increased incidence of age-related diseases. Caloric restriction remains the most reproducible measure known to extend the maximum lifespan and to retard a variety of age-associated changes in several species (Speakman and Hambly 2007). Recent evidence suggests that caloric restriction, besides preventing accumulation of energy-related metabolites like reactive oxygen species or advanced glycation end products, triggers a regulatory response (Koubova and Guarente 2003). As a result of the limited energy intake, a modulated expression of genes involved in various regulatory integrative functions has been discovered. In a recent study we described age-related changes in the expression of the prolyl-4-hydroxylase domain (PHD) 3 and as a consequence in the expression of the hypoxia-inducible factor (HIF)-1α transcription factor (Katschinski 2006; Rohrbach et al. 2005).
PHD3 and HIF belong to the cellular oxygen sensing system and regulate therefore the adaptation of cells towards a decreased oxygen supply via hypoxia-inducible gene expression. This involves genes associated with angiogenesis, pH control, glucose metabolism, oxygen transport etc. (Wenger et al. 2005). In this regard, it should be noted that some ageing-associated diseases such as ischemic diseases, atherosclerosis and cancer are associated with an altered oxygen supply. Moreover, the tolerance to ischemia and hypoxia is reduced in elderly (Abete et al. 1999; Bosch-Marce et al. 2007; Mariani et al. 2000; Paolucci et al. 2003).
HIF is a heterodimer comprising the oxygen labile α-subunit and the oxygen-independently expressed β-subunit, which is also called ARNT. The expression of HIF-1α is regulated by three described PHDs, i.e. PHD1, PHD2 and PHD3, at the protein level (Ivan et al. 2001; Jaakkola et al. 2001). PHD-dependent hydroxylation is directly depending on the oxygen availability (Bruick and McKnight 2001; Epstein et al. 2001). Hydroxylation of HIF-1α allows binding of the von Hippel Lindau tumor suppressor protein, which triggers rapid ubiquitination and degradation (Maxwell et al. 1999). An age-dependent decline of HIF-1α protein levels and HIF-target gene expression has been described in different organs like rat cerebral cortex, mouse heart, carotid body and smooth muscle cells isolated from rabbit aorta (Di Giulio et al. 2005; Rivard et al. 2000; Rohrbach et al. 2005). The age-related expression pattern of PHDs in other organs besides heart is not known.
Caloric restriction has been consistently described to challenge biological ageing (Rohrbach et al. 2006b). Regarding the HIF system, it is interesting to note that caloric restriction counteracts age-related changes in the angiogenic response and can protect cardiomyocytes from hypoxic death in rodent models of ischemic diseases (Ahmet et al. 2005; Facchetti et al. 2007). To gain more insight into the impact of nutrient availability on the adaptive transcriptional response towards hypoxia, we quantified the expression of the HIF-1α regulating PHD1–3 in heart, liver and skeletal muscle in young and old rats undergoing a well-defined protocol of caloric restriction.
Materials and methods
Animals and diet protocol, 40% caloric restriction for 6 months
Male young (4 months) and senescent (22 months) Sprague-Dawley rats were obtained from Charles River (Germany), caged individually with a light/dark cycle of 12 hrs and with tap water ad libitum. Food (AltrominR 1244; 2550 cal/g) was offered ad libitum. Prior to the application of the specific diet protocols, daily food intake of the normal standard diet was monitored for 14 days and averaged for each rat individually. Thereafter, rats at the age of 6 months (young rats) or at the age of 24 months (old animals) were randomly assigned to one of the following diets for the next six months: rats on “control diet” received their individual prediet average of AltrominR 1244 (2,550 cal/g), but not more, in order to avoid any degree of diet-induced obesity. Rats subjected to caloric restriction received also their prediet average, but of a calorically reduced, fibre-rich diet (AltrominR 1344/1500; 1,550 cal/g). Thus, in young rats on control diet (n = 6), the daily energy intake during six months was 64.8 ± 2.2 kcal and in young rats on -40% caloric restriction (n = 6) the daily intake was 37.8 ± 1.9 kcal. In old rats the daily energy intake amounted to 57.9 ± 1.8 kcal with control diet (n = 3) and to 36.6 ± 0.1 kcal with 40% caloric restriction (n = 3). All animals were fasted for 12 hours before killing.
RNA extraction, RT-reaction and quantification by PCR
The RNA was isolated from left ventricle, liver and skeletal muscle (M. gastrocnemicus) tissue as described before (Rohrbach et al. 2007). Integrity and quality of the RNA was confirmed by agarose gel electrophoresis and the concentration determined by measuring UV-absorption.
Real time PCR
Reverse transcription (RT) of RNA samples was carried out for 30 min at 42°C. Real-time PCR and data analysis were performed using the Mx3000P Multiplex Quantitative PCR System (Stratagene). DNA amplification was performed as follows: initial denaturation at 95°C for 10 min, 40 cycles of amplification (denaturation at 95°C for 30 s, annealing at 60°C for 60 s, and extension at 72°C for 60 s), followed by a denaturation at 95°C for 60 s and a melting curve over the range from 55°C up to 95°C. Fluorescence data were collected at the end of the annealing stage of amplification. We performed Real-time PCR of PHD1, PHD2, PHD3, MIF-1 and 18S rRNA (18S rRNA Control kit, Yakima Yellow®-Eclipse® Dark Quencher, Eurogentec) in samples derived from rat left ventricles, skeletal muscle and liver. The following primers were used: PHD1 sense CGTGAGGCATGTTGACAATC, PHD1 antisense AACACCTTTCTGTCCCGATG; PHD2 sense TACAGGATAAACGGCCGAAC, PHD2 antisense GGCTTGAGTTCAACCCTCAC; PHD3 sense GGCCGCTGTATCACCTGTAT, PHD3 antisense TTCTGCCCTTTCTTCAGCAT; MIF sense CAGAACCGCAACTACAGCAA, MIF antisense GAACAGCGGTGCAGGTAAGT. Each assay was performed in duplicate and validation of PCR-runs was assessed by evaluation of the melting curve. All data of mRNA are given as relative units of 18S rRNA concentrations.
Western blot
Liver tissue was rapidly homogenized in a buffer containing 50 mmol/l Tris·HCl, 1% SDS, 1 mmol/l sodium-orthovanadate, 5 mmol/l EGTA, 1 mmol/l PMSF, 1 μg/ml aprotinin, and 1 μg/ml leupeptin. Proteins were quantified using the bicinchoninic acid protein assay (Pierce). Protein (50 μg) in 2× Laemmli SDS sample buffer were boiled for 5 min and after centrifugation loaded onto a SDS-PAGE gel. After electrophoresis, proteins were transferred to a nitrocellulose membrane at 100 V for 90 min. The filters were blocked with 0.01% Tween, 2% nonfat milk, and then incubated with antibodies directed against MIF (Abcam) and GAPDH (Abcam).
Statistical analysis
For statistical comparison of age groups or the influence of caloric restriction, a non-paired t-test was used. A P-value < 0.05 was considered to represent a significant difference.
Results
Prevention of age-dependent increase of PHD3 in heart, liver and skeletal muscle
Age-dependent changes of PHD1-3 expression were investigated in three different organs obtained from young (12 months old) and old (30 months old) rats. PHD1 and PHD2 mRNA expression did not differ significantly between the two age groups neither in heart, liver nor in skeletal muscle (Figs. 1 and 2). However, in all three different organs investigated, there was a significant increase in PHD3 mRNA expression in the tissues obtained from the old rats compared to the young animals (Fig. 3).
Fig. 1Effect of age and caloric restriction on PHD1 mRNA expression. Tissue samples (heart, liver and skeletal muscle (M. gastrocnemicus)) were obtained from young (6 months old) and old (24 months old) rats with or without a 40% caloric restriction applied for 6 months. Subsequently, RNA was extracted and PHD1 mRNA quantitated by real time PCRFig. 2Effect of age and caloric restriction on PHD2 mRNA expression. Tissue samples (heart, liver and skeletal muscle (M. gastrocnemicus)) were obtained from young (6 months old) and old (24 months old) rats with or without a 40% caloric restriction applied for 6 months. Subsequently, RNA was extracted and PHD2 mRNA quantitated by real time PCRFig. 3Effect of age and caloric restriction on PHD3 mRNA expression. Tissue samples (heart, liver and skeletal muscle (M. gastrocnemicus)) were obtained from young (6 months old) and old (24 months old) rats with or without a 40% caloric restriction applied for 6 months. Subsequently, RNA was extracted and PHD3 mRNA quantitated by real time PCR
Young and old rats were subsequently challenged by caloric restriction. Daily caloric intake was reduced to 40% in young and old animals for 6 months. Control animals received their prediet average caloric intake. Using this regimen, we previously have observed that thioredoxin reductase 2 is significantly reduced in ageing skeletal and cardiac muscle and renormalized after caloric restriction (Rohrbach et al. 2006a). Whereas no significant effect of caloric restriction was observed on the expression of PHD1 or PHD2 (Figs. 1 and 2), the reduced food intake significantly counteracted the age-dependent increase in PHD3 expression in heart, liver and skeletal muscle (Fig. 3). PHD3 levels in old rats after caloric restriction was comparable to the PHD3 expression levels in young animals.
Caloric restriction affects the expression of the macrophage migration inhibitory factor
Numerous HIF-target genes, including erythropoietin, vascular endothelial growth factor, carbonic anhydrase IX etc., have been identified (Wenger et al. 2005). Recently, the expression of the macrophage migration inhibitory factor (MIF) has been demonstrated to be inducible by hypoxia. In addition, functional HIF binding sites in the promoter have been identified. In a recent study evidence was provided that HIF-1 plays a critical role in delaying the onset of senescence in rodent cells via transcriptional activation of MIF and thereby inhibition of the p53-mediated pathway (Welford et al. 2006). To gain insight into the functional consequences of age and caloric restriction-mediated changes on the HIF-induced signal transduction pathways, we analyzed the mRNA expression of MIF as a function of age and caloric restriction. In line with the suggestion that HIF-dependent gene expression is hampered with increasing age, MIF RNA and protein expression was significantly lower in the heart, liver and skeletal muscle obtained from the old rats compared to the expression found in the young rats (Figs. 4, 5). Restriction of food intake reversed the age-dependent decline in MIF expression (Fig. 4). MIF levels after caloric restriction were comparable to the expression levels in young rats in all three tissues investigated.
Fig. 4Effect of age and caloric restriction on MIF mRNA expression. Tissue samples (heart, liver and skeletal muscle (M. gastrocnemicus)) were obtained from young (6 months old) and old (24 months old) rats with or without a 40% caloric restriction applied for 6 months. Subsequently, RNA was extracted and MIF mRNA quantitated by real time PCRFig. 5Effect of age and caloric restriction on MIF protein levels. Liver tissue samples were obtained from young (6 months old) and old (24 months old) rats with or without a 40% caloric restriction applied for 6 months. Subsequently protein was extracted and MIF and GAPDH protein levels were investigated by immunoblot analysis
Discussion
Caloric restriction has been described to extend life in a variety of different mammalian and non-mammalian species (Masoro 2000). In addition, restriction of food intake delays the progression of several age-associated diseases. Identification of age-dependently expressed genes is one of the most direct approaches in ageing research. In a previous study, we identified PHD3 as one age-dependently expressed gene in human and mouse heart (Rohrbach et al. 2005). The data presented here demonstrate that PHD3 expression is additionally increased in rat heart, liver and skeletal muscle with increasing age. These findings indicate that age-dependent changes in PHD3 expression are tissue- and species-independent. Furthermore our data presented here demonstrate that PHD3 expression is decreased by caloric restriction in old rats, whereas no change in the expression of PHD1 or PHD2 was observed. Previous findings indicate that each PHD-isoform displays its own tissue and cell-line-specific expression pattern (Appelhoff et al. 2004). In addition, the susceptibility of the PHD isoform expression by change in the oxygen availability differs greatly. Whereas PHD3 is highly inducible by hypoxia, there is just a slight hypoxia-mediated induction described in case of PHD2. In case of PHD1 no hypoxia-inducible expression has been observed (Appelhoff et al. 2004; Marxsen et al. 2004). Including the age-dependent and caloric restriction-induced changes in PHD3 expression, PHD3 seems to be the most flexible PHD isoform regarding stimuli-induced change in expression.
In an unbiased screen using white adipose tissue it was found that energy restriction lowers the expression of genes linked to angiogenesis indicating that hypoxia-inducible gene expression is affected by caloric restriction (Higami et al. 2006). Similarly, the age-dependent and caloric restriction-induced expression of PHD3 described here seems to have consequences. Quantification of the HIF-target gene MIF demonstrates a decrease of MIF expression in heart, liver and skeletal muscle in the old animals, which was reversed by caloric restriction. In line with our observation, in previously published microarray experiments increased levels of MIF RNA were found in liver lysates from young adult mice treated with caloric restriction or with a methionine-deficient diet as well as in young mice of the long-lived Snell dwarf stock (Dozmorov et al. 2002; Miller et al. 2005; Miller et al. 2002). Since MIF affects cellular senescence (Welford et al. 2006), changes in PHD3 expression with increasing age seem to be one important feature of the molecular senescence phenotype. This conclusion is in line with a recent report demonstrating increased HIF-1α hydroxylation in aged versus young primary murine fibroblasts (Chang et al. 2007). Together with a previously published study demonstrating that under hypoxic conditions senescent cells failed to induce HIF-1α compared to presenescent cells our data indicate that the age-dependent changes in the oxygen sensing system may indeed have impact for the ageing process (Coppe et al. 2006).
Up to now a reduced protein expression of HIF-1α with increasing age has been demonstrated in smooth muscle cells isolated from rabbit aorta, rat cerebral cortex, mouse lung, liver, kidney, heart and in the carotid body (Di Giulio et al. 2005; Frenkel-Denkberg et al. 1999; Rivard et al. 2000; Rohrbach et al. 2005). A decreased HIF-1α expression was also demonstrated in tissue lysates from ischemic limbs comparing 20 months old mice with 2 months old mice (Bosch-Marce et al. 2007). Hwang et al described a diminished HIF-1α stabilization in old rats in response to hypoxia, although in nomoxic breathing animals there was an age-related increase in basal levels of HIF binding to DNA. Finally, mouse and human fibroblasts isolated from different age groups express less HIF-1α as a matter age (Chang et al. 2007). However, it also should be noted that Kang et al demonstrated an increased HIF activity and HIF-1α expression in old rats and conflicting to the presented results a decrease in HIF activity as a result of caloric restriction in liver lysates (Kang et al. 2005). Comparing the chow protocols, these rats were fed a soybean protein diet, which may have resulted in the described increase in redox metabolites and HIF-1α protein expression. The degree and management of caloric restriction is not described (Hwang et al. 2007). Taken collectively, several papers indicate a diminished HIF-response with increasing age with some exceptions, which may be tissue or diet-specific.
Whereas ageing thus is mostly associated with a decreased hypoxia-inducible gene expression, in endometrial cancer cells mutation of PHD2 has been associated with increased expression of HIF-1α and immortality (Kato et al. 2006). Downregulation of HIF-1α by overexpression of functional PHD2 altered the cancer cell phenotype and led to the occurrence of senescence markers (Kato et al. 2006). The impact of the HIF-system for cell fate decisions additionally was described by Welford et al demonstrating in a well defined nontransformed/nonimmortalized knock out cell model that HIF-1 delays the onset of senescence via transcriptional activation of MIF and inhibition of p53 dependent pathways (Welford et al. 2006). Alltogether these data demonstrate the necessity of tight regulation of the HIF system to prevent unrestricted cell proliferation or early cellular senescence.

ageing
prolyl-4-hydroxylase domain
hypoxia
hif-1α


p53 is upregulated in Alzheimer's disease and induces tau phosphorylation in HEK293a cells

p53 and tau are both associated with neurodegenerative disorders. Here, we show by Western blotting that p53 is upregulated approximately 2-fold in the superior temporal gyrus of Alzheimer's patients compared to healthy elderly control subjects. Moreover, p53 was found to induce phosphorylation of human 2N4R tau at the tau-1/AT8 epitope in HEK293a cells. Confocal microscopy revealed that tau and p53 were spatially separated intracellularly. Tau was found in the cytoskeletal compartment, whilst p53 was located in the nucleus, indicating that the effects of p53 on tau phosphorylation are indirect. Collectively, these findings have ramifications for neuronal death associated with Alzheimer's disease and other tauopathies.
Tau is the major microtubule-associated protein (MAP) in neurons and functions in the formation and maintenance of axons by influencing microtubule organization. In adult human brain, there are six isoforms of tau generated by alternative mRNA-splicing. Tau has zero, one or two amino-terminal inserts and either three or four repeats of a microtubule-binding domain situated towards the carboxy-terminus [7]. Tau splicing and phosphorylation are developmentally regulated. Only the shortest tau isoform is expressed in foetal brain [8] and foetal tau is more extensively phosphorylated than tau from adult brain [10,15]. Phosphorylated tau is less efficient at promoting microtubule assembly [12,14] and elevated levels of phosphorylated tau correlate with increased microtubule dynamics associated with plasticity during development [1]. Increased tau phosphorylation is also a characteristic feature of Alzheimer's disease (AD) and tauopathies such as frontotemporal dementia with Parkinsonism linked to chromosome 17 (FTDP-17) [7]. In these disorders, normally soluble tau is present as paired-helical filaments (PHFs), which in turn aggregate to form neurofibrillary tangles (NFTs).
p53 is a tumor suppressor protein, which induces cell cycle arrest or apoptosis. Normally, p53 is maintained at low levels by murine double minute-2 (MDM2) or the human homologue (HDM2), which inhibit the transcriptional activity of p53 and promote degradation of p53 via the proteasome [2]. Activation of p53 involves stabilization of the protein by post-translational modifications, which disrupts the interaction between p53 and MDM2. Several studies have reported an increase in p53 immunoreactivity in sporadic AD [11,13] especially in subpopulations of cortical neurons undergoing neurofibrillary degeneration [5]. Furthermore, p53−/− mice display a reduction in tau phosphorylation [6]. These findings prompted us to investigate the effects of p53 on tau phosphorylation in vitro.
The following antibodies and plasmids were used in this study: mouse anti-β actin, which was from Sigma (UK). Rabbit anti-total tau from Dakocytomation (UK). Tau-1 monoclonal antibody, which was a gift from Professor L. Binder (Cognitive Neurology and Alzheimer's Disease Center, Northwestern University, USA). PHF-1 monoclonal antibody, which was a kind gift from Dr. P. Davies (Albert Einstein College of Medicine, NY, USA). AT270 and AT8 monoclonal antibodies, goat anti-mouse IgG and goat anti-rabbit IgG were from Santa Cruz Biotechnology (USA). Anti-p53 (clone DO7) was from Novocastra Laboratories (UK). Alexa Fluor 594 goat anti-mouse IgG and Alexa Fluor 488 goat anti-rabbit IgG were from Invitrogen (UK). p53, which was a generous gift from Dr B. Vogelstein (John Hopkins, USA). 2N4R tau was a gift from Professor J. Woodgett (Ontario Cancer Institute, Toronto, Canada) BAX-Luc and p21waf-Luc were from Dr. T. Soussi (Universite P.M. Curie, Paris).
Human embryonic kidney 293a cells (HEK293a) (Quantum Biotechnologies, Canada) were cultured in low glucose Dulbecco's modified essential medium (Invitrogen, UK) containing 10% fetal bovine serum (Autogen Bioclear, UK), 2 mM l-glutamine, 100 IU penicillin, and 100 mg/ml streptomycin.
Post mortem brain tissue from the superior temporal gyrus was provided by the MRC London Brain Bank for Research on Neurodegenerative Diseases. Use of human tissue was approved by the South London and Maudsley Research Ethics Committee and was conducted in accordance with the Declaration of Helsinki. Control subjects had no history or symptoms of neurological disorder. All AD cases were neuropathologically confirmed, using conventional histopathological techniques and diagnosis was performed using the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) criteria. Total protein was extracted (50 μg) from 17 non-demented elderly controls and 53 AD cases and p53 levels were determined by Western blotting using a specific anti-p53 antibody (1:1000). p53 expression was normalised to β-tubulin by standard densitometric procedures. Values for control and AD were compared using an unpaired two-tailed t-test, *p < 0.05. For tau phosphorylation experiments HEK293a cells plated in a 6 well plate were transfected with OptiMEM (100 μl) containing FuGene 6 (5 μl) and cDNA constructs (500 ng of each) encoding human 2N4R tau independently or in combination with p53. Cells lysates were harvested 24 h after transfection. Western blotting was performed using the following primary antibodies; rabbit anti-total tau (1:10,000), mouse anti-phospho-Ser199/Ser202/Thr205 (tau-1, 1:1000), mouse-anti-phospho-Ser202/Thr205 (AT8, 1:500), mouse anti-phospho-Ser396/Ser404 (PHF-1) (1:1000) or mouse anti-phospho-Thr181 (AT270, 1:1000) according to standard protocols with HRP-conjugated secondary antibodies and enhanced chemiluminescence reagents (Amersham Pharmacia, UK). To ensure equal loading membranes were reprobed with mouse anti-β actin. All experiments were performed in triplicate.
For sub-cellular localisation experiments HEK293a cells were transfected with human 2N4R tau independently or in combination with p53 as described above. The following day the cells were fixed in ice-cold methanol and stained according to standard protocols. Cells were incubated with rabbit anti-total tau (1:500) or mouse anti-p53 (1:250) before being incubated with the appropriate fluorescent secondary antibody (1:200). Nuclei were counter-stained with Hoescht 33342. Immunofluorescence was captured using a Zeiss LSM510 meta-confocal microscope. All experiments were performed in triplicate; therefore, figures shown are representative of a single experiment.
The transcriptional effects of p53 were examined by reporter gene assay. Four wells of HEK293a cells plated in a 48 well plate were transfected by adding 25 μl of a master-transfection mix to the culture medium. The master-mix contained 100 μl of OptiMEM (Invitrogen, UK), 4 μl FuGene 6 (Roche, UK), 400 ng of firefly either BAX-Luc or p21waf-Luc (luciferase-based reporter DNA), 50 ng phTK-Renilla luciferase (Promega, UK) to control for transfection efficiency and 800 ng of p53. Appropriate amounts of empty vector DNA were included where necessary to maintain constant DNA concentrations. Twenty-four hours post-transfection the firefly and Renilla luciferase activities were sequentially measured using Dual-Glo reagents (Promega, UK) in a Wallac Trilux 1450 Luminometer (Perkin-Elmer, UK). Firefly values were divided by the Renilla value from the same well to control for non-specific effects. Data for each set of four replica transfections was averaged, the control in each set normalized to 1 and data presented as fold increases over control. Each assay was repeated three times.
p53 levels were quantified by Western blotting in the superior temporal gyrus from a large cohort of patients comprising of 53 AD cases and 17 control subjects. In accordance with previous reports that had examined smaller patient numbers [5,11,13] we found that p53 immunoreactivity was indeed significantly elevated in tissue from AD patients compared to healthy elderly controls (Fig. 1A). Densitometry revealed an approximate 2-fold increase in p53 expression in AD (Fig. 1B). Next we explored the effects of p53 on tau phosphorylation. The 2N4R isoform of human tau was exogenously expressed in HEK293a cells (which do not contain endogenous tau) alone or in combination with p53. Co-expression of tau with p53 resulted in an increase in tau phosphorylation as demonstrated by a decrease in electrophoretic mobility using an anti-total tau antibody, which detects all tau isoforms independently of their phosphorylation state (Fig. 2A, panel a). Tau can be phosphorylated at a number of serine (Ser) and threonine (Thr) residues; therefore, we subsequently used a number of phosphorylation-specific anti-tau antibodies to examine the effects of p53 on tau further. There was an almost complete reduction of tau-1 immunoreactivity in HEK293a cells co-transfected with p53 and tau in comparison to cells expressing tau alone (Fig. 2A, panel b). Tau-1 recognises a number of amino acids including Ser199, Ser202 and Thr205 [4] when de-phosphorylated, therefore a decrease in immunoreactivity at this site reflects an increase in tau phosphorylation. Consistent with this, p53 induced an increase in tau phosphorylation as evidenced using the AT8 antibody, which recognizes tau phosphorylated at Ser202/Thr205 (Fig. 2A, panel c). In contrast, no changes in tau phosphorylation in the presence of p53 were observed using PHF-1 (Fig. 2A, panel d) or AT270 (Fig. 2A, panel e) monoclonal antibodies. Immunoblotting for β-actin illustrated that equal amounts of protein had been loaded across lanes (Fig. 2A, panel f).
Confocal microscopy demonstrated that p53 and tau are compartmentally separated (Fig. 2B). p53 (red) exhibits a diffuse nuclear localisation, whilst tau (green) is present in the cytoskeletal compartment when expressed both independently and in combination. This suggests that the effects of p53 on tau phosphorylation are indirect and most likely attributable to the transcription of a p53 target gene. We verified the transcriptional properties of p53 in HEK293a cells using BAX-Luc and p21waf-Luc (Fig. 2C), which are luciferase reporter constructs derived from known p53 target genes.
In summary, our findings demonstrate that p53 is upregulated in the superior temporal gyrus in AD and that p53 induces tau phosphorylation at the tau-1/AT8 epitope in HEK293a cells. We infer that the effects of p53 on tau phosphorylation are indirect as evidenced by the compartmental segregation of the two proteins. Therefore, pathological and sustained expression of p53 in adult brain might promote excessive and prolonged tau phosphorylation, which in turn might precipitate the formation of NFTs and neuronal death. Interestingly, we [9] have previously shown that TAp73 induces tau phosphorylation in HEK293a cells at the tau-1 and at the PHF-1 epitopes, which suggests that a similar mechanism of action might be shared by other p53 family members. Pertinent to our observations, it has recently been demonstrated that the expression of p53 is in part mediated by the transcriptionally active intracellular domain (ICD) of the β-amyloid precursor protein (APP), the generation of which is dependent upon the γ-secretase activities of presenilin-1 [3]. It is feasible then that in sporadic and familial AD, which both exhibit increased Aβ production, the concomitant increase in APP-ICD generation could lead to an increase in p53 expression and increased tau phosphorylation. Such a scheme forges a link between the two neuropathological hallmarks of AD, senile plaques and NFTs. In addition, it has also recently been demonstrated that Aβ itself, in particular the 42 amino acid form, binds the p53 promoter and enhances transcription [13]. Thus, p53 seems to play a pivotal role in AD, implying that modulation of cell death pathways might be of therapeutic benefit in AD and indeed in other age related neurological disorders.

p53
alzheimer's disease
tau
microtubules