Maarten Van Segbroeck
email: maarten.vansegbroeck@esat.kuleuven.be

Hugo Van

Unsupervised learning of time-frequency patches as a noise-robust representation of speech

Keywords: acoustic signal analysis, language acquisition, matrix factorization, automatic speech recognition, noise robustness

We present a self-learning algorithm using a bottom-up based approach to automatically discover, acquire and recognize the words of a language. First, an unsupervised technique using non-negative matrix factorization (NMF) discovers phone-sized time-frequency patches into which speech can be decomposed. The input matrix for the NMF is constructed for static and dynamic speech features using a spectral representation of both short and long acoustic events. By describing speech in terms of the discovered time-frequency patches, patch activations are obtained which express to what extent each patch is present across time. We then show that speaker-independent patterns appear to recur in these patch activations and how they can be discovered by applying a second NMF-based algorithm on the co-occurrence counts of activation events. By providing information about the word identity to the learning algorithm, the retrieved patterns can be associated with meaningful objects of the language. In case of a small vocabulary task, the system is able to learn patterns corresponding to words and subsequently detects the presence of these words in speech utterances. Without the prior requirement of expert knowledge about the speech as is the case in conventional automatic speech recognition, we illustrate that the learning algorithm achieves a promising accuracy and noise robustness.

Introduction

It is remarkable how babies exposed to a language acquire it naturally without deliberate efforts of teaching or learning. Before they can even speak, infants gather immense amounts of information while listening to human voices in their surroundings. During this stage, their brains are being tuned to a specific language. With only a surprisingly small amount of supervision, they succeed in learning new words from a spoken language. Most words are not being explained to them, but are learned from their significance in the world they live in. Although these streams of language data are large and appear to be unsegmented, infants show the ability to distinguish the different units of the language and to acquire how these units are linked in meaningful patterns, such as words.

Evidence exists [START_REF] Chomsky | New Horizons in the Study of Language and Mind[END_REF] that the basic ability to acquire language is innate to the child, e.g. the basics of human speech is built into babies' brains. However, no specific structural property of language has yet been proven to be innate and any infant seems equally capable of acquiring any language. Future research still has to reveal what in human language is inborn into the infant's brain and how they succeed in learning the language through experience and exposure to a specific speech community. Moreover, during their lifespan, humans are exposed to variations of what is being uttered. These variations can be acoustical (such as different speaking styles, accents or speech distortions caused by e.g. background noises) as well as on the level of interpretation in the context used. Nevertheless, humans have the ability to continuously learn and adapt to these new situations.

Although current systems for automatic speech recognition (ASR) show to be successful in some aspects, their performance can only be guaranteed if these systems are task-specifically programmed and adjusted to the (predicted) acoustic challenges in which they will operate. This way, ASR systems are unable to adapt to situations different from the one seen during training and are mostly unreliable in real life situations.

Our work is motivated by the idea that engineering approaches have fallen short in the design of ASR-systems and that inspiration has to come from human language learning and speech perception, an idea that was also postulated in other research work (Baker et al., 2006(Baker et al., -2007;;[START_REF] Scharenborg | How should a speech recognizer work?[END_REF]. This paper does not claim to explain human language learning, neither does it have the intention to learn grammar, world knowledge or pragmatics. However, we will show that a small vocabulary can be learned from scratch using a bottom-up approach from a spectral analysis of speech signals. To this end, we intend to build a system that automatically discovers the structure in the data, learns the patterns, links them with the words of a vocabulary and finally recognizes them in unseen (noisy) speech data. Our work is related to previously reported approaches of unsupervised language learning [START_REF] Scharenborg | Segmentation of speech: Child's play?[END_REF][START_REF] Qiao | Unsupervised phoneme segmentation using transformed cepstrum features[END_REF][START_REF] Brugnara | Automatic segmentation and labeling of speech based on hidden markov models[END_REF][START_REF] Aversano | A new text-independent method for phoneme segmentation[END_REF][START_REF] Siivola | Unlimited vocabulary speech recognition based on morphs discovered in an unsupervised manner[END_REF]. However, in these approaches the units are phones, phonemes or sub-word items, while in this work we search for recurring acoustic patterns in the time-frequency plane. Moreover, instead of acquiring the words of the language by concatenating these units, we assume that words can be represented by a sparse combination of these patterns and can be learned by discovering similarities in the activations of these patterns.

A first step in language acquisition is to build representations of speech that are to a great extent speaker independent and robust to noise. The first part of the paper explains how recurring acoustic patterns are discovered in speech data without supervision, a problem that was also addressed in a variety of other research work, see e.g. [START_REF] Park | Towards unsupervised pattern discovery in speech[END_REF][START_REF] Stouten | Discovering phone patterns in spoken utterances by nonnegative matrix factorisation[END_REF][START_REF] Smaragdis | Convolutive speech bases and their application to speech separation[END_REF][START_REF] Meyer | Optimization and evaluation of gabor feature sets for ASR[END_REF]. The learning algorithm involved makes use of non-negative matrix factorization (NMF) introduced by [START_REF] Lee | Algorithms for non-negative matrix factorization[END_REF]. Thanks to the non-negativity constraints, NMF decomposes a matrix in additive (not subtractive) components, resulting in a parts-based representation of the data. NMF can therefore be seen as a learning algorithm that, when applied to an appropriate feature space, finds the parts or objects that the training data are built of.

We will apply NMF to magnitude spectrograms in order to discover typical patterns in the time-frequency plane (the parts) that can be combined additively to form spectrograms of speech. We will consider spectral analyses over longer time windows than the centisecond scale usually considered in automatic speech recognition. Instead, the spectral patterns that are found have a duration in the order of hundreds of milliseconds. Other researchers have also observed that speech features spanning a longer time interval such as TRAPs [START_REF] Hermansky | TRAPS -classifiers of temporal patterns[END_REF] and its variants show improved robustness to noise [START_REF] Hermansky | Temporal patterns (TRAPs) in ASR of noisy speech[END_REF]. Other examples of long-span features are MRASTA filtering [START_REF] Hermansky | Multi-resolution RASTA filtering for TANDEM-based ASR[END_REF] or modulation spectra [START_REF] Kingsbury | Robust speech recognition using the modulation spectrogram[END_REF][START_REF] Tyagi | Mel-cepstrum modulation spectrum (MCMS) features for robust ASR[END_REF]. Some work also explicitly looks at time-frequency representations [START_REF] Meyer | Optimization and evaluation of gabor feature sets for ASR[END_REF][START_REF] Kleinschmidt | Localized spectro-temporal features for automatic speech recognition[END_REF][START_REF] Ezzat | Spectro-temporal analysis of speech using 2-D gabor filters[END_REF]. An important difference with the current work is that our time-frequency representation results from a parts-based representation that is learned without supervision. Other authors have used NMF for this purpose. Our approach is most closely related to that of [START_REF] Virtanen | Monaural sound source separation by nonnegative matrix factorization with temporal continuity and sparseness criteria[END_REF], who additionally imposes temporal continuity. Convolutional NMF by [START_REF] Smaragdis | Convolutive speech bases and their application to speech separation[END_REF] or the variant by O' Grady and Pearlmutter (2008) can also be used to find speech patterns in the time-frequency plane. The discovered speech units seem to be best described as phones, while our units are best described as "acoustic events", such as bursts or formant trajectories. The current work differs in other respects. Firstly, the patterns are discovered from a combination of two complementary feature representations that either reveal timing or frequency structure and which are derived from a time-frequency reassignment spectrogram. Subsequently, these spectral features are segmented into twodimensional overlapping time-slices which are stacked into column vectors. Recurring time-frequency dependent patterns and bases are then found by applying NMF on these vectors. By enforcing sparsity constraints in NMF, both timing and frequency information are modeled by the obtained bases. Secondly, we use conventional NMF instead of convolutive NMF (cNMF). Although the convolutive variant is appealing from a theoretical point of view, we have found from analyses of parts based on cNMF that it is less resistant to noise. Also, the computational requirements are significantly higher for cNMF. Thirdly, we also add a pattern recognition step to show speech recognition based on the discovered time-frequency patterns and demonstrate the robustness to noise thus obtained.

The bases are acoustic patterns and they will also be referred to as timefrequency patches of speech. From a neuroscience point of view, we could relate the process of discovering and acquiring these patches with the learning and/or evolutionary process by which humans have developed an auditory system that is exceedingly sensitive to speech sounds, though we do not claim that what we present here is a validated model of the neurophysiological mechanism. By describing speech in terms of these patches, we show how meaningful objects of the language such as words are linked to patch activation patterns. These patterns appear to be unrelated to speaker-specific properties and remain clearly visible when noise is added to the speech signals.

Similarly to our auditory system, the proposed model seems to be skilled in easily distinguishing speech from other environmental sounds, without the use of noise models or noise reduction techniques.

In the second part of this paper, our aim is to let a computer system acquire the vocabulary of a language by detecting, segmenting and learning the recurring activation patterns from the continuous stream of speech. To this end, the above mentioned speech model will be exploited in a language learning system. Similar to human speech recognition, the proposed system is able to acquire a language on clean training speech using weak supervision without knowing the words until after the acquisition process is completed. Key to the success of the system is the ability to discover recurring patterns in the activations of the time-frequency patches in speech across time. Therefore, the speech data is transformed into a high-dimensional vector representation, called "histograms of acoustic co-occurrence" (HAC) which are computed by accumulating the co-occurrence counts of acoustic events [START_REF] Van Hamme | Integration of asynchronous knowledge sources in a novel speech recognition framework[END_REF]. For this work, these events are quantized patch activation vectors. Subsequently, a learning algorithm with weak supervision and which is again based on non-negative matrix factorization (NMF) is proposed to discover recurring patterns through the use of HAC-features and link them with the lexical items of a language. Hence, in contrast to hidden Markov model (HMM) based speech recognition systems, no expert knowledge from audiology or phonology is incorporated in our system, neither do we need any a-priori information about what the words are and how they are composed. After the learning process, the system shows a remarkably good performance in detecting the words of the language in both clean and noisy speech data. Hence, the learning system could form the basis of an alternative framework for robust speech recognition.

Figure 1 shows the structure of the proposed system. In the learning stage, the NMF in the first layer is performed on the time-frequency representation to acquire time-frequency patches in clean training speech. From these learned patches, the patch activations along the time axis are then computed. In the second layer of the system, the patch activation vectors are quantized and transformed to the HAC representation. On these HACfeatures, another NMF is performed to learn the HAC of the vocabulary words. During testing, the first NMF computes the patch activations from the learned time-frequency patches and the second NMF computes the word activations from the learned HAC-models to detect the words in the utterance. To assess the efficacy of the language learning, a third layer was added to the system to detect the words from the word activations on unseen speech data. The outline of the paper is as follows: section 2 explains how the timefrequency patches of the first layer are learned from speech and how their activations along the time axis are computed. In section 3, the concept of HAC-models is restated from which the HAC of the words are learned in the second layer. Section 4 describes how this learning system can be applied to detect words in speech utterances. A small vocabulary word discovery experiment was conducted on the Aurora2 digit database and experimental results are given in section 5. Finally, conclusions can be found in section 6.

Layer 1: Time-frequency patch discovery

The production of human speech can be regarded as a process of combining a small number of spectral patterns into many more different sequences. In this section, our goal is to find a set of patterns by analyzing continuous speech recordings. Moreover, we would like that (i) these patterns accurately represent a wide variety of acoustic speech events with well localized energy regions to model e.g. formant tracks or energy bursts due to plosives, and in a later stage (ii) that they can be robustly detected in unseen noisy speech. To this end, we transform the speech data into a time-frequency reassignment spectrogram [START_REF] Auger | Improving the readability of time-frequency and time-scale representations by the reassignment method[END_REF] which is subsequently smoothed in time and frequency domain. The reassignment method (briefly restated in section 2.1) produces sharpened time and frequency estimates for each spectral component from partial derivatives of the short-time phase spectrum.

Instead of locating the spectral density value at the geometrical center of the analysis window, as in traditional short-time spectral analysis (e.g. STFT), the components are reassigned to the center of gravity of the energy distribution. In a next step, we perform a non-negative matrix factorization (NMF) on a data matrix containing consecutive spectral slices of this speech representation. By imposing non-negativity constraints, NMF generates parts that are additive, unlike factorization techniques such as PCA or SVD. Furthermore, NMF was chosen among other unsupervised learning method such as hierarchical clustering, self-organizing maps or neural networks, since (i) it is a recent and promising technique that has shown it merits in other research questions; (ii) it provides a more stable, intuitive and meaningful decomposition of non-negative data. By combining two complementary time-frequency reassignment representations that either reveal timing or frequency structure, the discovered speech patterns are acoustic patches of correlated energy that are well localized in both time and frequency. The involved smoothing process allows to make these patches speaker-independent. Besides the fact that the reassignment method produces highly detailed patterns, another motivation to use RTFR is the impact it has on the higher level of word learning (section 3). This will be illustrated in the experiments of section 5 by comparing the approach of RTFR with the approach where the patches are derived from the short time Fourier transform (STFT) of the speech.

Time-frequency reassignment

Time-frequency reassignment [START_REF] Auger | Improving the readability of time-frequency and time-scale representations by the reassignment method[END_REF][START_REF] Plante | Improvement of speech spectrogram accuracy by the method of reassignment[END_REF][START_REF] Hainsworth | Time-frequency reassignment: a review and analysis[END_REF] allows perfect localization of (wellseparated) impulses, cosines and chirps, which constitute a reasonable model for speech. The corresponding reassigned time-frequency representation (RTFR) has an increased sharpness of localization of the signal components without sacrificing the frequency resolution.

In this paper, the reassignment principle is applied to the short time Fourier transform (STFT) although it can be applied to different timefrequency representations each characterized by a different analysis kernel. The STFT is often used as the basis for a time-frequency representation of speech signals and is written as

STFT{x(t)} = +∞ -∞ x(u)h * (t -u)e -jωu du (1)
where x(t) is the analyzed signal and h(t) is the analysis kernel function. The spectrogram is then defined as the magnitude of the STFT and can also be expressed as a two-dimensional smoothing of the Wigner-Ville distribution [START_REF] Auger | Improving the readability of time-frequency and time-scale representations by the reassignment method[END_REF])

|STFT{x(t)}| 2 = 1 2π +∞ -∞ W x (u, ν)W h (t -u, ω -ν)dudν (2) with W x (t, f ) = +∞ -∞ x(t + 1 τ )x * (t - 1 τ )e -jωτ dτ. ( 3
)
From expression (2) it can be seen that the spectral density value of each time-frequency component is the weighted sum of all the Wigner-Ville distribution values at the points (tu, ων) and thus located at the geometrical center (t, ω) of the spectral analysis kernel function. The principle of the reassignment method is then to reallocated the energy from the geometric center of the kernel function to the center of gravity of the energy distribution. Therefore, the RTFR takes into account the phase of the STFT, which is omitted in the classical spectrogram, but contains important temporal information and this results in an improved localization of the energy in the time-frequency plane.

The reassignment points can be computed from the partial derivatives of the phase of the STFT using the principle of stationary phase [START_REF] Kodera | Analysis of time-varying signals with small bt values[END_REF]. According to this principle, the maximal contribution to the values of (2) occurs at the points where the phase is changing most slowly with respect to time and frequency. If φ(t, ω) denotes the short-time phase spectrum, then these points are computed as [START_REF] Plante | Improvement of speech spectrogram accuracy by the method of reassignment[END_REF] t, ω = t -1 2π

∂ ∂ω φ(t, ω), ω + 1 2π ∂ ∂t φ(t, ω) (4)
which represents the group delay and instantaneous frequency of the windowed signal. It has been shown in [START_REF] Auger | Improving the readability of time-frequency and time-scale representations by the reassignment method[END_REF], that a more efficient implementation is possible using two additional STFTs rather than using the derivatives of the phase. Let H(t, ω), D(t, ω) and T (t, ω) denote the STFT of the signal obtained with the window of choice h(t), the derivative of h(t) and the time weighted th(t) respectively and let ℜ(X) and ℑ(X) be the real and imaginary part of X, then the energy at (t, ω) is reassigned to the center of gravity [START_REF] Auger | Improving the readability of time-frequency and time-scale representations by the reassignment method[END_REF]:

t, ω = t -ℜ T (t, ω) H(t, ω) , ω + ℑ D(t, ω) H(t, ω) (5)
where the time and frequency offsets are now computed from the ratios of the three STFTs.

To improve the visibility of acoustic events with short duration, we further enhance the localization of the energy along the time axis. Therefore, we first search for zero-crossing points in the time offsets of ( 5) and only those of them that are connected in the vertical direction (i.e. along the frequency axis) are retained. Finally, the corresponding energy of the RTFR is assigned to the retained points. When applied to speech with a sufficiently short analysis window, the enhanced RTFR clearly shows the vertical (i.e. well-localized in time) lines that are related to the burst of plosives and affricatives and energy releases by the vocal folds. By repeating the same procedure using the frequency offsets of (5), the tracks of time-varying spectral features such as pitch and formants can be clearly localized in frequency. We have found that formant structure is more apparent if we use shorter windows in the RTFR.

The different steps of the enhanced reassignment procedure are shown figure 2 for the word "two". Firstly, a time-frequency representation is computed using a 128-point STFT. Subsequently, a RTFR is produced by reallocating the spectral energy to the gravity centers according to (5). The above mentioned enhancement steps are then applied to the RTFR to reveal either the timing or the frequency structure. Experiments have shown that an optimal choice for the window length is respectively 11 ms and 7 ms for male speakers and 6 ms and 4 ms for female speakers. The analysis window is shifted by 1 ms. To prevent ambiguity in later formulations, we will use the word subframe to denote a frame of the enhanced RTFR.

Constructing the input matrix

In this section, we explain how the input matrix is created to which we will apply NMF for finding acoustic time-frequency patterns in speech signals. These patterns are discovered on clean training data. After preemphasizing the speech signals, we compute the enhanced RTFRs by the approach described in 2.1. Both representations are used to exploit the spectral information that is more apparent in either the vertical or the horizontal direction. Two additional steps are also performed; a smoothing in time and frequency followed by a cube root compression. If these steps are not be applied, speaker-dependent "bases" will be learned to model the different pitch characteristics of training speakers. For reasons that become clear in the next section, however, we want to prevent overfitting to the training set, e.g. we want the resulting time-frequency patches not to be speaker dependent. Time smoothing is performed by reframing the enhanced RTFR by a sliding triangular window with a length of 30 subframes and a frameshift of 10 subframes. After conversion of the frequency axis from the Hertz scale to the Mel scale followed by a frequency smoothing using N = 128 triangular overlapping windows with a window size of 3 frequency bins using a weight of 1 for the center bin and 0.5 for the adjacent bins, we obtain the final N-dimensional feature vectors. Subsequently, spectral changes are emphasized by adding first and second order derivatives resulting in a static (S), a velocity (V ) and an acceleration (A) stream. For these feature representations we use the word frames and these are shown in figure 3(a) for the same uttered "two" as was used in figure 2. Note that the vertical lines corresponding to pitch bursts are dissolved by the smoothing process, but the overall energy bursts and releases are retained. In figure 3(b) we also show the feature vectors derived by applying the STFT using the same time window parameters as in the enhanced and smoothed RTFR. Note that, despite the smoothing process, formant contours still remain clear in figure 3(a) and are not confused with pitch harmonics as is the case in conventional Fourier transform. In section 5, we will compare the final accuracy results obtained by the STFT features with those of the enhanced and smoothed RTFR features.

The feature representations corresponding to the timing and frequency structure contain complementary information and therefore both will be used in the discovery of the speech patches. Let us now define the spectral vector at a certain frame t for a feature stream ρ (ρ = S, V or A) as v ρ,t and h ρ,t , corresponding to the feature representation that reveals respectively the timing (vertical) and frequency (horizontal) structure. Since NMF requires the data to be comprised of non-negative values only, we split these vectors into a positive (v + and h + ) and a negative (v -and h -) stream by zeroing out those values that are respectively < 0 and > 0 and taking absolute value of the negative stream. Finally, we stack all these vectors in one real and non-negative column vector of dimension 4N:

c ρ,t =       v + ρ,t |v - ρ,t | h + ρ,t |h - ρ,t |       (6)
For static features, v - ρ,t and h - ρ,t are all-zero vectors and their rows can be removed from (6). Note that the above mentioned procedure to handle input data with mixed sign in NMF, can also be seen as an alternative for the semi-NMF as was proposed by [START_REF] Ding | Convex and semi-nonnegative matrix factorizations for clustering and low-dimension representation[END_REF].

At each frame step t, we take k consecutive frames of c ρ,t representing the spectro-temporal structure of a short-time speech segment (i.e. of length 10k ms). These k frames are then reshaped into a column vector C k,ρ t of dimension 4kN as schematically illustrated in figure 4. From these column vectors, we construct a data matrix:

C k =     C k,S 1 . . . C k,S t . . . C k,S T C k,V 1 . . . C k,V t . . . C k,V T C k,A 1 . . . C k,A t . . . C k,A T     (7)
with T the total number of frames used from the clean training set.

Matrix factorization for unsupervised learning

By applying non-negative matrix factorization to the matrix C (dropping index k for notational convenience), it is approximated by the product of factors B and A which are of size 4kN × P and P × T :

C ≈ BA (8)
subject to the constraint that all matrices are non-negative and where the common dimension P of B and A is much smaller than T and 4kN. Hence, equation ( 8) contains only additive linear combinations such that the factorization leads to a parts-based representation, where P parts are found in the columns of B and their activation across time are given by the corresponding rows of A. In order to capture all feature streams in each basic vector, namely the timing and frequency spectral structure and their corresponding positive and negative part, additional sparsity constraints must be enforced on A. Otherwise, NMF tends to model all these parts in multiple columns of B. Therefore, we use sparse NMF [START_REF] Hoyer | Non-negative matrix factorization with sparseness constraints[END_REF] where the factorization is approximated by minimizing the objective function:

G(C||BA, λ) = D(C||BA) + λ i,j A ij (9)
The first term in ( 9) is a generalized version of the Kullback-Leibler divergence [START_REF] Lee | Algorithms for non-negative matrix factorization[END_REF], defined as:

D(C||BA) = ij C ij log C ij (BA) ij -C ij + (BA) ij (10)
The second term in equation ( 9) enforces sparsity on A by minimizing the L 1 -norm of its columns. The trade off between reconstruction accuracy and sparseness is controlled by the parameter λ.

An algorithm for finding B and A given C based on multiplicative updates and with the additional sparseness constraint can be found in (O' Grady and Pearlmutter, 2008). To address scaling, the constraint that each column of B sums to unity is imposed. Experiments have shown that with the settings used in this paper, a good choice for λ is 1000.

Interpreting the time-frequency patches and their activation in time

The columns of matrix B correspond to spectral patches which describe the recurrent time-varying spectra of speech. A selection of these patches are shown in figure 5 and6. Just for visualization, the rows of each feature stream were extracted from B and were reshaped back into N × k matrices, then the positive and negative parts were recombined and the feature representations corresponding to the timing and frequency structure were plotted onto each other by means of the max-operator. These figures illustrate the patches found for the static and velocity features. The parameter k of section 2.2 is set to 10 (figure 5) and 20 (figure 6) corresponding to a patch length of resp. 100 and 200 ms. Most patches describe formant movements over the duration of about a phone. A smaller set of time-frequency patches resemble wideband sounds and short-time energy bursts. Others are modeling the beginning or ending of phones and phone-pair transitions. Since we have discovered the acoustic patterns from recordings composed of a sufficiently large set of different speakers, the patches are assumed to be speaker independent (will be confirmed in section 5).

To discover the patches that are present in test utterances of the Aurora2 database, the same procedure as in training is used except that we compute A in equation ( 8) by holding B fixed to the one obtained from training. As an example, figure 7 shows the time-frequency representations (a) and their For the noisy speech examples, the babble noise type of the aurora2 database were added at different levels of signal-to-noise ratio (SNR) to the clean word utterances of figure 7(a), namely at 15, 10 and 5 dB SNR. In these noisy conditions, the activation patterns of the words remain similar and are hardly distorted by the different noise types. The noise robustness properties of the proposed speech model will be investigated in more detail later on (section 5).

Finally, figure 8 displays ten time-frequency patches for the word "four". The selected patches have a patch length of 100 ms and are those with the highest activation values in the utterance. The patches are ordered chronologically, e.g. patch 1 is activated from frames 31 to 33, patch 10 from frames 61 to 65.

Comparison with convolutive NMF

Alternatively, convolutive NMF (cNMF) could be used to obtain a partsbased representation of the data (O'Grady and Pearlmutter, 2008). Therefore, cNMF can be applied onto the sequence of feature vectors c ρ,t of equation (6). However, experiments show two major drawbacks in disfavor of cNMF. Firstly, the computational requirements for cNMF are higher than those of the NMF procedure as described in section 2.3. The training of the time-frequency patches with cNMF involves more computational time than NMF for the same number of iteration steps. During testing, the processing time spent per iteration is similar for both factorization techniques, but cNMF requires more iterations to convergence. Secondly, although cNMF also produces speaker independent activation patterns, it turned out that these less robust to additional noise sources. From figure 7(c) it can be seen that the activations patterns of cNMF are more distorted in noisy speech than the patterns of NMF. These observations will be confirmed in section 5 by discussing and comparing the final results.

Layer 2: Acquiring activation patterns of time-frequency patches

Our bottom-up approach for language learning is driven by the assumption that a particular language is characterized by similarities in the activation patterns of time-frequency patches, as was illustrated by figure 7. If we assume that the patches correspond with (groups of) auditory neurons, each sensitive to a specific time-frequency pattern, then a "snapshot" of their firing rate at a certain time is represented by a column of A. Hence, if recurring acoustic patterns in speech correspond to recurring neural firing patterns, one can hypothesize that the meaningful objects in a language (e.g. words) are characterized by similar activation patterns of time-frequency patches.

In this section, a learning algorithm is proposed that is able to acquire the objects of a language. The objects that are found are words, but could also be phone-like units in a different setting. The algorithm will discover the latent structures in patch activations by using "histograms of acoustic co-occurrence" (HAC) which are described by Van hamme (2008a) and will be restated next. HAC-features can represent a given segment of speech in a unique high-dimensional vector without requiring a segmentation of the segment, time warping or a constraint of its duration. Moreover, each word in the utterance contributes additively to the HAC of the utterance which motivates to apply the HAC representation in association with NMF. Word identities are being provided to the algorithm to bring the discovered activation patterns in relation with the words in the vocabulary.

Histograms of Acoustic Co-occurrences

HAC-models can describe speech by the co-occurrence statistics of acoustic events. In this work, these models are used to recover recurring patterns in patch activation events which are here the occurrence of quantized vectors in the activation matrix A.

The activation data of the time-frequency patches will be characterized by its similarity to examples. Therefore, the columns of the activation matrix A are clustered into Σ centroids using the K-means algorithm. Given the Euclidean distance metric used in clustering, each centroid can be represented by a Gaussian with spherical covariance. As a consequence, the posterior probabilities P i,n of all centroids n characterize any frame of A in terms of its similarity to each of the centroids. For each frame i, the posterior probabilities satisfy:

Σ n=1 P i,n = 1 (11)
A special case is obtained in a "winner takes all" setting, where all posteriors are zero except for the centroid closest to the observation, which is assigned the value 1. This setting is related to a vector quantization (VQ) approach where the centroids are the codebook entries labeled from 1 to Σ. After decoding, each frame of the activation matrix A is then replaced by the best matching centroid of the codebook, which allows to reduce the activation matrix to a single row vector of VQ-labels.

The HAC-representation is then the number of times all VQ-label pairs (m, n) ∈ Σ × Σ are observed τ frames apart. In other words, a histogram of lag-τ co-occurrences is constructed where each co-occurrence signifies that the input of activation frames is encoded into a VQ-label m at time i, while encoded into VQ-label n at time i + τ . For a given utterance u, the lag-τ co-occurrence is weighted with the (approximated) probability of the event

[V τ u ] mn = Iu-τ i=1 P i,m P i+τ,n with m, n = 1 . . . Σ ( 12
)
where I u is the number of frames in the utterance. Also note that [V τ u ] mn = [V τ u ] nm , such that these co-occurrences are directed. By stacking all (m, n)-combinations, each utterance can be represented by a single column vector V τ u where the elements express the sum of all Σ 2 possible lag-τ co-occurrences. We will refer to this vector as a HAC (histogram of acoustic co-occurrence).

This procedure can be performed for different τ -values and for a given set of time-frequency patches with a patch length of k frames. a set of U utterances, the data matrix for a choice of k and τ is then formed by

V k,τ = V k,τ 1 . . . V k,τ u . . . V k,τ U . ( 13
)
Note that thanks to the vector quantization approach, matrix V k,τ has a high sparsity. Furthermore, all its entries are non-negative such that NMFmethods can be applied.

Semi-supervised learning with NMF

Suppose that the utterances are composed of R recurring acoustic events such as words, each constructed from the set of time-frequency patches. Since (13) is a sum over time of activations, the words will contribute additively to the corresponding column of V k,τ . As each word is characterized by a HAC, the HAC of each utterance will be a (integer) linear combination of these histograms.

If the HAC of the words are placed in separate columns of a matrix W, and if the corresponding rows of H would contain the presence of each word in each utterance, one would have (leaving out indices k and τ ):

V ≈ WH (14)
Given their interpretation, all entries of W and H are constrained to be positive or zero. Because of these constraints and given the fact that equation ( 14) will not hold exactly since the observed symbols are subject to variability and uncertainty, W and H are estimated by NMF. Factorization of V is performed using the approach of section 2.3 without enforcing sparsity constraints, e.g. we set λ to 0. Once W is estimated on a training set, new utterances can be analyzed with factorization ( 14) by estimating H. The degree to which each discovered activation pattern is present in each new utterance is then found by examining the columns of H.

In this work, the words are unknown and NMF is used to separate them out from the utterances. However, since utterances can be seen as a sequence of words, but also as, for instance, a sequence of phones, constraints have to be imposed on ( 14) by exploiting grounding information. If it is known which words occur in each utterance, this information can be exploited to associate a word identity to each column of W. Therefore, the L × U grounding matrix G is formed, which holds in its l-th row and u-th column the number of times the l-th word occurs in the u-th utterance. Here, L is the number of word identities and U is the number of training utterances. Subsequently, we compute:

G V = W g W v H (15)
which expresses that word identities need to be explained jointly with the acoustic data by common model activations H. Given the properties of multiplicative updates [START_REF] Lee | Algorithms for non-negative matrix factorization[END_REF], grounding forces the NMF decomposition to associate word models in W v also to the utterances containing those words. Without augmenting V with the grounding matrix, NMF tends to spend columns of W v preferably on the more frequent acoustic patterns since this has the most impact on minimizing the modeling error. Experiments have shown that the common dimension R is better overestimated, hence R ≥ L. This allows to model acoustic events that have no relevance to grounding such as silence or filler words.

Improving learning by modeling multiple streams

As explained in section 3.1, the data matrix V k,τ contains lag-τ cooccurrences in the activation data of time-frequency patches with a duration of k frames. For each individual configuration (k, τ ), patterns can be learned using the approach of section 3.2 by connecting acoustic information and by assigning a meaning to these patterns. We may assume that the performance of the learning algorithm will increase if multiple configurations are combined in the input matrix. This idea has already been exploited by jointly capturing static, velocity and acceleration feature information in each time-frequency patch. On the activation level, the data matrix of ( 14) can be further extended by incorporating different sets of patches and including co-occurrence data at different time lags. By allowing the use of patches with different durations, we could also compensate for the time differences of phones. For instance, one can expect that plosives cause more neural activation at neurons modeling time-frequency patches with a duration around 50 ms, while neurons corresponding to patches of 100 ms better represent diphthongs and vowels. Units with even longer duration (e.g. 200 ms) can be used to model intra-and inter-phoneme transitions. Therefore P sets of time-frequency patches are included in the model, each exploiting Q values of τ , augmented with grounding information that relates to the spoken words:

     G V k 1 ,θ . . . V k P ,θ      = W g W v H with V k i ,θ =    V k i ,τ 1 . . . V k i ,τ Q    . (16)
For these joint streams, the generative parts-based model still holds: the joint stream co-occurrences of utterances can be written as an additive combination of parts. As we will show in the experiments of section 5, it is indeed advantageous to exploit multiple combination of k and τ in the HAC-model.

Layer 3: Detecting words in activation patterns

After the semi-supervised training procedure, W g and W v is known. Recognition on unseen test utterances (from which grounding information G is unknown), is achieved by first computing the histograms of co-occurrence V and then estimating the matrix factor Ĥ in V ≈ W v Ĥ by holding W v fixed. This matrix Ĥ reveals to which extent the internal representations of the trained words are present in the new test utterance. By estimating the grounding information as: Ĝ = W g Ĥ ( 17)

we obtain estimates for the presence of the words in the test utterances. Hence, for a word that is present, the corresponding element of Ĝ tends to 1 and to 0 if it is absent. This way, a word detection system can be build from the content of matrix Ĝ by comparing Ĝ with a threshold ξ. Two types of errors are involved in the system: missed detections ( Ĝij < ξ while utterance j contains word i) and false alarms ( Ĝij ≥ ξ while utterance j does not contain word i). The trade-off of both error types can be visualized by means of a Detection Error Trade-off (DET) curve [START_REF] Martin | The DET curve in assessment of detection task performance[END_REF].

In figure 9, the DET-curve is shown for the word detection task where the model is trained on lag-10 co-occurrence counts (τ =10) computed on a set of time-frequency patches with a length of 100 ms (k=10). The performance of the system in clean speech conditions is compared with a noisy test case where the speech is distorted by babble noise at 10 dB SNR. The estimated grounding matrix Ĝ of five different utterances for both test cases are shown in figure 10 where high values (black) indicate that the corresponding words have a high probability to be present in the utterance.

In the experimental evaluation of section 5, we will not apply this "per word detection" paradigm, which is relevant for tasks such as keyword spotting. Instead we will measure correct word recognition per utterance. Assuming that the number D u of different digits occurring in the u-th test utterance is given, the D u candidates with highest activation according to equation ( 17) are selected. Notice that the recognition result is unordered, a problem that is addressed in (Van hamme, 2008a) by a sliding window decoder that estimates at which time each word occurs in the utterance. Word error rate is thus defined as the sum of the number of incorrect digits that end up in the top D u , divided by the sum of D u over the complete test set.

Experiments

The speech data are taken from Aurora2, a small vocabulary, speaker independent database for connected digit recognition as defined by [START_REF] Hirsch | The AURORA experimental framework for the performance evaluation of speech recognition systems under noisy conditions[END_REF]. All utterances are derived from the TI-Digits database that contains recordings of male and female US-American adults, downsampled to 8 kHz sampling frequency. The database contains isolated digits and sequences of up to seven digits. The Aurora2 clean-condition training set consists of 8440 utterances that are used for the discovering the timefrequency patches and their activation patterns from which the HAC-models are derived. The test experiments are conducted on test set A consisting of 4004 utterances from the TI-Digits test data, split into 4 subsets of 1001 utterances each and to which 4 different noises are added at different SNRlevels: noise recorded in a subway (N1), babble noise (N2), car noise (N3) and noise recorded in an exhibition hall (N4).

Reference experiment

Baseline recognition results are produced by a conventional HMM-based recognition system using the complex back-end configuration as defined by the ETSI Aurora group ETSI standard document (2000b). Whole word digit models were trained on the clean speech training database using the HTK software package version 2.2 from Entropic [START_REF] Young | The HTK book -version2[END_REF]. The digit models have 16 emitting states with 20 Gaussians per state. The optional inter-word silence is modeled by 1 or 3 states with 36 Gaussians per state, while leading and trailing silence have 3 states. The total number of Gaussians is 3628. Feature were extracted by the Aurora WI007 front-end ETSI standard document (2000a), a cepstral analysis scheme where 12 Mel-scaled cepstral coefficients and c 0 (no log-energy) are determined for a speech frame of 25 ms length using a frame shift of 10 ms. These features are combined with their dynamic coefficients to yield in 39-dimensional feature vectors for recognition, as explained in [START_REF] Macho | Evaluation of a noise-robust DSR front-end on Aurora databases[END_REF].

Here, we assume that for each test utterance u the number N u of digit sequences is known. This information is then used in the language model u1), "zero seven nine six two" (u2), "six" (u3), "two five two" (u4) and "eight" (u5).

by forcing the decoder to recognize exactly N u digits. From the recognition result, only the different digits are retained to obtain an unordered string result of at most N u digits. Similarly as in section 4, a detection error is accounted for each digit from the set of D u different and correct digits in the utterance that is not present in the unordered recognition result. The error rate of this HMM-based word detection system is shown in table 1 2: Unordered word error rate results on the Aurora2 database averaged over the four noise types for the proposed recognition system using a combination of different sets of time-frequency patches and different lag-τ co-occurrence counts. The time-frequency patches are derived from the enhanced and smoothed RTFR features using the NMF procedure described in section 2.3. The ×-symbol indicates which configurations (k, τ ) are integrated in the input matrix of equation ( 16).

static, velocity and acceleration features as explained in section 2, while using the following values for k: 5, 10, 15, 20. For each set, the number of patches P to be discovered is 100. Experiments, not reported in this paper, have shown that this number suffices to model the different spectral patterns of the small vocabulary task of Aurora2. The obtained patches are modelled by the columns of the four matrices B k which are stored for the recognition task on test data.

For each set of time-frequency patches, the patch activation vectors in A k are quantized using a codebook of 250 entries, resulting in 4 sequences of VQ-labels. Subsequently, the VQ-label co-occurrence histograms are computed for all utterances using different lag-τ values: 5, 10, 15, 20. The VQ histogram counts are divided by a fixed constant (100) such that the acoustic and grounding information have roughly the same weight in the data matrix V k,τ . Experiments have shown that the value of this constant is not critical: it can be changed over several orders of magnitude without significant impact. To acquire all eleven words of Aurora2, namely the digits "one" to 4: Unordered word error rate results for Aurora2 averaged over the four noise types using time-frequency patches derived from the enhanced and smoothed RTFR features by the cNMF procedure discussed in section 2.5.

"nine", "zero" and "oh", the training procedure as described in section 3 was performed with R = 12 using the utterances of the clean training set. After factorization (15), W g and W v are stored for recognition.

Evaluating the results

To discover the digits that are present in the test utterances, the same procedure as in training is used except that we compute the patch activation matrix A in (8) by holding B fixed to the one obtained from training. Similarly, the word activation matrix H is found by holding W fixed in ( 14) to the one estimated from the training set.

Table 2 shows the unordered word error rate on the Aurora2 test set averaged over the four noise types, using different stream configurations (k, τ ). For clean speech, the self-learning algorithm performs worse than the HMMbased system that makes use of expert speech knowledge that arises from audiology and linguistics. However, our system performs comparably to the HMM-based recognizer at 15 dB SNR and has a remarkably higher accuracy for noisy speech at lower SNRs without using any noise compensation techniques. From the table 2, we can also observe that the robustness can be increased by exploiting more knowledge sources. The reason for this noise robustness is three-fold: (i) thanks to the parts-based representation of speech, the system easily detects, even in noisy conditions, which time-frequency patches are active; (ii) these time-frequency patches provide static and dynamic spectral information over large time windows; (iii) multi-window timefrequency representations can be exploited by the joint modeling of different streams.

For comparison, we added the results of the STFT features with the same time windows parameters for the timing and frequency structure (see table 3) and those where cNMF performed onto the feature vectors of equation ( 6) (see table 4). As can be seen from both tables, the word error rates are worse than those shown in table 2. This indicates respectively that the enhanced and smoothed RTFR features are more robust than STFT features and that the activation patterns acquired by the proposed approach using NMF to discover time-frequency patches deliver a more robust input stream to the word detection system than those obtained by the cNMF approach.

Conclusions

In this paper, we proposed a bottom-up approach for learning the words of a language. An unsupervised technique was presented to discover a set of spectral patches that can describe speech. We exploited the use of two complementary feature representations derived from a reassigned time-frequency spectrogram to obtain a representation that can cope with acoustic events with short and long durations. The non-negative matrix factorization (NMF) algorithm using sparsity constraints was applied to discover latent recurring patterns in static and dynamic features. The obtained basis vectors correspond to phone-sized spectral patterns which we referred to as time-frequency patches. Experiments on the Aurora2 database revealed that these patches are activated in speaker-independent patterns which are related to the words of a language.

Next, a learning algorithm was built that automatically discovers and acquires the recurring patterns in the activation data by applying NMF on the co-occurrence counts of activation events. The obtained patterns were associated with the words of a language and finally the system was able to detect the words in unseen (noisy) speech data.

Experimental evidence was given for the noise robustness of the proposed word detection system, based on the Aurora2 digit recognition task. Although a conventional HMM-based approach using cepstral features obtained better results on clean speech data, the proposed learning algorithm showed a superior performance for speech that is distorted by the noise down to 5 dB SNR. The NMF learning algorithm was shown to be sufficiently versatile to apply it at both levels of speech representations for discovering structure in the data. NMF has less parameters to be tuned in comparison to HMM-based systems. The most important parameters are the number of time-frequency patches P , the sparsity parameter λ in the NMF of the first layer and the number of VQ-labels Σ in the second layer. Moreover, experiments not reported here have shown that for the small vocabulary task as was considered in this paper, the performance of the system is not very sensitive to these parameters.

Inspired by research on the auditory cortex of mammals, researchers have suggested that ASR systems should trigger on the presence of spectrotemporal patches. Such biologically inspired systems might exhibit properties of human audition such as robustness to noise. In this paper, we have shown that such an auditory representation with good robustness can be obtained through unsupervised learning (the first layer). We have also shown how the activation patterns can be exploited to build a speech recognizer. Further work involves extending the word detection system to a speech recognition system that also provides information related to the order in which the words occur in the test utterances. Therefore, the HAC-models in the second layer can be extended by moving a sliding window over the utterance to detect the time of occurrence of the different words in the utterance and hence the word order. In our current implementation, the noise has been left uncompensated and we would like to investigate to which extent the performance of the recognition system can be further improved by exploiting noise reduction techniques. Though, the layered architecture offers scalability towards vocabularies in the sense that the patch set is reusable across words, more research is required to reveal how well the proposed system is suited for large vocabulary continuous speech recognition. At his point, the presented threelayered architecture is not capable to deal with these vocabularies because of the large data requirements per word. However, the scalability of the system can be increased by adding more layers in cascade to model the words as a combination of subword patches instead of learning all the words from scratch.

To conclude, we believe that the presented system is an ideal platform for future research as in its baseline implementation it already yields competitive results and could open new avenues of research on automatic speech recognition.

Figure 1 :

 1 Figure 1: Structural representation of the proposed learning system.

Figure 2 :

 2 Figure 2: STFT representation (top), RTFR (middle) and enhanced RTFR (bottom) for the word "two" with focus on timing (left) and frequency (right) structure. In the bottom left panel, vertical lines correspond to plosive burst and vocal fold bursts, while the enhanced RTFR at the bottom right reveals the horizontal structure in the word, e.g. pitch and formant contours.

  Figure 3: (a) Static and velocity feature vectors derived from the enhanced RTFRs that reveal timing (top) and frequency (bottom) structure for the word "two". (b) The corresponding STFT feature vectors using the same time window parameters.

Figure 4 :

 4 Figure4: Schematic representation of the construction of data matrix C k,ρ from feature vectors c ρ,t which contain the feature representations that contain timing (v ρ,t ) and frequency (h ρ,t ) structure.

Figure 5 :

 5 Figure5: A collection of the discovered time-frequency patches for static (top row) and velocity features (bottom row) with a duration of 100 ms. Some patches show formant patterns, wideband spectra and bursts; others model inter-phone or silence-phone transitions.

Figure 6 :

 6 Figure 6: A collection of the discovered time-frequency patches with a duration of 200 ms.

Figure 7 :

 7 Figure7: (a) Time-frequency representations of the word "four" uttered by three different speakers in clean conditions (left) and noisy conditions (right) and their corresponding patch activation matrix A for patches derived from the procedure with (b) NMF (section 2.3) and (c) cNMF (section 2.5).

Figure 8 :

 8 Figure 8: Time-frequency representation of the word "four" (top) and the time-frequency patches (bottom) with highest activation values in the utterance.

Figure 9 :

 9 Figure 9: DET-curves of the word detection system for clean and noisy speech (babble noise at 10 db SNR).

Figure 10 :

 10 Figure10: Estimated grounding matrix Ĝ for (a) clean and (b) noisy speech (babble noise at 10 db SNR) of the utterances "four six two five" (u1), "zero seven nine six two" (u2), "six" (u3), "two five two" (u4) and "eight" (u5).

Table 1 :

 1 . Results were averaged over the four noise types of Aurora2. Unordered word error rate of an HMM-based word detection system on the Aurora2 database averaged over the four noise types.

	clean 15dB 10dB 5dB
	0.16 2.98 11.92 34.88
	5.2. Training procedure
	In the experiments, multiple sets of time-frequency patches, modeling
	acoustic patterns of different durations, were trained on the clean training
	set of Aurora2. The patches are learned from a data matrix constructed from

Table

Table 3 :

 3 Unordered word error rate results for Aurora2 averaged over the four noise types using time-frequency patches derived from STFT features by the NMF procedure.

	k	τ
	5 10 15 20 5 10 15 20 clean 15dB 10dB 5dB
	×	× × × × × 3.45 6.57 10.23 18.16
	× × ×	× × × × 2.76 5.83 9.48 16.36
	× × × × × × × 3.18 5.90 9.44 16.49
	× × × × × × × × 2.85 5.50 9.08 15.78

Table

Acknowledgement

This research was funded by the Institute for the Promotion of Innovation through Science and Technology in Flanders, Belgium (I.W.T.-Vlaanderen) and by the European Commission under contract FP6-034362 (ACORNS).


B Luthringer

S Isbert

Weg Müller

C Zilberberg

N L Thakur

G Wörheide

R H Stauber

M Kelve

M Wiens
email: wiens@uni-mainz.de

Poriferan survivin exhibits a conserved regulatory role in the interconnected pathways of cell cycle and apoptosis

Keywords: survivin, IAP, caspase, sponges, Suberites domuncula, cadmium aa, amino acid, ABTS,

2,2'-azino-bis(3-ethylbenzthiazoline-6-sulfonic acid); Bcl-2, B-cell lymphoma-2; BIR, baculoviral IAP repeat; CDE, cell

Introduction

Apoptosis plays a crucial role not only in countless physiological processes (e.g., elimination of redundant cells during vertebrate embryonic development) but also during pathogenesis. Since apoptosis relies on the clear-cut function of numerous pro-and anti-apoptotic factors, any malfunction on their part or disruption of their respective interplay inevitably leads to dysfunctional cellular metabolism. The ensuing delayed or premature cell death often manifests in severe pathologies, including carcinogenesis and neurodegenerative diseases.

The groundbreaking discovery of the Caenorhabditis elegans proteins Ced-9, homologue of human anti-apoptotic Bcl-2, and Ced-3, homologue of human caspase-1, has demonstrated the molecular conservation of the apoptotic cell death program along a broad range of Metazoa, from nematodes to vertebrates. [START_REF] Hengartner | The biochemistry of apoptosis[END_REF] Since then, the fundamental role of apoptosis has been increasingly recognized in various physiological processes. In addition, several invertebrate model organisms revealed a significant potential for analyses of apoptotic mechanisms, not only under an evolutionary point of view. Thus, the identification of the human crumbs homologue (CRB-1) and its causative role in the manifestation of retinitis pigmentosa was based on the discovery of mutational variants of the Drosophila melanogaster protein crumbs. [START_REF] Hollander | Mutations in a human homologue of Drosophila crumbs cause retinitis pigmentosa (RP12)[END_REF] In addition, loss of function mutations of the C. elegans dys-1 gene confirmed the role of human dystrophin in the manifestation of the neuromuscular disease of Duchenne. [START_REF] Bessou | Mutations in the Caenorhabditis elegans dystrophin-like gene dys-1 lead to hyperactivity and suggest a link with cholinergic transmission[END_REF] During the last years a novel invertebrate model system has emerged: sponges (phylum Porifera), which are among the earliest-branching nonbilaterian animals. [START_REF] Philippe | Phylogenomics revives traditional views on deep animal relationships[END_REF] Recent biomarker analyses date the occurrence of probable stem-group poriferans back before the end of the Marinoan glaciation (~635 Myr ago) [START_REF] Love | Fossil steroids record the appearance of Demospongiae during the Cryogenian period[END_REF] and molecular clock analyses estimated that the main poriferan lineages diverged in the Cryogenian/Ediacaran 6 , well before the rapid appearance of major metazoan groups (Cambrian Explosion).

Until today, the model system "Porifera" has revealed several characteristics that have proven valuable for evolutionary studies within a comparative context. The major avail is the phylogenetically basal position of Porifera allowing a unique vantage point on the hypothetical common ancestral taxon, the Urmetazoa.

Moreover, comparative transcriptomics and proteomics divulged several examples of poriferan molecules, genetic features, protein domains, etc. that remained conserved throughout evolution, but otherwise have been lost in invertebrate model systems traditionally used, C. elegans and D. melanogaster. [START_REF] Müller | Contribution of sponge genes to unravel the genome of the hypothetical ancestor of Metazoa (Urmetazoa)[END_REF] Furthermore, the majority of poriferan genes display a closer sequence similarity to their human homologues than to the respective molecules of worm and fly (similar to some anthozoans), probably caused by an accelerated evolution of the latter animal lineages. [START_REF] Gamulin | Sponge proteins are more similar to those of Homo sapiens than to Caenorhabditis elegans[END_REF][START_REF] Kortschak | EST analysis of the cnidarian Acropora millepora reveals extensive gene loss and rapid sequence divergence in the model invertebrates[END_REF] The fine-tuned balance between apoptosis and proliferation is upheld via numerous regulatory molecules that maintain tissue integrity and, ultimately, ensure survival of the organism. The inhibitors of apoptosis (IAP) family comprises structurally related proteins (the presence of at least one baculoviral IAP repeat (BIR) domain is mandatory) that were originally discovered in baculovirus. In addition to their cell death regulating activities several IAP have a complementary role in cell division, in particular attributed to survivin. Accordingly, the dual role of human survivin is based upon its function as (i) a chromosomal passenger protein that controls segregation of chromatin/regulation of cytokinesis and (ii) a caspase inhibitor that prevents processing/activation of caspases. [START_REF] Knauer | Survivin's dual role: an export's view[END_REF] Since transcription from its single gene locus is up-regulated in most tumor cell types, survivin is a wellestablished analytical marker of neoplasms. [START_REF] Ambrosini | A novel anti-apoptosis gene, BIRC5, expressed in cancer and lymphoma[END_REF][START_REF] Caldas | Survivin splice variants regulate the balance between proliferation and cell death[END_REF] The function of survivin homologues in non-bilaterian Metazoa is much less well-defined. Accordingly, similar to vertebrate survivin the D. melanogaster homologue deterin deters cells from caspase-induced apoptosis and some studies propose an additional pro-proliferative effect. [START_REF] Jones | Deterin, a new inhibitor of apoptosis from Drosophila melanogaster[END_REF] On the other hand, the C. elegans survivin homologue BIR-1 appears to be exclusively involved in cytokinesis, in particular during embryogenesis. [START_REF] Fraser | Caenorhabditis elegans inhibitor of apoptosis protein (IAP) homologue BIR-1 plays a conserved role in cytokinesis[END_REF] Here we report the discovery and functional characterization of a survivin-like protein (SDSURVL) from a member of the ancestral-like clade Porifera, Suberites domuncula. SDSURVL displays both significant sequence homologies to human survivin (in particular concerning the BIR domain) and a conserved genetic structure.

Complementary studies in vivo and in transfected cells suggest a conserved dual role of survivin and, concurrently, indicate a loss of function in certain rapidly evolving and highly adapted metazoan taxa.

Results

Characterization of the poriferan survivin-like protein SDSURVL. The complete SDSURVL cDNA was isolated from a S. domuncula cDNA library by PCR and degenerate primers that were directed against a conserved region within the BIR domain. SDSURVL consists of 620 nt (excluding the poly(A) tail) that encompass an open reading frame (ORF) of 447 nt (excluding the first stop codon), beginning at nt 43-45 (Met start ). During Northern blot analyses, a DIG-labeled probe detected a transcript whose size was consistent with that of the cDNA (≈ 650 nt; Figure 4A). The deduced protein, SDSURVL, comprises 149 aa with an expected size of 17,003 Da.

Analyses of SDSURVL revealed significant sequence similarity to vertebrate survivin homologues, such as Gallus gallus survivin isoform 1 (with an expect value [E value] [START_REF] Coligan | Induction of immune responses[END_REF] of 2 e-20; 32% identical and 52% similar aa) and human survivin (8 e-18; 30%/49%) (Figure 1A).

[HKEPILVY]-x(2)-R-x(3,8)-[FYW]-x(11,14)-[STAN]-G-[LMF]-x- [FYHDA]-(x(4)│x(7))-[DESL]-x(2,3)-C-x(2)-C-x(6)-[WA]-x(9)-H-x(4)-[PRSD]-x-C-x(2)- [LIVMA]
. The putative NES (aa 95-103 ) loosely fits a consensus sequence proposed by [START_REF] Heger | Qualitative highly divergent nuclear export signals can regulate export by the competition for transport cofactors in vivo[END_REF] , L-x(2,3)-[FILVMP]-x(2)-[LI]-x-[LIV]. Furthermore, COILS algorithms indicate the typical COOH-terminal coiled-coil conformation (aa 127-147 ), usually present in survivin but missing in other IAP family members. Moreover, the conserved threonine residue, whose phosphorylation by p34cdc2 mediates human survivin activation, [START_REF] Wall | Suppression of survivin phosphorylation on Thr34 by flavopiridol enhances tumor cell apoptosis[END_REF] is present in SDSURVL (aa 26 ). Additionally, a ubiquitin ligase complex-binding destruction motif (APCC-D box; aa 136-141 ) was predicted.

To determine the phylogenetic relationship of survivin-like proteins, molecular phylogenetic analyses were performed by neighbor-joining, maximum likelihood, and Bayesian methods, including the so far known sequences of metazoan taxa as well as related BIR-containing sequences of viruses and yeast. Accordingly, the resulting composite consensus tree shows the poriferan SDSURVL protein at the base of a monophyletic metazoan clade, whereas viral sequences form a separate monophyletic group (Figure 1B).

Characterization of the poriferan survivin-like gene (SDSURVL). The

S. domuncula gene comprises four exons and three introns (Figure 2A). Each exon/intron border displays typical splicing signals (GT-intron-AG). The phases of introns vary: the first and third introns are in phase 0, while the second intron is in phase 2. The human survivin gene shares the same features (Figure 2B) except for the borders of the third intron (AT-intron-GG). Parts of the first and third exons and the complete second exon are encoding the BIR domain (Figure 2C). Analyses of the 5'-flanking region (Figure 2D) of human (1) and murine (2) survivin genes reveal the presence of a TATA-less promoter, containing a canonical CpG island and several transcription factor binding sites. [START_REF] Li | Transcriptional analysis of human survivin gene expression[END_REF] However, in silico analyses of the putative poriferan promoter region (3) predicted the presence of a TATA box (nt -64 to -73 ) in addition to other cis-regulatory elements, CAAT box (nt -109 to -113 ), and ribosomal binding site (nt -5 to -8 ). Moreover, similar to the human and murine promoter two putative Sp1 sites (nt -51 , -292 ) have been predicted, although in contrast to the former ones no cell cycle dependent elements (CDE) or cell cycle homology regions (CHR) have been found in the poriferan non-coding region.

Characterization of the poriferan caspase-like protease 2 (SDCASL2). Caspases

(cysteine-dependent aspartyl-specific proteases) are synthesized as zymogens that become activated by scaffold-mediated transactivation or by cleavage via upstream proteases. [START_REF] Yuan | A first insight into the molecular mechanisms of apoptosis[END_REF] Since the anti-apoptotic properties of vertebrate survivin have been functionally linked to inhibition of caspase-7 and -3, a S. domuncula cDNA library was screened for homologous molecules. The correct size of the resulting clone, SDCASL2, then was verified on Northern blots (Figure 4A). SDCASL2 (1,827 nt, excluding poly(A)) comprises an ORF between nt 175-177 (Met start ) and nt 1738-1740 (stop), coding for a putative protein of 521 aa that was termed caspase-like protease 2 (SDCASL2; calculated Mr 58,427). SDCASL2 revealed sequence homology not only to SDCASL (E value 7 e-31; 33% aa identical and 52% similar aa) but also to members of the caspase-7 subfamily, e.g., Xenopus laevis and human representatives (E value 3 e-16 [21%/33%] and 3 e-14 [19%/35%] respectively) (Figure 3).

All caspases contain a CASc domain, which is cleaved into two subunits (p10, p20) during activation. SDCASL2 represents a putative procaspase, comprising subunits p10 (aa 191-267 ) and p20 (aa 22-154 ), the latter containing the characteristic Cys and His active sites (Figure 3). The Cys active site consensus pattern is conserved

(PROSITE pattern PS01122) with two exceptions, marked in bold ([KL]-P-K-[LIVMF]- [LIVMFY]-[LIVMF](2)-[QPD]-[AF]-C-[RQG]-[GE]), whereas the histidine active site (PS01121) features three alterations, [HL]-x(2,4)-[SC]-x(1,2)-{A}-x-[LIVMFY](2)-[ST]- G-H-G.
The absence of a large NH 2 -terminal prodomain, characteristic of initiator caspases, suggests a classification to the group of effector caspases. However, similar to SDCASL, SDCASL2 carries an unusual long COOH-terminal stretch of 254 aa, containing a putative double-stranded RNA-binding motif (aa 441-514 ; PF0035

[Pfam]).

Evolutionary conserved function(s) of poriferan survivin.

To elucidate the controversially discussed ancestral function of survivin (or if indeed its dual role is conserved through metazoan evolution), functional analyses were performed in sponge tissue and primmorphs (aggregates of proliferating sponge cells). Moreover, functional conservation was assessed in a heterologous vertebrate cell model (SDSURVL-expressing transfected HEK-293 cells) that has been previously used to explore expression and functional mechanisms of survivin. [START_REF] Tamm | IAPfamily protein survivin inhibits caspase activity and apoptosis induced by Fas (CD95), Bax, caspases, and anticancer drugs[END_REF][START_REF] Torres | Caveolin-1 controls cell proliferation and cell death by suppressing expression of the inhibitor of apoptosis protein survivin[END_REF] Regulation of proliferation. Comparative Northern blot analyses were performed, showing an increasing level of SDSURVL transcripts during the first two weeks of primmorph growth, whereas the control (taken immediately after primmorph formation) revealed a considerably lower expression level (Figure 4A). After 14 d, survivin transcription reached the steady-state. On the other hand, in adult sponge tissue survivin transcripts were barely detected. Additionally, SDCASL2 expression was very low throughout the experiments, in both primmorphs and sponge tissue (Figure 4A). Subsequently, SDSURVL and SDCASL2 expression was investigated in aging primmorphs (two weeks of age) via complementary in situ hybridization studies (Figure 4B). For this purpose, primmorph cryosections were incubated with labeled, single-stranded sense (control) or antisense probes. Both sense probes did not produce specific signals. In contrast, the antisense SDSURVL probe detected survivin transcripts abundantly and homogenously throughout the sections, whereas only few SDCASL2 copies were identified, mainly in the surface cell layer and in cells lining the aquiferous canal system within the mesohyl (internal part of the sponge) (Figure 4B). Regulation of apoptosis. To explore the putative implementation of SDSURVL as a pro-survival factor, SDSURVL and SDCASL2 expression was analyzed in sponge tissue and primmorphs challenged with pro-apoptotic stimulants. Moreover, the viability of mock-transfected and HEK293-SDSURVL cell lines was assessed following exposure to the same stimuli. Pam 3 Cys-Ser-(Lys) 4 and cadmium were employed for their selective ability to trigger extrinsic (the former compound) or intrinsic (latter) cell death pathways. [START_REF] Aliprantis | The apoptotic signaling pathway activated by toll-like receptor-2[END_REF][START_REF] Mao | Cadmium induces apoptosis in human embryonic kidney (HEK) 293 cells by caspasedependent and -independent pathways acting on mitochondria[END_REF] Both lipopeptide and heavy metal stimulated the expression of SDSURVL and SDCASL2 in sponge tissue and primmorphs alike, already after 6 h (Figure 7). With prolonged incubation time, the expression level of both transcripts further increased; however, this effect was more pronounced in primmorphs than in tissue samples.

To assess putative effects of SDSURVL expression, mock-transfected HEK-293 and HEK293-SDSURVL cell lines (95.5% transfection efficiency) were exposed to Pam 3 Cys-Ser-(Lys) 4 and cadmium. Then, MTT assays, cell death detection ELISA, and Western blot analyses were performed. MTT assays revealed that the lipopeptide did not alter cell viabilities, neither in mock-transfected nor in HEK293-SDSURVL cells, compared to untreated control cells (Figure 8A).

Additionally, Pam 3 Cys-Ser-(Lys) 4 -treatment did not change the amount of survivin (endogenous as well as poriferan). In contrast, both cell lines were sensitive to cadmium. Thus, MTT assays showed that 46.7% of mock-transfected cells underwent cell death after 12 h of exposure. Nevertheless, SDSURVL-expression rescued HEK293-SDSURVL since only 25.6% cells were non-viable (Figure 8B; significance level P ≤ 0.05). Furthermore, Western blot analyses displayed an additional effect of cadmium, since increased immunodetection was observed of both, human survivin and its poriferan homologue after 6-12 h of challenge.

Concurrently, cell death detection ELISA could not reveal altered formation of mono-and oligonucleosomes, neither in mock-transfected nor HEK293-SDSURVL cells following Pam 3 Cys-Ser-(Lys) 4 -treatment, compared to untreated control cells (Figure 9A). In contrast, exposure to cadmium significantly induced apoptotic DNA fragmentation in both cell lines. However, SDSURVL-expression caused a reduced DNA fragmentation by 51% (Figure 9B; significance level P ≤ 0.05).

Discussion

Survivin represents a unique protein within the IAP family, containing a single BIR domain and a COOH-terminal coiled-coil structure. Thus, it differs from all other family members that comprise 2-3 BIR domains and a canonical COOH-terminal RING finger. [START_REF] Ambrosini | A novel anti-apoptosis gene, BIRC5, expressed in cancer and lymphoma[END_REF] In vertebrates, survivin not only controls cell cycle checkpoints and correct completion of cytokinesis but also regulates activation of caspases. [START_REF] Zangemeister-Wittke | An IAP in action: the multiple roles of survivin in differentiation, immunity and malignancy[END_REF] However, the evolutionary conservation of this dual role remains inconclusive since in Nematoda the survivin homologue appears to be implicated exclusively in cytokinesis. [START_REF] Li | Survivin study: What is the next wave?[END_REF] In addition, even though a fungal (S. pombe) survivin-like protein was proposed to be required for efficient cell division and exhibits anti-apoptotic activity, [START_REF] Uren | Role for yeast inhibitor of apoptosis (IAP)-like proteins in cell division[END_REF] it is considerably larger in size than other survivin proteins.

Key regulators of apoptosis and cell cycle progression have already been

discovered in the phylum Porifera, [START_REF] Wiens | Cell death in Porifera: molecular players in the game of apoptotic cell death in living fossils[END_REF] which appears to be the earliest-branching metazoan taxon. [START_REF] Philippe | Phylogenomics revives traditional views on deep animal relationships[END_REF] However, identification of poriferan survivin provides an important tool not only to investigate the primordial function(s) of this versatile regulator protein, but also to elucidate the putative ancient and intimate link between apoptosis, proliferation, and morphogenesis.

The newly discovered poriferan survivin homologue demonstrates a significant degree of sequence conservation. A consensus tree resulting of NJ, ML, and Bayesian analyses places SDSURVL at the base of a metazoan clade, confirming the common ancestry of metazoan survivin-like proteins, whereas viral sequences form a well-supported separate group. SDSURVL features all characteristics of survivins that facilitate proteininteraction and mediate both anti-apoptotic activity and proper kinetochore attachment. [START_REF] Johnson | Survivin as a cell cycle-related and antiapoptotic protein in granulosa cells[END_REF] However, it displays several additional putative domains that are known to be involved in cell cycle regulatory functions: (i) APCC-D box, required for ubiquitination and proteasome-mediated degradation via the anaphase-promoting ubiquitin ligase complex (APC/C); (ii) LIG_FHA1/2 motifs (aa 98-104 ; aa 100-106 ) that bind regulators of the G2/M checkpoint; (iii) USP7-binding motif (aa 33-37 ) that is a target of the UPS7 deubiquitinating enzyme, whose substrates are regulators of cell survival pathways. So far, no such feature has been described for other survivin molecules.

The gene structure (intron/exon positions, intron phases) is conserved between poriferan and vertebrate (human, murine) survivin, [START_REF] Li | Transcriptional analysis of human survivin gene expression[END_REF] but not within the D. melanogaster deterin, [START_REF] Jones | Deterin, a new inhibitor of apoptosis from Drosophila melanogaster[END_REF] which possesses only two introns. However, some differences to the vertebrate promoter region are noticeable: the TATA-less vertebrate promoter includes CpG islands and several Sp1, CDE, and CHR sites. By analogy with other cell cycle-regulated genes it was postulated that Sp1 regulates basal transcriptional expression of the survivin gene, further modulated by CDE/CHR elements, thus imparting cell cycle periodicity of survivin expression. [START_REF] Li | Transcriptional analysis of human survivin gene expression[END_REF] In contrast, the poriferan promoter features a canonical TATA box, two Sp1 sites, and is missing CDE/CHR elements. Therefore, considering the aforementioned protein interaction motifs, regulation of the poriferan survivin pool might also occur on a posttranscriptional/proteasomal level, e.g., via (de)ubiquitination or complex formation with other regulatory proteins.

Survivin is expressed in many human tumors and in rapidly dividing cells of fetal tissue during the G2/M phase, but is not detectable in terminally differentiated cells. It was proposed that survivin exerts its anti-apoptotic function via inhibiting caspases, particularly caspase-7 and -3, [START_REF] Shin | An anti-apoptotic protein human survivin is a direct inhibitor of caspase-3 and -7[END_REF] though other studies argue against a direct mechanism of caspase inhibition by survivin, see e.g., [START_REF] Banks | Survivin does not inhibit caspase-3 activity[END_REF] .

To approach the enigmatic role of survivin in non-bilaterian Metazoa, comparative functional studies of poriferan survivin were performed. Accordingly, the implementation of SDSURVL in cell proliferation and cell death was studied in sponges as well as in a human cell model (HEK-293; as SDSURVL-EGFP fusion). This strategy had already been successfully adopted to assess the anti-apoptotic potential of deterin and poriferan Bcl-2. [START_REF] Hawkins | Anti-apoptotic potential of insect cellular and viral IAPs in mammalian cells[END_REF][START_REF] Wiens | Sponge Bcl-2 homologous protein (BHP2-GC) confers distinct stress resistance to human HEK-293 cells[END_REF] Moreover, survivin-EGFP fusions have been widely used to study both the bifunctional role of survivin and its dynamic intracellular localization and appear fully functional. [START_REF] Delacour-Larose | Distinct dynamics of Aurora B and Survivin during mitosis[END_REF][START_REF] Colnaghi | Separating the anti-apoptotic and mitotic roles of survivin[END_REF][START_REF] Mahotka | Differential subcellular localization of functionally divergent survivin splice variants[END_REF] In contrast to sponge tissue that displays a very low steady-state expression level, proliferating cells of primmorphs contained a considerable amount of SDSURVL, indicative of its involvement in cell division. This assumption was supported by a significantly increased proliferation rate of HEK-293 cells expressing SDSURVL-EGFP. In Cytotoxicity mediated by cadmium is associated with the release of mitochondrial pro-apoptotic factors into the cytosol. [START_REF] Mao | Cadmium induces apoptosis in human embryonic kidney (HEK) 293 cells by caspasedependent and -independent pathways acting on mitochondria[END_REF] These factors cause activation of caspase-3/7, [START_REF] Ozben | Oxidative stress and apoptosis: impact on cancer therapy[END_REF] which is considered as a biochemical hallmark of the apoptotic cascade. However, survivin was proposed to interact with various caspases and, consequently, to inhibit further progression of the apoptotic cascade. The aforementioned observations in combination with significant similarities of sequence and structure between SDSURVL and its vertebrate counterparts propose a common regulatory function in the interconnected pathways of cell cycle and apoptosis. In HEK-293 cells, poriferan survivin possibly complements the endogenous molecule by sequestrating pro-apoptotic binding partners, consequently delaying cell death. In addition, the presence of putative target sites for ubiquitination might explain the elevated levels of human and poriferan survivin detected in challenged transfected cells. Accordingly, this effect is not the result of an induced expression but is indicative of a decreased proteasomal degradation of either protein. Therefore, the poriferan molecule replenishes the pool of endogenous survivin, concomitantly preventing degradation of its human counterpart by engaging/silencing elements of the proteasome. Furthermore, concurrent with previous observations during which elevated levels of survivin occur in most human tumor cell types and overexpression of survivin in transfected HEK-293 increases cell proliferation, [START_REF] Tamm | IAPfamily protein survivin inhibits caspase activity and apoptosis induced by Fas (CD95), Bax, caspases, and anticancer drugs[END_REF][START_REF] Torres | Caveolin-1 controls cell proliferation and cell death by suppressing expression of the inhibitor of apoptosis protein survivin[END_REF] SDSURVL-EGFP expression stimulates cell division. In addition, LMB treatment of SDSURVL-EGFP expressing HEK-293 cells causes significant nuclear accumulation of the fusion protein. This is consistent with previous studies in which CRM1 (chromosome region maintenance 1)-mediated, NES-dependent nuclear export of human survivin was blocked by LMB. [START_REF] Knauer | Survivin's dual role: an export's view[END_REF][START_REF] Heger | Qualitative highly divergent nuclear export signals can regulate export by the competition for transport cofactors in vivo[END_REF] Survivin is a subunit of the chromosomal passenger complex (CPC), consisting of Aurora-B, Borealin and INCENP, and is essential for proper chromosome segregation and cytokinesis. Since Crm1 is involved in tethering the CPC to the centromere by interacting with survivin's NES, [START_REF] Knauer | Survivin's dual role: an export's view[END_REF] SDSURVL might affect various stages of cell proliferation, from proper kinetochore attachment to spindle formation.

In conclusion, the identification of a bona fide poriferan survivin homologue

RNA extraction and Northern blotting analyses.

Total RNA was isolated through lysis of homogenized tissue/primmorphs using TRIzol Reagent (Invitrogen; Karlsruhe, Germany) as described earlier. [START_REF] Grebenjuk | Induction of (2'-5')oligoadenylate synthetase in the marine sponges Suberites domuncula and Geodia cydonium by the bacterial endotoxin lipopolysaccharide[END_REF] Subsequently, 5 µg of total RNA was size-separated and blotted on Hybond N1 membranes (Amersham; Freiburg, Germany). Labeled probes were generated with DIG-11-deoxyuridine triphosphate through the PCR-DIG-Probe Synthesis Kit (according to the manufacturer's instructions (Roche Applied Science; Mannheim, Germany)). Hybridization was performed with a S. domuncula survivin-like probe (SDSURVL, nt 1-357 ) or a caspaselike protease 2 probe (SDCASL2, nt 810-1480 ). Furthermore, a probe was designed to detect the expression of the housekeeping gene ß-tubulin (SDTUB, nt 83-483 ; NCBI accession number AJ550806) as internal reference. Hybridized probes were detected with anti-DIG Fab fragments (conjugated to alkaline phosphatase) and ultimately visualized by chemiluminescence, using the 1,2-dioxetane compound CDP Star (Roche) as substrate.

Cloning of S. domuncula survivin-like protein and caspase-like protease 2.

cDNA coding for the survivin-like protein SDSURVL was isolated from a S. domuncula cDNA library [START_REF] Wiens | Toll-like receptors are part of the innate immune defense system of sponges (Demospongiae: Porifera)[END_REF] by polymerase chain reaction (PCR) with degenerate primers that were designed against the conserved BIR domain (forward primer, 5'-GAT GGC IGA GGC T/CGG CTT-3'; reverse primmer, 5'-CT/CA TGT CCA GTT TT/CA AG/AA-3'; where I = inosine). PCR was carried out at an initial denaturation at 95°C for 5 min, followed by 35 amplification cycles at 95°C for 30 s, 54°C for 45 s, and 72°C for 1 min, and terminated with a final extension at 72°C for 10 min. Thus, a fragment of ≈ 209 bp was isolated and subsequently sequenced using an automatic DNA sequencer (Li-Cor 4300). Ultimately, the SDSURVL sequence was completed through primer walking. Subsequently, genomic sequences were extracted from a S. domuncula genomic DNA library. [START_REF] Seack | Identification of highly conserved genes: SNZ and SNO in the marine sponge Suberites domuncula: their gene structure and promoter activity in mammalian cells[END_REF] Similarly, SDCASL2 was isolated from the S. domuncula cDNA library: application of the degenerate forward primer 5'-T/CTI ATT/C/A CAA/G GCI TGT/C A/T/CG/TI GG-3' (directed against the caspase Cys active site L/F-I-D-A-C-R/L-G) Vector constructs, transfections, and leptomycin B treatment. The forward primer 5'-GAA TTC ATG GCT AAT ACG TAT GAG A-3' (EcoRI site is underlined) and the reverse primer 5'-GGA TCC CGA TGT TCG TGT CTT TTC A-3' (BamHI) were used to amplify the ORF of SDSURVL (nt 1-447 ), including M start and excluding the first stop codon. The cDNA was ligated in frame into the eukaryotic expression vector pEGFP-N2 (Clontech; Heidelberg, Germany). The recombinant protein, thus, contained a COOH-terminal EGFP-tag.

Human embryonic kidney (HEK-293; ATCC CRL-1573) cells were obtained from the American Type Culture Collection (LGC Promochem; Wesel, Germany) and grown in Dulbecco's Modified Eagle Medium (high glucose and L-glutamine; GIBCO; Karlsruhe, Germany) with 10% [v/v] fetal bovine serum. Cell counting was performed using a Neubauer chamber and trypan blue staining, three times for each sample.

On the eve of transfection, cells were seeded to allow for ≈ 60% confluency on the next day. The transfection technology was provided by IBA (Göttingen, Germany) and based on the combination of cationic liposomes and magnetic DNA-binding nanoparticles. Transfections were performed in 12-wells plates with 1.2 µg DNA per well. For constitutive expression, the cells were split 48 h post-transfection into selective medium, containing G418 at the appropriate concentration (Carl Roth; Karlsruhe, Germany; 200 µg/mL). SDSURVL expression was confirmed microscopically and immunologically, using anti-EGFP antibodies (Miltenyi Biotec GmbH; Bergisch Gladbach, Germany) and an AHBT3 light microscope with AH3-RFC reflected light fluorescence attachment (Olympus; Hamburg, Germany).

The nucleo-cytoplasmic trafficking of the SDSURVL fusion protein was analyzed through treatment of transfected cells with the nuclear export inhibitor leptomycin B (LMB), according to [START_REF] Heger | Qualitative highly divergent nuclear export signals can regulate export by the competition for transport cofactors in vivo[END_REF] . In short, 48 h post-transfection cells were treated with LMB (6 nM, 30 min), washed in phosphate buffered saline (PBS), and fixed in 4% [w/v] paraformaldehyde (PFA; 5 min, RT). Following additional washing of cells with PBS and subsequent counterstaining of nuclei with DRAQ5 (5 µM; Biostatus Ltd.; Shepshed, UK), images were acquired on a Zeiss 710 confocal LSM (Zeiss; Göttingen, Germany). The argon laser line of 488 nm was used to excite EGFP and a 633 nm HeNe laser was employed to excite DRAQ5. For illustration purposes, green and red channels were extracted from the merged pictures with the ZEN software (Zeiss).

In situ localization studies. The in situ hybridization method applied was based on a procedure described by [START_REF] Wiens | Toll-like receptors are part of the innate immune defense system of sponges (Demospongiae: Porifera)[END_REF] . In short, frozen sections (8 µm) of primmorph samples were fixed in 4% paraformaldehyde, then incubated with proteinase K (1 µg/mL), and fixed again in paraformaldehyde. Hybridization was performed with single-stranded SDSURVL DNA probes, overnight at 45°C in 2x SSC (sodium chloride/sodium citrate, supplemented with 50% formamide). The probes (236 nt each) were generate during two separate PCR reactions, either with forward primer 5'-TAT GAG AGT TGT GAC AGA GTA AA-3' (sense/control probe) or with reverse primer 5'-TGT TCA TCA CGG GGA TTG TCT GA-3' (antisense probe), using SDSURVL cDNA as template.

During the linear amplification the probes were labeled with the DIG-Probe Synthesis Kit. In a similar way SDCASL2 probes (292 nt) were obtained, using forward primer However, expression of SDSURVL-EGFP significantly reduced this effect compared to mock-transfected controls (P ≤ 0.05).

  Considerably lower was the similarity to survivin homologues of more basal Metazoa and fungi, e.g., D. melanogaster deterin (7 e-11; 45%/24%), placozoan (Trichoplax adhaerens) survivin-like protein (2 e-11; 19%/35%), and Schizosaccharomyces pombe survivin homolog (5 e-11; 34%/48% with respect to the BIR domain [aa 117-194 ]). Viral genomes often encode anti-apoptotic or cell cycle promoting proteins in order to bypass the host defense system. Viral survivin-like proteins generally display lower sequence homology to their metazoan counterparts and in particular to S. domuncula survivin, e.g., Epiphyas postvittana nucleopolyhedrovirus IAP-3 (4 e-07; 17% aa similarities) and Cydia pomonella granulosis virus ORF17 IAP-3 (3 e-06; 16%). Since SDSURVL features a single BIR domain, a putative nuclear export signal (NES), and a coiled-coil region, but is missing a RING domain (characteristic of non-survivin IAP), the molecule represents a novel member of the survivin subfamily of IAP. The poriferan BIR domain (aa 20-93 ) has an E value of 4.85 e-22 and is consistent with the PROSITE consensus sequence PS01282 with two exceptions (marked in bold):

  In parallel, a construct encoding a fusion of SDSURVL and COOH-terminal EGFP (SDSURVL-EGFP) was introduced into HEK-293 cells. Expression was confirmed microscopically (Figure5B) and by immunodetection, using anti-EGFP antibodies (Figure8; expected size of 45 kD corresponding to the SDSURVL-EGFP fusion). The transfection efficiency was determined by the percentage of EGFPpositive HEK-293 cells via fluorescence microscopy. An average transfection efficiency of 94.6% was calculated.Subsequently, the putative regulatory function of SDSURVL in cytokinesis was explored by monitoring the concentrations of SDSURVL-EGFP transfected (HEK293-SDSURVL) and mock-transfected HEK-293 cells, 48 h post-transfection (Figure5A).Whereas mock-transfection provoked no differences in cell concentration compared to the non-transfected control, SDSURVL-EGFP expression caused a 2.3-fold increase in number of HEK293-SDSURVL cells (significance level P ≤ 0.05). In addition, trypan blue staining did not display altered cell viability between control andSDSURVL-EGFP expressing populations.Finally, subcellular localization and nucleo-cytoplasmic trafficking of SDSURVL-EGFP in transfected cells was analyzed by confocal laser scanning microscopy (LSM). For this purpose, HEK293-SDSURVL cells were incubated with the nuclear export inhibitor leptomycin B (LMB) for 30 min or remained untreated (control). Following counterstaining of dsDNA with the far-red fluorescent dye DRAQ5, LSM showed that in the controls SDSURVL-EGFP predominantly localizes in the cytoplasm and, to a lesser degree in the nucleus (Figure6A). Consequently, the fluorescence pattern of the merge revealed a barely detectable co-localization of fusion (green) and nuclei (red). Upon LMB treatment, SDSURVL-EGFP accumulated in the nucleus. The merged channel clearly depicted a significant co-localization of nuclei and SDSURVL-EGFP (Figure6B; yellow), whereas the staining intensity of cytoplasmic SDSURVL-EGFP is decreased, compared to the untreated control.

  addition, application of two inducers of cell death (cadmium, Pam 3 Cys-Ser-(Lys) 4 ) indicated an anti-apoptotic function of SDSURVL since both compounds triggered the expression of SDSURVL in sponge tissue and primmorphs, possibly a countermeasure to the induced expression of the caspase-7 homologue SDCASL2. Concurrently, cadmium induced apoptosis in HEK-293 cells, although concomitant expression of SDSURVL-EGFP drastically reduced the occurrence of cell death (≈ 50%). However, Pam 3 Cys-Ser-(Lys) 4 incubation did not alter HEK-293 cell viability, probably due to the different repertoire of Toll-like receptors (mediating lipopeptide-induced innate immunity and apoptosis) found within HEK-293 and sponge cells. Future studies will investigate the effect of further inducers of cell death on sponge and HEK293-SDSURVL cells.

  represents a compelling opportunity to investigate the deep evolutionary original function(s) of survivin, while concurrently avoiding limitations of classical invertebrate model organisms. The data presented propose an evolutionary ancient dual regulatory role of survivin, implemented in cell cycle control and programmed cell death. Materials and Methods Preparation of S. domuncula tissue and primmorphs. Specimens of the marine sponge Suberites domuncula (Porifera, Demospongiae, Hadromerida) were collected near Rovinj (Croatia) in the northern Adriatic Sea and then kept in aquaria in Mainz (Germany) at 17°C. Primmorphs were obtained after tissue dissociation into single cells as described earlier. 37 They were cultured in natural sterile-filtered seawater (Sigma-Aldrich; Taufkirchen, Germany), supplemented with 0.2% [v/v] RPMI-1640 medium (Biochrom AG; Berlin, Germany) and 60 μM silicate. Cultures were shaken for 5 d to assist the formation of aggregates, consisting of proliferating cells. Then, two sets of experiments were performed. For the first set, primmorphs were exposed to cadmium (CdCl 2 , Sigma-Aldrich; 50 µM) or the synthetic lipopeptide (S)-[2,3-Bis(palmitoyloxy)-(2-RS)-propyl]-N-palmitoyl-(R)-Cys-(S)-Ser-(S)-Lys 4 -OH•3HCl (Pam 3 Cys-Ser-(Lys) 4 ; Axxora; Grünberg, Germany; 40 µg/mL) for 6 and 9 h, or they remained untreated as a control. For the second set of experiments, samples were taken 0 (control), 7, 14, and 21 d after primmorph formation. All experiments were performed under mild shaking. In a similar way, S. domuncula tissue was exposed to cadmium (50 µM) or Pam 3 Cys-Ser-(Lys) 4 (40 µg/mL) for up to 12 h.

5 'Figure 1

 51 Figure 1 Suberites domuncula survivin-like protein SDSURVL. (A) The deduced

Figure 2

 2 Figure 2 Suberites domuncula survivin-like gene. (A) Exon/intron architecture. Each

Figure 3

 3 Figure 3 Suberites domuncula caspase-like protease 2 SDCASL2. The CASc

Figure 4

 4 Figure 4 Expression of SDSURVL and SDCASL2 in Suberites domuncula

Figure 5 SDSURVL

 5 Figure 5 SDSURVL expression in HEK-293 cells. (A) Effect of SDSURVL expression

Figure 6

 6 Figure 6 Subcellular localization of SDSURVL-EGFP in transfected HEK-293 cells.

Figure 7

 7 Figure 7Gene expression analyses of challenged sponge tissue and primmorphs.

Figure 8

 8 Figure 8 SDSURVL-EGFP mediated effects on Pam 3 Cys-Ser-(Lys) 4 -and cadmium challenged HEK-293 cells. Viability (MTT assays, left; bars represent mean ± SD of

Figure 9

 9 Figure 9 Susceptibility of HEK-293 cells to Pam 3 Cys-Ser-(Lys) 4 -and cadmium. Following exposure to Pam 3 Cys-Ser-(Lys) 4 (PAM; A) or cadmium (B), apoptotic fragmentation of genomic DNA was assessed immunologically in HEK293-SDSURVL

Acknowledgements We thank F Natalio for excellent technical assistance and CU Pietrzik for access to LSM. This work was supported by grants from the Deutsche Forschungsgemeinschaft (WI 2116/2-2) and the European Commission's Marie Curie Research Training Network "BIOCAPITAL".

combined with a library-specific primer resulted in a fragment of ≈ 1,200 bp. PCR was carried out at an initial denaturation at 95°C for 5 min, followed by 35 amplification cycles at 95°C for 30 s, 54°C for 45 s, and 72°C for 2 min, and terminated with a final extension at 72°C for 10 min. The complete cDNA was isolated by using a combination of sequence-and library-specific primers.

Sequence analyses.