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Computer Science
Medicine

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Developmental Emergence of Sparse Coding: A Dynamic Systems Approach

During neocortical development, network activity undergoes a dramatic transition from largely synchronized, so-called cluster activity, to a relatively sparse pattern around the time of eye-opening in rodents. Biophysical mechanisms underlying this sparsification phenomenon remain poorly understood. Here, we present a dynamic systems modeling study of a developing neural network that provides the first mechanistic insights into sparsification. We find that the rest state of immature networks is strongly affected by the dynamics of a transient, unstable state hidden in their firing activities, allowing these networks to either be silent or generate large cluster activity. We address how, and which, specific developmental changes in neuronal and synaptic parameters drive sparsification. We also reveal how these changes refine the information processing capabilities of an in vivo developing network, mainly by showing a developmental reduction in the instability of network’s firing activity, an effective availability of inhibition-stabilized states, and an emergence of spontaneous attractors and state transition mechanisms. Furthermore, we demonstrate the key role of GABAergic transmission and depressing glutamatergic synapses in governing the spatiotemporal evolution of cluster activity. These results, by providing a strong link between experimental observations and model behavior, suggest how adult sparse coding networks may emerge developmentally.

Results
To study developmental changes in postnatal neuronal activity, we used simulations of a spatially localized recurrent neural network with short-term synaptic plasticity (STP-RNN) 13 , where STP renders synaptic efficacies dynamic over time 14,15 ; see Methods. This network is a mean-field Wilson-Cowan-type model of one excitatory (E) and one inhibitory (I) neuronal population with recurrent dynamic synaptic connections (Fig. 1a). This model has the advantage of being biophysically interpretable and mathematically accessible. The activity rates (E r and I r ) can be properly scaled to represent locally the average recorded activities in the populations. Here, we use experimentally reported, postnatal changes in neurobiologically plausible parameters (Table 1) to assess how the network's spontaneous behavior and sensory processing properties are refined toward the postnatal onset of sensory transduction. To study the developmental states of visual cortical networks from before to after eye-opening, we selected four postnatal days (P) for modelling: P3 (period of physiological blindness), P10 (a few days before eye-opening), P14 (the day after eye-opening) and P20 (a few days after eye-opening) 3 . During this period, immature networks not only undergo the sparsification process but also a dramatic development in intrinsic neuronal and STP properties 10,12 ; see Table 1 and Supplementary Methods. To derive the results, we used system dynamics methods described in the Methods section (see also Supplementary Methods). For quick reference, all important technical terms are explained in Table 2.

Transient unstable state hidden in firing activities.
We first address what mechanisms may underlie the emergence of cluster activity in immature networks, by analyzing model behavior at P3 as a representative early postnatal stage.
Previous experimental 16,17 and modeling studies 18,19 usually considered adult networks to be bi-or multi-stable where, e.g., spontaneous (stimulus-absent) network activity can transition between two stable activity states: A relatively quiet activity state, and a higher activity state. For early postnatal stages, our stability analysis and simulations indicate that the proposed developing network model is actually mono-stable whose only stable state is a quiescent, spontaneous state (Fig. 1b). To demonstrate this, we plotted in Fig. 1b the so-called phase plane of network dynamics in terms of the average activity in the E-and I-populations (E r -I r -plane; Table 2). Figure 1b shows that the network's fixed point (FP; Table 2) is located at E r = I r = 0 which is essentially a stable activity state of the network (see Supplementary Methods). In the following, we will call this specific FP the rest state of the network. This analysis result fits well with experimental findings of near-zero hertz spontaneous activity of immature populations 3,20 , and the absence of spontaneous network's persistent activity states mainly during periods of physiological blindness 7,21 .
How can a quiescent, immature, mono-stable network generate large cluster activity? By analyzing network perturbations, we found strikingly that while a P3 network model was relaxed at its stable rest state, threshold-crossing perturbations of E-population were amplified profoundly (Fig. 1c, Supplementary Fig. 1c). This amplification resulted in population spikes (PSs , Table 2), which, in the model, we assume is the analogue to experimentally observed cluster activity. Note that this perturbation can be due to, e.g., a single-shock electrical stimulation 22 , the onset of a longer-lasting external input ( Supplementary Fig. 2c), or a random input driven by spontaneous retinal waves or the thalamus 3,20 .
While previous studies showed the underlying mechanism of PSs generation mainly in adult bi-stable network models 18,19 , here we sought to address this mechanism in an immature mono-stable network (Fig. 2). As the initial phase of PSs is known to be governed by the network's fast (i.e. firing activity) dynamics 18 , we re-plotted the E r -I r -plane after freezing the network's slow (i.e. STP) dynamics at the rest state (Fig. 2d); thus, it is turned to a Static-RNN (Table 2) with frozen synaptic efficacies (Frozen STP-RNN). Surprisingly, we found that in addition to the stable rest state, there is an unstable FP hidden in the network's fast dynamics (i.e. in the respective Frozen STP-RNN, Fig. 2e), which does not exist in the (non-frozen) STP-RNN (Fig. 1b). This unstable FP (black dot, Fig. 2e), which is located close to the rest state, can confine the attraction domain (grey region, Table 2) of the stable rest state in the E r -I r -plane of the corresponding Frozen STP-RNN (Fig. 2d); but, clearly, not in the E r -I r -plane of the STP-RNN itself (Fig. 2c). We call the outer border of this domain in Fig. 2d (before perturbation) the amplification-threshold. Almost exactly the same border exists for activity-perturbation domains of the STP-RNN (Fig. 2c). Cluster activity is initiated by an activity perturbation exceeding this threshold, where the activity will be moved far from the rest state in both corresponding networks with frozen (Frozen STP-RNN) or dynamic (STP-RNN) synapses. Strikingly, when freezing synaptic efficacies at different times during a cluster activity (Fig. 2a), we can see that this unstable FP disappears roughly after the peak of cluster activity (Fig. 2e), thereby allowing network activity to converge back to its stable rest state in the STP-RNN (Fig. 2a). This disappearance is mainly because of the weakening of the excitatory synapses at high activity rates (Fig. 2b); these synapses provide positive feedback to the network. Importantly, as it can be seen in Fig. 2e  network is again at the origin after cluster activity, sufficient recovery time is required for the re-emergence of the unstable FP.
Our analyses further show that the experimentally observed variability in size (i.e. the number of network neurons recruited by cluster activity) and duration of spontaneous cluster activities at each postnatal day 2,3 is effectively governed by the relatively long recovery time of immature excitatory synapses ( Supplementary Fig. 1). In addition, in our model, cluster activity was generated only if the perturbation strength was sufficiently large (Supplementary Fig. 1c and d) and was able to push network activity beyond the amplification-threshold ( Fig. 2c  and d). This is consistent with experimental reports of immature networks 7,23 , and may underlie a relatively all-or-none characteristic of postnatal cluster activity.  Table 2  The route towards sensory processing. The sparsification of activity patterns sets in around eye-opening 3,7 . We now use our model at the stages P10 to P20 to address the question what refinements of the developing network enable sparsification. Firstly, similarly as for P3, at all later stages up to P20 in the model we found that for a developing network relaxed at its stable rest state (Fig. 3a), again an unstable FP is hidden in the network's fast (i.e. firing activity) dynamics (Fig. 3b). Consequently, a threshold-crossing perturbation of the E-population at rest state led to cluster activity (Fig. 3c). In addition, our analysis revealed that the amplification-threshold tends to move toward higher E-activity rates, from P3 to P20 (Fig. 3b, Supplementary Fig. 3). This result suggests that a stronger input may be required to trigger (large) cluster activities at late development stages.
Experimental in vivo findings show that the cluster activity size starts to drop dramatically around the end of the second postnatal week 2,3 . Here, we consider this developmental decrease in cluster activity size as an indicator for the developmental transitioning from dense to sparse coding (sparsification process); similarly to experimental studies 2,3 . In our model, we approximate the cluster activity size by a measure of the average activity of both E-and I-populations (PS net amp ; see Table 2 and Methods). Using this measure, we observed, similarly as in experiments, that cluster activity size undergoes developmental reduction between P10 to P20 ( Fig. 3c and  ). Secondly, the results also show that immature networks probably lack any spontaneous, non-quiescent stable FP (i.e. a spontaneous attractor or persistent activity state) up to a few days prior to eye-opening. In Fig. 3a this is indicated by the existence of only one green dot at P3 and P10. However, after eye-opening, new spontaneous FPs with higher activity rates than the rest state emerged (note the non-origin FPs at P14 and P20 in Fig. 3a). This is compatible with experimental data showing the existence of spontaneous persistent cortical activity for stages mostly after the second postnatal week 3,21,24 and their absence during early development 7,21 . In our model, in the absence of a stimulus, these FPs emerge mainly because of the developmental increase and decrease in background activity 2,3,7 and membrane resistance 2,10 , respectively. Their combined effects determine the increase in population activity thresholds θ E and θ I (see also Supplementary Fig. 4). While in the model the network's FP domains (FP-domains; Table 2) are not affected by these thresholds (see "Characterization of operating regimes" section in Supplementary Methods and also Supplementary equation (17)), the developmental reduction in the network instability (yellow regions, Fig. 3a) allows for these FPs to be stable. This means that attractors start emerging, under a wide range of E-activity; note the non-origin green dots at P14 and P20 in Fig. 3a.
Thirdly, the emergence of spontaneous attractors not only turns the developing mono-stable network (at P3 and P10) into a bi-stable (at P14 and P20) network (Fig. 3a), but also has a striking effect on the dynamical trajectory of cluster activity. That is, in the model, we found a fundamental difference between spontaneous postnatal cluster activities and those in more mature networks (Fig. 3c): before eye-opening (P3 and P10), the cluster activity is of the so-called mono-stable type (see Supplementary Methods), while after eye-opening the cluster activity can be also of the bi-stable type. The difference is that a mono-stable cluster activity (P3 and P10 in Fig. 3c) is initiated and terminated at the stable rest state, while a so-called bi-stable cluster activity is initiated at the stable rest state but converges to a spontaneous attractor (P14 and P20 in Fig. 3c  interpreted as a first expression of information processing where the newly emerged attractors, which a bi-stable cluster activity converges to, can be seen as representative states that are the basis of perception. In the model, the time required for an effective transition to the attractor, and thus an effective representation of perceptual stimuli, is probably equal to the duration of cluster activity (see dashed grey lines, Fig. 3c): ~240 ms at P14 and ~180 ms at P20; these transition times are consistent with experimental observations at these stages 17,24 .
Moreover, our simulations showed that the spatiotemporal characteristics (i.e. size and duration) of mono-stable cluster activities are in general considerably more robust to interfering perturbations (P3 and P10, Supplementary Fig. 5), as compared to the bi-stable ones (P14 and P20, Supplementary Fig. 5). This suggests that the trajectories underlying mono-stable cluster activities (P3 and P10) can be seen as signals with high signal-to-noise ratio, which may act as a reliable neuronal communication mechanism in neonatal networks. The decreased robustness of bi-stable cluster activities after eye-opening (P14 and P20) can in principle augment the network's information processing capability by making it more flexible in responding to sensory input.
In addition, we found that at the new spontaneous attractors (non-origin green dots; Fig. 3a) the network operates as a so-called inhibition-stabilized network (ISN, light-yellow regions, see also Table 2) 25, 26 . An ISN regime is thought to allow the cortex to process complex computations 27 , and some experimental evidence indicates that adult cortical networks operate as ISNs 25,28 . Our results show that the ISN regime becomes effectively accessible after eye-opening, as the unstable FP-domain, which is confined to low E-activity ranges, is developmentally substituted by an ISN FP-domain. This indicates a developmental increase in the ratio of areas of the ISN (AOD ISN ) and unstable FP-domains (AOD Unstable ), which we quantified by AOD ISN/Unstable (Table 2) in Fig. 3e. In practice, this means that in parallel to the strengthening of the sensory inputs after eye-opening, a larger set of potential stimulus-evoked ISN attractors will be accessible for the network, presumably to perform complex sensory computations. Overall in our model, the process of sparsification is translated not only to a potent reduction of postnatal network instability but also to a potential emergence of new attractors and information processing capabilities.
Spatiotemporal evolution of postnatal cluster activity. What mechanisms govern the spatiotemporal evolution (i.e., size and duration) of cluster activity during development in vivo? We addressed this question by analyzing how a postnatal cluster activity, once on a trajectory away from the rest state, is brought back to either the rest state or another FP with an activity level higher than the rest state.
In our model, there are two important factors: (i) the inhibitory transmission, which is believed to play a critical role in the stabilization of adult networks 25, 28 , and (ii) STP, which can dynamically control the gain of neuronal responses 14 and acts strongly depressing in immature networks 10,12 .
Firstly, we found that cluster activity initiated at the rest state can still converge back to this state even when blocking GABAergic receptors, at all stages from P3 to P20 (Fig. 4a). This finding suggests that inhibitory E r -I r -plane A 2D phase plane of E-and I-population activity rates (I r vs. E r ) ‡ .

FP
A fixed point (FP) is the steady state of a network, and is determined as the intersection of (quasi) E rand I r -nullclines in the E r -I r -plane ‡ .
Spontaneous activity Network activity in the absence of stimulus (or sensory input)*.
Attractor A non-quiescent stable FP (in this paper); also known as memory or persistent activity state.
Attraction domain For a stable FP (in this paper), a region in phase plane comprising all initial conditions that lead to that FP.

Amplification-threshold
The outer border of the attraction domain of the rest state in the Frozen STP-RNN, beyond which network perturbations undergo an overall continuous growing (in the Frozen STP-RNN) or will effectively trigger cluster activity (in the STP-RNN).

Nullcline
For example, an excitatory nullcline is a set of points (a curve) in E r -I r -plane for which

FP-domain
For example, an unstable FP-domain is a domain of all potential FPs in the E r -I r -plane, at which the network will be unstable ¶ . The scaled amplitude of PS net , which provides a qualitative approximation to the cluster activity size # . processing may not be necessary for stability in the developing network with depressing excitatory synapses. However, blockage of GABAergic receptors yielded an increase in cluster activity size ( Fig. 4a and c), consistent with in vivo reports 20,29 . This blockage effect on cluster activity size became more pronounced during the course of development (Fig. 4c), in spite of the developmental reduction in the network instability (e.g. see AOD ISN/Unstable in Fig. 3e). This finding may underlie a developmental enhancement of an effective contribution of inhibitory transmission to network activity (see also Supplementary Fig. 2d showing the ability of inhibitory inputs to turn off activity at the attractor 22 , after eye-opening). In addition, we found that the blockage of GABAergic receptors shortened the cluster activity duration, for example, from approximately 330 to 320 ms at P3 (Fig. 4a and b) and from approximately 265 to 210 ms at P10 (Fig. 4a). The range of these cluster activity durations in our model is compatible with previous experimental reports 7,30 . Moreover, we found that this blockage effect on cluster activity duration increased from P3 to P20 (Fig. 4d). Secondly, in contrast to GABAergic transmission, we found that at P3 the simulated removal of the STP effect led to run-away excitation (Fig. 4e). After freezing the synaptic efficacies (see Supplementary Methods) at the stable rest state, a threshold-crossing perturbation of the E-population (similarly as in Fig. 1c) caused a seizure-like surge of network activity (Fig. 4e). Importantly, this means that the rest state in the corresponding Frozen STP-RNN is a locally, but not globally, stable FP (see Methods). Similar results were observed for P10 to P20 (data not shown). Accordingly, we conclude that the strongly depressing immature STP, but not inhibitory synaptic transmission, guarantees the stabilization of postnatal cluster activities during the course of development. This finding also holds, when GABAergic transmission is considered as excitatory at the network level during the first postnatal week (see Supplementary Methods and Supplementary Fig. 6b). Under this assumption, blocking GABAergic receptors led to a decrease in cluster activity size, which is not compatible with in vivo data 20,29 . Therefore, our modeling results (Fig. 4b) are in agreement with recent in vivo findings that while GABA  r max r max ) at [10,10] Hz. Moreover, the developmental decrease in − AOD Non ISN just means that the E-activity rate after which the Non-ISN FPdomain starts in the E r -I r -plane, was shifted to higher levels. See Table 2 for details about technical terms.
SCiEntifiC REPORTS | 7: 13015 | DOI:10.1038/s41598-017-13468-z acts as a mainly depolarizing transmitter during the first postnatal week, it exerts an inhibitory effect on the underlying network activity in the immature cortex and hippocampus 20,31 .
Moreover, cluster activity was abolished by simulated blockage of glutamatergic receptors (data not shown), consistent with experimental in vivo data 20,29 . This is because the main source of the network instability in our model is determined by glutamatergic synapses which provide positive feedback to the network. Therefore, blocking glutamatergic receptors removed all unstable FPs as well as the attractors in Fig. 3a and forced spontaneous network activity to the rest state (as observed experimentally 17,22 ), and removed the unstable FP hidden in the network's fast dynamics in Fig. 3b, throughout P3 to P20 (data not shown). In addition, note that the GABAergic transmission can play an important modulatory effect on this instability (see Supplementary Methods), where an increase in the efficacies of inhibitory synapses can effectively attenuate the instability (not shown). This implies that relatively weak GABAergic inhibition (Table 1) is permissive for the generation of the unstable states (e.g. see Fig. 3a and b) in developing networks; note that in our model these synapses strengthen during development (Table 1).
In sum, we found that strongly depressing excitatory synapses have a key role in the termination of postnatal cluster activities, whereas GABAergic transmission mainly regulates their spatiotemporal evolution.
Key maturational processes mediating sparsification. The most influential, maturational processes mediating sparsification are still not clearly understood 2,3 . By using our modelling approach, we mechanistically quantified the impact of different network parameters on the emergence of sparse coding.
To do this, we replaced single parameters or parameter combinations of the P10 network model by their respective values at P20 and measured to what extent this modification can account for the normal decrease in PS net amp and normal increase in AOD ISN/Unstable , when transitioning from P10 to P20. To quantify effects we computed ratio PS and ratio AOD as the ratios of the modification-induced changes in PS net amp and AOD ISN/Unstable , relative to their respective normal developmental changes during this period (Fig. 5). We found that only three parameters caused a substantial decrease in PS net amp (Fig. 5a): the two absolute synaptic efficacies (J E and J I ) caused the largest decrease close to that in the normal transition and, to a minor degree, the excitatory release probability U E . Strikingly, for the excitatory synaptic time constant (τ E ) the modification led to an increase in PS net amp , i.e. when this parameter is changed on its own, it tended to reverse sparsification (see also Supplementary Figs 6c and 7). We obtained qualitatively similar conclusions when analyzing the effect of replacing single parameters or parameter combinations of the P20 network model by their values at P10 (Supplementary Fig. 8a). When testing for changes in AOD ISN/Unstable , we found that the inhibitory synaptic depression time constant τ r I had the strongest contribution to the normal transition from P10 to P20, whereas all other single parameter substitutions had relatively weak effects (Fig. 5b). Note that the change in τ r I appears to be a necessary but not a sufficient condition for the normal transition from P10 to P20, as substituting J E and J I in the P20 model by their respective P10 values virtually abolished the developmental change in AOD ISN/Unstable (Supplementary Fig. 8b). Figure 5 further shows that the modification effect of the population activity thresholds (θ E and θ I ) was virtually negligible for ratio PS and zero for ratio AOD . However, as shown above, their maturation plays an important role for the emergence of spontaneous attractors (see also Supplementary Fig. 4).

Discussion
We modelled the in vivo activity of a developing cortical network during the first postnatal month by combining an extended Wilson-Cowan model with experimentally reported trajectories of neuronal and synaptic parameters. We revealed mechanistically that a particular combination of a transient, hidden unstable state in firing activities and strong synaptic depression enables an immature network to generate large cluster activity while otherwise being mostly silent. We further found that the normal developmental transition from dense to sparse coding is strongly dependent on an elaborate, parallel refinement of absolute synaptic efficacies, both short-term synaptic plasticity (STP) and intrinsic membrane properties, and background activity. Strikingly, in our model, sparsification translates not only to a reduction of postnatal instability of network activity but also to an effective availability of the inhibition-stabilized network (ISN) regime as well as the emergence of spontaneous attractors, providing a novel mechanistic explanation for how the network's information processing is refined towards eye-opening.
How can immature networks be quiescent for relatively long periods and occasionally generate large cluster activity 3,32 ? Surprisingly, we found that while the developing networks (P3 to P20) are operating at their rest state (Fig. 3a), an unstable state is formed in their fast (i.e. firing activity) dynamics (Figs 2e and 3b). This may be a key to this biphasic behavior ( Fig. 3c and Supplementary Fig. 2c). In addition, we found that at early stages prior to eye-opening (P3 and P10) immature networks are mono-stable, where the only FP of the network is its stable rest state (Fig. 3a). Accordingly, the underlying mechanism of cluster activity emergence in these networks is in stark contrast to models of cluster activity proposed for adult networks, based on the usual assumption of a bi-stable (or multi-stable) network 18,19 . Our finding about the strong effect of the hidden unstable state (Fig. 3b) during the initial phase of network activity at the rest state (see Figs 2 and 3c) may provide an explanation why neonatal networks, e.g. at P3 and P10, are more susceptible to seizures than mature networks 32,33 . Moreover, the lack of any spontaneous attractor in these networks (Fig. 3a) might also contribute to this susceptibility, since the attractors can, in principle, aid in stabilizing a network's activity.
Sparsification coincides with the peak of GABAergic and glutamatergic synaptogenesis 11,12 and overlaps with a developmental reduction in release probabilities, particularly at glutamatergic synapses 10,12 . Using a computational study, we here revealed how these changes are suited to drive the transition from dense to sparse coding during network maturation (e.g. see Figs 3 and 5). Since sensory deprivation affects the total synapse numbers only modestly 34 , our results might also explain previous experimental findings: While sensory experience may have a modulatory effect on sparsification during the first days after eye-opening 3 , this process is largely mediated internally, i.e. independent of sensory inputs 2,3,7 .
How does the sparsification process prepare in vivo developing networks for effective sensory processing? While the answer to this question remains poorly understood by experimental studies, our computational study provides two mechanistic insights: Firstly, we found that Non-ISN and instability may play key roles as the dominating operating regimes prior to eye-opening (Fig. 3a), possibly reflecting an immaturity of inhibitory transmission. That is, our modeling results imply that during stimulation (e.g. in response to long-lasting external inputs) the immature networks will operate under a Non-ISN regime, rather than an ISN regime (P3 and P10; Supplementary Fig. 2b), due to the lack of an effective availability of the ISN regime during early development (P3 and P10; Fig. 3a). The Non-ISN regime will enable the immature networks to maintain their stability without requiring inhibitory transmission effects (see Methods). For adult networks, however, some experimental evidence indicates that cortical networks operate as ISNs 25,28 . Strikingly, in the model, during sparsification, the unstable FP-domain is effectively replaced by that of an ISN regime (Fig. 3a and e). This may enable cortical networks to process complex sensory computations 27 in parallel to the developmental strengthening of sensory inputs.
Secondly, we found that spontaneous attractors start emerging around eye-opening, which, in our model, is mainly due to the combined effect of developmental increase in background activity 2,3,7 and the developmental decrease in membrane resistance 2,10 . In general, attractors can represent the solution to a specific neural computation 35 . They are also thought to be the substrate of, and used to model, e.g., the working memory 36 , eye position stability 37 , and orientation selectivity 38 . Besides, in our developing model, the attractor emergence phenomenon can also underlie three developmental mechanisms. First, it provides the basis for transitioning between FPs (here, via bi-stable cluster activity at P14 and P20; Fig. 3c) and may be thought of as an early step towards representation of perceptual stimuli. Second, it enables inhibitory transmission to contribute effectively more to network activity, e.g. through providing a sufficiently strong balancing of the intrinsic instability of the E-activity under an ISN regime 26,27 (P14 and P20; Fig. 3c), or by becoming amenable to terminate activity at the attractor 22 ( Supplementary Fig. 2d). This may be important for a presumably more effective processing of sensory inputs. Third, this phenomenon may also initiate the effective interaction of spontaneous activity with sensory cortical responses, as observed experimentally 17 .
Evidence from in vitro studies suggests that GABA depolarizes neonatal neurons, e.g. in rodents during the first postnatal week 20,32 . However, the effect of GABA at the network level is still an open question. While most of the previous in vitro studies reported that GABA is excitatory (or both excitatory and inhibitory) at the network level 32 , recent in vivo studies harnessing new experimental techniques found that GABA inhibits intact neonatal networks in neocortex and hippocampus 20,29,31 . In particular, GABAergic transmission was shown to limit the spatial extent of cluster activity 20 . In our model, we tested these two hypotheses by blocking the GABAergic receptors in two P3 networks with either excitatory (Supplementary Fig. 6b) or inhibitory GABAergic transmission (Fig. 4b). This simulated manipulation led to an increase in the cluster activity size only when GABAergic transmission was inhibitory at the network level. Therefore, our results support the recent in vivo findings 31,33 . Importantly, this finding implies that even when GABA depolarizes immature neurons, GABA still can attenuate the instability effect on network activity, thereby restricting cluster activity size.
To our knowledge, there are only few previous modelling studies covering this developmental period (e.g. see refs 39,40 ), where the authors' main focus was on the potential mechanism of the cluster activity generation under in vitro condition. These studies were focused on a single postnatal stage and this stage was mostly restricted to the first postnatal week, like P5. Moreover, these authors probably did not consider some important biophysical considerations which have only recently been revealed by in vivo studies: the abolishment of cluster activity following the removal of glutamatergic synapses 20,29 , the existence of a profound inhibitory effect of GABAergic transmission at the network level 20,29 and its non-excitatory (but depolarizing) effect at the neuron level 20,31 during the first postnatal week, or that the loss of large cluster activity occurs around eye-opening 2,3 and not around P7 41 . In contrast, here, (i) we used a unified computational model which takes into account most of these recently reported biophysical considerations (see Supplementary Method and Table 1), (ii) our work shows results based on, and mainly in accordance to the recent in vivo studies, where these studies revealed some critical features different from those reported for in vitro data, (iii) we studied the whole developmental period, comprising the time course of sparsification, and not only a single postnatal stage, (iv) we not only address the possible underlying mechanism of the postnatal cluster activity generation, but also the developmental changes in the operating regimes and information processing capabilities, as well as the possible refinements in network properties governing the sparsification.
There are advantages and limitations of our modeling approach. Clearly, as compared to a mean-field model as employed here, a spiking network model can in principle provide more detailed results, e.g. one could model the temporal sparseness of spiking activities, or the specific patterns of sensory inputs at the neuron level. However, for this early developmental period, we are not aware of any established neuron model. For such a model, a considerable amount of unspecified neurobiological parameters is required to be measured experimentally first. Instead, using a mean-field model enabled us to forgo such detailed parameterizations by considering only their average characteristics over the network. Although this procedure comes at the price of removing biological details, this enabled us to use an extended Wilson-Cowan-type model 13 ; a well-established model and extensively used before to study adult networks behavior 18,42 . This model enabled us to incorporate the available, experimentally reported developmental trajectories of intrinsic neuronal and synaptic parameters (see Supplementary  Method and Table 1), including short-term synaptic plasticity. In addition to its biophysical interpretability, this model is in general mathematically tractable which allowed us to derive analytical expressions, e.g. in our stability analyses (see Supplementary Methods). Moreover, this model is readily extendable to incorporate other biophysical mechanisms like spike-frequency adaptation, or to build a spatial graph of multiple homogenous and heterogeneous inter-connected networks to study, e.g. the activity propagation over different brain areas.
In sum, we have shown that by establishing and extending a novel application of an existing computational model to immature networks and integrating recent experimental findings, new mechanistic insights into the development of neural networks can be obtained. We expect that, in the future, this modelling approach can also guide research by providing for concrete predictions that can be tested experimentally.

Methods
Here, we briefly describe the main components of our model and the analyses. A detailed description can be found in Supplementary Methods. STP-RNN model. This model (Fig. 1a) is an extended version of Wilson-Cowan's recurrent neural network (RNN) mean-field model 43 , for which the short-term plasticity (STP) of synaptic connections were also modeled 13 . The equations governing the model dynamics over time are (dots denote the time derivatives): r , j is the index of presynaptic population, E r (=A E ) and I r (=A I ) are the average activity (in hertz) of E-and I-populations which receive, respectively, the external inputs e E and e I , e.g., from other brain regions, G i is the linear input-output gain above population activity threshold θ i (otherwise = G 0 i ), and x ij and u ij are the dynamic variables of short-term synaptic depression and facilitation mechanisms. The parameter definitions and values are listed in Table 1. A STP-RNN with synaptic efficacies frozen at the FP is called Frozen STP-RNN (a Static-RNN-type model).
Phase plane components. E r -I r -plane is the 2D phase plane of E-and I-population activity rates (I r vs. E r ), in which the E r -and I r -nullclines are represented as the set of points for which = dE dt / 0 r and = dI dt / 0 r , respectively. For a STP-RNN, the depicted quasi E r and I r nullclines in E r -I r -plane are based on the reduced STP-RNN (Supplementary equation (7)). Any intersection of these two quasi nullclines (i.e. the FP) can represent the steady state of the whole network (i.e. the 10D STP-RNN).

Stability of FPs.
To determine the stability of any FP of interest we applied linear stability analysis to our 10D STP-RNNs (or 2D Frozen STP-RNNs): We investigated whether all eigenvalues of the network's system of equations linearized around the FP (using the Jacobian matrix) have strictly negative real parts (if so, the FP is stable), or whether at least one eigenvalue with positive real part exists (if so, the FP is unstable). This can be re-stated as: A FP is stable if following a small perturbation from that FP (at which the network was before perturbation), the network dynamics converge back to it. Conversely, if the dynamics move away from the FP or dies out, that FP is unstable.
SCiEntifiC REPORTS | 7: 13015 | DOI:10.1038/s41598-017-13468-z Operating regimes and FP-domains. The stable operating regimes of a RNN at a FP can be classified as an inhibition-stabilized network (ISN) vs. a Non-ISN 25,26 . To discriminate between these two regimes three criteria were defined: (A) Excitatory instability: For the inhibitory activity rate fixed at the FP, the recurrent excitation is strong enough to render the E-population intrinsically unstable. (B) Excitatory stability: In contrast to (A), the E-population is stable per se, i.e. even with a feedback inhibition fixed at its level at the FP. (C) Overall stability: The dynamic feedback inhibition to the E-population is strong enough to stabilize the whole network activity. At a FP, a network operating under the (A) and (C) criteria is an ISN, while a network operating under the (B) and (C) criteria is a Non-ISN. A network, which is neither ISN nor Non-ISN at the FP, operates under an unstable regime.
We partition the E r -I r -plane into different domains of operating regimes (FP-domains). Each FP-domain contains all potential steady states (i.e. FPs) at which the network could operate under the corresponding regime. The area of each FP-domain is computed numerically as AOD (area of domain). Note that the borders between the FP-domains (Fig. 3a) were determined by using numerical simulations, when we plotted the FP-domains of operating regimes based on their stability criteria (A-C) obtained analytically in Supplementary Methods (see "Characterization of operating regimes" section in Supplementary Methods).
Cluster activity size and duration. In a RNN, the cluster activity (network spike; PS net ) usually involves the population spikes 44,45 in both E (PS E ) and I (PS I ) populations. To approximate cluster activity size, i.e. the total number of neurons synchronized during a cluster activity, we calculate ω = × − PS A A ( ) net amp sum amp sum 0 (a dimensionless parameter), where A sum = E r + I r is the sum of the activity of E-and I-populations (in hertz), A sum amp and A sum 0 are the maximal activity during the cluster activity and the preceding activity, respectively, and ω is a scaling factor (in units of [Hz −1 ]) to convert the activity rate to an approximate number of activated neurons during the cluster activity. For simplicity, we set ω = 1, since its veridical value is not defined for the mean-field model. We measure the cluster activity duration as its termination time minus its onset time.
Computation of ratio PS and ratio AOD . To investigate the impact of specific maturational processes on sparsification (as used in Fig. 5), we first substituted single parameters or a small combination of parameters at P10 by their values at P20 ( , where for ratio PS we set γ = PS net amp with ϖ = −1, and for ratio AOD we set γ = AOD ISN/Unstable with ϖ = +1. γ P10 res is the value of γ measured after the parameter(s) value(s) substitution.
Data availability. Custom Matlab and Mathematica codes for our model are available upon request from the corresponding author.


s2fieldsofstudy: Biology Chemistry Medicine

text:
Crotonis Fructus Extract Inhibits 12-O-Tetradecanoylphorbol-13-Acetate-Induced Expression of Matrix Metalloproteinase-9 via the Activator Protein-1 Pathway in MCF-7 Cells

Purpose Metastatic cancers spread from the primary site of origin to other parts of the body. Matrix metalloproteinase-9 (MMP-9) is essential in metastatic cancers owing to its major role in cancer cell invasion. Crotonis fructus (CF), the mature fruits of Croton tiglium L., have been used for the treatment of gastrointestinal disturbance in Asia. In this study, the effect of the ethanol extract of CF (CFE) on MMP-9 activity and the invasion of 12-O-tetradecanoylphorbol-13-acetate (TPA)-treated MCF-7 cells was examined. Methods The cell viability was evaluated using the 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) assay. The expression of MMP-9 was examined by Western blotting, zymography, and real-time polymerase chain reaction. An electrophoretic mobility gel shift assay was performed to detect activator protein-1 (AP-1) DNA binding activity and cell invasiveness was measured by an in vitro Matrigel invasion assay. Results CFE significantly suppressed MMP-9 expression and activation in a dose-dependent manner. Furthermore, CFE attenuated the TPA-induced activation of AP-1. Conclusion The results indicated that the inhibitory effects of CFE against TPA-induced MMP-9 expression and MCF-7 cell invasion were dependent on the protein kinase C δ/p38/c-Jun N-terminal kinase/AP-1 pathway. Therefore, CFE could restrict breast cancer invasiveness owing to its ability to inhibit MMP-9 activity.

INTRODUCTION
Breast cancer is the most common cancer and the second most common cause of cancer-related death in women [1]. Despite the successful eradication of the primary tumor, cancer can still occur in other areas of the body; therefore breast cancer metastasis is the main cause of death in breast cancer patients [2]. Invasion, which is the initial step in cancer metastasis, allows the tumor to invade into the surrounding tissue and metastasize. Thus, it is a popular target in the study of metastasis [3].
The degradation of the extracellular matrix, in which matrix metalloproteinases (MMPs) play a key role, is a critical step during cell invasion [4]. MMPs are a family of zinc endopeptidases, of which 26 members have been discovered. Among these, MMP-9 can degrade type IV collagen, which is a major structural component of basement membranes; thus, the control of MMP-9 expression is thought to be important in the regulation of metastasis [5,6].
Crotonis fructus (CF) is the mature fruit of Croton tiglium L., which is a leafy shrub native to Southeast Asia. CF has been used to attenuate gastrointestinal diseases such as constipation, visceral pain, and intestinal inflammation [7,8]. Isoguanosine from C. tiglium L. was shown to possess antitumor activity against implanted S-180 ascitic tumors in mice [9]. However, it has remained unclear whether CF extract (CFE) inhibited the invasion of breast cancer cells.
In the present study, the effects of CFE on 12-O-tetradec-

Crotonis Fructus Extract Inhibits 12-O-Tetradecanoylphorbol-13-Acetate-Induced Expression of Matrix Metalloproteinase-9 via the Activator Protein-1 Pathway in MCF-7 Cells
Purpose: Metastatic cancers spread from the primary site of origin to other parts of the body. Matrix metalloproteinase-9 (MMP-9) is essential in metastatic cancers owing to its major role in cancer cell invasion. Crotonis fructus (CF), the mature fruits of Croton tiglium L., have been used for the treatment of gastrointestinal disturbance in Asia. In this study, the effect of the ethanol extract of CF (CFE) on MMP-9 activity and the invasion of 12-Otetradecanoylphorbol-13-acetate (TPA)-treated MCF-7 cells was examined. Methods: The cell viability was evaluated using the 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) assay. The expression of MMP-9 was examined by Western blotting, zymography, and real-time polymerase chain reaction. An electrophoretic mobility gel shift assay was performed to detect activator protein-1 (AP-1) DNA binding activity and cell invasiveness was measured by an in vitro Matrigel invasion assay.
Results: CFE significantly suppressed MMP-9 expression and activation in a dose-dependent manner. Furthermore, CFE attenuated the TPA-induced activation of AP-1. Conclusion: The results indicated that the inhibitory effects of CFE against TPAinduced MMP-9 expression and MCF-7 cell invasion were dependent on the protein kinase C δ/p38/c-Jun N-terminal kinase/ AP-1 pathway. Therefore, CFE could restrict breast cancer invasiveness owing to its ability to inhibit MMP-9 activity.
anoylphorbol-13-acetate (TPA)-induced MCF-7 cell invasion were evaluated through measurements of cell invasiveness and MMP-9 activity. In addition, the signaling mechanism was investigated.

METHODS
Cells and materials MCF-7 cells were obtained from the American Type Culture Collection (Manassas, USA). The cells were grown in Dulbecco's Modified Eagle's Medium (DMEM) supplemented with 10% fetal bovine serum (FBS) and 1% antibiotics. TPA and anti-β-actin antibodies were obtained from Sigma-Aldrich (St. Louis, USA). FBS, high-glucose DMEM, and phosphatebuffered saline were obtained from Gibco-BRL (Gaithersburg, USA).

Preparation of crotonis fructus extract
The mature fruits of C. tiglium L. were purchased from Kwangmyungdang Medicinal Herbs Co., Ltd. (Ulsan, Korea) and authenticated by professor G.S.L. The fruits were powdered and extracted with 70% aqueous ethanol under reflux for 2 hours. The solvent was removed by evaporation and the extract was freeze-dried. The yield of the final extract was 11.48% (w/w).

Western blot analysis
The cells were incubated for 24 hours with TPA, which was followed by treatment with 10 or 30 μg/mL CFE for 1 hour. The cells were lysed with ice-cold radioimmunoprecipitation assay (RIPA) buffer (Thermo, Rockford, USA), and the protein lysates (20 μg) were resolved by sodium dodecyl sulfatepolyacrylamide gel electrophoresis. The separated proteins were transferred to polyvinylidene difluoride (PVDF) membranes using Western blotting apparatus. The membranes were blocked for 2 hours with 2% bovine serum albumin or 5% skim milk and then incubated with 1 μg/mL primary antibody overnight at 4°C. The primary antibodies used for detection were: protein kinase C (PKC)α, PKCβ, PKCδ, p38, phos-phorylated p38 (p-p38), c-Jun N-terminal kinase (JNK), p-JNK, extracellular signal-regulated kinase (ERK), p-ERK, p-c-Jun (Cell Signaling Technology, Beverly, USA), sodium potassium ATPase (Na-K ATPase) (Abcam, Cambridge, UK), MMP-9, p50, p65, and proliferating cell nuclear antigen (PCNA) (Santa Cruz Biotechnology, Dallas, USA). Horseradish peroxidase conjugated IgG was used as a secondary antibody and the protein expression levels were evaluated by signal analysis using a MINI HD6 image analyzer (Uvitec, Cambridge, UK).

Gelatin zymography assay
First, the medium was collected and mixed with sample buffer. The gel for electrophoresis contained 0.1% (w/v) gelatin. The gel was washed with 2.5% Triton X-100 solution at 18°C to 23°C (room temperature) for 30 minutes and then incubated at 37°C in 5 mM CaCl2, 0.02% Brij, and 50 mM Tris-HCl (pH 7.5) for 16 hours. The gel was stained using Coomassie brilliant blue solution and then photographed using an image analyzer (Fujifilm, Tokyo, Japan). Proteolysis was identified by the presence of white zones in dark blue fields. Densitometric analysis was performed by using Multi Gauge Software for Image Analysis (Fujifilm).

Preparation of nuclear extract
The cells were treated with CFE in the presence or absence of TPA for 3 hours and then pelleted by centrifugation at 1,800 × g for 4 minutes. The isolation of the cytoplasmic and nuclear extracts was performed using the NE-PER ® Nuclear and Cytoplasmic Extraction Reagents (Thermo Scientific, Rockford, USA).
Membrane fractionation MCF-7 cells (5 × 10 7 ) were pretreated with 30 μg/mL CFE for 1 hour and then incubated with TPA for 1 hour at 37°C. The membrane proteins were prepared from cells using the MEM-PER TM plus kit (Thermo Scientific).

Invasion assay
The invasion assay was conducted in 24-well chamber dishes (8-μm pore size) coated with 20 μL of Matrigel that was rehydrated in 0.5 mL DMEM for 30 minutes prior to the experiments. Cells (2 × 10 5 ) and chemoattractant (conditioned medium; 0.5 mL) were added to the upper and lower chamber of the dishes, respectively. After incubation, the invading cells were fixed and stained with a Toluidine blue solution. The analyzed data were presented as the mean ± standard error from three individual experiments performed in triplicate.

Statistical analyses
The statistical analyses of data were performed using analysis of variance and Duncan test using the Microsoft 2010 Excel program (Microsoft Corp., Redmond, USA). Values of p < 0.05 were considered statistically significant.

Effect of crotonis fructus extract on MCF-7 cell viability
To evaluate the cytotoxicity of CFE on MCF-7 cells, the viability of untreated and CFE-treated cells was compared. At all tested concentrations, CFE treatment for 24 hours did not result in any significant changes in cell viability. CFE also did not affect MCF-7 cell proliferation at 100 μg/mL for up to 72 hours ( Figure 1A and Supplementary Figure 1A, available online). Thus, the optimal nontoxic concentrations (10 and 30 μg/mL) of CFE were used for all subsequent experiments.

Effect of crotonis fructus extract on TPA-induced MMP-9 expression/secretion in MCF-7 cells
To examine the effect of CFE on TPA-induced MMP-9 expression, MCF-7 cells were analyzed by Western blot analysis, real-time quantitative polymerase chain reaction (RT-qPCR), and gelatin zymography. Western blot analysis revealed that CFE treatment blocked the upregulation of TPA-induced MMP-9 protein expression in MCF-7 cells. Zymography was performed to determine the effects of CFE on TPA-induced MMP-9 secretion and showed that TPA increased MMP-9 secretion in MCF-7 cells. However, CFE significantly diminished TPA-induced MMP-9 secretion ( Figure 1B). RT-qPCR revealed that TPA induced an increase in MMP-9 expression levels in MCF-7 cells, whereas CFE blocked TPA-induced MMP-9 upregulation in a dose-dependent manner ( Figure  1C). These results indicated that CFE exerted potent inhibitory effects against the TPA-induced upregulation of MMP-9 expression in MCF-7 cells.

Effect of crotonis fructus extract on TPA-induced PKC activation in MCF-7 cells
To determine the effects of CFE on TPA-induced PKC activation, we analyzed the PKC isotype levels in the membrane fraction. CFE inhibited the membrane localization of PKCδ induced by TPA (Figure 2), which suggested that CFE inhibited TPA-induced MMP-9 expression via the suppression of PKCδ activation in MCF-7 cells.

Effect of crotonis fructus extract on TPA-induced MAPK activation in MCF-7 cells
To further understand the signaling pathway involved in the inhibitory action of CFE on TPA-induced MMP-9 activation, we used Western blotting to evaluate the effect of CFE on the TPA-induced activation of mitogen-activated protein kinase (MAPK). The treatment with CFE showed inhibitory effects on p38 and JNK, but not on ERK ( Figure 3A). These results suggested that the p38 and JNK pathways were involved in the suppression of TPA-induced MMP-9 expression by CFE.

Effect of crotonis fructus extract on TPA-induced activation of NF-κB and AP-1 in MCF-7 cells
It has been reported that the MMP-9 promoter has nuclear factor-κB (NF-κB) and AP-1 binding sites; these are known to be involved in MMP-9 gene induction by TPA [10,11]. To clarify the transcription mechanism by which CFE inhibited MMP-9 expression, the effect of CFE on the TPA-induced activation of NF-κB and AP-1 was evaluated using Western blotting. As shown in Figure 3B, TPA upregulated the expres-sion of p65 and p50 (NF-κB subunits) and p-c-Jun (AP-1 subunit) in the nuclear fraction. The pretreatment of cells with CFE resulted in the inhibition of TPA-induced p-c-Jun expression, but not that of NF-κB activation. Additionally, the DNA binding activity of AP-1 was confirmed. The treatment with CFE suppressed TPA-induced AP-1 DNA binding activity ( Figure 3C). These results indicated that CFE specifically blocked AP-1 activation in MCF-7 cells.

Effect of crotonis fructus extract on TPA-induced cell invasion in vitro
The upregulation of MMP expression is known to contribute significantly to cancer invasion [12,13]. In this study, we investigated the inhibitory effects of CFE on the invasive ability of MCF-7 cells induced by TPA by performing a Matrigel invasion assay. The cells pretreated with 20 nM TPA were subsequently incubated with CFE for 24 hours. TPA-induced MCF-7 cell invasion was suppressed by CFE treatment ( Figure  4)

DISCUSSION
The main components of CF, the mature fruit of C. tiglium L., are croton oils, which are traditionally used as a laxative or purgative. This oil contains a toxic resin, which is a highly toxic substance that must be treated with great caution. Thus, croton oil can lead to initial skin sloughing, inflammation, and pain [14]. For that reason, the croton oils in CF are removed. Although there are limited studies on the effects of CFE, the effectiveness of CFE for the therapy of peptic ulcers and for the stimulation of lipolysis has been reported [15,16].
MMP-9 is a well-known proteinase that disrupts the extra-cellular matrix, which induces the invasion of cancer cells; thus, it has been widely studied as a major target of cancer metastasis. In the present study, we found that CFE treatment suppressed the TPA-induced protein, mRNA expression, and secretion of MMP-9 ( Figure 1). These results were the first to demonstrate the effect of CFE on MMP-9.
As described above, MMP-9 expression was closely correlated with cancer cell invasion. The present findings showed that CFE blocked TPA-induced MCF-7 cell invasion ( Figure  4). Additionally, CFE inhibited TPA-induced MCF-7 migration (Supplementary Figure 1B, available online). As this was the first study to demonstrate the inhibitory activity of CFE on cell invasion, the mechanism of action was also evaluated. The activation of the three major MAPK families, JNK, ERK, and p38 kinase, is required for MMP-9 induction [17,18]. The MMP-9 gene promoter contains both NF-κB and AP-1 binding sites. AP-1, composed of Jun and Fos subunits, can be activated by p-JNK [19,20]. As shown in Figures 2 and 3, CFE inhibited the TPA-induced phosphorylation of p38, JNK, and c-Jun. These results showed that CFE suppressed TPA-induced MMP-9 expression through the MAPK/AP-1 pathway in MCF-7 cells.
It was previously reported that PKC isoforms contributed to the TPA-induced invasion and migration of MCF-7 cells, suggesting that the TPA-induced upregulation of MMP-9 expression and cell invasion involved the activation of PKCδ and PKCα [21]. In this study, the translocation of PKCδ into the cell membrane was slightly inhibited by CFE treatment (Figure 2A). These results suggested that CFE possessed the ability to regulate the activation of TPA-responsive PKC isozymes, especially PKCδ, in MCF-7 cells.
This study demonstrated that CFE exhibited anti-invasive activity in TPA-induced MCF-7 cells and suppressed TPA-induced MMP-9 expression via the activation of the PKCδ/p38/ JNK/AP-1 pathway. Therefore, the anti-invasive effect of CFE on breast cancer cells at nontoxic concentrations indicated its potential as a chemopreventive agent against breast cancer.


extfieldsofstudy: Chemistry Medicine

text:
A valuable peroxidase activity from the novel species Nonomuraea gerenzanensis growing on alkali lignin

Graphical abstract Treatment of 50 μM RBBR with 60 μg of N. gerenzanensis peroxidase preparation: the dye decolorizing activity was clearly observed.

Introduction
Lignocellulose, consisting of a complex of three main polymers, i.e., lignin, cellulose, and hemicellulose, is the major structural constituent of plant biomass and represents the most abundant renewable carbon feedstock on earth [1,2]. Lignin, which accounts for ca. 20% of the lignocellulosic material, has a complex and heterogeneous molecular architecture, derived from the oxidative coupling of three main phenylpropanoid monomers (p-coumaryl, coniferyl, and synapyl alcohols) [3]. Due to its complex structure, Abbreviations: LiP, lignin peroxidase; MnP, manganese peroxidase; VP, versatile peroxidase; DyP, dye decolorizing peroxidase; MM-L, minimal salt medium plus lignin; MAM, mannitol agar medium; ABTS, 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid); RB5, reactive black 5; 2,4-DCP, 2,4-dichlorophenol; 2,6-DMP, 2,6dimethoxyphenol; RBBR, remazol brilliant blue R. the lignin polymer is highly resistant to chemical and biological degradation. Therefore, removing and using lignin constitute a central issue for industrial exploitation of plant biomasses to produce second-generation biofuels, chemicals, and new biobased materials [4,5]. Since most of the available chemical methods for fractionating and degrading lignin generate poisonous side-products, the development of a sustainable and ecologically favorable technology, based on the use of enzyme cocktails for breaking down this polymer (and for its valorization), represents a great biotechnological challenge. The list of benefits using biotreatment methods includes energy savings during defibration/ refining steps, increase in delignification rate and facilitated access of hydrolytic enzymes to carbohydrates moieties, decrease of alkali consumption, decrease in hexenuronic acid content in kraft pulps, removal of inhibitory phenolic compounds and other toxic intermediates in lignocellulose hydrolysates for biofuel production [4,5]. Additionally, microbial fermentation/enzymatic degradation of lignin fractions may funnel lignin-derived aromatics to products having potential of industrial applications as: (i) vanillic acid (then converted into vanillin flavor) [6], or (ii) medium chain-length (C6-C14) polyhydroxyalkanoates (used as plastics or adhesives, or depolymerized and converted to chemical precursors or methylester-based fuels) [7] or (iii) adipic acid (a polymer precursor for nylon, plasticizers, lubricants, and polyester polyols) via muconic acid [8].
Ligninolytic microbes have developed a unique strategy to circumvent the natural resistance of lignin and to degrade and mineralize the polymer. They secrete an array of oxidative enzymes, such as laccases (EC 1.10.3.2), lignin peroxidases (LiP, EC 1.11.1.14), manganese peroxidases (MnP, EC 1.11.1.13), and versatile peroxidases (VP, EC 1.11.1.16) [1,[9][10][11][12]. Laccases are copper-containing enzymes that catalyze the oxidation of various phenolic and non-phenolic compounds and concomitantly reduce molecular oxygen to water. LiPs, MnPs, and VPs are structurally related enzymes, belonging to class II peroxidases within the heme peroxidase superfamily, which use hydrogen peroxide as electron acceptor to catalyze multi-step oxidative reactions and hydroxylation. Notably, LiPs use H 2 O 2 as the co-substrate in addition to a mediator such as veratryl alcohol to oxidize soluble semi-degraded lignin derivatives and other phenolic compounds, while MnPs oxidize Mn(II) to Mn(III), thus enhancing the degradation of phenolic compounds. VPs are hybrids of LiPs and MnPs, with bifunctional characteristics (thus being capable of using both Mn (II) and veratryl alcohol) and a broad substrate preference [10][11][12].
The best characterized peroxidases are those secreted by whiterot families, such as Phanerochaete chrysosporium [13]. Nevertheless, the large-scale applications of fungal enzymes have been limited by the challenge of producing these post-translationally modified proteins in commercially viable amounts [14]. By contrast, bacterial peroxidases should be much easier to produce. Additionally, bacterial enzymes might offer advantages such as better stability and activity under conditions compatible with industrial applications, as already reported in the case of bacterial laccases and dye-decolorizing peroxidases (DyPs) [15][16][17][18][19]. Peroxidase activities were identified within members of different bacterial taxa, especially Proteobacteria, Firmicutes, Acidobacteria, and Actinobacteria [1,15,17]. Particularly, filamentous actinomycetes, which are myceliar, multicellular soil bacteria that grow similarly to fungi and share the same ecological niche, represent an attractive group for isolating novel peroxidase enzymes putatively involved in lignin degradation [16,19]. Actinomycetes are aerobic, chemoorganotrophic, Gram-positive bacteria that play an important role in degrading organic polymers in nature, including lignin [20][21][22]. The first secreted peroxidase enzyme reported to be produced by a filamentous actinomycete was the extracellular LiP from Streptomyces viridosporus T7A [23]. Since then, it was reported that Streptomyces spp. produced a few laccases [24,25] and most recently peroxidase activity [26].
There are also reports of peroxidase secretion by other soil (notfilamentous) actinomycetes (i.e., Nocardia and Rhodococcus) [19,27]. Among the filamentous actinomycetes, streptomycetes can be easily isolated and cultivated by the commonly and traditionally used microbiological methods, but increasing evidence is showing that other less known genera of filamentous actinomycetes might be widespread in specific environments, where they play a role in lignin degradation [28].
In the present study, we report on the screening of 43 filamentous actinomycetes belonging to different genera/families, including representatives of more difficult-to-handle actinomycetes [29,30]. Following this approach, we discovered and investigated the biochemical properties of a novel and efficient peroxidase activity produced by a Nonomuraea strain (Streptosporangiaceae family) recently classified as Nonomuraea gerenzanensis [31] that might be involved in the natural metabolism of lignin.

Plate assays
Forty-three filamentous actinomycetes belonging to the culture collection of The Protein Factory research center [32] were used in screening. Escherichia coli DH5a (Invitrogen, Carlsbad, CA USA), cultivated according standard procedures [33], was used as a control. For primary screening, agar plates containing minimal salt medium and alkali lignin (also known as

Growth conditions for actinomycetes
Nonomuraea gerenzanensis [31] and Streptomyces coelicolor A3 (2) working cell banks (WCBs) were prepared as previously described [29]. Preinoculum cultures were set up by inoculating 0.75 ml of the WCB into 15 ml VM liquid medium (in g/l: 24 soluble starch, 1 glucose, 3 meat extract, 5 yeast extract, and 5 tryptose) in 100 ml Erlenmeyer flasks, incubated at 28 C and 200 revolutions per minute (rpm) for 72 h. An aliquot of 1.8 ml of these cultures was transferred into 300-ml baffled Erlenmeyer flasks containing 50 ml of two different basal media, VM and MM-L (liquid version, without agar), to which the following components could be added: 0.8 or 1.5 g/l alkali lignin, 6 or 12 g/l yeast extract, 2 mM MnCl 2 , 2 mM CuSO 4 , 0.2 mM FeSO 4 Á7H 2 O, 5 mM tryptophan, 0.5 g/l mannose, 0.5 g/l glucose, 6 g/l meat extract, 1 g/l hydrolyzed casein, and 3.5 or 5.0% v/v ethanol. Flask cultures were incubated at 28 C and 200 rpm, up to 480 h and regularly sampled. The growth curves were determined by collecting 5 ml of the culture, centrifuged at 1900 Â g for 10 min at room temperature: on the supernatant, pH and residual glucose were measured with pH Test Strips 4.0-10.0 (Sigma-Aldrich, St. Louis, MO USA) and Diastix strips (Bayer, Leverkusen Germany), respectively; biomass was measured as wet weight on the pellet.

Enzyme assays
Enzyme activities were assayed spectrophotometrically at 25 C as follows. Laccase and MnP activity was measured by monitoring the oxidation of ABTS (e 420nm = 36,000 M À1 cm À1 ) at 420 nm for 5 min. The laccase activity was assayed on 0.5 mM ABTS in 50 mM sodium acetate, pH 5.0, and the MnP activity on 0.5 mM ABTS, 0.05 mM H 2 O 2 , 0.16 mM MnCl 2 , in 40 mM sodium citrate buffer, pH 4.5 (100 ml protein sample in 1 ml final volume). One unit of activity was defined as the amount of enzyme that oxidized 1 mmol of ABTS per min at 25 C. Furthermore, enzymatic activity was assayed by monitoring the enzymatic oxidation of 2,4-dichlorophenol (2,4-DCP) as substrate in the presence of H 2 O 2 and 4-aminoantipyrine; a 1 ml of reaction mixture containing 200 ml protein sample, 5 mM 2,4-DCP (dissolved in ethanol), 3.2 mM 4-aminoantipyrine, 10 mM H 2 O 2 in 20 mM potassium phosphate, pH 7.0, was used. The reaction was monitored for 5 min following the absorbance change at 510 nm (e 510nm = 21,647 M À1 cm À1 ). One unit of enzyme activity corresponded to an increase of 1.0 absorbance unit per min. The presence of peroxidase activity in the broth was also detected with 0.125 mM H 2 O 2 and 2 mM 2,6-dimethoxyphenol (2,6-DMP) in 50 mM sodium acetate buffer, pH 5.0 (1 ml final assay volume, e 468nm = 49,600 M À1 cm À1 ).

Enzyme preparation
N. gerenzanensis was grown in 500-ml baffled Erlenmeyer flasks containing 100 ml VM medium supplemented with 0.8 g/l alkali lignin and 2 mM CuSO 4 . Cells were removed after 20 days by centrifugation at 10,000 Â g for 15 min at 4 C. Cell-free broth was filtered twice on cotton and paper filters and then concentrated by means of tangential flow microfiltration cassette Pellicon-XL (Millipore, Billerica, MA USA) with a 10-kDa cut-off membrane and washed several times, adding 50 mM sodium acetate buffer, pH 5.0. Fractional precipitation was performed at 30, 50, and 75% w/v of (NH 4 ) 2 SO 4 saturation. The protein precipitated at 75% w/v of (NH 4 ) 2 SO 4 saturation was resolubilized in 50 mM sodium acetate buffer, pH 5.0, and dialyzed against the same buffer.

Kinetic properties
The kinetic parameters of the sample obtained by 75% (NH 4 ) 2 SO 4 precipitation were determined at room temperature in the presence of different concentrations of H 2 O 2 and ABTS (2-1000 mM), catechol (2-10,000 mM, e 410nm = 2211 M À1 cm À1 ), or 2,6-DMP (2-1000 mM) in 50 mM sodium acetate buffer, pH 5.0, at 25 C. The LiP activity was assayed on 2.5 mM veratryl alcohol (e 310nm = 9300 M À1 cm À1 ) in the same buffer, at pH 5.0 or 3.0. The specific activity was expressed as unit per mg of total protein (determined by Biuret analysis). The activity on H 2 O 2 was assayed in the presence of 2 mM 2,6-DMP; the activity on ABTS, 2,6-DMP, veratryl alcohol, and catechol was assayed in the presence of 0.125 mM H 2 O 2 . The kinetic data were fitted to the Michaelis-Menten equation or to the one modified to account for substrate inhibition [34,35].
The effect of pH on the peroxidase activity towards 2,6-DMP and H 2 O 2 was determined in 100 mM multicomponent buffer (33 mM Tris-HCl, 33 mM Na 2 CO 3 , 33 mM H 3 PO 4 ), in the 3.0-9.0 pH range [36]. The pH dependence of peroxidase activity was fitted using Eq.
where a is the limiting activity value at acidic pH, b is the calculated intermediate value, and c is the limiting activity value at basic pH.
The effect of NaCl, dimethyl sulfoxide (DMSO), and Tween-80 concentration on the peroxidase activity toward 2,6-DMP was determined in 50 mM sodium acetate buffer, pH 5.0. Temperature dependence of peroxidase activity was determined by measuring the enzymatic 2,6-DMP oxidation in the 10-70 C temperature range. Enzyme preparation stability was measured at 25 and 37 C by incubating the enzyme solution in 50 mM sodium acetate buffer, pH 5.0: samples were withdrawn at different times and residual activity was determined using the 2,6-DMP assay. The peroxidase activity was also assayed in the presence of 0.125 mM e 270nm = 11,590 M À1 cm À1 for Mn 3+ -malonate complex, e 595nm = 8300 M À1 cm À1 for RBBR.

SDS-PAGE and native-PAGE
Laemmli sample buffer was added to the proteins from the fermentation broth and the proteins were then separated by SDS-PAGE using 14% w/v acrylamide. They were visualized by staining with Coomassie Brilliant Blue R-250. Native-PAGE analysis was performed on a 14% w/v acrylamide-resolving gel without SDS. Molecular markers were from Thermo Fisher Scientific (Waltham, MA USA). Two different staining procedures were employed: a) dye decolorizing peroxidase activity was visualized by incubating the gel in 50 mM sodium acetate buffer, pH 5.0, containing 0.1 mM RBBR for 15 min; the gel was then washed and incubated with 50 mM sodium acetate buffer, pH 5.0, containing 0.125 mM H 2 O 2 at 25 C; b) peroxidase activity was visualized by incubating the gel in 50 mM sodium acetate buffer, pH 5.0, containing 0.125 mM H 2 O 2 and 2 mM ABTS, at 25 C.

Screening for ligninolytic activity
A total of 43 actinomycetes belonging to different genera (Actinoplanes, Streptomyces, Nonomuraea, Microbispora,and Planomonospora, see list in Appendix A Supplementary data Table A.1) were screened for their ability to grow on alkali lignin as sole carbon source in solid media. Alkali lignin is a commercially available preparation of soluble, semi-degraded (molecular mass ca. 10,000) and chemically modified derivative of insoluble highmolecular mass lignin (for its preparation see [37]). Here, 33 strains grew in the presence of alkali lignin, with two of them, i.e., Streptomyces coelicolor A3(2) and Nonomuraea gerenzanensis (former Nonomuraea sp. ATCC 39727), forming a clear degradation halo around the colony (not shown). The genome of S. coelicolor A3 (2) contains a gene for a two-domain laccase, called SLAC [25,38,39], whose role in degrading lignocellulosic biomass was recently demonstrated [16]. N. gerenzanensis is a recently classified species [31] and its genome is not yet available; to our knowledge, this is the first report on its ability to use soluble lignin derivatives for growing.
N. gerenzanensis was therefore selected for further analyses and its ability to produce laccase and peroxidase enzymes was tested on agar plates supplemented with differently colored indicator compounds (ABTS, guaiacol, and the dyes RB5 and azure B). Tests were run in parallel with S. coelicolor A3(3) and E. coli DH5a, used as positive and negative controls, respectively. MAM medium was selected since it supports growth of actinomycetes but reduces pigment production, which otherwise interferes with the detection of oxidative activity [40]. N. gerenzanensis was able to oxidize ABTS and decolorize both dyes, but lacked oxidative activity on guaiacol (Table 1 and Fig. 1).
E. coli DH5a was not active on the indicator compounds, while the enzymes secreted by S. coelicolor A3(2) rapidly oxidized ABTS and guaiacol and decolorized RB5 and azure B (Table 1 and Fig. 1). For both of the selected actinomycetes, the enzymatic activity was pH-dependent: activity appeared enhanced at basic pH in N. gerenzanensis, that from S. coelicolor A3(2) in more acidic environment (Table 1).

Production of oxidative enzymes by N. gerenzanensis
N. gerenzanensis was cultivated in liquid VM (limpid and rich medium usually employed for growing this microorganism) and MM-L (salt minimal medium containing alkali lignin as carbon source) media supplemented with the specific inducers listed in the Material and Methods section. The addition of metal cations to cultivation media, in particular Mn 2+ , Cu 2+ and Fe 2+ , might induce laccase and peroxidase production since metal cations represent the main cofactors of these enzymes [41]. Indeed, the use of ethanol and aromatic compounds was previously reported to stimulate laccase activity [42]. Finally, different nitrogen sources and their concentrations can influence the production of peroxidase enzymes in filamentous microorganisms and fungi [43]. For N. gerenzanensis, peroxidase activity was medium-dependent, whereas laccase activity was never detected (Table 2). In particular, relevant peroxidase activity (53.4 and 3.0 U/l on ABTS and 2,4-DCP as substrate, respectively) was observed in the minimal MM-L medium (containing alkali lignin) to which yeast extract and ethanol were added, whereas in the nitrogen-and carbon-rich VM medium the highest activity (65.9 and 13.8 U/l on ABTS and 2,4-DCP, respectively) was achieved by supplementing 0.8 g/l alkali lignin and 2 mM CuSO 4 . Since agitation rates can influence peroxidase production in filamentous microorganisms and fungi [43], N. gerenzanensis was grown at different shaking conditions: without shaking, no peroxidase activity was recorded, while it increased with the shaking (the measured activity was higher at 200 rpm than at 100 rpm, data not shown).
Time courses for N. gerenzanensis growth and peroxidase activity production in the medium that performed better (i.e., VM added with 0.8 g/l alkali lignin and 2 mM CuSO 4 ) are reported in Fig. 2. In the first phase of growth, glucose was consumed, pH increased to almost 9.0, and biomass production reached its maximum of 68 g/l wet weight ( Fig. 2A). Peroxidase production started when cells, after 192 h from inoculum, entered into the stationary phase of growth, reaching the maximum volumetric productivity after about 20 days of growth (Fig. 2B). Peroxidase activity production during stationary phase of growth and at prolonged cultivation time is typical also for several peroxidaseproducing fungi [13,14].

Enzyme preparation
The peroxidase activity in the crude broth was ca. 14, 65, and 140 U/l on 2,4-DCP, ABTS, and 2,6-DMP as substrates, respectively. The broth was clarified (to eliminate aggregates and cell residues) by centrifugation and a two-step filtration; the sample was then concentrated 10-fold. Following a fractional precipitation with ammonium sulfate, the peroxidase activity was recovered in the precipitate at 75% of saturation: this sample contained 36 mg of Table 1 Screening for oxidase activities from S. coelicolor A3(2) and N. gerenzanensis on agar plates. E. coli DH5a did not produce any detectable oxidase activity in the same cultivation conditions. The activity is classified on an arbitrary scale as intense (+++), medium (++), weak (+) or absent (À). The days required for the appearance of the activity are reported in parentheses. protein from 1 l of fermentation broth, with a specific activity of 1.98 U/mg protein on 2,6-DMP as substrate (Table 3).
Although the enzyme preparation in SDS-PAGE showed many protein bands, the peroxidase activity was clearly observed in native-PAGE analyses (Fig. 3, lanes 3 and 4). Notably, the staining for dye-decolorizing peroxidase and classical peroxidase (on ABTS) activity co-localized, thus suggesting that both activities originated from the same enzyme.

Kinetic properties
The kinetic parameters of the peroxidase preparation from N. gerenzanensis were determined on H 2 O 2 , the nonphenolic ABTS, and the phenolic 2,6-DMP and catechol as substrates (Table 4). In all cases, the dependence of the activity values on the substrate concentration followed Michaelis-Menten kinetics, the only exception being H 2 O 2 , which showed a substrate inhibition effect (K i % 340 mM). The highest activity was observed on catechol as substrate (% 3.8 U/mg protein).
N. gerenzanensis peroxidase preparation also possessed a dyedecolorizing activity: notably, it showed a high affinity for RBBR (K m % 13 mM, significantly lower than for the other canonical substrates, Tables 4 and 5). The same preparation also showed a manganese peroxidase activity, although the catalytic efficiency was low (Table 5). Indeed, no lignin peroxidase activity was observed on veratryl alcohol as substrate.

Effect of pH and temperature
The activity of the peroxidase preparation from N. gerenzanensis on 2,6-DMP was determined at different pH and temperature values. The maximal activity occurred at acidic pH values (Fig. 4A)  MM-L a 4.4 0 6 g/l yeast extract 5.0 1.6 12 g/l yeast extract 4.1 3.9 6 g/l yeast extract + 2 mM CuSO 4 9.0 4.4 6 g/l yeast extract + 2 mM MnCl 2 1.8 1.6 6 g/l yeast extract + 0.2 mM FeSO 4 1.1 0 6 g/l yeast extract + 5 mM tryptophan 1.8 2.0 6 g/l yeast extract + 0.5 g/l glucose 2.5 2.0 6 g/l yeast extract + 0.5 g/l mannose 4.0 1.7 6 g/l yeast extract + 6 g/l meat extract 1.1 1.7 6 g/l yeast extract + 1 g/l hydrolyzed casein 2.5 1.2 6 g/l yeast extract + 3.5% v/v ethanol 53.4 3.0 6 g/l yeast extract + 5.0% v/v ethanol 42.2 2.6 6 g/l yeast extract + 3.5% v/v ethanol + 2 mM CuSO 4 15. and enzymatic activity could also be detected in the 7.0-9.0 pH range (a pK a value of 5.8 for the second ionization was determined based on a two-ionizations equation). Peroxidase preparation from N. gerenzanensis possessed a good stability in the 3.0-7.0 pH range following incubation for 24 h at 25 C (Fig. 4B), showing the highest residual activity at pH 4.0-5.0. The trend of pH stability resembled the one observed for pH activity. The N. gerenzanensis peroxidase preparation is quite thermophilic, showing an optimum at around 60 C (Fig. 4C), and is quite stable: after 24 h incubation at 25 and 37 C, peroxidase maintained ca. 90% of its initial activity.

Effect of NaCl, solvents, and detergents
A further main issue affecting peroxidase applications in decolorizing dye effluents is the presence of halide ions. Accordingly, the effect of sodium chloride concentration on enzymatic activity was investigated. Interestingly, the enzymatic activity of N. gerenzanensis peroxidase increased at increasing NaCl concentration, reaching a 1.6-fold increase in the presence of 1 M NaCl (Fig. 5A).
In order to verify the potential for using the peroxidase in processes requiring solvents, the effect of DMSO on enzymatic activity was also investigated. In the presence of 30% v/v DMSO, N. gerenzanensis peroxidase retained ca. 20% of the activity value assayed in the presence of buffer only (Fig. 5B). Indeed, the enzymatic activity was strongly affected by the presence of Tween-80 in the reaction medium: in the presence of 1% v/v of the detergent, the activity was halved (Fig. 5C).

Discussion
Peroxidases represent one of the main components of the ligninolytic system and comprise several members, namely LiPs, VPs, and MnPs. These enzymes are oxidoreductases that utilize hydrogen peroxide for catalyzing oxidation of structurally diverse substrates. Since a single peroxidase can act on a wide range of substrates by employing different modes of oxidation, peroxidase classification based on structure-function relationships is not simple. In addition, peroxidases having diverse molecular structures may catalyze the same reaction.
The white-rot fungus P. chrysosporium secretes an exceptional array of peroxidases, which act synergistically during ligninolysis; Table 4 Kinetic parameters of N. gerenzanensis peroxidase preparation on canonical substrates. The activity was assayed at pH 5.0 and 25 C.  Table 5 Kinetic parameters for manganese oxidation and dye decolorization activity of N. gerenzanensis peroxidase preparation. The activity was assayed in 50 mM sodium malonate, pH 4.5, at 25 C.   these enzymes are currently used in other biotechnology applications including transformation of environmental pollutants and biobleaching of pulp water. Production of differentlycomposed peroxidase cocktails was also reported in other white-rot fungi, especially in Pleurotus spp.; recently, sequencing of the Pleurotus ostreatus genome revealed a comprehensive picture of the peroxidase gene family, consisting of three VPs and six short-MnPs [44,45]. Notably, the production of MnP is apparently limited to certain basidiomycetous fungi [44], whereas P. chrysosporium wild-type does not produce VPs [46]. Indeed, white-rot fungi produce diverse patterns of LiPs, and/or VPs, and/ or MnPs depending on the species, medium composition and cultivation period [47]. Genes encoding such enzymes were found to be differentially regulated in response to a wide variety of environmental signals such as the concentration and origination of bioavailable nitrogen and carbon [13]. Several fungal species such as P. chrysosporium produce LiPs and MnPs in liquid media under nutrient-limited conditions; in contrast, for other species such as P. ostreatus and Trametes trogii, MnPs are preferentially produced in the presence of high concentration of nutrient nitrogen [13]. Additionally, P. ostreatus, Pleurotus eryngii and T. trogii were found to produce relatively low amounts of peroxidases when grown on solid wheat straw medium [47].
Here we demonstrated that a novel bacterial species belonging to Nonomuraea genus produces during stationary phase (when grown in liquid media containing alkali lignin) a peroxidase activity, whose features favorably compare with the fungal enzymes. In fact, when compared to P. chrysosporium MnP, our N. gerenzanensis peroxidase preparation shows a significantly higher stability at pH >6.5 and a higher thermostability (P. chrysosporium MnP is fully inactivated in % 3 min at 55 C) [46,48]. Although more active at acidic than at basic pHs, N. gerenzanensis peroxidase activity is more stable at higher pHs than the fungal counterparts. This finding is coherent with the ecological niche from which this actinomycete was isolated: it was demonstrated that it grows easily at pHs of 10.0 and 11.0 [31], whereas fungi usually prefer acidic environments. From a kinetic point of view, N. gerenzanensis peroxidase activity shows a K m for H 2 O 2 similar to that of P. chrysosporium and Bjerkandera MnPs (% 30-55 mM) and a lower affinity for Mn 2+ (K m % 50-80 mM) [46,49]. Our preparation also differs from P. ostreatus MnPs: the six known MnP isoenzymes significantly differ in K m for H 2 O 2 (23-530 mM) and for Mn 2+ (% 7-101 mM) [50]. Indeed, in contrast to the Bjerkandera MnP, no activity was apparent for the N. gerenzanensis peroxidase on veratryl alcohol (although both enzymes were able to oxidize ABTS and 2,6-DMP), even when the activity was assayed at pH 3.0. This result demonstrates that N. gerenzanensis does not produce LiP or VP-like activities. Furthermore, N. gerenzanensis peroxidase preparation shows a dye-decolorizing activity that expands its substrate range and paves the way for using this enzyme in industrial sectors, including the textile (for bleaching) and dye industry. Indeed, dye-decolorizing peroxidases show activity on lignin model compounds [19], a further valuable field of application.
In conclusion, we discovered a valuable peroxidase activity produced by a novel species belonging to the Nonomuraea genus by screening 43 filamentous actinomycetes from different genera/ families that are considered a yet-poorly-exploited promising source for enzymes involved in lignin modification and degradation [28]. Successful ingredients for such screening were (i) assaying enzyme activity on diverse compounds that act as preferential substrates for different families of oxidases (laccases, LiP, MnP and VP peroxidases), and (ii) exploiting a novel bacterial group that is involved in lignin degradation and resembles fungal life style. To our knowledge, this is the first report on a secreted peroxidase activity from a microorganism belonging to Nonomuraea taxon. Further studies will be devoted to the purification to homogeneity of the enzyme(s) responsible of the peroxidase activity detected in N. gerenzanensis. We are aware that understanding the role of such enzyme(s) in lignin degradation in nature is not straightforward since the model compounds (such as the commonly used alkali lignin) are water soluble and can be degraded by substrate-specific enzymes that could differ from those attacking complex, water-insoluble lignin. Moreover, in white-rot fungi growing on wheat straw medium the rate of lignin degradation was found as not necessarily correlated with the level of laccase and peroxidase activities, suggesting that additional activities should be involved [47]. Although we cannot simply correlate peroxidase production with lignin degradation, we are confident that genome sequencing of this novel microbial species, Fig. 4. Effect of pH and temperature on the activity and stability of N. gerenzanensis peroxidase preparation. (A) pH effect on the enzymatic activity assayed on 2 mM 2,6-DMP and 0.125 mM H 2 O 2 as substrates and at 25 C. The value at pH 5.0 was taken as 100%. The data were fitted using Eq. (1), based on two ionizations: pK a2 is 5.8 AE 0.1 (and pK a1 is estimated 3.0). (B) Effect of pH on the stability of peroxidase activity determined by measuring 2,6-DMP oxidation. The residual activity was assayed after 24 h of incubation at 25 C: the activity value at time = 0 at each pH value was taken as 100%. (C) Effect of temperature on the peroxidase activity determined as in panel (A). The value at pH 5.0 and 25 C was taken as 100%. Values represent the means of three independent experiments (mean AE standard error). able to grow on alkali lignin as unique carbon source, will contribute to understand ligninolytic system in actinomycetes. Additionally, production of a peroxidase cocktail in a single bacterial strain clearly remains a desirable trait for degrading a complex substrate as lignin and for converting chemically diverse compounds in biotechnological applications.