**7. All together**

Recent developments in brain imaging techniques include brain *tractography* with MRI, which has been promptly used to study human language. Brain tactography can be achieved through *diffusion tensor* and *diffusion spectrum* techniques. The main difference between the two of them depends on the deterministic vs. probabilistic approaches used to analyze the movement of water molecules within the main tracts substantiating cortico-cortical connections (de Schotten et al., 2011). It must be noted here, however, that the novelty and the relative scarcity of studies approaching language with this technique may explain certain inconsistencies between studies (for an extensive review, see Friederici, 2009).

Although it is well known after Karl Wernicke, the relevance for human language of the *arquate fasciculus* (AF) connecting Broca's and Wernicke's areas appears largely strengthened by tractographic techniques. The data also stress the relevance for language processing of other fascicles connecting anterior and posterior brain areas. A detailed description of all these connections is also emerging.

One of the first studies applying tractography to approach language was developed by Catani et al. (2005). These authors reported a direct strong connection between Broca's and Wernicke's areas through the FA, but given the fact that the areas actually connected covered a wider territory than the classical Broca's and Wernicke's areas (though, as shown earlier, the precise limits for these two areas may vary depending on the author), Catani and colleagues suggested to call them Broca's and Wernicke's *territories*, respectively. Their results also revealed the existence of two additional but indirect pathways connecting Broca's and Wernicke's territories. One would run laterally, consisting of an anterior segment connecting Broca's territory and the inferior parietal cortex. The other would be a posterior segment connecting the inferior parietal cortex with Wernicke's territory. Given the apparent relevance of these two indirect segments, and the fact that the inferior parietal cortex appears the main meeting point for these indirect connections, Catani et al. (2005) suggested to call this region the *Geschwind's territory*, in the memory of Norman Geschwind, who already proposed a relevant role of the inferior parietal cortex in language. This region largely overlaps with semantic areas involved in word and discourse processing, as we have seen.

The AF seems to have evolved substantially in the human brain from tiny tracts connecting the IFG with the posterior part of BA 22 and the inferior parietal regions, already present in the macaque brain. These connections appear more robust and abundant in the chimpanzee, thereafter reaching the plainest robustness of the human brain (Rilling et al., 2008). Actually, one of the main differences between the human and the chimpanzee brains in this regard is the notable expansion of the posterior ramifications of the AF, which spread not only to involve Wernicke's area and surrounding parietal regions, but also posterior portions of the MTG. The development of these connections, occurring particularly and noticeably within the left hemisphere, seem to have played a critical role in the evolution of human language.

Interestingly, two other tracts connecting anterior and posterior regions seem relevant in language processing. One is the superior longitudinal fasciculus (SLF), connecting Broca's area (particularly, BA 44) with the posterior temporal lobe, namely in the STG and the MTG and also involving portions of BA 40. As this tract runs parallel to the AF, several authors (e.g., Rilling et al., 2008) consider both as representing together a functional unit called the *dorsal stream*. The other connection is more primitive; part of it is actually the most developed fronto-temporal connection in the macaque brain and conveys the ventral portion of the extreme capsule and the uncinate fasciculus. Through these connections, the IFG is connected with the anterior and posterior STG (Frey et al., 2008; Rilling et al., 2008), and it is indeed possible that at least part of this *ventral stream* is preferentially used in simpler grammar such as finite-state, relatively accessible to other non-human primates (Friederici et al., 2006).

Finally, there are evidences for an additional number of connections importantly involved in language, most of them located locally within the IFG and the STG (Friederici, 2009).


TABLE 3 | Back-analysis of the landslides dataset: each landslide is associated to a hazard class according to the susceptibility class of its location and to the rainfall rate provided by the thresholds of the warning system.

TABLE 2 | Class break values for the susceptibility classes of each alert zone. Alert zone S1–S2 (%) S2–S3 (%) A3 4 18

would have occurred in H4 class, 14 in H3, 15 in H2, 1 in H1 and none in H0. These numbers are in accordance with the criteria used for the hazard assessment calibration: no landslides would have occurred in the "no hazard class," less than 10% (namely, a single landslide) in the H1 class and 38 out of 39 would have been associated to one of the hazard levels interpreted as very likely to be associated with landslides.

Although the validation results are encouraging, we are aware that the dataset used for validation is limited; therefore, further tests should be performed before deeming the proposed procedure ready for the operational employ in the risk management.

# DISCUSSION

### Hazard Interpretation

Given the physical meaning of the input susceptibility and rainfall classes and the outcomes of the back-analysis, the qualitative hazard classes obtained with the proposed approach could be interpreted as follows:

	- R1/S2. Landslides occurred here represent an error of the proposed hazard model, because harmless rainfalls (no alarms issued by the rainfall thresholds) actually triggered some landslides in a medium susceptibility area.
	- R2/S1. Landslides occurred here represent an error of the proposed hazard model, because mild rainfalls (medium criticality level provided by the rainfall thresholds) triggered some landslides in an area where landslides should not be expected (low susceptibility).
	- R1/S3. Landslides are located in the highest susceptibility class but in the low rainfall rate class. These situations

represent errors of the rainfall thresholds model, for example situations related to snow melting, which is not taken into account in the rainfall thresholds definition. Therefore, with this class the proposed dynamic hazard approach is capable of accounting also for occurrences that would be missed by the original warning system.

	- R3/S2. The interpretation of this class is that when the rainfall rate is at the maximum level, landslides can be expected also in areas with medium landslide susceptibility.
	- R2/S3. Where the susceptibility to landslides is at the highest level, landslides can be triggered also when the rainfall rate is at a medium level of criticality.

# Possible Use

The methodology presented could be easily integrated into the regional landslide warning system and used to obtain real time dynamic hazard maps. Since the warning system combines rainfall forecasts and real time rainfall data recorded at hourly time step by a network of automated rain gauges, the hazard scenarios could be displayed both for the real-time condition and for the future.

In addition, the dynamic hazard scenario may change as soon as new outputs of the warning systems are provided. If the system is running in now-casting mode, every hour the dynamic hazard map can be refreshed and a new scenario can be built using the new rainfall data coming from the regional network of rain gauges providing hourly rainfall measures. If the system is run in forecast mode, whenever a new forecast of distributed rainfall field is available (normally, twice a day), the dynamic hazard map can be refreshed and updated.

Traditionally, regional warning systems provide a spatially constant alert level for the whole area of application or for large subdivisions called alert zones. The actual consequence of the application of our methodology is that when a rainfall rate (R1, R2, and R3) is recorded/forecasted in a given alert zone, its territory is automatically partitioned in three hazard classes, thanks to the availability of the susceptibility map (**Figure 5**). Therefore, a double advantage is obtained:

1. It is possible to better hypothesize where landslides are expected and with which hazard degree, thus fostering a more

effective hazard and risk management (e.g., setting priorities of intervention);

2. The spatial resolution of the warning system is markedly refined because from time to time the areas where landslides should be expected are only a fraction of the alert zone.

**Table 4** quantifies to which extent the spatial resolution of the warning system can be refined by the proposed approach: at every time, the territory of each alert zone is partitioned into three out of the five proposed hazard classes, depending on the current rainfall rate. As instance, when the maximum rainfall rate is encompassed (R3: rainfall above the highest threshold), in 26% of the territory of the A3 alert zone the landslide hazard is medium, in 52% is high and in 21% is very high. Taking into account the rainfall threshold warning system alone, the whole alert zone territory would be considered at the maximum hazard level. Therefore, the proposed methodology can be used to obtain a consistent refinement of the spatial resolution of the hazard assessment providing a dynamic hazard classification, which can be used in hazard and risk management.

# Comments on the Physical Settings

Rainfall thresholds and susceptibility maps are products that are traditionally created independently for different needs (namely: temporal forecasting for warning purposes and spatial assessment for land management). The objective of this work is finding a dependence between the two approaches to couple the temporal and the spatial forecasting. Form this point of view, rainfall thresholds and susceptibility maps can be considered as complimentary products that can be conveniently coupled to have a dynamic hazard assessment. These two methodologies can be considered complimentary also from another point of view: while rainfall thresholds relate landslide initiation with the main triggering factor (rainfall), susceptibility maps relate landslide occurrence to the predisposing factors (e.g., morphometry, lithology, and land use). It is important to note that both approaches neglect an explicit analysis of the physic behind the landslide initiation: both rainfall thresholds and susceptibility maps used a statistical approach and not a physically based one. As a consequence, the same can be said for the resulting hazard matrix and hazard assessment proposed in this work. However, some characteristics of the hazard assessment can be put into close correlation with some physical features of the test site.

The study area is wide and presents heterogeneous physical features; therefore the approach of subdividing the site into alert zones calibrated independently allowed strengthening the statistical correlation between landslide initiation and triggering/predisposing factors. The very different threshold equations (**Table 1**) and the difference in susceptibility values used to separate susceptibility classes (**Table 2**) corroborates further the effectiveness of the approach consisting in partitioning the study area into independent physiographic units. Among these, A3 is the most prone to landslides: here landslide density is higher than in the other alert zones (Segoni

TABLE 4 | Spatial extension of the hazard classes determined in each alert zone in case of rainfall rates R1, R2, or R3.

et al., 2014b; Battistini et al., 2017), due to the higher slope gradients, the highest altitudes and the presence of lithologies very susceptible to landslides (pelitic flyschs and shistose metamorphic rocks). This is also reflected by the outcomes of the calibration procedure: A3 has the lowest rainfall thresholds combined with the lowest susceptibility, while B3 shows the highest ones. The subdivision of the study area into alert zones to be monitored independently allow also the better encompass the spatial and temporal variability of the rainfall variable: a check on the validation period revealed that in 39% of the days, the rainfall amounts were so different from an alert zone to another that the systems returned different rainfall rate classes (R1, R2, R3), and this occurred mainly during storms: during the validation period it never happened that all the alert zones were at the R2 or R3 level during the same day.

According to the validation procedure, the dynamic hazard assessment proposed in this work underestimated landslide hazard only in one circumstance out of 39. A thorough investigation of this error revealed that it is related to a single landslide event occurred in a S2 spot of the alert zone A3, in a day when the warning system state was in R1 mode, thus providing a H1 (low hazard) matrix output. In this case, both the rainfall threshold system and the susceptibility map underestimated the hazard: the former because no threshold was overcome, the latter because probably some anthropic predisposing factor were not take into account properly (indeed, the landslides occurred close to a mountain road and to a drainage system, which both could have favored the triggering mechanism). This was the only circumstance for which the proposed hazard assessment could not encompass properly the inherent uncertainty of the two original models that were combined into the matrix, but it is a flaw within the error limits imposed by the calibration and optimization procedure explained in section Hazard Assessment.

# CONCLUSION

We propose a dynamic landslide hazard assessment procedure based on the combination of rainfall thresholds and susceptibility maps. Inside each alert zone, the dynamic but spatially constant input provided by a warning system based on rainfall thresholds is combined with the spatially variable but static input provided by a susceptibility map into a matrix defining five possible hazard levels.

The proposed hazard classification scheme underwent a calibration procedure against a large landslide dataset, counting 1,761 landslides, to ensure a good constraint between hazard classification and experimental data. This allowed minimizing the number of landslide occurrences in the lowest hazard classes and to provide an interpretation of the hazard classes. A validation procedure was performed against an independent dataset simulating an operational employ of the dynamic hazard assessment through a 16 months period. The validation provided encouraging results, as 38 landslides out of 39 would have been associated to a consistent hazard level.

The proposed procedure could be easily applied to early warning systems based on rainfall thresholds bringing two main advantages: a consistent refinement of the spatial resolution of the forecasts and a robust tool to assist hazard and risk management and spatial-temporal forecasting of rainfall induced landslides at regional scale.

# AUTHOR CONTRIBUTIONS

SS has written the manuscript, conceived the work and assisted the data interpretation. VT has conceived the work and she has provided a specific contribution to the methodological part with special reference to the susceptibility assessment. AR has contributed to the rainfall thresholds assessment and to the data elaboration. FC and NC have supervised the work.

# ACKNOWLEDGMENTS

This research has been performed in the framework of the project SARnet, funded by the Italian Department of Civil Protection, Presidency of the Council of Ministers.

REFERENCES

regional scale based on space–time-variable rainfall thresholds. Natl. Hazards Earth Syst. Sci. 15, 853–861. doi: 10.5194/nhess-15-853-2015

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Segoni, Tofani, Rosi, Catani and Casagli. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# The Weather Radar Observations Applied to Shallow Landslides Prediction: A Case Study From North-Western Italy

Roberto Cremonini\* and Davide Tiranti

Department of Natural and Environmental Risks, Regional Agency for Environmental Protection of Piemonte, Turin, Italy

In northern Italy rainfall-triggered shallow landslides are recurrent hazardous phenomena that cause casualties and extensive damages. In the last decades several early warning systems (EWSs) have been developed based on rainfall intensity–event duration (I-D) thresholds derived by long rain gages time series. However, rain gages density and their representativeness limit reliability of such EWSs. In the past decades, several studies explored successfully the usefulness of reliable quantitative precipitation estimates (QPEs) by weather radar. The availability of high spatial and temporal resolution QPEs with short latency of the data makes those observations appealing as input data to automatic EWSs. Nevertheless, weather radar based QPEs can be affected by several sources of errors and uncertainties (miscalibration, partial beam blocking, overhanging precipitation, and so on). Analyzing the heavy precipitations that hit Piemonte, north-western Italy, on November 2016, causing floods and triggering widespread shallow landslides, this work presents a fruitful case study of operational weather radar application in shallow landslides early warning system.

Keywords: slope processes, early warning system, QPE, remote sensing, heavy rainfall

# INTRODUCTION

Shallow landslide early warning systems are normally implemented using rain gage network recording as input for rainfall values (Aleotti, 2004; Bosco et al., 2007; Guzzetti et al., 2007; Brunetti et al., 2010; Capparelli and Tiranti, 2010; Tiranti and Rabuffetti, 2010; Mercogliano et al., 2013; Rosi et al., 2015; Segoni et al., 2015, 2018; Devoli et al., 2018).

 In fact, the small size of the initiation catchments of debris flows, also caused by shallow landslides contribution, makes spatial resolution of observed precipitation the most relevant factor (Marra et al., 2017). Availability of high resolution observation of rainfall fields is thus mandatory in threshold-based EWSs. Especially in case of convective and localized precipitations, weather radar quantitative estimation of precipitations can overcome the poor representativeness of ground measurements, due to low density and geometry of the gage networks (Morrissey et al., 1995). Moreover, huge ground weather station networks may have longer latency of the data due to longer data transmission time. Weather radar typically scan the atmosphere close

#### Edited by:

Steven L. Forman, Baylor University, United States

#### Reviewed by:

Stefano Luigi Gariano, Istituto di Ricerca per la Protezione Idrogeologica (IRPI), Italy Milad Janalipour, K. N. Toosi University of Technology, Iran Samuele Segoni, Università degli Studi di Firenze, Italy

#### \*Correspondence:

Roberto Cremonini<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Quaternary Science, Geomorphology and Paleoenvironment, a section of the journal Frontiers in Earth Science

> Received: 16 March 2018 Accepted: 17 August 2018 Published: 03 September 2018

#### Citation:

Cremonini R and Tiranti D (2018) The Weather Radar Observations Applied to Shallow Landslides Prediction: A Case Study From North-Western Italy. Front. Earth Sci. 6:134. doi: 10.3389/feart.2018.00134

**124**

to the ground up to 150 km, with 1-km resolution or less, within few minutes. Both data resolution and short delay in data availability increase effectiveness of EWSs based weather radar.

Weather radar is thus successfully used in the real-time observations of rainfall triggering values for slope phenomena caused by short-duration and intense rainfalls, such as channelized debris flows (Borga et al., 1998; Tiranti et al., 2008, 2014; Marra et al., 2014; Berenguer et al., 2015); in fact, the distinguishing features of rainstorms are easier detected and characterized by weather radar than by rain gages. However, weather radar based QPEs in complex orography are affected by several errors and uncertainties residual clutter, partial beam blocking, rain-induced attenuation, variability of drop size distribution (DSD) and wet radome. Montopoli et al. (2017) provide a comprehensive review of limiting factors in QPEs in complex orography by ground weather radar. Due to nonuniform spatial distribution of errors, reliability of weather radar based EWSs in impervious areas or data quality of long-duration

Cremonini and Tiranti Weather Radar to Predict Landslides

triggering rainfalls must be carefully evaluated. This paper discusses a successful operational implementation of weather radar utilization in shallow landslides EWSs in Piemonte. The areas examined in this study have good weather radar coverage and the performing weather radar data elaborating chain guarantees errors and uncertainties reduction in QPEs.

On November 2016, heavy rainfalls hit Piemonte in north-western Italy and triggered shallow landslides widely, mainly in the Alps. To estimate the rainfall parameters that triggered shallow landslides, rainfall-fields derived by weather radars observations merged with dense rain gage network data have been used. Based on higher number of shallow landslides occurred during the rainfall events, two study areas have been selected: the Pellice and Chisone Valleys and the Alto Tanaro catchments (**Figure 1**).

### MATERIALS AND METHODS

## The Operational Weather Radar in Piemonte

Arpa Piemonte (the Regional Agency for Environmental Protection of Piemonte) operates two polarimetric Doppler C-band weather radar, located on the hill nearby Turin (Piemonte), installed on 1998, and in the Apennines near Savona (Liguria) installed on 2000. **Table 1** summarizes the main characteristics of the weather radars.

Bric della Croce and Monte Settepani radars complete polarimetric volume scans, 170 km range, every 5 min, with, respectively, 11 and 8 elevations. Given the good visibility southward, Monte Settepani operates every 5 min an additional volume scan 250 km range by single polarization, composed of two elevations. A semisupervised robust hydrometeor classification is performed on polarimetric raw volumes (Bechini and Chandrasekar, 2015) to identify clutter, anaprop, attenuation, and meteorological echoes. Finally, completely free lowest beams are used to derive the product of surface rainfall intensity (SRI), according to beam visibility. During winter season, the radar reflectivity is later corrected for vertical profile reflectivity (VPR) according to Koistinen (1991). Single radar SRIs are merged in a 800 m resolution composite considering maximum reflectivity, where more than one weather radar observation is available. Additional weather radar data are merged to Piemonte composite, including the polarimetric weather radar in Monte Lema (Swiss) operated by MeteoSwiss, and weather radar in Carpiano (Italy) operated by the National Flight Assistance Company (ENAV).

Finally, composite radar reflectivity is converted in instantaneous rainfall intensity (mm/h) according to the well-known Z-R relationship:

$$Z = \operatorname{ar} \mathbf{R}^{\mathbf{b}} \tag{1}$$

where Z is the linear weather radar reflectivity expressed in mm/m3 , R is rainfall intensity in mm/h and a = 300 and b = 1.5 are coefficients experimentally derived (Joss and Waldvogel, 1990). Further details on the operational weather radar operated

by ARPA Piemonte can be found in Davini et al. (2011) and in Cremonini and Bechini (2010). Daily rainfall accumulations are corrected with tipping-bucket quality-controlled rain gage measurements by radar-gages assessment factors (Goudenhoofdt and Delobbe, 2009). On daily basis, the 24-h weather radar quantitative precipitation estimation (QPE) is compared with rain gages observations and for each ground weather station the following ratio is derived:

$$AF\_{\mathbf{x},\mathbf{y}} = 10\ ^\ast\,\log 10\ (R\_{\mathbf{x},\mathbf{y}}/G\_{\mathbf{x},\mathbf{y}}) \tag{2}$$

where Rx,y is the 24-h rainfall accumulation QPE co-located with the raingauge (Gx,y) placed at x,y coordinates. Only precipitation accumulations greater than 1.0 mm are considered and the outliers are also excluded and flagged as suspect ratios because they can be due to mechanical failure of raingauge or too large errors in weather radar QPE. Point assessment factors (AFs) are interpolated on the same weather radar composite grid using Ordinary Kriging method (Li and Heap, 2014) to derive the adjustment field. Finally, hourly weather radar QPEs are multiplied by assessment factor field to obtain the best unbiased hourly rainfall estimation.

**Figure 2** shows the weather radar coverage in north-western Italy: stars indicate weather radar sites, circles the radar coverage and red polylines the two study basins of Pellice-Chisone and Alto Tanaro. Theoretical visibility maps, derived in standards propagation atmosphere, demonstrate that Piemonte, Lema and Carpiano weather radar have good visibility over target areas, being first completely free radar beams very close to the ground.

#### The Shallow Landslides EWS

On 2006, Tiranti and Rabuffetti (2010) developed a shallow landslide triggering model named "Shallow landslides Movements Announced through Rainfall Thresholds" (SMART) based on I-D empirical rainfall thresholds derived by statistical analysis on historical shallow landslides occurred in Piemonte from 1990

to 2006. SMART operates since 2008 both in forecast and monitoring modes and it is part of the regional warning system for natural hazards.

For shallow landslides, SMART recognizes two homogeneous areas (**Figure 3**), characterized by two different empirical thresholds (Equations 3 and 4) for two different environments (respectively, Alpine-Appennine and hylls), and defined on rainfall accumulation longer than 12 h. For shorter durations other slope phenomena can occur like diffused and accelerated soil erosion caused by run-off or channelized debris flows in the Alpine small catchments. Based on expected landslides density three levels of scenarios have been defined: (i) isolated landslides or soil erosion due to run-off; (ii) less than ten landslides per km2 expected; (iii) more than ten landslides per km2 expected.

$$\text{Area I (Alps and Apennines)} \; : \; I = \; 25 \; D^{-0.45} \tag{3}$$

$$Area\ 2\ (hills)\ :\ I = \ 40\ D^{-0.65}\tag{4}$$

where l is the accumulated rainfall on D duration, the numerical coefficients are experimentally derived from statistical analysis of collected historical landslides and as exponent the Montana Coefficients (Boni and Parodi, 2001; Tiranti and Rabuffetti, 2010; Devoli et al., 2018) define rainfall effects according to the altitude.

The model rainfall inputs are the quantitative rainfall forecast and observed values by rain gage network, consisting in more than 400 weather stations distributed on about 10 km mesh grid for a total area of 25,402 km2 . Thresholds are linked to each raingauge with an assigned buffer range of 5 km from rain gage centroid. For this reason, shallow landslides triggered at a distance > 5 km from rain gage are characterized by major uncertainty in forecasting. Recently, the weather radar observations have been introduced as new data input of SMART to fill this gap in ground network to identify triggering rainfall.

# RESULTS

#### Synoptic Forcing and Precipitations

From 21st to 25th November 2016, Piemonte was hit by heavy and persistent rainfalls, that have been initially localized in

the southern areas, closed to the Ligurian Sea and then, they shifted to north-western Piemonte. On 21st November 2016 a deep trough approached western Europe, causing intense and humid south-westerly wind over the region. Low level wind convergence over Ligurian sea and diffuse atmospheric instability caused heavy localized precipitations over Apennines with more

FIGURE 5 | (a) Accumulated rainfall from 23rd November at 21:00 UTC in Alto Tanaro. (b) In red, areas interested by precipitations that overpassed thresholds of Equation 3 (critical rainfall accumulations) and rain gages overpassing threshold (yellow asterisks) and related buffers (yellow circles); green star labels indicate occurred landslides.

FIGURE 6 | (a) Accumulated rainfall from 24th November at 03:00 UTC in Alto Tanaro. (b) In red, areas interested by precipitations that overpassed thresholds of Equation 2 (critical rainfall accumulations) and rain gages overpassing threshold (yellow asterisks) and related buffers (yellow circles); green star labels indicate occurred landslides.

than 250 mm in 24 h. The following day, the low-pressure system isolated (cut-off ), losing momentum, and becoming stationary over Iberian Peninsula. This atmospheric synoptic circulation caused intense stationary flow from Southeast over Piemonte, with heavy and diffuse precipitations. On 23rd and 24th November, low level flows turned from east, forcing heaviest rainfall in the western area of Piemonte. On 24th November rain gages recorded more than 350 mm in 24 h and, due to warm air advection, the snow level raised up to 2,000 m asl. Finally, on 25th November, the deep pressure system started to dissipate and precipitations over Piemonte progressively reduced. The largest rainfall amount recorded during 5 days was 632.6 mm; this accumulated precipitation corresponds to more than 50% mean annual rainfall in the region. The whole event was characterized by heavy convective rainfall, high spatial variability and peaks of more than 120 mm in 3 h. On 21st November, the snow level was located around 1500–1700 m asl and it increased on the following days, settling up to 1800–2000 m asl. On 24th November new cold air aloft caused a drop in the snow level up to 1400–1600 m asl, locally at lower altitude in the north-western areas of Piemonte. Rivers in the region had large increases in discharge recording maxima up to more than 50–100 years return period. About 150 shallow landslides were triggered, causing wide damages to residential

FIGURE 7 | (a) Accumulated rainfall from 24th November at 15:00 UTC in Alto Tanaro. (b) In red, areas interested by precipitations that overpassed thresholds of Equation 4 (critical rainfall accumulations); green star labels indicate occurred landslides.

overpassed thresholds of Equation 4 (critical rainfall accumulations) and rain gages overpassing threshold (yellow asterisks) and related buffers (yellow circles); green star labels indicate occurred landslides.

buildings and infrastructures. These landslides were reported several days later after ground surveys by local authorities and Civil Protection. Their reports contain information about landslide type, location, reported damages but the triggering datetime of landslides is not available. **Figure 4** shows rainfall accumulation from 21st to 25th November 2016 derived by weather radar observations and adjusted on daily basis with rain gages of meteorological ground network managed by Arpa Piemonte.

**Figure 4** demonstrates that the Piemonte raingauge network is not able to accurately describe the spatial variability of convective rainfall peaks, due to low spatial density. In Pellice-Chisone area the maximum rainfall accumulation recorded by rain gages over the whole period was 580.4 mm, meanwhile rainfall estimation by weather radar shows three peaks overpassing 700 mm.

An early warning system based trusting in rain gages observations would have probably failed where rainfall peaks are missed.

#### Rainfall Triggered Landslides

To evaluate the effectiveness of SMART thresholds in forecasting the triggering time of shallow landslides, the QPEs recorded during the event and derived by weather radar observations have been compared and filtered with the thresholds defined by Equation 3 in the Pellice-Chisone and Alto Tanaro mountain areas, where most landslides occurred (98 reported), and by Equation 4 for the hilly environment of Alto Tanaro (7 reported). It is possible to reconstruct the spatial-temporal evolution of phenomena, locating most prone areas to landslide triggering.

FIGURE 9 | (a) Accumulated rainfall from 24th November at 15:00 UTC in Pellice-Chisone. (b) In red, areas interested by precipitations that overpassed thresholds of Equation 3 (critical rainfall accumulations) and rain gages overpassing threshold (yellow asterisks) and related buffers (yellow circles); green star labels indicate occurred landslides; red circle include reported debris flows that reached the alluvial fans' areas generated from shallow landslides triggered at the basins' head and propagated along channel networks.

During night on 23rd November, in Alto Tanaro heavy rainfall started to overpass triggering thresholds. **Figures 5a,b** shows the accumulated rainfall estimated by weather radar from the beginning of event to 23rd November at 21:00 UTC, the green stars show the whole reported landslides after the rainfall event for which the triggering time is unknown. **Figure 5b** reports in red areas where thresholds were overpassed according to Equation 3. The yellow asterisk represents the raingauge that recorded an overpassing.

The critical rainfall interested wider areas in Alto Tanaro basin, causing an increase in landslides occurrence since 24th November 2016 at 03:00 UTC (**Figures 6a,b**). Only one raingauge issued a warning for threshold exceeding.

In the Alto Tanaro, where the higher number of shallow landslides has been recorded, wider area interested by overpassing has been reached on 24th November at 15:00 UTC (**Figures 7a,b**). It is interesting noting that no raingauge recorded threshold exceedance at that time.

Some shallow landslides also occurred in hilly areas of Alto Tanaro. The same approach has been followed, applying the proper equation describing the behavior of intensity according to rainfall duration (Equation 4). **Figure 8** shows that extent of rainfall critical values during the event.

The total number of landslides in this hilly area has been seven but both rain gages and weather radar catched only five of them.

Over Pellice-Chisone the first critical rainfalls were reached on 24th November at 15:00 UTC (**Figures 9a,b**), also interesting the upper part of some catchments, where channelized debris flows were triggered due to the propagation of shallow landslides into channel network and observed in alluvial fans' areas (stars in the red circle in **Figure 9b**). Three rain gages recorded threshold overpassing missing most of landslide sites.

The area of threshold exceeding reached maximum extent on 25th November 2016 at 04:00 UTC, and then rainfall intensities decreased until the end of event. **Figure 10** shows reported landslides, the area of overpassing threshold according to weather radar QPEs, the rain gages and their datetime of overpassing threshold values.

### DISCUSSION

**Figures 5–9** show the evolution of severe rainfall event that hit Piemonte on November 2016. All shallow landslides in Alto Tanaro's Alpine environment occurred between 400 and 1500 m asl. The four highest shallow landslides (1400–1500 m asl) have been checked with surrounding ground weather stations, assessing that no snow at ground was reported during the whole period. In Pellice-Chisone basin the seven landslides occurred between 500 and 1150 m asl, excluding a snow contribution in the phenomena triggering.

Given weather radar QPEs high spatial resolution (0.64 km2 ), the warning system is more effective in landslide triggering detection than the one exclusively based on rain gages. 14 landslides were reported in Pellice-Chisone basin of which 10 have been properly detected by weather radar, while only five ones were detected by rain gages (**Figure 10a**). In Alto Tanaro's Alpine environment, 84 landslides correspond to proper detection by weather radar QPEs, while only 72 landslides were detected by rain gages, mainly due to low rain gages network density (**Figure 10b**). Nevertheless, five rain gages reported threshold exceeding corresponding to false alarms. In the hilly area, just five over seven landslides occurred were correctly detected by both radar QPEs and rain gages: this result suggests that a radar and rain gages integrated approach could have better performances in landslides triggering detection.

On 24th November 2014, temperature dropped and liquid precipitations turn to snowfall above 1200–1400 m asl in

Pellice-Chisone basin. The following day, a sudden increase in temperature caused a new turn to liquid precipitation and melting of the snow accumulated over rain gages. Weather ground stations named Pragelato, Clot della Soma, Pra Catinat, Praly, and Massello (**Figure 11**) recorded rainfalls exceeding triggering threshold when heavy precipitation occurred but with a significant contribution due to snowmelting over the gage. These conditions determined false alarms in their surrounding

area. It is worth noting that weather radar QPEs provided better estimations of the actual precipitation, correctly identifying that any threshold was not overpassed.

# CONCLUSION

Heavy precipitation interested Piemonte (Italy) between 21st and 25th November 2016, causing floods and diffuse shallow landslides in Alto Tanaro and Pellice-Chisone basins. The illustrated case study shows that the operational use of QPEs, derived by merging weather radar observations and quality-controlled rain gages, is more reliable and effective in real-time determining a representative precipitation field responsible for triggering shallow landslides. The availability of QPEs with appropriate spatial and temporal resolution allowed characterizing the initiation of the torrential phenomena (debris flow) occurred in Pellice-Chisone basin by the observation of evolution of critical rainfalls occurred over the head of basins, where the mass transport was located. In Alto Tanaro basin weather radar based QPEs identified correctly the triggered shallow landslides distribution.

Furthermore, the widespread nature of triggers, which occurred during November 2016 heavy rainfalls, confirmed the effectiveness of the pre-announcement model of shallow landslides developed and operated by the Arpa Piemonte. The subsequent use of the distribution of critical rainfall values has finally allowed reconstructing with a good degree of reliability the timing of landslides triggering.

Given a long-time weather radar QPE dataset, a possible next development will be to derive weather radar based I-D thresholds and to compare them with gages-based ones. Although, weather radar based quantitative precipitation forecast (QPF) is not currently implemented into the Arpa Piemonte operational EWS, the real-time forecast of landslides is possible by making use of the time lag between slope failure and rainfall peak, as demonstrated during November 2016 heavy rainfall. A further development will consist in the application of weather radars QPFs to foresee the shallow landslides initiation one or 2 h ahead, improving the effectiveness of warnings.

# AUTHOR CONTRIBUTIONS

RC provide weather radar data and analysis and developed algorithm for QPEs. DT developed shallow landslide early warning system.

# REFERENCES

feart-06-00134 August 30, 2018 Time: 17:9 # 12

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Cremonini and Tiranti. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.


Midpoint Approach

The midpoint approach of the ReCiPe method provides a complete environmental profile of each stage of the bridge life-cycle represented by 18 impact categories: agricultural land occupation (ALO), climate change (GWP), fossil depletion (FD), freshwater ecotoxicity (FEPT), freshwater eutrophication (FEP), human toxicity (HTP), ionizing radiation (IRP), marine ecotoxicity (MEPT), marine eutrophication (MEP), metal depletion (MD), natural land transformation (NLT), ozone depletion (OD), particulate matter formation (PMF), photochemical oxidant formation (POFP), terrestrial acidification (TAP), terrestrial ecotoxicity (TEPT), urban land occupation (ULO) and water depletion (WD). This large amount of information makes the results difficult to interpret. Although it is difficult to achieve a global assessment of the environmental impact of the bridge with the information obtained by means of the midpoint approach, it is very helpful to obtain more accurate knowledge of the impact of each category and the contribution of each process to the different impact categories.

As explained above, the data used for the environmental impact assessment do not correspond with the real data. This implies that the uncertainty associated with the different products or processes should be taken into account to obtain more realistic results. Table 3 shows the mean and coefficient of variance of each impact category for each bridge life-cycle phase. Although it is not possible to carry out a global assessment for each bridge life-cycle phase, it is possible to obtain information about the phase in which each impact category is the most significant and the variance of the information obtained. In this way, it can be observed that the manufacturing phase is the phase in which there are a higher number of impact categories with the highest contribution followed by the use and maintenance phase. The impact categories with the highest contribution in the manufacturing phase are ALO, GWP, FEPT, FEP, HTP, IRP, MEPT, MD, TETP, ULO and WD and the impact categories with the highest contributions to the use and maintenance phase are FD, MEP, NLT, ODP, PMFP, POFP and TAP. Neither the construction phase nor the end of life phase has impact categories with the highest contribution. All of this can be seen better in Figures 5 and 6, in which the bars represent the ratio of the contribution of each impact category to each life-cycle phase in relation to the highest contribution. In addition, Table 3 shows the variance of each result. In this way, although the GWP has the highest variance in the manufacturing phase, the manufacturing phase is the one in which more impact categories have the lowest variance, with a mean of 7.13%. The construction phase has the highest mean of variances (17.15%), followed by the end-of-life phase (13.16%) and the use and

maintenance phase (10.58%). Furthermore, the impact category with the highest coefficient of variation is the ULO (17.28%) and the impact category with the lowest coefficient of variation is the ALO (8.04%).

Another type of information that can be obtained by the midpoint approach is the contribution of the different products or processes to each impact category. For illustrative purposes, only three of the most popular impact categories (GWP, OD and PMF) will be studied more exhaustively and will display the contribution of the different products or processes to each bridge life-cycle phase. Figures 7–10 show the contributions of the most important processes for each bridge life-cycle phase. Figure 7 corresponds to the manufacturing phase and it is possible to see that the most important associated processes are the cement production, steel production and transport. Cement production makes the highest contribution to the GWP, namely 46.49% of the total but in the PMF and OD categories, steel production has the higher ratio with percentages of 76.14 and 57.44% respectively. Furthermore, it can be seen that, although the GWP has a low percentage of other processes (6.07%), the cement production, steel production and transport represent a larger part of the environmental impact of this bridge life-cycle phase. Figure 8 corresponds to the construction phase and the processes that lead to practically all the environmental impacts are those due to the manipulation of fresh concrete and the transport and elevation of the precast beams. Figures 9 and 10 show the use and maintenance phase and end-of-life phase, in which the CO2 fixed is taken into account. In the GWP impact category, it can be seen that there is a positive impact. On one hand, in the use and maintenance phase, the amount of CO2 fixed is much lower than the CO2 eq produced by the maintenance activities and the traffic detour because the concrete surface in contact with the environment represents a very low proportion of the total of amount of concrete in the bridge. The percentage of the CO2 fixed is −3.84%, while the percentages of maintenance activities and traffic detour are 89.95% and 13.89%, respectively, adding a total of 100% due to that the global GWP impact in this phase is positive. The ratio of the contribution of the maintenance activities and traffic detour can be modified considerably in function of the features of the traffic diversion (distance, average daily traffic and percentage of trucks). On the other hand, in the end-of-life phase, the amount of CO2 fixed is higher ( −254.05%) than the CO2 eq produced by the demolition activities (22.40%), the waste treatment (36.21%) and the associated transport (96.18%). The total contribution of the processes in the end-of-life phase is negative, adding a total of −100%. In the other impact categories (PMF and OD), the maintenance activities and transport make the major contribution to each bridge life-cycle.

**Table 3.** Midpoint approach.

**Figure 5.** Impact categories of manufacturing and construction stage.

**Figure 6.** Impact categories of use and end of life stage.

**Figure 7.** Manufacturing phase.

**Figure 8.** Construction phase.

**Figure 9.** Use and maintenance phase.

**Figure 10.** End-of-life phase.


*2.1. Patient Recruitment*

Inclusion criteria were age ≥ 50 years, displaced or unstable three- or four-part fracture of the proximal humerus (except isolated displaced fractures of the greater or lesser tuberosity) treated with a plate and screw osteosynthesis (PHILOS locking plate—with or without screw augmentation) within 10 days after injury, ability to understand the content of the study and the patient consent form and voluntary signed informed consent.

Exclusion criteria were previous proximal humerus fracture on the ipsilateral limb, humeral head impression/splitting fracture, fibula grafting, bone block or any other noncement augmentation of the PHILOS locking plate fixation, associated nerve or vessel injury, serious fracture fixation issues such as too long screw, screw perforation through the humeral head, or a broken screw or implant recognized directly on the first postoperative X-ray. Other exclusion criteria were severe systematic diseases rated in class 4 and higher of the American Society of Anesthesiologists (ASA) physical status classification, substance abuse, prisoner, participation in another medical device or product study in the past month that could affect this study, pregnancy, or pacemaker.

Patient data including age, gender, height, weight, residential status, injury side, arm dominance and fracture type were collected at recruitment.

#### *2.2. Postoperative Protocol*

The two university hospitals used different postoperative rehabilitation protocols according to their standard of care. In hospital H1, the patients were treated with a sling for 3 weeks and were only allowed passive and active-assisted mobilization under supervision of a physiotherapist for the first 3 weeks. Patients in hospital H2 were treated without a sling and allowed to mobilize without restrictions immediately. Physiotherapy was started immediately postoperatively and prescribed 2–3 times per week in both hospitals.

#### *2.3. Activity Tracking Apparatus and Procedure*

Accelerometer sensors (AX3, Axivity Ltd., Newcastle upon Tyne, UK) [23] (Figure 1, left) were used to measure shoulder activity continuously (24/7) for 6 weeks after the operation in two consecutive 3-week periods. The length of the measurement period was determined by the sensor's battery and memory capacities. The first period started at the latest 4 days postoperatively and ended at 21 ± 3 days, the second period started at the same visit and ended at 42 ± 3 days. Two sensors were used for each patient and period. One sensor was attached to the upper arm of the treated side, and another was located at

the chest and served as a reference (Figure 1, right), allowing evaluation of the shoulder angle as the orientation difference between both devices. Data recording was performed at 50 Hz frequency within ±4 g limits that were deemed suitable in a pilot evaluation. The sensors were attached to the skin using a dedicated certified medical-grade adhesive tape (3M 4077, 3M Medical Materials & Technologies, Oakdale, CA, USA). Attachment (directly postoperatively and at the 3-week follow-up visit) and detachment (at the 3-week and 6-week follow-up visits) were performed by trained study personnel according to standard operating procedures to ensure consistent sensor location and alignment. The start and end time points of a given period were marked by knocking five times synchronously at both arm and chest sensors. The patients were allowed to follow their normal daily activities including showering with the attached device. At the end of the measurement periods and after detachment, sensor data were downloaded using the Open Movement GUI software (Open Movement project).

'

" " " " – – **Figure 1.** Accelerometer sensors (Axivity AX3, (**left**), source: https://axivity.com/) were attached to the patients (illustrated on the (**right**)), on the chest ("1") and on the back of the upper arm on the treated side ("2"). Each sensor had its own coordinate system, indicated with the red–green– blue arrows.


**3. Preliminary study**

The proposed security framework uses the SHA-3 for the initial key generation, the AES algorithm for the whole image encryption–decryption, and the RSA algorithm for the key exchange and authentication. More information on the suggested algorithms is given in this section.

#### **3.1 Keccak**

Hash functions get a finite arbitrary length of data as an input argument and produce an output data digest of a fixed length of bits. The keccak hash function is selected by the NIST as the contest winner SHA-3 [7]. The keccak enables the integrity of information such that a tiny alteration in the input data, with one bit, will cause a greatly significant change in the output. As a sequence, each data has its own data digest. It is considered a sponge function because it is based on construction sponge as shown in **Figure 1**. It can produce pseudo-random output with the desired length from an arbitrary length entry.

The keccak algorithm is the permutation f, which is repeatedly applied to a fixed data length (b = r + c bits), where c and r are, respectively, the capacity and the throughput binary. Higher values of c correspond to a higher level of security then those higher values of r improve the speed. The sponge function consists of three phases: the first is the padding phase, where the entry is completed by a padding rule that produces an output length multiple of r, the bit rate. Second, the message is divided into blocks with each of r bits in length such that each block is XOR with the bit rate r. The result will then be concatenated with the capacity c to constitute a state that is an entry of the absorbing phase. In this phase, the state is absorbed by a function f, then, a calculation is repeated for a number determined of rounds. Finally, the output of the absorbent phase is extracted from its state (phase of compression) and constitutes the digest with the desired length. More details are given in [8].

**Figure 1.** *The construction mechanism of the sponge.*

#### **3.2 AES**

AES is a symmetric encryption standard considered by the FIPS as the rapid and the more efficient among symmetric algorithms [9]. AES is a block cipher algorithm; data is processed in 128-bit blocks for plaintext and ciphertext. The secret key is 128 bits long, hence the version name is AES 128 (there are two other variants whose keys are 192 and 256 bits). The key with 256 bits of length is the most secure to perform high security. The AES is composed of four main operations to perform both confusion and diffusion for the Shannon Principe, which are the AddRound key, the Subbytes, the Shiftrows, and the MixColumns. The key expansion is used to generate keys from an initial secret key. More information is given in [9]. The flow design of the AES is shown in the **Figure 2**.

#### **3.3 CTR encryption mode**

In this mode, the key flow is obtained by encrypting the successive values of a counter. This mode combines many advantages because it allows stream encryption and is pre-computable. In addition, it allows random access to data, is parallelizable, and only uses the encryption function. The counter used can be a pseudo-random sequence that will be easy to find from the seed (initialization vector). Encryption architecture is detailed in **Figure 3**.

**Figure 2.** *Flow design of the AES.*

*Hardware Implementation of an Improved Hybrid Cryptosystem for Numerical Image Encryption… DOI: http://dx.doi.org/10.5772/intechopen.105207*

#### **Figure 3.**

*General architecture of CTR encryption mode.*

## **3.4 RSA algorithm**

The RSA cryptosystem provides a digital signature scheme (sign + verify), based on the math of the modular exponentiations and discrete logarithms and the computational difficulty of the RSA problem (and its related integer factorization problem). The RSA sign/verify algorithm works as described below.

1.Key Generation Procedure

1.Select two distinct big random prime numbers p and q.

#### 2.RSA Sign

	- 1.Verifying a signature 's' for the message A with the public key exponent e

2.Compute the hash: h = hash(A)

3.Decrypt the signature: *h*0 =*se*(mod*n*)

4.Compare h with h' to find whether the signature is valid or not

5. If the signature is correct, then the following will be correct:

$$h' = \operatorname{se}(\operatorname{mod} n) = (\operatorname{hd})\,e(\operatorname{mod} n) = h'$$


**18. New development of the National Drug Strategy in Europe**

4 EU Member States (Czech Republic, Estonia, Hungary, Finland), Turkey and Norway adopted new drug action plans in the second half of 2007. The documents cover a period of 3–4 years and, with the exception of the Turkish action plan, were preceded by other plans or programs. In 2007, Spain adopted a complementary national action program against cocaine (2007–2010). In 2008, 3 more Member States adopted new policy documents. The first Italian Drugs Action Plan has a time frame of one year, followed by a four-year Action Plan (2009–2012), which will be synchronized with the new EU Drugs Action Plan. Malta's first national drug document does not set a timeframe of almost 50 measures. The new 10-year UK drugs strategy (2008–2011) is complemented by a three-year action plan (2008–2011) setting out key actions [25].


 Normally, Transcription factors recruit coactivators with HAT activity to regulatory DNA sites, whereas transcriptional repressors recruit corepressors with HDAC activity (Sun et al., 2001). A summary of known HAT proteins is presented in Table 1 (Sterner et al., 2000; Yang, 2004; Kimura et al., 2005).

Many HATs have also be showed to be involved in breast cancer. Among of them, p300/CBP and NCOAs are the most important and well-characterised HAT proteins associated with breast cancer.

#### **<IP_ADDRESS> p300/CBP**

p300 and its close homolog CBP (CREB-binding protein) are often referred to as a single entity. p300 and CBP share several conserved domains: (1) the bromodomain (Br), which is frequently found in mammalian HATs; (2) three cysteine-histidine (CH)-rich domains (CH1, CH2 and CH3); (3) a KIX domain; and (4) an ADA2-homology domain, which shows extensive similarity to Ada2p, a yeast transcriptional co-activator. The N- and C-terminal domains of p300/CBP can act as transactivation domains, and the CH1, CH3 and the KIX domains are likely to be important in mediating protein-protein interactions, and a number of cellular and viral proteins bind to these regions. The acetyl-transferase domain is located in the central region of the protein, and the Br domain could function in recognising different acetylated motifs (Fig 3A, B) (Chan et al., 2001). p300/CBP contribute to acetylation of H3-K56 and promotes the subsequent assembly of newly-synthesized DNA into chromatin (Das et al., 2009). It is a non-DNA-binding transcriptional coactivator which stimulates transcription of target genes by interacting, either directly or through cofactors, with numerous promoter-binding transcription factors such as CREB, nuclear hormone receptors, and oncoprotein-related activators such as c-Fos, c-Jun, c-Myb and AML1 (Fig 3C) (Kitabayashi et al., 1998; Sterner et al., 2000).

Histone Modification and Breast Cancer 325

Fig. 3. Organisation of p300/CBP proteins. (A) Comparison of p300 and CBP. The dark regions indicate the areas of highest homology; (B) The functional domains in p300; (C) One

p300/CBP is a ubiquitously expressed, global transcriptional coactivator that is involved in most important cellular programs, such as cell cycle control, differentiation, and apoptosis. Mice nullizygous for p300 or double heterozygous for p300 and CBP showed defects in neurulation and heart development, and then exhibited embryonic lethality, and mutations in p300 and CBP are associated with certain human disease processes (Giles et al., 1998; Yao et al., 1998; Giordano et al., 1999). A role for p300 in tumor suppression has been proposed by the fact that disturbance of p300 function by viral oncoproteins is essential for the transformation of rodent primary cells and, consistent with this hypothesis, mutations of p300 have been identied in certain types of human cancers, including breast carcinomas

It showed that both the localization of p300 and the recruitment to aggresomes differ between breast cancers and normal mammary glands. The expression level of p300 in breast cancer epithelia is higher than that in normal mammary gland. Cytoplasmic localization of p300 was also observed in tumor epithelia whereas nuclear localization was found in normal mammary glands in both animal models and in non-malignant adjacent areas of human breast cancer specimens. Proteasomal inhibition induced p300 redistribution to aggresomes in tumor but not

The regulation of gene expression by nuclear receptors (NRs) controls the phenotypic properties and diverse biologies of target cells.  Upon E2 treatment, gene transcription is widely impacted, creating highly complex regulatory networks whose ultimate goal is the stimulation or suppression of specic biological processes. p300/CBP can function as a transcriptional cofactor of ERs and other

in normal mammary gland-derived cells (Fermento et al., 2010).

of the potential model for the action of p300/CBP in the transcriptional regulation

(Kitabayashi et al., 1998; Sterner et al., 2000).

(Gayther et al., 2000).

Table 1. Summary of major human HATs

Basic functions

H3/H4 Nuclear receptor coactivators

Hat1 H4 Histone deposition, chromatin

Tip60 H2A/H3/ H4 Transcriptional co-regulator, DNA

MORF H2A/H3/ H4 Transcriptional coactivator (strong

HBO1 H3/H4 DNA replication, transcriptional

Gcn5 H3/H4 Transcriptional coactivator PCAF H3/H4 Transcriptional coactivator

MOZ H3 Transcriptional coactivator

TAFII250 H3/H4 TBP-associated factor, transcription

TFIIIC H3/H4 RNA polymerase III transcription

initiation TFIIIC220

ATF-2 H4/H2B Transcriptional activator CIITA H4 Transcriptional coactivator

CDY H4 Histone-to-protamine transition

TFIIIC110 TFIIIC90

Table 1. Summary of major human HATs

Global transcriptional coactivator

(transcriptional response to hormone

assembly and gene silencing

repair and apoptosis

homology to MOZ)

during spermatogenesis

initiation, kinase and ubiquitin ligase

corepressor

specificity

3/H4

signals) NCOA1

Family Members Histone

(SRC-1) NCOA2 (SRC-2) NCOA3 (SRC-3)

Nuclear receptor coactivators (p160, SRC)

GNAT

MYST

P300/CBP H2A/H2B/H

Fig. 3. Organisation of p300/CBP proteins. (A) Comparison of p300 and CBP. The dark regions indicate the areas of highest homology; (B) The functional domains in p300; (C) One of the potential model for the action of p300/CBP in the transcriptional regulation (Kitabayashi et al., 1998; Sterner et al., 2000).

p300/CBP is a ubiquitously expressed, global transcriptional coactivator that is involved in most important cellular programs, such as cell cycle control, differentiation, and apoptosis. Mice nullizygous for p300 or double heterozygous for p300 and CBP showed defects in neurulation and heart development, and then exhibited embryonic lethality, and mutations in p300 and CBP are associated with certain human disease processes (Giles et al., 1998; Yao et al., 1998; Giordano et al., 1999). A role for p300 in tumor suppression has been proposed by the fact that disturbance of p300 function by viral oncoproteins is essential for the transformation of rodent primary cells and, consistent with this hypothesis, mutations of p300 have been identied in certain types of human cancers, including breast carcinomas (Gayther et al., 2000).

It showed that both the localization of p300 and the recruitment to aggresomes differ between breast cancers and normal mammary glands. The expression level of p300 in breast cancer epithelia is higher than that in normal mammary gland. Cytoplasmic localization of p300 was also observed in tumor epithelia whereas nuclear localization was found in normal mammary glands in both animal models and in non-malignant adjacent areas of human breast cancer specimens. Proteasomal inhibition induced p300 redistribution to aggresomes in tumor but not in normal mammary gland-derived cells (Fermento et al., 2010).

The regulation of gene expression by nuclear receptors (NRs) controls the phenotypic properties and diverse biologies of target cells.  Upon E2 treatment, gene transcription is widely impacted, creating highly complex regulatory networks whose ultimate goal is the stimulation or suppression of specic biological processes. p300/CBP can function as a transcriptional cofactor of ERs and other

Histone Modification and Breast Cancer 327

or TIF2) and NOCA3 (SRC-3, p/CIP, RAC3, ACTR, AIB1 or TRAM-1). These three members have an overall sequence similarity of 50–55% and sequence identity of 43–48%. They contain three structural domains. The N-terminal basic helix-loop-helix-Per/ARNT/ Sim (bHLH-PAS) domain is the most conserved region and is required for interact with several transcription factors (such as myogenin, MEF-2C and TEF, but not be obligator for NRs) and then enhance the transcription (Onate et al., 1995; Belandia et al., 2000). The central region contains three LXXLL (L, leucine; X, any amino acid) motifs, which form an amphipathic αhelix and are responsible for interacting with NRs (Heery et al., 1997; Darimont et al., 1998). The C-terminus contains two intrinsic transcriptional activation domains (AD1 and AD2). The AD1 region binds p300/CBP (but not interact with NRs), and this recruitment of p300/CBP to the chromatin is essential for NCOA-mediated transcriptional activation (Yao et al., 1996). The AD2 domain interacts with histone methyltransferases, coactivatorassociated arginine methyltransferase 1 (CARM1) and protein arginine methyltransferases (PRMT1) (Koh et al., 2001). Based on such molecular features, NCOAs interact with ligandbound nuclear receptors and recruit histone acetyltransferases and methyltransferases to specific enhancer/promotor regions, which in turn results in chromatin remodeling, assembly of general transcription factors and recruitment of RNA Polymerase II for transcriptional activation (Fig 5) (Zhang et al., 2004; Xu et al., 2009). Furthermore, The Ctermini of NCOAs itself also contain HAT activity domains (Chen et al., 1997; Spencer et al., 1997), and the poly Q encoding sequence in the C-terminal of NCOA3 gene is genetically unstable and is an easy target for somatic mutations in cancer cells (Wong et al., 2006).

Fig. 5. Molecular structure of NCOAs and their functional mechanisms in steroid hormoneinduced gene expression. Abbreviations: H, hormone; NRID, NR interaction domain; TBP, the TATA binding protein; TAFIIs, TBP-associated general transcription factors (GTFs).

Except of NRs, NCOAs also serve as coactivators for many other transcription factors associated with breast cancer, such as HIF1, NF-κB, E2F1, p53, RB and MRTFs (Zhang et al., 2004; Xu et al., 2009). By regulating a broad range of gene expression controlled by NRs and non-NR transcription factors, NCOAs regulate diverse events in the development of breast cancer. Either NCOA1 or NCOA2 deficiency can reduce ductal side branching and alveologenesis in the mammary gland (Xu et al., 1998; Mukherjee et al., 2006), and NCOA3−/− mice show growth retardation, delayed puberty, reduced female reproductive

In normal human breast, the levels of the three NCOA proteins in epithelial cells are usually low or undetectable (Hudelist et al., 2003). NCOA1 is overexpressed in 19% to 29% of breast cancers and plays important roles in cell proliferation, lymph node metastasis, disease recurrence and poor disease-free survival (DFS) (Fleming et al., 2004). Therefore, elevated

function and blunted mammary gland development (Xu et al., 2000).

nuclear hormone receptors (Hanstein et al., 1996). Compared to CBP, NRIP1 and NCOAs, which play more gene-specic roles in the ER-dependent transcription, p300 seemed to be the only cofactor that appeared to be recruited at all the target genes of ER and plays a central role in both transcriptional activation and repression. After E2 treatment, ERα recruits coactivator complexes including of p300 and initiates transient stimulation of transcription via binds to ERα binding sites of target genes. If it could offer a more stable nucleation site for coactivator proteins (i.e. SRC-3), leading to histone acetylation and engagement of RNA polymerase II (Pol II), the transcriptional activation status would be maintained. Alternatively, ERα can cause transcriptional repression by recruiting, via p300, CtBP1-containing repressor complexes which lead to RNA polymerase II dismissal and histone deacetylation (Fig 4) (Stossi et al., 2009). In addition, the breast cancer susceptibility gene BRCA1 can strongly inhibits the transcriptional activity of ERα in human breast and prostate cancer cell lines, and this event is correlates with its down-regulation of p300 (but not CBP) (Fan et al., 2002). p300 also plays roles in the regulation of CYP19 I.3/II (aromatase), the key enzyme in estrogen biosynthesis and an important target in breast cancer (Subbaramaiah et al., 2008).

Fig. 4. Proposed model for ERα-mediated activation or repression of target genes via p300 (Stossi et al., 2009).

Another important role of p300 in breast cancer is the regulation of p53, a famous tumor suppressor. p53 can be acetylated by p300 in response to DNA damage to regulate its DNAbinding and transcriptional functions (Yuan et al., 1999). What's more, the N terminus of p300/CBP exhibits the ubiquitin ligase E3/E4 activity and is required for physiologic p53 polyubiquitination and degradation. Depletion of CBP or p300 could enhance the stabilization of p53 (Grossman et al., 2003; Shi et al., 2009).

Furthermore, p300/CBP has also been identified as a coactivator of HIF1α (hypoxiainducible factor 1 alpha), and thus plays a role in the stimulation of hypoxia-induced genes (such as VEGF, GLUT1, etc) and development of glycolysis, which is the most important metabolic marker of cancer (Ruas et al., 2005).

#### **<IP_ADDRESS> Nuclear receptor coactivators**

The Nuclear receptor coactivator family (NCOA), also named as p160 or steroid receptor coactivator, contains three homologous members: NCOA1 (SRC-1), NCOA2 (SRC-2, GRIP1

nuclear hormone receptors (Hanstein et al., 1996). Compared to CBP, NRIP1 and NCOAs, which play more gene-specic roles in the ER-dependent transcription, p300 seemed to be the only cofactor that appeared to be recruited at all the target genes of ER and plays a central role in both transcriptional activation and repression. After E2 treatment, ERα recruits coactivator complexes including of p300 and initiates transient stimulation of transcription via binds to ERα binding sites of target genes. If it could offer a more stable nucleation site for coactivator proteins (i.e. SRC-3), leading to histone acetylation and engagement of RNA polymerase II (Pol II), the transcriptional activation status would be maintained. Alternatively, ERα can cause transcriptional repression by recruiting, via p300, CtBP1-containing repressor complexes which lead to RNA polymerase II dismissal and histone deacetylation (Fig 4) (Stossi et al., 2009). In addition, the breast cancer susceptibility gene BRCA1 can strongly inhibits the transcriptional activity of ERα in human breast and prostate cancer cell lines, and this event is correlates with its down-regulation of p300 (but not CBP) (Fan et al., 2002). p300 also plays roles in the regulation of CYP19 I.3/II (aromatase), the key enzyme in estrogen biosynthesis and an important target in breast

Fig. 4. Proposed model for ERα-mediated activation or repression of target genes via p300

Another important role of p300 in breast cancer is the regulation of p53, a famous tumor suppressor. p53 can be acetylated by p300 in response to DNA damage to regulate its DNAbinding and transcriptional functions (Yuan et al., 1999). What's more, the N terminus of p300/CBP exhibits the ubiquitin ligase E3/E4 activity and is required for physiologic p53 polyubiquitination and degradation. Depletion of CBP or p300 could enhance the

Furthermore, p300/CBP has also been identified as a coactivator of HIF1α (hypoxiainducible factor 1 alpha), and thus plays a role in the stimulation of hypoxia-induced genes (such as VEGF, GLUT1, etc) and development of glycolysis, which is the most important

The Nuclear receptor coactivator family (NCOA), also named as p160 or steroid receptor coactivator, contains three homologous members: NCOA1 (SRC-1), NCOA2 (SRC-2, GRIP1

stabilization of p53 (Grossman et al., 2003; Shi et al., 2009).

metabolic marker of cancer (Ruas et al., 2005).

**<IP_ADDRESS> Nuclear receptor coactivators**

cancer (Subbaramaiah et al., 2008).

(Stossi et al., 2009).

or TIF2) and NOCA3 (SRC-3, p/CIP, RAC3, ACTR, AIB1 or TRAM-1). These three members have an overall sequence similarity of 50–55% and sequence identity of 43–48%. They contain three structural domains. The N-terminal basic helix-loop-helix-Per/ARNT/ Sim (bHLH-PAS) domain is the most conserved region and is required for interact with several transcription factors (such as myogenin, MEF-2C and TEF, but not be obligator for NRs) and then enhance the transcription (Onate et al., 1995; Belandia et al., 2000). The central region contains three LXXLL (L, leucine; X, any amino acid) motifs, which form an amphipathic αhelix and are responsible for interacting with NRs (Heery et al., 1997; Darimont et al., 1998). The C-terminus contains two intrinsic transcriptional activation domains (AD1 and AD2). The AD1 region binds p300/CBP (but not interact with NRs), and this recruitment of p300/CBP to the chromatin is essential for NCOA-mediated transcriptional activation (Yao et al., 1996). The AD2 domain interacts with histone methyltransferases, coactivatorassociated arginine methyltransferase 1 (CARM1) and protein arginine methyltransferases (PRMT1) (Koh et al., 2001). Based on such molecular features, NCOAs interact with ligandbound nuclear receptors and recruit histone acetyltransferases and methyltransferases to specific enhancer/promotor regions, which in turn results in chromatin remodeling, assembly of general transcription factors and recruitment of RNA Polymerase II for transcriptional activation (Fig 5) (Zhang et al., 2004; Xu et al., 2009). Furthermore, The Ctermini of NCOAs itself also contain HAT activity domains (Chen et al., 1997; Spencer et al., 1997), and the poly Q encoding sequence in the C-terminal of NCOA3 gene is genetically unstable and is an easy target for somatic mutations in cancer cells (Wong et al., 2006).

Fig. 5. Molecular structure of NCOAs and their functional mechanisms in steroid hormoneinduced gene expression. Abbreviations: H, hormone; NRID, NR interaction domain; TBP, the TATA binding protein; TAFIIs, TBP-associated general transcription factors (GTFs).

Except of NRs, NCOAs also serve as coactivators for many other transcription factors associated with breast cancer, such as HIF1, NF-κB, E2F1, p53, RB and MRTFs (Zhang et al., 2004; Xu et al., 2009). By regulating a broad range of gene expression controlled by NRs and non-NR transcription factors, NCOAs regulate diverse events in the development of breast cancer. Either NCOA1 or NCOA2 deficiency can reduce ductal side branching and alveologenesis in the mammary gland (Xu et al., 1998; Mukherjee et al., 2006), and NCOA3−/− mice show growth retardation, delayed puberty, reduced female reproductive function and blunted mammary gland development (Xu et al., 2000).

In normal human breast, the levels of the three NCOA proteins in epithelial cells are usually low or undetectable (Hudelist et al., 2003). NCOA1 is overexpressed in 19% to 29% of breast cancers and plays important roles in cell proliferation, lymph node metastasis, disease recurrence and poor disease-free survival (DFS) (Fleming et al., 2004). Therefore, elevated

Histone Modification and Breast Cancer 329

methylation at H3K4 or H3K36, mono- methylations of H3K27, H3K9, H4K20, H3K79, and H2BK5 is associated with transcriptional activation, whereas trimethylations of H3K27, H3K9 H3K79, and H4K20 are linked to transcriptional repression (Rea et al., 2000; Kouzarides, 2007; Wang et al., 2007). Many HKMTs have been isolated and characterized (Tab 2). Up to now, except of Dot1, all the HKMTs contains a conserved SET [Su(var), Enhancer of zeste, trithorax] domain that is responsible for catalysis and binding of cofactor S-adenosyl-l- methionine (AdoMet), and many of them has been shown to play roles in the

NSD3 is amplied in human breast cancer cell lines and primary tumors and identied at the breakpoint of t(8;11)(p11.2;p15), resulting in a fusion of the NUP98 and NSD genes

SMYD3 is a novel SET-domain-containing lysine histone methyltransferase which has been regarded as an important factor in carcinogenesis. Formed a complex with RNA polymerase II through an interaction with the RNA helicase HELZ, SMYD3 specically methylates H3K4 and activates the transcription of a set of downstream genes (including of Nkx2.8, hTERT, WNT10B, VEGFR1, c-Met, etc) containing a ''5′ - CCCTCC - 3′" or "5′ - GGAGGG - 3" sequence in the promoter region (Fig 6) (Hamamoto et al., 2004; Hamamoto et al., 2006; Kunizaki et al., 2007; Zou et al., 2009). It seems that the N-terminal region of SMYD3 plays an important role for the regulation of its methyltransferase activity, and the cleavage of 34 amino acids in the N-terminal region or interaction with heat shock protein 90 alpha (HSP90α) may enhance the histone methyltransferase (HMTase) activity compared to the full-length protein (Silva et al., 2008). Enhanced expression of SMYD3 is essential for the growth of many cancer cells (such as breast cancer, colorectal carcinoma, hepatocellular carcinoma, etc), and it also could stimulate cell adhesion and migration, whereas suppression of SMYD3 by RNAi or other reagents induces apoptosis and inhibits cell proliferation and migration (Hamamoto et al., 2004; Hamamoto et al., 2006; Luo et al., 2007; Wang et al., 2008; Luo et al., 2009; Zou et al., 2009; Luo et al., 2010). SMYD3 may be an important coactivator of estrogen receptor (ER) in the estrogen signal pathway. It can directly interact with the ligand binding domain of ER, in turn augments ER target gene

Fig. 6. SMYD3-mediated histone H3-K4 methylation and transcriptional regulation. (Sims et

EZH2 overexpression has been found in breast cancer, its elevation is associated with poor prognosis. It seems that EZH2 might be associated with the regulation of pRB–E2F pathway and genes involved in homologous recombination pathway of DNA repair (Zeidler et al., 2005). However, the detailed mechanism of EZH2 in cancer is not yet clear. Another study has shown that EZH2 is also overexpressed in preneoplastic breast lesions and morphologically normal breast epithelium adjacent to the pre-invasive and invasive lesions, indicating that it might be a marker of epithelium at higher risk for neoplastic

breast cancer.

al., 2004)

transformation (Ding et al., 2006).

(Angrand et al., 2001; Rosati et al., 2002).

expression via histone H3-K4 methylation (Kim 2009).

NCOA1 has been regarded as an independent predictor of breast cancer recurrence following therapy (Redmond et al., 2009). Although the evidence were not very sufficient, NCOA2 overexpression might also promote proliferation and invasion of breast cancer cells (Kishimoto et al., 2005). The amplification (in less than 10%) and elevated expression (in over 30%) of NCOA3 were be detected in breast cancer, and its overexpression in breast cancer usually correlates with the expression of ERBB2 , matrix metalloproteinase 2 (MMP2), MMP9 and PEA3 and with larger tumor size, higher tumor grade, and/or poor DFS (Anzick et al., 1997; Hudelist et al., 2003; Harigopal et al., 2009; Xu et al., 2009). What's more, elevated NCOA3 is able to promote estrogen-independent cell proliferation depends on the function of E2F1 and the association between NCOA3 and E2F1, but not ER (Louie et al., 2004).

In addition, NCOAs play important roles in the chemotherapy resistance of breast cancer. Increased expression levels of the ER-NCOA3 complex were found in tamoxifen-resistant cells, and such overexpression of NCOA3 could enhance the agonist activity of tamoxifen and therefore, reduces its antitumor activity in patients with breast cancer (Smith et al., 1997; Zhao et al., 2009).

#### **<IP_ADDRESS> HDACs**

The 18 HDACs identied so far can be categorized into four classes: class I (HDAC1–3, HDAC8), class II (HDAC4–7, 9–10), class III (Sirtuin1–7) and class IV (HDAC11). Class I, II, and IV HDACs share homology in both sequence and structure and all require a zinc ion for catalytic activity. In contrast, class III HDACs shares no similarities in their sequence or structure with class I, II, or IV HDACs and requires nicotinamide adenine dinucleotide (NAD+) for catalytic activity (Ellis et al., 2009; Mottet et al., 2010). HDACs remove the acetyl groups from histone lysine tails and are thought to facilitate transcriptional repression by decreasing the level of histone acetylation. Like HATs, HDACs also have non-histone targets (Bolden et al., 2006; Wang et al., 2007).

Several HDACs have been found to be involved in breast cancer. In ER-positive breast cancer MCF-7 cells, expression of HDAC6 was increased after being treated by estradiol, and the elevated HDAC6 could deacetylate alpha-tubulin and increase cell motility. While the ER antagonist tamoxifen (TAM) or ICI 182,780 could prevent estradiol-induced HDAC6 upregulation, and then reduce cell motility. The *in vivo* assays showed that the patients with high levels of HDAC6 mRNA tended to be more responsive to endocrine treatment than those with low levels, indicating that the levels of HDAC6 expression might be used as both as a marker of endocrine responsiveness and also as a prognostic indicator in breast cancer (Zhang et al., 2004; Saji et al., 2005).  HDAC4 overexpression and mutations have also been found in breast cancer samples (Sjoblom et al., 2006).

#### **2.2.2 Histone methylation in breast cancer**

Histones can be mono-, di-, or tri-methylated at lysine or arginine residues by histone methyltransferases (HMTs). Many HMTs, including both lysine-specific HMTs (eg. SMYD3) and arginine-specific HMTs (eg. PRMT1 and CARM1), have been shown to act as ER coactivators and be involved in breast cancer.

#### **<IP_ADDRESS> Histone lysine methyltransferase (HKMTs)**

Histone lysine methylation occurs on histone H3 at ε-amino group of lysines 4, 9, 14, 27, 36, and 79 and on histone H4 at lysines 20 and 59 (Strahl et al., 2000; Lee et al., 2005). In general,

NCOA1 has been regarded as an independent predictor of breast cancer recurrence following therapy (Redmond et al., 2009). Although the evidence were not very sufficient, NCOA2 overexpression might also promote proliferation and invasion of breast cancer cells (Kishimoto et al., 2005). The amplification (in less than 10%) and elevated expression (in over 30%) of NCOA3 were be detected in breast cancer, and its overexpression in breast cancer usually correlates with the expression of ERBB2 , matrix metalloproteinase 2 (MMP2), MMP9 and PEA3 and with larger tumor size, higher tumor grade, and/or poor DFS (Anzick et al., 1997; Hudelist et al., 2003; Harigopal et al., 2009; Xu et al., 2009). What's more, elevated NCOA3 is able to promote estrogen-independent cell proliferation depends on the function of E2F1 and

In addition, NCOAs play important roles in the chemotherapy resistance of breast cancer. Increased expression levels of the ER-NCOA3 complex were found in tamoxifen-resistant cells, and such overexpression of NCOA3 could enhance the agonist activity of tamoxifen and therefore, reduces its antitumor activity in patients with breast cancer (Smith et al., 1997;

The 18 HDACs identied so far can be categorized into four classes: class I (HDAC1–3, HDAC8), class II (HDAC4–7, 9–10), class III (Sirtuin1–7) and class IV (HDAC11). Class I, II, and IV HDACs share homology in both sequence and structure and all require a zinc ion for catalytic activity. In contrast, class III HDACs shares no similarities in their sequence or structure with class I, II, or IV HDACs and requires nicotinamide adenine dinucleotide (NAD+) for catalytic activity (Ellis et al., 2009; Mottet et al., 2010). HDACs remove the acetyl groups from histone lysine tails and are thought to facilitate transcriptional repression by decreasing the level of histone acetylation. Like HATs, HDACs also have non-histone

Several HDACs have been found to be involved in breast cancer. In ER-positive breast cancer MCF-7 cells, expression of HDAC6 was increased after being treated by estradiol, and the elevated HDAC6 could deacetylate alpha-tubulin and increase cell motility. While the ER antagonist tamoxifen (TAM) or ICI 182,780 could prevent estradiol-induced HDAC6 upregulation, and then reduce cell motility. The *in vivo* assays showed that the patients with high levels of HDAC6 mRNA tended to be more responsive to endocrine treatment than those with low levels, indicating that the levels of HDAC6 expression might be used as both as a marker of endocrine responsiveness and also as a prognostic indicator in breast cancer (Zhang et al., 2004; Saji et al., 2005).  HDAC4 overexpression and mutations have also been found in breast cancer samples

Histones can be mono-, di-, or tri-methylated at lysine or arginine residues by histone methyltransferases (HMTs). Many HMTs, including both lysine-specific HMTs (eg. SMYD3) and arginine-specific HMTs (eg. PRMT1 and CARM1), have been shown to act as ER

Histone lysine methylation occurs on histone H3 at ε-amino group of lysines 4, 9, 14, 27, 36, and 79 and on histone H4 at lysines 20 and 59 (Strahl et al., 2000; Lee et al., 2005). In general,

the association between NCOA3 and E2F1, but not ER (Louie et al., 2004).

Zhao et al., 2009). **<IP_ADDRESS> HDACs**

(Sjoblom et al., 2006).

targets (Bolden et al., 2006; Wang et al., 2007).

**2.2.2 Histone methylation in breast cancer**

coactivators and be involved in breast cancer.

**<IP_ADDRESS> Histone lysine methyltransferase (HKMTs)**

methylation at H3K4 or H3K36, mono- methylations of H3K27, H3K9, H4K20, H3K79, and H2BK5 is associated with transcriptional activation, whereas trimethylations of H3K27, H3K9 H3K79, and H4K20 are linked to transcriptional repression (Rea et al., 2000; Kouzarides, 2007; Wang et al., 2007). Many HKMTs have been isolated and characterized (Tab 2). Up to now, except of Dot1, all the HKMTs contains a conserved SET [Su(var), Enhancer of zeste, trithorax] domain that is responsible for catalysis and binding of cofactor S-adenosyl-l- methionine (AdoMet), and many of them has been shown to play roles in the breast cancer.

NSD3 is amplied in human breast cancer cell lines and primary tumors and identied at the breakpoint of t(8;11)(p11.2;p15), resulting in a fusion of the NUP98 and NSD genes (Angrand et al., 2001; Rosati et al., 2002).

SMYD3 is a novel SET-domain-containing lysine histone methyltransferase which has been regarded as an important factor in carcinogenesis. Formed a complex with RNA polymerase II through an interaction with the RNA helicase HELZ, SMYD3 specically methylates H3K4 and activates the transcription of a set of downstream genes (including of Nkx2.8, hTERT, WNT10B, VEGFR1, c-Met, etc) containing a ''5′ - CCCTCC - 3′" or "5′ - GGAGGG - 3" sequence in the promoter region (Fig 6) (Hamamoto et al., 2004; Hamamoto et al., 2006; Kunizaki et al., 2007; Zou et al., 2009). It seems that the N-terminal region of SMYD3 plays an important role for the regulation of its methyltransferase activity, and the cleavage of 34 amino acids in the N-terminal region or interaction with heat shock protein 90 alpha (HSP90α) may enhance the histone methyltransferase (HMTase) activity compared to the full-length protein (Silva et al., 2008). Enhanced expression of SMYD3 is essential for the growth of many cancer cells (such as breast cancer, colorectal carcinoma, hepatocellular carcinoma, etc), and it also could stimulate cell adhesion and migration, whereas suppression of SMYD3 by RNAi or other reagents induces apoptosis and inhibits cell proliferation and migration (Hamamoto et al., 2004; Hamamoto et al., 2006; Luo et al., 2007; Wang et al., 2008; Luo et al., 2009; Zou et al., 2009; Luo et al., 2010). SMYD3 may be an important coactivator of estrogen receptor (ER) in the estrogen signal pathway. It can directly interact with the ligand binding domain of ER, in turn augments ER target gene expression via histone H3-K4 methylation (Kim 2009).

Fig. 6. SMYD3-mediated histone H3-K4 methylation and transcriptional regulation. (Sims et al., 2004)

EZH2 overexpression has been found in breast cancer, its elevation is associated with poor prognosis. It seems that EZH2 might be associated with the regulation of pRB–E2F pathway and genes involved in homologous recombination pathway of DNA repair (Zeidler et al., 2005). However, the detailed mechanism of EZH2 in cancer is not yet clear. Another study has shown that EZH2 is also overexpressed in preneoplastic breast lesions and morphologically normal breast epithelium adjacent to the pre-invasive and invasive lesions, indicating that it might be a marker of epithelium at higher risk for neoplastic transformation (Ding et al., 2006).

Histone Modification and Breast Cancer 331

PRDM2 (RIZ1) was originally identied as a pRb-binding protein, and its inactivation and underexpression via mutations or promoter hypermethylation had been found in a number of tumors including breast, colon, liver and lung cancers, as well as neuroblastoma, melanoma and osteosarcomas (Kim et al., 2003; Wang et al., 2007). Overexpression of PRDM2 induces G2/M cell-cycle arrest and apoptosis in tumor cell lines, while PRDM2-/ mice are prone to developing B cell lymphoma and stomach cancer (Steele-Perkins et al.,

The protein arginine methyltransferase (PRMT) family is the major HRMTs up to now. The PRMTs are classified into four groups depending on the type of methylarginine they generate: Type I PRMTs (PRMT1, PRMT2, PRMT3, PRMT4, PRMT6 and PRMT8) catalyze the formation of ω-NG, monomethylarginines (MMA) and ω-NG, NG-asymmetric dimethylarginines (aDMA); Type II PRMTs (PRMT5, PRMT7 and PRMT9) catalyze the formation of MMA and ω-NG, N'G-symmetric dimethylarginines (sDMA); Type III PRMTs (remained unclear) catalyze only the monomethylation of arginine residues in proteins; Type IV PRMTs (only be found in *Saccharomyces cerevisiae* up to date) catalyze the methylation at delta (Δ) nitrogen atom of arginine residues (Niewmierzycka et al., 1999;

Compared to HKMTs, The evidence for the involvement of HRMTs in human cancers is not as solid. However, underexpression of PRMT1 has been observed in breast cancer (Scorilas et al., 2000). PRMT4, also known as coactivator-associated arginine methyltransferase-1 (CARM1), is a coactivator for nuclear receptors and is oversexpressed in prostate and breast cancers (El et al., 2006). PRMT4 plays an important role in estrogeninduced cell cycle progression in the MCF-7 breast cancer cell line. Upon estrogen stimulation, the E2F1 promoter is subject to PRMT4-dependent dimethylation on H3R17, and this recruitment of PRMT4 by ERα are dependent on the presence of the NCOA3

It used to be considered that histone methylation was a permanent and irreversible histone modification. However, in recent decade, many enzymes have been identified with the ability to demethylate methylated histone lysine/arginine residues via amine oxidation, hydroxylation or deimination (Cloos et al., 2008). The histone demethylases could be divided into three distinct classes. The rst class (petidylarginine deiminase 4, PADI4) converts a methyl-lysine to citrulline. The second class (lysine-specic demethylase 1, LSD1) reverses histone H3K4 and H3K9 modications by an oxidative demethylation reaction. The third class of demethylases is the family of Jumonji C (JmjC)-domain containing histone demethylases (JHDMs). Contrast to LSD1, JHDMs can demethylate all three methylated states (mono- di- and tri-methylated lysine). Up to now, JHDMs have been found to demethylate H3K36 (JHDM1), H3K9 (JHDM2A) and H3K9/K27 (JHDM3 and JMJD2A-D)

Histone demethylase JARID1B (PLU-1) is shown to be overexpressed in breast cancers but low expressed in normal adult tissues, and it is essential for the proliferation of the MCF-7 breast cancer cell line and for the tumor growth of mammary carcinoma cells in nude mice. Several target genes of JARID1B have also been identified to be associated with breast cancer proliferation, such as 14–3–3σ, BRCA1, CAV1, and HOXA5 (Lu et al., 1999; Yamane

2001; Gibbons, 2005).

Boisvert et al., 2005; Bachand, 2007).

**<IP_ADDRESS> Histone arginine methyltransferase (HRMTs)**

Table 2. Summary of major human HKMTs (Pan et al., 2010)

NDS2 H4K20 Transcriptional

SETD2 H3K36 Transcriptional

SMYD2 H3K36 Transcriptional

SMYD4 Unclear Transcriptional

SETD8 H4K20 Transcriptional

SETDB1 H3K9 Transcriptional

H3K27

SMYD3 H3K4 Mainly be

SMYD5 Unclear Unclear EZ EZH2 H3K27 Transcriptional

SUV4~20 SUV4~20H1, SUV4~20H2 H4K20 Heterochromatin PRDM2 H3K9 Transcriptional

Others SET7/9 H3K4 Transcriptional

Dot1 Dot1L H3K79 Transcriptional

EHMT1 H3K9,

Table 2. Summary of major human HKMTs (Pan et al., 2010)

Non-SET domain-containing proteins

SMYD SMYD1 H3K4 Transcriptional

SET1 MLL1, MLL2, MLL3 H3K4 Transcriptional

specificity

H4K20

H3K27

Basic functions

H3K9 Transcriptional

repression

activation

activation

Mainly be transcriptional repression

activation

repression

activation

repression

repression

activation

activation

repression

repression

repression

Transcriptional repression

transcriptional activation

Transcriptional activation

Family Members Histone

SET2 NSD1 H3K36,

NSD3 H3K4,

SUV39 SUV39H1, SUV39H2, SULT1E1, G9A,

**SET domain-containing proteins**

CLLL8

PRDM2 (RIZ1) was originally identied as a pRb-binding protein, and its inactivation and underexpression via mutations or promoter hypermethylation had been found in a number of tumors including breast, colon, liver and lung cancers, as well as neuroblastoma, melanoma and osteosarcomas (Kim et al., 2003; Wang et al., 2007). Overexpression of PRDM2 induces G2/M cell-cycle arrest and apoptosis in tumor cell lines, while PRDM2-/ mice are prone to developing B cell lymphoma and stomach cancer (Steele-Perkins et al., 2001; Gibbons, 2005).