Modelling and regulating of cardio-respiratory response for the enhancement of interval training

Background The interval training method has been a well known exercise protocol which helps strengthen and improve one’s cardiovascular fitness. Purpose To develop an effective training protocol to improve cardiovascular fitness based on modelling and analysis of Heart Rate (HR) and Oxygen Uptake (VO2) dynamics. Methods In order to model the cardiorespiratory response to the onset and offset exercises, the (K4b2, Cosmed) gas analyzer was used to monitor and record the heart rate and oxygen uptake for ten healthy male subjects. An interval training protocol was developed for young health users and was simulated using a proposed RC switching model which was presented to accommodate the variations of the cardiorespiratory dynamics to running exercises. A hybrid system model was presented to describe the adaptation process and a multi-loop PI control scheme was designed for the tuning of interval training regime. Results By observing the original data for each subject, we can clearly identify that all subjects have similar HR and VO2 profiles. The proposed model is capable to simulate the exercise responses during onset and offset exercises; it ensures the continuity of the outputs within the interval training protocol. Under some mild assumptions, a hybrid system model can describe the adaption process and accordingly a multi-loop PI controller can be designed for the tuning of interval training protocol. The self-adaption feature of the proposed controller gives the exerciser the opportunity to reach his desired setpoints after a certain number of training sessions. Conclusions The established interval training protocol targets a range of 70-80% of HRmax which is mainly a training zone for the purpose of cardiovascular system development and improvement. Furthermore, the proposed multi-loop feedback controller has the potential to tune the interval training protocol according to the feedback from an individual exerciser.

http://www.biomedical-engineering-online.com/content/13/1/9 Introduction Heart rate (HR) and oxygen uptake (VO 2 ) are key indicators of functional health status; their measurements can aid early detection of cardiac diseases [1,2]. Furthermore, these cardio respiratory endurances have long been recognized as one of the fundamental components of physical fitness.
To build effective training protocols, VO 2 and HR are often applied to assess the exercise intensity for achieving different training goals, such as fat burning or cardiovascular system improvement [3].
One of the most effective training regimens is called interval training which was first described by Reindell and Roskamm [4] and was popularized in the 1950s by the Olympic champion, Emil Zatopek. Swedish physiologist Per OlØf Astrand provided the first scientific study [5] on interval training in 1960. Since then, it has been the basis for athletic training programs for many years.
Nowadays, interval training method becomes a well known exercise protocol which helps strengthen and improve cardiovascular fitness. Previous researchers in this area [6,7] have investigated the effectiveness of interval training and its significance in improving factors associated with O 2 transport along with muscle uptake. Interval training has been shown to raise skeletal muscle enzyme activity [8,9], improve vascular health [10], and decrease the risk of cardiovascular-disease in obese adolescents [11]. Additionally, interval training increases proteins that transport fatty acids across the mitochondrial membrane [12]. This study aims to develop an effective interval training protocol for an individual exerciser to improve cardiovascular fitness based on modelling and analysis of HR and VO 2 dynamics.
Interval training interleaves high intensity exercises with recovery or rest periods [13]. The idea behind this approach is breaking up a set amount of work into smaller segments rather than performing a greater volume of work at a higher intensity [5]. In addition, it is well known that exercise at varying intensities helps improve exerciser's aerobic capacity to exercise longer [14]. A well designed interval training protocol can guide the exerciser to timely shift from low to high training intensities and vice versa within the exercise zone to achieve desired exercise targets with safe.
Any interval training protocol has at least three different periods, warm-up, exercise (switching between high intensity period and recovery period), and cool-down. The most important characters for the design of an appropriate interval training protocol are time (or distance), intensity level (speed level in our case at onset segment), time of each recovery period (offset), and number of repetitions within the exercise period. For example, both the time intervals of the onset and offset segments should be properly selected based on their transient properties to avoid reaching a steady state value during interval training.
To achieve better interval training effect, this study first investigates the dynamic responses of VO 2 and HR by building a model, which can depict both the onset and offset dynamics. Although existing literatures [15,16] have studied the HR and/or VO 2 responses to the onset and offset exercises, we need to re-explore the onset and offset dynamics with a certain maximum intensity (70 percent of VO 2max ) for the purpose of improving cardiovascular fitness. We noticed the distinct differences of two dynamic characteristics (time constants and steady state gains) for the onset and offset of running exercises. http://www.biomedical-engineering-online.com/content/13/1/9 A switching RC circuit model for healthy male subjects has then been developed, which can well accommodate these observed differences. Based on this model, an interval training protocol has been proposed for this group of subjects. In order to adapt to different individual exerciser, a multi-loop integral control scheme has been proposed and achieved desired simulation results. It should be pointed out that in moderate intensity exercise the relationship between HR and VO 2 is nearly linear [17]. As the proposed interval training protocol is particularly designed in moderate intensity range, HR can be used as an indicator of exercise strength during the proposed training.
This paper is organized as follows. Section 'Experimental equipment and exercise protocol' describes the experimental equipment and exercise protocol setup for model estimation. Section 'Results and discussion' presents the main results and discussions including the proposed switching model and interval training protocol. Section 'Individualized adaptation for the proposed interval training protocol' describes the adaption framework as well as the multiloop PI controller design. Section 'Control system verification' provides a practical method to examine and verify the functionality as well as performance of the proposed controller. Finally, Section 'Conclusion' gives the conclusions.

Subjects
Eight healthy non-smoking males were invited to join the experiments. They were free from any known cardiac or metabolic disorders, hypertension, and were not under any medication. The UTS Human Research Ethics Committee (UTS HREC 2009000227) approved the study and an informed consent was obtained from all participants before each experiment. The physical characteristics of the participants are presented in Table 1.
Since both HR and VO 2 can vary with the time of the day, emotional state, food and caffeine intake, previous activity, fatigue, temperature, humidity, altitude and dehydration [18], the participants were asked to have a light meal at least two hours before the experiment and not to engage in intense or prolonged exercise for 24 hours prior to each experiment. Environmental conditions were the same for all participants. The temperature of the laboratory was set at 25 degrees Celsius and the humidity was set at approximately 50%. All subjects were requested to be familiar with an automated treadmill before experiments.

Experimental settings
All laboratory analyses were performed using a portable gas analyzer (K 4 b 2 , Cosmed). The analyzer comes with a compatible HR probe which consists of two parts: the elastic belt containing the transmitter, and the receiver.
Using chest electrodes to monitor HR signals is considered to be valid, accurate and reliable during physical activity monitoring [2].
The K 4 b 2 gas analyzer was used because it has previously been reported to be valid, accurate and reliable [19].
The experiments have been done on healthy however untrained subjects. A submaximal running protocol, that is practical and easy to apply, was implemented in the setup. It is required that the subjects where to perform at less than 80% of their maximum heart rate (HR max ), this is also a recommendation for unfit people and for those with respiratory or cardiac risks [20].
The relationship between HR and VO 2 becomes non-linear during light and very highly intense activity [2]. Therefore, and for the values of HR and VO 2 to remain located in the linear part, a moderate speed of 9 km/h was examined and set to be the highest level in the training protocol; each participant, at varying times, reached a reading of 80% of his HR max at this speed.
Each subject completed two running exercises on separate occasions. In each session and as a warming up interval, the subjects were requested to walk on a treadmill at 5 km/h for 4 minutes to avoid any kind of stiffness or fatiguing effect may occur during the running interval. Then, they were asked to run at 9 km/h for 6 minutes, before a recovery time or cooling down period of 5 minutes at 5 km/h speed.
During experiments, HR was monitored to ensure the safety of the subjects. Whenever noticeable change in heart rhythm is detected the experiment will be terminated.

Experimental results
It is known that many internal and environmental factors may have drastic effects on the readings during the experiment [2], even when all of the laboratory conditions have been standardized and set to be the same for all participants. Some environmental and undetectable factors such as dehydration, heat loss mechanisms as well as altitude may still affect the results. To reduce the influences of these uncontrollable factors, the proposed training protocol was repeated twice and the data was interpolated, averaged and filtered.
Original signals of HR and VO 2 for all subjects are shown in Figures 1 and 2 respectively. According to literatures [21,22], heart rate response to exercise can be approximated as a first-order process. Since the response of VO 2 has similar shape with HR, we model both HR and VO 2 as first-order linear systems for both the onset and offset exercises. The first order system model can be written as follows: Where k and τ are the steady state gain and the time constant respectively. http://www.biomedical-engineering-online.com/content/13/1/9

Figure 1 Measured HR signals for 8 male subjects running on a treadmill under a predefined protocol.
Matlab System Identification Toolbox has been used to identify the parameters of the model for each step response data. Time constants and steady state gains of HR and VO 2 for each subject are shown in Tables 2 and 3.
By observing the original data for each subject we can clearly identify that all subjects have similar HR and VO 2 profiles. For each individual subject, steady state gain of offset is smaller than steady state gain of onset for both HR and VO 2 .
Average values of onset and offset time constants as well as steady state gains for VO 2 and HR profiles are shown in Table 4. These values will be used to simulate the VO 2 and HR responses during the proposed interval training protocol.  Based on the results listed in Tables 2, 3 and 4, it can be seen that the time constant of offset is larger than that of onset for both HR and VO 2 . Paper [16] has shown the same result for HR response during walking exercises; nevertheless, it did not investigate the dynamic characteristics of a running protocol.

The proposed interval training protocol
In this subsection, a sensible interval training protocol will be built based on the experimental results. To build a training protocol, targets have to be set first.
For instance, if the aim of a training protocol is to develop basic endurance and aerobic capacity, all running intervals should be completed at a maximum of 70% of HR max while developing cardiovascular system can be done by training at a range of 70% to 80% of HR max .
On the other hand, training in anaerobic zone which is 80% to 90% of HR max will develop the lactic acid system [23]. Therefore, determining an actual HR max is the key to constructing a well-designed training program. According to [24], a population specific formula should be used for better estimation of HR max for an individual subject. However, the most accurate general equation to estimate HR max is that of Inbar [25]: It is well known that HR is a valid measure of exercise intensity only if it reflects the metabolic rate which can be measured by VO 2 . Improving aerobic capacity can be  achieved by increasing oxygen delivered to the muscles during exercise. Table 5 shows the relationship between VO 2max and HR max [3]. Table 6 shows the training zones and their corresponding value for both HR max and VO 2max .
As mentioned before, a linear relationship between VO 2 and HR exists during moderate intense activity and that is why our training protocol will target the aerobic zone (70-80% of HR max or 50-70% VO 2 max) which aims to develop the exerciser's cardiovascular system. Based on equation (2), for an averaged model HR max = 205.8-0.685 × (average age) = 205.8-0.685 × (30.63) = 184.82 ≈ 185 bpm. Thus, building an interval training which reaches a 148 bpm (80% of HR max ) in each interval will guarantee achieving ∼70% of VO 2max .
The experiment results show that for an average model, the exerciser exceeds an 80% of his HR max at the 297 th second which means 57 seconds after the start of the running period, also 1 minute at speed 5km/h is considered to be the recovery period after each running interval. Figure 3 shows an interval training protocol based on the above calculations.
In order to verify the new proposed interval training protocol shown in Figure 3, more subjects need to be recruited, and this will be the next step of this study.
Before verifying this protocol by experiment, we tested it by simulation first. Switching RC circuit as shown in Figure 4 was used to simulate both HR and VO 2 responses.
The parameters of the model are tuned based on Table 4. R On multiplied by C (R On ×C) represents the time constant for the onset response, while R Off multiplied by C (R Off ×C) represents the time constant for the offset response.
The gain value can be controlled by manipulation of the voltage sources V On and V Off . It should be noted that when the steady state gains of the onset and offset dynamics are approximately the same, V On will be constant and V Off is constant zero. As another special case, if both the onset and offset exercises reach steady state, then both V On and V Off can be constant.
The switch is utilized to replicate the switching behavior between the two responses based on the training protocol segments. National Instruments Multisim has been used to simulate the proposed interval training protocol.  New male subject (Subject No.9, Age = 30 year, Weight = 84 Kg, and Height = 185 cm) was asked to run on the treadmill under the new interval training protocol. Experimental results for subject 9 are shown in Figure 5.
The experimental results approximately match the simulation results. However, in contrast with HR, the experimental results for VO 2 were skewed due to the presence of extensive noise. As the intensity of this protocol is anticipated in the moderate range for the purpose of improving cardiovascular fitness, the relationship between HR and VO 2 is nearly linear [2]. Furthermore, HR is easier to be measured and has wider frequency bandwidth. Therefore, HR is suitable and therefore recommended to be used as an indicator of exercise strength for the proposed interval training protocol.

Individualized adaptation for the proposed interval training protocol
The proposed interval training protocol is based on the established average model for eight healthy young male subjects. However, for an individual exerciser (who is young and healthy) the proposed protocol may need to be adjusted due to the differences of the intra-and inter-subjects.
In this section, we present a hybrid system model (a dynamic system that exhibits both continuous and discrete dynamic behaviors) to describe the adaptation process and propose a multi-loop PI (Proportional and Integral) control approach for the tuning of interval training protocol.

The adaptation framework
The final goal of the adaptation of the proposed interval training protocol is tuning the duty cycle ( t 1 ) as shown in Figure 6, and the period ( t 1 + t 2 ) to achieve desired training effects. The adaptation process is a set of successive interval training experiments for a particular user. This process can be described by using a hybrid system model [26]. Specifically, each single experiment can be considered as a discrete event and the dynamics of cardio-respiratory responses to exercise can be depicted by a continuous linear model. It will be shown under modest assumptions, we can simplify this special hybrid system as a simple discrete time system.
The intermission between the interval training experiments can be hours or days. However, in order to simplify the process, we first assume that the intermission is constant (similar as the sampling time of a discrete time system) given that the subject has similar physiological conditions before each training (i.e., the intermission between the interval training is long enough to recover the exercisers).
For each training, the cardio respiratory responses (HR/VO 2 ) are actually the outputs of continuous systems. In order to simplify the system as a static discrete time system, we first pick up some key characteristics from the continuous processes, which are associated with exercise effects, such as mean value and standard deviation. The proposed training protocol (see Figure 3) contains three-period square wave. In this study we pick up the lowest point and the highest point of the third period HR response y(t 4 ) and y(t 5 ) respectively as the reflections of exercise effects (see Figure 6).
We can then treat every single interval training experiment as a simple static system, which has two inputs (the duty cycle ( t 1 ) and the period ( t 1 + t 2 )) and two outputs  ( t 1 ) and period( t 1 + t 2 )) and HR response during interval training protocol.
(y(t 4 ) and y(t 5 )). Under the above assumptions and simplification, the overall adaptation process can now be simply treated as a two-input two-output (2I2O) discrete time system.
In order to determine the model of the 2I2O static system, we investigate HR response for the proposed interval training protocol. Figure 6 shows the protocol and its response.
Based on the previous model, the outputs y(t 4 ) and y(t 5 ) can be obtained as follows: Where k 1 , k 2 , T 1 and T 2 are the onset, offset gains and time constants respectively, and

Controller design and simulation study
As the adaptation can be depicted as a discrete time system, we can design a discrete time multi-loop controller (Proportional and Integral controller) to adjust the two inputs (the duty cycle ( t 1 ) and the period ( t 1 + t 2 )) to regulate the two outputs (y(t 4 ) and y(t 5 )) to reach the desired reference values. Figure 7 is the block diagram of the proposed control system. Two discrete PI controllers C 1 and C 2 have been built and connected to the HR model to control the desired output values y(t 4 ) and y(t 5 ) separately. The controller parameters (the coefficients of P and I actions) of these two simple multi PI controllers are tuned by trial and error. We simulated the control performance in Matlab Simulink.
Up to now, we can see even though our aim is to regulate a continuous process; we proposed a simple discrete time control technique to fulfill the goal. During interval training of an individual subject, at the first iteration the exerciser is asked to run under the predefined training protocol. HR response at t 4 and t 5 (as shown in Figure 6) will then be measured and feedbacked to the multi-loop control system to update controller outputs' inputs u 1 and u 2 (the duty cycle ( t 1 ) and the period ( t 1 + t 2 )). This modification will try to adjust the measured output values (y(t 4 ) and y(t 5 )) to approach the desired setpoints for the next training exercise. This recursive process will be repeated until we finally reach the desired set points.
The self-adaption feature gives the exerciser the opportunity to reach his desired setpoints after a number of iterations. Figure 8 below shows the simulated controller outputs, y(t 4 ) and y(t 5 ).
In simulation, from Figure 8, we conclude that after at least 12 iterations (12 training exercises) the system will reach the desired setpoints.
In other words, t 4 and t 5 have to be observed 12 times and re-entered to the controller to achieve the desired outputs. Figure 9 below shows the HR response for the 13 th iteration. The third waveform in Figure 9 shows that the output signal finally reached the desired reference values R 1 and R 2 at t 4 and t 5 respectively.
Since our controller is based on discrete events regulating a continuous process, it can perform one time between two training experiments. Therefore, it is very easy to be implemented in low cost portable devices which have limited computation power. Heart rate response Time(Seconds) Heart rate(bpm) Figure 9 Continuous interval training HR response simulation from the controller. http://www.biomedical-engineering-online.com/content/13/1/9

Control system verification
This section provides a practical method to examine and verify the functionality as well as performance of the proposed controller. The new interval training protocol combined with the designed controller have been implemented and tested on stair climbing exercise as a free environment activity. This can show how the proposed interval training protocol that has been created earlier can be implemented using different exercising techniques, such as stairs climbing, cycling and even swimming. The setup, concept and structure of the interval training remain the same. However, the change would be within the type of exercise during the onset and offset stages. Subjects in the new implementation will be requested to ascend a staircase of steps without any pause; the exercisers will attempt to achieve the training protocol without holding the banisters or the handrail.
The experiments were carried out at free speed and the participants were given the instruction to walk at their normal pace. However, each exerciser was instructed to stay within 4-5 km/h climbing speed. This will represent the onset stage in our training protocol. On the other hand, offset stages can be represented as walking aside on the same step the exercisers reach when the onset period finishes; this continuous walking on the same step is the perfect match of the offset stage in the treadmill interval training exercise. All participants were instructed to use their left legs for the first step, to only place one foot on each step (foot-over-foot ascent) and to continue walking in straight line.
Based on the simulation results in the previous section, the exerciser needs 12 iterations to reach his desired setpoints. Practically, the experiments results for a new male subject (Subject No.10, Age=31 year) show that the participant has almost reached his desired setpoints after three iterations only.
Based on the exerciser's age, the controller R 1 and R 2 are set to be 148 bpm(80% of HR max ) and 111 bpm(60% of HR max ) respectively. In the first iteration, 60 seconds is set to be the onset and offset times. At the end of the first training session, the reading of HR at t 4 and t 5 were observed and feed backed to the multi-loop control system to update the controller output's inputs (the duty cycle and the period). Accordingly, the controller adjusts the time of onset and offset periods for the next training session. Table 7 shows the controller parameters and their corresponding experimental results for Subject No.10 in three training sessions. Figure 10 shows the new subject's HR response under the stairs climbing activity.
Referring to the results from Table 7 and Figure 10, we can conclude that the exerciser has almost reached his desired setpoints after only 3 iterations, when the simulation results showed that it can be done after 12 iterations. This shows how our controller has accomplished its duty with minimum number of iterations.
It is worth mentioning that for a proposed training regime based on an averaged model; we expect each subject to respond differently to a particular exercise or training protocol, of interest was Subject 10 who reached his desired set points after only three iterations. We may reach the desired set points or a steady state for another subject after 10, 11 or even 12 iterations, it depends totally on the intra-and inter-model uncertainties.
During the study, it was considered that after a certain number of training sessions, the training capacity of the subject will improve and this may affect the result of his HR response. In the next step of the study, we will recruit more subjects in order to verify our results and to even improve the performance of our controller.

Conclusion
A designed square-wave exercise protocol was applied to investigate the dynamic characteristics of HR and VO 2 responses for both the onset and offset exercises. The K 4 b 2 portable device was used to measure breath-by-breath VO 2 and beat-by-beat HR. According to the experimental results, it was concluded that the time constants and steady state gains of the onset exercise for both HR and VO 2 are distinctively different with those of the offset exercise. Based on the identified dynamic characteristics for both HR and VO 2 , we proposed an interval training protocol to improve cardiovascular fitness. A switching RC circuit model was presented to simulate HR and VO 2 responses for the proposed interval training protocol. In order to adapt to individual users, a multi-loop integral control scheme has been proposed to regulate the HR response for interval running exercise under free living conditions. In the next step of the study, we will recruit more subjects for the validation of the proposed models, interval training protocol and the feedback control based adaption scheme.


extfieldsofstudy:
Medicine
Materials Science

text:
Preparation of Tween 80-Zn/Al-Levodopa-Layered Double Hydroxides Nanocomposite for Drug Delivery System

We incorporated anti-Parkinsonian drug, levodopa (dopa), in Zn/Al-LDH by coprecipitation method to form dopa-LDH nanocomposite. Further coating of Tween-80 on the external surfaces of dopa-LDH nanocomposite was achieved through the oxygen of C=O group of Tween-80 with the layer of dopa-LDH nanocomposite. The final product is called Tween-dopa-LDH nanocomposite. The X-ray diffraction indicates that the Tween-dopa-LDH nanocomposite was formed by aggregation structure. From the TGA data, the Tween-80 loading on the surface of LDH and dopa-LDH was 8.6 and 7.4%, respectively. The effect of coating process on the dopa release from Tween-dopa-LDH nanocomposite was also studied. The release from Tween-dopa-LDH nanocomposite shows slower release compared to the release of the drug from dopa-LDH nanocomposite as done previously in our study, presumably due to the retarding shielding effect. The cell viability study using PC12 showed improved viability with Tween-80 coating on dopa-LDH nanocomposite as studied by mitochondrial dehydrogenase activity (MTT assay).

Introduction
Nanotechnology is gaining more acceptability in the area of biomedical sciences especially in the field of drug delivery due to increase precision in specific tissue drug delivery and decrease toxicity among other advantages compared to conventional drug delivery system [1]. Layered double hydroxides (LDHs) are one of nanolayer materials used in drug delivery; its synthesis is relatively easy in the laboratory for drug delivery, usually with controllable size and versatile compositions. It has a very good biocompatibility as well as low cytotoxicity profile, giving perfect cover to the intercalated drug by virtue of its interlayer structure [2]. However, poor water solubilisation, agglomeration, inadequate targeted delivery, minimal thermal stability, and surface charges of nanoparticles (LDHs inclusive) intended for drug delivery may be a hindrance [3]. Significant influence in the physicochemical and pharmaceutical properties of the nanoparticles through surface modification with surfactants or polymer or both can alter particle size, size distribution, particles morphology, surface chemistry, surface hydrophobicity, zeta potential, and drug encapsulation efficiency. These will likely improve the water solubility of a nanoparticles, decrease agglomeration, and lead to a more targeted delivery of the nanoparticles with minimal toxicity [4]. Surface interaction and adherence of nanoparticles is significantly different compared to submicron sized particles as such nanoparticles have an extremely high tendency of adhesion and aggregation [3]. The choice of surface coating material is important and very critical for a successful nanoparticle formulation in drug delivery and other biomedical applications. For example, synthesized uncoated iron oxide nanoparticles were shown 2 The Scientific World Journal to aggregate in biological solutions due to their large surface area to volume ratio, forming large clusters and rendering them unsuitable for biomedical applications [5]. A number of materials have been used in the past as surface coating including dextran, polyethylene glycol (PEG), phospholipids, polyethyleneimine (PEI), and most recently, chitosan [5]. These coating are sometimes used as targeting material, helping in nanoparticle delivery to specific areas, increasing the stability, and/or decreasing the toxicity potentials.
In the past, the synthesis of polymer-coated LDH has been reported [6,7], either for targeted drug delivery and/or increasing stability. Several methods were used in the coating process, among which are reconstruction, ionic exchange, and spontaneous self-assembly method [6]. Unlike ion exchange and reconstruction method, spontaneous selfassembly produces dextran coated LDH maintaining the initial size and crystalline structure base on XRD result [7]. Maintaining the hydrotalcite-like compounds structure of LDH with positively charged metal oxide/hydroxide sheets intercalated with anions and water molecules is of essence in drug delivery via ion exchange [7].
However, in nanoparticle delivery to brain, Tween 80 appeared to be very helpful in mediating transport across the blood brain barrier (BBB) [8]. Apolipoprotein E adsorption by the surface of Tween 80 coated nanoparticles is believed to be responsible for this transport. Particles attached with this plasma protein seem to mimic low density lipoprotein (LDL) whose receptors are in abundant in the endothelial cells lining the BBB leading to their uptake into the brain [8].
Hence, the choice of Tween-80 surfactant as coating material in this study for levodopa delivery to the brain is timely. Here, we aimed at studying the stability, realizing ability, and toxicity profile of LDHs intercalated with levodopa and coated with Tween 80.

Synthesis.
Zn/Al-LDH was synthesized by adding a solution of 1 Mol/L sodium hydroxide dropwise to a mixture solution of zinc and aluminum nitrate, with initial molar ratio 2 : 1, in deionized water under a nitrogen atmosphere while vigorously stirring until a pH of 7.0 was reached. The mixture was aged in an oil bath for 18 hours at 70 ∘ C. The white precipitate obtained was centrifuged, washed three times with deionized water, and dried in an oven overnight at 70 ∘ C. The product was denoted as Zn/Al-LDH.
Dopa-Zn/LDH nanocomposite was synthesized as described in our previous work using a direct coprecipitation method [9]. In brief, a solution of levodopa (0.08 molar) was added to a Zn(NO 3 ) 2 ⋅6H 2 O and Al(NO 3 ) 3 ⋅9H 2 O solutions, at a ratio of 2 : 1, under constant stirring in the presence of a sustained nitrogen supply at room temperature, and the pH was adjusted to 7.0 using 1.0 molar NaOH. The experiment was protected from direct sunlight exposure because of the sensitivity of levodopa to light. The mixture was aged at 70 ∘ C in an oil bath for 18 hours and then centrifuged, filtered, washed with deionized water three times, and dried in an oven overnight. The product was denoted as dopa-LDH nanocomposite (levodopa intercalated into Zn/Al-LDH).
Coated dopa-LDH nanocomposite by Tween-80 was synthesized according to literature [10]. Dopa-LDH nanocomposite (0.2 g) was added to Tween-80 dissolved solution (5 mM and 100 mL) and reacted with stirring for 18 hour. The precipitate was washed with deionized water three times and dried in an oven over night. The product was denoting as Tween-dopa-LDH nanocomposite (Tween-80 coated dopa-LDH nanocomposite). The coated Zn/Al-LDH by Tween-80 was synthesized similar to Tween-dopa-LDH nanocomposite and the final product was denoting as Tween-LDH.

Controlled-Release Study. Levodopa release profiles from
Tween-dopa-LDH nanocomposite were determined at room temperature using phosphate-buffered saline at pH 4.8 and 7.4. Approximately 300 mg of the nanocomposite was added to 500 mL of the medium. The accumulated amount of levodopa released into the solution was measured at preset time intervals and at max = 280 nm using an ultraviolet-visible spectrophotometer (Lambda 35, Perkin-Elmer, Boston, MA, USA).

Cell Culture and Treatment.
A dopaminergic cell model (PC12) was obtained from American Type Culture Collection (ATCC) Manassas, VA. USA. To maintain the cell line we used RPMI 1640D medium enriched with 10% fetal bovine serum (FBS), 15 mmol/L L-glutamine, 100 units/mL penicillin, and 100 g/mL streptomycin, cells were kept in a humidified incubator at 37 ∘ C and 5% CO 2 . Cells were seeded into 96-well plate at 1 × 10 5 cells/mL and kept overnight for cells attachment.

Preparation of Nanocomposite for Viability
Assay. Dopa-LDH nanocomposites as well as the Zn/Al-LDH carrier were dispersed in PBS. A stock suspension of 10 mg/mL of each nanocomposite was made by sonication for 5 minutes and culture medium was used to obtain the desired concentration via serial dilution. The nanocomposite were further dispersed through vortex agitation for 2 mins before usage. A dose range 25 /mL to 800 g/mL used and cells were exposed for 72 hours to assess the impact of our treatment on their viability for toxicity effects. Well contained cells and media only were used as control to compare. Experiment was done in triplicate and each time a freshly prepared nanoparticle is used to treat cells and results were presented as mean ± SD.
The Scientific World Journal 3 2.6. Cell Viability Study. MTT assay was used to assess the viability of PC 12 after exposure to different doses of the synthesized nanoparticles. In this assay, the MTT reagent (3,(4,5dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) is converted to an insoluble and brightly coloured formazan by viable cells in the culture which correlate with the reading taken for viable cells [11]. It takes 2-4 hours for this conversion to happen and a detergent (DMSO) is then added to the cells to stop the conversion and solubilize the formazan. The plate was shaken in the dark for thirty to sixty minutes and absorbance of the formed formazan is taken at a wavelength of 570 nm using a suitable multiwall microplate reader. In brief, 20 L of MTT solution (5 mg/mL in PBS) was added to each well and kept in an incubator for 2 hour. The MTTcontaining medium was removed gently and replaced with dimethyl sulfoxide (100 L/well) to mix with the formazan crystals until dissolved. Absorbance at 570 nm and 630 nm (background) was measured with a microplate Elisa reader (ELx800 from BioTek Instruments):

Measurement of Homovalinic Acid by ELISA. An in vitro
drug metabolism using the neurogenic PC-12 cells was used to study possible delivery of levodopa and its metabolism by measuring the drug end product called homovalinic acid (HVA) from treated cells. Monoamine oxidases (MAOs) A and B are the enzymes present in PC 12 cells and capable of deamination of biogenic amines including dopamine from levodopa into HVA [12]. RPMI-1640 media containing nerve growth factor (NGF) (NGF-7S, Sigma-Aldrich, Inc., St Louis, MO) at 100 ng/mL was used to differentiate the cells for this purpose as reported earlier [1]. We chose doses from the viability assay that allow for more than eighty percent cell vitality. Zero concentration used as control from which we measured the endogenously produce HVA from the neuronal cells. Cell lysate obtained by mechanical disruption of the treated and control cells was centrifuged at 15000 rpm for 15 minutes at 40 ∘ C.  OR, USA) was used to determine the surface morphology of the samples.  (Figure 1(D)) and Tween-dopa-LDH nanocomposite (Figure 1(C)). This is because the additional Tween-80 molecules into the sample resulted in the adsorption of the former on the surfaces of LDH particles and were not exchanged with dopa intercalated into the interlayer of dopa-LDH nanocomposite.

Results and Discussion
Generally, polymer-LDH nanocomposite can be obtained from the association of LDH with polymers by different ways. The first type is called intercalated nanocomposite, where the polymeric chains were encapsulated into the interlayers of LDH and in this case, the interlayer spacing could be increased. The exfoliated nanocomposite is the second type, in which the materials present no ordering along the stacking axis of the layer. The third type is called aggregation, where there is no expansion of the basal spacing in the XRD; diffraction peaks can be observed. In the Tween-dopa-LDH nanocomposite, the sample will follow the last type ( Figure 2) and the XRD diffraction peaks confirm that.

Infrared Spectroscopy.
The FT-IR spectrum of Tween-80 is shown in Figure 4(D). Tween-80 shows many intense, sharp absorption peaks that are due to the different functional groups present in the molecules (Figure 3). Methyl group (-CH 3 ) shows absorption band at 2920 cm −1 , while the band at 2864 cm −1 is due to -CH 2 -stretching. The band at 1735 cm −1 can be attributed to C=O and the band at 1095 cm −1 is due to stretching of C-O-C [13]. The Tween-LDH gives absorption bands at 2924, 2851, and 1096 cm −1 which is related to -CH 3 , -CH 2 -, and C-O-C of Tween-80, respectively; whereas, the Tween-dopa-LDH nanocomposite shows bands at 2924, 2855, and 1090 cm −1 for the same functional groups. These results indicate the coating of the Zn/Al-LDH and dopa nanocomposite by the Tween-80. The absorption bands due to C=O stretching at 1735 cm −1 in Tween-80, however, cannot be observed in the surface modified-Zn/Al-LDH and dopa nanocomposite which might be due to the chemical interaction of Tween-80 with the surface of LDH via the oxygen of C=O group [13].
Figure 4(C) shows also different absorption peaks related to the dopa molecules, which indicate that the coating process was not affected on the intercalated dopa molecules [9].

Thermal Analysis.
Thermal behavior of Zn/Al-LDH and dopa-LDH nanocomposite before and after coating by Tween-80 was examined using thermogravimetric and differential thermogravimetric analyses ( Figure 5). For Tween-80 (Figures 5(a) and 5(d)), one main thermal event was clearly observed which occurred in the region of 240-480 ∘ C and was attributed to the combustion of Tween-80, corresponding to a sharp peak in the differential thermogravimetric curve at 415 ∘ C with 96.8% weight loss [14]. Figure 5(b) shows that the thermal decomposition of Zn/Al-LDH progressed through four major steps of weight loss at 100 ∘ C, 226 ∘ C, 302 ∘ C, and 787 ∘ C, with total weight losses of 32.3% [15]. Figure 5(c) shows the thermal behavior of Tween-LDH with four stages of weight loss and total weight loss of 40.9%. This result indicates that Tween-80 loading on the surface of Zn/Al-LDH is around 8.6%.
The thermal decomposition characteristics of the dopa-LDH nanocomposite are shown in Figure 5(e). The thermal decomposition was characterized by two weight loss events, one at 35 ∘ C-162 ∘ C with a weight loss of 31.2%, due to the removal of the external surface-adsorbed and interlayer water molecules, and a second event at 162 ∘ C-960 ∘ C with a weight loss of 30.1%. The second weight loss was due to dehydroxylation of the layers and decomposition of levodopa [9]. Figure 5(f) shows the thermal properties of tween-dopa-LDH nanocomposite. After the coating process, the thermal decomposition characteristics of the tween-dopa-LDH nanocomposite were similar to the precursor. The difference was only in the total weight loss, where the total weight loss in case of tween-dopa-LDH nanocomposite was 68.7%, compared to 61.3% for precursor of dopa nanocomposite. This result indicates that the dopa-LDH was coated with 7.4% Tween-80.

Surface Morphology.
The surface morphologies of Tween-LDH and coated dopa nanocomposite were illustrated in Figure 6. The Zn/Al-LDH and the dopa-LDH nanocomposite showed nonuniform, irregular agglomerates with compact and nonporous plate-like structures [9]. Tween-LDH and Tween-dopa-LDH nanocomposite show the same surface morphology with uncoated Zn/Al-LDH and the dopa-LDH nanocomposite with more agglomeration. the results are shown in Figure 6. The release behavior at pH 4.8 (Figure 7(A)) is faster compared to pH 7.4 (Figure 7(B)), which can be attributed to the partial dissolution of LDH layer under acidic environment [16,17] and ion-exchange process between the intercalated anions in interlayer and phosphate anions in the buffer solution. The release step was characterized as released percentage of 75% after 5550 minute. At pH 7.4, the release of dopa is slower and persistent, and 78% of released percentage is obtained after 9550 minute. This slow and sustained release process may also be interpreted on the basis of the ion-exchange process between the dopa anions and phosphate anions in the buffer solution [17,18]. In our previous report of controlled release of dopa from uncoated nanocomposite, release was reported to be 76% after 2400 and 8600 minutes at pH 4.8 and 7.4, respectively [9]. This difference is attributed to the retarding effect which results from Tween-80 molecules which was adsorbed and coated nanocomposite [10].

Release Kinetics of Levodopa from the Tween-Dopa-LDH
Nanocomposite. Data obtained from in vitro release studies were fitted to various kinetic equations to find out the mechanism of dopa release from the coated nanocomposite. The kinetic models used were [19,20]. Pseudo-first-order: Pseudo-second-order: Parabolic diffusion: where and are the equilibrium release amount and the release amount at any time ( ), respectively, and are the initial amount and the amount of the drug in the nanocomposite at time , respectively. By using the three kinetic models in the release kinetic data of dopa, it was found that the parabolic diffusion model and pseudo-second-order are more satisfactory for describing the release kinetic processes of dopa from the coated dopa nanocomposite at pH 7.4 and 4.8, respectively, with correlation coefficient values 0.9828 and 0.9803, respectively, ( Figure 8 and Table 1). The rate constant for the parabolic diffusion model is 9.8 × 10 −3 min −1 , whereas the rate constant for the pseudo-second-order was 8.2 × 10 −6 mg/min (Table 1).

Cell Viability Assessment of Tween-80 Coating on PC 12
Cells. Cell viability study was done using a dopaminergic neuronal cell line PC 12 which is commonly used as in vitro Parkinson disease model [21,22]. The cells were exposed to different concentrations of coated and uncoated nanocomposite (dopa-LDH, Zn/Al-LDH, tween-dopa-Zn/Al-LDH, and tween-LDH) over a period of 72 hrs, and at the end of the exposure period cell proliferation assay shows sustained cell survival even at higher doses ( Figure 9). No significant cell death (cytotoxicity) was observed with all the four agents exposures within the dose range of 25-800 g/mL compared to the control (untreated wells). In most of the treatments, more than 95% of the exposed cells survived compared to control. The tween 80 coated nanocomposite (tween-dopa-LDH and tween-LDH) demonstrated higher cells survival at the highest concentration (800 g/mL) used compared to their corresponding uncoated nanocomposite (dopa-LDH and Zn/Al-LDH), respectively.
The slight difference in viability assay at higher concentration between coated and the uncoated may be the result of changes imposed by the surface coating on the nanoparticles. It was reported earlier that, physiochemical characteristics of nanoparticles like their surface charges, crystalline morphology, thermal stability, sustain drug release ability, cellular/tissue uptake, target delivery, and ultimately toxicity profile are shown to be affected with introduction of surface coating [23].
In a related study, iron oxide coated with dextran and polymer polyethylene glycol (PEG) demonstrated decrease of cytotoxicity compared to their corresponding uncoated nanoparticles; not only the toxicity but also the cell morphology were better maintained with the coated nanoparticle 8 The Scientific World Journal  than the uncoated particles [24]. This result shows that our nanodelivery system containing levodopa coated with tween 80 to have similar or even better survival potential on PC 12 than the uncoated nanoparticles. Nanotechnology in drug delivery system has the advantage of targeted delivery in a sustained release fashion compared to the conventional drug delivery system [6]. HVA is a metabolite produce from dopamine metabolism at the peripheral level or in the central nervous system; in Figure 10, we use a Parkinson's disease model (PC12) to show levodopa release from zinc-aluminum nanocomposite via its metabolite compared to pure levodopa. In release study above (Figure 7), levodopa release from the coated nanocomposite (Tween-dopa-LDH) was demonstrated to be slow and sustain, that lasted several days. This may explain the lower metabolite (HVA) production by the coated nanoparticle compared to pure levodopa ( Figure 10). In the case of pure levodopa, its metabolism may be rapid and immediate since almost hundred percent of it is available for metabolism after uptake by the dopaminergic cells. This experiment further strengthens sustain releasing ability of this coated nanocomposite and its possible uptake and metabolism of tween-dopa-LDH nanocomposite by the neural cells (PC 12).

Conclusion
Tween-dopa-LDH nanocomposite was prepared using Tween-80 water-soluble. The dopa-LDH nanocomposite was coated by Tween-80 in an aqueous solution. The release of levodopa from Tween-dopa-LDH nanocomposite suffered a retarding effect, which is due to coating with Tween-80. The viability study showed improved cell survival in Tween-80 coated nanocomposite than the corresponding uncoated Statistically there is no significant difference between all the groups tested ( > 0.05) as tested by one-way ANOVA. Tween-dopa-LDH showed slightly higher viability than dopa-LDH at 200-800 g/mL concentration. Tween-LDH also had less toxicity effect on the cell between 200-800 g/mL concentrations. nanocomposite. Levodopa metabolism from Tween-dopa-LDH nanocomposite possibly followed a slow and sustained release fashion demonstrated in the release study. Thus, the successful coating of depo-LDH with Tween-80 may help in delivering LDH nanocomposite to the brain via Apo lipoprotein acquisition and subsequent LDL receptor attachment on BBB surface.


extfieldsofstudy: Biology Medicine

s2fieldsofstudy: Biology Medicine

text:
MicroRNA-339-5p inhibits colorectal tumorigenesis through regulation of the MDM2/p53 signaling

Tumor suppressor p53 plays a central role in tumor suppression. To ensure its proper function, the levels and activity of p53 are under a tight regulation in cells. MicroRNAs are short non-coding RNAs that play an important role in regulation of gene expression. Recently, microRNA-339-5p has been reported to be frequently down-regulated in colorectal cancer, and furthermore, its down-regulation is associated with poor prognosis in cancer patients, which strongly suggests a tumor suppressive function of microRNA-339-5p in colorectal cancer. In this study, we found that microRNA-339-5p directly represses the expression of MDM2, a key negative regulator of p53, through binding to MDM2 3′-UTR in colorectal cancer cells. Through the down-regulation of MDM2, microRNA-339-5p increases p53 protein levels and functions, including p53 transcriptional activity and p53-mediated apoptosis and senescence in response to stress. Furthermore, microRNA-339-5p inhibits the migration and invasion of colorectal cancer cells and the growth of colorectal xenograft tumors in a largely p53-dependent manner. Our results highlighted an important role of microRNA-339-5p in suppression of colorectal tumorigenesis, and also revealed that regulating the p53 function is an important mechanism for microRNA-339-5p in tumor suppression.

INTRODUCTION
The tumor suppressor p53 and its signaling pathway play a central role in suppression of tumorigenesis, including colorectal cancer [1][2][3]. In response to stress, p53 is activated and accumulated in cells, which in turn transcriptionally regulates many target genes to initiate various cellular responses to maintain genomic stability and prevent tumorigenesis, including apoptosis, cell cycle arrest and senescence [1][2][3]. p53 also plays a critical role in inhibition of migration, invasion and metastasis of cancer cells [1,2,4]. To ensure its proper function in tumor suppression, the levels and activities of p53 are under a tight and complex regulation by many different regulators and mechanisms in cells [2,5,6]. MDM2, an E3 ubiquitin ligase, is the most critical negative regulator for p53. MDM2 binds to p53 and degrades p53 through ubiquitination to keep the p53 at a low level under the non-stress conditions [7,8]. Importantly, MDM2 itself is a transcription target of p53. Thus, p53 and MDM2 form an auto-regulatory negative feedback loop, which is tightly controlled to allow the appropriate response of p53 to stress in cells [5,6]. MDM2 is frequently overexpressed in various types of tumors, including colorectal cancer, which attenuates p53 function and promotes tumorigenesis [9][10][11] .
MicroRNAs (miRNAs) are a class of endogenously expressed, small (20-25 nucleotides) non-coding regulatory RNA molecules in cells. MiRNAs bind to the 3'-untranslated regions (3'-UTRs) of mRNAs in a sequence-specific manner, reducing mRNA stability and inhibiting translation, and thereby negatively regulate the expression of genes at the posttranscriptional level [12][13][14]. Recently, emerging evidence has shown that miRNAs are an important component of the p53 signaling pathway in addition to protein-coding genes [15][16][17]. p53 positively or negatively regulates the expression of certain miRNAs, which in turn mediates p53 function. At the same time, miRNAs also regulate p53 levels and functions through regulating p53 or regulators for p53 in cells. For instance, miR-125b and miR-504 have been recently identified as the direct negative regulators of p53 [15,18,19]. miR-192, miR-194, miR-25, miR-32 and miR-661 have been reported to directly repress MDM2 expression to activate p53 in cells [20][21][22]. These findings demonstrate that as a new component of the p53 signaling pathway, miRNAs play an important role in regulating and/or mediating p53 function in tumor suppression. The expression of many miRNAs is frequently altered in tumors [23,24]. The altered expression of certain miRNAs could lead to the disruption of the fine balance between miRNAs and the p53 signaling pathway, and thus attenuates p53 function and contributes to tumorigenesis [15,17].
Recently, microRNA-339-5p (miR-339-5p) was reported to be frequently down-regulated in different types of cancers, including colorectal cancers, which is associated with cancer metastasis and poor prognosis in cancer patients [25][26][27]. In this study, we found that miR-339-5p directly represses MDM2 expression, which in turn increases p53 protein levels and enhances p53 functions in regulating apoptosis and senescence in colorectal cancer cells in response to stress. Furthermore, miR-339-5p inhibits the migration and invasion of colorectal cancer cells as well as the growth of colorectal xenograft tumors in mice largely through the up-regulation of p53 functions. These results demonstrated that the up-regulation of p53 function is an important mechanism by which miR-339-5p inhibits colorectal tumorigenesis.

MiR-339-5p down-regulates MDM2 expression through its binding to the 3'-UTR of MDM2
Recently, miRNA-339-5p was reported to be frequently down-regulated in colorectal cancer, and furthermore, its decreased expression is associated with poor prognosis in colorectal cancer patients, which strongly suggests a potential role of miR-339-5p in suppression of colorectal cancer [25]. However, its underlying mechanism is not well-understood. To further understand the mechanism by which miR-339-5p exhibits tumor suppressive function in colorectal cancer, we performed a computational search for the potential targets for miR-339-5p. MDM2 was identified as a potential target for miR-339-5p, which contains multiple putative binding sites for miR-339-5p in its 3'-UTR.
Computational analysis predicted three putative binding sites for miR-339-5p located in the first 1.6 kb of 3'-UTR of the human MDM2 mRNA ( Figure 1E). To determine whether miR-339-5p binds to these three sites to down-regulate MDM2, a firefly luciferase reporter vector was constructed by inserting the first 1.6 kb of the 3'-UTR cDNA sequences of the human MDM2 gene containing these three sites into the 3' end of the luciferase gene. The vector was transfected into HCT116 p53+/+ or RKO p53+/+ cells together with either miR-339-5p mimic or miR-con. Compared with miR-con, miR-339-5p significantly decreased (by ~2-2.5-fold) the luciferase RKO cells. HCT116 p53+/+, HCT116 p53-/-, RKO p53+/+ and RKO p53-/-cells were transfected with miR-339-5p mimic or scrambled miRNA control (miR-con), and the MDM2 and p53 protein levels were measured at 24 h after transfection by western-blot assays. (B) MiR-339-5p decreased MDM2 mRNA levels in HCT116 and RKO cells. The MDM2 mRNA levels were measured by Taqman real-time PCR in cells transfected with miR-339-5p mimic or miR-con, and normalized with actin. The levels of the MDM2 mRNA in control cells transfected with miR-con were designated as 1. Data are presented as mean ± SD (n=3). #: p<0.05, *: p<0.01, two-tailed Student t-tests. (C) MiR-339-5p decreased MDM2 protein levels and increased p53 protein levels in human lung H460 and breast MCF7 cells. (D) The miR-339-5p inhibitor increased MDM2 protein levels and reduced p53 protein levels in HCT116 and RKO cells. Cells were transfected with the miR-339-5p inhibitor or miR-con inhibitor, and the MDM2 and p53 protein levels were measured at 24 h after transfection by western-blot assays. (E) The sequences of miR-339-5p, its putative binding sites and their mutants in the 3'-UTR of human MDM2. The positions of putative binding sites are labeled. The drawing is not to scale. (F) miR-339-5p inhibited the luciferase activities of the luciferase reporter vectors containing the wild-type (WT) human MDM2 3'-UTR which includes the 3 putative binding sites. HCT116 p53+/+ and RKO p53+/+ cells were transfected with luciferase reporter vectors containing WT or different mutant human MDM2 3'-UTR together with miR-339-5p mimic or miR-con. Luciferase activities were measured at 24 h after transfection. Data are presented as mean ± SD (n = 3). *: p<0.01, two-tailed Student t-tests. activities of the vectors containing the WT MDM2 3'-UTR sequences in both HCT116 p53+/+ and RKO p53+/+ cells ( Figure 1F). We further constructed serial luciferase vectors containing the mutant MDM2 3'-UTR sequence by mutating different putative binding sites for miR-339-5p. As shown in Figure 1F, mutating either putative binding site 1 (Mut 1) or site 2 (Mut 2) partially rescued the luciferase activities reduced by miR-339-5p, whereas mutating the putative binding site 3 (Mut 3) alone failed to do so. Consistently, mutating the putative binding sites 1 and 2 together (Mut 1+2) almost completely abolished the inhibitory effect of miR-339-5p on the luciferase activities, whereas mutating these 3 putative binding sites together (Mut 1+2+3) did not further increase the luciferase activities. These results indicated that miR-339-5p targets MDM2 through direct binding to the first two binding sites in MDM2 3'-UTR.
MiR-339-5p increases p53 protein accumulation and its transcriptional activity in response to stress p53 protein is maintained at a low level in cells under the non-stressed conditions mainly through proteasomal degradation of p53 protein by E3 ubiquitin ligases, particularly MDM2 [5,6,32]. In response to stress signals, p53 protein is accumulated in cells, which in turn leads to the transcriptional activation of p53 target genes to exert p53 functions in tumor suppression [1][2][3]. To investigate whether miR-339-5p reduces the p53 protein accumulation and p53 transcriptional activity in response to stress in colorectal cancer cells, HCT116 p53+/+ and HCT116 p53-/-cells transfected with miR-339-5p mimic were treated with chemotherapeutic agent 5-Fluorouracil (5-FU), which acted as stress signal to activate p53. 5-FU is the most widely-used chemotherapeutic agent for colorectal cancer [33,34]. The miR-339-5p mimic induced p53 protein levels in HCT116 p53+/+ cells under both non-stressed and stressed conditions (5-FU treatment) ( Figure 2A). Consistent with the increased p53 protein accumulation, the levels of p21 protein, a well-known p53 target, was also increased by miR-339-5p under both non-stressed and stressed conditions in a p53-dependent manner; p21 was clearly induced by miR-339-5p in p53+/+ but not p53-/-HCT116 cells (Figure 2A). This result demonstrated that the induction of p21 by miR-339-5p is due to the activation of p53 through MDM2 downregulation. The increased p53 transcriptional activity by miR-339-5p was also confirmed by examining the mRNA levels of several p53 target genes, including p21 (involved in cell cycle and senescence), Puma and Fas (involved in apoptosis). MiR-339-5p induced the mRNA levels of these genes in HCT116 p53+/+ cells with or without 5-FU treatment as detected by real-time PCR assays ( Figure  2B, upper panel). Furthermore, the mRNA levels of these genes were not affected by miR-339-5p in HCT116 p53-/-cells treated with or without 5-FU ( Figure 2B, lower panel). In addition to HCT116 cells, miR-339-5p mimic reduced the MDM2 protein levels, which in turn increased the protein levels of p53 and p21, in RKO p53+/+ cells treated with or without 5-FU ( Figure 2C). These results together showed that miR-339-5p enhances p53 protein accumulation and p53 transcriptional activity in response to stress in colorectal cancer cells.
It has been reported that chemotherapeutic agent Doxorubicin induces senescence in a largely p53dependent manner in many different types of cell lines [35,36]. To investigate whether miR-339-5p affects p53 function in inducing senescence, HCT116 p53+/+ and p53-/-cells transfected with the miR-339-5p mimic were treated with Doxorubicin, and senescent cells were detected by senescence associated β-galactosidase (SAβ-gal) staining. Doxorubicin induced senescence in a largely p53-dependent manner in HCT116 cells ( Figure  3C, D), which is consistent with previous reports [35,36].

MiR-339-5p inhibits migration and invasion of colorectal tumor cells in a largely p53-dependent manner
p53 plays an important role in inhibition of tumor metastasis [1,4,37]. Cell migration and invasion are two critical steps for tumor cell metastasis [38,39]. Recently, miR-339-5p was reported to be associated with cancer metastasis and poor prognosis in cancer patients [25][26][27]. To investigate whether miR-339-5p inhibits tumor cell migration and invasion through the activation of p53, chamber transwell assays were employed to determine the effects of miR-339-5p on the abilities of cell migration and invasion in HCT116 p53+/+ and p53-/-cells. Compared with HCT116 p53+/+ cells, HCT116 p53-/-cells displayed enhanced abilities of migration and invasion ( Figure  4A, B), demonstrating that p53 inhibited migration and invasion of tumor cells, which is consistent with previous reports [4,37,40,41]. Notably, miR-339-5p greatly inhibited migration and invasion of HCT116 p53+/+ cells ( Figure 4A, B). The inhibitory effects of miR-339-5p on migration and invasion were much less pronounced in HCT116 p53-/-cells ( Figure 4A, B). Furthermore, the miR-339-5p inhibitor significantly promoted the migration and invasion of HCT116 p53+/+ cells, but displayed a much less pronounced effect on migration and invasion in HCT116 p53-/-cells ( Figure 4C, D). Taken together, these results indicated that miR-339-5p inhibits migration and invasion of colorectal cancer cells in vitro through its activation of p53 function.

Figure 4: MiR-339-5p inhibits the migration and invasion of colorectal cancer cells in a largely p53-dependent manner.
(A) MiR-339-5p inhibited the migration of colorectal cancer cells in a largely p53-dependent manner. HCT116 p53+/+ and p53-/-cells transfected with miR-339-5p mimic or miR-con were seeded into chambers for migration assays. Left panels: Representative images of migrated cells; Right panel: Quantifications of average number of migrated cells per field. (B) MiR-339-5p inhibited the invasion of colorectal cancer cells in a largely p53-dependent manner. HCT116 p53+/+ and p53-/-cells transfected with miR-339-5p mimic or miRcon were seeded into matrigel-coated chambers for invasion assays. Left panels: Representative images of invading cells; Right panel: Quantifications of average number of invading cells per field. (C, D) The miR-339-5p inhibitor promoted the migration (C) and invasion (D) of colorectal cancer cells in a largely p53-dependent manner. HCT116 p53+/+ and p53-/-cells transfected with miR-339-5p inhibitor or miR-con inhibitor were used for migration and invasion assays as described in A and B, respectively. Data are presented as mean ± SD (n=3). #: p<0.05; *: p<0.01; **: p<0.001; two-tailed Student t-tests. MDM2 protein levels in both HCT116 p53+/+ and p53-/-tumors, and increased p53 protein levels in HCT116 p53+/+ tumors ( Figure 5C). These results indicated that miR-339-5p inhibits tumor growth in a largely p53-dependent manner in vivo through its direct downregulation of MDM2.
MDM2 overexpression and amplification is observed in many types of human cancers, including colorectal cancer, which contributes to tumorigenesis through attenuating p53 function [9][10][11]. In cells, many signals and factors can regulate the MDM2 protein, including genotoxic stress signals, oncogenic activation, ribosomal stress, chronic stress, neurohormones [11,44,45]. Recently, several miRNAs targeting MDM2 have been identified, including miR-192, miR-194, miR-25, miR-32 and miR-661, which revealed that miRNAs are a new component in the MDM2/p53 signaling pathway [20][21][22]. In this study, we found that miR-339-5p directly down-regulates MDM2 through binding to the 3'-UTR of MDM2. Through the negative regulation of MDM2, miR-339-5p increases p53 protein levels and functions, including p53 transcriptional activity, p53-mediated apoptosis and senescence in cells in response to stress. Thus, our results clearly demonstrated that miR-339-5p is an important negative regulator for MDM2 and a positive regulator for p53 in cells.
Recently, several studies reported that the expression of miR-339-5p was decreased in several different types of cancers, including colorectal cancer and breast cancer. Furthermore, lower levels of miR-339-5p were associated with cancer metastasis and poor prognosis in cancer patients [25][26][27]. These findings strongly suggest a potential role of miR-339-5p in tumor suppression. However, its mechanism is not well understood. In this study, we identified that MDM2 is a direct target for miR-339-5p. Through the negative regulation of MDM2, miR-339-5p increases p53 protein levels and functions. Considering the critical role of p53 in tumor suppression, our findings strongly suggest that activation of p53 is an B) MiR-339-5p inhibited the growth of HCT116 xenograft tumors in nude mice in a largely p53-dependent manner. Xenograft tumors were established by s.c. injection of HCT116 p53+/+ and HCT116 p53-/-cells into nude mice. When tumor volumes reached ~60mm 3 , tumors were injected with miR-339-5p mimic or miR-con once every two days for 6 times. (A) Representative tumors were photographed at 12 days after the first treatment with miR-339-5p mimic or miR-con. Scale bar: 10 mm. (B) Upper panels: The growth curves of HCT116 p53+/+ and p53-/-tumors after miR-339-5p injection. The relative volumes of the tumors before treatment at day 0 were designated as 1. Lower panel: The fold reduction of tumor volumes by miR-339-5p injection in both HCT116 p53+/+ and p53-/-tumors. Data are presented as mean ± SD (n=12 for each group). *: p<0.01; **: p<0.001; two-tailed Student t-tests. (C) Injection of miR-339-5p reduced MDM2 protein levels and increased p53 protein levels in HCT116 xenograft tumors. Six tumors from each group were used for western-blot analysis, and very similar results were observed. Represented are 2 tumors from each group. important mechanism by which miR-339-5p displays a tumor suppressive function. Consistently, our results in this study showed that miR-339-5p inhibits the migration and invasion of colorectal cancer cells in vitro as well as the growth of colorectal xenograft tumors in mice in a largely p53-dependepnt manner through its repression of MDM2 expression. These results strongly suggest an important role of miR-339-5p in suppression of colorectal tumorigenesis through its regulation of the MDM2/p53 signaling.
Some interesting questions still remain unclear. For instance, the regulation of miR-339-5p in normal and tumor cells is largely unknown. It is unclear whether miR-339-5p itself can be regulated by stress signals in cells. It is also unclear why the expression of miR-339-5p is frequently down-regulated in different types of tumors. Our results showed that miR-339-5p inhibits tumor cell migration/invasion and xenograft tumor growth in a largely p53-dependenet manner. However, miR-339-5p also displays some p53-independent effects on both tumor cell migration/invasion and tumor growth, although these effects are much less pronounced compared with the p53-dependent effects of miR-339-5p. One miRNA can regulate many different targets [12][13][14]. Previous studies have identified several other miR-339-5p targets, including BCL-6 and PRL-1 [25,26]. These identified targets and some additional unidentified targets for miR-339-5p could contribute to the p53-independent activities of miR-339-5p in tumor suppression. Furthermore, recent studies have reported that MDM2 also exhibits some p53-independent functions [9,46]. The repression of MDM2 by miR-339-5p could also lead to the changes of activities and functions of other MDM2-regualted proteins in addition to p53, which may also contribute to the p53-independent activities of miR-339-5p in tumor suppression. Further studies are needed in the future to address these interesting questions.
In summary, our results in this study demonstrated that miR-339-5p negatively regulates MDM2 in colorectal cancer cells. Through the negative regulation of MDM2, miR-339-5p increases p53 protein levels and functions, including the p53 transcriptional activity and p53mediated apoptosis and senescence in response to stress in colorectal cancer cells. Furthermore, miR-339-5p inhibits the migration and invasion of colorectal cancer cells in vitro and the growth of colorectal xenograft tumors in vivo in a largely p53-dependepnt manner. Thus, our results highlighted an important role of miR-339-5p in suppression of colorectal tumorigenesis through regulating the p53 activity and function.

Construction of luciferase reporter vectors and luciferase reporter assays
The human MDM2 3'-UTR sequences (1580 bp, 38-1617 nt from the start of 3'-UTR) containing three putative miR-339-5p binding sites were amplified by PCR using following two primers: Forward primer 5'-ACT AGT TAT AAC CCT AGG AAT TTA GAC AAC C -3' and Reverse primer 5'-AAG CTT ACA TCA TTA CTC CCA TCC CTT AC-3'. These two primers contain HindIII and SpeI recognization sites at the 5' end of the primers, respectively. The PCR products were subcloned into the 3' end of the pMIR-luciferase reporter vector (Ambion) at HindIII and SpeI sites. The mutations of three putative miR-339-5p binding sites were introduced using a Quikchange II XL Site-Directed Mutagenesis Kit (Stratagene/Agilent Technologies). Luciferase reporter assays were performed as previously described [18]. In brief, firefly pMIR-luciferase reporter vectors (100 ng) were transfected into cells in 6-well plates together with miR-339-5p mimic (100 nM) or scrambled miRNA mimic as a negative control by using Lipofectamine 2000. pRL-SV40 vectors (5 ng) that express Renilla luciferase (Promega) were co-transfected to normalize the transfection efficiency. Luciferase activities were measured at 24 h after transfection by using the Dual Luciferase Assay Kit (Promega).

Taqman Real-Time PCR Analysis
Total RNA from cells was prepared by using an RNeasy kit (Qiagen). cDNA was prepared with random primers using TaqMan reverse transcription kit (Applied Biosystems) as we previously described [18]. Gene expression levels were determined by real-time PCR using Taqman PCR master mixture and primers. The expression of genes was normalized to actin gene.

Cellular apoptosis and senescence analysis
Cellular apoptosis and senescence assays were performed as previously described [49]. For apoptosis analysis, cells were treated with 5-FU (300 µM), and collected at different time points (24 and 36 h) after treatment. Cells were washed with PBS, stained with a Alexa Fluor® 488 annexin V/Dead Cell Apoptosis Kit (Life Technologies) before being analyzed in a flow cytometer (Beckman Coulter). For senescence analysis, senescent cells were detected by using a Senescence β-Galactosidase Staining Kit (Cell Signaling) according to the manufacturer's protocol.

Cell migration and invasion assays
The transwell systems (24 wells, 8 μM pore size, BD Biosciences) were used for cell migration and invasion assays as we previously described [50]. For migration assays, 5 ×10 4 cells in 300 μl of serum-free medium were seeded into upper chambers, and the lower chamber was filled with 750 μl medium supplemented with 10% FBS. For invasion assays, transwell membranes were precoated with 50 µl Matrigel (BD Biosciences), and 1 ×10 5 cells were seeded into upper chambers. After cells were cultured at 37 °C for 24 h (for migration assays) or 36 h (for invasion assays), cells in the upper surface of the membrane were removed and cells on the lower surface were fixed with methanol and stained with crystal violet. The number of cell was counted in at least five randomly selected fields under a microscope.

Xenograft tumorigenicity assays
Xenograft tumorigenicity assays were performed as previously described with the approval of the Institutional Animal Care and Use Committee [47]. HCT116 p53+/+ and HCT116 p53-/-cells (5×10 6 in 0.2 ml PBS) were injected (s.c.) into seven-week-old BALB/c nu/nu male athymic nude mice (Taconic, NY). When the volumes of xenograft tumors reached ~60 mm 3 , miR-339-5p mimic (0.5 nmol) or scrambled miRNA-con were injected directly into the tumors every two days for 6 times (n=12 per group), and mice were sacrificed at the day 12 (the day for the first injection was designated as day 0), and tumors were collected for analysis. Tumor volume = ½ (length × width 2 ).

Statistical analysis
The differences in tumor growth among groups were analyzed for statistical significance by ANOVA, followed by Student's t-test using a GraphPad Prism software. All other P values were obtained using two-tailed Student t-tests.