**3. Results**

#### *3.1. The Effect of Farmyard Manure (FYM) on Sugar Beet Beetroot and Top Yield*

If we compare the effect of manure application, we find that the beetroot yield in the observed period (2016–2018) was significantly affected by both the fertilization treatment (d.f. = 1, F = 13.58, *p* < 0.001) and especially the weather conditions (d.f. = 2, F = 73.48, *p* < 0.002). The effect of the interaction between the treatment and year was also significant (d.f. = 2, F = 4.29; *p* < 0.03). The conditions of the year had the highest impact on beetroot yield (80%), followed by the fertilizer treatment (15%), and their interaction (5%).

The application of the FYM provided comparable results as the control. Significantly higher yields were recorded only in 2016 (Table 2). The average beetroot yield was 52.9 t ha−1 in the control, and 61.2 t ha−1 in the FYM treatment (2016–2018, Table 2). Comparing the years, the average yield was 66.2 t ha−1 and 67.2 t ha−1 in 2016 and 2017, respectively (without a statistical difference), while the significantly lower yield was recorded in 2018 (37.8 t ha−1) (Table 2).

**Table 2.** The beetroot and top yield as affected by the fertilizer treatment (control and farmyard manure (FYM)) and year(2016–2018).

Note: The mean values with the standard error of the mean followed by the same letter (small letters "a", horizontally; and big letters "A", vertically) are not significantly different (*p*, 0.05).

> In the individual years, the top yield was not affected by the FYM application (Table 2). However, for the entire evaluated period (2016–2018), the differences among the compared treatments (d.f. = 1, F = 5.5, *p* < 0.03) and years (d.f. = 2, F = 113.0, *p* < 0.001) were significant.

While the effect of the year was 95%, the effect of fertilization was only 5%. As in the case of beetroots, this means that the differences between the compared fertilization treatments were very low, while the fluctuation between the years was very high (caused mainly by the severe drought in 2018). The average top yield was 17.4 t ha−1 in the control, while it was 19.4 t ha−1 in the FYM treatment. Comparing the years, the highest yields were recorded in 2017 (23.8 t ha−1), followed by 2016 (22.1 t ha−1), and 2018 (9.3 t ha−1) (Table 2).

#### *3.2. The Effect of Mineral NPK on Sugar Beet Beetroot and Top Yield*

If we compare the entire period (2016–2018), the application of mineral NPK fertilizers generally increased the beetroot yield significantly (Table 3). According to MANOVA, the beetroot yield was mainly affected by the year (d.f. = 2, F = 146.3, *p* < 0.0001, 92%), showing a very high fluctuation among the years. The highest average yield was recorded in 2017 (72.2 t ha−1), followed by 2016 (68.2 t ha−1), and 2018 (44.4 t ha−1). The effect of the fertilizer treatment was also significant (d.f. = 4, F = 11.4, *p* < 0.001), but the only significant difference was recorded between the control and NPK treatments. However, no significant differences among NPK1–4 treatments were recorded over the entire period (Table 3). The average beetroot yield was 52.9 t ha−1 (control), 61.3 t ha−1 (NPK1), 62.7 t ha−1 (NPK3), 63.3 t ha−1 (NPK2), and 67.7 t ha−1 (NPK4). When only NPK treatments were considered, yield response to N rates across three years plateaued at 112 kg ha−1 N with a corresponding beetroot yield of 66 t ha−1 (Figure 2, left).

**Table 3.** The beetroot and top yield as affected by the fertilizer treatment (control, NPK1–4) and years (2016–2018).

Note: The mean values with the standard error of the mean followed by the same letter ( small letters "a", horizontally and big letters "A", vertically) are not significantly different (*p*, 0.05).

**Figure 2.** Means (black dots) of sugar beet beetroot yield (**left**) and top yield (**right**) at different N rates of NPK treatments in 2016, 2017, and 2018 combined and their linear-plateau regression (blue line).

In the case of the sugar beet top yield, the effect of the year (d.f. = 2, F = 425.9, *p* < 0.0001), fertilizer treatments (d.f. = 4, F = 34.3, *p* < 0.001), and their interactions (d.f. = 8, F = 3.8, *p* < 0.002) was significant. The lowest average top yield over the evaluated period was provided by the control treatment (17.4 t ha−1). Significantly higher top yields were

recorded in NPK treatments, with the highest top yield in the NPK4 treatment (26.8 t ha−1) (Table 3). The year again had the greatest impact on the top yield (92%), followed by the fertilization treatment (7%). Comparable top yields were recorded in the years 2016 (28.3 t ha−1) and 2017 (29.1 t ha−1), while a significantly lower top yield was recorded in the dry year 2018 (11.9 t ha−1) (Table 3). According to the linear-plateau model, the mean top yield response to N rates across three years plateaued at 122 kg ha−1 N, with a corresponding top yield of 25 t ha−1 (Figure 2, right).

#### *3.3. Comparison of the FYM and FYM + NPK Treatments*

Over the entire evalutated period (2016–2018), the combined application of the FYM with mineral NPK fertilizers significantly increased the beetroot yields (d.f. = 5, F = 19.6, *p* < 0.001) (Table 4). The lowest yield was recorded in the control (52.9 t ha−1), followed by the FYM treatment (61.2 t ha−1). The addition of mineral NPK fertilizers significantly increased the beetroot yields as compared with the control and FYM treatments (Table 4), ranging from 65.5 t ha−1 (FYM + NPK3) to 66.3 t ha−1 (FYM + NPK1). The differences among all FYM + NPK treatments were insignificant. The effect of the year was also significant (d.f. = 2, F = 333.7, *p* < 0.0001), as well as the year\*treatment interaction (d.f. = 10, F = 2.5, *p* = 0.014). The comparison of years indicated the same results as the previous evaluation. While in the years with relatively favourable conditions (2016 and 2017) the differences were not significant (the average yields were 71.4 t ha−1 in 2016 and 72.4 t ha−1 in 2017), the conditions of the year 2018 sharply reduced the beetroot yield to an average value of 45.1 t ha−1. The beetroot yield response to different rates of FYM and NPK fertilizers plateaued at 165 kg ha−1 N, with a corresponding beet yield 66 t ha−1 (Figure 3, left).

A similar effect of mineral fertilizers was found for the top yields. The top yield was significantly affected by the year (d.f. = 2, F = 493.8, *p* < 0.0001), fertilization treatment (d.f. = 5, F = 41.8, *p* < 0.0001), and their interaction (d.f. = 10, F = 4.5, *p* < 0.001). The lowest yields were provided by the control and FYM treatments (17.4 and 19.4 t ha−1, respectively) (Table 4). The addition of mineral fertilizers increased the top yields significantly, ranging from 25.2 t ha−1 (FYM + NPK2) to 26.9 t ha−1 (FYM + NPK3). The differences between the FYM + NPK treatments were insignificant. Comparing the years, dry conditions during 2018 resulted in the lowest yield of the tops (12.1 t ha−1), while significantly higher yields were recorded in 2016 and 2017 (28.8 and 29.7 t ha−1, respectively). According to the linear-plateau model, the response of the sugar beet tops plateaued at 181 kg ha−1 N, with a corresponding yield of 24 t ha−1 (Figure 3, right).

**Figure 3.** Means (black dots) of sugar beet beetroot yield (**left**) and top yield (**right**) at different N rates applied with the FYM and FYM + NPK treatments in 2016, 2017, and 2018 combined and their linear-plateau regression (blue line).

**Table 4.** The beetroot and top yield as affected by the fertilizer treatment (control, FYM, FYM + NPK1–4) and years (2016–2018).

Note: The mean values with the standard error of the mean followed by the same letter ( small letters "a", horizontally; and big letters "A", vertically) are not significantly different (*p*, 0.05).

#### *3.4. The Effect of Fertilization on Sugar Content (SC) and Chemical Elements Concentration*

We must admit that due to limited funds, analyses of sugar beet in reduced quantities were performed over the years 2016–2018. This means that no repeated measurements were performed from each fertilizer treatment every single year. Therefore, the results of the statistical analysis presented here represent the average results for the entire analysed period. It is, therefore, necessary to take the results with a grain of salt.

According to the statistical analysis, no significant differences were recorded between the fertilizer treatments for any analysed parameter (the SC and the concentration of N, P, K, Ca, Mg, and Na) of the sugar beetroots. The SC varied from 19.7% (NPK4) to 21.9% (NPK1) (Table 5). The concentration of N, P, K, Ca, Mg, and Na was not affected by the fertilizer treatment (Table 5).

**Table 5.** The sugar content (%) and concentrations of N, P, K, Ca, Mg, and Na (%) in sugar beet beetroots as affected by the fertilizer treatment and over the years 2016–2018.

Note: The mean values without letters were not significantly different.

Similar results were recorded in the case of the sugar beet tops, where the concentrations of N, P, K, Ca, and Mg were analysed. Except for P, the effect of the fertilizer treatment was insignificant. All results are shown in Table 6. In the case of P, the mean concentration varied from 0.15% (control) to 0.23% (NPK4 and FYM + NPK1 treatments). Higher concentrations of the P were found in the FYM + NPK treatments as compared with the control, FYM, and NPK treatments (Table 6).

**Table 6.** The concentrations of N, P, K, Ca, and Mg in sugar beet tops as affected by the fertilizer treatment and over the years 2016–2018.

Note: The mean values with the standard error of the mean followed by the same letter are not significantly different (*p*, 0.05). Mean values without letters were not significantly different.

#### *3.5. The Effect of the Fertilizer Treatments on the Soil Properties*

The application of different combinations and doses of fertilizers did not affect the value of the soil pH. The average values ranged from 6.08 (NPK3) to 6.60 (FYM). The concentration of N was slightly affected by the fertilizer treatment. The lowest concentrations

were recorded in the control and FYM treatments (0.13%), while the highest concentrations were recored in the FYM+NPK4 treatment (0.16%). All other treatments provided results fitting within these extreme limits. In the case of soil carbon content, the distribution of the fertilizer treatments is clearer. The lowest C concentration was recorded in the control treatment (0.99%). All FYM + NPK treatments differed significantly from this value, and ranged from 1.26% to 1.35%, while the FYM and all NPK treatments filled the space between the control and FYM + NPK treatments. The concentration of soil P significantly varied among the treatments with lowest concentration in the control (20 mg kg−1) and FYM (29 mg kg−1) treatments and highest concentrations in NPK4 (70 mg kg−1) and FYM + NPK4 (93 mg kg−1) treatments. A similar pattern was recorded in the case of K (lowest concentrations were in the control and FYM treatments, while the highest concentrations were in the NPK4 and FYM + NPK2 treatments) (Table 7). The concentrations of Ca and Mg were not affected by the fertilizer treatment (Table 7).

**Table 7.** The basic soil chemical properties as affected by the fertilizer treatment and over the years 2016–2018.

Note: The mean values with the standard error of the mean followed by the same letter are not significantly different (*p*, 0.05). Mean values without letters were not significantly different.


**8. Techniques of pulmonary artery flush**

Following instillation of the Perfadex solution, the cannula is removed. The lungs are then recruited with Valsalva maneuver at 30 cm H2O and both lungs are sequentially examined in standard fashion. The heart is then excised in standard fashion. In certain instances, there is consent for homograft valves and sufficient care needs to be exercised while excising the heart to preserve the valves [20].

Next, dissection begins first by dividing the inferior pulmonary ligament (bilateral) and the posterior pericardium. The main and right pulmonary artery are dissected away from the aorta. Bilaterally, the inferior pericardium is then divided at

the level of the esophagus. Then the posterior pericardium is divided in a horizontal fashion just above the esophagus to the level of the pulmonary veins. The aortic arch is then transected. The superior aspect of the arch is then exposed and divided as distal as possible along the arch vessels so that a portion of the wall of the arch and descending aorta are left as a cuff to prevent dividing the ligamentum arteriosum.

Attention is then turned to the trachea which is isolated just above the carina with blunt dissection. A TA-30 stapler is passed around the trachea 3 rings above the carina [21]. The lungs are inflated to 60% tidal volume-to avoid baro-trauma (especially if the lungs are being flown back to the recipient institution), and the endotracheal tube is withdrawn. The stapler is used to divide the trachea. The lung bloc is removed from the donor after division of any remaining attachments.

The lung bloc is taken to the back table. Retrograde flush is performed at 250–500 cc/vein x4. If lot of clots/thrombi are seen exiting the PA during retro flush one can take a call regarding quality (small clots are not uncommon).

The double lung bloc is then examined for compliance, color, any areas of inadequate perfusion, atelectasis etc. (refer to DBD procurement of lungs section). Once the procurement surgeon is satisfied that the lungs are suitable for transplantation the recipient surgeon is called, and if accepted the lungs are then split on the back table and packed in standard fashion. The main PA is divided at the insertion site of the pulmonary artery cannula. The atrial cuff is then created approximately 1 cm from insertion of the pulmonary veins into the left atrium. The atrium is then divided in the midline with scissors. The left mainstem bronchus is divided with a TA-30 stapler near the hilum. An additional liter of cold Perfadex solution is instilled bilaterally via the pulmonary veins with a balloon catheter. The lungs are packed first in a bag containing Perfadex, followed by 2 bags containing ice cold saline slurry. The organs are subsequently transported to their respective transplant centers.


*Influence of Landsat Revisit Frequency on Time-Integration of Evapotranspiration… DOI: http://dx.doi.org/10.5772/intechopen.80946*

(3) imagery from only one Landsat 5 from one path only. The first integration run approximated a condition, where four images are collected each 16 days. This is a condition that would occur with four Landsat satellites in orbit with the current path width or with two Landsat satellites in orbit, each having a 'double-wide' path of approximately 300 km. The second and third integration runs approximated the condition where two currently formulated Landsats are in orbit at any one time, for the center of a WRS path. The last condition represents the condition, where only one Landsat satellite is in orbit.

In the Idaho study area, ET was integrated over the April 1–October 31 period to form monthly ET for April through October. The absence of clear images for the study area during late March and early April and during late October and early November for some of the time integration runs required the use of 'synthetic' images to represent ET conditions during these periods. The synthetic images were required to anchor the spline function prior to April and following October. The synthetic images were created for the Idaho study area by applying a daily soil water balance model for a bare soil condition [11] representing surface conditions during Idaho winters and late falls, where nearly all vegetation is dormant due to freezing. The daily soil water balance model used the FAO-56 evaporation model [11] and was applied to 18 weather stations in the region and an evaporation surface was created using inverse distance interpolation. The average ETrF during the late March to early April and from late October to early November periods was determined by averaging the simulated evaporation rates over those periods. Those synthetic images were then used as beginning and ending points for the spline interpolation process.

No synthetic images were required for the full two-Landsat/two-path integration for the Idaho study area, as sufficient image dates during early April and late October were available. In the double satellite/single path integration, however, synthetic ETrF images were required at the end of the growing season to provide endpoints for the cubic spline, and were placed on dates November 01, 2000 (ETrF averaged 0.25) and November 10, 2000 (ETrF averaged 0.1). For the run using only Landsat 5 data and for path 40 only, synthetic images were required at both the beginning and end of the growing season. In this run, four synthetic images were placed on dates March 20, 2000 (ETrF averaged 0.1); March 31, 2000 (ETrF averaged 0.1); November 01, 2000 (ETrF averaged 0.25); and November 10, 2000 (ETrF averaged 0.1) to provide endpoints for the cubic spline.

For the Nebraska study area, ET was integrated over the May–September period, representing the shorter growing season for the predominately corn and soybean crop rotation there, as opposed to the April–October growing period for Idaho crops. As with Idaho, ETrF for bare soil was also estimated for the Nebraska study area using the FAO-56 style evaporation model for the purpose of creating synthetic images for April 1, April 15, October 15, and November 15. These dates and synthetic images were used in each of the time-integration analyses to approximate ET conditions during those general periods so that the spline function could be applied with spans covering May–September.

In both study areas, about 1500 data points were sampled. The points were selected from the interiors of irrigated fields, with one point per field. Pixels were located far from field edges to avoid contamination of thermal pixels from thermal information from outside the field. Nearly all of the Idaho sample locations were in agricultural fields, with about 15 sample points taken from desert rangeland. Irrigated agriculture was emphasized in this study due to its importance in water resources management. In the Nebraska data set, about 100 sample pixels were selected from rangeland and riparian areas each. The rest were from irrigated agriculture.

*Advanced Evapotranspiration Methods and Applications*

**Time integration using both paths and landsats**

L7 \* \*

L5 \* \*

L7 \* \*

L7 \* \*

L7 \* \*

L5 \* \*

L7 \* \*

L5 \* \*

L7 \* \*

L5 \* \*

**Run 2: Time integration using path 30 and both landsats**

**Run 3: Time integration using path 29 and both landsats**

L7 \* \* \*

L7 \* \* \*

L7 \* \* \*

L7 \* \* \*

L7 \* \* \*

L5 \* \* \*

L5 \* \* \*

L5 \* \* \*

L5 \* \* \*

**Run 4: Time integration using path 29 and landsat L5**

**Run 5: Time integration using path 29 and landsat L7**

**Dates Sensor Run 1:**

1 April 24, 2002

2 May 02, 2002

3 May 03, 2002

4 June 11, 2002

5 June 27, 2002

6 June 28, 2002

7 July 22, 2002

8 July 29, 2002

9 July 30, 2002

10 August 6, 2002

11 August 14, 2002

13 August 15, 2002

14 August 23, 2002

15 August 31, 2002

16 September 7, 2002

17 September 8, 2002

18 September 15, 2002

19 September 16, 2002

20 September 23, 2002

**56**

**Table 2.**

*platform.*

Three to four integration runs were made for the study areas, as described in the next section. The integration runs utilized (1) both Landsat 5 and 7 imagery from both paths; (2) both Landsat 5 and 7 imagery from one path or the other; and

*Selection of Landsat images used to time-integrate ET for the Nebraska study area, showing collection path and*

*Asterisks indicate the dates used in a particular integration run to estimate monthly and growing season ET.*

## **2.4 Model runs**

The first baseline model runs used all 21 ETrF images listed in **Table 1** for Idaho and all 20 images listed in **Table 2** for Nebraska. These runs, representing a condition with four traditional Landsat satellites in orbit or two 'double-wide' Landsats providing four images every 16 days, served as baselines for comparing against sparser image data sets. There were seven times in Idaho and six times in Nebraska when image dates were only 1 day apart, as shown in **Tables 1** and **2**, due to the scheduling of the two Landsat systems and geometry of the WRS path system. In cases where images were 1 day apart, we subtracted 2 days from the first image and added 2 days to the second image in the baseline spline model run 1. This was required to keep the spline function from creating large vertical components caused by a time difference of only 1 day. In cases where images were 1 day apart, the additional information afforded by the second image was deemed to be of much less value than if it had been 4 days apart. Four days apart, larger changes would have occurred in ETrF due to vegetation development and wetting conditions in addition to larger differences in cloudiness. Images 1 day apart typically had similar cloud conditions and ETrF behavior.

Four other integration runs were carried out for the Idaho study area as indicated in **Table 1**. These runs represented conditions where fewer than four revisits per 16-days were available. Runs 2 and 3 were made using Landsat 5 and Landsat 7 images from only one path, either path 39 or path 40. These runs represent scenarios where two Landsat satellites are in orbit and the focus includes the center two-thirds of a path so that the revisit time is each 8 days. Runs 2 and 3 represent two replicates of the same scenario of 8 day revisit, which is possible in the path overlap area.

Run 4 for the Idaho study represents the scenario presented when only one Landsat is in orbit, collecting data every 16 days. This represents the actual scenario for the USA during the late 1980s and 1990s when only Landsat 5 was collecting data and again in 2012 when only Landsat 7 was collecting data. Run 4 was constructed by using imagery for path 40 and Landsat 5. Additional runs 5, 6, and 7, would have represented three additional replicates of a single satellite having 16-day revisit, via combinations of path 40 with Landsat 7 and path 39 with Landsat 5 and path 39 with Landsat 7. However, runs 5, 6, and 7 were not possible to implement because too few images were available during the April–October to apply the ETrF interpolation process without applying what was considered to be too much speculation on the evolution and trends in ETrF over time.

Nebraska runs 2 and 3 were made using a combination Landsat 5 and Landsat 7 images from only path 30 or from only path 29. These runs represent scenarios, where two Landsat satellites are in orbit so that the revisit time is each 8 days. Model runs 4 and 5 applied Landsat 5 and Landsat 7, respectively, to path 29, only. Each of these runs represented conditions where only a single Landsat is in orbit, with revisit of 16 days for the majority of a path area. This represents the actual scenario for the USA during the late 1980s and 1990s when only Landsat 5 was collecting data and again in 2012 when only Landsat 7 was operational. Model run 4 was setup to only process imagery from Landsat 5 for path 29 and model run 5 was setup to only process imagery from Landsat 7 for path 29.


*Edited by Dennis Wat*

Cystic Fibrosis in the Light of New Research provides the latest research and clinical evidence that will be useful for clinicians, scientists and researchers to further their knowledge around this fascinating condition. The authors have brought along their expertise and wealth of knowledge to produce this book, including the basic science that underlies the disease, the burden of bacterial and viral infections, immunologic aspects of CF, a variety of clinical measurements to predict prognosis and novel therapies including gene therapy. This book will be invaluable and entertaining for anyone who is involved in the care of patients with cystic fibrosis.

ISBN 978-953-51-2152-7

Cystic Fibrosis in the Light of New Research

Photo by Tabthipwatthana / DollarPhoto


**Pathophysiology of Non-Dopaminergic Monoamine Systems in Parkinson's Disease: Implications for Mood Dysfunction**

Nirmal Bhide and Christopher Bishop *Department of Psychology, Binghamton University, Binghamton, NY USA*


**1.1. Definition of risk**

358 Risk Management – Current Issues and Challenges

Source: Adapted from ISO Guide 73

**Figure 1.** The process of risk management

for analysis to help manage and thus minimize risk.

irrespective of the stature of an individual or an organization.

most. The general process and steps involved is presented in Figure 1.

technology, innovation, human resources and time to name a few. In order to adequately address an impending risk, it is important to gather as much factual information as possible

Risk can be classified into both voluntary and involuntary [1]. This classification depends on how an individual or an organization judges the situation. For example, a person with a habit of smoking or drinking fails to associate the habits as involving risks; yet often the habit becomes hazardous and they can significantly affect a person's quality life. Involuntary risk places a person or the organization in a state of ambiguity, where the people involved in the decision making process have not been exposed to a particular circumstance or they lack knowledge and awareness of the particular risk situation. The ability to deal with such risks is a crucial factor in determining successful outcomes

For some individuals, the ability to deal with risk appears to be built in their character but for the rest of us it seems, it is knowledge that can be acquired through training. In order to gain the skill set required so that one to deal with risk, it is important to step out of one's comfort zone and be willing to change, learn, develop new skills, or be challenged to manage risk. Risk management is a methodical approach that could be taught and learnt by

This paper is organized in the following manner: In the next few sections risk is defined and risk management explored focusing on types of risks associated with real estate market. The Australian real estate market is then reviewed and possible risks involved are explored in some depth particularly in terms the global financial crisis. The paper compares the market In the international context, the ISO 31000/ISO Guide 73: 2009 [2] defines risk as the "effect of uncertainty on objectives" (p. 1). When there is a lack of knowledge or exposure to a certain event then such a situation can be termed uncertain. Taking decision on an uncertain event or situation may or may not be successful, which is what risk is about. Many definitions of risk exist in common usage [3-4]; however the ISO definition of risk was developed by an international committee representing over 30 countries and is based on the input of several thousand subject matter experts.

Risk is defined in Australia by the Australia/New Zealand standard for risk management [2] as "the possibility of something happening that impacts on your objectives. It is the chance to either make a gain or a loss. It is measured in terms of likelihood and consequence…" (p. 2). Risk can also be defined as the uncertainty of future events that might influence the achievement of one or more objectives such as an organization's strategic, operational and financial objectives [3]. Risk management may produce positive opportunities for developers although the negative aspects of risk are usually the once that are emphasized [4].

Likelihood of risk occurring varies from industry to industry and how complex a job maybe. Some areas where there is a high chance of risk are construction, transport, mining, health care, sports, finance and banking, insurance and superannuation.

Risk can be broadly understood and explained in three different scenarios [5]: risk versus probability; risk versus threat; and all outcomes versus negative outcomes. It is believed that any risk can be managed through the engagement of a proper risk management process.

#### **1.2. Risk management**

There seems to be an increasing demand of organizations to meet and exceed the financial expectations of shareholders. In the pursuit of growth, many organizations (for example: Toyota) have adapted and responded to expectations of the shareholders by becoming lean and efficient. It is always easy to think that risks and their potential consequences could have been predicted and managed. This is clearly not true when it comes to success in a business. Business success usually requires some acceptance of risk and, as such any risky strategy undertaken may lead to a failure.

In large organizations and corporations there are designated personnel; namely, risk managers. Hillson [6] argued that risk is mostly managed "continuously, both consciously an unconsciously, though rarely systematically" (p. 240). Risk manager's main role is to be

aware of the market, collect data and predict forthcoming threats so that a company can manage the risks in a successful manner. Risk manager duties include developing and communicating risk polices and process, building risk models involving market, conducting credit and operational risk analysis, coordinating with concerned stakeholders involved in the process and creating a risk awareness culture in the organization.

Importance of Risk Analysis and Management –The Case of Australian Real Estate Market 361

To establish context and define goals is an important step. Once the context is established it is critical that the risk is defined and the objectives are set. Also important is to know the limitations of the risk strategies proposed. An effective risk management team understands the needs of the organization and the way it operates. Once the goal is defined there is a need to identify the scope of the context. In general, these factors can be classified into strategic and operational risks. Strategic risk management includes economic, social, environmental, political, legal and public issues; while operational risk management includes technological, human resource, financial, reputation and other relevant strategic issues. Clearly, management may not be able to totally control the many factors but the risks

The process of risk management has to be simple, precise and effective. For it to be effective, organizations should consider strength, weakness, opportunities and threats (SWOT) type analysis of the situation. By conducting SWOT analysis, the management can identify and analyze different situations [7]. Once threats are identified, appropriate measures and decisions may then be taken to convert the threat into an opportunity. The organizational context provides an understanding of the organization, its capability and goals, objectives and strategies. In establishing the context the identification of stakeholders is critical; these

**1.3. The steps involved in managing risk**

Source: AS/NZS 4360:2004

**Figure 2.** The steps in risk management

*1.3.1. Establish goals and context*

posed by them could indeed be minimized.

Risk management not only prevents organizations from entering a dangerous and uncertain territory, which could lead to a catastrophic failures, but also ensure the development and growth of the business. The depth and clarity with which a risk is defined is critical for risk management. In an event where an organization has a low risk situation at hand and decides to postpone rather than resolve the issue involved for financial or other reasons, the risk may eventually become a threat of moderate to high level and this could prove to be disastrous for management. Ignoring the risks that apply to the business activities or the events that have been planned could impact on the following:

A systematic approach to managing risk is now regarded as best management practice. The approach taken almost always benefits the organization irrespective of type of risk involved. Once the risk is identified it is documented in detail; subsequently the concerned stakeholders undertake possible risk management and mitigation processes. A comprehensive review of the situation and critical feedback are usually required that may ultimately lead to changes in the organizational polices and structures; particularly in case of a major events.

Organizations that thrive to be successful constantly monitor themselves and willfully undertake only calculated risks. In doing so, they enjoy a competitive advantage in addition to meeting their business objectives. In era of globalization, companies are often expanding their business opportunities and in the process, they may undertake challenging and ambitious projects. In most cases, they need to take a number of risks. In this regard, businesses such as Microsoft, Google, and Wal-Mart appear to have been successful global players mainly because they were able to manage risk in a timely manner.

Risk management decisions should be a part of business objectives. Every new project, policy or invention should include all the possible anticipated risks that one may possibly confront. Decision making process needs to consider threats identified, its impact and reaction on the business. By making a careful analysis, companies will have fewer surprises and thus may in the end spend less time recovering from the losses that may be inevitable at times. When companies do not have "a keen eye on the kind of risk", risk retention can become a legitimate way of managing the risk. Figure 2 shows the six steps involved in the risk management process: establish the context, identify the risk, analyze the risk, evaluate the risk, and manage and review the risk.


Data are shown as the mean ± SD or median with interquartile range. Normal distribution of the variables was evaluated using the Shapiro–Wilk *W* test. Comparison of variables between two groups was performed by either the independent sample *t*-test or Mann–Whitney *U* test according to the data distribution. Univariate and multivariate logistic regression analyses were used to estimate the odds ratio (OR) and 95% confidence intervals (CIs) of each variable for CRC. Cutoff point analysis, defined by the largest distance from the diagonal line of the ROC curve [sensitivity × (1 − specificity)], was used to identify the optimal value of serum ZAG levels that differentiated healthy people from patients with CRC. The sensitivity and specificity of the index for the cutoff point were also calculated. Stepwise multiple regression analysis was performed to explore the variables independently related to ZAG levels in serum and WAT. All statistical computations were run on SPSS 20.0 for Windows (SPSS Inc., Chicago, IL, USA). *P* < 0.05 was considered statistically significant.

## RESULTS

## General Characteristics of Subjects in NW **+** CRC, OW/OB **+** CRC, and Control Groups

The characteristics of the CRC patients and healthy controls have been summarized in **Table 1**. Generally, NW + CRC patients have a significantly higher age but lower HDL-C levels when compared with control subjects (all *P* < 0.05). As expected, patients in the OW/OB + CRC group presented with a higher body weight, BMI, SBP, and TG than those in the NW + CRC group (all *P* < 0.05). However, no significant difference was observed with regard to height, DBP, FBG, TC, or LDL-C in these two groups.

## Serum Levels of ZAG, Leptin, HMW-ADPN, and TNF-**α** in CRC Patients and Healthy Controls

As shown in **Figure 1A**, serum ZAG levels in NW + CRC patients were 11.7% higher than in healthy controls (1.53 ± 0.30 vs. 1.37 ± 0.31 μg/mL, *P* < 0.05). In addition, serum levels of leptin in NW + CRC patients had a tendency to be lower compared with healthy controls (1.82 ± 1.85 vs. 2.13 ± 1.36 ng/mL, *P* = 0.07) (**Figure 1B**). No significant difference was found in serum HMW-ADPN and TNF-α levels between NW + CRC patients and healthy controls (**Figures 1C,D**).

As shown in **Figures 1E–H**, serum HMW-ADPN levels in OW/OB + CRC patients were significantly decreased by 23.5% (2.02 ± 1.19 vs. 2.64 ± 1.30 μg/mL, *P* < 0.05), while the leptin levels were significantly increased by 57.7% (2.87 ± 1.47 vs. 1.82 ± 1.90 ng/mL, *P* < 0.01) when compared with NW + CRC patients. However, no significant difference was observed in serum ZAG and TNF-α levels between these two groups.

Next, serum levels of the four adipokines were further analyzed in male and female subjects, separately. As shown in Figure S1 in Supplementary Material, for men, serum ZAG levels in NW + CRC patients were 22.2% higher (1.57 ± 0.31 vs. 1.28 ± 0.25 μg/mL, *P* < 0.05), while the leptin levels were 30.1% lower (1.20 ± 1.36 vs. 1.71 ± 1.10 ng/mL, *P* < 0.05) than in healthy controls. Serum HMW-ADPN levels in male OW/ OB + CRC patients were significantly decreased by 37.5% (1.67 ± 1.05 vs. 2.68 ± 1.41 μg/mL, *P* < 0.05), while the leptin levels were significantly increased by 86.1% (2.22 ± 1.29 vs. 1.20 ± 1.36 ng/mL, *P* < 0.01) when compared with NW + CRC patients. However, no significant difference was observed in serum ZAG, leptin, HMW-ADPN, and TNF-α levels in women across these three groups (Figure S2 in Supplementary Material).

## Expression of *ZAG*, *Leptin*, *HMW-ADPN*, and *TNF-***α** in sWAT and vWAT of NW **+** CRC and OB **+** CRC Patients

In our present study, the mRNA levels of *ZAG*, *leptin*, *HMW-ADPN*, and *TNF-*α were also measured in sWAT and vWAT from nine NW + CRC and nine OB + CRC patients. Baseline characteristics of the patients have been summarized in Table S2 in Supplementary Material. Our results showed that *ZAG* mRNA levels in sWAT were significantly lower in OB + CRC patients than in NW + CRC patients (reduced by 78.8%, *P* < 0.01) as presented in **Figure 2A**. Additionally, *TNF-*α mRNA levels in vWAT of OB + CRC patients were significantly increased by 2.8-fold when compared with NW + CRC patients (*P* < 0.05) (**Figure 2H**). No significant differences in *HMW-ADPN* or *leptin* mRNA levels in sWAT and vWAT were observed between these two groups (**Figures 2B–G**).

## The Relationships Between Clinical Parameters and Serum ZAG Levels and *ZAG* mRNA Levels in sWAT and vWAT in CRC Patients

As shown in **Table 2**, a significant positive correlation between circulating ZAG levels and TNF-α levels was found after adjusting for age and sex in CRC patients (*r* = 0.27, *P* = 0.03). *ZAG* mRNA levels in sWAT were negatively associated with BMI (*r* = −0.55, *P* = 0.03), whereas *ZAG* mRNA levels in vWAT were positively correlated with BMI (*r*= 0.60, *P*= 0.01). In addition, *ZAG* mRNA levels in sWAT were also found to be positively associated with serum HMW-ADPN levels (*r* = 0.54, *P* = 0.03). No significant relationship between serum ZAG levels and BMI was found in CRC patients.

Next, stepwise multivariate linear regression was performed. As displayed in **Table 3**, CRC, TNF-α, and HDL-C were independent factors associated with serum ZAG levels after adjusting for age, gender, BMI, TC, TG, SBP, DBP, FBG, HDL-C, and LDL-C. Among them, the presence of CRC was found to be independently positively associated with serum ZAG levels (*B* = 0.24, *P* < 0.01), which was consistent with the higher serum ZAG levels in CRC patients as displayed in **Figure 1A**. Serum TNF-α levels were also independently positively associated with serum ZAG levels (*B*= 0.01, *P*< 0.01), which was also in accordance with the results shown in **Table 2** by partial correlation analysis. In addition, BMI was independently negatively related to *ZAG* mRNA levels in sWAT but independently positively related to *ZAG* mRNA levels in vWAT, which was also consistent with the results demonstrated in **Table 2**.

## Association of ZAG, Leptin, HMW-ADPN, and TNF-**α** with CRC Risks

Next, all subjects were stratified into trisections according to ZAG tertiles (lowest: <1.30 μg/mL; median: 1.30–1.51 µg/mL; highest: ≥1.51 μg/mL). As shown in **Table 4**, the CRC risk was 2.43-fold higher in subjects with the high ZAG level than those with the low serum ZAG levels (OR = 3.43, 95% CI 1.24–9.49, *P* = 0.02) after adjusting for age and gender (Model 1). This increased probability of CRC risk still remained after further adjusting for BMI, SBP, DBP, and FBG based on Model 1 (Model 2, OR = 3.96, 95% CI 1.28–12.27, *P* = 0.04) and TC, TG, HDL-C, and LDL-C based on Model 2 (Model 3, OR = 6.84, 95% CI 1.70–27.54, *P* = 0.03). In addition, serum leptin levels were also categorized into tertiles (lowest: <1.12 ng/mL; median: 1.12–2.92 ng/mL; highest: ≥2.92 ng/mL). As presented in **Table 4**, the probability of CRC

weight; OW/OB, overweight/obese; CRC, colorectal cancer. All values are expressed as the median with the interquartile range.

risk decreased by 94.0% in participants with the highest leptin level compared with those with the lowest serum leptin levels (OR = 0.06, 95% CI 0.01–0.41, *P* = 0.01) after adjusting for age, gender, BMI, SBP, DBP, and FBG (Model 2). This decreased risk of CRC remained after further adjusting for TC, TG, HDL-C, and LDL-C based on Model 2 (Model 3, OR = 0.11, 95% CI 0.01–0.89, *P* = 0.04). However, no significant differences were found in the OR of CRC risks between the tertiles of HMW-ADPN and TNF-α levels.

## Diagnostic Values of Serum ZAG, Leptin, HMW-ADPN, and TNF-**α** for CRC

Subsequently, ROC curve analysis was used to investigate the potential application of the four serum adipokines for the discrimination between patients with CRC and healthy people. Our results indicate that only ZAG can effectively discriminate between CRC patients and healthy individuals within ROC curve areas of 0.655 (95% CI 0.544–0.767, *P*< 0.05) (**Figure 3A–D**). At the cutoff value of 1.42 µg/mL for ZAG, the sensitivity and specificity for the discrimination of CRC are 62.2 and 69.2%, respectively.

## DISCUSSION

Zinc-α2-glycoprotein is a 41-kDa secreted glycoprotein that was first identified in human plasma in 1961 (30). Previous studies have shown that ZAG is expressed at high levels in a variety of malignancies, such as prostate, breast, and lung cancer (20–22). Serum ZAG was found to be a potential biomarker for prostate cancer (23). Early in 2012, Agesen et al. found increased *ZAG* gene expression at the transcriptional levels in CRC tissues from Western populations, including Norwegian, American, and Australian populations (26). Further studies in Chinese populations also reported that ZAG was upregulated at the transcriptional and posttranscriptional levels in fresh colon cancer tissues (27), suggesting that ZAG might be a potential biomarker for CRC in both Western and Eastern populations. Consistent with its elevated expression in cancer tissues, Xue et al. also found higher serum ZAG levels in CRC patients than in healthy controls (28). Further analysis showed that there was a positive association between serum ZAG levels and CRC clinical stages (28). Another study performed by Ji et al. found that serum ZAG was elevated in CRC patients, and CRC patients with higher ZAG levels

zinc-α2-glycoprotein; HMW-ADPN, high-molecular-weight-adiponectin; TNF-α, tumor necrosis factor-alpha; sWAT, subcutaneous white adipose tissue; vWAT, visceral white adipose tissue; NW, normal weight; OB, obese; CRC, colorectal cancer. All values are expressed as the median with the interquartile range.

showed worse clinical outcomes (27). In our present study, we also found that serum ZAG levels were significantly increased in CRC patients with NW and CRC was found to be independently associated with serum ZAG levels. Based on these data, it is thus reasonable to assume that the elevated expression of ZAG in CRC tissues results in high serum ZAG levels, which may further promote CRC development. In addition, our study showed that the significantly higher serum ZAG levels only presented in male CRC patients, which is consistent with the epidemiological findings that men are at a higher risk of CRC.

Next, all subjects were stratified into trisections according to their serum ZAG tertiles. The results showed that the CRC risk was 2.43-fold higher in subjects with the highest ZAG level than those with the lowest serum ZAG levels after adjusting for age and gender (Model 1). This increased probability of CRC remained after further adjusting for BMI, SBP, DBP, and FBG based on Model 1 (Model 2, OR = 3.96) and TC, TG, HDL-C, and LDL-C based on Model 2 (Model 3, OR = 6.84), suggesting that ZAG overexpression is a significant risk factor for CRC, independent of other clinical pathological factors. Further analyses using ROC curves showed that the AUC of ZAG was 0.655. At the cutoff value of 1.42 µg/mL, the diagnostic value of ZAG had 62.2% sensitivity and 69.2% specificity. In accordance with our results, Ji et al. reported that serum ZAG was a useful biomarker for CRC within ROC curve areas of 0.9572 (95% CI 0.9173–0.9971) in a cohort of 534 Chinese individuals (27). Studies conducted by Xue et al. further found that the AUC of ZAG was 0.742 (95% CI 0.656–0.827), which was lower than the AUC of CEA (0.746, 95% CI 0.665–0.827) but higher than the AUC of CA19-9 (0.676, 95% CI 0.578–0.774) in a total of 160 Chinese subjects (28). All of these findings suggest that ZAG could be used as a potential serum biomarker for CRC.

Zinc-α2-glycoprotein is also a novel adipokine that can be secreted by adipose tissue. Decreased ZAG levels in sWAT of obese patients and its negative association with BMI have been previously reported (19, 31–33). In our present study, we observed for the first time that *ZAG* mRNA levels in sWAT of OB + CRC patients were also significantly decreased when compared with Table 2 | Partial correlation analysis between ZAG levels in both serum and WAT and clinical parameters in CRC patients.

*Values are age-sex-adjusted Spearman partial correlation coefficients.  Bold font means p < 0.05.*

Table 3 | Multiple regression analysis for the variables independently related to serum ZAG and *ZAG* mRNA in sWAT and vWAT in CRC patients.

*Stepwise multiple regression analysis was used. Variables also entered into multiple regression analysis but not included in the equation: age, gender, BMI, total cholesterol, triglycerides, SBP, diastolic blood pressure, fasting blood glucose, low-density lipoprotein cholesterol, serum high-molecular-weight-adiponectin, and serum leptin.*

*B, regression coefficient;* β*, standardized regression coefficient; sWAT, subcutaneous white adipose tissue; vWAT, visceral white adipose tissue; 0; non-cancer; 1; colorectal cancer; ZAG, zinc-*α*2-glycoprotein; CRC, colorectal cancer; HDL-C, high-density lipoprotein cholesterol; BMI, body mass index; SBP, systolic blood pressure.*

NW + CRC patients. Further partial correlation and multiple regression analysis found a negative relationship between *ZAG* mRNA levels in sWAT and BMI, and BMI was independently negatively related to *ZAG* levels in sWAT. These results suggest that the decreased ZAG levels in sWAT and its negative relationship with BMI were observed both in simple OW/OB patients and in CRC patients. By contrast, *ZAG* mRNA levels in vWAT were found to be positively related with BMI in our present study. In contrast with our findings in CRC patients, previous studies in Table 4 | Unconditional logistic regression analysis of colorectal cancer risks according to tertiles of zinc-α2-glycoprotein (ZAG), leptin, high-molecularweight adiponectin (HMW-ADPN), and tumor necrosis factor-alpha (TNF-α) in all subjects.

*Multivariate ORs and 95% CIs from unconditional logistic regression models were used in the analysis.*

*Model 1: basic model, adjusted for age and gender.*

*Model 2: further adjusted for body mass index, systolic blood pressure, diastolic blood pressure, and fasting blood glucose based on the model 1.*

*Model 3: full model, further adjusted for total cholesterol (*<*5.18 mmol/L,*

≥*5.18 mmol/L), triglycerides (*<*1.7 mmol/L,* ≥*1.7 mmol/L), high-density lipoprotein cholesterol (*<*1.04 mmol/L,* ≥*1.04 mmol/L), and low-density lipoprotein cholesterol (*<*3.37 mmol/L,* ≥*3.37 mmol/L) based on the model 2. Bold font means p < 0.05.*

simple obese patients performed by Mracek and Selva et al. found that ZAG expression in vWAT was significantly lower in obese patients and showed a negative correlation with BMI (19, 34). Given these results together, we speculate that *ZAG* mRNA levels in sWAT and vWAT might play a different role in CRC patients. Previous studies by Balaz have shown that ZAG in sWAT, but not in vWAT, is associated with whole-body insulin sensitivity (31). Although the biological mechanisms of the different role of sWAT and vWAT in CRC are still not well known, a possible explanation for this difference may be attributed to the much more severe insulin resistance of vWAT compared to sWAT (31). In addition, *ZAG* mRNA levels in vWAT were for the first time found to be negatively correlated with SBP in our present study. Further studies need to be done to validate this phenomenon.

Figure 3 | ROC curves of serum ZAG (A), leptin (B), HMW-ADPN (C), and TNF-α (D). ROC curves were derived by plotting the relationship between the specificity and the sensitivity at various cutoff levels. Abbreviations: ZAG, zinc-α2-glycoprotein; HMW-ADPN, high-molecular-weight-adiponectin; TNF-α, tumor necrosis factor-alpha; ROC, receiver operating characteristic; AUC, area under the curve.

After stratifying all subjects into trisections according to serum leptin tertiles, the probability of CRC risk was found to decrease by 94.0% in subjects with the highest leptin level compared to those with the lowest serum leptin levels after adjusting for age, gender, BMI, SBP, DBP, and FBG (Model 2, OR = 0.06). This decreased risk of CRC remained even after further adjusting for TC, TG, HDL-C, and LDL-C based on Model 2 (Model 3, OR = 0.11), suggesting that leptin might be a protective factor against CRC, which is independent of other clinical pathological factors. In line with our results, a case–control study performed by Joshi et al. in a South Korean population found a negative association between leptin and CRC risk (13). Additionally, studies in Western populations performed by Kumor and Salageanu et al. observed significantly lower serum leptin levels in CRC patients than controls (11, 12). Our present study also demonstrated that serum leptin levels have a lower trend in NW + CRC patients than healthy controls (*P* = 0.07).

Next, the CRC patients were further divided into OW/OB or NW groups. Interestingly, a significantly higher serum leptin levels was observed in OW/OB Chinese CRC patients when compared with NW CRC patients. Consistent with our results, studies performed by Stachowicz et al. in a total of 146 Caucasians with CRC also demonstrated that OW/OB CRC patients had statistically higher serum leptin levels than NW patients (35). It is well known that obese patients have markedly increased circulating leptin levels compared with NW controls (36). In our present study, we first found that the OW/OB CRC Chinese patients also had higher serum leptin than NW CRC patients.

ADPN is a 30-kDa protein hormone secreted exclusively from adipose tissue (37), and HMW-ADPN is now considered the most active form of adiponectin (38). ADPN has been shown to be decreased in obese subjects and is supposed to exert anti-inflammatory and anticancerous activity (39). Our present study also observed significantly decreased serum levels of HMW-ADPN in OW/OB + CRC patients compared to NW + CRC patients, similar to its profile in obese patients (40).

Tumor necrosis factor-alpha is a key pro-inflammatory cytokine produced by macrophages cells and secreted by adipocytes (41). It has been widely accepted that an obesity-associated low grade of chronic inflammation is an important contributing factor in CRC pathogenesis (42). However, in this study, no significant differences were found in the serum TNF-α level between NW + CRC patients and healthy controls, or between NW + CRC and OW/OB + CRC patients. In accordance with our results, Amor et al. measured the plasma levels of TNF-α in lean and obese subjects with and without CRC also found no significant change in plasma TNF-α levels between patients with CRC or obesity (43). Additionally, our study observed that *TNF-*α mRNA levels were upregulated in vWAT from OB + CRC patients compared to NW + CRC patients. Studies performed by Delgado et al. also reported that vWAT, but not sWAT, was an indicator of inflammation (44). These results suggest that TNF-α secreted by vWAT, instead of sWAT, may be involved in obesity-related CRC development.

In conclusion, our study found that serum ZAG levels were significantly increased in CRC patients. *ZAG* mRNA levels in sWAT were found to be significantly reduced in OB + CRC Chinese patients in comparison with NW + CRC patients. The patients with the highest tertile ZAG serum levels were more likely to have CRC. At the cutoff value of 1.42 µg/mL for serum ZAG, the sensitivity and specificity for differentiating patients with CRC from controls were 62.2 and 69.2%, respectively. Additional and more comprehensive studies are needed to explore the detailed mechanisms of the role of ZAG in CRC development.

#### REFERENCES

## ETHICS STATEMENT

Informed consent was signed by all participants and the study was approved by the ethics committee of Peking Union Medical College Hospital (No. S-364).

## AUTHOR CONTRIBUTIONS

HZ designed the experiments and revised the primary manuscript. ML analyzed the data and wrote the primary manuscript. NZ performed the molecular biological experiments. HP, GL, NL, LW, HY, and KY collected the clinical materials and serum samples and finished the clinical and biochemical parameters measurements. FG designed the experiments, supervised the whole study, and revised the primary manuscript.

#### FUNDING

The study was supported by grants from the National Natural Science Foundation of China (Nos. 30600836, 81471024 for HZ, Nos. 30540036, 30771026, 81370898 for FG), the Beijing Natural Science Foundation (No. 7082079 and 7182130 for FG), the National Key Program of Clinical Science (WBYZ2011-873 for FG and HZ), and the PUMCH Foundation (2013-020 for FG).

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at https://www.frontiersin.org/articles/10.3389/fendo.2018.00190/ full#supplementary-material.

an observational study in Chinese male. *Med Oncol* (2012) 29:3129–35. doi:10.1007/s12032-012-0280-2

and by tumour necrosis factor-alpha in adipocytes. *J Endocrinol* (2010) 204:165–72. doi:10.1677/JOE-09-0299

**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 Zhu, Liu, Zhang, Pan, Lin, Li, Wang, Yang, Yan and Gong. 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.*

# Mechanistic Insights Into the Interaction Between Transcription Factors and Epigenetic Modifications and the Contribution to the Development of Obesity

#### Qi Huang1†, Chaoyang Ma2†, Li Chen<sup>1</sup> , Dan Luo<sup>3</sup> , Rui Chen<sup>4</sup> \* and Fengxia Liang<sup>1</sup> \*

#### Edited by:

*Johannes le Coutre, Imperial College London, United Kingdom*

#### Reviewed by:

*Sarah J. Spencer, RMIT University, Australia Sean L. McGee, Metabolic Research Unit, Deakin University, Australia*

#### \*Correspondence:

*Rui Chen<EMAIL_ADDRESS>Fengxia Liang<EMAIL_ADDRESS>
*†Equal co-authorship.*

#### Specialty section:

*This article was submitted to Obesity, a section of the journal Frontiers in Endocrinology*

Received: *09 February 2018* Accepted: *19 June 2018* Published: *06 July 2018*

#### Citation:

*Huang Q, Ma C, Chen L, Luo D, Chen R and Liang F (2018) Mechanistic Insights Into the Interaction Between Transcription Factors and Epigenetic Modifications and the Contribution to the Development of Obesity. Front. Endocrinol. 9:370. doi: 10.3389/fendo.2018.00370* *<sup>1</sup> Department of Acupuncture and Moxibustion, Hubei University of Chinese Medicine, Wuhan, China, <sup>2</sup> Department of Rehabilitation, The Central Hospital of Wuhan, Tongji Medical College of Huazhong Science and Technology University, Wuhan, China, <sup>3</sup> Department of Traditional Chinese Medicine, Huazhong University of Science and Technology Tongji Medical College, Wuhan, China, <sup>4</sup> Department of Integrated TCM and Western Medicine, Union Hospital, Tongji Medical College of Huazhong Science and Technology University, Wuhan, China*

Objective: The development of obesity is inseparable from genetic and epigenetic factors, and transcription factors (TFs) play an essential role in these two mechanisms. This review analyzes the interaction of TFs with epigenetic modifications and the epigenetic mechanisms underlying peroxisome proliferator-activated receptor (PPAR)γ, an important transcription factor, in the development of obesity.

Methods: We describe the relationship between TFs and different epigenetic modifications and illustrate the several mechanisms described. Next, we summarize the epigenetic mechanisms of PPARs, an important class of transcription factors involved in obesity, that induce obesity with different triggering factors. Finally, we discuss the mechanisms of epigenetic modification of PPAR-related ligands in lipid metabolism and propose future avenues of research.

Results: TFs participate in epigenetic modifications in different forms, causing changes in gene expression. The interactions between the different epigenetic modifications and PPARs are important biological developments that affect fat tissue differentiation, lipogenesis, and lipid metabolism, thereby inducing or inhibiting the development of obesity. We then highlight the need for more research to understand the role of epigenetic modifications and PPARs.

Conclusions: Epigenetic mechanisms involved in the regulation of PPARs may be excellent therapeutic targets for obesity treatment. However, there is a need for a deeper understanding of how PPARs and other obesity-related transcription factors interact with epigenetic modifications.

Keywords: obesity, transcription factors, epigenetics, DNA methylation, histone modifications, chromatin remodeling, non-coding RNA, interaction

## INTRODUCTION

Obesity is currently the world's most conspicuous nutritional disease. It is currently considered a serious threat to human health because of its high prevalence (1). The expansion of fat mass, adipocyte size increase, and to a lesser extent cell proliferation (hyperplasia) are important features of obesity (2). The increase in adipose tissue corresponds to increase in size and number of adipocytes (3), i.e., lipogenesis and differentiation. Factors that regulate fat tissue differentiation such as insulin, growth hormone, and glucocorticoids promote differentiation of pre-adipocytes (4, 5) and genetic and transcriptional regulation (6, 7). Among regulatory factors, transcription factors (TFs) play a critical role in the regulation of adipocyte differentiation. Studies have shown that lipogenesis and differentiation are regulated coordinately by several TFs, mainly including the peroxisome proliferator-activated receptor γ (PPARγ), CCAAT enhancer binding proteins (C/EBPs), and the transcription factor sterol regulatory element binding protein-1 (SREBP-1) (8, 9). PPARγ is the most critical TF for the regulation of adipocyte differentiation, being a necessary and sufficient condition for the differentiation of adipose tissue (10). The CAAT/enhancer family has the function of activating CAAT repeats of specific gene DNA enhancers. Its family members, C/EBPα, C/EBPβ, and C/EBPδ, are considered important factors in adipocyte differentiation. C/EBPδ and C/EBPα can induce the expression of the PPARγ gene (11), which is probably achieved through a transcriptional effect of the C/EBP binding site in the PPARγ promoter (12). Activated PPARγ, in turn, increases the expression of the C/EBPα gene. In the absence of C/EBPα, cells can only express PPARγ at low levels and cannot form adipocytes (13). In mammals, SREBP is classified into two types: SREBP1 and SREBP2. SREBP1 is also known as adipocyte determination and differentiation-dependent factor 1 (ADDl), and can act independently as a TF to regulate adipocyte differentiation and transcription of genes related to cholesterol metabolism (14). Studies have shown that SREBP promotes the synthesis of triglycerides by up-regulating the expression of genes such as fatty acid synthase and lipoprotein lipase (14). SREBP can also activate PPAR expression or induce the expression of an endogenous ligand of PPAR to increase its lipogenic activity and promote adipocyte differentiation (15). In addition to the above TFs, cyclic AMP response element binding protein (CREB), nuclear factor of activated T cells (NFAT), kruppellike factor (KLF) family, and forkhead transcription factor (FOX) proteins have been demonstrated to affect adipogenesis (16, 17).

However, the complex mechanisms of adipocyte differentiation remain to be further elucidated (18). Epigenetics is a likely candidate to elucidate the missing links that lead to obesity. Specific dietary behavior leads to change in epigenetic patterns and regulates gene expression via epigenetic modifications (19, 20). These changes interfere with metabolic homeostasis and result in body adiposity (21, 22). Epigenetic changes in chromatin also occur during adipocyte differentiation (23, 24). Some studies indicated that the epigenetic changes at the gene locus of a specific TF correlate with the body-mass index (BMI) in humans (25, 26). This suggests that the development of obesity may be inseparable from the interaction between TFs and epigenetic modification. However, it is still mostly unclear how cells establish the connection between TFs and epigenetic modifications and how it leads to regulation of gene expression. Several specific mechanisms in biological processes, from epigenetic changes to gene regulation, are still not fully understood. A systematic investigation of the link between TFs and epigenetics will provide a deeper understanding of pathogenesis in the metabolism and thereby provide a basis for more appropriate treatment regimens. This review describes the critical link between epigenetic modifications and TFs and their contribution to the development of obesity.

## INTERACTION BETWEEN EPIGENETIC MODIFICATIONS AND TFS

Epigenetics can be described as reversible and heritable changes to the DNA that regulates chromatin structure and gene expression without altering the DNA sequence (27). The genome of each organism contains both DNA sequence information and epigenetic information. The interaction between the two specific factors maintains the function of organs and cells. Research has confirmed that there are specific genes in the body that can confer different susceptibilities to disease. However, owing to the stability of the genomic structure, most environmental factors cannot cause gene mutations or DNA sequence changes (28, 29). Therefore, some authors speculate that the changes in gene expression mediated by epigenetic modifications cause genomic abnormalities in the body during development, or result in increased susceptibility to certain diseases (30, 31). However, this process requires the participation of TFs. The currently most studied epigenetic modifications are DNA methylation, histone modification, chromatin remodeling, and non-coding RNA. The relationship between epigenetic modifications and TFs are described below.

## DNA METHYLATION AND TFS

DNA methylation is a process in which cytosine is converted to 5-methylcytosine (5-mC) by DNA methyltransferases (DNMTs) with S-adenosylmethionine (SAM) as a methyl donor (32, 33). In mammals, DNMTs comprise three active enzymes, DNMT1, DNMT3A, and DNMT3B, and one related regulatory protein, DNMT3L (33). DNMTs can promote DNA methylation and cause gene silencing. In contrast, the ten-eleven translocation (TET) enzyme is associated with DNA demethylation by reactivating the expression of the silenced gene (34). In most vertebrates, DNA methylation occurs predominantly at highly aggregated CpG dinucleotides, termed CpG islands (CGI), which overlap with gene promoter regions and can be bound by ubiquitous TFs (35). The association between methylation and TFs can be described in the following four ways.

## DNA Methylation Interferes With the Binding of TF to Target Genes

DNA methylation prevents the binding of TFs to their binding sites and influences gene transcription (36, 37). Studies showed that CpG methylation of the cAMP-responsive element (CRE) leads to loss of the binding site and of transcriptional activity of some TFs in vitro and in vivo (38). However, the methylation of the CRE sequence, which is often associated with promoters of cell type-specific genes, creates a TF binding site for C/EBPα, thereby activating transcription during adipocyte differentiation (39, 40) (**Figure 1A**).

## Competition Between TFs and Transcriptional Repressors for mCpG Sites

Transcriptional repressors compete with TFs for binding to methyl-CpG sites in the DNA sequence and block transcription. In mammals, one of the main transcriptional repressors are the methyl-CpG-binding domain (MBD) proteins, which comprise eleven known proteins containing the MBD domain (41, 42). These proteins are recruited to a specific sequence containing methylated cytosines, and prevent TFs and RNA polymerase from forming the initial complex at the corresponding methyl-CpG site (43, 44), thereby inhibiting gene transcription (**Figure 1B**).

## Association of (de)Methylation Enzymes and TFs in DNA Methylation

The expression of DNMT and demethylase genes are affected by TFs. TFs modulate promoter methylation levels by recruiting and forming complexes with DNMTs (45). Knocking out some TFs can reduce the recruitment of DNMTs and reduce gene methylation levels in the glioma cell line (46) (**Figure 1C**).

## Methylation of TFs

DNA methylation can also modify TFs. Methylation of TFs belongs to non-histone methylation. Previous studies have indicated that non-histones can be methylated at lysine residues (47). For example, the RelA (p65) subunit of nuclear factor kappa-light-chain-enhancer of activated B cells (NFκB) is methylated by lysine methyltransferase Set9 at lysine residues 314 and 315 in cells. It inhibits the activity of NFκB and prevents it from binding to DNA, thereby causing transcriptional repression (48). In contrast, nuclear receptorbinding SET domain-containing protein 1 (NSD1, a lysine methylase) can methylate RelA at K218 and K221 and active NF-κB transcriptional activity in cell line (49) (**Figure 1D**).

## HISTONE MODIFICATION AND TFS

Histone modifications are another essential component of epigenetics. Histones comprise core histones (H3, H4, H2A, and H2B) and linker histones, H1/H5. Two copies of the four core histones are wrapped around ∼165 bp of DNA to constitute nucleosomes and form eukaryotic chromatin with the linker histone H1 (50, 51). In histones, flexible charged tails extend from the core region as N- or C-terminal ends and can be covalently modified by various enzymes. This modification is termed post-translational modification (PTM) and may lead to changes in chromatin structure and further genome regulation. Known modifications of N-terminal tails of histones include methylation, acetylation, phosphorylation, ubiquitination, ADP ribosylation, and sumoylation (52). Histone acetylation and deacetylation are the most studied (52, 53), and are considered reversible processes controlled by histone acetyltransferases (HATs) (54) and histone deacetylases (HDAC) (55), respectively. In addition, histone methylation and demethylation are catalyzed by histone methyltransferases (HMTs) and histone demethylases (HDMs), respectively. The modification on histone tails occurs sequentially or in combination, termed "histone code" (56). This code is read by binding of specific TFs. Associations between histone modifications and TFs are divided into the following categories.

## Histone Modification Enzymes Interact With TFs

TFs act as a "porter" to recruit histone modification enzymes to regulate gene expression. For example, during histone acetylation, specific TFs repress or activate gene expression through HDAC or HAT recruitment, respectively (57). The activator or repressor role of these TFs on the promoter may depend on its binding site or time on DNA in the nuclear extracts of murine L929 cells (58) (**Figure 2A**). In some cases, TF recruits HATs or HDACs and then binds to these enzymes to form a complex that increases the enzymatic activity and further regulates the expression of related genes (59, 60). Changes in expression of these genes may result from changes in chromatin structure caused by histone modifications (56). The condensed state of chromatin leads to an inability of TFs to bind to target genes when histone is acetylated (61). Coincidentally, HATs may also act on TFs and induce acetylation of these proteins (62–64). Expression of related genes is regulated by the acetylation levels of these TFs (62, 65, 66) (**Figure 2B**).

## Crosstalk of TFs With Histone Modifications and PTMs

Modification of the histone code widely occurs in PTM of histone tails. A modification may either enhance or inhibit another (67–69). For instance, the phosphorylation of H3S10 leads to acetylation of H3K14 (67) but blocks acetylation of H3K9 in cell cycle progression (68). Similarly, this modification pattern may occur in TFs. For example, phosphorylation at serine residues 276 and 536 of the P65 subunit of NFκB facilitates P65 acetylation at lysine 310 in a mammalian one-hybrid system (70, 71) (**Figure 2C**). On the other hand, phosphorylation of P65 also promotes P65 ubiquitination and leads to the degradation of P65 and repression of transcription in cells. However, this process may require the participation of GCN5 (a HAT) (72) (**Figure 2D**). Posttranslational modifications of NF-κB were reviewed previously (73).

FIGURE 1 | Interaction between DNA methylation and transcription factors (TFs). (A) Methylated DNA inhibits the binding of TFs to DNA but also creates binding sites for some specific TFs and activates transcription. (B) Transcriptional repressors occupy an mCpG site, thereby blocking transcription. (C) TFs recruit DNA methyltransferases to form a complex and repress transcription. (D) Methylase acts on TF and regulates its activity and transcription.

## CHROMATIN REMODELING AND TFS

Chromatin remodeling is a dynamic modification of chromatin architecture, which opens the access for regulatory transcription machinery proteins and condenses DNA to control gene expression. Remodeling is accomplished in three ways: (1) covalent histone modifications by enzymes such as HATs, HDACs, and HMTs; (2) ATP-dependent chromatin-remodeling complexes (74), such as the SWI/SNF family (BAF60a, BAF250, and BAF57), the ISWI family, the Mi-2/CHD family, and the INO80 family; and (3) utilization of histone variants (75). Histone modifications loosen or tighten the DNA wrapped around histones, and thereby allow or prevent binding of TFs to the DNA (75). In contrast, ATP-dependent chromatin-remodeling complexes have a common ATPase domain and utilize the energy of ATP hydrolysis to mobilize nucleosomes along DNA. These complexes can expel histones from DNA or facilitate the exchange of histone variants, which modulate DNA accessibility and alter nucleosome structure (76). From these mechanisms, the connection between chromatin remodeling and TFs is probably reflected in the following aspects.

## Role of Pioneer Factors in Opening Chromatin

Genes in eukaryotic cells are packaged in chromatin. TFs must reorganize local nucleosome structure and create an open site for successful interaction with genomic regulatory elements during transcription. TFs with this characteristic are termed "pioneer factors" (77). Examples include the forkhead box (FOX) proteins and the glucocorticoid receptor (GR) (78– 80). These TFs recruit SWI/SNF and form a complex to open chromatin (81). The resident time of the complex is short, and the opened chromatin state is maintained for a time (82). The site then becomes accessible to other adjacent TFs to bind after dissociation of the complex (82, 83). (**Figure 3A**).

## Binding of TFs to Chromatin Remodeling Complexes

TFs cannot induce chromatin remodeling independently during transcriptional regulation, but need to recruit chromatin remodeling complexes. The PRotein Interactions by Structural Matching (PRISM) algorithm shows that GR binds to several subunits of SWI/SNF such as BAF60a, BAF250, and BAF57 (84). C/EBPα and/or C/EBPβ have also been shown to recruit the SWI/SNF complex to target promoters and regulate the differentiation of adipocytes, neutrophil granulocytes, and hepatocytes in mice (85, 86) (**Figure 3B**).

## Histone Modification by TFs Induce Chromatin Remodeling

Chromatin remodeling is closely related to modifications of histone N-terminal tails, and these modifications affect chromatin stability (87). During histone modification, histone variants are inserted into nucleosomes that alter their structure and function, which further affect the highly ordered structure of chromatin (88). In addition, activated TFs can mediate the acetylation of histones and evict nucleosomes, which remodel chromatin and induce transcription initiation or elongation in vitro (89) (**Figure 3C**).

## NON-CODING RNA AND TFS

It is well known that <2% of transcripts in the mammalian genome have a protein-coding function, with the remaining 98% being non-coding RNA (ncRNA) (90). ncRNA are mainly classified into two types: microRNA (miRNA) and long noncoding RNA (lncRNA). Studies have found that the promoter region of miRNA and lncRNA genes can contain different epigenetic modifications and are involved in many biological processes (91–94) through the interaction with TFs. Any abnormality in these transcriptional processes can lead to disease (95).

## Interaction Between miRNA and TFs

TFs and miRNA play an important role in gene transcription and post-transcriptional regulation. Studies found that genes regulated by the same TF and miRNA present a significant coexpressed pattern (96, 97). During regulation, TFs bind to the upstream promoter region of the miRNA gene and exert an influence on transcription. In addition, some miRNAs conversely affect translation of TFs (98). Therefore, TFs and miRNA make up a complex regulatory network termed feed-forward loop (96) in stabilizing gene regulation (9). Ten kinds of interactions have been described between TFs, miRNA, and genes (9, 99) (**Figure 4A**).

## Interaction Between lncRNA and TFs

LncRNAs can alter the transcription of genes by interacting with TFs, chromatin-modifying complexes, or mRNA (100, 101). The interaction between lncRNA and TFs can be divided into the following categories.

ultimately causing chromatin remodeling.

and cause high levels of HIF-1a expression (113). This is a positive feedback loop between TF and lncRNA (**Figure 4D**).

## ROLE OF TFS IN EPIGENETIC REGULATION IN THE DEVELOPMENT OF OBESITY

### Role of PPARγ in Obesity

PPARγ is a critical transcriptional regulator of adipogenesis in mammals, is closely related to regulation of lipids and glucose metabolism, and is associated with the control of obesity and related diseases (114, 115). Siersbaek et al. analyzed the PPARγ and C/EBPα binding sites and found that PPARγ and C/EBPα regulate the expression of most genes associated with adipogenesis (116). The BPro12Ala and 6CAC478CAT exon polymorphisms of the PPARγ gene are significantly related to the incidence of severe obesity (117). In the USA, mutations in the PPARγ gene can severely cause certain types of sexual obesity in women (118). Animal studies also provide more direct evidence for research in this area. For example, Japanese researchers reported that, in high-fat diets, mice lacking PPARγ exhibited significant dysplasia of adipocytes, smaller cell morphology, and lower fat content, causing pathological features such as obesity and severe insulin resistance (119). PPARγ not only promotes the proliferation and differentiation of adipocytes but also confers insulin sensitivity to adipocytes (120). An increase in insulin sensitivity can in turn promote the expression of the PPARγ gene in adipose tissue, thereby positively accelerating the differentiation of adipocytes. Both these aspects greatly increase the efficiency of adipocyte synthesis and storage and are important factors for obesity.

In addition, PPARs also play an important role in lipid metabolism. After feeding, carbohydrates and fats are converted into glucose and chylomicrons, respectively, and enter circulation. Most of the glucose is absorbed by the liver. If hepatic glucose storage capacity is reached, the remaining glucose is used for lipogenesis. Studies have shown that rats in the fed state increase the amount of SREBP1, which promotes the conversion of glucose to acetyl-CoA, followed by synthesis of fatty acids. Fatty acids are further converted into triglycerides and very low-density lipoproteins and are stored in the body (120, 121). This may result from the fact that PPARγ is a target gene for SREBP1 (122), which is involved in the production of endogenous PPARγ ligands (possibly fatty acids). SREBP1 stimulates the uptake of glucose and fatty acids, which are subsequently converted to triglycerides (14, 15).

and promoting gene transcription.

## ROLE OF PPARγ IN EPIGENETIC FACTORS IN ADIPOSE TISSUE

## DNA Methylation and PPARγ

Studies have shown that DNA methylation regulates the expression of PPARγ. Two studies examined the CpG methylation of the PPARγ gene in 9–year–old overweight children (123, 124), and both confirmed the presence of different levels of methylation. In addition, one group pointed out that the level of CpG methylation is negatively correlated with the expression of PPARγ (124), whereas the other group suggested that PPARγ methylation levels may be related to child body size, with an upward trend with age (123).

Studies on PPARγ in animals have revealed that the PPARγ2 gene promoter is highly methylated in mouse 3T3-L1 preadipocytes (125). However, with the differentiation of 3T3-L1 pre-adipocytes, the PPARγ2 promoter is gradually demethylated, and the expression of PPARγ2 mRNA is gradually increased. The DNA methylation inhibitor 5-aza-2′ -deoxycytidine interferes with the normal differentiation of 3T3-L1 pre-adipocytes and inhibits lipid accumulation in adipocytes (125). Compared with wild-type mice, methylation levels of the PPARγ2 promoter in visceral adipocytes increase, whereas PPARγ2 mRNA levels decrease in obese diabetic mice (125). This suggests that DNA methylation of the PPARγ promoter can inhibit the expression of the PPARγ gene and is associated with the occurrence of obesity and diabetes. However, no studies mentioning the involvement of DNMT or demethylase in the regulation of PPAR gene during fat differentiation or obesity were found.

## Histone Modification and PPARγ

Histone methylation regulates PPARγ gene expression. Several HMTs and HDMs are known to be involved in the regulation of PPARγ expression and lipogenesis (126). Myeloid/lymphoid or mixed-lineage leukemia protein 3 (MLL3) and myeloid/lymphoid or mixed-lineage leukemia protein 4 (MLL4) are major H3K4 methyltransferases in mammalian cells (126). Studies have shown that MLL4 can bind to the PPARγ locus and increase PPARγ gene expression and lipogenesis. The early adipogenic TF C/EBPβ serves as a pioneer TF and recruits MLL4 to the PPARγ gene loci and induces PPARγ and C/EBPα expression. MLL4 is then recruited to cooperate with other adipogenic TFs for adipocyte gene expression (126). In 3T3-L1 pre-adipocytes and mature adipocytes, the TF tonicityresponsive enhancer binding protein (TonEBP) binds to the PPARγ2 promoter directly, giving rise to H3K9me2 in the PPARγ2 promoter, thereby inhibiting PPARγ2 activity (127). PPARγ2 expression decreases, thereby inhibiting adipogenesis. The binding of TonEBP to the PPARγ2 promoter is associated with blocking C/EBPβ binding to H3K9m2 (127). In addition, PPARγ is also regulated by SET domain family proteins (128). Importantly, SET domain bifurcated 8 (SETD8) is a direct target of PPARγ and is induced during adipocyte differentiation. SETD8 regulates many target genes that depend on PPARγ activation by increasing the monomethylation levels of H4K20 during adipogenesis (128).

Histone acetylation also regulates PPARγ expression. It was found that the epigenetic modification of the PPARγ gene is regulated by C/EBPs and GR in vitro in a lipogenic cell model (129). The transient recruitment of GR and C/EBPβ by a complex consisting of MED1 and p300 (HAT) in the enhancer region of PPARγ2 causes a significant increase in the levels of H3K9 acetylation in this region, which enhances the expression of PPARγ2 and becomes a major driver of adipogenesis (130). Analysis at the genome level revealed that, during differentiation of 3T3-L1 cells, H3K9 and H3K27 acetylation at the PPARγ locus increases significantly, and both acetylation at both sites are positively correlated with the expression of the PPARγ gene. However, the roles of H3K9 and H3K27 acetylases in lipogenesis and PPARγ expression have not been determined (131). In contrast to the functions of HATs, HDACs repress PPARγ expression by deacetylating histones (132). Several HDACs are significantly downregulated during adipogenesis (133). SIRT1, a class III NAD-dependent HDAC, is an inhibitor of PPARγ (134). In vivo, fasting-induced SIRT1 binds to the PPARγ binding site of a fat-specific gene, enhancing its inhibitory function and thereby blocking adipogenesis (134). Studies have also confirmed that SIRT1 plays a role in the browning of white adipose tissue (135). This may be related to the deacetylation effect of SIRT1 on the regulation of PPARγ, whereas PPARγ promotes the production of brown fat by inducing the transcription-assisted regulator PRDM16 (135).

## Chromatin Remodeling and PPARγ

During lipogenesis, the chromatin environment at the PPARγ locus also needs to be correctly remodeled to allow expression of PPARγ target genes. Within a few hours after differentiation of 3T3-L1 preadipocytes, chromatin remodeling occurs in the PPARγ locus leading to an open state (129). This chromatin opening may be related to histone modifications at the PPAR locus and binding of pioneer TFs (127). Studies have shown that remodeling and opening of the PPARγ2 promoter region are dependent on protein kinase A (PKA). Knockout of the PKA gene using shRNA results in decreased chromatin accessibility in the PPARγ2 promoter region (136). In addition, studies have shown that specific binding to and function of the PPARγ promoter in adipocytes requires the participation of G-protein suppressor 2 (GPS2) (137). GPS2 primes a local chromatin environment via inhibition of the ubiquitin ligase RNF8 and stabilization of the H3K9 histone demethylase KDM4A/JMJD2 (137). Moreover, the SWI/SNF chromatin remodeling complex regulates PPARγ2 expression during adipogenesis. PPARγ activity also depends on components of a chromatin remodeling SWI/SNF complex with ATPase BRG1 and BAF60c subunits (138). A dominant mutant of Brg1 inhibits the transdifferentiation of fibroblasts into adipocytes induced by PPARγ, C/EBPα, and C/EBPβ (139). However, it remains unclear how Brg1 is recruited to the PPARγ promoter.

Chromatin remodeling can be regulated by replacement of canonical histones by histone variants. Genome-wide localization studies have shown that the variant H2A.Z, which is mainly involved in the regulation of gene expression, can be preferentially located in the promoter and enhancer regions

↑ *Promotes lipogenesism,* ↓ *Inhibits lipogenesis.*

(140). Studies have shown that, during adipogenesis, the E1Abinding protein p400 complexed with subunit bromo-containing protein 8, the p400/Brd8 complex, influences the expression of PPARγ target genes by inserting the histone variant H2A.Z into the transcriptional regulatory region (141). This is crucial for differentiation of fat tissue.

## Non-coding RNA and PPARγ

Non-coding RNA is an important post-transcriptional gene expression regulator that modulates many physiological and pathological processes (142). Multiple obesity-associated miRNAs have been identified in adipose tissue in obese humans, rats, and mice (143, 144). Several of these miRNAs can regulate PPARγ, and are classified into two categories: inhibition and stimulation of PPAR transcription (**Table 1**). Five miRNA can bind to the 3′ UTR of the PPARγ gene mRNA or interact with PPARγ at the cellular level to inhibit PPARγ protein expression and adipocyte differentiation (142, 145–151). In contrast, miR-103, miR-143, miR-200a, miR-335, and miR-375 have a lipogenic effect during adipocyte development resulting from elevated PPARγ expression (152–156). The ectopic expression of miR-103 increases the accumulation of triglycerides in adipocytes and up-regulates the expression of PPARγ2 (152). MiRNA, PPARγ gene, PPARγ, and related transcription factors form a cyclic network that regulates fat tissue formation.

In the process of 3T3-L1 cell differentiation, lncRNA can also affect lipogenesis by affecting PPARγ transcription. It was found that, in 3T3-L1 adipocytes, lncRNA U90926 inhibits the activity of the PPARγ2 promoter, thereby inhibiting PPARγ expression and lipogenesis (157). In contrast, lncRNA NEAT1 plays a regulatory role in alternative splicing events of PPARγ during differentiation of mouse 3T3-L1 cells.  In addition, overexpression of lncRNA HOTAIR promotes the expression of PPARγ and the differentiation of pre-adipocytes into mature adipocytes, although the mechanism remains unclear (159). The mechanism underlying the role of lncRNA in lipogenesis and obesity still needs to be further investigated.

### PARTICIPATION OF PPARS IN EPIGENETIC MODULATION OF LIPID METABOLISM

Fatty acids can be oxidized to CO<sup>2</sup> and H2O in the presence of oxygen, releasing large amounts of energy and becoming one of the main energy sources for the body. Most of the fatty acids in the human body are exogenous fatty acids derived from foods and can be utilized through transformation and processing. The body can also convert sugars and proteins into endogenous fatty acids and triglycerides to store energy. Fatty acid synthase catalyzes the first step in de novo lipogenesis.

PPARs are ligand-activated receptors that heterodimerize with the retinoid X receptor (RXR), bind reactive elements in target genes, and induce transcription. PPARγ can modulate fatty acid metabolism, promote differentiation of adipocytes, and regulate lipid storage through transcription of a series of lipid-related proteins in the posterior segment (160). In animals, it has been demonstrated that fatty acids act as ligands for PPARs and activate the expression of genes involved in fatty acid metabolisms such as the transmembrane proteins CD36, lipoprotein lipase, fatty acid-binding protein 4, and long-chain acyl-CoA synthetase 1 (161). Studies have found that multiple miRNAs are involved in the regulation of fatty acids. MiR-122 is the first miRNA confirmed to be involved in lipid regulation and is the most abundant miRNA identified in liver tissue (162). MiR-122 plays a prominent role in maintaining the phenotype of liver cells and fatty acid metabolism (162). In mice, inhibition of miR-122 expression can lead to a sustained reduction in plasma cholesterol levels, increased hepatic fatty acid oxidation, and decreased liver fatty acid and cholesterol synthesis rates. Ultimately, high-density lipoprotein and apolipoprotein A1 levels increase, and low-density lipoprotein and apolipoprotein B levels are reduced (163). In addition, inhibition of miR-205- 5p expression in genetically improved farmed tilapia liver can increase fatty acid synthase and PPARα mRNA levels and thus regulate hepatic lipid metabolism (164). Increased PPARα levels have been shown to increase liver fatty acid oxidation and reduce circulating triglyceride levels to regulate rodent obesity (165). In addition to miRNA regulation, studies in rodents have shown that decreased PPARα promoter methylation contributes to enhanced expression of carnitine palmitoyl transferase-1, a regulatory enzyme in fatty acid oxidation, and decreased expression of fatty acid synthase (166). However, no PPARmediated lipid metabolism modulation by mechanisms such as histone modification and chromatin remodeling has been described. Another important transcription factor, SREBP1, also plays an important role in lipid metabolism. NAD-dependent deacetylase sirtuin-1(SIRT1) can remove acetyl groups from lysine at positions 289 and 309 of SREBP-1c, inhibiting SREBP-1c activity, decreasing its stability, and reducing lipid production (167).

 Knocking out PPARγ in adipose tissue of mice can prevent high-fat diet-induced obesity and insulin resistance (168). Because of its complex and diverse biological functions, it has grown up to be a therapeutic target for obesity-related diseases (114). For example, PPARγ agonists have been applied to treat hyperlipidemia, hyperglycemia, diabetes (114, 169). However, these drugs, such as Thiazolidinedione (TZD), have reduced insulin resistance but also increased lipid accumulation in skeletal muscle by promoting adipocyte hypertrophy and hyperplasia (170). Therefore, long-term use of PPARγ agonists may trigger unexpected effects on systemic metabolism, including insulin sensitivity. Other more appropriate treatments are needed. With the development of epigenetics, epigenetic markers have become a useful tool to assess the risk of obesity and metabolic disorders (171). Perhaps, in the future, the epigenetic modification of PPARγ may be intervened to

to decrease adipogenesis. The pioneering transcription factor (e.g., C/EBPβ or GR) recruit histone acetyltransferases or histone methyltransferases in the vicinity of the PPARγ promoter and cause histone methylation or acetylation, thereby regulating PPARγ expression. Under various factors such as histone modifications, histone variants, and chromatin remodeling complexes, the chromatin remodeling in the PPARγ promoter induces changes in the expression of PPARγ. The miRNA and lncRNA acting on the PPARγ promoter region also govern the expression of PPARγ. Inhibited expression of PPARγ reduces the production of adiposity. Conversely, promoting the expression of PPARγ increases in adipogenesis and leads to obesity. In addition, methylation and miRNA modification of the PPARs promoter region regulates the expression of fatty acid synthase and participate in lipid metabolism. Deacetylation of the transcription factor SREBP-1c is some other important way in regulating lipid metabolism. Disorders of lipid metabolism can also induce obesity.

regulate the expression of genes involved in lipogenesis without compromising metabolism in vivo, thereby reducing the occurrence of obesity and obesity-related diseases (**Figure 5**).

## CONCLUSION

A variety of physiological, biochemical, genetic, and behavioral factors can cause obesity. To control the occurrence of this disease, it is necessary to reasonably control the diet and perform a moderate amount of exercise so as to reduce the storage of residual energy within the body. In addition, it is also necessary to control the excessive proliferation and differentiation of adipocytes. Knowledge of epigenetic modifications provides novel insights into the pathogenesis of obesity. Owing to the importance of PPARγ in lipogenesis and lipid metabolism in humans and other animals, the epigenetic mechanisms underlying transcriptional regulation of PPARγ will remain a research focus in the field of lipid biology and medicine and may result in improved treatments for obesity. Although much evidence for epigenetic regulation has been reported in recent years, few studies have shed light on the role of epigenetic modifications, transcription factors, and upstream and downstream mechanisms involved in obesity. In the future, a better understanding of the interrelationship between regulation of obesity-related transcription factors and epigenetics will be needed to explore effective therapeutic methods for the treatment of obesity using epigenetic modifications.

#### AUTHOR CONTRIBUTIONS

QH and CM prepared the first draft and wrote the final version of the manuscript. LC and DL were involved in literature searches.

#### REFERENCES

FL and RC critically revised the manuscript and gave constructive opinions on articles.

#### FUNDING

The study was supported by the National Natural Science Foundation of China (No. 81574065; No.81473787).

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**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.

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