*3.4. Latent Heat*

The DSC endothermic and exothermic curves of ternary nitrate and modified nitrate are shown in Figure 7. The endothermic peak of the melting process and the exothermic peak of the solidification process can be seen in the figure. Table 3 shows the latent heat of phase change, the onset temperature, and the melting temperature of the ternary nitrate and the modified nitrate. The phase transition temperature of different samples is unchanged basically, indicating that the addition of trace nanoparticles has little effect on the phase transition process of the material. Compared with ternary nitrate, the latent heat of nitrate doped with nanoparticles is slightly lower. On the whole, there is little difference in the latent heat of phase change. In the field of medium and low-temperature energy storage, the latent heat of phase change materials is about 130 J/g, so this modified salt can meet the requirements.

**Figure 7.** The DSC endothermic and exothermic curves of ternary nitrate and modified nitrate.

**Table 3.** Result of latent heat, onset temperature, and melting temperature.


**Author details**

Wilson Abreu\* and Margarida Abreu ESEP-CINTESIS (University of Porto), Porto, Portugal

\*Address all correspondence to<EMAIL_ADDRESS>
© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Current Perspectives on Frailty in the Elderly, Evaluation Tools and Care Pathways DOI: http://dx.doi.org/10.5772/intechopen.92281*


**5.1 Spatial feature extraction performance**

In grayscale, we tested the performances of kICA versus nICA algorithms and the effect of feature overlaps (shadow effects) on image segmentation. An example of PCA and ICA procedures is presented in **Figure 7**. We simulated and evaluated the feature extraction processes on an overlapped photo that is mixed with three images: a human face, fruits, and vegetable scenes (features **f**1, **f**2, and **f**3 in **Figure 7**). The problem can be formulated as the extraction of the human face (feature **f**2) from the mixed images (**g**1, **g**2, and **g**3) without any prior information

### **Figure 7.**

*An example of independent component analysis with kurtosis and negentropy maximization on linear mixtures of three independent images (f1, f2, and f3 with 180 180 pixels). ICA goes a step further and rotates the principal components to maximize non-Gaussianity. The results are recovered original images. The column on the right side represents the cross plots of the variables.*

from the original face. This simulation helps us to evaluate the performance of the SFE2D algorithm before the implementation on more complex applications.

While PCA whitening fails to recover all three features (y1, y2, y3), the Fast-ICA algorithm could separate the latent features inside the mixed images (^*f* 1, ^*f* 2, ^*f* 3). Further analysis of the non-Gaussianity maximization methods revealed that the Fast-ICA through negentropy maximization (nICA algorithm) compared to kurtosis maximization (kICA algorithm) is more effective and recovers more details of each original photo. The kICA converges at 50 iterations, while the nICA converges at iteration 5 (**Figure 8**). The kICA algorithm is prone to outliers and thus fails to converge to an effective solution. However, restarting the kICA each time might improve the results. Therefore, for large images, the efficiency of the nICA method is superior to the kICA, with about 10 times faster convergence and more accurate results.

Another influencing factor with the same level of added unpredictable noise is the sampling interval. This is crucially important in the geophysical sense since the sampling interval directly influences the geophysical survey budget. Large regionalscale data sets are sampled in smaller intervals and give more details about geological structures. As is shown in **Figure 9**, increasing the number of samples reduces

### **Figure 8.**

*Performance of kICA algorithm (a) compared to nICA algorithm (b). The kICA results converge at 10 iterations while the nICA results are converged at iteration 5. The nICA method is superior to the kICA, with about two times faster convergence and more accurate results (180 180 pixels).*

### **Figure 9.**

*Performance of kICA algorithm (a) compared to nICA algorithm (b) on large images with 720 720 pixels. For the kICA algorithm, a 16 times increase of pixels results in eight times slower convergence with numerous local minima. Therefore, for larger images with smaller sampling intervals, negentropy maximization is superior to kurtosis maximization.*

*SFE2D: A Hybrid Tool for Spatial and Spectral Feature Extraction DOI: http://dx.doi.org/10.5772/intechopen.101363*

the performance of kICA compared to nICA and indicates that for large datasets with smaller sampling intervals, negentropy maximization is superior to kurtosis maximization.

However, there are two inherent ambiguities in the ICA framework: (1) Ambiguity in permutations of the recovered sources. This problem states that the order of the estimated independent components is unspecified. (2) Ambiguity in the recovered amplitudes of the sources. One can partially solve this problem by assuming unit variance for all sources. However, there is still a 50% chance that the polarities of the sources are not determined correctly. In cases where we have enough prior information, we can multiply sources by 1 to achieve the best results.

The effect of mixing on image segmentation is also explored in **Figure 10**. ICA significantly reduces the impact of feature overlaps and enhances the performance of image segmentation. The proposed approach integrates information from multiple geo-images and makes sure that the combined images are maximally independent and unique. In other words, the feature overlaps are minimal. This has an important implication in image segmentation since a slight presence of image overlaps and artifacts distort the segmentation output. As shown in **Figure 10**, the segmentation after negentropy maximization improves the human face detection.

### **5.2 Spectral feature extraction**

Further feature extraction can be performed on the images to extract spectral features like edges and lineaments inside the photos. **Figure 11a** shows the results of CWT with Cauchy mother wavelet and 20 scales on the second independent component (recovered human face). As can be seen, different spectral features appear in different CWT scales/directions. Low-level features like edges appear in higher frequencies, and high-level features appear in lower frequencies. However, the transition from each scale/direction is relatively smooth, and that allows some features from each scale/direction leak into the following scale/direction, creating unwanted spectral features' overlap. To reduce these effects, we perform source separation methods like PCA and ICA algorithms again. The results are shown in **Figure 11b**. As is shown, spectral source separation with nICA has improved the low/high-level features with crisper edges and more visible boundaries.

### **Figure 10.**

*Effect of ICA as a preprocessing step before grayscale image segmentation. (a) Segmentation on the mixture image (***g**2 *in Figure 6). (b) Segmentation after negentropy maximization nICA (*^**f**2 *in Figure 6).*

**Figure 11.**

*(a) Space-frequency representation with Cauchy mother wavelet in four scales on the second independent component (recovered human face). Different spectral features appear in different CWT scales/directions. (b) Separation of spectral features with nICA. The results are crisper and have more distinct features.*


**Author details**

26 Atmospheric Aerosols – Regional Characteristics – Chemistry and Physics

(Grinsted et al., 2004; Jevrejeva, 2003)

**9. Conclusions**

estimations:

The continuous WT (wavelet transformation) expands the time series into time frequency space. Cross wavelet transform (XWT) finds regions in time frequency space where the time series show high common power. Wavelet coherence (WTC) finds regions in time frequency space where the two time series co-vary (but does not necessarily have high power); See

The application of the AOD and T estimationtechnique for processing the results of observations at the Russia actinometric network stations allows obtaining qualitatively new and detailed information on the level of aerosol content of the atmosphere in separate regions and in Russia as a whole. Our analysis has made it possible to formulate the following conclusions about the spatiotemporal distribution of AOD over Russia. The spatial distribution of the AOD values averaged over the 35-year period underconsideration generally corresponds to the model of global aerosol distribution over Eurasia, which is represented in the IPCC third and fourth reports. This is manifested in the AOD decrease from the southwest to the northeast in the presence of regions with continuous increasing aerosol turbidity in southwestern and southeastern Russia. Against this background, regions with increased troposphere aerosol loads are pronounced that have been more noticeable under the global purification of the atmosphere from the stratospheric aerosol layer that started in 1995. These troposphere sources are related either to an anthropogenic load (cities in southern Russia, western Siberia, and Primorskii Krai) or to forest and tundra fires in Siberia, in particular, at the Tura station in the Evenki Area. One more cause of the decreased transparency in the atmosphere over eastern Russia, which is manifested in the annual means of AOD, is the volcanoes of Kamchatka. On the whole, for Russia, the trends of multiyear variations have been negative in the last decades. However, there are stations at which the AOD trends are positive; this is particularly true of the stations of Kamchatka and the Far East.We have ascertained the peculiarities of spatial variations in the air turbidity factor in summer 2010 year in comparison with the long-term average spatial variations, which have been manifested in both distribution character and the value of the anomalies of the turbidity factor at ETR. Also we have ascertained the peculiarities of spatial and seasonal variations in the air turbidity factor T and aerosol optical depth AOD in Trans-

Baikal and Central Siberia region of RF for the year and different season

model and to the estimates obtained from satellite data (0.16);

The analysis of AOD variationsduring the last 35 years shows the following concrete

1. Total averaged AOD over all stations and the whole period under study (0.14) is very close to the annual mean global AOD value (0.14) calculated from the ECHAM-HAM

2. Maximum annual mean AOD (0.29) is reached in Krasnodar (№ 4) and the minimum one (0.07) is observed at the "aerosol pure" station of Srednekolymsk (№ 51). The

**Notice:**

Inna Plakhina *Oboukhov Institute of Atmospheric Physics, RussianAcademy of Science, Russia*


**2. Swedish preschools**

The ECEC institutions in Sweden are educational settings that aim to give education and care to children in their early years. The ECEC institution is divided into preschools for children aged 1-5 years and preschool classes for 6-year-olds before formal schooling starts at seven. All Swedish children from 1 year have the right

#### *Preschool Improvement Practices DOI: http://dx.doi.org/10.5772/intechopen.113143*

to be educated and cared for in ECEC institutions. The School Act (2010:800) [11] establishes that education within the school system, which includes preschool, aims to promote the development of all children and a lifelong desire to learn. According to statistics [14] in 2022, there were 508.815 children in Swedish preschools, 115.309 were 4 years old, and 116.092 were 5. From the age of one, children must be offered preschool to the extent necessary concerning the parents' gainful employment or studies or if the child has their own needs due to the family's situation in general. The municipalities must also offer preschool to all children for at least 525 free hours per year from and with the autumn term in the year the child turns three (general preschool). The home municipality is responsible for ensuring that preschool education is provided for all children in the municipality who must be offered preschool and whose guardian wishes it. Individuals may, after application, be approved as the principal of a preschool. Preschools with an individual principal can be operated as, for example, a parent cooperative or staff cooperative by a foundation or a limited company. The municipalities ensure the business meets good quality and security requirements. Most Swedish preschools run by a municipal principal make up 70% of the preschools, and those with an individual principal, as in this study, amount to 30% of the Swedish preschools. The groups of 4-years to 5-year children consist of 16 children in preschools driven by municipalities. In independent preschools, the groups are slightly smaller [14]. The Swedish preschool has a national curriculum [11], which should reflect the values and rights expressed in the UN Convention on the Rights of the Child (CRC) [15]. It is fundamental that all children must be involved in the activities and that they are planned and carried out to promote their development, health, and well-being [11, 16]. There are conditions for well-being in preschools where all children can interact with other children and staff. Responsive staff who create a positive learning environment and take the children's opinions into the teaching are essential for the children's involvement in the actual preschool activities. It provides a sense of social belonging to cope with things and be independent, which is necessary for well-being [17]. Play should play a vital role in education, and an approach by the work team and an environment that encourages play confirm the importance of space for children's development, learning, and well-being. Listening to the children's voices is a part of everyday activity, and in Sweden, children are encouraged to express their thoughts about the experiences provided by the adults [11].


**4. Biosensors for aflatoxins**

Aflatoxins are harmful organisms. Their toxicity is due to their capacity to covalent binding DNA and proteins. The most acutely and chronically toxic aflatoxin is the B1. The legal lim‐ its set for AFB1 or for total aflatoxins vary from country to country [25]. The detection and quantification of aflatoxins are the first steps in the challenging task of controlling such or‐ ganisms.

The rearing of livestock, the storage of grains, and the stock of their derivatives are daily life activities which are susceptible to be infected with pests and diseases. Such infections may cause human death and economical losses.

Every topic exposed in this chapter is so vast that could be by itself a single chapter. The aims of this chapter are to give a general overview of all the existing methods for the meas‐ urement and quantification of aflatoxins; to signalize their principles of operation; and to ex‐ pose their tendencies.

Biosensors are multidisciplinary tools with an enormous potential in detection and quantifi‐ cation of aflatoxins. Thus, such devices have a huge impact in healthcare, food management, agronomical economy and bio-defense [26].

#### **4.1. Biosensors**

**3.4. Recent advances**

296 Aflatoxins - Recent Advances and Future Prospects

increase its sensitivity.

reported materials.

ganisms.

**4. Biosensors for aflatoxins**

cause human death and economical losses.

ELISA has been modified by using electrochemical techniques. Antibodies or antigens may be immobilized on an electrode with a free and enzyme-conjugate. So in a competitive as‐ say, some enzyme-conjugate will bind to the electrode, and enzyme density can be shown by current produced from the catalytic oxidation reaction of the enzyme with the substrates. In a non-competitive assay, the formation of an Ab-Ag complex generates a barrier of direct electrical communication between the immobilized enzyme and the electrode surface.

Some authors have reported the use of electrochemical sensors. In [19] developed a sensor based on enzymatic silver deposition amplification to detect AFB1 in rice. A linear sweep voltage was done in order to read the sensor response. In [20] the authors proposed the use

Optical ELISAs often uses surface plasmon resonance (SPR). They are similar to electro‐ chemical sensors, but in SPR, Ab or Ag is immobilized in an optical-sensitive surface. As AFB1 changes, the angle of Spectral Power Distribution (SPD) varies. A combination of com‐ petitive-direct ELISA and an immunochromatographic assay was done by [21], in order to

In recent years, some articles have developed modifications on ELISAs (e.g. with the using of nano particles). In [22] the authors refined the ELISA process for aflatoxin detection by using anti-AFB1 single chain fragment variables, in order to detect only free AFB1 instead of an AFB1-protein conjugate. In the references [23] developed an ultra-sensitive ELISA by cou‐ pling a micro plate ELISA with sensitive magnetic particles. An important feature of this hy‐ brid system is its small column size, high capture efficiency and lower cost over other

A combination of a competitive direct ELISA and gold nanoparticle immunochromatograph‐ ic strip was done by [21], with a detection limit of 1.0 ng/ml for AFM1 in milk. Immunochro‐ matographic assay (ICA) is rapid and simple, and can be carried out by untrained personnel without using electronic devices. However, this type of assay has low selectivity, so in [24],

Aflatoxins are harmful organisms. Their toxicity is due to their capacity to covalent binding DNA and proteins. The most acutely and chronically toxic aflatoxin is the B1. The legal lim‐ its set for AFB1 or for total aflatoxins vary from country to country [25]. The detection and quantification of aflatoxins are the first steps in the challenging task of controlling such or‐

The rearing of livestock, the storage of grains, and the stock of their derivatives are daily life activities which are susceptible to be infected with pests and diseases. Such infections may

an improved ICA by using a new monoclonal antibody against AFB1 was developed.

of electrical impedance spectroscopy and free-labeled molecules.

The International Union of Pure and Applied Chemistry (IUPAC) define biosensor as:

"A device that uses specific biochemical reactions mediated by isolated enzymes, immuno‐ systems, tissues, organelles or whole cells to detect chemical compounds usually by electri‐ cal, thermal or optical signals".


On the third day of embryo development, the number of embryos that reached the stage of 6–8 was assessed. Pick-up report (IVF + ICSI), fertilization and Pregnancy rate (PR) are shown in **Table 4**.

The results obtained support the theory that a high concentration of the p53 protein in spermatozoa DNA is associated with a low percentage of embryos able to reach the 6–8 cell stage on day three and a lower pregnancy rate (**Figure 4**).

Our work fits well with prediction models and the importance of having objective and repetitive data prior to conception, both in vivo and in vitro [134].

#### **6. Conclusions**

Cytochemistry, fluorescence and electrophoresis techniques have so far been used to assess DNA damage. For our studies, we employed an innovative method called 'quantitative proteomics', an analytical chemical technique for determining the amount of protein in a given sample. The methods for identifying proteins are identical to those used in general proteomics, but include quantification as an additional dimension. We used p53, a protein already known as the 'guardian of the genome', to assess the effect of environmental and/or dietary toxic factors on human bodies through DNA damage. From our studies, we have identified the spermatozoon as a sentinel cell of environmental impact, as its DNA damage is strongly correlated with pollution. Inevitably, the evolution of these preliminary

#### **Figure 5.**

*Schematic representation of the effects of the different concentrations of the p53 protein on human reproduction.*

studies turned to understanding whether DNA damage could influence the fertilizing capacity of males. We think that given our results, this protein can be used as an indicator of environmental impact, and given the renewal characteristics of spermatogenesis, it can also be used as a prevention and follow-up index for environmental remediation. A more extensive use would be to understand whether sperm DNA is compatible with the couple's optimal reproductive project both in vivo and in vivo (**Figure 5**).

### **Author details**

Salvatore Raimondo1,2\*, Mariacira Gentile1,2, Tommaso Gentile1,2 and Luigi Montano2,3

1 Laboratory "Gentile s.a.s." Research Department, Gragnano, Naples, Italy

2 Unit of the Network for Environmental and Reproductive Health (EcoFoodFertility Project), "Oliveto Citra Hospital", Oliveto Citra, Salerno, Italy

3 Andrology Unit and Service of Lifestyle Medicine in UroAndrology, Local Health Authority (ASL) "St. Francis of Assisi Hospital", Oliveto Citra, Salerno, Italy

\*Address all correspondence to<EMAIL_ADDRESS>
© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Presence of p53 Protein on Spermatozoa DNA: A Novel Environmental Bio-Marker… DOI: http://dx.doi.org/10.5772/intechopen.99559*

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#### **Chapter 6**

## A Study of p53 Action on DNA at the Single Molecule Level

*Kiyoto Kamagata*

#### **Abstract**

The transcription factor p53 searches for and binds to target sequences within long genomic DNA, to regulate downstream gene expression. p53 possesses multiple disordered and DNA-binding domains, which are frequently observed in DNA-binding proteins. Owing to these properties, p53 is used as a model protein for target search studies. It counters cell stress by utilizing a facilitated diffusion mechanism that combines 3D diffusion in solution, 1D sliding along DNA, hopping/jumping along DNA, and intersegmental transfer between two DNAs. Singlemolecule fluorescence microscopy has been used to characterize individual motions of p53 in detail. In addition, a biophysical study has revealed that p53 forms liquidlike droplets involving the functional switch. In this chapter, the target search and regulation of p53 are discussed in terms of dynamic properties.

**Keywords:** p53, single molecule, fluorescence, DNA, disordered, diffusion, jumping, intersegmental transfer, sliding, hopping, target search, liquid–liquid phase separation

#### **1. Introduction**

p53 is a multifunctional transcription factor that induces cell cycle arrest, DNA repair, and apoptosis, thereby suppressing cell cancerization [1, 2]. It is referred to as a guardian of the genome that determines cell fate. When p53 is activated by various stress factors, it searches for and binds to target DNA sequences and regulates the expression of downstream genes. p53 is composed of an N-terminal (NT) domain, core domain, linker, tetramerization (Tet) domain, and C-terminal (CT) domain. The core and Tet domains possess specifically folded structures, while other domains are intrinsically disordered [3–5]. p53 forms a tetramer via Tet domains [5]. Core and CT domains are involved in its binding to DNA sequences in a specific and nonspecific manner, respectively [6]. Fifty percent of gene mutations in tumor cells were found in p53, and many of the identified mutations were located in structured domains, which inhibited target DNA binding [3]. Comprehensive mutagenesis analysis supports the correlation between the structured domains of p53 and its function [7]. Since p53 possesses common properties frequently observed in DNA-binding proteins, including oligomerization, disordered regions, and multiple DNA-binding domains [8], it is used as a model protein in the target search study described below [9–11].

The target DNAs for p53 were ~ 20 bp, while the genomic DNA was ~109 bp. Accordingly, p53 was required to search for small targets efficiently from within vast lengths of non-target DNAs. This is known as a target search problem for

**Figure 1.**

*Target search dynamics of DNA-binding proteins and visualization of p53 dynamics on DNA by singlemolecule fluorescence microscopy. (a) Schematic diagram of four target search dynamics. (b) Schematic diagram of single-molecule fluorescence microscope and flow cell. In the flow cell, one end of the DNA is tethered to the surface and it is stretched by buffer flow. p53 molecule labeled to a fluorescence dye is illuminated by TIRF and the fluorescence is detected by EM-CCD through an objective lens. Panels (a) and (b) are adapted from ref. [12] and ref. [13] with some modifications, respectively.*

sequence-specific DNA-binding proteins. To solve this problem, a facilitated diffusion mechanism has been proposed for DNA-binding proteins. The facilitated diffusion is the integration of three-dimensional (3D) diffusion in solution, onedimensional (1D) diffusion along DNA, hopping/jumping along DNA, and intersegmental transfer between two DNAs (**Figure 1a**). In 3D diffusion, p53 diffuses in solution, altering the search sites on genomic DNA. In 1D sliding, it moves along the DNA, while maintaining continuous contact. In addition, p53 hops or jumps along DNA (within 100 bp of jump). Intersegmental transfer enables p53 to move from one DNA to another without dissociation. Theoretical studies suggest that the integration of multiple search dynamics, while not requiring all dynamics, can facilitate the target search [14–17]. The facilitation factor depends on various physical parameters, such as diffusion coefficient along DNA, residence time on DNA, dissociation time in solution, and frequency of transfer and jump.

How does p53 solve the target search problem using facilitated diffusion? How is the target search and binding of p53 regulated? In this chapter, I explain the facilitated diffusion and regulation of p53 based on recently accumulated singlemolecule data.

#### **2. Single-molecule fluorescence microscopy**

Single-molecule fluorescence microscopy enables the differentiation and characterization of individual search dynamics of DNA-binding proteins, including p53, as reported previously [18–24]. In general, the system combines a fluorescence microscope and a flow cell (**Figure 1b**). In the flow cell, one end of the DNA is tethered to the surface, and it is stretched by buffer flow. Several methods have been proposed for tethering DNAs [18, 25–29]. For example, a DNA garden is a simple method for producing DNA arrays, in which neutravidin molecules are printed in a line on polymer-coated coverslips, and biotinylated DNAs are tethered to the printed neutravidin [29]. p53 molecules labeled with a fluorescence dye are introduced into the flow cell using a syringe pump. The fluorescent p53 bound to DNA is selectively illuminated by total internal reflection fluorescence (TIRF). p53 molecules on DNA are detected as fluorescent spots on the sequential images of an electron-multiplying charge-coupled device (EM-CCD). The positions of molecules were tracked using an appropriate analysis program to visualize the search dynamics of p53.

### **3. Target search dynamics of p53**

In 2008, 1D sliding of p53 along DNA was observed for the first time using single-molecule fluorescence microscopy [30]. This observation was consistent with a reported indirect evidence that p53 dissociated rapidly from short DNA in the absence of blocks at ends by sliding off from DNA [31]. In 2011, a study of p53 mutants deleting either of two DNA-binding proteins revealed that p53 can slide along DNA using disordered CT domains [32]. This is consistent with the fact that a designed peptide targeting CT domains suppressed the 1D sliding of p53 [33]. Furthermore, 1D sliding of p53 was supported by molecular dynamics simulations [34, 35]. In 2012, it was shown that 1D sliding dynamics of p53 depends slightly on DNA sequence, suggesting that p53 feels the energy landscape based on DNA sequence through interactions between core domains and DNA [36]. In 2015, a detailed analysis of 1D sliding dynamics demonstrated that p53 possesses two sliding modes on non-target DNA [37, 38]. In the fast mode, it interacts with DNA loosely using CT domains. In contrast, in the slow mode, it binds tightly to DNA using core and CT domains (**Figure 2a**). In 2017, the disordered linker was

#### **Figure 2.**

*Target search dynamics of p53. (a) Schematic diagram of two modes for 1D sliding p53 along DNA. p53 is composed of the NT (purple), Core (green), linker (black), Tet (yellow), and CT (pink) domains. The switch between two modes is triggered by the linker. (b) Typical single-molecule data showing intersegmental transfer of p53 between crisscrossing DNAs. (c) Schematic diagram of intersegmental transfer of p53 between two DNAs. p53 uses CT domains (pink) for the transfer. (d) Typical single-molecule data showing jumping of p53 along DNA (white traces). Arrows denote the jumping events. (e) Schematic diagram of encounter complex formation of p53 and conversion from the encounter complex to long-lived complex. Panel (b) is adapted from ref. [39] with some modifications. Panels (d) and (e) are adapted from ref. [40] with some modifications.*

identified to trigger the switch between the two modes (**Figure 2a**) [41]. In 2016, the target recognition process of p53 was characterized in detail [42]. The results demonstrated that target recognition occurs mainly via 1D sliding. The target recognition of p53 was quite low (the successful recognition probability was 7%), but it was enhanced two-fold upon a post-translational modification. Accordingly, 1D sliding is considered as one of the important dynamics in the target search and binding of p53.

In 2018, intersegmental transfer of p53 was examined using ensemble kinetic and single-molecule fluorescence measurements [39]. After the solutions of p53 bound to fluorescently labeled DNA and non-labeled DNA were mixed, the transfer reaction of p53 was monitored between the two DNAs. The observed reactions included the dissociation of p53 from one DNA and its transfer to the other. Actually, as the concentration of non-labeled DNA increased, the observed rate constant increased, suggesting intersegmental transfer. The rate constant of the transfer was ~108 M−1 s−1, which is close to the diffusion limit. Furthermore, single-molecule tracking of p53 on crisscrossed DNAs demonstrated that p53 moves along the first DNA and then moves along the second DNA through the transfer at the intersection (**Figure 2b**). A study of p53 mutants deleting either of two DNAbinding domains identified that p53 binds to the first DNA and then to the second DNA using disordered CT domains at the same time; it then releases the first DNA, resulting in a transfer between the two DNAs (**Figure 2c**). This mechanism is supported by molecular dynamics simulations of p53 [43].

In 2020, the hopping/jumping of p53 on DNA was investigated [40]. Hopping/ jumping was expected to occur at a time scale that is faster than the time resolution of the microscope (ex. 33 ms). To detect these events, the time resolution of the microscope was improved to 500 μs by optimizing the fluorescence excitation based on critical angle TIRF illumination and by utilizing the time delay integration mode of the EM-CCD [40]. Using the sub-millisecond-resolved microscope, jumping events of p53 along DNA were directly detected (arrows in **Figure 2d**). The jump frequency of p53 was ~6 s−1, and the jump time was 2.2 ms. Based on the study of p53 mutants deleting either of two DNA-binding domains, disordered CT domains were identified to be indispensable for the jumping of p53 along DNA [13]. Furthermore, 1D diffusion along DNA was enhanced upon increasing the salt concentration, suggesting that p53 moves along DNA by hopping DNA-binding domains. Thus, it was revealed that p53 possesses hopping and jumping dynamics along DNA.

In 2016, 3D diffusion of p53 was characterized using ensemble kinetic measurements [42]. Association rate constants for target and non-target DNAs were determined to be ~109 M−1 s−1, comparable to the diffusion limits. The difference in affinity for target and non-target DNAs was attributed to the dissociation rate constants. In 2020, the association process of p53 with non-target DNA was further investigated at the single-molecule level using a sub-millisecond resolved fluorescence microscope [40]. Kymographs demonstrated that short-lived traces of p53 with an average residence time of 2.8 ms were detected in addition to long-lived traces moving along DNA. The short-lived complex was interpreted as an encounter complex. Disordered CT domains of p53 were identified to participate in the transient complex formation and in the conversion from the transient complex to the long-lived complex [13] (**Figure 2e**). The long-lived complex was further stabilized by core domains [13].

Overall, single-molecule fluorescence microscopy revealed that p53 possesses all four search dynamics proposed theoretically: 3D diffusion, 1D sliding, hopping/jumping, and intersegmental transfer. The unique structure of p53, which is a tetramer of two DNA-binding domains, enables these search dynamics. This is the first study to examine all search possibilities for a single model protein.

### **4. Liquid–liquid phase separation of p53**

Target search and binding of p53 might be regulated by a liquid-like assembly of p53 molecules. In a liquid–liquid phase separation (LLPS), p53 molecules, which disperse in the bulk phase, assemble and form a condensed phase called liquid droplets. In the droplet phase, p53 can move fluidly while maintaining a high concentration. This fluid property in the condensed phase differs from the solid aggregation that causes malfunction of p53. Early *in vivo* studies demonstrated that p53 is recruited into cellular droplets such as Cajal and promyelocytic leukemia protein (PML) bodies [44–46]. These facts suggest that LLPS might be involved in the cellular functions of p53.

In 2020, this possibility was extensively examined using *in vitro* measurements such as scattering, DIC microscopy, and fluorescence microscopy [47]. p53 formed micrometer-sized droplets at neutral and slightly acidic pH and low salt concentrations. The fusion events of at least two droplets into a single large droplet were observed, confirming the fluidity of p53 inside the droplets (**Figure 3a**). High fluorescence intensity was detected in the droplets of p53 labeled with a fluorescent dye, supporting the high concentration of p53 in the droplets (**Figure 3b**). The droplet formation of p53 was affected by pH and salt concentrations. This suggests that attractive electrostatic interactions among local parts of p53 and repulsive net charges among whole molecules of p53 are balanced, resulting in droplet formation. Deletion of either of the disordered NT and CT domains suppressed the droplet formation of p53. This suggests multivalent electrostatic interactions between the oppositely charged NT and CT domains in p53 droplets.

The structural properties of p53 in solution and in droplet form were investigated using fluorescence resonance energy transfer (FRET) between two fluorophores labeled at two residues of p53. Since FRET depends on the distance

#### **Figure 3.**

*Liquid droplet formation of p53 regulates its function. (a) Time course of a typical fusion event of three p53 droplets into a single droplet using DIC microscopy. (b) DIC and fluorescence images of the droplets of Alexa488-labeled p53 and non-labeled p53. Scale bars in panels (a) and (b) represent 10* μ*m. (c) Schematic diagram of p53 conformation in the droplet. p53 is composed of the NT (purple), Core (orange), Tet (yellow), and CT (red) domains. In the droplets, the NT and CT domains interact electrostatically. Arrows denote the structural changes on the different domains of p53 that are induced by the intermolecular interactions in a droplet. The dimer structure is displayed for clarity. (d) Functional switch model of p53. The panels (a)-(d) are adapted from ref. [47] with some modifications.*

between the two fluorophores, it was used to measure the conformational changes. The distance between the core domains of p53 was slightly longer in the droplets, while the distance between the CT domains became slightly shorter (**Figure 3c**). Accordingly, p53 adopted a new tertiary structure, forming interactions with the adjacent molecules in the droplets.

Does p53 maintain binding to the target DNA after experiencing the droplet formation? The reactions of p53 binding to the target DNA were similar before and after the droplet formation. These results indicate that droplet formation of p53 is reversible, and p53 dispersed in solution from the droplets retains its DNA binding ability.

Droplet formation of p53 was found to be regulated by molecular crowding, endogenous molecules, and post-translational modification. Molecular crowding agents, mimicking the cellular crowding condition, promoted droplet formation. In contrast, ssDNA, dsDNA, and ATP suppressed it. The p53 mutant mimicking post-translational phosphorylation did not form droplets. Based on these results, a functional switch model was proposed (**Figure 3d**). Under normal cell conditions, the compartmentalization of p53 into the droplets suppresses its function as a transcriptional regulator. Under stress conditions, the activation of p53, triggered by posttranslational phosphorylation, releases p53 from the droplets and promotes target search and binding.

#### **5. Target search and regulation model of p53**

In this section, the current model of p53 is described in terms of target search and regulation. p53 functions as a transcription factor that responds to various emergency situations in cells. Under normal cell conditions, p53 turns off through the following mechanisms. First, the copy number of p53 is maintained at a low level, allowing dimers with a low affinity to target DNAs in an oligomeric state [48, 49]. Second, post-translational modifications for activating p53 are not added, for example, suppressing the target recognition of p53 [42]. Third, p53 is stored in liquid droplets [47]. These actions of p53 prevent its malfunction under normal conditions.

Under cellular stress, p53 is activated by post-translational modifications [1, 2, 50–56] and by a change in its oligomeric state from dimers to tetramers, with a high affinity for target DNAs [48, 57–59]. Phosphorylation of the CT domain of p53 triggers its release from the droplets, allowing it to engage in target search [47]. The increase in the copy number of p53 also facilitates the target search [60]. As explained above, p53 utilizes facilitated diffusion combining four search dynamics. Using 3D diffusion, p53 associates randomly with nonspecific sites of DNA, followed by dissociation. Until p53 associates with the target sequence by chance, it repeats such association and dissociation motions. If the search motion of p53 is limited to 3D diffusion, it would be a time-consuming endeavor. After p53 associates with the nonspecific site of DNA by 3D diffusion, it can search for the target sequence along DNA near the bound site using 1D sliding and hopping/jumping. The search distance of p53 per association event is estimated to be 700 bp [40], corresponding to approximately 35-fold facilitation of the target search.

In cells, genomic DNAs are covered by many DNA-binding proteins, including histones and other nucleoid proteins. These DNA-binding proteins may act as obstacles in the target search of p53. For example, when the sliding p53 collides with other DNA-binding proteins on DNA, it may not be able to bypass these obstacles due to steric hindrance, thereby limiting the search distance on DNA. However, p53 possesses two bypass mechanisms: the jumping along DNA [40] and the intersegmental transfer between two DNAs [39]. Using these motions, it can overcome such obstacles and continue its search for targets in cells. Overall, the search and regulation strategies of p53 could satisfy various cellular requirements.

*A Study of p53 Action on DNA at the Single Molecule Level DOI: http://dx.doi.org/10.5772/intechopen.96163*

#### **Figure 4.**

*Schematic diagram of target search of p53. Pink and gray circles are p53 and obstacle bound to DNA, respectively. Four search mechanisms are illustrated.*

#### **6. Conclusions**

The target search and binding of p53 and its regulation have been characterized using single-molecule fluorescence microscopy and relevant biophysical methods. The accumulated data demonstrate that p53 searches for target DNAs utilizing four search dynamics: 3D diffusion in solution, 1D sliding along DNA, hopping/jumping along DNA, and intersegmental transfer between two DNAs (**Figure 4**). Especially, hopping/jumping and intersegmental transfer between two DNAs are required to bypass obstacles bound to DNA. It was reported that other DNA-binding protein with a disordered DNA-binding domain bypasses obstacles through obstacle-unbound region of DNA [24].  Target search and binding are regulated by copy number, post-translational modifications, and liquid droplet formation. Considering that p53 can interact with many partner proteins, the partner proteins may affect the target search. Complexity in the target search and regulation of p53 would enable a response to various emergency situations in cells and be required to satisfy various cellular requirements.

#### **Acknowledgements**

I thank the corroborators for their helpful discussions on our studies of target search by p53.

*p53 - A Guardian of the Genome and Beyond*

### **Author details**

Kiyoto Kamagata Institute of Multidisciplinary Research for Advanced Materials, Tohoku University, Sendai, Japan

\*Address all correspondence to<EMAIL_ADDRESS>
© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*A Study of p53 Action on DNA at the Single Molecule Level DOI: http://dx.doi.org/10.5772/intechopen.96163*

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*Edited by Mumtaz Anwar, Zeenat Farooq, Mohammad Tauseef and Vijay Avin Balaji Ragunathrao*

p53 is a protein that plays an important role in the regulation of cell division and thereby offers protection to the cell against malignant transformation. This is the most well-studied and most appreciated role of p53, due to which the protein is famously known as the "guardian of the genome." It is the most studied protein in all forms of cancer research, with much of this research investigating p53's role in tumor suppression. However, research has also shown that p53 plays a role in a wide range of other cellular functions, like serving as a biomarker for environmental pollution. Even as a tumor suppressor and transcription factor, many aspects of p53 function are still obscure, and research in the field is continually attempting to shed light on these functions. This book examines and discusses the myriad roles of p53 and different aspects of its functions. Chapters examine p53 in cancer prevention, DNA repair, gene regulation, and more.

Published in London, UK © 2022 IntechOpen © selvanegra / iStock

p53 - A Guardian of the Genome and Beyond

p53

A Guardian of the Genome and Beyond

*Edited by Mumtaz Anwar, Zeenat Farooq, Mohammad Tauseef*

*and Vijay Avin Balaji Ragunathrao*